diff --git a/.DS_Store b/.DS_Store new file mode 100644 index 0000000..992d58c Binary files /dev/null and b/.DS_Store differ diff --git a/.gitignore b/.gitignore index 4db1aa2..9ceeb75 100644 --- a/.gitignore +++ b/.gitignore @@ -1,5 +1,4 @@ -data/coco/ -data/flickr30k/ +data/ trash/ .virtual_documents/ dataset/__pycache__/ @@ -8,3 +7,11 @@ gptq_results_first_run/ slurm_files/1_quantization_run/ slurm_files/2_quantization_run_smaller_bits/ +**/*.pyc +**/__pycache__ + +awq/**/ +.vscode/ + +llava_runs/**/ + diff --git a/__pycache__/blip_quantizer.cpython-310.pyc b/__pycache__/blip_quantizer.cpython-310.pyc index 5cd7811..883ab50 100644 Binary files a/__pycache__/blip_quantizer.cpython-310.pyc and b/__pycache__/blip_quantizer.cpython-310.pyc differ diff --git a/__pycache__/blip_quantizer.cpython-312.pyc b/__pycache__/blip_quantizer.cpython-312.pyc index 50f8a32..64ef81e 100644 Binary files a/__pycache__/blip_quantizer.cpython-312.pyc and b/__pycache__/blip_quantizer.cpython-312.pyc differ diff --git a/__pycache__/inference_pipeline.cpython-310.pyc b/__pycache__/inference_pipeline.cpython-310.pyc index f99bb67..c130c11 100644 Binary files a/__pycache__/inference_pipeline.cpython-310.pyc and b/__pycache__/inference_pipeline.cpython-310.pyc differ diff --git a/__pycache__/inference_pipeline.cpython-312.pyc b/__pycache__/inference_pipeline.cpython-312.pyc new file mode 100644 index 0000000..f1fc585 Binary files /dev/null and b/__pycache__/inference_pipeline.cpython-312.pyc differ diff --git a/__pycache__/quant_functions.cpython-310.pyc b/__pycache__/quant_functions.cpython-310.pyc index 002d4d8..959c9a4 100644 Binary files a/__pycache__/quant_functions.cpython-310.pyc and b/__pycache__/quant_functions.cpython-310.pyc differ diff --git a/__pycache__/quant_functions.cpython-312.pyc b/__pycache__/quant_functions.cpython-312.pyc new file mode 100644 index 0000000..5f89e01 Binary files /dev/null and b/__pycache__/quant_functions.cpython-312.pyc differ diff --git a/__pycache__/utils.cpython-310.pyc b/__pycache__/utils.cpython-310.pyc index 52e7870..ba737ff 100644 Binary files a/__pycache__/utils.cpython-310.pyc and b/__pycache__/utils.cpython-310.pyc differ diff --git a/__pycache__/utils.cpython-312.pyc b/__pycache__/utils.cpython-312.pyc new file mode 100644 index 0000000..08df777 Binary files /dev/null and b/__pycache__/utils.cpython-312.pyc differ diff --git a/awq/__init__.py b/awq/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/awq/__pycache__/__init__.cpython-310.pyc b/awq/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 0000000..2cd0738 Binary files /dev/null and b/awq/__pycache__/__init__.cpython-310.pyc differ diff --git a/awq/__pycache__/__init__.cpython-312.pyc b/awq/__pycache__/__init__.cpython-312.pyc new file mode 100644 index 0000000..95cb340 Binary files /dev/null and b/awq/__pycache__/__init__.cpython-312.pyc differ diff --git a/awq/__pycache__/awq_quantizer.cpython-312.pyc b/awq/__pycache__/awq_quantizer.cpython-312.pyc new file mode 100644 index 0000000..b4d6eb6 Binary files /dev/null and b/awq/__pycache__/awq_quantizer.cpython-312.pyc differ diff --git a/awq/__pycache__/quantizer.cpython-310.pyc b/awq/__pycache__/quantizer.cpython-310.pyc new file mode 100644 index 0000000..91b27d4 Binary files /dev/null and b/awq/__pycache__/quantizer.cpython-310.pyc differ diff --git a/awq/__pycache__/scaled_modules.cpython-310.pyc b/awq/__pycache__/scaled_modules.cpython-310.pyc new file mode 100644 index 0000000..1395c03 Binary files /dev/null and b/awq/__pycache__/scaled_modules.cpython-310.pyc differ diff --git a/awq/__pycache__/scaled_modules.cpython-312.pyc b/awq/__pycache__/scaled_modules.cpython-312.pyc new file mode 100644 index 0000000..7c47eb0 Binary files /dev/null and b/awq/__pycache__/scaled_modules.cpython-312.pyc differ diff --git a/awq/__pycache__/utils.cpython-310.pyc b/awq/__pycache__/utils.cpython-310.pyc new file mode 100644 index 0000000..65765bc Binary files /dev/null and b/awq/__pycache__/utils.cpython-310.pyc differ diff --git a/awq/__pycache__/utils.cpython-312.pyc b/awq/__pycache__/utils.cpython-312.pyc new file mode 100644 index 0000000..f5656d1 Binary files /dev/null and b/awq/__pycache__/utils.cpython-312.pyc differ diff --git a/awq/awq_captioning.html b/awq/awq_captioning.html new file mode 100644 index 0000000..fa45480 --- /dev/null +++ b/awq/awq_captioning.html @@ -0,0 +1,14 @@ + + + +
+
+ + \ No newline at end of file diff --git a/awq/awq_image_captioning.csv b/awq/awq_image_captioning.csv new file mode 100644 index 0000000..6b0c71d --- /dev/null +++ b/awq/awq_image_captioning.csv @@ -0,0 +1,344 @@ +vit_bits,qformer_bits,llm_bits,METEOR,CIDEr,model_size +2,2,2,0.029884481134349352,0.000790216892799398,15727220992 +2,2,3,0.14985661382475526,0.3892738924855776,18244540672 +2,2,4,0.1837345249958186,0.5443518230890101,20761860352 +2,2,5,0.1886596236812807,0.577806426477473,23279180032 +2,2,6,0.19215876871645188,0.5940621735445684,25796499712 +2,2,8,0.1935281864323662,0.6015116071758554,30831139072 +2,2,16,0.19318000730820825,0.6016201760679405,50969696512 +2,3,2,0.02488881296409678,0.00174091879923525,15832311040 +2,3,3,0.1536184976125826,0.41995583557884075,18349630720 +2,3,4,0.19254725268146605,0.6413107364012529,20866950400 +2,3,5,0.1971479719875085,0.6644740132743288,23384270080 +2,3,6,0.2053801049843051,0.7005174822474439,25901589760 +2,3,8,0.2062730261379436,0.7060009487248382,30936229120 +2,3,16,0.20829024037502017,0.7114414564035497,51074786560 +2,4,2,0.029512816604721017,0.0015242110097923745,15937401088 +2,4,3,0.15372660118410247,0.42258480275870286,18454720768 +2,4,4,0.19648368648501646,0.6677658754857135,20972040448 +2,4,5,0.19983649487376928,0.6794140389055991,23489360128 +2,4,6,0.20676989347449634,0.7158724879010914,26006679808 +2,4,8,0.2085390794369036,0.7239213616454379,31041319168 +2,4,16,0.2096374717583629,0.7267086690055766,51179876608 +2,5,2,0.028747258227322558,0.0015383744706068737,16042491136 +2,5,3,0.156545753956854,0.43421880970831933,18559810816 +2,5,4,0.196610560416409,0.6643003856622003,21077130496 +2,5,5,0.20181309651240506,0.6880985619516059,23594450176 +2,5,6,0.20881490132915903,0.7235377941797142,26111769856 +2,5,8,0.21110624326329927,0.7375435379366277,31146409216 +2,5,16,0.21195715932298625,0.7375809567131982,51284966656 +2,6,2,0.03063110391338109,0.0020275794823421942,16147581184 +2,6,3,0.15356247751795993,0.424523049292371,18664900864 +2,6,4,0.19758305780734386,0.6760507495669246,21182220544 +2,6,5,0.2007745793399482,0.6885736800597454,23699540224 +2,6,6,0.20849863145595418,0.7263028445719076,26216859904 +2,6,8,0.2098694607988712,0.7315217945479447,31251499264 +2,6,16,0.21048954312798976,0.7339511837416856,51390056704 +2,8,2,0.02943548481173078,0.001666631696574353,16357761280 +2,8,3,0.15683632111521817,0.43771168324000265,18875080960 +2,8,4,0.19715300706681188,0.6719258108942063,21392400640 +2,8,5,0.20108814859611662,0.6872950083608445,23909720320 +2,8,6,0.20858989521160368,0.7246423668329751,26427040000 +2,8,8,0.21055393327787206,0.7350918293335355,31461679360 +2,8,16,0.21111491508569893,0.7362575171829725,51600236800 +2,16,2,0.02950987712164611,0.0016837862661927283,17198481664 +2,16,3,0.15691158896246368,0.43627960511248964,19715801344 +2,16,4,0.19727345249098036,0.6727059028707632,22233121024 +2,16,5,0.20106423910901167,0.6882361624647443,24750440704 +2,16,6,0.20860240485027726,0.7246993426668064,27267760384 +2,16,8,0.21057204891397074,0.734900136126526,32302399744 +2,16,16,0.21098317418817375,0.7357684637386981,52440957184 +3,2,2,0.03185703693757794,0.0022093327972838544,16711758208 +3,2,3,0.20029049990462205,0.7266863329828995,19229077888 +3,2,4,0.2484356580168853,1.0235981557690623,21746397568 +3,2,5,0.25535518504605753,1.0729311908769705,24263717248 +3,2,6,0.26137412436348845,1.1194833585777608,26781036928 +3,2,8,0.2592091197853552,1.1086451337916028,31815676288 +3,2,16,0.2599854702694635,1.1134807950614616,51954233728 +3,3,2,0.034032341758506536,0.004540930649061664,16816848256 +3,3,3,0.21138636679405406,0.8051281610518605,19334167936 +3,3,4,0.2650228454047996,1.1560284551343807,21851487616 +3,3,5,0.269467141951613,1.1923197774896415,24368807296 +3,3,6,0.27608791249093045,1.2347054039266734,26886126976 +3,3,8,0.27565966629376465,1.2272202650647102,31920766336 +3,3,16,0.2764047060750114,1.2313846308384797,52059323776 +3,4,2,0.03710586607227904,0.003412387388407898,16921938304 +3,4,3,0.21451697505007117,0.8208565360141244,19439257984 +3,4,4,0.2702844643529473,1.1794014085789621,21956577664 +3,4,5,0.27584509903784266,1.2266779752864407,24473897344 +3,4,6,0.2815846450851879,1.2572691461851668,26991217024 +3,4,8,0.2833953779681455,1.26358895663258,32025856384 +3,4,16,0.28439495440997603,1.2673159046347733,52164413824 +3,5,2,0.03681903996096765,0.0037738532167631528,17027028352 +3,5,3,0.21242035463087375,0.8106225186823535,19544348032 +3,5,4,0.2685017116853036,1.1758572483909744,22061667712 +3,5,5,0.27492709888820605,1.224376698596568,24578987392 +3,5,6,0.28087712948026294,1.2613331346868197,27096307072 +3,5,8,0.2823379329904332,1.261397607201865,32130946432 +3,5,16,0.28386210564334813,1.2687618310593374,52269503872 +3,6,2,0.03836227417524412,0.004072541652402495,17132118400 +3,6,3,0.21427363142651198,0.8236695721105687,19649438080 +3,6,4,0.27011229843322315,1.1856491875247703,22166757760 +3,6,5,0.27460669541124244,1.2213518706277067,24684077440 +3,6,6,0.2806354693035315,1.259917659736496,27201397120 +3,6,8,0.2836407999143544,1.2663044193020636,32236036480 +3,6,16,0.2843478419605554,1.2708232038912224,52374593920 +3,8,2,0.03810343749151561,0.004033343246625629,17342298496 +3,8,3,0.21370157121906094,0.8190620216054207,19859618176 +3,8,4,0.26953744087505843,1.1808923852510342,22376937856 +3,8,5,0.27484708512194606,1.225699556061882,24894257536 +3,8,6,0.2809750487176385,1.260754669664881,27411577216 +3,8,8,0.2836878203740132,1.2666116687606168,32446216576 +3,8,16,0.2845303570314229,1.2737401966926578,52584774016 +3,16,2,0.037998972341977605,0.004006166141518532,18183018880 +3,16,3,0.2134457657549776,0.8184339490345491,20700338560 +3,16,4,0.26930225234336214,1.180122405297351,23217658240 +3,16,5,0.2749129889020977,1.226209348185821,25734977920 +3,16,6,0.28071187087992566,1.2599670609394376,28252297600 +3,16,8,0.2836549420341586,1.268297667693004,33286936960 +3,16,16,0.28453632151027247,1.2752890935916952,53425494400 +4,2,2,0.03554405202439207,0.0020596108397078843,17696295424 +4,2,3,0.2007969629020746,0.7295886421565306,20213615104 +4,2,4,0.25022377059788276,1.0417863722989882,22730934784 +4,2,5,0.2552756428745414,1.089604655273231,25248254464 +4,2,6,0.26173111696962725,1.1243699092711588,27765574144 +4,2,8,0.26275616027627124,1.1321620826054106,32800213504 +4,2,16,0.2626553112263047,1.1325403602869566,52938770944 +4,3,2,0.03792766458441041,0.004751817549633914,17801385472 +4,3,3,0.21101031865318656,0.8002507737119463,20318705152 +4,3,4,0.26631072776290265,1.1676678353269516,22836024832 +4,3,5,0.27016142253095093,1.201189986792182,25353344512 +4,3,6,0.2764764962597095,1.2413131851952102,27870664192 +4,3,8,0.2767816837784037,1.2364159506260926,32905303552 +4,3,16,0.27811341803978223,1.246123613154921,53043860992 +4,4,2,0.038647645311473806,0.0041302318948400855,17906475520 +4,4,3,0.21139620273553503,0.8040498221201324,20423795200 +4,4,4,0.2696296967613541,1.1818920290369954,22941114880 +4,4,5,0.2743049084227653,1.2187256913286244,25458434560 +4,4,6,0.2820665632442025,1.2693462940552265,27975754240 +4,4,8,0.2837285042274702,1.26753571103515,33010393600 +4,4,16,0.2840855765190654,1.2679401319860708,53148951040 +4,5,2,0.03816679850970886,0.004180429754729747,18011565568 +4,5,3,0.20800549611357294,0.7936760412704565,20528885248 +4,5,4,0.2684271879621022,1.1769199968051363,23046204928 +4,5,5,0.2741510572230004,1.2177552194123182,25563524608 +4,5,6,0.28128887015849,1.2691464532836039,28080844288 +4,5,8,0.28232846393243993,1.2661784066985244,33115483648 +4,5,16,0.28310202287995034,1.2694800484392854,53254041088 +4,6,2,0.03947246512330218,0.004496874060880208,18116655616 +4,6,3,0.20844173270297314,0.7959372573564164,20633975296 +4,6,4,0.26820924509813787,1.1741282222425378,23151294976 +4,6,5,0.2741500493132533,1.21868239273846,25668614656 +4,6,6,0.28115868685524437,1.2662373768159405,28185934336 +4,6,8,0.28178260704857505,1.25967838492472,33220573696 +4,6,16,0.2832089336877907,1.2677578930557416,53359131136 +4,8,2,0.03932832614478332,0.004533730593880283,18326835712 +4,8,3,0.21092989137155516,0.8051917158643872,20844155392 +4,8,4,0.2686142296604581,1.1791031207801064,23361475072 +4,8,5,0.2742289723913308,1.2171637489140836,25878794752 +4,8,6,0.2816999624424486,1.269949088841136,28396114432 +4,8,8,0.28294566286056905,1.2685095577773473,33430753792 +4,8,16,0.2839275915866569,1.2725369264593078,53569311232 +4,16,2,0.039870783460907666,0.004488325986947271,19167556096 +4,16,3,0.2107603494156979,0.8049525065074827,21684875776 +4,16,4,0.268773824997628,1.1793997074725706,24202195456 +4,16,5,0.2742171149298761,1.217838638892368,26719515136 +4,16,6,0.2818531035064126,1.270626685213267,29236834816 +4,16,8,0.28304734623205957,1.268790043613231,34271474176 +4,16,16,0.28379253997143766,1.2723116809585964,54410031616 +5,2,2,0.03539568644904178,0.003264698962190208,18680832640 +5,2,3,0.19857547135910164,0.7180181623782547,21198152320 +5,2,4,0.2488564578980053,1.0329624946451361,23715472000 +5,2,5,0.25265624301184025,1.0707977126380381,26232791680 +5,2,6,0.26038460954808534,1.1182626611582889,28750111360 +5,2,8,0.25916154259240265,1.1127242409207425,33784750720 +5,2,16,0.2597450882896447,1.115818110946458,53923308160 +5,3,2,0.03852835005945664,0.005044279655373526,18785922688 +5,3,3,0.20919428953205838,0.7879097938908757,21303242368 +5,3,4,0.26359356710436166,1.1485486724193579,23820562048 +5,3,5,0.26684549637944127,1.1764723409323512,26337881728 +5,3,6,0.2730030358969137,1.2155479022667492,28855201408 +5,3,8,0.2736482751434616,1.2161199154649738,33889840768 +5,3,16,0.2742002799959992,1.2207863371539902,54028398208 +5,4,2,0.0383106603039449,0.004182410951920071,18891012736 +5,4,3,0.20945680883980095,0.7941648429911843,21408332416 +5,4,4,0.2681996749926065,1.1692669018889825,23925652096 +5,4,5,0.271236873072046,1.1960020285283905,26442971776 +5,4,6,0.2795632422324446,1.249905599427292,28960291456 +5,4,8,0.2807453214549563,1.2512046604157983,33994930816 +5,4,16,0.2821228770619992,1.2581493339973435,54133488256 +5,5,2,0.038812903989368874,0.004414734647868175,18996102784 +5,5,3,0.20827855506579382,0.7886177826050037,21513422464 +5,5,4,0.2657672271375958,1.1574830881644504,24030742144 +5,5,5,0.270453762144429,1.1903072050381271,26548061824 +5,5,6,0.2785285750157541,1.248833370994669,29065381504 +5,5,8,0.27973573483009506,1.2505910698213745,34100020864 +5,5,16,0.28100653715595897,1.2559031668672331,54238578304 +5,6,2,0.03937630053597519,0.004761893448042619,19101192832 +5,6,3,0.20881127024669635,0.7933442754900087,21618512512 +5,6,4,0.2660535139642068,1.1612856969921397,24135832192 +5,6,5,0.26956692669777055,1.1875149732346562,26653151872 +5,6,6,0.2783926218102539,1.2446303376282275,29170471552 +5,6,8,0.2794284812688384,1.247600525639272,34205110912 +5,6,16,0.2805120454493048,1.252184355894695,54343668352 +5,8,2,0.0392876594512647,0.004715707717172699,19311372928 +5,8,3,0.20505358929816234,0.7715007986326152,21828692608 +5,8,4,0.26715058027909827,1.167161012774189,24346012288 +5,8,5,0.2700647922767295,1.189471154011835,26863331968 +5,8,6,0.2791323330204765,1.2486870632772915,29380651648 +5,8,8,0.28043141176226755,1.2538222735773836,34415291008 +5,8,16,0.28147200907029796,1.2590312903179968,54553848448 +5,16,2,0.03935728728751011,0.004699991121996324,20152093312 +5,16,3,0.20857656444229464,0.7910105903131062,22669412992 +5,16,4,0.26650447697030605,1.1645621691200578,25186732672 +5,16,5,0.269994073154192,1.1891130623911599,27704052352 +5,16,6,0.27947041159223907,1.2499655271501364,30221372032 +5,16,8,0.2800480375813525,1.250759578071996,35256011392 +5,16,16,0.28148906254024053,1.2574020173423825,55394568832 +6,2,2,0.033689045247177904,0.002135514876681239,19665369856 +6,2,3,0.1995702866972476,0.7264676632473416,22182689536 +6,2,4,0.2475822019731219,1.0232920884230814,24700009216 +6,2,5,0.2529732771470224,1.0704198052787794,27217328896 +6,2,6,0.2598567516851389,1.1160500908441646,29734648576 +6,2,8,0.2592645753079949,1.1131496180075628,34769287936 +6,2,16,0.25979623972425103,1.1176110659095753,54907845376 +6,3,2,0.03886168980413313,0.005116810542135994,19770459904 +6,3,3,0.20894836040125647,0.78766789683324,22287779584 +6,3,4,0.2631136294006772,1.1458078798333984,24805099264 +6,3,5,0.26602992622951516,1.1743586936521027,27322418944 +6,3,6,0.2734671202303975,1.2184402901688431,29839738624 +6,3,8,0.27393127108089294,1.213608212193691,34874377984 +6,3,16,0.27548844448051657,1.2253157599692892,55012935424 +6,4,2,0.03881370840278316,0.004372741260313818,19875549952 +6,4,3,0.2079392854106619,0.7863488675261142,22392869632 +6,4,4,0.26577511268863374,1.154364944176871,24910189312 +6,4,5,0.2694405705313831,1.1868886054730516,27427508992 +6,4,6,0.27871376274464843,1.2440943544657643,29944828672 +6,4,8,0.2799813598846214,1.2469043049851019,34979468032 +6,4,16,0.28039466173437994,1.2495600801266238,55118025472 +6,5,2,0.03915732687638075,0.004528508012128144,19980640000 +6,5,3,0.20672796264015827,0.7822532071076522,22497959680 +6,5,4,0.26466748384828653,1.1466218886814121,25015279360 +6,5,5,0.2692422909529576,1.182464151264467,27532599040 +6,5,6,0.27770066951734496,1.239097227163636,30049918720 +6,5,8,0.2782405905869657,1.2374097592485436,35084558080 +6,5,16,0.279889958988931,1.2451443958139716,55223115520 +6,6,2,0.03974397974387037,0.0047738714292881795,20085730048 +6,6,3,0.2076556413002826,0.7869627504029372,22603049728 +6,6,4,0.2645534527788851,1.1448253129962436,25120369408 +6,6,5,0.2693868110143037,1.1849927369299327,27637689088 +6,6,6,0.2769359695011477,1.2359611482185155,30155008768 +6,6,8,0.2774697283891806,1.2345152710805227,35189648128 +6,6,16,0.2791530049253948,1.2410981814756032,55328205568 +6,8,2,0.03950222184763547,0.004710927469749527,20295910144 +6,8,3,0.20714260422318853,0.7831441749206127,22813229824 +6,8,4,0.26569207939692235,1.1532797092887275,25330549504 +6,8,5,0.2694899075089131,1.1870631294646565,27847869184 +6,8,6,0.27772646142003016,1.2378950190538096,30365188864 +6,8,8,0.27826385994290326,1.2396680301427143,35399828224 +6,8,16,0.2795140452687747,1.2464283971977719,55538385664 +6,16,2,0.03954398606318085,0.00471070789410733,21136630528 +6,16,3,0.20722071899092814,0.7846150320891636,23653950208 +6,16,4,0.2655337977316027,1.151739526032029,26171269888 +6,16,5,0.2697323199765623,1.1888011112017192,28688589568 +6,16,6,0.2778688652580702,1.2381501818428504,31205909248 +6,16,8,0.2783466182494372,1.2406648192914698,36240548608 +6,16,16,0.2795925000876874,1.2469064852182756,56379106048 +8,2,2,0.0344184962623263,0.0020926826069756223,21634444288 +8,2,3,0.19717382514919235,0.7116309448572241,24151763968 +8,2,4,0.24669864044450215,1.0096640742263971,26669083648 +8,2,5,0.2504880541245433,1.051832240994192,29186403328 +8,2,6,0.25848988087002034,1.1031148303323675,31703723008 +8,2,8,0.2581235372562963,1.0996036917317866,36738362368 +8,2,16,0.25851087051156696,1.1026112693765862,56876919808 +8,3,2,0.03886992764461106,0.00527665016687497,21739534336 +8,3,3,0.20474475574756268,0.7587296632897987,24256854016 +8,3,4,0.26041139847619543,1.1278018779745882,26774173696 +8,3,5,0.2637886450865388,1.1535606055527943,29291493376 +8,3,6,0.2702609965385433,1.1953810071788358,31808813056 +8,3,8,0.2710305582860595,1.1949338492083719,36843452416 +8,3,16,0.2724762395803599,1.2026468908277965,56982009856 +8,4,2,0.038960864382997014,0.004082857671826092,21844624384 +8,4,3,0.20461539027052877,0.760952128204789,24361944064 +8,4,4,0.2637425407875613,1.1428027133910008,26879263744 +8,4,5,0.2682072256800668,1.1751184948781388,29396583424 +8,4,6,0.2754944544086485,1.222517587510634,31913903104 +8,4,8,0.27624204160350824,1.2236798072660213,36948542464 +8,4,16,0.27704278070614685,1.2239900971983946,57087099904 +8,5,2,0.03893135030367744,0.004346702386261458,21949714432 +8,5,3,0.20336768009167425,0.7570584897619376,24467034112 +8,5,4,0.26169397783359366,1.1319068641144796,26984353792 +8,5,5,0.2672903069142515,1.1726682788050742,29501673472 +8,5,6,0.2750725549338091,1.2251675741742547,32018993152 +8,5,8,0.2755511614969632,1.2213806795583368,37053632512 +8,5,16,0.27644192316048954,1.2248409116679573,57192189952 +8,6,2,0.03960992968666858,0.0046426558906571165,22054804480 +8,6,3,0.20509843285659402,0.7657076774627696,24572124160 +8,6,4,0.26235259118565024,1.135895470847944,27089443840 +8,6,5,0.2673520382984019,1.1721543852482914,29606763520 +8,6,6,0.2750965863700415,1.2232652165288425,32124083200 +8,6,8,0.2748874626975029,1.217991990088048,37158722560 +8,6,16,0.27618632109382835,1.2256323022273348,57297280000 +8,8,2,0.039621741979294495,0.004641359059321113,22264984576 +8,8,3,0.20420840408325575,0.7609812697499186,24782304256 +8,8,4,0.2629765025163644,1.1398554135838508,27299623936 +8,8,5,0.2677249093127373,1.1744269293359073,29816943616 +8,8,6,0.2753843775356539,1.2246538612050695,32334263296 +8,8,8,0.27573558703942475,1.2223636948702057,37368902656 +8,8,16,0.27710555751369514,1.2289172931774284,57507460096 +8,16,2,0.03954255419394073,0.004702792715368278,23105704960 +8,16,3,0.20421498189194914,0.7611484990145555,25623024640 +8,16,4,0.2629573909208676,1.1390067988226158,28140344320 +8,16,5,0.2677163506866694,1.1752137486977983,30657664000 +8,16,6,0.2754273250795791,1.224003727431766,33174983680 +8,16,8,0.2756801593566381,1.221797226097161,38209623040 +8,16,16,0.2769747471271154,1.2286821685059492,58348180480 +16,2,2,0.03397363194774226,0.002116580094303056,29510742016 +16,2,3,0.19975265550071775,0.7274376374824795,32028061696 +16,2,4,0.24856422239386056,1.0252398540053982,34545381376 +16,2,5,0.2530323290592932,1.0706442589700385,37062701056 +16,2,6,0.2602453575218017,1.1196220237774295,39580020736 +16,2,8,0.26002046752749525,1.1172477652537727,44614660096 +16,2,16,0.26060486267934874,1.1214114229504835,64753217536 +16,3,2,0.03867871996288693,0.0050311480090812315,29615832064 +16,3,3,0.20985452742306104,0.7920462665699586,32133151744 +16,3,4,0.2636281741650799,1.1504623141490604,34650471424 +16,3,5,0.26694422153877856,1.1773655292386453,37167791104 +16,3,6,0.27403367873791135,1.2227150746235125,39685110784 +16,3,8,0.27462384815130203,1.2225423326577731,44719750144 +16,3,16,0.27600821574598505,1.2288258774986203,64858307584 +16,4,2,0.038567322448854933,0.004301490966441779,29720922112 +16,4,3,0.20969386118044714,0.7959052930969952,32238241792 +16,4,4,0.2667684943363454,1.1621039256838672,34755561472 +16,4,5,0.2711871713179565,1.196929399809687,37272881152 +16,4,6,0.279923563615494,1.253046170170458,39790200832 +16,4,8,0.28139842107549934,1.256735727756202,44824840192 +16,4,16,0.28219241680364565,1.2609753126243561,64963397632 +16,5,2,0.03912346101002547,0.0043702656502771576,29826012160 +16,5,3,0.2079609378623707,0.7879401066468005,32343331840 +16,5,4,0.2656879108724494,1.1556677145197873,34860651520 +16,5,5,0.269967086318697,1.1893109363512375,37377971200 +16,5,6,0.2787494875664804,1.245958819839066,39895290880 +16,5,8,0.27949801793201345,1.2457846506159767,44929930240 +16,5,16,0.28069307628564694,1.2528649424602132,65068487680 +16,6,2,0.039684002166362545,0.004644940031994874,29931102208 +16,6,3,0.2085110100872384,0.7919767269675149,32448421888 +16,6,4,0.26570523625178344,1.1567217842367485,34965741568 +16,6,5,0.27054993547141293,1.1935809173708705,37483061248 +16,6,6,0.2782112952274305,1.2420703577665526,40000380928 +16,6,8,0.27944128130410667,1.2436823085127202,45035020288 +16,6,16,0.2810511926614303,1.2516247225738375,65173577728 +16,8,2,0.03968199177932108,0.00458078299074695,30141282304 +16,8,3,0.20820295178369366,0.7895426734487146,32658601984 +16,8,4,0.26662869456601357,1.164603641736421,35175921664 +16,8,5,0.27073700933850925,1.193852243546073,37693241344 +16,8,6,0.27883645953844804,1.2451381600762323,40210561024 +16,8,8,0.28022910967278475,1.2495830587956385,45245200384 +16,8,16,0.2811955411594185,1.253045592341763,65383757824 +16,16,2,0.039602095057516065,0.004599127353840183,30982002688 +16,16,3,0.20801327394510427,0.7887153055212033,33499322368 +16,16,4,0.26641324792044385,1.1638367480835288,36016642048 +16,16,5,0.27086551011189425,1.1950595426281183,38533961728 +16,16,6,0.27898890725873554,1.2452834367954881,41051281408 +16,16,8,0.280146845145009,1.2493832157443017,46085920768 +16,16,16,0.28128207621616275,1.2541976582736079,66224478208 diff --git a/awq/awq_image_text_retrieval.csv b/awq/awq_image_text_retrieval.csv new file mode 100644 index 0000000..9376f7e --- /dev/null +++ b/awq/awq_image_text_retrieval.csv @@ -0,0 +1,50 @@ +vit_bits,qformer_bits,txt_r1,txt_r5,txt_r10,txt_r_mean,img_r1,img_r5,img_r10,img_r_mean,r_mean,agg_metrics,model_size +2,2,67.5,83.0,88.1,79.53333333333333,61.32,81.88,86.72,76.64,78.08666666666667,79.53333333333333,3103760704 +2,3,83.8,95.7,97.6,92.36666666666667,70.5,89.62,93.62,84.58,88.47333333333333,92.36666666666667,3265519936 +2,4,84.5,95.4,97.4,92.43333333333334,71.22,89.9,93.62,84.91333333333334,88.67333333333335,92.43333333333334,3427279168 +2,5,83.9,95.6,97.5,92.33333333333333,71.42,89.74,93.86,85.00666666666666,88.66999999999999,92.33333333333333,3589038400 +2,6,83.7,95.3,97.4,92.13333333333333,71.1,89.82,93.7,84.87333333333333,88.50333333333333,92.13333333333333,3750797632 +2,8,84.0,95.1,97.3,92.13333333333333,71.2,89.94,93.66,84.93333333333332,88.53333333333333,92.13333333333333,4074316096 +2,16,84.1,95.1,97.4,92.2,71.24,89.98,93.68,84.96666666666667,88.58333333333334,92.2,5368389952 +3,2,87.8,94.2,95.5,92.5,82.1,94.94,96.64,91.22666666666667,91.86333333333334,92.5,4088297920 +3,3,97.2,100.0,100.0,99.06666666666666,88.54,98.18,99.02,95.24666666666667,97.15666666666667,99.06666666666666,4250057152 +3,4,97.5,100.0,100.0,99.16666666666667,88.52,97.88,99.06,95.15333333333332,97.16,99.16666666666667,4411816384 +3,5,97.1,100.0,100.0,99.03333333333335,88.76,97.8,98.98,95.18,97.10666666666668,99.03333333333335,4573575616 +3,6,97.3,100.0,100.0,99.10000000000001,88.82,97.88,98.92,95.20666666666666,97.15333333333334,99.10000000000001,4735334848 +3,8,97.4,100.0,100.0,99.13333333333333,88.62,97.84,98.9,95.12,97.12666666666667,99.13333333333333,5058853312 +3,16,97.4,100.0,100.0,99.13333333333333,88.68,97.86,98.92,95.15333333333335,97.14333333333335,99.13333333333333,6352927168 +4,2,87.4,94.7,95.5,92.53333333333335,83.32,95.46,96.88,91.88666666666666,92.21000000000001,92.53333333333335,5072835136 +4,3,97.6,100.0,100.0,99.2,89.3,98.28,99.06,95.54666666666667,97.37333333333333,99.2,5234594368 +4,4,97.6,100.0,100.0,99.2,89.68,98.22,99.08,95.66000000000001,97.43,99.2,5396353600 +4,5,97.3,100.0,100.0,99.10000000000001,89.5,98.22,98.98,95.56666666666666,97.33333333333334,99.10000000000001,5558112832 +4,6,97.4,100.0,100.0,99.13333333333333,89.6,98.26,99.04,95.63333333333334,97.38333333333333,99.13333333333333,5719872064 +4,8,97.4,100.0,100.0,99.13333333333333,89.64,98.2,99.04,95.62666666666667,97.38,99.13333333333333,6043390528 +4,16,97.4,100.0,100.0,99.13333333333333,89.66,98.2,99.02,95.62666666666667,97.38,99.13333333333333,7337464384 +5,2,88.1,94.6,95.3,92.66666666666667,83.18,95.54,96.88,91.86666666666667,92.26666666666668,92.66666666666667,6057372352 +5,3,98.2,100.0,100.0,99.39999999999999,89.44,98.22,99.06,95.57333333333334,97.48666666666666,99.39999999999999,6219131584 +5,4,97.9,100.0,100.0,99.3,89.58,98.18,99.12,95.62666666666667,97.46333333333334,99.3,6380890816 +5,5,97.6,100.0,100.0,99.2,89.44,98.18,99.08,95.56666666666666,97.38333333333333,99.2,6542650048 +5,6,97.9,100.0,100.0,99.3,89.46,98.2,99.04,95.56666666666666,97.43333333333334,99.3,6704409280 +5,8,98.0,100.0,100.0,99.33333333333333,89.4,98.18,99.08,95.55333333333334,97.44333333333333,99.33333333333333,7027927744 +5,16,97.8,100.0,100.0,99.26666666666667,89.36,98.18,99.08,95.54,97.40333333333334,99.26666666666667,8322001600 +6,2,87.8,94.9,95.4,92.7,83.22,95.76,97.04,92.00666666666667,92.35333333333334,92.7,7041909568 +6,3,98.2,100.0,100.0,99.39999999999999,89.46,98.3,99.1,95.62,97.50999999999999,99.39999999999999,7203668800 +6,4,97.9,100.0,100.0,99.3,89.68,98.26,99.06,95.66666666666667,97.48333333333333,99.3,7365428032 +6,5,97.8,100.0,100.0,99.26666666666667,89.58,98.14,99.06,95.59333333333332,97.42999999999999,99.26666666666667,7527187264 +6,6,97.9,100.0,100.0,99.3,89.54,98.2,99.04,95.59333333333335,97.44666666666667,99.3,7688946496 +6,8,97.9,100.0,100.0,99.3,89.42,98.2,99.06,95.56,97.43,99.3,8012464960 +6,16,97.9,100.0,100.0,99.3,89.4,98.2,99.06,95.55333333333334,97.42666666666668,99.3,9306538816 +8,2,87.8,94.9,95.5,92.73333333333333,83.7,95.62,97.06,92.12666666666667,92.43,92.73333333333333,9010984000 +8,3,98.2,100.0,100.0,99.39999999999999,89.5,98.3,99.1,95.63333333333333,97.51666666666665,99.39999999999999,9172743232 +8,4,97.9,100.0,100.0,99.3,89.66,98.2,99.1,95.65333333333335,97.47666666666667,99.3,9334502464 +8,5,97.9,100.0,100.0,99.3,89.68,98.2,99.08,95.65333333333332,97.47666666666666,99.3,9496261696 +8,6,97.9,100.0,100.0,99.3,89.48,98.16,99.04,95.56,97.43,99.3,9658020928 +8,8,97.9,100.0,100.0,99.3,89.44,98.2,99.04,95.56,97.43,99.3,9981539392 +8,16,97.9,100.0,100.0,99.3,89.44,98.2,99.04,95.56,97.43,99.3,11275613248 +16,2,88.0,94.8,95.4,92.73333333333335,83.28,95.78,97.08,92.04666666666667,92.39000000000001,92.73333333333335,16887281728 +16,3,98.2,100.0,100.0,99.39999999999999,89.5,98.28,99.08,95.62,97.50999999999999,99.39999999999999,17049040960 +16,4,97.9,100.0,100.0,99.3,89.64,98.22,99.1,95.65333333333335,97.47666666666667,99.3,17210800192 +16,5,97.8,100.0,100.0,99.26666666666667,89.66,98.18,99.06,95.63333333333333,97.44999999999999,99.26666666666667,17372559424 +16,6,97.9,100.0,100.0,99.3,89.5,98.16,99.04,95.56666666666666,97.43333333333334,99.3,17534318656 +16,8,97.9,100.0,100.0,99.3,89.46,98.2,99.04,95.56666666666666,97.43333333333334,99.3,17857837120 +16,16,97.9,100.0,100.0,99.3,89.46,98.22,99.04,95.57333333333334,97.43666666666667,99.3,19151910976 diff --git a/awq/awq_retrieval.html b/awq/awq_retrieval.html new file mode 100644 index 0000000..1e6b655 --- /dev/null +++ b/awq/awq_retrieval.html @@ -0,0 +1,14 @@ + + + +
+
+ + \ No newline at end of file diff --git a/awq/captioning_multi_sbatch.py b/awq/captioning_multi_sbatch.py new file mode 100644 index 0000000..23a5c5d --- /dev/null +++ b/awq/captioning_multi_sbatch.py @@ -0,0 +1,379 @@ +import os +from datetime import datetime +import argparse +import shutil +import math +import time +import socket +import itertools +import subprocess + + +def run(cmd): + return subprocess.check_output(cmd, shell=True).decode('UTF-8').splitlines() + +def present_in_list(string, gpu_list): + return any([x in string for x in gpu_list]) + +def split(a, n): + k, m = divmod(len(a), n) + return (a[i*k+min(i, m):(i+1)*k+min(i+1, m)] for i in range(n)) + +def get_exclude_string(gpu_list, default_exclude=None): + if gpu_list[0] == 'any': + if default_exclude is None: + return '' + else: + return '#SBATCH --exclude='+','.join(default_exclude) + memdata = run('sinfo -O nodehost,gres -h') + superset = set([x.split()[0] for x in memdata]) + blacklist = [] + for x in memdata: + nodehost, gres = x.strip().split() + if present_in_list(gres, gpu_list): + blacklist.append(nodehost) + + exclude_list = superset - set(blacklist) + if default_exclude: + exclude_list = exclude_list.union(set(default_exclude)) + exclude_string = ','.join(sorted(exclude_list)) + if exclude_string: + exclude_string = '#SBATCH --exclude='+exclude_string+'\n' + return exclude_string + else: + return '' + +def get_include_string(gpu_list, default_include=None): + if gpu_list[0] == 'any': + raise Exception("That's too much, man! (It's a Bojack reference. Watch it if you haven't already, you degenerate)") + memdata = run('sinfo -O nodehost,gres -h') + include_list = [] + for x in memdata: + nodehost, gres = x.strip().split() + if present_in_list(gres, gpu_list): + include_list.append(nodehost) + include_string = ','.join(sorted(include_list)) + if include_string: + include_string = '#SBATCH --nodelist='+include_string+'\n' + return include_string + else: + return '' + +# Function to chec for validity of QOS +#TODO: Add time check for QOS + +qos_dict = { + "scav" : {"nhrs" : 72, "cores": 32, "mem":256}, + "high" : {"gpu":4, "cores": 16, "mem":128, "nhrs": 36}, + "medium" : {"gpu":2, "cores": 8, "mem":64, "nhrs": 72}, + "default" : {"gpu":1, "cores": 4, "mem":32, "nhrs": 168}} + + +def check_qos(args): + + for qos in args.qos: + for key, max_value in qos_dict[qos].items(): + val_from_args = getattr(args, key) + if val_from_args != None: + if val_from_args > max_value: + raise ValueError("Invalid parameter for {} for {}".format(key, qos)) + else: + setattr(args, key, max_value) + return args + + +#TODO: Add day funtionality too +parser = argparse.ArgumentParser() +parser.add_argument('--nhrs', type=int, default=None) +parser.add_argument('--base-dir', default=f'{os.getcwd()}') +parser.add_argument('--output-dirname', default='outputs') +parser.add_argument('--partition', default='vulcan', choices=['vulcan','cml','nexus']) +parser.add_argument('--dryrun', action='store_true') +parser.add_argument('--qos', default=None, type=str, nargs='*', help='Qos to run') +parser.add_argument('--env', type=str, help = "Set the name of the dir you want to dump") +parser.add_argument('--gpu', default=None, type=int, help='Number of gpus') +parser.add_argument('--gpu-type', type=str, help='Type of gpu to use (can be multiple)', default=['any'], + choices=['any','p6000','gtx','rtx2080','a4000','a5000','a6000'], nargs='*') +parser.add_argument('--cores', default=None, type=int, help='Number of cpu cores') +parser.add_argument('--mem', default=None, type=int, help='RAM in G') +parser.add_argument('--single', action='store_true') +parser.add_argument('--filename', default=None, type=str, help='Slurm file name') +parser.add_argument('--max_jobs', default=80, type=int, help='Maximum number of jobs running in parallel') +parser.add_argument('--offset', default=0, type=int, help='Offset') +parser.add_argument('--batchsize', default=500, type=int, help='Offset') + +args = parser.parse_args() + +if args.filename is None: + args.filename = args.env + +output_dir = os.path.join(args.base_dir, args.output_dirname, args.env) +if os.path.exists(output_dir): + shutil.rmtree(output_dir) +if not os.path.exists(output_dir): + os.makedirs(output_dir) +print("Output Directory: %s" % output_dir) + +if "nexus" in socket.gethostname(): + root = 'root' ## TODO +else: + raise Exception("Not on nexus") + + +# print(f"Starting a batch of {args.batchsize} from offset {args.offset}") +params = { + # 'config_file': ['', '', [f'./configs/{i}.json' for i in range(args.offset, args.batchsize+args.offset)]] + 'config_path': ['--config_path', 'config_path', [f'captioning_configs/awq_{i}' for i in range(7**3)]], + 'task': ['--task', 'task', ['image_captioning']] +} +####################################################################### + +class Argument(object): + + def __init__(self, name, cmd_line, string_id, val): + self.name = name + self.val = val + if isinstance(val,list): + if len(val) == 0: + + if isinstance(cmd_line, list): + self.cmd_string = '' + for cur_line in cmd_line: + self.cmd_string += ' '+cur_line+' []' + else: + self.cmd_string = ' '+cmd_line+' []' + else: + if isinstance(cmd_line, list): + self.cmd_string = '' + for cur_line in cmd_line: + self.cmd_string += ' '+cur_line+' '+','.join([str(e) for e in val]) + else: + self.cmd_string = ' '+cmd_line+' '+','.join([str(e) for e in val]) + else: + + if isinstance(cmd_line, list): + self.cmd_string = '' + for cur_line in cmd_line: + self.cmd_string += ' '+cur_line+' '+str(val) + else: + self.cmd_string = ' '+cmd_line+' '+str(val) + if isinstance(val,bool): + if not val: + self.job_string = '' + self.cmd_string = '' + self.name = '' + else: + self.job_string = '_'+string_id if string_id else '' + if isinstance(cmd_line, list): + self.cmd_string = '' + for cur_line in cmd_line: + self.cmd_string += ' '+cur_line+' ' + self.cmd_string = ' '+cmd_line+' ' + elif isinstance(val,list): + self.job_string = '_'+string_id+'_'.join([str(v) for v in val]) + else: + self.job_string = '_'+string_id+str(val) + if string_id == 'none': + self.job_string = '' + + def copy(self): + new_arg = Argument(self.name, cmd_line='', string_id='', val=self.val) + new_arg.cmd_string = self.cmd_string + new_arg.job_string = self.job_string + return new_arg + + +os.makedirs(f'{args.base_dir}/{args.output_dirname}/{args.env}',exist_ok=True) +n_jobs = 0 +# Making text files which will store the python command to run, stdout, and error if any +with open(f'{args.base_dir}/{args.output_dirname}/{args.env}/now.txt', "w") as nowfile,\ + open(f'{args.base_dir}/{args.output_dirname}/{args.env}/log.txt', "w") as output_namefile,\ + open(f'{args.base_dir}/{args.output_dirname}/{args.env}/err.txt', "w") as error_namefile,\ + open(f'{args.base_dir}/{args.output_dirname}/{args.env}/name.txt', "w") as namefile: + + arg_list = [] + for key, param in params.items(): + cur_arg_list = [] + if not isinstance(param[2],list): + param[2] = [param[2]] + + if len(param[2])>1 and key!="dataset": + assert param[1]!='none', f"{param[0]} set to none with multiple values!" + + for value in param[2]: + cur_arg_list.append(Argument(key, param[0],param[1], value)) + + arg_list.append(cur_arg_list) + + arg_list = list(itertools.product(*arg_list)) + n_jobs = 0 + for idx,job_args in enumerate(arg_list): + + # Allows modification of current set of args + job_args = {arg.name:arg.copy() for arg in job_args} + + job_string = '' + python_cmd = 'python ../run_awq.py ' + for arg_name, arg in job_args.items(): + python_cmd += arg.cmd_string + job_string += arg.job_string + + job_string = f'{n_jobs}_'+job_string + cmd_line_str = python_cmd + + # cmd_line_str = python_cmd + + n_jobs += 1 + + nowfile.write(f'{cmd_line_str}\n') + namefile.write(f'{(os.path.join(output_dir, job_string))}.log\n') + output_namefile.write(f'{(os.path.join(output_dir, job_string))}_log.txt\n') + error_namefile.write(f'{(os.path.join(output_dir, job_string))}_error.txt\n') + if args.single: + break + +########################################################################### +if len(args.qos)>1: + splits = split(range(0,n_jobs), len(args.qos)) + for qos in args.qos: + cur_dir = os.path.join(args.base_dir, args.output_dirname, args.env, qos) + if os.path.exists(cur_dir): + shutil.rmtree(cur_dir) + if not os.path.exists(cur_dir): + os.makedirs(cur_dir) + + with open(f'{args.base_dir}/{args.output_dirname}/{args.env}/log.txt', "r") as output_namefile,\ + open(f'{args.base_dir}/{args.output_dirname}/{args.env}/err.txt', "r") as error_namefile: + logs = output_namefile.read().splitlines() + errs = error_namefile.read().splitlines() + + with open(f'{args.base_dir}/{args.output_dirname}/{args.env}/log.txt', "w") as output_namefile,\ + open(f'{args.base_dir}/{args.output_dirname}/{args.env}/err.txt', "w") as error_namefile: + for i,log in enumerate(logs): + qos_idx = math.floor(i/math.ceil(n_jobs/len(args.qos))) + folder, basename = os.path.split(log) + new_log_name = os.path.join(folder, args.qos[qos_idx], basename) + folder, basename = os.path.split(errs[i]) + new_err_name = os.path.join(folder, args.qos[qos_idx], basename) + output_namefile.write(f'{new_log_name}\n') + error_namefile.write(f'{new_err_name}\n') + + + +########################################################################### +#slurm_script_path = os.path.join(output_dir, '%s.slurm' % name) +id = args.env.split('run')[-1] +filenames = [] +if len(args.qos)==1: + filenames = [f'{args.qos[0][:2]}_r{id}.slurm' if not args.filename else args.filename] +else: + for qos in args.qos: + filenames.append(f'{qos[:2]}_r{id}.slurm' if not args.filename else qos[0]+args.filename) + +print("Filenames:") +print(filenames) +slurm_script_paths = [os.path.join(output_dir, filename) for filename in filenames] +slurm_commands = ["sbatch %s" % slurm_script_path for slurm_script_path in slurm_script_paths] +shutil.copyfile(os.path.abspath(__file__), + os.path.join(output_dir, + os.path.basename(os.path.abspath(__file__)))) + + +idx = 0 +start_idx, end_idx = [], [] +for i in range(len(args.qos)): + start_idx += [idx+1] + idx += math.ceil(n_jobs/len(args.qos)) + end_idx += [min(idx, n_jobs)] + +for i,slurm_script_path in enumerate(slurm_script_paths): + print(f"writing to {slurm_script_path}") + with open(slurm_script_path, 'w') as slurmfile: + slurmfile.write("#!/bin/bash\n") + if args.max_jobs>0: + slurmfile.write(f"#SBATCH --array={start_idx[i]}-{end_idx[i]}%{args.max_jobs}\n") + else: + slurmfile.write(f"#SBATCH --array={start_idx[i]}-{end_idx[i]}\n") + slurmfile.write("#SBATCH --output=/dev/null\n") + slurmfile.write("#SBATCH --error=/dev/null\n") + slurmfile.write("#SBATCH --requeue\n") + args = check_qos(args) + + default_include_list = [] + default_exclude_list = [] + if args.qos[i] == "scav": + if "vulcan" in args.partition: + slurmfile.write("#SBATCH --account=vulcan\n") + slurmfile.write("#SBATCH --partition=vulcan-scavenger\n") + slurmfile.write("#SBATCH --qos=vulcan-scavenger\n") + default_exclude_list = ["janus[02-04]"] + elif "nexus" in args.partition: + slurmfile.write("#SBATCH --account=scavenger\n") + slurmfile.write("#SBATCH --partition=scavenger\n") + slurmfile.write("#SBATCH --qos=scavenger\n") + elif "cml" in args.partition: + slurmfile.write("#SBATCH --account=cml-abhinav\n") + slurmfile.write("#SBATCH --partition=cml-scavenger\n") + slurmfile.write("#SBATCH --qos=cml-scavenger\n") + elif args.qos[i] == "high" or args.qos[i] == "medium" or args.qos[i] == "default": + if "vulcan" in args.partition: + slurmfile.write("#SBATCH --account=vulcan-abhinav\n") + slurmfile.write("#SBATCH --partition=vulcan-ampere\n") + slurmfile.write(f"#SBATCH --qos=vulcan-{args.qos[i]}\n") + default_exclude_list = ["janus[02-04]"] + elif "nexus" in args.partition: + slurmfile.write("#SBATCH --account=nexus\n") + slurmfile.write(f"#SBATCH --qos={args.qos[i]}\n") + elif "cml" in args.partition: + slurmfile.write("#SBATCH --account=cml-abhinav\n") + slurmfile.write("#SBATCH --partition=cml-dpart\n") + slurmfile.write(f"#SBATCH --qos=cml-{args.qos[i]}\n") + + slurmfile.write("#SBATCH --time=%d:00:00\n" % args.nhrs) + slurmfile.write("#SBATCH --cpus-per-task=%d\n" % args.cores) + slurmfile.write("#SBATCH --mem=%dG\n" % args.mem) + + + if not args.gpu is None: + if len(args.gpu_type)==1: + if 'any' in args.gpu_type: + slurmfile.write("#SBATCH --gres=gpu:%d\n" % args.gpu) + elif "rtx2080" in args.gpu_type: + slurmfile.write("#SBATCH --gres=gpu:rtx2080ti:%d\n" % args.gpu) + elif "gtx" in args.gpu_type: + slurmfile.write("#SBATCH --gres=gpu:gtx1080ti:%d\n" % args.gpu) + elif "p6000" in args.gpu_type: + slurmfile.write("#SBATCH --gres=gpu:p6000:%d\n" % args.gpu) + elif "a4000" in args.gpu_type: + slurmfile.write("#SBATCH --gres=gpu:rtxa4000:%d\n" % args.gpu) + elif "a5000" in args.gpu_type: + slurmfile.write("#SBATCH --gres=gpu:rtxa5000:%d\n" % args.gpu) + elif "a6000" in args.gpu_type: + slurmfile.write("#SBATCH --gres=gpu:rtxa6000:%d\n" % args.gpu) + else: + assert len(args.gpu_type)>1 + slurmfile.write("#SBATCH --gres=gpu:%d\n" % args.gpu) + # slurmfile.write(get_include_string(args.gpu_type,default_include_list)) + slurmfile.write(get_exclude_string(args.gpu_type,default_exclude_list)) + else: + raise ValueError("Specify the number of gpus") + + slurmfile.write("\n") + if "vulcan" in socket.gethostname() or "nexus" in socket.gethostname(): + slurmfile.write(f"cd {root}") #TODO + # slurmfile.write('conda activate {env}\n') #TODO + slurmfile.write('source ~/.bashrc') + slurmfile.write('micromamba activate blip\n') + + num_exps = 1 + for n in reversed(range(num_exps)): + slurmfile.write(f"srun --output=$(head -n $SLURM_ARRAY_TASK_ID {args.base_dir}/{args.output_dirname}/{args.env}/log.txt | tail -n 1) $(head -n $(expr {num_exps} \* $SLURM_ARRAY_TASK_ID - {n}) {args.base_dir}/{args.output_dirname}/{args.env}/now.txt | tail -n 1)\n") + slurmfile.write("\n") + +for i,slurm_command in enumerate(slurm_commands): + print(slurm_command) + print("Running on {}, with {} gpus, {} cores, {} mem for {} hour".format(args.qos[i], args.gpu, args.cores, args.mem , args.nhrs)) + +if not args.dryrun: + for slurm_command in slurm_commands: + os.system("%s &" % slurm_command) diff --git a/awq/captioning_multi_sbatch_submit.sh b/awq/captioning_multi_sbatch_submit.sh new file mode 100755 index 0000000..9676b7f --- /dev/null +++ b/awq/captioning_multi_sbatch_submit.sh @@ -0,0 +1,12 @@ +python captioning_multi_sbatch.py --env slurm_files \ + --nhrs 2 \ + --qos scav \ + --partition nexus \ + --gpu 1 --gpu-type a5000 a6000 \ + --cores 1 \ + --mem 64 \ + --output-dirname captioning_output \ + # --dryrun + # --base-dir awq/ \ + + diff --git a/awq/coco_captioning.html b/awq/coco_captioning.html new file mode 100644 index 0000000..3a37595 --- /dev/null +++ b/awq/coco_captioning.html @@ -0,0 +1,14 @@ + + + +
+
+ + \ No newline at end of file diff --git a/awq/compute_scores.ipynb b/awq/compute_scores.ipynb new file mode 100644 index 0000000..ce68660 --- /dev/null +++ b/awq/compute_scores.ipynb @@ -0,0 +1,234 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import json\n", + "import os\n", + "import sys\n", + "sys.path.append('..')\n", + "\n", + "from scoring_pipeline import ScoringPipeline" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Adding current path to python system paths\n" + ] + } + ], + "source": [ + "sp = ScoringPipeline()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "base_dir = '/fs/cfar-projects/low-bit-vision/awq_results/image_text_retrieval'\n", + "\n", + "results_dir = os.path.join(base_dir, 'image_text_retrieval_results')\n", + "scores_dir = os.path.join(base_dir, 'image_text_retrieval_scores')\n", + "\n", + "os.makedirs(scores_dir, exist_ok=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Scoring result awq_0\n", + "Scoring result awq_1\n", + "Scoring result awq_2\n", + "Scoring result awq_3\n", + "Scoring result awq_4\n", + "Scoring result awq_5\n", + "Scoring result awq_6\n", + "Scoring result awq_7\n", + "Scoring result awq_8\n", + "Scoring result awq_9\n", + "Scoring result awq_10\n", + "Scoring result awq_11\n", + "Scoring result awq_12\n", + "Scoring result awq_13\n", + "Scoring result awq_14\n", + "Scoring result awq_15\n", + "Scoring result awq_16\n", + "Scoring result awq_17\n", + "Scoring result awq_18\n", + "Scoring result awq_19\n", + "Scoring result awq_20\n", + "Scoring result awq_21\n", + "Scoring result awq_22\n", + "Scoring result awq_23\n", + "Scoring result awq_24\n", + "Scoring result awq_25\n", + "Scoring result awq_26\n", + "Scoring result awq_27\n", + "Scoring result awq_28\n", + "Scoring result awq_29\n", + "Scoring result awq_30\n", + "Scoring result awq_31\n", + "Scoring result awq_32\n", + "Scoring result awq_33\n", + "Scoring result awq_34\n", + "Scoring result awq_35\n", + "Scoring result awq_36\n", + "Scoring result awq_37\n", + "Scoring result awq_38\n", + "Scoring result awq_39\n", + "Scoring result awq_40\n", + "Scoring result awq_41\n", + "Scoring result awq_42\n", + "Scoring result awq_43\n", + "Scoring result awq_44\n", + "Scoring result awq_45\n", + "Scoring result awq_46\n", + "Scoring result awq_47\n", + "Scoring result awq_48\n" + ] + } + ], + "source": [ + "for i in range(7**2):\n", + " result_path = os.path.join(results_dir, f'awq_{i}')\n", + "\n", + " scores = {}\n", + " with open(result_path) as f:\n", + " result = json.load(f)\n", + "\n", + " # some json post processing to get scoringpipeline to work\n", + " result['scores_i2t'] = np.array(result['scores_i2t'])\n", + " result['scores_t2i'] = np.array(result['scores_t2i'])\n", + " result['txt2img'] = {int(k):v for k,v in result['txt2img'].items()}\n", + " result['img2txt'] = {int(k):v for k,v in result['img2txt'].items()}\n", + "\n", + " print(f'Scoring result awq_{i}')\n", + " scores = sp.compute_scores(result, 'image_text_retrieval')\n", + " scores['model_size'] = result['model_size']\n", + " \n", + "\n", + " # print(scores)\n", + " scores_path = os.path.join(scores_dir, f'awq_{i}')\n", + " with open(scores_path, 'w') as f:\n", + " json.dump(scores, f, indent=2)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "results_dir = './image_captioning_results'\n", + "scores_dir = './image_captioning__scores'\n", + "\n", + "os.makedirs(scores_dir, exist_ok=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Scoring result awq_0\n", + "Tokenizing...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "PTBTokenizer tokenized 307315 tokens at 517060.12 tokens per second.\n", + "PTBTokenizer tokenized 46137 tokens at 206863.65 tokens per second.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Computing scores...\n", + "Computing METEOR score...\n", + "Computing CIDEr score...\n", + "{'METEOR': 0.029884481134349352, 'METEOR_per_caption': [0.046109510086455335, 0.0, 0.04597701149425287, 0.035555555555555556, 0.029850746268656716, 0.025157232704402517, 0.01716738197424893, 0.04071246819338423, 0.0, 0.022792022792022793, 0.023529411764705882, 0.05111821086261982, 0.040816326530612256, 0.04968944099378883, 0.03761981078829165, 0.05925925925925926, 0.03458595507955845, 0.03088803088803089, 0.0, 0.06397163529035275, 0.04597701149425287, 0.026229508196721315, 0.02826855123674912, 0.025, 0.04456824512534818, 0.041558441558441565, 0.022535211267605635, 0.022857142857142854, 0.034261241970021415, 0.035634743875278395, 0.02094240837696335, 0.041343669250646, 0.0, 0.018648018648018648, 0.033264033264033266, 0.04968944099378883, 0.022662889518413595, 0.020779220779220783, 0.02318840579710145, 0.0, 0.0, 0.04199475065616798, 0.04790419161676647, 0.0, 0.032, 0.02094240837696335, 0.019002375296912115, 0.026755852842809364, 0.0449438202247191, 0.06521739130434782, 0.027027027027027025, 0.03433476394849786, 0.04953560371517029, 0.027366020524515394, 0.020253164556962026, 0.03193612774451098, 0.05111821086261982, 0.015065913370998116, 0.0399002493765586, 0.027874564459930314, 0.028070175438596492, 0.03940886699507389, 0.021220159151193633, 0.03143418467583497, 0.023054755043227668, 0.047619047619047616, 0.02197802197802198, 0.0, 0.025806451612903226, 0.05369127516778524, 0.04383561643835616, 0.017660044150110375, 0.028985507246376815, 0.04266666666666667, 0.02555910543130991, 0.0, 0.0, 0.02857142857142857, 0.023255813953488372, 0.0, 0.024844720496894415, 0.033684210526315796, 0.0, 0.05245901639344263, 0.03292181069958848, 0.0, 0.030947775628626696, 0.021164021164021163, 0.028169014084507046, 0.0, 0.020202020202020204, 0.0, 0.024844720496894415, 0.022346368715083803, 0.024464831804281342, 0.0, 0.023809523809523808, 0.0316205533596838, 0.06629834254143645, 0.046109510086455335, 0.046109510086455335, 0.020202020202020204, 0.0273972602739726, 0.023255813953488372, 0.019047619047619046, 0.0, 0.018518518518518517, 0.022099447513812154, 0.020512820512820513, 0.0, 0.04833836858006042, 0.02439024390243903, 0.02693602693602694, 0.037037037037037035, 0.05351170568561873, 0.03695150115473441, 0.051172707889125806, 0.035555555555555556, 0.03143418467583497, 0.02110817941952507, 0.027027027027027025, 0.021447721179624665, 0.02572347266881029, 0.05263157894736842, 0.02507836990595612, 0.05517241379310345, 0.021798365122615803, 0.041558441558441565, 0.028169014084507046, 0.02318840579710145, 0.038461538461538464, 0.04848484848484849, 0.05111821086261982, 0.0, 0.06382978723404255, 0.021164021164021163, 0.035955056179775284, 0.022408963585434174, 0.023809523809523808, 0.027027027027027025, 0.05908560537390478, 0.024539877300613498, 0.03371586815931799, 0.02826855123674912, 0.050955414012738856, 0.04060913705583757, 0.0, 0.031496062992125984, 0.04790419161676647, 0.029304029304029307, 0.0, 0.03827751196172249, 0.0, 0.06106870229007633, 0.04395604395604396, 0.02318840579710145, 0.0, 0.03855421686746988, 0.03076923076923077, 0.021621621621621623, 0.0399002493765586, 0.04050632911392405, 0.02439024390243903, 0.022988505747126436, 0.02572347266881029, 0.03934426229508198, 0.0, 0.025, 0.02110817941952507, 0.022222222222222223, 0.023255813953488372, 0.021276595744680854, 0.032, 0.0449438202247191, 0.049792531120331954, 0.06486486486486485, 0.039024390243902446, 0.06349206349206349, 0.04113110539845759, 0.056074766355140186, 0.03305785123966942, 0.052060737527114966, 0.041025641025641026, 0.02439024390243903, 0.022598870056497175, 0.0, 0.0, 0.018181818181818184, 0.04776119402985075, 0.02572347266881029, 0.06315789473684211, 0.05263157894736842, 0.019801980198019806, 0.022535211267605635, 0.02631578947368421, 0.029850746268656716, 0.020512820512820513, 0.013445378151260505, 0.02631578947368421, 0.0, 0.020356234096692113, 0.024539877300613498, 0.0625, 0.03143418467583497, 0.06685236768802227, 0.018306636155606407, 0.021739130434782608, 0.0533946517662506, 0.023255813953488372, 0.05504587155963303, 0.0, 0.04071246819338423, 0.0, 0.03950617283950617, 0.05245901639344263, 0.04804804804804805, 0.02588996763754045, 0.014260249554367202, 0.062135922330097085, 0.02373887240356083, 0.0, 0.02693602693602694, 0.017467248908296946, 0.023054755043227668, 0.02061855670103093, 0.0, 0.0, 0.0, 0.04199475065616798, 0.0, 0.03493449781659389, 0.04383561643835616, 0.025806451612903226, 0.058536585365853655, 0.019704433497536946, 0.015444015444015444, 0.024024024024024024, 0.02588996763754045, 0.050955414012738856, 0.024464831804281342, 0.023323615160349857, 0.023255813953488372, 0.035398230088495575, 0.01724137931034483, 0.0, 0.027303754266211608, 0.03669724770642201, 0.04953560371517029, 0.04923076923076924, 0.017857142857142856, 0.0, 0.049844236760124616, 0.035010940919037205, 0.050955414012738856, 0.02388059701492537, 0.05517241379310345, 0.0449438202247191, 0.03433476394849786, 0.03347280334728034, 0.04878048780487806, 0.04221635883905014, 0.022222222222222223, 0.025, 0.04371584699453552, 0.029850746268656716, 0.02373887240356083, 0.0471976401179941, 0.0199501246882793, 0.02572347266881029, 0.02797202797202797, 0.02439024390243903, 0.0, 0.02072538860103627, 0.019559902200489, 0.0, 0.035010940919037205, 0.032520325203252036, 0.027303754266211608, 0.04481792717086835, 0.027027027027027025, 0.026058631921824105, 0.04848484848484849, 0.04571428571428571, 0.03931203931203932, 0.029520295202952032, 0.032, 0.0463768115942029, 0.04071246819338423, 0.022598870056497175, 0.04113110539845759, 0.025316455696202535, 0.04092071611253197, 0.049079754601226995, 0.04678362573099415, 0.04733727810650887, 0.0, 0.05263157894736842, 0.05594405594405594, 0.0, 0.02110817941952507, 0.0, 0.025477707006369428, 0.05405405405405405, 0.02122015915119364, 0.032128514056224904, 0.049844236760124616, 0.04255319148936171, 0.0, 0.04923076923076924, 0.04968944099378883, 0.013468013468013471, 0.024844720496894415, 0.020253164556962026, 0.028169014084507046, 0.0, 0.0, 0.07493424178039172, 0.04071246819338423, 0.02416918429003021, 0.028169014084507046, 0.029962546816479405, 0.02507836990595612, 0.04923076923076924, 0.025974025974025972, 0.0272108843537415, 0.043596730245231606, 0.0, 0.022099447513812154, 0.0, 0.02439024390243903, 0.024922118380062308, 0.06685309805897692, 0.0427807486631016, 0.026490066225165566, 0.0, 0.04804804804804805, 0.04199475065616798, 0.04571428571428571, 0.0273972602739726, 0.02150537634408602, 0.03855421686746988, 0.04733727810650887, 0.022222222222222223, 0.030947775628626696, 0.027303754266211608, 0.021164021164021163, 0.011173184357541902, 0.03931203931203932, 0.022662889518413595, 0.032, 0.02373887240356083, 0.024464831804281342, 0.0, 0.03411513859275053, 0.046109510086455335, 0.0, 0.0, 0.03747072599531616, 0.02150537634408602, 0.02388059701492537, 0.05970149253731343, 0.026578073089701, 0.05309734513274337, 0.020671834625323, 0.02110817941952507, 0.041666666666666664, 0.04597701149425287, 0.020253164556962026, 0.04123711340206186, 0.020408163265306128, 0.0, 0.04804804804804805, 0.07179305086467387, 0.0, 0.020460358056265986, 0.0, 0.04199475065616798, 0.04507042253521127, 0.02555910543130991, 0.05111821086261982, 0.0221606648199446, 0.0, 0.02072538860103627, 0.04419889502762431, 0.021447721179624665, 0.038461538461538464, 0.03088803088803089, 0.0221606648199446, 0.06611570247933883, 0.025316455696202535, 0.035555555555555556, 0.020100502512562818, 0.0221606648199446, 0.04266666666666667, 0.03571428571428571, 0.02150537634408602, 0.04833836858006042, 0.018912529550827423, 0.025, 0.05783132530120482, 0.02388059701492537, 0.03773584905660378, 0.024539877300613498, 0.027303754266211608, 0.06233766233766234, 0.06382978723404255, 0.04060913705583757, 0.024024024024024024, 0.02777777777777778, 0.02909090909090909, 0.025157232704402517, 0.020887728459530026, 0.05063291139240507, 0.0213903743315508, 0.05714285714285714, 0.04199475065616798, 0.04289544235924933, 0.05620608899297424, 0.0399002493765586, 0.025477707006369428, 0.04878048780487806, 0.024464831804281342, 0.045605700712589084, 0.046109510086455335, 0.026755852842809364, 0.024464831804281342, 0.01436265709156194, 0.02197802197802198, 0.0, 0.0, 0.04968944099378883, 0.02346041055718475, 0.0427807486631016, 0.027027027027027025, 0.027303754266211608, 0.027027027027027025, 0.023255813953488372, 0.021276595744680854, 0.0, 0.028520499108734405, 0.04395604395604396, 0.04289544235924933, 0.03931203931203932, 0.021621621621621623, 0.0, 0.06217616580310881, 0.06685236768802227, 0.023809523809523808, 0.0, 0.021680216802168018, 0.03375527426160337, 0.0625, 0.022988505747126436, 0.023255813953488372, 0.05111821086261982, 0.0, 0.0273972602739726, 0.05985037406483791, 0.02946593001841621, 0.029197080291970802, 0.04733727810650887, 0.01724137931034483, 0.0, 0.02191780821917808, 0.05111821086261982, 0.03931203931203932, 0.040201005025125636, 0.02555910543130991, 0.028985507246376815, 0.04878048780487806, 0.02439024390243903, 0.05063291139240507, 0.022727272727272728, 0.025806451612903226, 0.021680216802168018, 0.022727272727272728, 0.04071246819338423, 0.035794183445190156, 0.04733727810650887, 0.0, 0.024464831804281342, 0.026229508196721315, 0.022792022792022793, 0.06106870229007633, 0.050314465408805034, 0.029962546816479405, 0.05405405405405405, 0.02857142857142857, 0.027874564459930314, 0.024242424242424246, 0.0469208211143695, 0.014388489208633094, 0.017817371937639197, 0.018181818181818184, 0.022598870056497175, 0.025316455696202535, 0.0, 0.05351170568561873, 0.05315614617940199, 0.022222222222222223, 0.03137254901960784, 0.01735357917570499, 0.022662889518413595, 0.0, 0.03532008830022075, 0.0418848167539267, 0.026490066225165566, 0.05263157894736842, 0.03747072599531616, 0.07600950118764846, 0.05985037406483791, 0.02797202797202797, 0.04289544235924933, 0.02110817941952507, 0.03644646924829157, 0.0, 0.025, 0.013513513513513513, 0.02507836990595612, 0.02191780821917808, 0.0471976401179941, 0.043596730245231606, 0.0221606648199446, 0.017937219730941704, 0.022222222222222223, 0.02318840579710145, 0.041558441558441565, 0.0, 0.02318840579710145, 0.052401746724890834, 0.05211726384364821, 0.024844720496894415, 0.04040404040404041, 0.050955414012738856, 0.046109510086455335, 0.024464831804281342, 0.027874564459930314, 0.035010940919037205, 0.02439024390243903, 0.022222222222222223, 0.059040590405904064, 0.04584527220630373, 0.0683943392494953, 0.042440318302387266, 0.019464720194647202, 0.04289544235924933, 0.03738317757009346, 0.04833836858006042, 0.014625228519195614, 0.026490066225165566, 0.0471976401179941, 0.0, 0.0463768115942029, 0.022727272727272728, 0.0471976401179941, 0.02461538461538462, 0.020050125313283207, 0.02439024390243903, 0.024922118380062308, 0.0, 0.025764895330112725, 0.04968944099378883, 0.038461538461538464, 0.0, 0.02228412256267409, 0.023809523809523808, 0.030710172744721688, 0.045454545454545456, 0.023255813953488372, 0.0, 0.028402366863905328, 0.023809523809523808, 0.0316205533596838, 0.021680216802168018, 0.03258655804480652, 0.02373887240356083, 0.02247191011235955, 0.024539877300613498, 0.025157232704402517, 0.0798185941043084, 0.030901287553648068, 0.0, 0.02318840579710145, 0.020833333333333332, 0.02572347266881029, 0.02247191011235955, 0.07207207207207206, 0.041558441558441565, 0.02388059701492537, 0.04848484848484849, 0.022662889518413595, 0.035398230088495575, 0.027303754266211608, 0.0, 0.024729520865533233, 0.026490066225165566, 0.0, 0.02555910543130991, 0.02185792349726776, 0.05015673981191224, 0.04790419161676647, 0.014814814814814815, 0.03493449781659389, 0.064, 0.025806451612903226, 0.036281179138321996, 0.0718562874251497, 0.02388059701492537, 0.02797202797202797, 0.023121387283236997, 0.03532008830022075, 0.03470715835140998, 0.021164021164021163, 0.024464831804281342, 0.028169014084507046, 0.03018867924528302, 0.02359882005899705, 0.027303754266211608, 0.02247191011235955, 0.0, 0.0, 0.03773584905660378, 0.0, 0.02359882005899705, 0.0, 0.027586206896551724, 0.02247191011235955, 0.017505470459518602, 0.05111821086261982, 0.06779661016949153, 0.02572347266881029, 0.03864734299516908, 0.025477707006369428, 0.01941747572815534, 0.04597701149425287, 0.028169014084507046, 0.024464831804281342, 0.016597510373443983, 0.03827751196172249, 0.019230769230769232, 0.0, 0.022099447513812154, 0.04597701149425287, 0.015122873345935728, 0.019801980198019806, 0.026490066225165566, 0.050473186119873815, 0.0, 0.05555555555555556, 0.020050125313283207, 0.030534351145038163, 0.022857142857142854, 0.017543859649122803, 0.044692737430167606, 0.03143418467583497, 0.047713717693836984, 0.019047619047619046, 0.024464831804281342, 0.040201005025125636, 0.01965601965601966, 0.043596730245231606, 0.0316205533596838, 0.05620608899297424, 0.02777777777777778, 0.022662889518413595, 0.022662889518413595, 0.023054755043227668, 0.020671834625323, 0.0, 0.01568627450980392, 0.021739130434782608, 0.03292181069958848, 0.024242424242424246, 0.047058823529411764, 0.03738317757009346, 0.023255813953488372, 0.027303754266211608, 0.032520325203252036, 0.024844720496894415, 0.019801980198019806, 0.026490066225165566, 0.04221635883905014, 0.0, 0.043596730245231606, 0.021621621621621623, 0.0182648401826484, 0.020833333333333332, 0.07748184019370462, 0.03180914512922465, 0.07673860911270984, 0.020151133501259445, 0.03278688524590164, 0.028169014084507046, 0.0399002493765586, 0.0, 0.06682134570765662, 0.046647230320699715, 0.019464720194647202, 0.041343669250646, 0.04383561643835616, 0.02197802197802198, 0.04383561643835616, 0.02689075630252101, 0.024464831804281342, 0.043126684636118594, 0.026490066225165566, 0.04060913705583757, 0.02461538461538462, 0.04266666666666667, 0.02946593001841621, 0.022662889518413595, 0.05647058823529412, 0.059040590405904064, 0.027874564459930314, 0.0, 0.03931203931203932, 0.04383561643835616, 0.04938271604938272, 0.05985037406483791, 0.05985037406483791, 0.023529411764705882, 0.01652892561983471, 0.038461538461538464, 0.018518518518518517, 0.07453416149068325, 0.04030226700251889, 0.03940886699507389, 0.04878048780487806, 0.02439024390243903, 0.043596730245231606, 0.02439024390243903, 0.037296037296037296, 0.03470715835140998, 0.05015673981191224, 0.022662889518413595, 0.024539877300613498, 0.023529411764705882, 0.035398230088495575, 0.052060737527114966, 0.04507042253521127, 0.04968944099378883, 0.03125, 0.017977528089887642, 0.026490066225165566, 0.02388059701492537, 0.025157232704402517, 0.0, 0.04678362573099415, 0.04923076923076924, 0.02507836990595612, 0.026578073089701, 0.04923076923076924, 0.04678362573099415, 0.025806451612903226, 0.02061855670103093, 0.041558441558441565, 0.031683168316831684, 0.0, 0.0, 0.020253164556962026, 0.04199475065616798, 0.0, 0.03088803088803089, 0.051063829787234054, 0.028119507908611598, 0.04733727810650887, 0.03931203931203932, 0.029962546816479405, 0.03940886699507389, 0.05565217391304348, 0.023809523809523808, 0.03747072599531616, 0.022727272727272728, 0.0636892882696434, 0.04507042253521127, 0.06648199445983378, 0.04199475065616798, 0.04790419161676647, 0.07667731629392971, 0.05144694533762058, 0.058608058608058615, 0.04571428571428571, 0.0, 0.0471976401179941, 0.043478260869565216, 0.019512195121951223, 0.049844236760124616, 0.0, 0.03980099502487562, 0.02247191011235955, 0.026229508196721315, 0.049896049896049906, 0.026402640264026403, 0.05985037406483791, 0.05063291139240507, 0.031189083820662773, 0.03636363636363637, 0.050473186119873815, 0.017543859649122803, 0.024922118380062308, 0.02461538461538462, 0.028169014084507046, 0.0, 0.021563342318059297, 0.02439024390243903, 0.024844720496894415, 0.026578073089701, 0.0316205533596838, 0.05594405594405594, 0.027874564459930314, 0.028368794326241138, 0.035634743875278395, 0.044444444444444446, 0.025, 0.04210526315789473, 0.05970149253731343, 0.0, 0.05581395348837209, 0.0, 0.04289544235924933, 0.0, 0.02588996763754045, 0.027303754266211608, 0.05063291139240507, 0.04395604395604396, 0.043478260869565216, 0.021447721179624665, 0.024242424242424246, 0.029962546816479405, 0.027027027027027025, 0.025316455696202535, 0.04040404040404041, 0.02228412256267409, 0.0, 0.02769230769230769, 0.023121387283236997, 0.01869158878504673, 0.0, 0.037825059101654845, 0.0, 0.0273972602739726, 0.03695150115473441, 0.04968944099378883, 0.0625, 0.0, 0.03773584905660378, 0.0196078431372549, 0.02572347266881029, 0.0625, 0.024844720496894415, 0.0, 0.020100502512562818, 0.0399002493765586, 0.05128205128205128, 0.023668639053254434, 0.02555910543130991, 0.02572347266881029, 0.021621621621621623, 0.022662889518413595, 0.04733727810650888, 0.05063291139240507, 0.02888086642599278, 0.03818615751789976, 0.0, 0.0, 0.027027027027027025, 0.0, 0.03960396039603961, 0.020202020202020204, 0.04938271604938272, 0.029962546816479405, 0.021621621621621623, 0.021563342318059297, 0.03180914512922465, 0.0273972602739726, 0.0, 0.06993862566169894, 0.02461538461538462, 0.0, 0.03018867924528302, 0.0, 0.041558441558441565, 0.025157232704402517, 0.020000000000000004, 0.03433476394849786, 0.025316455696202535, 0.04790419161676647, 0.022222222222222223, 0.042440318302387266, 0.06611570247933883, 0.02228412256267409, 0.0, 0.0, 0.05245901639344263, 0.02359882005899705, 0.024844720496894415, 0.03433476394849786, 0.0449438202247191, 0.03470715835140998, 0.043478260869565216, 0.043596730245231606, 0.017543859649122803, 0.019370460048426155, 0.07127050817101066, 0.029520295202952032, 0.05405405405405405, 0.043478260869565216, 0.018348623853211007, 0.04092071611253197, 0.045584045584045586, 0.021563342318059297, 0.026490066225165566, 0.05111821086261982, 0.0, 0.0, 0.025682182985553775, 0.025974025974025972, 0.08247422680412372, 0.03960396039603961, 0.0, 0.021739130434782608, 0.05063291139240507, 0.02572347266881029, 0.046109510086455335, 0.027027027027027025, 0.022408963585434174, 0.0, 0.07594936708860761, 0.023054755043227668, 0.03747072599531616, 0.02318840579710145, 0.05211726384364821, 0.041666666666666664, 0.04456824512534818, 0.023809523809523808, 0.023054755043227668, 0.031189083820662773, 0.0, 0.025157232704402517, 0.0, 0.043243243243243246, 0.04733727810650887, 0.03940886699507389, 0.019138755980861243, 0.040506329113924044, 0.016632016632016633, 0.02797202797202797, 0.0, 0.02318840579710145, 0.022099447513812154, 0.02507836990595612, 0.0, 0.04507042253521127, 0.021739130434782608, 0.02359882005899705, 0.0, 0.0, 0.02507836990595612, 0.022598870056497175, 0.041025641025641026, 0.04060913705583757, 0.029850746268656716, 0.02888086642599278, 0.026578073089701, 0.02197802197802198, 0.018604651162790697, 0.028169014084507046, 0.022727272727272728, 0.0399002493765586, 0.04199475065616798, 0.05111821086261982, 0.05183585313174946, 0.0, 0.033542976939203356, 0.029520295202952032, 0.03041825095057035, 0.0365296803652968, 0.032, 0.03603603603603603, 0.02572347266881029, 0.025396825396825397, 0.026755852842809364, 0.023054755043227668, 0.026229508196721315, 0.021447721179624665, 0.03076923076923077, 0.021164021164021163, 0.049844236760124616, 0.04301075268817204, 0.025157232704402517, 0.017897091722595078, 0.014260249554367202, 0.025806451612903226, 0.0, 0.0221606648199446, 0.04833836858006042, 0.021563342318059297, 0.020253164556962026, 0.019138755980861243, 0.02373887240356083, 0.04747774480712166, 0.0, 0.02247191011235955, 0.025806451612903226, 0.03695150115473441, 0.042440318302387266, 0.024242424242424246, 0.029850746268656716, 0.021680216802168018, 0.053571428571428575, 0.02150537634408602, 0.02318840579710145, 0.02416918429003022, 0.024464831804281342, 0.040201005025125636, 0.04481792717086835, 0.05405405405405405, 0.032520325203252036, 0.05660377358490566, 0.061855670103092786, 0.06539509536784742, 0.0, 0.015625, 0.0418848167539267, 0.039024390243902446, 0.0427807486631016, 0.0, 0.04597701149425287, 0.05015673981191224, 0.03931203931203932, 0.022099447513812154, 0.0, 0.0418848167539267, 0.042440318302387266, 0.038929440389294405, 0.03931203931203932, 0.019370460048426155, 0.017897091722595078, 0.0, 0.025157232704402517, 0.04833836858006042, 0.024024024024024024, 0.046109510086455335, 0.035555555555555556, 0.04395604395604396, 0.018604651162790697, 0.0625, 0.04456824512534818, 0.04060913705583757, 0.042440318302387266, 0.0213903743315508, 0.03678160919540229, 0.03960396039603961, 0.0636604774535809, 0.026490066225165566, 0.02359882005899705, 0.0, 0.058679706601467, 0.04938271604938272, 0.024096385542168672, 0.02588996763754045, 0.0449438202247191, 0.027874564459930314, 0.02203856749311295, 0.0, 0.01702127659574468, 0.07563636363636364, 0.025, 0.02572347266881029, 0.020304568527918784, 0.0469208211143695, 0.024922118380062308, 0.0729483282674772, 0.023255813953488372, 0.039024390243902446, 0.03319502074688797, 0.021052631578947364, 0.05620608899297424, 0.021447721179624665, 0.04383561643835616, 0.04848484848484849, 0.0, 0.0, 0.025316455696202535, 0.051948051948051945, 0.02228412256267409, 0.02857142857142857, 0.04456824512534818, 0.021680216802168018, 0.03931203931203932, 0.043360433604336036, 0.06868686868686869, 0.06803074920471523, 0.03818615751789976, 0.049079754601226995, 0.0471976401179941, 0.02507836990595612, 0.0588235294117647, 0.05263157894736842, 0.02373887240356083, 0.022099447513812154, 0.02461538461538462, 0.04879069963201136, 0.02469135802469136, 0.025974025974025972, 0.026578073089700994, 0.0, 0.04848484848484849, 0.00988875154511743, 0.0, 0.02359882005899705, 0.024464831804281342, 0.021798365122615803, 0.02318840579710145, 0.0, 0.020565552699228794, 0.022222222222222223, 0.028169014084507046, 0.045584045584045586, 0.018433179723502304, 0.02191780821917808, 0.03669724770642201, 0.020202020202020204, 0.053670748541834325, 0.022662889518413595, 0.0, 0.06685236768802227, 0.050955414012738856, 0.043321299638989175, 0.022662889518413595, 0.03864734299516908, 0.038929440389294405, 0.03827751196172249, 0.07748881820472325, 0.04301075268817204, 0.04383561643835616, 0.025236593059936908, 0.019512195121951223, 0.03088803088803089, 0.046109510086455335, 0.0, 0.0402205372575787, 0.03678160919540229, 0.03931203931203932, 0.02416918429003021, 0.04923076923076924, 0.0, 0.040100250626566414, 0.0, 0.028169014084507046, 0.02110817941952507, 0.021164021164021163, 0.04833836858006042, 0.0273972602739726, 0.021680216802168018, 0.04507042253521127, 0.0440771349862259, 0.06569636075161225, 0.0, 0.02439024390243903, 0.023255813953488372, 0.014492753623188408, 0.035955056179775284, 0.023809523809523808, 0.03404255319148936, 0.04289544235924933, 0.021798365122615803, 0.04863221884498481, 0.0, 0.03433476394849786, 0.05063291139240507, 0.045283018867924525, 0.020671834625323, 0.020512820512820513, 0.024539877300613498, 0.021798365122615803, 0.03980099502487562, 0.023391812865497075, 0.02684563758389262, 0.039024390243902446, 0.03361344537815126, 0.04968944099378883, 0.02416918429003021, 0.019464720194647202, 0.041025641025641026, 0.041558441558441565, 0.0, 0.0, 0.0418848167539267, 0.038461538461538464, 0.018648018648018648, 0.023255813953488372, 0.032, 0.022099447513812154, 0.045714285714285714, 0.023809523809523808, 0.0, 0.028622540250447227, 0.031496062992125984, 0.017857142857142856, 0.024922118380062308, 0.025806451612903226, 0.0, 0.046109510086455335, 0.0, 0.040100250626566414, 0.02318840579710145, 0.023809523809523808, 0.041558441558441565, 0.019704433497536946, 0.028985507246376815, 0.02110817941952507, 0.04863221884498481, 0.02191780821917808, 0.03800475059382423, 0.0712166172106825, 0.07667731629392971, 0.04266666666666667, 0.02439024390243903, 0.04456824512534818, 0.016161616161616165, 0.0, 0.027303754266211608, 0.0, 0.05687203791469195, 0.028169014084507046, 0.025477707006369428, 0.0, 0.045584045584045586, 0.0835509138381201, 0.02507836990595612, 0.0, 0.043478260869565216, 0.024024024024024024, 0.030710172744721688, 0.022857142857142854, 0.03809523809523809, 0.026755852842809364, 0.024922118380062308, 0.020565552699228794, 0.028070175438596492, 0.049844236760124616, 0.02909090909090909, 0.0399316427361755, 0.04383561643835616, 0.04571428571428571, 0.0736196319018405, 0.027027027027027025, 0.025806451612903226, 0.0, 0.031496062992125984, 0.020202020202020204, 0.04953560371517029, 0.026578073089701, 0.04140030441400305, 0.03319502074688797, 0.059040590405904064, 0.05111821086261982, 0.04301075268817204, 0.0, 0.04210526315789473, 0.0469208211143695, 0.0273972602739726, 0.02110817941952507, 0.06233766233766234, 0.041025641025641026, 0.050739957716701915, 0.02631578947368421, 0.04980625872912706, 0.040816326530612256, 0.04923076923076924, 0.027118644067796613, 0.022535211267605635, 0.023255813953488372, 0.025806451612903226, 0.03669724770642201, 0.026229508196721315, 0.0392156862745098, 0.022662889518413595, 0.018604651162790697, 0.026755852842809364, 0.01762114537444934, 0.026490066225165566, 0.02439024390243903, 0.026229508196721315, 0.024539877300613498, 0.03143418467583497, 0.04383561643835616, 0.02888086642599278, 0.02318840579710145, 0.040100250626566414, 0.022099447513812154, 0.05263157894736842, 0.049844236760124616, 0.04833836858006042, 0.0, 0.0, 0.05955334987593052, 0.022662889518413595, 0.02197802197802198, 0.0399002493765586, 0.04624277456647399, 0.03082851637764933, 0.0, 0.026490066225165566, 0.0427807486631016, 0.031496062992125984, 0.0418848167539267, 0.04301075268817204, 0.02588996763754045, 0.03305785123966942, 0.016632016632016633, 0.042440318302387266, 0.029962546816479405, 0.0, 0.02461538461538462, 0.0, 0.019370460048426155, 0.0, 0.05405405405405405, 0.04804804804804805, 0.025316455696202535, 0.05741626794258373, 0.021563342318059297, 0.04790419161676647, 0.0, 0.021680216802168018, 0.026229508196721315, 0.0, 0.0273972602739726, 0.0, 0.04953560371517029, 0.025157232704402517, 0.02777777777777778, 0.020779220779220783, 0.015625, 0.04923076923076924, 0.05955334987593052, 0.026755852842809364, 0.04571428571428571, 0.05333333333333334, 0.0, 0.024242424242424246, 0.0449438202247191, 0.05111821086261982, 0.025477707006369428, 0.02366863905325444, 0.019704433497536946, 0.04071246819338423, 0.022662889518413595, 0.03219315895372234, 0.07667731629392971, 0.034261241970021415, 0.01873536299765808, 0.021739130434782608, 0.024464831804281342, 0.024024024024024024, 0.05925925925925926, 0.0418848167539267, 0.02072538860103627, 0.019801980198019806, 0.0, 0.04255319148936171, 0.028419182948490232, 0.05333333333333334, 0.022662889518413595, 0.03433476394849786, 0.028169014084507046, 0.02507836990595612, 0.02555910543130991, 0.03493449781659389, 0.04030226700251889, 0.035087719298245605, 0.056074766355140186, 0.024767801857585144, 0.04833836858006042, 0.05211726384364821, 0.022727272727272728, 0.028673835125448036, 0.06539509536784742, 0.022792022792022793, 0.02388059701492537, 0.04395604395604396, 0.0272108843537415, 0.0, 0.05263157894736842, 0.044692737430167606, 0.04221635883905014, 0.025157232704402517, 0.022099447513812154, 0.021447721179624665, 0.05970149253731343, 0.03603603603603603, 0.021798365122615803, 0.0, 0.04199475065616798, 0.03773584905660378, 0.04597701149425287, 0.024242424242424246, 0.02777777777777778, 0.022857142857142854, 0.03088803088803089, 0.046109510086455335, 0.0, 0.0, 0.04968944099378883, 0.0794701986754967, 0.023809523809523808, 0.02388059701492537, 0.0, 0.037037037037037035, 0.0, 0.04968944099378883, 0.04383561643835616, 0.0, 0.04790419161676647, 0.0, 0.07407407407407407, 0.03931203931203932, 0.022792022792022793, 0.0, 0.04071246819338423, 0.02439024390243903, 0.013008130081300815, 0.03292181069958848, 0.054421768707483, 0.046109510086455335, 0.02588996763754045, 0.038461538461538464, 0.046109510086455335, 0.03827751196172249, 0.02110817941952507, 0.028985507246376815, 0.04519774011299435, 0.03219315895372234, 0.0, 0.05211726384364821, 0.024539877300613498, 0.025806451612903226, 0.06091370558375634, 0.021333333333333336, 0.05925925925925926, 0.045283018867924525, 0.0, 0.04199475065616798, 0.040201005025125636, 0.06486486486486485, 0.027303754266211608, 0.02318840579710145, 0.026229508196721315, 0.0, 0.022535211267605635, 0.0, 0.021447721179624665, 0.06486486486486485, 0.025477707006369428, 0.06486486486486485, 0.02572347266881029, 0.03695150115473441, 0.07513812154696135, 0.0, 0.0, 0.0, 0.0, 0.02150537634408602, 0.046647230320699715, 0.05620608899297424, 0.020304568527918784, 0.01990049751243781, 0.027633851468048365, 0.026490066225165566, 0.04507042253521127, 0.02572347266881029, 0.021563342318059297, 0.02094240837696335, 0.0, 0.016842105263157898, 0.05405405405405405, 0.05245901639344263, 0.024024024024024024, 0.021563342318059297, 0.07250755287009063, 0.03180914512922465, 0.0, 0.020202020202020204, 0.027303754266211608, 0.020512820512820513, 0.019851116625310174, 0.022535211267605635, 0.02191780821917808, 0.04395604395604396, 0.02507836990595612, 0.035634743875278395, 0.07570977917981074, 0.0, 0.025157232704402517, 0.044444444444444446, 0.0, 0.020100502512562818, 0.023391812865497075, 0.025316455696202535, 0.05063291139240507, 0.0, 0.026490066225165566, 0.02388059701492537, 0.02247191011235955, 0.028985507246376815, 0.050473186119873815, 0.02416918429003021, 0.023154848046309694, 0.0, 0.02228412256267409, 0.064, 0.02247191011235955, 0.06233766233766234, 0.020253164556962026, 0.04733727810650887, 0.029962546816479405, 0.06075949367088607, 0.02099737532808399, 0.049079754601226995, 0.0, 0.012158054711246199, 0.039119804400978, 0.05263157894736842, 0.04571428571428571, 0.02247191011235955, 0.028070175438596492, 0.04255319148936171, 0.02909090909090909, 0.04923076923076924, 0.028985507246376815, 0.046511627906976744, 0.023391812865497075, 0.042440318302387266, 0.06746987951807229, 0.016161616161616165, 0.02247191011235955, 0.0, 0.07594936708860761, 0.0, 0.0, 0.020202020202020204, 0.040100250626566414, 0.02197802197802198, 0.024767801857585144, 0.02416918429003021, 0.035955056179775284, 0.04289544235924933, 0.023054755043227668, 0.037296037296037296, 0.020565552699228794, 0.0, 0.022598870056497175, 0.0272108843537415, 0.04678362573099415, 0.022662889518413595, 0.026186579378068744, 0.019370460048426155, 0.03389830508474577, 0.05633802816901409, 0.03809523809523809, 0.02072538860103627, 0.0399002493765586, 0.047058823529411764, 0.04804804804804805, 0.02857142857142857, 0.032454361054766734, 0.06629834254143645, 0.023809523809523808, 0.019753086419753086, 0.026229508196721315, 0.02318840579710145, 0.08510638297872342, 0.0898876404494382, 0.02398578540193092, 0.0, 0.041558441558441565, 0.03738317757009346, 0.03354297693920335, 0.046647230320699715, 0.03478260869565218, 0.03206412825651302, 0.08304498269896195, 0.043596730245231606, 0.024024024024024024, 0.026229508196721315, 0.04301075268817204, 0.024464831804281342, 0.04571428571428571, 0.046647230320699715, 0.022099447513812154, 0.05620608899297424, 0.064, 0.017467248908296946, 0.02228412256267409, 0.0418848167539267, 0.03493449781659389, 0.049844236760124616, 0.025157232704402517, 0.04419889502762431, 0.021164021164021163, 0.028169014084507046, 0.0, 0.02957486136783734, 0.02388059701492537, 0.05245901639344263, 0.022988505747126436, 0.02777777777777778, 0.025806451612903226, 0.02150537634408602, 0.027027027027027025, 0.019370460048426155, 0.027303754266211608, 0.06666666666666665, 0.05734767025089607, 0.04255319148936171, 0.04678362573099415, 0.026229508196721315, 0.04968944099378883, 0.046109510086455335, 0.0, 0.021680216802168018, 0.027874564459930314, 0.07017543859649121, 0.028169014084507046, 0.018604651162790697, 0.016632016632016633, 0.022727272727272728, 0.023668639053254434, 0.04301075268817204, 0.024844720496894415, 0.03478260869565218, 0.026229508196721315, 0.0221606648199446, 0.021563342318059297, 0.09065155807365441, 0.022662889518413595, 0.026402640264026403, 0.038461538461538464, 0.024922118380062308, 0.04833836858006042, 0.05333333333333334, 0.04395604395604396, 0.0, 0.04199475065616798, 0.029520295202952032, 0.023952095808383235, 0.016985138004246284, 0.017937219730941704, 0.0, 0.03470715835140998, 0.02318840579710145, 0.046109510086455335, 0.035955056179775284, 0.04804804804804805, 0.025806451612903226, 0.02507836990595612, 0.04519774011299435, 0.024464831804281342, 0.03678160919540229, 0.0552533689054157, 0.04199475065616798, 0.040100250626566414, 0.05741626794258373, 0.02777777777777778, 0.06106870229007633, 0.023391812865497075, 0.039119804400978, 0.06525374164608273, 0.04092071611253197, 0.020565552699228794, 0.02507836990595612, 0.050955414012738856, 0.03636363636363637, 0.03611738148984199, 0.023054755043227668, 0.06299212598425197, 0.02318840579710145, 0.02555910543130991, 0.022099447513812154, 0.039119804400978, 0.05714285714285714, 0.0449438202247191, 0.040268456375838924, 0.058679706601466985, 0.05925925925925926, 0.022099447513812154, 0.032, 0.02507836990595612, 0.0, 0.021447721179624665, 0.0, 0.027303754266211608, 0.024242424242424246, 0.03347280334728034, 0.03636363636363637, 0.025236593059936908, 0.028070175438596492, 0.026402640264026403, 0.0, 0.02631578947368421, 0.0, 0.03695150115473441, 0.03669724770642201, 0.02318840579710145, 0.02318840579710145, 0.024024024024024024, 0.04848484848484849, 0.05111821086261982, 0.023255813953488372, 0.044444444444444446, 0.05263157894736842, 0.03493449781659389, 0.04240282685512368, 0.04678362573099415, 0.027491408934707903, 0.024922118380062308, 0.0449438202247191, 0.04833836858006042, 0.02826855123674912, 0.051063829787234054, 0.04833836858006042, 0.030534351145038163, 0.02572347266881029, 0.0, 0.04419889502762431, 0.014059753954305799, 0.027303754266211608, 0.03603603603603603, 0.0, 0.04113110539845758, 0.058536585365853655, 0.08579088471849866, 0.02416918429003021, 0.035634743875278395, 0.04289544235924933, 0.01927710843373494, 0.025974025974025972, 0.019464720194647202, 0.05542725173210161, 0.0, 0.049844236760124616, 0.020151133501259445, 0.0, 0.023255813953488372, 0.056838365896980464, 0.04301075268817204, 0.018223234624145785, 0.05245901639344263, 0.03773584905660378, 0.023255813953488372, 0.06122448979591837, 0.03855421686746988, 0.030947775628626696, 0.051948051948051945, 0.025157232704402517, 0.04678362573099415, 0.03143418467583497, 0.04123766866379788, 0.02946593001841621, 0.0471976401179941, 0.05351170568561873, 0.021447721179624665, 0.020100502512562818, 0.019512195121951223, 0.028169014084507046, 0.031007751937984496, 0.04221635883905014, 0.018181818181818184, 0.02318840579710145, 0.0199501246882793, 0.02366863905325444, 0.054982817869415807, 0.028673835125448036, 0.02388059701492537, 0.0, 0.04571428571428571, 0.050473186119873815, 0.02110817941952507, 0.0, 0.046109510086455335, 0.02346041055718475, 0.0, 0.045584045584045586, 0.06106870229007633, 0.02821869488536155, 0.02318840579710145, 0.021333333333333336, 0.04395604395604396, 0.02450229709035222, 0.06956521739130433, 0.0311284046692607, 0.025974025974025972, 0.03433476394849786, 0.0, 0.02318840579710145, 0.018957345971563982, 0.0, 0.04848484848484849, 0.04456824512534818, 0.06837606837606837, 0.040100250626566414, 0.0418848167539267, 0.059040590405904064, 0.03773584905660378, 0.02203856749311295, 0.01941747572815534, 0.022792022792022793, 0.05776173285198556, 0.018604651162790697, 0.0, 0.026229508196721315, 0.02572347266881029, 0.01652892561983471, 0.03773584905660378, 0.03883495145631068, 0.026578073089701, 0.06593406593406594, 0.0, 0.02191780821917808, 0.022222222222222223, 0.020100502512562818, 0.06936416184971099, 0.0, 0.05263157894736842, 0.04833836858006042, 0.044692737430167606, 0.018223234624145785, 0.050955414012738856, 0.05245901639344263, 0.02346041055718475, 0.023880597014925373, 0.0, 0.03940886699507389, 0.026800670016750423, 0.04199475065616798, 0.022408963585434174, 0.026229508196721315, 0.022598870056497175, 0.0418848167539267, 0.01886792452830189, 0.06315789473684211, 0.026490066225165566, 0.044036697247706424, 0.041025641025641026, 0.040201005025125636, 0.03855421686746988, 0.020356234096692113, 0.024242424242424246, 0.01965601965601966, 0.022535211267605635, 0.026490066225165566, 0.03669724770642201, 0.059040590405904064, 0.04532577903682719, 0.019138755980861243, 0.024922118380062308, 0.0365296803652968, 0.049079754601226995, 0.04071246819338423, 0.05111821086261982, 0.04395604395604396, 0.04395604395604396, 0.05517241379310345, 0.04113110539845759, 0.03678160919540229, 0.02439024390243903, 0.026058631921824102, 0.039024390243902446, 0.04199475065616798, 0.04938271604938272, 0.03047619047619048, 0.02507836990595612, 0.02555910543130991, 0.05568445475638051, 0.04000000000000001, 0.0, 0.02359882005899705, 0.0, 0.05111821086261982, 0.04071246819338423, 0.037296037296037296, 0.0221606648199446, 0.0, 0.037914691943127965, 0.0449438202247191, 0.0, 0.0, 0.02388059701492537, 0.04923076923076924, 0.05660377358490566, 0.019801980198019806, 0.0221606648199446, 0.025157232704402517, 0.02572347266881029, 0.02555910543130991, 0.06539509536784742, 0.02094240837696335, 0.0, 0.03375527426160337, 0.026578073089701, 0.03827751196172249, 0.02555910543130991, 0.04584527220630373, 0.026578073089701, 0.05298013245033113, 0.023391812865497075, 0.019230769230769232, 0.044692737430167606, 0.03571428571428571, 0.032, 0.05351170568561873, 0.05574912891986063, 0.01032258064516129, 0.041025641025641026, 0.024922118380062308, 0.0, 0.0, 0.023668639053254434, 0.025806451612903226, 0.0, 0.0, 0.02191780821917808, 0.0632411067193676, 0.0, 0.017977528089887642, 0.04383561643835616, 0.04532577903682719, 0.05, 0.05309734513274337, 0.05010438413361169, 0.024767801857585144, 0.0418848167539267, 0.035955056179775284, 0.05084745762711865, 0.04060913705583757, 0.021563342318059297, 0.027027027027027025, 0.054982817869415807, 0.04507042253521127, 0.034261241970021415, 0.025477707006369428, 0.01724137931034483, 0.019753086419753086, 0.019559902200489, 0.020460358056265986, 0.04678362573099415, 0.04597701149425287, 0.018648018648018648, 0.025806451612903226, 0.05015673981191224, 0.03773584905660378, 0.021447721179624665, 0.02110817941952507, 0.0449438202247191, 0.04597701149425287, 0.026229508196721315, 0.04123711340206186, 0.0, 0.022598870056497175, 0.023391812865497075, 0.02110817941952507, 0.016597510373443983, 0.04383561643835616, 0.02555910543130991, 0.022662889518413595, 0.04678362573099415, 0.024242424242424246, 0.0, 0.02666666666666667, 0.036613272311212815, 0.024539877300613498, 0.024922118380062308, 0.035634743875278395, 0.035398230088495575, 0.027874564459930314, 0.021276595744680854, 0.04395604395604396, 0.022857142857142854, 0.026490066225165566, 0.051948051948051945, 0.054771212867595544, 0.017582417582417586, 0.05574912891986063, 0.04733727810650888, 0.07894736842105263, 0.0316205533596838, 0.024024024024024024, 0.02228412256267409, 0.026058631921824102, 0.032, 0.019002375296912115, 0.04848484848484849, 0.04210526315789473, 0.056737588652482275, 0.04255319148936171, 0.024539877300613498, 0.02777777777777778, 0.037122969837587005, 0.022408963585434174, 0.041666666666666664, 0.020100502512562818, 0.024539877300613498, 0.024024024024024024, 0.024242424242424246, 0.04113110539845759, 0.021276595744680854, 0.02857142857142857, 0.03773584905660378, 0.025, 0.046875, 0.022727272727272728, 0.029304029304029307, 0.027027027027027025, 0.02072538860103627, 0.029850746268656716, 0.0, 0.028169014084507046, 0.022598870056497175, 0.019851116625310174, 0.021621621621621623, 0.051948051948051945, 0.04289544235924933, 0.027874564459930314, 0.048879837067209775, 0.0, 0.026229508196721315, 0.025157232704402517, 0.040201005025125636, 0.041025641025641026, 0.031067961165048542, 0.02416918429003021, 0.041025641025641026, 0.022727272727272728, 0.04092071611253197, 0.0, 0.020779220779220783, 0.028169014084507046, 0.017857142857142856, 0.01918465227817746, 0.02555910543130991, 0.03187250996015937, 0.05063291139240507, 0.031683168316831684, 0.04301075268817204, 0.058394160583941604, 0.0221606648199446, 0.02061855670103093, 0.0, 0.04383561643835616, 0.026490066225165566, 0.03970223325062035, 0.0, 0.028985507246376815, 0.027874564459930314, 0.027874564459930314, 0.014084507042253523, 0.0, 0.02555910543130991, 0.03686635944700461, 0.034261241970021415, 0.022099447513812154, 0.025316455696202535, 0.040201005025125636, 0.022662889518413595, 0.024464831804281342, 0.043596730245231606, 0.02888086642599278, 0.026755852842809364, 0.0272108843537415, 0.02507836990595612, 0.05529953917050692, 0.02318840579710145, 0.037825059101654845, 0.02318840579710145, 0.01603206412825651, 0.024844720496894415, 0.013722126929674101, 0.0418848167539267, 0.0, 0.02439024390243903, 0.0471976401179941, 0.04733727810650887, 0.050955414012738856, 0.04571428571428571, 0.027586206896551724, 0.0632411067193676, 0.10827259800393975, 0.02631578947368421, 0.05369127516778524, 0.027027027027027025, 0.027303754266211608, 0.02507836990595612, 0.04289544235924933, 0.04733727810650888, 0.05574912891986063, 0.032454361054766734, 0.06382978723404255, 0.0469208211143695, 0.018604651162790697, 0.04030226700251889, 0.04289544235924933, 0.030534351145038163, 0.04092071611253197, 0.02555910543130991, 0.041366574330563254, 0.017582417582417586, 0.017094017094017092, 0.0, 0.04923076923076924, 0.0, 0.02388059701492537, 0.0449438202247191, 0.021333333333333336, 0.022792022792022793, 0.04221635883905014, 0.0, 0.024464831804281342, 0.025157232704402517, 0.02197802197802198, 0.023054755043227668, 0.03827751196172249, 0.03041825095057035, 0.044692737430167606, 0.05245901639344263, 0.02946593001841621, 0.03678160919540229, 0.03950617283950617, 0.020304568527918784, 0.027027027027027025, 0.04507042253521127, 0.024844720496894415, 0.05769230769230769, 0.032454361054766734, 0.02318840579710145, 0.036613272311212815, 0.03931203931203932, 0.0, 0.0, 0.0, 0.0469208211143695, 0.04571428571428571, 0.043596730245231606, 0.0273972602739726, 0.04395604395604396, 0.028673835125448036, 0.02461538461538462, 0.06997084548104957, 0.02555910543130991, 0.0418848167539267, 0.02110817941952507, 0.025157232704402517, 0.027027027027027025, 0.02247191011235955, 0.0418848167539267, 0.04395604395604396, 0.0, 0.02572347266881029, 0.026229508196721315, 0.0, 0.02962962962962963, 0.024844720496894415, 0.0, 0.027874564459930314, 0.022662889518413595, 0.02191780821917808, 0.025806451612903226, 0.0, 0.0, 0.03047619047619048, 0.02373887240356083, 0.04571428571428571, 0.043478260869565216, 0.04878048780487806, 0.04301075268817204, 0.04848484848484849, 0.024464831804281342, 0.010443864229765015, 0.023391812865497075, 0.02318840579710145, 0.0, 0.04255319148936171, 0.020671834625323, 0.050955414012738856, 0.019753086419753086, 0.041025641025641026, 0.04255319148936171, 0.046109510086455335, 0.02857142857142857, 0.026229508196721315, 0.03773584905660378, 0.022099447513812154, 0.04013377926421405, 0.04968944099378883, 0.02122015915119364, 0.043243243243243246, 0.02388059701492537, 0.03493449781659389, 0.06818181818181818, 0.03193612774451098, 0.0, 0.0, 0.024464831804281342, 0.02359882005899705, 0.04289544235924933, 0.022222222222222223, 0.022408963585434174, 0.02507836990595612, 0.025, 0.019002375296912115, 0.02857142857142857, 0.025157232704402517, 0.02461538461538462, 0.0, 0.01941747572815534, 0.01322314049586777, 0.020671834625323, 0.0, 0.02359882005899705, 0.07317073170731707, 0.024024024024024024, 0.022222222222222223, 0.03609022556390978, 0.02366863905325444, 0.0, 0.023054755043227668, 0.04597701149425287, 0.02359882005899705, 0.033126293995859216, 0.05797101449275363, 0.0, 0.035010940919037205, 0.0, 0.025316455696202535, 0.021164021164021163, 0.02110817941952507, 0.02416918429003021, 0.021563342318059297, 0.026578073089701, 0.050955414012738856, 0.0, 0.02247191011235955, 0.04571428571428571, 0.023054755043227668, 0.02359882005899705, 0.0, 0.05769230769230769, 0.04301075268817204, 0.02469135802469136, 0.018058690744920995, 0.021621621621621623, 0.0449438202247191, 0.0718562874251497, 0.026578073089701, 0.028673835125448036, 0.02185792349726776, 0.0687679083094556, 0.025806451612903226, 0.024242424242424246, 0.0, 0.027586206896551724, 0.057279236276849645, 0.016359918200409, 0.046109510086455335, 0.02572347266881029, 0.04804804804804805, 0.05111821086261982, 0.019801980198019806, 0.04199475065616798, 0.04507042253521127, 0.044036697247706424, 0.0221606648199446, 0.0471976401179941, 0.08759124087591241, 0.027303754266211608, 0.025157232704402517, 0.04395604395604396, 0.06539509536784742, 0.0, 0.024316109422492405, 0.05734767025089607, 0.025974025974025972, 0.02439024390243903, 0.022535211267605635, 0.05400228655303967, 0.022099447513812154, 0.06666666666666665, 0.016877637130801686, 0.02373887240356083, 0.0, 0.04113110539845759, 0.03931203931203932, 0.026490066225165566, 0.07769308749755885, 0.0, 0.0, 0.05144694533762058, 0.025806451612903226, 0.026578073089701, 0.046109510086455335, 0.0471976401179941, 0.025477707006369428, 0.02061855670103093, 0.024922118380062308, 0.024242424242424246, 0.04395604395604396, 0.018140589569160998, 0.02777777777777778, 0.026229508196721315, 0.012393493415956624, 0.048192771084337345, 0.031746031746031744, 0.026490066225165566, 0.021798365122615803, 0.02191780821917808, 0.07594936708860761, 0.05405405405405405, 0.01990049751243781, 0.044444444444444446, 0.05574912891986063, 0.02572347266881029, 0.03678160919540229, 0.04383561643835616, 0.023809523809523808, 0.0, 0.020202020202020204, 0.0471976401179941, 0.05405405405405405, 0.020671834625323, 0.043360433604336036, 0.05970149253731343, 0.02416918429003021, 0.029197080291970802, 0.03864734299516908, 0.04289544235924933, 0.014184397163120569, 0.05351170568561873, 0.0718562874251497, 0.020671834625323, 0.020202020202020204, 0.02061855670103093, 0.03800475059382423, 0.037037037037037035, 0.02150537634408602, 0.04255319148936171, 0.027303754266211608, 0.039024390243902446, 0.028673835125448036, 0.04804804804804805, 0.04395604395604396, 0.05369127516778524, 0.02197802197802198, 0.02247191011235955, 0.020671834625323, 0.023054755043227668, 0.033684210526315796, 0.027303754266211608, 0.029962546816479405, 0.023054755043227668, 0.0, 0.0, 0.02416918429003021, 0.054982817869415807, 0.054607508532423216, 0.02857142857142857, 0.025316455696202535, 0.031034482758620686, 0.035010940919037205, 0.02909090909090909, 0.0316205533596838, 0.0, 0.023054755043227668, 0.04848484848484849, 0.0, 0.020050125313283207, 0.03292181069958848, 0.04113110539845759, 0.023255813953488372, 0.022662889518413595, 0.026490066225165566, 0.04804804804804805, 0.04113110539845758, 0.024242424242424246, 0.027027027027027025, 0.031496062992125984, 0.05574912891986063, 0.021164021164021163, 0.03470715835140998, 0.026229508196721315, 0.028368794326241138, 0.020100502512562814, 0.0636604774535809, 0.032520325203252036, 0.040100250626566414, 0.016842105263157898, 0.041025641025641026, 0.028169014084507046, 0.0469218646593572, 0.048929663608562685, 0.025157232704402517, 0.047761334620930976, 0.021164021164021163, 0.047244094488188976, 0.0, 0.0418848167539267, 0.01909307875894988, 0.0, 0.03773584905660378, 0.02857142857142857, 0.08465608465608465, 0.046647230320699715, 0.0, 0.0, 0.021680216802168018, 0.0, 0.020202020202020204, 0.0469208211143695, 0.054982817869415807, 0.029038112522686028, 0.0, 0.0, 0.027027027027027025, 0.028169014084507046, 0.025157232704402517, 0.028169014084507046, 0.051948051948051945, 0.020725388601036267, 0.027027027027027025, 0.04597701149425287, 0.05575606720597386, 0.0, 0.0199501246882793, 0.02359882005899705, 0.026755852842809364, 0.03225806451612904, 0.04071246819338423, 0.03340292275574113, 0.0, 0.05555555555555556, 0.016842105263157898, 0.02110817941952507, 0.045454545454545456, 0.05529953917050692, 0.0221606648199446, 0.03047619047619048, 0.033684210526315796, 0.02185792349726776, 0.05111821086261982, 0.02882882882882883, 0.041666666666666664, 0.026755852842809364, 0.033684210526315796, 0.04507042253521127, 0.04678362573099415, 0.024242424242424246, 0.024922118380062308, 0.025806451612903226, 0.046109510086455335, 0.041666666666666664, 0.0, 0.043478260869565216, 0.043478260869565216, 0.04519774011299435, 0.05263157894736842, 0.0, 0.027027027027027025, 0.04571428571428571, 0.0632411067193676, 0.05620608899297424, 0.025236593059936908, 0.046109510086455335, 0.02572347266881029, 0.03773584905660378, 0.018604651162790697, 0.03041825095057035, 0.03931203931203932, 0.023391812865497075, 0.0, 0.025316455696202535, 0.02507836990595612, 0.040816326530612256, 0.05063291139240507, 0.0213903743315508, 0.04532577903682719, 0.02797202797202797, 0.029197080291970802, 0.0, 0.02150537634408602, 0.02909090909090909, 0.018306636155606407, 0.022857142857142854, 0.0718562874251497, 0.024316109422492405, 0.048192771084337345, 0.06486486486486485, 0.026755852842809364, 0.029906542056074764, 0.06037735849056604, 0.041025641025641026, 0.0, 0.0, 0.017777777777777778, 0.035242290748898675, 0.06521682527167348, 0.021621621621621623, 0.022988505747126436, 0.064, 0.024464831804281342, 0.03738317757009346, 0.0, 0.03818615751789976, 0.03960396039603961, 0.03874092009685231, 0.038461538461538464, 0.020671834625323, 0.0221606648199446, 0.05351170568561873, 0.04733727810650887, 0.059113300492610835, 0.04113110539845758, 0.02693602693602694, 0.02228412256267409, 0.04289544235924933, 0.02056555269922879, 0.04678362573099415, 0.0, 0.026058631921824105, 0.02359882005899705, 0.03125, 0.07461506575523076, 0.019370460048426155, 0.026490066225165566, 0.05517241379310345, 0.0, 0.023668639053254434, 0.04733727810650887, 0.024464831804281342, 0.026578073089701, 0.0316205533596838, 0.02469135802469136, 0.023054755043227668, 0.0, 0.0, 0.0, 0.064343163538874, 0.03773584905660378, 0.02439024390243903, 0.0, 0.02366863905325444, 0.05111821086261982, 0.04301075268817204, 0.04584527220630373, 0.05111821086261982, 0.024242424242424246, 0.02507836990595612, 0.03470715835140998, 0.02197802197802198, 0.022857142857142854, 0.0418848167539267, 0.025806451612903226, 0.03855421686746988, 0.02072538860103627, 0.035794183445190156, 0.02507836990595612, 0.03088803088803089, 0.032128514056224904, 0.024922118380062308, 0.04733727810650887, 0.024922118380062308, 0.020512820512820513, 0.04833836858006042, 0.02191780821917808, 0.01909307875894988, 0.019138755980861243, 0.030534351145038163, 0.0272108843537415, 0.019464720194647202, 0.042440318302387266, 0.022598870056497175, 0.08421052631578947, 0.020833333333333332, 0.050955414012738856, 0.02882882882882883, 0.022662889518413595, 0.059040590405904064, 0.027303754266211608, 0.046109510086455335, 0.0, 0.021220159151193633, 0.0, 0.05405405405405405, 0.0547945205479452, 0.0, 0.045584045584045586, 0.021276595744680854, 0.023856858846918492, 0.04456824512534818, 0.04395604395604396, 0.024922118380062308, 0.027303754266211608, 0.0, 0.06521739130434782, 0.0418848167539267, 0.04030226700251889, 0.02909090909090909, 0.020100502512562814, 0.0, 0.01941747572815534, 0.025806451612903226, 0.04733727810650887, 0.032520325203252036, 0.025236593059936908, 0.040201005025125636, 0.023668639053254434, 0.020725388601036267, 0.04289544235924933, 0.022727272727272728, 0.01932367149758454, 0.023809523809523808, 0.04395604395604396, 0.031929046563192905, 0.022099447513812154, 0.026359143327841845, 0.0196078431372549, 0.022727272727272728, 0.0, 0.10031347962382445, 0.03319502074688797, 0.05633802816901409, 0.0471976401179941, 0.028070175438596492, 0.027303754266211608, 0.050473186119873815, 0.05405405405405405, 0.022099447513812154, 0.025806451612903226, 0.04733727810650887, 0.04597701149425287, 0.04301075268817204, 0.0463768115942029, 0.02061855670103093, 0.05351170568561873, 0.021563342318059297, 0.058394160583941604, 0.06857142857142856, 0.0, 0.03855421686746988, 0.0, 0.0, 0.0, 0.049079754601226995, 0.0, 0.020304568527918784, 0.04456824512534818, 0.019441069258809233, 0.0, 0.06629834254143645, 0.02228412256267409, 0.0, 0.0637291215141649, 0.03433476394849786, 0.01965601965601966, 0.0, 0.027874564459930314, 0.0, 0.0, 0.0, 0.03931203931203932, 0.029850746268656716, 0.021680216802168018, 0.04199475065616798, 0.046109510086455335, 0.02110817941952507, 0.022662889518413595, 0.0, 0.0, 0.02572347266881029, 0.0863166582533626, 0.02094240837696335, 0.0, 0.046109510086455335, 0.04040404040404041, 0.02572347266881029, 0.050955414012738856, 0.02388059701492537, 0.04395604395604396, 0.02359882005899705, 0.041025641025641026, 0.021164021164021163, 0.0, 0.021447721179624665, 0.04199475065616798, 0.022099447513812154, 0.027874564459930314, 0.05555555555555556, 0.023255813953488372, 0.036281179138321996, 0.037037037037037035, 0.016632016632016633, 0.02373887240356083, 0.0463768115942029, 0.0471976401179941, 0.040201005025125636, 0.02572347266881029, 0.043243243243243246, 0.021621621621621623, 0.05263157894736842, 0.020565552699228794, 0.025039123630672924, 0.032128514056224904, 0.02203856749311295, 0.026229508196721315, 0.043596730245231606, 0.018058690744920995, 0.022598870056497175, 0.0, 0.0, 0.022857142857142854, 0.0418848167539267, 0.040100250626566414, 0.04923076923076924, 0.05517241379310345, 0.029520295202952032, 0.024242424242424246, 0.043596730245231606, 0.04571428571428571, 0.023809523809523808, 0.04733727810650887, 0.024242424242424246, 0.025157232704402517, 0.05517241379310345, 0.017897091722595078, 0.023391812865497075, 0.0, 0.03874092009685231, 0.02318840579710145, 0.0, 0.024024024024024024, 0.02507836990595612, 0.04519774011299435, 0.06091370558375634, 0.019002375296912115, 0.043478260869565216, 0.026490066225165566, 0.030534351145038163, 0.04040404040404041, 0.06122448979591837, 0.028070175438596492, 0.02631578947368421, 0.030534351145038163, 0.03493449781659389, 0.0449438202247191, 0.040100250626566414, 0.019047619047619046, 0.03818615751789976, 0.05687203791469195, 0.03619909502262444, 0.04383561643835616, 0.0, 0.016666666666666666, 0.024844720496894415, 0.022598870056497175, 0.036324056185071237, 0.023054755043227668, 0.0, 0.026229508196721315, 0.0, 0.022662889518413595, 0.0418848167539267, 0.02099737532808399, 0.023255813953488372, 0.0335195530726257, 0.0, 0.026229508196721315, 0.06106870229007633, 0.06685236768802227, 0.02439024390243903, 0.04289544235924933, 0.054421768707483, 0.043126684636118594, 0.0, 0.0, 0.021563342318059297, 0.02555910543130991, 0.027027027027027025, 0.024242424242424246, 0.05144694533762058, 0.04733727810650887, 0.02439024390243903, 0.05734767025089607, 0.0221606648199446, 0.024464831804281342, 0.08725484140338292, 0.04050632911392405, 0.017543859649122803, 0.02318840579710145, 0.0, 0.023255813953488372, 0.05734767025089607, 0.019801980198019806, 0.02110817941952507, 0.0, 0.02857142857142857, 0.04938271604938272, 0.0, 0.021798365122615803, 0.046109510086455335, 0.03125, 0.048780487804878044, 0.044444444444444446, 0.059040590405904064, 0.020779220779220783, 0.03232323232323233, 0.03931203931203932, 0.02191780821917808, 0.017467248908296946, 0.026578073089700994, 0.0449438202247191, 0.025157232704402517, 0.026578073089701, 0.03874092009685231, 0.0, 0.02359882005899705, 0.02507836990595612, 0.020202020202020204, 0.03433476394849786, 0.025236593059936908, 0.02946593001841621, 0.018957345971563982, 0.04266666666666667, 0.026229508196721315, 0.025, 0.05405405405405405, 0.05714285714285714, 0.04199475065616798, 0.03125, 0.023809523809523808, 0.046109510086455335, 0.02572347266881029, 0.0, 0.0399002493765586, 0.015779092702169626, 0.04092071611253197, 0.020779220779220783, 0.04092071611253197, 0.041025641025641026, 0.02197802197802198, 0.0547945205479452, 0.032, 0.043596730245231606, 0.02099737532808399, 0.05734767025089607, 0.0625, 0.019801980198019806, 0.0, 0.017543859649122803, 0.0, 0.031746031746031744, 0.0, 0.0, 0.050955414012738856, 0.0, 0.02666666666666667, 0.022662889518413595, 0.04199475065616798, 0.022222222222222223, 0.023054755043227668, 0.03206412825651302, 0.024024024024024024, 0.026229508196721315, 0.0199501246882793, 0.05111821086261982, 0.05633802816901409, 0.0, 0.02056555269922879, 0.05405405405405405, 0.0, 0.05063291139240507, 0.041558441558441565, 0.0, 0.02631578947368421, 0.01171303074670571, 0.02150537634408602, 0.02110817941952507, 0.01873536299765808, 0.025157232704402517, 0.02228412256267409, 0.03382663847780127, 0.05144694533762058, 0.021164021164021163, 0.025157232704402517, 0.036281179138321996, 0.05063291139240507, 0.09169054441260746, 0.019002375296912115, 0.022662889518413595, 0.03018867924528302, 0.023952095808383235, 0.02797202797202797, 0.025, 0.04669260700389105, 0.0469208211143695, 0.05647058823529412, 0.03411513859275053, 0.0399002493765586, 0.03747072599531616, 0.029962546816479405, 0.0471976401179941, 0.025974025974025972, 0.07643312101910828, 0.0625, 0.022662889518413595, 0.028070175438596492, 0.03463203463203463, 0.028368794326241138, 0.07594936708860761, 0.03931203931203932, 0.0632411067193676, 0.035010940919037205, 0.04507042253521127, 0.025806451612903226, 0.030947775628626696, 0.022099447513812154, 0.02631578947368421, 0.018957345971563982, 0.02777777777777778, 0.022662889518413595, 0.0, 0.022857142857142854, 0.023323615160349857, 0.02888086642599278, 0.05574912891986063, 0.04833836858006042, 0.0, 0.049844236760124616, 0.02631578947368421, 0.040100250626566414, 0.0, 0.02416918429003021, 0.030534351145038163, 0.04260985352862849, 0.027303754266211608, 0.02416918429003021, 0.0, 0.05536332179930795, 0.020304568527918784, 0.020202020202020204, 0.022662889518413595, 0.02888086642599278, 0.04199475065616798, 0.03773584905660378, 0.04456824512534818, 0.040201005025125636, 0.06106870229007633, 0.07453416149068325, 0.020050125313283207, 0.03088803088803089, 0.024464831804281342, 0.02909090909090909, 0.04221635883905014, 0.0517799352750809, 0.02359882005899705, 0.04733727810650887, 0.020565552699228794, 0.04289544235924933, 0.020253164556962026, 0.04584527220630373, 0.03137254901960784, 0.0, 0.02888086642599278, 0.04597701149425287, 0.023809523809523808, 0.043596730245231606, 0.048786455949380234, 0.02555910543130991, 0.029038112522686028, 0.03874092009685231, 0.020833333333333332, 0.05543915448793209, 0.024242424242424246, 0.02388059701492537, 0.024464831804281342, 0.041025641025641026, 0.026490066225165566, 0.04071246819338423, 0.02359882005899705, 0.026490066225165566, 0.02110817941952507, 0.04289544235924933, 0.021563342318059297, 0.02318840579710145, 0.042328042328042326, 0.02439024390243903, 0.04289544235924933, 0.027027027027027025, 0.017937219730941704, 0.0, 0.0, 0.02203856749311295, 0.0, 0.022727272727272728, 0.0221606648199446, 0.027874564459930314, 0.02631578947368421, 0.04255319148936171, 0.02318840579710145, 0.0273972602739726, 0.0, 0.04092071611253197, 0.05, 0.03960396039603961, 0.0273972602739726, 0.04199475065616798, 0.06685236768802227, 0.03636363636363637, 0.0, 0.02439024390243903, 0.037647058823529415, 0.10964467005076145, 0.0, 0.0418848167539267, 0.040100250626566414, 0.06537634408602151, 0.02416918429003021, 0.04456824512534818, 0.06015037593984962, 0.023809523809523808, 0.028169014084507046, 0.027303754266211608, 0.041025641025641026, 0.048780487804878044, 0.0632411067193676, 0.025477707006369428, 0.02631578947368421, 0.041025641025641026, 0.02318840579710145, 0.026229508196721315, 0.02507836990595612, 0.029850746268656716, 0.025974025974025972, 0.022535211267605635, 0.02094240837696335, 0.033684210526315796, 0.028169014084507046, 0.0, 0.020100502512562818, 0.04948453608247424, 0.040201005025125636, 0.029850746268656716, 0.0221606648199446, 0.022988505747126436, 0.04878048780487806, 0.05776173285198556, 0.04113110539845759, 0.03206412825651302, 0.045283018867924525, 0.04848484848484849, 0.022662889518413595, 0.028673835125448036, 0.024242424242424246, 0.03347280334728034, 0.02359882005899705, 0.02777777777777778, 0.023809523809523808, 0.04968944099378883, 0.04395604395604396, 0.01965601965601966, 0.05111821086261982, 0.02247191011235955, 0.024242424242424246, 0.01909307875894988, 0.04678362573099415, 0.049844236760124616, 0.02572347266881029, 0.0, 0.01639344262295082, 0.05351170568561873, 0.0, 0.026755852842809364, 0.050955414012738856, 0.024242424242424246, 0.0, 0.02318840579710145, 0.02507836990595612, 0.04638786395929011, 0.05351170568561873, 0.03137254901960784, 0.036281179138321996, 0.021621621621621623, 0.059040590405904064, 0.025, 0.04678362573099415, 0.02439024390243903, 0.0, 0.023809523809523808, 0.016701461377870565, 0.04092071611253197, 0.0213903743315508, 0.028419182948490232, 0.04395604395604396, 0.021220159151193633, 0.025157232704402517, 0.046109510086455335, 0.024024024024024024, 0.0, 0.04790419161676647, 0.02588996763754045, 0.025477707006369428, 0.0, 0.04395604395604396, 0.020253164556962026, 0.02588996763754045, 0.04301075268817204, 0.017977528089887642, 0.033333333333333326, 0.023952095808383235, 0.019370460048426155, 0.0, 0.019047619047619046, 0.0525164113785558, 0.026578073089701, 0.023668639053254434, 0.022099447513812154, 0.04507042253521127, 0.04000000000000001, 0.04395604395604396, 0.020253164556962026, 0.043478260869565216, 0.024464831804281342, 0.02197802197802198, 0.024024024024024024, 0.03193612774451098, 0.05263157894736842, 0.024539877300613498, 0.0, 0.047151277013752456, 0.0, 0.05144694533762058, 0.0, 0.033542976939203356, 0.0, 0.047619047619047616, 0.032, 0.03980099502487562, 0.025974025974025972, 0.05263157894736842, 0.03470715835140998, 0.022727272727272728, 0.043596730245231606, 0.02684563758389262, 0.07771428571428574, 0.023391812865497075, 0.013745704467353953, 0.0625, 0.05970149253731343, 0.023809523809523808, 0.04597701149425287, 0.022099447513812154, 0.05734767025089607, 0.02416918429003021, 0.0365296803652968, 0.0, 0.049844236760124616, 0.02416918429003022, 0.029197080291970802, 0.024024024024024024, 0.021220159151193633, 0.021220159151193633, 0.06106870229007633, 0.04040404040404041, 0.04532577903682719, 0.03470715835140998, 0.02197802197802198, 0.017699115044247787, 0.0, 0.059082277356709505, 0.03603603603603603, 0.032, 0.06106870229007633, 0.06382978723404255, 0.02072538860103627, 0.06976744186046512, 0.0, 0.02962962962962963, 0.024464831804281342, 0.04733727810650887, 0.04507042253521127, 0.024242424242424246, 0.023809523809523808, 0.026490066225165566, 0.025806451612903226, 0.02962962962962963, 0.02346041055718475, 0.05769230769230769, 0.028673835125448036, 0.043596730245231606, 0.023255813953488372, 0.025974025974025972, 0.022099447513812154, 0.04266666666666667, 0.03125, 0.0449438202247191, 0.02588996763754045, 0.03470715835140998, 0.024767801857585144, 0.024922118380062308, 0.01990049751243781, 0.05309734513274337, 0.0, 0.018306636155606407, 0.0, 0.026578073089701, 0.04301075268817204, 0.02318840579710145, 0.03980099502487562, 0.03076923076923077, 0.0, 0.058679706601467, 0.06521739130434782, 0.024205748865355526, 0.040201005025125636, 0.04255319148936171, 0.017937219730941704, 0.03678160919540229, 0.01965601965601966, 0.023529411764705882, 0.06451612903225805, 0.0418848167539267, 0.04289544235924933, 0.04519774011299435, 0.02318840579710145, 0.021621621621621623, 0.022598870056497175, 0.07640449438202249, 0.029850746268656716, 0.02228412256267409, 0.044444444444444446, 0.034261241970021415, 0.0471976401179941, 0.0, 0.03088803088803089, 0.04747774480712166, 0.020565552699228794, 0.0, 0.014336917562724018, 0.02197802197802198, 0.035955056179775284, 0.021276595744680854, 0.022988505747126436, 0.039024390243902446, 0.02909090909090909, 0.0, 0.05620608899297424, 0.022988505747126436, 0.036281179138321996, 0.04456824512534818, 0.02439024390243903, 0.04395604395604396, 0.05821962313190383, 0.02359882005899705, 0.024844720496894415, 0.041025641025641026, 0.026490066225165566, 0.04199475065616798, 0.04571428571428571, 0.02318840579710145, 0.056737588652482275, 0.028368794326241138, 0.040201005025125636, 0.05574912891986063, 0.04804804804804805, 0.022222222222222223, 0.0, 0.019851116625310174, 0.09380267781857499, 0.04383561643835616, 0.0, 0.04430432262257488, 0.05970149253731343, 0.037825059101654845, 0.05714285714285714, 0.03225806451612904, 0.026058631921824105, 0.029520295202952032, 0.021680216802168018, 0.04968944099378883, 0.044444444444444446, 0.023054755043227668, 0.022662889518413595, 0.019002375296912115, 0.0, 0.04395604395604396, 0.023809523809523808, 0.027586206896551724, 0.025157232704402517, 0.02439024390243903, 0.02555910543130991, 0.02318840579710145, 0.040816326530612256, 0.07594936708860761, 0.024464831804281342, 0.04507042253521127, 0.023054755043227668, 0.054176072234762986, 0.020779220779220783, 0.0471976401179941, 0.023255813953488372, 0.0, 0.025316455696202535, 0.04177545691906005, 0.02777777777777778, 0.037037037037037035, 0.021447721179624665, 0.04145077720207253, 0.022988505747126436, 0.026229508196721315, 0.04456824512534818, 0.029962546816479405, 0.025559105431309903, 0.02197802197802198, 0.03686635944700461, 0.023054755043227668, 0.0, 0.03041825095057035, 0.026755852842809364, 0.022727272727272728, 0.02061855670103093, 0.03931203931203932, 0.029962546816479405, 0.0, 0.027874564459930314, 0.0, 0.028985507246376815, 0.024464831804281342, 0.022099447513812154, 0.028985507246376815, 0.051063829787234054, 0.02388059701492537, 0.04678362573099415, 0.0272108843537415, 0.023809523809523808, 0.040100250626566414, 0.04968944099378883, 0.05263157894736842, 0.0443213296398892, 0.034188034188034185, 0.05063291139240507, 0.026490066225165566, 0.0449438202247191, 0.024844720496894415, 0.022662889518413595, 0.016427104722792612, 0.02197802197802198, 0.029520295202952032, 0.022988505747126436, 0.06916426512968299, 0.02962962962962963, 0.024242424242424246, 0.038929440389294405, 0.02857142857142857, 0.025236593059936908, 0.02555910543130991, 0.02359882005899705, 0.02110817941952507, 0.03433476394849786, 0.0, 0.0, 0.029962546816479405, 0.029962546816479405, 0.020304568527918784, 0.04678362573099415, 0.03131115459882584, 0.04733727810650887, 0.021680216802168018, 0.038461538461538464, 0.02439024390243903, 0.023255813953488372, 0.05555555555555556, 0.04968944099378883, 0.021333333333333336, 0.0418848167539267, 0.027350427350427354, 0.025236593059936908, 0.023809523809523808, 0.022222222222222223, 0.021621621621621623, 0.020460358056265986, 0.022408963585434174, 0.023391812865497075, 0.02507836990595612, 0.024844720496894415, 0.04848484848484849, 0.0, 0.024242424242424246, 0.043596730245231606, 0.04456824512534818, 0.06217616580310881, 0.023809523809523808, 0.04419889502762431, 0.028985507246376815, 0.0, 0.020304568527918784, 0.01509433962264151, 0.04255319148936171, 0.021739130434782608, 0.028985507246376815, 0.015037593984962409, 0.029962546816479405, 0.022857142857142854, 0.0, 0.024844720496894415, 0.02555910543130991, 0.0, 0.017738359201773836, 0.026058631921824105, 0.03187250996015937, 0.024844720496894415, 0.0, 0.0, 0.05111821086261982, 0.020253164556962026, 0.022099447513812154, 0.025157232704402517, 0.03747072599531616, 0.02228412256267409, 0.0, 0.022408963585434174, 0.03644646924829157, 0.0, 0.02507836990595612, 0.018518518518518517, 0.05387205387205388, 0.035010940919037205, 0.03611738148984199, 0.022099447513812154, 0.0, 0.023255813953488372, 0.0, 0.023054755043227668, 0.020253164556962026, 0.024844720496894415, 0.022662889518413595, 0.020202020202020204, 0.026755852842809364, 0.022346368715083803, 0.02572347266881029, 0.05405405405405405, 0.04938271604938272, 0.04301075268817204, 0.06106870229007633, 0.0, 0.02631578947368421, 0.023668639053254434, 0.05351170568561873, 0.03931203931203932, 0.020100502512562818, 0.023255813953488372, 0.022598870056497175, 0.020512820512820513, 0.029962546816479405, 0.020565552699228794, 0.02507836990595612, 0.027027027027027025, 0.02572347266881029, 0.06539509536784742, 0.04848484848484849, 0.0547945205479452, 0.023809523809523808, 0.031372549019607836, 0.021447721179624665, 0.03125, 0.02191780821917808, 0.021680216802168018, 0.04255319148936171, 0.04071246819338423, 0.021621621621621623, 0.029850746268656716, 0.05693950177935944, 0.07973421926910298, 0.022099447513812154, 0.0, 0.0, 0.047058823529411764, 0.0, 0.03305785123966942, 0.05111821086261982, 0.022857142857142854, 0.02318840579710145, 0.019801980198019806, 0.020253164556962026, 0.02439024390243903, 0.04804804804804805, 0.030075187969924817, 0.01673640167364017, 0.02909090909090909, 0.0, 0.016632016632016633, 0.02228412256267409, 0.05925925925925926, 0.04113110539845758, 0.0, 0.029411764705882356, 0.0712166172106825, 0.043478260869565216, 0.03375527426160337, 0.027303754266211608, 0.019704433497536946, 0.039024390243902446, 0.022535211267605635, 0.05405405405405405, 0.06075949367088607, 0.0, 0.028169014084507046, 0.022099447513812154, 0.031496062992125984, 0.06521739130434782, 0.024922118380062308, 0.03404255319148936, 0.03864734299516908, 0.018604651162790697, 0.0469208211143695, 0.02094240837696335, 0.0272108843537415, 0.020202020202020204, 0.0, 0.019801980198019806, 0.02110817941952507, 0.037825059101654845, 0.024024024024024024, 0.035010940919037205, 0.04289544235924933, 0.06629834254143645, 0.024464831804281342, 0.020408163265306128, 0.03088803088803089, 0.022099447513812154, 0.024539877300613498, 0.02359882005899705, 0.0, 0.02099737532808399, 0.026402640264026403, 0.032, 0.02439024390243903, 0.026229508196721315, 0.06539509536784742, 0.0, 0.025806451612903226, 0.022988505747126436, 0.02588996763754045, 0.01609657947686117, 0.048192771084337345, 0.0, 0.023952095808383235, 0.04301075268817204, 0.04301075268817204, 0.030303030303030304, 0.0, 0.03940886699507389, 0.024242424242424246, 0.018140589569160998, 0.025806451612903226, 0.0, 0.028169014084507046, 0.0, 0.0, 0.0, 0.04790419161676647, 0.0, 0.025806451612903226, 0.04938271604938272, 0.049079754601226995, 0.06837606837606837, 0.0, 0.028169014084507046, 0.028169014084507046, 0.04383561643835616, 0.04456824512534818, 0.05985037406483791, 0.02191780821917808, 0.04968944099378883, 0.023255813953488372, 0.025806451612903226, 0.023054755043227668, 0.0, 0.04395604395604396, 0.0, 0.0449438202247191, 0.026058631921824105, 0.0, 0.0, 0.023809523809523808, 0.0, 0.0712166172106825, 0.04507042253521127, 0.0, 0.050955414012738856, 0.04395604395604396, 0.02572347266881029, 0.024464831804281342, 0.024464831804281342, 0.022535211267605635, 0.020202020202020204, 0.035955056179775284, 0.0, 0.02439024390243903, 0.05351170568561873, 0.023809523809523808, 0.05063291139240507, 0.04507042253521127, 0.028169014084507046, 0.035010940919037205, 0.05542725173210161, 0.018058690744920995, 0.04804804804804805, 0.021447721179624665, 0.035634743875278395, 0.041025641025641026, 0.02572347266881029, 0.020202020202020204, 0.021621621621621623, 0.02631578947368421, 0.0449438202247191, 0.040100250626566414, 0.024539877300613498, 0.01716738197424893, 0.04923076923076924, 0.023391812865497075, 0.027027027027027025, 0.032520325203252036, 0.025, 0.02122015915119364, 0.018181818181818184, 0.041558441558441565, 0.05245901639344263, 0.043596730245231606, 0.02572347266881029, 0.03686635944700461, 0.0, 0.0, 0.0, 0.0449438202247191, 0.024242424242424246, 0.0, 0.022662889518413595, 0.025236593059936908, 0.024844720496894415, 0.022099447513812154, 0.024096385542168672, 0.041558441558441565, 0.04040404040404041, 0.04040404040404041, 0.02777777777777778, 0.0, 0.04383561643835616, 0.035634743875278395, 0.0, 0.045159046370964545, 0.04597701149425287, 0.02094240837696335, 0.025157232704402517, 0.02359882005899705, 0.016064257028112452, 0.0, 0.022222222222222223, 0.024242424242424246, 0.0, 0.054982817869415807, 0.024242424242424246, 0.0, 0.0, 0.025236593059936908, 0.05660377358490566, 0.025, 0.051063829787234054, 0.047713717693836984, 0.0, 0.021621621621621623, 0.013961605584642234, 0.0, 0.03493449781659389, 0.054607508532423216, 0.02061855670103093, 0.05517241379310345, 0.017777777777777778, 0.02373887240356083, 0.025806451612903226, 0.0, 0.0, 0.03493449781659389, 0.0449438202247191, 0.049844236760124616, 0.021798365122615803, 0.015625, 0.0, 0.023323615160349857, 0.017582417582417586, 0.06233766233766234, 0.023391812865497075, 0.0, 0.024464831804281342, 0.023054755043227668, 0.05405405405405405, 0.04289544235924933, 0.0, 0.02439024390243903, 0.0, 0.022099447513812154, 0.02572347266881029, 0.04301075268817204, 0.02197802197802198, 0.018058690744920995, 0.02318840579710145, 0.028985507246376815, 0.024242424242424246, 0.024464831804281342, 0.021563342318059297, 0.03792910467671071, 0.03088803088803089, 0.0, 0.0, 0.05714285714285714, 0.04633204633204634, 0.018648018648018648, 0.0, 0.03809523809523809, 0.05063291139240507, 0.02777777777777778, 0.039024390243902446, 0.04289544235924933, 0.0399002493765586, 0.03738317757009346, 0.046109510086455335, 0.020779220779220783, 0.03669724770642201, 0.04456824512534818, 0.019230769230769232, 0.03855421686746988, 0.02391629297458894, 0.06685236768802227, 0.046109510086455335, 0.043126684636118594, 0.02777777777777778, 0.023391812865497075, 0.059040590405904064, 0.01724137931034483, 0.03747072599531616, 0.020253164556962026, 0.05581395348837209, 0.024024024024024024, 0.05111821086261982, 0.03855421686746988, 0.04395604395604396, 0.0, 0.03611738148984199, 0.0469208211143695, 0.025477707006369428, 0.027118644067796613, 0.04968944099378883, 0.05351170568561873, 0.0471976401179941, 0.0, 0.018518518518518517, 0.022662889518413595, 0.0, 0.023809523809523808, 0.018912529550827423, 0.02191780821917808, 0.021798365122615803, 0.029962546816479405, 0.02110817941952507, 0.021447721179624665, 0.0, 0.032520325203252036, 0.0, 0.026058631921824105, 0.03644646924829157, 0.020512820512820513, 0.023054755043227668, 0.0, 0.0213903743315508, 0.04624277456647399, 0.04471057884231537, 0.025806451612903226, 0.05144694533762058, 0.04790419161676647, 0.021052631578947364, 0.03292181069958848, 0.045584045584045586, 0.02555910543130991, 0.050473186119873815, 0.0, 0.029520295202952032, 0.025316455696202535, 0.022662889518413595, 0.027303754266211608, 0.021276595744680854, 0.0, 0.0, 0.0, 0.03931203931203932, 0.05144694533762058, 0.041666666666666664, 0.04678362573099415, 0.04968944099378883, 0.043596730245231606, 0.0, 0.0, 0.016949152542372885, 0.02439024390243903, 0.0, 0.03931203931203932, 0.023809523809523808, 0.0316205533596838, 0.0182648401826484, 0.043126684636118594, 0.0, 0.0273972602739726, 0.02631578947368421, 0.030534351145038163, 0.017094017094017092, 0.0, 0.04571428571428571, 0.05236703682057277, 0.04507042253521127, 0.05985037406483791, 0.02572347266881029, 0.03695150115473441, 0.05351170568561873, 0.06495726495726496, 0.027303754266211608, 0.020000000000000004, 0.0, 0.028070175438596492, 0.04113110539845759, 0.02197802197802198, 0.02857142857142857, 0.023323615160349857, 0.014084507042253523, 0.04050632911392405, 0.023054755043227668, 0.050473186119873815, 0.02072538860103627, 0.037037037037037035, 0.022099447513812154, 0.02247191011235955, 0.025316455696202535, 0.06233766233766234, 0.02826855123674912, 0.03695150115473441, 0.02461538461538462, 0.05111821086261982, 0.024242424242424246, 0.02461538461538462, 0.04923076923076924, 0.0, 0.025236593059936908, 0.023809523809523808, 0.02572347266881029, 0.04923076923076924, 0.02359882005899705, 0.03292181069958847, 0.05063291139240507, 0.05309734513274337, 0.025157232704402517, 0.02572347266881029, 0.020460358056265986, 0.021680216802168018, 0.025236593059936908, 0.050473186119873815, 0.019002375296912115, 0.01909307875894988, 0.04733727810650887, 0.06857142857142856, 0.027538726333907054, 0.0, 0.02150537634408602, 0.020304568527918784, 0.021621621621621623, 0.02507836990595612, 0.046109510086455335, 0.03738317757009346, 0.023668639053254434, 0.037647058823529415, 0.05245901639344263, 0.0, 0.0, 0.023255813953488372, 0.020000000000000004, 0.02507836990595612, 0.0794701986754967, 0.02318840579710145, 0.040100250626566414, 0.04507042253521127, 0.0, 0.021680216802168018, 0.0, 0.021621621621621623, 0.0, 0.026578073089701, 0.04804804804804805, 0.04571428571428571, 0.0, 0.02099737532808399, 0.028419182948490232, 0.032, 0.03809523809523809, 0.059040590405904064, 0.0, 0.051282051282051294, 0.046109510086455335, 0.058394160583941604, 0.04013377926421405, 0.0641711229946524, 0.0471976401179941, 0.06470287262685201, 0.0, 0.02644628099173554, 0.05714285714285714, 0.041666666666666664, 0.05127079700763643, 0.025806451612903226, 0.026490066225165566, 0.023255813953488372, 0.032128514056224904, 0.0, 0.020253164556962026, 0.0, 0.024464831804281342, 0.019704433497536946, 0.025806451612903226, 0.018306636155606407, 0.0449438202247191, 0.0, 0.0, 0.0, 0.035955056179775284, 0.035634743875278395, 0.013840830449826988, 0.03669724770642201, 0.024316109422492405, 0.042440318302387266, 0.026490066225165566, 0.02228412256267409, 0.022222222222222223, 0.038929440389294405, 0.0, 0.01646090534979424, 0.049281314168377825, 0.03493449781659389, 0.05111821086261982, 0.025477707006369428, 0.02359882005899705, 0.04923076923076924, 0.046511627906976744, 0.02572347266881029, 0.022598870056497175, 0.0, 0.012924071082390954, 0.0273972602739726, 0.05405405405405405, 0.0392156862745098, 0.020202020202020204, 0.020460358056265986, 0.04289544235924933, 0.019230769230769232, 0.039024390243902446, 0.03695150115473441, 0.02439024390243903, 0.0, 0.022099447513812154, 0.04289544235924933, 0.023809523809523808, 0.051063829787234054, 0.025157232704402517, 0.06037735849056604, 0.02388059701492537, 0.045454545454545456, 0.04081632653061224, 0.06997084548104957, 0.02909090909090909, 0.0, 0.0, 0.021621621621621623, 0.050955414012738856, 0.0, 0.023255813953488372, 0.06666666666666665, 0.028673835125448036, 0.021052631578947364, 0.02359882005899705, 0.0, 0.02671614100185529, 0.01990049751243781, 0.05245901639344263, 0.033126293995859216, 0.0, 0.04790419161676647, 0.020725388601036267, 0.05, 0.0, 0.02359882005899705, 0.04456824512534818, 0.04804804804804805, 0.0, 0.021680216802168018, 0.0, 0.02110817941952507, 0.02572347266881029, 0.019464720194647202, 0.02373887240356083, 0.0, 0.02150537634408602, 0.02416918429003021, 0.024844720496894415, 0.0, 0.027303754266211608, 0.02094240837696335, 0.04210526315789473, 0.023529411764705882, 0.037037037037037035, 0.028673835125448036, 0.025974025974025972, 0.032854209445585224, 0.046109510086455335, 0.03773584905660378, 0.03773584905660378, 0.04597701149425287, 0.03980099502487562, 0.02507836990595612, 0.029962546816479405, 0.05263157894736842, 0.0, 0.008592910848549946, 0.02359882005899705, 0.025157232704402517, 0.030379746835443033, 0.028169014084507046, 0.0213903743315508, 0.0221606648199446, 0.06075949367088607, 0.0, 0.0, 0.0350109409190372, 0.021621621621621623, 0.0418848167539267, 0.018390804597701146, 0.020356234096692113, 0.0, 0.024922118380062308, 0.0471976401179941, 0.0, 0.05111821086261982, 0.0, 0.020671834625323, 0.0, 0.0, 0.06400982731142163, 0.03773584905660378, 0.024464831804281342, 0.02572347266881029, 0.04383561643835616, 0.023668639053254434, 0.0, 0.05144694533762058, 0.05568445475638051, 0.025, 0.08333333333333334, 0.0463768115942029, 0.037914691943127965, 0.032, 0.027027027027027025, 0.02962962962962963, 0.07453416149068325, 0.02197802197802198, 0.041666666666666664, 0.03018867924528302, 0.0418848167539267, 0.037825059101654845, 0.0418848167539267, 0.022857142857142854, 0.04456824512534818, 0.05405405405405405, 0.0, 0.04210526315789473, 0.04040404040404041, 0.01724137931034483, 0.019801980198019806, 0.04383561643835616, 0.0471976401179941, 0.0486815415821501, 0.023564064801178203, 0.02318840579710145, 0.01927710843373494, 0.037647058823529415, 0.03193612774451098, 0.0, 0.025, 0.027303754266211608, 0.025316455696202535, 0.019370460048426155, 0.023809523809523808, 0.0, 0.019801980198019806, 0.022099447513812154, 0.05015673981191224, 0.023809523809523808, 0.06837606837606837, 0.022988505747126436, 0.04395604395604396, 0.027303754266211608, 0.020512820512820513, 0.021739130434782608, 0.024242424242424246, 0.05574912891986063, 0.022662889518413595, 0.05574912891986063, 0.04060913705583757, 0.028673835125448036, 0.017316017316017316, 0.022662889518413595, 0.0, 0.0, 0.0, 0.026186579378068744, 0.044692737430167606, 0.02439024390243903, 0.0, 0.0, 0.04456824512534818, 0.03433476394849786, 0.027681660899653977, 0.03747072599531616, 0.018018018018018014, 0.03686635944700461, 0.020671834625323, 0.020050125313283207, 0.03818615751789976, 0.024464831804281342, 0.021276595744680854, 0.05245901639344263, 0.03470715835140998, 0.021621621621621623, 0.0, 0.04255319148936171, 0.0, 0.0, 0.026578073089701, 0.025806451612903226, 0.035634743875278395, 0.035555555555555556, 0.022598870056497175, 0.03773584905660378, 0.020671834625323, 0.024844720496894415, 0.04199475065616798, 0.024922118380062308, 0.0, 0.0, 0.06539509536784742, 0.01735357917570499, 0.02359882005899705, 0.06685236768802227, 0.0, 0.04199475065616798, 0.022662889518413595, 0.050473186119873815, 0.02197802197802198, 0.025806451612903226, 0.025806451612903226, 0.024539877300613498, 0.026229508196721315, 0.05405405405405405, 0.061855670103092786, 0.04678362573099415, 0.02318840579710145, 0.04968944099378883, 0.027303754266211608, 0.0, 0.0, 0.037209302325581395, 0.020512820512820513, 0.04776119402985075, 0.0, 0.05111821086261982, 0.0, 0.0, 0.05063291139240507, 0.032520325203252036, 0.025477707006369428, 0.027027027027027025, 0.022598870056497175, 0.02247191011235955, 0.03936956942960754, 0.04733727810650887, 0.023255813953488372, 0.024464831804281342, 0.02826855123674912, 0.022727272727272728, 0.040100250626566414, 0.0, 0.027303754266211608, 0.023255813953488372, 0.045584045584045586, 0.041558441558441565, 0.0, 0.022346368715083803, 0.044444444444444446, 0.0, 0.03470715835140998, 0.0427807486631016, 0.02572347266881029, 0.02507836990595612, 0.023054755043227668, 0.023255813953488372, 0.0449438202247191, 0.02572347266881029, 0.05063291139240507, 0.023809523809523808, 0.026578073089701, 0.03773584905660378, 0.0, 0.027874564459930314, 0.027303754266211608, 0.027027027027027025, 0.04507042253521127, 0.020512820512820513, 0.03404255319148936, 0.039024390243902446, 0.025157232704402517, 0.023809523809523808, 0.04968944099378883, 0.025236593059936908, 0.035794183445190156, 0.07792207792207793, 0.0399002493765586, 0.05405405405405405, 0.028673835125448036, 0.0427807486631016, 0.025157232704402517, 0.0443213296398892, 0.02247191011235955, 0.022988505747126436, 0.022598870056497175, 0.0, 0.05405405405405405, 0.041025641025641026, 0.033126293995859216, 0.0, 0.03855421686746988, 0.04289544235924933, 0.022988505747126436, 0.025806451612903226, 0.0, 0.04532577903682719, 0.022598870056497175, 0.026143790849673207, 0.05351170568561873, 0.03293900483767472, 0.022662889518413595, 0.043596730245231606, 0.040201005025125636, 0.0, 0.046109510086455335, 0.021220159151193633, 0.04371584699453552, 0.037209302325581395, 0.0, 0.0, 0.0272108843537415, 0.02197802197802198, 0.04210526315789473, 0.039119804400978, 0.0, 0.0, 0.020671834625323, 0.0, 0.02150537634408602, 0.0, 0.026402640264026403, 0.0, 0.029850746268656716, 0.0449438202247191, 0.02072538860103627, 0.023668639053254434, 0.02359882005899705, 0.019230769230769232, 0.023054755043227668, 0.04507042253521127, 0.0, 0.0399002493765586, 0.04456824512534818, 0.0, 0.025477707006369428, 0.01965601965601966, 0.025974025974025972, 0.046109510086455335, 0.025806451612903226, 0.0471976401179941, 0.05594405594405594, 0.022346368715083803, 0.02247191011235955, 0.02439024390243903, 0.023809523809523808, 0.0, 0.051948051948051945, 0.05263157894736842, 0.0, 0.0, 0.0, 0.025236593059936908, 0.04113110539845759, 0.026578073089701, 0.05161290322580645, 0.026936026936026942, 0.05063291139240507, 0.029739776951672865, 0.04678362573099415, 0.0, 0.0, 0.049844236760124616, 0.015296367112810709, 0.023952095808383235, 0.04383561643835616, 0.04571428571428571, 0.02359882005899705, 0.05415824983550334, 0.01886792452830189, 0.0199501246882793, 0.051063829787234054, 0.05985037406483791, 0.0, 0.022988505747126436, 0.05970149253731343, 0.021798365122615803, 0.03493449781659389, 0.05405405405405405, 0.0, 0.026229508196721315, 0.022598870056497175, 0.02507836990595612, 0.04733727810650887, 0.023255813953488372, 0.021739130434782608, 0.05245901639344263, 0.02388059701492537, 0.04255319148936171, 0.036281179138321996, 0.025559105431309903, 0.022598870056497175, 0.02359882005899705, 0.01436265709156194, 0.058608058608058615, 0.0, 0.037825059101654845, 0.03125, 0.034261241970021415, 0.04833836858006042, 0.02359882005899705, 0.04571428571428571, 0.02318840579710145, 0.0, 0.046511627906976744, 0.035398230088495575, 0.04571428571428571, 0.02416918429003021, 0.0471976401179941, 0.025477707006369428, 0.03818615751789976, 0.0449438202247191, 0.029850746268656716, 0.017817371937639197, 0.07058823529411766, 0.0471976401179941, 0.0, 0.04301075268817204, 0.02555910543130991, 0.03669724770642201, 0.04968944099378883, 0.032, 0.04071246819338423, 0.02797202797202797, 0.054176072234762986, 0.02572347266881029, 0.03463203463203463, 0.03931203931203932, 0.019002375296912115, 0.0, 0.021164021164021163, 0.019047619047619046, 0.03874092009685231, 0.05714285714285714, 0.018561484918793503, 0.029304029304029307, 0.0, 0.0, 0.05925925925925926, 0.02197802197802198, 0.02555910543130991, 0.06857142857142856, 0.0449438202247191, 0.04804804804804805, 0.026058631921824105, 0.0221606648199446, 0.04571428571428571, 0.0221606648199446, 0.024844720496894415, 0.0, 0.048192771084337345, 0.0221606648199446, 0.0, 0.03404255319148936, 0.03695150115473441, 0.052173913043478265, 0.04584527220630373, 0.02388059701492537, 0.02388059701492537, 0.058394160583941604, 0.060453400503778336, 0.022727272727272728, 0.03143418467583497, 0.0, 0.026755852842809364, 0.0, 0.04395604395604396, 0.0, 0.023809523809523808, 0.03800475059382423, 0.03855421686746988, 0.022662889518413595, 0.04678362573099415, 0.026578073089701, 0.027303754266211608, 0.02359882005899705, 0.02555910543130991, 0.026229508196721315, 0.05925925925925926, 0.022988505747126436, 0.0, 0.03258655804480652, 0.020202020202020204, 0.03773584905660378, 0.04221635883905014, 0.027303754266211608, 0.041558441558441565, 0.04289544235924933, 0.05263157894736842, 0.028169014084507046, 0.027027027027027025, 0.02507836990595612, 0.04092071611253197, 0.026229508196721315, 0.02572347266881029, 0.024922118380062308, 0.025, 0.0, 0.04289544235924933, 0.04804804804804805, 0.04060913705583757, 0.025806451612903226, 0.0399002493765586, 0.04255319148936171, 0.018223234624145785, 0.022408963585434174, 0.022099447513812154, 0.04289544235924933, 0.05111821086261982, 0.02094240837696335, 0.03931203931203932, 0.0, 0.024464831804281342, 0.02388059701492537, 0.043596730245231606, 0.02359882005899705, 0.0, 0.029520295202952032, 0.026490066225165566, 0.024316109422492405, 0.025477707006369428, 0.0418848167539267, 0.05063291139240507, 0.026578073089701, 0.05925925925925926, 0.0, 0.06539509536784742, 0.04383561643835616, 0.02572347266881029, 0.033542976939203356, 0.02318840579710145, 0.0449438202247191, 0.04255319148936171, 0.03695150115473441, 0.03587443946188341, 0.021447721179624665, 0.026490066225165566, 0.0463768115942029, 0.022598870056497175, 0.04301075268817204, 0.04733727810650887, 0.0273972602739726, 0.03931203931203932, 0.02359882005899705, 0.038461538461538464, 0.020304568527918784, 0.04123711340206186, 0.020671834625323, 0.0, 0.02588996763754045, 0.048929663608562685, 0.0221606648199446, 0.0, 0.028673835125448036, 0.0, 0.027006665175473857, 0.022535211267605635, 0.0, 0.028169014084507046, 0.05504587155963303, 0.016842105263157898, 0.021739130434782608, 0.0, 0.024464831804281342, 0.04938271604938272, 0.022408963585434174, 0.02150537634408602, 0.025, 0.05333333333333334, 0.035555555555555556, 0.028169014084507046, 0.026229508196721315, 0.042628774422735355, 0.022662889518413595, 0.04383561643835616, 0.040201005025125636, 0.017897091722595078, 0.06685236768802227, 0.03931203931203932, 0.024242424242424246, 0.041558441558441565, 0.016842105263157898, 0.0, 0.022408963585434174, 0.03232323232323233, 0.022535211267605635, 0.02247191011235955, 0.021447721179624665, 0.049844236760124616, 0.035555555555555556, 0.06315789473684211, 0.022792022792022793, 0.04137931034482759, 0.032, 0.06593406593406592, 0.0, 0.02572347266881029, 0.02247191011235955, 0.025477707006369428, 0.0, 0.021164021164021163, 0.04584527220630373, 0.024096385542168672, 0.11497392085473383, 0.04113110539845759, 0.02572347266881029, 0.0, 0.02416918429003021, 0.05542725173210161, 0.022727272727272728, 0.04383561643835616, 0.04519774011299435, 0.0418848167539267, 0.04733727810650887, 0.039024390243902446, 0.04733727810650887, 0.0, 0.03980099502487562, 0.04848484848484849, 0.059040590405904064, 0.021680216802168018, 0.02318840579710145, 0.023809523809523808, 0.037209302325581395, 0.041025641025641026, 0.03305785123966942, 0.0, 0.04790419161676647, 0.0, 0.019464720194647202, 0.05783132530120482, 0.022662889518413595, 0.023323615160349857, 0.04289544235924933, 0.07272727272727274, 0.028673835125448036, 0.025157232704402517, 0.020202020202020204, 0.02555910543130991, 0.0, 0.04071246819338423, 0.024464831804281342, 0.02631578947368421, 0.0, 0.0, 0.06037735849056604, 0.08515406162464986, 0.0, 0.02946593001841621, 0.050473186119873815, 0.03827751196172249, 0.04071246819338423, 0.04071246819338423, 0.04968944099378883, 0.028070175438596492, 0.0, 0.017817371937639197, 0.05405405405405405, 0.03738317757009346, 0.046109510086455335, 0.06799568142666557, 0.0, 0.02857142857142857, 0.027303754266211608, 0.037122969837587005, 0.02388059701492537, 0.0, 0.01873536299765808, 0.0, 0.0, 0.04804804804804805, 0.04289544235924933, 0.022598870056497175, 0.0, 0.0, 0.04481792717086835, 0.0, 0.036281179138321996, 0.022662889518413595, 0.02005012531328321, 0.01639344262295082, 0.020512820512820513, 0.03333333333333333, 0.0, 0.021164021164021163, 0.04804804804804805, 0.0, 0.02572347266881029, 0.02461538461538462, 0.04678362573099415, 0.022662889518413595, 0.028135990621336454, 0.023054755043227668, 0.021052631578947364, 0.03193612774451098, 0.024922118380062308, 0.03088803088803089, 0.035010940919037205, 0.022727272727272728, 0.0, 0.02631578947368421, 0.025806451612903226, 0.022727272727272728, 0.022792022792022793, 0.03493449781659389, 0.028169014084507046, 0.02388059701492537, 0.026490066225165566, 0.020460358056265986, 0.022792022792022793, 0.05387205387205388, 0.05755395683453238, 0.0, 0.03018867924528302, 0.02555910543130991, 0.02318840579710145, 0.02150537634408602, 0.05263157894736842, 0.02572347266881029, 0.03143418467583497, 0.050473186119873815, 0.0, 0.03827751196172249, 0.023054755043227668, 0.022988505747126436, 0.01990049751243781, 0.04266666666666667, 0.02318840579710145, 0.045584045584045586, 0.06539509536784742, 0.051948051948051945, 0.020833333333333332, 0.020202020202020204, 0.018181818181818184, 0.04456824512534818, 0.028169014084507046, 0.017057569296375266, 0.0625, 0.04848484848484849, 0.017699115044247787, 0.025316455696202535, 0.018475750577367205, 0.04833836858006042, 0.021164021164021163, 0.027027027027027025, 0.03258655804480652, 0.025974025974025972, 0.03864734299516908, 0.0182648401826484, 0.0517799352750809, 0.04678362573099415, 0.03747072599531616, 0.043596730245231606, 0.02318840579710145, 0.027586206896551724, 0.08684902945860466, 0.021680216802168018, 0.05405405405405405, 0.0, 0.016949152542372885, 0.039119804400978, 0.04848484848484849, 0.050955414012738856, 0.0, 0.03738317757009346, 0.025236593059936908, 0.054421768707483, 0.0, 0.049844236760124616, 0.02909090909090909, 0.020356234096692113, 0.029520295202952032, 0.02359882005899705, 0.04923076923076924, 0.0, 0.017937219730941704, 0.0, 0.0, 0.016359918200409, 0.024024024024024024, 0.0, 0.0, 0.02346041055718475, 0.0, 0.07619012292754912, 0.0, 0.029962546816479405, 0.024464831804281342, 0.019138755980861243, 0.025806451612903226, 0.023255813953488372, 0.0, 0.02110817941952507, 0.02555910543130991, 0.06153846153846154, 0.04571428571428571, 0.04848484848484849, 0.02072538860103627, 0.017467248908296946, 0.03636363636363637, 0.02359882005899705, 0.0, 0.0, 0.051948051948051945, 0.04833836858006042, 0.024464831804281342, 0.020050125313283207, 0.024242424242424246, 0.020887728459530026, 0.058536585365853655, 0.07256702166727874, 0.040100250626566414, 0.026490066225165566, 0.0175054704595186, 0.026490066225165566, 0.03455723542116631, 0.03855421686746988, 0.03258655804480652, 0.030360531309297913, 0.019704433497536946, 0.0, 0.040816326530612256, 0.023391812865497075, 0.04221635883905014, 0.03619909502262444, 0.02072538860103627, 0.019047619047619046, 0.041025641025641026, 0.0, 0.043478260869565216, 0.02507836990595612, 0.04507042253521127, 0.01484230055658627, 0.0, 0.041025641025641026, 0.023668639053254434, 0.026578073089701, 0.018957345971563982, 0.02572347266881029, 0.025, 0.026229508196721315, 0.021164021164021163, 0.0418848167539267, 0.043478260869565216, 0.07142857142857142, 0.030534351145038163, 0.024844720496894415, 0.0213903743315508, 0.03931203931203932, 0.04678362573099415, 0.05155269829702551, 0.023054755043227668, 0.03041825095057035, 0.019801980198019806, 0.05111821086261982, 0.0, 0.03855421686746988, 0.045368620037807186, 0.04383561643835616, 0.022346368715083803, 0.03695150115473441, 0.01965601965601966, 0.06106870229007633, 0.02439024390243903, 0.04804804804804805, 0.021563342318059297, 0.02094240837696335, 0.021447721179624665, 0.0, 0.02507836990595612, 0.04597701149425287, 0.01990049751243781, 0.04923076923076924, 0.051948051948051945, 0.040201005025125636, 0.0377975187816438, 0.02588996763754045, 0.06539509536784742, 0.0213903743315508, 0.023054755043227668, 0.050955414012738856, 0.023952095808383235, 0.01941747572815534, 0.0, 0.0449438202247191, 0.029520295202952032, 0.02346041055718475, 0.027303754266211608, 0.025157232704402517, 0.03375527426160337, 0.028985507246376815, 0.0, 0.036281179138321996, 0.02439024390243903, 0.025316455696202535, 0.0, 0.05517241379310345, 0.02507836990595612, 0.04395604395604396, 0.05896805896805897, 0.024464831804281342, 0.03836930455635492, 0.022988505747126436, 0.02099737532808399, 0.02888086642599278, 0.05992509363295881, 0.0, 0.024844720496894415, 0.02110817941952507, 0.026490066225165566, 0.028169014084507046, 0.01436265709156194, 0.04199475065616798, 0.04199475065616798, 0.018604651162790697, 0.050955414012738856, 0.019801980198019806, 0.0, 0.019464720194647202, 0.041558441558441565, 0.06121739130434783, 0.0, 0.026490066225165566, 0.04878048780487806, 0.0449438202247191, 0.05647058823529412, 0.02857142857142857, 0.02335766423357664, 0.021680216802168018, 0.022662889518413595, 0.0, 0.032520325203252036, 0.023054755043227668, 0.03747072599531616, 0.0, 0.024844720496894415, 0.04255319148936171, 0.04507042253521127, 0.024539877300613498, 0.023054755043227668, 0.053333333333333344, 0.02359882005899705, 0.022099447513812154, 0.023809523809523808, 0.026490066225165566, 0.03773584905660378, 0.022662889518413595, 0.0, 0.02318840579710145, 0.0, 0.0449438202247191, 0.030947775628626696, 0.02197802197802198, 0.02439024390243903, 0.024096385542168672, 0.023323615160349857, 0.022662889518413595, 0.02555910543130991, 0.02373887240356083, 0.044692737430167606, 0.02555910543130991, 0.032, 0.028169014084507046, 0.02507836990595612, 0.027303754266211608, 0.033684210526315796, 0.023054755043227668, 0.02373887240356083, 0.021447721179624665, 0.02072538860103627, 0.05574912891986063, 0.020253164556962026, 0.018518518518518517, 0.0471976401179941, 0.05111821086261982, 0.0, 0.021798365122615803, 0.02439024390243903, 0.02857142857142857, 0.023809523809523808, 0.03931203931203932, 0.0, 0.0418848167539267, 0.0, 0.025806451612903226, 0.029962546816479405, 0.043596730245231606, 0.03125, 0.026755852842809364, 0.01335559265442404, 0.02228412256267409, 0.03818615751789976, 0.03950617283950617, 0.05574912891986063, 0.04923076923076924, 0.03827751196172249, 0.02150537634408602, 0.04383561643835616, 0.04289544235924933, 0.028419182948490232, 0.031007751937984496, 0.07384615384615385, 0.022662889518413595, 0.05387205387205388, 0.0, 0.024316109422492405, 0.059040590405904064, 0.046109510086455335, 0.0, 0.029795158286778395, 0.04289544235924933, 0.04395604395604396, 0.04030226700251889, 0.024539877300613498, 0.018058690744920995, 0.023809523809523808, 0.04678362573099415, 0.02346041055718475, 0.02548672566371681, 0.020000000000000004, 0.027303754266211608, 0.028169014084507046, 0.022099447513812154, 0.0449438202247191, 0.024242424242424246, 0.026578073089701, 0.0, 0.040201005025125636, 0.019370460048426155, 0.04833836858006042, 0.04225352112676056, 0.04113110539845759, 0.026936026936026942, 0.023323615160349857, 0.02631578947368421, 0.046511627906976744, 0.019047619047619046, 0.02359882005899705, 0.058679706601467, 0.02110817941952507, 0.021621621621621623, 0.022988505747126436], 'CIDEr': np.float64(0.000790216892799398), 'CIDEr_per_caption': [0.00021961744254830342, 0.0, 0.00015558086569721853, 0.00026761981439404724, 8.893055578801344e-08, 0.0003826662119210622, 0.00011460288789810656, 0.0006997774538164142, 0.0, 3.6633513155234957e-08, 0.00025519686721972096, 1.3936851918886213e-07, 0.000365238969381815, 0.0016899072984714514, 1.169919319318089e-06, 0.0030317999784472945, 0.000897636673956804, 0.0016747337742429587, 0.0, 0.06601909949371758, 0.0004485449235152541, 0.0036562482625870224, 3.110603806150541e-08, 0.0013112116676427654, 3.501010506536126e-08, 0.0004219342779662717, 0.0006859529083565537, 5.612677504745359e-08, 0.00012133623051092936, 0.007675593788048137, 0.0003133385102312052, 0.003632983541854149, 0.0, 0.0003807203093769665, 0.000481979466075854, 0.009170323864934332, 3.9244444869138336e-08, 1.3706054265801557e-08, 5.0306832418178314e-08, 0.0, 0.0, 9.238192951439506e-08, 0.00045864392605829765, 0.0, 4.3039934147700674e-08, 0.0002242627444875594, 9.911546504163411e-09, 0.0011030099284323155, 0.00042940599805217904, 0.0003232017606694953, 0.0003718094967387005, 0.0005783442256767987, 6.56624489078815e-08, 0.00016117038082869, 4.562823665515406e-08, 0.0005596261801047225, 0.00031500905432588916, 2.113767924827314e-09, 0.00048740934978346875, 3.36272640845305e-08, 2.5054682321508947e-08, 0.00045088467367777326, 5.753993682813795e-08, 0.001148381200607008, 6.594020990779129e-08, 0.001996177472722121, 2.4059849107660488e-08, 0.0, 4.617281364201496e-08, 8.974963322347352e-08, 9.493025066319573e-08, 0.00013897156360430265, 5.47904069014504e-05, 0.0007748637791304513, 7.70912805501021e-08, 0.0, 0.0, 3.572003471542344e-08, 1.516937970730958e-08, 0.0, 8.671958639295487e-08, 0.0015540522268575755, 0.0, 0.000227090771106749, 0.00028192215657831194, 0.0, 0.00010201734853873267, 0.0005612076443833483, 0.00015785852176227307, 0.0, 2.056957719964822e-08, 0.0, 0.0033407056852920777, 0.0004641140602352682, 5.777880474226295e-08, 0.0, 5.427531018897289e-08, 0.0009591925849304916, 0.002081960184988537, 1.6140093525718917e-07, 0.0010842189075238075, 2.5860448053517926e-08, 0.00022529525072497703, 3.209875046266438e-08, 0.00019334931072386585, 0.0, 0.0004521569550336971, 2.644817565826703e-08, 0.00020606078325115972, 0.0, 0.0007770367670472945, 5.317345424144807e-08, 3.986927752551034e-08, 0.00019432870605637337, 1.1139787282087118e-07, 3.5494575005143444e-08, 0.0007594847580466537, 0.005589381231235308, 0.00012824261834574674, 9.178062335826234e-09, 0.00021060376628976327, 1.2697445371800244e-08, 2.122670339987997e-08, 0.0006454778145830897, 2.0995313080684415e-08, 1.2971038188124855e-07, 5.6158827373978626e-08, 0.00036074881719418475, 3.340210049935213e-08, 0.00017915273784898143, 0.000228061561069352, 1.3981200847255615e-07, 0.0013655934337963214, 0.0, 2.1500258330989188e-07, 0.006157019380974862, 0.0031769833681811505, 0.00014915551613356288, 3.880586651011461e-08, 7.480028882538742e-08, 8.245867164831355e-08, 9.421552133603466e-08, 0.0, 0.0001566028088544786, 1.5418497702617758e-07, 0.003824663989857577, 0.0, 0.00018869266774147254, 0.0007175623884106092, 0.0001900605019421547, 0.0, 0.001304882042795054, 0.0, 0.0009057392726968005, 1.0264292492228243e-07, 1.7909563894109354e-08, 0.0, 0.008126561986239464, 3.370131408887294e-08, 0.0007768481857496207, 0.0004225237608019548, 0.011298220490009947, 2.804071603742715e-08, 0.0004146853794516781, 0.00048469811064147026, 0.0009729537370293398, 0.0, 4.3105986635776847e-08, 0.000382498855538204, 6.72395972797116e-08, 4.9251874144900195e-08, 0.00023453820274758994, 6.624029288031139e-08, 5.433157315291107e-08, 0.0028187459969866, 2.0659090212729877e-07, 0.00026380041117144776, 0.010258739227864551, 1.293745759248114e-07, 9.10550030430423e-08, 0.00040066655106316866, 0.0003694893249407982, 0.00029987659009598355, 5.924186814815173e-08, 0.0005180637294680979, 0.0, 0.0, 2.458840842665104e-08, 0.00013070049953035873, 0.00043823472049484105, 0.0011304968957992893, 0.0005276415506590327, 0.0002484292414069998, 0.0010986784683389825, 0.000369531823263661, 6.472782318557703e-08, 0.00023564167625515287, 0.00010980637305660207, 4.090133136415053e-08, 0.0, 0.0010214044735527565, 0.00023293512737883136, 0.0007712818986183283, 0.00010394558245207799, 1.9833913242327993e-07, 6.190578091815053e-09, 5.2850581585711825e-08, 0.00324166825181745, 2.9563789644481735e-08, 8.560017765041008e-08, 0.0, 0.0006302290048952794, 0.0, 0.00046687021107278976, 0.000440048235740811, 1.5771437079391305e-07, 0.00028962132042379757, 9.484186651798746e-05, 0.0004566067287939175, 4.3809385349891694e-08, 0.0, 3.366926157062091e-08, 0.00044620582232333136, 7.731842370933439e-08, 3.009905018425195e-08, 0.0, 0.0, 0.0, 1.0033446702566746e-07, 0.0, 0.0001674107218800999, 6.476799405825638e-08, 0.0004672357716773956, 1.5969498924508868e-07, 0.0001951919085101336, 9.382446966201019e-05, 6.757395139965758e-08, 0.00012038358207756681, 0.002513221087319247, 3.0541353925022935e-08, 1.9303421072694918e-08, 5.267964369417331e-08, 0.00018491649320194074, 0.0001320300976971839, 0.0, 0.00041351489763652617, 0.00021328535901144217, 7.594206094679518e-08, 0.0024421499848687475, 3.517375863962903e-08, 0.0, 0.00036573217231608513, 0.000363230723076697, 0.000363648311642297, 1.39385743691846e-08, 0.001917259064829843, 8.949663757111602e-08, 0.0007222793356760173, 0.0009845691602431723, 0.0008419281856135165, 0.00016954747425836845, 0.0021818115519231488, 0.0004031318090883717, 2.6209730454262844e-08, 3.958815034261919e-08, 2.0233014439759695e-08, 0.0004194149504940422, 0.0013488869201416934, 6.292082004102217e-08, 0.0006882555511282465, 3.403938694811007e-08, 0.0, 2.6505991125100522e-08, 5.344533219541207e-07, 0.0, 0.00010845814043107486, 2.5197086734767953e-08, 6.291199972375799e-08, 0.0013388781212479036, 8.294706955958617e-08, 1.230223876694684e-07, 1.5425818154957777e-07, 9.972628912307367e-08, 4.811363097507713e-08, 4.5985125305437895e-05, 0.0003440067324931605, 0.0012801935655527203, 0.00038003252250797074, 1.0022184135384708e-08, 0.0033727869811752513, 4.964549964260878e-08, 0.0007047061114610278, 0.0003325524159746861, 0.0021701246124103245, 0.00032619229574455634, 0.0, 0.0007653894729638486, 0.0009400802290185778, 0.0, 4.368842658545607e-08, 0.0, 3.961211924514836e-08, 0.0011108517570034891, 0.00012476308296635922, 4.2261224004536254e-08, 0.0008471344459393493, 0.001894226536661781, 0.0, 0.0006084470065720993, 6.171176471045884e-08, 5.334476097806631e-05, 5.2719963368148825e-08, 3.5266125568546616e-08, 5.258347452622572e-08, 0.0, 0.0, 0.0011995563733984856, 0.00025483827540896036, 0.00020421328642745613, 0.0005659565462875903, 3.595513573820997e-08, 0.0010811515008199027, 0.0018728894273179505, 0.0009749165240673669, 0.0006631716149894104, 0.00035433247703493776, 0.0, 3.5163452025371145e-08, 0.0, 2.2553821385382518e-08, 1.1301493334160755e-07, 3.846236139371619e-08, 0.005294909364059731, 2.5843090761731562e-08, 0.0, 0.0008048974038164225, 0.00023585505090273606, 0.0004856555474961745, 0.00017063948096219356, 7.203848200974676e-08, 0.00023027913604906194, 9.101817529328487e-08, 9.94904221734918e-08, 0.0002495392969606182, 5.248581178950431e-08, 2.822681117651863e-08, 2.8157249798601964e-05, 6.09274938164904e-08, 3.6001590439920526e-08, 6.755448270965389e-08, 0.0006311043631767235, 5.404712699854656e-08, 0.0, 0.0004007331879330771, 0.0006720481679167415, 0.0, 0.0, 5.046521215355932e-08, 3.035834714550534e-08, 5.669574323013283e-08, 0.0006020640628872673, 1.1155167005148003e-07, 1.9490035546863654e-07, 5.18776040681221e-08, 0.00038023198613643095, 1.01744833875515e-07, 0.0009501720910318923, 0.0001455187500044548, 1.4165281068004602e-07, 0.0009129561358672087, 0.0, 0.0018257859143846083, 0.003989374899965077, 0.0, 0.00036341667649419224, 0.0, 0.0008339648012528504, 0.0002761317983782781, 0.0009031263318781944, 0.0012443528459369952, 3.8270868092526924e-08, 0.0, 2.4829840892864924e-08, 0.0021291625712565425, 3.768326961441097e-08, 0.00014664192790410108, 0.0007793317170681662, 0.000283670093671543, 0.0016172411667115046, 0.0007170251352319637, 0.00045027454988003297, 0.0002556909820235896, 0.00016222323173680872, 7.56496401533398e-08, 0.00010724314075064584, 0.00016126443040022007, 0.0007788396953473302, 0.0001946285981924394, 2.550552562818015e-08, 0.009931762483154315, 3.885107484414521e-08, 0.0004445311146674079, 0.00019061957978127767, 4.2637211974754555e-08, 0.0010937018337645477, 2.72117679672908e-07, 0.00029246106386100605, 0.000666734219097666, 3.3119263947189896e-08, 0.03293008489152826, 0.00043177618592395704, 3.565191886560173e-08, 0.0003356498816135169, 1.673154823202259e-08, 4.0813870011513576e-08, 0.0002197024455790332, 1.1302806735013357e-07, 0.004179789676905156, 0.0007083245823483659, 0.0006816284946810449, 1.0059587021683211e-07, 3.021981429483763e-08, 0.0009147087501440383, 0.00033862794031745355, 0.001095523722933053, 2.5411414216512495e-08, 9.390008897840489e-05, 0.00038719490538512017, 0.0, 0.0, 5.1338725888764106e-08, 0.00018958000726948057, 6.887693617563547e-08, 3.94749975529389e-08, 2.7789283968819423e-08, 7.529513647368895e-08, 3.4678163528943525e-08, 4.6591925075388934e-08, 0.0, 0.0007316659913163316, 0.0003512481663232601, 0.0007406266191056858, 0.0007464476413365045, 0.000220415234154359, 0.0, 0.0006478755432400758, 2.1610838054769144e-07, 5.2984848045461806e-08, 0.0, 0.00024792007125105724, 4.054506848646639e-08, 8.655919559065952e-08, 4.9420190617009546e-08, 3.354710354195195e-08, 8.221834268825125e-08, 0.0, 0.00020847537185778983, 1.542628455353541e-07, 2.5490962183774883e-08, 0.001043066785671531, 0.0011988862355378038, 0.00017366282304498144, 0.0, 3.71855048833727e-08, 0.0006102128190995767, 1.4048821073059736e-07, 2.0016145712443636e-08, 8.362928529587679e-08, 4.891026270477886e-08, 0.0013828310745848415, 5.52579648436474e-08, 5.179754631822283e-08, 3.663405811450372e-08, 0.000640576583048441, 2.0290099346762034e-08, 3.528319981854836e-08, 9.576728027202571e-08, 0.0018319034092165178, 7.939975640627414e-08, 0.0, 2.6936542480850107e-08, 0.00016186423189177565, 6.262157616012321e-08, 2.2220104822396076e-07, 0.0007352682093783119, 3.185885802352345e-08, 0.00024422238051652274, 6.177551403274493e-05, 9.08522749184972e-08, 0.0002747674606497627, 0.0008725948307061309, 4.713212179270803e-05, 0.00012749914244143424, 0.00016444676980584142, 5.5001726793268355e-08, 0.004767796346322552, 0.0, 1.198706273264712e-07, 0.0003216750851403208, 0.0007442522769447841, 0.0013961619760011154, 0.00014623938300918269, 0.0005207122115428105, 0.0, 0.00018852137984949298, 0.0002918370902920569, 4.0340287178883205e-08, 0.0037879585916943516, 0.0009284801636112463, 0.006489273375172305, 0.0008903923765119983, 2.2907928686773433e-08, 0.0015300855797682907, 3.9225984500000364e-08, 1.0705465393878321e-07, 0.0, 1.653352900552684e-08, 8.603042303091495e-05, 0.0006307441094382726, 7.523117163444773e-09, 0.0006812801338471621, 0.015596858258290103, 2.9047977569646537e-08, 0.003353497695156262, 0.013952006251359787, 2.768844403117445e-08, 0.0005952934473493691, 0.0, 1.3976307838203008e-08, 0.015028423290405154, 0.0010348573500144076, 0.00010042373560236812, 0.002648211681138216, 0.005976527192175066, 0.0002114552632213068, 5.325127392392103e-08, 7.580763112638196e-08, 1.825321863201332e-08, 1.992806833711624e-08, 1.4497987406723084e-08, 0.007876293735916758, 0.0003870867823813186, 0.0014799158130181708, 0.0004997226726684858, 4.483293304514834e-09, 0.00024129422922642798, 0.0036127388836798844, 0.0005250562702682014, 0.0006775347904677176, 9.950026779279327e-08, 0.0006892193070020211, 0.0, 0.0003188835978483885, 0.00021779447267888214, 0.0005610082131365764, 0.000701786587989533, 0.0011042323744550433, 0.00023197784344084407, 2.036335796257185e-08, 0.0, 0.0013245504358033022, 1.0258305029506226e-07, 0.0001292767231418592, 0.0, 0.0003896726167901812, 6.991964714871002e-08, 0.0004689774823630489, 0.0006072042927476618, 0.001581441855887504, 0.0, 0.0007143734632431867, 2.5360333093289503e-08, 0.0007840656297673538, 7.186069373246459e-05, 0.000763714036144226, 0.00021751611522754217, 0.00025594514982803473, 0.000687407676573278, 0.0007195810887616078, 1.2733017241080058e-07, 0.0005468677299384504, 0.0, 4.4940281824013296e-08, 2.9341765386398974e-08, 2.176638862045875e-08, 1.8145953819139202e-07, 0.0006336882956072499, 5.643915329495439e-08, 0.0006971540396703764, 0.0009293042396243058, 1.1695198851750405e-08, 0.0001529526330449339, 3.548642150448656e-08, 0.0, 5.5075603621836146e-05, 1.0961843937464504e-07, 0.0, 4.160673151266341e-08, 7.253558580661824e-08, 0.002150446453290612, 0.0001412656993797008, 5.980574834815505e-05, 0.0004115747851385422, 2.2113854804287766e-07, 5.5698083991800166e-08, 0.000376777125176421, 2.53065047646477e-07, 5.16200942634858e-08, 2.3164765765447188e-08, 3.0112657482542996e-08, 6.293302571155012e-08, 0.00011742200258699669, 2.3541861324949177e-08, 5.2749294999938745e-08, 4.8321083027815897e-08, 0.0003208703520490468, 4.0674109737110394e-08, 6.718912045786494e-08, 0.0003893153804152743, 0.0, 0.0, 5.2965689284197536e-08, 0.0, 0.000191729213676946, 0.0, 8.478594602151091e-08, 0.000346803140827561, 9.677594834422537e-05, 0.0007298634379208626, 0.0025371187213251278, 0.0018691533036775303, 8.48364436340792e-08, 2.4042711658732622e-08, 0.0002070492397841627, 1.931034406703044e-07, 4.307507026693004e-08, 4.3683196773874e-08, 8.667997907806858e-05, 0.004410356168340838, 0.000398771636940788, 0.0, 2.0705261339165375e-08, 0.005684893545136288, 0.012163816386223795, 0.00019970325758030116, 3.538736276559614e-08, 0.0007133823342270249, 0.0, 0.00029527967747525966, 0.0004656691787544589, 4.4672828868769616e-08, 0.00329402965098192, 0.0001158597749586731, 0.0009026875689891628, 0.0003643366371002758, 6.766596028537458e-08, 1.861060067037957e-08, 4.549915464008733e-08, 0.0004538223375077161, 0.0002814662098674817, 0.00021901201342179534, 8.075443793455085e-08, 2.371287412913073e-07, 5.450919763899773e-06, 4.671459807602558e-08, 1.7908448970435097e-08, 0.00020409714588909622, 3.1987227316791894e-08, 0.0, 0.00024950478553002265, 9.185887869340971e-05, 0.0010282471020395683, 0.000512519663627227, 0.0003100822783983956, 3.235503641247131e-08, 4.195849214952761e-08, 4.1779591476321864e-08, 0.00011249530552578707, 0.0005498927001562728, 0.00015828507979798753, 0.00022042101981979825, 0.0005672386774248851, 0.0, 0.0006103912047295055, 0.0006628710492922, 1.2481483745445119e-08, 4.3242115361384335e-08, 0.02343719772217869, 0.0001295812826184201, 0.0006727620154138522, 0.00022826402221952342, 0.00023836380085003593, 5.934450388048354e-08, 0.00042103147424924197, 0.0, 0.003660606395869132, 0.0004928136054351687, 0.0014965743301748342, 0.0023780668297218735, 0.002470154090054179, 8.117048181969602e-08, 8.418881654444105e-08, 0.0007702103618681642, 0.00089418739870265, 0.0019462611919048235, 4.003459889522008e-08, 0.0004018596037414457, 3.245524543339441e-08, 0.000413768537568475, 0.00011349234881748397, 4.503412695266108e-08, 0.008948180963128757, 1.0692744848361899e-07, 4.333751995696558e-08, 0.0, 0.002556414197721792, 4.5319966745271495e-08, 1.344525888489475e-07, 1.8179766183810401e-07, 1.7728875057421855e-07, 2.7936320746824776e-08, 1.5746905524359443e-08, 0.00015482805481368243, 0.00015151108469532395, 4.5707154331246975e-07, 0.0002212062788916353, 7.411392573616672e-05, 0.00248766453873144, 3.9237936339038195e-08, 0.0007990222784014186, 7.163646163798422e-08, 0.0015905034144678702, 4.635629307806105e-08, 0.006269005167894401, 0.0012296033588607529, 0.0001580828063131351, 0.0001804564983208735, 0.00016096282998776944, 0.0011822545755157193, 0.0013837376385380064, 0.00035396385130479223, 0.0006345677848078738, 3.397812012402281e-08, 4.9124760166240474e-08, 3.481613255871372e-08, 6.715467958870943e-08, 0.0, 0.0004429766181866014, 0.0006966266613339365, 7.497721658180795e-08, 4.362657247783595e-08, 0.0014708783145113689, 0.002518453400897882, 5.41423280695383e-08, 0.000948391724869107, 0.0008785081201829849, 0.006292396038676493, 0.0, 0.0, 6.20332928244924e-08, 0.0008479768595006955, 0.0, 0.00012325923651652706, 0.0005901183957460291, 1.7809600557519968e-08, 0.0030573194662279458, 0.0010301128466458677, 6.183322127733279e-08, 0.0007285519387929112, 0.001807494260653744, 4.408413108040088e-08, 0.00022499101219550764, 6.224795248337582e-08, 0.0005233061828858994, 0.0008810359852605986, 0.0035829214602509218, 1.1711149730249037e-07, 7.856130916688581e-08, 0.019491425861089696, 0.003488766379820275, 0.001998279519214079, 0.001452469997044869, 0.0, 0.002940813025926519, 0.000838496633233893, 0.0003276797418928138, 2.0422023182823586e-07, 0.0, 5.513442378683885e-08, 0.0006936742905784411, 4.265406591203438e-08, 9.04077946425832e-08, 1.5583409209006137e-08, 1.0884029703108175e-07, 0.0008674519149028908, 0.00042100482751713153, 0.00047268595714998344, 7.94913303613773e-08, 0.0002876090000659873, 0.007625557474774807, 6.062545088352586e-08, 3.489912402084974e-08, 0.0, 0.0002477026038704131, 5.0796265961936416e-08, 4.501472032874753e-08, 7.68341983021722e-05, 0.00030654145355356544, 0.01684715665092456, 0.0008933236505290893, 0.0004983780941893862, 6.663991688150017e-08, 0.00257064900621582, 5.19421537840191e-08, 0.0015712015771146763, 0.0004888693658798628, 0.0, 0.001052372340115995, 0.0, 0.0018852643331678791, 0.0, 0.00032073658021450325, 0.0005424784420674275, 1.82266584735348e-07, 1.3055366240119447e-07, 0.0006025355900281347, 4.0382566470949854e-08, 5.5068297841426124e-08, 3.299807615784695e-08, 4.680456453243119e-05, 0.002185594938778019, 0.0017864895909347304, 0.0019156771325890237, 0.0, 0.0001418868477887985, 3.101451974869658e-08, 0.0023549547948752314, 0.0, 1.1772406589749631e-07, 0.0, 0.00028528750137860607, 0.00044190719975558735, 0.0009430795893405905, 0.00045064826795962866, 0.0, 0.00022736862753106244, 4.00638525763115e-08, 4.4986304414614026e-08, 0.003128074556835266, 0.005621492903851506, 0.0, 8.452082793889069e-08, 0.00022474912699247277, 0.0008989191719204885, 4.669278051807559e-08, 0.000318101644438715, 0.0013202990598644654, 1.113899220108158e-08, 2.9670130833661525e-08, 0.00038541169664731893, 1.0516866225369093e-07, 4.145620988248599e-08, 0.0005062317626092573, 0.0, 0.0, 8.76012087558765e-08, 0.0, 0.011982572879702927, 1.6320742333752188e-08, 0.0007363896972861892, 4.9838112747290723e-08, 3.1129345621106385e-08, 1.6406653796176954e-07, 0.000696726689584543, 0.00024710764626785216, 0.0, 0.0008014209224948591, 0.0005940573169698727, 0.0, 0.002152083441954899, 0.0, 0.0002171899428644391, 3.252028310413794e-08, 0.0002543397701808068, 0.0003642442171057109, 8.238446110897501e-08, 0.00016021526225623535, 4.6003452766455494e-08, 0.002282060514124447, 0.003732293757642442, 0.0022300944630155722, 0.0, 0.0, 8.41275363934575e-08, 0.0004986471012258621, 6.01465924475551e-08, 0.00011392874308212262, 0.0007021867942263683, 8.217233209872544e-08, 0.0014571388851027972, 0.0016463965022500062, 9.444378124627997e-09, 0.00020189556646212743, 7.569835437636127e-08, 6.268188032952853e-08, 0.0011337445343202815, 1.055557596148266e-07, 4.3359833254249203e-08, 0.0005864071684232373, 9.149223124768665e-08, 1.458524316112113e-08, 0.000512020702158178, 0.0003444525417786178, 0.0, 0.0, 5.643415006928514e-05, 2.6572012625840377e-08, 3.418783885162784e-07, 7.395352775152431e-08, 0.0, 0.00032962856339557216, 2.1546236951642438e-07, 8.671539574174897e-09, 1.1947951489720373e-07, 0.0006045609991843133, 1.639440111013053e-05, 0.0, 0.004168251475420082, 3.999201295638278e-08, 6.627440900891366e-08, 3.7493355894601425e-08, 0.0010379806510875907, 0.0005006823603513547, 0.0005074519950315959, 0.0002216929800203112, 4.904409716631203e-08, 0.0007994797529435598, 0.0, 1.9599861686612407e-08, 0.0, 0.0014176627634600353, 0.0017276956094166452, 0.0002910472238376536, 0.0005633557453735764, 0.00017633338151874705, 0.00014307455408009133, 3.043378709637598e-08, 0.0, 2.0577396629274475e-08, 1.0814292749752878e-08, 5.635692033216923e-08, 0.0, 2.152295849742879e-07, 3.80511205337536e-08, 0.0001529699072037454, 0.0, 0.0, 4.456936216470421e-08, 4.8036563803736274e-08, 0.00037499007071077124, 0.0008361998260144859, 3.740336722685214e-08, 3.884998195823386e-08, 5.8096368489195797e-08, 5.2859593984495453e-08, 1.2623320193638534e-08, 3.711614167417147e-08, 1.4673321527302486e-08, 0.0020397532373414525, 0.000558239211059566, 2.1424064292370461e-07, 0.00016640124732717344, 0.0, 0.00018862277408170672, 0.0005431227407570897, 2.4255836148183997e-08, 0.0001677617098871313, 0.00046715297143019754, 0.0002261837189810498, 2.526304906250332e-08, 0.00011273537481590635, 4.7166901763257405e-08, 0.00017220671311362457, 5.680805218044284e-08, 2.5261221639251872e-08, 2.4543153896089412e-08, 1.5067065960859643e-08, 1.7481878960596552e-07, 0.00029543915599526146, 3.76746436969039e-08, 0.00019015684186041336, 6.66453470746244e-05, 8.510125040955475e-08, 0.0, 0.00025837207397711804, 0.0008714486879934078, 3.122369122674879e-08, 1.7957667019741156e-08, 0.0017938580143837942, 3.023233966603007e-08, 0.0006775669245460323, 0.0, 0.00028616078579586906, 0.0015306801282728103, 0.0004283152966427464, 0.0026460865299685467, 3.853747605591949e-08, 4.394164210313732e-08, 3.8843946496283476e-08, 0.0006862266266383589, 2.4817809474912877e-08, 1.631527590514641e-08, 3.3057504982187195e-05, 5.6994506828670474e-08, 0.0008975078706931518, 2.7490111544088522e-08, 0.001270659848461222, 3.294173665750591e-08, 0.0050685082063974315, 0.0018940471486241504, 0.02173725856316162, 0.0, 0.0001447491679268512, 0.0016452893037583486, 0.00037768524907328543, 6.674493235182702e-08, 0.0, 0.0007555992528805666, 0.0035529930623875404, 0.00027708635407717113, 2.5717373631191093e-08, 0.0, 9.85080736939041e-08, 0.0036477819545383053, 0.001069486434857295, 0.0005965476481541192, 1.1632808616653211e-08, 0.0005758227688526054, 0.0, 5.569257199020464e-08, 1.1890839757756886e-07, 5.2117664874095864e-08, 0.0004994063589890615, 0.0002945073100246446, 0.00014623253380150332, 2.6691913506717404e-08, 0.0016059179975075849, 0.0012156434480999788, 0.0006992783293372812, 0.0025138582746396, 2.9566635480082898e-08, 0.00041994586799213505, 0.001569852743250094, 0.002566246784827387, 0.0005523429198246647, 5.132094951393855e-08, 0.0, 1.0616443961676265e-07, 0.0026640565229766066, 0.0003759362439904211, 0.00039008473801126523, 2.8537563845416766e-08, 0.00042378039516764786, 4.54899510059972e-08, 0.0, 0.001925993661281308, 0.0018159748094462372, 0.0014278982493960107, 2.1664847426208632e-08, 7.320852751482939e-08, 1.4018811015700183e-07, 2.96392181722125e-08, 0.0004702462268205452, 7.380114706654487e-08, 0.00017001845226739183, 0.00017223626870745742, 0.00047544695529846775, 0.000758754122706796, 4.916339394900162e-08, 0.004563185393147469, 0.02303479916373987, 0.0, 0.0, 9.293207463565736e-08, 0.00110997930546096, 0.0005657827161925349, 0.00033845511274057305, 0.0003100784704908895, 3.345221069190731e-08, 0.0006473244009722607, 0.0007875535966694223, 3.8107652469066724e-08, 0.0020045616839457314, 0.0003174199206355643, 0.00035862835160386456, 0.00035219420244825815, 2.126086615346967e-08, 1.7222568346198083e-07, 1.679137996578403e-07, 3.819897784375294e-08, 1.8824715985359793e-08, 3.0581068706212545e-08, 0.0026356189589733115, 8.363472059754774e-08, 0.0003292382322361657, 0.00036862360279429224, 0.0, 0.0025426892679473396, 1.9216988994877352e-06, 0.0, 8.171336958981432e-08, 4.2081650302130253e-08, 0.00033061180291114666, 0.0011165141901906136, 0.0, 0.0002071100402851614, 1.0986267982011054e-07, 3.264058816765331e-08, 0.0027901653738425453, 0.00024357278923270486, 6.149901771461795e-08, 0.0001621970527177014, 2.590459880743998e-08, 0.07039795811618639, 0.00042240423736823393, 0.0, 1.8122147479967055e-07, 7.041802508905293e-08, 0.0008935173507566372, 2.7074225779272552e-08, 0.00021852634526737502, 0.0039979839237860365, 4.662669584504528e-08, 0.0004952992147969462, 5.088135071719815e-08, 0.0003372617767188285, 0.0013342245039384294, 0.00019243612496430647, 0.0001791708381364353, 1.336934292430815e-07, 0.0, 6.564257680631607e-05, 0.0002194366916753686, 5.022124109136242e-08, 3.020074990409568e-08, 0.0009869521831113012, 0.0, 0.00025840977921392355, 0.0, 5.164577304853557e-08, 3.850219964026298e-08, 0.0001637611240191043, 1.3215690580699326e-07, 0.00033648055797024835, 0.0003863063394329726, 0.0008398299185253519, 0.0002058600062297738, 0.01057172085542897, 0.0, 0.00058000302317564, 1.976445192170673e-08, 6.383750457978696e-05, 0.0032921694106081006, 3.66574182121121e-08, 0.0022987327336253275, 0.0006092725278639276, 0.00036246134481955487, 0.00020288542652365925, 0.0, 0.0002859550416315408, 1.6768643162525877e-07, 0.00013201024847901894, 3.485174527249296e-08, 0.00013938977416507473, 3.4725348754967745e-08, 0.00275449724680813, 0.00034319837012613706, 0.0002757246426521594, 0.00020957741976811682, 0.00022354755722002847, 0.0007760807139523536, 0.00110539046627503, 0.0003435626661714963, 0.0002517096743704442, 0.0003103946064042654, 0.0011744599595188373, 0.0, 0.0, 6.082701393936876e-08, 0.00039238499133175395, 2.1805603051753673e-08, 5.1904131676873635e-08, 9.667724177287552e-08, 1.693867854748579e-08, 0.010059192038343701, 2.0228279611123972e-08, 0.0, 2.526559156148169e-08, 7.940867740259257e-05, 0.0002117347275557004, 0.00023178277730464023, 0.006735081804412962, 0.0, 0.0005701584809776944, 0.0, 0.0005765038296146178, 0.00017584496834598183, 5.685547848290062e-08, 0.00023961297028804487, 0.0001817425097257406, 3.952055026112824e-07, 1.7482277528685126e-08, 0.0011499007717082504, 3.4348196360415025e-07, 0.001334523606321609, 0.003853607237869928, 0.028031585868540543, 0.0002357371125005497, 2.7305681745214367e-08, 0.00021540856592303987, 0.00023152286631960417, 0.0, 1.6797811955942758e-08, 0.0, 0.0005266935608913583, 5.345264557930033e-08, 3.416741208141534e-05, 0.0, 0.0007724375166719482, 0.08823316805606243, 5.77550113275424e-08, 0.0, 0.000670830574442485, 8.406054282853638e-08, 0.0003467458059014621, 2.9494863199077756e-08, 0.0005200248652283322, 1.2162178884033535e-07, 2.6108987945782985e-08, 0.001503715142321203, 0.0015052198803683732, 0.0005126199689690877, 0.0003119816979451597, 0.0, 1.4966258814711967e-07, 8.19917853989698e-08, 0.001539391629793044, 5.343335742118524e-08, 3.500916271513168e-08, 0.0, 5.853115052680423e-05, 2.1600240517655747e-08, 0.0012852595410234044, 0.00026404251796249167, 0.00046033932340597706, 0.0004266608119993401, 0.0009359205321843834, 0.001196876854576876, 1.652146006813085e-07, 0.0, 0.001101475796262453, 1.417815191889669e-07, 0.0003140021482049139, 0.000548406945440707, 1.1215035022269844e-07, 0.00043914924318470285, 1.0039569390641342e-07, 8.036014035495857e-08, 0.00040152684660862774, 0.0005171317202376649, 0.0002953354964288828, 0.00020008711185088687, 0.0003430988847906672, 5.469569587007945e-08, 7.958380870834814e-08, 0.0002950120344059195, 0.00016207479267145767, 0.001072101101236276, 2.7664817250372425e-08, 0.00021254681021443968, 1.5527517757281876e-07, 0.0001490894290221151, 5.09770584607854e-08, 4.8760959967884914e-08, 0.00026935525473067133, 0.0003654143859767213, 0.0104396744714116, 0.0003379954046250196, 5.546532901451651e-08, 1.6438898594081582e-08, 0.0005049853093400671, 0.00031813794510416465, 0.0009302416654492103, 0.0005794087948286018, 0.0011823563346289982, 0.0, 0.0, 1.431570009605624e-07, 1.906300197480149e-08, 5.0729301956967564e-08, 0.00014878828985988293, 0.0005744496747417548, 6.772210191800866e-05, 0.0, 6.645693309901485e-08, 0.0002718335530485146, 1.3825527758927125e-08, 0.00026761539693991336, 1.0577168125220459e-07, 6.11750391951106e-08, 2.2103028110573655e-08, 0.00011330891699443046, 0.0006326173442630938, 8.658839453389699e-05, 0.0, 0.0003797583125995509, 0.0, 2.6657069208043978e-08, 0.0, 0.0016522115182974481, 0.0005672837678063929, 7.319413727178485e-08, 1.4410444494827128e-07, 0.0004073634638969007, 0.000564460473334853, 0.0, 4.6742917747021266e-08, 0.0005662203715398423, 0.0, 0.0002945047917312526, 0.0, 0.0015268067234875896, 3.9335972287821564e-08, 3.542770258004271e-08, 0.0007525219215495701, 0.00019876457658478397, 0.0017425792975880095, 0.0005285945548741446, 0.0007930130592131949, 9.986160921959055e-08, 0.0014939307319039746, 0.0, 0.00037623775576990084, 0.0004839754661117259, 0.000521942359325524, 3.0176931979954764e-08, 7.790941802748023e-05, 9.023694481296955e-05, 0.0007770811882327965, 0.0012245100161678564, 0.00013932913675658083, 0.04023387503311142, 4.545023005611818e-08, 2.7519518149859408e-08, 0.00012598910823897054, 3.3193468340912724e-08, 0.00029496850550952963, 0.0011946705089598301, 4.6053516423740204e-08, 7.524336290709812e-09, 0.002275434369491817, 0.0, 7.811920051848509e-08, 0.00011048595737197798, 0.0007763841720164421, 5.134123238772482e-08, 0.00016799013285244695, 3.9475999675477474e-08, 6.06649067082055e-08, 7.775061900663662e-08, 0.0009205315548869677, 0.0010363698794552795, 0.0009200904928790548, 0.004663292904752975, 1.0909673360087314e-07, 0.0002379469644578861, 1.2106947764066355e-07, 6.204899147477962e-08, 3.581394737581447e-08, 0.0007734812392707941, 2.2116110064098966e-08, 4.139440154740399e-08, 0.0005722748599959581, 1.4725697191334166e-08, 0.0, 8.757954554277448e-08, 8.013168271527708e-08, 0.002215490983674663, 5.390107676194298e-08, 4.328509717325765e-08, 3.953885375922041e-08, 0.0006831463213592741, 0.0005437929810922656, 0.0022761281327830984, 0.0, 0.00031716428718883813, 0.0005361946616159857, 0.0007656156095168058, 0.0002632666115189702, 0.00041330329315415553, 5.495244313373571e-07, 5.2637331355440825e-05, 0.0004615382179965879, 0.0, 0.0, 0.0010227920414585304, 0.006582044598407669, 2.9617907903119903e-08, 4.642525286691466e-08, 0.0, 0.0008555685738171986, 0.0, 0.000860132432788766, 0.0004116761216783579, 0.0, 0.0003854257864469553, 0.0, 0.00342785185562747, 0.00034198525630130174, 0.0002950838692588907, 0.0, 7.014838106617999e-08, 3.1822837145094505e-08, 4.347346107895053e-05, 4.863503240069989e-08, 0.0008984283587570225, 1.5410307526913418e-07, 0.0002519947521207944, 0.0006663750587455049, 1.050294467054099e-07, 0.00044567122830921064, 1.4220448115131554e-08, 0.0005211660805958473, 0.0012878784270069108, 0.0001101053719378439, 0.0, 0.014550074107652173, 0.0002979341771750626, 5.0222230620047856e-08, 0.0020762455991236105, 1.3033742040286922e-08, 8.982948334460867e-08, 0.0014162474490461928, 0.0, 0.0008409186891332803, 0.00024972539781192816, 2.2633296722800505e-07, 3.0817129646420614e-08, 2.638012383861917e-08, 0.000354548279505125, 0.0, 8.748534642385023e-08, 0.0, 0.0050178272003535244, 0.00033493799083218116, 8.12437816054054e-08, 0.004802930804279463, 5.0766118745011e-08, 2.1199444214670144e-08, 1.0723604187407946e-07, 0.0, 0.0, 0.0, 0.0, 4.78120688345359e-09, 1.0880279266638299e-07, 0.0007256297863108725, 0.0002521551141222104, 2.8651171892235113e-08, 0.0002058462112191397, 0.0005816288148253432, 1.7957765812562126e-07, 6.844648492685544e-08, 0.0006386257503283859, 0.00015384626202996401, 0.0, 0.00022721759502235825, 0.0007034898081723979, 0.003012951041673829, 7.683084117771764e-08, 0.00020959744814106715, 0.00539747539180406, 0.00014533351686063986, 0.0, 0.0003775732211668077, 0.0010339756131927092, 3.150243554312651e-08, 0.00014676602990475445, 1.947785125661845e-08, 4.3679725646214445e-08, 0.0002752443317061585, 3.3336217908548895e-08, 4.609033781448212e-08, 2.1633286602441768e-07, 0.0, 6.884650968385172e-08, 0.0010333360672267486, 0.0, 0.0001427642050463264, 0.002000338452285911, 7.519432009501844e-08, 1.2850735991336365e-07, 0.0, 6.275055608910695e-08, 5.1206437611683035e-08, 2.7904161184183037e-08, 5.108712146860645e-08, 0.001658235282075194, 4.993723606343234e-08, 0.000263790423038687, 0.0, 5.0101167193069874e-08, 1.7949791782441426e-07, 4.109988130421319e-08, 1.2580211302693974e-07, 0.008445486211146054, 1.521292777128736e-07, 6.16940524459559e-08, 0.024678683364987304, 0.0005696346595887117, 6.533627142902502e-08, 0.0, 1.4669446986950137e-05, 0.0006020737009959953, 0.0017076645060124709, 0.0010446631943640987, 2.0239773555597112e-08, 0.0011479558121561084, 0.005096641213401105, 0.00031285016108038226, 0.0005281884296421987, 7.357158363656453e-08, 0.013078185348778977, 3.848396661713077e-08, 5.041551184847749e-08, 0.006507849313700303, 9.352366498725274e-05, 7.732092214308934e-08, 0.0, 0.0014093066516477361, 0.0, 0.0, 0.00021168310376725754, 0.0031830004218988974, 0.000991380817706462, 2.3578218020903553e-08, 0.0006701569755171666, 3.789415404149691e-08, 0.0003013941592736539, 7.14359424606538e-08, 0.0020024944773909736, 4.960212418682681e-08, 0.0, 0.0005371247696281157, 0.0005309385096111268, 0.0004340216154080295, 0.0002684109867741152, 0.0007765624270553454, 7.481907818355264e-09, 0.0016114325790336113, 0.0010165614523075502, 0.00012231312139502422, 0.00427436629357316, 0.0003099508128863645, 0.0010525022958277797, 0.000696083667893076, 1.8533415248372223e-07, 0.0002506850067930162, 2.66391608999106e-07, 0.007288613241188187, 3.125704500562161e-08, 6.925938645250304e-08, 0.001565556991542993, 1.9608084734202668e-07, 0.0020963459435451434, 0.0001572083886627044, 0.0, 0.0004097666929244925, 0.0006893994393477518, 1.947332116943654e-08, 0.0006057581177763605, 3.147939420033453e-08, 0.0014993064303014908, 0.0018178324756708839, 0.0016267468030920639, 2.2848849057261356e-08, 0.00035918032380166773, 0.002163958756151977, 6.87166669644621e-09, 0.0004458252010486354, 0.0010858263992843797, 4.552612662294691e-08, 1.6918900285080804e-07, 0.001990397621470146, 0.00042341309985599617, 0.000212454979768655, 6.829988863288343e-08, 0.0001252495265452755, 1.405935914340045e-07, 3.982582027284969e-08, 0.0010732373326153247, 5.2590258599461356e-08, 7.332631500822462e-08, 0.0, 0.00025974204004343535, 6.366929718479861e-08, 1.671935733974559e-07, 0.0005801765907882152, 0.0015171695178469533, 0.0022575995653492807, 2.8971608752964827e-08, 4.9819059796787e-08, 0.0011014785320150183, 0.0038519390839569408, 0.0026499278512179423, 0.0014871497231485047, 0.0013867356965805086, 1.6816817198608853e-07, 0.0016415354581742066, 0.002551495065690367, 0.0018620532809693934, 0.0, 2.7154016480698498e-08, 0.0003989696966017011, 1.773529587640311e-07, 3.802698655276116e-08, 3.234623471922267e-08, 0.00016894446631772884, 4.293723199814696e-08, 1.4634531683153446e-08, 2.2567222989230365e-07, 4.7432324039997497e-07, 0.00019787531610359005, 5.5691051981436355e-08, 6.99661973580334e-08, 2.7272888710048912e-08, 0.041025120626032086, 0.00020488863081427773, 3.7329729332021484e-08, 0.001797671719452072, 0.00018693070050247544, 1.127880140071288e-07, 0.01180764834473413, 5.867867328199298e-08, 0.0, 0.00039374608015298976, 0.0010570880024203075, 4.4451489595717616e-08, 0.00021736813303067077, 0.0005788757957231928, 0.0, 0.0003040053291489435, 2.58739541337702e-08, 0.00021177762226338813, 0.0005090272301827998, 6.954896522929109e-08, 1.2252185574798337e-08, 0.0004013534117980616, 0.0015565784538456454, 7.700153639196386e-08, 0.00024700151065978865, 0.005707125929010818, 0.00022611285359796848, 0.0013237660173776489, 0.0021674616703050505, 0.00016568312267580427, 0.0030667337384590334, 0.0006684226283964288, 0.0007490354080513194, 0.001939705001355323, 0.00040185979076720634, 2.85438644022127e-07, 6.44488045111542e-08, 0.0004234562892919371, 0.00027102150970841776, 0.0005051670936431408, 0.0016812111377510113, 0.026735080220316676, 4.062795643363129e-08, 0.0004507749090912757, 7.249381029942386e-09, 0.0027886169203952304, 0.0003546690183347114, 7.964025178097016e-08, 0.0006373961355239674, 8.36832643937888e-08, 2.0446321977284826e-07, 2.071845628017577e-08, 6.482001976707718e-08, 8.008179141612198e-08, 0.0, 0.00038560238898290434, 0.0, 6.146762715466564e-08, 2.5166321840688315e-08, 0.00021613825474859217, 7.907698260827252e-08, 3.117185096977505e-08, 1.508508876463229e-08, 4.75807351214342e-08, 0.0, 0.0004572290687211814, 0.0, 0.00014812268459625958, 0.000402728337978597, 2.782295827744022e-08, 3.536285362464618e-08, 1.8569656844865072e-08, 0.000595583165029148, 0.00083382066057984, 2.8044401672351826e-08, 0.004368073253544033, 0.0010928132112284833, 0.000280977654591229, 0.0013241135410895887, 0.0003931080209041987, 0.0009922738286564838, 4.256420505358672e-08, 0.0008490088021659483, 0.00033028019386261033, 0.0003279909601896549, 9.135891945311836e-08, 0.0010768341870962261, 3.09880758062676e-08, 3.511802324069296e-08, 0.0, 0.0004948254196219048, 9.140830436717369e-05, 2.6797341499849863e-08, 0.00028733937165980103, 0.0, 0.00033903982550161294, 0.004274884816922671, 0.005979915452609071, 0.0011623313044782234, 0.004381127399098461, 0.0006854847577438988, 3.307680308136686e-08, 4.08046028482879e-08, 0.0009383554231236152, 0.001986106279074691, 0.0, 1.6255129231640297e-07, 0.00018279803893803497, 0.0, 2.1391730152266054e-08, 0.002747974346570031, 0.000704252420555152, 0.0005031076923497659, 0.0004242928975771568, 0.0001300898874999365, 5.743251418788495e-08, 1.5057339962718777e-07, 1.2180494529782307e-07, 0.0006646696132646436, 0.002584241579342926, 5.595227308117496e-08, 5.2874653403214776e-05, 0.0001269766083226342, 0.00033311499242601194, 2.8312614708616765e-08, 9.843907704692704e-08, 0.0005021428811443423, 0.0005250742453899736, 2.995303797888749e-08, 0.0004779026425390384, 3.2527069364471656e-08, 0.00018131213065256178, 0.0014395871985728365, 5.88248211307225e-09, 0.0002651319854148213, 0.00015021064851693786, 2.8219830847349912e-08, 0.00029186655318270434, 0.0006925927205683783, 4.645115483494994e-08, 0.0, 1.1511338313978266e-07, 0.00026082704909464706, 2.996973074702869e-08, 0.0, 0.00108248184874946, 0.0003890424938063539, 0.0, 0.00021295618381814082, 1.127182826677916e-07, 0.0001317083552756665, 0.0002102584320540044, 0.00038484624926907933, 0.0010894675001219582, 7.131417597366697e-05, 3.2762327284748185e-07, 4.2293915396510526e-08, 4.524394655735598e-07, 0.0003951295931156595, 0.0, 3.5034558038144204e-08, 9.828930769510082e-09, 0.0, 0.001896164574440075, 0.0009269153896159361, 0.0023870153092525096, 0.00039033729943783237, 0.0001870940205697324, 0.002764871919136774, 2.792301914692307e-08, 2.6925878280182286e-08, 0.00022842971927469416, 0.00035655438630955187, 0.002218429390367916, 0.00022609466811545059, 0.0, 6.46678487390562e-08, 1.3317666287249228e-08, 8.849906549378378e-05, 0.0013059633406845338, 0.001690944961430508, 8.867928152206572e-08, 5.27187786701286e-08, 0.0, 3.460019845204423e-08, 0.00034081947412506106, 6.485384858540057e-08, 0.0008188415254852988, 0.0, 9.335559960739905e-08, 0.0008433283511009278, 0.001330228420138141, 1.8104735526339934e-08, 1.331392044287466e-07, 1.5327629015087917e-07, 0.0005069992816944317, 0.00013253517146689342, 0.0, 0.0007851320903946738, 2.3414855557642783e-08, 9.573949640035381e-08, 7.990531942437228e-05, 0.0010423159427198736, 3.322544089901219e-08, 0.0003378599207884592, 5.107250316320349e-08, 0.0022649394667745197, 6.569422536609733e-08, 0.0009499752699926943, 1.2151478732785965e-07, 0.00024805457324170995, 0.0009678183456357631, 6.012591349295172e-08, 2.8381712179873954e-08, 2.55802705185419e-08, 0.00041747696486865314, 6.05051719701471e-08, 0.0009134240584194057, 1.943582789135939e-07, 0.00046567964422995035, 3.036280755869695e-08, 7.071026440643531e-08, 0.0009476902361903038, 0.007232045343743292, 0.0018656782113596925, 1.1474782811129846e-07, 0.0019715457177157587, 0.0003302338243380966, 2.978197148896816e-07, 0.00032562984605355563, 0.0009597039598902781, 2.369942119894741e-08, 8.88299166540014e-05, 0.003489531545048518, 7.986693129669535e-08, 0.0013093960716171113, 2.981985458708711e-05, 1.5921372474630776e-08, 0.0002898873742621503, 0.004377877433407632, 0.0004521126837847084, 0.0, 5.907184923637796e-08, 0.0, 1.3883640354576394e-07, 6.674246825405462e-08, 0.0014837471590695797, 2.3456144483675293e-08, 0.0, 0.00020545702722554596, 0.0002283377626666475, 0.0, 0.0, 5.206635657691757e-08, 0.00056558978180333, 0.0014052584135863383, 2.3531847589389916e-08, 2.201448217499298e-08, 3.646841899001125e-08, 4.0745060094643826e-08, 0.0009998116413198308, 1.5818753084864507e-07, 3.615358482097167e-08, 0.0, 0.00011382230983375308, 5.2150843932826176e-08, 0.0011931684526600061, 0.0004652712661690173, 0.020927043781219082, 4.1980525356273275e-08, 0.000542977908010739, 4.301810132047336e-08, 2.7953764996321225e-08, 0.00047379339966192066, 0.0004235821406831968, 7.855794968435437e-08, 1.1065261031188675e-07, 4.8952643836805346e-08, 0.00023988948147214651, 0.002220217320742562, 7.634408310551846e-08, 0.0, 0.0, 0.0002988455652386368, 0.0004283344793385483, 0.0, 0.0, 1.1308032336524869e-07, 0.001508107190445826, 0.0, 0.00020590917894171122, 0.0009479179851100165, 0.005094914836436016, 0.00032664711545286684, 1.0104126216647727e-07, 5.169103860099663e-08, 0.0003118134751310732, 0.0006169250958503924, 0.002212299249159082, 0.011848743728766452, 0.0002176812015705433, 1.3373168677059242e-08, 0.0004364343987423676, 6.569234901420949e-08, 0.0006229927673799607, 4.076988854206734e-08, 0.0002121933478087971, 1.7480886474507665e-08, 0.00016372184683424815, 0.00025765342566140504, 1.0170410534613091e-08, 1.0573166456173488e-07, 0.001283435374975655, 0.00010388546520012747, 6.773532977255143e-08, 0.005156687891311776, 8.326612405316528e-08, 4.214752502445031e-08, 0.000276455203966323, 1.1604683442891632e-07, 0.0009969369292469193, 0.0004283944297794342, 0.0018401264131462083, 0.0, 0.00041049948644819806, 3.027007276566167e-08, 0.00020647654229545104, 0.00032754705618844254, 0.00033864095247222346, 7.72615598936864e-08, 4.0906256976595317e-08, 7.510097947103747e-08, 2.3023953303341077e-08, 0.0, 0.0004117613576203508, 1.894492495732523e-08, 7.98293760821076e-05, 8.991565325715903e-08, 0.00012592322314654766, 3.9363902479670426e-08, 0.0004902742963899247, 0.0005929782559975001, 0.0007987294180840811, 9.738041707411604e-08, 4.851512990723324e-08, 0.0011001058986386842, 0.00016296414585521254, 0.00026612212535654934, 0.0009944474646625694, 4.40888520828302e-08, 0.00011204136821319207, 0.0009315758400654069, 3.9156730482424556e-08, 8.73104092310675e-08, 0.00011605398214094509, 5.09423128264456e-08, 0.00012087276123680295, 0.0005582318989124944, 0.0016507139686911224, 0.001337636547609038, 0.0005227113322671639, 0.0003725656175759735, 5.421329396160616e-08, 0.0018468248509595042, 0.00023086723596053691, 0.0005290214707355234, 1.924055822349344e-08, 0.0013606431544414153, 2.5804784345019276e-08, 0.00032356552091432323, 0.00038816749253814875, 7.587153528908263e-08, 0.000898986303163538, 0.00045210185962030446, 1.896450785373022e-08, 4.238953530847078e-08, 0.00022493600591284017, 6.283492535897266e-05, 2.6925022856429585e-08, 2.7436128135545894e-08, 0.001222781747352835, 0.0, 5.493011644895413e-08, 0.0006211832616055248, 4.4295076811781896e-08, 0.0006160973655464272, 0.0009973021248643706, 1.0258236557597766e-07, 1.1105796033842291e-07, 1.0889958487131753e-07, 0.0, 0.00019158728036647115, 0.0010060275096524353, 0.0005665155460065401, 0.0006157188751433998, 0.00014695734742352568, 0.002032178736526507, 0.0003518767116721448, 3.0648663267915205e-08, 0.001125986318387508, 0.0, 0.00040404396408643176, 3.5961113612588045e-08, 3.576402027341427e-08, 2.391380132335738e-08, 2.637883136876718e-08, 2.195924065173409e-08, 0.001073821884934363, 0.00013520024275268292, 0.0016411423991461995, 9.819518818847093e-08, 0.0005199810776164733, 0.0003141590223292689, 0.0, 0.000302508936861098, 2.2031959890871748e-08, 0.0006193227847972852, 0.0, 2.059012552881152e-08, 2.0673782060617033e-08, 1.1554093050569074e-07, 4.641913689660003e-05, 0.0, 0.0004884281155495627, 0.0003183658604749566, 0.0003122207830028093, 2.7778706310213686e-08, 0.0014133498227447828, 6.505543221609334e-08, 1.7992637916400344e-08, 0.0005419500818081193, 0.0004979296479371912, 1.0631861519190547e-08, 6.808333976930298e-08, 2.5085329457748336e-08, 5.257860427502987e-08, 0.001385120393616741, 2.997149304073631e-08, 0.0004343440100124914, 4.1085192585878464e-08, 8.30275805037927e-09, 0.0037693294975410033, 8.311774626615247e-05, 0.0003587318492811792, 0.0, 2.1379113346097744e-08, 0.0006011256531257594, 0.0018378581270201823, 0.000781606831916867, 1.2514620323931394e-07, 4.9913171749934956e-08, 0.0031603867002800096, 0.00201784568152973, 5.285506007836914e-08, 0.0017831062174418217, 0.0003880764049996982, 4.151796922221248e-08, 2.0379667938283004e-08, 0.00033250558336341343, 0.0019724584095573653, 0.0008597216352683765, 0.0002683424634066936, 0.0002760845742620231, 1.1881755750276356e-07, 4.834492140040922e-08, 0.00021730914296256502, 0.0001641016591353295, 8.729679891371106e-08, 0.0004379694413155847, 0.000803873525915355, 0.00015997966349680896, 0.00010382328027653809, 0.00022801264066118587, 0.0, 0.002336005767581466, 0.0, 0.0012149169295963623, 0.0014541011591707653, 1.649283510035514e-08, 1.8352347282890634e-08, 0.0012706469975882378, 0.0, 0.0004512672489930912, 6.6423500877727e-08, 3.697901050845255e-08, 4.8617312859673086e-08, 2.2889642154111716e-08, 0.00027632305023225757, 4.1830365148057374e-08, 0.0015819488539900914, 0.001703978655689237, 0.001456175595882446, 0.00030498044240864796, 1.1129038471242006e-08, 9.047892027884367e-08, 9.391455125957366e-08, 3.595908461032646e-08, 0.0005642586954884624, 0.0028874387982749665, 1.3069055462782715e-08, 0.0003098042824380915, 0.0003608682381856389, 0.0, 0.0, 0.0, 0.0009262383932408136, 1.0305832139955481e-07, 0.000640570875416799, 0.0015095884370970852, 6.227408376285525e-08, 0.0012230159627261055, 9.260318697980564e-08, 0.004567519657800813, 1.0094018672914448e-07, 0.0003529269228052127, 0.00015382356421904202, 5.8124363517027156e-08, 0.00026463186221583945, 0.0002616805413493401, 0.00023049513011776766, 0.0002218043716080675, 0.0, 4.788280251603382e-08, 3.396236627770099e-08, 0.0, 0.0008969784806859746, 3.802823899085591e-08, 0.0, 1.2287838428070194e-07, 0.0006015393806350918, 3.026861080636413e-08, 3.2872522691826494e-08, 0.0, 0.0, 0.0004166575410877475, 3.4182558991038515e-08, 1.7617039903975844e-07, 0.0008895777983324207, 0.001640373393135476, 0.0008935684451636268, 1.337663938628125e-07, 1.0792138526734103e-08, 2.7263229999941487e-06, 0.002047135138300784, 4.631905860772427e-08, 0.0, 0.0010039895699426133, 0.006059517487301069, 0.00039878580901963213, 1.3326345017381408e-08, 1.0879660526685875e-07, 0.002249089078084236, 0.0007436788996404448, 0.00020200857032392553, 2.7553079943014205e-08, 0.00017036754268932378, 4.3197953711490804e-08, 0.0007788876839329633, 0.0007581568258777887, 0.009205612931992511, 0.001988351870358408, 0.005776750858353592, 0.0008029116487743177, 0.00015534623207930543, 0.00021921127465681799, 0.0, 0.0, 3.097951535839508e-08, 2.802983043148553e-08, 5.134063553162263e-08, 0.0008404646820595234, 0.00019162361812933068, 5.357036818230413e-08, 1.0798478744395356e-08, 3.08606399012651e-08, 2.6968083465003688e-08, 6.477435245986196e-08, 3.9438754967716714e-08, 0.0, 1.9043023466669386e-08, 0.00014530837210131132, 2.0567079289897228e-08, 0.0, 3.171605631237339e-08, 0.002248996928694762, 8.829834489624729e-08, 6.132582729723664e-08, 0.00246429069921352, 0.00021184504046359992, 0.0, 0.0005953721948352565, 6.972860075646438e-08, 0.00016480571887209485, 0.00013615132621166634, 0.0009467456191410853, 0.0, 3.6940976417032045e-08, 0.0, 5.891980810371853e-08, 2.7125699234445506e-08, 3.601454682725698e-08, 0.0002267789668825447, 0.00016253724313164362, 8.13911059138556e-08, 5.616700357287627e-08, 0.0, 0.00025050253693179205, 0.03529149223887033, 9.193258247124781e-08, 8.274472623475712e-08, 0.0, 0.00042435990648262553, 0.0006496766995288204, 9.547178262357226e-08, 0.0002811355427850461, 2.7172439823516855e-08, 1.3292155544235184e-07, 2.2259858249080396e-07, 8.188817389025896e-08, 7.837472397244606e-08, 4.749894167866771e-08, 2.1331350748506238e-07, 4.931327394702586e-08, 3.7666973228923555e-08, 0.0, 3.7729339525157653e-08, 1.2273258031135452e-07, 4.328671457879696e-09, 1.6678284970668478e-07, 4.1598119064543846e-08, 0.0007056314497696898, 0.0007212943953306034, 0.00032500814284473696, 0.0023276259266877524, 1.5363163131585773e-07, 0.00044930949672882165, 1.147560112018438e-07, 0.001584378741294685, 0.0009334365808450129, 3.784350974439252e-08, 2.8629291525885466e-08, 0.000925043719665581, 2.0730034224734472e-07, 0.0, 3.314824704837871e-08, 0.000510174385079252, 0.0002161178415131374, 6.587757982428249e-08, 7.43048652161837e-08, 0.0006767643739970237, 2.356797655923583e-08, 0.001985763828023302, 0.00026815355165224144, 0.00018723764278108466, 0.0, 0.00019117797528557122, 0.0003531057387390502, 6.13995190374553e-08, 0.0007052969359380528, 0.0, 0.0, 0.0024019309401418845, 0.014133140288237162, 0.00039584551710541304, 0.0023751923373359542, 0.0016502203093435334, 4.712246181894566e-08, 1.638441450963266e-08, 7.261221925271564e-08, 9.576561084049526e-08, 0.00047650261622584874, 0.0026766653196360042, 0.00032180484522753995, 0.0008290614754661511, 3.7361840036921375e-09, 0.0016294175040220877, 0.00012571087962388194, 1.637007934965832e-08, 7.606827821620932e-08, 5.632234887599323e-08, 0.001148449697967353, 0.003133955204091093, 0.00022050341800915955, 0.0007163638044323252, 7.803613796652811e-08, 0.00041608769215570123, 0.00045337761404955006, 2.936019818676646e-08, 2.648286945862487e-08, 0.0, 1.8945743981837245e-08, 0.0007480601968350303, 0.0008431940706465548, 0.0006591717454745283, 2.312701087253567e-08, 0.007284886454433054, 4.797689489626751e-08, 5.2840950208103184e-08, 0.0007989090898264134, 0.00024083890673521344, 7.805162003856003e-05, 0.0011679254215438597, 0.0005766994333607076, 5.89428104643182e-09, 0.00014189423179166414, 1.1136238750399522e-08, 0.0006258930908475417, 3.729705138998289e-08, 5.824355371675774e-08, 0.0006916146717741282, 4.242551059311835e-08, 0.0003113131630026677, 0.00015142803032476218, 9.216335624177387e-08, 0.0007475028668863378, 0.0067014043398922135, 3.3218544356365596e-08, 2.1628906194523285e-08, 6.82324706456325e-09, 0.0007348454383034181, 0.00022511376231618, 0.0010282965202721929, 6.242390846787863e-08, 5.7577471811836526e-08, 0.0, 0.0, 4.084047938753787e-08, 0.0009542246606741409, 0.002963192040306247, 6.257119025138098e-08, 1.2003253079941873e-07, 0.0, 0.0008233528601316231, 0.00026524665495342235, 1.3389912103740488e-07, 0.0, 0.0002837967567964807, 0.0008198570290273388, 0.0, 3.5750846498402475e-08, 0.00022111444626124655, 0.0004196277057426324, 3.731101296478974e-08, 0.0005141057515831909, 2.6900707170877283e-08, 0.0004172832723653597, 6.996716161819032e-08, 0.0002835579629202498, 5.514695014258538e-08, 7.167954735977625e-05, 1.5062511501751583e-07, 0.0007844640565876056, 0.0001785808866015291, 0.000990593261313175, 5.8498512230148715e-08, 0.0001895608784684176, 0.0019671135762549326, 0.0002894928001576189, 0.005038167022925219, 2.971989060955788e-08, 0.0002140233393859348, 4.670192865030095e-08, 8.459822362587963e-05, 0.00352662977863665, 1.6691711723121558e-08, 0.00021771722155057316, 1.8162546023968272e-08, 0.00017434631649773297, 0.0, 5.7089957622601403e-08, 1.2272792414005511e-08, 0.0, 0.0008508937919900236, 1.4379125143947551e-08, 0.02673828262143927, 0.0014523947044411583, 0.0, 0.0, 0.00019617171480962125, 0.0, 2.1694336969582855e-08, 1.1959380417363816e-07, 1.2841771857536647e-07, 6.452948576691033e-05, 0.0, 0.0, 0.00042072108906399846, 6.314737093377085e-08, 4.771516876467543e-08, 0.00040340996190104143, 0.0033751267045641865, 9.943605275703381e-05, 4.093753645168737e-08, 5.925682647964092e-08, 0.006167989608159537, 0.0, 1.5058940411523073e-08, 3.8433083212239504e-08, 1.1192954360508903e-07, 0.0023107574719835335, 0.0013201218209828506, 0.0028134512222284143, 0.0, 0.0007805384286512445, 0.00024017298243445733, 3.1236899454897034e-08, 1.7792066030956175e-08, 0.00025031467815843383, 3.338597626608557e-08, 0.00022051165664362256, 0.0005809095935730615, 1.701620733403186e-08, 0.0003315380646650471, 0.0015235410914704092, 0.0007657522433824301, 0.0014426022085256406, 0.0053581267816007715, 1.2414779501274917e-07, 1.1144964088461286e-07, 0.00022780635499308435, 1.061767580060528e-07, 7.728743700569745e-08, 0.011971480113025474, 0.0015113714758986027, 0.0, 0.0003611663376615486, 8.70624871528817e-08, 1.5568657510351188e-07, 1.046983467800537e-07, 0.0, 9.490419761729121e-08, 0.0017747869340072095, 1.1049936545111391e-07, 1.0815897433652775e-07, 3.4060963009528e-08, 0.0003104389863848089, 2.5912425934500885e-08, 0.00021096429555341535, 1.6101358515526127e-08, 4.671482269150964e-08, 0.0003183817718629469, 6.120610190807064e-08, 0.0, 1.4171623414418361e-07, 0.0008321220045974733, 0.0010267718787603372, 0.0009173626630966762, 0.0004431480334534289, 0.0004276888449054982, 5.805009428479347e-08, 0.0030411944963594792, 0.0, 0.0004132299402101392, 0.0003814931231933934, 8.980427978918379e-05, 0.00043087492969407935, 0.0016879364157003308, 0.0007092457677402282, 0.00117255927522647, 0.006879967965060181, 0.0008764689432941696, 8.770647021425068e-05, 0.001126254914191907, 0.0003961022400110299, 0.0, 0.0, 0.00013315833285670775, 0.0006631467967898915, 0.008207438349913691, 0.0007589177476120521, 0.0001387125343111284, 3.3968927980546594e-07, 5.425147091427888e-08, 0.00048275952840639354, 0.0, 1.523479638562896e-07, 0.000933020863180622, 0.0009151449036558026, 0.005674930328035184, 0.00074906771651941, 4.996397270534157e-08, 0.001458646694899352, 1.461695132398472e-07, 0.0012568551647702147, 0.00045974264340835184, 1.5431888081987617e-08, 0.00036261887414528333, 0.0006640297335197898, 1.8493247175888044e-08, 0.0019163989492099838, 0.0, 9.419234730827808e-08, 2.424737258745389e-07, 0.0003141977766197503, 0.0034818223043833092, 0.00016635463138133416, 3.377341933631813e-08, 1.8709665359591201e-07, 0.0, 0.01196969511051971, 0.0005085316704416515, 3.2303454835299953e-08, 0.0004395153416719194, 0.0007318089691003107, 1.3397381249499622e-07, 0.0002672138787425861, 0.0, 0.0, 0.0, 0.002285227780815767, 0.00014065686092016853, 2.5059713161484183e-08, 0.0, 0.00048248458755179263, 1.2833684936215198e-07, 0.0003385671555514817, 0.0005794174781243889, 0.008973278689846683, 7.825660953036888e-08, 3.8584874774534104e-08, 0.00020435254156085056, 4.476709656142844e-08, 0.003094859264322902, 0.00451519272369784, 1.1378176922548511e-07, 0.009340368177953098, 1.9067061038201657e-08, 0.0003705024796998222, 3.218881357036421e-08, 0.00016989226168710736, 0.0023324050339833956, 2.300649055810071e-08, 0.0005367122154055808, 0.0030794222482850046, 0.0001721141515380207, 0.0011560336288194849, 1.8716739150921388e-07, 0.0005229215584069921, 0.0013421444633326606, 4.353223073884217e-07, 0.0010828052486911482, 0.0005925552961648947, 0.0003513548684597067, 2.8914746797512307e-08, 0.025608308909579103, 8.308310457065534e-08, 0.0006363372956030553, 0.00031199152990170863, 2.770507498736331e-08, 0.004454838496404531, 3.315272196403524e-08, 0.00028390296210191495, 0.0, 7.236138210051256e-08, 0.0, 0.0014009995288480183, 0.002811250785234109, 0.0, 0.0010785039267886887, 1.5829322624942002e-07, 0.0008067537130433658, 0.0011472272079293478, 8.299503479829206e-08, 0.00020279170610411275, 6.332975799078478e-09, 0.0, 0.0005014614318841812, 3.8573074380682105e-08, 4.221368732035114e-08, 0.00017198625182488237, 9.14325475813605e-05, 0.0, 0.00041251566441866713, 4.844157061829761e-08, 9.454147218293989e-08, 0.0001044543474361248, 0.0006947128153567936, 0.0016302899231767066, 6.87585134983586e-08, 5.524419796878481e-08, 0.0031716233504106147, 2.0766283001588243e-08, 1.3625085512054091e-08, 3.0612959575999836e-08, 0.0007289116466762778, 0.0010171457048030771, 2.224962926575617e-08, 0.0006999020663844653, 2.9502428196408093e-09, 1.958748618296329e-08, 0.0, 0.01761599590933532, 0.00045345976296400634, 0.000663134858071435, 0.00036881558477589024, 5.22845925641959e-08, 5.5716491964966074e-08, 0.0002100090970064603, 1.3322549379814458e-07, 0.0003655248408468793, 2.5815412665238123e-08, 0.0005042776494537885, 3.6976523640674384e-08, 9.433208903239174e-08, 0.003606264386344531, 2.5242663812714946e-08, 0.0026800892476011927, 1.6237428926655407e-08, 0.002866044178660295, 2.1545172335608814e-07, 0.0, 7.971552221793767e-08, 0.0, 0.0, 0.0, 0.0018587894684518127, 0.0, 0.0007114099347679458, 0.0008881091213164623, 5.360965395409897e-06, 0.0, 2.8056716091173583e-07, 2.0313336927151153e-07, 0.0, 0.0026109313092993534, 0.0003525932129218349, 0.00020430861813166027, 0.0, 1.0851823071862448e-07, 0.0, 0.0, 0.0, 0.0033161018807818477, 0.005617715777142843, 2.6233667264557978e-08, 0.000734581998478211, 1.0125937592678625e-07, 0.0006072186393590271, 1.3887008450826344e-08, 0.0, 0.0, 6.689332886272464e-08, 5.821901064836371e-08, 0.00021990386284798655, 0.0, 9.102013791932588e-08, 0.0007116640813914388, 1.532299840284477e-08, 0.002115309116630613, 5.526856243407159e-08, 0.0005060760022712596, 0.0007130884814938068, 0.0005270659684357957, 3.8686963031521e-08, 0.0, 0.0007701828366429139, 5.673960272465441e-08, 0.0006590664201954297, 0.00029314080451730835, 9.375332965374454e-08, 0.00011687512986959593, 0.00040081236665307954, 0.00018098553156861903, 6.331541784924255e-05, 8.057937582151683e-08, 0.00021561558930857828, 0.00028806756420777705, 0.0003002633616835223, 4.3089384325675856e-08, 0.0014088857973512444, 4.453945940425865e-08, 0.0005959186253769562, 6.825448011427522e-08, 2.6689144733138294e-05, 0.0010106065441555323, 2.29732575162233e-08, 0.00018473910397105603, 0.0009079774927242257, 0.00034759818673100226, 1.3980301408789926e-08, 0.0, 0.0, 0.0002879688322461837, 6.671021953622045e-08, 0.0007226579955448814, 0.00042050963228151963, 0.0018064816698989006, 0.00022787677645109183, 5.982274345990579e-08, 0.0003634998002765533, 1.0439998831601975e-07, 0.0007552139944123476, 8.99294396457494e-08, 9.648079765642987e-08, 4.056723495482383e-08, 2.687322629984178e-07, 0.0003115997615997061, 4.535818415573691e-08, 0.0, 0.0009595238563852709, 1.212891522836319e-07, 0.0, 4.3133099132472825e-08, 0.0015526301269934012, 0.0008910744239745476, 0.000500259532131672, 0.004637215169637226, 0.001146070951759737, 0.0007394032451298771, 4.150299051916477e-08, 0.002991778512086128, 0.0020417532698302748, 6.894076939810804e-08, 0.00025724558871889706, 4.510586806052437e-08, 0.00018884320082831582, 0.000412163754827048, 7.16361668624256e-08, 0.00023710351133718972, 0.00022219481733151724, 3.7038338917639765e-07, 0.0007972193167558226, 7.118097047894124e-08, 0.0, 0.00010674754971996787, 0.00015196478953443003, 3.256682314591935e-08, 0.0016203080467508935, 1.937450913838679e-06, 0.0, 5.335609767360572e-08, 0.0, 1.613738002910143e-08, 0.00047612720428023787, 4.696058655652595e-08, 4.167279978034995e-08, 0.00030725648952080887, 0.0, 0.0007871154245317035, 0.0006291859120121964, 0.0006452391131939514, 3.682320987729214e-08, 0.00020020141076387707, 1.3608316429823495e-07, 0.0011879965014511995, 0.0, 0.0, 4.7459123866841036e-08, 0.0010202505445616361, 1.1781558764498736e-07, 5.7831427547961016e-08, 0.0013356469612105107, 1.5812869524565536e-07, 6.416568845532269e-08, 0.00036256546313778444, 3.502295931678596e-08, 3.635713863710865e-08, 0.010975186085072199, 0.0005602300192551414, 9.683095512805876e-09, 4.273471345732514e-08, 0.0, 3.692254869648197e-08, 1.364203727431856e-07, 0.000529656377817914, 2.382936080208311e-08, 0.0, 0.00041998210525338853, 0.0003614162426464164, 0.0, 0.008809422610350332, 0.0004032634442114471, 0.0006305908273363605, 0.000846607225799262, 2.134888259692963e-08, 0.0013782562832590122, 0.0001607427957094523, 0.0011321108587546347, 0.000737807234763346, 0.0036606695042301993, 0.0003098796421170489, 1.1903319821139067e-08, 0.001203505596821486, 6.037219778726565e-08, 6.531123476628732e-08, 0.0048447397103046266, 0.0, 0.0006465353921494526, 0.00999679309841851, 3.274161148786941e-08, 4.271370566841363e-08, 3.8232021684348906e-08, 8.708496881421877e-05, 0.00011897787240516998, 0.02066077856526692, 2.871065293406739e-08, 1.310277815113786e-08, 0.0008596338591574256, 0.0032338743819711937, 0.00017774515065151328, 5.393112165065902e-05, 2.7595708167236164e-08, 7.912325061159487e-08, 4.4677030986219916e-08, 0.0, 0.000227562659195211, 0.00010445907759027968, 9.862376462925997e-08, 0.00018374631154667326, 0.008159226111332542, 0.000580776703461296, 3.632052100883265e-08, 1.1795827471266054e-07, 5.774538764696377e-08, 0.0003352085071410454, 6.286495776181815e-08, 0.0008125686799100953, 2.4062446405856406e-07, 2.4840154775146583e-08, 0.0, 0.0003675818916811398, 0.0, 0.00014289865201426007, 0.0, 0.0, 0.007697608607617151, 0.0, 3.106273079241975e-08, 5.059283740858935e-08, 0.000370599183417188, 0.00019974508918770318, 0.0011342461642933218, 0.000993751642058381, 2.7858640746498153e-05, 2.770229343852765e-08, 0.00046369935052863745, 0.0007612459395077899, 0.00077749094061284, 0.0, 7.260742516168412e-09, 1.1499925463691889e-07, 0.0, 0.001367921098606159, 0.0010975612881719944, 0.0, 0.00040776163363580645, 0.000780228579910677, 0.0005717322049342468, 6.632385730401569e-08, 0.0004602068216823516, 0.0003515764510841373, 3.049959449369766e-08, 9.167278992201768e-08, 0.008981462869794676, 0.00023239638312990714, 0.0005754971010327438, 0.0010031714335473766, 0.0009677119304721019, 0.0028536918611270752, 2.7734475912270404e-08, 2.1109723471864958e-08, 0.0016631528178403798, 0.0009384428157309942, 0.00041913989775342565, 0.004822155023333887, 0.0006378027226164117, 0.000671415295343343, 8.522826035200237e-08, 0.0006436727453077609, 0.00057486055739932, 0.0002358997889266983, 2.8588310894963578e-08, 0.0003669619735906959, 2.2394710293931103e-08, 0.003699141977853049, 1.9940352701675572e-07, 5.8956116063739454e-08, 8.761872746635834e-08, 3.607533894024984e-08, 0.0003168000843390044, 2.607007725880433e-07, 0.00022555330161433418, 9.52697578539928e-08, 5.02475914334581e-08, 0.0002710215194407256, 1.96899189364983e-08, 0.0004111431460427224, 3.002496133767377e-08, 4.5800138218913625e-08, 0.00011877740293733018, 6.997262854316914e-08, 1.4468416515562238e-08, 0.0, 4.764984560743923e-08, 0.00022062882926757372, 2.2939393776123644e-08, 0.0008352268337846238, 0.00031315205553293163, 0.0, 0.0022505392242344207, 9.539032212775873e-08, 0.00017758421767665377, 0.0, 2.8802654305603114e-08, 0.0005195739642535131, 0.010489222996546284, 7.083567504624519e-08, 0.00017048322074917624, 0.0, 0.0006753602201903987, 0.00015505899132259115, 2.4439093205364135e-08, 0.002430358902212466, 3.443125967402508e-08, 0.0007251089206370244, 0.00014163478132404842, 0.0002851509092730129, 0.0005178303208407373, 0.0005888365729337674, 0.007067523635954077, 5.115407990305181e-08, 2.8472551693612568e-08, 4.188150981387641e-08, 0.00044699137875773426, 0.013374930959257765, 0.00117985371628, 4.134890124860354e-08, 0.0005275104542051092, 0.0002584766160256213, 0.00038189127392439866, 0.00018004535550854693, 0.00035830872654844836, 0.00015562701361719969, 0.0, 3.126093507918601e-08, 5.054989166931257e-08, 0.0002107408590715224, 0.0012354802002735514, 0.0005214577454700979, 3.2518691480624016e-08, 0.00015699317671989958, 0.00023061671092497796, 0.0013577729316868433, 0.0036116877591708552, 8.955055660793222e-08, 3.4496767431810617e-08, 9.961394387902976e-08, 0.00063062460189687, 4.087952053425995e-08, 0.0005373652637195452, 0.0009132911760253084, 5.741779478491225e-08, 3.5801296405041394e-08, 0.0006108289562957009, 2.3164896287798386e-08, 0.004442832510124735, 0.0016401081279555606, 5.8386213039365455e-08, 0.00427569226391695, 4.6164682963774834e-08, 0.00012037888924321765, 0.0, 0.0, 0.003269264450812405, 0.0, 0.00024193890427897578, 0.0032475281016308634, 0.001257381835170268, 0.0005138631107409498, 0.0006227250733724931, 2.066589616806135e-08, 0.000259366565809726, 0.0, 0.00015758927510349367, 0.0013792747673896013, 0.0014320995829207512, 1.20701699708231e-08, 0.00030690013314354494, 0.003341950538635649, 0.0005640506345457792, 0.0, 5.6525940066992425e-08, 0.0005809940665668412, 0.0009679354369031116, 0.0, 4.2865357453646e-08, 0.00033024526219803206, 5.635590066383407e-08, 7.158183795766288e-08, 0.0012100234564170215, 0.0012130152253226542, 4.38741691546818e-08, 8.497851666468747e-08, 6.634567550605261e-08, 0.003200400863915542, 1.3944948520004936e-07, 2.4772587966518116e-07, 1.4177899631941178e-08, 0.00043704233510857723, 0.002587442176238043, 1.295194653808095e-08, 9.1230673323808e-08, 4.048897793116589e-08, 0.0007832889557233, 0.0020669170725399946, 5.825132484123745e-08, 8.09702132566935e-09, 0.0006868148939603233, 8.637647755425126e-08, 0.0, 3.39625108998282e-08, 0.002105906047664278, 0.0001921747062377081, 5.679918448016691e-08, 0.00018231128396793038, 0.0022546971345556075, 0.0010393864895032157, 8.493697695244425e-08, 0.00020198570249301044, 0.002549410826990836, 0.002287202803652429, 0.00044786956764864146, 3.1666296467079744e-08, 1.5918005238089506e-08, 7.657639171834143e-08, 1.0274299649639771e-07, 4.419594411159257e-08, 2.6645164094948298e-08, 6.17902395191836e-08, 0.0003038241189047992, 1.2880393072813666e-07, 0.00019515628315404913, 0.0006646993893001566, 4.1373977743975984e-05, 3.66976246392613e-08, 0.0017933271971924299, 0.000890879307183216, 0.0006961720683280573, 3.052696771453706e-08, 0.0, 0.00010082848777900832, 2.2456902297498644e-07, 0.0, 0.0017007328090726265, 0.0004358585693821122, 0.0023914323332767154, 0.0, 4.735583449354795e-08, 4.1356651924962965e-08, 0.00033089521936569834, 0.0023843199557750583, 0.0021889141772332417, 0.0003858590062214602, 0.003268158397090578, 0.0005491785347839233, 3.6513857323198394e-08, 0.00044256033321033296, 2.4572489448817116e-08, 0.0, 4.1287337266685924e-08, 0.0004001070036950305, 0.0016778440523845801, 0.0008793422966338837, 0.0004069509966372475, 0.000782295759290071, 0.00038408822771440136, 2.9323598871652174e-08, 0.00021369049248651462, 7.863434480419893e-08, 0.0, 1.2326845385418563e-07, 0.0002355547575015911, 5.283330189610941e-08, 0.0, 0.004607221867193834, 1.6150559612303123e-08, 0.04313592972845968, 0.0004395674984958801, 0.0003446190889003199, 2.9683778206516617e-08, 0.00023726272322779256, 2.568616180947057e-08, 0.0, 0.00014327869544283065, 0.0010636624091793034, 6.674620712879471e-08, 5.591078232999523e-08, 4.769002872715516e-08, 0.0003413667094474669, 0.0025118038489447807, 0.0008177842070481124, 0.00023942656115632214, 0.00041335513828806146, 8.519544892529032e-08, 8.170711496051437e-08, 1.141271952779935e-07, 0.00010903482617239352, 0.0005265022846680134, 0.0019349135725987658, 0.0, 0.009034998020376817, 0.0, 0.0012827780008906825, 0.0, 0.00016147614280672892, 0.0, 0.0014110299668751258, 4.989457418726517e-08, 0.0008866201957832084, 1.0905976511363717e-07, 2.826305982559263e-07, 0.0014375356361731218, 3.042360100078082e-08, 0.0011412490619525867, 0.0003799046109916643, 0.004777629889256037, 3.746217851720752e-08, 4.4016581888506285e-05, 1.0336237997608437e-07, 0.0010160759441136629, 0.0012650706634307223, 0.0017760865896730585, 0.0009361915875980682, 0.0011616020375173042, 5.5528883341088435e-08, 0.0016716226246001366, 0.0, 1.6947967531094915e-07, 0.0005603331956054077, 0.0013768602676393043, 0.0009765455230843379, 0.0006647300858736202, 0.000520132922361073, 1.1621013178816588e-07, 0.00262251434913294, 0.0037990565184257965, 0.00023431557572970384, 0.0007811246657843745, 7.106123294934264e-05, 0.0, 0.001360872749503908, 0.0003351867234263179, 7.052396457923458e-08, 1.0326010712066804e-07, 2.4205383964506655e-07, 2.8882382797363735e-08, 4.1467619655675377e-07, 0.0, 7.60278494230578e-08, 1.535156349090992e-08, 0.0009058476895360305, 0.00044744517658482503, 7.118642187971863e-08, 7.052081703012899e-08, 3.6858007267068155e-08, 0.00038769556231466236, 5.048562292365115e-08, 6.440938491068637e-08, 0.0028888411315912106, 0.0009190839753170502, 0.0007961908804751821, 2.7563805984478795e-08, 4.8085156800954124e-08, 2.6726190971036266e-08, 8.425106365816642e-08, 2.7017954055129976e-08, 0.00042548443441657694, 0.00020010007142420936, 0.00015837699170519236, 0.002286344882518417, 6.835508051214334e-08, 2.893657002298205e-08, 0.0016440586500702094, 0.0, 0.0001338547028109684, 0.0, 0.00036399418198196804, 6.085164676122101e-08, 4.044757134475433e-08, 1.1655004069252777e-07, 0.0008847103186097219, 0.0, 0.0002889731398278795, 0.001188889003433661, 0.00024676603213825374, 0.001974686903729865, 0.00036515864393154397, 7.195091735138375e-09, 0.0003990377202962211, 2.985183795199714e-08, 2.8890238553911378e-08, 0.005269206039539006, 0.0002659407243142329, 0.00024955688814228005, 0.0016172576738726395, 3.0909601273877766e-08, 2.032143082942705e-08, 2.6137673332920924e-08, 0.0008401944325359576, 3.889392354345149e-05, 0.0007308925611482127, 0.00253514257429535, 0.00044096670599666596, 0.00023300727999789165, 0.0, 0.004169197332535175, 0.0011158172857043095, 2.5485335966644618e-08, 0.0, 4.3991388888750595e-05, 4.298463299142357e-08, 0.0007265662273725751, 5.032260712584022e-08, 0.0006873281677688093, 0.0004990334737233539, 0.0002657158455884645, 0.0, 2.0461859726184635e-07, 0.0014383129397496379, 0.00010401876593337545, 7.228829813465988e-08, 5.906515309371345e-08, 0.0005029643351415295, 0.0004842660601425143, 4.833981764432287e-08, 5.9270410113921967e-08, 1.3181172751702492e-07, 0.0004594281375767039, 0.00038034614791998317, 0.0009327621085613403, 1.3117174876092689e-08, 7.272303411971802e-08, 7.835118603385196e-08, 0.0009226645206099758, 0.0009676122122944885, 0.00037868570216695194, 3.8300483732021924e-08, 0.0, 1.4586516253954635e-08, 4.9853962757103657e-08, 0.0010719674367465606, 0.0, 0.0, 2.3954294027710107e-07, 0.0007530054497232107, 0.001970898890541379, 0.0022937014594598504, 0.0009510473014553151, 6.196424706318708e-08, 4.390708129344214e-08, 6.907939419893539e-07, 0.0021612125465611505, 0.0003671228692672226, 1.8524413269323817e-08, 0.0003161476186076023, 0.0, 0.0003829841211084857, 4.8036493944756755e-08, 7.013011919012704e-08, 0.00176269445178649, 0.0011621579604513671, 7.217505147305924e-08, 5.648316736019577e-08, 0.0024593888539106478, 3.0252264605197304e-07, 5.7326312187913254e-08, 1.6144505390351062e-07, 4.303178235901714e-08, 0.002555207850289327, 4.355986107787503e-08, 0.00039466985632839003, 4.9947630148714103e-08, 0.0, 2.8952639543962086e-08, 0.0011113148574084832, 0.00023990495317250616, 0.0016195353878743013, 4.516015471124637e-08, 0.00015491085234293117, 0.0002688330123864099, 5.90874361797834e-08, 9.719470122385986e-07, 8.579674886548987e-08, 3.3859349381397086e-08, 3.311049565825678e-08, 3.561433364184368e-08, 6.388000474075281e-08, 0.0, 0.000814427122268998, 0.00041869478369452084, 4.249850309690672e-08, 2.7054912751064887e-08, 0.0001558450205265019, 3.9428848101588645e-08, 0.0, 1.3459493015314428e-07, 0.0, 8.603608589564849e-08, 3.2951713554659594e-08, 0.00016314687978941878, 4.5720367408544276e-08, 0.001307927115410186, 1.934605531065519e-08, 0.0013606823038299068, 7.888601409967734e-08, 5.157414282360872e-08, 0.00043432170948972044, 1.5672822653271826e-07, 1.2906160529739824e-07, 1.6383041971631272e-07, 0.0020093543851249962, 0.00023177407959865755, 0.0004733179669238547, 0.0005698942319604199, 4.041578302390137e-08, 0.0003130627699116771, 0.0002842875477619178, 0.0008200004172805538, 0.0008880661652716658, 0.0008243408573921059, 0.04713678210166808, 0.0005799599807548399, 4.6965387161275204e-08, 0.0030055542033520575, 0.0006318115648729823, 0.0005116518978112373, 6.4604409132943e-08, 5.104907507199991e-08, 0.0022232779971202215, 0.0014442173040529705, 0.0, 0.0, 7.542413868784342e-05, 8.237873412752007e-08, 0.0005575058859499885, 8.408237821892519e-08, 0.00021204294727920434, 6.711413302408053e-08, 4.5901281923946817e-08, 0.006004312460303536, 3.380480060149051e-08, 2.9319125278541593e-08, 7.840635482267828e-08, 0.0005367363281910996, 9.014285276201393e-08, 0.0004901868540262249, 6.463433387559685e-05, 3.7839038177769125e-08, 2.8805319148299562e-08, 0.0002722699450128358, 7.758939096869124e-09, 0.00030704879424749447, 0.00029367159417506924, 1.2754721564576633e-08, 0.00035333243056040025, 1.0868676666063092e-07, 0.0014576361377840626, 0.0, 0.00018031574457411345, 0.0007232454670949439, 0.001953772241400977, 0.00013936414030136422, 5.095225630424085e-08, 0.0012832012883369982, 5.523353099876587e-08, 0.0, 0.0005737701961838417, 0.0002614797841916217, 0.0004778844368910055, 0.00020268218221388085, 0.011662800245745213, 5.2586167324897327e-05, 6.412422691126742e-08, 0.0007969249135685197, 0.0, 3.838452980850451e-05, 9.468680525133428e-08, 0.0, 0.0001689791031230217, 0.0017574343060631679, 0.0014215898175726676, 4.728503944771591e-08, 0.0, 0.0, 0.0004221003999129796, 1.517729309965798e-08, 3.39217601780255e-08, 4.969750560380204e-08, 0.001685587061945966, 2.8222888714917888e-08, 0.0, 0.0001950723230321532, 0.00013494445696046883, 0.0, 4.553863829546402e-08, 0.0012633219239924338, 6.764837991684679e-08, 0.0007035727676782358, 0.00033926308377789567, 1.4573854215242853e-08, 0.0, 1.8388389913956392e-08, 0.0, 3.143719839237522e-08, 1.6271392301184567e-08, 1.6904885843384205e-08, 2.4335587839098218e-08, 0.0001902901878616075, 4.759428798880239e-08, 0.0007530524760653774, 0.004838349962659493, 0.006200440171312489, 1.604677885340003e-07, 1.4051969167208436e-07, 0.0011641631457339696, 0.0, 0.00047576254875531627, 0.00022894500854409302, 0.013951780270214442, 0.0002609382235914258, 0.00023093213444415003, 6.791965653449785e-08, 1.7544509523783107e-08, 0.005616432094916406, 6.655982324936025e-08, 0.0003114755337152134, 4.746899627975053e-08, 4.3467884170883264e-08, 3.1212002367421924e-08, 0.001077916733941672, 1.5367014921208512e-07, 0.005160075483530927, 0.0005513096652827026, 0.0002526509898095599, 1.5291441631772083e-08, 0.00011760844475068996, 2.150029456779673e-08, 1.940598202483812e-08, 0.0021714649900414335, 0.0002976795703698894, 2.2780273939711085e-07, 3.2113630478224016e-08, 0.042657434305199825, 0.0009903267191671992, 2.764258325074265e-08, 0.0, 0.0, 0.00021608189469440403, 0.0, 0.0002914152248220477, 0.0012409715686786797, 1.804457783684978e-07, 2.3960968104503565e-08, 3.4114374820693284e-08, 0.0003293780354286837, 2.89587926683624e-08, 3.935303930583356e-05, 0.0002720713251893415, 0.0013153683686711044, 0.00024212220950680887, 0.0, 0.0010837976848970096, 6.445668935797497e-08, 0.0005979593740151339, 0.002050418271560414, 0.0, 0.0007869879100520015, 0.000503960690953294, 2.325283217345602e-08, 0.0018152692937445467, 5.527034107295394e-08, 2.626688741234941e-08, 1.1941681315663886e-07, 0.0005617607632398377, 0.001438369848240235, 0.04854948614588406, 0.0, 5.932883270560321e-08, 3.081650695146146e-08, 2.2078337758797313e-08, 1.5707955996741196e-07, 1.220964120432114e-07, 3.151651557264563e-08, 0.0004000288454964313, 0.0002516641273907995, 0.006975716323104116, 0.00029074588763690275, 4.940888885474711e-08, 3.631470973117795e-08, 0.0, 3.972893570171052e-08, 4.828093443304932e-08, 0.00015506229889384555, 0.0005383645420885154, 5.109867730656933e-08, 0.0005121723887773715, 0.0007822226109582342, 6.573847871091026e-08, 1.5649278867998365e-08, 1.718468410615115e-08, 2.17403675872298e-08, 0.031192423399225906, 3.990940767306147e-08, 0.0, 3.2126945406931866e-08, 2.861573238983676e-08, 4.065235390941513e-08, 0.0008726141457560249, 0.0003777351119978986, 0.0031553194293509677, 0.0, 3.373970716810724e-08, 4.067986302657965e-08, 0.00011547031115502528, 4.964851641378045e-09, 0.00048670788083424155, 0.0, 3.273999353109375e-08, 0.00012850652651687412, 4.7180160213955627e-08, 0.00012621251176525645, 0.0, 0.0002160623598883276, 3.710230539887013e-08, 0.00011135301595140058, 5.6256621801789146e-08, 0.0, 3.970406633165221e-08, 0.0, 0.0, 0.0, 0.0009045129574732228, 0.0, 2.087253635621325e-08, 0.0003216311857949051, 0.0015743922362023224, 3.030756811559609e-07, 0.0, 7.509362018458592e-08, 5.460440936296524e-08, 6.910775378751179e-08, 0.0004736662130008708, 2.152537090913719e-07, 0.0005233479203535343, 0.0006629281288965186, 5.487883599668918e-08, 4.211204612225615e-08, 4.313052425432245e-08, 0.0, 0.0008919885069145313, 0.0, 0.0008215922807777154, 0.0004277208766242428, 0.0, 0.0, 3.322124437913543e-08, 0.0, 0.005050129447319699, 8.777011275570286e-08, 0.0, 1.0746157395864591e-07, 0.00035135110110956686, 1.2471442080589423e-08, 1.8183820811413977e-05, 3.976235785578742e-07, 1.0086350869581681e-08, 0.0041334174053227505, 0.0033685016557692743, 0.0, 4.739462614145293e-08, 0.00044882971468573234, 3.482100209265451e-08, 0.0004848389355237236, 0.000788802351910498, 4.485051807722945e-08, 0.0002756492006119589, 0.001820734132444382, 8.66243373111396e-05, 0.00639157692073121, 1.8188406477692146e-08, 0.00039572599215004315, 0.0001570302829402256, 2.0872019799301158e-08, 1.9283956818782468e-08, 0.003918375365772828, 1.0243958449484325e-07, 0.0002218754650812262, 0.0015214870512737287, 0.0002511179607200093, 0.0003093526983337775, 0.000977226835302143, 5.116233579603598e-08, 3.888151330128373e-08, 0.0001631299013477589, 3.829772086521368e-08, 0.00014620013282901656, 0.00028405420032588983, 5.716918202072274e-08, 1.7242864993986306e-07, 0.0011063067695821268, 8.909219996646141e-08, 0.00011485072680023206, 0.0, 0.0, 0.0, 0.0006036807681191798, 0.000696857456613805, 0.0, 0.00109038715116492, 6.232044853307787e-08, 0.0002726648275775424, 1.6198907251652633e-08, 4.93504911428062e-08, 0.0004261109997331499, 0.0013205204144345402, 0.0005105227610164495, 3.005935843816605e-08, 0.0, 8.941763514050936e-08, 0.00012003098247434258, 0.0, 4.3674198572180604e-08, 8.267649712796894e-08, 0.00015874335539409825, 4.873998617816569e-08, 4.416405403366126e-08, 5.3978520765010776e-05, 0.0, 0.00011703564671299917, 5.451150742048513e-08, 0.0, 0.0004689611788666441, 4.7495999590315386e-08, 0.0, 0.0, 0.0004773836983220798, 0.006131404950795496, 2.8206963699382337e-08, 0.0004362474071636913, 0.0004051134301655473, 0.0, 6.202557152581663e-08, 3.391324437156137e-05, 0.0, 0.0001665081576238562, 0.0006761772042976752, 4.3549209287679486e-08, 0.0007765474360955563, 0.00013261322374820146, 0.0016123278054115528, 0.0010891109937700733, 0.0, 0.0, 0.0006051347374294574, 6.651718736746088e-08, 1.1477709360403485e-07, 4.420321291686035e-08, 0.0006847187234205389, 0.0, 0.00017656369660973373, 0.00026831035870772987, 0.005094454473850876, 1.0209712004584618e-07, 0.0, 2.68885147237468e-08, 4.978674703755113e-08, 1.2274843020279142e-07, 0.0008044140265582118, 0.0, 2.3185699371313178e-08, 0.0, 3.266749841766268e-08, 5.166866608814507e-08, 6.975245726262954e-08, 2.016786885450615e-08, 0.00014719134633593426, 2.607465309783879e-08, 0.0004165976888657514, 0.0004599726222490161, 3.0387517666974974e-08, 1.46308363569121e-08, 0.0009952759150658756, 4.7167849031232994e-08, 0.0, 0.0, 0.0006130125729858907, 0.000713111841125626, 0.00024438625667773324, 0.0, 5.861807924220328e-08, 0.0019318859889733522, 5.075400748731254e-08, 0.00015354521090787192, 0.0003917644991189122, 0.00024263353474065683, 0.00013709091955014442, 1.0016422423693932e-07, 4.0299075482737844e-08, 0.00020402013813582042, 0.002995544558709086, 0.00021244161191873384, 0.00023212054764008657, 0.00010392463776254598, 0.0022328098956398592, 0.0007674064118603852, 0.0017506932291080387, 5.031645556947415e-08, 0.000425968130758107, 0.01748851195083405, 0.00041186520300093006, 0.0007910656409253452, 5.667002714564805e-08, 1.4011030279736533e-07, 0.0002977045812015674, 0.0007696836556865634, 0.0003174456574028395, 0.0006430124603549494, 0.0, 0.0012384486408811256, 0.004999400960087598, 0.00029897647558984106, 3.917357754280282e-09, 0.001130130471798692, 0.000562801095251044, 7.014421239343083e-08, 0.0, 0.00068134964339795, 0.0042258190858492405, 0.0, 9.174690410291459e-09, 0.0005324856567199681, 0.00011831362441260871, 0.00023124251835512374, 0.00014203981307449372, 1.0401585393883135e-08, 6.95325245096741e-08, 0.0, 0.0002527071556133373, 0.0, 2.344217782451977e-08, 0.00020702153279914464, 0.00048602450600644046, 5.727699583542869e-08, 0.0, 0.0014196225494921707, 0.00012368080172311203, 1.3398768868304123e-07, 3.592874423693081e-08, 0.010637714474236386, 0.002343468186806619, 0.0007965672146245408, 0.00016276241014620037, 1.0218074934612086e-07, 6.751569641930115e-08, 0.001265070555163541, 0.0, 5.1871758966187837e-08, 0.0005090148320940303, 6.965325255547408e-08, 6.216854345265115e-08, 3.116210565365054e-08, 0.0, 0.0, 0.0, 0.0002614332761279542, 0.0010179828152676502, 0.002139025381334837, 0.0010741698999148807, 0.00042042382741368483, 1.0110587790608047e-07, 0.0, 0.0, 9.631990375991404e-05, 0.0004228061063829419, 0.0, 0.0003252223523174282, 8.5172870323811e-08, 6.985027198302396e-08, 2.1888672806133518e-08, 8.292671264783357e-08, 0.0, 0.0382924666719962, 0.0012322876161508342, 8.580488000558805e-05, 6.473877965526651e-05, 0.0, 1.2392111638874324e-07, 0.0017742282153467234, 0.0002234878866759181, 0.0012946684811547433, 2.9596216632287958e-08, 0.0001595792876626221, 0.0014037038799454493, 0.003531594227891432, 7.008585284512789e-08, 0.0002878788730415272, 0.0, 3.97571856410452e-08, 1.2339410424797848e-07, 3.672873486372294e-08, 4.1963243422806274e-08, 6.852921815961314e-08, 0.0001466253272216156, 0.0021483595739782892, 1.0274580964673772e-07, 0.00465525154441982, 0.005151807727894451, 0.00019066398981184177, 3.484828439312574e-08, 0.0007124688179013782, 6.405309048896194e-08, 0.006431106127560017, 0.00013196567191421706, 0.00010764464251412193, 0.0002879082307790686, 0.0012314032347146577, 1.937211294018234e-08, 4.693769020271556e-08, 0.0013363453220571102, 0.0, 0.00042827014804119705, 2.996698759454313e-08, 6.4791366654096e-08, 0.0013518125483049758, 3.7074584376462376e-08, 4.4361335564297975e-08, 0.0007195698703275288, 0.0014517236971061769, 4.087851823215678e-08, 3.2656706671620753e-08, 9.97459775589553e-05, 2.933416218495872e-08, 4.2310724002851674e-08, 0.00025118317235775023, 2.281484288872214e-08, 0.0003022018024407164, 9.10221530270322e-08, 0.013219289216173881, 1.5678712938502667e-08, 0.0, 0.00013557341226105464, 0.0001698083917992967, 0.0001924184645014097, 5.3497066595690055e-08, 0.0002603972796853298, 0.0016928886949229998, 7.547400600421237e-08, 0.00024563617529182926, 0.0003854456692331334, 0.0, 0.0, 5.2664929242206354e-08, 0.0005783544746623312, 3.185728415820659e-08, 0.0028485005944463796, 0.0008250004306229585, 0.0019402138886054632, 0.00042126389688216154, 0.0, 4.506544342220908e-08, 0.0, 0.0010386691467453059, 0.0, 3.4131275703228956e-08, 0.0005835182827398743, 0.0018628200921575544, 0.0, 0.0017716598592106836, 0.00015316236567434102, 6.856931900611402e-08, 0.00012188279008835091, 1.1965936192360537e-07, 0.0, 0.0003024099591933069, 0.0007832334757494326, 0.0008499025351926342, 0.00047275227025332276, 0.000561148777835942, 0.00044136614794581044, 0.001138269006713997, 0.0, 0.000144139668956352, 0.0003595671937619226, 1.3379979912558415e-07, 3.474209313330468e-08, 3.2904954903894727e-08, 2.0174272125701297e-08, 2.397810656777345e-08, 0.0009285867374781418, 0.0, 0.0003115895943982963, 0.0, 2.8666958728310977e-08, 0.0032210370903345505, 0.0017205593762621035, 0.00015039687737886735, 0.0010463920240882279, 0.0, 0.0, 0.0, 0.0010591559532855813, 3.9123296547787504e-08, 0.0013774222546709093, 0.000329903231716538, 1.523438708541343e-08, 0.003754208752651759, 0.08113106330205555, 0.0002279667794273882, 8.927238110040513e-09, 0.002325885482715447, 0.0, 0.00020803390968750782, 0.0004412508343876564, 0.0002886617733973038, 0.001966888457840709, 0.0004599621693083297, 2.9515931753982764e-08, 0.0008853576705356383, 0.00034002658303401717, 2.1111685759539896e-08, 2.9722324077186496e-08, 0.0, 1.8279264504153246e-05, 1.06787435970307e-07, 0.0018829016869523318, 0.00028993198788107127, 0.0005508028657860034, 0.00022007400373053043, 0.0003446346308054745, 3.1335035363183306e-08, 0.0003946675283204311, 0.0002803195407847408, 3.775776705016905e-08, 0.0, 1.985455284505536e-08, 0.0025630954408158236, 2.7976927718897323e-08, 0.0006003304648818335, 6.354773176910734e-08, 1.9593828237935623e-07, 3.415522313231265e-08, 0.00037637571582310617, 0.0001460939940433576, 4.80538195655204e-07, 0.0002790760072157535, 0.0, 0.0, 0.00014652390015523325, 0.0025185720427328283, 0.0, 3.920713145240624e-08, 0.007017686340880668, 0.0006340361209805014, 1.685556536733644e-08, 0.00030132169054455834, 0.0, 0.0, 1.7416983575651255e-08, 9.785596554594969e-08, 0.0008825009009327527, 0.0, 6.001895136459047e-08, 0.00016593254006150006, 0.0018439486852300953, 0.0, 0.00010768303825682565, 0.0003186551534364629, 0.004207047732188689, 0.0, 1.529211284988752e-08, 0.0, 0.0001670504517927727, 0.0007098086749430729, 1.6077697350068651e-07, 2.3688075497821943e-08, 0.0, 3.925707726457409e-05, 0.0003814678218630437, 0.0015787251161913714, 0.0, 0.0008771626360789247, 0.0033815875017612525, 0.0006966470574181864, 0.00027326823194692403, 0.00046215861923368137, 3.259359340249444e-05, 0.0006364821224879876, 0.0006343019752130361, 0.000803162866728847, 3.608466205942382e-08, 5.5759034216513414e-08, 0.0006783667626353439, 0.0009096113002341536, 2.573427838213168e-08, 9.48062477693597e-05, 2.702614307841943e-07, 0.0, 8.347842049439894e-07, 0.00018250855181588228, 4.76936753664367e-08, 0.0012413361029591982, 3.44978418441161e-08, 0.001365269281156341, 4.376154091277597e-08, 0.002031772103697348, 0.0, 0.0, 0.0009340176186105922, 0.00025374902615529836, 5.965429104291339e-08, 0.00019838622330868607, 0.0002551797757794366, 0.0, 2.3071787567577235e-08, 9.691947052788893e-08, 0.0, 0.0009390816746711434, 0.0, 4.766003010641515e-08, 0.0, 0.0, 0.009798831617579988, 4.855184504965562e-08, 4.2363985350569354e-08, 2.9144869348262058e-08, 0.0004919064569492913, 3.42954512584634e-08, 0.0, 0.0010061995382365295, 0.0010488718296429865, 3.119083896602419e-08, 0.017276348228688298, 0.0015145293167128128, 9.821792069499204e-08, 1.137872522211752e-07, 0.0007163541169107478, 1.5786352951743243e-06, 0.017119155273140816, 1.0056069192645114e-07, 0.0006382722651223968, 0.00035830353567646177, 0.0003496581704769271, 0.0017439602082730849, 0.001076008052904283, 6.289832756490256e-08, 0.0005330503384486001, 9.792312317824037e-08, 0.0, 0.00020340627338788798, 0.0009807688736790303, 5.228278616961894e-09, 1.571830135163856e-08, 9.493881677424123e-08, 9.917438147502464e-08, 0.0018120136359774243, 8.260660534755114e-05, 3.305568137944724e-08, 0.00016772815317082598, 0.00015192494038374375, 0.001169156031280562, 0.0, 1.2202044780320043e-07, 3.4928508634747204e-08, 4.774235705557138e-05, 0.00015947312155477073, 1.9030279341090776e-08, 0.0, 0.0007188413561631226, 1.2359539356548535e-08, 0.0006689952064198584, 4.792549102175592e-08, 0.0003590117095336169, 0.0, 0.0005835808418886493, 3.9928041409581844e-08, 2.9145158552568254e-08, 5.710800181041702e-08, 7.243426719876335e-08, 1.3816660382077115e-07, 3.522036902098843e-08, 4.434287039760586e-08, 0.0011784005103319048, 7.109557691964004e-08, 0.00014401385025221707, 3.629663011973186e-08, 0.0, 0.0, 0.0, 2.0398698086566923e-05, 0.0014898558695072776, 6.904676911990583e-08, 0.0, 0.0, 0.00028413566078803657, 0.000220931166449595, 0.0006871002376309982, 0.00017893015915709998, 0.00035637627701805253, 0.0014333550533820864, 1.2523571566771056e-08, 0.0019001571491262495, 0.0019510997840287614, 1.2811514402562705e-08, 0.0008873826197568447, 0.0013834958425438279, 0.0005704893149111542, 2.0019448379960404e-08, 0.0, 0.00030721004340273077, 0.0, 0.0, 5.154618122292986e-08, 0.0004471807794858213, 7.533916155904806e-08, 0.0003275664265063217, 4.010549421646047e-08, 7.529999302383984e-08, 2.823963804746989e-08, 0.00040748097198181415, 0.0007261532589206252, 3.894482145386423e-08, 0.0, 0.0, 2.494625769404212e-07, 0.00020017390901388182, 0.0004289434883394233, 2.648224201158358e-07, 0.0, 0.0007862381242782953, 2.989732781015124e-08, 0.000207154909114103, 4.5893637379991435e-08, 5.568952237510367e-08, 7.12590774111358e-08, 0.00027404135446054494, 3.976668798671574e-08, 8.203221067885475e-08, 0.0010597740971260167, 5.250234526543778e-08, 5.974002046148299e-09, 5.726757712012966e-08, 4.386540184147607e-08, 0.0, 0.0, 0.0006312388802951879, 1.0561889477114829e-08, 0.00015691981861615408, 0.0, 1.4089887656983048e-07, 0.0, 0.0, 1.1425942441183819e-07, 0.00015298819955470529, 3.311362931789652e-05, 0.0007003728376416441, 1.5558134765635194e-08, 0.012541662517985969, 0.00035585635443794014, 0.0006421150990819552, 2.9747191400297838e-08, 8.641958351674613e-08, 9.349911959653775e-08, 7.608525897708715e-08, 4.541095117465354e-08, 0.0, 2.8961578587029053e-08, 6.382771672742235e-08, 0.00018401047211868247, 0.0023952949714386566, 0.0, 0.0006482313387658003, 5.782211692802305e-08, 0.0, 0.0013659659075716817, 0.0002224503160896057, 5.2964846415086195e-08, 8.027033777068518e-08, 0.00026899454041613105, 3.1707258847758686e-08, 0.0012466842375721643, 0.0026243225410232055, 0.0010244051797014962, 0.001348938776518505, 2.3704537798622467e-08, 0.0003268510064616192, 0.0, 0.0014616976243811064, 6.070287885056419e-08, 2.9659626056292535e-08, 0.0009032362841806443, 0.0004951511676408642, 0.0010657290968202648, 0.0004553289198247738, 4.95767150434796e-08, 3.252812465112083e-08, 1.0266940114212031e-07, 0.00016764885889330436, 0.004881154894987314, 1.4865137488896969e-07, 0.00026594781487636907, 1.20093613413418e-07, 0.000951600225419214, 0.0001451300549854658, 0.0005022135085304569, 0.00256986955264248, 3.866344467738877e-08, 3.909056473408294e-08, 1.7778455436861794e-08, 0.0, 8.379189214895577e-08, 5.3043179307795304e-08, 0.00015866180221275113, 0.0, 0.000542577104854901, 0.00015747325947157326, 3.7411517266485154e-08, 5.6004413942050806e-08, 0.0, 7.025051453298e-08, 1.4066038689239028e-08, 0.00012617621805034124, 0.0029271082993364774, 0.0013211636787867306, 3.1824226428348294e-08, 0.0008924704185761264, 0.0001901230493031603, 0.0, 0.0003848859583678101, 0.00025934344615146076, 0.0001234270040446973, 0.001598792943215363, 0.0, 0.0, 3.0976298657767506e-08, 0.0008625711070856248, 0.0010110905157424417, 0.00041961881176892874, 0.0, 0.0, 4.4099311299112976e-08, 0.0, 9.226899833686324e-05, 0.0, 0.00521571363833725, 0.0, 4.6902649672154515e-08, 0.000533082067045788, 1.5741952489827177e-08, 3.945324922883129e-08, 3.752189795987139e-08, 0.0003670179555901234, 8.298635387762114e-08, 8.42924610023664e-08, 0.0, 0.00045038166414474324, 0.0018515857945343413, 0.0, 1.5638406195526288e-08, 0.0002154824222070006, 5.44150457680727e-08, 1.811131709737889e-07, 6.760130286169782e-07, 0.00018274055236892943, 0.0053771669650879185, 2.9305181636083294e-08, 0.00020958443837995418, 1.9861290122246602e-08, 0.00037335785564184194, 0.0, 0.0012226830055613981, 0.0005919529532546111, 0.0, 0.0, 0.0, 5.510834411170809e-08, 0.0003287110238890572, 4.5074346559069255e-08, 0.0017570352506685438, 0.00012846932592822918, 0.001843784107809168, 0.00010124876987736229, 0.0014148743361647887, 0.0, 0.0, 0.0009354440651951806, 0.00013917604853773934, 4.759565953369175e-08, 1.0853779483525891e-07, 0.0003087747870212334, 1.6683572366468254e-08, 0.0002880644791435748, 0.0004457165699895978, 1.8089462891903094e-08, 0.00016597133288481054, 0.0013697573202875722, 0.0, 0.0006202538793688317, 0.0013207072010293238, 0.0030022419677922744, 0.0002071057223694098, 0.0007310649888364832, 0.0, 6.601607817090093e-08, 9.499311568445908e-09, 2.6712254872656186e-08, 0.00024170677742647417, 0.0013273706181100528, 0.0007850265428813179, 1.3521073422777734e-07, 6.210933806372214e-08, 0.0005979508134824299, 3.690907552316046e-08, 0.00033723048518518337, 2.843274240742656e-08, 1.4830355269921544e-08, 9.451604811686775e-05, 6.193461929929038e-08, 0.0, 2.6355141559201297e-08, 0.00010732015693996552, 3.940401081342265e-08, 1.2801999851854248e-07, 2.5705076339261867e-08, 0.0012294702222093615, 1.164832254234658e-08, 0.0, 0.0012128171222750073, 4.4464826773165876e-08, 0.0008377235293176549, 0.000433207501285954, 1.710348499141921e-07, 3.5551565068535974e-08, 0.00017084790038063278, 7.597789808698119e-08, 3.578144102111282e-08, 0.00018009757776197237, 0.008330547627423053, 1.3822272904976904e-07, 0.0, 0.00047968042965442403, 9.191541952013678e-08, 0.0004524732949647758, 7.720651858456246e-08, 3.2238888243082064e-08, 0.00019502981956273202, 0.0007269385715342348, 9.946527509878309e-08, 0.0014208155243897918, 0.002945066330027472, 9.14138252424234e-08, 0.00015251115174992825, 0.0, 9.868397316917767e-08, 1.8179286046378153e-08, 0.00017571450182449986, 1.4322554397011814e-07, 1.2286724246354834e-05, 3.740973185855181e-08, 0.0, 0.0, 1.3184176928553492e-07, 0.00048494518056900525, 4.2165036205174816e-07, 0.0005407775832355645, 1.424431002239507e-07, 9.176101165624114e-08, 0.0018551199653027194, 0.00031105462036453563, 0.00030115756207921133, 0.000506969871470803, 3.13241092729511e-08, 0.0, 0.00014495869896320268, 0.0005413335581504563, 0.0, 7.937885546509789e-08, 9.00443595102461e-05, 8.574003782744258e-08, 6.098657227718163e-08, 6.276041083277423e-08, 3.5391655051745715e-08, 0.001426139882539694, 0.0012481026637222185, 2.9289684245112176e-08, 0.00014482941832156824, 0.0, 0.00027208490365754714, 0.0, 1.0220177544351729e-07, 0.0, 1.9104791446357102e-08, 0.0012240439729884508, 0.0001593220392899258, 5.41437370660764e-08, 0.0009741061436708134, 4.672247380869536e-08, 5.289660150223401e-08, 5.385264991512575e-08, 2.79514002837214e-08, 2.6470819877121627e-08, 1.7055786154505997e-07, 7.918637445496158e-05, 0.0, 0.00014440384872457018, 2.192305006197127e-08, 0.0007286326419112562, 0.0016122172350583508, 5.741782508891516e-08, 1.25026353630038e-07, 9.8729980297128e-07, 0.000976340522832034, 7.176408534667659e-08, 4.329329696312638e-07, 3.968350507944677e-08, 4.242130468796635e-08, 0.00023863086364630132, 1.832074088926111e-08, 2.6606006730750433e-08, 5.1948459467153434e-08, 0.0, 0.0003228591956897205, 1.3055584476068668e-07, 0.0010527860338697139, 3.211205654684359e-08, 0.0003831385636064142, 0.0015832330797837188, 0.0003793486372439541, 0.0004920589142134149, 2.8961028799605487e-08, 6.765804195781639e-08, 0.0006045040934370331, 0.0001824002809185207, 0.0006306491895470977, 0.0, 3.3121516387681714e-08, 3.3496367132035865e-08, 7.026443173587521e-07, 1.745348727868312e-08, 0.0, 7.24495939044464e-08, 0.0018414681639802566, 0.00021567798739314555, 0.0006376036208145611, 0.0038724115650174682, 0.00200125824146984, 0.0006234810641908378, 1.9498856326329276e-07, 0.0, 1.2764928699692526e-07, 0.000575809180181281, 1.206823319041186e-08, 3.794771224627902e-08, 1.53369151417656e-08, 0.0018440331918789655, 0.0002677923500348219, 0.00010937626348342039, 0.0006489411978981324, 0.0004587164912037548, 8.505517659981421e-08, 0.0016493550305657374, 2.734797905482212e-08, 0.0003943336915583595, 0.0007613061553602262, 3.9785338485744196e-08, 5.602047779390763e-08, 2.8943603546698027e-08, 0.001600478620141127, 0.0024915249214027086, 0.0008765594893558284, 2.2076620156533334e-08, 0.0, 0.000847705425832529, 0.0004019436188305082, 4.1889818616925186e-08, 0.0, 0.0008503457021584356, 0.0, 0.0, 0.0003075253728168233, 0.0, 8.47054508492786e-08, 0.0009975675163288375, 0.00011447223912868912, 0.0003697817365493465, 0.0, 6.079270143898723e-08, 0.0002510185215814122, 1.1737338427742038e-07, 3.031866634983782e-08, 2.2367405269353434e-08, 1.2221424886739645e-07, 0.00022799981690213637, 4.9783839816411385e-08, 3.2925242635530835e-08, 0.00011545502841930273, 4.8074445825570824e-08, 0.000676121757709953, 9.490225029727348e-08, 0.00018209968970975054, 1.967084217215907e-07, 1.8156639539282614e-07, 8.207820214689116e-08, 0.00023022558928659502, 0.00011636635315607759, 0.0, 6.709912114164006e-08, 0.00038695786508320874, 8.370462106058794e-08, 0.00021425954878553135, 0.0003585090776113427, 1.0873060372795189e-07, 0.0005232441001576138, 0.0009600930096751699, 4.809983803352643e-08, 0.000677731538377441, 6.479693175716483e-08, 0.00552840360222194, 0.0, 4.470275443897049e-08, 4.811405118800588e-08, 2.2888272210325487e-08, 0.0, 3.495748497283359e-08, 0.0001783202690545692, 0.00010717296233649963, 0.0019961377751372277, 8.49621896508931e-08, 3.0377774698899084e-08, 0.0, 1.5360254033508542e-08, 0.012731573849396407, 1.7728742003565655e-08, 0.0009716704470020556, 0.016415794492426986, 3.563293674004021e-08, 0.00033418665580361333, 0.0006421617208201313, 0.0008088716064539606, 0.0, 0.000517171510348419, 0.000272803145972168, 1.2596012288969322e-07, 0.00026566807493752964, 0.0003177799160806538, 4.299330892386266e-08, 0.00032155702436257395, 0.0003828580335898151, 0.00033297756139319824, 0.0, 0.002279634345102666, 0.0, 0.0003804794184971869, 1.1041375223166782e-07, 4.4625803036201836e-08, 0.0001756570311676969, 0.002644909838758947, 0.004093903743834382, 2.573647817676108e-08, 3.8433370230323594e-08, 0.000546030145777443, 4.7154284728002164e-08, 0.0, 0.0003437817249876603, 3.63009223722506e-08, 0.0007501620537536855, 0.0, 0.0, 0.0005487000459114972, 0.005914898279046163, 0.0, 7.319717551569913e-05, 6.196695616591386e-08, 0.0002219076066561026, 0.0006857316957235545, 0.0007562190204121698, 7.673116256193057e-08, 3.188025537250408e-08, 0.0, 0.0001292907821447127, 0.0006995686774962815, 6.866234870453762e-08, 1.0846797833071111e-07, 0.015614637505120873, 0.0, 0.0039455582900706455, 0.006051125203254831, 0.0013497861199043306, 5.721812389613447e-08, 0.0, 0.0001483427900028475, 0.0, 0.0, 0.00038543935253192613, 3.8474921006540256e-08, 3.1069061746084e-08, 0.0, 0.0, 9.134869785023965e-08, 0.0, 0.00018187813923466412, 5.077966077081914e-08, 0.00015598444081782215, 0.0001314066575703701, 4.072954151561025e-08, 0.0002897738291058927, 0.0, 1.092904300768318e-08, 9.13435250127299e-08, 0.0, 2.8727184167956584e-08, 2.8175208261317485e-08, 1.0851013643107248e-07, 2.859508004790721e-08, 2.0579045028703938e-05, 6.106207057194884e-08, 4.577070690983037e-08, 0.00026570845541081515, 8.05859034205381e-08, 4.276828231553818e-08, 0.00015798070035647038, 0.00023347544892537178, 0.0, 7.445880565950075e-08, 6.900847849047232e-08, 5.061436428717341e-08, 0.00032634472277183794, 0.00048126650576983324, 7.980438114659456e-08, 7.373571344940602e-08, 3.98687768444011e-08, 0.0005552886910923192, 0.0002318842311379397, 0.0005509866340592632, 0.0006772686967109543, 0.0, 0.0022869842624598565, 7.938824977889715e-07, 7.669144394786147e-08, 8.717470341007248e-09, 0.012348326924779805, 3.389934474936235e-08, 0.012821609532410981, 0.0011538723624561053, 0.0, 0.0002667454130400497, 0.0010857412728451298, 3.118914388869927e-08, 0.00037831577623718255, 0.0005288827394393026, 4.099514007274427e-08, 0.00013369046247478032, 0.002344331760969977, 0.0006583986095972942, 0.0011332956396082475, 4.230285125323426e-08, 0.0014877700195063087, 0.0001223711527601583, 2.520891575358259e-08, 0.0010635451933884367, 0.005367016515372302, 0.0004005313014684032, 0.00017161433230442223, 0.0048479562277055415, 0.0018660241557760574, 8.029379893171165e-08, 0.005006710325142627, 0.004255869019001842, 0.004966309126993195, 0.0025617516232132685, 0.0018989997536560496, 0.00025830648208165994, 7.632029454693536e-08, 0.0018488194345071258, 0.0012564461030826865, 1.1655639897141504e-07, 2.8308672411806316e-08, 8.871886947169212e-08, 6.21680309963242e-08, 9.94297015063897e-05, 1.7030309967057465e-07, 0.0, 7.4014146903003075e-09, 0.0032893652223510757, 0.0008988227610154621, 0.0008147654356405454, 0.0, 0.00027794483985712115, 2.8481414139119143e-07, 0.0022260772390646343, 0.0, 0.002234875279886544, 0.0003041266702308377, 0.00030009095934806766, 4.2427746752817746e-07, 4.7001572156141506e-08, 0.0013535123652110648, 0.0, 4.70460446588709e-09, 0.0, 0.0, 6.536536024427053e-05, 5.290844587287618e-08, 0.0, 0.0, 0.0005877773485364997, 0.0, 6.989494540520459e-08, 0.0, 7.464981560620699e-08, 4.5138949075079755e-08, 0.0028262158474128213, 4.8949225381204424e-08, 4.538935125741588e-08, 0.0, 3.529839734878069e-08, 0.004704849439922414, 0.002141700152910413, 0.0005186628733703111, 0.0019077070556526557, 4.714616754869793e-08, 0.00010470395069868257, 0.000190592130092112, 0.00047125149392458543, 0.0, 0.0, 0.0037556501795688686, 0.007355938046920415, 0.000804644966643615, 5.8682114094453305e-08, 3.3831331681181024e-05, 0.00022343756690147387, 1.547430367788929e-07, 0.0001810994647336061, 0.0021963673735968903, 0.0003096219445635418, 9.655194659988642e-05, 4.288604080073523e-08, 0.00014818842345284765, 0.00030772246164660643, 0.00018977570130657487, 0.0001730146752336331, 0.00026438437219123854, 0.0, 0.0015409840486044462, 7.514792480494803e-08, 0.0019248122642393185, 3.306920678160923e-08, 0.000587092127095451, 1.843459991979858e-08, 0.000329966919259649, 0.0, 0.001535570589409958, 4.174766709449017e-08, 0.0005672996067725385, 3.629711116164701e-05, 0.0, 0.0009558944720527898, 6.756518496575874e-08, 0.0010163362919205737, 9.584777146532716e-05, 3.2293381130300166e-08, 0.00020985605259227262, 0.0003940768614732157, 0.00035496119629076987, 0.00025523590862219265, 0.00030522772412488455, 0.0031046561743009486, 0.0009589879850117743, 0.00046064058349137556, 2.507353589929961e-08, 0.00015178277614013785, 0.00163508812881737, 4.742079790202365e-08, 0.0002761664277871204, 0.0026746420080839533, 1.4578773647847253e-08, 0.0016553083152564898, 0.0, 5.7839603806420394e-08, 9.690727451737069e-08, 0.00022804418970162832, 0.00018839127411119587, 0.0005389240469427616, 0.0003927954918179909, 0.0003753345194367256, 1.9175590053317813e-08, 0.0013290746984471673, 3.2711738277002456e-08, 1.2977820378725416e-07, 8.802046532759371e-08, 0.0, 2.520660978973447e-08, 0.0010127247236638986, 0.000196373810733929, 0.0016138001515265477, 0.0005916632893584492, 0.0002821464394396902, 4.665177366199796e-08, 0.000141682066675896, 1.3499818370048656e-07, 0.0007573657260720966, 3.179032162906273e-08, 0.00044063234021505084, 2.4463324424312092e-08, 0.0044593838555797224, 0.0, 7.220846151655641e-08, 4.7004794426380104e-08, 0.00041144812735349344, 6.732916672509939e-08, 5.261488260489547e-08, 0.00020267214146876966, 0.00017633041033878673, 0.0, 0.000962200555902809, 3.670894527808416e-08, 0.0007482787490238449, 0.0, 0.00041416563179316805, 0.00025666362728778684, 0.005822303637876698, 0.0023044279219401847, 4.9537599342064665e-08, 0.00018254400803347187, 0.0008424793344820713, 5.801332870347815e-08, 0.00037437302680382137, 0.0032610835609494463, 0.0, 5.2166301813128024e-08, 0.00013160187930179011, 0.0006661729298450275, 3.6541025022796126e-08, 2.4264024738014565e-05, 0.0005043976364927017, 4.8870820225882766e-08, 0.0001653999709769671, 1.0282458148692307e-07, 0.0002695584141328893, 0.0, 0.0008582759670870735, 1.5281951313518987e-07, 0.00022958726694971026, 0.0, 0.0007429186506407311, 0.0020242279457362926, 0.000786146691986072, 0.0058335849206016845, 0.0007425052542547272, 0.0006702156266464349, 2.0168494765750598e-08, 3.538222474379818e-08, 0.0, 5.023921964591465e-08, 8.133154226423938e-09, 0.0011221857913352243, 0.0, 1.5614264995007762e-05, 0.00020751821723660995, 1.4997157426739878e-07, 0.0002859417895182792, 1.266656742540997e-08, 0.0, 2.4474652356062205e-08, 3.096995592499822e-08, 5.636185680769056e-08, 6.93440166051257e-08, 0.00018939870121379272, 6.425944738097643e-08, 0.0, 4.5052100741306755e-08, 0.0, 0.0006881945956062636, 0.00013777331124306073, 0.0002446927846062621, 5.210987132333524e-08, 4.3551824092522086e-08, 1.943927660194445e-07, 0.00021091505562369743, 4.244742469496818e-08, 1.3694152266182822e-08, 0.0002018290245497998, 0.0003785556841122044, 0.0002797697687784655, 3.328225524553998e-08, 4.116293874337907e-08, 2.84615212682164e-08, 0.00020587771069443904, 6.843632775894319e-08, 2.5826697017954593e-08, 0.00019035034460864586, 0.0002196583296767111, 2.3434846898714417e-07, 0.00022619856452848976, 0.00030537981339528943, 0.0007232444596990666, 0.001698954115967092, 0.0, 0.0013628874203857387, 5.729321403794396e-08, 0.0005102374664405568, 0.00034304908132387017, 0.0003477575969698999, 0.0, 0.0008576657570163741, 0.0, 3.578727159262907e-08, 0.00016006761874867305, 0.0012165719472385932, 0.001353072756789562, 4.2409347201456544e-08, 2.7077645903417527e-09, 0.0005031026824607453, 0.0005147397655593503, 5.43093427340422e-08, 0.002165576683916845, 0.0023864776181568813, 0.0001903928475745959, 8.766674520870659e-05, 0.0001315129226927102, 0.0003082270695875456, 0.0005824300029574213, 0.0009040627397587779, 0.0016136737112221258, 1.8069696822424904e-08, 0.0008530661190781115, 0.0, 0.00029880177002747973, 1.0499160041826043e-07, 0.000364331630199059, 0.0, 0.00035978292591514567, 0.0006242517093259986, 0.005899583247777585, 0.0004259845720857797, 0.00028157880679760685, 1.397976972879652e-08, 1.8916920844754506e-08, 6.032303698934513e-08, 0.000101382902157473, 0.0, 0.0004758478952525237, 1.057663141426386e-07, 7.390864009235868e-08, 0.0006255153284882194, 0.0011452065349217997, 6.685360197420484e-08, 4.46425470166334e-08, 0.0, 0.00024238230560857596, 3.468985073446399e-08, 0.00028824005927261345, 0.00025372412871898104, 5.6385371177864426e-08, 5.2152561665717765e-05, 6.331382866780953e-08, 0.0007976667413819388, 0.0016163239687259669, 0.00011380107629838104, 0.0004272272542133307, 0.0009772967675132122, 1.1319505284090871e-08, 4.422009455060336e-08, 6.74624691799259e-08], 'model_size': 15727220992}\n" + ] + } + ], + "source": [ + "for i in range(7**3):\n", + " result_path = os.path.join(results_dir, f'awq_{i}')\n", + "\n", + " scores = {}\n", + " with open(result_path) as f:\n", + " result = json.load(f)\n", + "\n", + " # some json post processing to get scoringpipeline to work\n", + " # result['scores_i2t'] = np.array(result['scores_i2t'])\n", + " # result['scores_t2i'] = np.array(result['scores_t2i'])\n", + " # result['txt2img'] = {int(k):v for k,v in result['txt2img'].items()}\n", + " # result['img2txt'] = {int(k):v for k,v in result['img2txt'].items()}\n", + "\n", + " print(f'Scoring result awq_{i}')\n", + " scores = sp.compute_scores(result, 'image_captioning')\n", + " \n", + " for k,v in scores.items():\n", + " if type(v) == np.ndarray:\n", + " scores[k] = v.tolist()\n", + "\n", + " scores['model_size'] = result['model_size']\n", + "\n", + " # print(scores)\n", + " # break\n", + "\n", + " \n", + " # print(scores)\n", + " scores_path = os.path.join(scores_dir, f'awq_{i}')\n", + " with open(scores_path, 'w') as f:\n", + " json.dump(scores, f, indent=2)\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.14" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/awq/generate_awq_configs.ipynb b/awq/generate_awq_configs.ipynb new file mode 100644 index 0000000..1e07520 --- /dev/null +++ b/awq/generate_awq_configs.ipynb @@ -0,0 +1,324 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Generate AWQ Config Files" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import json\n", + "import os\n", + "import itertools" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "dir_name = 'retrieval_configs'\n", + "os.makedirs(f\"./{dir_name}\", exist_ok=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "model_part2modules = {\n", + " 'self_attn' : ['self_attn', 'self_attn_output'],\n", + " 'fc': ['fc1', 'fc2'],\n", + " 'cross_attn': ['cross_attn', 'cross_attn_output'],\n", + " 'img_ff' : ['intermediate_query', 'output_query'],\n", + " 'txt_ff' : ['intermediate_txt', 'output_txt']\n", + "}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Info-retrieval Configs" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "49" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "bit_options = [2,3,4,5,6,8,16]\n", + "\n", + "vit_model_parts = ['self_attn', 'fc']\n", + "\n", + "# bit_widths = [bit_options for _ in range(len(vit_model_parts))] \n", + "# vit_bits = list(itertools.product(*[bit_widths]))\n", + "\n", + "qformer_model_parts = ['self_attn', 'txt_ff', 'img_ff', 'cross_attn']\n", + "# bit_widths = [bit_options for _ in range(len(qformer_model_parts))]\n", + "# qformer_bits = list(itertools.product(*bit_widths))\n", + "\n", + "all_bits = list(itertools.product(*[bit_options, bit_options]))\n", + "len(all_bits)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "for i,bits in enumerate(all_bits):\n", + "\n", + " json_config = {}\n", + " json_config['vit_layers'] = {}\n", + " json_config['qformer_layers'] = {}\n", + "\n", + " vit_bits, qformer_bits = bits\n", + "\n", + " # ['self_attn', 'fc']\n", + " # for vit_model_part, bit_width in zip(vit_model_parts, vit_bits):\n", + " # modules = model_part2modules[vit_model_part]\n", + " # for mod in modules:\n", + " # json_config['vit_layers'][mod] = bit_width\n", + "\n", + " for vit_model_part in vit_model_parts:\n", + " modules = model_part2modules[vit_model_part]\n", + " for mod in modules:\n", + " json_config['vit_layers'][mod] = vit_bits\n", + "\n", + "\n", + " # ['self_attn', 'text_ff', 'img_ff', 'cross_attn']\n", + " # for qformer_model_part, bit_width in zip(qformer_model_parts, qformer_bits):\n", + " # modules = model_part2modules[qformer_model_part]\n", + " # for mod in modules:\n", + " # json_config['qformer_layers'][mod] = bit_width\n", + "\n", + " for qformer_model_part in qformer_model_parts:\n", + " modules = model_part2modules[qformer_model_part]\n", + " for mod in modules:\n", + " json_config['qformer_layers'][mod] = qformer_bits\n", + " \n", + " filename = os.path.join(dir_name, f'awq_{i}')\n", + " with open(filename, 'w') as f:\n", + " json.dump(json_config, f, indent=2)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "test_config = json.load(open('/nfshomes/vla/low_bit_vision/mmq/awq/retrieval_configs/awq_20'))" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'vit_layers': {'self_attn': 4, 'self_attn_output': 4, 'fc1': 4, 'fc2': 4},\n", + " 'qformer_layers': {'self_attn': 16,\n", + " 'self_attn_output': 16,\n", + " 'intermediate_txt': 16,\n", + " 'output_txt': 16,\n", + " 'intermediate_query': 16,\n", + " 'output_query': 16,\n", + " 'cross_attn': 16,\n", + " 'cross_attn_output': 16}}" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "test_config" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Caption Generation Configs" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "dir_name = 'captioning_configs'\n", + "os.makedirs(f\"./{dir_name}\", exist_ok=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "343" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "bit_options = [2,3,4,5,6,8,16]\n", + "\n", + "vit_model_parts = ['self_attn', 'fc']\n", + "\n", + "# bit_widths = [bit_options for _ in range(len(vit_model_parts))] \n", + "# vit_bits = list(itertools.product(*[bit_widths]))\n", + "\n", + "# NOTE: no text ff for caption generation\n", + "qformer_model_parts = ['self_attn', 'img_ff', 'cross_attn']\n", + "# bit_widths = [bit_options for _ in range(len(qformer_model_parts))]\n", + "# qformer_bits = list(itertools.product(*bit_widths))\n", + "\n", + "llm_model_parts = ['self_attn', 'fc']\n", + "\n", + "all_bits = list(itertools.product(*[bit_options, bit_options, bit_options]))\n", + "len(all_bits)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "for i,bits in enumerate(all_bits):\n", + "\n", + " json_config = {}\n", + " json_config['vit_layers'] = {}\n", + " json_config['qformer_layers'] = {}\n", + " json_config['llm_layers'] = {}\n", + "\n", + " vit_bits, qformer_bits, llm_bits = bits\n", + "\n", + " for vit_model_part in vit_model_parts:\n", + " modules = model_part2modules[vit_model_part]\n", + " for mod in modules:\n", + " json_config['vit_layers'][mod] = vit_bits\n", + "\n", + "\n", + " # ['self_attn', 'text_ff', 'img_ff', 'cross_attn']\n", + " # for qformer_model_part, bit_width in zip(qformer_model_parts, qformer_bits):\n", + " # modules = model_part2modules[qformer_model_part]\n", + " # for mod in modules:\n", + " # json_config['qformer_layers'][mod] = bit_width\n", + "\n", + " for qformer_model_part in qformer_model_parts:\n", + " modules = model_part2modules[qformer_model_part]\n", + " for mod in modules:\n", + " json_config['qformer_layers'][mod] = qformer_bits\n", + "\n", + " # ['self_attn', 'fc']\n", + " # for llm_model_part, bit_width in zip(llm_model_parts, llm_bits):\n", + " # modules = model_part2modules[llm_model_part]\n", + " # for mod in modules:\n", + " # json_config['llm_layers'][mod] = bit_width\n", + "\n", + " for llm_model_part in llm_model_parts:\n", + " modules = model_part2modules[llm_model_part]\n", + " for mod in modules:\n", + " json_config['llm_layers'][mod] = llm_bits\n", + " \n", + " filename = os.path.join(dir_name, f'awq_{i}')\n", + " with open(filename, 'w') as f:\n", + " json.dump(json_config, f, indent=2)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "test_config = json.load(open('/nfshomes/vla/low_bit_vision/mmq/awq/captioning_configs/awq_55'))" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'vit_layers': {'self_attn': 3, 'self_attn_output': 3, 'fc1': 3, 'fc2': 3},\n", + " 'qformer_layers': {'self_attn': 2,\n", + " 'self_attn_output': 2,\n", + " 'intermediate_query': 2,\n", + " 'output_query': 2,\n", + " 'cross_attn': 2,\n", + " 'cross_attn_output': 2},\n", + " 'llm_layers': {'self_attn': 16, 'self_attn_output': 16, 'fc1': 16, 'fc2': 16}}" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "test_config" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.14" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/awq/llava_quantizer.py b/awq/llava_quantizer.py new file mode 100644 index 0000000..6663f1a --- /dev/null +++ b/awq/llava_quantizer.py @@ -0,0 +1,257 @@ +from awq.quantizer import BaseAWQQuantizer +from awq.scaled_modules import ScaledModule +from awq.utils import * +import random + +class LlavaAWQQuantizer(BaseAWQQuantizer): + + def __init__(self, model, device, inputs_processor, dataset, config, + dataset_name = 'VQAv2'): + super().__init__(model, device, inputs_processor, dataset, config) + self.group2modules = self._get_group2modules() + # TODO: change, testing for now + self.n_samples = 128 + self.dataset_name = dataset_name + + # keep track of excluded modules for model size computation + self.excluded_mods = [] + + def _get_group2modules(self): + + group2modules = {} + + if 'vision_layers' in self.config: + group2modules['vision_layers'] = { + 'self_attn': ['self_attn.q_proj', 'self_attn.k_proj', 'self_attn.v_proj', 'self_attn.out_proj'], + 'mlp': ['mlp.fc1', 'mlp.fc2'] + } + + if 'llm_layers' in self.config: + group2modules['llm_layers'] = { + 'self_attn': ['self_attn.q_proj', 'self_attn.k_proj', 'self_attn.v_proj', 'self_attn.o_proj'], + 'mlp': ['mlp.gate_proj', 'mlp.up_proj', 'mlp.down_proj'] + } + + return group2modules + + def _get_model_layer_groups(self): + + layer_groups = {} + + quant_vision_flag = 'vision_layers' in self.config + if quant_vision_flag: + layer_groups['vision_layers'] = self.model.vision_tower.vision_model.encoder.layers + + self.excluded_mods.extend(get_mods(self.model.vision_tower, non_linears_only=quant_vision_flag)) + + + quant_llm_flag = 'llm_layers' in self.config + if quant_llm_flag: + layer_groups['llm_layers'] = self.model.language_model.model.layers + + self.excluded_mods.extend(get_mods(self.model.language_model, non_linears_only=quant_llm_flag)) + + print(f'layer_groups: {layer_groups}') + return layer_groups + + def _group_modules_for_scaling(self, layer, linear_inputs, layer_group): + + grouped_mods = [] + + if layer_group == 'vision_layers': + + if 'self_attn' in self.config[layer_group]: + + # self_attn input + grouped_mods.append( + dict( + prev_op = layer.layer_norm1, + modules = [ + layer.self_attn.k_proj, + layer.self_attn.q_proj, + layer.self_attn.v_proj, + ], + inp = linear_inputs['self_attn.q_proj'], + parent_module = layer.self_attn, + layer_kwargs = self.layer_kwargs, + w_bits = self.config[layer_group]['self_attn'] + ) + ) + # self_attn output + grouped_mods.append( + dict( + prev_op = layer.self_attn.v_proj, + modules = [ + layer.self_attn.out_proj + ], + inp = linear_inputs['self_attn.out_proj'], + parent_module = layer.self_attn.out_proj, + layer_kwargs = self.layer_kwargs, + w_bits = self.config[layer_group]['self_attn'] + ) + ) + + if 'mlp' in self.config[layer_group]: + # fc1 + grouped_mods.append( + dict( + prev_op = layer.layer_norm2, + modules = [layer.mlp.fc1], + inp = linear_inputs['mlp.fc1'], + parent_module = layer.mlp.fc1, + layer_kwargs = self.layer_kwargs, + w_bits = self.config[layer_group]['mlp'] + ) + ) + # fc2 + grouped_mods.append( + dict( + prev_op = layer.mlp.fc1, + modules = [layer.mlp.fc2], + inp = linear_inputs['mlp.fc2'], + parent_module = layer.mlp.fc2, + layer_kwargs = self.layer_kwargs, + w_bits = self.config[layer_group]['mlp'] + ) + ) + + # TODO: vision_layers, projector + if layer_group == 'llm_layers': + if 'self_attn' in self.config[layer_group]: + # self_attn input + # grouped_mods.append( + # dict( + # prev_op = layer.input_layernorm, + # modules = [ + # layer.self_attn.q_proj, + # layer.self_attn.k_proj, + # layer.self_attn.v_proj, + # ], + # inp = linear_inputs['self_attn.q_proj'], + # parent_module = layer.self_attn, + # layer_kwargs = self.layer_kwargs[layer_group], + # w_bits = self.config[layer_group]['self_attn'] + # ) + # ) + + # self_attn query + grouped_mods.append( + dict( + prev_op = 'self_attn.q_proj', + modules = [ + layer.self_attn.q_proj + ], + inp = linear_inputs['self_attn.q_proj'], + parent_module = layer.self_attn.q_proj, + layer_kwargs = self.layer_kwargs[layer_group], + w_bits = self.config[layer_group]['self_attn'] + ) + ) + + # self_attn key + grouped_mods.append( + dict( + prev_op = 'self_attn.k_proj', + modules = [ + layer.self_attn.k_proj + ], + inp = linear_inputs['self_attn.k_proj'], + parent_module = layer.self_attn.k_proj, + layer_kwargs = self.layer_kwargs[layer_group], + w_bits = self.config[layer_group]['self_attn'] + ) + ) + + # self_attn value + grouped_mods.append( + dict( + prev_op = 'self_attn.v_proj', + modules = [ + layer.self_attn.v_proj + ], + inp = linear_inputs['self_attn.v_proj'], + parent_module = layer.self_attn.v_proj, + layer_kwargs = self.layer_kwargs[layer_group], + w_bits = self.config[layer_group]['self_attn'] + ) + ) + + # self_attn output + # NOTE: sometimes skipped in the AutoAWQ implementation according to: https://github.com/mit-han-lab/llm-awq/pull/67#issue-1850622696 + if layer.self_attn.v_proj.weight.shape == layer.self_attn.o_proj.weight.shape: + grouped_mods.append( + dict( + prev_op = layer.self_attn.v_proj, + modules = [ + layer.self_attn.o_proj + ], + inp = linear_inputs['self_attn.o_proj'], + parent_module = layer.self_attn.o_proj, + layer_kwargs = self.layer_kwargs[layer_group], + w_bits = self.config[layer_group]['self_attn'] + ) + ) + + + if 'mlp' in self.config[layer_group]: + # linear 1 + grouped_mods.append( + dict( + prev_op = layer.post_attention_layernorm, + modules = [layer.mlp.gate_proj, layer.mlp.up_proj], + inp = linear_inputs['mlp.gate_proj'], + parent_module = layer.mlp, + layer_kwargs = self.layer_kwargs[layer_group], + w_bits = self.config[layer_group]['mlp'] + ) + ) + + # linear 2 + grouped_mods.append( + dict( + prev_op = layer.mlp.up_proj, + modules = [layer.mlp.down_proj], + inp = linear_inputs['mlp.down_proj'], + parent_module = layer.mlp.down_proj, + layer_kwargs = self.layer_kwargs[layer_group], + w_bits = self.config[layer_group]['mlp'] + ) + ) + + + + return grouped_mods + + + # NOTE: assuming VQAv2 dataset for now + def _get_calibration_set(self): + random.seed(self.seed) + indices = random.sample(range(len(self.dataset)), self.n_samples) + + # if self.dataset_name == 'VQAv2': + imgs = [] + prompts = [] + for i in indices: + + # short answer prompting according to: https://github.com/haotian-liu/LLaVA/blob/main/docs/Evaluation.md + # prompt = 'USER: \n' + self.dataset.qa_pairs[i]['question'] + '\nAnswer the question using a single word or phrase. ASSISTANT:' + prompt = self.dataset[i]['text_input'] + prompts.append(prompt) + + imgs.append(self.dataset[i]['image']) + + # apply inputs processor + samples = self.inputs_processor(images = imgs, + text = prompts, + return_tensors='pt', + padding=True).to(self.model.device) + + return samples + + + def _run_model(self, calibration_set): + out = self.model.generate(**calibration_set, use_cache=False) + out = out.to('cpu') + clear_memory(out) + + diff --git a/awq/plot_results.ipynb b/awq/plot_results.ipynb new file mode 100644 index 0000000..92f1c11 --- /dev/null +++ b/awq/plot_results.ipynb @@ -0,0 +1,15314 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "import os\n", + "\n", + "import plotly.express as px" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Image Text Retrieval" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "uniform_results_path = '/fs/cfar-projects/low-bit-vision/uniform_results/image_text_retrieval/blip2_flickr_results.csv'\n", + "df_uniform = pd.read_csv(uniform_results_path)\n", + "df_uniform = df_uniform.dropna(axis = 1, how = 'all')\n", + "\n", + "# with pd.option_context('display.max_rows', 10):\n", + "# display(df_uniform)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
vit_bitsqformer_bitstxt_r1txt_r5txt_r10txt_r_meanimg_r1img_r5img_r10img_r_meanr_meanagg_metricsmodel_size
02267.583.088.179.53333361.3281.8886.7276.64000078.08666779.533333387.970088
12383.895.797.692.36666770.5089.6293.6284.58000088.47333392.366667408.189992
22484.595.497.492.43333371.2289.9093.6284.91333388.67333392.433333428.409896
32583.995.697.592.33333371.4289.7493.8685.00666788.67000092.333333448.629800
42683.795.397.492.13333371.1089.8293.7084.87333388.50333392.133333468.849704
\n", + "
" + ], + "text/plain": [ + " vit_bits qformer_bits txt_r1 txt_r5 txt_r10 txt_r_mean img_r1 \\\n", + "0 2 2 67.5 83.0 88.1 79.533333 61.32 \n", + "1 2 3 83.8 95.7 97.6 92.366667 70.50 \n", + "2 2 4 84.5 95.4 97.4 92.433333 71.22 \n", + "3 2 5 83.9 95.6 97.5 92.333333 71.42 \n", + "4 2 6 83.7 95.3 97.4 92.133333 71.10 \n", + "\n", + " img_r5 img_r10 img_r_mean r_mean agg_metrics model_size \n", + "0 81.88 86.72 76.640000 78.086667 79.533333 387.970088 \n", + "1 89.62 93.62 84.580000 88.473333 92.366667 408.189992 \n", + "2 89.90 93.62 84.913333 88.673333 92.433333 428.409896 \n", + "3 89.74 93.86 85.006667 88.670000 92.333333 448.629800 \n", + "4 89.82 93.70 84.873333 88.503333 92.133333 468.849704 " + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "awq_results_path = '/fs/cfar-projects/low-bit-vision/awq_results/image_text_retrieval/awq_image_text_retrieval.csv'\n", + "df_awq = pd.read_csv(awq_results_path)\n", + "# bits --> megabytes\n", + "df_awq['model_size'] = df_awq['model_size'] / 8e6\n", + "\n", + "# df_awq['vit_bits'] = df_awq['vit_bits'].astype(str)\n", + "# df_awq['qformer_bits'] = df_awq['qformer_bits'].astype(str)\n", + "\n", + "df_awq[:5]" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + " \n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "customdata": [ + [ + 2, + 2, + 83, + 88.1, + 79.53333333333333, + 81.88, + 86.72, + 76.64, + 78.08666666666667, + 79.53333333333333 + ], + [ + 3, + 2, + 94.2, + 95.5, + 92.5, + 94.94, + 96.64, + 91.22666666666667, + 91.86333333333334, + 92.5 + ], + [ + 4, + 2, + 94.7, + 95.5, + 92.53333333333336, + 95.46, + 96.88, + 91.88666666666666, + 92.21, + 92.53333333333336 + ], + [ + 5, + 2, + 94.6, + 95.3, + 92.66666666666669, + 95.54, + 96.88, + 91.86666666666667, + 92.26666666666668, + 92.66666666666669 + ], + [ + 6, + 2, + 94.9, + 95.4, + 92.7, + 95.76, + 97.04, + 92.00666666666667, + 92.35333333333334, + 92.7 + ], + [ + 8, + 2, + 94.9, + 95.5, + 92.73333333333332, + 95.62, + 97.06, + 92.12666666666668, + 92.43, + 92.73333333333332 + ], + [ + 16, + 2, + 94.8, + 95.4, + 92.73333333333336, + 95.78, + 97.08, + 92.04666666666668, + 92.39, + 92.73333333333336 + ] + ], + "hovertemplate": "qformer_bits=%{customdata[1]}
img_r1=%{x}
txt_r1=%{y}
model_size=%{z}
vit_bits=%{marker.color}
txt_r5=%{customdata[2]}
txt_r10=%{customdata[3]}
txt_r_mean=%{customdata[4]}
img_r5=%{customdata[5]}
img_r10=%{customdata[6]}
img_r_mean=%{customdata[7]}
r_mean=%{customdata[8]}
agg_metrics=%{customdata[9]}", + "legendgroup": "2", + "marker": { + "color": [ + 2, + 3, + 4, + 5, + 6, + 8, + 16 + ], + "coloraxis": "coloraxis", + "size": 4.5, + "symbol": "circle" + }, + "mode": "markers", + "name": "2", + "scene": "scene", + "showlegend": true, + "type": "scatter3d", + "x": [ + 61.32, + 82.1, + 83.32, + 83.18, + 83.22, + 83.7, + 83.28 + ], + "y": [ + 67.5, + 87.8, + 87.4, + 88.1, + 87.8, + 87.8, + 88 + ], + "z": [ + 387.970088, + 511.03724, + 634.104392, + 757.171544, + 880.238696, + 1126.373, + 2110.910216 + ] + }, + { + "customdata": [ + [ + 2, + 3, + 95.7, + 97.6, + 92.36666666666667, + 89.62, + 93.62, + 84.58, + 88.47333333333333, + 92.36666666666667 + ], + [ + 3, + 3, + 100, + 100, + 99.06666666666666, + 98.18, + 99.02, + 95.24666666666668, + 97.15666666666668, + 99.06666666666666 + ], + [ + 4, + 3, + 100, + 100, + 99.2, + 98.28, + 99.06, + 95.54666666666668, + 97.37333333333332, + 99.2 + ], + [ + 5, + 3, + 100, + 100, + 99.4, + 98.22, + 99.06, + 95.57333333333334, + 97.48666666666666, + 99.4 + ], + [ + 6, + 3, + 100, + 100, + 99.4, + 98.3, + 99.1, + 95.62, + 97.51, + 99.4 + ], + [ + 8, + 3, + 100, + 100, + 99.4, + 98.3, + 99.1, + 95.63333333333333, + 97.51666666666664, + 99.4 + ], + [ + 16, + 3, + 100, + 100, + 99.4, + 98.28, + 99.08, + 95.62, + 97.51, + 99.4 + ] + ], + "hovertemplate": "qformer_bits=%{customdata[1]}
img_r1=%{x}
txt_r1=%{y}
model_size=%{z}
vit_bits=%{marker.color}
txt_r5=%{customdata[2]}
txt_r10=%{customdata[3]}
txt_r_mean=%{customdata[4]}
img_r5=%{customdata[5]}
img_r10=%{customdata[6]}
img_r_mean=%{customdata[7]}
r_mean=%{customdata[8]}
agg_metrics=%{customdata[9]}", + "legendgroup": "3", + "marker": { + "color": [ + 2, + 3, + 4, + 5, + 6, + 8, + 16 + ], + "coloraxis": "coloraxis", + "size": 4.5, + "symbol": "diamond" + }, + "mode": "markers", + "name": "3", + "scene": "scene", + "showlegend": true, + "type": "scatter3d", + "x": [ + 70.5, + 88.54, + 89.3, + 89.44, + 89.46, + 89.5, + 89.5 + ], + "y": [ + 83.8, + 97.2, + 97.6, + 98.2, + 98.2, + 98.2, + 98.2 + ], + "z": [ + 408.189992, + 531.257144, + 654.324296, + 777.391448, + 900.4586, + 1146.592904, + 2131.13012 + ] + }, + { + "customdata": [ + [ + 2, + 4, + 95.4, + 97.4, + 92.43333333333334, + 89.9, + 93.62, + 84.91333333333334, + 88.67333333333335, + 92.43333333333334 + ], + [ + 3, + 4, + 100, + 100, + 99.16666666666669, + 97.88, + 99.06, + 95.15333333333332, + 97.16, + 99.16666666666669 + ], + [ + 4, + 4, + 100, + 100, + 99.2, + 98.22, + 99.08, + 95.66, + 97.43, + 99.2 + ], + [ + 5, + 4, + 100, + 100, + 99.3, + 98.18, + 99.12, + 95.62666666666668, + 97.46333333333334, + 99.3 + ], + [ + 6, + 4, + 100, + 100, + 99.3, + 98.26, + 99.06, + 95.66666666666669, + 97.48333333333332, + 99.3 + ], + [ + 8, + 4, + 100, + 100, + 99.3, + 98.2, + 99.1, + 95.65333333333336, + 97.47666666666667, + 99.3 + ], + [ + 16, + 4, + 100, + 100, + 99.3, + 98.22, + 99.1, + 95.65333333333336, + 97.47666666666667, + 99.3 + ] + ], + "hovertemplate": "qformer_bits=%{customdata[1]}
img_r1=%{x}
txt_r1=%{y}
model_size=%{z}
vit_bits=%{marker.color}
txt_r5=%{customdata[2]}
txt_r10=%{customdata[3]}
txt_r_mean=%{customdata[4]}
img_r5=%{customdata[5]}
img_r10=%{customdata[6]}
img_r_mean=%{customdata[7]}
r_mean=%{customdata[8]}
agg_metrics=%{customdata[9]}", + "legendgroup": "4", + "marker": { + "color": [ + 2, + 3, + 4, + 5, + 6, + 8, + 16 + ], + "coloraxis": "coloraxis", + "size": 4.5, + "symbol": "square" + }, + "mode": "markers", + "name": "4", + "scene": "scene", + "showlegend": true, + "type": "scatter3d", + "x": [ + 71.22, + 88.52, + 89.68, + 89.58, + 89.68, + 89.66, + 89.64 + ], + "y": [ + 84.5, + 97.5, + 97.6, + 97.9, + 97.9, + 97.9, + 97.9 + ], + "z": [ + 428.409896, + 551.477048, + 674.5442, + 797.611352, + 920.678504, + 1166.812808, + 2151.350024 + ] + }, + { + "customdata": [ + [ + 2, + 5, + 95.6, + 97.5, + 92.33333333333331, + 89.74, + 93.86, + 85.00666666666666, + 88.66999999999999, + 92.33333333333331 + ], + [ + 3, + 5, + 100, + 100, + 99.03333333333336, + 97.8, + 98.98, + 95.18, + 97.10666666666668, + 99.03333333333336 + ], + [ + 4, + 5, + 100, + 100, + 99.1, + 98.22, + 98.98, + 95.56666666666666, + 97.33333333333334, + 99.1 + ], + [ + 5, + 5, + 100, + 100, + 99.2, + 98.18, + 99.08, + 95.56666666666666, + 97.38333333333333, + 99.2 + ], + [ + 6, + 5, + 100, + 100, + 99.26666666666668, + 98.14, + 99.06, + 95.59333333333332, + 97.43, + 99.26666666666668 + ], + [ + 8, + 5, + 100, + 100, + 99.3, + 98.2, + 99.08, + 95.65333333333332, + 97.47666666666666, + 99.3 + ], + [ + 16, + 5, + 100, + 100, + 99.26666666666668, + 98.18, + 99.06, + 95.63333333333333, + 97.45, + 99.26666666666668 + ] + ], + "hovertemplate": "qformer_bits=%{customdata[1]}
img_r1=%{x}
txt_r1=%{y}
model_size=%{z}
vit_bits=%{marker.color}
txt_r5=%{customdata[2]}
txt_r10=%{customdata[3]}
txt_r_mean=%{customdata[4]}
img_r5=%{customdata[5]}
img_r10=%{customdata[6]}
img_r_mean=%{customdata[7]}
r_mean=%{customdata[8]}
agg_metrics=%{customdata[9]}", + "legendgroup": "5", + "marker": { + "color": [ + 2, + 3, + 4, + 5, + 6, + 8, + 16 + ], + "coloraxis": "coloraxis", + "size": 4.5, + "symbol": "x" + }, + "mode": "markers", + "name": "5", + "scene": "scene", + "showlegend": true, + "type": "scatter3d", + "x": [ + 71.42, + 88.76, + 89.5, + 89.44, + 89.58, + 89.68, + 89.66 + ], + "y": [ + 83.9, + 97.1, + 97.3, + 97.6, + 97.8, + 97.9, + 97.8 + ], + "z": [ + 448.6298, + 571.696952, + 694.764104, + 817.831256, + 940.898408, + 1187.032712, + 2171.569928 + ] + }, + { + "customdata": [ + [ + 2, + 6, + 95.3, + 97.4, + 92.13333333333333, + 89.82, + 93.7, + 84.87333333333333, + 88.50333333333333, + 92.13333333333333 + ], + [ + 3, + 6, + 100, + 100, + 99.1, + 97.88, + 98.92, + 95.20666666666666, + 97.15333333333334, + 99.1 + ], + [ + 4, + 6, + 100, + 100, + 99.13333333333333, + 98.26, + 99.04, + 95.63333333333334, + 97.38333333333333, + 99.13333333333333 + ], + [ + 5, + 6, + 100, + 100, + 99.3, + 98.2, + 99.04, + 95.56666666666666, + 97.43333333333334, + 99.3 + ], + [ + 6, + 6, + 100, + 100, + 99.3, + 98.2, + 99.04, + 95.59333333333336, + 97.44666666666669, + 99.3 + ], + [ + 8, + 6, + 100, + 100, + 99.3, + 98.16, + 99.04, + 95.56, + 97.43, + 99.3 + ], + [ + 16, + 6, + 100, + 100, + 99.3, + 98.16, + 99.04, + 95.56666666666666, + 97.43333333333334, + 99.3 + ] + ], + "hovertemplate": "qformer_bits=%{customdata[1]}
img_r1=%{x}
txt_r1=%{y}
model_size=%{z}
vit_bits=%{marker.color}
txt_r5=%{customdata[2]}
txt_r10=%{customdata[3]}
txt_r_mean=%{customdata[4]}
img_r5=%{customdata[5]}
img_r10=%{customdata[6]}
img_r_mean=%{customdata[7]}
r_mean=%{customdata[8]}
agg_metrics=%{customdata[9]}", + "legendgroup": "6", + "marker": { + "color": [ + 2, + 3, + 4, + 5, + 6, + 8, + 16 + ], + "coloraxis": "coloraxis", + "size": 4.5, + "symbol": "cross" + }, + "mode": "markers", + "name": "6", + "scene": "scene", + "showlegend": true, + "type": "scatter3d", + "x": [ + 71.1, + 88.82, + 89.6, + 89.46, + 89.54, + 89.48, + 89.5 + ], + "y": [ + 83.7, + 97.3, + 97.4, + 97.9, + 97.9, + 97.9, + 97.9 + ], + "z": [ + 468.849704, + 591.916856, + 714.984008, + 838.05116, + 961.118312, + 1207.252616, + 2191.789832 + ] + }, + { + "customdata": [ + [ + 2, + 8, + 95.1, + 97.3, + 92.13333333333333, + 89.94, + 93.66, + 84.93333333333332, + 88.53333333333333, + 92.13333333333333 + ], + [ + 3, + 8, + 100, + 100, + 99.13333333333333, + 97.84, + 98.9, + 95.12, + 97.12666666666668, + 99.13333333333333 + ], + [ + 4, + 8, + 100, + 100, + 99.13333333333333, + 98.2, + 99.04, + 95.62666666666668, + 97.38, + 99.13333333333333 + ], + [ + 5, + 8, + 100, + 100, + 99.33333333333331, + 98.18, + 99.08, + 95.55333333333334, + 97.44333333333331, + 99.33333333333331 + ], + [ + 6, + 8, + 100, + 100, + 99.3, + 98.2, + 99.06, + 95.56, + 97.43, + 99.3 + ], + [ + 8, + 8, + 100, + 100, + 99.3, + 98.2, + 99.04, + 95.56, + 97.43, + 99.3 + ], + [ + 16, + 8, + 100, + 100, + 99.3, + 98.2, + 99.04, + 95.56666666666666, + 97.43333333333334, + 99.3 + ] + ], + "hovertemplate": "qformer_bits=%{customdata[1]}
img_r1=%{x}
txt_r1=%{y}
model_size=%{z}
vit_bits=%{marker.color}
txt_r5=%{customdata[2]}
txt_r10=%{customdata[3]}
txt_r_mean=%{customdata[4]}
img_r5=%{customdata[5]}
img_r10=%{customdata[6]}
img_r_mean=%{customdata[7]}
r_mean=%{customdata[8]}
agg_metrics=%{customdata[9]}", + "legendgroup": "8", + "marker": { + "color": [ + 2, + 3, + 4, + 5, + 6, + 8, + 16 + ], + "coloraxis": "coloraxis", + "size": 4.5, + "symbol": "circle" + }, + "mode": "markers", + "name": "8", + "scene": "scene", + "showlegend": true, + "type": "scatter3d", + "x": [ + 71.2, + 88.62, + 89.64, + 89.4, + 89.42, + 89.44, + 89.46 + ], + "y": [ + 84, + 97.4, + 97.4, + 98, + 97.9, + 97.9, + 97.9 + ], + "z": [ + 509.289512, + 632.356664, + 755.423816, + 878.490968, + 1001.55812, + 1247.692424, + 2232.22964 + ] + }, + { + "customdata": [ + [ + 2, + 16, + 95.1, + 97.4, + 92.2, + 89.98, + 93.68, + 84.96666666666667, + 88.58333333333334, + 92.2 + ], + [ + 3, + 16, + 100, + 100, + 99.13333333333333, + 97.86, + 98.92, + 95.15333333333336, + 97.14333333333336, + 99.13333333333333 + ], + [ + 4, + 16, + 100, + 100, + 99.13333333333333, + 98.2, + 99.02, + 95.62666666666668, + 97.38, + 99.13333333333333 + ], + [ + 5, + 16, + 100, + 100, + 99.26666666666668, + 98.18, + 99.08, + 95.54, + 97.40333333333334, + 99.26666666666668 + ], + [ + 6, + 16, + 100, + 100, + 99.3, + 98.2, + 99.06, + 95.55333333333334, + 97.42666666666668, + 99.3 + ], + [ + 8, + 16, + 100, + 100, + 99.3, + 98.2, + 99.04, + 95.56, + 97.43, + 99.3 + ], + [ + 16, + 16, + 100, + 100, + 99.3, + 98.22, + 99.04, + 95.57333333333334, + 97.43666666666668, + 99.3 + ] + ], + "hovertemplate": "qformer_bits=%{customdata[1]}
img_r1=%{x}
txt_r1=%{y}
model_size=%{z}
vit_bits=%{marker.color}
txt_r5=%{customdata[2]}
txt_r10=%{customdata[3]}
txt_r_mean=%{customdata[4]}
img_r5=%{customdata[5]}
img_r10=%{customdata[6]}
img_r_mean=%{customdata[7]}
r_mean=%{customdata[8]}
agg_metrics=%{customdata[9]}", + "legendgroup": "16", + "marker": { + "color": [ + 2, + 3, + 4, + 5, + 6, + 8, + 16 + ], + "coloraxis": "coloraxis", + "size": 4.5, + "symbol": "diamond" + }, + "mode": "markers", + "name": "16", + "scene": "scene", + "showlegend": true, + "type": "scatter3d", + "x": [ + 71.24, + 88.68, + 89.66, + 89.36, + 89.4, + 89.44, + 89.46 + ], + "y": [ + 84.1, + 97.4, + 97.4, + 97.8, + 97.9, + 97.9, + 97.9 + ], + "z": [ + 671.048744, + 794.115896, + 917.183048, + 1040.2502, + 1163.317352, + 1409.451656, + 2393.988872 + ] + } + ], + "layout": { + "coloraxis": { + "colorbar": { + "ticks": "outside", + "title": { + "text": "vit_bits" + }, + "x": 0, + "y": 1, + "yanchor": "top" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ] + }, + "legend": { + "title": { + "text": "qformer_bits" + }, + "tracegroupgap": 0 + }, + "scene": { + "domain": { + "x": [ + 0, + 1 + ], + "y": [ + 0, + 1 + ] + }, + "xaxis": { + "autorange": "reversed", + "title": { + "text": "Text-->Img R@1" + } + }, + "yaxis": { + "autorange": "reversed", + "title": { + "text": "Img -->Text R@1" + } + }, + "zaxis": { + "title": { + "text": "Model Size (MB)" + } + } + }, + "template": { + "data": { + "bar": [ + { + "error_x": { + "color": "#2a3f5f" + }, + "error_y": { + "color": "#2a3f5f" + }, + "marker": { + "line": { + "color": "#E5ECF6", + "width": 0.5 + }, + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "bar" + } + ], + "barpolar": [ + { + "marker": { + "line": { + "color": "#E5ECF6", + "width": 0.5 + }, + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "barpolar" + } + ], + "carpet": [ + { + "aaxis": { + "endlinecolor": "#2a3f5f", + "gridcolor": "white", + "linecolor": "white", + "minorgridcolor": "white", + "startlinecolor": "#2a3f5f" + }, + "baxis": { + "endlinecolor": "#2a3f5f", + "gridcolor": "white", + "linecolor": "white", + "minorgridcolor": "white", + "startlinecolor": "#2a3f5f" + }, + "type": "carpet" + } + ], + "choropleth": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "type": "choropleth" + } + ], + "contour": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "contour" + } + ], + "contourcarpet": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "type": "contourcarpet" + } + ], + "heatmap": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "heatmap" + } + ], + "heatmapgl": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "heatmapgl" + } + ], + "histogram": [ + { + "marker": { + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "histogram" + } + ], + "histogram2d": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "histogram2d" + } + ], + "histogram2dcontour": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "histogram2dcontour" + } + ], + "mesh3d": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "type": "mesh3d" + } + ], + "parcoords": [ + { + "line": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "parcoords" + } + ], + "pie": [ + { + "automargin": true, + "type": "pie" + } + ], + "scatter": [ + { + "fillpattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + }, + "type": "scatter" + } + ], + "scatter3d": [ + { + "line": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatter3d" + } + ], + "scattercarpet": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattercarpet" + } + ], + "scattergeo": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattergeo" + } + ], + "scattergl": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattergl" + } + ], + "scattermapbox": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattermapbox" + } + ], + "scatterpolar": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatterpolar" + } + ], + "scatterpolargl": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatterpolargl" + } + ], + "scatterternary": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatterternary" + } + ], + "surface": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "surface" + } + ], + "table": [ + { + "cells": { + "fill": { + "color": "#EBF0F8" + }, + "line": { + "color": "white" + } + }, + "header": { + "fill": { + "color": "#C8D4E3" + }, + "line": { + "color": "white" + } + }, + "type": "table" + } + ] + }, + "layout": { + "annotationdefaults": { + "arrowcolor": "#2a3f5f", + "arrowhead": 0, + "arrowwidth": 1 + }, + "autotypenumbers": "strict", + "coloraxis": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "colorscale": { + "diverging": [ + [ + 0, + "#8e0152" + ], + [ + 0.1, + "#c51b7d" + ], + [ + 0.2, + "#de77ae" + ], + [ + 0.3, + "#f1b6da" + ], + [ + 0.4, + "#fde0ef" + ], + [ + 0.5, + "#f7f7f7" + ], + [ + 0.6, + "#e6f5d0" + ], + [ + 0.7, + "#b8e186" + ], + [ + 0.8, + "#7fbc41" + ], + [ + 0.9, + "#4d9221" + ], + [ + 1, + "#276419" + ] + ], + "sequential": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "sequentialminus": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ] + }, + "colorway": [ + "#636efa", + "#EF553B", + "#00cc96", + "#ab63fa", + "#FFA15A", + "#19d3f3", + "#FF6692", + "#B6E880", + "#FF97FF", + "#FECB52" + ], + "font": { + "color": "#2a3f5f" + }, + "geo": { + "bgcolor": "white", + "lakecolor": "white", + "landcolor": "#E5ECF6", + "showlakes": true, + "showland": true, + "subunitcolor": "white" + }, + "hoverlabel": { + "align": "left" + }, + "hovermode": "closest", + "mapbox": { + "style": "light" + }, + "paper_bgcolor": "white", + "plot_bgcolor": "#E5ECF6", + "polar": { + "angularaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "bgcolor": "#E5ECF6", + "radialaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + } + }, + "scene": { + "xaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + }, + "yaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + }, + "zaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + } + }, + "shapedefaults": { + "line": { + "color": "#2a3f5f" + } + }, + "ternary": { + "aaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "baxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "bgcolor": "#E5ECF6", + "caxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + } + }, + "title": { + "x": 0.05 + }, + "xaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + }, + "yaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + } + } + }, + "title": { + "text": "AWQ Blip-2 Flickr30k Information Retrieval" + } + } + }, + "text/html": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = px.scatter_3d(df_awq, x='img_r1', y=\"txt_r1\", z = 'model_size',\n", + " color = 'vit_bits',\n", + " symbol = 'qformer_bits',\n", + " hover_data = list(df_awq.columns),\n", + " title = 'AWQ Blip-2 Flickr30k Information Retrieval',)\n", + "\n", + "\n", + "fig.update_traces(marker=dict(size=4.5))\n", + "\n", + "fig.update_layout(scene = dict(\n", + " xaxis_title='Text-->Img R@1',\n", + " yaxis_title='Img -->Text R@1',\n", + " zaxis_title='Model Size (MB)',\n", + " \n", + " xaxis = dict(autorange='reversed'),\n", + " yaxis = dict(autorange='reversed')),)\n", + "\n", + "fig.update_layout(coloraxis_colorbar = dict(yanchor=\"top\", y=1, x=0,\n", + " ticks=\"outside\"))\n", + "\n", + "fig.write_html(\"awq_retrieval.html\")\n", + "\n", + "fig.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "vit_bits 5.000000\n", + "qformer_bits 3.000000\n", + "txt_r1 98.200000\n", + "txt_r5 100.000000\n", + "txt_r10 100.000000\n", + "txt_r_mean 99.400000\n", + "img_r1 89.440000\n", + "img_r5 98.220000\n", + "img_r10 99.060000\n", + "img_r_mean 95.573333\n", + "r_mean 97.486667\n", + "agg_metrics 99.400000\n", + "model_size 777.391448\n", + "Name: 22, dtype: float64" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_awq.sort_values(by='txt_r1', ascending=False).iloc[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "vit_bits 4.0000\n", + "qformer_bits 4.0000\n", + "txt_r1 97.6000\n", + "txt_r5 100.0000\n", + "txt_r10 100.0000\n", + "txt_r_mean 99.2000\n", + "img_r1 89.6800\n", + "img_r5 98.2200\n", + "img_r10 99.0800\n", + "img_r_mean 95.6600\n", + "r_mean 97.4300\n", + "agg_metrics 99.2000\n", + "model_size 674.5442\n", + "Name: 16, dtype: float64" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_awq.sort_values(by='img_r1', ascending=False).iloc[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
vit_bitsqformer_bitstxt_r1txt_r5txt_r10txt_r_meanimg_r1img_r5img_r10img_r_meanr_meanagg_metricsmodel_size
164497.6100.0100.099.289.6898.2299.0895.6697.4399.2674.5442
\n", + "
" + ], + "text/plain": [ + " vit_bits qformer_bits txt_r1 txt_r5 txt_r10 txt_r_mean img_r1 \\\n", + "16 4 4 97.6 100.0 100.0 99.2 89.68 \n", + "\n", + " img_r5 img_r10 img_r_mean r_mean agg_metrics model_size \n", + "16 98.22 99.08 95.66 97.43 99.2 674.5442 " + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_awq[(df_awq.vit_bits == 4.0) & (df_awq.qformer_bits == 4.0)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Captioning" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
vit_bitsqformer_bitsllm_bitsmeteorcidermodel_sizequant_type
02220.0298840.0007901965.902624AWQ
12230.1498570.3892742280.567584AWQ
22240.1837350.5443522595.232544AWQ
32250.1886600.5778062909.897504AWQ
42260.1921590.5940623224.562464AWQ
\n", + "
" + ], + "text/plain": [ + " vit_bits qformer_bits llm_bits meteor cider model_size \\\n", + "0 2 2 2 0.029884 0.000790 1965.902624 \n", + "1 2 2 3 0.149857 0.389274 2280.567584 \n", + "2 2 2 4 0.183735 0.544352 2595.232544 \n", + "3 2 2 5 0.188660 0.577806 2909.897504 \n", + "4 2 2 6 0.192159 0.594062 3224.562464 \n", + "\n", + " quant_type \n", + "0 AWQ \n", + "1 AWQ \n", + "2 AWQ \n", + "3 AWQ \n", + "4 AWQ " + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "awq_results_path = './awq_image_captioning.csv'\n", + "gptq_results_path = '../gptq_scores.csv'\n", + "\n", + "df_awq = pd.read_csv(awq_results_path)\n", + "# bits --> megabytes\n", + "df_awq['model_size'] = df_awq['model_size'] / 8e6\n", + "df_awq = df_awq.rename({'METEOR': 'meteor', 'CIDEr': 'cider'}, axis = 1)\n", + "df_awq['quant_type'] = 'AWQ'\n", + "\n", + "\n", + "df_awq[:5]" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
vit_bitsqformer_bitsllm_bitscidermeteorquant_type
03561.1877800.269797GPTQ
116840.8838120.218899GPTQ
22780.0079280.034667GPTQ
36271.0875260.253127GPTQ
48871.1984310.272075GPTQ
\n", + "
" + ], + "text/plain": [ + " vit_bits qformer_bits llm_bits cider meteor quant_type\n", + "0 3 5 6 1.187780 0.269797 GPTQ\n", + "1 16 8 4 0.883812 0.218899 GPTQ\n", + "2 2 7 8 0.007928 0.034667 GPTQ\n", + "3 6 2 7 1.087526 0.253127 GPTQ\n", + "4 8 8 7 1.198431 0.272075 GPTQ" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_gptq = pd.read_csv(gptq_results_path)\n", + "df_gptq['quant_type'] = 'GPTQ'\n", + "df_gptq[:5]" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "df_captioning = pd.merge(df_awq, df_gptq, how = 'outer')" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
vit_bitsqformer_bitsllm_bitsmeteorcidermodel_sizequant_type
02220.0298840.0007901965.902624AWQ
12230.0101510.001498NaNGPTQ
22230.1498570.3892742280.567584AWQ
32240.0088340.000763NaNGPTQ
42240.1837350.5443522595.232544AWQ
\n", + "
" + ], + "text/plain": [ + " vit_bits qformer_bits llm_bits meteor cider model_size \\\n", + "0 2 2 2 0.029884 0.000790 1965.902624 \n", + "1 2 2 3 0.010151 0.001498 NaN \n", + "2 2 2 3 0.149857 0.389274 2280.567584 \n", + "3 2 2 4 0.008834 0.000763 NaN \n", + "4 2 2 4 0.183735 0.544352 2595.232544 \n", + "\n", + " quant_type \n", + "0 AWQ \n", + "1 GPTQ \n", + "2 AWQ \n", + "3 GPTQ \n", + "4 AWQ " + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_captioning[:5]" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "vit_bits 3\n", + "qformer_bits 16\n", + "llm_bits 16\n", + "meteor 0.284536\n", + "cider 1.275289\n", + "model_size 6678.1868\n", + "quant_type AWQ\n", + "Name: 209, dtype: object" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_captioning.sort_values(by='meteor', ascending=False).iloc[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
vit_bitsqformer_bitsllm_bitsmeteorcidermodel_sizequant_type
208316160.2543431.101888NaNGPTQ
209316160.2845361.2752896678.1868AWQ
\n", + "
" + ], + "text/plain": [ + " vit_bits qformer_bits llm_bits meteor cider model_size \\\n", + "208 3 16 16 0.254343 1.101888 NaN \n", + "209 3 16 16 0.284536 1.275289 6678.1868 \n", + "\n", + " quant_type \n", + "208 GPTQ \n", + "209 AWQ " + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_captioning[(df_captioning.vit_bits == 3) & (df_captioning.qformer_bits == 16) & (df_captioning.llm_bits == 16)]" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
vit_bitsqformer_bitsllm_bitsmeteorcidermodel_sizequant_type
1363440.2298850.941375NaNGPTQ
1373440.2702841.1794012744.572208AWQ
\n", + "
" + ], + "text/plain": [ + " vit_bits qformer_bits llm_bits meteor cider model_size \\\n", + "136 3 4 4 0.229885 0.941375 NaN \n", + "137 3 4 4 0.270284 1.179401 2744.572208 \n", + "\n", + " quant_type \n", + "136 GPTQ \n", + "137 AWQ " + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_captioning[(df_captioning.vit_bits == 3) & (df_captioning.qformer_bits == 4) & (df_captioning.llm_bits == 4)]" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "vit_bits 6\n", + "qformer_bits 7\n", + "llm_bits 6\n", + "meteor 0.284343\n", + "cider 1.311675\n", + "model_size NaN\n", + "quant_type GPTQ\n", + "Name: 493, dtype: object" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_captioning.sort_values(by='cider', ascending=False).iloc[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
vit_bitsqformer_bitsllm_bitsmeteorcidermodel_sizequant_type
4936760.2843431.311675NaNGPTQ
\n", + "
" + ], + "text/plain": [ + " vit_bits qformer_bits llm_bits meteor cider model_size \\\n", + "493 6 7 6 0.284343 1.311675 NaN \n", + "\n", + " quant_type \n", + "493 GPTQ " + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_captioning[(df_captioning.vit_bits == 6) & (df_captioning.qformer_bits == 7) & (df_captioning.llm_bits == 6)]" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['vit_bits',\n", + " 'qformer_bits',\n", + " 'llm_bits',\n", + " 'meteor',\n", + " 'cider',\n", + " 'model_size',\n", + " 'quant_type']" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "hover_data = list(df_awq.columns)\n", + "# hover_data.remove('METEOR_per_caption')\n", + "# hover_data.remove('CIDEr_per_caption')\n", + "hover_data" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "customdata": [ + [ + 2, + 2, + 2 + ], + [ + 2, + 2, + 3 + ], + [ + 2, + 2, + 4 + ], + [ + 2, + 2, + 5 + ], + [ + 2, + 2, + 6 + ], + [ + 2, + 2, + 8 + ], + [ + 2, + 2, + 16 + ], + [ + 3, + 2, + 2 + ], + [ + 3, + 2, + 3 + ], + [ + 3, + 2, + 4 + ], + [ + 3, + 2, + 5 + ], + [ + 3, + 2, + 6 + ], + [ + 3, + 2, + 8 + ], + [ + 3, + 2, + 16 + ], + [ + 4, + 2, + 2 + ], + [ + 4, + 2, + 3 + ], + [ + 4, + 2, + 4 + ], + [ + 4, + 2, + 5 + ], + [ + 4, + 2, + 6 + ], + [ + 4, + 2, + 8 + ], + [ + 4, + 2, + 16 + ], + [ + 5, + 2, + 2 + ], + [ + 5, + 2, + 3 + ], + [ + 5, + 2, + 4 + ], + [ + 5, + 2, + 5 + ], + [ + 5, + 2, + 6 + ], + [ + 5, + 2, + 8 + ], + [ + 5, + 2, + 16 + ], + [ + 6, + 2, + 2 + ], + [ + 6, + 2, + 3 + ], + [ + 6, + 2, + 4 + ], + [ + 6, + 2, + 5 + ], + [ + 6, + 2, + 6 + ], + [ + 6, + 2, + 8 + ], + [ + 6, + 2, + 16 + ], + [ + 8, + 2, + 2 + ], + [ + 8, + 2, + 3 + ], + [ + 8, + 2, + 4 + ], + [ + 8, + 2, + 5 + ], + [ + 8, + 2, + 6 + ], + [ + 8, + 2, + 8 + ], + [ + 8, + 2, + 16 + ], + [ + 16, + 2, + 2 + ], + [ + 16, + 2, + 3 + ], + [ + 16, + 2, + 4 + ], + [ + 16, + 2, + 5 + ], + [ + 16, + 2, + 6 + ], + [ + 16, + 2, + 8 + ], + [ + 16, + 2, + 16 + ] + ], + "hovertemplate": "qformer_bits=%{customdata[1]}
METEOR=%{x}
CIDEr=%{y}
model_size=%{z}
vit_bits=%{customdata[0]}
llm_bits=%{marker.color}", + "legendgroup": "2", + "marker": { + "color": [ + 2, + 3, + 4, + 5, + 6, + 8, + 16, + 2, + 3, + 4, + 5, + 6, + 8, + 16, + 2, + 3, + 4, + 5, + 6, + 8, + 16, + 2, + 3, + 4, + 5, + 6, + 8, + 16, + 2, + 3, + 4, + 5, + 6, + 8, + 16, + 2, + 3, + 4, + 5, + 6, + 8, + 16, + 2, + 3, + 4, + 5, + 6, + 8, + 16 + ], + "coloraxis": "coloraxis", + "size": 3.5, + "symbol": "circle" + }, + "mode": "markers", + "name": "2", + "scene": "scene", + "showlegend": true, + "type": "scatter3d", + "x": [ + 0.0298844811343493, + 0.1498566138247552, + 0.1837345249958186, + 0.1886596236812807, + 0.1921587687164518, + 0.1935281864323662, + 0.1931800073082082, + 0.0318570369375779, + 0.200290499904622, + 0.2484356580168853, + 0.2553551850460575, + 0.2613741243634884, + 0.2592091197853552, + 0.2599854702694635, + 0.035544052024392, + 0.2007969629020746, + 0.2502237705978827, + 0.2552756428745414, + 0.2617311169696272, + 0.2627561602762712, + 0.2626553112263047, + 0.0353956864490417, + 0.1985754713591016, + 0.2488564578980053, + 0.2526562430118402, + 0.2603846095480853, + 0.2591615425924026, + 0.2597450882896447, + 0.0336890452471779, + 0.1995702866972476, + 0.2475822019731219, + 0.2529732771470224, + 0.2598567516851389, + 0.2592645753079949, + 0.259796239724251, + 0.0344184962623263, + 0.1971738251491923, + 0.2466986404445021, + 0.2504880541245433, + 0.2584898808700203, + 0.2581235372562963, + 0.2585108705115669, + 0.0339736319477422, + 0.1997526555007177, + 0.2485642223938605, + 0.2530323290592932, + 0.2602453575218017, + 0.2600204675274952, + 0.2606048626793487 + ], + "y": [ + 0.0007902168927993, + 0.3892738924855776, + 0.5443518230890101, + 0.577806426477473, + 0.5940621735445684, + 0.6015116071758554, + 0.6016201760679405, + 0.0022093327972838, + 0.7266863329828995, + 1.0235981557690623, + 1.0729311908769703, + 1.1194833585777608, + 1.1086451337916028, + 1.1134807950614616, + 0.0020596108397078, + 0.7295886421565306, + 1.0417863722989882, + 1.089604655273231, + 1.1243699092711588, + 1.1321620826054106, + 1.1325403602869566, + 0.0032646989621902, + 0.7180181623782547, + 1.032962494645136, + 1.070797712638038, + 1.1182626611582889, + 1.1127242409207423, + 1.115818110946458, + 0.0021355148766812, + 0.7264676632473416, + 1.0232920884230814, + 1.0704198052787794, + 1.1160500908441646, + 1.1131496180075628, + 1.1176110659095753, + 0.0020926826069756, + 0.7116309448572241, + 1.0096640742263971, + 1.051832240994192, + 1.1031148303323677, + 1.0996036917317866, + 1.1026112693765862, + 0.002116580094303, + 0.7274376374824795, + 1.0252398540053982, + 1.0706442589700385, + 1.1196220237774297, + 1.117247765253773, + 1.1214114229504837 + ], + "z": [ + 1965.902624, + 2280.567584, + 2595.232544, + 2909.897504, + 3224.562464, + 3853.892384, + 6371.212064, + 2088.969776, + 2403.634736, + 2718.299696, + 3032.964656, + 3347.629616, + 3976.959536, + 6494.279216, + 2212.036928, + 2526.701888, + 2841.366848, + 3156.031808, + 3470.696768, + 4100.026688, + 6617.346368, + 2335.10408, + 2649.76904, + 2964.434, + 3279.09896, + 3593.76392, + 4223.09384, + 6740.41352, + 2458.171232, + 2772.836192, + 3087.501152, + 3402.166112, + 3716.831072, + 4346.160992, + 6863.480672, + 2704.305536, + 3018.970496, + 3333.635456, + 3648.300416, + 3962.965376, + 4592.295296, + 7109.614976, + 3688.842752, + 4003.507712, + 4318.172672, + 4632.837632, + 4947.502592, + 5576.832512, + 8094.152192 + ] + }, + { + "customdata": [ + [ + 2, + 3, + 2 + ], + [ + 2, + 3, + 3 + ], + [ + 2, + 3, + 4 + ], + [ + 2, + 3, + 5 + ], + [ + 2, + 3, + 6 + ], + [ + 2, + 3, + 8 + ], + [ + 2, + 3, + 16 + ], + [ + 3, + 3, + 2 + ], + [ + 3, + 3, + 3 + ], + [ + 3, + 3, + 4 + ], + [ + 3, + 3, + 5 + ], + [ + 3, + 3, + 6 + ], + [ + 3, + 3, + 8 + ], + [ + 3, + 3, + 16 + ], + [ + 4, + 3, + 2 + ], + [ + 4, + 3, + 3 + ], + [ + 4, + 3, + 4 + ], + [ + 4, + 3, + 5 + ], + [ + 4, + 3, + 6 + ], + [ + 4, + 3, + 8 + ], + [ + 4, + 3, + 16 + ], + [ + 5, + 3, + 2 + ], + [ + 5, + 3, + 3 + ], + [ + 5, + 3, + 4 + ], + [ + 5, + 3, + 5 + ], + [ + 5, + 3, + 6 + ], + [ + 5, + 3, + 8 + ], + [ + 5, + 3, + 16 + ], + [ + 6, + 3, + 2 + ], + [ + 6, + 3, + 3 + ], + [ + 6, + 3, + 4 + ], + [ + 6, + 3, + 5 + ], + [ + 6, + 3, + 6 + ], + [ + 6, + 3, + 8 + ], + [ + 6, + 3, + 16 + ], + [ + 8, + 3, + 2 + ], + [ + 8, + 3, + 3 + ], + [ + 8, + 3, + 4 + ], + [ + 8, + 3, + 5 + ], + [ + 8, + 3, + 6 + ], + [ + 8, + 3, + 8 + ], + [ + 8, + 3, + 16 + ], + [ + 16, + 3, + 2 + ], + [ + 16, + 3, + 3 + ], + [ + 16, + 3, + 4 + ], + [ + 16, + 3, + 5 + ], + [ + 16, + 3, + 6 + ], + [ + 16, + 3, + 8 + ], + [ + 16, + 3, + 16 + ] + ], + "hovertemplate": "qformer_bits=%{customdata[1]}
METEOR=%{x}
CIDEr=%{y}
model_size=%{z}
vit_bits=%{customdata[0]}
llm_bits=%{marker.color}", + "legendgroup": "3", + "marker": { + "color": [ + 2, + 3, + 4, + 5, + 6, + 8, + 16, + 2, + 3, + 4, + 5, + 6, + 8, + 16, + 2, + 3, + 4, + 5, + 6, + 8, + 16, + 2, + 3, + 4, + 5, + 6, + 8, + 16, + 2, + 3, + 4, + 5, + 6, + 8, + 16, + 2, + 3, + 4, + 5, + 6, + 8, + 16, + 2, + 3, + 4, + 5, + 6, + 8, + 16 + ], + "coloraxis": "coloraxis", + "size": 3.5, + "symbol": "diamond" + }, + "mode": "markers", + "name": "3", + "scene": "scene", + "showlegend": true, + "type": "scatter3d", + "x": [ + 0.0248888129640967, + 0.1536184976125826, + 0.192547252681466, + 0.1971479719875085, + 0.2053801049843051, + 0.2062730261379436, + 0.2082902403750201, + 0.0340323417585065, + 0.211386366794054, + 0.2650228454047996, + 0.269467141951613, + 0.2760879124909304, + 0.2756596662937646, + 0.2764047060750114, + 0.0379276645844104, + 0.2110103186531865, + 0.2663107277629026, + 0.2701614225309509, + 0.2764764962597095, + 0.2767816837784037, + 0.2781134180397822, + 0.0385283500594566, + 0.2091942895320583, + 0.2635935671043616, + 0.2668454963794412, + 0.2730030358969137, + 0.2736482751434616, + 0.2742002799959992, + 0.0388616898041331, + 0.2089483604012564, + 0.2631136294006772, + 0.2660299262295151, + 0.2734671202303975, + 0.2739312710808929, + 0.2754884444805165, + 0.038869927644611, + 0.2047447557475626, + 0.2604113984761954, + 0.2637886450865388, + 0.2702609965385433, + 0.2710305582860595, + 0.2724762395803599, + 0.0386787199628869, + 0.209854527423061, + 0.2636281741650799, + 0.2669442215387785, + 0.2740336787379113, + 0.274623848151302, + 0.276008215745985 + ], + "y": [ + 0.0017409187992352, + 0.4199558355788407, + 0.6413107364012529, + 0.6644740132743288, + 0.7005174822474439, + 0.7060009487248382, + 0.7114414564035497, + 0.0045409306490616, + 0.8051281610518605, + 1.156028455134381, + 1.1923197774896417, + 1.2347054039266734, + 1.2272202650647102, + 1.2313846308384797, + 0.0047518175496339, + 0.8002507737119463, + 1.1676678353269516, + 1.201189986792182, + 1.2413131851952102, + 1.2364159506260926, + 1.246123613154921, + 0.0050442796553735, + 0.7879097938908757, + 1.148548672419358, + 1.1764723409323512, + 1.2155479022667492, + 1.2161199154649738, + 1.2207863371539902, + 0.0051168105421359, + 0.78766789683324, + 1.1458078798333984, + 1.1743586936521029, + 1.2184402901688431, + 1.213608212193691, + 1.2253157599692892, + 0.0052766501668749, + 0.7587296632897987, + 1.1278018779745882, + 1.1535606055527945, + 1.1953810071788358, + 1.194933849208372, + 1.2026468908277963, + 0.0050311480090812, + 0.7920462665699586, + 1.1504623141490604, + 1.1773655292386451, + 1.2227150746235125, + 1.2225423326577731, + 1.2288258774986205 + ], + "z": [ + 1979.03888, + 2293.70384, + 2608.3688, + 2923.03376, + 3237.69872, + 3867.02864, + 6384.34832, + 2102.106032, + 2416.770992, + 2731.435952, + 3046.100912, + 3360.765872, + 3990.095792, + 6507.415472, + 2225.173184, + 2539.838144, + 2854.503104, + 3169.168064, + 3483.833024, + 4113.162944, + 6630.482624, + 2348.240336, + 2662.905296, + 2977.570256, + 3292.235216, + 3606.900176, + 4236.230096, + 6753.549776, + 2471.307488, + 2785.972448, + 3100.637408, + 3415.302368, + 3729.967328, + 4359.297248, + 6876.616928, + 2717.441792, + 3032.106752, + 3346.771712, + 3661.436672, + 3976.101632, + 4605.431552, + 7122.751232, + 3701.979008, + 4016.643968, + 4331.308928, + 4645.973888, + 4960.638848, + 5589.968768, + 8107.288448 + ] + }, + { + "customdata": [ + [ + 2, + 4, + 2 + ], + [ + 2, + 4, + 3 + ], + [ + 2, + 4, + 4 + ], + [ + 2, + 4, + 5 + ], + [ + 2, + 4, + 6 + ], + [ + 2, + 4, + 8 + ], + [ + 2, + 4, + 16 + ], + [ + 3, + 4, + 2 + ], + [ + 3, + 4, + 3 + ], + [ + 3, + 4, + 4 + ], + [ + 3, + 4, + 5 + ], + [ + 3, + 4, + 6 + ], + [ + 3, + 4, + 8 + ], + [ + 3, + 4, + 16 + ], + [ + 4, + 4, + 2 + ], + [ + 4, + 4, + 3 + ], + [ + 4, + 4, + 4 + ], + [ + 4, + 4, + 5 + ], + [ + 4, + 4, + 6 + ], + [ + 4, + 4, + 8 + ], + [ + 4, + 4, + 16 + ], + [ + 5, + 4, + 2 + ], + [ + 5, + 4, + 3 + ], + [ + 5, + 4, + 4 + ], + [ + 5, + 4, + 5 + ], + [ + 5, + 4, + 6 + ], + [ + 5, + 4, + 8 + ], + [ + 5, + 4, + 16 + ], + [ + 6, + 4, + 2 + ], + [ + 6, + 4, + 3 + ], + [ + 6, + 4, + 4 + ], + [ + 6, + 4, + 5 + ], + [ + 6, + 4, + 6 + ], + [ + 6, + 4, + 8 + ], + [ + 6, + 4, + 16 + ], + [ + 8, + 4, + 2 + ], + [ + 8, + 4, + 3 + ], + [ + 8, + 4, + 4 + ], + [ + 8, + 4, + 5 + ], + [ + 8, + 4, + 6 + ], + [ + 8, + 4, + 8 + ], + [ + 8, + 4, + 16 + ], + [ + 16, + 4, + 2 + ], + [ + 16, + 4, + 3 + ], + [ + 16, + 4, + 4 + ], + [ + 16, + 4, + 5 + ], + [ + 16, + 4, + 6 + ], + [ + 16, + 4, + 8 + ], + [ + 16, + 4, + 16 + ] + ], + "hovertemplate": "qformer_bits=%{customdata[1]}
METEOR=%{x}
CIDEr=%{y}
model_size=%{z}
vit_bits=%{customdata[0]}
llm_bits=%{marker.color}", + "legendgroup": "4", + "marker": { + "color": [ + 2, + 3, + 4, + 5, + 6, + 8, + 16, + 2, + 3, + 4, + 5, + 6, + 8, + 16, + 2, + 3, + 4, + 5, + 6, + 8, + 16, + 2, + 3, + 4, + 5, + 6, + 8, + 16, + 2, + 3, + 4, + 5, + 6, + 8, + 16, + 2, + 3, + 4, + 5, + 6, + 8, + 16, + 2, + 3, + 4, + 5, + 6, + 8, + 16 + ], + "coloraxis": "coloraxis", + "size": 3.5, + "symbol": "square" + }, + "mode": "markers", + "name": "4", + "scene": "scene", + "showlegend": true, + "type": "scatter3d", + "x": [ + 0.029512816604721, + 0.1537266011841024, + 0.1964836864850164, + 0.1998364948737692, + 0.2067698934744963, + 0.2085390794369036, + 0.2096374717583629, + 0.037105866072279, + 0.2145169750500711, + 0.2702844643529473, + 0.2758450990378426, + 0.2815846450851879, + 0.2833953779681455, + 0.284394954409976, + 0.0386476453114738, + 0.211396202735535, + 0.2696296967613541, + 0.2743049084227653, + 0.2820665632442025, + 0.2837285042274702, + 0.2840855765190654, + 0.0383106603039449, + 0.2094568088398009, + 0.2681996749926065, + 0.271236873072046, + 0.2795632422324446, + 0.2807453214549563, + 0.2821228770619992, + 0.0388137084027831, + 0.2079392854106619, + 0.2657751126886337, + 0.2694405705313831, + 0.2787137627446484, + 0.2799813598846214, + 0.2803946617343799, + 0.038960864382997, + 0.2046153902705287, + 0.2637425407875613, + 0.2682072256800668, + 0.2754944544086485, + 0.2762420416035082, + 0.2770427807061468, + 0.0385673224488549, + 0.2096938611804471, + 0.2667684943363454, + 0.2711871713179565, + 0.279923563615494, + 0.2813984210754993, + 0.2821924168036456 + ], + "y": [ + 0.0015242110097923, + 0.4225848027587028, + 0.6677658754857135, + 0.6794140389055991, + 0.7158724879010914, + 0.7239213616454379, + 0.7267086690055766, + 0.0034123873884078, + 0.8208565360141244, + 1.179401408578962, + 1.2266779752864407, + 1.2572691461851668, + 1.26358895663258, + 1.2673159046347733, + 0.00413023189484, + 0.8040498221201324, + 1.1818920290369954, + 1.2187256913286244, + 1.2693462940552265, + 1.26753571103515, + 1.2679401319860708, + 0.00418241095192, + 0.7941648429911843, + 1.1692669018889823, + 1.1960020285283903, + 1.249905599427292, + 1.2512046604157985, + 1.2581493339973435, + 0.0043727412603138, + 0.7863488675261142, + 1.154364944176871, + 1.1868886054730516, + 1.2440943544657643, + 1.246904304985102, + 1.2495600801266238, + 0.004082857671826, + 0.760952128204789, + 1.1428027133910008, + 1.1751184948781388, + 1.222517587510634, + 1.223679807266021, + 1.2239900971983946, + 0.0043014909664417, + 0.7959052930969952, + 1.1621039256838672, + 1.196929399809687, + 1.253046170170458, + 1.256735727756202, + 1.260975312624356 + ], + "z": [ + 1992.175136, + 2306.840096, + 2621.505056, + 2936.170016, + 3250.834976, + 3880.164896, + 6397.484576, + 2115.242288, + 2429.907248, + 2744.572208, + 3059.237168, + 3373.902128, + 4003.232048, + 6520.551728, + 2238.30944, + 2552.9744, + 2867.63936, + 3182.30432, + 3496.96928, + 4126.2992, + 6643.61888, + 2361.376592, + 2676.041552, + 2990.706512, + 3305.371472, + 3620.036432, + 4249.366352, + 6766.686032, + 2484.443744, + 2799.108704, + 3113.773664, + 3428.438624, + 3743.103584, + 4372.433504, + 6889.753184, + 2730.578048, + 3045.243008, + 3359.907968, + 3674.572928, + 3989.237888, + 4618.567808, + 7135.887488, + 3715.115264, + 4029.780224, + 4344.445184, + 4659.110144, + 4973.775104, + 5603.105024, + 8120.424704 + ] + }, + { + "customdata": [ + [ + 2, + 5, + 2 + ], + [ + 2, + 5, + 3 + ], + [ + 2, + 5, + 4 + ], + [ + 2, + 5, + 5 + ], + [ + 2, + 5, + 6 + ], + [ + 2, + 5, + 8 + ], + [ + 2, + 5, + 16 + ], + [ + 3, + 5, + 2 + ], + [ + 3, + 5, + 3 + ], + [ + 3, + 5, + 4 + ], + [ + 3, + 5, + 5 + ], + [ + 3, + 5, + 6 + ], + [ + 3, + 5, + 8 + ], + [ + 3, + 5, + 16 + ], + [ + 4, + 5, + 2 + ], + [ + 4, + 5, + 3 + ], + [ + 4, + 5, + 4 + ], + [ + 4, + 5, + 5 + ], + [ + 4, + 5, + 6 + ], + [ + 4, + 5, + 8 + ], + [ + 4, + 5, + 16 + ], + [ + 5, + 5, + 2 + ], + [ + 5, + 5, + 3 + ], + [ + 5, + 5, + 4 + ], + [ + 5, + 5, + 5 + ], + [ + 5, + 5, + 6 + ], + [ + 5, + 5, + 8 + ], + [ + 5, + 5, + 16 + ], + [ + 6, + 5, + 2 + ], + [ + 6, + 5, + 3 + ], + [ + 6, + 5, + 4 + ], + [ + 6, + 5, + 5 + ], + [ + 6, + 5, + 6 + ], + [ + 6, + 5, + 8 + ], + [ + 6, + 5, + 16 + ], + [ + 8, + 5, + 2 + ], + [ + 8, + 5, + 3 + ], + [ + 8, + 5, + 4 + ], + [ + 8, + 5, + 5 + ], + [ + 8, + 5, + 6 + ], + [ + 8, + 5, + 8 + ], + [ + 8, + 5, + 16 + ], + [ + 16, + 5, + 2 + ], + [ + 16, + 5, + 3 + ], + [ + 16, + 5, + 4 + ], + [ + 16, + 5, + 5 + ], + [ + 16, + 5, + 6 + ], + [ + 16, + 5, + 8 + ], + [ + 16, + 5, + 16 + ] + ], + "hovertemplate": "qformer_bits=%{customdata[1]}
METEOR=%{x}
CIDEr=%{y}
model_size=%{z}
vit_bits=%{customdata[0]}
llm_bits=%{marker.color}", + "legendgroup": "5", + "marker": { + "color": [ + 2, + 3, + 4, + 5, + 6, + 8, + 16, + 2, + 3, + 4, + 5, + 6, + 8, + 16, + 2, + 3, + 4, + 5, + 6, + 8, + 16, + 2, + 3, + 4, + 5, + 6, + 8, + 16, + 2, + 3, + 4, + 5, + 6, + 8, + 16, + 2, + 3, + 4, + 5, + 6, + 8, + 16, + 2, + 3, + 4, + 5, + 6, + 8, + 16 + ], + "coloraxis": "coloraxis", + "size": 3.5, + "symbol": "x" + }, + "mode": "markers", + "name": "5", + "scene": "scene", + "showlegend": true, + "type": "scatter3d", + "x": [ + 0.0287472582273225, + 0.156545753956854, + 0.196610560416409, + 0.201813096512405, + 0.208814901329159, + 0.2111062432632992, + 0.2119571593229862, + 0.0368190399609676, + 0.2124203546308737, + 0.2685017116853036, + 0.274927098888206, + 0.2808771294802629, + 0.2823379329904332, + 0.2838621056433481, + 0.0381667985097088, + 0.2080054961135729, + 0.2684271879621022, + 0.2741510572230004, + 0.28128887015849, + 0.2823284639324399, + 0.2831020228799503, + 0.0388129039893688, + 0.2082785550657938, + 0.2657672271375958, + 0.270453762144429, + 0.2785285750157541, + 0.279735734830095, + 0.2810065371559589, + 0.0391573268763807, + 0.2067279626401582, + 0.2646674838482865, + 0.2692422909529576, + 0.2777006695173449, + 0.2782405905869657, + 0.279889958988931, + 0.0389313503036774, + 0.2033676800916742, + 0.2616939778335936, + 0.2672903069142515, + 0.2750725549338091, + 0.2755511614969632, + 0.2764419231604895, + 0.0391234610100254, + 0.2079609378623707, + 0.2656879108724494, + 0.269967086318697, + 0.2787494875664804, + 0.2794980179320134, + 0.2806930762856469 + ], + "y": [ + 0.0015383744706068, + 0.4342188097083193, + 0.6643003856622003, + 0.6880985619516059, + 0.7235377941797142, + 0.7375435379366277, + 0.7375809567131982, + 0.0037738532167631, + 0.8106225186823535, + 1.1758572483909744, + 1.224376698596568, + 1.2613331346868195, + 1.261397607201865, + 1.2687618310593374, + 0.0041804297547297, + 0.7936760412704565, + 1.1769199968051365, + 1.2177552194123182, + 1.269146453283604, + 1.2661784066985244, + 1.2694800484392854, + 0.0044147346478681, + 0.7886177826050037, + 1.1574830881644504, + 1.1903072050381271, + 1.248833370994669, + 1.2505910698213745, + 1.2559031668672331, + 0.0045285080121281, + 0.7822532071076522, + 1.146621888681412, + 1.182464151264467, + 1.239097227163636, + 1.2374097592485436, + 1.2451443958139716, + 0.0043467023862614, + 0.7570584897619376, + 1.1319068641144796, + 1.1726682788050742, + 1.2251675741742547, + 1.2213806795583368, + 1.2248409116679573, + 0.0043702656502771, + 0.7879401066468005, + 1.1556677145197871, + 1.1893109363512375, + 1.245958819839066, + 1.2457846506159769, + 1.2528649424602132 + ], + "z": [ + 2005.311392, + 2319.976352, + 2634.641312, + 2949.306272, + 3263.971232, + 3893.301152, + 6410.620832, + 2128.378544, + 2443.043504, + 2757.708464, + 3072.373424, + 3387.038384, + 4016.368304, + 6533.687984, + 2251.445696, + 2566.110656, + 2880.775616, + 3195.440576, + 3510.105536, + 4139.435456, + 6656.755136, + 2374.512848, + 2689.177808, + 3003.842768, + 3318.507728, + 3633.172688, + 4262.502608, + 6779.822288, + 2497.58, + 2812.24496, + 3126.90992, + 3441.57488, + 3756.23984, + 4385.56976, + 6902.88944, + 2743.714304, + 3058.379264, + 3373.044224, + 3687.709184, + 4002.374144, + 4631.704064, + 7149.023744, + 3728.25152, + 4042.91648, + 4357.58144, + 4672.2464, + 4986.91136, + 5616.24128, + 8133.56096 + ] + }, + { + "customdata": [ + [ + 2, + 6, + 2 + ], + [ + 2, + 6, + 3 + ], + [ + 2, + 6, + 4 + ], + [ + 2, + 6, + 5 + ], + [ + 2, + 6, + 6 + ], + [ + 2, + 6, + 8 + ], + [ + 2, + 6, + 16 + ], + [ + 3, + 6, + 2 + ], + [ + 3, + 6, + 3 + ], + [ + 3, + 6, + 4 + ], + [ + 3, + 6, + 5 + ], + [ + 3, + 6, + 6 + ], + [ + 3, + 6, + 8 + ], + [ + 3, + 6, + 16 + ], + [ + 4, + 6, + 2 + ], + [ + 4, + 6, + 3 + ], + [ + 4, + 6, + 4 + ], + [ + 4, + 6, + 5 + ], + [ + 4, + 6, + 6 + ], + [ + 4, + 6, + 8 + ], + [ + 4, + 6, + 16 + ], + [ + 5, + 6, + 2 + ], + [ + 5, + 6, + 3 + ], + [ + 5, + 6, + 4 + ], + [ + 5, + 6, + 5 + ], + [ + 5, + 6, + 6 + ], + [ + 5, + 6, + 8 + ], + [ + 5, + 6, + 16 + ], + [ + 6, + 6, + 2 + ], + [ + 6, + 6, + 3 + ], + [ + 6, + 6, + 4 + ], + [ + 6, + 6, + 5 + ], + [ + 6, + 6, + 6 + ], + [ + 6, + 6, + 8 + ], + [ + 6, + 6, + 16 + ], + [ + 8, + 6, + 2 + ], + [ + 8, + 6, + 3 + ], + [ + 8, + 6, + 4 + ], + [ + 8, + 6, + 5 + ], + [ + 8, + 6, + 6 + ], + [ + 8, + 6, + 8 + ], + [ + 8, + 6, + 16 + ], + [ + 16, + 6, + 2 + ], + [ + 16, + 6, + 3 + ], + [ + 16, + 6, + 4 + ], + [ + 16, + 6, + 5 + ], + [ + 16, + 6, + 6 + ], + [ + 16, + 6, + 8 + ], + [ + 16, + 6, + 16 + ] + ], + "hovertemplate": "qformer_bits=%{customdata[1]}
METEOR=%{x}
CIDEr=%{y}
model_size=%{z}
vit_bits=%{customdata[0]}
llm_bits=%{marker.color}", + "legendgroup": "6", + "marker": { + "color": [ + 2, + 3, + 4, + 5, + 6, + 8, + 16, + 2, + 3, + 4, + 5, + 6, + 8, + 16, + 2, + 3, + 4, + 5, + 6, + 8, + 16, + 2, + 3, + 4, + 5, + 6, + 8, + 16, + 2, + 3, + 4, + 5, + 6, + 8, + 16, + 2, + 3, + 4, + 5, + 6, + 8, + 16, + 2, + 3, + 4, + 5, + 6, + 8, + 16 + ], + "coloraxis": "coloraxis", + "size": 3.5, + "symbol": "cross" + }, + "mode": "markers", + "name": "6", + "scene": "scene", + "showlegend": true, + "type": "scatter3d", + "x": [ + 0.030631103913381, + 0.1535624775179599, + 0.1975830578073438, + 0.2007745793399482, + 0.2084986314559541, + 0.2098694607988712, + 0.2104895431279897, + 0.0383622741752441, + 0.2142736314265119, + 0.2701122984332231, + 0.2746066954112424, + 0.2806354693035315, + 0.2836407999143544, + 0.2843478419605554, + 0.0394724651233021, + 0.2084417327029731, + 0.2682092450981378, + 0.2741500493132533, + 0.2811586868552443, + 0.281782607048575, + 0.2832089336877907, + 0.0393763005359751, + 0.2088112702466963, + 0.2660535139642068, + 0.2695669266977705, + 0.2783926218102539, + 0.2794284812688384, + 0.2805120454493048, + 0.0397439797438703, + 0.2076556413002826, + 0.2645534527788851, + 0.2693868110143037, + 0.2769359695011477, + 0.2774697283891806, + 0.2791530049253948, + 0.0396099296866685, + 0.205098432856594, + 0.2623525911856502, + 0.2673520382984019, + 0.2750965863700415, + 0.2748874626975029, + 0.2761863210938283, + 0.0396840021663625, + 0.2085110100872384, + 0.2657052362517834, + 0.2705499354714129, + 0.2782112952274305, + 0.2794412813041066, + 0.2810511926614303 + ], + "y": [ + 0.0020275794823421, + 0.424523049292371, + 0.6760507495669246, + 0.6885736800597454, + 0.7263028445719076, + 0.7315217945479447, + 0.7339511837416856, + 0.0040725416524024, + 0.8236695721105687, + 1.1856491875247703, + 1.221351870627707, + 1.259917659736496, + 1.2663044193020636, + 1.2708232038912224, + 0.0044968740608802, + 0.7959372573564164, + 1.1741282222425378, + 1.21868239273846, + 1.2662373768159405, + 1.25967838492472, + 1.2677578930557416, + 0.0047618934480426, + 0.7933442754900087, + 1.1612856969921397, + 1.1875149732346562, + 1.2446303376282275, + 1.247600525639272, + 1.252184355894695, + 0.0047738714292881, + 0.7869627504029372, + 1.1448253129962436, + 1.184992736929933, + 1.2359611482185155, + 1.2345152710805227, + 1.2410981814756032, + 0.0046426558906571, + 0.7657076774627696, + 1.135895470847944, + 1.1721543852482914, + 1.2232652165288425, + 1.217991990088048, + 1.2256323022273348, + 0.0046449400319948, + 0.7919767269675149, + 1.1567217842367483, + 1.1935809173708705, + 1.2420703577665526, + 1.2436823085127202, + 1.2516247225738375 + ], + "z": [ + 2018.447648, + 2333.112608, + 2647.777568, + 2962.442528, + 3277.107488, + 3906.437408, + 6423.757088, + 2141.5148, + 2456.17976, + 2770.84472, + 3085.50968, + 3400.17464, + 4029.50456, + 6546.82424, + 2264.581952, + 2579.246912, + 2893.911872, + 3208.576832, + 3523.241792, + 4152.571712, + 6669.891392, + 2387.649104, + 2702.314064, + 3016.979024, + 3331.643984, + 3646.308944, + 4275.638864, + 6792.958544, + 2510.716256, + 2825.381216, + 3140.046176, + 3454.711136, + 3769.376096, + 4398.706016, + 6916.025696, + 2756.85056, + 3071.51552, + 3386.18048, + 3700.84544, + 4015.5104, + 4644.84032, + 7162.16, + 3741.387776, + 4056.052736, + 4370.717696, + 4685.382656, + 5000.047616, + 5629.377536, + 8146.697216 + ] + }, + { + "customdata": [ + [ + 2, + 8, + 2 + ], + [ + 2, + 8, + 3 + ], + [ + 2, + 8, + 4 + ], + [ + 2, + 8, + 5 + ], + [ + 2, + 8, + 6 + ], + [ + 2, + 8, + 8 + ], + [ + 2, + 8, + 16 + ], + [ + 3, + 8, + 2 + ], + [ + 3, + 8, + 3 + ], + [ + 3, + 8, + 4 + ], + [ + 3, + 8, + 5 + ], + [ + 3, + 8, + 6 + ], + [ + 3, + 8, + 8 + ], + [ + 3, + 8, + 16 + ], + [ + 4, + 8, + 2 + ], + [ + 4, + 8, + 3 + ], + [ + 4, + 8, + 4 + ], + [ + 4, + 8, + 5 + ], + [ + 4, + 8, + 6 + ], + [ + 4, + 8, + 8 + ], + [ + 4, + 8, + 16 + ], + [ + 5, + 8, + 2 + ], + [ + 5, + 8, + 3 + ], + [ + 5, + 8, + 4 + ], + [ + 5, + 8, + 5 + ], + [ + 5, + 8, + 6 + ], + [ + 5, + 8, + 8 + ], + [ + 5, + 8, + 16 + ], + [ + 6, + 8, + 2 + ], + [ + 6, + 8, + 3 + ], + [ + 6, + 8, + 4 + ], + [ + 6, + 8, + 5 + ], + [ + 6, + 8, + 6 + ], + [ + 6, + 8, + 8 + ], + [ + 6, + 8, + 16 + ], + [ + 8, + 8, + 2 + ], + [ + 8, + 8, + 3 + ], + [ + 8, + 8, + 4 + ], + [ + 8, + 8, + 5 + ], + [ + 8, + 8, + 6 + ], + [ + 8, + 8, + 8 + ], + [ + 8, + 8, + 16 + ], + [ + 16, + 8, + 2 + ], + [ + 16, + 8, + 3 + ], + [ + 16, + 8, + 4 + ], + [ + 16, + 8, + 5 + ], + [ + 16, + 8, + 6 + ], + [ + 16, + 8, + 8 + ], + [ + 16, + 8, + 16 + ] + ], + "hovertemplate": "qformer_bits=%{customdata[1]}
METEOR=%{x}
CIDEr=%{y}
model_size=%{z}
vit_bits=%{customdata[0]}
llm_bits=%{marker.color}", + "legendgroup": "8", + "marker": { + "color": [ + 2, + 3, + 4, + 5, + 6, + 8, + 16, + 2, + 3, + 4, + 5, + 6, + 8, + 16, + 2, + 3, + 4, + 5, + 6, + 8, + 16, + 2, + 3, + 4, + 5, + 6, + 8, + 16, + 2, + 3, + 4, + 5, + 6, + 8, + 16, + 2, + 3, + 4, + 5, + 6, + 8, + 16, + 2, + 3, + 4, + 5, + 6, + 8, + 16 + ], + "coloraxis": "coloraxis", + "size": 3.5, + "symbol": "circle" + }, + "mode": "markers", + "name": "8", + "scene": "scene", + "showlegend": true, + "type": "scatter3d", + "x": [ + 0.0294354848117307, + 0.1568363211152181, + 0.1971530070668118, + 0.2010881485961166, + 0.2085898952116036, + 0.210553933277872, + 0.2111149150856989, + 0.0381034374915156, + 0.2137015712190609, + 0.2695374408750584, + 0.274847085121946, + 0.2809750487176385, + 0.2836878203740132, + 0.2845303570314229, + 0.0393283261447833, + 0.2109298913715551, + 0.2686142296604581, + 0.2742289723913308, + 0.2816999624424486, + 0.282945662860569, + 0.2839275915866569, + 0.0392876594512647, + 0.2050535892981623, + 0.2671505802790982, + 0.2700647922767295, + 0.2791323330204765, + 0.2804314117622675, + 0.2814720090702979, + 0.0395022218476354, + 0.2071426042231885, + 0.2656920793969223, + 0.2694899075089131, + 0.2777264614200301, + 0.2782638599429032, + 0.2795140452687747, + 0.0396217419792944, + 0.2042084040832557, + 0.2629765025163644, + 0.2677249093127373, + 0.2753843775356539, + 0.2757355870394247, + 0.2771055575136951, + 0.039681991779321, + 0.2082029517836936, + 0.2666286945660135, + 0.2707370093385092, + 0.278836459538448, + 0.2802291096727847, + 0.2811955411594185 + ], + "y": [ + 0.0016666316965743, + 0.4377116832400026, + 0.6719258108942063, + 0.6872950083608445, + 0.7246423668329751, + 0.7350918293335355, + 0.7362575171829725, + 0.0040333432466256, + 0.8190620216054207, + 1.1808923852510342, + 1.225699556061882, + 1.260754669664881, + 1.2666116687606168, + 1.2737401966926578, + 0.0045337305938802, + 0.8051917158643872, + 1.1791031207801064, + 1.2171637489140836, + 1.269949088841136, + 1.2685095577773473, + 1.2725369264593078, + 0.0047157077171726, + 0.7715007986326152, + 1.167161012774189, + 1.189471154011835, + 1.2486870632772915, + 1.2538222735773836, + 1.2590312903179968, + 0.0047109274697495, + 0.7831441749206127, + 1.1532797092887277, + 1.1870631294646563, + 1.2378950190538096, + 1.2396680301427143, + 1.246428397197772, + 0.0046413590593211, + 0.7609812697499186, + 1.1398554135838508, + 1.1744269293359073, + 1.2246538612050697, + 1.2223636948702057, + 1.2289172931774284, + 0.0045807829907469, + 0.7895426734487146, + 1.164603641736421, + 1.193852243546073, + 1.2451381600762323, + 1.2495830587956385, + 1.253045592341763 + ], + "z": [ + 2044.72016, + 2359.38512, + 2674.05008, + 2988.71504, + 3303.38, + 3932.70992, + 6450.0296, + 2167.787312, + 2482.452272, + 2797.117232, + 3111.782192, + 3426.447152, + 4055.777072, + 6573.096752, + 2290.854464, + 2605.519424, + 2920.184384, + 3234.849344, + 3549.514304, + 4178.844224, + 6696.163904, + 2413.921616, + 2728.586576, + 3043.251536, + 3357.916496, + 3672.581456, + 4301.911376, + 6819.231056, + 2536.988768, + 2851.653728, + 3166.318688, + 3480.983648, + 3795.648608, + 4424.978528, + 6942.298208, + 2783.123072, + 3097.788032, + 3412.452992, + 3727.117952, + 4041.782912, + 4671.112832, + 7188.432512, + 3767.660288, + 4082.325248, + 4396.990208, + 4711.655168, + 5026.320128, + 5655.650048, + 8172.969728 + ] + }, + { + "customdata": [ + [ + 2, + 16, + 2 + ], + [ + 2, + 16, + 3 + ], + [ + 2, + 16, + 4 + ], + [ + 2, + 16, + 5 + ], + [ + 2, + 16, + 6 + ], + [ + 2, + 16, + 8 + ], + [ + 2, + 16, + 16 + ], + [ + 3, + 16, + 2 + ], + [ + 3, + 16, + 3 + ], + [ + 3, + 16, + 4 + ], + [ + 3, + 16, + 5 + ], + [ + 3, + 16, + 6 + ], + [ + 3, + 16, + 8 + ], + [ + 3, + 16, + 16 + ], + [ + 4, + 16, + 2 + ], + [ + 4, + 16, + 3 + ], + [ + 4, + 16, + 4 + ], + [ + 4, + 16, + 5 + ], + [ + 4, + 16, + 6 + ], + [ + 4, + 16, + 8 + ], + [ + 4, + 16, + 16 + ], + [ + 5, + 16, + 2 + ], + [ + 5, + 16, + 3 + ], + [ + 5, + 16, + 4 + ], + [ + 5, + 16, + 5 + ], + [ + 5, + 16, + 6 + ], + [ + 5, + 16, + 8 + ], + [ + 5, + 16, + 16 + ], + [ + 6, + 16, + 2 + ], + [ + 6, + 16, + 3 + ], + [ + 6, + 16, + 4 + ], + [ + 6, + 16, + 5 + ], + [ + 6, + 16, + 6 + ], + [ + 6, + 16, + 8 + ], + [ + 6, + 16, + 16 + ], + [ + 8, + 16, + 2 + ], + [ + 8, + 16, + 3 + ], + [ + 8, + 16, + 4 + ], + [ + 8, + 16, + 5 + ], + [ + 8, + 16, + 6 + ], + [ + 8, + 16, + 8 + ], + [ + 8, + 16, + 16 + ], + [ + 16, + 16, + 2 + ], + [ + 16, + 16, + 3 + ], + [ + 16, + 16, + 4 + ], + [ + 16, + 16, + 5 + ], + [ + 16, + 16, + 6 + ], + [ + 16, + 16, + 8 + ], + [ + 16, + 16, + 16 + ] + ], + "hovertemplate": "qformer_bits=%{customdata[1]}
METEOR=%{x}
CIDEr=%{y}
model_size=%{z}
vit_bits=%{customdata[0]}
llm_bits=%{marker.color}", + "legendgroup": "16", + "marker": { + "color": [ + 2, + 3, + 4, + 5, + 6, + 8, + 16, + 2, + 3, + 4, + 5, + 6, + 8, + 16, + 2, + 3, + 4, + 5, + 6, + 8, + 16, + 2, + 3, + 4, + 5, + 6, + 8, + 16, + 2, + 3, + 4, + 5, + 6, + 8, + 16, + 2, + 3, + 4, + 5, + 6, + 8, + 16, + 2, + 3, + 4, + 5, + 6, + 8, + 16 + ], + "coloraxis": "coloraxis", + "size": 3.5, + "symbol": "diamond" + }, + "mode": "markers", + "name": "16", + "scene": "scene", + "showlegend": true, + "type": "scatter3d", + "x": [ + 0.0295098771216461, + 0.1569115889624636, + 0.1972734524909803, + 0.2010642391090116, + 0.2086024048502772, + 0.2105720489139707, + 0.2109831741881737, + 0.0379989723419776, + 0.2134457657549776, + 0.2693022523433621, + 0.2749129889020977, + 0.2807118708799256, + 0.2836549420341586, + 0.2845363215102724, + 0.0398707834609076, + 0.2107603494156979, + 0.268773824997628, + 0.2742171149298761, + 0.2818531035064126, + 0.2830473462320595, + 0.2837925399714376, + 0.0393572872875101, + 0.2085765644422946, + 0.266504476970306, + 0.269994073154192, + 0.279470411592239, + 0.2800480375813525, + 0.2814890625402405, + 0.0395439860631808, + 0.2072207189909281, + 0.2655337977316027, + 0.2697323199765623, + 0.2778688652580702, + 0.2783466182494372, + 0.2795925000876874, + 0.0395425541939407, + 0.2042149818919491, + 0.2629573909208676, + 0.2677163506866694, + 0.2754273250795791, + 0.2756801593566381, + 0.2769747471271154, + 0.039602095057516, + 0.2080132739451042, + 0.2664132479204438, + 0.2708655101118942, + 0.2789889072587355, + 0.280146845145009, + 0.2812820762161627 + ], + "y": [ + 0.0016837862661927, + 0.4362796051124896, + 0.6727059028707632, + 0.6882361624647443, + 0.7246993426668064, + 0.734900136126526, + 0.7357684637386981, + 0.0040061661415185, + 0.8184339490345491, + 1.180122405297351, + 1.226209348185821, + 1.2599670609394376, + 1.268297667693004, + 1.2752890935916952, + 0.0044883259869472, + 0.8049525065074827, + 1.1793997074725706, + 1.217838638892368, + 1.270626685213267, + 1.268790043613231, + 1.2723116809585964, + 0.0046999911219963, + 0.7910105903131062, + 1.1645621691200578, + 1.18911306239116, + 1.2499655271501364, + 1.250759578071996, + 1.2574020173423823, + 0.0047107078941073, + 0.7846150320891636, + 1.151739526032029, + 1.1888011112017192, + 1.2381501818428504, + 1.2406648192914698, + 1.2469064852182756, + 0.0047027927153682, + 0.7611484990145555, + 1.1390067988226158, + 1.1752137486977985, + 1.224003727431766, + 1.221797226097161, + 1.2286821685059492, + 0.0045991273538401, + 0.7887153055212033, + 1.1638367480835288, + 1.1950595426281183, + 1.245283436795488, + 1.2493832157443017, + 1.254197658273608 + ], + "z": [ + 2149.810208, + 2464.475168, + 2779.140128, + 3093.805088, + 3408.470048, + 4037.799968, + 6555.119648, + 2272.87736, + 2587.54232, + 2902.20728, + 3216.87224, + 3531.5372, + 4160.86712, + 6678.1868, + 2395.944512, + 2710.609472, + 3025.274432, + 3339.939392, + 3654.604352, + 4283.934272, + 6801.253952, + 2519.011664, + 2833.676624, + 3148.341584, + 3463.006544, + 3777.671504, + 4407.001424, + 6924.321104, + 2642.078816, + 2956.743776, + 3271.408736, + 3586.073696, + 3900.738656, + 4530.068576, + 7047.388256, + 2888.21312, + 3202.87808, + 3517.54304, + 3832.208, + 4146.87296, + 4776.20288, + 7293.52256, + 3872.750336, + 4187.415296, + 4502.080256, + 4816.745216, + 5131.410176, + 5760.740096, + 8278.059776 + ] + } + ], + "layout": { + "coloraxis": { + "colorbar": { + "ticks": "outside", + "title": { + "text": "llm_bits" + }, + "x": 0, + "y": 1, + "yanchor": "top" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ] + }, + "legend": { + "title": { + "text": "qformer_bits" + }, + "tracegroupgap": 0 + }, + "scene": { + "domain": { + "x": [ + 0, + 1 + ], + "y": [ + 0, + 1 + ] + }, + "xaxis": { + "autorange": "reversed", + "title": { + "text": "METEOR" + } + }, + "yaxis": { + "autorange": "reversed", + "title": { + "text": "CIDEr" + } + }, + "zaxis": { + "title": { + "text": "Model Size (MB)" + } + } + }, + "template": { + "data": { + "bar": [ + { + "error_x": { + "color": "#2a3f5f" + }, + "error_y": { + "color": "#2a3f5f" + }, + "marker": { + "line": { + "color": "#E5ECF6", + "width": 0.5 + }, + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "bar" + } + ], + "barpolar": [ + { + "marker": { + "line": { + "color": "#E5ECF6", + "width": 0.5 + }, + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "barpolar" + } + ], + "carpet": [ + { + "aaxis": { + "endlinecolor": "#2a3f5f", + "gridcolor": "white", + "linecolor": "white", + "minorgridcolor": "white", + "startlinecolor": "#2a3f5f" + }, + "baxis": { + "endlinecolor": "#2a3f5f", + "gridcolor": "white", + "linecolor": "white", + "minorgridcolor": "white", + "startlinecolor": "#2a3f5f" + }, + "type": "carpet" + } + ], + "choropleth": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "type": "choropleth" + } + ], + "contour": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "contour" + } + ], + "contourcarpet": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "type": "contourcarpet" + } + ], + "heatmap": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "heatmap" + } + ], + "heatmapgl": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "heatmapgl" + } + ], + "histogram": [ + { + "marker": { + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "histogram" + } + ], + "histogram2d": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "histogram2d" + } + ], + "histogram2dcontour": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "histogram2dcontour" + } + ], + "mesh3d": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "type": "mesh3d" + } + ], + "parcoords": [ + { + "line": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "parcoords" + } + ], + "pie": [ + { + "automargin": true, + "type": "pie" + } + ], + "scatter": [ + { + "fillpattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + }, + "type": "scatter" + } + ], + "scatter3d": [ + { + "line": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatter3d" + } + ], + "scattercarpet": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattercarpet" + } + ], + "scattergeo": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattergeo" + } + ], + "scattergl": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattergl" + } + ], + "scattermapbox": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattermapbox" + } + ], + "scatterpolar": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatterpolar" + } + ], + "scatterpolargl": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatterpolargl" + } + ], + "scatterternary": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatterternary" + } + ], + "surface": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "surface" + } + ], + "table": [ + { + "cells": { + "fill": { + "color": "#EBF0F8" + }, + "line": { + "color": "white" + } + }, + "header": { + "fill": { + "color": "#C8D4E3" + }, + "line": { + "color": "white" + } + }, + "type": "table" + } + ] + }, + "layout": { + "annotationdefaults": { + "arrowcolor": "#2a3f5f", + "arrowhead": 0, + "arrowwidth": 1 + }, + "autotypenumbers": "strict", + "coloraxis": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "colorscale": { + "diverging": [ + [ + 0, + "#8e0152" + ], + [ + 0.1, + "#c51b7d" + ], + [ + 0.2, + "#de77ae" + ], + [ + 0.3, + "#f1b6da" + ], + [ + 0.4, + "#fde0ef" + ], + [ + 0.5, + "#f7f7f7" + ], + [ + 0.6, + "#e6f5d0" + ], + [ + 0.7, + "#b8e186" + ], + [ + 0.8, + "#7fbc41" + ], + [ + 0.9, + "#4d9221" + ], + [ + 1, + "#276419" + ] + ], + "sequential": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "sequentialminus": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ] + }, + "colorway": [ + "#636efa", + "#EF553B", + "#00cc96", + "#ab63fa", + "#FFA15A", + "#19d3f3", + "#FF6692", + "#B6E880", + "#FF97FF", + "#FECB52" + ], + "font": { + "color": "#2a3f5f" + }, + "geo": { + "bgcolor": "white", + "lakecolor": "white", + "landcolor": "#E5ECF6", + "showlakes": true, + "showland": true, + "subunitcolor": "white" + }, + "hoverlabel": { + "align": "left" + }, + "hovermode": "closest", + "mapbox": { + "style": "light" + }, + "paper_bgcolor": "white", + "plot_bgcolor": "#E5ECF6", + "polar": { + "angularaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "bgcolor": "#E5ECF6", + "radialaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + } + }, + "scene": { + "xaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + }, + "yaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + }, + "zaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + } + }, + "shapedefaults": { + "line": { + "color": "#2a3f5f" + } + }, + "ternary": { + "aaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "baxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "bgcolor": "#E5ECF6", + "caxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + } + }, + "title": { + "x": 0.05 + }, + "xaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + }, + "yaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + } + } + }, + "title": { + "text": "AWQ Blip-2 COCO Captioning" + } + } + }, + "text/html": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = px.scatter_3d(df_awq, x='METEOR', y=\"CIDEr\", z = 'model_size',\n", + " color = 'llm_bits',\n", + " symbol = 'qformer_bits',\n", + " hover_data = hover_data,\n", + " title = 'AWQ Blip-2 COCO Captioning',)\n", + "\n", + "\n", + "fig.update_traces(marker=dict(size=3.5))\n", + "\n", + "fig.update_layout(scene = dict(\n", + " xaxis_title='METEOR',\n", + " yaxis_title='CIDEr',\n", + " zaxis_title='Model Size (MB)',\n", + " \n", + " xaxis = dict(autorange='reversed'),\n", + " yaxis = dict(autorange='reversed')),)\n", + "\n", + "fig.update_layout(coloraxis_colorbar = dict(yanchor=\"top\", y=1, x=0,\n", + " ticks=\"outside\"))\n", + "\n", + "fig.write_html(\"awq_captioning.html\")\n", + "\n", + "fig.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "customdata": [ + [ + 2, + 2, + 2, + 1965.902624, + "AWQ" + ], + [ + 2, + 2, + 3, + 2280.567584, + "AWQ" + ], + [ + 2, + 2, + 4, + 2595.232544, + "AWQ" + ], + [ + 2, + 2, + 5, + 2909.897504, + "AWQ" + ], + [ + 2, + 2, + 6, + 3224.562464, + "AWQ" + ], + [ + 2, + 2, + 8, + 3853.892384, + "AWQ" + ], + [ + 2, + 2, + 16, + 6371.212064, + "AWQ" + ], + [ + 2, + 3, + 2, + 1979.03888, + "AWQ" + ], + [ + 2, + 3, + 3, + 2293.70384, + "AWQ" + ], + [ + 2, + 3, + 4, + 2608.3688, + "AWQ" + ], + [ + 2, + 3, + 5, + 2923.03376, + "AWQ" + ], + [ + 2, + 3, + 6, + 3237.69872, + "AWQ" + ], + [ + 2, + 3, + 8, + 3867.02864, + "AWQ" + ], + [ + 2, + 3, + 16, + 6384.34832, + "AWQ" + ], + [ + 2, + 4, + 2, + 1992.175136, + "AWQ" + ], + [ + 2, + 4, + 3, + 2306.840096, + "AWQ" + ], + [ + 2, + 4, + 4, + 2621.505056, + "AWQ" + ], + [ + 2, + 4, + 5, + 2936.170016, + "AWQ" + ], + [ + 2, + 4, + 6, + 3250.834976, + "AWQ" + ], + [ + 2, + 4, + 8, + 3880.164896, + "AWQ" + ], + [ + 2, + 4, + 16, + 6397.484576, + "AWQ" + ], + [ + 2, + 5, + 2, + 2005.311392, + "AWQ" + ], + [ + 2, + 5, + 3, + 2319.976352, + "AWQ" + ], + [ + 2, + 5, + 4, + 2634.641312, + "AWQ" + ], + [ + 2, + 5, + 5, + 2949.306272, + "AWQ" + ], + [ + 2, + 5, + 6, + 3263.971232, + "AWQ" + ], + [ + 2, + 5, + 8, + 3893.301152, + "AWQ" + ], + [ + 2, + 5, + 16, + 6410.620832, + "AWQ" + ], + [ + 2, + 6, + 2, + 2018.447648, + "AWQ" + ], + [ + 2, + 6, + 3, + 2333.112608, + "AWQ" + ], + [ + 2, + 6, + 4, + 2647.777568, + "AWQ" + ], + [ + 2, + 6, + 5, + 2962.442528, + "AWQ" + ], + [ + 2, + 6, + 6, + 3277.107488, + "AWQ" + ], + [ + 2, + 6, + 8, + 3906.437408, + "AWQ" + ], + [ + 2, + 6, + 16, + 6423.757088, + "AWQ" + ], + [ + 2, + 8, + 2, + 2044.72016, + "AWQ" + ], + [ + 2, + 8, + 3, + 2359.38512, + "AWQ" + ], + [ + 2, + 8, + 4, + 2674.05008, + "AWQ" + ], + [ + 2, + 8, + 5, + 2988.71504, + "AWQ" + ], + [ + 2, + 8, + 6, + 3303.38, + "AWQ" + ], + [ + 2, + 8, + 8, + 3932.70992, + "AWQ" + ], + [ + 2, + 8, + 16, + 6450.0296, + "AWQ" + ], + [ + 2, + 16, + 2, + 2149.810208, + "AWQ" + ], + [ + 2, + 16, + 3, + 2464.475168, + "AWQ" + ], + [ + 2, + 16, + 4, + 2779.140128, + "AWQ" + ], + [ + 2, + 16, + 5, + 3093.805088, + "AWQ" + ], + [ + 2, + 16, + 6, + 3408.470048, + "AWQ" + ], + [ + 2, + 16, + 8, + 4037.799968, + "AWQ" + ], + [ + 2, + 16, + 16, + 6555.119648, + "AWQ" + ], + [ + 3, + 2, + 2, + 2088.969776, + "AWQ" + ], + [ + 3, + 2, + 3, + 2403.634736, + "AWQ" + ], + [ + 3, + 2, + 4, + 2718.299696, + "AWQ" + ], + [ + 3, + 2, + 5, + 3032.964656, + "AWQ" + ], + [ + 3, + 2, + 6, + 3347.629616, + "AWQ" + ], + [ + 3, + 2, + 8, + 3976.959536, + "AWQ" + ], + [ + 3, + 2, + 16, + 6494.279216, + "AWQ" + ], + [ + 3, + 3, + 2, + 2102.106032, + "AWQ" + ], + [ + 3, + 3, + 3, + 2416.770992, + "AWQ" + ], + [ + 3, + 3, + 4, + 2731.435952, + "AWQ" + ], + [ + 3, + 3, + 5, + 3046.100912, + "AWQ" + ], + [ + 3, + 3, + 6, + 3360.765872, + "AWQ" + ], + [ + 3, + 3, + 8, + 3990.095792, + "AWQ" + ], + [ + 3, + 3, + 16, + 6507.415472, + "AWQ" + ], + [ + 3, + 4, + 2, + 2115.242288, + "AWQ" + ], + [ + 3, + 4, + 3, + 2429.907248, + "AWQ" + ], + [ + 3, + 4, + 4, + 2744.572208, + "AWQ" + ], + [ + 3, + 4, + 5, + 3059.237168, + "AWQ" + ], + [ + 3, + 4, + 6, + 3373.902128, + "AWQ" + ], + [ + 3, + 4, + 8, + 4003.232048, + "AWQ" + ], + [ + 3, + 4, + 16, + 6520.551728, + "AWQ" + ], + [ + 3, + 5, + 2, + 2128.378544, + "AWQ" + ], + [ + 3, + 5, + 3, + 2443.043504, + "AWQ" + ], + [ + 3, + 5, + 4, + 2757.708464, + "AWQ" + ], + [ + 3, + 5, + 5, + 3072.373424, + "AWQ" + ], + [ + 3, + 5, + 6, + 3387.038384, + "AWQ" + ], + [ + 3, + 5, + 8, + 4016.368304, + "AWQ" + ], + [ + 3, + 5, + 16, + 6533.687984, + "AWQ" + ], + [ + 3, + 6, + 2, + 2141.5148, + "AWQ" + ], + [ + 3, + 6, + 3, + 2456.17976, + "AWQ" + ], + [ + 3, + 6, + 4, + 2770.84472, + "AWQ" + ], + [ + 3, + 6, + 5, + 3085.50968, + "AWQ" + ], + [ + 3, + 6, + 6, + 3400.17464, + "AWQ" + ], + [ + 3, + 6, + 8, + 4029.50456, + "AWQ" + ], + [ + 3, + 6, + 16, + 6546.82424, + "AWQ" + ], + [ + 3, + 8, + 2, + 2167.787312, + "AWQ" + ], + [ + 3, + 8, + 3, + 2482.452272, + "AWQ" + ], + [ + 3, + 8, + 4, + 2797.117232, + "AWQ" + ], + [ + 3, + 8, + 5, + 3111.782192, + "AWQ" + ], + [ + 3, + 8, + 6, + 3426.447152, + "AWQ" + ], + [ + 3, + 8, + 8, + 4055.777072, + "AWQ" + ], + [ + 3, + 8, + 16, + 6573.096752, + "AWQ" + ], + [ + 3, + 16, + 2, + 2272.87736, + "AWQ" + ], + [ + 3, + 16, + 3, + 2587.54232, + "AWQ" + ], + [ + 3, + 16, + 4, + 2902.20728, + "AWQ" + ], + [ + 3, + 16, + 5, + 3216.87224, + "AWQ" + ], + [ + 3, + 16, + 6, + 3531.5372, + "AWQ" + ], + [ + 3, + 16, + 8, + 4160.86712, + "AWQ" + ], + [ + 3, + 16, + 16, + 6678.1868, + "AWQ" + ], + [ + 4, + 2, + 2, + 2212.036928, + "AWQ" + ], + [ + 4, + 2, + 3, + 2526.701888, + "AWQ" + ], + [ + 4, + 2, + 4, + 2841.366848, + "AWQ" + ], + [ + 4, + 2, + 5, + 3156.031808, + "AWQ" + ], + [ + 4, + 2, + 6, + 3470.696768, + "AWQ" + ], + [ + 4, + 2, + 8, + 4100.026688, + "AWQ" + ], + [ + 4, + 2, + 16, + 6617.346368, + "AWQ" + ], + [ + 4, + 3, + 2, + 2225.173184, + "AWQ" + ], + [ + 4, + 3, + 3, + 2539.838144, + "AWQ" + ], + [ + 4, + 3, + 4, + 2854.503104, + "AWQ" + ], + [ + 4, + 3, + 5, + 3169.168064, + "AWQ" + ], + [ + 4, + 3, + 6, + 3483.833024, + "AWQ" + ], + [ + 4, + 3, + 8, + 4113.162944, + "AWQ" + ], + [ + 4, + 3, + 16, + 6630.482624, + "AWQ" + ], + [ + 4, + 4, + 2, + 2238.30944, + "AWQ" + ], + [ + 4, + 4, + 3, + 2552.9744, + "AWQ" + ], + [ + 4, + 4, + 4, + 2867.63936, + "AWQ" + ], + [ + 4, + 4, + 5, + 3182.30432, + "AWQ" + ], + [ + 4, + 4, + 6, + 3496.96928, + "AWQ" + ], + [ + 4, + 4, + 8, + 4126.2992, + "AWQ" + ], + [ + 4, + 4, + 16, + 6643.61888, + "AWQ" + ], + [ + 4, + 5, + 2, + 2251.445696, + "AWQ" + ], + [ + 4, + 5, + 3, + 2566.110656, + "AWQ" + ], + [ + 4, + 5, + 4, + 2880.775616, + "AWQ" + ], + [ + 4, + 5, + 5, + 3195.440576, + "AWQ" + ], + [ + 4, + 5, + 6, + 3510.105536, + "AWQ" + ], + [ + 4, + 5, + 8, + 4139.435456, + "AWQ" + ], + [ + 4, + 5, + 16, + 6656.755136, + "AWQ" + ], + [ + 4, + 6, + 2, + 2264.581952, + "AWQ" + ], + [ + 4, + 6, + 3, + 2579.246912, + "AWQ" + ], + [ + 4, + 6, + 4, + 2893.911872, + "AWQ" + ], + [ + 4, + 6, + 5, + 3208.576832, + "AWQ" + ], + [ + 4, + 6, + 6, + 3523.241792, + "AWQ" + ], + [ + 4, + 6, + 8, + 4152.571712, + "AWQ" + ], + [ + 4, + 6, + 16, + 6669.891392, + "AWQ" + ], + [ + 4, + 8, + 2, + 2290.854464, + "AWQ" + ], + [ + 4, + 8, + 3, + 2605.519424, + "AWQ" + ], + [ + 4, + 8, + 4, + 2920.184384, + "AWQ" + ], + [ + 4, + 8, + 5, + 3234.849344, + "AWQ" + ], + [ + 4, + 8, + 6, + 3549.514304, + "AWQ" + ], + [ + 4, + 8, + 8, + 4178.844224, + "AWQ" + ], + [ + 4, + 8, + 16, + 6696.163904, + "AWQ" + ], + [ + 4, + 16, + 2, + 2395.944512, + "AWQ" + ], + [ + 4, + 16, + 3, + 2710.609472, + "AWQ" + ], + [ + 4, + 16, + 4, + 3025.274432, + "AWQ" + ], + [ + 4, + 16, + 5, + 3339.939392, + "AWQ" + ], + [ + 4, + 16, + 6, + 3654.604352, + "AWQ" + ], + [ + 4, + 16, + 8, + 4283.934272, + "AWQ" + ], + [ + 4, + 16, + 16, + 6801.253952, + "AWQ" + ], + [ + 5, + 2, + 2, + 2335.10408, + "AWQ" + ], + [ + 5, + 2, + 3, + 2649.76904, + "AWQ" + ], + [ + 5, + 2, + 4, + 2964.434, + "AWQ" + ], + [ + 5, + 2, + 5, + 3279.09896, + "AWQ" + ], + [ + 5, + 2, + 6, + 3593.76392, + "AWQ" + ], + [ + 5, + 2, + 8, + 4223.09384, + "AWQ" + ], + [ + 5, + 2, + 16, + 6740.41352, + "AWQ" + ], + [ + 5, + 3, + 2, + 2348.240336, + "AWQ" + ], + [ + 5, + 3, + 3, + 2662.905296, + "AWQ" + ], + [ + 5, + 3, + 4, + 2977.570256, + "AWQ" + ], + [ + 5, + 3, + 5, + 3292.235216, + "AWQ" + ], + [ + 5, + 3, + 6, + 3606.900176, + "AWQ" + ], + [ + 5, + 3, + 8, + 4236.230096, + "AWQ" + ], + [ + 5, + 3, + 16, + 6753.549776, + "AWQ" + ], + [ + 5, + 4, + 2, + 2361.376592, + "AWQ" + ], + [ + 5, + 4, + 3, + 2676.041552, + "AWQ" + ], + [ + 5, + 4, + 4, + 2990.706512, + "AWQ" + ], + [ + 5, + 4, + 5, + 3305.371472, + "AWQ" + ], + [ + 5, + 4, + 6, + 3620.036432, + "AWQ" + ], + [ + 5, + 4, + 8, + 4249.366352, + "AWQ" + ], + [ + 5, + 4, + 16, + 6766.686032, + "AWQ" + ], + [ + 5, + 5, + 2, + 2374.512848, + "AWQ" + ], + [ + 5, + 5, + 3, + 2689.177808, + "AWQ" + ], + [ + 5, + 5, + 4, + 3003.842768, + "AWQ" + ], + [ + 5, + 5, + 5, + 3318.507728, + "AWQ" + ], + [ + 5, + 5, + 6, + 3633.172688, + "AWQ" + ], + [ + 5, + 5, + 8, + 4262.502608, + "AWQ" + ], + [ + 5, + 5, + 16, + 6779.822288, + "AWQ" + ], + [ + 5, + 6, + 2, + 2387.649104, + "AWQ" + ], + [ + 5, + 6, + 3, + 2702.314064, + "AWQ" + ], + [ + 5, + 6, + 4, + 3016.979024, + "AWQ" + ], + [ + 5, + 6, + 5, + 3331.643984, + "AWQ" + ], + [ + 5, + 6, + 6, + 3646.308944, + "AWQ" + ], + [ + 5, + 6, + 8, + 4275.638864, + "AWQ" + ], + [ + 5, + 6, + 16, + 6792.958544, + "AWQ" + ], + [ + 5, + 8, + 2, + 2413.921616, + "AWQ" + ], + [ + 5, + 8, + 3, + 2728.586576, + "AWQ" + ], + [ + 5, + 8, + 4, + 3043.251536, + "AWQ" + ], + [ + 5, + 8, + 5, + 3357.916496, + "AWQ" + ], + [ + 5, + 8, + 6, + 3672.581456, + "AWQ" + ], + [ + 5, + 8, + 8, + 4301.911376, + "AWQ" + ], + [ + 5, + 8, + 16, + 6819.231056, + "AWQ" + ], + [ + 5, + 16, + 2, + 2519.011664, + "AWQ" + ], + [ + 5, + 16, + 3, + 2833.676624, + "AWQ" + ], + [ + 5, + 16, + 4, + 3148.341584, + "AWQ" + ], + [ + 5, + 16, + 5, + 3463.006544, + "AWQ" + ], + [ + 5, + 16, + 6, + 3777.671504, + "AWQ" + ], + [ + 5, + 16, + 8, + 4407.001424, + "AWQ" + ], + [ + 5, + 16, + 16, + 6924.321104, + "AWQ" + ], + [ + 6, + 2, + 2, + 2458.171232, + "AWQ" + ], + [ + 6, + 2, + 3, + 2772.836192, + "AWQ" + ], + [ + 6, + 2, + 4, + 3087.501152, + "AWQ" + ], + [ + 6, + 2, + 5, + 3402.166112, + "AWQ" + ], + [ + 6, + 2, + 6, + 3716.831072, + "AWQ" + ], + [ + 6, + 2, + 8, + 4346.160992, + "AWQ" + ], + [ + 6, + 2, + 16, + 6863.480672, + "AWQ" + ], + [ + 6, + 3, + 2, + 2471.307488, + "AWQ" + ], + [ + 6, + 3, + 3, + 2785.972448, + "AWQ" + ], + [ + 6, + 3, + 4, + 3100.637408, + "AWQ" + ], + [ + 6, + 3, + 5, + 3415.302368, + "AWQ" + ], + [ + 6, + 3, + 6, + 3729.967328, + "AWQ" + ], + [ + 6, + 3, + 8, + 4359.297248, + "AWQ" + ], + [ + 6, + 3, + 16, + 6876.616928, + "AWQ" + ], + [ + 6, + 4, + 2, + 2484.443744, + "AWQ" + ], + [ + 6, + 4, + 3, + 2799.108704, + "AWQ" + ], + [ + 6, + 4, + 4, + 3113.773664, + "AWQ" + ], + [ + 6, + 4, + 5, + 3428.438624, + "AWQ" + ], + [ + 6, + 4, + 6, + 3743.103584, + "AWQ" + ], + [ + 6, + 4, + 8, + 4372.433504, + "AWQ" + ], + [ + 6, + 4, + 16, + 6889.753184, + "AWQ" + ], + [ + 6, + 5, + 2, + 2497.58, + "AWQ" + ], + [ + 6, + 5, + 3, + 2812.24496, + "AWQ" + ], + [ + 6, + 5, + 4, + 3126.90992, + "AWQ" + ], + [ + 6, + 5, + 5, + 3441.57488, + "AWQ" + ], + [ + 6, + 5, + 6, + 3756.23984, + "AWQ" + ], + [ + 6, + 5, + 8, + 4385.56976, + "AWQ" + ], + [ + 6, + 5, + 16, + 6902.88944, + "AWQ" + ], + [ + 6, + 6, + 2, + 2510.716256, + "AWQ" + ], + [ + 6, + 6, + 3, + 2825.381216, + "AWQ" + ], + [ + 6, + 6, + 4, + 3140.046176, + "AWQ" + ], + [ + 6, + 6, + 5, + 3454.711136, + "AWQ" + ], + [ + 6, + 6, + 6, + 3769.376096, + "AWQ" + ], + [ + 6, + 6, + 8, + 4398.706016, + "AWQ" + ], + [ + 6, + 6, + 16, + 6916.025696, + "AWQ" + ], + [ + 6, + 8, + 2, + 2536.988768, + "AWQ" + ], + [ + 6, + 8, + 3, + 2851.653728, + "AWQ" + ], + [ + 6, + 8, + 4, + 3166.318688, + "AWQ" + ], + [ + 6, + 8, + 5, + 3480.983648, + "AWQ" + ], + [ + 6, + 8, + 6, + 3795.648608, + "AWQ" + ], + [ + 6, + 8, + 8, + 4424.978528, + "AWQ" + ], + [ + 6, + 8, + 16, + 6942.298208, + "AWQ" + ], + [ + 6, + 16, + 2, + 2642.078816, + "AWQ" + ], + [ + 6, + 16, + 3, + 2956.743776, + "AWQ" + ], + [ + 6, + 16, + 4, + 3271.408736, + "AWQ" + ], + [ + 6, + 16, + 5, + 3586.073696, + "AWQ" + ], + [ + 6, + 16, + 6, + 3900.738656, + "AWQ" + ], + [ + 6, + 16, + 8, + 4530.068576, + "AWQ" + ], + [ + 6, + 16, + 16, + 7047.388256, + "AWQ" + ], + [ + 8, + 2, + 2, + 2704.305536, + "AWQ" + ], + [ + 8, + 2, + 3, + 3018.970496, + "AWQ" + ], + [ + 8, + 2, + 4, + 3333.635456, + "AWQ" + ], + [ + 8, + 2, + 5, + 3648.300416, + "AWQ" + ], + [ + 8, + 2, + 6, + 3962.965376, + "AWQ" + ], + [ + 8, + 2, + 8, + 4592.295296, + "AWQ" + ], + [ + 8, + 2, + 16, + 7109.614976, + "AWQ" + ], + [ + 8, + 3, + 2, + 2717.441792, + "AWQ" + ], + [ + 8, + 3, + 3, + 3032.106752, + "AWQ" + ], + [ + 8, + 3, + 4, + 3346.771712, + "AWQ" + ], + [ + 8, + 3, + 5, + 3661.436672, + "AWQ" + ], + [ + 8, + 3, + 6, + 3976.101632, + "AWQ" + ], + [ + 8, + 3, + 8, + 4605.431552, + "AWQ" + ], + [ + 8, + 3, + 16, + 7122.751232, + "AWQ" + ], + [ + 8, + 4, + 2, + 2730.578048, + "AWQ" + ], + [ + 8, + 4, + 3, + 3045.243008, + "AWQ" + ], + [ + 8, + 4, + 4, + 3359.907968, + "AWQ" + ], + [ + 8, + 4, + 5, + 3674.572928, + "AWQ" + ], + [ + 8, + 4, + 6, + 3989.237888, + "AWQ" + ], + [ + 8, + 4, + 8, + 4618.567808, + "AWQ" + ], + [ + 8, + 4, + 16, + 7135.887488, + "AWQ" + ], + [ + 8, + 5, + 2, + 2743.714304, + "AWQ" + ], + [ + 8, + 5, + 3, + 3058.379264, + "AWQ" + ], + [ + 8, + 5, + 4, + 3373.044224, + "AWQ" + ], + [ + 8, + 5, + 5, + 3687.709184, + "AWQ" + ], + [ + 8, + 5, + 6, + 4002.374144, + "AWQ" + ], + [ + 8, + 5, + 8, + 4631.704064, + "AWQ" + ], + [ + 8, + 5, + 16, + 7149.023744, + "AWQ" + ], + [ + 8, + 6, + 2, + 2756.85056, + "AWQ" + ], + [ + 8, + 6, + 3, + 3071.51552, + "AWQ" + ], + [ + 8, + 6, + 4, + 3386.18048, + "AWQ" + ], + [ + 8, + 6, + 5, + 3700.84544, + "AWQ" + ], + [ + 8, + 6, + 6, + 4015.5104, + "AWQ" + ], + [ + 8, + 6, + 8, + 4644.84032, + "AWQ" + ], + [ + 8, + 6, + 16, + 7162.16, + "AWQ" + ], + [ + 8, + 8, + 2, + 2783.123072, + "AWQ" + ], + [ + 8, + 8, + 3, + 3097.788032, + "AWQ" + ], + [ + 8, + 8, + 4, + 3412.452992, + "AWQ" + ], + [ + 8, + 8, + 5, + 3727.117952, + "AWQ" + ], + [ + 8, + 8, + 6, + 4041.782912, + "AWQ" + ], + [ + 8, + 8, + 8, + 4671.112832, + "AWQ" + ], + [ + 8, + 8, + 16, + 7188.432512, + "AWQ" + ], + [ + 8, + 16, + 2, + 2888.21312, + "AWQ" + ], + [ + 8, + 16, + 3, + 3202.87808, + "AWQ" + ], + [ + 8, + 16, + 4, + 3517.54304, + "AWQ" + ], + [ + 8, + 16, + 5, + 3832.208, + "AWQ" + ], + [ + 8, + 16, + 6, + 4146.87296, + "AWQ" + ], + [ + 8, + 16, + 8, + 4776.20288, + "AWQ" + ], + [ + 8, + 16, + 16, + 7293.52256, + "AWQ" + ], + [ + 16, + 2, + 2, + 3688.842752, + "AWQ" + ], + [ + 16, + 2, + 3, + 4003.507712, + "AWQ" + ], + [ + 16, + 2, + 4, + 4318.172672, + "AWQ" + ], + [ + 16, + 2, + 5, + 4632.837632, + "AWQ" + ], + [ + 16, + 2, + 6, + 4947.502592, + "AWQ" + ], + [ + 16, + 2, + 8, + 5576.832512, + "AWQ" + ], + [ + 16, + 2, + 16, + 8094.152192, + "AWQ" + ], + [ + 16, + 3, + 2, + 3701.979008, + "AWQ" + ], + [ + 16, + 3, + 3, + 4016.643968, + "AWQ" + ], + [ + 16, + 3, + 4, + 4331.308928, + "AWQ" + ], + [ + 16, + 3, + 5, + 4645.973888, + "AWQ" + ], + [ + 16, + 3, + 6, + 4960.638848, + "AWQ" + ], + [ + 16, + 3, + 8, + 5589.968768, + "AWQ" + ], + [ + 16, + 3, + 16, + 8107.288448, + "AWQ" + ], + [ + 16, + 4, + 2, + 3715.115264, + "AWQ" + ], + [ + 16, + 4, + 3, + 4029.780224, + "AWQ" + ], + [ + 16, + 4, + 4, + 4344.445184, + "AWQ" + ], + [ + 16, + 4, + 5, + 4659.110144, + "AWQ" + ], + [ + 16, + 4, + 6, + 4973.775104, + "AWQ" + ], + [ + 16, + 4, + 8, + 5603.105024, + "AWQ" + ], + [ + 16, + 4, + 16, + 8120.424704, + "AWQ" + ], + [ + 16, + 5, + 2, + 3728.25152, + "AWQ" + ], + [ + 16, + 5, + 3, + 4042.91648, + "AWQ" + ], + [ + 16, + 5, + 4, + 4357.58144, + "AWQ" + ], + [ + 16, + 5, + 5, + 4672.2464, + "AWQ" + ], + [ + 16, + 5, + 6, + 4986.91136, + "AWQ" + ], + [ + 16, + 5, + 8, + 5616.24128, + "AWQ" + ], + [ + 16, + 5, + 16, + 8133.56096, + "AWQ" + ], + [ + 16, + 6, + 2, + 3741.387776, + "AWQ" + ], + [ + 16, + 6, + 3, + 4056.052736, + "AWQ" + ], + [ + 16, + 6, + 4, + 4370.717696, + "AWQ" + ], + [ + 16, + 6, + 5, + 4685.382656, + "AWQ" + ], + [ + 16, + 6, + 6, + 5000.047616, + "AWQ" + ], + [ + 16, + 6, + 8, + 5629.377536, + "AWQ" + ], + [ + 16, + 6, + 16, + 8146.697216, + "AWQ" + ], + [ + 16, + 8, + 2, + 3767.660288, + "AWQ" + ], + [ + 16, + 8, + 3, + 4082.325248, + "AWQ" + ], + [ + 16, + 8, + 4, + 4396.990208, + "AWQ" + ], + [ + 16, + 8, + 5, + 4711.655168, + "AWQ" + ], + [ + 16, + 8, + 6, + 5026.320128, + "AWQ" + ], + [ + 16, + 8, + 8, + 5655.650048, + "AWQ" + ], + [ + 16, + 8, + 16, + 8172.969728, + "AWQ" + ], + [ + 16, + 16, + 2, + 3872.750336, + "AWQ" + ], + [ + 16, + 16, + 3, + 4187.415296, + "AWQ" + ], + [ + 16, + 16, + 4, + 4502.080256, + "AWQ" + ], + [ + 16, + 16, + 5, + 4816.745216, + "AWQ" + ], + [ + 16, + 16, + 6, + 5131.410176, + "AWQ" + ], + [ + 16, + 16, + 8, + 5760.740096, + "AWQ" + ], + [ + 16, + 16, + 16, + 8278.059776, + "AWQ" + ] + ], + "hovertemplate": "quant_type=%{customdata[4]}
meteor=%{x}
cider=%{y}
vit_bits=%{customdata[0]}
qformer_bits=%{customdata[1]}
llm_bits=%{customdata[2]}
model_size=%{customdata[3]}", + "legendgroup": "AWQ", + "marker": { + "color": "#636efa", + "line": { + "color": "DarkSlateGrey", + "width": 2 + }, + "symbol": "circle" + }, + "mode": "markers", + "name": "AWQ", + "opacity": 0.75, + "orientation": "v", + "showlegend": true, + "type": "scatter", + "x": [ + 0.0298844811343493, + 0.1498566138247552, + 0.1837345249958186, + 0.1886596236812807, + 0.1921587687164518, + 0.1935281864323662, + 0.1931800073082082, + 0.0248888129640967, + 0.1536184976125826, + 0.192547252681466, + 0.1971479719875085, + 0.2053801049843051, + 0.2062730261379436, + 0.2082902403750201, + 0.029512816604721, + 0.1537266011841024, + 0.1964836864850164, + 0.1998364948737692, + 0.2067698934744963, + 0.2085390794369036, + 0.2096374717583629, + 0.0287472582273225, + 0.156545753956854, + 0.196610560416409, + 0.201813096512405, + 0.208814901329159, + 0.2111062432632992, + 0.2119571593229862, + 0.030631103913381, + 0.1535624775179599, + 0.1975830578073438, + 0.2007745793399482, + 0.2084986314559541, + 0.2098694607988712, + 0.2104895431279897, + 0.0294354848117307, + 0.1568363211152181, + 0.1971530070668118, + 0.2010881485961166, + 0.2085898952116036, + 0.210553933277872, + 0.2111149150856989, + 0.0295098771216461, + 0.1569115889624636, + 0.1972734524909803, + 0.2010642391090116, + 0.2086024048502772, + 0.2105720489139707, + 0.2109831741881737, + 0.0318570369375779, + 0.200290499904622, + 0.2484356580168853, + 0.2553551850460575, + 0.2613741243634884, + 0.2592091197853552, + 0.2599854702694635, + 0.0340323417585065, + 0.211386366794054, + 0.2650228454047996, + 0.269467141951613, + 0.2760879124909304, + 0.2756596662937646, + 0.2764047060750114, + 0.037105866072279, + 0.2145169750500711, + 0.2702844643529473, + 0.2758450990378426, + 0.2815846450851879, + 0.2833953779681455, + 0.284394954409976, + 0.0368190399609676, + 0.2124203546308737, + 0.2685017116853036, + 0.274927098888206, + 0.2808771294802629, + 0.2823379329904332, + 0.2838621056433481, + 0.0383622741752441, + 0.2142736314265119, + 0.2701122984332231, + 0.2746066954112424, + 0.2806354693035315, + 0.2836407999143544, + 0.2843478419605554, + 0.0381034374915156, + 0.2137015712190609, + 0.2695374408750584, + 0.274847085121946, + 0.2809750487176385, + 0.2836878203740132, + 0.2845303570314229, + 0.0379989723419776, + 0.2134457657549776, + 0.2693022523433621, + 0.2749129889020977, + 0.2807118708799256, + 0.2836549420341586, + 0.2845363215102724, + 0.035544052024392, + 0.2007969629020746, + 0.2502237705978827, + 0.2552756428745414, + 0.2617311169696272, + 0.2627561602762712, + 0.2626553112263047, + 0.0379276645844104, + 0.2110103186531865, + 0.2663107277629026, + 0.2701614225309509, + 0.2764764962597095, + 0.2767816837784037, + 0.2781134180397822, + 0.0386476453114738, + 0.211396202735535, + 0.2696296967613541, + 0.2743049084227653, + 0.2820665632442025, + 0.2837285042274702, + 0.2840855765190654, + 0.0381667985097088, + 0.2080054961135729, + 0.2684271879621022, + 0.2741510572230004, + 0.28128887015849, + 0.2823284639324399, + 0.2831020228799503, + 0.0394724651233021, + 0.2084417327029731, + 0.2682092450981378, + 0.2741500493132533, + 0.2811586868552443, + 0.281782607048575, + 0.2832089336877907, + 0.0393283261447833, + 0.2109298913715551, + 0.2686142296604581, + 0.2742289723913308, + 0.2816999624424486, + 0.282945662860569, + 0.2839275915866569, + 0.0398707834609076, + 0.2107603494156979, + 0.268773824997628, + 0.2742171149298761, + 0.2818531035064126, + 0.2830473462320595, + 0.2837925399714376, + 0.0353956864490417, + 0.1985754713591016, + 0.2488564578980053, + 0.2526562430118402, + 0.2603846095480853, + 0.2591615425924026, + 0.2597450882896447, + 0.0385283500594566, + 0.2091942895320583, + 0.2635935671043616, + 0.2668454963794412, + 0.2730030358969137, + 0.2736482751434616, + 0.2742002799959992, + 0.0383106603039449, + 0.2094568088398009, + 0.2681996749926065, + 0.271236873072046, + 0.2795632422324446, + 0.2807453214549563, + 0.2821228770619992, + 0.0388129039893688, + 0.2082785550657938, + 0.2657672271375958, + 0.270453762144429, + 0.2785285750157541, + 0.279735734830095, + 0.2810065371559589, + 0.0393763005359751, + 0.2088112702466963, + 0.2660535139642068, + 0.2695669266977705, + 0.2783926218102539, + 0.2794284812688384, + 0.2805120454493048, + 0.0392876594512647, + 0.2050535892981623, + 0.2671505802790982, + 0.2700647922767295, + 0.2791323330204765, + 0.2804314117622675, + 0.2814720090702979, + 0.0393572872875101, + 0.2085765644422946, + 0.266504476970306, + 0.269994073154192, + 0.279470411592239, + 0.2800480375813525, + 0.2814890625402405, + 0.0336890452471779, + 0.1995702866972476, + 0.2475822019731219, + 0.2529732771470224, + 0.2598567516851389, + 0.2592645753079949, + 0.259796239724251, + 0.0388616898041331, + 0.2089483604012564, + 0.2631136294006772, + 0.2660299262295151, + 0.2734671202303975, + 0.2739312710808929, + 0.2754884444805165, + 0.0388137084027831, + 0.2079392854106619, + 0.2657751126886337, + 0.2694405705313831, + 0.2787137627446484, + 0.2799813598846214, + 0.2803946617343799, + 0.0391573268763807, + 0.2067279626401582, + 0.2646674838482865, + 0.2692422909529576, + 0.2777006695173449, + 0.2782405905869657, + 0.279889958988931, + 0.0397439797438703, + 0.2076556413002826, + 0.2645534527788851, + 0.2693868110143037, + 0.2769359695011477, + 0.2774697283891806, + 0.2791530049253948, + 0.0395022218476354, + 0.2071426042231885, + 0.2656920793969223, + 0.2694899075089131, + 0.2777264614200301, + 0.2782638599429032, + 0.2795140452687747, + 0.0395439860631808, + 0.2072207189909281, + 0.2655337977316027, + 0.2697323199765623, + 0.2778688652580702, + 0.2783466182494372, + 0.2795925000876874, + 0.0344184962623263, + 0.1971738251491923, + 0.2466986404445021, + 0.2504880541245433, + 0.2584898808700203, + 0.2581235372562963, + 0.2585108705115669, + 0.038869927644611, + 0.2047447557475626, + 0.2604113984761954, + 0.2637886450865388, + 0.2702609965385433, + 0.2710305582860595, + 0.2724762395803599, + 0.038960864382997, + 0.2046153902705287, + 0.2637425407875613, + 0.2682072256800668, + 0.2754944544086485, + 0.2762420416035082, + 0.2770427807061468, + 0.0389313503036774, + 0.2033676800916742, + 0.2616939778335936, + 0.2672903069142515, + 0.2750725549338091, + 0.2755511614969632, + 0.2764419231604895, + 0.0396099296866685, + 0.205098432856594, + 0.2623525911856502, + 0.2673520382984019, + 0.2750965863700415, + 0.2748874626975029, + 0.2761863210938283, + 0.0396217419792944, + 0.2042084040832557, + 0.2629765025163644, + 0.2677249093127373, + 0.2753843775356539, + 0.2757355870394247, + 0.2771055575136951, + 0.0395425541939407, + 0.2042149818919491, + 0.2629573909208676, + 0.2677163506866694, + 0.2754273250795791, + 0.2756801593566381, + 0.2769747471271154, + 0.0339736319477422, + 0.1997526555007177, + 0.2485642223938605, + 0.2530323290592932, + 0.2602453575218017, + 0.2600204675274952, + 0.2606048626793487, + 0.0386787199628869, + 0.209854527423061, + 0.2636281741650799, + 0.2669442215387785, + 0.2740336787379113, + 0.274623848151302, + 0.276008215745985, + 0.0385673224488549, + 0.2096938611804471, + 0.2667684943363454, + 0.2711871713179565, + 0.279923563615494, + 0.2813984210754993, + 0.2821924168036456, + 0.0391234610100254, + 0.2079609378623707, + 0.2656879108724494, + 0.269967086318697, + 0.2787494875664804, + 0.2794980179320134, + 0.2806930762856469, + 0.0396840021663625, + 0.2085110100872384, + 0.2657052362517834, + 0.2705499354714129, + 0.2782112952274305, + 0.2794412813041066, + 0.2810511926614303, + 0.039681991779321, + 0.2082029517836936, + 0.2666286945660135, + 0.2707370093385092, + 0.278836459538448, + 0.2802291096727847, + 0.2811955411594185, + 0.039602095057516, + 0.2080132739451042, + 0.2664132479204438, + 0.2708655101118942, + 0.2789889072587355, + 0.280146845145009, + 0.2812820762161627 + ], + "xaxis": "x", + "y": [ + 0.0007902168927993, + 0.3892738924855776, + 0.5443518230890101, + 0.577806426477473, + 0.5940621735445684, + 0.6015116071758554, + 0.6016201760679405, + 0.0017409187992352, + 0.4199558355788407, + 0.6413107364012529, + 0.6644740132743288, + 0.7005174822474439, + 0.7060009487248382, + 0.7114414564035497, + 0.0015242110097923, + 0.4225848027587028, + 0.6677658754857135, + 0.6794140389055991, + 0.7158724879010914, + 0.7239213616454379, + 0.7267086690055766, + 0.0015383744706068, + 0.4342188097083193, + 0.6643003856622003, + 0.6880985619516059, + 0.7235377941797142, + 0.7375435379366277, + 0.7375809567131982, + 0.0020275794823421, + 0.424523049292371, + 0.6760507495669246, + 0.6885736800597454, + 0.7263028445719076, + 0.7315217945479447, + 0.7339511837416856, + 0.0016666316965743, + 0.4377116832400026, + 0.6719258108942063, + 0.6872950083608445, + 0.7246423668329751, + 0.7350918293335355, + 0.7362575171829725, + 0.0016837862661927, + 0.4362796051124896, + 0.6727059028707632, + 0.6882361624647443, + 0.7246993426668064, + 0.734900136126526, + 0.7357684637386981, + 0.0022093327972838, + 0.7266863329828995, + 1.0235981557690623, + 1.0729311908769703, + 1.1194833585777608, + 1.1086451337916028, + 1.1134807950614616, + 0.0045409306490616, + 0.8051281610518605, + 1.156028455134381, + 1.1923197774896417, + 1.2347054039266734, + 1.2272202650647102, + 1.2313846308384797, + 0.0034123873884078, + 0.8208565360141244, + 1.179401408578962, + 1.2266779752864407, + 1.2572691461851668, + 1.26358895663258, + 1.2673159046347733, + 0.0037738532167631, + 0.8106225186823535, + 1.1758572483909744, + 1.224376698596568, + 1.2613331346868195, + 1.261397607201865, + 1.2687618310593374, + 0.0040725416524024, + 0.8236695721105687, + 1.1856491875247703, + 1.221351870627707, + 1.259917659736496, + 1.2663044193020636, + 1.2708232038912224, + 0.0040333432466256, + 0.8190620216054207, + 1.1808923852510342, + 1.225699556061882, + 1.260754669664881, + 1.2666116687606168, + 1.2737401966926578, + 0.0040061661415185, + 0.8184339490345491, + 1.180122405297351, + 1.226209348185821, + 1.2599670609394376, + 1.268297667693004, + 1.2752890935916952, + 0.0020596108397078, + 0.7295886421565306, + 1.0417863722989882, + 1.089604655273231, + 1.1243699092711588, + 1.1321620826054106, + 1.1325403602869566, + 0.0047518175496339, + 0.8002507737119463, + 1.1676678353269516, + 1.201189986792182, + 1.2413131851952102, + 1.2364159506260926, + 1.246123613154921, + 0.00413023189484, + 0.8040498221201324, + 1.1818920290369954, + 1.2187256913286244, + 1.2693462940552265, + 1.26753571103515, + 1.2679401319860708, + 0.0041804297547297, + 0.7936760412704565, + 1.1769199968051365, + 1.2177552194123182, + 1.269146453283604, + 1.2661784066985244, + 1.2694800484392854, + 0.0044968740608802, + 0.7959372573564164, + 1.1741282222425378, + 1.21868239273846, + 1.2662373768159405, + 1.25967838492472, + 1.2677578930557416, + 0.0045337305938802, + 0.8051917158643872, + 1.1791031207801064, + 1.2171637489140836, + 1.269949088841136, + 1.2685095577773473, + 1.2725369264593078, + 0.0044883259869472, + 0.8049525065074827, + 1.1793997074725706, + 1.217838638892368, + 1.270626685213267, + 1.268790043613231, + 1.2723116809585964, + 0.0032646989621902, + 0.7180181623782547, + 1.032962494645136, + 1.070797712638038, + 1.1182626611582889, + 1.1127242409207423, + 1.115818110946458, + 0.0050442796553735, + 0.7879097938908757, + 1.148548672419358, + 1.1764723409323512, + 1.2155479022667492, + 1.2161199154649738, + 1.2207863371539902, + 0.00418241095192, + 0.7941648429911843, + 1.1692669018889823, + 1.1960020285283903, + 1.249905599427292, + 1.2512046604157985, + 1.2581493339973435, + 0.0044147346478681, + 0.7886177826050037, + 1.1574830881644504, + 1.1903072050381271, + 1.248833370994669, + 1.2505910698213745, + 1.2559031668672331, + 0.0047618934480426, + 0.7933442754900087, + 1.1612856969921397, + 1.1875149732346562, + 1.2446303376282275, + 1.247600525639272, + 1.252184355894695, + 0.0047157077171726, + 0.7715007986326152, + 1.167161012774189, + 1.189471154011835, + 1.2486870632772915, + 1.2538222735773836, + 1.2590312903179968, + 0.0046999911219963, + 0.7910105903131062, + 1.1645621691200578, + 1.18911306239116, + 1.2499655271501364, + 1.250759578071996, + 1.2574020173423823, + 0.0021355148766812, + 0.7264676632473416, + 1.0232920884230814, + 1.0704198052787794, + 1.1160500908441646, + 1.1131496180075628, + 1.1176110659095753, + 0.0051168105421359, + 0.78766789683324, + 1.1458078798333984, + 1.1743586936521029, + 1.2184402901688431, + 1.213608212193691, + 1.2253157599692892, + 0.0043727412603138, + 0.7863488675261142, + 1.154364944176871, + 1.1868886054730516, + 1.2440943544657643, + 1.246904304985102, + 1.2495600801266238, + 0.0045285080121281, + 0.7822532071076522, + 1.146621888681412, + 1.182464151264467, + 1.239097227163636, + 1.2374097592485436, + 1.2451443958139716, + 0.0047738714292881, + 0.7869627504029372, + 1.1448253129962436, + 1.184992736929933, + 1.2359611482185155, + 1.2345152710805227, + 1.2410981814756032, + 0.0047109274697495, + 0.7831441749206127, + 1.1532797092887277, + 1.1870631294646563, + 1.2378950190538096, + 1.2396680301427143, + 1.246428397197772, + 0.0047107078941073, + 0.7846150320891636, + 1.151739526032029, + 1.1888011112017192, + 1.2381501818428504, + 1.2406648192914698, + 1.2469064852182756, + 0.0020926826069756, + 0.7116309448572241, + 1.0096640742263971, + 1.051832240994192, + 1.1031148303323677, + 1.0996036917317866, + 1.1026112693765862, + 0.0052766501668749, + 0.7587296632897987, + 1.1278018779745882, + 1.1535606055527945, + 1.1953810071788358, + 1.194933849208372, + 1.2026468908277963, + 0.004082857671826, + 0.760952128204789, + 1.1428027133910008, + 1.1751184948781388, + 1.222517587510634, + 1.223679807266021, + 1.2239900971983946, + 0.0043467023862614, + 0.7570584897619376, + 1.1319068641144796, + 1.1726682788050742, + 1.2251675741742547, + 1.2213806795583368, + 1.2248409116679573, + 0.0046426558906571, + 0.7657076774627696, + 1.135895470847944, + 1.1721543852482914, + 1.2232652165288425, + 1.217991990088048, + 1.2256323022273348, + 0.0046413590593211, + 0.7609812697499186, + 1.1398554135838508, + 1.1744269293359073, + 1.2246538612050697, + 1.2223636948702057, + 1.2289172931774284, + 0.0047027927153682, + 0.7611484990145555, + 1.1390067988226158, + 1.1752137486977985, + 1.224003727431766, + 1.221797226097161, + 1.2286821685059492, + 0.002116580094303, + 0.7274376374824795, + 1.0252398540053982, + 1.0706442589700385, + 1.1196220237774297, + 1.117247765253773, + 1.1214114229504837, + 0.0050311480090812, + 0.7920462665699586, + 1.1504623141490604, + 1.1773655292386451, + 1.2227150746235125, + 1.2225423326577731, + 1.2288258774986205, + 0.0043014909664417, + 0.7959052930969952, + 1.1621039256838672, + 1.196929399809687, + 1.253046170170458, + 1.256735727756202, + 1.260975312624356, + 0.0043702656502771, + 0.7879401066468005, + 1.1556677145197871, + 1.1893109363512375, + 1.245958819839066, + 1.2457846506159769, + 1.2528649424602132, + 0.0046449400319948, + 0.7919767269675149, + 1.1567217842367483, + 1.1935809173708705, + 1.2420703577665526, + 1.2436823085127202, + 1.2516247225738375, + 0.0045807829907469, + 0.7895426734487146, + 1.164603641736421, + 1.193852243546073, + 1.2451381600762323, + 1.2495830587956385, + 1.253045592341763, + 0.0045991273538401, + 0.7887153055212033, + 1.1638367480835288, + 1.1950595426281183, + 1.245283436795488, + 1.2493832157443017, + 1.254197658273608 + ], + "yaxis": "y" + }, + { + "customdata": [ + [ + 2, + 2, + 3, + null, + "GPTQ" + ], + [ + 2, + 2, + 4, + null, + "GPTQ" + ], + [ + 2, + 2, + 5, + null, + "GPTQ" + ], + [ + 2, + 2, + 6, + null, + "GPTQ" + ], + [ + 2, + 2, + 7, + null, + "GPTQ" + ], + [ + 2, + 2, + 8, + null, + "GPTQ" + ], + [ + 2, + 2, + 16, + null, + "GPTQ" + ], + [ + 2, + 3, + 3, + null, + "GPTQ" + ], + [ + 2, + 3, + 4, + null, + "GPTQ" + ], + [ + 2, + 3, + 5, + null, + "GPTQ" + ], + [ + 2, + 3, + 6, + null, + "GPTQ" + ], + [ + 2, + 3, + 7, + null, + "GPTQ" + ], + [ + 2, + 3, + 8, + null, + "GPTQ" + ], + [ + 2, + 3, + 16, + null, + "GPTQ" + ], + [ + 2, + 4, + 3, + null, + "GPTQ" + ], + [ + 2, + 4, + 4, + null, + "GPTQ" + ], + [ + 2, + 4, + 5, + null, + "GPTQ" + ], + [ + 2, + 4, + 6, + null, + "GPTQ" + ], + [ + 2, + 4, + 7, + null, + "GPTQ" + ], + [ + 2, + 4, + 8, + null, + "GPTQ" + ], + [ + 2, + 4, + 16, + null, + "GPTQ" + ], + [ + 2, + 5, + 3, + null, + "GPTQ" + ], + [ + 2, + 5, + 4, + null, + "GPTQ" + ], + [ + 2, + 5, + 5, + null, + "GPTQ" + ], + [ + 2, + 5, + 6, + null, + "GPTQ" + ], + [ + 2, + 5, + 7, + null, + "GPTQ" + ], + [ + 2, + 5, + 8, + null, + "GPTQ" + ], + [ + 2, + 5, + 16, + null, + "GPTQ" + ], + [ + 2, + 6, + 3, + null, + "GPTQ" + ], + [ + 2, + 6, + 4, + null, + "GPTQ" + ], + [ + 2, + 6, + 5, + null, + "GPTQ" + ], + [ + 2, + 6, + 6, + null, + "GPTQ" + ], + [ + 2, + 6, + 7, + null, + "GPTQ" + ], + [ + 2, + 6, + 8, + null, + "GPTQ" + ], + [ + 2, + 6, + 16, + null, + "GPTQ" + ], + [ + 2, + 7, + 3, + null, + "GPTQ" + ], + [ + 2, + 7, + 4, + null, + "GPTQ" + ], + [ + 2, + 7, + 5, + null, + "GPTQ" + ], + [ + 2, + 7, + 6, + null, + "GPTQ" + ], + [ + 2, + 7, + 7, + null, + "GPTQ" + ], + [ + 2, + 7, + 8, + null, + "GPTQ" + ], + [ + 2, + 7, + 16, + null, + "GPTQ" + ], + [ + 2, + 8, + 3, + null, + "GPTQ" + ], + [ + 2, + 8, + 4, + null, + "GPTQ" + ], + [ + 2, + 8, + 5, + null, + "GPTQ" + ], + [ + 2, + 8, + 6, + null, + "GPTQ" + ], + [ + 2, + 8, + 7, + null, + "GPTQ" + ], + [ + 2, + 8, + 8, + null, + "GPTQ" + ], + [ + 2, + 8, + 16, + null, + "GPTQ" + ], + [ + 2, + 16, + 3, + null, + "GPTQ" + ], + [ + 2, + 16, + 4, + null, + "GPTQ" + ], + [ + 2, + 16, + 5, + null, + "GPTQ" + ], + [ + 2, + 16, + 6, + null, + "GPTQ" + ], + [ + 2, + 16, + 7, + null, + "GPTQ" + ], + [ + 2, + 16, + 8, + null, + "GPTQ" + ], + [ + 2, + 16, + 16, + null, + "GPTQ" + ], + [ + 3, + 2, + 3, + null, + "GPTQ" + ], + [ + 3, + 2, + 4, + null, + "GPTQ" + ], + [ + 3, + 2, + 5, + null, + "GPTQ" + ], + [ + 3, + 2, + 6, + null, + "GPTQ" + ], + [ + 3, + 2, + 7, + null, + "GPTQ" + ], + [ + 3, + 2, + 8, + null, + "GPTQ" + ], + [ + 3, + 2, + 16, + null, + "GPTQ" + ], + [ + 3, + 3, + 3, + null, + "GPTQ" + ], + [ + 3, + 3, + 4, + null, + "GPTQ" + ], + [ + 3, + 3, + 5, + null, + "GPTQ" + ], + [ + 3, + 3, + 6, + null, + "GPTQ" + ], + [ + 3, + 3, + 7, + null, + "GPTQ" + ], + [ + 3, + 3, + 8, + null, + "GPTQ" + ], + [ + 3, + 3, + 16, + null, + "GPTQ" + ], + [ + 3, + 4, + 3, + null, + "GPTQ" + ], + [ + 3, + 4, + 4, + null, + "GPTQ" + ], + [ + 3, + 4, + 5, + null, + "GPTQ" + ], + [ + 3, + 4, + 6, + null, + "GPTQ" + ], + [ + 3, + 4, + 7, + null, + "GPTQ" + ], + [ + 3, + 4, + 8, + null, + "GPTQ" + ], + [ + 3, + 4, + 16, + null, + "GPTQ" + ], + [ + 3, + 5, + 3, + null, + "GPTQ" + ], + [ + 3, + 5, + 4, + null, + "GPTQ" + ], + [ + 3, + 5, + 5, + null, + "GPTQ" + ], + [ + 3, + 5, + 6, + null, + "GPTQ" + ], + [ + 3, + 5, + 7, + null, + "GPTQ" + ], + [ + 3, + 5, + 8, + null, + "GPTQ" + ], + [ + 3, + 5, + 16, + null, + "GPTQ" + ], + [ + 3, + 6, + 3, + null, + "GPTQ" + ], + [ + 3, + 6, + 4, + null, + "GPTQ" + ], + [ + 3, + 6, + 5, + null, + "GPTQ" + ], + [ + 3, + 6, + 6, + null, + "GPTQ" + ], + [ + 3, + 6, + 7, + null, + "GPTQ" + ], + [ + 3, + 6, + 8, + null, + "GPTQ" + ], + [ + 3, + 6, + 16, + null, + "GPTQ" + ], + [ + 3, + 7, + 3, + null, + "GPTQ" + ], + [ + 3, + 7, + 4, + null, + "GPTQ" + ], + [ + 3, + 7, + 5, + null, + "GPTQ" + ], + [ + 3, + 7, + 6, + null, + "GPTQ" + ], + [ + 3, + 7, + 7, + null, + "GPTQ" + ], + [ + 3, + 7, + 8, + null, + "GPTQ" + ], + [ + 3, + 7, + 16, + null, + "GPTQ" + ], + [ + 3, + 8, + 3, + null, + "GPTQ" + ], + [ + 3, + 8, + 4, + null, + "GPTQ" + ], + [ + 3, + 8, + 5, + null, + "GPTQ" + ], + [ + 3, + 8, + 6, + null, + "GPTQ" + ], + [ + 3, + 8, + 7, + null, + "GPTQ" + ], + [ + 3, + 8, + 8, + null, + "GPTQ" + ], + [ + 3, + 8, + 16, + null, + "GPTQ" + ], + [ + 3, + 16, + 3, + null, + "GPTQ" + ], + [ + 3, + 16, + 4, + null, + "GPTQ" + ], + [ + 3, + 16, + 5, + null, + "GPTQ" + ], + [ + 3, + 16, + 6, + null, + "GPTQ" + ], + [ + 3, + 16, + 7, + null, + "GPTQ" + ], + [ + 3, + 16, + 8, + null, + "GPTQ" + ], + [ + 3, + 16, + 16, + null, + "GPTQ" + ], + [ + 4, + 2, + 3, + null, + "GPTQ" + ], + [ + 4, + 2, + 4, + null, + "GPTQ" + ], + [ + 4, + 2, + 5, + null, + "GPTQ" + ], + [ + 4, + 2, + 6, + null, + "GPTQ" + ], + [ + 4, + 2, + 7, + null, + "GPTQ" + ], + [ + 4, + 2, + 8, + null, + "GPTQ" + ], + [ + 4, + 2, + 16, + null, + "GPTQ" + ], + [ + 4, + 3, + 3, + null, + "GPTQ" + ], + [ + 4, + 3, + 4, + null, + "GPTQ" + ], + [ + 4, + 3, + 5, + null, + "GPTQ" + ], + [ + 4, + 3, + 6, + null, + "GPTQ" + ], + [ + 4, + 3, + 7, + null, + "GPTQ" + ], + [ + 4, + 3, + 8, + null, + "GPTQ" + ], + [ + 4, + 3, + 16, + null, + "GPTQ" + ], + [ + 4, + 4, + 3, + null, + "GPTQ" + ], + [ + 4, + 4, + 4, + null, + "GPTQ" + ], + [ + 4, + 4, + 5, + null, + "GPTQ" + ], + [ + 4, + 4, + 6, + null, + "GPTQ" + ], + [ + 4, + 4, + 7, + null, + "GPTQ" + ], + [ + 4, + 4, + 8, + null, + "GPTQ" + ], + [ + 4, + 4, + 16, + null, + "GPTQ" + ], + [ + 4, + 5, + 3, + null, + "GPTQ" + ], + [ + 4, + 5, + 4, + null, + "GPTQ" + ], + [ + 4, + 5, + 5, + null, + "GPTQ" + ], + [ + 4, + 5, + 6, + null, + "GPTQ" + ], + [ + 4, + 5, + 7, + null, + "GPTQ" + ], + [ + 4, + 5, + 8, + null, + "GPTQ" + ], + [ + 4, + 5, + 16, + null, + "GPTQ" + ], + [ + 4, + 6, + 3, + null, + "GPTQ" + ], + [ + 4, + 6, + 4, + null, + "GPTQ" + ], + [ + 4, + 6, + 5, + null, + "GPTQ" + ], + [ + 4, + 6, + 6, + null, + "GPTQ" + ], + [ + 4, + 6, + 7, + null, + "GPTQ" + ], + [ + 4, + 6, + 8, + null, + "GPTQ" + ], + [ + 4, + 6, + 16, + null, + "GPTQ" + ], + [ + 4, + 7, + 3, + null, + "GPTQ" + ], + [ + 4, + 7, + 4, + null, + "GPTQ" + ], + [ + 4, + 7, + 5, + null, + "GPTQ" + ], + [ + 4, + 7, + 6, + null, + "GPTQ" + ], + [ + 4, + 7, + 7, + null, + "GPTQ" + ], + [ + 4, + 7, + 8, + null, + "GPTQ" + ], + [ + 4, + 7, + 16, + null, + "GPTQ" + ], + [ + 4, + 8, + 3, + null, + "GPTQ" + ], + [ + 4, + 8, + 4, + null, + "GPTQ" + ], + [ + 4, + 8, + 5, + null, + "GPTQ" + ], + [ + 4, + 8, + 6, + null, + "GPTQ" + ], + [ + 4, + 8, + 7, + null, + "GPTQ" + ], + [ + 4, + 8, + 8, + null, + "GPTQ" + ], + [ + 4, + 8, + 16, + null, + "GPTQ" + ], + [ + 4, + 16, + 3, + null, + "GPTQ" + ], + [ + 4, + 16, + 4, + null, + "GPTQ" + ], + [ + 4, + 16, + 5, + null, + "GPTQ" + ], + [ + 4, + 16, + 6, + null, + "GPTQ" + ], + [ + 4, + 16, + 7, + null, + "GPTQ" + ], + [ + 4, + 16, + 8, + null, + "GPTQ" + ], + [ + 4, + 16, + 16, + null, + "GPTQ" + ], + [ + 5, + 2, + 3, + null, + "GPTQ" + ], + [ + 5, + 2, + 4, + null, + "GPTQ" + ], + [ + 5, + 2, + 5, + null, + "GPTQ" + ], + [ + 5, + 2, + 6, + null, + "GPTQ" + ], + [ + 5, + 2, + 7, + null, + "GPTQ" + ], + [ + 5, + 2, + 8, + null, + "GPTQ" + ], + [ + 5, + 2, + 16, + null, + "GPTQ" + ], + [ + 5, + 3, + 3, + null, + "GPTQ" + ], + [ + 5, + 3, + 4, + null, + "GPTQ" + ], + [ + 5, + 3, + 5, + null, + "GPTQ" + ], + [ + 5, + 3, + 6, + null, + "GPTQ" + ], + [ + 5, + 3, + 7, + null, + "GPTQ" + ], + [ + 5, + 3, + 8, + null, + "GPTQ" + ], + [ + 5, + 3, + 16, + null, + "GPTQ" + ], + [ + 5, + 4, + 3, + null, + "GPTQ" + ], + [ + 5, + 4, + 4, + null, + "GPTQ" + ], + [ + 5, + 4, + 5, + null, + "GPTQ" + ], + [ + 5, + 4, + 6, + null, + "GPTQ" + ], + [ + 5, + 4, + 7, + null, + "GPTQ" + ], + [ + 5, + 4, + 8, + null, + "GPTQ" + ], + [ + 5, + 4, + 16, + null, + "GPTQ" + ], + [ + 5, + 5, + 3, + null, + "GPTQ" + ], + [ + 5, + 5, + 4, + null, + "GPTQ" + ], + [ + 5, + 5, + 5, + null, + "GPTQ" + ], + [ + 5, + 5, + 6, + null, + "GPTQ" + ], + [ + 5, + 5, + 7, + null, + "GPTQ" + ], + [ + 5, + 5, + 8, + null, + "GPTQ" + ], + [ + 5, + 5, + 16, + null, + "GPTQ" + ], + [ + 5, + 6, + 3, + null, + "GPTQ" + ], + [ + 5, + 6, + 4, + null, + "GPTQ" + ], + [ + 5, + 6, + 5, + null, + "GPTQ" + ], + [ + 5, + 6, + 6, + null, + "GPTQ" + ], + [ + 5, + 6, + 7, + null, + "GPTQ" + ], + [ + 5, + 6, + 8, + null, + "GPTQ" + ], + [ + 5, + 6, + 16, + null, + "GPTQ" + ], + [ + 5, + 7, + 3, + null, + "GPTQ" + ], + [ + 5, + 7, + 4, + null, + "GPTQ" + ], + [ + 5, + 7, + 5, + null, + "GPTQ" + ], + [ + 5, + 7, + 6, + null, + "GPTQ" + ], + [ + 5, + 7, + 7, + null, + "GPTQ" + ], + [ + 5, + 7, + 8, + null, + "GPTQ" + ], + [ + 5, + 7, + 16, + null, + "GPTQ" + ], + [ + 5, + 8, + 3, + null, + "GPTQ" + ], + [ + 5, + 8, + 4, + null, + "GPTQ" + ], + [ + 5, + 8, + 5, + null, + "GPTQ" + ], + [ + 5, + 8, + 6, + null, + "GPTQ" + ], + [ + 5, + 8, + 7, + null, + "GPTQ" + ], + [ + 5, + 8, + 8, + null, + "GPTQ" + ], + [ + 5, + 8, + 16, + null, + "GPTQ" + ], + [ + 5, + 16, + 3, + null, + "GPTQ" + ], + [ + 5, + 16, + 4, + null, + "GPTQ" + ], + [ + 5, + 16, + 5, + null, + "GPTQ" + ], + [ + 5, + 16, + 6, + null, + "GPTQ" + ], + [ + 5, + 16, + 7, + null, + "GPTQ" + ], + [ + 5, + 16, + 8, + null, + "GPTQ" + ], + [ + 5, + 16, + 16, + null, + "GPTQ" + ], + [ + 6, + 2, + 3, + null, + "GPTQ" + ], + [ + 6, + 2, + 4, + null, + "GPTQ" + ], + [ + 6, + 2, + 5, + null, + "GPTQ" + ], + [ + 6, + 2, + 6, + null, + "GPTQ" + ], + [ + 6, + 2, + 7, + null, + "GPTQ" + ], + [ + 6, + 2, + 8, + null, + "GPTQ" + ], + [ + 6, + 2, + 16, + null, + "GPTQ" + ], + [ + 6, + 3, + 3, + null, + "GPTQ" + ], + [ + 6, + 3, + 4, + null, + "GPTQ" + ], + [ + 6, + 3, + 5, + null, + "GPTQ" + ], + [ + 6, + 3, + 6, + null, + "GPTQ" + ], + [ + 6, + 3, + 7, + null, + "GPTQ" + ], + [ + 6, + 3, + 8, + null, + "GPTQ" + ], + [ + 6, + 3, + 16, + null, + "GPTQ" + ], + [ + 6, + 4, + 3, + null, + "GPTQ" + ], + [ + 6, + 4, + 4, + null, + "GPTQ" + ], + [ + 6, + 4, + 5, + null, + "GPTQ" + ], + [ + 6, + 4, + 6, + null, + "GPTQ" + ], + [ + 6, + 4, + 7, + null, + "GPTQ" + ], + [ + 6, + 4, + 8, + null, + "GPTQ" + ], + [ + 6, + 4, + 16, + null, + "GPTQ" + ], + [ + 6, + 5, + 3, + null, + "GPTQ" + ], + [ + 6, + 5, + 4, + null, + "GPTQ" + ], + [ + 6, + 5, + 5, + null, + "GPTQ" + ], + [ + 6, + 5, + 6, + null, + "GPTQ" + ], + [ + 6, + 5, + 7, + null, + "GPTQ" + ], + [ + 6, + 5, + 8, + null, + "GPTQ" + ], + [ + 6, + 5, + 16, + null, + "GPTQ" + ], + [ + 6, + 6, + 3, + null, + "GPTQ" + ], + [ + 6, + 6, + 4, + null, + "GPTQ" + ], + [ + 6, + 6, + 5, + null, + "GPTQ" + ], + [ + 6, + 6, + 6, + null, + "GPTQ" + ], + [ + 6, + 6, + 7, + null, + "GPTQ" + ], + [ + 6, + 6, + 8, + null, + "GPTQ" + ], + [ + 6, + 6, + 16, + null, + "GPTQ" + ], + [ + 6, + 7, + 3, + null, + "GPTQ" + ], + [ + 6, + 7, + 4, + null, + "GPTQ" + ], + [ + 6, + 7, + 5, + null, + "GPTQ" + ], + [ + 6, + 7, + 6, + null, + "GPTQ" + ], + [ + 6, + 7, + 7, + null, + "GPTQ" + ], + [ + 6, + 7, + 8, + null, + "GPTQ" + ], + [ + 6, + 7, + 16, + null, + "GPTQ" + ], + [ + 6, + 8, + 3, + null, + "GPTQ" + ], + [ + 6, + 8, + 4, + null, + "GPTQ" + ], + [ + 6, + 8, + 5, + null, + "GPTQ" + ], + [ + 6, + 8, + 6, + null, + "GPTQ" + ], + [ + 6, + 8, + 7, + null, + "GPTQ" + ], + [ + 6, + 8, + 8, + null, + "GPTQ" + ], + [ + 6, + 8, + 16, + null, + "GPTQ" + ], + [ + 6, + 16, + 3, + null, + "GPTQ" + ], + [ + 6, + 16, + 4, + null, + "GPTQ" + ], + [ + 6, + 16, + 5, + null, + "GPTQ" + ], + [ + 6, + 16, + 6, + null, + "GPTQ" + ], + [ + 6, + 16, + 7, + null, + "GPTQ" + ], + [ + 6, + 16, + 8, + null, + "GPTQ" + ], + [ + 6, + 16, + 16, + null, + "GPTQ" + ], + [ + 7, + 2, + 3, + null, + "GPTQ" + ], + [ + 7, + 2, + 4, + null, + "GPTQ" + ], + [ + 7, + 2, + 5, + null, + "GPTQ" + ], + [ + 7, + 2, + 6, + null, + "GPTQ" + ], + [ + 7, + 2, + 7, + null, + "GPTQ" + ], + [ + 7, + 2, + 8, + null, + "GPTQ" + ], + [ + 7, + 2, + 16, + null, + "GPTQ" + ], + [ + 7, + 3, + 3, + null, + "GPTQ" + ], + [ + 7, + 3, + 4, + null, + "GPTQ" + ], + [ + 7, + 3, + 5, + null, + "GPTQ" + ], + [ + 7, + 3, + 6, + null, + "GPTQ" + ], + [ + 7, + 3, + 7, + null, + "GPTQ" + ], + [ + 7, + 3, + 8, + null, + "GPTQ" + ], + [ + 7, + 3, + 16, + null, + "GPTQ" + ], + [ + 7, + 4, + 3, + null, + "GPTQ" + ], + [ + 7, + 4, + 4, + null, + "GPTQ" + ], + [ + 7, + 4, + 5, + null, + "GPTQ" + ], + [ + 7, + 4, + 6, + null, + "GPTQ" + ], + [ + 7, + 4, + 7, + null, + "GPTQ" + ], + [ + 7, + 4, + 8, + null, + "GPTQ" + ], + [ + 7, + 4, + 16, + null, + "GPTQ" + ], + [ + 7, + 5, + 3, + null, + "GPTQ" + ], + [ + 7, + 5, + 4, + null, + "GPTQ" + ], + [ + 7, + 5, + 5, + null, + "GPTQ" + ], + [ + 7, + 5, + 6, + null, + "GPTQ" + ], + [ + 7, + 5, + 7, + null, + "GPTQ" + ], + [ + 7, + 5, + 8, + null, + "GPTQ" + ], + [ + 7, + 5, + 16, + null, + "GPTQ" + ], + [ + 7, + 6, + 3, + null, + "GPTQ" + ], + [ + 7, + 6, + 4, + null, + "GPTQ" + ], + [ + 7, + 6, + 5, + null, + "GPTQ" + ], + [ + 7, + 6, + 6, + null, + "GPTQ" + ], + [ + 7, + 6, + 7, + null, + "GPTQ" + ], + [ + 7, + 6, + 8, + null, + "GPTQ" + ], + [ + 7, + 6, + 16, + null, + "GPTQ" + ], + [ + 7, + 7, + 3, + null, + "GPTQ" + ], + [ + 7, + 7, + 4, + null, + "GPTQ" + ], + [ + 7, + 7, + 5, + null, + "GPTQ" + ], + [ + 7, + 7, + 6, + null, + "GPTQ" + ], + [ + 7, + 7, + 7, + null, + "GPTQ" + ], + [ + 7, + 7, + 8, + null, + "GPTQ" + ], + [ + 7, + 7, + 16, + null, + "GPTQ" + ], + [ + 7, + 8, + 3, + null, + "GPTQ" + ], + [ + 7, + 8, + 4, + null, + "GPTQ" + ], + [ + 7, + 8, + 5, + null, + "GPTQ" + ], + [ + 7, + 8, + 6, + null, + "GPTQ" + ], + [ + 7, + 8, + 7, + null, + "GPTQ" + ], + [ + 7, + 8, + 8, + null, + "GPTQ" + ], + [ + 7, + 8, + 16, + null, + "GPTQ" + ], + [ + 7, + 16, + 3, + null, + "GPTQ" + ], + [ + 7, + 16, + 4, + null, + "GPTQ" + ], + [ + 7, + 16, + 5, + null, + "GPTQ" + ], + [ + 7, + 16, + 6, + null, + "GPTQ" + ], + [ + 7, + 16, + 7, + null, + "GPTQ" + ], + [ + 7, + 16, + 8, + null, + "GPTQ" + ], + [ + 7, + 16, + 16, + null, + "GPTQ" + ], + [ + 8, + 2, + 3, + null, + "GPTQ" + ], + [ + 8, + 2, + 4, + null, + "GPTQ" + ], + [ + 8, + 2, + 5, + null, + "GPTQ" + ], + [ + 8, + 2, + 6, + null, + "GPTQ" + ], + [ + 8, + 2, + 7, + null, + "GPTQ" + ], + [ + 8, + 2, + 8, + null, + "GPTQ" + ], + [ + 8, + 2, + 16, + null, + "GPTQ" + ], + [ + 8, + 3, + 3, + null, + "GPTQ" + ], + [ + 8, + 3, + 4, + null, + "GPTQ" + ], + [ + 8, + 3, + 5, + null, + "GPTQ" + ], + [ + 8, + 3, + 6, + null, + "GPTQ" + ], + [ + 8, + 3, + 7, + null, + "GPTQ" + ], + [ + 8, + 3, + 8, + null, + "GPTQ" + ], + [ + 8, + 3, + 16, + null, + "GPTQ" + ], + [ + 8, + 4, + 3, + null, + "GPTQ" + ], + [ + 8, + 4, + 4, + null, + "GPTQ" + ], + [ + 8, + 4, + 5, + null, + "GPTQ" + ], + [ + 8, + 4, + 6, + null, + "GPTQ" + ], + [ + 8, + 4, + 7, + null, + "GPTQ" + ], + [ + 8, + 4, + 8, + null, + "GPTQ" + ], + [ + 8, + 4, + 16, + null, + "GPTQ" + ], + [ + 8, + 5, + 3, + null, + "GPTQ" + ], + [ + 8, + 5, + 4, + null, + "GPTQ" + ], + [ + 8, + 5, + 5, + null, + "GPTQ" + ], + [ + 8, + 5, + 6, + null, + "GPTQ" + ], + [ + 8, + 5, + 7, + null, + "GPTQ" + ], + [ + 8, + 5, + 8, + null, + "GPTQ" + ], + [ + 8, + 5, + 16, + null, + "GPTQ" + ], + [ + 8, + 6, + 3, + null, + "GPTQ" + ], + [ + 8, + 6, + 4, + null, + "GPTQ" + ], + [ + 8, + 6, + 5, + null, + "GPTQ" + ], + [ + 8, + 6, + 6, + null, + "GPTQ" + ], + [ + 8, + 6, + 7, + null, + "GPTQ" + ], + [ + 8, + 6, + 8, + null, + "GPTQ" + ], + [ + 8, + 6, + 16, + null, + "GPTQ" + ], + [ + 8, + 7, + 3, + null, + "GPTQ" + ], + [ + 8, + 7, + 4, + null, + "GPTQ" + ], + [ + 8, + 7, + 5, + null, + "GPTQ" + ], + [ + 8, + 7, + 6, + null, + "GPTQ" + ], + [ + 8, + 7, + 7, + null, + "GPTQ" + ], + [ + 8, + 7, + 8, + null, + "GPTQ" + ], + [ + 8, + 7, + 16, + null, + "GPTQ" + ], + [ + 8, + 8, + 3, + null, + "GPTQ" + ], + [ + 8, + 8, + 4, + null, + "GPTQ" + ], + [ + 8, + 8, + 5, + null, + "GPTQ" + ], + [ + 8, + 8, + 6, + null, + "GPTQ" + ], + [ + 8, + 8, + 7, + null, + "GPTQ" + ], + [ + 8, + 8, + 8, + null, + "GPTQ" + ], + [ + 8, + 8, + 16, + null, + "GPTQ" + ], + [ + 8, + 16, + 3, + null, + "GPTQ" + ], + [ + 8, + 16, + 4, + null, + "GPTQ" + ], + [ + 8, + 16, + 5, + null, + "GPTQ" + ], + [ + 8, + 16, + 6, + null, + "GPTQ" + ], + [ + 8, + 16, + 7, + null, + "GPTQ" + ], + [ + 8, + 16, + 8, + null, + "GPTQ" + ], + [ + 8, + 16, + 16, + null, + "GPTQ" + ], + [ + 16, + 2, + 3, + null, + "GPTQ" + ], + [ + 16, + 2, + 4, + null, + "GPTQ" + ], + [ + 16, + 2, + 5, + null, + "GPTQ" + ], + [ + 16, + 2, + 6, + null, + "GPTQ" + ], + [ + 16, + 2, + 7, + null, + "GPTQ" + ], + [ + 16, + 2, + 8, + null, + "GPTQ" + ], + [ + 16, + 2, + 16, + null, + "GPTQ" + ], + [ + 16, + 3, + 3, + null, + "GPTQ" + ], + [ + 16, + 3, + 4, + null, + "GPTQ" + ], + [ + 16, + 3, + 5, + null, + "GPTQ" + ], + [ + 16, + 3, + 6, + null, + "GPTQ" + ], + [ + 16, + 3, + 7, + null, + "GPTQ" + ], + [ + 16, + 3, + 8, + null, + "GPTQ" + ], + [ + 16, + 3, + 16, + null, + "GPTQ" + ], + [ + 16, + 4, + 3, + null, + "GPTQ" + ], + [ + 16, + 4, + 4, + null, + "GPTQ" + ], + [ + 16, + 4, + 5, + null, + "GPTQ" + ], + [ + 16, + 4, + 6, + null, + "GPTQ" + ], + [ + 16, + 4, + 7, + null, + "GPTQ" + ], + [ + 16, + 4, + 8, + null, + "GPTQ" + ], + [ + 16, + 4, + 16, + null, + "GPTQ" + ], + [ + 16, + 5, + 3, + null, + "GPTQ" + ], + [ + 16, + 5, + 4, + null, + "GPTQ" + ], + [ + 16, + 5, + 5, + null, + "GPTQ" + ], + [ + 16, + 5, + 6, + null, + "GPTQ" + ], + [ + 16, + 5, + 7, + null, + "GPTQ" + ], + [ + 16, + 5, + 8, + null, + "GPTQ" + ], + [ + 16, + 5, + 16, + null, + "GPTQ" + ], + [ + 16, + 6, + 3, + null, + "GPTQ" + ], + [ + 16, + 6, + 4, + null, + "GPTQ" + ], + [ + 16, + 6, + 5, + null, + "GPTQ" + ], + [ + 16, + 6, + 6, + null, + "GPTQ" + ], + [ + 16, + 6, + 7, + null, + "GPTQ" + ], + [ + 16, + 6, + 8, + null, + "GPTQ" + ], + [ + 16, + 6, + 16, + null, + "GPTQ" + ], + [ + 16, + 7, + 3, + null, + "GPTQ" + ], + [ + 16, + 7, + 4, + null, + "GPTQ" + ], + [ + 16, + 7, + 5, + null, + "GPTQ" + ], + [ + 16, + 7, + 6, + null, + "GPTQ" + ], + [ + 16, + 7, + 7, + null, + "GPTQ" + ], + [ + 16, + 7, + 8, + null, + "GPTQ" + ], + [ + 16, + 7, + 16, + null, + "GPTQ" + ], + [ + 16, + 8, + 3, + null, + "GPTQ" + ], + [ + 16, + 8, + 4, + null, + "GPTQ" + ], + [ + 16, + 8, + 5, + null, + "GPTQ" + ], + [ + 16, + 8, + 6, + null, + "GPTQ" + ], + [ + 16, + 8, + 7, + null, + "GPTQ" + ], + [ + 16, + 8, + 8, + null, + "GPTQ" + ], + [ + 16, + 8, + 16, + null, + "GPTQ" + ], + [ + 16, + 16, + 3, + null, + "GPTQ" + ], + [ + 16, + 16, + 4, + null, + "GPTQ" + ], + [ + 16, + 16, + 5, + null, + "GPTQ" + ], + [ + 16, + 16, + 6, + null, + "GPTQ" + ], + [ + 16, + 16, + 7, + null, + "GPTQ" + ], + [ + 16, + 16, + 8, + null, + "GPTQ" + ], + [ + 16, + 16, + 16, + null, + "GPTQ" + ] + ], + "hovertemplate": "quant_type=%{customdata[4]}
meteor=%{x}
cider=%{y}
vit_bits=%{customdata[0]}
qformer_bits=%{customdata[1]}
llm_bits=%{customdata[2]}
model_size=%{customdata[3]}", + "legendgroup": "GPTQ", + "marker": { + "color": "#EF553B", + "line": { + "color": "DarkSlateGrey", + "width": 2 + }, + "symbol": "circle" + }, + "mode": "markers", + "name": "GPTQ", + "opacity": 0.75, + "orientation": "v", + "showlegend": true, + "type": "scatter", + "x": [ + 0.0101509943402546, + 0.0088340686285083, + 0.0155899811097696, + 0.0163295442428401, + 0.0110204231682136, + 0.0159338480384932, + 0.0090890703312112, + 0.0104842683424739, + 0.0168004506613121, + 0.0237587431495462, + 0.0303619535963857, + 0.0211129285151924, + 0.0264902040194135, + 0.0188223479333816, + 0.0073740024940355, + 0.0232869785720877, + 0.0220397507632954, + 0.0294781863795688, + 0.018317311015308, + 0.03933140383517, + 0.0458451262466043, + 0.0132893244084989, + 0.0273426277430907, + 0.0288074283656735, + 0.0406086582271537, + 0.0313302071479879, + 0.0301855537831424, + 0.0314790841594325, + 0.0131576518105608, + 0.0323455580987091, + 0.0293674505179078, + 0.03106697829786, + 0.0330154428007336, + 0.0449142809285116, + 0.0389898562051129, + 0.0109839806517981, + 0.0259534843230406, + 0.0261528609370051, + 0.0272663976841582, + 0.0306502504756865, + 0.0346673083886235, + 0.021996984425321, + 0.0108562692128552, + 0.0287163810820384, + 0.0257699189437937, + 0.0291298607639057, + 0.0372198376814247, + 0.0396617673154379, + 0.0358534421567968, + 0.0119990321350278, + 0.0377454068624469, + 0.0292756387483196, + 0.0332713784675424, + 0.0277954597387067, + 0.0372764695714126, + 0.0276568484814306, + 0.0788191689546042, + 0.2003803860621313, + 0.2326558425059257, + 0.2428395614867973, + 0.2408200676383683, + 0.2483277762538575, + 0.2525719457891249, + 0.090075527149698, + 0.2149445122673331, + 0.2564913130952467, + 0.2584498904179683, + 0.2612442646290462, + 0.251760226051615, + 0.2573178398129164, + 0.0741479055540712, + 0.2298850447398329, + 0.2553255261637969, + 0.25908517162403, + 0.2649083418495246, + 0.2557713813971211, + 0.2699935729788946, + 0.069534308111828, + 0.2271970206198886, + 0.247020556569343, + 0.2697969766290725, + 0.2697811686538955, + 0.2584998184627469, + 0.253529667887787, + 0.0557137084482638, + 0.2317569698627178, + 0.247132795695003, + 0.2649794928880195, + 0.2707627624006676, + 0.2685535767129971, + 0.2650512078010595, + 0.0629859242279032, + 0.1970062958044801, + 0.2493200170122143, + 0.2596946951417015, + 0.2537172067906273, + 0.2659037308448395, + 0.2670324835158698, + 0.0795256606053799, + 0.2252194788495606, + 0.2436577923334506, + 0.2622039344269986, + 0.256775792367136, + 0.2734121132546192, + 0.2669032243726221, + 0.0830986348994234, + 0.2376183097975369, + 0.243315703377886, + 0.2630536959962511, + 0.2523986103737525, + 0.2583456344178121, + 0.2543431309903181, + 0.0587030704460059, + 0.2158155672518252, + 0.2505609286926828, + 0.2557093725600452, + 0.2658015071013662, + 0.2479954527960229, + 0.2523615071882862, + 0.0604147972463985, + 0.2313389641334878, + 0.2557728900771275, + 0.2676588292399841, + 0.2716002004995171, + 0.2787450915956347, + 0.2689197654037695, + 0.065858662539599, + 0.2390865681473539, + 0.2470320558619711, + 0.2706426299085686, + 0.2671504907791259, + 0.2740877707595741, + 0.2631789964727932, + 0.076471220558264, + 0.2345871456314205, + 0.2711508362664911, + 0.2747517938488452, + 0.2789393594902043, + 0.2687634359588921, + 0.2709278637840276, + 0.0842805423236264, + 0.2379463768648862, + 0.2711945995333918, + 0.2762657683924261, + 0.271389154455358, + 0.2676905668213665, + 0.2722348680601927, + 0.0816393709418319, + 0.2245187254662377, + 0.2622265729359909, + 0.2794676154312032, + 0.2628991957098335, + 0.2666393244944598, + 0.2728891807844822, + 0.0707099029100885, + 0.2361111023174682, + 0.2603424081735628, + 0.2599532362418715, + 0.2724394622348405, + 0.2602363364445407, + 0.2763630910172273, + 0.0827294290340496, + 0.2174998009671489, + 0.2567920116196879, + 0.2742908495873926, + 0.2797749913422573, + 0.2636694858593554, + 0.2687273635096571, + 0.0871045467791697, + 0.2289849494251449, + 0.2421822278524677, + 0.2543048294389661, + 0.2495244733252055, + 0.2469314057259962, + 0.2475156923224727, + 0.0912689859850269, + 0.2186404328787877, + 0.2657506256744, + 0.2670165932668172, + 0.2778917398348947, + 0.2683749961933498, + 0.2748676929433147, + 0.0746141244948362, + 0.2211305607068668, + 0.2659073412095775, + 0.2751784174163265, + 0.2724649163253744, + 0.266441057478906, + 0.2708438492553079, + 0.0874917332190431, + 0.227734256017508, + 0.2584462740316047, + 0.2732186399164084, + 0.2676579568976495, + 0.2730460603075249, + 0.2775924477351711, + 0.0828187958688651, + 0.2156931256664336, + 0.2668471913148609, + 0.2684936712785205, + 0.2647503991586364, + 0.2651613077624922, + 0.2717228744886364, + 0.1029451517181668, + 0.2310156802192286, + 0.2680004933587172, + 0.2641765203739215, + 0.2718966892006794, + 0.2808078047226223, + 0.2699106833880589, + 0.0742612269577198, + 0.2261561856766414, + 0.2674631142059365, + 0.2715965898310001, + 0.2736429370573359, + 0.2696549936072608, + 0.2662342132475261, + 0.0652822107412974, + 0.2404317854904731, + 0.2552071975396662, + 0.2624534094334289, + 0.2708390962743312, + 0.2761532531547833, + 0.269432443190383, + 0.0687089860831234, + 0.2204599058293365, + 0.2521709439600205, + 0.2615788391450334, + 0.2531266441008886, + 0.2598311053136787, + 0.23435788558248, + 0.0657241045887942, + 0.2102692607738765, + 0.2557483832286708, + 0.2725237897747631, + 0.2724646998419084, + 0.273512601638023, + 0.2725200183102257, + 0.0818720249051902, + 0.2306111246669006, + 0.2723812439776036, + 0.2704254526503866, + 0.2782082158522669, + 0.2709685496890975, + 0.265392647226198, + 0.0803667132233428, + 0.2410466998845678, + 0.2632530696447148, + 0.2711009529966307, + 0.2715956191194985, + 0.2743719192582209, + 0.2775787041926977, + 0.0884670544799035, + 0.2193032503330496, + 0.2649796351377491, + 0.2714712883914125, + 0.2647204432752614, + 0.2781276147110718, + 0.2743287284584625, + 0.0745039985801638, + 0.2440112625304193, + 0.2614833074481487, + 0.284343158660314, + 0.2709824987307294, + 0.2672381240286334, + 0.2677031829832452, + 0.0646805475631668, + 0.2318439349830785, + 0.2683625241050587, + 0.2716406255483246, + 0.2696658834641076, + 0.2711115862468866, + 0.2723706157745775, + 0.084317355899782, + 0.2153438196031828, + 0.2538414576307057, + 0.2676743933780552, + 0.2704608917926687, + 0.2712589059297048, + 0.271365761010262, + 0.0678532152158794, + 0.2281933718929868, + 0.2437996754965493, + 0.2606156034891746, + 0.2473916595553342, + 0.2495957964699268, + 0.2585905401299266, + 0.067997354820574, + 0.2248028817926027, + 0.2651440429738932, + 0.2765460667134314, + 0.2636544321411984, + 0.2688940225081606, + 0.2621688670800611, + 0.0862964133973605, + 0.2305325626646963, + 0.2576139351483844, + 0.273594026958449, + 0.2750415337195191, + 0.2716768445377813, + 0.2773558845911573, + 0.0572685829999058, + 0.2432540856116504, + 0.2557620331428388, + 0.2744452122084377, + 0.2707574733904774, + 0.2718745072654312, + 0.2689391431576329, + 0.0466229576655524, + 0.2379179638936858, + 0.2646278486053692, + 0.2732536619103621, + 0.2622254326931058, + 0.2770415800639479, + 0.2752533066517958, + 0.0793901869208519, + 0.2342326049359317, + 0.2572167750380091, + 0.2745186892085989, + 0.2722458206275437, + 0.2655370120288739, + 0.2736891023545228, + 0.088618876628914, + 0.228709380145255, + 0.2584115280970096, + 0.2729032763256883, + 0.2744261426461032, + 0.2760072074535579, + 0.2720321936099723, + 0.0907009353496895, + 0.2374034111204564, + 0.2716626569454359, + 0.2703046558414297, + 0.2598861049936764, + 0.2706242260938302, + 0.2660990617905922, + 0.0737552147330567, + 0.2126328289285691, + 0.2394012804880039, + 0.2527580534386101, + 0.2554295257339779, + 0.2534110581462355, + 0.2524231509889801, + 0.0779721381688259, + 0.2355874777986923, + 0.2659790768103025, + 0.2729566128373987, + 0.2739969526173593, + 0.2625575696644953, + 0.2686609148293443, + 0.0790041968947412, + 0.224102018410639, + 0.2613700524241101, + 0.2688828798826286, + 0.2759182589087557, + 0.2676394608254692, + 0.2725318330777681, + 0.0998711248530587, + 0.2359895635004501, + 0.2700548123912901, + 0.2777449491864727, + 0.2664110686013836, + 0.2701059955912535, + 0.2728000178992855, + 0.0735086550237912, + 0.2125618933887206, + 0.2657040215767418, + 0.2685224114522399, + 0.2726240080683363, + 0.2734984798006242, + 0.2695102734871265, + 0.0674492976334643, + 0.2264319956642847, + 0.2503175570706385, + 0.2695614249608655, + 0.2678938089641989, + 0.2828159543957243, + 0.2707307944762384, + 0.063681630012908, + 0.2290983630393371, + 0.2611509041502507, + 0.2760476559720744, + 0.2720754059114277, + 0.2762099474468814, + 0.2741317897100238, + 0.0713590856030923, + 0.243269833003398, + 0.2699379303029676, + 0.2629192273436857, + 0.2722565233646085, + 0.2684260170350768, + 0.2687378413722184, + 0.0742959830663597, + 0.2201544375456314, + 0.2401482461681527, + 0.257680425387034, + 0.2559361027621114, + 0.2568440416607038, + 0.2576102345713943, + 0.0671484707527612, + 0.2261394244660083, + 0.2660949396945497, + 0.2675059409212252, + 0.270637411183011, + 0.2687833021476288, + 0.2641382719366991, + 0.0736289249779437, + 0.2124194616737727, + 0.264839292928929, + 0.2672784276300824, + 0.2689917363104334, + 0.2562203946308231, + 0.2729090395227818, + 0.0805556493753058, + 0.2271327775648592, + 0.2702433548323473, + 0.2673536775181908, + 0.2694406662183629, + 0.2761238899905744, + 0.2632252808096829, + 0.0663642256782712, + 0.2265332719815047, + 0.2599035098953106, + 0.2712483665958033, + 0.2732084244766888, + 0.273955994454751, + 0.2618383170640967, + 0.0955013272849004, + 0.2340593375513878, + 0.2583525972449989, + 0.2725515939269699, + 0.2728564384949101, + 0.2722806676656806, + 0.2696388187846983, + 0.0611796481276367, + 0.2188992853999682, + 0.2666147494454983, + 0.2739511507976573, + 0.271085807311084, + 0.2605690360448486, + 0.273379216643398, + 0.0855598645630358, + 0.2367057762580658, + 0.2637882023941424, + 0.2754226399118956, + 0.2734460153941674, + 0.2723981673958693, + 0.2732587979152673 + ], + "xaxis": "x", + "y": [ + 0.0014981143422107, + 0.0007632907925214, + 0.0029097210518471, + 0.0030354149772694, + 0.0025668698698922, + 0.0030735740840323, + 0.0020890877629372, + 0.0018951746387372, + 0.0018590175578755, + 0.005198639089948, + 0.0072295798856813, + 0.0036126575089534, + 0.007039247664862, + 0.002683819793735, + 0.0012702407280795, + 0.0034645741619075, + 0.0037753888063183, + 0.0056896234897692, + 0.0023662530900511, + 0.0114297868146668, + 0.0138734291629878, + 0.0025550459238047, + 0.0026203769315841, + 0.0058617693802487, + 0.010800692027131, + 0.0055000025982668, + 0.0067763210081299, + 0.005107330731382, + 0.0019383550169702, + 0.0054299568091096, + 0.0086967464032185, + 0.0078579783935992, + 0.0080612479494432, + 0.0119806392945841, + 0.008670659151602, + 0.002439013630685, + 0.0044144376249749, + 0.0043435194831127, + 0.0041871486933038, + 0.0059891148893778, + 0.0079281999841042, + 0.0029726843980808, + 0.001977523536491, + 0.0035625793106057, + 0.004736129177439, + 0.0078927075815822, + 0.0086144818317023, + 0.0101693239823326, + 0.0046641041683276, + 0.0025626072217407, + 0.0075453261632182, + 0.004612071739868, + 0.0062282072846321, + 0.0071140644524548, + 0.0124216773978934, + 0.0052042264550185, + 0.1763110541830811, + 0.7435859729333998, + 0.9556618659385248, + 0.9900979258960908, + 1.001086957572305, + 1.0270025443927695, + 1.0503642434792058, + 0.2404521287537404, + 0.8555236815607831, + 1.1375354779064342, + 1.148099214270454, + 1.1259916712493478, + 1.1023284180088568, + 1.132292255681916, + 0.1780812958205132, + 0.9413754988940553, + 1.0993993491699223, + 1.169094337541494, + 1.1888011894279025, + 1.1108485242424908, + 1.2315916750788476, + 0.1668425730009947, + 0.9460161517985276, + 1.079244888608979, + 1.1877800395137463, + 1.173319140705028, + 1.1016627972550836, + 1.11668528221346, + 0.114097165671628, + 0.9405291722030128, + 1.0639627730826708, + 1.1845369820653993, + 1.2081219126001308, + 1.1948539611615718, + 1.1717024307484611, + 0.1617543111375243, + 0.7240441536600299, + 1.0891566054150577, + 1.1495293307394048, + 1.1090357313388717, + 1.158481714753464, + 1.2167491806051265, + 0.2170981415167454, + 0.9034303489910208, + 1.0384376163419362, + 1.1317793439920232, + 1.119106598440014, + 1.198123821990452, + 1.2134377823922555, + 0.1811481137844586, + 1.0095257799776172, + 1.0424479291621664, + 1.1537201669852142, + 1.0981747640322346, + 1.159218653444308, + 1.1018880173653458, + 0.107853308953933, + 0.8575086430691543, + 1.0854342491694076, + 1.0905237788855002, + 1.166748462602317, + 1.0663395553619197, + 1.083000643794271, + 0.135897697221761, + 0.9658588506696048, + 1.1123560707245743, + 1.1794193797983346, + 1.2217093402495611, + 1.2623600889788325, + 1.1869865261348789, + 0.1376968990637526, + 1.0090488581719883, + 1.064529571365553, + 1.2102845743724164, + 1.2020217637788508, + 1.2268006856484162, + 1.191515824193017, + 0.1650873220093012, + 0.9928843318818326, + 1.22736530478368, + 1.260129423158903, + 1.260374148148236, + 1.1840129923018252, + 1.2097532110432354, + 0.1787854549058433, + 0.968964111986822, + 1.2362160577748775, + 1.2392733077705964, + 1.1906590587348784, + 1.1618218136847105, + 1.2299690158070715, + 0.2063856589963886, + 0.9165061427547144, + 1.130258409573858, + 1.2347910157879871, + 1.1636854976973123, + 1.2177800514996835, + 1.2201137943609357, + 0.1590339919281581, + 1.0177291405119682, + 1.136284296118951, + 1.1752117336776349, + 1.1941423897240375, + 1.1376532744999528, + 1.2509424889629197, + 0.1281928432329332, + 0.8744929323158068, + 1.1320771459391303, + 1.2427731808191953, + 1.2635652372814536, + 1.177837358161243, + 1.1989707077013283, + 0.2165226570684773, + 0.9128057827967246, + 1.028861448166121, + 1.0850514208988504, + 1.0868847759147533, + 1.052917364386642, + 1.0338461965377357, + 0.216410938255457, + 0.894614386423719, + 1.157947187501875, + 1.1840085497272086, + 1.2707460705325784, + 1.2275586465711037, + 1.199261667260656, + 0.180723469672694, + 0.915899160424718, + 1.2089247433808703, + 1.253590668326296, + 1.231728583681874, + 1.1874105693070511, + 1.225906340176104, + 0.1955785089978277, + 0.9323524560420912, + 1.152766192237074, + 1.2350794722867804, + 1.1869321888382165, + 1.2247690382770977, + 1.2442057677915968, + 0.2076940282225752, + 0.8301156370839514, + 1.2006194007947442, + 1.195257847925561, + 1.1558882709844454, + 1.1832983838931983, + 1.2513308224676296, + 0.2555046839311619, + 0.959081941183304, + 1.186071068714201, + 1.1651901236278697, + 1.178306979078529, + 1.281644197189058, + 1.1787467032981338, + 0.1595829376046691, + 0.9286498118978196, + 1.2111247798585558, + 1.202570074300962, + 1.2227444627419934, + 1.223414057406298, + 1.2037422091771472, + 0.1508263506843169, + 1.0232735437492295, + 1.1319042816625422, + 1.1365122096337794, + 1.2021506183099633, + 1.2504827992538128, + 1.192311591073823, + 0.1704670806165085, + 0.8987450927407257, + 1.08753001165231, + 1.0917924665813956, + 1.087525654099781, + 1.1099596548903006, + 0.9899235726230634, + 0.145781990722824, + 0.8297361824726199, + 1.134032414016971, + 1.212802940243195, + 1.2340670509539098, + 1.2428391332397744, + 1.2087609811790982, + 0.1798172861389938, + 0.954757423862689, + 1.2180539240226955, + 1.2243791050481736, + 1.246668454565147, + 1.203447004630302, + 1.1816423770216555, + 0.1767723755618828, + 1.0213632068025982, + 1.1519794100031873, + 1.229260492826878, + 1.2470671527030857, + 1.2578807851881026, + 1.2690602196702392, + 0.2174637209050114, + 0.8930947614846669, + 1.2068774137802114, + 1.2232110342453228, + 1.181580316622216, + 1.2694081603725658, + 1.2350612554176572, + 0.174732230504834, + 1.0554319533068557, + 1.1684350157389969, + 1.3116746859405015, + 1.2314925272857469, + 1.1804167568816246, + 1.2043693529469417, + 0.1495674390094394, + 0.9888658273595976, + 1.200024231802605, + 1.233694649310816, + 1.196685338421508, + 1.21230174585923, + 1.2256504949775913, + 0.2159991135117441, + 0.8720587264171022, + 1.1438827485991472, + 1.1931721916471685, + 1.2220770250819528, + 1.2024072390890814, + 1.228346028974431, + 0.1342832635254595, + 0.9253276993503023, + 1.0316045545447667, + 1.1329806342607385, + 1.0480489054757316, + 1.084270927624952, + 1.1126799899404909, + 0.1547422354664931, + 0.9316434648593828, + 1.180872255103929, + 1.24489362857649, + 1.1670822710917634, + 1.2001918530465114, + 1.1640762633540294, + 0.1933812838580858, + 0.9263476265489226, + 1.1210583466708013, + 1.2257011547489942, + 1.236176449531025, + 1.2291973664802989, + 1.2699707769410336, + 0.142542229691284, + 1.0370516854872192, + 1.13874686951824, + 1.229207378610874, + 1.2175073789651472, + 1.2291754408666713, + 1.1828776230311044, + 0.0798700918791185, + 1.0208929792846844, + 1.1820081959737552, + 1.2279906199783797, + 1.1472824478973895, + 1.2450764075334604, + 1.2419168454190808, + 0.1829389512212232, + 0.991811765320798, + 1.165784298399422, + 1.2348591428320188, + 1.241401176048452, + 1.2098878167446625, + 1.2379668300701336, + 0.2338981271629903, + 0.9480400430209774, + 1.139536479246261, + 1.2164117074784129, + 1.2316961191853544, + 1.212445548303819, + 1.2238468094052424, + 0.1908940714344191, + 1.0004328914382348, + 1.2077185789853484, + 1.2264882918500986, + 1.1409979281342473, + 1.225464278869398, + 1.1847660240581028, + 0.1792986585307673, + 0.8567531381164782, + 1.0034922635793084, + 1.0738888291991668, + 1.1014715922197496, + 1.0920897759596704, + 1.080186176697299, + 0.1923711030478928, + 0.9863122460089556, + 1.1770236857652483, + 1.243137112325582, + 1.2298901264685953, + 1.1735158569026929, + 1.2033224350942384, + 0.1918131233109666, + 0.9313286178586512, + 1.1448803520482245, + 1.2116104186721062, + 1.249521921843218, + 1.1820179109105813, + 1.242770845855084, + 0.21270914955261, + 0.9710307282692724, + 1.2340727504936653, + 1.254317381165604, + 1.1870970556751406, + 1.211995555056387, + 1.2570078393285125, + 0.1709777779275192, + 0.8504220903021797, + 1.1905720632525283, + 1.200444966874689, + 1.2012101789343608, + 1.271895612162015, + 1.2363214391313964, + 0.1225809888745334, + 0.9874202053764954, + 1.103509702437675, + 1.1840161840314511, + 1.1995434091534587, + 1.290008701862969, + 1.2167489830125944, + 0.1422852072129863, + 0.9514618233018948, + 1.1631882215550675, + 1.2284112959393994, + 1.1984314708469743, + 1.2613365236063074, + 1.2506597043638663, + 0.1563196457904491, + 1.005277270311129, + 1.1943610368874242, + 1.1474747123354696, + 1.2164352423636062, + 1.1917130132453635, + 1.222319305877811, + 0.1571274152390572, + 0.882084260463904, + 1.0007023744865855, + 1.100525711555948, + 1.0932060088008742, + 1.1130401432170078, + 1.0887634700014392, + 0.1716040712052062, + 0.924918300575842, + 1.170622143120521, + 1.1835066291271872, + 1.2251305443461566, + 1.205892766775016, + 1.2117156509744598, + 0.1896608505984979, + 0.8468217150076025, + 1.1864202796898224, + 1.1813909947229932, + 1.1831315986385844, + 1.1181780114585291, + 1.2478711268998126, + 0.1942927421013412, + 0.9413785015728308, + 1.2571947982408829, + 1.2085300720707322, + 1.2222815436533736, + 1.2342251900207906, + 1.1449582627752588, + 0.0854341687644978, + 0.9292886965623748, + 1.1593742644436358, + 1.1974495478377032, + 1.2358388888930194, + 1.231585705655647, + 1.1586491973936648, + 0.224712676142666, + 0.9587373925800132, + 1.14377073893113, + 1.1956600123012044, + 1.2576968269117694, + 1.2344344482293506, + 1.2288687484304457, + 0.1383072403061505, + 0.8838120957889313, + 1.2052100960395609, + 1.2494692702192391, + 1.1993464653681143, + 1.157881699329323, + 1.2112818124966749, + 0.2146362843781857, + 0.9970359461494256, + 1.1751902386696005, + 1.2421918731774717, + 1.2375228987700988, + 1.2279879334930202, + 1.204082654861252 + ], + "yaxis": "y" + } + ], + "layout": { + "legend": { + "title": { + "text": "quant_type" + }, + "tracegroupgap": 0 + }, + "template": { + "data": { + "bar": [ + { + "error_x": { + "color": "#2a3f5f" + }, + "error_y": { + "color": "#2a3f5f" + }, + "marker": { + "line": { + "color": "#E5ECF6", + "width": 0.5 + }, + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "bar" + } + ], + "barpolar": [ + { + "marker": { + "line": { + "color": "#E5ECF6", + "width": 0.5 + }, + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "barpolar" + } + ], + "carpet": [ + { + "aaxis": { + "endlinecolor": "#2a3f5f", + "gridcolor": "white", + "linecolor": "white", + "minorgridcolor": "white", + "startlinecolor": "#2a3f5f" + }, + "baxis": { + "endlinecolor": "#2a3f5f", + "gridcolor": "white", + "linecolor": "white", + "minorgridcolor": "white", + "startlinecolor": "#2a3f5f" + }, + "type": "carpet" + } + ], + "choropleth": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "type": "choropleth" + } + ], + "contour": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "contour" + } + ], + "contourcarpet": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "type": "contourcarpet" + } + ], + "heatmap": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "heatmap" + } + ], + "heatmapgl": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "heatmapgl" + } + ], + "histogram": [ + { + "marker": { + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "histogram" + } + ], + "histogram2d": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "histogram2d" + } + ], + "histogram2dcontour": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "histogram2dcontour" + } + ], + "mesh3d": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "type": "mesh3d" + } + ], + "parcoords": [ + { + "line": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "parcoords" + } + ], + "pie": [ + { + "automargin": true, + "type": "pie" + } + ], + "scatter": [ + { + "fillpattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + }, + "type": "scatter" + } + ], + "scatter3d": [ + { + "line": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatter3d" + } + ], + "scattercarpet": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattercarpet" + } + ], + "scattergeo": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattergeo" + } + ], + "scattergl": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattergl" + } + ], + "scattermapbox": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattermapbox" + } + ], + "scatterpolar": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatterpolar" + } + ], + "scatterpolargl": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatterpolargl" + } + ], + "scatterternary": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatterternary" + } + ], + "surface": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "surface" + } + ], + "table": [ + { + "cells": { + "fill": { + "color": "#EBF0F8" + }, + "line": { + "color": "white" + } + }, + "header": { + "fill": { + "color": "#C8D4E3" + }, + "line": { + "color": "white" + } + }, + "type": "table" + } + ] + }, + "layout": { + "annotationdefaults": { + "arrowcolor": "#2a3f5f", + "arrowhead": 0, + "arrowwidth": 1 + }, + "autotypenumbers": "strict", + "coloraxis": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "colorscale": { + "diverging": [ + [ + 0, + "#8e0152" + ], + [ + 0.1, + "#c51b7d" + ], + [ + 0.2, + "#de77ae" + ], + [ + 0.3, + "#f1b6da" + ], + [ + 0.4, + "#fde0ef" + ], + [ + 0.5, + "#f7f7f7" + ], + [ + 0.6, + "#e6f5d0" + ], + [ + 0.7, + "#b8e186" + ], + [ + 0.8, + "#7fbc41" + ], + [ + 0.9, + "#4d9221" + ], + [ + 1, + "#276419" + ] + ], + "sequential": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "sequentialminus": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ] + }, + "colorway": [ + "#636efa", + "#EF553B", + "#00cc96", + "#ab63fa", + "#FFA15A", + "#19d3f3", + "#FF6692", + "#B6E880", + "#FF97FF", + "#FECB52" + ], + "font": { + "color": "#2a3f5f" + }, + "geo": { + "bgcolor": "white", + "lakecolor": "white", + "landcolor": "#E5ECF6", + "showlakes": true, + "showland": true, + "subunitcolor": "white" + }, + "hoverlabel": { + "align": "left" + }, + "hovermode": "closest", + "mapbox": { + "style": "light" + }, + "paper_bgcolor": "white", + "plot_bgcolor": "#E5ECF6", + "polar": { + "angularaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "bgcolor": "#E5ECF6", + "radialaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + } + }, + "scene": { + "xaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + }, + "yaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + }, + "zaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + } + }, + "shapedefaults": { + "line": { + "color": "#2a3f5f" + } + }, + "ternary": { + "aaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "baxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "bgcolor": "#E5ECF6", + "caxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + } + }, + "title": { + "x": 0.05 + }, + "xaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + }, + "yaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + } + } + }, + "title": { + "text": "Blip-2 COCO Captioning" + }, + "xaxis": { + "anchor": "y", + "domain": [ + 0, + 1 + ], + "title": { + "text": "meteor" + } + }, + "yaxis": { + "anchor": "x", + "domain": [ + 0, + 1 + ], + "title": { + "text": "cider" + } + } + } + }, + "text/html": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "hover_data = list(df_captioning.columns)\n", + "\n", + "fig = px.scatter(df_captioning, x='meteor', y=\"cider\",\n", + " color = 'quant_type',\n", + " hover_data = hover_data,\n", + " title = 'Blip-2 COCO Captioning',)\n", + "\n", + "fig.update_traces(marker=dict(\n", + " line=dict(width=2,\n", + " color='DarkSlateGrey')),\n", + " opacity = 0.75\n", + " )\n", + "\n", + "fig.write_html(\"coco_captioning.html\")\n", + "\n", + "fig.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.14" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/awq/postprocess_results.ipynb b/awq/postprocess_results.ipynb new file mode 100644 index 0000000..442e85d --- /dev/null +++ b/awq/postprocess_results.ipynb @@ -0,0 +1,413 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import json\n", + "import os\n", + "import pandas as pd" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "awq_results_path = '/fs/cfar-projects/low-bit-vision/awq_results/image_text_retrieval'" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "gather = []\n", + "for i in range(7**2):\n", + "\n", + " config_path = os.path.join(awq_results_path, \n", + " 'image_text_retrieval_configs',\n", + " f'awq_{i}')\n", + " config = json.load(open(config_path))\n", + "\n", + " scores_path = os.path.join(awq_results_path,\n", + " 'image_text_retrieval_scores',\n", + " f'awq_{i}')\n", + " scores = json.load(open(scores_path))\n", + "\n", + " # merge dicts :O\n", + " row = config | scores\n", + " gather.append(row)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
vit_bitsqformer_bitstxt_r1txt_r5txt_r10txt_r_meanimg_r1img_r5img_r10img_r_meanr_meanagg_metricsmodel_size
02267.583.088.179.53333361.3281.8886.7276.64000078.08666779.5333333103760704
12383.895.797.692.36666770.5089.6293.6284.58000088.47333392.3666673265519936
22484.595.497.492.43333371.2289.9093.6284.91333388.67333392.4333333427279168
32583.995.697.592.33333371.4289.7493.8685.00666788.67000092.3333333589038400
42683.795.397.492.13333371.1089.8293.7084.87333388.50333392.1333333750797632
\n", + "
" + ], + "text/plain": [ + " vit_bits qformer_bits txt_r1 txt_r5 txt_r10 txt_r_mean img_r1 \\\n", + "0 2 2 67.5 83.0 88.1 79.533333 61.32 \n", + "1 2 3 83.8 95.7 97.6 92.366667 70.50 \n", + "2 2 4 84.5 95.4 97.4 92.433333 71.22 \n", + "3 2 5 83.9 95.6 97.5 92.333333 71.42 \n", + "4 2 6 83.7 95.3 97.4 92.133333 71.10 \n", + "\n", + " img_r5 img_r10 img_r_mean r_mean agg_metrics model_size \n", + "0 81.88 86.72 76.640000 78.086667 79.533333 3103760704 \n", + "1 89.62 93.62 84.580000 88.473333 92.366667 3265519936 \n", + "2 89.90 93.62 84.913333 88.673333 92.433333 3427279168 \n", + "3 89.74 93.86 85.006667 88.670000 92.333333 3589038400 \n", + "4 89.82 93.70 84.873333 88.503333 92.133333 3750797632 " + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = pd.DataFrame(gather)\n", + "df['vit_bits'] = [x['self_attn'] for x in df['vit_layers']]\n", + "df['qformer_bits'] = [x['self_attn'] for x in df['qformer_layers']]\n", + "df = df.drop(['vit_layers', 'qformer_layers'], axis = 1)\n", + "cols_to_move = ['vit_bits', 'qformer_bits']\n", + "df = df[cols_to_move + [ col for col in df.columns if col not in cols_to_move]]\n", + "df[:5]" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "df.to_csv(os.path.join(awq_results_path, 'awq_image_text_retrieval.csv'), index = None)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "awq_results_path = '/fs/cfar-projects/low-bit-vision/awq_results/image_captioning'" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "gather = []\n", + "for i in range(7**3):\n", + "\n", + " config_path = os.path.join(awq_results_path, \n", + " 'image_captioning_configs',\n", + " f'awq_{i}')\n", + " config = json.load(open(config_path))\n", + "\n", + " scores_path = os.path.join(awq_results_path,\n", + " 'image_captioning_scores',\n", + " f'awq_{i}')\n", + " scores = json.load(open(scores_path))\n", + "\n", + " # merge dicts :O\n", + " row = config | scores\n", + " gather.append(row)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
vit_bitsqformer_bitsllm_bitsMETEORMETEOR_per_captionCIDErCIDEr_per_captionmodel_size
02220.029884[0.046109510086455335, 0.0, 0.0459770114942528...0.000790[0.00021961744254830342, 0.0, 0.00015558086569...15727220992
12230.149857[0.10727330075156825, 0.3090046331141728, 0.07...0.389274[0.0019240011690538441, 0.8408578974436829, 0....18244540672
22240.183735[0.14292774501859082, 0.14546094907199703, 0.1...0.544352[0.2696444495408537, 0.46867603603345265, 0.00...20761860352
32250.188660[0.14780028178058824, 0.13799920208691527, 0.0...0.577806[0.33973916406447047, 0.47594926119454817, 0.0...23279180032
42260.192159[0.14780028178058824, 0.18947368421052632, 0.0...0.594062[0.33973916406447047, 0.6514179363760448, 0.00...25796499712
\n", + "
" + ], + "text/plain": [ + " vit_bits qformer_bits llm_bits METEOR \\\n", + "0 2 2 2 0.029884 \n", + "1 2 2 3 0.149857 \n", + "2 2 2 4 0.183735 \n", + "3 2 2 5 0.188660 \n", + "4 2 2 6 0.192159 \n", + "\n", + " METEOR_per_caption CIDEr \\\n", + "0 [0.046109510086455335, 0.0, 0.0459770114942528... 0.000790 \n", + "1 [0.10727330075156825, 0.3090046331141728, 0.07... 0.389274 \n", + "2 [0.14292774501859082, 0.14546094907199703, 0.1... 0.544352 \n", + "3 [0.14780028178058824, 0.13799920208691527, 0.0... 0.577806 \n", + "4 [0.14780028178058824, 0.18947368421052632, 0.0... 0.594062 \n", + "\n", + " CIDEr_per_caption model_size \n", + "0 [0.00021961744254830342, 0.0, 0.00015558086569... 15727220992 \n", + "1 [0.0019240011690538441, 0.8408578974436829, 0.... 18244540672 \n", + "2 [0.2696444495408537, 0.46867603603345265, 0.00... 20761860352 \n", + "3 [0.33973916406447047, 0.47594926119454817, 0.0... 23279180032 \n", + "4 [0.33973916406447047, 0.6514179363760448, 0.00... 25796499712 " + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = pd.DataFrame(gather)\n", + "df['vit_bits'] = [x['self_attn'] for x in df['vit_layers']]\n", + "df['qformer_bits'] = [x['self_attn'] for x in df['qformer_layers']]\n", + "df['llm_bits'] = [x['self_attn'] for x in df['llm_layers']]\n", + "\n", + "df = df.drop(['vit_layers', 'qformer_layers', 'llm_layers'], axis = 1)\n", + "cols_to_move = ['vit_bits', 'qformer_bits', 'llm_bits']\n", + "df = df[cols_to_move + [ col for col in df.columns if col not in cols_to_move]]\n", + "df[:5]" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "df.to_csv(os.path.join(awq_results_path, 'awq_image_captioning.csv'), index = None)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.14" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/awq/quantizer.py b/awq/quantizer.py new file mode 100644 index 0000000..70d6272 --- /dev/null +++ b/awq/quantizer.py @@ -0,0 +1,1075 @@ +import torch +import numpy as np +import torch.nn as nn +from transformers import Blip2ForConditionalGeneration, Blip2ForImageTextRetrieval + +from tqdm import tqdm +from collections import defaultdict +from functools import partial +from typing import Tuple, List +import random + +from awq.scaled_modules import ScaledModule +from awq.utils import * + + +# ==================================================== +# Base AWQ Quantizer Class +# ==================================================== +class BaseAWQQuantizer(): + + def __init__(self, model, device, inputs_processor, dataset, config, **kwargs): + self.model = model + self.device = device + self.inputs_processor = inputs_processor + self.dataset = dataset + self.config = config + + # QUANTIZATION SETTINGS + self.group_size = 128 + self.grid_search_size = 20 + self.zero_point = True + + # Calibration set size, AutoAWQ uses 128 for LLMs + self.n_samples = 128 + + # seed for sampling dataset + self.seed = 42 + + self.excluded_mods = [] + + + def pseudo_quantize_tensor(self, w: torch.Tensor, w_bits): + org_w_shape = w.shape + if self.group_size > 0: + assert org_w_shape[-1] % self.group_size == 0 + w = w.reshape(-1, self.group_size) + assert w.dim() == 2 + assert torch.isnan(w).sum() == 0 + + # zero point quantization + if self.zero_point: + max_val = w.amax(dim=1, keepdim=True) + min_val = w.amin(dim=1, keepdim=True) + max_int = 2**w_bits - 1 + min_int = 0 + scales = (max_val - min_val).clamp(min=1e-5) / max_int + zeros = (-torch.round(min_val / scales)).clamp_(min_int, max_int) + w = ( + torch.clamp(torch.round(w / scales) + zeros, min_int, max_int) - zeros + ) * scales + zeros = zeros.view(org_w_shape[0], -1) + else: + max_val = w.abs().amax(dim=1, keepdim=True) + max_val = max_val.clamp(min=1e-5) + max_int = 2 ** (w_bits- 1) - 1 + min_int = -(2 ** (w_bits - 1)) + scales = max_val / max_int + zeros = None + w = torch.clamp(torch.round(w / scales), min_int, max_int) * scales + + assert torch.isnan(scales).sum() == 0 + assert torch.isnan(w).sum() == 0 + + scales = scales.view(org_w_shape[0], -1) + w = w.reshape(org_w_shape) + + return w, scales, zeros + + + @torch.no_grad + def quantize(self): + ''' + Apply AWQ to self.model, pseudo-quantizing weights in-place + ''' + + self.model_size = 0 + + layer_groups = self._get_model_layer_groups() + self._add_mods_to_model_size(self.excluded_mods) + + print(f'Calibration set size: {self.n_samples}') + calibration_set = self._get_calibration_set() + + # Run calibration set through model + first_inputs, self.layer_args, self.layer_kwargs = self._gather_first_inputs(layer_groups, calibration_set) + + del calibration_set + gc.collect() + torch.cuda.empty_cache() + + + self.model.to('cpu') + + # print(first_inputs) + for layer_group, modules in layer_groups.items(): + self.inps = first_inputs[layer_group] + + # quantize layer-by-layer + for i in tqdm(range(len(modules)), desc= f"Quantizing {layer_group}"): + + pass + + # move layer inputs to gpu + self.inps = self.inps.to(self.device) + + layer = modules[i] + # move layer to gpu + layer = layer.to(self.device) + + # nn.linear modules within layer to quantize + named_linears = get_named_linears(layer) + # two dicts with the same keys, mapping module name to nn.linear and module name to bit_width + named_linears, w_bits_dict = self._filter_named_linears(named_linears, layer_group) + # gather inputs to each nn.Linear via pytorch hooks + # NOTE: also setting self.inps for next iteration + linear_inputs = self._gather_linear_inputs(layer, named_linears, layer_group) + + # group weights together as appropriate for model type + # TODO: can probably just move this to outer for loop, unless supporting per-layer granularity + grouped_mods = self._group_modules_for_scaling(layer, linear_inputs, layer_group) + + # compute scales over each group of modules (weight blocks) to quantize + scales = [ + self._compute_scales(layer, **group) + for group in grouped_mods + ] + + # apply scales to prev_op and modules + for group, scale in zip(grouped_mods, scales): + assert torch.all(scale) + self._apply_scales(scale, group['prev_op'], group['modules'], layer) + + scale = scale.to('cpu') + clear_memory(scale) + + + # solve for and apply clipping + clips = self._search_best_clip(named_linears, linear_inputs, w_bits_dict) + self._apply_clip(named_linears, clips) + + # apply pseudo_quant to linear weights + for name, module in named_linears.items(): + # module = module.to(device).half() + module = module.to(self.device) + module.weight.data, scales, _ = self.pseudo_quantize_tensor( + module.weight.data, w_bits_dict[name] + ) + module = module.to('cpu') + clear_memory(module) + scales = scales.to('cpu') + clear_memory(scales) + + layer = layer.to('cpu') + clear_memory(layer) + + self.model.to('cpu') + gc.collect() + torch.cuda.empty_cache() + pass + + + clear_memory(first_inputs[layer_group]) + + + + + def _gather_first_inputs(self, layer_groups, calibration_set): + ''' + Gather initial inputs (+other positional args, kwargs) to each layer group + Runs calibration set through model up until the last layer group + ''' + + first_inputs = {} + layer_args = {} + layer_kwargs = {} + + # get input and kwargs to layer 0 (for each group of layers) + # use this Catcher hack cause forward hooks cannot capture kwargs + class Catcher(nn.Module): + def __init__(self, module, layer_group, is_last): + super().__init__() + self.module = module + self.layer_group = layer_group + self.is_last = is_last + + def forward(self, *args, **kwargs): + # assume first input to forward is hidden states + if len(args) > 0: + hidden_states = args[0] + # del args + else: + first_key = list(kwargs.keys())[0] + hidden_states = kwargs.pop(first_key) + + first_inputs[self.layer_group] = hidden_states.to('cpu') + + # preserve rest of positional arguments + layer_args[self.layer_group] = args[1:] + layer_kwargs[self.layer_group] = kwargs + + # early exit for last group of layers + if self.is_last: + raise ValueError + + return self.module.forward(*args, **kwargs) + + keys = list(layer_groups.keys()) + + for i in range(len(keys)): + layer_group = keys[i] + is_last = True if i == len(keys) - 1 else False + + modules = layer_groups[layer_group] + modules[0] = Catcher(modules[0], layer_group, is_last) + + self.model = self.model.to(self.device) + + if type(calibration_set) == torch.tensor: + calibration_set = calibration_set.to(self.device) + + # NOTE: catching raised ValueError to stop inference early + try: + self._run_model(calibration_set) + except ValueError: + pass + + + if type(calibration_set) == torch.tensor: + calibration_set = calibration_set.cpu() + clear_memory(calibration_set) + else: + for key in calibration_set.keys(): + calibration_set[key] = calibration_set[key].to('cpu') + clear_memory(calibration_set[key]) + + del calibration_set + + for _, modules in layer_groups.items(): + # restore proper module at beginning of layer group + modules[0] = modules[0].module + + return first_inputs, layer_args, layer_kwargs + + + def _gather_linear_inputs(self, layer, named_linears, layer_group): + ''' + Gather inputs to linear layers using pytorch forward hooks + ''' + + def input_hook(module, input, output, module_name, inputs): + x = input[0] + x = x.detach().cpu() + inputs[module_name].append(x) + + + inputs = defaultdict(list) + hooks = [] + + for name, mod in named_linears.items(): + hooks.append( + mod.register_forward_hook(partial(input_hook, + module_name = name, + inputs = inputs)) + ) + + # compute next set of inputs, grabbing linear inputs through the hooks + # self.inps = layer(self.inps, *self.layer_args[layer_group], **self.layer_kwargs[layer_group]) + out = layer(self.inps, *self.layer_args[layer_group], **self.layer_kwargs[layer_group])[0].to('cpu') + self.inps = self.inps.to('cpu') + clear_memory(self.inps) + + # self.inps = self.inps[0].to('cpu') + self.inps = out + + # remove hooks from model + for hook in hooks: + hook.remove() + + inputs = {k: torch.cat(v, dim=0) for k, v in inputs.items()} + + return inputs + + def _compute_scales(self, layer, prev_op, modules, inp, parent_module, layer_kwargs, w_bits): + + ''' + Grid search for scales to preserve salient weights, + Minimizes L2 loss between full-precision and quantized output + ''' + + inp = inp.to(self.device) + + # block of weights concatted together + W = torch.cat([mod.weight for mod in modules], dim = 0) + orig_shape = W.shape + W = W.view(-1, self.group_size) + + # rescale W to 0-1 scale + W_scale = W.abs() / (W.abs().amax(dim=1, keepdim=True) + 1e-6) + W_scale = W_scale.view(orig_shape) + # per channel mean of normalized weights + W_mean = W_scale.mean(0) + W_mean = W_mean.view(-1) + + clear_memory(W) + + # per channel mean of input (activation) + X_mean = inp.cpu().abs().view(-1, inp.shape[-1]).mean(0) + X_mean = X_mean.view(-1) + + kwargs = sanitize_kwargs(layer_kwargs, parent_module) + + # compute full precision output + with torch.no_grad(): + fp_output = parent_module(inp, **kwargs)[0] + + + # Grid search for best scales + n_grid = self.grid_search_size + history = [] + best_ratio = -1 + best_scales = None + best_error = float("inf") + + org_sd = {k: v.cpu() for k, v in parent_module.state_dict().items()} + + for ratio in range(n_grid): + scales = X_mean.pow(ratio).clamp(min=1e-4).view(-1) + + # avoid scaling values that overflow + scales[torch.isinf(scales)] = 1 + scales[torch.isnan(scales)] = 1 + + scales_view = scales.view(1, -1).to(self.device) + + + fail_flag = False + # Q(W * s) / s + # pseudo-quantize modules (nn.linear) + for mod in modules: + + # mod.weight.data = mod.weight.data.to(torch.double) + + # TODO: this operation produces an inf sometimes... + mod.weight.mul_(scales_view) + + if torch.isinf(mod.weight.data).sum() != 0: + fail_flag = True + break + + # mod.weight.data = mod.weight.data.to(torch.float16) + + mod.weight.data = ( + self.pseudo_quantize_tensor(mod.weight.data, w_bits)[0] / scales_view + ) + + if fail_flag: + parent_module.load_state_dict(org_sd) + continue + + with torch.no_grad(): + # Q(W * s) / s * X + q_output = parent_module(inp, **kwargs)[0] + + # Compute loss (L2 NORM) + loss = compute_loss(fp_output, q_output) + + history.append(loss) + if loss < best_error: + best_error = loss + best_ratio = ratio + best_scales = scales.clone() + + # reset to original weights + parent_module.load_state_dict(org_sd) + + assert best_ratio != -1, "best scales ratio never set" + assert torch.isnan(best_scales).sum() == 0, best_scales + + # NOTE: trying this to save memory... + inp = inp.to('cpu') + clear_memory(inp) + fp_output = fp_output.to('cpu') + clear_memory(fp_output) + q_output = q_output.to('cpu') + clear_memory(q_output) + scales_view = scales_view.to('cpu') + clear_memory(scales_view) + + return best_scales.detach().cpu() + + + def _apply_scales(self, scale, prev_op, modules, layer): + + ''' + Applies scales to weights in modules and fuses scales + to prev_op/adds wrapper module + ''' + + scale = scale.to(self.device) + + # define custom pytorch wrapper module to apply scaling for input + # doing this when there isn't a convenient module to fuse scaling with prior to reaching modules + if isinstance(prev_op, str): + module = rgetattr(layer, prev_op) + scaled_mod = ScaledModule(scale, module) + module = rsetattr(layer, prev_op, scaled_mod) + else: + prev_op = prev_op.to(self.device) + + # fuse scales with previous LayerNorm + if isinstance(prev_op, torch.nn.LayerNorm): + prev_op.weight.div_(scale) + + if hasattr(prev_op, "bias") and prev_op.bias is not None: + prev_op.bias.div_(scale) + + # fuse scales with previous Linear module + elif isinstance(prev_op, torch.nn.Linear): + prev_op.weight[-scale.size(0) :].div_(scale.view(-1, 1)) + + # SANITY check + for p in prev_op.parameters(): + assert torch.isnan(p).sum() == 0 + prev_op.cpu() + + # store (W*s), quantization applied later + for fc in modules: + fc.weight.mul_(scale.view(1, -1)) + + # SANITY check + for p in fc.parameters(): + assert torch.isnan(p).sum() == 0 + + for fc in modules: + fc.cpu() + scale.cpu() + + + @torch.no_grad() + def _apply_clip(self, modules, clip_list: Tuple[str, torch.Tensor]): + ''' + Clamp outlier values before quantization according to computed clipping values + ''' + for name, max_val in clip_list: + module: nn.Linear = modules[name] + module.to(self.device) + + max_val = max_val.to(module.weight.device) + org_shape = module.weight.shape + + module.weight.data = module.weight.data.reshape(*max_val.shape[:2], -1) + module.weight.data = torch.clamp(module.weight.data, -max_val, max_val) + module.weight.data = module.weight.data.reshape(org_shape) + module.cpu() + + def _search_best_clip(self, modules, linear_inputs, w_bits): + ''' + Grid search for best clipping ranges for each module in modules + ''' + + clip_list = [] + + # NOTE: awq libraries seem to avoid clipping some attention modules + avoid_clipping = ["q_", "k_", "query", "key", "Wqkv"] + + for name in modules: + # due to qk bmm, it is hard to clip precisely + if any([_ in name for _ in avoid_clipping]): + continue + + modules[name].to(self.device) + max_val = self._compute_best_clip( + modules[name].weight, linear_inputs[name], w_bits[name], n_grid = self.grid_search_size + ) + clip_list.append((name, max_val)) + modules[name].cpu() + + return clip_list + + + @torch.no_grad() + def _compute_best_clip( + self, + w: torch.Tensor, + input_feat: torch.Tensor, + w_bits, + n_grid=20, + max_shrink=0.5, + n_sample_token=512, + ): + + ''' + Compute best clipping ranges (grid search) for w + Minimizes MSE b/w original and quantized values + ''' + + assert w.dim() == 2 + org_w_shape = w.shape + # w [co, ci] -> [co, 1, n_group, group size] + # input_feat [n_token, ci] -> [1, n_token, n_group, group size] + group_size = self.group_size # if self.group_size > 0 else org_w_shape[1] + input_feat = input_feat.view(-1, input_feat.shape[-1]) + input_feat = input_feat.reshape(1, input_feat.shape[0], -1, group_size) + + # Compute input feature step size (minimum 1) + step_size = max(1, input_feat.shape[1] // n_sample_token) + input_feat = input_feat[:, ::step_size] + + w = w.reshape(org_w_shape[0], 1, -1, group_size) + + oc_batch_size = 256 if org_w_shape[0] % 256 == 0 else 64 # prevent OOM + assert org_w_shape[0] % oc_batch_size == 0 + w_all = w + best_max_val_all = [] + + for i_b in range(org_w_shape[0] // oc_batch_size): + w = w_all[i_b * oc_batch_size : (i_b + 1) * oc_batch_size] + + org_max_val = w.abs().amax(dim=-1, keepdim=True) # co, 1, n_group, 1 + + best_max_val = org_max_val.clone() + min_errs = torch.ones_like(org_max_val) * 1e9 + input_feat = input_feat.to(w.device) + org_out = (input_feat * w).sum(dim=-1) # co, n_token, n_group + + for i_s in range(int(max_shrink * n_grid)): + max_val = org_max_val * (1 - i_s / n_grid) + min_val = -max_val + cur_w = torch.clamp(w, min_val, max_val) + q_w = self.pseudo_quantize_tensor(cur_w, w_bits)[0] + cur_out = (input_feat * q_w).sum(dim=-1) + + # co, 1, n_group, 1 + err = (cur_out - org_out).pow(2).mean(dim=1).view(min_errs.shape) + del cur_w + del cur_out + cur_best_idx = err < min_errs + min_errs[cur_best_idx] = err[cur_best_idx] + best_max_val[cur_best_idx] = max_val[cur_best_idx] + best_max_val_all.append(best_max_val) + + best_max_val = torch.cat(best_max_val_all, dim=0) + + clear_memory(input_feat) + clear_memory(org_out) + + return best_max_val.squeeze(1) + + + def _filter_named_linears(self, named_linears, layer_group): + ''' + Filter nn.Linear modules according to what is defined in config + ''' + + def flatten(xss): + return [x for xs in xss for x in xs] + + + valid_modules = [] + w_bits_by_linear = {} + + for k in self.config[layer_group]: + + module_names = self.group2modules[layer_group][k] + valid_modules.append(module_names) + + for name in module_names: + w_bits_by_linear[name] = self.config[layer_group][k] + + + valid_modules = [self.group2modules[layer_group][k] for k in self.config[layer_group]] + valid_modules = flatten(valid_modules) + + # filtered_linears = {k:v for k,v in named_linears.items() if k in valid_modules} + + filtered_linears = {} + for k,v in named_linears.items(): + + if k in valid_modules: + filtered_linears[k] = v + # quantized size (in bits) + element_size = w_bits_by_linear[k] + + else: + # bytes --> bits + element_size = (param.element_size() * 8) + + # add to model size + for _,param in v.named_parameters(): + self.model_size += param.nelement() * element_size + + return filtered_linears, w_bits_by_linear + + + def _add_mods_to_model_size(self, mods): + + size = 0 + + for layer in mods: + for name, param in layer.named_parameters(): + # NOTE: element_size in bits + element_size = param.element_size() * 8 + size += param.nelement() * element_size + + # model buffers (not quantized) + for buffer in self.model.buffers(): + size += buffer.nelement() * (buffer.element_size() * 8) + + self.model_size += size + + def _get_calibration_set(self): + raise NotImplementedError('_get_calibration_set') + + # return layers of model to consider for quantization (modify with config file) + def _get_model_layer_groups(self): + raise NotImplementedError('_get_model_layers') + + def _run_model(self, input): + raise NotImplementedError + # process calibration set inputs + def _prepare_input(self): + raise NotImplementedError('_prepare_input') + + # return groups of modules for weight grouping, scales calculation + def _group_modules_for_scaling(self, layer, linear_inputs, layer_group): + raise NotImplementedError('_group_modules_for_scaling') + + + +class Blip2AWQQuantizer(BaseAWQQuantizer): + + def __init__(self, model, device, inputs_processor, dataset, config): + super().__init__(model, device, inputs_processor, dataset, config) + self.group2modules = self._get_group2modules() + + def _get_group2modules(self): + + group2modules = {} + + if 'vit_layers' in self.config: + group2modules['vit_layers'] = { + 'self_attn': ['self_attn.qkv'], + 'self_attn_output' : ['self_attn.projection'], + 'fc1' : ['mlp.fc1'], + 'fc2': ['mlp.fc2'] + } + + if 'qformer_layers' in self.config: + group2modules['qformer_layers'] = { + 'self_attn': ['attention.attention.query', 'attention.attention.key', 'attention.attention.value'], + 'self_attn_output':['attention.output.dense'], + 'intermediate_txt': ['intermediate.dense'], + 'output_txt': ['output.dense'], + 'intermediate_query': ['intermediate_query.dense'], + 'output_query': ['output_query.dense'], + 'cross_attn': ['crossattention.attention.query', 'crossattention.attention.key', 'crossattention.attention.value'], + 'cross_attn_output': ['crossattention.output.dense'], + + # TODO: how to handle these since they aren't a part of a particular layer like + # all the other modules + # 'vision_proj':4, + # 'txt_proj':4, + # 'itm_head': 4, + } + + if 'llm_layers' in self.config: + group2modules['llm_layers'] = { + 'self_attn': ['self_attn.k_proj', 'self_attn.v_proj', 'self_attn.q_proj'], + 'self_attn_output': ['self_attn.out_proj'], + 'fc1':['fc1'], + 'fc2': ['fc2'] + } + + return group2modules + + def _group_modules_for_scaling(self, layer, linear_inputs, layer_group): + grouped_mods = [] + + if layer_group == 'vit_layers': + + # vit self-attn + if 'self_attn' in self.config[layer_group]: + grouped_mods.append( + dict( + prev_op = layer.layer_norm1, + modules = [layer.self_attn.qkv], + inp = linear_inputs['self_attn.qkv'], + parent_module = layer.self_attn, + layer_kwargs = self.layer_kwargs[layer_group], + w_bits = self.config[layer_group]['self_attn'] + ) + ) + + if 'selt_attn_output' in self.config[layer_group]: + grouped_mods.append( + dict( + prev_op = 'self_attn.projection', + modules = [layer.self_attn.projection], + inp = linear_inputs['self_attn.projection'], + parent_module = layer.self_attn.projection, + layer_kwargs = self.layer_kwargs[layer_group], + w_bits = self.config[layer_group]['selt_attn_output'] + ) + ) + + if 'fc1' in self.config[layer_group]: + grouped_mods.append( + # vit fc1 + dict( + prev_op = layer.layer_norm2, + modules = [layer.mlp.fc1], + inp = linear_inputs['mlp.fc1'], + parent_module = layer.mlp.fc1, + layer_kwargs = self.layer_kwargs[layer_group], + w_bits = self.config[layer_group]['fc1'] + ) + ) + + # vit fc2 + if 'fc2' in self.config[layer_group]: + grouped_mods.append( + dict( + prev_op = layer.mlp.fc1, + modules = [layer.mlp.fc2], + inp = linear_inputs['mlp.fc2'], + parent_module = layer.mlp.fc2, + layer_kwargs = self.layer_kwargs[layer_group], + w_bits = self.config[layer_group]['fc2'] + ) + ) + + elif layer_group == 'qformer_layers': + + # Qformer self-attn QKV + if 'self_attn' in self.config[layer_group]: + grouped_mods.append( + dict( + prev_op = 'attention.attention', + modules = [ + layer.attention.attention.query, + layer.attention.attention.key, + layer.attention.attention.value + ], + inp = linear_inputs['attention.attention.query'], + parent_module = layer.attention.attention, + layer_kwargs = self.layer_kwargs[layer_group], + w_bits = self.config[layer_group]['self_attn'] + ) + ) + + # Qformer self-attn output + if 'self_attn_output' in self.config[layer_group]: + grouped_mods.append( + dict( + prev_op = 'attention.output.dense', + modules = [layer.attention.output.dense], + inp = linear_inputs['attention.output.dense'], + parent_module = layer.attention.output.dense, + layer_kwargs = self.layer_kwargs[layer_group], + w_bits = self.config[layer_group]['self_attn_output'] + ) + ) + + + # Qformer intermediate (txt) + if 'intermediate_txt' in self.config[layer_group]: + grouped_mods.append( + dict( + prev_op = 'intermediate.dense', + modules = [layer.intermediate.dense], + inp = linear_inputs['intermediate.dense'], + parent_module = layer.intermediate.dense, + layer_kwargs = self.layer_kwargs[layer_group], + w_bits = self.config[layer_group]['intermediate_txt'] + ) + ) + + # Qformer output (txt) + if 'output_txt' in self.config[layer_group]: + grouped_mods.append( + dict( + prev_op = 'output.dense', + modules = [layer.output.dense], + inp = linear_inputs['output.dense'], + parent_module = layer.output.dense, + layer_kwargs = self.layer_kwargs[layer_group], + w_bits = self.config[layer_group]['output_txt'] + ) + ) + + # Qformer intermediate_query (img) + if 'intermediate_query' in self.config[layer_group]: + grouped_mods.append( + dict( + prev_op = 'intermediate_query.dense', + modules = [layer.intermediate_query.dense], + inp = linear_inputs['intermediate_query.dense'], + parent_module = layer.intermediate_query.dense, + layer_kwargs = self.layer_kwargs[layer_group], + w_bits = self.config[layer_group]['intermediate_query'] + ) + ) + + # Qformer output_query (img) + if 'output_query' in self.config[layer_group]: + grouped_mods.append( + dict( + prev_op = 'output_query.dense', + modules = [layer.output_query.dense], + inp = linear_inputs['output_query.dense'], + parent_module = layer.output_query.dense, + layer_kwargs = self.layer_kwargs[layer_group], + w_bits = self.config[layer_group]['output_query'] + ) + ) + + # Qformer cross-attn (only present every other layer) + if hasattr(layer, 'crossattention'): + # NOTE: Qformer cross-attn QKV cant be grouped together (unlike self-attn) + # because of different shapes of hidden_states and encoder_hidden_states + if 'cross_attn' in self.config[layer_group]: + grouped_mods.append( + dict( + prev_op = 'crossattention.attention.query', + modules = [ + layer.crossattention.attention.query, + ], + inp = linear_inputs['crossattention.attention.query'], + parent_module = layer.crossattention.attention.query, + layer_kwargs = self.layer_kwargs[layer_group], + w_bits = self.config[layer_group]['cross_attn'] + ) + ) + grouped_mods.append( + dict( + prev_op = 'crossattention.attention.key', + modules = [ + layer.crossattention.attention.key, + ], + inp = linear_inputs['crossattention.attention.key'], + parent_module = layer.crossattention.attention.key, + layer_kwargs = self.layer_kwargs[layer_group], + w_bits = self.config[layer_group]['cross_attn'] + ) + ) + grouped_mods.append( + dict( + prev_op = 'crossattention.attention.value', + modules = [ + layer.crossattention.attention.value + ], + inp = linear_inputs['crossattention.attention.value'], + parent_module = layer.crossattention.attention.value, + layer_kwargs = self.layer_kwargs[layer_group], + w_bits = self.config[layer_group]['cross_attn'] + ) + ) + + # Qformer cross-attn output + if 'cross_attn_output' in self.config[layer_group]: + grouped_mods.append( + dict( + prev_op = 'crossattention.output.dense', + modules = [layer.crossattention.output.dense], + inp = linear_inputs['crossattention.output.dense'], + parent_module = layer.crossattention.output.dense, + layer_kwargs = self.layer_kwargs[layer_group], + w_bits = self.config[layer_group]['cross_attn_output'] + ) + ) + + + elif layer_group == 'llm_layers': + + assert layer.do_layer_norm_before, "llm do_layer_norm_before set to false" + + # llm attn + if 'self_attn' in self.config[layer_group]: + grouped_mods.append( + dict( + prev_op = layer.self_attn_layer_norm, + modules = [ + layer.self_attn.q_proj, + layer.self_attn.k_proj, + layer.self_attn.v_proj, + ], + inp = linear_inputs['self_attn.q_proj'], + parent_module = layer.self_attn, + layer_kwargs = self.layer_kwargs[layer_group], + w_bits = self.config[layer_group]['self_attn'] + ) + ) + + # llm attn output + if 'self_attn_output' in self.config[layer_group]: + grouped_mods.append( + dict( + prev_op = layer.self_attn.v_proj, + modules = [layer.self_attn.out_proj], + inp = linear_inputs['self_attn.out_proj'], + parent_module = layer.self_attn.out_proj, + layer_kwargs = self.layer_kwargs[layer_group], + w_bits = self.config[layer_group]['self_attn_output'] + ) + ) + + # LLM FC1 + if 'fc1' in self.config[layer_group]: + grouped_mods.append( + dict( + prev_op = layer.final_layer_norm, + modules = [layer.fc1], + inp = linear_inputs['fc1'], + parent_module = layer.fc1, + layer_kwargs = self.layer_kwargs[layer_group], + w_bits = self.config[layer_group]['fc1'] + ) + ) + + # LLM FC2 + if 'fc2' in self.config[layer_group]: + grouped_mods.append( + dict( + prev_op = layer.fc1, + modules = [layer.fc2], + inp = linear_inputs['fc2'], + parent_module = layer.fc2, + layer_kwargs = self.layer_kwargs[layer_group], + w_bits = self.config[layer_group]['fc2'] + ) + ) + + return grouped_mods + + + +# ====================================================================== +# Blip2ForConditionalGeneration (captioning task) AWQ Quantizer Class +# ====================================================================== +class Blip2ForConditionalGenerationAWQQuantizer(Blip2AWQQuantizer): + + def __init__(self, model, device, inputs_processor, dataset, config): + assert isinstance(model, Blip2ForConditionalGeneration) + super().__init__(model, device, inputs_processor, dataset, config) + self._run_model = model.generate + + # keep track of excluded (not quantized) modules for model size calc + self.excluded_mods = [self.model.language_projection] + + + def _get_model_layer_groups(self): + + + # NOTE: should ensure that keys are defined sequentially for early quitting of calibration set run + layer_groups = {} + + if 'vit_layers' in self.config: + layer_groups['vit_layers'] = self.model.vision_model.encoder.layers + self.excluded_mods.extend(get_mods(self.model.vision_model, non_linears_only=True)) + else: + self.excluded_mods.extend(get_mods(self.model.vision_model, non_linears_only=False)) + + if 'qformer_layers' in self.config: + layer_groups['qformer_layers'] = self.model.qformer.encoder.layer + self.excluded_mods.extend(get_mods(self.model.qformer, non_linears_only=True)) + else: + self.excluded_mods.extend(get_mods(self.model.qformer, non_linears_only=False)) + + + if 'llm_layers' in self.config: + layer_groups['llm_layers'] = self.model.language_model.model.decoder.layers + self.excluded_mods.extend(get_mods(self.model.language_model, non_linears_only=True)) + # exclude this final projection still + self.excluded_mods.append(self.model.language_model.lm_head) + else: + self.excluded_mods.extend((get_mods(self.model.language_model, non_linears_only=False))) + + return layer_groups + + + def _prepare_input(self, inp): + X = self.inputs_processor(images=inp, return_tensors="pt").to(self.device) + return X['pixel_values'] + + + def _get_calibration_set(self): + ''' + Sample n_samples from self.dataset and return as single tensor + ''' + + samples = [] + random.seed(self.seed) + indices = random.sample(range(len(self.dataset)), self.n_samples) + + for i in indices: + data = self.dataset[i] + sample = self._prepare_input(data[0]) + samples.append(sample) + + samples = torch.cat(samples, dim = 0) + return samples + + +# ====================================================================== +# BLip2ForImageTextRetrieval (retrieval task) AWQ Quantizer Class +# ====================================================================== +class Blip2ForImageTextRetrievalAWQQuantizer(Blip2AWQQuantizer): + + def __init__(self, model, device, inputs_processor, dataset, config): + assert isinstance(model, Blip2ForImageTextRetrieval) + super().__init__(model, device, inputs_processor, dataset, config) + + # TODO: TEST + self.n_samples = 128 + + self.excluded_mods = [self.model.embeddings, + self.model.vision_projection, + self.model.text_projection, + self.model.itm_head] + + + + def _get_model_layer_groups(self): + + # NOTE: should ensure that keys are defined sequentially for early quitting of calibration set run + layer_groups = {} + + if 'vit_layers' in self.config: + layer_groups['vit_layers'] = self.model.vision_model.encoder.layers + self.excluded_mods.extend(get_mods(self.model.vision_model, non_linears_only=True)) + else: + self.excluded_mods.extend(get_mods(self.model.vision_model, non_linears_only=False)) + + if 'qformer_layers' in self.config: + layer_groups['qformer_layers'] = self.model.qformer.encoder.layer + self.excluded_mods.extend(get_mods(self.model.qformer, non_linears_only=True)) + else: + self.excluded_mods.extend(get_mods(self.model.qformer, non_linears_only=False)) + + + return layer_groups + + + def _get_calibration_set(self): + + random.seed(self.seed) + indices = random.sample(range(len(self.dataset)), self.n_samples) + + images = [] + texts = [] + for i in indices: + images.append(self.dataset[i]['image']) + texts.append(self.dataset.text[i]) + + samples = self.inputs_processor(images=images, text = texts, + padding=True, truncation=True, + return_tensors="pt").to(self.device, torch.float16) + + return samples + + + def _run_model(self, calibration_set): + itm_out = self.model(**calibration_set, use_image_text_matching_head=True) + calibration_set.to('cpu') + clear_memory(calibration_set) diff --git a/awq/retrieval_multi_sbatch.py b/awq/retrieval_multi_sbatch.py new file mode 100644 index 0000000..158dff2 --- /dev/null +++ b/awq/retrieval_multi_sbatch.py @@ -0,0 +1,379 @@ +import os +from datetime import datetime +import argparse +import shutil +import math +import time +import socket +import itertools +import subprocess + + +def run(cmd): + return subprocess.check_output(cmd, shell=True).decode('UTF-8').splitlines() + +def present_in_list(string, gpu_list): + return any([x in string for x in gpu_list]) + +def split(a, n): + k, m = divmod(len(a), n) + return (a[i*k+min(i, m):(i+1)*k+min(i+1, m)] for i in range(n)) + +def get_exclude_string(gpu_list, default_exclude=None): + if gpu_list[0] == 'any': + if default_exclude is None: + return '' + else: + return '#SBATCH --exclude='+','.join(default_exclude) + memdata = run('sinfo -O nodehost,gres -h') + superset = set([x.split()[0] for x in memdata]) + blacklist = [] + for x in memdata: + nodehost, gres = x.strip().split() + if present_in_list(gres, gpu_list): + blacklist.append(nodehost) + + exclude_list = superset - set(blacklist) + if default_exclude: + exclude_list = exclude_list.union(set(default_exclude)) + exclude_string = ','.join(sorted(exclude_list)) + if exclude_string: + exclude_string = '#SBATCH --exclude='+exclude_string+'\n' + return exclude_string + else: + return '' + +def get_include_string(gpu_list, default_include=None): + if gpu_list[0] == 'any': + raise Exception("That's too much, man! (It's a Bojack reference. Watch it if you haven't already, you degenerate)") + memdata = run('sinfo -O nodehost,gres -h') + include_list = [] + for x in memdata: + nodehost, gres = x.strip().split() + if present_in_list(gres, gpu_list): + include_list.append(nodehost) + include_string = ','.join(sorted(include_list)) + if include_string: + include_string = '#SBATCH --nodelist='+include_string+'\n' + return include_string + else: + return '' + +# Function to chec for validity of QOS +#TODO: Add time check for QOS + +qos_dict = { + "scav" : {"nhrs" : 72, "cores": 32, "mem":256}, + "high" : {"gpu":4, "cores": 16, "mem":128, "nhrs": 36}, + "medium" : {"gpu":2, "cores": 8, "mem":64, "nhrs": 72}, + "default" : {"gpu":1, "cores": 4, "mem":32, "nhrs": 168}} + + +def check_qos(args): + + for qos in args.qos: + for key, max_value in qos_dict[qos].items(): + val_from_args = getattr(args, key) + if val_from_args != None: + if val_from_args > max_value: + raise ValueError("Invalid parameter for {} for {}".format(key, qos)) + else: + setattr(args, key, max_value) + return args + + +#TODO: Add day funtionality too +parser = argparse.ArgumentParser() +parser.add_argument('--nhrs', type=int, default=None) +parser.add_argument('--base-dir', default=f'{os.getcwd()}') +parser.add_argument('--output-dirname', default='outputs') +parser.add_argument('--partition', default='vulcan', choices=['vulcan','cml','nexus']) +parser.add_argument('--dryrun', action='store_true') +parser.add_argument('--qos', default=None, type=str, nargs='*', help='Qos to run') +parser.add_argument('--env', type=str, help = "Set the name of the dir you want to dump") +parser.add_argument('--gpu', default=None, type=int, help='Number of gpus') +parser.add_argument('--gpu-type', type=str, help='Type of gpu to use (can be multiple)', default=['any'], + choices=['any','p6000','gtx','rtx2080','a4000','a5000','a6000'], nargs='*') +parser.add_argument('--cores', default=None, type=int, help='Number of cpu cores') +parser.add_argument('--mem', default=None, type=int, help='RAM in G') +parser.add_argument('--single', action='store_true') +parser.add_argument('--filename', default=None, type=str, help='Slurm file name') +parser.add_argument('--max_jobs', default=80, type=int, help='Maximum number of jobs running in parallel') +parser.add_argument('--offset', default=0, type=int, help='Offset') +parser.add_argument('--batchsize', default=500, type=int, help='Offset') + +args = parser.parse_args() + +if args.filename is None: + args.filename = args.env + +output_dir = os.path.join(args.base_dir, args.output_dirname, args.env) +if os.path.exists(output_dir): + shutil.rmtree(output_dir) +if not os.path.exists(output_dir): + os.makedirs(output_dir) +print("Output Directory: %s" % output_dir) + +if "nexus" in socket.gethostname(): + root = 'root' ## TODO +else: + raise Exception("Not on nexus") + + +# print(f"Starting a batch of {args.batchsize} from offset {args.offset}") +params = { + # 'config_file': ['', '', [f'./configs/{i}.json' for i in range(args.offset, args.batchsize+args.offset)]] + 'config_path': ['--config_path', 'config_path', [f'retrieval_configs/awq_{i}' for i in range(7**2)]], + 'task': ['--task', 'task', ['image_text_retrieval']] +} +####################################################################### + +class Argument(object): + + def __init__(self, name, cmd_line, string_id, val): + self.name = name + self.val = val + if isinstance(val,list): + if len(val) == 0: + + if isinstance(cmd_line, list): + self.cmd_string = '' + for cur_line in cmd_line: + self.cmd_string += ' '+cur_line+' []' + else: + self.cmd_string = ' '+cmd_line+' []' + else: + if isinstance(cmd_line, list): + self.cmd_string = '' + for cur_line in cmd_line: + self.cmd_string += ' '+cur_line+' '+','.join([str(e) for e in val]) + else: + self.cmd_string = ' '+cmd_line+' '+','.join([str(e) for e in val]) + else: + + if isinstance(cmd_line, list): + self.cmd_string = '' + for cur_line in cmd_line: + self.cmd_string += ' '+cur_line+' '+str(val) + else: + self.cmd_string = ' '+cmd_line+' '+str(val) + if isinstance(val,bool): + if not val: + self.job_string = '' + self.cmd_string = '' + self.name = '' + else: + self.job_string = '_'+string_id if string_id else '' + if isinstance(cmd_line, list): + self.cmd_string = '' + for cur_line in cmd_line: + self.cmd_string += ' '+cur_line+' ' + self.cmd_string = ' '+cmd_line+' ' + elif isinstance(val,list): + self.job_string = '_'+string_id+'_'.join([str(v) for v in val]) + else: + self.job_string = '_'+string_id+str(val) + if string_id == 'none': + self.job_string = '' + + def copy(self): + new_arg = Argument(self.name, cmd_line='', string_id='', val=self.val) + new_arg.cmd_string = self.cmd_string + new_arg.job_string = self.job_string + return new_arg + + +os.makedirs(f'{args.base_dir}/{args.output_dirname}/{args.env}',exist_ok=True) +n_jobs = 0 +# Making text files which will store the python command to run, stdout, and error if any +with open(f'{args.base_dir}/{args.output_dirname}/{args.env}/now.txt', "w") as nowfile,\ + open(f'{args.base_dir}/{args.output_dirname}/{args.env}/log.txt', "w") as output_namefile,\ + open(f'{args.base_dir}/{args.output_dirname}/{args.env}/err.txt', "w") as error_namefile,\ + open(f'{args.base_dir}/{args.output_dirname}/{args.env}/name.txt', "w") as namefile: + + arg_list = [] + for key, param in params.items(): + cur_arg_list = [] + if not isinstance(param[2],list): + param[2] = [param[2]] + + if len(param[2])>1 and key!="dataset": + assert param[1]!='none', f"{param[0]} set to none with multiple values!" + + for value in param[2]: + cur_arg_list.append(Argument(key, param[0],param[1], value)) + + arg_list.append(cur_arg_list) + + arg_list = list(itertools.product(*arg_list)) + n_jobs = 0 + for idx,job_args in enumerate(arg_list): + + # Allows modification of current set of args + job_args = {arg.name:arg.copy() for arg in job_args} + + job_string = '' + python_cmd = 'python ../run_awq.py ' + for arg_name, arg in job_args.items(): + python_cmd += arg.cmd_string + job_string += arg.job_string + + job_string = f'{n_jobs}_'+job_string + cmd_line_str = python_cmd + + # cmd_line_str = python_cmd + + n_jobs += 1 + + nowfile.write(f'{cmd_line_str}\n') + namefile.write(f'{(os.path.join(output_dir, job_string))}.log\n') + output_namefile.write(f'{(os.path.join(output_dir, job_string))}_log.txt\n') + error_namefile.write(f'{(os.path.join(output_dir, job_string))}_error.txt\n') + if args.single: + break + +########################################################################### +if len(args.qos)>1: + splits = split(range(0,n_jobs), len(args.qos)) + for qos in args.qos: + cur_dir = os.path.join(args.base_dir, args.output_dirname, args.env, qos) + if os.path.exists(cur_dir): + shutil.rmtree(cur_dir) + if not os.path.exists(cur_dir): + os.makedirs(cur_dir) + + with open(f'{args.base_dir}/{args.output_dirname}/{args.env}/log.txt', "r") as output_namefile,\ + open(f'{args.base_dir}/{args.output_dirname}/{args.env}/err.txt', "r") as error_namefile: + logs = output_namefile.read().splitlines() + errs = error_namefile.read().splitlines() + + with open(f'{args.base_dir}/{args.output_dirname}/{args.env}/log.txt', "w") as output_namefile,\ + open(f'{args.base_dir}/{args.output_dirname}/{args.env}/err.txt', "w") as error_namefile: + for i,log in enumerate(logs): + qos_idx = math.floor(i/math.ceil(n_jobs/len(args.qos))) + folder, basename = os.path.split(log) + new_log_name = os.path.join(folder, args.qos[qos_idx], basename) + folder, basename = os.path.split(errs[i]) + new_err_name = os.path.join(folder, args.qos[qos_idx], basename) + output_namefile.write(f'{new_log_name}\n') + error_namefile.write(f'{new_err_name}\n') + + + +########################################################################### +#slurm_script_path = os.path.join(output_dir, '%s.slurm' % name) +id = args.env.split('run')[-1] +filenames = [] +if len(args.qos)==1: + filenames = [f'{args.qos[0][:2]}_r{id}.slurm' if not args.filename else args.filename] +else: + for qos in args.qos: + filenames.append(f'{qos[:2]}_r{id}.slurm' if not args.filename else qos[0]+args.filename) + +print("Filenames:") +print(filenames) +slurm_script_paths = [os.path.join(output_dir, filename) for filename in filenames] +slurm_commands = ["sbatch %s" % slurm_script_path for slurm_script_path in slurm_script_paths] +shutil.copyfile(os.path.abspath(__file__), + os.path.join(output_dir, + os.path.basename(os.path.abspath(__file__)))) + + +idx = 0 +start_idx, end_idx = [], [] +for i in range(len(args.qos)): + start_idx += [idx+1] + idx += math.ceil(n_jobs/len(args.qos)) + end_idx += [min(idx, n_jobs)] + +for i,slurm_script_path in enumerate(slurm_script_paths): + print(f"writing to {slurm_script_path}") + with open(slurm_script_path, 'w') as slurmfile: + slurmfile.write("#!/bin/bash\n") + if args.max_jobs>0: + slurmfile.write(f"#SBATCH --array={start_idx[i]}-{end_idx[i]}%{args.max_jobs}\n") + else: + slurmfile.write(f"#SBATCH --array={start_idx[i]}-{end_idx[i]}\n") + slurmfile.write("#SBATCH --output=/dev/null\n") + slurmfile.write("#SBATCH --error=/dev/null\n") + slurmfile.write("#SBATCH --requeue\n") + args = check_qos(args) + + default_include_list = [] + default_exclude_list = [] + if args.qos[i] == "scav": + if "vulcan" in args.partition: + slurmfile.write("#SBATCH --account=vulcan\n") + slurmfile.write("#SBATCH --partition=vulcan-scavenger\n") + slurmfile.write("#SBATCH --qos=vulcan-scavenger\n") + default_exclude_list = ["janus[02-04]"] + elif "nexus" in args.partition: + slurmfile.write("#SBATCH --account=scavenger\n") + slurmfile.write("#SBATCH --partition=scavenger\n") + slurmfile.write("#SBATCH --qos=scavenger\n") + elif "cml" in args.partition: + slurmfile.write("#SBATCH --account=cml-abhinav\n") + slurmfile.write("#SBATCH --partition=cml-scavenger\n") + slurmfile.write("#SBATCH --qos=cml-scavenger\n") + elif args.qos[i] == "high" or args.qos[i] == "medium" or args.qos[i] == "default": + if "vulcan" in args.partition: + slurmfile.write("#SBATCH --account=vulcan-abhinav\n") + slurmfile.write("#SBATCH --partition=vulcan-ampere\n") + slurmfile.write(f"#SBATCH --qos=vulcan-{args.qos[i]}\n") + default_exclude_list = ["janus[02-04]"] + elif "nexus" in args.partition: + slurmfile.write("#SBATCH --account=nexus\n") + slurmfile.write(f"#SBATCH --qos={args.qos[i]}\n") + elif "cml" in args.partition: + slurmfile.write("#SBATCH --account=cml-abhinav\n") + slurmfile.write("#SBATCH --partition=cml-dpart\n") + slurmfile.write(f"#SBATCH --qos=cml-{args.qos[i]}\n") + + slurmfile.write("#SBATCH --time=%d:00:00\n" % args.nhrs) + slurmfile.write("#SBATCH --cpus-per-task=%d\n" % args.cores) + slurmfile.write("#SBATCH --mem=%dG\n" % args.mem) + + + if not args.gpu is None: + if len(args.gpu_type)==1: + if 'any' in args.gpu_type: + slurmfile.write("#SBATCH --gres=gpu:%d\n" % args.gpu) + elif "rtx2080" in args.gpu_type: + slurmfile.write("#SBATCH --gres=gpu:rtx2080ti:%d\n" % args.gpu) + elif "gtx" in args.gpu_type: + slurmfile.write("#SBATCH --gres=gpu:gtx1080ti:%d\n" % args.gpu) + elif "p6000" in args.gpu_type: + slurmfile.write("#SBATCH --gres=gpu:p6000:%d\n" % args.gpu) + elif "a4000" in args.gpu_type: + slurmfile.write("#SBATCH --gres=gpu:rtxa4000:%d\n" % args.gpu) + elif "a5000" in args.gpu_type: + slurmfile.write("#SBATCH --gres=gpu:rtxa5000:%d\n" % args.gpu) + elif "a6000" in args.gpu_type: + slurmfile.write("#SBATCH --gres=gpu:rtxa6000:%d\n" % args.gpu) + else: + assert len(args.gpu_type)>1 + slurmfile.write("#SBATCH --gres=gpu:%d\n" % args.gpu) + # slurmfile.write(get_include_string(args.gpu_type,default_include_list)) + slurmfile.write(get_exclude_string(args.gpu_type,default_exclude_list)) + else: + raise ValueError("Specify the number of gpus") + + slurmfile.write("\n") + if "vulcan" in socket.gethostname() or "nexus" in socket.gethostname(): + slurmfile.write(f"cd {root}") #TODO + # slurmfile.write('conda activate {env}\n') #TODO + slurmfile.write('source ~/.bashrc') + slurmfile.write('micromamba activate blip\n') + + num_exps = 1 + for n in reversed(range(num_exps)): + slurmfile.write(f"srun --output=$(head -n $SLURM_ARRAY_TASK_ID {args.base_dir}/{args.output_dirname}/{args.env}/log.txt | tail -n 1) $(head -n $(expr {num_exps} \* $SLURM_ARRAY_TASK_ID - {n}) {args.base_dir}/{args.output_dirname}/{args.env}/now.txt | tail -n 1)\n") + slurmfile.write("\n") + +for i,slurm_command in enumerate(slurm_commands): + print(slurm_command) + print("Running on {}, with {} gpus, {} cores, {} mem for {} hour".format(args.qos[i], args.gpu, args.cores, args.mem , args.nhrs)) + +if not args.dryrun: + for slurm_command in slurm_commands: + os.system("%s &" % slurm_command) diff --git a/awq/retrieval_multi_sbatch_submit.sh b/awq/retrieval_multi_sbatch_submit.sh new file mode 100755 index 0000000..753a7e1 --- /dev/null +++ b/awq/retrieval_multi_sbatch_submit.sh @@ -0,0 +1,12 @@ +python retrieval_multi_sbatch.py --env slurm_files \ + --nhrs 2 \ + --qos scav \ + --partition vulcan \ + --gpu 1 --gpu-type a5000 a6000 \ + --cores 1 \ + --mem 64 \ + --output-dirname retrieval_output \ + # --dryrun + # --base-dir awq/ \ + + diff --git a/awq/scaled_modules.py b/awq/scaled_modules.py new file mode 100644 index 0000000..f48bd5a --- /dev/null +++ b/awq/scaled_modules.py @@ -0,0 +1,13 @@ +import torch.nn as nn + +# Custom pytorch module to apply AWQ weight scaling before forward pass of another module +class ScaledModule(nn.Module): + + def __init__(self, scales, module): + super().__init__() + self.scales = nn.Parameter(scales.data) + self.module = module + + def forward(self, x, *args, **kwargs): + x = x / self.scales.view(1, 1, -1).to(x.device) + return self.module(x, *args, **kwargs) diff --git a/awq/test_run.sh b/awq/test_run.sh new file mode 100644 index 0000000..e4856b8 --- /dev/null +++ b/awq/test_run.sh @@ -0,0 +1,29 @@ +#!/bin/bash + +#SBATCH --job-name=blip2_awq_test # sets the job name +#SBATCH --output=blip2_awq_test.%j # indicates a file to redirect STDOUT to; %j is the jobid. If set, must be set to a file instead of a directory or else submission will fail. +#SBATCH --error=blip2_awq_test.%j # indicates a file to redirect STDERR to; %j is the jobid. If set, must be set to a file instead of a directory or else submission will fail. +#SBATCH --time=02:00:00 # how long you would like your job to run; format=hh:mm:ss + +#SBATCH --partition=vulcan-scavenger +#SBATCH --qos=vulcan-scavenger # set QOS, this will determine what resources can be requested +#SBATCH --account=vulcan-abhinav +#SBATCH --gres=gpu:rtxa5000:1 + +#SBATCH --nodes=1 # number of nodes to allocate for your job +#SBATCH --ntasks=1 +#SBATCH --ntasks-per-node=1 +#SBATCH --mem=128gb # (cpu) memory required by job; if unit is not specified MB will be assumed + +module load cuda +source ~/.bashrc +# eval "$(micromamba shell hook --shell bash)" +micromamba activate MMQ + +python ../run_awq.py \ + --config_path retrieval_configs/awq_26 \ + --task image_text_retrieval + +wait # wait for any background processes to complete + +# once the end of the batch script is reached your job allocation will be revoked diff --git a/awq/test_run_caps.sh b/awq/test_run_caps.sh new file mode 100644 index 0000000..3e070e4 --- /dev/null +++ b/awq/test_run_caps.sh @@ -0,0 +1,29 @@ +#!/bin/bash + +#SBATCH --job-name=blip2_awq_test_caps # sets the job name +#SBATCH --output=blip2_awq_test_caps.%j # indicates a file to redirect STDOUT to; %j is the jobid. If set, must be set to a file instead of a directory or else submission will fail. +#SBATCH --error=blip2_awq_test_caps.%j # indicates a file to redirect STDERR to; %j is the jobid. If set, must be set to a file instead of a directory or else submission will fail. +#SBATCH --time=02:00:00 # how long you would like your job to run; format=hh:mm:ss + +#SBATCH --partition=vulcan-scavenger +#SBATCH --qos=vulcan-scavenger # set QOS, this will determine what resources can be requested +#SBATCH --account=vulcan-abhinav +#SBATCH --gres=gpu:rtxa5000:1 + +#SBATCH --nodes=1 # number of nodes to allocate for your job +#SBATCH --ntasks=1 +#SBATCH --ntasks-per-node=1 +#SBATCH --mem=128gb # (cpu) memory required by job; if unit is not specified MB will be assumed + +module load cuda +source ~/.bashrc +# eval "$(micromamba shell hook --shell bash)" +micromamba activate MMQ + +python ../run_awq.py \ + --config_path captioning_configs/awq_21 \ + --task image_captioning + +wait # wait for any background processes to complete + +# once the end of the batch script is reached your job allocation will be revoked diff --git a/awq/utils.py b/awq/utils.py new file mode 100644 index 0000000..011086b --- /dev/null +++ b/awq/utils.py @@ -0,0 +1,59 @@ +import torch +import torch.nn as nn +import gc +import inspect +import functools + +def clear_memory(weight=None): + if weight is not None: + del weight + gc.collect() + torch.cuda.empty_cache() + +@torch.no_grad() +def compute_loss(fp_output, q_output): + fp_output_flat = fp_output.view(-1) + q_output_flat = q_output.view(-1) + L2 = torch.linalg.norm(fp_output_flat - q_output_flat, ord=2) + return L2 + +# returns all nn.linear within module (a layer) +def get_named_linears(module): + return {name: mod for name, mod in module.named_modules() if isinstance(mod, nn.Linear)} + +def get_mods(model, non_linears_only = True): + children = list(model.children()) + + if non_linears_only: + return [model] if len(children) == 0 else [ci for c in children for ci in get_mods(c, non_linears_only) if type(ci) != torch.nn.Linear] + else: + return [model] if len(children) == 0 else [ci for c in children for ci in get_mods(c, non_linears_only)] + + +def sanitize_kwargs(inputs_kwargs, module): + """ + Remove the arguments that are not supported in the module's + forward pass + + Args: + inputs_kwargs (`dict`): + The input dictionary to pass to the model layer + module (`torch.nn.Module`): + Target module to quantize. + """ + module_signature = inspect.signature(module.forward).parameters + sanitized_kwargs = {} + for k, v in inputs_kwargs.items(): + if k in module_signature: + sanitized_kwargs[k] = v + return sanitized_kwargs + + +def rsetattr(obj, attr, val): + pre, _, post = attr.rpartition('.') + return setattr(rgetattr(obj, pre) if pre else obj, post, val) + +def rgetattr(obj, attr, *args): + def _getattr(obj, attr): + return getattr(obj, attr, *args) + return functools.reduce(_getattr, [obj] + attr.split('.')) \ No newline at end of file diff --git a/awq_demo.ipynb b/awq_demo.ipynb new file mode 100644 index 0000000..d5a2996 --- /dev/null +++ b/awq_demo.ipynb @@ -0,0 +1,8403 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "import torch\n", + "from transformers import Blip2Processor, Blip2ForConditionalGeneration, AutoProcessor, Blip2ForImageTextRetrieval\n", + "from dataset import COCODataset\n", + "from awq.quantizer import Blip2ForConditionalGenerationAWQQuantizer, Blip2ForImageTextRetrievalAWQQuantizer\n", + "from inference_pipeline import InferencePipeline\n", + "import time\n", + "from scoring_pipeline import ScoringPipeline\n", + "\n", + "from dataset import Flickr30kEvalDataset\n", + "import torchvision.transforms as transforms\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "device(type='cuda')" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "if torch.backends.mps.is_available():\n", + " device = torch.device(\"mps\")\n", + "else:\n", + " device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", + " \n", + "device" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "def model_size(model):\n", + " # returns all layers of model\n", + " def get_layers(model):\n", + " children = list(model.children())\n", + " return [model] if len(children) == 0 else [ci for c in children for ci in get_layers(c)]\n", + " \n", + " layers = get_layers(model)\n", + " size = 0\n", + " \n", + " # model params\n", + " for layer in layers:\n", + " for name, param in layer.named_parameters():\n", + " # NOTE: element_size in bits\n", + " element_size = param.element_size() * 8\n", + " size += param.nelement() * element_size\n", + " \n", + " # model buffers (not quantized)\n", + " for buffer in model.buffers():\n", + " size += buffer.nelement() * (buffer.element_size() * 8)\n", + "\n", + " # bits --> megabytes\n", + " size /= (8e6)\n", + " return size" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## AWQ Blip-2 Caption Generation" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "357ca5e745954910be293ec1d3ba7fae", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Loading checkpoint shards: 0%| | 0/2 [00:00" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "coco_dataset[0][0]" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 0%| | 0/1 [00:00 len(self.question_ids): + return ValueError(f"Max_samples: {max_samples}, is larger than the current size of the dataset {len(self.question_ids)}") + self.question_ids = self.question_ids[:max_samples] + + def collater(self, samples): + samples = [s for s in samples if s is not None] + + if not samples: + return None + + question_ids = [] + images = [] + questions = [] + gt_answers = [] + for sample in samples: + question_ids.append(sample["question_id"]) + images.append(sample["image"]) + questions.append(sample["text_input"]) + gt_answers.append(sample["gt_answer"]) + + return {"question_id": question_ids, "image": images, "text_input": questions, "gt_answer": gt_answers} + + + def __getitem__(self, index): + question_id = self.question_ids[index] + question = self.questions[question_id] + + image_path = os.path.join(self.image_root, f"{question['imageId']}.jpg") + image = Image.open(image_path).convert("RGB") + + if self.img_transform: + image = self.img_transform(image) + + if self.text_processor: + question = self.text_processor(question) + + return { + "question_id": question_id, + "image": image, + "text_input": self.prompt.format(question["question"]), + "gt_answer": question["answer"] + } + + \ No newline at end of file diff --git a/dataset/vqa.py b/dataset/vqa.py index 7e2f26d..af429c5 100644 --- a/dataset/vqa.py +++ b/dataset/vqa.py @@ -1,17 +1,117 @@ +import json import os +import re + import pandas as pd from PIL import Image -import torch -from torch.utils.data import Dataset, DataLoader +from torch.utils.data import Dataset +from pathlib import Path + +class VQAv2Eval(Dataset): + def __init__( + self, + image_root, + ann_root, + q_root, + img_transform=None, + text_processor=None, + prompt=None, + ): + self.annotation_dict = json.load( + open(os.path.join(ann_root, "v2_mscoco_val2014_annotations.json")) + ) + + self.annotations = self.annotation_dict["annotations"] + self.question_dict = json.load( + open(os.path.join(q_root, "v2_OpenEnded_mscoco_val2014_questions.json")) + ) + self.questions = self.question_dict["questions"] + self.image_root = image_root + self.img_transform = img_transform + self.text_processor = text_processor + self.prompt = prompt if prompt else "Question: {} Short answer:" + + self.qa_pairs = [] + self._create_qa_pairs() + + def set_max_samples(self, max_samples): + if max_samples > len(self.annotations): + return ValueError(f"Max_samples: {max_samples}, is larger than the current size of the dataset {len(self.annotations)}") + self.qa_pairs = self.qa_pairs[:max_samples] + question_ids = set([qa["question_id"] for qa in self.qa_pairs]) + + self.annotations = [anno for anno in self.annotations if anno["question_id"] in question_ids] + self.annotation_dict["annotations"] = self.annotations + + self.questions = [question for question in self.questions if question["question_id"] in question_ids] + self.question_dict["questions"] = self.questions + + def collater(self, samples): + samples = [s for s in samples if s is not None] + + if not samples: + return None + + images = [] + questions = [] + question_ids = [] + for sample in samples: + images.append(sample["image"]) + questions.append(sample["text_input"]) + question_ids.append(sample["question_id"]) + + return {"image": images, "text_input": questions, "question_id": question_ids} + + def _create_qa_pairs(self): + # image_id -> path map + image_to_path = [entry.name for entry in os.scandir(self.image_root) if entry.is_file()] + image_to_path = {int(re.search(r"_(\d+)\.jpg$", path).group(1)): path for path in image_to_path} + + question_to_annotation = {int(anno["question_id"]): anno for anno in self.annotations} + + for question in self.questions: + image_id = question["image_id"] + question_id = question["question_id"] + annotation = question_to_annotation[question_id] + self.qa_pairs.append( + { + "question_id": question_id, + "question": question["question"], + "answer": [answer_dict["answer"] for answer_dict in annotation["answers"]], + "image": image_to_path[image_id], + } + ) + + def __len__(self): + return len(self.annotations) + + def __getitem__(self, index): + ann = self.qa_pairs[index] + + image = Image.open(self.image_root + "/" + Path(ann["image"]).name).convert("RGB") + question = ann["question"] + + if self.img_transform: + image = self.img_transform(image) + + if self.text_processor: + question = self.text_processor(question) + + return { + "image": image, + "text_input": self.prompt.format(question), + "question_id": ann["question_id"], + } + class VQA(Dataset): def __init__(self, csv_file, img_dir, transform=None): self.data = pd.read_csv(csv_file, delimiter="|", skipinitialspace=True) self.img_dir = img_dir self.transform = transform - + # Group captions by image - self.grouped_data = self.data.groupby('image_name') + self.grouped_data = self.data.groupby("image_name") self.image_names = list(self.grouped_data.groups.keys()) def __len__(self): @@ -20,11 +120,11 @@ def __len__(self): def __getitem__(self, idx): img_name = self.image_names[idx] img_path = os.path.join(self.img_dir, img_name) - image = Image.open(img_path).convert('RGB') - + image = Image.open(img_path).convert("RGB") + if self.transform: image = self.transform(image) - - captions = list(self.grouped_data.get_group(img_name)['comment']) - - return image, captions \ No newline at end of file + + captions = list(self.grouped_data.get_group(img_name)["comment"]) + + return image, captions diff --git a/datasets/__pycache__/__init__.cpython-312.pyc b/datasets/__pycache__/__init__.cpython-312.pyc new file mode 100644 index 0000000..075ad35 Binary files /dev/null and b/datasets/__pycache__/__init__.cpython-312.pyc differ diff --git a/datasets/__pycache__/coco.cpython-312.pyc b/datasets/__pycache__/coco.cpython-312.pyc new file mode 100644 index 0000000..19fd9cb Binary files /dev/null and b/datasets/__pycache__/coco.cpython-312.pyc differ diff --git a/datasets/__pycache__/flickr30k.cpython-312.pyc b/datasets/__pycache__/flickr30k.cpython-312.pyc new file mode 100644 index 0000000..085dafb Binary files /dev/null and b/datasets/__pycache__/flickr30k.cpython-312.pyc differ diff --git a/datasets/__pycache__/vqa.cpython-312.pyc b/datasets/__pycache__/vqa.cpython-312.pyc new file mode 100644 index 0000000..4746dad Binary files /dev/null and b/datasets/__pycache__/vqa.cpython-312.pyc differ diff --git a/datasets/flickr30k.py b/datasets/flickr30k.py new file mode 100644 index 0000000..dd39757 --- /dev/null +++ b/datasets/flickr30k.py @@ -0,0 +1,62 @@ +import os +import pandas as pd +from PIL import Image +import torch +from torch.utils.data import Dataset, DataLoader +import json +import re + +class Flickr30kEvalDataset(Dataset): + def __init__(self, ann_file, img_dir, img_transform=None): + self.annotation = json.load(open(ann_file)) + self.img_transform = img_transform + self.image_root = img_dir + + self.text = [] + self.image = [] + self.txt2img = {} + self.img2txt = {} + + txt_id = 0 + for img_id, ann in enumerate(self.annotation): + self.image.append(ann["image"]) + self.img2txt[img_id] = [] + for i, caption in enumerate(ann["caption"]): + self.text.append(self._process_caption(caption)) + self.img2txt[img_id].append(txt_id) + self.txt2img[txt_id] = img_id + txt_id += 1 + + def __len__(self): + return len(self.annotation) + + def __getitem__(self, index): + image_path = os.path.join(self.image_root, self.annotation[index]["image"].split("/")[-1]) + image = Image.open(image_path).convert("RGB") + if (self.img_transform): + image = self.img_transform(image) + + return {"image": image, "index": index} + + def _process_caption(self, caption): + max_words = 50 + + caption = re.sub( + r"([.!\"()*#:;~])", + " ", + caption.lower(), + ) + caption = re.sub( + r"\s{2,}", + " ", + caption, + ) + caption = caption.rstrip("\n") + caption = caption.strip(" ") + + # truncate caption + caption_words = caption.split(" ") + if len(caption_words) > max_words: + caption = " ".join(caption_words[: max_words]) + + return caption diff --git a/demo_vqa.ipynb b/demo_vqa.ipynb new file mode 100755 index 0000000..c6dc1c5 --- /dev/null +++ b/demo_vqa.ipynb @@ -0,0 +1,465 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "596b33cf-382f-4121-ac88-92799c65e284", + "metadata": {}, + "source": [ + "# Demo of Blip2 Quantization, Inference, and Scoring" + ] + }, + { + "cell_type": "markdown", + "id": "25de58b3-4ee9-4267-80c5-1a41582fe3cb", + "metadata": {}, + "source": [ + "## 1. Load Model and Quantize" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "8c695ac4-7895-4a53-b4da-579984a1899b", + "metadata": {}, + "outputs": [], + "source": [ + "from blip_quantizer import BlipQuantizer, QuantConfig, ModelPart, LayerGroup, LayerType\n", + "from quant_functions import uniform_quantization\n", + "import torch\n", + "from transformers import Blip2Processor, Blip2ForConditionalGeneration, AutoTokenizer\n", + "from datasets import VQAv2Eval\n", + "from tqdm import tqdm\n", + "from PIL import Image\n", + "from torch.utils.data import DataLoader\n", + "from utils import print_model_structure\n", + "from lavis.models import load_model_and_preprocess" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "e13309c9-5353-41f8-8bec-8351f4c82504", + "metadata": {}, + "outputs": [], + "source": [ + "vqav2_dataset = VQAv2Eval(\n", + " image_root=\"./data/vqav2/val2014\",\n", + " ann_root=\"./data/vqav2/annotations\",\n", + " q_root=\"./data/vqav2/questions\"\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "c25cac40-c06e-470e-a24f-8fcfe0a9b065", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "a2a4293a02a848f0b83411f190033419", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Loading checkpoint shards: 0%| | 0/2 [00:00" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "What are the people in the background doing?\n", + "true answers: ['spectating', 'watching', 'watching', 'watching', 'watching', 'watching', 'watching', 'watching', 'watching', 'watching']\n", + "pred answers: ['nothing']\n" + ] + } + ], + "source": [ + "show_results(1, data, vqav2_dataset)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "bf2051df-dde4-4f25-bdd8-c780e17cf864", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "What website copyrighted the picture?\n", + "true answers: ['foodiebakercom', 'foodiebakercom', 'foodiebaker', 'foodiebakercom', 'foodiebakercom', 'http://foodiebakercom', 'foodiebakercom', 'foodiebakercom', 'foodiebakercom', 'foodiebaker']\n", + "pred answers: ['none']\n" + ] + } + ], + "source": [ + "show_results(3, data, vqav2_dataset)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "8d57dacb-bd85-4b0f-8b92-edca066ecf7e", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAigAAAF8CAYAAADl+kD5AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy80BEi2AAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOz9T6xtW3bmBf7GmHPtvc+59/2JCNvhtLCTqpJIEBIgJWAsQQclStFAQmSjlA2EEI3qOBsENHCHxC2rWtDA9BBUByXQRqKBq4cyhZS0smFEZqnKdtoRjogX77177zlnrzXnHNUYY8y19rn3vQhDZoZDPst+cc/ZZ++111pzzjG/8Y1vjCFmZrwcL8fL8XK8HC/Hy/Fy/Ck69Kd9AS/Hy/FyvBwvx8vxcrwcz48XgPJyvBwvx8vxcrwcL8efuuMFoLwcL8fL8XK8HC/Hy/Gn7ngBKC/Hy/FyvBwvx8vxcvypO14AysvxcrwcL8fL8XK8HH/qjheA8nK8HC/Hy/FyvBwvx5+64wWgvBwvx8vxcrwcL8fL8afueAEoL8fL8XK8HC/Hy/Fy/Kk7XgDKy/FyvBwvx8vxcrwcf+qOF4DycrwcL8fL8XK8HC/Hn7rjpwpQfvu3f5t//B//x7lcLvzqr/4q/8v/8r/8NC/n5Xg5Xo6X4+V4OV6OPyXHTw2g/Lf/7X/Ld77zHf76X//r/K//6//KP/vP/rP85b/8l/njP/7jn9YlvRwvx8vxcrwcL8fL8afkkJ9Ws8Bf/dVf5V/4F/4F/vP//D8HYIzBL//yL/PX/tpf4z/6j/6jn8YlvRwvx8vxcrwcL8fL8afkqD+NL13Xlb/9t/82v/EbvzFfU1X+0l/6S/zNv/k333v/9Xrler3O38cYfPbZZ3zrW99CRP6RXPPL8XK8HC/Hy/FyvBz/5w4z482bN/zSL/0Sql8fxPmpAJQf/OAH9N759re/ffP6t7/9bX73d3/3vff/1m/9Fr/5m7/5j+ryXo6X4+V4OV6Ol+Pl+Id4/P7v/z7/2D/2j33te34qAOVPevzGb/wG3/nOd+bvX3zxBb/yK7/C/+v/+f/g1d3CspygnEAqWhe0VkyKv7lvWFsxFKxj/RHbVsyEcrpD64miFT1fkOXO34egogyDMRqqFdXKMGP0DbMV2zZGu1Kk07cVRoPinxVRkIIhYANEEBEGA0EpqkjfsNEYY4AUpFSGib8X6CJYN/wuGmM0igjlckHrCV0uyOk1ermnnu6gXCjLBa1npJwQPQEFBP8PBYMknIyBIBiGGfHvbbTP7MOvzZ/n7+O998+f49/99Y6ZIYfPjzEgvn+M2+8aDCzPP8bNOTAwG3Emic8bImDD4nVn3MbxuuO6ENtfN2PY4cuZHwdkXqMUmeeMB+f3L/NBk5ye3/N+bXkuEf/dDu9F4lRmID6PEEHj2Yn4WAn+VQNBJcevI1L8o6JInHXYwMzZSVHB8rqJ1w7XqyL+XlGGDUQEEb8WUQVRSikUVSgV1QWVSimVpS7UUqjLiaLKokJlUK0htiHWoK/YtjL6xtha/Lz62hxbPCf/Hl0qimKxbvpotDXeZxulCMpAZFAkx3sDa4zR0XJiOX+Enj+inu9RKfNJ997pW2Nbr1yfVkbv8UyFYWAIgsZ8HGjx5yyimBZE1VlbY65ri/EV1TmQquqfiXEXhTE6vXd/1qPHuvB55PPJr8XHO+aKFj8P8X0xFj7OPt7+XTll85yAyZxb/pA0rsWwMbDRD2vC3yLTVsXcEJ/7cw0PA+vQO+36yPr4jrY98nS90rdBW2Ndbp2Hd0/0zWd5KQJiIBVB0VJBlVIXyrKgy2vq5Y5yvvDq029QX91xupzQ84V6OlFrRYrPwXmNz8nzfYnNa89/9cC0W+/0ttG2jdaubNeVx/UdT49veXr3BZ9//gN+8IPv8cff/4zPPn/Hw3Vw3Yx13djWztqMtY/4fGN0MJGwRfuCtuPanWO621CJtca0wxbPO/++j42vZ0PYbUGuZWEfZxGh9x73LvQxUPW1nfbfzK9H9fY8iMb7/Dskfh4j7UFei58s5wXCfu/+6+FZWJjafSww4w/+P3+Xjz76iB93/FQAys/93M9RSuF73/vezevf+973+MVf/MX33n8+nzmfz++9/vEnH3O/+OKUegIpbtSXSllOYRXO0BsAvV1hNai+4KUuDlCWM7KckXqOLcANjKGYnXzilIqNQVs7qmesCu3aqaUgl4K1DVRBC2Y+yXxjUEQqRZWeE9MGo7nB6G1jACL+zariG4sosjhwKefX1NOJ5XTBzt+gnO/Q5Q5dXiHLnRvkUhEtDBG/b1HEYuITazY20zRi9kwjvW+oCSrmh+bEne9NQPMM3OSiPP4+JrDYgQjz78NBSH4u53mcFwbdBtyAmN0QjIORnQvmcC87UBFM3HDb4fMOFBKkOJAZFobaEuDcgps0OPGtB4MTpiIsy/Pn8DwceQtafDMzMQe4cSi33zXPpcUBizDHwQ1UifPt3+/nOwBNy00tDL3lrE8DNm6Qk+Q8KX5eoaMIVQkjB926j52eqfUMpUIpVIWCURgoG2yPsD0xrg+M7YHRrthYY8A1Ni/N3ZLeO60pVo3ejDaUZanUotRiLAXUBkbzTb9vft9qnM5KvVtQPc1nOsZgtMa2LWznJ65PT2CGaol1U9CykBuMljLH08GAhEHOx3MAIeFO+Pj4fLDhQFhF6H2jt+62xQa9d98MMHrf/P5jTEQOQETDrgU4MRT1L0AnaIpxswTzCcIlxnIH0ODgZPR+Ow/FEPwaLJya3KjGcf4MY/SOXc9s54X1qXKqynrdGNXtRt8GS6lcHxujDWA4wD1fUC2oLqAOQhGhVsExjFH64ISyqIPf5XSmnhbqsqBF0QApiAMDvwn/H+Hw2pzTMud73PAEKb01tu2J6/qa69NHXF9/xKtXr/no1Sd8+up7/OF3f8APPn/L26eVazvxeF2p186pGe1UeVo32mb0udbD4TvYhDknDj9POyjV1+20S4qq//12DQvNBiU2+DGMUuvNuS3Ai88vx5JFyw2wECnvXY9a7gcy36fxITGQMRD1eS5z4huiBzts+1xKm3Vzv2GjfE2Mm2fzdcdPBaCcTif+4l/8i/zO7/wO/+a/+W8Cbjh+53d+h1//9V//ic8jWnAkZxQb4Xn6RNTw8nrvzmb0zTeBcnIvZAy0FLRIWOXOaI9u+E1oXSYoGWbI6Ize6G2lA8UcmQKgJQiKHmi9YNbdgNQTWI2tpWHWaeFJlvBQLbwnVN1z0cppObNcXsHlNfXVx+jpFVovlOUVpTpjJHpCdQkPK5iaudnG/9hhr/GLDSNWbhYABPK9YVJ2IAG+gI6LfAcZsckHozEBCWO+zSfpOICBWBS2g5cEQckuGOOGXbHhn0lPwtgNgf/dz9VHj+dgO7CKhXEEVSIcwFOCkeHG2XYP6H3jcvQu02Mdc1MdI/mh+VDnkMgN4JhPNv4bbqjoMI3F0bgcNpP0wOM6diPVw9sOz0rwuTgPAXrc987YmTz39gKYHMdsdExwIykba2ygqKK1ULXSdWGtZ0o5UcvJwURdOC2VpVwoy0LRO/R0j7ZH2N5hbcP6irTBGBtmndHFvWUz6A2LtTfGoImhegJdkCqIDbDiBlMV6xvDOn1755v6ORyFBFpxvSc5o1p8szWdbNFAw7s15OCxp7eYe/7uWcphnsa87QOxfW508fVj3X9WUSTnfKwd1eIbiDhzYsHCiMicWyIFif9D87XDNCI9+cP84wA85xy8nYE+dw6/W/zPXHe2A5d8DqVSlgt1NE7mEKbTJ6A3YFs3J11azqI4J4b1Nm3s1h/BBtI3ngQ4YOoqyhChiwA15tw+v4/XnHP/ZnXdsACxdoO98fseOxjMPURPLFoRWVD9Y/jRZ/A0GGNBTB1s42DZGFgbPo8gwGXaSV+nqbWw4Rydzfmkfm0wweFkWMO5cMLOqFrc7o3hIEbSlueo7v/rdizByq1zNB0Wi+chMS8s5oCMnV0V0BJsppuog9OTP4OYxuUcnR2Z9jIB5dGB+0mOn1qI5zvf+Q7/zr/z7/DP//P/PP/iv/gv8p/9Z/8Z796949/9d//dn/wkSWEGTVpLAV1QrfskEUVKPG4zhiiMBaw5UzHCI4yNry6nME7uTeWkQ2C0Fe2NoYVhRhEflEQBMhrWOhQNytzorYOKbxLW4jqCghV1ryA8GV3O6Pmeer5H7j5luXuFXO4dmJQ7SrkDlWBK3KAS3hgmoJo2hbnpyZjg4fDqXMQTnASqT+MRf4yXbhmE5//ac+biaOkm3Xz7nklvs3/v8fvMjGE9KFCbC8P/c/D3/FocrLpXOj+HOL1ogwwNWQKW43eNMRcz5uGRXFBJz+cDtJh3+3MdE1iRzzYN4YRSbgDGkeq8eY4JIvI8EZLJE0oyOWG0DuNxa4D2TcWty/uAKMMSZDgiKeZn71HN8Uzw46ff4u8S7IqIQC90aahcoTyiZaFopVQHKGU5cVrOnJcLJ71Q9URd7qnnT6E/oe0R7Su2PdC2J+y6+dobw6+jG8JA+pXOxiodrUrhhJSKjIYESPdx2WBc6W2BeqbK2Tc1EYoWN7qjUkqnb53ejWHioCDmgtizZzbsvXWT8zgBigOE7v/1Ppk/6DsXZY4twCKMVTCglIpo9bOU6g7O3Mhyc5VnjNjBYzdDAmiO3v07RvHw3gHt7uTKrXc/QwV55vR049zj+DyAUQSRhSJnTsUwGWyy0jdft4so/VIoDdancFDahpV93mo4KVoWtqeNupwZKqxvi7NaCkutHlpVBxbSO2LHcNf7zORXs5X7DYgKSmGRMyoaobwEXwXFaDY8Ul478oMvwDoFdfwk2wztiAx6MH5jxLgFC5Wr2F+Q3bHSZMsEPbCeaTNuQMWceOEY7a5osFw+L8ZoPm+S+TOZ6yKvQRzdQX6HyQRou7N6cMSI8KeI73cWgEoKyQaJ5L/7NSaT896zf89B++rjpwZQ/u//9/873//+9/mP/+P/mO9+97v8c//cP8f/+D/+j+8JZ7/usDDAjEFvnVIbpZ58XIbFIMfCkOELtQNhnnvbMDrl5HbEP1eQuiCyAL4gemsgPYzGCaSh1ECvDUZzBmC4ZkJs2z2fscHYJqKtRSKuvVBrpZSCXRY4nSj3n1DuPqGeP0KXV5TqYScpAbrSfUtDN+evTqyWG/509AP5Pt+AEgwcX/TYJBOVW76P3aDs39IP7AR+/+kZmVPF+fPzcMhIjyw3Y7PpwTswsdBPjAmAIL2KY6inMyyX7h7Px3pe/LwmR/9xTnHNRU8dSdxnUt6+qY8ZN3WvIBibMUhNQMb63bndgUfv4TWV4uAthsI3hTCq8Xyg3xij6eWKzjmMuCH1Q/xa8/nPQ7Dx3PuSsI3z6m88sxKaiXH0rp8BtwmwYr4ITI0K6curYGOjW2gEmoBcUVVKraxa0FK51jPX5YKWhWVZuJxOXOqZslwop4/BVrQ/sLQnyuM7xvZEvz4wru6dMgwbjT460Gm1spwWfz7L4oazG8KJMaD3wehPyFagLLj3LcGmFMwaTdVBQetYN9fgSKwflQCe0HtzD3NEkDSYkTF22nvaHASsQR8THJRSYqlFiALXF4kKJor1Gs7UycHTxEIj5lfOOQfox80r2cVc0GNsMeYacz4BEfMacpyBCB2BjAjhqWtkRoKv/K7czCCuQTEFrWfAOI2BDMPdMGVdV5b7Bdk6A8UaMIye4fDQ3LXWqYc5V5aFvj6yPVaqFHo5IW5xfbXUBa0LRQQZxpBkd9wWFEIqk47BAbzfADNRpCql7/dnqbuSMhmFsmiEMgt8/zPe2gBTsArWsOoAYYiH3CzZCQObrIj6ucPeaTAQbYBQwj64/RmHtThXd67r/F1l2pQ5wCaTgSNXZymM3vZzpR4FCW2K22rfRnNeVZ97aozu9iujBYbFe2/1etO5E6at8f0qQbvf858AmwA/ZZHsr//6r/+JQjrPD8EN4B47wzenocAKY5ub7LCk4FzP0NqVsV19EEejLCcfTOsUPGZKoNI0xqUsUArWlZFe89YZ/eqiPcJoxUSRUsKou+jWtSjuDdiyoOdXlLvXlNO9C3bPr9B6jy4XqhTQGpYlryFo+eeejj+BaUwn6gibdfSeOHx6goMJLA6fjfd/iDHJ73zuRe6AxgLAvK8/uRHcze8OcBfnG8Fm7eEf8w1CbkFK3uM4sA/J1Jjls2IHY3mtmAvbblZLbtzOVCSw4PkziXM4ZZz3vnsMSSUfn9/xNR8nSPd8BrOC5rXYtEhQIMAYjPxbeHlpmIzdeB2G2BmXwzQ4Hs62HgFZmc9UUqB7+L79c/Le9yF4OCOYC2sZinIjWlpzY6rKple2+ujCyLJwXRYeyoXT6czpdOZc7ljqhVIHenqkbE+U61t4/JwhP6I/bO7N9sFoV9brA7KcQE8s4loXpCLF9z9Cl9LbyrY+UC9KqRc34viGX6VixTUs0BjdJqPheFdIeO/hpxEhWUPTBnDQ8mQ4xiy0IR5O2kXJEsY+gLq4J+qESYTLdPdMRzcIu5IbCypkMNe99wgDjJxP+4Z7gDGHOeL2bXrLHkPANMDnCHsqgmglbWHOR/eSI1xmillF6sV5hSGIXJHSMXFhaQ1A2K6d7eHq5+5CKZXRfIzaasFuVdanJ0qAbbp/YWVwkkHFUPMtVAAtqfVLxiIYpVsTuS8Kct1naMznpgZ49VeUIhpMm6A1GQyliPH9H33Jm3crJhKav4FKR1RYW4c2duJsPv3DGjrYnaNjk4zdYHfKdnFqrv2j4Db3grn5hVDb5226EFrKvGdNpyR0JQIO/M0iGURvrs+sTa5GmHzAB67dHLOlvbTd+EwTks7ynwCk/Exk8XzVIeoCMqnu8fY+EHG2ww19QZfzTk3mhmnN49S9wRjI6EHxKWLGGC1yXBxMqJb5nblpjm4Riw+VP0Ipl7lxqDpIGeF2lrqg5Z4SYRy5e4VeXlHO9yzLK7ScXTgm1ZkaDrQ6Ochfj0DfD3l8AFjsSI5kKA5nuPnc85DM/i7b59/xbwk4DsDiVmOyf6dre/L7d9bkyJTsIZ30WI/fl0DoALQOAOU50MrPZnAjcP0ERcfN2A7iwdvNP99j8wxHxmKq6uM8Ry/j9lzdLahABs7lsJCtD5Bbb4/Mqzg8k/iyCRreP3bFsQVQRp7T3xF+nOO1ZwDko3s/Lh4GUD4A8lI/gwURF0xUA5GN3gu6OX2/lorWC3U7Udezh4DqHadl4VQ/YtF7ZHlFOb3ivLhuZX34jLG9Y2yPjL6xrU+UeqaWs9sC7TglpKgpysoYjb69dWemnJC6xONf4h4GZYlRtQ2PzBjWD1kKozPGSjKlEq8rOBpKml5dgGwhbjVc64AEMAkD7vuLh1+dHfENMEW3mdVjNmB09gwzp+hN3PkxsT1LA99cE+BOwW3MhedM5vP5HeRcgOd9zaj4PEuQnNt1Hy6s9KzBZWYOaqno+kTamLZ1ejHKooylMpqP0egNCaGnFs8yVBG29YoRGiSE64OwWZt2p1gEFw2EGgBx1z7EVDz4J7faqpu3OZL271cXmKs6E2iS2VE+h1UE1U4pisgXztBBMC45nrnBu22x1OPYroHxBIyAD1M4M2cFZqDldGOrYQS4cGDLYV0mleLi2oNfE4DWM8GOmjab2VBmtpsi8QQNbNc17YL9tJ3+N9Vdz7M7cTbnztGWOPjRsAcJkn6y42caoBjKMBeljj6cOmRHqaUaohenxy294kgVjlkxbNAxalCE1oM9KYNuGxqhnhR0mkFvK9a7x4xFEKk+ePUexRDrJHRctDC0UO9eUe8/pVxeo+c7ZLmjVE+RLnrHTEsW3EMmJv0c/A8jz+ee808CTt7bwL/inPnzzpocQiLsG+VRaDpGvwE+t2zJIWwzz5HvTQByACLDwx8jNnWz/dpGiERF8vd9YRwPiXNOeyW7N/BVz2l+9hl78Bx45F81gOp7Bv/ZOY6bgn8+gECCDvZxsYxX34z/ft7UzCRoCcFIwqb3tCcyvcv9GWXIcP89veLdwE56Ot8jKQiVAClyc8/P7z/HPlmjHnS/qNC1IOWJtlV0OXGtZx7LhaWeuVzuOS+VU7mwXM7o8prL5Vssr3+B9vgZ7eGHrOtbRrvS1ze0IujpTJECOlz3V5xVkd48Vfn6AHJ2drJGmEfBut9jUaELjO7p/54JE0zZGGjQ3bk23UFyBkRE6DbC8BfG/Hs8I82wynCPFRfkeqqvxDTI7/ONy8W7kQ58DP31Q1gDcQYxwrOqKdCM9ZCxa/a593yMfGYIdNvXeFAAKjCs+IZkB0Afk6poRaT6lCkDlchlMl+TS29YN7oObFHKRWDdaGvHBftCqQ5Yxxh06473rNPWB4jEAxnDHcic7oQ3LhHSYc9g2UHZbuXyvV95BMgoJdencDqNsC8DxcOyIhYalIJtPwB7QsVFz8aewemYwjAZkRV4cIDmpi+704B4yrtkSvSHWLAEJBb66J3t9OW9a/pSW3K852RjcmwMInvNQzmiitoOZOLr4z+Z89Cfc502aEx9hMU1CKIGI8Gz+VgnePmgM/Xh42caoAAgQm89UvhWbDSWZUFqCc9nQKmBSgtlFAbF7flJkebsyDYapYf4ieYLoCzza3yfyLj8QEp6KguiriWhqAs7w4CX04VyecXl/DHL/SfY/Ufo6Q4tC4rTvm5QznOBZOjE/ZVMH/wA6v/A8VXA4vi3CQZ+zOeeAwz3NMaPeU+IUEewIbYbvNvP2A2guMnmGQemJR68MKahFTHohoxdkGXDMyZu7uo5GAsvULjdTA8StnjukaUSr3+ImUgatNywCr4JpdYo/xYfuA39PB83MlzAAUg5D9HnQzjcrx28l/jbGD1MVYinD/GdmXmRrpXf/IHlCAN08wRTOJnhJD7I1Owhht0rOm4Q86cESANnWQae9t2Uvja0bki5UssjrZ5o6xPXWlmWM8ty5nK6cLq7Y3n1Lcr6c5yePmd5+hHrw2cwHtnaBlo41UqROtenxOZBX+n9StveIkXCKch5YZmZ7++3jo12SEf355D36Zk9GtrCEFeCG3Bxj9XZFBcV7kAOT9El9VdHcC4uIyD0U2ZT6yJE1k8C4RzYvDJLIMKNB7wDkSPw/RB49hB0H20/Z46XuEBSYs4ITAZADKTovhGaeh0mPL11iNHHQCiU2umbIdIRhQ6M5vdfxUEKXW7E6m1dGa1jCIsomwgewo9sN9sZVLXQ82Wc5OhcWP5PbJQ8n8MxBlIQXTz5QRTRyXOgUoES2SwFZXHd4g9+wOdvnxyMVQErM5NFergLdrB1kXgh4vTbBPDxXHd90C3bo1LdjooTPRHU3+8RceYQc3Age50ji3WaaMXXhU3GJPdGTw7J62V+dpI0BCuY9o4WDvXxee41eWLyxx6Q83Gfuz/J8TMNUFKx3vpK31ase869yqDICZOC9QYW2726aKgsJ09NHINuK7QNiQJELeLpVc9oPQVNCxL1E8Ao9TIVyjkWI9TNUJClUi73nD76JsurTzmdPqIud4xyRqWGwtsnplNmETckvc0wAPHqHFDhTzK2X3Hchh2+Dpy8/773gYn/G8g9vIbdGD5PIz4CmvfBToKcBDfz7s1Qds1Ehhnm9RFg49nCAnZD9RV0r2dV7EACs5nzr7EZKPviSqp2Ps1D+qKI09L5Wn5HGt00vM+feaa2788lvFz2rIx0UBJk3Q7p8PksILHBWXhXXoYj2ZhDhkB47fm00nAcQzklY9eWac8y24vujEyyOfmk8u8SU+JZhlFyPOIi4TGae4/W0d4wXRntidFWmhautVJOdzyd77iczpyXM6flI06n19y9+jlOH31Of/qMtn1JNw8blmB9ssZEDr/2Rt8eQGMjKorXXhnzOmspsFSuMdcmKBCFGtqySAdOGJvgQyWYQWV6wUKwJ8gsWZBM7g7CdyDEYeMR8O+yEXkjbjemuDwe6cyimxqAPb3UnQO/zucM3/TogyVyca7DJyKrRaW4mDfYHwmROOKCYRuyg2cBhmDlBCIscZ29FGRrlMVQvbIWD7G1pw1rBr3TZQuwBa01Stmvs12fUK1R58bndxtwnjbCKJZgAih7lhuSYD6f6vthHgkhp2+mERJRoSJYlZjXysDo0p11KJWVjsnA5DP/nHREGiIFrmNqY3rrtCy+BpF9tDs6Uws2tYb7GkoQlrVfRmrjJktyGMv47C4NiL9Flo6P9bM1HplSyah4RtyhgB/7dSO3actjKBmt+TD77PZL7GALbyvq/NjjZxqgGHuVQ3B1eEmUJh5bH1EAycRV7W4wKjIGqJG1SWwM1AqlLD7BOLIYhbJ4+h9joIsXGxpt8/PHu6mF8/1rLq8/oV4+oVw+QuvFq9tG5c3kaAYOdmJVT+8k7+w5GtknwP7vT4pGn3/2QwzMzcZpFp5ahmMyVfl91iRzFm/ee8OOfAjcHK8jvBdCpIlNsVh6j/LsmvcwQ6D7XGBBlVogx6ORur0Obr5fwou+fb7xUQ7S5GCzkmlQ9EBhDnr3TT3vdX+Uu+E4Pu9ZG+HwTPZnuzM8SZ0cRv4wX3YGx/UfcSZzXmZ/EIdnLnCsxntkPyzGXsTFr+khJUiaHvt8n0S4zZ/UXiQqK7D6U8xQ0fv36Wn8asIYJdZso7eGSNSqaFeu6wPX5cRpecX5dOF8vnBZXnG6v7CcPqa0L7H+DhlPYJvXPRoDRRyoFMMrzTZse8BUsfNHUJZYbWHwa0GpeL2iAKUBNEzdk5YojgaBLcLFTB3EXMlHpEEK9ZNR7PG5Z3PCT+Tn0ASw1YGI3dqF55qnBIQ3QMRSfJ1hSZnXZTk/I13XszhKFADL61Jn9xgwYoNK8KUy51d+X6bnUwq6nKjj3jMQdaVvzUsx2GCcCksV1ocNW43WNmeJtGB9oBW6gfVGM0AeOQFNQN4Jo48JCBcAE4rovlaDvdprkuyi0TSvO0ux6yyO2F+1UJczmXnn4M2rgdsYfMs2tIAUxfgM3rzzasMGDKEFgLAqaNNZqTjHLMNaonul2xxitXRSYp2k3ZLIvsn3D0841mfhniy+aPEsCOC621G5ARe1Fi82R8yHnEM+efxcsX5lHOxxJoQQ1yi2O3EBahGZBJYIfyKI8jMNUDwO1jCU5XRBLahyUacWzUA8nJPVGMOuhhy5z22RMWjXlVM5R056LD634R51LCViv4AYIzyr5XzhdP+a090rzvcfUc4fQb1DihdRk9xFD5wIpuy2wpCD95kbyM0LvL+RHTfC/e+3xy3b8f57P8SW5L/GUcj6fmpZApadHfH/hmUGj2/c7zEnZmR+/ixyll5kWk3SpOy+wIeoz2G79zaGRW7GfqrbH/ZneXu/FhvAOHxguqOH5+F/s8i4GWLTe931Kf09MJUGsHfXJmXWxfGZHCFIzr2MR8+N6XDpCU7NfOMUwb3ZPI+lZ6s7ZZusTD6SA3AVmNWPM5xmOfnnNe2hBEjdj9+Dsy2RdhuGeRw0KglO8l72eHicY0Aqhd0YR/Xn0ZG2Ibqy1cq2bDwtJ5brPZfzhfvLmUt9zVLvWdhQe4eOR7Q9Ius7bHvE7IqaQj17Qa3RaesbrBRUX0XBQwcEasJQ4SSCaqO1Rh8xI6RE5dYyQZ3NnNYDM3kDsvGCbDdMnpAVPY/zxLN3mBxKFkizDN/n2c0iNGl4cchkumRuYIcTz13PSS2Zote0h1PkO+/rAJwz22zOA53P4kM1dggQKyhWFpazIMuJpldWeULMwezWNkotVFUev3yiPV4ppaIBcgyvrltLpMC2lfU6KNYRgda2iGTGfAt7X2yhAGo6vf4E9AUX4n6VBuK4NkQEU3OHsi6BCx2oedK/4AXNYl7EuNmbJ/TsNbKeGDTL8G6hDW/lcXyWIuAlSxJQ+h9T1yY3QGoHoznbtNa5vie7ATdWbjJmh3E1szQR07YlYCo3mrMSoc6DnkQyxB4C2OkwWoQpjw7hQcg7DK31/ZYiX3P8zAOUpShWPC45FsO2R2R4Khoq9NAqlOLhGkZUq2wb4ELXMU70dp3oL6nOMcZM6SNyxD323EEL9f6e8+UVl1efUC6vWOprdDlhuoSKXw6T5RBbJIWH73uUx+OW+ZCvfB8cJvjhPe+/7+sBTSJ1UV8oqUD3v71/3lkL4qA32VmTMQs85d92b+54jn1D3+HIvKvDdx/ASrAPx/MIu3bkJ31G+3d86LXn57gFernBH1miD4oPzUHa8Xyq+f4eGxITDAr7pj4X97N7yH+dPewOdrFZr6KQBayCij8+J917fSSoM3IDtRyufd4KDsiKZzgYPYrmHoWIAZQsmR+dgCu9VnnPKEXI6PZpzZ9mpVszX3t4GfuxrWgtbOsD63rH0/Wey+Weu+Weu+U1l/oKWRpyuSLtDXb9El3fwPqAtCcog63jeov1AdGKnk6YROacCioutjXZsCtYlDKfYzEBWvextSgQOFFJakYSlMWY4iLSZF+eH77Pdm7WF2D9qDcYs4JOFkGccD4HjMOmJBFOmOUSbDpgJgenI1BQzuujmNb8a2P8nZkb2F4nKN8Ps74HiLMJNcLpAWhQL/F/OVfWbWPtG3pSTmOhtxTcK9t2je8Wr2GF0ruzLbUUSjVsfcSKQvWaVFbERc+AVW8JkJ68SOK3fc7tIa7dMTiCAYOw/0qhIicPWWgUA6V4SPd8OlMVai0s3/+M73/+lkGnYtzVgq7KKgat08n1dQs4jmHi9x2cfQfwbe3Z+0QoqnvLCtn3nXC1JugKOQgAfVgULPRspDyvhxRjXQeosHEI13O7/o/Xnm/wRI/QW+UbNTJl5eh8f/3xMw1QLJA2EvnowZKM3im1ULRgUmYsUNRfky6zEWBdTqDKphYxV9lVyf2RIhdMlB7SNllO1MtHnC6vWC53/u/pHuoFkVOkCHPYaMKz8SveB2sfXo7A4UMba3729t5vF9Vxg/wwCPnwdxyNYdLD759j35jzv0y3dHQd2VGHSTzGiPCEb9DvVZs9gJnn96mxifomnPfpz+p438eneHztOcvy4ed6G3K6NQ63z+r2VLfPMOzfvqE+Y2iefyc3z9hfHeYeuFOk0arO3OPJktgfMmTOVKTMNwqvRTzdq0XKfv7Dcy/lCPziOgwX+RJhMjFs7BtOhntugdjRKB1B2NHApmjl+bzLH1Jro6g8G9tkWMwBXDehq/r6HS3SjK+s25Wn05Wn8x335zPn5cT5dOZ0uiDlNVrfQvkhPH1GlUeMwdYHbX3AUJAT5XSPZ+M4BBADLQNdFsQ60nbxdpn9eNIdfn/NTnC2U2U340cWrQqd1gSD7I0tj88hQ2hjDCL/Zc4hzwTKvjZ6+9mYB6LFw48HtnIP+8RckcO4sIPgEa0jjpoVv+bCcaNMfObpuJVmFuGCjtZOsUHpjbp4k8i2bQF5PfvJxmDbIkMs57gqYwTLkDVpRsNGwVqjPz2xSXXBrSrVBBtGpWLRLFVU4AgKn9mS4/Pa56fdvN+zg5YYsy2W8mvEDJXiFWTNIATAn33xFtXBtoaDKx6y7AO0e/hqWIaE9+98XofkaHymHUgwGBlLI6pGqzxzkiZQOQxs2efiUXid+j1VDYAB2fajaBQJRHa7TujvpoMYsNlR3bz0FAgrIch9dk8/7viZBijALEKz7/uhnejhnZbiyNBCXIZ6B1YVrsPj1FIq3mysMfBwtY9I96SIpUL1zseXu3uW+29Rz3dOSZbimpZyctMuGa4J/Pq1uW0wN4jjKx8AKV/FMnwIdHyYKfgwMIFQ/ie0tmedf8fYha9hmMYBYHADNvZQj/IckOyHCDx3qA/8SLxnP99xPotkt04moHp+j0eNyvO/HYu8Ha9XyHjvbrj3zz0/zw5c5vvFN+Pj994avXiPycwO2T2SnZZ1oOeiVCEFtIexepYNlNT+HiLoEDV98u+zCqRZhHHcFx4jNgAzMmWxxPOF6KidYxiF6far3cFHsmLHzdi9rLxHYTdaz9ipWbnJwxW+cTDv6fn4ZfFDGwNrHVFnQ1t7ZN1OrOs959MrTucLd6eFc3nFcjpR9IzKBVm/pMjndHsDHUZ7oq9v3U7Uy+51p0NTQ1M1XGPkYFvDSDBrSCAS5cYThOjNfab2weLBzPtP8G+2F1ozB5zIc68+veldBSW6Azwy/DI349yY0x75ZpTC1sNEJcW8hxfZLc3OrOQYTtqehMMJcCzWUWqW8nSK1hN1aVi9MsojE39aZ9s2zIhS8aC9UmuBGrVqeqebUag0bZgpy6khUunXlT6AGl2rR49yDw6itY7JDnzd8VUsqD9qT8GtlZm1JWE30EJHMKmUemLoABpvvqw82qDWBqqUIrQGIl7xuD0DJDvbI7l45u/pEOD03GRgQCglx9xu5kquKrJx7cGBNbMJknP9ZjHAGX4UYYind7uG00J3RICYox7N5vzg4HjOlyOz0IybumI/7viZBiilFLScGKOhQTuPUh1YHB6YmSFjQ4swcAGYd9SsWIQzynLyCrEmXtBJK1bPyOU15dUnyOme0+kVl9M99fyRd2sVvGgPzuAErj0Y4V1Bvk8wDr8zfz8eX8UEOBCLFFaiENCHvLfjBHm24J7/vIuMHaBk/Hdv2X70+PtMS5vWdmpHDoWgPrC+b+5tn8fsHrIfw3ZPfEwK2zfhPbwyJkiY0dg4V2oWb5mgW1hnWQAriyQZER7Zxyr9Tze4yYv6uWSeLtulZwdPT/XdQyj5Rtu9mQlixvSYkm2zHE9JZkr3DWyyFULvoZwK7ym9JLEaVR5H4AGZ353XrVm+PcfC/eBIHbVZ/RQEzayTw9gk6Jitfg79Np5T1DtQdOB+01H2MAd3Ru34h/29x/NKZBu0vjnb0zutb5R2otVCXx55Wt6xnF7xdLnzcvrLiXP9hHq5UMtHUM6IVPTxS3p7ouHAx7MzThM4ugZIPdun4hkqMEOXHgr2i06RI7YzJxkmPj4TM/FU49y8Q6B/zHKTnMPzPDsglMiuSaFqbnApksy5ks1D93mTISmfW6L7sxcZ/h12ADY5P1QR6uH+mC0SMiTktaPYP4OH0PweUjzrY6qi1EVZZQCdQmfQ0AKjd84naJuxrY1hJ28MWU5o8Sye0b3AG2a09TQrtkof3rbADOue6lxz7kfKrWdyaqyWDx/vO1P7O4eIi3/Vy+6j6s+n5Lj4noIKl3Li+9/7Ed//7C1fPm5czoVaLO7bOxbrEPrBgVXVeY1HVnwClt3wxN906kXk4CBlGviwKJJYdudgghHSriaoDCftoKlzp8eBT2yTjDFRKZH+894es9fFSocqvlM1wvBfs0E8O37GAUqlLidaE3rbEFVO9Y6m4qlSxt41dBjWXeCWc2Kq1c1LXUsRuinUE+dXn6Kvvkm9e81y9zFSzyzLhaqVvZa2xh5iE5wAN4bW44LpRYbxmH9/nz3JQ8JPzf3VjWUUnhIvxt/HoPVbUebOEuzfj+1sxETKxOWkfuT4dzGy/PEEK9GPJn048rzEI7TDAkhA8YGJuJd0J7xGm8/B++QcX4uJPvbrEJGDNmYEvRnn8t1jAoSstZLPej6j4S0P9q6e+/Xnsxy2f3bfXPZN3c/f5zXtrNMRUAYTIjtrsqvrD+c22LOg8nvDY0nglQLWuJcUB/tn0jv3547EpiER8smEkfByRwDdOa+0Ts/fEHTxue1UuusvhnhDPcmqpaN7JdH5TIiwXAKSA1ALT7DbbVdZ/+7bMF6G9iwGxcckYd7+fMw8HVVUKQzGavRN6duKlCfq8sS63vN4unC5vObV3YX75QyXxcvd62uUP8CuX9C3R9rTl4gslFN0OM/70kIpAzHvrNwHEUo5eLscn8Fgrx6a15ogOccra1QYfXRsdG5VOxzOmSCTOZdQT//F9rL4x5ot8c6b1TfZrwTuswt3fmeE9FRivTEBmBco05u5LzZuq+bEIKWOal9DPcJEbnfG8GB5qYW7y0IRo+pwoFYLSmGcjLV22tq9rZZo1JvxVO7ZKqCtrpFAvdDmWtgwzwIKYLdYQWlz/iR4vMlJeK4JyrX83DmMMTdA6sISgNsjfgESVKmlctJCEUAHfAZv1k7R4aUu1GhDaN24Np22QyN7JhmwWYxxspyJan18Z2XiuQaNUuvOsgaY0GBEbvYJonaSqtdlSTt1eJ/GOW9adRxDUGpYP8z7eD37Th27YLuek2BSvgoevn/8TAMUb/qm3u1yVIb6A/SMwqt7eEmGDvOW0OUEWnd0aIKNSPlVKKc76qtvcv7oF6h3H7OcLpR655+Jxl4z9XNO4ONEvvUmfO88imEzZJFA5fkmvnvds8OlJjgRqoBppSNel6UfQxb2zCjG9djhb8/ec+yVczRe+w35fxH+daaA3Cz8Hnv+fCwOZvs1Hb/D49m5ofbb701wZAly9g0ue/sw3j/33BxIQ/38eQASjMWNhx8i0bkRHpmtg0dwEK8eUzvzOcLBcznUSblpwDcO42kBMDRJYoGRGQJ5Pd7dqWefm+nBjIPdzOfo3vJghBeUmTQu4vT7jk9E+XXH5TZr8swNTdQBOEotylCnys0aS/SqGiMN4phpiPmdjkUcQMgBvFh6WrgnuoMU5liAzvkwa7AkaAk28jYDwIG0Mzt+93241997o7eVtl1o25W23bOe77k7n7lbPmZ5/Ro9vaI+/QDe/SFje6Cvb6L66yu/oOxKLvHcZKNvfYpD+ziOtT9LB5PdU2B9wA5D5Ru8jQZaDn6836eQmYYO6PIZiAC5Mcz6Szvwzec6n4odqHh/4oelFGv1pqutO1eeyBR9xvDxK1FDKnUuoJi602UjGlam1x3rZ4SzIDGuPYW/5kxVOd0hGMuycL5ceXqoWHlidKFvzu7cLyfevb3S+mBsqztMInND9efUUamMsdG6IZugo9NHZxWhmrMKxqAyEKLlAJHlE2DvuHHnePqnnolm7cAslL1ibeI2E0CFIga2TvAP32V8ceXx2iJTyti6Id3oZHfpBBO6f0cyNBISBbllKLPc/BEA705MNKWNrLzn9svYZTkTfBpIzLm0bd5jJwATRB2agLRjhMaIIFPGvI7JysRn53reF/tPdPxMAxQJ44EWpJyo6RFXN7DgXoCWBZGNsigsZ0TPoMKwBqNjvaDnj1juP+L06hPq3afU00eUReKzyw3FJnM+7IgWjh72B6+WI3Dw99vNX/2echALBajRaM2RusdkTbwOgPTb7JCdPTmGNw7hECN+z/d8+Dr29hBhiGAi67l9H0CI0MM4+flTo5Ki2QRiYzxna1LTsr/n6H2mcc4um9lQ8Lm+5Bjq2u/p9tkcx2h+7oD6b895C3ziyt57XnkfvgZvRcJHwwZQs46CyX4/yUypHtK5o7Jj1ihg91j6pNJ3wzlDH4Z7a8eCTFg0UrTZuVlKjR4jMo1+z0JyUcNFXdLv96tKQRmmThz2gWmnmFG07+NhgyEOEkcwAs83TZL/OKwTlewUmyGIAL2hg/ECd0eGiKn7sBAHJ7BJgMTwwlh93ejrlb480rcHtvUV1/WO9e41d5c7zpdf5HT5hNP9J7R332NsX9LHFzA0NCuL0+Ph4ZeY471Hl9du/pzyJkV80x6dEczBnDRSAvzt44e6VkVKjQ2k3HrzmgXHjt2Lo8w+erNGkh0YlpqxMucCCUokH75fg2S10cg4VBJ4C86sxkY57ckIwBiFAc0b/Y22ubMxPKNMVegtNuPosSPRV0fk7J2nT2es3THWJ0q9MPgR69riu0H1xIVCv66sTxu9b6DCdvUuyno6H2xUp2++cZayYGOlW/P5ZINqZwbmhdcMrJa9Ci6++c+6ImkPnoGT9w8HaiU7ZAfAFpQctRI9ipCK1b/PDz9befsw0DNIH9hmmBi9D1o3LGsHRbLHrhHxhn/HPjcz9PP8qqKmDZKsbtYjiufqQ0/8hWEjwoPmYtiwJWnLJodn5gX72PeCQUwxM+h7uHfke2Mu5X6Twu4/M714AtsDvomrLpgZiwBF6KOhpbLUE6b3yHJB6iUS9YZT28uJulw4vfoG9f4TynJPiYqvlD0ev2tJuFWEH8ywO1By89qz/ZGdIdmNdDaKE3G60r1MUPMwlizVq+KaMlBa62ytsbZsIraHbvw7DwYlCqfdgpjo/zGvf2cxXCwnYdQC3BzYjJ19OWpFAsKIBZ2bGou+18oYFl4YkdZ2e47jtR2ZjARCRzCRV22HxXr7GTtsgjtzlLqkD4HEGxpTjs9wpyjdqynx/r2q7dwU5HAe8eeiQcu23vbxwT1VeoRxkANwO4yFeGZOnrPNTTifqcwKtTIsknkOQMsidhzrxEFQDwCanjoM2xARFk6MDt7V1H1OKXXqUoaZN8+0KOVudZ9nEfLpw5mMHKVhLeZa6CKO68IpUHpzgNRtz2pIdivHfQc0ZbJMMVDzu3ZBLkBjmHj2XW/0fqX3lbY9sW5XHq93XC6vub9cuD//ErV+Stl+hK1fYOMtRgWiaKMADMoS5lKiM3p0Ts8x8YhijxBij3CmTA9VVfzZhNvtAksHWA4Wyo23vs/0+Nr5t1sW0MIZcPYsdSYOUkope1aQJiDx8UjHB3yz8vFye+COBsGsyQwJqcS67luEzge9Xycwk6Hu/LUNqRUbFUp1gC7FEwtUPQtL1OtLobweKw8PT5TS6N3obVCqIKPSWvd2Jq0jpTLa6vU0emeLuSsCfX3y1PBlQYf3Y9pGhLMcQ1POgzIq0WLNh0IdTt/UR9lJiQ8AFd94M41f1JvBLsfeVADBVppBqZ1aPkc+e8O7xw0TT4NWGTT1Rn9jBCgRmXuM49jDvAg7605MfF9c6AibVzTuJcpklJHA+JZFz1CvqmJZfj/YYncKMmd7THaMmB1zfsb5nD2Mjt6jI+b7WDe3S2U6ZOPPDkBREWp1j1BVKWHurQmjGUVdVZ01IUSFIY2hAuqphafLxyyXjynn10g9o8WFRyoFC8/taBh2SjqBxsHLTm/mPVCyH/6x/TMOvP1zKjv6dp2IMKJeShvuQbftkd5bCMVC7GkHTy03Y+tffyHcGsCdftsZidv/G5MlOWa/jJtOq/m3PvUZ89wZSgkgJTD1I0dK8Qg0nncD9qsNIyp7iCqFb8/Bys5IjJmFshv25+LhnWmaf4sF7FFzF5yNBHfuADO9+likYjv7MiL0kgLa3ER9c13ILrYemmpkjN8mZ7WzUCISXbfDCIR4LTH67iHHDYVux/cjmfc849h5rx5Y8LBFcyZsdA9BoOraC8m6Km4MPQW0TMBmpl4sbhAsgz/vEQB1mDGO8y08O7PhdYoQZv23qMngYZtgTw5rzBvjMel1Xyr9wLP7nOk5zhhDlG6N3jutXunrlbE+0dYnxvUjtstr7i6vudxdqKdvINsPYGwMa+i4Bnio7n5WMBpe9TaAQHiW3MyvSik+MK4jcK9YzdKlivfnpqY3dsHSW322hlVKzIw+x7O1fe1mbyCL8d47He8b59ELjo8xLEStY0yANe8vWobY6J7vy8Da5pyNCCI1PPzu3ZVFoFiUefBK3GgJcJnCXmcKyvnOPfhx5U4r5enKer2i4m0QrBjnuxPr08baVmTA9frEkMIYQi2LFw2L8L7RHeT2hm0rzUL4P0aE8i8+Xgl488EKezhFPqyfy3GBw9yLdVHEQ2ZHIDNfF7x0vy6IDH7w2TvePbq9X1Wp3dBmtC4eurcYxwyfyCGde6a4+/yYdWck+orN1PGcd95FODVsDA8jx8LxZ6BCDzvlc67fzEUCnIo527OHobMxooU+K5wr0dmdmtCJkeyVEDWVfrLjZxuglBPL+UIzN0Riwx+gnR2xDW80ZRjooEuHekaWe+r5Y07332A5f+SMSVmiQuSB5gwD74dnItiNEZk/3VzXcfPL+Xob59w/khvG3r5aZyjDhtG3Rt8aW++MbmAtAEgCk/37j2GTrzueb843rwWYmA36iO/KazpustMrP35/niOYHdgFt2ZzcVjQ3+PZZ28Evke9hzDFsj28xP2+M0Qk3IaVYsGkVzm/4/0xmyWsE6CMiL8CKWHfjVZu1HEf4lqAMQc2PBjbN+cIYsT1wtCGWBY/Gzf36l5dgOLRprdjvdMlTsCJjK8n23IDrvK5jdAjhAGlR6XfA0Vs3ZAooCWxbsYAyuIMQWwo2T1ZyhIAUSad7J6bxGNVhgzf2KxG6uhwr9lCsTS7pkYI0cYsGQC7QM+ig7CTDnqwm8mSsbNQOaqHAmLEBqEGvW0+Tn3QW2NdV8bauLaN63jFq8uZ+3rP+fxLMB7R8RZrD8FOJfvhoKmoYMXDYyns9XlaYnORneGRna0YY0SzuDmL8nbiuYzdJRg72M754T2PkjbfwVuCxdQagA+NqtJ7bNBklkY8HwmdgxAOj+tjxlgd0I8MUx1Q8HJ273/4eGkpzrKF9z2ixUCOhZYAuqLOzJVgu02xUkn6uNonWFlBH1EV1us2GTQUzrpAOXN92uh9pWyFNgZyuoC52PN0OtFGp/QlwLlrUsBoozHMG0QmcA9jH2NKrHHm3Pnq8E5WCwrxKQHSqlLnePjKzsqqIgpFKQKLfsb3P3vkzYMDQZ8r6uNl6iX+cx2UEAhroVui+P26MntrAq6Yb8lEiwim6q+bOaszvNHpzmNYCGm9NcQYOvcot7nNHQgVlH4oOeB7YrLmDlL1plXJbOdBXnf5SvD3oeNnGqBIjaqt6bGOjgWlXPQEal6OXAw736N3n3C6+ybL5WNOp1focofUipQpVQznWKJg1tEljX3BhB20vr/RP/89/ZUjZ5Ib0NGDyfOP4ZqMFkbdWqBiG17yOYsmmU8Ad6LDmOdrx+u4uZ4dVAAz9HNkLzx+mJu+Pft3BHofh8/k39mNme3nwo40vXu2DhpC2HncmGWn9fN63TjjnsPhHiMARVV2DxJlr8Tpr2VqZbIdt+wKMJuvwRgt0s/3cR9mWIv4ukQ6p4S4dYLY0JUQm6V4V2udq9xAbIr7RAp9a2hx8OA10MoN4B3NgYmL5uJ52QgvR+ndvVURuamVsnc1lglCeh+7Jz1vzWJjd/ZjCuvN19BkbaR4nForWebeWkOyXFg8D8BLx4fwUkuZ81rVx6R3ZfTGsDHFyb03fzwle8HEQ0ht6QxLxNzOwnEjwLl4KnAaPZs+RYKUCHFKigSFbitbH+horO3Kqb2jbvc8PX1Mu/uIu7s7zvXEub6m1kdk+5LR3noNmwAbpShmBeuGdyA2YM82yXDB7vlyc0gwREJoBSL91IHJ++nJOX5mNp0ZDUFBVqf1jrs4YxFhmuM6mnWFLJ9hx4aDpNY2ClEAr11hNJ8qYyBjIyF8V0HKhVJPviEVjTGXKOlvMDolmuQ5QHEgpdlsUUDrssP9qCV1ao26nGnLiXJ6QPQJKQ9sm3v0ZbmjlMLTwwbWsD5oqzHqQqmV3p2Ja+0KppzUsF4cdPUFLz/hm3jacszDPyxlB40JXg7HEfTnOneWKYBBphkXbzKYu0em1qu66LmiFBNEPqf3L+kitCRlVTyRI3xCMntJvQ9cSc3MgTWZNbfSpkmA+1gTRZStB5ObGTvm7xmxYHxOaWiI9iw7zBgyospytOnI+W0hR0hGSRJqpyzA09Fl7PuDSWYJfb0DfTx+tgGKwOire1bdn0oRTyrrIpTl7B1IT3ec7r/B6e5Tzpeog1AXSlmYJcEPh95Mzmco+vmv8gwDPDucGsv35mTeKVghTMlwg9GDjiQ7SppNYdOMM9tz5mIf8PdAU2xKMjfo2zRIM19ocqCqDeb5s0HWrHAZRvKY634DYMbB87jx/nwLGXPCBiuR34PF5pPhgAOrAL45sAMYEX9ofQIhf5LvAa7uhnaIzevfx6alC87sDdQHrs/MHVKi74Q/zGENyywWi9g1Y6djbU8B9ocTqXxjQHEJHVhsxAOIhmE4Q+C4Lc4Z98AYiPUQtSpWomZJXuWIYkqx0R15HvfjJDJObHp9MlmkMdkXx1EJIDsq2VVW6L1NwyUSc0FcFOhfKHSLOhBhGL2GyLKDFqkMzRBg8z474oJYy2fs9ZyCKcAr2BKepAwvwTnneeCt9Cxj7ifVv48tHk43f+4SoI910LRDH56dsDVoG2v/hMvdifPpxH35iLMuaF3g+gbrG9YGLAtVBWWwboMxSsTdR4DUgufdWe4fc65npoWvLeazV8n37utztxu+yQkSINkXdooqj/qJDCcSRQGz0FzqC25COPGMbLhmh5zLVMwGpVbECr2vUZG1gFQvAVJqsA/ucdsANS/dYGMLUJubtHl7EelYKV53priGqYpiS8fq4v3L6gWtF0S/RNTQ0mnbhjG43J8oFPpmbFuj9XdIXxj9RNHiuqPeqaq0dXXSpzTK6C7ctULpRh3CMBeJytkz2dQKFDCUmfb27EhWLg93HoTkV0UEKUqRCnIOoOAT23UaG90aVhQtjT/4/srjw+pYSQulC+twsGLsDJdK8bL+CVCyJ1xoYczSUjJtVc6tpRKZQlBLjck4vCUGu5heS3GB+MGJS9s1a6yIoRIspLUAnOGE5vOJ8+ee458N+3LjrP/442caoPTevRYCB2qYwZCBnE6U+9fU82vOl2+y3L2ins6UekeK0Vy1cptt8NW03of+ljTzTil/iFXJz+b7Dn6Ne+7WD11Ogx05fNXRUD3PYPlxDI5Ng2c3fzuGVHawcGREdo/UAcT+uaNg1cMCB1YivmsXy+a9+slGeuERQsputg7E7dl3x3UdzpH30mf4KQ1/GNopCm4HvYtEZeHYjIfFd/kGmYs30dkRYBF1Orzw1W6bHM6EDsDwDZygvlUmEwXG7HURKZy5SY0xoHvvJxfV+rXvJayHgyizAKxGxqOR6jMpQZ3ZZDgOT3yC7wTS+Qw0aHrX5R3uN85jY4B63yqoUYwq5u8Y87z7GIcXZdsEmx0OWQL5HgIgeEi1FC8T7kxKXLMGc3XU+w4LxijS1GfNIR+4ZINy/TmA3AcsrzPTHZNpc61Mo/UrrW2M1tj6yrXdc395BXevkeXCslQKC7R3dHmgtM0xUe0sBq3hmRjg12Y44D2sP8YO+glHQ1Vn9sQYm2tV8mkFg+VCaG7StolwV9ovJ1N31sXw5ymA9cZoG+6qx7rNjCkNWl7AtCJaKVI9jSAYWrHmGSeEYFOzdUjoSKpX922Yhy1FYo0NZkhlrlHXrMWsdEArEuHOEaDfvXTJ7IGHB4oKyyL0buEgeKh3rMPBkwmbPsUm7yGnGj1ktuHhUepCe3ig940S6fSm5lmg+czN0EVCXC7vOaT7nNvtvdvW6KQc7IULZ9PunmK8hbsBRkHEgd/Wvs8PWqevACeaDZbi1SOycaPhWTwWurAEsZk1o6rv7RkTm4c9LRPExC2NPsM9NhqZ9G7q83QWyRzuYEv1MFHv6aAFQD3sDw5GM2kgss1kzGvMAnSj/xnpxdN6o2/RfChCBFYr9e4j9P4TL0l/+Yjz8jFl8WwELYsPRRi2KRr8B3CkQdhJwDi17IZlsG/C++a/e/2TBTic8/iv//yTX0/8lHtPbPpu7EYwI0c2Y2QmydjPYc8YjSNLge2l8bNIlcCz944DI5OszTiIa0MnlBqT3rgFVG6M9BAWywJfI/oBWaCcHXDt2UBZOiqFm/6VaTzymQiH/GpSAKYWAtccBzKVz8FVvNnHd3QHJTNUvKfLiqh3vw4PokeBvdk9mDEp0BGsmwOtHiW7M6SRwuDDpnecGOHh7TVY9mcMFl5tZ8yGhRFameDFkyVNhN5WtFSy8VeCo73A3S1Ahz7n5hgtf2AEYyDpbcf2VNRZhloJgNKnXqVIoR9TpFPkLBFmwcOyuVKyNomQfUNyPmQYLJ2IOWSEixAAafP3904bG1u7sq0bWxu0u1fcnc/cL5+ievJnqO/Q7s/XtSdGtz7lGrn+j+ziHpa16aGbebrypOfJUJygUidYTSYG4YYBzQk757/6M8/n6QBlw7YN6y00CjE+Ip5EUCpavMFpKQ4aVcWFsVHES82wHiFHjeqpGh2NCV0O2aygevbhaGTn730Ghj3o3pKBAMsSYaCssq7lgqlwLh7iaE/v2FYH7e3a6FunLK4DuT41RlvZBHQ50/uglHFwkjw8bg10vXoo6vRE1wWpkVFnoMEY+UZaPHnBUYc/5Q84jfsL/ZYpEfGaLzDXjCdqRGaPx3TADO1K//yBL68dqb4lK+YZg1JosaZFFallshp7bZFI1bddG3JzaZYatT3LUKjOqiqM4WM3bbM0NNfTCKdqDGZ14hivEqxKzscElJOdFXOtUTy8/Vp/ctjxMw1Q6N0nG3jI5nSmXj7l9Po1y+Wb6Okj6qlQ1WOcSKb0gUnkcR9O91Xsx9cfGRRhb2+RLx1BBTa7hyZIOKCGeSEjfnbq+n3WI8/2/JqPLMvtZ44aEQtDOaYn7/MojEaGYlKoN+aVT+NtQa27BqBPjzsuPrz4PbRj+3YRXmsny6dnyIrJpmSfmNThtMOG22fsfEZPxkRRQHdBZWwAXiXRvZYxLMpz70Zd5rDFM/ELhNF3YzQML8+doMK1GWhlrxBMuraxAcmsfOmeZI/iTEmtxxjFYvfnnp4XPkcttCQSmwQ78JjCPLMJkHIzzrLUMuuC+IY9hk1dRGq0JjYMLUd6Xy7EDg9/eBlyUQePon7vJvvGT4AZRIMxkjkH/RE3bOxepkVGi6CRsh18iLjYslYfj97XGXNvbdsBF+GdJzSRo6jYn2UuINEE2kKWqYdjJdv08gTGoNnG6NDaRt822rqybVdPTb5/zXp5xV2953SqXuNifeMbmCnFGtWMbXM21L3JDEv2A/bdnaL8N+nxLCI4hdKT2LJwJAKIR7hmBKj3uRVetcSTaS3O4WtAtDjjEc9RQ3ysWjCpqHp4JTM/UuNiokBBzRlDX7HdwwOxBg3DijiA1WSyFKG7jQiA6d5zgqlBkv2ZYZnZISPGuaTWxrzJq2plW6+cziuGUZZCX1s06oMj5eaOyzYdih7PTkZDt8H2+IS3PTG4y2d6mjZUdLhOJvveSArv9zE5/usjmyHqmNVSvA5XZGfu/ZmG2zl1kamWM3b5Ifzocx4fgVFdO0mk9VMY6F7gMESzGSIT8XpZObfnxOEWlJtxKPxoriWTLGePZzlZND9Mhg1BSgCu4et9RNHL1IGJurTCHQncmKhn+0iJ7xoOTgR4Lqn4uuNnGqDYaJicsFqodx9zev0NTnff5HR3R62vPJW4HJTUpADqiCTSRP74430AI2TVzN1g+3HTcG8i0/TWASyd+N3b4/3PHH+/ufcPLJAPHx/4nCUQSYA0JnDa6fEIa3D0fPy/PoWsYxrLBEEkMEkmhgQmoT2YItq8Z+Ja3HKNHuW/bQctXrHQ6GQfIl9omXKbwCcLAgWCCkCjcd7MHgmNgGXXYM9WAItUyhbrO+HrXo+AWQU32YO9aSGZShpjO3oCJPDMrO7ABTees2JupmHiegtRF84mKMnQDKQhd+/LGMyGenOupEAtn8Wu5u/m83xgURjMH9XM/onN4JimOMNhqfHIORQgNb1CbGCSzytSWzN9OM7NHOkCowWgqRQph4yiBA4FlcsU0grZfyi84tudwg1ygtUEejENooJdjBlkSOwGeAdDkZlGGtPIRaQb1ld6W9lap18u3J8XTuUTyqni7WHfovZIGQ3rRutZTTWDUcnS7puUyG4vpkZp+PXuYZqdhUxWRNNRiHEdvQew8bk8ImPGjMlylCjA5xorZyK1ebVd1RJlF7xQnxdXi947XRh983Etvkn3AEbSG1F4g1EEKLsuKcJGRSuZ2WO2+QY4QUmZYUMNbYuIHTx5ZzDKEiH5WkFPDH3LqTeQJ54eV2xR7j+5Z3vyTsjWG327OntjPsqihVNmvbWVXgrt+oRXBw+b0BuLjewB6W1QYqxmdda038/tKfvUy/WfMRaLjC6tudkO4OyhkIB7ooWhBmp89tkjXz51Nq2M4Ru8p/wX2gh7USJTLNaxligVwD6n7HB9eUk2XLjaY10tS0lTdgjbDp8TFmFWsXgM4fzMqugRrs0mPSa+HcjAmxmWkEmN3bmJDFU9GpMfc/xMA5Ru3g79/NE3OH3851hefYtleUWpQpGCZIwx8u+n4QpbOoHJT/C85AOTEw7gw2LQ+Ir3sW/K8YKHPWaFCGGas+HZR0dty/Pve/768e+374XUaJhl9k+kv6ao9Qhi5oZ//J49RDMsvyN3gDDyBzbDJiDxEIzHIUfQvXtoZxfIDgi2JPUibbRgUPxhjTGo0eMBQuhYqi9KBtmuwDDvxXGkXFUjTbcdblN8M7ABffMNYHh9maLp5SiYiwLz2jJbbHQXsSIetssaAZ7ZEnoHSjAvm49rZDBM8JNZBDIOlSxTpOsbW2tHcWrUExgOQhwcZBrfCKZiD3vs2VYpl8UpeduzOSzYkTG8+V3OVdcJRHv11BKEoRlZX8GGJ0ZIiAoD0JhJhBDd8JUoVjUzIEQCKEKn+TNTL1yXgssqJ68hocJSTl66fnTaWMkHdCO4TnAT4zsyTGk+PzwF0p/9kaVwkJJx/ajPg7L1LaqRblE23+itMdprev+Eu8s9l1o8zDA6MlZK8Y3cWsjKhwvcVXID3gX5gx4e967nIlk7ZYYttQRb2RujNQfl2xbJAUzg6wM3ECpaqoOOomj1/mMK2GhMv1gLEnoptEzmJDdDB33NwZt5SMfDim4bSmxUI7OYRAPPi88x8ZTrFK8j3Su3hp3Iiqd7eYNwBSLl1+3GgonrhmiVoguXWlEZlOIC4XePV0YXlruKFtjWjb49QvfnPWLvrJGp2UzRZaEVQbfKSvM53jsDvMhnlnyI9RaXdbCR+c9uh9NRkMPmnfvNQFwbs8QysT0bT8TDK7UoSz1x0c/QH33Bjx69/tWsVKyRYh/AxNSzerQ4O1N8uCfQxyYEB5hCdJcGJaPOzPCLVUMWixPwdPFwxCxd/BBne0QgbV3kLKmHdMCvo/fuazpYHQtn4s9Mobbz629w/wv/N++bc37FqZ7nYmNOlUgWTrp0cqeTPw1CJcHKV6GV95HzMQacYsnn78nvdKJiz5LZ2YN9cuT3jHmi54LViYs/CE7263JWIwvsDNtDLjI1GmOvV3D4jBca231MAknPqpJjF7fmu3oArUxP7rYzJioaOoyBEBUo4zmrKdu2gm2INQauEbE+6C0UMvHcGIr1jVqEy8kr/ZayuE2PNL0hUSAOo7XBtl4R2yjlzOhKOfm1VBQbW2xKIUYlU1+D4SCZ6R7tzIPetJ0p6Xg9AeetZRpEwpPZtg0ToZQabMtOQc/wSICVUnwpOhXq19NiDozhgrRu5puzBFNjgyFtZysCKNz0UiKdOWOIn18lKsDS6aPNTIuj4NXMnN4WIIqjDSQ+C6ON6Ql51VRcZIkw+g63gZlBhCmjjUlT+xrYsxA8rOgFvTb1GHz2THFjLEiHUlynpOIC1x6FAb2onH+3V8IM5iefF0z21GuCWIxVml9/9q6Fgh4ZIa25ToTxxOhXtr7Rxidw94pz+YRyugO5Q+17FHtCWmPYRnaTtRIG37yI2RDx9Eu6Z2r1hgqhv3FNhAQ7Mpob99E3aJvPCdt3P583zm6IlYhiK7Wc0GUJQavrIkaPOWcdzGuSGHuZe2cTveJxF8WkRvO5npRa6KycsVF8Po60JxEKKoRzWAUdeLYMDlasX30ujQZW0FhvVtoEBNlbCineIWAs1AD3Q+MecX2GiPD02LGq2FnRR6VtnfX6SK0LLjD1xpe9G4tWRoe+Cl0q0he2bUWXywxRCwLD0LM5M6GLhwglcZjNqXTQcQcgcOA5Z1TYhVnErQj1JCDVmVOFqhWtZ7RcWMrC3blw+fItn33RWa1GXtjgVE+u51LD664snJbKSZ3g8iaXcgPcLexY1UIfsI1OcWRCQxwEIQ6uyYye/Z48qSFSlG3MKrc+P0eMQQKdQlIyNgZVw15KVOLGWaKvypD60PEzDVA+/oX/Cx9/45fgdE8piyNt2+syyOHfD23nuVFmHM9s4mWO/3w1rffVdN/Na0lhB60v2N5PxNKbfJ8VeX66rwcl8fmRgryYKAdwsscCbSdyjgyQBQOhyX70w9+ZwCR1Hh5msMkajShnLwFeRu90ydo0+2cVc/zUwcZGb0+MvoIZvW9TKOg0Y6RVD6hqiFW0GFUN1YEU4VwunttfvHHkdl158+4L+ra5MewbSqE9OcvSY9MtWiO1MnpdEEaIwV5PBUbrDLEQd0l4k9ywSkrYVME3oNBXJNHUet8LnYnMmiganrVrTohn0BzwxYLPMIzrHw1CFG45BQ1GAqB0xqeAwV/K2LRZABV28I54Rdeih66nYVS4mRvibE+ifc1mdsGYtD6vOcv8W/BYGbpQ8ZoOFnVc3NsMwXB1yrnbQMpwDYz480kvT0WpqgytjN5oXVHr9MhaMWsYikYFXBvm+CQ3vUlW2DS+O5cS/2boLEIuoze27ckBwxD6UEY3Wu+8vr/n9amwlE99HORHqL3htBnbtXlIPubYCEA6QuskZjQb9G317+1RJn+GWN1LdRYj7l89ddvXenq2Gtc9wsNe0FLJXjhZgNLH0jURXpbQ9UkMo0dGWh/d527xEIsXnktxdIDJSN0n2FKCjVKVmbFl5oAsQ0cu7u7ocgpbBLuPP7DmoMg1WOagMmxkpj1TF4SCjAt23zBWhm2cZWEMo62DUjTmsdFbiLZVvZ2CKL11qg5G29jkyVmN8CCbeH82jarIJu79d4hQmWZ29zx8ZHdnN+M8kxQ7ZNE5axctDWJNmrogvIZjrUUoy0I5/YhavuT7X6xsvSLFm4FWXRjFHZFahapCLcqpOnBLLZI7FOFw9WTpnLcj+BvXsjjz28M+DZxZO2pTRt9t4V5NPTRK8frxZ6/FFIxagNvcgyOa+BMfP9MAZXn985TTx0itUSfBvbTjMSnEI1iJAdzfFL/KPtmeQ4Hjhi6HF74KRNyACYs3GuEx2/7yDRH3/DwfZkqef8fUjhgh7NxZE98sDkxMsiV5SUdwg2+6I+PbB4aFpPpC5yES4aI0jNk4rDd/jmZRdbAz+hOZVRNLAxmD1gZrW+ntim2ri3dHm8K8bnuz5jaiZLKBsdGkuzhrqSyney6nVyzne5blzKgbtV5YloW3b3/A9ekdY3tg9BWtnqpnNpB6Bqp7mqUgVuYm5eJSX7iZQUSxUL2nOsAFo0i0VJxprbtw1PfEcL3m87KoceKLWdU3G3+usdkM815SiGNuDVCjxbUqWUoamR5L9lBKy25RO8ZFlC6a9Roj+DOIlGwSGI29Rsg+p2wKHGeaewoHQ3i4GymdYQEwercpFA1ERLMtwgmZaSRz8xutsQvoOj3DYqqM4SJP31xdf1Oi+vOwgbTVQ3RjN8aWdLNlTYZ9XVuf5Q0DaMcfRMBSl+Of75GNwiKs16eYF5t/HwOzO16d7jjd/yJaF7oMqqMVthbrghoQBWB4J/LIZhltY1eP7kyqiHj0K8rba3FGQ4M1MWtkFVuv9BwApS4e/ixlMihen8OiOnIKHwvaXbidRbzGGN7HhiSfoumg7tVFxYqPfSfm3DRppPDXi9fpfKYmxesA6RLFu1w8OwtWznBkgNYSG3r2hJHhbE/xRoFldM60CEteWdcrogPPqiosiwPl3objeQU9+XPTWDO9O8Mpm7GczEXZq2HXMw3X4XhxTHPQoM4eatF9usx/5T1L7nuFxeMZUXSRCLkpNTbxIouPsYTdKJWqlaLCqTzyg8+vPA0HHKVALTsrdipCVQftpfjz6TN7yZ99i7CciodcmvXQjGlwPtmHTOYdPS+pkQ6OFAumydhHi5lCbOlQaYCcmkRA7B8D9M8Kg3I6v/YN6RBPTor3+XFkS7AbEMyx6NN+7Ch4zrqxr8Q0ynL4+b1/84u+AmQ8Z2LeP89Xvf99cOILnhsmxsILm2wKO0A6MiezGJvt4j4/vcZ5DyyLUxKerjsNavdUxr556GQCH0/fdFo3QA+umzBRD+VsV8ZYIWrBeBl2P+s2QqjVYd2eppE2XadBXk6F5SOo5485L3fockZOr6iXV5wu9yBKa9/l6fGB9ektRQt1uUTMuINWSkk9Q84D9zJcNBkb23CWAhHKaYnNK4y2eggHInPDgNnFM+ffziaMnplCMrUcezpyw5v1qdd8cMFJ6hF3AG0uqnSSHoY197It94vMliE6GY+5BoaBztYFASbVO4DPtPNnU98/mt6hbxZkvQR2p8DMqFnLQ3agvReDS23GLjzUyPrwW80sgwQvAyyLtB3Ei+baBFGPwS9ypvYTrW+0vvn9qsUcNlLcrMe1HoxWUuESDEt6iATolHjefWzY5o0HR7/6XB8N2ie0e+X13R13l29QFQY/QMbnqHlKrGTae2y6Fl6p32/MBQF08cnWfT1LyVohkelS64GdE7R0DyOpQTtskOl0BXh1bY9nlZjZbCtgfW/oyQjhq3agYObvUU2A4kyOr4csOFfobc8IHHOuRdpr6L8wIm1Wd0AuyaxaaNhSeFmDbXNgrxL2wuKe65nlrJRaKFqR8YAW5Tre0FavjrssSq1nHt9do9tyoUTfttYdyEppaOhBVAulVSfNyiOlC1U826bEhBFhNrM8HvtmvoPt4/pJscGufXIAhghLUVQ3srZMqZW6nDnXE3WpXE4/4iQ/4vtvB19sXivnVF2vgkRqtJTJ5JTiqeJZiRygVg0WBG/RoB6K7qP5nBr79Tp4OIZ7c27GNQ8X92Yx03TYRzoz0w7szyVzmyzCZH9mRLKlRuoccuORPT8mvQ0TnOxq5yNgkf3jNwSI7eDEPsR3fN2xi2Dz9M/xyjE76Dlw+XEMyjEEM8jdlJ0BkdhQJiMzpqB1lgGflSfnTkxucxJaEkk6OdsJDK8bIWae6bBdyV4reyp1MCJ9i7Q1n/xe/VVpI0NPwUoM3/j68JoSmzn96F5m57GtdIxVlEUXZwQ2WJcLfXuk9zusVooWlvM9UirfRLHh1PSXY7A9PbGuj24IhiJFItnwFvxZAKZjSwLXNETsFe9EOua88Nvbgcbuiew/OwerpYT4NPzpg9efOphSFt9Mp/hY2Ks6CrQRKaPBaOXzjWH0GLdLiBlZOj2uxwyb7R0806b35loZQhirKWDMJpGRDp0JMZK9iyY5Oddf1rHZ2UsgiuJ5xdtBa82N/TCGKKPL9MKACGPkxun3aQLbZFmUYcW90qK+caly0hO1Fg8vjub9Sw73lGsFKWSLDCZ4DFAy16xO4+uAsYWezMXezjANWuusvdHtI8blNa9P36R8tNClYF/8AOzKiKqmIg42TDItXJEarMLoUCvSO0O2WL9RdVXrHkfMAncjNAQRphHNcero8HDJPncJcBCbT4TNTNTBUDIrs4dQ+tMyNyjXu7iLM2xAVcooqO6tCwTIhoOqipVkJcEsiwxaAHPmuA1C15IF4NJudZshphStCwM9FWQDPQuqF3SpMBoilbYZrTVqOSGqPL59dIalFupyPgCzaKyohdY2tC3es+npidF8zmW5IUToIeqV4WsxAf+HbPMtUEnGMWdWvEcFsUKpzLWpkU7t9WhOVFGqCrI80T5/4DpC2D8adTntIOBQRkFVWGqdDLDPGUXHoIgx1PVIs/SB9RAmezgon/W+ZnxcsgzFtAvCZDw9bG+Tg5lhUnGnB8Gb7w5jVkn/CY5/4ADlP/lP/hN+8zd/8+a1v/AX/gK/+7u/C8DT0xP/wX/wH/A3/sbf4Hq98pf/8l/mv/gv/gu+/e1v/4m/a4jM3HDMZjrf7mXas0nCTbW9w6tMZBJGddjBwNo+qY7HhxiNZ+84/Ps+ADnWMPnw59Nj/TrmZGc2bn5Pr3DsAOX2Xiw8neBVZmaHOZsRYMdGC0+xx0bVvTdH87TB+V1tDaCTXrV4SWdzY+hZDTAEWos6Jy3qloiHF0La4vHk0elAb94ssbdBXx9YrXEuldene2pZKIvQ25Xr0xuknqgGLAYno5YT95dPKd8S7s8X7s+v+MEP/og3X/6Q9fpE7UapjT62feMXIo3VJojo3euo1OIlz/u6MQRqTS9735zB+w1p2Y3qUbVu4ckf6xXssVubi94LIAm11BCt7UBpHMZJcL0KZtB1D/PYLLqe9nV6NWYdouppem42QnMkenNNOVcs7jGTDEPsH4ZozxKbxhecAcjwTXjJRfaN7CgwFNEozMYMSfn1Ztp11IUJoOYWzxM1yewlcY+66OJ9abqHnPassVwfQs1MmRwbM9dc+Jl2xoeDMzOcAemWgaENe3qgjs7AmUbpCvf3vDp9wvKRi3yfrhvt8Z3bpqX4OGtqmaKSLOZhn9Qp5QYQG7ZXG9YZ7vMwge3izcxsGd3ZM5PJfBKMJGKzJs/cQCPsAIZq9+dx3GSDhUvWRAIkzY60HgNBD9k5Fl72aFGcLlKfcxVk76A+otS8OLuSxQtJp0ktNGGFDDsmcIYRacIXpERLEwb1dGW7blyfrozWKVU431/Qp43RNnoIwj3EvCEmLJcTfRNaKRSMsgnWXZBsPde1BJiRSEtP4MYtSJEjo/KBw/b3EefREOGLeBkF7+vje5PJtxllYdXPaGZ8/+1Kj/u3MTxLS3Z2Zo5bAu8MVx72QQ2QuK+92IciBTjTkNN+mQkcgIuDk+IpxalJkbBfZkTRJWd2Ney6yM6+/LSzeP7pf/qf5n/6n/6n/Uvq/jX//r//7/M//A//A//9f//f88knn/Drv/7r/Fv/1r/F//w//8//B74pFdIw6WQ7/Hxz5KK7ZTCSzrXEKLHQ3MCOtOp+hh8DSHYm5AB65tveByFfDUxuAUfGTH0CSGw8zz6XwtgRm1Ma43keL2tOhHMw2zObRoKVBBdtsjBeaKlD9wZxYzRX4I/m7bUTUau4gJVE2grF2CKNb5TugCVYEjfwmX4MbRsToKxt9eJcdE/d7d7MzK6PjLVjBU6vF+5eX3h9d8/dR5967n670tcThqDbyvnunnqq1OWbnC+v0XrPdfNw0fblj3h8eqAsQl0FGzWey4gy2xJkkqHFvE/Icp79J1QLrcPpcnYDHC3CIChNM1pv2FConnGkxZvlOYW6keQCIhStIYKNWixBF3smj2dFzDFT8dj6aIy+Yn2lao36JGMaT7Qg5RT0eMyDIUFVx3W2EWGH2HDCS7L0dAMciWRbA2bmaDjAEYrytZf1VPLeXBC86yp6eNa7UQXLLJzIdDAAdZAwAIY3WhtjTF0IweIgOJukFSs1RJ4VLQ5WvKZDn97kMJ+P2+aGUm1fox7S69S6MIkEGzODTchwRXiSwGZ7WXAZBFvVGa8/5v78CadPF+7Ec7K2hzeodY/L68nDLVF3RPBxpRtSospnIEsJgW+GZwzzcGPYP5MxK5AeK7e6jcn+OwEehQmek2HLruVKYUiEKgldmQlYxZrQNdg/cPGsEFVgHXxOtjXMX7dGSVGoFCzCO1Ii26p7eM4ifCB9BKMWnXiHsxtYC5ArAR6FogtIYbBixTgtNTbNz8HeMtrGGvquuijYibZ22nZF0dAshq0aFvOiI32w2koWEext9WsOQJ7aT8xBmsY6RZM9kJibsXXEM581pSQZ3VhIkuUQCqUKs5RAWhLRyKI0Rht0+5LP3j4y5IRpOpEdatRSGq4PLKHXSgY4K8+20abNlmRVcNCdxRyL+JpzcBtC2slEuqOZVaEd+/sdLEtkbA4vrQ8uME/Q6/ZDGfUnhx3/UABKrZVf/MVffO/1L774gv/yv/wv+W/+m/+Gf/Vf/VcB+K/+q/+Kf+qf+qf4W3/rb/Ev/Uv/0p/oe45akw+Ker7qcx/8U3rCH2A0njEez3/ez/AMBIVxP7Inedz0qXkPuDA/45ji8N6M2c5z9gAl4eEdGRK8O2UaVLXUNFgYrkxPDVAzxmRFJC7EMmZgvml5Vo2islDVM0I6BepBr0FkTIyBSnOjZxuIMGyldS861XpzjcoYbE9XWmtOzXdnNfbnovR1ZaxX7Nq4vLrj1eXCtz79lG99689RP/4mNsRrILTBdX0D6wPr5cz59Wvq8gqRhcvlNR999Alf/OjE07tHF51enYJUCjauQEdtgHh9gFIKHaGeLozLhVLP4bkp53pCunvuWoo3MYtn5URGi3TYM3IqUz8gGNo9Na/qAggtipeZCH1bQ1XvqdhlucNGC7bQM0gYXlyK0aB3uu0g2j1NkOIdY7tl7D8ZQaVFWC/TfDXYiiNXKBGWsZGMUIJWF2FPVY0e0ueH7g6C7CmPeW1zU3hGTXs6985UdPP6OSU8uAHB9IzJbs4sHTP3dK17H64J4MMr1QgFiXo7+95mSf0xzENNcT2pMZjrM2ti3DgYxhgCNK/7YLAasYYy1b0z7BX3pzvuPv6/8pFeePjR/5f27i2MitQSYtbINrLuWTXmHXJTnOrj4c0eSebD0hsNtVRk7iCKxvcTHnEybzOEkO4uHk5wgBkZFlooLK6P8YHwEEB3oKKlMKwGgHTNUgIeE6IKccydYALy2zTTc8eIecz0tBH1zRnxDS0cKC+LEP/G+EmJsdWCyuKVSlvDulKWC8vlFWKDvg1661DcseujoUUYXVmfrpRaWM53iCrb1kAF7ZszpkMYkQmELqz1ca5NEWendIEinu6bDm6GT3OcfCGkAmW3+0L2A2NfB3l/+VkN7U3sBZMBBaw3fviwYj00O5zc9musyQ79cP7JoMR39dCO2WFtTnAlvv4ma5h7RZynRLbjXDeFGdoBY6l11ptyObLN+84+bB/SiH7V8Q8FoPzv//v/zi/90i9xuVz4tV/7NX7rt36LX/mVX+Fv/+2/zbZt/KW/9Jfme//Jf/Kf5Fd+5Vf4m3/zb34lQLler1yv1/n7l19+CTAnv+SE/wmOD7EYHzo+BEC+6nO3dHheWQAVMg33QKWZfeX5P3ReTaOO5zZM9sRsFl6bn8svI/QJ1pwFSfo8ActR8Duykuw+YWcGT8YdNatKZgn1gDfi6F+zBoYZRqMNL0/ezRjNa0m0vnFdn9i2J6w5E9O7U6/btrI+PYGt9C2YluGGutSFOob3pFDltJy4Oy18/PqeT7/5i8jH36J14+2bL9nefMnj28/pb77L+Vyw7ecpd9+EcvHiW71BN/q68vDui+lNqAJ9RWWwVEOk08Xoqoje0bYHpF3oyxmTgqlidx/RtDNKQW1Bowx9FaWtG9enR/po1IuHpWpWUYy4r5vszul09oKLbQsjMUB6eGd4JVOL0IKN3XCNDZFBjTqS7uW5QfaCTB3YK09O7Kq+6Xlaam5aPn+DGJ5zxSu3ygxD+P/bzOwxcYOY/TUcuBwEgRkyeDbfx7O5n4zlbjDduLfhXpcEi0LMO0QO/YQEFYu021hj6hu66yZKABWlSqFrYcPLoFvopNyGRDgjU7zxmHwKW4+OSq5PZ6Vc7L1GBpz3uWnuld8LXF5x+eTPcb8I64/+iOubL4Cc317NVHFho8XGTpVgcN3QGzb1TVr2Ympze5kbW9l7Nykhag2Yc6iv5OLh8OrVNTxmw0XoAUqJDZMRYA5nWb3ysUBms8Q4W0+dkQSACcAyvIItGvofUk+ioa92qbfWJUDuRlFlRPaJ4OJmZ8tjLJJ5MWWYC7+plXq5d1CzenZU708M67R+he6sqGuso5aOVzyL2kad1q6xEgx4TSlgbWO7XtFloUdxSAdJO8h2JivtYui73Mvj9shFmHPIPzMgQi9e+2WJOXae6yQAwmi0deXaPuexbfShiFZ6i0aJJYS+tpcAdUHsoI0ePI5fw3O9zPMQ0W2Y58Doq8z1IYB4vBQzr1RrUpzVMXM7aR6aqwlk9aeoQfnVX/1V/uv/+r/mL/yFv8Af/dEf8Zu/+Zv8K//Kv8Lf+Tt/h+9+97ucTic+/fTTm898+9vf5rvf/e5XnvO3fuu33tO1wBGY5MT/kx/PgcLR+Dw/vhLM6PH7D5OPvLZnxvjwnc81JQkAHKUmXXs0iPF7eGy3N+7gwz2QHvqR1b3s3J2CEhzz9wAVdgRRGd3a+2hIZO0k+o1v2ePxg8in9yJVbayM0WitYVujbVe29ZHt+o716ZGxbe7xto1tu3qJ6vWJ3h4ZWwuDqZS6oKezP8suXC733F/uOS+V01Iia+dCNeHUGtvjA+v6xPrmC8ajhzTqJsj5I18cW0elstSKGFyfvsS4soQAUGUg3QWHAoyiuM4D1qeK6ILnzQjb+QtYFnpRVBdX00eBGxfZNvow6uUV14dHBpXL6Uypbti1FrQsDC3uBY4etTB8jG1kDLexbhutrWhqHaggg6KFspzJir2zh0bQ95q1JUKYSYyxD7h4WKLkmB/SyINxE/HtUegztdA3Cos5thuu9C4zq8c3mxT52v7aB9bUjSA4XtcsMDYaWkrUsigh4DPokIUZU7TrGTBRVbdEaX8i40RAxPu6UN3LHBYl0YP9kPCU8xlO7JYe6eH65n9EOMq2eR4V9x/FBNGBne65u/8l7rw7He3hTWg3Ii17gguNDd/XnR1swJiZGQGmwkufYuioTDt1JXMhpz3y0JBS51jPTUl1Ohwy6mRYMdf6EUBMIl149/QlmK30/p0hFC1oCMo9fDe8m3LMka6Zyl0Y4gxC1gkqHe9/JcJ23Ty0guuP3Gw5MPMtMvRIAiIVKYYundPlytgeuV4fMVuBlT689ks9LYw+6G2lnhYPa2hUG4+6BobR1ifGArp6WKguJxfKZnaeiIvyq6cEO3sS1a6fgdnjXEd2xiF7a+1rQzyLSZUltURpc/uA1vj51rhuK9/7cuXN1hFpLOG0dEDVwaUFPuzdWxS0Ho7ogSd9L1oQbHxWf80QWLKIc0GYTdbHX9fYJ9Kh1cnQzLkSBaOyL9pPcvwDByj/+r/+r8+f/5l/5p/hV3/1V/nzf/7P89/9d/8dd3d3/4fO+Ru/8Rt85zvfmb9/+eWX/PIv//KkLfcHPInm946fBHC8DxZuz//855zI8en4393TSgcivc7noOT97+w3r+dGcqTJE2R4t9s9iyM+5Aaa5oAi9CKOZnt8dEwx2m7gdE48xyOpw4lJKsIYoLZMTwrzuOhmAy8HPtjaxugbo1+9+NpYaeuKtZX1+sT2+Ja+PtCuj/TVxYvJnhhGb6uDqtAstM3YMN/8TxfuX/0cr19/RNFC72tkDTU0NpbTaWG73FNOd8hyz9aujHcr53plWV6xnE9c7j7mG9/8RbDGUi/88R8/8vDuh9jmRa2q4kp5cc+1lIUhj96OvBVE1phnCk9fenGnfI6Gq/7VaMPLktdyYpQTT+LP7p06MDFZKPXC+f41b+sZ04XL+eyps4dUX62V9brRrlf69a2zWDaotaK1MJZLVChVXLMTjIMKaKcuuBGMSr2e51du5mfKACfedbeObdv27IBU9IuHIbyvS2oXUtXvnVrTlh3PPTUusgtPZ7gnriV/38M++waaJf/NDGqZDIxiEXYsBwFosjwEqOoh1Cx0Hb55qrpNjcqb+Z+Zuec+BJVkppKub8FUBIMQh7c6GMjQWFvwJO/ci21GG0Z7VbHLHfd3v8C5LAi/x/Xtl1h/QlgYUjALbQWADIZ4iBRCC2SHVM70ZqONRwpPzTKzLjfDBFnJ3vrGbLnBaLIg2YpBcexrXt159GBrMkyU//k4axL5Ala89w5mkZLqLJ4zN55VlXujhGbKgUmJQom+YUvq6cK+9u7MWImeP2n3+vAwSIaKRve5QFF0OXO6e8Vlc1vUqle6tQGlKsWU9amxbk+c6hJ6rB56FHPQPhpju9JK9WZ468JGhMExqjgANRGGelM/U5uBjRny3y30bvMnaXkbTpk2GUUL1OU09w5vjeLhql/sDStvGD965LpdvTZNGcgodKkQqcTJpqIOvFu3SE6w9/4TYxZYnKBPvUCcL7ZCa75H1dCEEaLXFFAXKfSwF3IEImPcPIOf9PiHnmb86aef8k/8E/8Ef/fv/l3+tX/tX2NdVz7//PMbFuV73/veBzUreZzPZ87n8wf/dqs1+XqNyJ/k+HF6k/y+49SzQz75jifkAGJuAcmxENqHGJX8Vw+fzSqt2R8nU1+n92QuZt2rySZI0p1CTy8ky9nGFY4URuXqEe8H4RoAZRudHr1JGBujD5oNbLu6bqKtjHZlrI/+b2u0beO6XdmuD7TrA6M9wlhZ15XeHJ333r0J2QRR7i308BbLMnh19w0++vhTTudzeJkhRtyusD7iAMFYljN3H/0ctq1c3/2IgWLlxNAKeuZ0t/CxVi73r0DPPGzv2LYHru++QHrDinsgbvgWtt6dIavVhYPDNzjDw1fexiWe7TCGwtjarOa8tZWDNcIifVq0cEW51hPDFCmVU/X0wqHOyCynE1JP2DYY65XRn4IMNkzFmZPTa6Q3p3l7o22+kZdaqaeLb9Bbc+880lpH9a61uXllj5fpqxkuZhWZIGVEV+aZCize3VkI9iNqcZgUUv/hHrJ7uDKM2SzTX4m/+e+DpMr3FMfn4dBs2tYn05NrUCDTa8XbwFuCbjGgMYazcdYEk+bMnBSqVC/6VxRVL8C2NQ9VOECokYklh3E81MxJQCSSLZgwg219BDqbDTaxyPzocLnjcvk5lm/AkN9j/fIHWNNgACyu2/9T9RRdrxliHsYKvRjdN4SJnQzXDKW9iistxeeqxaMScKYutSDUHA0XumuJjKE+ha+Ca0gIIGa9o9LC+3e9EDMUEOGhaBApxQ6OU2iF8HYNzjKF/gJnY1xjl0Or1Jhng9TSNQeE4nV+RtYgCjZIEKws2HJGbXDG1289VUYTrk+d3nFgZ0LbBn17co3MsaqzecC2942+Xj3NfC2IdcbWnGgQ12KYnX0jdQLF9UXx+7wZ3Vm53d7H3HnOtpiRUmVRr/1TbbDYGWzQ+zd8fkjlosLv/fANb9aN2kPQj8+3PqDhYLJv3g8sQX8yKfmdWTU61/fRKZ6OhDn7kZ/Rw8+egVTmORCZ1WnzXhPI/OT8yT8CgPL27Vv+3t/7e/zb//a/zV/8i3+RZVn4nd/5Hf7KX/krAPxv/9v/xu/93u/xa7/2a3/icxfZH9Bkzw6A5etAxocAx/N0ra977/5HuOF+ye3e5r88Y08yJryDm1uaLZFs3li+3ywKPg03uhKb+uzFMmLxk9QzQakCuEcGOJoO4sUp5DCMFh5nFETqGXvug9ZXX6ztyUHG9kRvV18wT+/o6zXSga+MttLbRttWtrVxjZb1o11DiBqVODc3FmOYK2ssFlMWsYrKplqEy/mOy8U73DYGYgum1ctjXx8d1ETDurrccb7/FJHBum10BFuvbNvqG5Mq9fSK+48/4e6jn+fN5z/k+vDWK9qaG2Mvdx1MUoooY0hKsT0MF2UGSrSat7BSPUBklAJzr0/dA8wUGMNTqLUouhmteenzpPGvxTMfxBx+asTOa6k0AdkWlrbOuiyjPWBtRbTQ64l2uqec7pFydgZDBLSip4t7y+G5ChrPLnvYBOMxhBQLg2uICjVCKeZgLTZDzwRrswCVz70ICFqwGJZsim8CE5zMegtZK8ei/oXvvh6+cXZPMnxk+dnAIcNL55sI3UsqR02PDF24qDjXhHVjSCX7uRQtHo3SwlJsGmzYw1jONIYjImlmw2b4rPaePRitg21Cs4fwP4czfXRMX3F/9w3uisFoXL98g5kgmbZOFs4z/x4FAnxYPE/EW0aIRMaeu8nzeedmTaTFTlZUHI5Y0GUiw0XfBAhLVlrCsRLXHDAifJZzdwDmwM+bRXo2EsGmeBgwWJExPJvNXG0xRkOG13cxhGEd/6mG05RgWdBywmwNULXRt+bMlLh+Z4QmL5tMel+hhXKCLnCuXkdERXh8eKJWvHeTGFaEMqqHznqAdJE9xdfEQ888oVrYiJozGn3DZLAMB40JFv1ZHxri7Zh2z5qce8z7+9E+A1Ke46Cx1sIYC4JxN2KM+6AWofXOd794x5cPV08yQNwho0ZSZ2izJrlvcz3lxqkIR47jqENJ5+B47XbYn2a3Z+wgS/BnyIEJkzBkP9VKsv/hf/gf8m/8G/8Gf/7P/3n+8A//kL/+1/86pRT+6l/9q3zyySf8e//ev8d3vvMdvvnNb/Lxxx/z1/7aX+PXfu3X/sQZPLDnjj9nHeRr7v//DKvy4fPlT2FYzbgtZ//h0I6j5/dDSU7lZiyP2MhcU4KFkbU9XXJ6mWPgfUiOheHc8FhsphrFrvbJNrxz8kgqcET/ih5ZNN4nxMaVbXuE/ki7vqNfHxjrgwMOM/r1kbattK153ZK+sbUrfdto2+YpwpGCaGMNj+j4vcR/RhZownzjWpbKaTkj4vUjtr6hohS5+MLoa9RQaJTlzNoN2zbEoC5nusH69ER78znX9ZH7jz5iOb2KbU4oemKpd7H5aHhEbmTca3VDlU6hxubrNOjYhWiiLqiL5+gpojY3BM868M+k+FCSXTOwZMCcywJceEl4OWn4AGxkiMYY6xP6+M4ZiPHghrqcaOsCT2/Q5R5dLhHfF9AFKSdEvaaLlkpZzk71yy4YLMVDJlprhH8k5mDO89ioJJ5LUS/yZN39cSmTrRtm854dK1sAb+ZGaVEq1wgcFRk64HqgARTz+aLBvMQjduOnwaodvGuvERJuLQQoIjb14aXfZSBSI43S1+FpOUemT9T5iU1wbizBzEgUoZIw+ikUNrOZNq/DMHMtQY2qtKgg5zvuz9/g7psdG7/P9eENw0K3osVTpQkWcT4UjUy8mHO9ueAaIlQTRdEmfSJexE4LpR97aN1YoT0kGGOkopGMtZC9qaQ4k5PczK5jy+9WKCefU6ETcg0QeNkCB109w8B4VpCo+jgRgu5g1kZkE2b5d+OgnevdJUH1BDqg1Cg/79lmtRS0F5rAsIraYGyNbeu09UrJhjADZ2Ox6KAOy1lYlnpIx08hr2dH9W0Dafu1mM+xWhYXhIpMIJI1QxCZ6frJPs6NHuO4XR3F4yaZpVWAhSWif+eY8ypAVX6hD4oC7Q0/elzpqtA8o83Hz4WrmSGWXbuzblrO+x30H7RJz8JPx7BsKYVsHuvZfiPsZWZexa5ovoL8RPN/fqLjHzhA+YM/+AP+6l/9q/zwhz/k53/+5/mX/+V/mb/1t/4WP//zPw/Af/qf/qeoKn/lr/yVm0Jt/0cOS4SXW/KPAWZfJ4D9utd/kowb2JmTDzMmt+9//zUX8xEbOVl8y2A2rxv9IMTLrBvfIkdUdp3luSS0DCFxnBBeFIniPb5PKG20yF9vjO0JG2/p29WLFV29g2trj9j1S7brk1dtbStj8066bTS2baNvHv4ZAVDWdY2UUU/ndHTt19ta+JVDoufOiMUZFVaDTSnlRFkWUOW6XtnWDSnCwwNcH77kcr9Ry+vY9AqPbePd2z/m7Wd/jOjgcj4jrbO+/Yynhy8p7ZF+/ohhwpvHNw6s2uZl93uuVqZS3aTi1W3d8AwB61ECWw8AtYA3SHY9hGfbFN8gIWK5NuuEgBxYBd/QNTqLOpvV6bZFwa6d1cuCVj7Nujeak3dxzYOiJ7RtrhvQim3v6JpVZaFE2ED15L2IykJdLmg9I/WMLBekVjdypVJLZbPQ16jglJHGhu1dp0upaBQ5c4u3AZEmHiAZ8c67kzpW3QW0z9ZEb14o0LOAwrhblOaWEkxKOqeRCt27A4QQ5xGgyJ9ziJ6TRQpljJr45zU2+Wywp4rqiVKqp76PzdcXkQoaglI/u2/qvXfUHAwd67z0rSGsrMN4a4NueF2IAXZ3x6v7X+L+5xX50e+xvnsk2zn10b22iUaFWZgxfbG9zlH28JkF1gJATT2JlIgumKcBkw9uxN8DBAqhNdDIFmMyK0LzIm5dp/PUuwu6w3TF3LIJsCw6JOvQ/VqDYQBDR4ft6gCrOBNq3XULGvYtsqQdPKU5HAPrm2viJMXprkUptRL0h9tFUcwKLHeUu87FBOELeHhg3UY4VF7bpWoJG+INByne5DBTsK0PerC/2crBrldagLpRz95UMNgjZywi5CR7hsz7u8hg5s49YydydWiMpxZjESOLrHnadfEiCeXMogV+8Bk/fOMZO1IaZgGcGPSZEBGsSUYgEuQfQqm5HnO9ish+pfG+1tpNmCcPES9zEB5IvBacmPLe+7/u+AcOUP7G3/gbX/v3y+XCb//2b/Pbv/3b/6e/q9ssfwPcDu6HjvdifYfXPgQebuOFHz7Xze+WCun9tQ+9b389PBobybeRQjazLcAJeDqHNxdLeO7x6epC0wgfCMy0YDSBCRCghWhs1607KGkbdA/FjH6F7Yqtb+ntS8b2RN+urmRvqwth1yttu9Lb1T/TvI/Haq596FnXpDsLI5Fr7/HOmNR9z0qIukz0brN6oVPzblzPp3NsasK2Nbb2iNmgWOXp4YnHt2+o9z9kHXBezgzg8zcP/NEf/v+4/vD3OZ8q2/1HlHLGtisyOteHd6ytMWTh4eGBL7/4nDdvvmBrKYAUPAVT6WNfuABtOADMWGqQCJMC731MengMry+TDneJLBoZOQfcKy+aSviKFPe4bHhPI4uNI5kFS2YG6NGITSW7R3tlzt43RPZCb+kdZxqlJUDSE7KdGFIxPaH1DOUO6hldTtGA84wtZ4jKrLUqVqrrYgxPOh0+ZjY0rtNFhCk21Gqe+TQEJLLCzKYnnhkos6IrQZUHE2LhpRJhFM+uinNIUMgRlsDG1IH46wYhWfRPRzwugUv3nlIaLIMMmWtGRF2jUsWzIJLJCCdCJTs5w2SUwhE4lFBBVOlt9Wu7+vqT4tdtItil8vruW9zZQPgu68NbXwN4kzfMszrSgTH11g8plpVh3mclNCXeqygcEfZwCXjBLX+eOtNEcy76I43xKTUASqYMa8Axz67L1OUUFvu4OM0/bFASsQQgmanfiNd+ocEYjHaF3iKmWqFUBhpMZYg7Z8n04m+LtdgDpVb1754FBUkdU581ZgRB74SoOMR2feDpwUO+W+vYBneXjzmVSzAEfQqOXUwdad7mISZ0oQxijvvaaqdzPAfXtyAS7QacprCcozE1b2gTgnW3PZ33uEeNmI+i3sBwMXVRbKzxHmNQtLOxMdoX/OgBnjaQ4uG0UHmFY+MAtNv7VV2P372zJqEji31k9L3YYjInaW9sdAfuNv3rfZ6NYJyStvkJjp/pXjz78T7IyOOrQMbXAZkfx5g8P9+Hfv/677KZjuW7XJ9siHcTbkikP7r9y8qRhkidHktShyodizokKhaeUDbhgqxDkOrz1jfa9oStj97ld3Xxqq0P9PUd1p/Y1kcswjQj6O5rb7S2eQhnu04j1aLoWOpV5r9Z8Ck2snXtc9GPMbwXj0WY5zBpBeN0KtR6opQFM+H6+EAphtZC7x7Pvj48cnn4PmIN2e64jsL3f//v84d/7+9Qrz/k448/5sSgvvoGJgMrlac1QBmVh4cnHlN74hWHcnTm/w0EunsE2VTRjYJFZUx1Bqi5R+rZqGlc/W6yAJp7QgfHIjw8wz1BIgNGpXg5a3NtTjbXGqHXkTDgU7iZU4kgyXLDNL8mr5IehleMWoShV3z5R6pzW0A9m2iUBa0nul1oyxlZ7l2QW89wWig9umUHkPCbCbZPslNvzPTRXXBs4h5ybPLWexhtT6nsve8MAUb2XyHW1hhRtC02pmxWN9OTVUP/0XfDqUwgpOl9w2RSLDxHL+XtYQPvveKARUuhiEDUvkgQ7RWdvbCcWPqVvgac7bIQfCqMTvZNFjG4Ehu+h6UEY7n7mLvzL3D5dDC2le3pnTd1s8rAQxbpgXp0IrqGx6bvtF6MgeBhnYPTprHBjA6GN8jcxas5HwfWGmiDUR0iScWKzsqvGk0OGR2RPpmKFl23RQS6BAMVDpIQbF5WtAWblWK7O2ICSIF6ZkhliIdZMjRaMswzSswfhd4c647Q9kQdlmGDbD4pZQkwi4eC6hlZztTzBXn7QGuPbK2j5UQfG5zuKIs3FPTnnAyc95cao1NEvfSANp9vTWjXyvrw6BO+QA3GckhoUcqtfkNkZ0duHWQHpD4sA7FD4TbcXigFqWUyZC5uHxR1QP7zFMygfv8N3/18cO1Qi4u6RTLjbm87srU2Q3Jpm49OdPbasZj/YJHJx9RmpcDeAnntxUC5SQTxwns5V3+y42caoAiB1GIxHo/noOAnzep5/r4blfMHjptzyvsP/7lGZrIl2d001OnuUce/vbkxCyp3r1kAKhHnTjSMG3m3wECcr7cNwfUAo1+hPWDro/fM2Z6w7S3j+ibAyVOIX6/07YHWN6ytwZZEDn3buG7e9K9blKDH0fDoRgs2ZQyjd/N+UKa01iJDJxiIMEypO8mQhs0UCCDKY5eyUOtCa42n7Udczo1qZ2p0+8VgrBumT6xb5+HxiTff/V2uX/w+VGNtC9vY0O4ZRq01Hq/Nr9+Eh4cr0leUgeqSPFMwIwFOmoXnGdEBmbgDE6UTHpIdPHvf9siGcF7+n0lvS2RoSLDyvbsxHETsXlyPYhKbpygqRsa+/c+D0S3AlP+eIc8pQovv62EYMvPF3+PzA5PIYshrKkBB6wm4x0tmntHlnrLcMU4XSl0oyznq5VeswJBQ0QjOklmWZh/0CVj8asPMoSVCGIZ7olOYCnhm6y5cTMOJobbHwVUXN55thLh5N7DeMdnPn3268v5d12OTZXDhrIfcbDidniE5/2yNqqR7mr4DPk+vnptorMG+jdDzpNja6M2iEaZPsvQ0RQrjfOb+7tvcfbPRfvD32Z7eUpag3wWsLJMN0xToWoayXKDrrwWLEFWDbQysJAvh2XjeIdkFoLMKaGTnjN5Dm1OxElVtCS/aNCJzWYU32NsxfPzNwgwNmipF68wWsRAhO4h2AOjhmjW0PlDOhiwXivq9luItCxQXPvfmDI9pACHcKx+jR0deZ5iHFcYkBmKTLwplwdRT+++uDzxcH2jDAbuKOJAoLpoNfomsm6MiPD1toF7+vkfFt+yCXkuEUUtByjbneFm8bQLHUIjlVvGhPSskCxaTw8rMhvE0f/+wUqkBhjVYX6iInJFhFP4I0bf88ReDLcDCrOVjCbtKfF8PYnOPQlh8JvIlYpqF/i56d2Vlaot1LwFO+kjga2EbU/j/1UTCVx0/0wBlbtDTT/rJb/z58aG42FeFf3yMJ3l1+DfCF3b4e7jL09O0rCIaC9fcmyS8CnDKMiu/ku8JQ9SHp+jtXx2zfTCNZmtPWHsC24COtSvt8Q398Uv69R1je8foT4z1gb49MNrqWTpto/WVbVsRMbbrla11ByUjm/uFXRwez2ybK9q31umte9gmqDwRmfHs1OdMYBObkmsubafFswkKGp5jdKbtK+v6jsFA64WH6yOXtXBaL5zOoYOolVd393z6jZ+j24ahPDytyOmKCWxPDzy8fWDtDdXC2DptfYw6DhoLLPBB6gSmVxDhGg3xaKluxNPTAG/AZz3CbSHAJLylYZQSICB0EDZkzpM+RlSB1Jgv0X8nIr8jqPlhnilSxDvAOos2fP4IAXafY+VILySU/ANAorgUNGtszYJCjwZ2tmDySNGF0Sp9XWh6Zguwspw/QusFrSd0WRAtLMuCap3ndwAbWpvshh1AIbM14hcHEyWFwuLCxs6M4++2PXRVZp6JQwoB3Wj24Zk6RbNWSlDWOZYaVYNJzQXQ8SwJR3q+jrqnmhIp36XUCHI0nBH0mjOlZIly9d5UYeQdaLroUQMkjdhMR8wXb593QoqGRuYj7l//PPet8/DDP2D0a2iWKini1gzV9S2WiWslJATU3o8rORummDPL7SV4cU+WsDNR+0OcIREdEFoBSVyXm6fBvoEF4BzO+mIhZO2e0TM0dCSejuLjMtOeY4PUgg4H4youStXiPb1KgEiC4RnWcD2T1xtJx653c1YntT+R/QbqDJiBoJR6YpzuEGu8+sTf/+7tW54eCcbLnMjRkytv2uosWYQu/dkLiDHahrOuziaUZaEDp1LQWhzgCP5MUQquj4J0WuNUx73GjgKBfH3sLIbZnrZfDJU6Pz9wEbKaIOMbdG1QF4p9znffdNYxvNzkkP2LQ9Sa4UsPb+3lL46A3mzXPXk0NdHmCOAbjQZ7tlcJxzwccgvNpH/tT7GS7D/K4zal+GiQ32dCPsSofOhvX6U/ec6E5KRNgBKhRi+aZZ7iNkVsKXLjGVjxs8W/6qxJeNg2cicJERaO1HMz9d0osj0mWt3o6wM9mBEbV98obdAf39KfPqddv2Bc32B9ZdseHZREc74xOq1vE/32IdGJXRnNY+u9eQrlGF61dFiPdOQU3e635pM1Cnd1bwbXU/kWAGXY/pxLUadnzTeRYUYxWJY7D3GJF5wrDLbWuK6P9O2MaqWe7nl1es0v/BKcTvf+t9b54RefM97+CK0X1vWRdX3Dtl1pzTeY0a/U0ylqesRGFqMiAZ5K8c1Gi0RqbkVKxbvnapTPB8SwvrlRHcNpcHxORBa335MoXn49H4V5l9WSojqfLSMMa+o85AD2Yq+dYQsC2OxpuAEmYX6349nIFInCXCPb2s+CToZZNISUwpASxk9QWTA5Yds9sj2g9TWl3rEVF9z2k2dxlFqBzugOuohwy2gj6OAy6X4VZ56atVkYzTsjgWerBYMRpeadEXBNDyZewE01DN/YwztE1dQACi2K/zkR4CXwZW4gI7Q76lqY3EBGZBAV8dCBVor4BjSGsK5eLDB5IWfB/DpK2WPtjgXU+wSJQN/om7KWipQ3DlAIr/hyYfn4z3Fnjacf/X2n+TVFxg6OR9T8UEq2Prqxfzaaa6AAqdXDmxZpvkUnSHF863PVLHo82YC2+RhpdUFu38WQkSfl66AawzZn8iyawQ2HXlZOiHQPc5RCrYvrfCxqp2qwPAJdlNE3Dxn04fV0bERfntBSBcPnczrEsLb6egstGyNCg6cTjUqpxQX2kZlWtaAF2rJQTveUemJR4TN7YtuM1p7orVLkztlBN+gM7XP8/bq8MOW6dpbTBZYTupwowVDU4gsuq7FCRRgOuD6w/7iN8AV6TPXNEMvNO7PRYNgOLUINe63ibN1g0CWY9QFDPud7n19ZoyWFcNBrhv31RrEHdo6wM6OF85hATyK047ZRD4B1WAD0Z8BXclMIXVp2F/9Jjp9pgLIf74OIf+jfZpB82fxOm+Q+qXa3qLqJjcPkBLMSHoBNNm//AosSx9HvQMr0wtzzJoq0NaxH2KU9MbYH7Pol/elz+uMbsC0AAJ55E2Gdtr7FemPbrtE0Lf/zrIyWQs5uc3IOg4FGuMZoPRu6Cb2nWDI8fTKOGml2ISQbQaGDBpOSkzsBgebTo/fuKvHTGa2Fs75i9BVVo/cnrBhtM69TII1STyAnzq+/wTdOr7FmfPep8e4H/2+4vqOePRNDVCJDJ0SmeueIXpTejVqFLHWOZVppbE7VBaLIgmiliYWR8Lh3bw3tJxc0y4YLFT2Pyj1zHzt3fPWQ4hcagbEDCyR7bLiR8Kfm2pes1ZAMwQ0wh2ntzCJFMd5Xgv0ZvXsabBW8a6lMzUx6SK658VRFBI+3ywY8YdsV2oqe3tLaGeSE6olWL2i9UM/3SKmU85mlhgjYmN6fSvGNUQajEetgQK1oPTHwPikEZWziwDXLvnt/zGggZ88rOBBzOvqChMGU9E7FAXBrzfvOEJT1XM5RgyTG3UV9Xiit9wAJEaI4LSfXZEU2Q4ojpx2K8/k1+fVqUe8z0zvbegXE2RNxwXSRV1zO91y+8ctYb7S3X5ACRwmGx8cnmk4KM7VVMkPK3EuNlbiLXEVQTh5aE4nuyz2YJJvCxj4Gg82F3SYMKdHTiHl+T2keyRnP9TJapGaXhoi3cRA9IVI9pV0WYMH6Bl0nEBxR7TpT61lOIMroNSc1GRMdJKh2Zg4zLBwtKQtFP3ZNyvD5VpZo0jk6vXm13lEq0lakb3zUv+Ddu6u3PhidPh4wzsGqAq3Fc69ehqD3aM2xIqNBO/FuDE4XL1+w1r1VwBIG30SiM0M5hGCJEQogy+0xa9kcDiPF2AFyxIFiJdgVi8rWIXCWCLENvmT7rLGaAhuaVXfxeYOoAwj2UKUJUXbhOMS7kz5F4jNDk3CQIjHioD/JzCz4M8SgwPtMx9e9D95nUj50fLXI9atezwEL1iMKGomEWHXqTPZc8h2sqBvqnrRn6hdGIBlHri60T+OzMfqKtacQtV7p6yO2PWDXL+iPP2K7vvEsnOHdOW1sMK5Yf3RRaIhZW/OS8z3iyb3D1olU2mQ4HF1vmzf9C7WFe2ljYFJowxdO7z4RM2aKeKzZq396RoKzgzpD6GnMBRfH1VJjc/GQVtHhJcBHQxis1zfIuLLImXb9hPXxAcoXGJXt+o6+fIxcvsXT9hnWVhf8hfE9lUpVpVcPWfV4BqBoCZMew5hjJQpaF5bThbK4d2WmXPuGBVMgw7t4d23QC0rFM7G8oJZ3iXZh4TjMHQmBGaKT+mduLZ5NhJTJWImRSSjeIp2oZsnBkw6wo9Fx1iusehqzny893gBE4xAWinFwwKKMHmE36w7I6FAf0PUBbScoZ2q9Y8hCLRcod7Tr/5+8fwu1bl3vesHf8x5a672P8Z3mXPO0ji5dSXaylYRK2MHSDVqm0HjjCYqAF6JgQIggXgiCIgZBUCk0XihYUCrorYIUlSrRC6vYIWXiduvWlNscd7Ky5vE7jEPvvbX38NTF87ytjznXWslam9plLdJZc81vjjG+Mfpo7W3v+zz/53/Yo3FC88S0vyal3TYGiTEj8YIo9hbQVq0gqkq3i72tiVGU9+7PhtrvLQQ3auz07hu5/xKiioyRaVeXf/pz12y9WuFjB7OKELqTWH1UYwZ25iar3SD6LkYyFldnxJCQFJC22jPUmiMT9twMLxwr0sN2fRH7ulLOdC1ItPGbkRUjKhNxd+Dw2uc56Zdp54+sWHQTvq0ABVQiw7VVnUC9Ff1+IAhi1u0M7gqb74mObnwUCs0Jjj6SCd0QLVJmOKfi3bN6VadhXH/odXW5MIRk/iSD2xSdO2ZrzryQrA+LSEteTHboNsIqTSB0j5IY+6z/40gYTbZnRXDyuIhxP/znWyBj8KbARquETpivSSjXHZQXnI6NXhfWUljrRM5XIIHQbR+f8mR2Aj2gzZEwrcaB7RO9LixnIc8Z36jH1bdrPtnvMHhV4iNuGU+84I3pw47Dz4OHZ9CAXv17h5Agid1T/Bn2vB9DAQNxmmh8wEcvzpyretHv95jgAIchpc0LDuOQDUT/4Tl3KUjG+HqoeYyT1LZfYUPl/Dmwa/EbCEH5egXH/xIk5eurcXyTGetizNccODfovbsDrLlFjtPXFiJ87ZrRgF0VIPjDOU4aoncq46E0/X8rZyO1lnt0vactN1BO9NWCAft6S1tv6eXMup6oPpoZm4Jqsa7JIdnWneDqD/5aOrV5JorIli2yemHS2jgkjHFTvY4yYy7rusdlM5a9+gFq38sORbaNzgzOjGSYcmbKiZzjpjxQbeZOS0C10tuKiFC0oLtE18J6uvEDeTZtf2+c1jPl+S+xk4U8XzFNO2KPpGTvr9QOUpHeCdEKyNqqmxnp1gmHECBkpmnHtNuR5h2IUGoltkgthSYGM1O9kApGalWNbqY1CKzbI20PuBMeLZPkUpoADqc7lOGqKFAfJapHrYfNYGzraJxA6zgNuPkVauvACnofS2GFGaMTwjqg7otuM2zyNbkRk1snkDjkA4dHz5gP17ReWZeV0k5Odl1pq9CWezTNkLLJlqcDmmckm+hT/GePA1G8u3oQHoyhS+7KK2JEcRkcEisgRQf0/FCJYKqaocbwC2Wdtxo5UIKgTe3AwyBzUBdUiKM+9uyIF4Nj3IAfelmyKeOaEZcHBD/GE2MPGSPD3juNxrAuqeuZlcg5JtJkvh5rOBDnK6Ynb1H6keV0h2ojxMxQkhni6MiKBIi+FzV3ndUO3hzL2Lx8H1F1gqQfTKpqjsnV0DrtzdaIWBPVW9lGshKGsiggKWOGmSDaCL1RdPWNy8dzaqO5HhXBxACmwY5o8HFVx0wee6MtC1KKrf04Mez/JYStQJOU/Rl1iXGI6LDpD8avGgGAYTxX4tykoEiciLN9bOrKrhXW9SXn2zvO6wpxopTK9f4JKU+McEZrnDLDobCrx1CUzv5RIKRIXc+A59LEM9WlyiG4IKAP9EM2ubh60xocgR5HuBUIAz3/ONF2FCsdrAD0PWPyYvFjlhRhR6uFnXzIuy8KNwUfBzk/Sa3u1W5ryMA6K/BVxhnydc7HTWLPdq/tc454qgs9BmH6N0qBcikSLq8xQfuksmd85SeRlK+Hvnzs87Bd/+1l1Qi2edkBJ87qV232ZxU3yRHGkHgD87zQsa3DIXs/kMfqNJe+ahB/Waw4WV7Qlluod2g9ocs9dbmHahbSxS3lzVzKZHy9Nkc8RkaPGaWVWmmYFYF16OaA2fz3aowxgtBUfAOxSsZQALsw4yAbhNLosrrexsL1Dk9dYucdyXjgYsqkkIjRipOcRmKtyZu7VD+AKhbYFkjzgd1uj6RAzBNpmiHuiJrQ3ji9+Fn46D/z9DCT93ty3hFkR1U7ZM6lIqfFDOb84Qndx1AYAhTDsIOfyLsd87wnTRkElrqSWqREU2f06tkmAtqjFWrdCjDzTDE0wz7eL9fOO+0mfvDJIDPa5w2qBiGCo2pKuxQKLh/e1q2rMkbRGEOmazUCs5rEfGua1Ui9g7NhtY1vRh3PFDRuiogFvYpGdrsdzx6/wVtv/xaefuo3sXv0jPV84vjyPY43N1TfoM7ribvTPeV4Z3yGOFPSjrx7TMpXKEJK+KEzSMqOPqa4beLa64NnJ5gKOFh3PJ4TCznzwsQ7uezWm7buGynZdmejPPMW2Xgihuds47OxlkEhRHq1vB4cDRkHxihSkmQQG0tqb0729sJIxzPgqIbq8KaFFjFTuzMSMiEGEmY6JjFxdXjC1N+hLj/Pcj5ve5q6V5AMQ8GxZ3nquDoKoVW27QaJ3lx10Lx13OMZrd0ysUZYp71fM4AMzZJ8VQIpmF+IRMuDCVhIoPYKk43rWmuoGwZGtcKnNUWnkVrsvh4+QlOpdC++6ZW+FuNx7R5BshERKZBTsHtnGwkSF9vTYqS7F1THisDk2zTd5e2evBxjpI8iQCuaD6T9Nfl4R2uvOJ8XeljY5UrJEykloiQ6EL2Z6L0aYtqKhyAq9XwkTRN1XaFDcsWlJTuHraDqnngcsKwd/HmFYUPwtZpr/cQf7aEcqLz/ICPKZtuczcMGR+QTURq7nIn5JfLBLbeL+waFYLJ2f1a8bba9Ce93Bmr2YGxjSjlbPX04FW9fbNe89ep+WKNp52Lw9w28vqULlPHa4PLx/xtUPrpSvRQaD14X6fDWt3797+27eu/dixOHQsf83P1MBu9ksJsNIfHuWb7W9/efLMZJsUh1S+nVttLLivYVrUfKcqTff0Q9v0TaEbRRy8L5dE9ry6Uz1G7z62pkx+oWx027RdGPAqVZ5DwSaV0ptXuX714PXtW3QaBk0AE/7jBoahO75uMQsMAw/OvYRhkmUbM55zDIynkip+xdmcPSZpBi5DlHBnK0EcU87zgcHBWZHzHt30R2j+lpIolw/ujLTMuXeW3XqG2PJJjmxNWUKGlHb418XhkDuIjxNFrzpFRHj0annPc78jQbbDw8IeJEbCspZmqBtlY0+whBld4DhnZaQdSqp/D0SKduHAK8+x38Bbt4BkvnyZRHSCLEyYuH6mvF0ZRu+TXDowW9mL9pVxtRoK4w8fsgbkk/9jVrse3AHBD65lfgUt0q7HfXfPqd38Q773yJ1974PI/e+BK6e5PORD0e2e9/mUdPn9PWQqsn1vMrjrfPOS0njsuZtVaEQj+/Yj3fE/PMOZjyJ+XZDMJ6c9AobwdRd58NcJQxYNEwrQMXD4cuFwQlDolz78QUoJuXRYiD7zOK6ots0oohY1YMufe2mQa5INOC30PxwjO5m669j1IqOjwsBkKqD/YbAUGdRG5PVNXCIkdSVO7j3rxoYiIxMx3e4OrpK85f+RULokx+/51LZcWmjRrpTlqvhbaekW6Bd6EVKwhioseAxos9ufbm6Jp5nozxbxA15KQWEhDybOsym6rJCmobJ3R3d+3OdTEov9HXxWwT8mReOsWQJx2Fm0baIGEOxErF/m4rUFZyzMScIJntQErZijJXhvVWIHZzut1GIN6SaoOGoSaOYKo3WE0EQqK7Mm1/feDqdMdaOudS6H2hlDM5742vpFBqQVthSgmdEuelEBVLaycw7a+pcUWrEkjE1okxU1I0Vznf80x6bMjR6Cg2tZo3EBc0nY+dHZtI40EhM4qUgdSIFygG9AgpClN+hxivIXyZ/aT8/Hsnbu8XSisgzmkKivYRgmgZPs1HqIIg3fLCxNGfzedkdDWGdfv7HGek+1z5+TYSkb+R17d0gSK9O4v4wcfkUm5sSjqDTraukQf/3tip+snPqXdUI90RcHlcsAIRVevoXaBGH3JKn7nZ5R00e9/8wSV+1rENyXFv3f1FTvbPckTXW/p6prZCX0705Q49f0gpdwiFQPYRjvmSaOvubWDBaW10w2r24a2ZWqPU7ryThkiilGaVfS+M8MGPzRCRreM39a2pLC62yZbDEv1BU8ytVjxoLgTvaJJ7IUhHYvYiyDYVKyBHLo/B7wo2t3ZIOEYhp0jKEykf0PSIMD1B9q8h8zXSF/R8wy7A/Ox1zofE8XiiVSXFTk+RHI30N7xIQrSMH8S8DDZiDK4qksA0TcRk8LGZtBmxMCBMMRN7o88Jk7naKEskUItQKAx7U9XoyFrakIvelUiAaNfW5NsY/C3dvEdagpjpq6kcemvEaP4IFtxnKoxt9OES3eSgS2sDsfGFCwwPh+AdFn24tqpvlMNRE3Lasd9f8/kvfBff8R3/LdfvfCfp8CaaZtZyRE53aD0a8oQrg/I1U9gzT095VBeWcjLnzt5Yq5n+lVZJqlBW0LptbRJA2kRvyezeGc+xKZlaXUgpo3G237e2DRGxjbtvBfbGO9FLmipesAW3tt+2AkyxYsiWFXwheaHWQWWgOrZmezXX3h673dsQXGodKKVsaAmeE2PjNkeKPFQQaVbEKlDOLGtEji9AlGkzitsxPf0ih/OZ8uIjVMV4NA0a3Q+VSnfn5xGsJ8WLgwq9ZSRkimRT9syzkY5dB9xjRsYaShC0Q6tInk3ZFCIpzkicwfNrLmZmaghZ6JA6wmy8hmbvw4wnm5nhxbEnxi2E03gok10bqU7qFXos9BCI2ZK9dfjKODKA4p4q2Yt6G2EFL5xaK1bwtIYGS9nuMhpFZZj65SlTdE/mUzyuSs6vuLu953ystLZQ+2r+S56gjhhhO+eJEISyrM7/6bTzHSetzPtrpASiVkiBeQvRtLFcwtKpu9jITBArDsVG0MOh5DLq+Tg59pNFygXzsgonpAwSmCVaFEBMxHhGRJnyZ9jlPRO/ws+92/jgvrodxJCL+9hXrfAwbxt77yPBfPxs82gZyL8VOL1d0qXbhpw4181yTb7Wcf41X9/SBcp4fZVka5vnfy2o7ONfNzbur0bVXC71QEpsHx6H8mU0dNn8w4YKt02907zjDZcbNb6f2sLTfjY/kl5hPcN6pJw+oB1foOsdtay09Qh1pTW3pVdL9BVJ9gB2L0Jq22bSRgBVpAtlbdt7FRzhIGyH6UA6xi9gUO9lLGMnhCtfMNhSsSj0EALdCxODKQPGeB+GVpWh0EE6oV+OSoODq7ft2eeoEKN3h0GJCCEZVyWnTMp7QpoJaaaFGU074nQAdlTN5PkxSSvp/JK4f8F6vGFdbihlJaldl3VdrYNFzZ/Jkv546F6aHE6WbF26PZy2dUQCEQFpxLzz+2rIVGvVisNWSTHZge08iO5r4xKI6D8bvIh15r2OzdCIxSjmqttXkAdeBYZLEcJljXY/hKs2fLi3dWBWeJjkEZcwj/UwbvPI/klx5tH1Y549foN3PvslPv1b/jc8eft7CPtPodPOlF+nG/r9+7S7D9DzC9rdDfW0osEs9DUm+nRFnq+Z1MmbrVLryrqeKL2xrKttkNEktL0uRpjVhvRKV7WOVRu9ms+MuvV595RfC168jFW+lqKvtUumzVBxPRxPjn0jpegFCj5es+tnf2WMUeL292G4jEQ/yM3xtIywwd782RiF6iWrx+T8zRRaVSjLQgqZJdxxTBM5JlLMpCmze+2L1OWeen8mpJnaF0I1DVOvK3U9U043tPWOKA3RCrXaeytmfqgho9MeysFN+RTSDkkzMQmi0XxM6GjP5kE0nK67yY+j8yhsbUVv4iDGCdQjLKKhOL1Vql/jHhd62m1FRZSdjU9StKK9N9BK6JWeIq0YcTY4ImwNgxegDWIwFLbLSLa3tTUap17XrfExkzNzyI1eRKqTdnsUQrhGUyaqkGKE3iinW2pZKOuZHGc0iwuJhJBMkaQ9k1MmSKA2RXqnnY8srdGnlTTvSfNEOZ29JFLmAISOkogp2/Pmv6cDSb6eHwAnv8ZR9jGUn2147miveD0YTCIvwRqxEOgS+Gz7JUr9iI9OxleKrtQUGWN5K5g2ewj/p3k0yPYM6UDxFNHmZHSMfzfUVo6iaP8NUqBsSMnXgLoefv7Xlx/L9tWD3KOjn9sgmA37vqwVg2QuiyhE0Ojw+Nj0xpfaBjpGMOBs577Q2pm13NPXE/V0pJ9vaMf3aaeX5vhaV1M6MEZAHVSwALPm5E69fM7fW2uuVGlmm2yOjc609uLM9lf3MBBTEtTe7dAMYduA5xh8nOBdtkslNcXLOE2Cd5nJCLF1JH7aWjbvkhXVleqKh5EJUls1nkNzv4IwIHIlitomhvmHhDgT84E4H5h3V6QYiUGMxzJfgxiykOfHyHRNCO/S+0qtiyNWYp3uspL9/T9MAI9y8V4QJ0j21nxDH4e5mCNnzCQvxkylomYf3RpoIYj9vK7mFdM6PsZwv41u24mhfH7vWtu6c3OBDV7gRlMgqGeR+No2tfNwWu0fW3ddPaBRdRs3CpfDUUTo1aWb/r2mmLneX/P662/z1me+yJuf/k7e/Mx3kR+/g0xPCSFRpdHagixH+t1zYr0nqiLrmfbqV9F2opOp01N094SQdhZEGCc0RPJ+JkwHUu8ctLHUs/WL3XgT57WiNGqpPjZZQA3dCHlydNPXFtBbNat6H28ZZI2jRKNo9obEn4HQXIYazaTtoS33hYKiD+o7J+WaLsxPEn+y3R1ZVJGYSCn5aM2CODU8kFer5xRtz9f4PYRaCms8g8ApTaSYCTETw455esL1G1/g5flnqLd39ODjjQ61enFyeomen0NbiAIpKnmId0IixoneDsj8GjFd00StqBL3EBkIQzcfH1sjyfc+J9gGQGzk0X0Wsa2ljpNp8esDohZ/0MuKToKkTAxWJISUSDnbpW4NejTeTO+otHGJLz9/IAniNyj4fhsUVZcWDzNMHxH3WmlBCNO179G270g07kXvher8mrhbyfXE9fUVVHhxc0cr95Q0g3RSzOSQfN0k5z9BSo0Q7fnuzQ5vTZF1UcJdIhfFVy0dyH3HtDMOWui2v436eox5AnJJAZaL5PhrjXgePs+DYC8SIDqCIpHkaduGWhntQOtCbwvrr97zcnFavQiltwuZ11U5A5nUB2fbSC0G3TiTwQvFgV72Xu258BFP64Vv9PUtXaCMgm5suA9fG7qBftXHvu5rK0SGh8lAStzDQi+oi/1zkfaNThYRI2MN62XEzWkUxbglrS3Qi/FDWqGuJ8rplra8otx+QD+/INQTtAVty7b5Vi8oerMupbYCCE0rWof1dHNjruB+EO618mAhId3yV5r6mErc5TTQaiOntKUL27w5bmOCQSIcLH7coyJE8wYJwbIstJm7rI65qgqn5czN3Q0c7w3Wb8Ml0TNJohmfxWg290PeakqMRAiZIBOEGcJkIYASaaokEVLMTLtEqQuNhsQdmdcsav18Z4WBiKMgq/0eQdgM8tQxIM8NQdtGtmtqklgJ5lKpBNLm5OjunBLcOMqRp5CMLwBOnC10mnsh2KPX/WdYF+oIlQgdIzlb0JxYgmtbfaxz8TcZlu0jpl28k7R/wraBWpHoqJ90H58ZZL5qIZGY857D/hFPn77G2+98ls9//ju5fu23MD39PPnqKcQZCZkYA62eLDphPSHdXC1bUOvEpdGP7yPLHciEzlcwXcPudfr8hBYnj6ZPTkIW9jla8cJEK5lpLpQ6DPkspLI3y23J0ukSaSFBML8MVd34EIaE+bXw+2q+IMN63saIHVOsNIwnMJrE6t3hkFmOp9syCYAWtyJfvEs16aqCJHqrLqt1mf14/rU7eXbsLZfvr85dq01gPVtBU46Ec0JiJIeA7Cfmq7c5vP6SVz//M9S+stQTrVTOp3vWuxfU+4+Q0wuCFnKK7HYZdsbnCXkCmcmto80KRnNEbsR2hnSNMFAJI39LgJRnJ0Eq0qqPKRoqgbbJ+EYv52PbmL0oHnnqTpAUIeeZMO3M1C/a/jKanK6d7iOJNBlS2MVDC3o3lZEXVd2FCsoYN/v+PN6HMWKsqOlOyHf7fBm+RiLECoRCDZ2YE2F/TdJOkkzIyt19Zzm9pNSJlK7YTdfWFClIjMzxgIRAWQtaG21d0dDI/j7qeraIg16J60r2cTsqTPi0ygNHx/jk4dnmB9l2hg2e1cPXJ8UfGydFxGXWhu5tKGMIxn/phsbfl1+kfXDifrH7lCRsZOMuwUZ0ToLVgToO6ed4g32Mcux5C6OBVydt94/7o3wjr2/pAmWMCQIfR06+6uu+TmHysY/7mEbGGMZRjrEgLtKosTDk68BuflC5qZBtTs2rz0KrC62c6PVErYV1XWinO/ryin5+QT++D+XWDkEdbq02q6wdD+GDWhvVN0xVN6dyL5XgBmnarDMLPjaxUY6jH/7rDFKrjRACUaBr8IdLNlOhHAJVhRwNwp6niWk+gAY0ZPOuIJDSRAiBWirrugLYpiCB4/lER4wD49D55pkixo7PObpjpqtoxFwYQ4jkvEclo2RU3ZI9JEptxNbJs3lxrL3Ty9nmqR0kmsqn12S5Qa1c4Ea1BOIYDAIfTEjVOu4yzQvB3orbuicu0j/rpoxbawWE9korwwVzcFysu49qKaK1FGfMc/k3WLEYxIpSJ+Spdk+UPsOmNAmbCqP3ZryeELzIcn5Tt2LMuDPqvKlRNLpEvMM8PeadNz/Np9/+LM+evc3u6nWePHub60efJV+9huwekfI1knZUXTjXSjkX6vGWen6FelqvSifMM+nqCW39kH62f3SNaD6g6y1h/ynCdPADLFJRk/bmGfLeM2ciOVn3nOJEkM5ZC+dSqfVMLyemnXVoYRIvbPz37s6hamwoR5C0cSXsGcEKFbVCtPsoRjDb+K52Lcd4II5uVXDuiCOMMVqXrkZctuO4IcEgw5HjY2qPtsnsx3ofhcx4NUdJW4NaInVdWOXIOSZOeUaSEHcHpqe/md2TL/Pez/331PXM+XzH3cvnnG6fU46vSL3w+DATdztanGA6ECWaX0fAIfg7pCZyeARERM/0Pta2cYGCBx5qUEPxHNlrxTpgHQnEYoV7TGkzuTP/k4TGTNRqfkSlbqTNIO553CtareAQsQJGQ0DIfv8qdLuvvRi3r2MZUPbqSEz294OZ4BECrQr4KA2/d6JWsAQd+7yhm8E6LnoLxGmHE7MIrZPWe+LdLeV0pC5KjCt6Jex2B6ZpRv3vT7s9JoVeaX2i1Mq6nIGAtIpkU3aWdaGsK7WsSLeQv8jOoZOOErY+l4HifY3zarMA4DIh+OQZuBUr4SJPTiR6n0AULY3r/oTSK59dV2hf5leeH7k9DZGAFSHJz0bzXfKm3ZPbu4/JL2jjGAtZ7Anj7PRR/uCAfaOvb+kC5eu9LlXkgwLv636x/Ss4AVbHzN7snRhl0KXTaSDJb3jYxjb2A3k4CTIod6hyeqG1lVbO1NXycNpyot+/op1vaOstrdyh5WgejdJpHVoTVw52c//07Al1yLMVI0iWdvGtaF3ppTBWt4iQUqa1Ts7JjavUbMLFNlAZckvVbYNuvVuRoO7s2U0CPOfMPO3Y7a7Mhj4fCDH7ewDtnRg7qve2CbsYLYZIzjM5zbTUMR+O6oiAFSfBiYbm2GnXOWCS0TQfaCSQjCqUZTEvGIms/vfoAYLS18VCvYDez5ZdEc3t9WHQmXhxYkVKdI7s8IVx2JJOU88R6pUgiRQjXSrd/RgQ+961FENwamEQ4tR5EUZatutuclRzmyxlZZTaveNkTieOdiM997ai6hJhJ2te/D5wVQUGzcPmNxPCUAcYV6EOFMmdT6/2B770uS/y+S99H29/9reR5yegyi4n0v4JMj1F552RVdW8Kur9c/rdHXL/PnH9iLXd2w+rBWlKzDNpPqBpJp7P9NZIsZkFeBQ0KSEaFC5loS3FFDzTDqbJ3GQlbh1IEiFHoYiivVjOVAxIntCQaCqb3FfG89rH4ZMwErq6QsQ6bq32DIhbrtfWtnGjOKlVXbG3dasPutPBLRmmd+IxCKamEugmjbeufRQhw3EZzJZdtm5YXfVnCrlKLSt1LeSwUs93HNNMzDMxKGl+zP7t38z08z/Fy6/8LOflzP2r5yynG+iVNM/0ljzxeGJDdzHFUScQ9IyUF75ZGeRPOyEyo14cBIm2E2qx9dyt+O7uVxLSRJj2qJsM5mkiptkQZAFSRWtBtZq0Vs/WAKhZJwjJeFy90l2NZESfDGLIn7biHXj3cZnZDIS8JyQLihRppGjIniFmwe5Lc7LzGI90c94mmneK1gYhUMU4LCntQZTShZayjSDTRBRo9cSpnsmTRW+ElLiK14hzkVKy5iZqRyarLLp2ellYlhNhV1nPR1Qieb1CtRNFSdn4Spud/xjBWmf5YN392g341yLODjO4gVwhQkyJrD7F2zcKV1y3FXl2htpI8iE//+Edp6V4gyCUtSLSN9diAX++cGSFBw385c9duxGl6ZjNvaEn7TcKSXZw4n+timyrMn3j6A8qFoPxvSJ0QtDoqu3LBoLyNX7GmOioyQ9N5FOtQ3Pdd+urFShtoWpjWc/U+48Iyw26niys7/TKLOjbCe2L3UARSjVPBRw1GbJRU9w42RKhNKW1cilOBhTr7zEEs9furdOwBNnk6plpmpDQ7cFWy1yRTXFiV9hQPUWkMgWD8kM0n4aQEnHakfKBkHdIaSyn4wUVSUYc6806rtoaQRIx7YhZae1kgbjuX5DixC4nTw8dIWzYzwnmQhrDwQq3deV8vue4PGKWo3UkKhyuI7XcUtdXaFktA6idkdrp1TJXarFrNjrEjR/v4ySTwenlWkq0cVRvWFFV6BV6EGiGvNShotIOdfUOTWjNCk1V8wSoWpwrrR5RYIZYMsY0aqTmcShqN/M4m0lnTHngIx4rRYjpQV5HMEM7B3zpfcX8UPZIqgQCc7zmnXfe4NmTZ7z5qc/yzme+xOGNb2c6fIrUGlHV0LGrZ0jc0YN75tzc0G8/JN38AnJ6DvWeooW4NlgrXYt5ZcRMCBMpBjSbHfq025EPj+m7a5gyEjPaAylFWl2o5xv07gW6P1D3jxF2vm4CvSihK/ucKXKg19XSnXsh4BlNGMKcHH0aRD/V0ahYEW4cFTyN1sddPWwZJFYQQkrZSsZoRWtXQ03sOVM3FwzuFeHFSnWzM4ARehlt/JNCRrRRe7UiJbj3UbPkWxxVsPFgN6TqZIUnIRGPJ3K+I5NpaYc8/RKvfem38e5//u84vXpBLI2ojR4LJSTWdbXn+rgyTXtiVEKG1J2n1CJRGpSXoCtJHluOnBtzjcFMrW3TCbTmxXqvaDnTW2FKmWl+hIbJzM+m7B27QhMazYrBnH0k4AiWFuiZ5nyI4InGTdXl4zYCE7FnwC5SQ9uCViNujjFNiO5Ym7I1Q20lUOguUtDeLbtJ1IjDfbIxhFQyM10tTC8Ava2+rswrJU0HpqtbDidYXnXqes/p/AKCsJSn7GIm4N5GISDTbONqP5TPa7GxbD1RfeSNrgidOSbKbjI+TmpoXMw7RSebYqlsRSy4bHhrhofAw0e5jnI8PPPsBOsur/b9lECKgHamaWLfOuyf0B4pz8i0KLRY+ZWvrBxLQEMnJTFTSrG1Y0KmYEZvIxzwwWsYtW2npjfYAaH6uPUbfX1LFyg+pvuq8uSi4uHBZ4fUz14XSeVATdS5CPa1OPfkcrPFv8aJpSqoRreCbzYGaAu9nY1r4HyQWo60sjrkd08/fgin50g9GzmrnCx8qphFdHeZa3diWavul2C9gU8mlOJZNReX1r4VJdoNjkvJNtfqBcOUIlPMFmUvxheLKbivQPbf0xZba3XjnahCbsm7fvuSXjt1XRHO5DDTJdBKodaFFMRCrGIk7HbUWilldP9CSpHcs40wuvW8UcTGQzEaIVas8h+XP8RAoNH7Ca0mZ42yR9cjtVVUOmc9EvWeEDJSG7Xc0ftL95OptLIYgtIAVaYU7SCjO4oy1DuJWsuDe+9dLnbNU4wbulLMUveSQYQCQ700xoM2WrBQRbuupkB10rOjbL4wiSFuCAhOsh2cirEr1do2B8/4gCuBZ2eYzBws+TpYgd46++nAb/2vfivf9V3fx+PXvgTTFfu0I6YZ0U6cMpr36PSIkDym4LSgLz8gvvvvCR/8v6j3/5kkV4TwOkUe0fLeR4smI7eAu4kwXTPRLFRx/4yQ9laYqG1uIGgy6mBOidpWUzV1pUklqNqB3pxbRWCe9rRgY7OczNis9mo8kpSp3ci+QU3mKpK2Z7f35mOVsPEmWje1lhH8lOjk1loLY5xrKpBBsPXLvJGUo3/oYrAo28eV3pQgnR5sTJkSF1L7IOX6fR+EdNz5tfXGsp4IMbKsN6STktJMWBq73YGnn//tfPo7/x3Hn/i/0XK1CIc2I2tliUooiZQb5/Pioxp3LUYhGM9shEuWBQKBHs50JlP7xAkh0cW4Fklsz4sS0GDmjSLBnteUDTl5sP9a9lCkdVePzOJEfRsft1qMXEywcFV70vB5jDWQMRu/x/kOwXkj3cfx0Ym94oiyBCfuiu/xPqroPn4w3X0hUFE6RQCqkVtd+YhE4+tMO5I+4TDQuHzDze2CliPLKXI8PYLdNYdkfL7WrYiY5z0xZgtiXQu9FZbj0Uf/gtZq47tlYTou1HgiJiMwqwiarahQ5eKCoRfV6NgnNmSIcU7JV3FRHvwFe95EnPeVSakxTQPVuEaHjYZkev8y7390y919R1OkSgc39ESURqdptWvbnGei25vd1nPvxj8RjNvVW0Vb5Rt9fUsXKN/syw7wS0XqNoOM5EV5eCK6sdoFXBsjIEMZjPjqsH2zQkPrnY1pdEV7ojfhfL6ln26gFaSeCeeXSLk1u/p2pnlHpd1GCKNrV2xmXd34y0hWQw4tFtbXt9/KfB1EnGdgUN+QlY2NbzzMyf0EQoKUJ/K8s4cDhwV7MPJuLV4hi6M4C11tRBDdUMms3u+RsLCulVoLYcpMcSIeDghQSuV0vMfcPSM5uaRwminOxTCkJzJo7IoRce2+eG5QOVsqrcK8e0ROQmsLvTemFM0HpbyiS6Cvd7TTDb0dGU6kVnRaGbGbMxei4uAI9e3PF/TETc+ojlxgXZkOQz6/Bx+Tj/tmKe5tokpz46nNL2Cb5g6lzUW+vsUAdBuFjDBB9a/Z3E81YH5iF9ms7cndJKDdiLqtrIQ0Me8OfP5zb/Nd3/t7eOvz/1t6gKSR1BdUq6kp5mvIB5TkhL+FcPshu1/9KXjvJynn/0Q8f0DRRORT7NIXuN+/RZ8iAZf8BiHNV+jVG8juqfk7xB0aDxAyGjzeb+sMYdiGSynEVOlTIoVMb5WAjUdVAxofWPfXSswrWl0JxYyIcVgQ3Qo3GQ7GbvoXxfgvMrg5jHrFkBB5uEeM5lCtMdl8ONzQzc4F/5hLx4cZ9AilHDN4Se5HIQLOe9FtrQR/H7Kt094rZV2YcqamzOl0QqZ7C4ebJpbdW3z6e/4QL/7nn+a9X/plppRZW0fjYiPQVjkvhRBXlGCjqyDMs/ufaDekcmwjIl7QZYh7mB6b4kYSJHVCqtJbop0Dg1XcnUthmWImmTZ+VHB3XBsRu70G6gW5NnOLVUDS7KGHkTSZIZyBUAdCqD6zFDfjWxnRH715c6XdGh4vkowIauZj0cc5dk3tZ/bVgjBD2FsDgXmHiHNZNCZyEMizh2zafamlcrw/Uxc4HV/QWyFePyOQCGJS+BAi05woQMyR1IN7E3VqV9LcCNop5zP9vFLikTRlehr8xogmT3Yf1hWfeI3m286jB7zI8Se5FCsOD9seNwZIwawgUuqw8+LdlTuDvJ3kPd7TF9wtHVf3m8/cqO/VbsvDkc9GhRhk2M3GwDyMhn3+N/r6li9QHs7bPim7+iTBaBvmqKMm6ruJDkhxFCjm/miF4FgKQ5bbjZjafcG1Siv3tOUWXV9SllfWsffkvgb36OkjQl8IVHo9ou1M7QulGoO6FQs8a62YtftmBWyjmdKaSyhNOVNq3eR8tVUjfnlXSTdDtBQTk4dljWTTFBMxBvNWyJmQExIzed4TQqKreW2IQmwVqYvxVNR5MHEyW2ffVEjJOpOyUnWlVZNmhzCTsjlNtmL5P0YmC8zTTN317fqNFGQjcl6Sb8d9Qm3zU7fhptu8eb+bmPezBatJZ54zc54NJ2snen0J7Z6kCdVgULWq+auIEdtGbH3vUEqjKNtBH2O039XHfL01K84QWlt9UT1cZ7pBm/a57qMBR9z89xnz1+CIiI1vdLTRgBHPxN/j+LiN77ojeJdRhTpkKuLSbEd3onQizq1JBz795lt85p23+cJv/h7e/vz3EZ88oSw37CWg6RpVJcwHWtwRJJO6cuKeTCAuQoz3hH0i6GdYeqLLakhUP5IohLC/2F3HiOyu7X1XG++FmJC0h7yn52SxCutCpFJKd/7KGeoZ6YWmbxhxuXUidt60WmnVCsqgahLJBVop5ouxu0bTjKS9oYob/8NIluNZ126HNV5gNDe+G+Z0I+V43FNDNF2pNaTaMrradokjECWo2GihARK9A7ZGqLcG0QIBYwiGpPROq8WLc0vaVVVXEHV6t1GmyowAp9Mtc0z0qVB2kf2bv5Xv+O3/B27e/T9S60xKJ5aeiIqRx0PhdF4s8yp7uCFGKk/BRqfJ358iaEgIE10F4sGiJJxIvykCRSCph+h5gaJuJ0/1Oi260ReoJOu6Vb2xMy5XL+ZSK6rILhInU+XFkIk5I0QkKCoryEKvCRUnn6v691idkKubeo4RAzDECmLopziHRnuxBlE7eYeNp5z/EbOFIqLWHDSFMO3I9cDucODRE7OxP6+KliMrcJ4mInvmaQ9d6MFZdzEyzbOhBos77bZGXSshV0cMG8tyQo7Jihmr8Akajd8S/UwKDwqO7VBjO+PGHz9JlpWPnYGOWvrdjjlvBfFut3O+VEaSrfOEEGLnyx+8oh+VKmarUFZXpMVA6I0mQgseHDuK9AG9DwS5j6DATyBBv87rW75AeWh4NF4XQisfG+uMA8C6nYcdaSNuEfXDcCxsX/8x9rRCK0BbTVlRz/TTS/T0EZyfw+k5bb33zadBr9DOlLpgfhSdqhfpb29K7d0PUDfoUosF33TnDHKdy7UUCyZTyx/p1QqA7H4CDGWKm5sF9xxIQ1YXIzFlQtpZGqpb3StC1GCbkit6jBDsbrplQlph4EopJTtgS7POEyVKZJ5N+tmaUpuZxZn9uEmAxaXEcOkwTeZsMKVqd7MkK3hSCOQpGZciJqbdnsOjp6TdI6TDNAmH6ydMu8fEPBFUOZ+fcHf7Pm19hRZn7nfzhRgFZkqmalKBmBKdi+TXRiwXdEqCINVl2Q/WRHNPGltdA9Yc/2C+B3p5eC8eG+N+wmbi57/76H4GCXZc//Ex+ZjE8NJBjc0GhDlMEJScJz73uW/j27/03bz56S8yP/mcSYZVmHbXaM6EsPMNdaA6gqZE5hG667Rpj7z6IrLeMBGgH/0QuUbZoSFQe6fFiWZUXIPm80TTQug2wlOMsBjywbxmCGg7sRkG9hUtR1hfknojTFcENb4KIaOpsZTGWkxu3FdhrRV6IxCop06cr+25j9nItqoYKbPTqqVhmxdGgGZxg8P/RYbqQ4zHsqEfBB8hXMYyIhgxVsY8we0H3DRFgtj37+pS5ODFDlbABTbTrFILhiSNwse/lztz1rrCcssUr6jlnvW84zxdQVhJ04Hdt/1+PvudP8HP/dt/gwYhEyndXGSPXS1Hy4upVlf6bse0U8LUiSnSq+1FsUOIExKN76StIL0AyUMtre4KIZr0uFf7dURMfuwOt+JGZIPMP8biXYeFunf426TNxus5J3OUDWIj3OgxC8LHXKslmorONjsb2YxgTdFoJmJerIxLrr55a3AUDHtTWithv98KlPF8KRA00+OK5EQ8PGKnQJhJ6RU3r245L2d6rdwppOkREhPzNDGQFtFOniZaq+RaSVFZbs7UciaVxLk17uc9kx6IObEEd6hVBc0WHKjRUCENHytULtQGHwXZHz/2Uh87+/++xvmo5gWjk31fR48kBYIKOQbCbOOd8O4dd0d3e3bOIklY24XjshXzo5D1YtToB+75JJdg1G/k9S1doPxa0uKv/mK93DB/becEsJmQSUCHYdb4ugdVYKuV2k6mHil39HI0Y6Tjh/TlObrc05d7Wjk9qCYNAutiB1ZtFg6u3VKCu48Hxg2OIdHoRpLFNlAwkpopTfwgVJziebkWIcimMgkhmCR43tmh5l4d0SWBMU+oJAt3wzZ58zbJiARS3m2eIK11sq7WKXonKSESaYTYjHznRLMQgnVLQG1GOi2lspbV5dEWHPbxezGk3JcZagriv49Npqd5x7S7Ik478nQgzTuiNPb7x+wff47p0aeIc0Q0kNaVdP0Rp5tf4fTyK2h5ic2/nDAXx0Puh0sbEjorOlKIvskpGgPrujhCYnyeWt0tdtP0G8Rv3B3rjIcLhIVAXorj3jrlE2ZF6gXzpiSx087lxMOWflwPQ3gMkbEwtwvULqQQnGQM+6tr3vn8d/DOb/pe0tU1YX+gxUCnEtQ5Af0WwmRQee9ISKijCb2cmDQxhR2aJnoEppkgV6TdM+a0Q2pg6YFaV5OhOqwbwkwIHSjbBtolbBJz0U6TRJ72EDNNOr0dkbYy9YVer0jpKY3XIczWDUaB1lhKtVXjqJKKmbqNdFvJngYtyeCXcZ27W3E7cqdiZmFD0SUqIFakWhCjXpCq1kebigRTfYGR2KOPJMyPxdewb8giQtNi46dgfFqcPTDlbPC/Edtovr667x2tWTff18IaKxrOnOSGvNsR0jVzSKy7Z3z6e/8AH/7yz/HixUtyqrSW6JjU31Q2xrWSXjfEOIbm464OwdxaQ6yEpGiMEI9Im6EnJE4uD7blFnKENpqoTl/M5atLtKYmRtvn4kBggqGX4znX5uGagaBq2UpaCWJpzdoLKkbch24eMwEk2fenNuxQbFZgNTzN2MaC2sXNz2ysJhiKV8UUDaYe8nGyuGGkjFPBNm0h+nuf6CGCZuaYIKhzCm85n08stZB2zU0qnzDlaL4t/lymPDNPC5XKnIVeF+pZCLFyOt4hMZCWRA2JnKKPQE0EIQDuoDsKk/Ea5p+DRzSamK8+F+XBGTGmB+ZqKxi5OKgZ2CGG/EgzXyqijbvn9j5f+eAFN6dirr1JDF3PNk4N9WI8eLHm0O2ndz/b9GN75q//+pYuULZKULY9GnlQHQIPkkmtKGgbGiKWMSA4AesTcJj/n/ZAUz9waqO2I+30Ek5m7W3Jm7fo+QN6uXHfihXdeAr+QI/MF7X30Z2A2h/csNqqqQlUtwN+jJkG92DtbD4iqtb15inTWmOKiRRtkU05mTX8lIl5cnVBcOIa5mpJcHKsmS+JTIS0p4mldwYzDbA8BQwelZARV5I0T8bNORJz9FGGW9WLuDRQKbWxLJfud1kq51LMir/b6KW3ThEj0OVkRUntjaRiv2OE3S6THz9j2r/Obt6T91eEmJl2T0hXrxGunkCYkCSkqZLTNWvI9OMJ7l8RJZgkFeMnmJFdsdFBa9T2IOxwsIF9YeUQvWgcD5tsD6Opqryg3O7dgDUV1bB1qSNSoD1A93ozU6MhBxZG567eZVqXnmIkpUDOYVP8BEeDxMdAMQRyjAQ3/wy9MaUE0w7JEykYP0fB4O8YTXY5GadAXOHVtRH7mfz8V9ifX9KP7xMplGmG9Db0QAwTOSQ0m89HPr9Cj3fktKOlK3S6JqQd7XyH9rNvpwrNIxuqW5sDIU8UMRSLUgntFmVFtJGa0NNjapqIITLFCe1wLid6bYRpZ6ifKG29J/SVSKP3a0gBkea5O7jjshkfRncy1eoy4WgHLj5h7SPwT61L7AJC8PsV6dXuUfA0ZuLloKA1H69231gESV6A9+qcGzugc56otVDqaqMejKPkbwJFKXomtkzoE+taWE735JSJeeZKMvlz38db//V/w8uf+L8TUHKeKVWJwRCGqo3VD6gQxCSuwQoWiZbPY1EK3cP9ikH4daaVyYoOhlqse/ikNR9BMI8TNSJ4mg5onG19i9nAS4rEEOl9NcdYLTRXrklthGbyZZFCiM6XEE8tVoDoDZaiXQhhMdVcx+wbeoU4E2d8pG0y6YvhoyK9kLshbkqgl2SIZSv2HIiNwwMmP5akBDHCMNKIPdBaIc3PmA+F/enIupw4n8zfBi9Or/cH5uS/f+9MKcN8AM48enzF8e6O5XhHyJV+f0eeZnb7K9BOPS9EzEzQTIejhZmOy0AcodQMrH/w3UYXsJGDP3FOfvxwGyWy/zkG8jS5VF8I19dIFHoI9C6EJubs+/4rXnShaiV1Q3RIYnw3sYiSMVK3xrxv71GHnPGbeH1rFyhsCrjtBg6w3Vjg9mdrer4O2qLm9rhVlg6ZDefH1iuWJmwbqtY79PwB7firsNyadW91o7V6MpSl1e17GZ/Ovmd13oVIoKpBoKZuEUdExNQxtfpowOBl8EKm6eb30LWTYjAYLtrBNA6wMIL1cvJAq2CbkDPkBfMrEbHOKYh4p5GJeU9gBKjZhmBujEZmHMogUJ9SmMooxYmU3amw4ahQ8C7f0JO1FM7nhfOysC7F5NGtmcqh27asvdOr0pKNrJJblEuIpJTJ84H56im7q2ekqycWokdkDYLWs2X5tOFQY4gO7iWSp8nSnKvNflsrlLLS6jBVC85oL5sLbxDAPWYGN6h3I7KKYjyb1rYNG19r5lbbqe1SqOKbypDgdZfnxRS8kO7mmCuRIXmP0eDTEIw0nKeAX2BEhHlK2/gEIEYluVNklMaT6x1XV9ebFFlLdedX63iyZGLcWVEkiejoGB5FINfXaHlJbi/otZLyUyTP1Gbr3Cz5C1GEjNLrCZFG3V9R9o+BTMrXtPUlva228SZXqbVCrwulL4h2UpiRGOlZnDegSD+jvEJLBx5bcJ6PV9qqlNVI0uz2xJwBtSwfuhUE2ulkevZu3XONqodqBu0okeTjNb9DjhKORkXd7XQEDjaXqdoWYpzXaqmvYTiWmicKYtyyGDP05rynuJFvW6+EkFzCbCPT1hRTKz/g1QVlXc/W7SKcT/ekvCPliTkEdvkZ73z3H+ajn/vvefnec1QW29u0WUwAw7NpcrK8EYR7z6TcCD2Z+2lMaFgRqeiaUZm8GG9o2tk42J/5Qeo3eW71f8wnKkQL2MSN02K2QiX4deulMYITgW1suXG61LOovKEwQNFdYuVhY2pj71pXaG6jkCcINg6OadpytraCEqFKBYkmV8cLGN/T7LHsl3Gnmluz5EzoeyKd/aMn0BdDD25PHM+vWO5XQ4jWR7TDo+1epRCZ5gMpz9Riasl2e8d5WUj5nl6uaWXhfFTi9TXrqkivRO2EPiGSN4mx1WxxQ4DtbLt4vSjebPvCvHAoHxx5+vHpg/rFlJDMl0XwpHnc8sFHYtG9cd59zquTwi4QV5ASqNnEDg+Vrw428pCPYufcN16kfGsXKNsdsv9+WJx8rFLbvka2g0C94yRYfkMfN1VdKNrNt6OXE73douWMlgXqCb1/n376gFDM56S1e3oxF1GDa33mOkYx2KGl3e3fJVgku+vhzLzLbm6tFrRkf+divKOI1ypq/IwB54Wwwf5Tzi4bNsJfzsZKR6KT+HyTBghOCBVss3JqthkbDW7GIOrZZhFDIKeJLgHtlXI+WphWV2KsJIk+xwS60Hql1EYphVpXyrqylMp5rbRSfDxhycu2psMG5QYSkvN2C4M7XMYYiFMmTHtkeoKmQF9X+vmeev+SLDsnx1kGxHLzHm25taJMbQTWUNZl8fGGITiirtChP3h47cAanXcgOKhinBnLMLqEkz3cDLoXucPyvLW+jQIkmJ9NDEYeHE1i7zZfty5bkWhpzjEGc8XVSkqW+tqDEoMw52wGaE74lGDGZkJingLvvPM5Xnvt06SY3celIVSCdGJXmyV7F61hss2rrcS2UtMOrt9mkQN6vEPvb+gRVGckTYRUSXJD7Ge7XLuMHiYso67Z3Hz3iJ6uLNCx3Noh7es2R1i7UtcTlDPiTqsSE2n3Jilmio5S39Ztdcm9aGNy47Z1OVO1of1gJGGsqYi9QjtBfoTEK9Rt8QndinMnLQZPwTUU1e0IhszTVXAi5ukx0JBhsAZshfrYU8IwhFO8k/Xv3x0+R7cif3Asgpjcf63FDlP3mhgjvyCWpVXWE0lgjYmynijrjpITa7gmvvld/Obv/UH+zf/l/wSaSQFaqDTFvD7WxbkMtlfVGji0Hfu9MouA2sBYm6K9EnqwYlsr9AVN1/RkCq/u+2QYXLGRJu1PQAgwTRONwetRVIafj2wHpsSAqu1RqBPEh2KyugpR3bQtDCt4Q/6CJPueTUlYQxTEEplDsr0oPFC4CZnSuyFh2ROXVbe0ae02NjRaWHK1nCHC42NxvkKiFz9uPDnvXvLh+x9xOt5xVqWe7yl95XD1hJx2zJh1wpwjrZgIoSsQznRdKed76rInBTFzyd6QZSV3Jag1iLUrIWeCeGaPb1iDM6UbwmLGnZ8UjXxSfnzxynrQuPtYOKbZiiD1kScD0WIbd+pHL7i/L2jMZGw6kJJV3b3bGbW21e7kRq62Ff8biiR7UU08GCFewK3t62R8UkddvH3QPQ7GnMiWqLZOa3fU9Ya+3KDLPaxnWl3odx9Q1xdEhVqVUo/UpXhU9rA9NwmyEcUuapVS+gMOBg8ONuuURYQudnCORdb6hZktvjhTDBvKo2quoMPwTESIyUc7YrkvrStVh8omkrMx17vPX0WiEYBboWszFn8IJiMulhs0pUSc9qZwKcXs2pczIt1daaN3U/Z79bJYQJg7ohqMXV0P37z7ssTd4Ju0bfKGIGjrxnkQQw16r/S2Upc71pDJMZP2T8iq1LJQT68I0bo8lWYJ0cf3CeVovCKxrKGBu5mhnkGQEqyb7QM1ihfUTfHwsQfracxUHz7gw61X1SV7aoRCG59FYnIlROtMU9zub0wRFTvUjemvINFHdBMx2EMvCPOULcU1WNDZlCPJJZDRyCkEbaQwc/3oGW+8+e3k/etUbO6e/DkIdFNhqMmndTU+khUyZ/rphik/puXHaIwUiUxRgUCLEaZr1EgBSLlBpBL2QlQ4v7ql3d0Qwx6dDtR0hcozS50tR4QGMSF1XAOo5YiUO5BInp8RHn0WiQfvQgq1dmC2EWox3yB6JdKg3qMjp2jaoaGjwbuEWOxrxrgyDPmqeCeujiRUaN45uvdGx/i0qM/WwRFXsUI1WDc+PDu6m5x1xD1qzNgtCpb/Y6mONvpRbB3qBbkZ4zmTuA8Je/BRsO1LtayUEEhxYjkfyXlHmmdyrOxz5sl3/n7e+R//r7z7i+/TYiQ0+77qfjstVM5LB00EsSTeGMzteZqjL2eXT/cFafeEFUMhs0dH5CsLyAxWcI15cxNBg9nZi49SggSTq9Mv+xM4Ud7KCvUCz0zyKq0aWmkjyGRrP0/EtLfnKCSCOlrWC9KEGPA91fh04go2i7tozgsyFYriqjc3v2xD1u8NS+uKRPUiwsaAMWVU3UG8T/Q4kyURROk0Hp3P9HLD6XRDjYsR7/PENF8RppkYsitnYH9lY18kG7qUItIa1EpdCpKg1UJTMddXFULv/vwmR7zNf0eHbczHCpCPFwGWwWVr6eHrQW9/mUBI8MZILuckI0zUkuh7hx7hg/de8upVoceBGFqRqq3beg+WsVQ9XmITCHwTr2/tAsXBkst/j65lQOl94HX+sQHl2QcGrGjM8rhBX711al1op1va+QX9+Nz+vb6ilTNSXlLrHY3o/iSLi/R8pFE7pY45rSt36qguuczndJiw4WZplnZamz1Ag9QkD37JGAyJERHm5DNUsSJL3O593u+sm4jDE8I6bG2dpp0pZ1KePLLdwu1kEGR18AIiXSOtFdZyRnslhSu7bhixbJhNBbGMjkqH6unEtVOXE70eCWIOrQ+l3b2bomJwLcZDZk6VZkLWeiXVgDYhRtgfrtgfnkAI9NNzFPNlibKzJEW7yag22vqKdvwIPT9H9GxcIVW6kUjAi6JubbM9QL1vT61gHAVQyto2I6ILN0SdhzAkvz4+k+GdMsY65jewmYNFJaTuPIGJlC/y6lqqObY2V7zQjFgYYJ6vyEnIQchTYjdPhGAhiTE6Qx6MUN07+/0jnr35eQ5PvoCGHYpFqasbznVVkoBIRzCkIdaMsDP7ajq6vKJLIn/ws4Qv/4TxqMITWtxB2NFkRucZJBPakdhWEjOxJsL6Hu3VLwOR+OgdJE6UMIGeXP1hPkL4aK01s7AXElN+g75/jZSviUQjapfF0K5a6OezuzTbWtJ2RtvRuBP6CJmSxTNoNxmkYkTPJMaJiRmRDGEYRskWhmeJuLiE0hyCTbptyGBzdQ+Ia1CDhUi6wsIOvOJkTt94kkuPNZjUFYyIjHm00JUubSM/tzZQPR+hhOhkeOenVYvOqGWhrLcc1x2HmOlhD48/xxe+749w89H/mZvb47aGBWFtNu4RNft/VJ2LZtB9iAUl2lhRxMciJydcW/4TYUfIe7MYcEWJ0S/GcyTWWNRCW8/EDBCRKoQULmgEhohod/PJ1lwk0CyvrFaTC+8OZg+PpZWHNBFFCFpRrdTSQC38Tz31HRE7NN2rqXeXuXsT17Y0XksZt9Rme0+tmplfUIU4O9IgW7Dg4AJqW2CKyDQzHa55/FohpcTNqzvO92eW43NCyuz2j+jyBElWWIg6ElwaO0fHWjee03o+Gfqwu7LCZ11Ym8nQY5ktE0cDw3pQol1bIts4QXDyChfBwaVfHx/bZg0XlJ5PkGudPB5VySgHN4UMGIkXESYVWnvOy2PdRtFCsvGDQu8JLd5c+1ktH3tDv/7rW7pAuVzqB0iKWFEy5LrS/XD0g3oLhgtpq0I7iV6jW2Q31nrm/niLvvoVtLxAllv09CGtfESvZyOu9W4ul8qm5rDMHWi1u6maPYq9XVRAqJHsJMSvzWb2+TV+IIYB44ETmKzeiO6nMGC+nDNTymY+FYJZjecZHe6ewbge2gfBNqBELxyMLy6qBFe5qARLCo7R7Mu9o7eaqaN1QduKYHP8XleDhg0gN47C+YiW1b63G5ZJtxh00XFfZFMHIA4H+u9kRYKQUuDqsOfZa2+TX/sMtQvr/XuU4weAEvITgjo6UBe0FcrpOedX79KXG+cFmaW8tubjNt3m3XZNrPMIBGofh5U/vF4Q4vwiwGXSgZQz1MpaqnXfQ6EhaTPqStFSniVYdzIl83HJOTHNE9NkeSnmPFxZVytol2qOl0GEFAMxKDna9djNJrUMo8BzXgx0AjuuH73O7vpTxN0jz4mR8aC4Kswj2TGyeNRm/hBtIuQd4XqmlZV0vmP+1X9Deu+nKM8+Z/clHRBZCFpofaUtd7C8sntKYjpco71y//JD1g9/iXh3A0/fQqe9Qdjl6N4hzQoy/3uEg5Hu5ivC9JiQzRk2xB0pTrSykIfkmsZZKyyBUCzzpK8N1ULiytQF3XwkpEHjzuMHCtqmB0m+shWUYx/RXlEiTbARqa9JdRfMkJwz0gZPxVDJh0qt1hpGI4kwko6H59YYUxkL3btKG4EENR5QdQ6UOOlQWzO5bYs06ZRSiOlMLpn5fGaJMzFM7KeZw2/53/Pp7/r39J/519y+qrQzFp8gmGHgOhR3QgyLkeYT5OzGfylvoy6heRNQCboizUzStoZCL3ub7Ueens1ApVa7tyogiaGGsqdtmHm5JxJ6CfLrzTNqItO099Gm8TkkQejROvVgRbc9W8kbDDXEKDinSIQuRnSuzoGQgVgH388A3NDPIT1Cnm2NBCviTLVlnCViooVInK+Z42z8jWlHnmeOL17w8tUdy/1zjvevMe+fMk07W0saCBmm/cE9WqCezqir+moRQ/DqmZgyEhNliXDMTD7+z72TdIbJEA8rHOXShPuaHCPH0Xj7bcGaXjYnDeOvqF+H4Ci9bNdTHN02GkTYzP1SEKpGeP8jXt0WQ55R6E6aTwnLbrJ70LZT+xt/fdMFyr/6V/+Kv/7X/zo//dM/zVe+8hX+yT/5J/zBP/gHt8+rKn/pL/0l/t7f+3u8fPmS3/E7fgd/5+/8Hb7t275t+5rnz5/zp//0n+af/bN/RgiBP/JH/gh/62/9La6vr7+5N7ONZGy5W6eg3p05uVVtoeN+H+L/hBhpPjurvdqBWhrLcs9y8xXa3fum1KkvYL2l13v66pJOnwapGpzVmylrDM5qDlnaQ+cNkiv7bOzjY0MGFPdV0dkhuCFYtZrUQ7GMsMTmKbLNgLEY8ZgvG26MVoCNmaUJB2z+rpgyRzcy77h6FzRgHGI5JmSagM40ZUSMNW95F42cgkumG6oebNfZirjh2zJ8UnCJ4bhv0TdtI8cKKY6tw4qTeUpc7Sam3Z4QJy7+imYlLuXWbkZXtJrHQK+Ven7J6f4FfT0iDKGW0koxVIlOKSsfi2nfYHfv5rDR30jCRWAE7W0PvJNl7Rp0CGJkXO/IECMq76bElCPzbs/+cGC/y8y7mWl3sFFciN4RFtbTSq3mH9DqYuTDXhG16y1BiGEUe4N42Y2PSGPePWG6eouQzQnU1qu3L17sdh9JBOxgsDWZIRyMGJkykUL8yk8zvf//QNv7cHci9oVpOdKxoiBSaOdb+ukVLc20+Bphfkqer9hdQ/nwfdYXv0xsKzx+m1Yq/XwkpkDV5E7F1uG2sLPDMWVEkvGmCIQ4u+LlIs2OMTFPO3Q+kBZL6i3FLAA6nUlnJFrabNdCrytSj5D2kK4t5yfvICZI2Z9nJzqHaARNMQ+eQcQcMkytlvUTJNJQ31cMeY0poDpk4ca2aM0dWwcQKu7v0UHCQF5t3NNoHutgOTjiXiiOvzLsEixwsrGUwm49cZ52TPNEK5F89SZPv+2/5e6jX6EtHdqZ2laUROhQuiWfr2vxiAmYMqyrGaPFGOhS7cxTsXFNs2DPoGe03aMtEsJhSAEcQcI5Ng+I/bUQawEJ9DYbQmKOkD7ycTMyxe0EhmGeINHQ7pH7ZYfliP3w2yEBQkJTIEpkQOjaLa7ACshAy1h2jFrRaUZ9QojRiNmO3iBKUbURW2umWvTxk4hL2ruaoV7ekySD79MxzhicsVBb4ea2cLx/Tj5cE6NwdXhCipOpxnxfDslIykqnLCviaqhSV284LW5CxYpWp0rZmDC4Cs/pCduZ8rEj8sJjtLXnRYyfYQ6jXPicfLxpttG07cjzuNeKr39xz5mCaOP53cnoBikQcwcKqSd6F5TqKs/Gxfn213990wXK/f093/3d382f+BN/gj/8h//wV33+r/21v8aP/diP8Q/+wT/gi1/8In/xL/5Ffu/v/b38x//4H9ntdgD80T/6R/nKV77CP//n/5xSCn/8j/9xfviHf5h//I//8Tf7dgB85GCd+ajKzSTGq3FVwFGLEKEqtSu1W/LvUhfW9cy63tJOd+jLXyS1M63e09d7kxH7QyYhbR4YtZZxNgJeHnXZNjqLq34wZRIBaRZA5oVJCFaAdCexbvbYvSPqniXDHTQa/GpqBHOevMS1+wM3ugkZBCp4sPrcxdLercckG9o0EA5JvsQ7vRe0F0Iwfw2RTq+N3hdEC1HMmdXM5By87c33UfuZivgB27x4aV4IyQYxdn8fgWweHi6tTTGSs7ld9lY4n56j0TgdWs8kUSIVrbe0urAud6ynW9pqSbB1OdF7IWLFRxuHuRPl1Jn641qO99fbBfonRFJK9vsJXJQZtmkMkmNM5qKSR6Z9d3+FNDFPE1dz5Ppqx+H6CYerR0yHHWm3I+Y9EmcrIttiadeHM22tfkDtKcvJikKtPhsWQx2KO2Vqsbqqd1I+sHv8BtOTt0m7x4SUrT1+0Lmobiwc4/qomPugrAStXlwJTbJ5ZewyTZ6Rff0WqTRWSn9Br2dC76h0pJzg/Jy6FPr+KTnN7A7X3JxvWV58mVgbIU/U4w09KH26tme1KK1FephJ6YBqtuZCzGTQ1nswUYoNS0DNLyhNe3bTnt5dzdM6upyMc5Q94C5ASBW0WBZPBw3Vmph88K7RxnO4bNvcYl29oj5/d9RD/VprsEyoEIUBhppvio8Pujoyo4RuktmOIYbd0UaLePDnpXeTfIs3JR2qml8MIdnh6CofEfPmSSlxms6kslDqnj5VJO24/sz38Prn/x39fOa0vGtmhRF6E5Pbd8v5qjFSV6FMkTWatDhGM1IUDb6XVVpbEZkIfUXqGdLkhHhDCkemlnqhYsBEN55aXZEYaUkJwYbhKTiXJERIiZgH70ZBArF1uhRwpViIybQPTWyk4xCASETd+E4k2oinK/RG68WQFALCwZuaYdeetsajt0oQG/12YTs7Yq/W4BINqezqjS4QEmGK5NQ21Zgln0ZHgoUuR5a+sBw/4g5rZA77R4irjrSa18s87wydXW1sF8XQUGsCPLWdTiShIRNzYt7vIVt+lAbXN+nDrX5UIH42PCB/6LYbXP70cPCz1TKDtxPMlG1iFKPKI1EboXXnsIk9ay/6QiAzK5xVib2T1fiSrakZW36shPq1X990gfKDP/iD/OAP/uDX/Jyq8jf/5t/kL/yFv8Af+AN/AIB/+A//IW+99Rb/9J/+U37oh36In/mZn+HHf/zH+df/+l/zfd/3fQD87b/9t/n9v//38zf+xt/g05/+9DfxbtRGOii0hmJ21sqYbWdH/HyheehYU/czKScjXbbF8iXakX7zPuH0P1NaoJ0+otU7I4i5SVMtZjqGS2Mt4dNVL119ZhxIMdvoxw/jERwXg8NdzUYcKWVyzhfbdwEVKxHyPDsr3cdSYp4glyRbMJhSLOzKTaXCyCDxz1tI2ZhFW7E2ECCcBzISRLVXh/YavVZ6bX4NTUliUsIVWiWJ8SSUiwW7MtIqfaNLgtYxRrGfkVL0AxV6NxfNGIXsnlrRZdS1NnwixOn2jt6/QlluiXmy3yvOaIuOvhTK+Y71dEcrhkD0XjzFOmwyYUUpDg2DlVDJ5/4pYN4RYgRJvNAzzxFxqSmGQmTr3kptrM1SiWMSl4va5puyRQocDgeur3dcHXbsD4+Yrp8Rd48gz2xGYr0T2olLAGBBFktNzjKTWqA3RwQl0JtD9uLk3W7y9Xx4g/nJZ5mu3zDbaklbwdFRC+lzKHZ0oaMtC90CL1ULnRlNifb6b2b97P+O8P7/0wwE42Pa/BlDcZYjra72rPVmniulo8vKeS2U1d5TnGbW+1vq3fvovKecXyJaiXUBormbdvU8qEApBc735Gk2DkM9M1yXVaIdBM2K7yiBKc+0mhGUWDqlAKVY0RkbhJ0rMjpdT8bnSAttOTujWwh5tvsgtiasAAHtjqCImbpJH8+MrYOkaiibGyOaCdDgjTZiVLQZgTRga8Zda2xjb9XuS0xOhreOP8RkIYYPMntEjN8h8bKeay3UdWFZFpa5GOk1RvaPPs/15/8byv0L7pY7Xny5E7VSQzf1iuP7a1ltH5oyQVZGflQcow1RNLQN/RGxWArzDFkRaUSNdEzxY+OV6s96Q2o1lVaIxKsdYEVJjNFVhoYmxdapfaXVFW2BkDxB3J3tuj9fWhtEizEAG2gOQzQJgYdBuepW+EpEUoK42wQBw5XavtCLPnVEBnHk2RtbPEfGmxKz7O9ITGaqFiHuEnGayNNMTgemvGPaPef21cL5fM/S1YqtsrDb7dxDKjgXz/aK/dU163JCtXG+u6NEWydlNWVPjnvi7pq6LPRSkCnbuRfN6Vi9EbNfwdeXunT6E6OVC79/g/UuKMv2Nb7mxBrfIZsO2/nqfydFJNqoWN77iJvjSumJWfdEiYgUu2bdxmOt/6+IoPxar1/4hV/g3Xff5Qd+4Ae2jz158oTv//7v5yd+4if4oR/6IX7iJ36Cp0+fbsUJwA/8wA8QQuAnf/In+UN/6A991fddFnsAx+vm5sb/ZJUyVMDgUGQoXoLVLw7nD7fWrp21KefziXZ8gbSjHSrsacuK3H6ZVG6pXaAa8Q51TEHNln54XnQVG+2I8U5EhGnaYR2uddoyDgJPuIWBtNtGEAQ7nGLYguFsVtlJG/nSDnCcyY/D8+4yBxo2NAIMBtUY6BS7HiPETXCI2DgyY9fR4fGhphyieg5Md7ShGSRZR4HTmyWRSvcOQ72IqwwBofmI6NZtWsdrypMRDmdok5NLw7gePnToUFrjuCw2tsiR1s3xdbfbEfMOpowCZTlT1jN1XahrpRfPzRF3//UkXJMFm5Ot9PFA2iE2yGXbGaM25hrjD9WRuGxeLzFnUpw4rStSCjVWQhDLPwqReZ6Z55mUM/vDFVfXj5l2M3l/Tdw/hekK4rBiN6JeDBONFVULNIuhWvmnhgxKDObJ4u/FAtdWyx5aK2n3lOnqDeLuGcLsxF6LWzBSpmdMIb6LJff7cPRLu3EMWrG8uF4gHuj7LxDm/zeaDqz7t+jxNeMXpEIS89QxwrSgrZLWO+b7j9CXN7T1jlhWzJyw0GqgtE5bT+TeIFhIZe2VHgJSDUVIa6SLFfelmXeIxECIOwShrCdTu4g4PyoxSUcxNKk26GUF6eZOqvac0G2UK31Fwh5I9GjhdBJs++1qSjN/YGwkLC73dQTLCirziDDlmpNfVZEeffTKJlEeHjoSlCiYg6voBVH0Q9GUZe7FIsMKf2XY3zsk6k1A3wzeSl1Y1pMhjjEi88zuzd/K4aNf4o1yQzv+IscXd6TYUS1bgaVqztbn88j0ElISclxt7/GWPEi3SUxXa1rCioRspmskhEwXS2BWRxvGc+6XDAnqZmAm0RW8mRrkfDeWkRhszAebZFvUxhtNDYlVGcixulMuJkRP2VUkAjRi7NvIPQR3lw3DAdf3O3RrCmNIZM/PGRYNYwSNo41dzGHJbF6iK+oi6A6dVkI6EqNFiwR5Di/OLOuZskRuWgd5aghrZ7MwMPm7FW61NnJQ1vOZtTZKLbTeWKc7Dk+q7QGl0NZi9zoEJ/sbf6S7gsk4T/b7few1xjyMgdAYEX2iSBnXxnd18SIlCcwyxtqyEb1VzTfly+9/xEccEUf9mipZTaBBEDf+/MZe/18tUN59910A3nrrrY99/K233to+9+677/Lmm29+/E2kxGuvvbZ9zSdff/Wv/lX+8l/+y1/1cYPkC4KZWolXgdst8T8oAproWswNtMG6rpTTDbGdma/eIFy9jpaXBBZqgSrFzHEwtGOjf4YAWJCcPeTBD/BLVRniRA6RWldaUJZSTd7qCIIMvXmwvJsYIaTENM2kaYfJE424quDEzmZwZS0MJjlcUBCDvW2xNyybR8Zoy5eh5fO4UyqySZ2t5jBWvPiDO8LoxFjAxqh3eFN7p6uRN1Vw4yqTCI4gNO1uUubuuaiae2WyTc44JgHVvI1ehqrJ8m4CKwXLVlVECqFE8proKZNSRdXuVVlOrOvJ3Cz9XglGXrYN0w7h5imqEi7Qvc3/zRlUPwbNG2xfe7OC5QGKNWbXYLbyyYO2EHfwTYF5yuz2O3O8nQ9Mhyek/TU679F0BWmPSEK0QG+otG3DTCmZ/0FtUE40gaJmLCeqRKJ15XWl1SO93SM6sds/Yjo8JcQ9ZrfWbFTSI5cYA1+zYsmwPWSTi6qpscRVXKLGT9HlBsoNzM/Q+S3Yfc74L62AFkMnmxV0kcCyLqytoXlPTkeUlXM7omuhpxkJ12icWeoNRc/mcuv8H0MmA10W6FZMhZgtmdndSAPQymoFynp0+Wz16+/GX8JGujQvgIUuEW3OE5KAtOZeNkfLpAqjeMvGSxhNSO/biPVizz4aAR8H9LY504qPCsGQULzQjZLMpNEPApHqcDkbx8X8WHyMZW6DBLGRzJZFs61P8+ARgbIurPnEskzM80ztE0U7+fpNdm/8Vzw6v6J9ofNe+QWWm4o66qHaaVgRtanHmjtml9WLA9s7jWjVUCkIRwTjXGhTc3zNV5Z8HM3mHg2+7zTUlYfq461hJmneI8Z/kGAH4EN103DV7qilwffVmkyJSJgszT0EUjaSqqAET17WUpwfpvTSqK0RusmpcaSh+/MPQgzJvYYgZAstDMqWH0RvJnFHsViI4NcgOScw2mgyTf6+LadoX6GUD2i3DUKj1iOncyTHPXmyYltCcEQjGDE2wBqgloXj8UzXRsqT+R0NAnFXqNWQwgCqZr7Xg0BQ+7eaOCE8PA/xMY37XT0c/X6918cc2wMIkaR5sxsbvMdR0NtZ1Png5o4YrShMmphUWVEXGHxjr28JFc+f//N/nj/7Z//s9t83Nzd87nOfQ+k+t6xei1w2qbFxgPMscIfQvtKbUNcj5fTKO4G3WcNjzusLplJp5Q4NI3raHpzefTZf/RDtJmMEd3x1olGSaZuZh5BYW2EExcUR9CRiuRFiB9w870i7mXl3TZoOPNSr11p9BFNZyxlZg0F+2IFmhz9sapPuP8NNh9Q/r1rNp6BnUzCBV99xGz8FtWAyM5dz5YdvjE0buK+D9mYjgj426OIeBp2m6m6oVjk393+ZknUiyQ/iQLdwMDpLgbVYJ1d69cJSabXRYnTo0orQ3ldqCahUtCxoFysE63oh58ImYZRgJnW1jTRbG7PFnMzFF7926HZvco4+qsNhZtkMn9Q3rAFNHw4zKXoYY7bOOXtS9JSzRw3skd0Bdo+RvLMo+7DHHIEdtpdG6NGcccU2oZBgf/WYWhdCyX4gBera0LIQQqB1k3PmvCPvnxKnK0I0n/veC41AzLPvQ4a6qXdr1Y36cNlslGGsZNeqpcmQqggtPTPLb7VGV/MO0Uc+Ay8O5gmpNHbHF/SPfpF6+xzqmTncE9otR72irTu62phtXVembKhZQAzGzxMmbFzINSOOUKpWeu1Gsj3eousN1LMb/h2p7UxvSindONgynlsrVioWuSDBDhVB7ECShSkezXzQjhzybvZCoPNxB2pb+0M5p8MVdtsjrKgJvl9sG7deFFaigRjdv0MjISVf7mOM4l4SMmIUhsvsRQOhmL9I7xXVSK2FVhbW9cS67NjljKZEnSZ2b3yJdvwIaCzHW07nL7OWIWfG5LR0VFek22zKJMLKqkrKndYEaZFMJHZBajWkLe0N2ZGExkSMMzlMiAzH0G73sltjZUhERXs0rop2Q/cADc7NkWTry2MDxmHcy2IorZqHjmZQ4tigzfdJDOmR3qjaoAYjmZoPvY+COyF1vPpAgpNKxcZe3Q9gIZiyZigcfB8FQ00kzz4GMwKvxGDNXTVzxpBm0i4y9c61VCTdsyyVdS3U5RUtLiCP2e+ebpyPAJTeWWvbzpTemkV+ZGyfcURdWkdqo69mE6HRVWHRxlk9BkuIJ1jjFYStddfBPRk4yni5pcWoZsbXjcJGxN2zIzF76J+LBqKPpq0INZFG4yvc3pZt3GQ/Qun5vxCC8vbbbwPw3nvv8c4772wff++99/ie7/me7Wvef//9j/29WivPnz/f/v4nXwMu/+RrA6kcAjcYNrqbq1KdWIibY1EXVFcjjNUF+kot99TTHef1fdr9z1PPH6L1DgjUbU3aYRkQynpJs+3Nxj0jHExEKNUcYnuPlFK82/GCRfDUXpPGGfPbkoWnvCPnmZR3WD6OdQ7THExxVAu7tlJOdxyP93A+m823S/5iDF4QdEsWFTE5Y2dDQ8b7pnVfrJ3osmfGCGz4acBWoIF1WUF8pKZqRF+Mg7Ol9/r8uYkpUNQVS4f9zqS0anHx6+mO3ioph0veiUCopnxQPyqMO2NqnRT2hJDQ1lmWhdQHbhTccMuMlyQLuPGWSc3tIAnSXGorlpIqAjk7I9+Ns8QfwiDkYFK55Im4cThSihVM4iqEnJM76AohRWLOxLQjTpMTDiNp3iHzDtKMxh0hWZGoI/jRV5nERNAAfUFEkSnT5Nq+vzbKejbEIFfazYebSkTVOAxxfoQkD9XT5p4d9ojb+LNbFHyQTfasKM3Hil3ESMeYQqgQCId30MdfINRK76+Q8hVCmVENaDS4nrAjxD0lKKGc2X3l31F/8d8SyhGePuY6z+ijTO8Td93CFM/riVe3d0zTvTlyBmvP9vKEMO2tsK0d1tV6XFVqPVNPR+rpBi2vkL66EmtxFMNiDlAzpes2pTQyqD8bxr9oJrtNgpYV1TuSBhITMSmtYmoUxdeQEtTyp7pWolj4XR9xAABBHEGxYDcjadp4IeXs3AB7CC+hg90lzNbtGkrhWK0jv6rmgxLVoPHoKMsoVnuP1Coe1LhS1jOl7KhptqL58Cnm196h1Xve+OwL1tM9H/7qh6yrmZeF5F10NwJ8KytFoiOOSq2dmBTJ1rTkWZFYrEDJizWDaYfWHaFfuZLQMoCqmkO2hLTl6+BjR6/XsDtlnkp06GNcvfm+DE8Y3ZojrY0gmTQbamM2BfZcb8UeA+wWH0uYpN7IQSY51qZbNpshPBii4sUStdpAXQw1195snKHmB2JGJLan2BgJO+AFJO4IeSUfDiBvkvJLjje3LPdnzkthXSq1rsTwmHk3+ZhwtdHXVrGYu7B0Oyf814GulFqsIOlKi6uruAMhT9CzZR9pwjxi7H0OvxM/rByhH/L67UBlWE7og5GPNcAP/ixW/EWUyZufK7xgEZN8a+v8Mh+it4Zm9jEKmv8LFShf/OIXefvtt/kX/+JfbAXJzc0NP/mTP8mf+lN/CoDf/tt/Oy9fvuSnf/qn+d7v/V4A/uW//Jf03vn+7//+b/InukRUQRjabqEX40jUttLqiVALoVWExRZfgzlAmw+sr15x997/ACKk5UO03PqM02R/g33sP8FRG9fRqxlwG2+i2aJFzRuiG2wXBvEuGVqQpokQ3WDNVrR/32F8ZmOSECfnpcyEtCOrbY75sJAOd5SjBU6Vcqa2hepMd9XK2qwoi5I2NCmMIDOPvrY5OjQpjGC8gKFEo9vWhxUKuCzSKnjFDjkk2Ey7C7WMMUqhqzDFyG6/4/rRY+bdHhHheDpwvMnU5QgI98czFuplHW5tAlw8a1IIzDnbwS+m6gkp2caEXd/aFTzgMKdsIprWKCWZ70NVWjnSaieobFyD5ETZEJMVMpjCypAjnLAqG4Jif4728EkgJSEEI9XavcqENBHyTMxmKhXiZFJWDPYeDUz3mbqZuO1RmWgs9OWEtNU20byD+Ahl8kJzQcuR3I7INKMhkFOiJfO8yWlHJDlCZRs+pSJpNWTRM0psE3Hp69iMtHuhYuiLtIXATI+ZcvUOUu6R5UC4e5dwfo9QV9p0TcuP0OkdejyYt8bpBfH2F+nnI8xCy50w7ZmefprH8XV4ecMHLz7gXE4sp5W6roQo5GDz/06kysTV7oA2JfVGR6hlQY8v6LcfUZcbSj1bcddHE+EJwOAbr497g43pqoswTPILEpWglZCgl0KPhZYKbTnRFNJkHg6COSxHTAXWvIA3h2Ah2tDfME95cLD6jTapPeA2+ao2Wg05WsGoDemmRhG1RmBwJNRmXIa+YsqbLt1HKA3zVLL9wtLCC7mslLVScyOlRs97wqPPMC8vCfXbeXYs1PO/5/n7L0Cq8x+MExMdFerNw0x7odVGlYLEE+V8JE4H4rT33K5ESom8e4zIhOYr1KXbihcKomicsELE7pSlXVtT1KSaZb1aEYmbqQEuvR8RDslm4UPyrDYuSxIAy5fSkO3gFB+vSYA0A0p0BCSKvS8t9vx154xZ0eF7QzC/J5FO8DF4FzWqozboK7RkHCJtIJWoRlS1htHCOu2bXtOxgnZXhXJe6W0x2weE+/UGDdfMyUS8zcfrMZrCNKZEwrKNqJV6PlHzxDLZc9pEaO5vFXImzIWwmxFPyRYM0R9Fj43ldbi52Ch72Nn3gSbDBfK4jHh48PWjkTPUyvcRMUdsEJIXeEY3aJQbpaoVo7qpJH/91zddoNzd3fGzP/uz23//wi/8Av/23/5bXnvtNT7/+c/zZ/7Mn+Gv/JW/wrd927dtMuNPf/rTm1fKd37nd/L7ft/v40/+yT/J3/27f5dSCj/yIz/CD/3QD32TCh4QmptDuUdITCxESj1TypmlV5b7e9L5FVNYifkxPSdT7miBXojnBV1+Hk0roe3dRMjJe81Qkj5cYL2bEgmEHAgbhcNmpl3VTM4cTpSYGKFcoeOV5aXqRmQzW2u9Ia0Q02QHY5wIeSJOOyTNECZSmEh1IV+dKMc75vM9db3nePeSstwjqqzrSlAbSwgmZ2w6QgrFZJtOtDV10/A+8Zam48ojVy/4pm8zVi6eIBiiIAqaEqpC8o26aSGEwPXVjqevPWP/6Bni5MZ5l9hPSl0nWlVyCmb93wu1d2qd6S1QmpvREZjSRJwSOSXSlLYId4k2748yGQqVdkzzgZAmC6Fbb1nWM3Wt1KLU1fwjjOvhI8EQiDGT8kxT25CNpnHxWohiP9NCGM03ATHPiO1jWCBjyDMhGI8hjITYOEwBXdbr6i4JFyOlPpCvthC0QRAkZONgdIud7yPOQM1zJ6cMyZRCeZ6QbD9jtEOGkFR6K8R0yTVi/HyGW674qMbrU1GUautIhbp7Qt6/RtSJlm+J53e9MVBCX1EKrVXS6Z5w8/OwvoceFN1HQt5Trt+kv/FbifkpO/4T0/mWw9XrTNm62apKWU7U5US9v6GI25PERFrNkLCVYjB/eYmW84iDto5XbeY+Or4h4UV9/WPoirk+mzLEjP1MEVVZYTlDOGEtR6NotlElYtxawnafmpv92ah2ILhsULYp9PCxabLOW8Kw/7Fb0IZNnn3/6KTxTgdJrigxFLSJkNNEcLmmPOCfDZ5MayZRrWU1UmWrqGa6Qp5fg+u3kdZ5vK705RV1Xan3ZzRY0RVDI8ow6bLfIcgYk9vzzHKk88IKBwkmI99fcfWsEuMBLffUZUamvRUpEof7gRUYzqujNWoxIriESA32zDVMES9xskYB0BrseVCLZuhVtoiP0ApoImpywY2YaZuqjRjU1kTMTuz1vaz7yAlXHYZk/A2CGPKQkzVgdJMqayeoh72W4oe9p3Mn4za1Hnzvs72VYA6vKkLIIFUJ+057dGLtK/XeRpbr+hH0gu6eeKaTcd4kJBsTx8A0TRQfxQWUspzpd1DjkUQgTMF+vzQRcibuZvLVwdB73HZioEJcig0d/ydfY9jziaLk4cc3NB6nVEQbis3+/Ucx1LohVBUo/UP0plFVqHnhG3190wXKT/3UT/G7f/fv3v57cEP+2B/7Y/z9v//3+XN/7s9xf3/PD//wD/Py5Ut+5+/8nfz4j//45oEC8I/+0T/iR37kR/g9v+f3bEZtP/ZjP/bNvhVXWPjhGiJdMmsxzb72M6021vMd7e59e0CevElZH1Nuf5b1/C7n++fI+mWgoiXQyp1V5XmiqVCaLaLNBhu8rriQ8cDY42M0MRaBpuE8GNEkPru3rqi606xBwqb4iGnaDMDMSGgysmOaIE9IukbCI8Jc0LaQ90/J64l+viPvH9OWO3pZKcuZ5kOD3syZtDaDE1XV4GRX1nSt3qEk6N0dVC9zdHCVEWavHURoxTbFlCx5VR2ajRJM/ogQp8Bhv+Nw9YirR4+Zrh6DTKhWRGayHNAqaFt5NBuRs2uitErtwSFrY/cHSaQ4QZiNDBfN0Cw6WiZhT8wzeT6Qp72FGYZIryt5DeRzYI0LJTZKaLQaXNlj3XecJnb7a1Les5Qjy+nM5hwrDvUTfVzjGTgpGaQfRxiZ+YxoSIToxYxgfAVpdl/Vrm3rjS7NoHwnBeMS9d5WQrPsIuOKmM014DK9YiZhbUF7weTdkZx25PnKuliMVGngXHAy7eJ5H3GTWT70xlF3yOxtEAeV2M9WACal58gyP2a/nMnTFT08RuWI9grLCel3hBm4/Xna8ZfQq5ny9DGNiZCekV77EvnpdxCIxNfv6WHP9dPfRN5foflAaZ3blx9w/+Ev8OrDX+J0/IgUG7teCfVMisZjoK80AmHeM+kYgppiqzWT2o+DvTWTjBsTy8c/w0FYDHbV0Kg+RoEFiSfbXJeIzqYUkxggRZAM7pdiXBNLKG7NzL2sM79wx8bIYNggaB/SdnH+BabGkO4SUQO8zCOpYSNLUwNKGP4dwX2IHMHZCKSNECqlrqzriVoNUW0t03IipWvi/g20ntk9azwpJ6SdePlLX+Z4fzaX4uhJtFjR16xDwOwCfH10Qy5oTipdT5zXeyQIu3RFSNdIviKmnZuNyFCw2z1wY0BELeCzrtbE5Z3tf+D8hXzhfEUxC/uy2mh1yITH/jsgdLVxNho91C6iZqhkv5dBYFbcMHxDDDnWYNd92PmHTe0GIyhVhtGNWGG+kVW9UEbM48bI2QHEVHOQCDnYKREjV0EJU2LKd9y9OoPCcnpOrydSvN5Sr0tTrq4eI0RLO68FjdDWI6Ws1H6m5EQgsOOa2EHriXI6stxH5taNpxJMqRcBUmK4kdvlG8GYv/Y5+1Dm7h+xS2EjC2vUxOXHOjQ/rk7ClJGJxJf1I17dnmjz4df+gQ9e33SB8rt+1+/6WHX1yZeI8KM/+qP86I/+6Nf9mtdee+1/sSnbw1fr6tkjQmkWob4uR0I7Qz0jpdHXox3eKFO6Z9E7uPsF9PQeen5JqCe6dpevySbZGg6X/ktthKCNENs7eYPLxW/WyJZxi/NkFucako9vGrXavy/FSNi6sRDN2piQHX3xdiZECBmNGZUdIV0RaDBVmO9J+9eg3NHOJ4MPezETOZeorWWhFMu30NaceFu8C+10sUCt4VUypGkCjEhzcPjaSabdk5wt2ROm3Z4YdiDXTCmx3++Zrh4TpivStDepZy+kWOmpQw+0taC7bs6EPbEWN2EjoWRqMy8bNBLzFSozIU/u3lvodGKaydOV8Tx8BKMp0+JECJXIQtBOCBM5dVqJ1FZZVhsduCscMDbH6HN5u/0xZTv0YzQZdBBCjht7H8RIqcOpeDOAcu6TKqLGF0CSDx+iMe09rG5UvorB0oJ9nRU4C0ohqAB+37RAX83noWNIiwSke64OEWVwKIz4nKoVT8SIBM9o6p0uAYmXsZ+q2d6H3untFS0/sgNrfkaTD0itWpd5vIfliPZG1fcJIaP1I8LuCczfQ0iBqUKd9yyvfzvp6k1iW9A3vsTu8DZyPFrOSroiAbu3vsjjN94h/vye937pP3B/f4MAqZkpGJJsJLN7Ys+Jc0k6la6dqRf6urCsi6vu3LZbLXbBOJHqREwrVOpQekgkxU5dzmY1ECcbJ3ZFQoP5QA/BXI99rYziMmCPZ1eQpg/2AkexFPvkiBrAYHY7Wwehd3T+xt1sXemYvYGNfi7+SjEGU6/4e1CfP/VuxWwpq5lO1oW5z9TWmPJEnB+RyjVcr1A/g5QbQ1J+dUXbYuiCc2fMe8hN5OQS22HrP2zIsqhS18Z6uiWdb0jzHTIvkAoBKxSaO2e4k5pbGpjhoKL0at4tKSbvxo00PK5zDIEmMMzxDVmZ7FkJhuoaOux7Wah238URHD9EY8pWlDYnXQ8O0OamPTgvxr/yGdXlfoMV+TJtewVqTeAoXrSrcWhCMhfc4OTZAORIj4kUhUOKBBKUj7i5KWgrrK1wajfM0yN207U1Y/48ppzJux1LKYiuHO9eoefE7voRadqbMMF5amijLYX17o6AkKfZeGcmk2IEWV4Ak8u6/GTi8dd7Pfz8lkslaiGDTExySYAWOqEHqCa1F4Gy/BfioPz//NUt/E21opJYe6OVE7QFamHqnV0ILGm2ufXz/4HWb8n1htBWQj+hdQECQU1C27pQF+ON9FoYsk9gK1Ja9ZyMMDw8TI5nRF2F3r1AyUjIZibU6sbWxjeosZHVWol5NsQlZJtZhrhJxEQ9EZcOMhHEDY6iQjpAPhPaAcl3SDdicC0rvVZSreRWqetqY4/lyHK+o2J29601N7lTRtqkat2qa4MB7QFsvRIxK+MQGjmJ2bUfduz3T0j5CokTaTqQ0o4wZYjG6BYJSK9ozeh8gH6LLlhXU090DczdTHxEMl0jtQtdI2DJqzI/IqSDQeha6WqS7jRdEdJk9sshIHlHq0qTld5ukG6FQwuzpdn2BrHRWqd2K0YLBRQzj3oQzigOYYYxc03J8zlGt+xKjRAZ8nEchVLpHrNQTVFAJhJAzOXSFwEGaVUMGt75vbBOzFCQirSG1ALrPdJO0AvQ3UcC6IVe75B68JgCU6IMbwQBIzj7OhQ1npWIQsubv4f6YRC0QTki8REiQk0Ta56QekfoJ1K9p5xewPIR8XwHh9dojz5Nf/rtdC1ETqTljhAjZfcO/fDExlv1CSW+ooeXlBSQtCdpp6VMfu0LPDufON284NVHP0etZ3pUCgUJe1JIxAiSdsQ0m/158Pl8UFpZSMeXrEd7T6FUlmoci6J6MRtTpXui7Yg0KGWF2ukSSU/eYpqe0dtL9HxD6EBSZDpcDNn8eRAfhdLMjXlwlTbTRXugTNbZdTPiuyhDuieKm3PoMGOUbvyGNKXtUBFvlBqmqBuPpoahWjSUY3ijrLVZUT4pIR2Q+QmprnD9Glq/yKPlBeX8P3J8vm4Fh70l46/F4AXWGIf679Jd5RWDHXraCv18R82vkPyMEA4EDTQJEE2FFIMdyrY9CtIbKWdrBFu14iyN5+diDLf5e4S4FSUSDGVQ50v0VrY9yhRqExqMq7MhI/ZZOmbeKL7OtXfErRvEkg3pnhqjPioH3JPHxvMbEu1NxHAv712tuRzPnRPpg3sNNQIt+D46r+T9Hfl8hGLXuJUz62I8uWm+Yp5mlvVs77M1k+TTSRFKWynHe3qH6eoJOVnzHGJAqnGpegiUnImjhhBXJ+FDehfx4aPRTxYmn/RFGZ4pQS5jnnEOWtRBsHsswuzPm6giaiNqDWo0jPP/iiOe/796dQup69qIk1X2ZTlRzveEuhBjJmogxMmktPe/QNI7YnhsiMvYRIJ4FY518Fq3B8QWrnMtAKovAldtQHRnR+vKjEnuEFg0yH3cfJtV20HXamOQLw0Etp+NJFNixAnisFM3Epdo8U7QwgAJiR4SPU5I3RuTXaulu6ZI6kpq5sjacjGoP0ZEOsU3bUQNQnX0pNZmVstqD10MY+4ol3llEHa7mavrA1fXj8n7Z4R0TY87JO0I8xUpz1sIYAh62YzzjPRraNcQE9Imermjt+aKIENQajdfjdotgTblAy1eQz6QHPnRMPwX9kiYLNk3ZZAJWU/Q75GSCGqHWI2BVjuxd6YZlmX1rSjZNUUI0WzkDaoENDjhzYqVoeYZFjIX1NOh44GfBrmgIF1MsthWNGSkJ7QEejS5stnvG0+hhRkwCTMhuESyIlqhnKCcSb1QXakQk3lUqK708gppjwhpj4QLMmOLXFwhNOD2RojGHNW1oynRQ2Cou4RG7GdCL2gCpNP3n0J2I/fzkgABAABJREFUr9NbIMiHlqqar2nLQjm+hHxApyeE3esEXkPLrxIoTGnPMu9hOpDPK1Mz35ykJqNVrRQibT5weP1TvPnpT9PWD1BtxCmS4kSYH7HfXRF3e1Nv5B1NAhUzbyNORJR0/5zw4pcIL38Vji9pp8VIrqq46cC2locXDiLUWq3Jj4G0/wJ5vma5u6WeX5FqRaeZzqd8lCaXez6IsPDgOfcDSq35SDm4/FQgWUGr9G2M0dURC7GRj40W3S6h+xiIfvk7jkSooxBBjfKovVNKNdR0Xc20cLIU4xB2xPQYyQvT3rhJof7XhKXwYfn3HF9VYvD9EB8F+ghx0OI3GbVDPb0bITQGCNqMG1TP5njcMpqjSY+nbInbNvE0hZZG0GpdNUopJ3LcWaffBHNY9kDL4MZtTUHipeizqtuiKboVGYSZOItzh9ynJPq67wX62MvF662OlOL7vXGA1Av1wfUZkIukaGTc5uRihnVBd4WLkeUlRXN3TVYgxWAGhD0Uk/zGiXx4xK4shBS4v72lro1WE8vSWMMtxMxumjxmw86Mvla2KWJthNTRWujr2WwVRkqzqn38fGKNAUmGnEi8FE9gPkhfZc724LUh5xuSNKTEY7o2qkiPhrDN3pDPPG0NPWI/SyMggbX8BilQaluoxbqPWpW1dpPJ3X1AKCdCfMRpeQ6nL8P5Q6QXuibO9daN0Kzi1WpkwFFJmuwNRs2onj4sRCQFCCZzsxttVvJdoadu6ptgxLguVuioJ3PiJLc+qvdqhg3ZE0uRaIfuyHOQCRUL6LPvEenhZJs6nRBm7w7EC5sdaCEEJQfv6iIQGjEYfyTFa2KKnOMEcks/doo2Z+3bPHvtjSmB5SyYIU8M5g+SEhwOgUeP9kzznrCbiFmMc5Mmcp6JU0JyNqajO8WGvhLUqnozfgrAI+jBYP56JtUF7cqqjaiJ3iCGPWH3Jn16gxzMzlxbM3g37wxBipEYJwgTMe0BhfWW2o+E8hwpy2bfn9LEhNBqJ8VM72bW34anhSrJ8PrNIZUxgttUPcNkzgm1rUMw4yMdX6dDsq32+/YGcoeuK6EXctyhPVEVkpgvxJBeWsecDHHT1Tw1hsRS24bU9WBQdFQjKff1nlSOML2JOJyt2s11tq02fiLTUzfpchuhcOryXCsKCQGVhJSFHu8gPIaQKYc9t2//NsJyZn3yWeT0wtZsvSc8/59ot19muvmAclhhfh30bMhXMg6OtE6XhkgnpZnQzZht1R2iZyZZqCmxe/IWz974DKqNq8dPSIenyO4pMj2mh8kDEBPSIHkRGPpCO59h94z42kzSHWX9T4RwZkpCU6F1ceMtUHcV7t0P5iQG1dfG+up/Ip6xtOx6T88LcCBIspHFfL2hiNqbjZB81DlGveJ8CQK0XlzpEKE1W1MRukZEZQurUwMIsNGQSTmNmG6eLeKy1pQm1mIRERZb4YGgKEqjFBcJtDO7OpHrnjYpcdpDSXRmUn8Nfbwwv/GbeFye09eVdWkEzII9xEinELoVUIplD6HdiwpBCCTUEr3TvPnvSLBCP6Zs/IMopJyMyIwi0szTBhvJxhCRYJ4oBEfzvJlTi5SmKXTMXdsKw0RvCyP12GwObI/U1gjTzhBIxewnghv34TyakOhSPZ1njLIrUn0UhfVUEqOV6zanMTdjKtqLk9mz81qKod1iv28X9wxRU+91sfMmACnNVBXyo0BKM5Jmbl98hNyeoXVK7bT+wvfcvaG62qgRC+eTQJhmwjyb8nFZqHkyBH4UBKVaw47FFgQiNc2+Pi1frAf92DgNLqjJxzkn/jk7Eu082ooaxUZjtncNDqUvYyZtIFcb4hcUyrJ+w2f8t3SBUuqR0qPB1uuClpVYXlG0sK53yPkXCe0jtN2bEkCjp4DW7QYIJo2/xFLb/FJaJwalDqLRgxRkEUMvDAIMWycD2WTBMlm4mJjng+J8AxO7b3Cg/V2ToOk4CIleCUcEG/dYaJeNsoJW43303cYSB2zDGJugF1MAQZQsyTJbtFtIl2SGlFcVIxj3I72dfeGpSS1TAum2wcTG1V7Z7Sf2+8RuJ55BZ8MnwUdSoRJi9YJuB2GiB9uMqZXQLHm3R6uwtSUIMxLMOr32Quruz4LQ0hP6/DY5XlsP2SuEjkxXaH5MSNFmndH8R0Ka7VAOgalV+voetFdID6b07B7YFkwxYH451WTogwDpJMoQbOTXWje/i0GA9hYi+HW2Tsylk60YOiFqYxt13wcqIjsIkVbuoZ5QD0wjZKr4Vjl8Gkb4mltJiwqhm839iDiQsWO4ZBMtxnfBR5BeZHc1T4sgZ7/ng8Bnh02T6r9vtnXjpmXUO8Lyrhm6hUckBZ2fUHZPqdevmVFe7cRWkWffgdz8CvrRfyAe30Nufo5+uqWmK8L9h75Jn23ktrwgc43GSOkd6StRFmo/Q4Dd9RM+9YXvNvLk7spGc0TLS1kXaL7GRRxravS10dYViYGYJ8LVa6w3j9HzKyOdBkw11a1r7l2dy2GFS6vGDQohsL78D7QUiKETRaHt0FLockTCjuak7bgFdbpM20frIxHcM4sBtcYG39zdqVgZhypWsIt9D7fisvFdt4gDEVMRhv8Pef8Sa+uW5XeBvzHm/L5vrf04r3vj3huvjHzZ5bSxDaRdLlNClem0cKYlJGNXwyoaPCRomQ4NJOhZINGADtCAHuUGSKVq0HBRpGUJLFNVictOyhibtHFGRj7icePee849j733Wuubc45RjTG+tfe58cgIhBshvswTEWeftfde63vMOcZ//B9n4qGeN4lhgzIGOoXE/Mw5ay2I/ln8oCWCKW0guwsme4w9+QJX60vG4cjLD38Hb9HcDAsPHSc9W8yJnLNELzTGmkwTUnaU+YI6X9BrZPjMU/j9RNW2yXE3z41EG7VSphyRsnHBPDpyi0GLe6hQwpGaPIfJ69hMyEzC26mtMUaoBbyiPiX5/IFdQqInaKFOC1jPIEI/G7qZS0iey4Lk36GGSkbDCE1GSJ5DqRXX0cbGCA7Ty3AtDlfs2NhL8lJCWFFFMFOmMdgvtxwlLC5sCPjK2g4RUqKw3+2Cb5N2CJ7I01QyHsEDDR1uEWbbk49z1yONXgvTHGvx8Mo5akVj5POQf/LZwuSzx3e+JhbEh54pohp+QdMcxYlH01ZUOf5vZcSzthW9e4msryhEUvFoR2rdI3WljzdwugkFjypjtAgi2x7skZaTHk6PUmpuOpJfDv5EqeFdEhBX6O1LndNwyXE0DYpSQqyapEZFvCAZJ342MzNLo6ycn7P5HoxwRNUjcyZjBtErOmcbHfET3mPsQV0TeQHXcjbeigc5PUqSn1YkQ5qGUWRh1vuUWyklEJTeYo7uIVyoYsxFKNrYTcrV3pmW4J7EOZpQjfA/DSMLpNYgTDqYHfFiOBeoXuBVcTlgvuJlAStI6WBHrB7xcYSxUpHkBCnMj9H6Ttg62ynGVAJOzyIxYcUyxVhMQnmg9YK6c8byFO8vkWF4OyLWI4WZGr4YZpQec3vLMYvZvWLrXpexFSeeXVuOYFS2lgLwDNtzxqbsyGRTCC6SFAU7gSRiVQINKDXsuuPHj/QkyXGhnfB+Yqx3yDhgdoeME05Y0rtFCmsdp8zmWXGvUdg84DxYXwN2LRcMqTE2sI6URA8MhgRZUIoy6gV6+CbVnK4/zpAJK0pVwWTOZNUB/cQoz2C5xpfH1E9/g/Lh/4vy8n/ErILt0Mc/Tb94l8GJisJ8wSoC1kK51N9QLHyKpqtH2KN36B6bg/rAjjfoesR1ByhuYVIo7oz1hK139PWAzvEMD1tp/RijykDok6ieI5gs6DfuRiAVUaRIBhGalHB0xSislHGLrzV4TCKMOnF2hs5CdDvCQyusCERyVr8htCLhImsw8hrWKTY/7552BVHwejqhBlmbuB88NpaRSIanKV/crKECchuMsYaax6JgHaVQ64z3Q4wB94+ovSOZou3rgZvnH0FGUZg7aHJltkH09v4lxph1juJE6wU6LTmSgNxNM/x3U804WwYXIuhU8Ry5bJwuHyOKA4/zINPCtoiFdD/dVL3RW5hTxjM2oDekONYLY1VkWoKcLyELV2DjZCEFnUsgizHPQVJ+3M2DQF9391Jz0bP1A9bChmBooKc+MD8xhlHEongQRQxEIgcsos8yjiXfBzrh9Q4pO3aXj7l+9w5eveLNzRry+HEAFe4OLQwr08F6MwYNgnZJY8BOa2Gvv55O6acT1vftcEClMJY7VByVPSORYK+Sxd65/vuOY7ve379w2cZBSTcWifFfnRGJCYUkl+r68eH7/Jy3jx/pAuXu0Oi3H7Mcv85+ekybnzCOL5Fu9MMr+jjQR7uHVUeE352JTxbEolDQ1IDZLYiDIilz1YoTjnwb4WqUCTTY3HHDVIpGoTHsiPsRF0eJirlKVL89GeMOoB6a9Rqafy+FIQPnBN2Rk1F84GWE86hZPkx3IXuuE0y7gOLxyDkpM6JzdiLBWpfs8twbOmtU/akImNyjg8DwdqKMUyh6fEUZTAXUG/tZWGZYJkU1ZtCbG2kpM1oE0YaUgZYdRS/p1qG/RJvjZQ8SBdHQBSl7cKGrBOrACh4cjdJvAmUizNC0LjEZ1ui+S7tFvIG/BgSv11FYVqGmzDaC9YQ677HlKdhdhL/VW2i3SX4Gcajp/BnoycjrVBkWZkJFJWSsaT+dtLxATcZAdLNyzgfYksBrnU6y3DUkqiFXDIQDb/hYYyfzVN1MO6TOsWBqWPVjA9oKaxQl6g33RqTIWhaka/JbFNoBbyvMGmNIovO1AW4HxA64ShaSG/ExHZgTXfASGS/US2q9Qk6vqOU57eIJ+Ez3izzHjk8TXiesFFhP8OjHOZVLFrtlPn6N6fXHjE/+Otz8Bv7uH2Y8+TI2vYeVNU3KhCKWaa0Bx2tPVUw/Ie0I7QSnI4rTa8D05kFi936ir3f09Q66wFjo/Uh7+U367Ru8Gd1BpFLUaRbjrJHju5DU37vA4ooXi5yibmHGlzksKoK1uP9dQHgUHkAk/B1PGlu0xSZ53hA2yHiJM4ck5vU2OpbQ/Gas6JkBlfSHKEqSxyY+cpyi4VY7JHgqZmdb/D7CD6WPtBkYI5ot3QGfBrl4fkS5HBT/AtZX/HTk1I4cP32VPI01TLkSBdoOTR+gaYr0Xp121N0lIkEyVbVAfCHuZbYCPo7wj4nizUTv0b5Nypsk3AgBFIqU8D1K9AKRtHpXROIZ2FRV4kqdYryJJvnfokochK1DSMuDwOpS8J5hs5pqKkkBxHwRCkRNBKCGtxHMeJ+QzcsFp2qBcop1oGzqzgw4NAsAyga9h2kgEoXBtOxQJsa8cD0r027HMr3izZsj6/FVJFD3QMJ39SJ8sZIMq7XilmGMo9HXA2bG6XgEBJkmhijL6JGFdTxhRSOUUwTLiAmqnp3BH6Ignz2+F7qyoe7na+ua966cpw7TTI6T4PLy+rvs5t/9+JEuUKytWLulH17Te5o9HX4THWvKYA/hHZGWyrp1PiO8BUQjFEw0ibS+ZRF4oA+pzgDBNBQDAdNNISeT+01aZE7/jJZ+C5bP5eajkp2+aiQ9lEyGrAvTsqfOC1rDAMnJcLDeiGCKlIE62PCQ2I4T3g5ZlYJOF+h0hVcP8qluSpOSSE9wZZB7uSUlJLleK8tc0DXQg6rgozOpMxVhWQq73UTNzAnVjIOfJuo0x/y+NJATyimKOTtBv0W94+NNbHxUpDxGymUQaucrqBepkqmxEp+e46fniI0Yf+Bgd4ifKNIoE9h6itHZ6SUqc3QAHumu2x+VII9qrVAvUF3jcyEgp1DBZO5PdNKFLWsHDWmcZ+sdxUpPBYzlAprdUEpaVTXUBep4yoDNHSWKkzFamHpt2SO24r6SNMDghnjDx5L3WI6ueqoUbFBSLeC94/2EjRVwxNdAZcYRbM3PNiEl5uPDQiVU7YTbAc8xpYe+LO45G5D+D4IxJDYopidgr8NJdhTgKrrRzZnYQnmmk+MUhhq9PKNN/zTT/inl8A3K66/Bpx9SP/k7LKeXHN75Ixz1XSZmRnWEKQpnO+HWw9J+HOjrgbbexcI2TXSD0e6Qk2OJmoz1Nb3f0lqnDKHZyun4Cju8YbQTI7OTfEvL3RK238rY2ThGadhlQs803qJEHhCJRMgRtx6aOivo/iqcjRMdCZM2S5PBGJXg90rAeyItbGox3RqjETkzkgZpReCe3GyZv0M8E2JnPtT5ePCzxzB6azHOzpFJ3NszLjB8Db+R5RI3o/bGrjWu+h1j/Yesb+7uuQhbHDHbeKXC5ppcI8KBskfniyBK60RNAjBYkkol+Xhp07ABvZv8eESek0oYEZ7HqgG6xHqVqhN5sBnatr5qOiV7ibXZHUZPXtfIwiRQ2e4SRnKplJQt8Vizy99oFdMlTLuQ5iOJemiMmMsOSjurwMp8ifY1eGJnEnWoqsx63AcWXCjXuNcUpdQ9pa60daJMM7VcUJkQPuHFyzfYMZx9b8ag7x6x88dcXVwRW1plqtu9FetSO90FEm5RFNb9ZaiLCHsIsYGva7r9RhyEeMktL8c8D5+MH2D085Ydfu6hnsU5sll0RCE/KxF78gMeP9IFSrGVSZVhlbvDgeEfI+1VmnAZvenZ10QlCZHbLLAEHLkmYceIGO5YvUaab8XMmhKVtUkJ2ZwEFO8iaRYXFS4lOgaVCGcTH3Q5naFfTwZ1rUqZJuo8M88X1PmKslxwjvcWjbktG0kzrYU8CE5OgdFo6wkVYyoa83krMbM1DxL89nniVow5sG3ZFtvDHptUkUBSmw9qCYvqucB+P0eEe1VUJ+pUoYY/SJ1qECBzhCAccf80IOSEXo0olNRWvN/h7U2c691T6vxTUN/F6+5sfmZlj7JS+huGlzSqGgG7s/F/dlF8lJit1jpBmbK4S35CqTHq0BFp0cRs2kaEMcq4d9INPkeuhC4UlZhf55iuWA1Jdl9p7YS1QEHEBbcTQbyr2ZEL4iPRmJijByISaIiPQxDrRgNZcVsDApclA+JGwN6S0j2PMUgQvE+4H6MYscZG0g2Vz4q1Iz5WxjghYwrrb0YS+jrYAW+vcJlwvcBtxkQzCJK8ZyJELngwhiwFpkeYGnY6UYsiE7hpOORuaiEh580d1QUre07TP0Xvr1luXqPPfhP55n9D+fR/YrE3DP4oh0e/NwvEipUe4y6/Q0plYsZqWPYnFR1fLe2+n9PvPqQfP2acbhnN6Bniaa0nF6DQPZQuBaW3UKDcQx2JtOn9/TtGjwVVw3V2KuGZUyS4SBMR6shwxmkHHq7FW4rxPTJrBL4f6FRJU7Ug5VqQSjXHahilTmnTH89MFMzBJZgkZq42Uj0oKf63iK+wHB+ZWYxF8/ePHn5HfQQPLn43eKm4TAx7lePnHToL02UUEo/6C/zuU14cf4feEox9oPQQiZK6pl8TZYK6w+sCdU+ZLkJVuHESfCTBexupB+dhS4nWzOlBPWII2EZxEYiIDeg9OX5xXmLEN85XcXOO1SWLRi2MbCJCGUWglPM+mkbJprLWIC+nR4iUED64hXIz7BwynVnDKXa7faQaosFlcgvVm9SRKfc50kp+FaMhsqLE2rxdY0UicqGDiNHpsFuol1dc9ztG7xzuVjAPa4hmrN1Zlj2lLsy7C+Y0gRzDc0RpUdj34AbOV4+jkawTjgeS1ju6rtvFhWqBytRy9gl6eD//7uOdOLYi5qHqxz1ECCLpI8P0v50Cxe9egN4g4xU2DlkpZ8Lr6NENygTEw775F7hEt6s6o4Wc2QcMKCVuysioCCRFNEx/jIJ4xSQWmAivkiAKag+2eJlQXYCKcYIxgfQg8HLCNHgBdbqI4mTaZbZFZvSUik4zphUZA00joM0i3aUCLcmT4QNj3UDDQZd+RKdLKHNsIEg6Hya5E4fRYl7bMiHYO0UGaKPuoapQZWG3c2qdUZmpZYUpZHuldLS+QVCKh7W7L/soGgyaNwYzVaeQx8k+zJ/kDmnPkfUWb0vA9GoIdzAKlX0Yse3ex1antFvUw/THW3pASHjTFDSvw4JMF5m5kV4xGmZP4dWwh3IRoxU/IGVghJIoItZb8Hhy4QRhePBYkBJy4k0WVysj4fhIbm5RoHiM37SsARObE4ZpuUgbuMeCWOsjpO7p4wY7fRyIR3Yy3kClUXMQuFIyIyXZ+1lguncYa3pr9ICkqfR2pLTXlPYSZGJIjBMYA+tHxnqDjVsqiuiCTVcghq6DMZ5QdiUEVqIR1CeGr8m7ygWwj1sYLXxIpiXSlzeoHKAorld4Cbt+XZVVL5HlCTov7Kb/Fj7+e+x/61vUJ/8nXn/u92PLdSjSxkyril9eQ2vU+YSMNYrDdsT9Lv5+a4z2knb7gn7qtJbk0uRPBSdxnFWgm5gKNk5ILORVgh1hyTkYmREyTRUkRrIqQDdKKklEDR0GfhPIlBqq79J9ZpoiybeWzfnXKQ8cg2uNzBkbg+Ie4YylxIa88S5a5P+UqZ5jGSafwrUZyQ8X6eJFiWcw+aVCKJRUgp9iozH6yql3pnlg3jCpoDN2bIyps8iCTzvcH1NHx+33cN1O9GPn1Ycf0iU4QiJT7s5OqVfodIXu99j8BFnewZcnjN0jyrS7d9PubXu7wXFzwaeKVqWWiCYpJbhgroTD8hj46Hg7EjEWCh4JyOoVqWm9sJ0PqZHkywUUQ/zI9gOjMAri+AAoC2XeU0uo/3TaIXMUU5KcCZdQRZ55F1FBs5mGbOO2kDRzRqc2VCcWuiStA3hHRj+Tm4M/FwWMEk2s6DH5LwXqPmwbbPCISpnfYK/ecHe443Q6cehOubjm8UVh0UpdFsxX6ukU6/2yMNYVs8FwQbQyT/tAWXxAiwBI9RLCwjn9aJaB2ITXaNTPPk8/BHn24evct1yiLHDQM6I9Lbvf9edsx492gXL6CJMb7PQyOkWXnGkGhHs//xVyQoKHmxJrCxNszM8qnZgVlPNsOq5HysukgtT877h4ZnHBhkWcey2xiGvGbSsToj2qeWl4P1DlEO9FEy6smozUgMrDqC2cMmstQfCyjo8I5Rt+ftvkmwCiyzALclyxFSlL8meyQ8iny5zgK4w1RwpHZKwU75TJmWal1plJJ6a5EHZhHnk3xdHpSNEDah3xRwy9xpYvUfefBz9i60vE1pwNQ9cNDahYd5BHkehMWFhPx9cBn2uhL48o0yU6PwbJBWisiC/IdAurEp4Dh3zIj7HRMhGqJw1DKCnheeAg01WQ6LwHemOnmOfTEU3r+a17ZVMTRMcU8zDowfTLgjUzQnzjmsRoySzdPtNnJhY2pxhgB8ZUKRdfpFx+AdU9st5h5TF++ibWXkUAmjrGxEjycvUVk8iziM4xdlz1ZDJtSoH0rxjWGcdXTPNLkB2b065Yo7dbvLcYF4yBrjcUN7RIwtBRkKhf4zphUtLG289QLaSapN8x2koZHV32UCRRoBxNbnB4mdIhXsPdV38/xxWW04n68X8Hv/1fcfXym7z54B+jP/0S6MzsC20YTQdQ8cwmYd6dUSjZ3WF3E30MBEvJt1NLjDsjhTcgbjNP9+ZENpAY9VnwUJwobsKVN9QxvY/gpdQI9hNJrxNxkIF5wS0jJdbkYOg1ZiWTssNcr48V6hyBag9UN5ALOZl5QxDc4xqE940XEskJLtqWSr2tS8IGrW+oTeSSqZcH46YwbOw98nbMgnRcyx7pJzrBqailUJYdwhU+3kXe/b1cHY8cTneMT19gUjKhe4mxzvKUsnvEdPGYuntCvf4cunsUIx7d0JaQcPvI2AYDl0IhGgPVGpwOiTG7eWbcWD8XVmYdL1MUMxu3Z2wmarE2O9uGmhJnD/6LsN0uzkDChbss6LJHpkukXqLTgkyhotya1/McaTu/OYI67yUkMkaswy7BWdrIyy4l5di5h3iFsr0vz5FHvH5LvQ7DQWUuM306QN3R6sTExN4G/XjLG07c3r3BT43lYsdOCxf9EbJcMJdrTJVRG9LXQCsydiRnLTEKM0dlRFL5zUtkXtA+M8qE2w6fQczQKdY4eXDP/qAus/cuylG8ieZ5k4cjH/2+P+Ph8SNdoHD6CC1H8AOrC+5542v00k4sAOYb3BSjnG3cI+JvQXwR6BbdbNgBbzdoptGmXMw2NRl6P5+VMH4THxQxikcwHzrF+EeiuAhkJpJNNZU6ZIorqmlFHjPFeL/ppyDbuEZysSWJ9ZISvA3yb4gdwyROAs7VLMAiytxhrGmmdIJ2Qx13wAmtxjwpU6mBLmkBej5QRtE3iL9I/f/7uFyHSoMgN0L+ziwWCwPpIxf2CDz03buIPU3J5wv88BIpF1AvsXKLlj1lvgz+hDXgZYSAyQTsUWk4N5idkHGKgsMC8tXM9ADBxwn3FrJKCYmzr2+QsaK0HKdYIitphc7AJdNNVUOFkaMLMUNHpfaB6QHr2elZ+IjIdjkMhnTEg6/jhDR02j1juvoJZHmGlYGUC4pe49Nj/PQNuP021m+AjtXLKKI8kn7PnciZFBt3n6eyoo+WxGWw9ho7foLKgouQGoI01wpLfqMEQRdHLdAC9zfYKbpI6gUmRkFAR8LwQYCMWi58LBAJg6bqFJ8xnbaV6MwviHgGgWKoPMKe/Th29zNof0F5eYN++28xf/jb2E/9HO3L/xhDlKICPtN6kHt76+dxCfMVZfcEmR4Hpymh/hBT+XZq7ic5kAVydLRCPNvFwWTL1SHUPslXiUls5rKoxfkf8UNVPFG8QB6lV+y0h5pRFCVMFVWEPlJ5Jyml9ZFju/TkfAiHJ1k0xo2KDaFMkbI9rKMjNuDgQ2yf08NYcGQz5Z4S5ihYel9z5DCyo42irJY9jDXyinQC3UXQZb2iXsUaM79/4nE7If7r9N6Q6RLmfSAQ+6fIfI0sT/DlMb48hfkKao3G5Iz4dqyfcr8qUfebh8IxpcXxvsKwzYflEheW9yppbumRV2UeSp1Qh1eQOcb57sHvqwVxRVjBeo5ZCsaMLJewXJ//aN1Fzla5dz8937e+OWjH/XPvsJ1jpnzu7jfrIDt7juiQlBW7E1tsNhhZT2bpEEiekFlYFdEdWi8o9YjNO2ZAfKWdDuz3d9weB6d+pK2vOZ0uuDteMy87ll1BpoqOOV2uC3WOkThj0Htjmmu861Rrel8Zt69h2YWwwZ8gJuhsFDckbazOZmt87+LkuyMr+SB6yTMT6yPIPUfnBzh+pAsUW28YUwvATGY8bXfMNmIg2JBz9SZaoluRSoG4mUgRXNIEtkvgOUOTrKDNiQ1N5cEYWxJ2jd+JG0hIUJU0IiK6bbPGRuQ8E88kRg2QVvCE3Ni8oWy+CcGExqFMNWK+CTIrGTIX424BBtKiayllRaTed8OS3BW3KExGKD+0vUG4RadB1SlGTmzyY48E5WIon1LtBXCDyg7TBfNCGbfo8UPcD8HHGUEojt99hZcJsqPWcomJppz4FTI+hn7CyjPw9xFmpJ6APa4zlD2M1zBOSF2opoxxCh8TRhQg/SXSDqhcMy07ap3owxE7IeM2+BZeQS+Qeh2f2+5iBOBxzYQoGMNMb5wLU5EwZqqWi5TmQtkPjDXGPDZGXmlirq2Eh4mfiMwQQeZHzI9+D7r/3PasY3UgOiP18xS9xmQHt9/Eji8wPwR0LQbtFI93xiko2wgJSPM2s4YxqKXidqSdPkV9Ry07dLoIaN1BdcGlMKJ1x0bD1ai7HWD09ZYiS3AZSpq/acE1xoujl+22D0TAHGtr3N8jUDvSrTIcmZNIXmZ0DFoVdP+Mu+svMt18kWofMd294PgP/x56mlgev8/d489hGuZWUozqjo3OaGvEA6jiZU+ZPkepTxiHj+mW1uUj1T+Q1y+kpZt7bIxeiA2oCINoSDTa+7PyIMiEjtNTc5e2+InGbt22eMPbgc5LyjxTp+WM5KlqIjsZe5BScNWS/IsgTmspPAwSjLEd9HXFIbwrPPx/hJr28pyjfYSwHh/J2xBJIreHKm2kimfkKAI8eCc6MQ4vkWmmpFpR6i6UbXtjefJFMNByzZvn30Z0icahLnhdkOUxZfdOhAPurinLLvorCS5LGKxJrsHBIRFR3BrWM1iVkkqyQfcReWgA056qcz6LINYzZd7Pa63pDqnJDUn5sdQJ8Zx3uabMfkLrHp0fwe4xslxQposwCtSwYVDe3jDfKv9tQy+zMfC0hHirQNmKmPsxSDQSMdoQAmmBrRj1t7k9roG8lAnXGdcFLXuqL1B2XE0XsLukXn6bly/fMPotrb1hba84HCfKpMH509zjSsXMmVWxdmI93lKLIL6EYWfv2Oi8/uRD6v4anS4pw7Dra+rFwrRfHoy9YrR+pkB8l+LisyTZ++3xu7vU/i4gzFvHj3SBEiS3CSkLRXf0NRaxtQcLXHUz/4kFuiQK4lmNbImg4V/i52LjYf/lSZKN3xcXQ/X+tbaZCo0IhyvF0GkfjPusGcc40fsxNsV7BCyq7AeQGLk4xXvZZM1JCLMYtUQOfcVaSWdMAtq0jJnXgenAraaV+cjPtw2DO24HdByQcUT9hJZwLCx1TylzBmoZIsccx6wIr1A+Bb3CteD+AmzG5RI9TPT+GC+PKOUdqFegF1i5wHVG7YiM16i/pPoJ7S8RX/FhVA54+xqtv8D3X6TMiiyXjMztECqmPTZ9DVOxiIEzhBXxFbFCcShUhJrFy8DHbc6DC9pzVOZ2Lk5EnPAj6cHpkRzdmIaDb6mxWOSi71KoNrBpT58u6KMhep/oDNGt+9jGh4NpvmJ5+pNMj77MWIK0KoMwy5PYSNhdQf1xYA4F2OkF7itlWmKOj6ezZ87eE+IPSkAsepYjPnMPYz+T+86sLKk2Ct+WLeisWw8kTSV5DIbrm5jxjx1Wo9AUHTFqTJWDlCVGqJsrp0cRoxke6ZLIoCSRcsvq6GGKJ7t3kMuvMG5eUC6u2F08p338a/Rv/V1898dAL2gSUvqBQa+IN8ZpwHqkt47IBaVesvrzQBBKKgcgUNARMHoRGFunSnhuhGtuvLZkYdBHjIpqqQlLZw7yGDk2CiS2kJ2vAkRDYv2OfnyFznvQSONFylnpRKJ127qyGcU9XGo8pah1Q0KcVP/xAB3pmBfUjFIqPUd/MZaK9ScItYOy8V0ya8qsJ0rkmBR0foy/+m3suASJUUvw38qessDiFuqOcgXzuxwPd6jMlFppOHW6Zto9ghpS4zLPcQ/5wEQYya2C4E+ZNapbmBOeF9ZoAKJhSySvaLhC43hfY+1wi6BTC0WSmeOLUufLINcX0CkCAYVIMR5Wcd/DXNH5GuZL6nIV1g5lOcu92cZl21jCt/+O9ygS2LklgmJuDzhDWyGyqafye+V+r5CNaiDhUC4asoSqiT4K6RpcIu2eFg20hArP5x3T7hFPHr3P/p0XPH35CW9evQzq2jhxfPMp4sJ+3sdIESgl1hmtiqW55+HmJU0nlnmmt2Osc+3Im8MhCpQeWW1Lv0TkCbXcp0Ejkh5bbxcj37Efv8VTETbO42fHQ37eAH/340e6QAnL6gnRXahFCCO2LTMiPE2y+Mgwv9iU5Oya6efC5N7iN9rN9A+BCAtLG3M3p414+ESUMaK6HtZQ6zFvJivn7GpCGhw3dR+b9EwoUwNWzCJQa4u0IpEXcpa7eW/YFqqlJY17lLM1VCI8vQ1MLMYIGtLZkmSvIHSdUD+Av6H4XcyWp8fU6RGue1wKUgdFtzTVW/DXFGnYdMmEMsYdqju07nAaRgd5H9FHeLlgyBIeJkSOTKhO3qC+Uvw1Ol5mIbBBvYLaDdK+hdu7yIYwqYDuUJ/ATpiulHlG+iW+NsSFgVM1Rm0hOUyFixlmDWmvkNGDTzFuwW/B7kIZ46dwXk3/kyC7xXlXmXENIa6i4VOhjmqlTjvm3TVqnUbwG4a1IEuLUZZLyu4SfDDt3qU++b2Mi6vsmDS8TIhRC9kFoo/Qq4JYjxDLdoOtA6EzegOfsK2v0y11eitSDKwzPEYE5iOQIi2szNTpOjZdmYg05Lj3Y2HujOMangglFjQxR2dHuaAUgOAiYZLIX6haxCJXSIZhxXBJ2baW6GbLjI/B0ErT6GipE2P3HserH2d6+VvQf4sxwzg1uP2YevOKw6VSxGmu9NaxmI1G97qG74n1E81ODDOGbSZcRHfqW/catuMhdoXuTqEwa5he9ZB3UGuqcIhu0bjfcLQ8IDqT76OFZLMUQasgYox+oJ1uqVKZ6jYySM4UGxIaz2jQKBKJyY1PE+0J345wr/butLUxzVPKV0PVErknCZsnGqNsnb8lF0rTQXjkH0tlWVjjl+Up9Bv8eEmTAjoHB6WGhFZ5xoxgJUUCLz9lnMLk0BkxEir3ab1aNJ1vyfc44p5JxMBsxYecDbvEDFpnjPBy6Q/M8wYxWi11wlom6ck9vK1KhPcJsZEWD7NBDWTPpKI2xXi77vH5CuZQXNUaKFQROaMk52wm921qHo2fhEtr4iVsVNjYZyW/ej++iGIlCxlyDJeTnaC2CGJKKeH+pBIUxLCKqEF3lEBApJ6QXqFO6LTHd0/R/fuUq1fs3rth/fQFrZ8Yp8br2+fYuKJooXggVWi4ysabi9TtdrxjHOB4CETldHfD7alTp4a6M08TpShtCXv9UoIbFUq3raD+/gjKtn9txelni5ONy/ODHj/SBYqjtBE5I84axLQ+Et6NMmGrfN1jeqjpzunD8ZId7/bzknS2WZ5L+kKMHNmkhD9HLBvpLO3u0+l1qvsYdVj4oWwERLNNMWesPTw9lJi1znWJZjp9OkLaTLiV5lBdzMP/wjpg6dkgkbOSlarnL+k2MFOgU8LWIW4ZG4ifgFtEj5SyMk+XqF7hMsXrRChlFyQ9H+h4Q5FIky39Ns9tQadneH2G2w0iAytXgEB7g3OKjkgfIdMV6ILLU0pasbt9Au0FuGLlGi973GvwQMYxGtNpju7NVsIV9wrKBVqvgX0Qia1T+hEflwwaxU8xc7ZTjOLKFLbo/Yh6g3Gi9xUZRzgbnoVR1+gDZcRC6h0pF7itoEuWjRauplpjMZwq07RH/EgfgfRMRZmv3qE8/f1MV+/R2w1OoVw8SSOuEl0sJTkO4BLtvJaOlQXdfQ76DW39LcQPURTTo4P0iB8wi453sw+3lnkkHlyNbj2u3eHbaDPK8g5eL/GyRFcmPTwYiqO1pzR3pRQodokS5149ninTSrm4Zp6f0NYbem9hqIaD95Azj5prl0fL2RWddsEd8inUZ6r00VmnGb38An75YxT5H+kLyMU7yOkWvf06vlyHQ+8I3pD1FRlOcacPh+OJdveGdho0z80ji43c84GYn5vH9cVgLjOmC82V0TqqIwoZlRh3edrmD8uty5L/5Vmsk7sXuFg0GR68FGsn/PgSKRXZ7aN4H56jlWhItEwpM4rn856EaSmbFXRz//Vcd7bRTKnnIlrFzjLjGB9zJjTaGFgNG39VEnVoMXbZxk2ALE8p1hjtiGphLTGSkrJHdAfTHmUw4agpg8J68wY73FAs/U1sRCCreDQFUnOMFFYNQKoSd5QUjAdpuSUnp+MMuq10L+FuO9WcZgpSYvzk44QkRGm9seWCh1NQcqQk1rfg7E3hQFv2MO0z+Xof+UBaQpAjUSyRvJKNo7RVFPcCi7zmvhUk+VffUJK3/2SNknvP/ViKHD+i2TyrUESxEkaRRTPpuiiwpI9VC86dFph36O4Cvbhi7ieW63dZD29otzec7u5Y10Y9xrpXFPDCqMp6OjHaHZNUWI+sttLXE+s6OK0HuhNr1OkQI9vTwO6O6H6Pt83JOHKRommMzy9ZZdhn6pX7QmR7Ch++4G2i+A9y/EgXKJYjjmAoW9iVk/KtbbSTJLitSIkTlpKxnBOeYdbPzBFHdj+jG+TvwIMJ//BEbxHr2XvF+0iyYKT09vPMv4+VdT1R0qFRapDhpMwULxuiFl1IfEqibImOwejnz6QiIVuONTM6liR8qjnDBp0eEK476p3KStUDdRpUvQS5TLRlouqjcK7VicKCtFcU+4fMfAvVzkkrIp8D36PjCOWITo/R4Wj7GvAGdaWMx4zpJxjzHKqpMiEswEU+6IZyGRJiP0aRIARCIRHqFwMyAaKoKUk4xUpyN4Dja7TdhJuWVpCOLyOUM3rJqBrSufEc65/gfsDsBvyYSELHR2O7K9zCMwTA7RYfBWdOeLMn2bonERrYT8h0ySQzOj1lt3uGXL2PXn8pwgmPb6I7nnY4YcIneEgVs+eV8zUmNiatlOkRLE8ZJw8zsraeg+ja4XBuJt16wtBBog1uSpw1szusH0BfMtZvUco1oiEDdS2xmZRcuFXC+G/EZliIWb4zQXF0vuTqvS+zf+cnefXtr+Gvvx0EXKvpaDpIT/MonkYqoUaMxUyjgNvk3BXFi3Lcfxnlc3BxxK6/hOljmkLptzn/N3wccB8R1GcN/A19fcHx9jmn45G2ht8DLgx3pjAajQ68FEaqgETDldh0wV0pFKqtjD5iYU4vkW0Eq9m8yNka/Z6HcM7cKcroa6wrYmhbGRnUt8wXsQnjcY8VQaiBduVsvpTgtfQ+cs0KDl1No8f4+R0tEdI5cv1JoGirlQBPnoqFO2qOdYbGeGWMfvblMctYj7qjTjvWYzQdNlVWnZilwFTRTFWvyxVqzt4VhrGud7R2QNoNUiaGeq45HmGpG/okUwQW+wh5vSw5slqDYJ3k5tFX1n5Adu+mD49S5oWqyX0QyfWgJkIaa6zoPUKtQxGN8ZZQIrhwusDrBTov1DJTy3QuTHK1J4cuubpmepkrW37S21upJGbC+ft5+Ipt5LONexJzOY81LL/btodXEwmS8/S92Bk7DysEqcExJSoirUvcV+aM3ROW/R3rxQ3L4ZZ+POC3rxnriWJKbRb38HZ/q2EYx7tbDocDTty71IX9xSWyXMR4spaIsmhp51Ail+tMbM199R5JuR+TPTgZOZp8uE/m8PV78FK+1/GjXaB4LPFbxoZIpEOOERbD5tmVlOCdbIF/yP08LSSh6RuwzfPPg99g9fcRPINYMI2xkRQhvEss+CJzmQKi7D1CBkWTTR8dzBg9O6ANBHPcGzZi/geCaFhSe7L58Q32HbmhptTOQhGgqUAKSDEKFMh02hEOhpLBdbM6Uw132KlOsWmVa2y6hPoFRC9B3uB2i7QPKf2vU/x/xLXSuKTyeVwuUGnAEcrA6w7x51T7f6DtlrA+/RJN/wTSHRmXWFlA93jdM2SHTJ/DyiPcB/RP0XHAmPH6BPQiNjw/gafbq68wJK2+QRioWKAP4xV+iJ/j4xaTH0PLDqREJyj7gKb6ioxbCuvWB4U6Ixf8qtBtBFLlK2N9iU8gssv8lyA/y1hxWnAF5opc/RTl8ovU/btMyxN6vcB80NsN1JrjoQzfU4j/2AK+iA7e4o9bC98GLeh8jY2QDVadsHbMImQwNBU21olAwi10LlA8BDQ7dR8nen+D66dM5RLrlyAVpku8PiZZgtusiH68wVqjTq+gVigL11/6Q+wurqFUlosn3Dz/Wiw0ZaFlqJwWiZGVK5rSdx2NcKDw4KIwM2QOkEWUdvke/v7PcvfbK60t8OgJow385iO8zPEce1iQj3aHr7eM25f0u1e0wy3rsbEeU1+nUEsQd8/UM/Pk2xgjn6OA8IMnMk2xbozWw8G0xDoQ52JT+HggS/XthXXjxA8PAmedLgJJ6Y12OlDmA7UsbMZq7mnf7kHMFYkAR9EwLMvJM46FURbRpapoKMam+3UjxtDhwWRu5wiO8+brg4xLzqIkxjuRBWYB/cvMtH+EvflGkKcPtzRC9osoUvdomRAuEDOqGct6AaeF482nSF8pOoEWXCXs0/GzPD9MLXMUNTouPYjAfQ1Up6+hfrMRIZQlEnYlx7VlmtNF1hhoSrgnrGyje0kU0hHC3NA0pSdJDi/znqnO1FJTmZjFgodh5+as7+f/YHsQvid/Qh6+lAcFyVsjDP/Ma7gvWjyVPgHToBbF/eb3pPkzN1kwJJfMnFLmLKI8DALLTJ13+O4S1iNcXOHthLaO3R7RfkKHM1QDtcKoOIzOacT4b5kvKFN4GkkRai0bQSGe4xYCgvAIDS5jqBM5f97vOCkPzs35NWzn/jtf9/2OH+kCZXjq3HMhUd2IpdkNjU0OFvPgiD/fEkjjpJmRi0U6E5ZwL41ZqtHN6D2JkD4ePBzhEDlNc4RI1bCCD7+D6GZdQg8sKYUzPPgg04JK5C7UuiSpceD9BNpxL4k0xEobBUp0+9bXs5lRFFchm6warozRZaWjIZmAjAOdWgt1jvfNtIP6BNVrvLyDLU8xVVg70/gYH7+B+g1FvoKVZ9j0bsC4NJQFKZfASu1fpdjfh/6texksv8m0fhW1A6ZXoM+w+i7GE6TuoOwx3UffInu83OFljyyfx6dHwYPxrYOtiKxYHyg9ORKH6GjrjI89Y3yKHz4Be4Mse/r8BUR6bOB2h7UXaH+DjIGZEPlFGRmfvKKtIxKVOL/9FlBEr3NzXdERduzKKWSQ+3eZHv0B5OrHIpOmKOrGOB1BIoY+bLeVquERYpIBgxbSbcSDe2QDW2+h30Zn456oxIh7gihOBZAxwhslERTY5JhxH/tGyHAlIUZMj3TvFBpaHwXZWQSjxes8EED1FdYb2qEzLzPL05+KaHdRvN1S91eIKv14g+opRkZlYmzJzOJ0D8OzSGiOzjLcive47GgIVhZkgv7OT2DPfwM9nGhMeDfGuMVqz6A7Q+i04yvG8ZbD609Yb2/oxwO9DbagWrfgcZxB0lRbbJ1edK+xQbsanghL0YLpNvqIfJwxLEDWtwh/W1e4/T7obVBqwjWM4ArpCV8PtNMdMqeluUgStNu9AVa+q0C74vfXKoEb5iY3Zd6Xj0E7rUy7QCGQGM/hG98mSNTxPlMl+KDD39Q8YZaWswiplOVd6P8AqY9hNVxrhFSm1F7KLmwS5gUda6i9Lq9Zbl7Tjqf0OMkE4BHeI6YBYZk5WxSIuTMk8r+07vGumMQ6JQjztHDKy1ZUmUqgC+KbCjIwDa0T1TUT4jckNpo3k7BqkJKOtnVHrTumqTKpMmXGDvm8i0Wcg8F9QbGp5MTPI5+3uv3zMvF2UQL3hcmZx7L93AcIwvZ7tq8PC4JzdadooZbwdDELtKNnEnU0TeFtUkpwn0QjS2uaaviZtBWWq2hk+sq8X+M+fPWK0lZ0hAv26Gusdz18pjSN8uZ5pmb4ZR8NGz2zp9YoqNzQqYLXcIvSck/e5h75+ez5ePvw81r1gx4/0gWKWah1tgs/RkJ+GinEbFVoWsfnXCch2axOP3OjhXuj3VfGm5zX0zoZmOeJeQq4bdk9gmkPJZxMwcN/oK+ZmROrWpYSQXDVwrTsmZYdpUYXQpI6kRWGnvMRzONmde/hjIjFbD6jwcmfXKUwtIZTat4AIoTpWinU4iwT1BKuhqIz7h3rz/FaEH8XkceIhqOg1Xdp5f/I8BX8SBnOkAU1x3WH66PYhO1TzCdcv4yWb+IudPlppF4yyh7Tx5g+jkyX6QpKFCiUS6DQ65vYGOdHMF2mmmAXi5I56km8Y8XGG2i3MALlcbcYpfiOYid8fcU4fIzLk+RFdFjv8P4S9buAxEUCiaLjhAonDO7S4SBJgIwG3EF9HeREX4Ns5o3BCC+Ji6+gl5+Lru2synCKFLRchGmZgWQu0GYyRRZGeHTJPlak3VD6Kxiv8R4kWRl3yBrqD51m1vWEDItua5zSBXeclQ0q95brIbcNKNw8LON9rLllzfh6xOucxhi7LAo7Nm4p4wYfB2R+h8df+D2Uyyt6O2LMWNlz+d5XGKcDWCjgbPMK8hLuuuMQPhvHO2h3uJ/oEEnPVMrF5+l9jw2hrw2WR5HhUydsug7p7fGQpK2VdvecfviIu9cvuLt7yWldubs90dd0i+Xe66VWxXqaqsWpzkuTZmHp7CrEIlrrFpKYfiNCehvFph8IimZfM9KY774rDIJrFgHtSC0L6oNxOjKkUHYXVK20vjJGo2iNghTJnxU27SC0tiI10LXzhpebmI1xX4B5tv9ZZMUmm8+8b2T4kaT+cY+e2LbWheRZlndhHBjtBpUL6IfgVmvBaigIRSe8VMruIpoiN66Gc/fyJWN4FsWpIOotnl2Zgt+RvjBaJkSmsHxnRN2tndFOQQieFpZpQTXsA8TWTAQuiaDpmYNDlQfqTAKdzrR60xnVPbXuqNPCMk1MU2GSGIerhIrrnECO4QGtxdc5w1jfscG+Zf2Onjkxb0mN+a5AwvmL25Bou66QpN68xiNN82KPskTf+xl16GboCAl7LZVaKkUXptLR0mFq2LjAe0OWE4zGvL+gv3nN+tIZxzukTkw7oY9DWNtrSL7rnOGhBAG8t7gGOireGjKFa61MhvsMU3CWcjP97IznM+jJ9zghP8DxI1+gWGr+N5JYZE5sSEfq+1XYItbv5WH6Vvey/bxh240Xqhk8q8Oc4caMTplKYZmXSNudrqDu4iEaLYhdTjxcRdMm21NxMNIILGCzbh1f+7mbUwKCjod/MGxlnJ0R03p5xOYtEqhbSDtjs4i4+BGdgN8/NFGcG9Kj4BC7A30Ri48foRu+fBlZFth/Hpl/H0Mv6etL5PQ/I/ZNzHs22xbKKXmE1/cx/T9Et+NHpCp9/n30+SlW9tGlewVZ0BpEwVJ2TNOEUnB7miqAiaoRz+0U2vGG4Qfcjoitwadot7C+gf4atyPhYBAcF/eC9xN6egnlBVYnpAPtDaXfoHYMgFNA3XNW3pM3tPF9hO4tpHniwAmxN9kkjvPEukhlmi/x+gwrFdFOkTBCCg+MyFbxdGUk8z0207VwxcwNbjTGeov352h/jp1eIm0g/UQ7vuLuw09QGtMUHCJZFJmDD6DbaI+UFid7z4ef4WtJI5ABdIepN8Z4iWBMAPooRpZ1Au+M0w2jv4Bxw6NnMfarvjJspfa7eC4u32X/ziUw09ZBPx0Y7RgLrSje1zDEG5/Sj68Zh+cRhLl7Qrn8AFud2l5zXDv99W+EJHX3hHm+oE87nAnRcMRUb5zuPuXm5dc53bzi7rDSu7OugEXRIZ7mZAO21JbYfth6kuQ5jlAblchnOaMNIlkkkIhWBkTma0QKUw3jPgBP3xs/myTmgjyC/yVmWDthdcJsSefUKHYiH4ogQaajZu8N1RJ5MkRYnWgQ7GsNFEVRRrdwPvUNMQtGzwaghzfNQDc5sT8Y7aRjsufm193ZLZ9jqXAYt8yj4i266S5hHukU6gToEuvYLs7VIopr4XhzG0XPegzCtytjDsM0JwoJnSLtXC2iGGycUrF0GaN18xwv1Dz94dCsRGrzVqCIBUK0kYw3N1e3RD+kQtkj8wXzvGM3LSzJp1CRMyKTCzo1tcBRvJ/rvbeKuOAD3m+w56JFstjwt+3fN+JofP+DTfjBCGRDGrYhUHCD7jlQTST5Ndto7uF9aon4F7rFaHDKhrfMBbUwt3QzmIPkX5Y9urtGdjNDnePHAx1G2U3M8466TEyTQnrNSBV87YzhjBztuBZ0qkx9oMtMTWSfUnJPio/3/TxSfrevfa/jR7pA2RCGSMKNr0lCcORDfu8u6bmowGYr7Uli3UzYtgc5fvbmkwEkc15VWabINtCyBBEszeESwiGcaCtbCnJ0cGmpPiJkbpiERHUdtITxqhamMlFF8wFOcl6xJHIGlDzO6cagErC6OOcIbdxwzcW1dwYjHdo7Ig3xhtir6GbKQhlX+PoNXJ9j7TXwE/j+C/hyRSnXWNnh3qjjS4zquB0x+wgZYcQ29Iquj2FasHIB9Sks+yjAHPAGw7I7D/h4ZKpv0YGUOWSM4vfmeNn9e2/09Y5iN3g/IuOU3VVwR6Lg71EQqGHilNHQ/iquRxvI+DSg9UScRLaWehtHBT0uForgvYiEy6+5B4ohaVyHBqeh5JzeTtBWoODacD/FZuk9uQ6R2bSBvsEpynaNkWO9Bu0ObW+wfou1uwjpY8X7if6tb1FvD8gEUzVkVuxC8SdX1P011QtDOi3Pb6S59hwzlgxZlLgPLLpH6R3lBhsT81SQMhA7JsfniPUj03TFxbu/l1kO+O0B7R2XHTJfUnaPcL+gLHuWSWPjObyMEZ8CXmNhrTO2f8p6+wL75DfZf/CY6eLH+OQbv4Ld/CZrP2JvnkMfcDxip6fo0ui6MNqBfrxjHF9x++rbvPr0Be00ON7F5YvFMGzp43LE4jj6ZoKVS0KiSQagMcYxjEGsCz6iINH0eSBzh0qZEIUh2cGfrx3Bb7PAbVyc0QdSBGTQUhlR5ytKneltje6zTlj6eAQa4+cmiq2Z8PBC8SRIaM17MJG20TtFBVeP0ec20iEb2M/OoFwJj6Z7mfHmmOs+0PmK6fJznG7vGP3uPH4ap1AXSim5hoYbtU5h6Oc+mCzQ4dPhEJ8L4rnUGZl38Z7UKTXGEmLx7HsP51iZNFV74WVVSg3EKmMTMAtlFZsQAkDRKkG9GjG6dhWszlD31PmCedoxTTPzVJhKmu5t5ye12JKImhahuDIChmMrH4KOv222D4uOvO8+wzc5H/lvZyltFkH+mX/fCpzt525/NlTlITp3/u58jSop1ggl2FBl0owryD+YxTPtke0mOlGrcFEiK04++RC7eUXVaWvTGW1FDweMQsco0xQNfx/hTTPP4UjbL9isIZgg7IEDbZRthCX35/wHL0W++/GjXaBsjPTCvYMkaeeehKiYUfv5prL0STCPgLXtwRoWZkbnTSxPtmro/IvOLNPEsizofIXoAvUS14nRB9gaiM00h926NVoqi1pbQ9nTA5InZ3vHduJ0imCnqU7spvsCZSpRtVYPzT5ubP5fQAZthReBO6hpZEBoCWlYCeKljsHsirYGkh4KvoLfxgiKD2F8TJEdZTyn2Gu8H+mnC6ydqGYMe03fPUbmL+Lrp2GVv7xLn55i0yVSr/B6gZcdaEUtipJAt2JcIm7BVyCSM+uoUYGrIXXCVWkO3SObxIsi81OEivoJGyfG6RPEP6SMQ5ip1QmfP4/qI6Tf4NMbZP4ctlzF6OP0EWW8isVOBm4HVComMeJRKWCEv8oUxGLtwQM4F63jhJaGaUooNT0uGPj4BD05bkuYLG0FsxZE9khm67hv2EtunkhiyoZYQ8ct1m7xNTKOxojPW4py+cEML++QBlqFSQvHdTA/f409XukXj0AKhezU50Jvnd5X0Egk1lwlw8dhU5ad8PEacadoGGahMzbeIEV55ys/z/zOH2ZwB+PI1Byvl9BXSnNMrjEN1K7MwrAjUhdG3VNkQUbjeLjBfGa+/AqG02+e8+nf/39y89v/X+rdc1bmyAYBOhOy3qGnp7gWer9lvXvJ8fAxh9cfsR4GvSlri0U8AoGjSC0qsennrN48yZlbwyxxzSwF4+5ODxOl6JgTfRWE3uPn11qpVSgeMmPRtOC3AY3I4WFbhD3Rv4iGGOsNHUcnRafCVMPB15KLphYcGNFtnLO9LxhtUOu9e2fvPQI6JZQvNgR0CgQ1uUaBEFuKAOTMv4seSVIlbXQbDBuJsBnUhfnqiyzrb3Nc13iuRPFeGOsdaMlCqKLzDqkThX3EQ5jFeM2d0/GU6qARvC+g1ilHNumfIOFxImSRLDXWtZbNFMHRE/ewU0j0aiR/0M6TBIniieRM1YrPV/juOkix08RUatwTkvzvrDXuN9BogJQtuVzOyp14NBOplrClGGO8VYx8duN9+G/byIZtL3JLufsGab5dnHzHbOT8dXvr993/m5wLF8t0bJO4n2vxKFRU0zk2GqlSJnye0DKl1B0owun2SD91lovKGEf85sTBjTrPCCM5XqGLvbu7ZVoWdq0F2qXhL3Y2I9T4LJvCMD499z5d/wuPH+kCZWz8Eg0OwDbLc4v46U5GUMtWfWaXtV1YD0fR0QeWeRXAWwUKG4EpSYCyudHKBFIZrhEG2GOhqRb92WhHej/R2onWGu3UwtpawVrDe+NwPHE8njCLRfquFOa5stTKPBXmeaKpMi9LNETuQSKr0zlwSzWVQyPzJ6SEUZaHRNI1wqtWceqUC8Z6i4yvUQV8KDIalTvG8W9ih/8eeSXMaSBkdaHqFXb342j9CaxMjMv3GJdfZF2eUevuHFMuOsUiYEd09HCO9BY3P2HuIn6CvmAyxSy4VJgX0B0uM9DTD0DQ3RWye8LoTltfQ/0SRb9J7/8tUma4/H2M3ecZvqB2R5UVpqfIfEU1w+szxqe3yOnbuVBVNgGgjArpTCt9B36bc2a/h/AzC0T8CKPGfZaQeiw6d9AUhoaCI2W0lAUtDa8n3C9wYoNyCU6Tpw03PTgyjNtw9uWE+xoFHgNkMD3b4ReVftORFaQ2LlDGqdBeHDmo0Kcd04jFyGxGXKiaHhOi4YnhG2cpkQN3xlhp/YZRwp20lx11CJcf/AH0g3+SVh8zmJnnLRlcg5tij6PQ62vwJo531OURXSpWdzhB8JzGI/rNp1GkXv0Y2lYuxop+8I9z+7X/jjdvfgfMmac5OrB2h919E0E4rAcOd6843B44vlnpp8i6CqlykNhFYarCVEvcy2JpAR+IR5iVRTEP0FqgZmP07JAd0Lds1GOdCPVcqErIsE9Pc0cLVDMRV5EkDGooc/BTuPb2Rrvrob6Tibq7Rkulkl0wHsWOaBa1sfncB/0Nqm7cufgTaJEyiTJ6Q+sWXBkbvJbgWPTe0SlpsnYfiBl3Z2y8gfDNTBfvMh9f0u0Vth7YBAX9BEZBiec0PER2IQiYr4BCdZiBwQ3t1FCL8EbJMVMgIbkmSTaNqoTRYKA9UlLRlpu6JdLppNBBZyjhtKqZW1aUM9fOpz1luYblkjItlDox1Rw9ZCEC95v8ubPPRsFVKe5hythTxannk3oe4zz8b7axxmf+bfvaw+PMW0mxw3eDFB6ORs57kCfcE1996/7M/3Hen1xi7+ujU7UyTxMkFYFJI6ywThllMlGWHeXyMbcvvsnh5ZHeVqa03jgcX1MOQp3mwJhNqHXBelzHk9+i0xyf13Z0QOfIhyKJ2tt59ixYvuPzfueXvufxo12guONn+3pjGOmmGTPFM4Qv2+z14fgmTH/C+2FkRHj8WyxYBHyVpmi40DdJoh2QqYBtacMjSF0+GKXQ00/DR4PNyfFMvFV8ON2N9dTo6yDC9Yy2rqwn5VAKu2lhvwzqVBg9/A3mUlCZzzwHrdHVIGH6MyzQCpUS4k5zqsGkHryY3oAeG6PFSKD3keTbWAxVOjqSnDYr+LuoXMI8aI9/P1x9hXH5Dja/w6RLxIfH0BxBCa5G+GiEFboj7HDv0eHaEfwOpIMOTHe4PY6cnLpE2q4KReo55XMIFFvocqC1Hex/irk+xpbP36ue5ifoXHHZUZnwMvDrn8L6AV0/xg/PMZljkXRQVoxDPESzYi2CtYpqdk+S8Lgl6lXSBr2QTkgJq1u41DKAKbgF7oGoGRSpiExBzsXu4VEH2XKB7MCgxXjJg4honr455nitlItCkYGOziwLZakc2g1yc8vxstF1Ypgy3DAdTLJDxw7zlXV01IVSzkBidOsjRkFFFRXD9I569UWu3/tZpvkxQwSpj4J8LRo8Ju30MeDNR1Eg377Bv/0PkIv3keVz1LHiukSquESx4v0NTWYQw+ue8vj3svvSkeUbjZcffpX1+JpSpygwNExwD8043Bl3t856As9xTh9pcJfKqyKhkEphVMpcM1GYTX6vNPNz0vnae3CHsiDd0stVJSByYmvo5sFhSKJxGHMmErBJJxOx6L2l429srAFu3DGOiu2u8Gmhe8nOPdYriA2aNLDyFJpqjkctSdWq231zZl1hONbTaK7WGHWYnQuQs6SVRBB8GydEcSUIQxWZn1H2T9kN59hfYOOIds2NrzKk4rXQdYZaKWVGywzVKMsF04hAR+RAH6E0o60hjRVSlTRxP1+XM4oBm79GGs71zqZACiTM0TnWu7j/KmUKZCZQWcGnPdQYuZdaqUUpMQ3Pa5BOJw8Kie0IBAUmV1xS1ZN7w2dpEg8LjU1v8d0Ki8+SQz8rwviBj/sq6lycfL/Ds2jvON6c1pWpVqY6MVcFC9RDamGeFq7mS+rFFcvFRxxevcCbUb1i3uinQGCH5/h7Dmfj3k5hzne4yz0xm36dzrXU5jyb1eF3FG1bIfeDHj/SBYq50rsFnA0Ji8J9ncwDuO0eitvCn9zuAaiHrwMSztfzTWKESsiHoWNlYsWt4KyB2KwnxFfqEimyZH4ODlu4m3MvVxx9W3y2MVS8ZXPDOxxtRbxRm9DHQq2KThPTvAuobtpRl0tKnYPYKyUs3XXNjgXcOn0cKJOwnyZ6C3v36i2KnCJhOGaEgZAJMiI8z5aCPf451id/Ep+eoMsTDo8/HyqFRGm0xA2skmZmmwy7RJFhLrglUjIGoYS5w32NhZUlioB2gyLUUpC6Q8sc4yBPXw1VylIpHTpC1x+jWVjGiwt13iHTLiS8DjpOuAxEFpbpEtl/juENLEISS1F8/xVghdtvwHiFyoxpbho529/EX4JSy45Sr/AyYzKngVTcZzHXjyImHGxTym4DtKG0GCdk4cO5gDnidsIz02aMcf7d1lvwDcpljOvkCP2AewtiMxNKpbwcyOhYbTBXrMyM7picmE9w+bozdecwg104lHtYObyCGmoRnKfLNY9/7Ocp7/8BXHfpmjvFKG1Z6BmAV9zobrkJObp+AgwGUxgHlqcMVdwL0m8Zp4+gPA5Ebv0Ut4WyvMfTZz/DuLvjzc3XOd6dOK3Ofh9heq/vnL5W7o4jum11Rg+0o9TwPAkYO9EgI4pkqXGrJRphBJG2bWTK7Rpwv3k85IFsChEtGWvRRjx721pgGYKYU2PfLASStK5nSCM8PugH1ttPgmc2X6FlxyjREI3YhbHM6BKR8Lcb4d80TRFvELLaVNeMweidOs9JjM/fm1wnQyGL7IdKoI2MuX1uzHEEnePZrt2opxOn9TaKehQnZMeUgukcFvIimEiE83ER/CEL0vc4rXgbwcPCg6xrfiZpb1xAJ9aH+OyJXplnOrhlaOs2EnJqifewOWdLfOBwJ647dFqo08yUxQk5fNsCYD+7Jb5lsglUiWJNNeTHMa3wB1yn++/z7a/bhvwQQXHPqIT7138vY7LvSr49v6cN9/EHhcpnf9b9oOktf54xUnKfzsLmMNWYAkxZKKqGZ9e8BOq033Pz0Ye0myMMpbdIRw9/rdiTqsZ5V42x3DgdOSUnBnYw59NVnSpvP2OfPff/myHJkixw34iyGt1gFBs5pvHsJsxyMUsHQ7MzavJQyQMPFq7seDfzIlIlM4aH3JYgr4qPIH55ByYETZvjmDWrSj6gaYs/IgJ9niuK0w16yxmmaCohTlhXlotHXDx+h2neMZXwCJj3V5EFU/doTZLsqnhtWA9n1rIZdbnRDisnu4NxAj0wX1zivIf7DdXC+0I8lE5SK/7oD9If/Rnk6R+mL08xTlAqlfAhUJliAS+GeEinBc18jCCUxgNSGWFWg8uKW+Y64BgxMjEGqEHZIeUC01TxjIDMa3Z8sVnumPZ76jLTT2/w9QYk5qViLVU6MZOFCt6xAnrxjCpOHQe6nZj279Ov/yhW94wXf5/x/L9kPtywlvh+R8IcNccESKHWMJujXhA5NjHuQUpu9JkHY2FtTqIOUbic4n1mKrVbqHTEjuDBQfJhMEbK+wJF8dao1WndOblysVyylIX9xY8zXX7Acvmcw923ON09h9sjdXQuy2Ce9yzzwnJzy65NvL4ofHM50KskOVRSuh2b9imdR99776d4//f8sxz2j3B7jcouHOymypiXCJOcr5gwfD3GSGtR2L9Pef1Ndm2lzXtaPaEysbBj3H6dfvM1RB9h9SoJm0L3CuWax+/+DGW65uXzb/Dpyxccjo7kPLsWYZqitm3NIudqVqYSyEYRvVfeeSpxNNKBfcs08TBaPPcAQhSQFs+2p+Oz5gIsRTODRBgWyKcMC/dbsVQBapiU0c/NjiUJrkxhpy4a6jD8xDg9B1+pvAu7Z5iH1NiGYXVbY+RszR8KklT9mKNTvS+e3LHe079iI32XCEuEBw6rhowROTXZrw0zhodfVKB5BuUSnZ8gA+bW6N7p6202ORMMZawS/ijuyDKiINA5Rplzo+AsUgGn3R1iQOoeihLXs9JGpJx5UJJU1ChUJAjBbJ4+AxJJlcTBRMj7IlAjF4nE87pD6hIqKK3RPMm2hm9uMP6ZEuX+XoAYAxUNYHQEtSYLsbdHMp/dVj87+tnSs7f95Ttex70X7Wf3m82PaUNnHr5h2caWG8fDHzRS3P+eh+80MplayN8xplpZZArfLg2y7FwCFS67PfOy5/jRh7x+/m3W1plKjHl6H1gZcQ4lzDL76cRcg0TbShT2k23F3hRreomRj36Xd/fDHD/SBYpsc0kPyHVknoVtPgA4Z4vn0ZMK/oD4VOu9Z0gmtY50jbUkawnCKJE5UUvFSzy0UsIRM+beyjRNoQjIvJXtBnO5nx8GSa5lp5UzS9V4/2f83WNsgLDMOz744Ce4fu/HkWmPt4aPF5S6Q+sedEFLpY9QslBmpM74vAO5xidFMYY15ss9F4++iO6fsb9+H/Qxbo1++y2Or38Nb58GG3z3DN75OeTyPZqvYV3fpyBclhmTwhBYSqH4Hi9K95bKmtzyTJPEuwt6UD+Ek6eHIkCSsCcWJLdSL6AuuAqjnxjJDapTjUTbMRh2CAi37pj1Hab5mrbeYO3AaIeQtdYSUm8Nx2CnIhc/DjrR7X+i1g9g/iJteRyutfOOZX6HW3tNa38Z2WBQE/AgEyIgkyNaMZkiSE2W6FR9hGzUB+49NozWwSd0vohrIoWhsfqJTKHQkcbZ9VfOeEYswilzDSlow9ZQGIjumc3Z6Ruulx1y/eOsX/inuaaw/MP/kv7ib7DHuHzlyJd+nvFTP0f75t+hf/wPGevH2PG3cWvpzeJ5LTw5C6FmePH8mzz/8O9x/f7vZ9pXfK7ItKcul4jO1GkPUjnRg5IDyP4R4/GPweE5+uofRGDm/veg0zXSlfrqt6lVuT3dcPvq26heYh2GGGY7TC6Z5x/j4rJxeXXH3SHQpiDBCn1WDnexrU1TqGvc+3mkuzlIA5Bkd1BK2bp02Jh70cBvLCTOBUB4nZTzeEdEz687c0IsuEl+ti9YKLUE+T1jKTZFjROFgKQpbRFBhmDrDi8X6HKZV9zvNy7LaAcVTMLOAL9fqxCn1hIo54PufduMgq+xBaOmmeS5AfMz0rOtbZJQgOiE1guYjflyZYw7vB9jziYrVgRtQpccmwnMUvAyE2Zv+xjbaMV90FqPe3asQUA3Em1UtOwwIXxRtFIq4CVk6ThbPeBjQy8A9+DuCRRdIGW3oywMrVAXpE7BYXrAeXDurYmSS3w/ZnprD+Eevdr+8HBUs31z7BFqcjZ3247PjnXkM8jJ90rzfThyClSIqBtlI+2e3+X9/3QeXDuJQg5jIyDHhC8acsv7w3rwqHofXMxzjERLOY/CpCrztGNXJqQ2+PAFa2tUncAGo3X2F1OcS9FA/3uPnB8bLPm5qsp5H83EsQeeM/kp/DvHZ9/v+JEuUIIkOjNGdKGxYMXCfy5O0qjI09ME/Owa23uPhyu9SR7eUJImCqMPVk5MLiAR1V3neChthHRzc530EgFu91LlnHOK0vPnbgvj9qwUKbH5Q3oWbEsONBthAnfxPq3smHYr6o8o5qhaKnIIQqhEp4Xskd2CTl+mTJfodInphM6XlP3TMFCrM1pj9iv9D1Jv/klYX1GqwnQJ8xVLmdiJ0tqK1RZy1LoLd0MfyVvQsJh2Tf19KpQsDJdGztTDph/w+IziHUbPHc6CKCuRQmraQ0Iojo1Gy2dTkUyObpH0mXbcopU675PgNzJbZpcQbUGqgDzF9ffRdY/MV3HBUgmgy8zy5Gdpz38FbR9iMqMUZH6XwYSOV6lWCCVQEQFd8OzqfEQBHGBbGKJtSdZOhXKJ6A7RGSiRi0RsECaCF6FQcC2MMz/EI//Jwn+k0Hhn2vGeNC7HCb/9iHb339GuPoT6iIu732F3LZSVGBE8fcbp+ius02+x+onD6Q1CYxNVlRpdbc/NwxFWd+zmI15//f/N4yfvwPJBdKXTLkwIVTHCWj8M4ArohJYd8uTHcVHqfIH/1i9Tbl/Rn/3vYXpCffENbHEunv4Bbl9+nTdv/i6zXND8Cp+ucH1MqQuXF+/Qnh5Zx4eM04nTCn0M1jWyr1RJWWoqKkj4Gc8NoWDpA6NFCCFLiy46IWdBUlUQG1lIj+N7zvlbfWTUBWgRaq1ZgAaqUafgZ9mAInOmPacTtcf5NQippxrTXPPZjt+Lh6STUkGFbp2plLOhY0nFEW6467mRIUfSmzWC9pFcKLZPFHtwjle2sDY5F0EPNsrEMMwk3I/LTJ/2SL9i3j+BfuB4+xrvd4FaeCAoSLxn1YJLoehCKQtSI8jR5x3L7sToN/R2zFFS3DeyIZBEEynThKiiPkKxMzoiIzlEBRsNekfrnKr8kUhuGMgNgcEUSKaUALj1O5ESP3/at9GQbYH2PHe6rfuyIe8PLCq2w+IniYcCyOTtIuNs8Clv31MPk8c3g0bLbANJO4mt2LQUdASS8l3GQ/FD8rORDU6Oe7bXb/vMtgnZCHd0d+5WwsCuVKTO4YauE1YmKHCpBew3ePHtjxmjMVoktIfxRcnCK5BeROlro+gRzCko3R3ZQwmXpZAsq54HUtF88QMfP3SB8tf/+l/n3/v3/j1+9Vd/lW9961v8F//Ff8Gf+TN/5vzv/+K/+C/yl/7SX3rre/7Un/pT/PIv//L57y9evOBf+9f+Nf7yX/7LqCp/7s/9Of6D/+A/4Orq6od6L607dfKzhG/jDfQM5ouNw6PjGkGUjfGHng3dtsCuew7KhvsJRTWked2AjsspWN9aIi4mZc6+wcO6jXEeVsxGrQX3yILow2mt3Y+hcgG5hwid1gd9dHp3DnfPuRwNr4/xqmCZsitv4oEeAUmPfbo1ch2Q53wN5ZIhmflZYs6KNXQM1EmtilD2V0yXj0FnmjsTI5UPSq0KdWaaSvzc3hk9z60Q4xkALGTaIkAm7vp9Abg9OAEfTcFhUc5kRqSwtobQYjbtnG3nLaXHGvG/oRIqQSbUMlHrgk9zFk+SCx/3kPd8FWReF1w0pcYlu5aJevUB/vSfpn3z/47Wmct3/ina1f8OY8Le/A5+/Bo2BsUFJUiy4kTB5OGEY9svrGEfrWWXSc3XMe5JjwmRGuTqEpsDUjAUNwEKbs7op9zpYh7/uF7z+eldlvFNxtWX8S/9X4KQ+fHfYfqd/4Z6+Cby+c9hp4/Qz7/H2D+lf/T38Od/l0P/hJO/CL8HsZzYRKqsNQviKINuhf28R0/Pabdfp1xdoeVZjj7u79FSSpiDpWrJtSAXT4FBI9xhOd3Qe2W++5R5PXLz9X/I8jM/wdMP/gnevPkdWvuUYiunHh16nZ8AM9P0iEePBrcvn/PmtkXExIaQbFEBPe7aWmOUiJfzJkQik9s41SxJmpqjnBKbg26ju2G01mN0Aunaec/pUCnn0c8YPYpiTTqVj7M3x9b63qMTeatj+bsiW6v3W2RdoqiTSpk287YWXkAP1oFt7bAxCC8dUqUYq/uwQfV6Xmc8W1PVGDEFh+MelfNEKOIb4nlFStAbtFDqHltWCk+Y7UDrK+vphI5YB72FTFsl0BS0hJU/M5QJdadUY9431nVlbSe8dyoFrDHcKHWPzEu8Jx9npQ3UGItKcjnKhBaPNOgRTY/GBw3flBIBlFFolnjet03Q74uKrRncNvUtQWbzKTn/O7yFasuGZn62NngwyskT/9Za//0QFM8CQnLcpXm/lnzvmhVMEK6zUX34ux6817ffU7jafodJWhZJkMTfvLe6x30yquHLwqQFnQRUqaSGQCNG5s0nz8NxeHTWuxumZYm10wdtDFycUgujRTp1O56oRcKFGMnnVu4LwXgokM+e2O9z/NAFyu3tLX/4D/9h/uV/+V/mz/7ZP/tdX/OLv/iL/Kf/6X96/vuyLG/9+z//z//zfOtb3+Kv/tW/SmuNf+lf+pf4V//Vf5X//D//z3+o97IsM3VewmoZxWi4x/zPxoacxELveM4pS4YJxsXv6Ydxf/H1XGTEokAaBikmJ9roTD0qSiTyb0TqWZK4FRxR/EjOC6NI0QJ+snOBsskJ47dy/nsgP8bhrvH8o2+wPP0Gy7NLxPQsH3WEUq+w+YLeQ6o8lZy/iuAUzqoQM2wQagyd8TFnEs0AdUwq3Sao+TCXIOdpkVBX2KB7kDFLDeLkGC2Y3jbAY16tGkQ+1QkzDzfOjYtDeEmITDmJdUZKEWMMdQw3VFvPoxE8YwMs4UaS12F6HuOoTFHglErRLBZMsnYa8YCVXf7eGB0JgawoDrbCUqjv/5MUfclER579ArZcUtuJdnFJv30Gb76NtNfYtEeWxwiF0l6i4xRFkyumlTLtkPkJTI+R6QrVJblO0YkKIW305CWdbc9ZY6FxD/5JOjsqC++NPbq+ZjRjmmf6t3+FC56yfP6PwPotKt+gnAZ9fkJ5/2fo11/i9PHXuHv1VQ7jJS1HEGb3s+5YLyWdWMPf5/0v/gyP3/kx1rtPmI6fMj36QtjTi4Y6iSk2frVUzTSGt3CfnC8Z1wbln8CPR6bf/P/Q/uf/Gxy+Tb1z2m/9A+of/T/z5MmP8+rlb9O6o+MOKdeMdkc7NUT3XF/vqXLJcbzEj7cUj6Kh9c6WXq5bUYuC3i/kZtmZeqjSPDkqUUwZxcPaW0uNccrweG4fjENE770dQo4cPit4FDvWozcNaf82m69JFowCYlNUOM66NuZM0u3jlmp71Fecy7gXxsALFK2YQG9rZnqFEk+rBM8rbffNjPIZRERzW91GD3LmsuTh2+a2fd/9P4XbbEd1RuYr1DuMlX3ruH0SfjlSQlE3GnY6BAm3zpkHU5AyIezQxSl0diNCLw9vXoWlwjgFulz2lDohxfC+Jt/rnieYDBTIZ0Q8VXQM1AIZ6iJ4WaDI2ahStZ6t7ItuvXpuzKSk2/0tpSbZyduGrvDAcdY2rsdnx0Fvj2q+5/GgOIiRkdO3mdODoFlRDb+dEgW/ujMSBVrb+IF/f+Ywx+fYiqGtSGJTG3Lel5o73RoDYT9lkKJoCBVQFglSOVTat34nXGVPEX+hcp1rvAYC6wEM9FMDPUIRpqlgHvd0etVFcfbdEKHf5fihC5Rf+qVf4pd+6Ze+72uWZeGDDz74rv/2a7/2a/zyL/8yf/Nv/k3+yB/5IwD8R//Rf8Sf/tN/mn//3//3+cIXvvADv5f9/pI6F1qrDGl0D3RiiyU/Z5QQN4Ph9IyUj7FoyunYqu6UHaafSvBR7l9Hus+OtobzoUSXH7Vn3HzbPDOcIw1lnHkoItnduNPbIIhM+V79Xl4XRU1n9MKnz5/Db/0PPDmeePLoHcr1NVbmeJjqBT49ptunqIclPumpgs6oz+e5cSiIBowTIiMKGDa6WsyQi0XcthKcGM3uxCiR0toOVJupdUkuQBBrwxqegH/LNnPc0KtQEGwSTk+78JpR9SMLEFsjxdkc3IxS0oR+hGnasHuXOpeNZFcw7WyxRJt53TSFj8XwkQV8Qq4E+TQ4BKSXn4I07Oo9ZPnTeHe6reiIDJkiPcZP8w6fJmy+guUakQXxR/hYo9hM6XepO6xeYeUyxkgb3GvAcJw1z0/OA3xENpIQkHvmPvURBl3PxjPG7UtMP2W/N+bTR1we/j79Duqb30t/9Ixjew/dP8X27zK44the4RePua0zt3d3VK10DcWTWZiBmUWX5CpcTM6yh93jDyhXnw9n1tNLOL1iiwOQuuR9PMd9o3o2AMMNc8GGx0a3OL0ISqOtjptS2g315W8yDkcm9rRxwI5HnAM6KzCYlwuG7Xj05BEnX3j5/GNOhzeIWEq3I58FiWsvUvIZDxQvxrQ8MKSzRK4CRo/7o2AijBaFj+YmEble7Qy1x2rgWWjE5+zJbwlrfGGe5/RTSuRQDZHOMKMUZVnmCDPthswlN4oT9Fu8XmMa1gAiTsXPXiBvOYn6xn2RswnYGOEzAnZv3w6b918iQuOMyJyLAL/fHDc1ivhg9CPUQtEZqVfo1Jn2K0s7crj9lN5XqiwIgVDSJsZ6jBGfztSp5noxM7GL+IZ+Yj0dGO02ybKdsd5FmKDPofSj5rXkHIg3tnGqwUax7D0aHcWwMmHEGLZIED6jPMtk441Usn3O/A8zME0kRrYx2EZqvZciq2og0w+QlR90T/0sghLKniBWi+WbyMgMtERml2SRIom+Wxhc6tisMX6QXxyfcUMR79/3d461Njm7mNDXE0cf2GQsZQqDUZzKIy5T0fl4NN588m2GHfA209sp3GhLQ8qUES16XresrZxunLIsmENdZmSSTED2s+PzD3r8I+Gg/LW/9td47733ePr0KX/iT/wJ/p1/59/hnXfeAeBXfuVXePLkybk4AfiTf/JPoqr8jb/xN/jn/rl/7jt+3ul04nQ6nf/++vVrAKZpYpkrtUBr0V0PM7QXTHrYkmssNNtceStGLJEWySJjs532RDY2KE4yKdjOfstG0YENx9WwNnDrSM9ZYkn1gCjWexLokpMywi/BczywthZfTxngubtJ2ao4HO+OvPjWVzm+ecF48h7v/uQ/znRxHaOV00SzCet3YLdshDstIdVsfoIyp3fKlEqmhBpl6yzA1cIKvTeKz8h8cY7/jrcekr4imwnextlJWHuTChKKieHhyivZpW/FyZT2ye7CVCeszKx6DDktQUTVLGDMAr6c6g5RiZk2nm6/Qnej9VO6iE6JznigE2w6fKUQD8ZZ2WMDxBALK3OplZmJITt82jP6kXp6A+0NotCG0ucZuKSMVH6IIPUaL+/lKJBAfHLzVKKrc4Qz8UMFkxbS0wBAEzUb4B0svHbw2Iz2u/fYTY+5vDswrx+x7I8sS2euil7HwjW//E2mNweW+i7j87/I4e636Uj43dwdg8Dpne5TIAEldNOtBzpWY5bB9dUz9s++zHL9RfTi8Vkt0g/PKb5GQSuXZ/jdSXKpJbKQXK9aKr4Gd2JcPEWv9/irCRlGe/MJ69/+K5i/Ri4EsUJnxQ7PoZ8QIsvItFJ14dHlFdZOvOinION5nmOS45HFVk+NvqB0C5R05Ka82ddH55Yds0cmVsTPR7Owrut5ZKkOo/dEWu5n5jHuCXDecrRbq5xRKIiNMgHb6NYtnm3XgcqOUiZaa5R6S7UTQizw6WWfJNGtoC+JygRaE2R+PW88gYJlETMMlx7IRi4iYvLW5nTPr3jQgbsEsbjdBRpZ92jdI7OBH5naHb2v2Ok2iLMpgPWu+FowLYxakaKBUuoEdUeZB9NuZblYOa6N9XSLuTOXBv2EaTx/7lFwWpKLree6GrOa8+cbIxDFgocdwhSGbeF0myOSnCZ8t8PzxyXNh4cvC8+jQDezlM1ruln5PSgAH4x43hrffPZrcG44IQNszSON3P3+stTgR22FkdkD1GWDPc4/8fscD4sQuSf53r+nLHi3l2zX3zqtR0EkU0jHyzQHufXS2ft7UIQyFW4/ec407TL/ywLp04q4nH268BGIGSHP9/SvIZtgiCnrd4yjvs/xv3qB8ou/+Iv82T/7Z/mJn/gJvvrVr/Jv/Vv/Fr/0S7/Er/zKr1BK4cMPP+S99957+03UyrNnz/jwww+/68/8d//df5e/+Bf/4nd8vWihiGDqTDU03mM0rK90CxJi5O6M83xvDEspco5hajkbkahsF1HOEr5AYxqFSplmxjCGNu79E6IIKjleWZY9LTdR8Rw8jTC3Gvlniz13i2HN1s2xJSYnEQkaswyqNcbNN5F6wG6+QhtHbBp4EVaWgF6HUmhUwksgxh4LMAUqsYGn6lHc+DaTTjjYeph8mQfnI1LCzrbGbo4MT5Z2FGokUVRRXGdEpjhf1oMUaBaujpnQua7hVyOS0fACWiMFWpBg9qucx0dFlGlaqGXGrCcZulElZIiBmhYmMdRrcA9Ssrk9GECiJyNRl0DJcMVYMWlUpnAdtR0yx+Jn8yWtd4Z+SLXX2OUVyB4xDXRqeoKWC1DlXhdyP2cNrsy2+AqmafTlho1ElyzyRkY74t4wj4Tieb7kvWf/GKU/Yr/+V+weH6iPvoLuHjGmivmEy8K4/hRefYK358hv/l+Z589xd/nT3L34DW5e/DanwzfxCiXwHQZGs+ApVIGpCLv9Ne//5M9T3v0D0T3Pjk01gh3Tq4U+4zoFgVgbUsKC/Bx46B2kMZYFL08pXZiu38cqlKlxEOH45pbDt+5gb/THwtiB+sxor8IwUXZ4VXopDD1SvHB1ecmpHZAi+EHoLThj9+Gg3EtTiRFWuDUXSgkSrSR/Ywyj9xO1lujePf5tc5XOejZQLO4bGUh+mmioylzODUTrK5uVUhBHPRDCRDoOh1Pea7DMzrTbcbq7YfQjsh7RukPrnIV3QDWl1vM4RkXC6r5WROtbXT0eRVI5d8p+3tjOKEm+/3tjuYeHh5miDawdMJ0YqczRaYfYJWV/zTxWrN3SxiFHII43Z0igb1I1C/MoLKXsKLMzjxbZY70z+oH1eIzmYKzYalgd0HNU0AeUmuuipHW/Q29hZ0AKHzxcujdvovC9CS5aLSV5Q4Hgbqql8/nK8+GS4/Q8B+fz9GAEtpFPzhTaB+fuXlW10ZI5F348GAtpIjm2/ew+kG3dz5st0P30qvEI+Pys5cVbV+x7fT2R8HsRxv3ro7iWM7oSJn3xEeP8xJTgaCcEZZ5K5CdxEedNYao7dtPXObx8TRuRAF3nKZxqCaDAeqMdVwYrvtEAPNbjySXQMNFEir+bNPq7H/+rFyh//s//+fP//oN/8A/yh/7QH+Knfuqn+Gt/7a/xC7/wC/+Lfua/+W/+m/zr//q/fv7769ev+fKXv0zcPJVJjWYNRZinHe10jJyajW9CQIiCgvasWMFM00/NKUVovT/Y2DaSmcZYqBi0NSrdAa2FHNJFqIkMlFJoa8sEX6OK0pJB3dcg/bUeUjwPyRG99eAIePw9LIUj0bTWjk+GD2EqisyVfvqUun7MdP2E8WQf83h9RPM7ZByx9hKlw7wnOBvRUSLjvttLJcT2IGNxc5usNO2xlZvmxh6mbKGQaIgUlvmCZXlEKTPdTljfSLCxgJQqIJW+3jCGUMpFytyOCEKZJjrheBsPVgERRm+BRlg4mwJxTjidkQt1w+lneakyoZ6uConoeDM2SpynGy3uZ7v3MRQ4oa6ox+hPxDFZczwWM9m5DEb9MqNe42NFy8KuXGc+SPCetrbEkiQZ60AUYKaJKw0IsxuLKPT2Js7N+gbaXXRWveHrHXKCZSlMNrh48VeZ9Xco119ELz6P7z6g+C1KxXdfpF8Z8zsr/vo3aa+/xXR7x/zh3+bueMsnN69icxQJTw4nr53mswJlWlgu34Gr95D5EcwXyG5H0RkvQq8X1N2UJMrI+jE7YSO4XPHhYvHb7R7T26ATAY/SG379JZb3X3JVO/rxyhiV296Q55VpP1iXKP6FBuUW6zfABUxXjOkRUy08eXwdplEUDrrJjSulVA6HA81JgvG2UIdvUahiDEXPaJwLIQ8fG5oa6OamUpbkrKhqFJDbhi8xbhQLFEqLMobkBhAoaCCKkpbpW74MseH2Rltv0BJ8LBsC9obWrlCpzFXyPs9nfZrOBdPZGgE7j6wkGyk3hzk8iDTRhMgZzDH2tI2J7IxkBqE/NgskkE47HTmpMuuMTPvglMzPqAZuCq1jt9/G7A7rilTF2yFGZemXIjrBHP4wOi9MfhnKyetGt0ex5nljjBnxQV+P0RC603VHkYXwS1J0t4AUSrUo0MywfqKZIbVkBRYOvGjk6YRkVthccrfC4m25sCeHabtfOKPWDws7sej3NcW+g7wHNnR9G9ty72+yFSd+LhJyXG+BnHtv5/G7i2JDkFGRMVhxRok1argF92kE0gz3aMh3O+5VO/dck/wUgCQKB8J94ZNlSTSaLogNVg86XmdhNxXqJIjsWaSwlDkKYfkd/JM3rIlGrqdb5kkx02hc1yOUwroORBqsx0jNvmpMXCAaCdafHTt9v+Mfucz4J3/yJ3n33Xf59V//dX7hF36BDz74gI8++uit1/TeefHixffkrSzL8h1EW4hqtpQS1ttKWuw68zzhUzz0I2e2vfVwUh1b1Wz3cKl7GIrFvwD3VfdZOubhWKta6L2fF7LNUOn8uvRdGDboCdkG56TR1s7aO30Yw42WY56tMHIbCNEVTXPhcn/BXAY+TtEx9SP9+HV0d4HXD4Ar3CSIimKRy9cPUBpFnjJ8YZingZonM3yrqO87DHXub3CzIKFKGDm5n9hQQvM45/F5JbxgAJGNEBvnK2TWgQgNM1rbAL4oItRLGMNZZ3g4gup5AZD7xT7/zyxQL6hYjnnUR3Rz3hhGvF8CLnc3+tj6iiVGQJutJLny+zhDqtFOn/ul+M/0whBRpvkpjEOqhmrSa/z8oMX33U+zczl4614dNgIBGivWjlh/A+OIj4b5Sut3LO0l77z6lDoJF/47YB8jux0yfxF2X0TLgvQDJidqf476Dvc72F0i0++hXhy48P8fl68+ZbLCSYUJxyrgIcmcykS1I0WNOW3E7dTwdqQu12hZ6FKgluikp0u8TBm9ng/F6ZO8f6DM1yzLOzTrdBpSwm5fl2vkc3+M9fIn6e+e0Cd/j+m3vkp5CYc7YRwyqjOJ5GO0CLssjreBU4GFqRQu9heMAcNPyUkKiHkzwzqrZ1TPDcWG3JvbvX29ewa/+Xk32kYfb1kM5N+VKMJaX8+d89mNkyhW7lGkjbtWzhuf5/2lRXJ8MSKSYjRGu0XLLTJNjJ4EVxGkjpQ2h3keORowl1CvmBOZP3JGgTXDAm30kOFqzTlYdv9nBOWBZDk/r1mnrUekVIYe0j4gXYTnC4pd4RfXVHvD8e51uB93x/UC6Styug2XWY3wP58kSLXTwrS/xL3RW2O9axxvbjG/BXcaBfXBGB1dDJ+3UYoivqHhwRvzseJjBZxhDTGn6v0ivXE17scZ95v5PZ8nXp6TsZAkf5fX50PMAyrS+d655/S8/S1+/iPn8cl2noePFBKE1YUQ1gKGwxh4c2zoWS1mFsnTI60yzmvMD0qEefAh7pEjf/DVz7xv2zxzIgEleDkLOhVqNsW6mfRZw1qjvbhh2Er1C8YR5MKoHnvX6fYW0ocoxqDxDNcy46WDhl/YD3r8Iy9Qvv71r/P8+XM+//nPA/DH//gf5+XLl/zqr/4qP/uzPwvAf/1f/9eYGX/sj/2xH/Knb/4IwlyWuPH9kMz8kkhdLJjr6cSBQ0JpYaplW5WbcOm2+arKeXPaPE0sL+T29bjh48Lqg0Wt9TVn0rBtyDaM0Tu9d9bWWYfRzemj5whK6L2hIuyWQi3CMte44McTF7PS28rt7Wsury6Rq2fY/DRknv1AtwOCIyNuEhNFZIfJcmb2nx9gJ6HorLq37jAfPgNUItsjUp8T3SGJhxaQYMiA54SP47O+9SBIcGFGH/QeCIikac+Z4+Mbdz9GZT6MgacyKhZdJ9JRt7h20YLbKa7HCMMul45omD+pGSYxYnODfnJKdUqdA9l42IkkrLtB5PEp7q9dUFliY9Ky5PKu4NENbkqwULiEVDjOq52TdD3vt2En+rjD2x1+fIOtL6C9wdoR7JiSdeNz/Ra5WhkoshPUGtJfUcYHiDXK+DrYLVqe0lkp5QKfnlKufxp/VNCl8Z79Lca37/jaSVitMPeVOntY07uwTDvKFBvhNC9I+5Rxmqn7BbFdjkAkQsY8FRc+KLs9ohegE+34imm5CBv5fmQQe6JQKbsLjA9w+eP4U2EdA3/6jzMtf5X93/9/05pxNKi9Z+qsBoQvRi2DNg54u0EYlGnPbolCe7hzamE41daVYYFaVhW6BwG+b1Ya2zXM53wbwQYMHuOUrRDY5MVbIQOcjds2OHpT221RDvM8h0fQGa4OFDH84QrmkXMkcC6+e+voNEen3I4wHbOzredxgorQ1pWqJUwcz2uPnjt/snNXz5JD4n+PzfvkfItLjl+2ImWzwA+U+GxC11a0rAy9YagglMz52iPzY3S3sowTY12xtSFTwbuBd0zusLViRekqFClI3eM6U2anjM58OdidOqfTSrt9GVemzJFZlkaHouX8fqVrmtKtYEf6esfoKxRFyz4fVXlwPnKN494X5rsf91wMSXnJVrQ8fM33Ykh8bxVNHCE257yeDLcoUIhstmEDUaVIzSLHsBGF6FnOblsKfDY+Oc47e51wP8L5rMT5s+vvw/8dvi58z9fEOjhobYshmcMivxZUoOJU7+zWA+32jrvTHd0VOzll/ySenbS2AsfWNZr+uyNjmvCpxR9VvP0jHPHc3Nzw67/+6+e/f+1rX+Nv/+2/zbNnz3j27Bl/8S/+Rf7cn/tzfPDBB3z1q1/l3/g3/g1++qd/mj/1p/4UAD/zMz/DL/7iL/Kv/Cv/Cv/Jf/Kf0FrjL/yFv8Cf//N//odS8AD46BF34hFm5JPiy44+EuqTiNZSGwyH2kcwwrfNcRsJbAS6YN9x9jTIfI2tO4oj5rBnOJjNiMdz/BOqApcNTUjJ87Ak5w1OrdOSEBaz8aiaDedgzjQp3lfm4lQd+DQxTZWqhT6EmSd0vaD1G4pHSKFZZ/Rbqs54uaLpnIuNnrstyE3E/a3q+v5GT7KaRI6pu4MF6TRAdjkjFKOfCG8YzZl/5b6Ai/PoUs4Ok26dqtMZTUIytVRBevhFhPGdIRha5uTrrBSdKGXHWTToFuoZXyliEWimFkZpGsQ73dCSEciaUsPmPa9xLtHxmrQwV/xckG62lrG2b10sFBeKBGmv55Z8NmUTZePGnLut0Rn9hLc7WN/A8RV+egVp1W/rLbRbar+jsOBPJ+zpjml6ymjfhvWEfvp3qYdfQ2qoC8qkFDHY7anzz8L8mLVGsvXlO3+U3ZM/xP7FV3n8rb/H1z76Np8ejGUWJu2IGCWpw6GMALEDOm6Q00u8LNT9I8QkvWdCBrm2O0Y/INIo8zVzvWSqC3enNUjiWikaMQ9rdfpuocizM8fG/MfgK/8s0+2J5fCrnG5i3DAX6O5hdS7C6J2JirPiTJh3prLj8uICLZXDqXE4HNMrYjBNU4wKe6AwG2bvlllKSaKMQnorSOMeuPc92brXez8JswdF9EOljIR5WxQvUTxvjU3c24ZIxCwEcTOUceFv0ZkzcNJdIwyyD7TOaL0vGs68hGHMu10o/cxiFFiiORpjUPNZ9mGZy3MvqT2HIEN+9vsuPdaEkkaLEakwpKEqdA2y95YczHRB2T3Bx4HLduI4PqH1u5D3C+FVst4FT+1s3jeFMWGZ0eWC6oN9b7T1lnZ6RT/dhXVBa2Arvh6oMlNqjBPNjoxCWPrTsH6KtHamIFmqvlVEPERQNo+TrfGQB0WduWcGUnzTw7HOdpxTl7/PHhptyn3mDg/W0vzNERngne6DlgTsoSlOECfCDO8LC7NNvPF28XOPovj5d51vxwevdU+FjPNgHT6/8FykvNWC3XfS5/t3w4NhYCzs5jks66eZenHN8vQD1pefcvutb7H6DW63rDeCTwveG6oa+VVlwjHUPe6v04GWqErYgvxgxw9doPytv/W3+Pmf//nz3zduyL/wL/wL/Mf/8X/M3/k7f4e/9Jf+Ei9fvuQLX/gC/8w/88/wb//b//ZbI5r/7D/7z/gLf+Ev8Au/8Atno7b/8D/8D3/Yt4L1O6yHY10gHwt1qiDhmaAapDZXpbpT55nS1gik0i2pdiMQJTEWYWxa9e1Cwxkx+SwkGPfJAJMHicWeJK0Yd5iHBXdrwUHpfTC6x9hpZHFiFjPZlDtaEWSnlHMeRMyNb25vqFdvmMYJbIr5eu+IvcHX52EZPy209HMppIW+c56P3lfiG4KSHzBnqbgnORjOEfBEenTJx3ODTDVXQtXta5uHxMC94IyQGY4Rbrr5u0LWR8jqREIO7TGFCQ5aTf+agUjNYm/FxhEZd/g4RAeH4jpwSeMnjVGOaRjNeY3wxeFOmeb4uTmu2Bj722P6Fsyvmk70uaBrpA27rXGvSaVIz8LWIpwQQPysoIgOqmPjAOsbZH2NnT6F9hLpd4x+A+Mmii0MK3G+FrtgLO8ivELWkaFzI8l9gpQgc2o94OuvoWoIl8zz+wxp2OV7lMsv8rl3fj8XH/0PfON3/nu+/eIlY6zs9pkO64WpTLHBrZ3SoLQ7aK+xKSB770d8vmDeP4KyRJHVjgyEqrswa9LsXE3CxMyhTPvwJGpR0FU7YHbktL/m9Owr+MX/RD29YahQNEaPzQaWQX3ika3UxykKP52pZWK/7ABlDGPuA6MFulWjoz+P5Uglm/ckuGYC8rYIi4Si6cF1f9iNmlnk+hDjxlo9c03sXMi0FpLkUvS88AesvfFROuYtR5T+4LnLIkQc7wesrch0kZturitb7IbFCGTjW5kbW6B9eJ2MxAPukR/JtexMmE3UKBQu989yriis4xSp663RiyB6DHWMFmTaB7dl3iPtimm/YuPA6c1H6Li5VyptRO8yoSVCBWWCWibKtI+15aKxf3TgdHjNq7vXtPUYp2OssS7Jc2R3idQpEN6W9gupzhENQ8NhEUJoZ/Q1rtlwp7jkErYVKd95bF8eSLjffpfXSDZCSTV5+/vf+kJURr6N+PJ9DLf7Nd+NRuSDeQnn8HPaL/eoh9l3vpPz2P1cAD3YebZ/OMMp974v/mAaIA++F95GXTaOC9taLBuvKkbk0uM50lpi7fRB2V2xfO59lrs72psbzAbr4Q3eGqowpNITFNepUmoU4u14iNy4IjGK/AGPH7pA+bmf+7nvgLgeHn/lr/yV3/VnPHv27Ic2Zftuh9khSKUakNkY2ygDtttseM6mU+paVPESqpRGdvFZfGwoydnpztJkLeFhS6tjw5N36Wfvgrihy4PfT4wsrNN7LGhRoGxOgRKSuXOWAplmGzfLQGhtoNbxixkHenf66ZbdxbeR4xuQXeTgrLdM6yfQn2PLJaaP6XpBLfXM4/TyNhy4HfeVcxxCEHRxO2vWzezs3Br1iESGR453Ai0aDwqU7amQmDcmwuS9ZSG5BakFWmIAGkiMWTi8miw4sP00t1ATWH+D2AGxFRiY7IIETAMfFBnhxVJ3DJmjwLIYf5V07NyC/FRrQv9RUNiwc4esWlIfIiA9NmAmXBpOA+8P313+v58/93m8s+X1bKOd06dYewX9yGi3YLfgnYGzDudYP8d0fInvVkr7/5P3J7+25Vl+H/ZZv2bvfc6553bvxYs2MyO7ymIVSywWJVkqCDBsEaYAwyNraJswYAjgmH+BRwUOPeHEA88MjmgIFgxKBkSDJdFmV5RVPauyMjIiMiJee9tzzt771ywP1m+feyMysqo4cBPwTrx89924zWl++/db67u+TUIkGTAjkTIWYheoJFxwrTh8Q9U3eL+jTD9G8p5Y3qKuvk/ZfouT9Skfnn/Ixad/yKef/WvGsqfv1oYyiUPrhHAH5Yw8XeNXKxCHi2ti3FjhWBNOIfrANM1InpndDeIj1a0NBROltHulZPObQQopJ6pzqDfn05k1VY2Et4pCDu21r9b9C2oHVM02ivUV0YJW8z0JPrBaDeQKKdej4sNiEB7m7rakHpCNWq1rPXpVfIV3sniMuMWVtJlRLQ3K4pVyPDSW+6UhqUsH71oeTgjBHK1rJZdyLIhSyUQBrSOW9p1wushaHVVs35Fm6X4shMKSD2T3sxNTIIUKPhpCosjxnn04etv9/qhYEVFT84khobUkK8Czp6TZfre3eyCGDqQn9JdoFUKZ6POe+TChMreicG6OxIKTYIGhzuMkIC7iwxrpM2U9sT47Je2uuTq8hqxGlo0R9XdMJFywJtOKA2lcNeM7qVv24OVV+DKCoO3eWwqzLzWSX0FVnGtREwua2vaAx6/d1x1zi/nZss8XWlq0Ln+3DDipZJRMJYtayjeuFSeu1RbLY/5y4VNbYbH8nqOgQZfPPSi2lue+EHUXJ+NlLS/P+/FI70vPZUF9HqMx1ZD0nIRJZoIbTLHjI90wMJ8+5eSdA4f0CW/e3OI629erOAoJ9R2hjw0NrHQhkvNMGsdGq/j/oorn/6NXydQiBN9Z19LIq9ocHrPSNlhtG0U2G1+pzLVQMC8U/2ghlzb/czioBR/ccf4MrXhRDJmAxrPIWDZLGxE0ICJXc8AsuTI3/knKVrjUrORajcRa6/GAd63jmqnoLBQPenugk8TZ6ZquG9jvrhl2r1C3hvwaf/gZZboB743gKK9MSt1f2A3Qgg2dFCsIqiXxWjpog71ZNi/rApFm5642frGbw7o4MzsrFLGEBinO/F0wOSaYmZ1qptYEZHOIXWDJAqKZwqFZ3VuFXlPCi6dS0GqBjCrmyaA1U9M15Ftcs85XKpQ9wfcmpdU9eCHLFk2KDUUzxWcIxZAxHa3DdFbdazzBiSXTNoNwIx8aUETOM2AhYYiD0DJMihEMlw7KXsdGXmwFi8sFSqbUBDmhaUJyglIbOmCeJ1oKQSMaHJ/lAz+qBbl6Tb78q4T6u4iMxFjJzmTJcQe6KuggOBVk95qa/u9IvSXkEen/CimuKN2vQPw2/p13ONv+iO78bT7743/CfXpD6NbmoFx3yOFT0ALdD5DVO4ThLXx/hsQTJAacBqomUlGYr6iHW9zqgtKfm+yzqYRcNdl3FXNtzczkan4aJS/oQEVawGbtKzps8Bpw04FQElUdYy5mulUzerjB1QrdKeqjGaB5g52zKtOYOdyPCB5Tsy+EcOsQvXPmxgpN6WKoRy62Nh9vzL65tZZqCo5Sso3/0ObjUI342rgpqhbg573dF0WtCPLeITU0k8a5jRsbcJ6TuZ1KxZUR5mt02IA/aaPEYgGcixdLEwKgWPNSK7hGUG5ug6Wl/9poyToG5y2SwbmAd85MwdyRIYBIBonoPBoa6QdqmSgJIOBlNIdpgejXSFij/YyrFwx1oqafUfKdrfnOQR6xvCpPlgXF8YiszT222xBXifV2R77Ys7vfsb+6std7Trg+WsOiytLz2ShZCM72FYfttaRsKHQrCFBDYStKaIf4l+gVwDK2PwZLVm0cs2Wk8aiAY1E6LYT/hizQPLGXIgQz7yvVCtFKS7te9nSpIGqp21a3HhEOMz2sx5NHafCx2mNaxj0Lkv+4GKOlNC+gyxEVqxbjsuxIRY1qEJbH/9Xx0fGfX36xlExVb6KNOTGKIMPAKnZoVbrhFHfxNkwTm5QhFWQ1MM07dEz4kwuKQl+wtSx2H5IOdvbOvxjg+Or1jS5QQvD0/QpxkdRcSxWhtGrWuvvGA5mLRaa7QKWQKUfHySPs1whKJqdsuvuGRi0pjWAcEVg8VuoxAVmcUnJ56OpqNcVONZVQqS3yvCzjn3r8PUCD1kxm7CU0glW7nGNOGWQmzQfKfI/P95T9FfPtC3QeCSFQ5gm/GvE1mwFWfwbxBHGDQegqNg5xYqMIsM0Eu7FspGKfVmwcK6JIbb4SC2pSMt6czsxwS9NR5mscH0MZtBacFkM8WkhgbhbXUW2u7kK07V89NbYZT75uiZvRTNbmOzRbgJkhMgJSEJ3JZaD6HpFqXgPzaA/cmdqn5GLS41BNfq5mLZ9ToaQJ7wfEiymKQgChmfw5jqTK1tEcPTHCgJbCkhC7jP+02gGJYlHmfc+wWiObC/LuCbvXjvz6FjeKebf4QOkjUqA/JHbzwO3qKaeH5yC/gq7+A4r7HUZfWMWRnDP51tFNlbIyt1zKC3R8SYiGAmn9XWJ3gfoTSvcBPqwJJ+/gvv0f8+H6CR//8X/O4e4GkYx0INME5RrdXtCLxQwkKi4npMzUMOO6njJnVFe4mFE8WhR1YlyBljckCNH3sPbMecTnV0i5o5JJpSOUSi4LwuQJ8RJ1PVF21PEOzQXnzJlYmpy8JEB868o93lu69vZEEXdAgXF/aNYBVix1PhzvzSXvZJmxL35EXyUS2j34la58ydZq72+MjXvxqDjJObWDZfkZ4H3AixlXPf55S+ds/iwVI1sXqIXqWujdo8e1cFf8wnVVC+arWvCYxHg5VFVt72v2dA1NELwLeOePUmRHU2YAmkcrtGtGsh2IXhwlmb2+jcd6K3DiiXmZ5JFuuGe8v6bmhPcLMzmT0x71rtngR9sj6HHe0/Vr9OSCkhKX00iaRtJhOuYqLejT8t5YRo2j1GxFohaUZL+zmD1DVx+JHZYX/4iefPkglEcb29IrLcjEgqQvhciDWqdBxAtK2u73UitzyaZeK1akFAy9cCrHfdssFOxzy/gJfVAEPR66S/uaIxLEw5pc+DMP6PSjD/WhuKpHmXU7NlS+8io8Wou/8PPSXg8THOTsmHM275OuY2BDcQVXE2WaObx+g9aZ6D1JkqEvpTLEvqF6DqlGxjbO5df+6q+9vtEFivge53twnQ1eKgjOvDRaqrAdKpbAuxjkmPOhp6ZMxd5UTVYpszhFloIPsSEbirSbHUCK2TCXOTVYz+alVaVxSRYrszZfz4WclTlX5tmq/+VaZt7Hv2slxhZt7YNV8eqp4hlTxcem+jnscf09ZRypUyEddhQvpCkQ55EwTbj+DX7zFBmeQLyEYPC9OuNg5KVz4PGmuNy9x1f5mE3hMMdTbS9CpRhx74hSJWhlH6W0jTejmg05YIZazEeCRgiTZNW6KlUSPpnEzjFCFiq9IRc1AxEJJ3aDF7MYF83kNOFywmOdNyFT3ZoqEReVIkZcrNkC54IYP6emkZpmQjAzrIJQiseXnq7zzfrZHdfR0lkJAhUjN/IwJhDkOBsXNUw6OAHZIvUJuv2A4fKHlHd/nfH1v2H/6o/Qq4/QwxtcnchlJgzwgonYbTh5/fvw1i9D9x6h7Ej5E6Saw6mKg7lappADDVCT8V9krgT+e3Ld4bb/Pmn9Q0qIxCi4i1/h27/c8+Yn/zXXb/6NHZASidv38CfvksqI1hkpsTnFZkqCunf4co8woa6nZLUbIdAag2r+DpqJvqP6Dh9WlPmA7zcETbhuzSwJ/J6sheB61A1UAup7NEzUJqkvmq3TzIWiMyIB8cEcfKsdYF0fWGlvJl2y8DWsSFsQLSPCy7GJWNb4Uqw8zN31eCjWYvi9/dvWpmuoW9d35NLI9m0Nm/rtgUxb68JrepCrayMkO+/N/kC0ISvFkJaGnBqn5eGxPB5Ref+I2N9IlVqbeaLdxCycKmVRVCxS40f3twA4nFbKfG9FWJlBOjRnipsh2R4RxewEJPS4MIAqrkx084immcNhR54P+K4dpnmC2ZHFxmRICxX0EYkDQc8Yts0SfRq5evGFPaQQoR3XIksR2Xxr1Jo8ZITokHGHmye6sqAodqj+edSDx2RZltXR9rnFbuaxqvPherB1q+3QLrWSa2YuM2P7k6s1MUE9QSzB2sQXbfxXi32/2Lvt1cwahUWh01An579UYDxGPurx0X/tE+RhxKzLO9/e71/4XV+6Hp63tKKnUipILUgyA8R18KZ+GzbE0yesL/fkw4H57s4ab7ECueSKduYvZkGOtmYFpTwq2v+i6xtdoLjuBIkrVCKh82iaKdNMCM5e3JJsrNOYw6ZJt66s6hJOJUZSbchHacPFhcytxz8PMmMweD/nzCOqFqrGim8DVKpaJZnygprYrL2Ueqx44WERPhD4jCuDKFmF+0PGjbZJdl1FCEzjDvY3SK1Ms5LmGdSKtH41EaeR0Ef84Zq4fk08eRuNZ1S3QuIGuq496mUmusgQaSMdPaIGshQtYqRPu6kKNYOThJOeY3I0GVr2Rq2GnLiqaJqpusNCFAdcWIHrTeUyXhP9gA9rNN+gUkCczYhdJnRbky6KlT/mWt8ksHppnX7eU/M1udyj8x3ebahhy4zDa6VPSmFPCQPFrxsyknC1mkNu9Yj35kBcshUXYB2kJaXQGhzgkScINJVI2wyqyUzBuDxOmiDAG2fEhw7dXBCe/pDNt/4jxpf/hutP/xX1839FLAkpPezvSA5cfUm9u4fuBMo9NRsXqG5Btm9R80uiB3UDdJfoeI/Mt+ZPsfuCXm+pmlqq9Qf2XFaOGH+Jd8KW1Y//C16++V2qBPqzH+FOfg0NnRV+00iRg43c8khXZ6TsKHFL9gHKBK2AxwaHNgp12tZuMPfMbsP22Q+5/exPuL/6MXX3iamReoFBqA7mXEhke7Gco9LUcItvfK1ImYygKx3qzUYgOOijI+DIvWeaSoPwLeuqVj2OVx58i3j4vC7E1AeypYi0EDt35CXIgnp4aZbzHDkrxy7bbqbjfzP43uTzduBmQyJt9ZinjrO1VvLYyKQPxN3HnLiFJ2VFMF9GWeTh+fGosKm6oJ2Lgsc9+hliyEbNlPHKCj7NoMGiLNIMztBmSQnnDzYeCgPqB1y3Jawvoe6Y8kiex8aZEWo2w0fkQAkR8b1lUrmACwEva2LNDGXios7kMnN3cwXHEVo9qhtVQcWcvqfURox+j3cbwnZHnqcjifhoasbPH+KLsZp+5SsUbR5KS6Oobf+XR1/3uFhZCpTUipM99+nA/Xxg1opzgUFWrBwEsdiVUgu5ZlKdyc2Tx1cltMZHmm+WtOBZM51s6cZfU2/9wiKsPjpPpKEtf84k5ecRwwWdeVSktCJ/SfKefSE2XpKo4HpldfYUHXfcjRZFclBFS8aVRMmREO3QS+OMwx2z8P6y1ze6QCFswXfGLWjqDAkGJ4UAXsGXDPOIsseXRNaMutQqOyXnRClmlRxiYC6FcZxbEFpqEGeDyVrHUsVmxbVYMVJKbpBhfSRNtio0ZzNjq/rVbJB6lJYtf5xrQX1ioXulVOY5UWumi5Gh75nLEsw2Mh5eIhXmmpkm80pRnZnSgbA/sF5t8P7AfH9Ft39Ft7pAunO0O0W7LRIHs8N3HSqhyXEdRZa5FkfnRcUQIuv4KqgdIIUD3inSEo2hUutILgdDTdI9eb6l5omqIzGe0vVPSdNzqmSiP0GnkVlv8erQ+YbarYnDE7p+ZZ1BKUhz7FR1x1tI27gF30HooKwh3aHpjn0aYXqNv7uhxBP2PhCZTPLZv4v4DThtBNSdFY3OZI4aVsZ5KQkfO5z0EPyxQG1A3PIojje5auPG1Ob22Q6UgEm3JTibmddM9AE9eYrvT+kufsT45EekT/9rePkHMEUOh8xNVzndH9B8T+mh+oBsvkUJF6T+fbruijr9P6H/LsV58APF71BJyCEic0byvya6gVr+A6S/IPuA78+ob32Pi83/Ev3sH7F//i9x+z+E3fvgv4/ka1xcgRvQfItPL6jlBfg16r9vazjv0dA1dAGo5RgYNtU9Xe3J84Fu/ZSb6+fcv/mEfP+c7sl36dWx/+R3yIgp2XI1L5U0NzVQj5TZpgZqxU/NGeczdd4R+jW5JlTFvH+0Hg/9BfFEHhFbtT5IbAWcX4qGhXtlo4SFwCePD32BLgZqNc+dlObjoZ/K4wExx5//YJ5mj984H48LJVn0/miZqPMBFzdI1x3Xk1HhFE9DzHQZLxb8ozV35LxJR8tSWJYlNjYxDosPwbgxzhQ9Xhw17cn7KyPoVvPq0EUgkK1IqS6S3b7Z3Ftyses21oTUA31O6O0bSt7hnSDeQ0loFsocwPU2mnMBcZbX4/oTujZ+uyiZXCv7u3tqamOFdojNs4UTlpyZ5sQ0z6QCQ+7oTt9lOD9Qcms4scJCZUE6aX8/FC5fLlIeXqrSRii1NZV1OZwfHfALCqZaqFoodWbOI4e0437ecygJcT3rUCEIgw9UzRTNTDUx14lSK50EC+pDjs+1oOAcno6oSnQdDwMeeIzyPSZ285X/vlxu+bd++eu++vVfX+wse5we15A2d9s8T4wCwfVEb27Out6wevI20+0tu5ev8c4zTzvmUhlWA2WekeDNC6g0V/V/ixnPN7pAmeeKlxnnA12IVBEIptDRZnntRZAuEYYEteVxzCM+TdT7W+brG5iMPzGsegbnyeWWecrUFlK6eH/QYNiueYQYImKmwQuTu9RqGvmyEJVaxoLqcVTwcKgtc9fFgwFiS+dMcz52XV0007aSE3c7zzrMqB/x2ey3p3Eilco4zQhKToAemA+FYYj4UZh29/TDNd3qFL85MZdQ1+HiGsK6bSSB4hxIbB1Xm2eL6f4VTy07SpkJYs6RuRzMwr45taqASCAyNGfPGVxAvbSMooIyU8fXpPtPmapHuicW8z69wDuh779naFY1lKTKPeYXvyA9i2+FQp3bqKgzl0JZodERaiDdfcR0/RO69ZYaT/FDxAXFpytq9x7ELRpiKyQS1FvrGruNSUlrpuoK5ybQiAsdXsyUrT6A+A8NtC74SrFi1pnyBMyMTAVE7XCwSbfDxQjrAX331wjhlNw/A/m/Ufafc/W6MqwDfVLIApfnVPc2RQKOSg7vUn0g+h1u/gzfv4u6kbx7gZSEqkOmirv+f9DVHax/CenfJfXn0K8hrjnp/lOG01/j/mf/JfnNvySe/oDsOzv40g0yf0qYX1hWy/AhxDWSDd6XWLAZT+t8q63HwsQ0T83ML3L/4nfp5jvq68+Z/Iq55XxUNdM7tJDnnRmGuXi8n+weM/8ZqQUtCb8YDB7TtA3JCMEcpUXAexvVamsWnLOgygd58ANauai2wHw3FsRjkRS7r2zmpVaca3lG3h8bE+QxF8kueyyelMuxKVlI+bWAOEvtRpMVwzyQO70zpBXXCl8x/eDx1DwevIp4MzZTLyy0MkNo/KM/j1EUO6RymkjjfSukqvFQXPsw2dcUbFxRxJLCXSeGiMQNbrhkyDPMB8b9DSUfTLnjO2pOMB1sfUiTLQs4t8KHFfRCv6lQC/M0Mu937Hf3ZkeQEiVlUlJSNu+fec7sdvfsppmVrlg9+y6rcU8pszWL1R2jJY4YytdwjB6KlOWfjVPSRjz1UV3y5eP7obSx8ZDtD1kLWQtzye119PTSEUQMZa+JXCdStTRxKdpM4qzJK5qYaqGow6N0gvGKXLCi8Cvjnq9+/AuVOY+meg//+Rcbun25WGmZSEDb0Nv9nUjZMYYOFzxdCJQ4wPqU4eKC/dU1IpYJl9OeaZqpjCStbFZrcGJr6JHo5C+6vtEFior5nDjX2wEVBAnrtgGUI4mR2OFjG+gUhWkklgk3rDmkwn43IiUzTplh0yEhkg+Zrh24C9HOrEGEaU6ts6Ml+1rnJt4eT1FzidXGeFrIowIPHR4Pi8OJbXTOmT2ObfgtCt7ZwWYbjDCnyuu7eyYtDKveFrCYi2ZKejwYgkLsJjalw5OJFOrpBXe7e/pbTz8MxBDwoadi+SJejLntfEBXl4iL1DQS+4jGnn77LhICGUV8oXMrdNxT0hvm8R4fe9anHxDXH+C7U1xcISHg6M1t1Rk5VlXReU8dr9i9/gmf/+R3uPnZ77N2e55++MtGVXF3ZO4bopTRsscTSOlgizyeUkM0XkJ15PmlKRu6HsIGjafE7YcgQs1XHF7+E2Z2xNUTuu6cbvtL1M0PqPECcT0aAJ2Q6lA54CnUfE+uZ/i4RXQwxCsY+dXxSOz46LCzxmWBq21OaHI72nslxu5v+m+tDh9WhAHK5Qd4/RtILbjbT5lu73i5T7zXOzhU5LRCLDgGyKaS0u7cBm5yhdctKhtc94TavzZvguqhZNzu90B3BEkwvc282iBeGE4uGPv/kBPn2P3kH+HufwKXl7j5Bj9/Ti2fonnGrb+DxnPU9bjoYN5bwrSW45gCvPEv/MycMl08odx+RPnsd8nzhN6/obgextfEThjTjNY9NZnPS80WgOl9h7pgSboVO9ycWDEK1DkjoTQTuY7SUJEQfUMtDe0oralQ5chBWUjP4pzB6c5k/kdSPQZnOrdIdvU43gMjxi7IS4ixhQ0qSyjpgrAtyGND8HkYIxmqKlLwPlDIoCO+Hqj1lCVFtxy5J6YqlCAg0faTUvFx4Ri4o/nggthp24sM6Ws5PceGzTw4qgOdR9KcwUdbq2IKK9QcrjUnKnuKV3IyTkkUgW5AwwrRc8Iq088jmiemed9WubNiKSdwI87fUX0gu47YRURWuBCJ/QYtl5ycT9y9ec7rl89J+z2KkFKlpkyaMynNFBVDolMmHW5J4x057Ul5JpdMyJ4aOA7W5NGfx0MMvoRGLOcIx3DB5fOq+uAMSxuXNdWNE8WJI4jH4wnO04WI4i2ZvfmgNBgHL6akseQwT0SgZEuSrgktMwklhYJGwbloOWHyENHw6NRrw6dHhYY84rG0NdYedDtn/pwz9GtRlMUdXI4FuGsFfymZOc9E3xNCh4+Jmge6s6esLl+xf/WGiKf6TJkPhCDkgzBr2xP7zgz9/pLXN7pA2aeM8w7GK2o54FenqBusUPDFfDcohJbNUZomnQqEgOsDl88KaU7cvn7BdH1H3E9Mc7J5JM7siUWa9M1mmVYA2htrDqj1iJAAbS66wLoPBLbYBXQ2ApgVJTbjE2f+DiYHXG4pkwQfZ+Xe8ie0gIgn58Lh7s4i4R1odhymbC5+EnCdYzN4uggu9jg81Qd8hWm3Yzxcs4omW646UevIqg+kaSbLgc6tiWHF6uIt+ou/wvryr8LqPWLYIt74Ol5WqMx2KKU9MKMSLMCtWX+rQnWWm+MkIBLwLqDDFn/+Fhdv/zJPf+k/4XD9Uz776T8l337CYfycOg30MePJaHXM6Yo4PGG/e0XZ3zKsnjF7RyjA8BbDaksXztjffkGMK1idoH6N236LOp8zlJ67n/0fSVcfM/cd8eafsX7y1+k2v0H232JabXEkXL0lri4MJYoV6jW1boGnwBYvSiWSnCe0sENLJbUD6HFSp7ROBJaxtrQds9EXBYITFGfz+q5H1xe481+l++A/opuvmF7ecuPhpCjdi3vyO1fIsEUKFOeQw8dkrujcOaX8CZXBjL42M24GnTMUG3fO7p6CQN4Ryi1FzqjB0deMv/x1pNwzvf4dfMmIm6nTJ3i3wXVbRhdR16NzxrmEbC6pb/7MRlZuhfiVmZkBMlo3nm6+4M2f/hP68pJSDcVA99zd/JSQDtQCU7lmnpbsEUOYpAYUQ8ycwwwXxe5FFSNZWwGYm6eNIR2C8TpiDMQI85wpzpR0lYVM6loBsnhEGPJTSjnKeUOMxk/Q0kYkYtL4duBI68IX+wFpfJIjgnbsYk3BYN/bxoIL7O7sHgkC6IzWGa/aRlX+ERLTCpVsQZ0uLAdtW2NN6bIcuk7kSNq30ZZrBZ753tjjs6anjDtqtRPUPJiW/6Y2sltGJMlML3HG0/I+4MSIs/RbwuaSLt8zl4lSRnCdPVmxfKCS9oZM+oG8jHu8w/cr46Nszrh88j7XLz/n+etXHA7LIdyKsHaG2vsaUAc67WE+QLYReC6BVAyRUoWqYqOwx6dz46IsO2z75zLJYDkalkuPPAx7z1zLCRMNRN+xiisL4pDAqvHWHI5BPH0rHkuLDVnFiqvgK4TazByzonNTcUllrhOVQJGOghWTj+38Fw8W9KGwMEVYe7y65Op85Tkfnw0PRbr+PJqy/I6fu1rjVY1ASamJVGxq4UPEdz1lfcrq/Al5t2O8m40UnO6ZiyAuMYt5+4Suw7n487/jF1zf6AJF1ZNTQtM1q35D6LYQbCZenYM+ohlicKZ6qErRZEz6Alom+n6g26zx92um+Y58mAHrflLriLy4FugnxzfZDqI2V25FBg318L5jnlOTB3rLzFFFMBlrDLHJ52ozBRNCcETnrEjxJtlzDZ4utRK8mTpp2+CGIVJSMuTGObpVJQ5nDCFwfrYhbAacX1mIV7lGcyVrpisTtXTtMaulCmMw6jTNOE0MReg2Z5x9+99j9e5v4FZv452nOqzjwxwRS6344NHo8fG8HRLQVUWzdQe5Gi8GLU190fwOmmxZXURCYPP2j/jRO7/G3dXPuH3xLyk3H5nLrANlz+AGk1AL1G6FxBWrfo1qoLhA0Yxbn7IZzji8+QMkHZD1W0h3iqOnngZWh/8Zu8//K9Lda8iBnf4zut1HhM2/A/VHlNoj6RU6vY0O75H7gbCq6HRrSbZRqUHQ2OG6HvwiXbVRgz2vetzwVOUR96hVko8pfNaeWJflIXrH6AUdNkxv/Q3i+Amy/x32es9qDUUT9f5nwJoQLyj5GnG3IPfk9YYqK/y8tzGLNkfe4JoN+lNq+E3q8NeoIeLrLTErVc6odYZVpH/nf4gLz3C7HzNd/altnOuZKhN13EJ3g9SRsFpT/Sll9xpXQeSUOdwgrsOrwjiS5xvGL34PXv4Rc+dxBebbN5R6g6QbUwahUJrtt/fHMSICQToe+F0Nrz6OIkqLcreNLngzvpIOds0fY0kdzkdFj121lnYwtYKyvT9Om3lWK1ZUsEwiZ8qK4BzzPOO8J3bGvSn1QX1DQyuWIC7nnB2mWNQDR26a3XfuCJsXRDK1TEidKEWOB+dj+N37eOxqjxyC5UnJA4wvTnBqhmA+xBZB4Y4jHtdMK53C/v6WSrDuuJHczU/FCLQKlrycHNXN1hi1WIsQfUs93uDKJaFMxDxRdtfUtDdytYiZv4mi3FNC1yYG3nx4QofvNgzrC9LZOzx77wOunr/i7uYWHz0hdrgQ6DSQ217jvZDEw7ynjnvqdKDME9p11ig6S3R22mjKj2+3nxuFLDfhQ/H5aB7ygPY+IpI6BC8R9cJKBUdkkGTmhFWRXPHVvH1NIRpQFpJzRWoipIxLSk3gksMVBW8qRqoZYFR5eH+PhUkrTh6jQsvzWgq6x8/v8XNebp8/B0z5BWjKw1WqolKRnEmSyM7OM1ciYdgQt09YXe6o5Ro3B3K28U7slFICpTqknDRJ+l/u+kYXKMwH8jRTtSDMsLsmSCR0p0jXU11knme8a/BqsZswTyMe2+hoUJxgmTO1mOW89VyLkqc2m3eOLnsGDS+mXIsfyuPNyXJnfAh4bZ1dk6728SFkb+gDzitd7x/ZUTvEBlVozqScLe8E10Za1llK5wkhmh+DFProWa23nDz9DvH0O+jqnEOuHD7+bfzuM1zY0AWHZsjjHdSDQbkpt98nDNtLzt/5a4S3fhU5+YAijlpmkjp8LTjJaBVcjSAFLSZDVkIjaopZVoSOSI/Lydwq69wyNSwx1YcB1zJhNGfj6vjE5uIdhtP/CXevP2X//Hcoh+fgT22zcZHN2Xegfwbh1GStInTzRE07xrsXCBkft8y7l40M6PG+R+Ml8uSHyPyK20//Ww7TxPmwYZw+YT3vOamFIvZ4qtsjvlB5SvKnuBiRdG/EaQHvTvEp2rpzD9b+DtrOoQ0y18ZBaXCyU2jw5tL1L+ZZqGfoBL+ZOOyvYTihPPkbhHlPefMRN/eveOuZIq5S5o9IdQ/DU0rtUBzzeODEr6AeKFOBBExYQvAQCf6cVDfUDNqfoiEgWigZ0IorFnwXth9Sh1NK/y519xPmwxeEWvHTH1MP9/Tv/Cbh8le5+ZP/C7r7Y5TvUkPCTdat53kPaUe9fU55/aekwxV5p7ha8HnEyR5XC1Ui+AkvlTD0ICtSdVhwnqEcUgRXvBUC9nIafF4LeMu40SNy8eAQagXaV/OkH0H6DV3w0bccG8GFxtPwltFTsewp76zAq7WCE2IX8S4al8W1jvyrh0H7jXUJk8TWqXNiWTILHK9KKeYfoYsXyTL2bXyRx2nqtSrOP6ACDxC/ITXqzE1W1dZ9CB0hRGLXE8JD1yoCrlbm+zdUgnEiyFDNllzwdsJqU5qUTM0zi0VDbpk5wa1svB63+GFiOBmpeWLeH9CyM+M5gjWDMlHGHYinuA71niADIawIQ2V1duBJ+h7f/1Gilj9gf9jTDwOuj3gqeUGDtBCcx+tIHm+ZpzvSvKUrHVUDtXqqPAQnNjnTV1bCn38t6II8+talERExlNsheCfmxSWRKgVJBZHUoimM/Gq2A6b6kyr4IvhUkbFYPIN6SjbflBSE6kzB4xp64prxGtr2lGWB6aNyY9lzju+vfOl5fN312FH5a5/713FT2pfWUkkuMddACB4XAi5E/PqEsDmlP1T6XLm7e4OIb+svWAzCX/Daf/X6RhcoVy9/iq+FfojENJHzjm1wFJfwdYtfnVloUUkIFt4VvHComTK3+XlO+DZfdEJz/zMhrc0ZbfKo2bTuDjtkLOXWZrWiSgyB6m1FhxAIMbA+WRNjJAYYhkg/dITgidG6mhgtC0WwTbFfbbl86wPmCru718z7OyQnajW5dJlnXE1mBe0cIXYEHxCEGDz9+oztuz+A7XtUd8pYlfHwKbjIsL6wA1UcDB4N5nUAHb4zpcPJ2bucvP1LTKffpYaAThPBN6dGAhJcm1O7Zuk8Q/XGXXHFpLq1cSucIjHifUfwPaqFUkZK3lPrSNEJUdcO6GO/gVShDxv6d37I3fqUVz/7bbr9Df78u7juouWWRKoKcz0wl1u6eoCc0fs3SN9Bd05mTz8fEAmk1Tv4boPMJ2wvv4ufXnP98r/n+tUd0WHuqOlf4egZNqegL+n0U2pZ4XffQ09/SB0uSHqDUwuzqxIIYtbeSxbKQ0dmviBoQwmq59hca4teEN8UFfZxJVFch+ctWL3E7z+B6NGnv06Vd5l2/w3PP7vn/H2H93vq9FMqmcIpMT7BlcTcwiNrgXpoM/M+UOL3md0vkynI9ALxK1QHVBLOZWS8JzCw79aoK7D5PrL9K4TrX2L47P+KpJck72H/KeWj/5KrH/+fyW9+Ql2v6edM9V/gyc350nE47Ci3NxxursBjnJqux8WBMmbEz+Rm4a4S8GFAZYVXQ09KrWhqG6UzVkOtVttJFaoT1BlKkBYzC2nJ5mIy/VwsxsKIqq6plWtbb41X0vYR83FpxYX31KzNi0iZ09QKCyuaUkpkMeUINHTMtKKE6A19KfUhQgE1aXiR4wimNCWPX8zS1KBz9Msd++NDola1kEojSLAoVKSpk1iKk/a4vF/GxoHgHwiy3tvBJyWR96+NGF9MneKccaO80FAXc6jVavskImjyFH8AZxyJEHuIK6ScEYeJfrWjpBfUcsB5wUtvRbEoJe1NreJ71LsWLjggsafbnDHk93n6HWWumY//7M/wEnFdNKsCvEmO60zwHnQi714zH25I61Pm2TgR1UWKc03ib8pD4avjjK/xTFnQzEejE/3K19n74QmueW61UVxwHiWDZlzFRje5xRy0QqIIUCs+K25SdMqIFnoVlA4vamGw0uEkEMQRG3JnPjtY4VUf1urXkV2PT+erz+9IHVjgueVfD/DbLypMHhfCqjb+K8WRSqGEQAgRiRnfD/h+A3GHiNBvL1E8nXOM04jiiULzy/rLXd/oAmVYr4iN5V5zZp5nDlcfMd16Qr8hbJ5SpcNVc5iV2FNVCNSW3yIEH1n3PfNmzaqPZlvcRjgOYYgdIXQcSuL+/pY8ToYaOCE0XolD6fvIsO6IXc96vUacZxh6uq47sv6dX7J6apNJKmk0u7ST7QWX73zI6uJ97qZEf/oW+6vnSNrhRImdGURN+2tAiN2K2G/o4grnI1489Bvk9G00nkCe8dMXbPMX6OkWyWu0jFg7E2E4PW5qgme1uqA/fY+pO8HlHakhTzVH1EXUm6Og0A6BYnLfIg4los5m5146Sy2WACngWnqr84EQBkIYjBOgM7XsbNQk0pRXgewzWXcEjZxu32Hzg/+U+93PSNNIzjPeFep8QNOEzwf8+IZ89wZNb5D0mmm3w/s15TAxuZnVKbj+BJEVw+aMVN9idfp9er1mv/uZcXn2hfHwAnHCcO9JVdmed/RD5nCtdHd/g7j9DeL2B3Ayod5RWmxbIBqpmAW2tAPiyAlw0jpw636d+IbNNYhZBCcm3a4ipJJx6yeU3TO4f4F0Fb3whPI/YPfRbyNfHNieV9z2GfhvkWVDnb9A9jt01eE5w8uBJT+InPFlQuoeKWuUe7TcIEVxOVPIyPyCHJ5S+g/IqxMbQ8yJbr6lv3+Bzl/gz3+ADueoOLyecN0NpMOecf8pVWzMkmfIOEoW7m9uEJ9BoetPUBet0+od5bCluHsrGtI94zyjUnF+ZdyG6lEMGTVpZ22kRVNqLMZjizy4iDMX1EeeJzY68a1oUErNx//ugrR6wDJzVNXM15LxUEzin6GZqfnWhCAcfV8W5Y+q+b6YKaPFXUgwL5VSaxvTOjwBXcbLzR/DO8NtFzRt+Xmq2si3j31QWpFB48E4MS7OcVTxcEg5aV8vnhgNRfGLgkfBi1CnkXn3ur1eHNOPq1Scr0Cmin+w1q8ZslDcwSRGzpMbwuRDB90Gr+d0+R4tE+PulRkoMiG+EZXzCAhJIqGhi66z/SH0lwxnxgF5W4WqjjfPvzD5f+Pz5MbdyGVG8g07/Ry/fYtuOMUPK0I/EGsPtYVQqiFWx3vtK9eXDuHl2H80xl9M1I6lrJF0bJzbCkRXrYjDGXKMN4K2JHOwZlm7Ns2EomhqO0Bw1ARd6M0eI0RK6G10FgLR+WNBawpG+1V1Kaj1Madk2Xt4xH98/GSXMc8DyXZ53keWylcK4+VaChf7FQrVphFzSszB+DjiM9J1hNUJrnvDPM/E4cQ4YvOBgDDPM9IXav6LMKyH6xtdoLi4IYZio4NUQD3j4dZmv5qZpgkVb9yOsCIMWxzepIreZokxdpysTnj3ydt0mw3qPXf7Azd3t6Ss7FPlrc7h+8CL65fcP/+C8faervP0fWS9XbMe1qzXG8Kqp/PeeCeieDHZsWB5PtZBW/WdS6WWGXGe1XDCarNGnHC3uyc5Rx8HTi6eUad7YqtSESHr+2Z21oaKC4nMDWvi9n0yHWXaUacrZByJfsMcAjknfL1o5NZAqELViSKZGNb0q3OKE1K5wc8HnPPU4sA1g6nkyGK8mKxmnoXhBIjaXBsneDzOW3S6+J4QepyLZLXxlgsd4oToPMoW1ZlSszHma8W7DqkgvvGFQmB78S6H/Y77N3/GdP+cmiYoIy5NeK1M0VFZ48qE5ucozYfBjczzS7rxGa5/C7c+JbjvUqc3cFjTEek0IdUz7gs5Q5oKBMf1mwOr6BmGStF/AfNPcPtfwY3/HvGsQ05uKOKAAbekhy7cAJHmJFts8xePtBPACG3OeAkOMqYIMJpCxtVKiGdw+gPKYYebft+UU9t3WT/7NfYf/3PyXli/p7i3fkTaPiP6AaZbqqu4eYfnBhc+o7//FHf7OfXVT+ByT3rnP0DiBeQZmZ8j+z+G4EiyYj55F0cylG2aiF/8Hus/+D+Q0nNS3yGrMwuHW53Td094+s5f5+rmC3ZXH9OJEE+f4E5OiG6gHPb0XcRHpcxKVdd8TIRUPWF4gj8t5KkihxvG/RWljqhORkpXk9p7MwDBeSgUU2mJjWJVHjw9ECGL3VO6TE+ghQM6UrFxjPdWjFQ1foVv9yqYaqax341o2jxQjgo9kSNvRR7l2tRaj5kwtq+b0mcpJmzTt/VRSrMjaPy1Wi3XyuFwTs2Mjs3DCIjlsHF4z9Hu3AqQVowc2bK+eblwHA04b+RY+6o2/mqFSj5cMx12ZFFcNhlz9YY82JhbkJqa06A5wi62+5Jmqussd8db+jEhIpzg8jldHslpz7y7pXAwlLA2FEscZb5HvDQPpkiMA+JXuH7DamtW/nk+kNLI9YvXVMy4rWSLp8i1IoyUQ0Y2T+k2TwmrLf2wZfbJ3r92P9ZHrcMvmHYYP6i94l/icSA8zuqxGrWhF07M1dkBxdLMTT7UpMHLeipmOUCprdixn+t8QIOpz9RZ4OPQd5QQ0dAIya4Vowi+ycGWR1p0yelZHu1XBprH8c5jRORBYv7wrY9tEr5OKr98/xL5sIBNSsnZXNI9BOcsYLLriV3HmG+ppdCdXKDOkxYuWc3I/zvTjP9/6dKSGecdIrYpWKW3JhXI2Rn5TyuHUohxR5j2BkGFlUl4g6UED/3AsD6FzRbF0R3uWJ1f8EW94MWnn/KtU0GiZ5v2+O0pT0+3bE5WrNY93WrA+UhwEWJHkADOkcpMGu9J6UCt5r1Qq5o1fBWCQBUhhp7V6qS94Qe6ftVMzxzdem3qDmy+7IIz5rrOljpaM6Ata2SD1sKUbinpFskH1BUUj6tdG2GZHXmqmTJN1JpYnVwS1qckKprvIY1mU97CAtFCTRO1Wq5N1gey1uJJosuN4BzFeWoZKbOpB4qPBN9b8F+OJmkUK2JiGCxvx9Fs8aGUZJbeFcSYQlCF1eoUd/ltrktCy2dmxe491AkXM0635PqKNB8sBsDZaClohekGpxUXtmjfQXhKKZk07onOHE2CDwRRnAr7YjfdvKuU6hry8Qbnfpf56ieQXhD830T9AE1Sqc63ADAbS6iagRgI1Vkx56tiRFoLapTaDlTErPvLZK8DRpJOiMltp0x1nvn0XfLFuxw+/xlyd83J5UgthRwi/uQ9ZucNfVidoXGFH2/ZfP7brD75LyyosN6ZwVq6I6QJ/+qfUdNEuPgBYV8J6V9wFwfk+qfI7ieoewWX38MPW7Iovu6QEqmrmeAjF+/+Kt3JOfP1T/D9JXr2aygrwvwFvCl0OjNtV1R15GlPOeyI2tnr5jrcJuDnt5Cb58y716TpnpQmPBPODyzSWG0HfC3WRTrfHEGqKQOcOIIPTHl8tIEukm/zSClNMOq9hc7V2jK7GpF9cWB1Fuhjfx9z5dqx8Mi2PueHoEEzYuN4IPim6kFa8GThyB8o1cy5FrWJR0CFWhMu7dB6bs2yLKZvtY2oFKTirc23pqTxFUxlrEfOhPPW1XoXmurGmYkaTVWkSrp/0UZbxkfJDkNL2qhjKfwQU2Spc9CCLUtOiJ/NXyVNdjDFFfgVvjvHrRJ9PlDSgTRPUDrEB7woWhLKniwOfI+p+jw1mMqn60HWM9snb3N5uObuzUvubmZKmagpMadMrgtldA9ffEQ8fYtuc8owbAihI3rbh5z7KuH4K1yh1lAsx7HdinL8+8goesQt0mNTuHxOwEFZ9kEnaOepWcyltmLy+aYaK0YQIriOsiCqovhgzcvyfrbwiIbqLLwQZ8b7SxGxkOJ4KBoePy/7WA3x0UdqHx499kejzuV7H/+9fMdSfIMVfaKVWoVcMlkrURzeRXLX4foOrSPj/pbqOk42K9zJmvEwMu5uGKf9zx/mv+D6Rhco+7vX9BH6vgcVg3JVLf492Zx4GvfWSWmPjwHnVnTR0a/P8CenJFU0Jca8g8Oe3vesqHR9B1V59q2nrPoNh6sveHrSwfYDvPN0wRKCC0otM85VToY1cf2MGnpSnbm/e0V5/RFpurNFFRxeLMZ8WeilJoomhn5LzrfoNHNy+l2KO6PkHeKKLSwHWZofgw3WDVJsSZ4lJVJ5biz5kojOU7wVRBShaOFQ9hxuDqS71wRXuHz2IeHknFRnmO8QFXxcgxhpr5TRlEO12GNtvBzvTSbI4jiwQOfOGSfDGfTsfQAtzOnWwvrEE8PKMo5cYJ4HJERc46k4b8TTnBPOJbw3SZqqHejD5i2e9Zdcvfgx4+5jQy6KR3RHlpF4OpHTxHTzM/LhNX52pGmFHJSL8x/a7FuEGjtKvjM3TQngKl5gwkHODPGci+/9Z9xP18wf/QP2twcyhU33Bp9vqPe/i65/Be3etkMuZLyLLQHZ40rLnkANY6qWHludBxdxYuS4h36MhyyjfCDPiXS4Ap3YxzXdlNFwj/oV3eklevsFVy8zMvxzIn+dUn9A7m7xcoqLm9ZlRerJO1x993/K9MH/yBAM7hnuPyPrHXm1oXMnuJczPDune/0H5Ps/ZBitYZ6e/Ij63r9LCSvq6m1W/buUfEutd4SwptLjh29xdvoDxvVT9revKPM1rt6Qxy+o+RY04WIgdpfEzQVpf0M+HMjV4/Dkmqmxpz97F+lO8Psb6s3npMMVnevxTvDOk/LiW9IQBSeIi4gfqEiz2G8Jw8s6dIvkv9hYpY1SfDBfkJRnSipHOHwpUKBtw21m/zhU8MuckHo8BJx3rSjVptrThjDS7gNHqfolXoxWxXehFSGt+yZbAStWIGhtkRtYoSBCE4K1cVa7z5ZOezH/E2dZY7Hr6GIkLAWY2P5DndhfPbfRky6hqY46zxQxF27nbULom1Eb1Q5irUrNQnbtPncCPphxo+9w3ZZKJdaZPo+U6ytKMmK8BBtvqZq/ivO2D9QQ7T4MHdL1+LJltX2Ly7f23L/5lKsXV8z7PZliTuDVUXOFsucm/RmszlltTlmtT4i97S8i0RAksbvMxq1frx5ZVDKyIJ8cS5njR4+/1oivDW3QVgyLFSnFCTVA6QTw4MRQUi14tTVMazhrkIci09n760Q5OoS2UZJW4021txlFLEeooXr6lcf4wGMy47kjB+f4Be2vx0gJXy1KHp7vV354uz/0eH/MJdGFYBb4IeI2a+Jm4HB/SxnvmT1EoMzWUE33V1/7Pnzd9Y0uUObDjpN+AyjzPCGl0A2B4M3F1MI4Tb8+rLasVmdI6OjXZ6zPn1jUu8D+5jV1HFl1HV4TuSpVCtv5DaUoad/TcYP0a4OHq5LzRG0a/Foy/WpFcI5+c07tt0gpDLKmTgd6L0yHHWM+UJ1B3cEHQlgTup5+vcGHE2DABYfTTM43zLsXoCMxDITuDGEFjQGPZGjGQeJdC9zD8mlqZa5mIV3KCNOePO9I0x317o5N7Dh799v47ROqjpCV4E8MztZKnXekcoCazPOAiBDRgHF0ioVH1QaNFjV3TIOePeoCLi6jntA4MgpV0Xmk5BEJHvEzqh2ldKhPeN8DbcPHW5RATYgzOqh3BRc6Lt/7AdevPIebzyBEglsRNkpKb7M9/6tsp3vy/cfk+Q1lfGNFjiRiAHFn6Po7+P4d+iGCv2BWQfKeOL+kuitkvuH1T/5zZk5hnpldos4Ov3fEVYcXT55e4tIN5rpoj8sKO2dwu2tmRzUbSVQw91OXqS5AjUcFkLZk65JGSHvKfKDO95YYjYf+FA4gZ29RqOj1T8j7W1799E/Z1kr//gmZLd2wJc9XBC1Ud4+enIPrGbtT3HaAlEnrD8hSEXri6V+j+/Aj4nzAXX2CrgfctscRKWfvUfp36JwjUZH5Djn9dvMkEUQj4oSqnuH019HwHD/doVpJr/8Y0he44Rkc3thGnjZ0zhj/sUamXMhlImsy1KlbEwis8Yx+TZ3uWsdXmnLOAHLvHdVHQlhBWB0PijlNjTgeKcU+LvXBr8R5G7MipgrKzbhtIY8uxQVYB3v0WtGHTXoJ71uKk+XAi9HMSb6UkCyNxwIsBI/gjycMwUyRSSkRSUAhy4grjbvQDi9xkEsy+/PGvUG8GbEt7jvSVIWty/au+VMET+ejeXc0dMg7gfHAdP2KuZjFfa52kKW5ojURo4JkggZDeNzQiCqG7lmRkszTxDt8ChRp+Ui+g3iKHyb6vEPnxH53S53vEecfGqya0LyjzpHUjA/FbS1nKa4Jq8rq9MCz936Zl1+85vM/25MrzDhDeqpDNFOmO+5f/4y71+9ycvmMMJzgQ4/3kVAaqu7kiDzYW/OLZj329ljCgjSp8pcRBrsePlfb/wpKRpmlMmtidhkNGWMfdXST7bOO0LgjEBp6gsPUj6JoyUgRfKlGDNZmfIcjOzVb/Edr0oJaHxRrX+bVAKqt0OT4fYuN/lKU8ZXvfVyQHw33j/DR4jDb/Hnaes0LDSFE3HrN6uKMdHfFmPakZITzECPTdCB8HSHoF1zf6ALFSTEJbC7kXAhUhvWWruvwoSeXbCx21zGcnBG6FcNmw+rigtpHKoU6T0gZOTk5Iwwr0jwjfkTLgSoOL5VUJpMQ1x2pJHSyw0Qkg9j8LdCjmhFmghdcvwU/4HVGDivKeMc839nmF3q868ywyAVzg5YKNVJyYjd/Sk0HpBzQMjMW8HGN8z0lnhGcR1TJeSbNc4MWbbOcp4TWRC0jIibRI92RDtcw7Xmy2dC/9S5y+g5ZI0EdoQukkqhlT51vGsTr8TKAGutaGSmzSbOdFkTNelxrI3g2upWImTlV76mhp4QB5wd8XOFjhzZioMuZOe0IYcb3A4IhNogH7Ql+xaLbVc14VbRaEJlzjsuLD3lThZuXv8vgegSz4q6riAyXhG6Njtf46YZOFBe2hNjhwwndyTtcjUC4JYRzXHgHCUqI71Dm51T9GXX6I/IcmEpl00FNlTR66Dqqv0WnF7jpzt5DMX8YISA+Ul1TN+IsJ0Yr1UUoCSkZ57qGjIXWTRsfqZaMKxM1742X4zscHbOvlOEpuMBq/ZukV7+Hvvx9NCvz7p6Q96R8QT1MhHwguwMxrtG0J2yfwLC29SuCdEZgVoR59X2m0x8Qpp8RDx/hXr6knH4A4Sn15JeReErSREw3uPKSXLaU/j3Er6ilQ4Ni/feMc6eEekeRLd3ph+jrz+nyNXNq6GY4NBO3HaoDuBObxaeZuWRK84wJ6y196ChXSm4Fj4ij5GqjQGe+EgRzm3XBzNVKMj5XTakVJtKARm/jNgHEzNfkSDw0xc9CRl1GPHNKlpDsA/PMl9yfj3vP4iviF/MzORaly8jIDNNo6IdFN6DNabZi+4U4lEIM4Yie0JRYJv3H7sFScEGOPACWcQ12SElzbFRxOB9wzrJSvLMRiuXxeLwoaX/NdH9FmkdEKzkrRSvTeKDkmRgsW8vRNz8YDxLssUkFzASzilAc1GYEV32guhXe90i3JQxP6DeWNj5OezTtrYFpo7KSBeY7VByhPQeRtTU03Qn95pLtxXt8+L0fMN7c8Pzza1RnvICLawusc0odb9i/+pTDs/cJw5qui3QhkL0DCTh8K+yOUzvgy4f5cmQf/90Oc9cKgsWcXY9fpXZ+qPnupJpINVm6MYkJs7kPWli5wJI47Y4jpWbKJxgZvI0ApZnslUoj43rAG6fGe8S3+AW3JGb//PUgd2+Fhi0SjqZ3D9OuR9/zc5969NosKNNXfofq0V02lUgXzYiNcoJuTxlON+iuFfIKoesZVlsO+8Mv+E0/f33DC5SFdAbUQt8HumgmaLUawQ3nGboVIXi6dc/qfIt0AVCzM6+FVb+ii56xHJjTgVgqvlQKkf10Tb17Q84jmiuUTBYoKZv/SHANRsU8CqZrfKsmfT/A9hmzS4ToCSkajO8CqsFId84WaMrT0XESyXjpLYLcC6EaNFxLoZYXlKWTK6ZeQsBVT86HY86Hq5NxU/AUVbr+BN+fEM6eoKfvI90psSqlCpr3SL6m5NE2B0yKWFMmz3tyPgAZnyu1JHKZzWlSM0twhzY/GQsUC22G7/GtOKE7oXYbpEGwi0tiSZblE8NA7FbgAnWeSX4mxLW9BtVM4dRZB+KyRRdcPP0udb7n+cf/iK1sUQLd+l2mtMepjVHEn+C84uMa8T3O9/j+hGkKlP0Nq/KCePIUcSf41ZrSPyP2HxLHHzPff0xMB2r2sLaMHidnaBY0JzsUuwQ4Sk0E1wLbXLD2WCcok5ENQw++s81em0dGg2HNyGs24700mvTcRYpbUUOllj2kDNMryu0rxJ8ybDuKO5DmN+T9azRuSdNryqyUdIdfBcLZd8j7inenEJ+CdzbGY6ZiqFb2kbT+Nlff/V9zVhyizzmc/SbSXQJ71A9k7enSZ/iCdWRlpq7fQjxI2sEw0HWniAo1JYL/G3D/h/j738evf0iNgbi6QGsl76/JeaR2nlKtS/VUnMMSiNVZVxk60sFQN99v2sgsG3cl9GjoIPRICwT0/cBUMqkVYjEEUkrt9TWUynt/tLwHiDGaJDnnB/LqkQioRx8T4IiumFTXDnzxX1ZuGcJixchiUQ9GOkWlfb5hQc6i6K1AKk3VY6/vguqUZtm/5HXJo+LjsWSWFqSg2Pg1dh2x7wixf3Co9q4peWD/+jNuXj1n1hEtlqic08Q8juScSUHQFNo9XulcJDiL8TBjPbWxRknUJOSG3uBN6u39AGEDq6f4XBnSnlpni6lwvTUhAHlqGHAgu6b2E3DxBEIgDFs2529x8e4PeP/+DfvDH3B/ZwGeMXb4YGulakFvP2P/4iPCygqUGDqct+PNYWM2oaFjPBzey3v6VZzksSkn8vD1Syab/SkUTZbFU2emOrGvew51Yiq2J3a14kqhqxby6trBLrOieYIYzB9JFLya2Vup6KzHtQIeHyPSddQuIME3AvCjx2+kPViKEB64Kks9shBlH57kw4dHqzf5uf/UDqWvI882f55SLGolBobY4csaWZ+TTrak+dZKudyk9f1g6p6/5PWNLlD6VU+MHrISg6PvQkMWErEPVFViDPigrFcDw3YLndkwRxfI2eSI8zyTxpGU9uTxQEoZnDLev+L+7rqlHGOdjRuI4hhWPathazBq5/FeqXmmjte26OKKrutgc4oLStkP1MNLJO2hSUrRCCo4iXR+1WDI5ggijSq1KGaqkSr7MlJroWqlCx3BZVt4NSJ+WZA9NWe0Tqb4weK/++GE1VvfY+7earLIHWW+QucdASH6FeqjMeinO7QccCUjpTYZ5tjIrBkV2zyr2mDciW8SWss4ATOoo2RyvbeE02mPn9Zov6bE1ZHYjKoR32q19OAQbEw1ZVweiN0AcYWrFUmJ5GZKGQjecfn+v8N0uOXus/+GQRy76xfGWSkHUDPIkvVAH88R11n3JpWLt7/D7Wd/wLi7Zsw/Zth+QJBzYhiQzSWl27KN54yv/5DsMsS3KOEEFcWLUHQ0749iUlrvIibybxuQD0gZYfcFOl0hISLrt2D1jh1+YtwiQSgloXmyw43FESZQZTAzs9xTqZBGxvELXLmjOMdUPO6usPv8DwmcgGwg9nTDJYebz1A+xocOSWtE31B9JMUTy3UhIEUgmDFhOnnC1Q//N5z/i/8t6+kfs//2f4J4Q6Y4+z7z3Z46bKisUTEErWhH6E6gzmQSYTinltdk3+E336Xe/SleHEUGMhF8h66e4nPBs6GvlTHfGYm8itn3l8QxHM9HQ9y6Nf0QSfOEAnHYkENH8aYcEDUOiiKWnKp2DKkKKRWU0oqT3Ob2Rqq3dOJFxrzsw7bt55w5hgq267FHiWvGNg+29LV5o/CVEZAlk9fSEAMA5JieKzVbYVMVHzwhhOPIacn1UUy9I+LAmVmaeN9GN8YFEZoZmBjHJviuHdL++BjFASVx9bOPePP5p7hBKNnbqCRNpGnPPJvCaR4H67qDx4fxwejNBbPHl4RTQXMmuwR+QpI5zQpmEufiKQyJWO7py558c0VNe0tOj5YhJLmgHCxl28fmY9IbeT4O1M05qyfv8b7+OnMa+emffG4Nl3c2MnPG96rljt2rj/CbMyteGjFfpHnYwFHyfTyLZSFSt/dY1AL9Gmy18FHsix5YKUf0oHFRCubSPdfErDOHkqhUgpgYolMlVgi5EKmQE8w29k0hoMGZKqgBPXWecfcJlxrPSRzarXCrip70TWLewhfFHF4frsdFyANU8vMusXJ8/seKRmhxCEth1Or1xtGyrfrLP+fYKJdiyiLnCd7k0sQOTSNzKniJZIRutWV9evHzh/kvuL7RBUpJ2eDhWokxUCq4mm127yrOBSgOH5W4GqBfNT6C2SEnEaLYCzvd3ZLGl2iebCPUhKaZzjlQIYrg40DfrYideaPYIqlHL4NQR3JK+Dni4glZerrhBIYzJhGmcsDXCa/mBZCUlr9hniKPLHjM5r0WUHs+jRVFUdfwx0qhUFp0dTVLUDPvKjNarDMVzXT9itXld4hnH1DClppm6nRLTfd26McNiKPkmbK7RtKeqgnRxLwfbXYZPPO8B8xbwEuwj2MHBCgjWsxeurjCnMC7QGy+z4pHvUPGO0MW0p7qO1y/aRktmZIrtU74MuADJkXUHZXRnBVDI4DWEc0jVUyJ9fa3f4OqE/Ob32MoGzKQtCB5QhoyFbGI8NkLEcfh9G0283/Izct/Trn7xLqdPhE2jiinuHiG7/8qhS317iMAxPUslDQplTqPFlDpTQI7u4p3lYAeU52ds5RlZIVqMLTJ9VQKrjo7qHI2Iq2ahXutTcLqAurNGlqCUsIZ0p0hYYvvtkh3gIOwf/MZ2/XP8GcfkueECxtq2DKNe+LrP0Hme/zmEglbQj9ZvEDcIPHEQvrE4WWmxIG77/9nrD/5bdxuTz1bUcPaXGDdGW73Ba4vrVDrkP4SqYXiBN8QtD5nKPfocEYMK7ImuuGc2XXmX9GfQnSQC3m8aWot8w1RJxaCOO7RNBN9pBAslSoEiLFxnAaD7l2wNNlSGtRsG3rRSsnJ7qV2IOVixNJlg31AU2o7rMAStLOhHU1J89hTwjl3LD5KrWgtZrYoVmQuG34pEONSGNgIRzWxmKvVxfZ++dNwBMXm+T5a12/BgWIsh+agKi3nxjnwodnJI0fCpveR2A340FsKejCisWsZOPn2JTc//THzzRUy2v7oRI2vVhNaLKJj9sph1xF7Txc7Yjc335Yl3sEM3BQoyX5HlZniIrgZqRam5/oTql7QlQN5npj3t5CdmT7StyJtgmQE8iQRdR1RHNV1+LhivX0KNfP+936dkgNvnr9cFL02fimZQiJd/4w7sfG5hAHpVrZvIHTd6ug1YyntcDy8ZaFX2Ikvx0JUjuuC+iDBtYRrORKPFyWjjYRcM27zrBBWRVlhJnuegksJSkZLgVJxKVM7YyWrE3CKmzMyHZD9aJ4hLsBKSd7Z6xQD6hUXLFPJFMjLSPGhkFgKLaunvkwStvXykMlz5Js8AuYe/IaOGAyPx2ALEVebjUbJhdyZMaAPnm7YIHKgTPfk7gzUE1Wo8vA4/qLrG12g7A87OgelKLUPdG6Fy3bDacrmXqkQwhrfr5DQG5mRpumf98g0EXxkRpB0Ty0jWjM6j8R+oJMO8RGcwasxBizsKNj8U2tj0ydKHvE6oNM9JTwHMurfI4ZTpDuHdUK90kslVIfMs/E7rCy3LrnNrSVEnAa02u9ZIGJ3lIzZZun6lqBaJ0tvLolYJ7Rket/Rnz7BnzyjDpfMWqjzHXW8o6ZC9D0urprkdkeZr6Hck+syD3bgExI64nBCmkeDn6uZLFU1B1zR0Wb/6WBdhVSo4LvIGHsG35PcHvEH1Pe4XIhRcRHmVGFzYvkegoVRlQol4eJg8mQJuOmVdb7dOT6sKeVAnvc4dYRhw8W3/n3eOE95/UcEeuNJ5D1aJvywAb8xeNdZoZk89Cff4TSsuH79+0zTC2pOhFoQDbhNB2FgOPsR4CjpU5xG0EJWwWWFueLjiFPrNGtWgqzMTKnd4DWuEPcUpENch1Qb4yiAzJZomvem2KjFxjy5tBm01aJVDHGrfoMfnpHXd5BeE6Z75nFP2Qvj53/EUCuyumR091CUWLPlZkzXrA9rQndKXV2im/fxpVA0Q+yQIgQXqeqYTy/QD/8mUjMlHYj5BlcScf4cLQfmrLg+Ifkev/sCqZ5u+wTNM2n3Odx9wrD/lFoqxUdcfkHOt4icE/tzasjofIBsibvBGwcCwDuhaKWmmTLd2cgw9OBDayyEVF3L4WnqG3Xkaom2NL7AQkIUcQ0pMZR0gbnNk6QF/X0NSfBxfsvXKT+c9410u3iatJGd6PH3mnNtPsqDF9XDMjLwS1G0HACCrQGB0qTP5sFSW2HzUPA8HJJtX3Deilhno+XgIzFEGzU7c532zrfxznOuPvmxKVGm2RQ/Tgghotmh3lHVxkvzOJGmmTRPxBSbsUxT6jXDOgVErCA0nawVjkWcZe3EgVgv8MNI3ewpaSale7wL0Nto1BGpk4Dac3AhUHxDn1zAD1uGmjhvTUHJid31lYkVigUSpnFmSiO393/ILB4X17iwNhXUong6hhiCpYw3R97jmWsH8fL/C91HtCmolrBPcQ1t8ThRgii96xCn+CBGei5Kj9JLJkqhc2oFXqsBqgPN9YGiIKDZVDySW6yKakuUzsYnmjpq30GnSGeP12qHhWsix2JkWc+PVWpf51D8+HpwjP3SX18ah331Wn5uKdnMBdWbh0voCMOKMDj0+h6pW9Q7prSj/uVtUL7ZBUrOGR9aXkUWtGRKcvjOU/NohKJuoDs5w3WN8KWQsVCyqkqd99R84HD/EenupY0nxBMEQmwuiW6Niyu6YUPfb5D1JVoyeX6F5ntkFoNJndD1PaUk8v6KKFAk4AbF+TWxvyAhHOYXiCaCX1HdQJUvw8e0hdbCru3/q21+TsrDBlwrwZlhjhaH6EysFlwYTy6I59+m9pdkLWi6Rcd7pBRi8GhnxMk8vSGPt5Q0WRdPRGsmz/fGYk8ZyZX9tG8+BpVSCvvplqKep29/F86/RbnLvPiz/woZX+JJwIwLWzYXT9G6x/drUoWb1y+oATYnJ8RhRYwbUr2gdOfUzpATXwt5LrhSzEJZB3u983O0TvTDU7zfkOrEnG8JbNl2G/x7/y63vmN888c49fTdOV48LjpcACXjVYheefrs1zm8+GM0VC7kN7h6/k+Zyw3l8AloZfDfw8sTxK3ozr/HeDMzjjfEIOB7giTi+AUaJqpuCbrF1YHiQIMajItaUB/OCpOaEda4WKl5puQJykytBwuJK5WaEyUXFjMyzQVyQsqCljkkbKmrt3HrCX3zMeNNQeYddf7vkHiKbN+j6zeMt68ZxbO92JCuZ/TsHL2sOLehdhO+RHBralyjVdC4xql15llOoG6Z51fU3e+Qb38X31+gLkIJ+Nb5a0nM4ys07ZD9z3CHN9Tdc0Kdcdt3qZ/9IbX7mLIpzGVNcNb95zLTx8iUM8F7cjU+mKjSB8c4ZWo1sy1DPjK1qBlciblE1Aw5VfJ0oKa5xUZYgUCLrAghkLPiWqHivLlzIkJtCcbLfVdrNUnysfvkSH713nhQog/yYrPDf0RKXLyO2sGwUDMsFsMOIre0/TwUKLUWQ2GdEINr5FdprrWKc7Gp4fyxO17GNrU1TqYcM6mn9x1d7BDxRGcIT3ABqYndZz9hvntF3KzxCpmFS9fhstDV3IznLPtKq6fkZtDmzfZfWcZNAVVvY9+cbAoyN5MxUePthxUSz3BDIuaJIRf0+gt03CGut0TkApDQdKDM3p6nt1HR0qTE1VusauXJO99jOuz56f6G3c098zyS88i0L8xTZdYbxlSBjfmsuOY5s9BjO8xiX8S8oOWRk+pxtMOxAJS2BkQXDsYyH/LNCFDs/g6O6CKlJAaXkVzoKIQw4Usyo7VgjrL4QC6F4rOhUNFM2UT1GKGgIcJgI0TNakTtNJupXq7YuGCJZFlGVUut8lCAPDZee/zxl5Q6jz/3qEj50rBoee6PCvqHn2kHUi6Zoh1VAho6/HDG9vJ90vUdu90bSujJcc94P//Fh3u7vtEFimsGNs5bJZnSTEXou9Bgvcjq9IJw8sSC7PJkna1z5IZYeKnU8Q16eIEWxfmOboh06zX96m3i6hQJa/A9LgyEbkUKPVECblqTD9f4MDJPX+BrMcKpdPgaYJ7JvCK4inSXBH8O/RZI1HRni0xBMBhaF9aTgrDYfGcURWsxuaSUNvKxQ84UO7PJcRH86pT1+XfxwxOqOsg7NO3QqoS4RXtPKTNl3lGme3S+h2IZEjVlUp4azG1W0bFfmdlUzdRamA57ynxPmXeknHg+vmH88T9nur3HlzesYqS6io+RoonbN59SRHB09Os1cndlaFPakoctU3fKepPwaUTrJaVbt+wQQSk4WuHUrewwLAlXEt3qGSGekOrINO7pu4Gh6yjP/grqhPTmJ5SaiAuhURRqAjE56XDyAX54m/H1H5D0jzm5+GXmm4/RdCAd3uDCKUM4gZMVoud0mw+Zrv8InUd8p9T5Hq7/GMo7hNW75qjZbe3xZ8taksewLdgGNM/kWW1NlEKZG+mYakVoLkhToSiLSVj7n2RUFMIJ4k+oq7eRk3tC/5L9rrDKIHLDfP2G1cZTDsX4iC8Ff1DWf/U7EFes3DmTq4QUEV8N6pUeX3fMvmemUusdXjbk9fto/z7Sfxc3/gSSKXKk21Jcj3Tg4hlFNoS0Q+8/R9Mr8u1z9MmH5H6Du/0ZPq7o6ChujRYrPrUoKWdSStYFT+kYTKcqdN2A+sCUD+Q2GqkzVG9x9IIlHnudLcQSi633weSsZnFvicOL0ZU2ssnxb3ggpbZZunNQ20ho+fySgfKYHwIPBYxd9VFhw5caDpr7q40GmpPw8l2lUPNMZmSe9nTD9ji6MHKnNOK1oSWLS6wVb64dTEKMHTFavEYMRmwVcYRGkpXpwNVnf8pwusWfn+OBVGdT+niHlmT3PpiyB0FrZBpBgn3e4SCa9FUiiDpLa68ZLUAZkeQojSeB6wku4PpTXDkQy0hNI4fda2q6w7nYClBMFTiP4O5QH0EiIMQ4gPPE1Tl6sufJ2++xu/mC2zdv2N1fGz9u9qRUKWWipBc8/+kfIF2HhEYyFsvDEhSNHUhoB/zC2zMZ+4KuLOM11+5D187nRdYNi+xcjBMkviGdkYBlvwXNNqouglSHRmcqNLWog1wDRQuzf1QcVFt/OnscDh8rLpXm2VPQeUZyRkrHEpDuRMCBVGc+YF9BSh5fj9GVr3OMfbC0X3gnj3/Gkh1lhITHP9OI3pVcK9E7Qhio3Ybu5BknZ58w7q9IWRHvKDr9oiP9565vdIEyNPXMlDJzTqgKg8A8Qd8HfAz4rodubfCeJkqZbeGpUOc9ethT5wwV+lVgvT1lffoevn/WurVAFYfrOnCe4ppenYrvtni/oeYDvuuR+y8w6zabB0pOqGag4CtIJzgf6fpTiovk+Q6p+WHDW4rUauxotKCtMNBqhYmqjXEoJid2WpCaGYYN8fQ93MkzI0POliyqVfFxi4bObt75Dt3fwzQSsC51zkKaE1oPODKhP8FLhziY5x05J+YxUeZr5sMtZbyj7O+pdWJfRhyVKIG+H+wmrR5fPPMyI66J27s917evOQicDoEfvl+5GCq7+cDIKzY1UzCpZQ5m+lR9xZWM9zMi4F1HLTOH+spIaLxLFzeMg2fOMyKJEDYM59+1Gcv+BVpHAIQKdUYoBD8wlwP9ao3/9m+Szj+Ez/81u7Am3fyMeb5hHN/gxjP69Tkh9MjqHdLhNenupyCjyZ7jG3RfbbwWB0TfwefOyMP+BIlb1DdY2XXWcdWRykTCZtGUjJAouT7Mvhs5obYkVq32GpZacWocDEfHKGvk7An+cE96PpFFkBpgKow5o97hOmWQwJgLYc74ecd8/7y5V26omgkIRSaq8wQ34ou0uHcljCN1uGD/9Dfw++/Q7T9Fyyu8JtSdUBGkFqoTUv8MXRUcGXRLcivqu+/gr59TD/eIvqFEJYUTWG3RaU+oHaqZ8X7CqUnnlYJ6S0g1gd5kLr1VyPR061NcWDGPe9J4T6E0M3c9zuKNK1Kbe/3CI5GjV8nRlK1t1N57QksgVyqLMdqiTls4K8v3Pt6kF1h9Idsu6MZRRryQcmtgHCecU0Jwx+weaNMTKnk+0PcbnHMkLViGk6njcIYsLGjKA7rUyImxMwVPjIQYTLrshN6b2dp485r9zXNW77x3lPb3CojDS6bmTC62Z+iUqUmZUyFVAzlUywPR1Duk2mjJHAqDNVwpU2U+qnJERkrscD7iugvc6kDMMynvmKa95XX1J4jaUSR5RueR6u+NgyWeIuBjj9c1cXXK+uJtnr3/PV49/5yXL59TElQVlJYGXSb2rz7iZXB0QzR0yDt8g561lR5488oyDuGXEYQHDxBtqPpDQftwGaEUNeL8IiVe3IFdBRcVM1xLVG/E7FwzsyaSBJJmDtiYXqvg8PRRIAuxQJon8mHEh0AVJVfb+42f6I/8xYVDc3QW5udHOEuxsYRmfnW8+Qv9YX7BSOjhMtSnFmuiqzO7BYkdfrNldfkeJ9oh+0p1A/Mv/Dk/f32jC5TQRaIzk6F5nkml0OXCfJiJfo0TpestpKxIbJp2U5/klKjjPTHvmPMdvhOeXnxI2HxI7jZUr6jrbIMBg9urI0jAuVNy2aG1ELyYp0m4RLs1tcxNPZMtZ6NW6jhBfW3E13COhh4NKzts0h2as3Ugaj4jJqk1pj4OXLWqW3xBiv2NZqiJkhLb7Sl6+i4+bJBSKeOdGS75wUYrCHm6p+xeQ7oDMjUqNc3kfE+tmTCs8fEScd58X9KeOh+Yd3eUvY2H5vme8XAP5UBJ90a8C6b0iMGRcmXKDsJsnXsN7PeZyMA+nnP2V36VP7z1/Jv/9l/z7L/7Cf+rv/kDnrx7yn6/o4z3dOeZWC4I/QkybBGNZkymjZwXaR2kMo035JKJ62es+nMmH6gp4/LE0A1w+jY1BNx0B/UOYTaflZKR2BG6U/Lhc/r1O/RPvsdw9h1WF9/h9uN/hrz5M+bDLXW8w81GMq7dCtl+h5v7zzgcbo1D407wuqMglPgttk9+Fc2V+er3cfUKJCOpoHmiiqd6c7iszlNKwqlH1JNKsjyPI4Rm8/hazI9GyEZgTlM7DBwlnhDijK4vqOeZlXyOjHvmNDONwqqLnJ9kVmuFkJieRHKMuOKY91e4mij9GaxW1DlRQofrhDAFiuvswM87S+qeR9zhCt2ccjj9PnF8izI9N4WYVmS+IbieSQv1yQ8p01u4zXNKvkG7c+6e/o+NCHnzMfnmE/o+IETGPCHeEUMgO0/1kLFsGx+Nc1JrxjKNKuJ7Yn+OXz2lG7b4/oYp75nH0TpiNSXNQpa1OIsHdCTnmbgUIUfSoDbn12LW6M4TgielqSGH5VGREY6+KTnnI0dEdSlmlCWl3HnHPNcHHkAbE0k74C2jy9LVbbsuaE0EUTRnql/Ijnbg+BBxLjTztlYgeJMVF6yAsuKkw3fRDBbFUIzgHbVmbl89J7hK2L5lomRf8SoUBVcPZA6UKhQ12XUplZxLI9C2sY/YcwnOzBTBzPdoicxKojpHduaY7eJkUvEw4LotUd8yS4hpJKcvqOWAlrXtaQAlU/MIs3FZkIh4j+Zo5pb9Od0msX2659s//BVevfic1y+uTRlXitmiOUj5jvuXP+H5sEF9b9YGNHc8zCZXuwh4xP/8gfkgM26jOFzjoSxxe0YceTjUpTUgjbxa7WN8RSLGVQxCqYVUKwnPLIUJYayWI1UyBDH5fO4g1YIfhBxBD4c24gfnFidhUGcuto/5q18qJBrp5c8tQPjy9z2odh7IKIaqPPran/tei3HIpViWkDizrBjOkcsPOV1fEve3pKTom1/k4PLz1ze6QDHH0WLjjpJaamyg63v6vsfVhA8disPXSvWK18AhFe52b8iv/gjJV6yGE7onv47rn1LEFCVaMfmix24UrIPBO8u6wUIATXGRkDqiwePkzOaGmi2xN004CjnvLeAu3EN3jkYjDYmscEzU1LJY0oE87g3qbqkMUgBMOYDQyFmF0Hm2T98nbN5Gq5DGmcJICJ4YTilVSPOBef8aLbdozogzCa+UGfEd3fY9RHpElJJn5vGOcrimjPdMuyvS4YYy75kOe8bpQC0Zz4TgmpmamEQ0bulWa652yuGw43vPPsCvTlnNmdKt+fbb3+Hqya/xr/53/3u2Lz+me/dd9rnnzHWojMzjDVy/wa0TWm6pekEIl4iPZFVcvSfUGd+tKXTUkpG6Y8qfU9cj3XCJiwMzZm4X4oq8ugRXCaXFrpeC5gO+D8jZM/KbHZquQDy+O+f0g9/EDW/jPv1t0tXH3N28oJtuGc6+hevPcedr+psv8NMnON8zjhA7j0qivPkp5f7/RBxOWzEZ0P1rgsck4r4DN4A/IVdAI7mkRu4UtEyUMuIVisBAIfsNoak4wOMw07qihVo9vVszByVcDHRxjUw/5u1hZt1nzt9S+gFq9Ygr5CmT9A27PDCWS+b9zGG8Qw4d/bDFd5eUaYUE8Jogm8+NTwFJCe8CZfUOfvsuqTvDdxvq/RVuNZA330anlpKsEQkratig6Zpp+IDiVubF8s534eRTDp/8U1K6ItVK6E8o1aDfPE+kbLJbwQy2ZqmNBO7QuEJW57gYECnkXBmGM/b7HXm+J8b+SF41ib47FiMPBcTCS1lcXqUhlA0ZqaVJj23Xf5BX1uOoc0FLwD0gIM4fC5SslTwmU3g0Y7icZ6omfAAWOXmljRiwIEGZ8Hluo8FiBbfrCD4Y7ylERDE0Rhzioul/nCfG4fin850VEMETQ5P03t+ze/ET5PJtujAAFmRX8w7qgZwS8ziy3+3RUkhFSckUUjkXchIoPZ1TYmheLBIRNzf3ZLBAwYLmGXWO7AU3OUQdiWLFYr/Fl0J/Uihlz+7uJYzX6OoSFcWFSp0nnDiSWMaViBI6qLLFxZ6w3rIqH/DWewd++Zc/53f3v8/N3QEXwUnj6tRKqTP59Z9w38N1COaAq9bQslJUBrKLDPhWcDXaQFOmUE3eXZeBq2jDK4RqNNZHhzqm7/P2e9QXVG0MWZ0VnKUkJs2MKTPVzF4L9zUxUSkCGhxeHdVFBgl4VZwa8lK8eS+JREIfCM4DEWVxFTdvGuSBuAo0x8if56A8HlM+dlSu1YIiDWniWJw7TKl0lGR/pVhRrbgq5FKZi3n8hNCjwxov56z6QLc5pUyjJUD/Ja9vdIFS5pEqmVzbJq+2QLphhe871AvVBqPWhZVMFk9NB/T2CwYmNqfPYP02EjYtNXOPuIiTAfFi8fI+4HxnhDB1qBTr6MW6WapDCfg8UinMxapH50/wbtMCujK13DOVPcyZUBVkZUQ+P+Cko7ie4gecX0EameY9qiPagrJqqVQPnVbWJ2/RPfke2p1Q88iUdyADsd/iXKTOmXR4TR7fUPLOcjgIBLeGzl4bCKRiHgh1uqMcrki7K+bxmjIfyIc7CzycD0zjHp0mvDPo3Ye+pRU7tN/g4ha3eo/3np7idGIYelw0dY6SObl4hy/Gkad+orvc8K3vfovhYt3UFNbZ1PmOWRJOByQlurXSDRdGJJ6TecLh8J2DEJkLaD6Qa6aUiWH1lC70GKNjIsQe9BTmDMy4OkM+UFxP322p2/eY7z9G5pcmTRw2bN/6JZTKjsoMjR8iSLel707JFz8kvbbCE58o6g3horA7vGFFxrvBZIkSEE4QsbVTc4Bs9tkitu0VEcDWZtCACw7fPWV/9Sf4fEXuzhEfKDVRU4JphmnHNib6KPTyObG/4mSzx8ueECBET0kZDtiMPILPEMMdw+qPKZyT8xl3u0QZM/O4ZXbP8KtnECOVTJCClzNqzYZU3d/jr34Clz/ADe+RXSU7IWdPHRPqOuguQTxZO2oY0O4crQ6RSg0e6TtIF5SwZrz/hHFObLSQ1EHskWnGuY4u9oy5tADGaCq6rIR+A06Y5j35cE+aRigWWqcIKZsjLSzqm8dqnQeH16VIKaWYp8RxZGNfk1JqoxxLAl4OokWxs0hQbZzjWuGyqCWaAWEjXNrvNjlqzY3b0hrTGB+KoBACqJgCrBYsbLIVV80UzgUbWxO8hff50MZPka7rbbQT4tGcLeCJEpDqmO7eMI8jfTwBqWgeSdOedLi1MVmqzIeRcdxbcnA1p+OSCynP5JzJ6QBszEfHB1NhOeNfiHhwVhRSrEipTsji7DVxwVBhifj+BMnn9PmckvdM+wmXd7h40ojQzgIJ5UCVgSqB4kPzfhnwYUNYOzaX3+K97/917g47/vD3PqJmiEHwIRiBu1ZKPXB49VOuw0BxZixZqr13ayodK1LsbNzTAh6Pxewj9aSomDqUxdLsawio7VwyubI3e4UWWllFyZKZKuxrYa8zd3VmXzOzKloDqCeYMb4Rn9s6a7gUWgJRItF5glO8UxtbNam+Laz6pQKl2Zocr8foymOFz3I9Vq19LeLS5kkPjsYcx6Kq7oGvFTwSDCn1nBDERCw1RtL4/ydhgfM8WYcKQMU7z8nJhs1mTYwOh5KnkZASNC+AuZQmnxxx8QRdvYPImpxnQ0Uk4kRx2IzPyKLa4L5KUW2Oj209COAj4tZQOnK+pZRMTZno11R/gmLKGhcviCFZMmfeoXKwzt51VvhgckLCQHURH9Y4reh0gN4SPcOcWT15B79+GxEhHV6hKLHf4OMJpRam8Zp0f01NtwQnxPUzJAy0gSs5FWPnz/douiHdPudw+5qaZgurmkfqOJpp3WEkJZMRazsIvHN0w0C3uiT0Az5uqGHDNFZOzy847K/wecfF5bu4k2cm01tf8PbK8b/4n/8t9Paa3hfc9Dml7KFCnkd8sA2csRIQdPLMClHewodIzplS9wziERctXAwlp4maX9trvrogdhuCPzHH0lo4IlxkyHcg5lzZn1ziqexvf4rOrwitSFm//UPqdEUlUve3qM644PFhhTv/DvX2OXn/MaHrTHIp3mSAYpS6ghLlFPyaIg7oEAZL1K0VkY4qtXVYllMRuw1ehSnf4uYbctlR5r1J4asnZUi5cObe8P3z12wvfkRxK9xnHzMcrtEqSFBUIq4byGWHE6VQjHxdbRYuWvH5hljuCK5AVFTfkPQTpkPHtDtlKmdM7gx32tH7njKsCEOH3r0m3/4eojcE/wGyecosET/tKd1ADgGtHep7cj9Qw6UFXGZgztT7T0njFSodpEBXC4fdjIQVhAG/XeH7iQlwWZrivoAokjPZd1ASKc3MOTPu9whKDD3aw25/aEj34yIFFvzb7rEHW3tze33wgTC5ZPmS/T1Y8WGyX3c8lB5v9KYEeviZJRuF98EAbvHMwNKUW+bVgyqExoOJrekxNZf9UG+zfG95Oj4GZEnzw9QpPkQjyIbu6I7rvCcGR/Tmp7S7+ow03eMQqmbyeMt8e0U63DPt75nH0lClVoypWZDlko08mwpjSTgHoY+tSYnN2TYZwlXEbAFUm6KyyaN9i/RwQuh6fByQ1RmxvM0qZ8r8gnm6o/OdcU4coIWQBZUdxZuMv7iAOEuO992GuLnk5Om3+fYP/jr7Q+GLj1/Qd+BCG6E1Xvw877j77E8Z80gaE5ctPLXUwlZNVk10VO+J7X1a1DAKiBl+G9oGSwKH/ftrRyeKirMvDBYMmHMhi2PMlckr+5K4Y+SgmbnSHJ4dmUgowRomqSiOLMLszF8qukBofiJdVQLVkqrFJPpHyXSbTxkC8pVH9zVqnq+TID/2SDla8fOleud4PZgVlpZw7OlCxMuA921clg5ITY8cmP/i69+qQPmt3/ot/uE//If80R/9EavVit/8zd/k7/29v8ePfvSj49eM48jf/bt/l3/wD/4B0zTxt/7W3+Lv//2/z9tvv338mo8//pi/83f+Dv/4H/9jTk5O+Nt/+2/zW7/1W9ZF/FtchynRB4/WSgzC0ydbnjy7JPYdYAvV+WhdlFaYC1pmdH9jb/T2LYofqJNxNoIL4IMl1DZVTAvNaAcd5j+ioe157siidhJJ0kFQRHfUcWR396c4ImF4CqEj+w4XBpCBQnNkLZlSdw8/X7yx4auRDzXNUGaKzrjoWL/zAdqfM5cdsvuczveU/m2kKvl+37KJbggx0J98SJJo8/UslNRcXcdryu4FOt+zv3vF/dVzUOi6LRnPYR4ZDzvm/YE8j20+H+iHNcP6gm41MKw3aIZ53JPHN4Qnb9OvPddf/CFzuiJstpAObHtPd/o+bnVKLDvO3fe53e+Y33yEu1tTp4l52jPurmG1pnMdnmLk5ZQJQxtxnTzBNWh+3N3RVfADuO4EXz05J8bdG3La0Q+XxOGSGAaLJZgFrQeoO6gJV+6YE/Q+/L/I+7NY29Lsrhf8ja+bczV779NHRGZENjjdYDpTFLeccl1EIWQeeMPPNBJPVtoSWCUhEA80AlRIJaSSXKgekHmocqlElRAqw6V84dIZ7OJibMDpNpvINuJEnDhnN6uZc37NqIfxrXVO2AmkKSHdFNOK9Dl777P23mvO+c3xjfH///7E9SNiUw7Xn0P0GTFGol+RHnw7ZZ5YeLfPtpWQRlb3PsJ0/RHy3VfN9tt5D8iID6PhvkOitA3OrU3cifE6VGze7SX1cd1iLePqqa3QmgVe5runSFYSO7ZlR82OoUw8CV/nweX7uKFC+wQ5fQo/fAf1g/8v4gVZFCmLWZab2ijJ259dc7SD2vU6VhZR1NPTVTMjjsDExbCj8i5z9RwPM0WeUNOWnffEi8d4qTRdqG2PLAkqLG6LtGgoeyoil2i8Qod7xLpQ/NwBZIFMog6P8BffhoQVl/c+wvTsi9y8+zlo5sbxx4nirYtRWjUs/DCAwrIcmOcjc55NrF6VaamUViwXRxzLkjnpA074edsRCyeQ1cleXFvl1efLqc0NLxfpEzflVY3QafdZSiOl8y7pQwu9FSfmoCk1d3K1aRece+kGUrXdrHfWsaV32JxLxm5KoxUg3grhU5xEQxAXXhnvDMY/cd6syT4hrjDf7bl5+jXKMlPLYknl8w3Hm+cshz01LyxLo1UTT+ppd9wZH7l3dGoWDofMcKyMm8Y8L3jvSc5burp2LUov4Fq1ZHLJi3Wlvad5h3jTo7jxCbEUhpypt08p8wE/RLrnDWqmyA6dQV0AIiIBCZ1CHTf4+4/Q8m18+7LHa+HuxREfjUuzLIUimVJn7m7epd7ccPviyLQoj6p1hVpVtttL0GaFLs5chL1QOZ1L6GMOtfwcnELtouzzeT89vntXwnua014geFS8dfmbFamnHJ6lNFoF7weigDO5CkG8Sb8tYRHpcnAVTxOTF3iFKELondjTz3uWpaiei+RXj/+Um+cb8VHcKx8/f43CacypesJhNCtQauxmh2g9J5lNI1az2aS/yeM3VRH8s3/2z/jMZz7D7/29v5dSCn/uz/05vv/7v59f/MVfZLPZAPCn//Sf5u///b/P3/k7f4erqyt+6Id+iD/yR/4I//Jf/kvAEj//8B/+w7z++uv8q3/1r3jnnXf4Y3/sjxFj5K/+1b/6m/lxrEXr7WZfr9dc3LsiJEuJ1WZWuVAzlB3ORzQ32ryH5YiTgSUXNF9Dm/DN0RBUZhOmVks2Fmf2wmbpXXaTaUHwuBDpe1RUbA7t2JLF4/yWOu9Zrr+Iv3tBWG+RuKb4LRK2qDc0vONkgV2M5KjNMPWlkJeduXiqZ7x4wubeG9Swpkw7RGaG7WPyMpHvfp62vCAMbyDj64zrhyArcm6UZYdM7zHvvk5Ia8LwwAKuvMetL7i4uMf2I9+JE1vQVKGUSp4nyjyRlxnUdrG4Ck2o5UiZjqhrkBo4zyAH9lXY395xe/0B4UHmo6/tcU5Jq4TGgXWp+HVF/Yrbu/cpfmCZj+zvXlCXIzMCEhl8QgRzIrUXOCrZe9L6Hs6ZS2Y53BFaIQAhXhDjQKkHlvnWir4yE1eXhOGCsLqi5kDNDmSh1RnRjPojbXCM2yfUMnPcvYPsBobN6wybB+SLjwENyQuu57T4sGK4fIvl5m3a9AznEk1WOG/5MCoRx2BeLqngVpzyO7xLnNwfzg1klNYOBIxujE+0snBvrTwOwsX8eaJ8yRJOF4cujdSg3QmtfQXNj4AV4iP1JBTNjZQLMqqB0lqlTQ3vm4lbgoepEgWkekpuSGqIa2YBrOBDYxUKQ/m3lHLBXB6R3RXFr6nhwgr+9hzyBbp+DRkruRzRFkEHmjvixNtyWTKthZ4eXKwpUGcqjrT5KNN+x/V7X2G6+Qq1Lrbo5UrtC7X6YHZRoMwT03y0gqOPbkqt5Fp6woAtyz4EWjWIorkj7P0/m0m1Jxw717+mnRfqU9flZYDgS8GgdqWgjSxOu0ChFHut04769P3ACpRlybievSNdcFjraVvev0fv+gT/MhnZeSXFgdBttk065r6H8znxhDAwDCMpDTbmCbFnBQWiD3g8hw++wO79d03wrsXGoscb5uOBfFgs+LPU3mdyHd5mWP3aKnHwSLTuMRKo1TEvwpDVrNy1onnGqY1zEDExtzajy3b8vneOKg5Jg41rhi2i9xl0R10OHA5HKEdjT4mNTUULmg8gp0JzRocLVC/MzcQV63sHnpRPkZeJt3/lbfa7HaUuHOeF43RgnmfmpbLsjhwOhSlX5mWm/BaztaMLrd2jDluCRoaYCN6s6q4/7KWfOOt+9WvJyYdorPRxSz/x/QroY6J+L0Y/MOcZL5EggSgNz0xpmcYMLhNoHJsSnRl7vXQeV1tMGyJQmiVZO9Fz1+3kBPPnK1PO6IpXi45Xi5Bv9PFXNSmnX4EP1zf9w3LuNHUlLqgRZXOtFtrpsGgEVVqdKPOBsvxX4qD8w3/4Dz/097/9t/82T5484Wd/9mf5fb/v93Fzc8Pf+lt/ix//8R/nD/yBPwDAj/3Yj/Fbf+tv5Wd+5mf43u/9Xn7yJ3+SX/zFX+Qf/aN/xGuvvcb3fM/38Jf/8l/mz/yZP8Nf+At/wfJrvsnDiRKjst1sefD4Dbb3HhCGAXrkuGsLLq7RvrO1NpoaB8QNlOUWmV4gQNWlt1PXpgHxnhY3piPwDTpTAAQ11SpaXwqlrJhsCBnnB2YvuM2nmO9uOe7eJs034A345oc14je4NKLOLLUOW5hsd7qYTqZZ3sX68gHD/TeowxX18AIoxLQlF0/Je5xuiKsntjORkSArXLjg0HbIZG01P6xIqysIF+j4kOHex6CnnHajXRfd0YW4xjwordG0GeFznshLJs/XzHfPKMcbOF7jj9fcvP+UL773ATfvztz3d1y9scHJjE8bqiTrCHlYO4cbHpMPL7g+fMD+9jmimdYgH/egfda+STgiNR853O1ZuYEWExpNICYtk6d9h9w1fNyYVkENMkedaXVPawvD+JBheMDiRnK+s6TmWmjlCD4QUmC4+DhLrRyP7yNxwK8eEi8/Ql2ekfNzXJ0QqXjvGDYPyRePLb84DTbi84b8lz7mcSq4FinazHKnzkZrNBoZdcr68g20NW5efAlxgVETF/593gxfYxy2tPAW9foLuNnRjkauzAv4qLD7LEO6hxJo4wX+ZrFCNwJzo3hBhit0mijTHr9q6CS4qCxFiWuzwfoKtThwSnIw1y4QFdupBXdH8BPz8iVCHch1xVyULI+ZloTOd9T0HKI5IvAbiigia7I4ShETuk57WhOKNtpyYDnu2T39Jabrr3O4/gDKDE4ozTgmLWyQAm5lkQrzkpnm2UYorYIKOS/Qxya0vtiK4IBKo9I7KIhpOvpM9iVIzYSRr4oIy0mk24uVUx1iHRPO/95osSeKbD1DIBt6vn/Oh1jnFYOvnscFvQbgpGehF1AWdGgPgJRWuE6yFu+REE1c74yB4kMgxKGPeE7FiceSASptUZ595Ve4ff+LaDnSnO9AtINpuuoJca64lCz3ypl+wOB29rs3raYhcZEYByBSi2Ud+WB8IfGlE2aNv+PsuQk1U5eJIoL3ilZziqUwQrpHHA+MmyOtVab5iOQtfhhpzlyNUieLEqg7pF3i1XgjxDX4DXH9Oqt7lQev77i5vub65hm73S21wDQX5rlRsqOUmbo8Y/5SIReoRSHPppuojdVGSW2NKqSQiB5wrucgnXsSJqZFoG+E+ym2TzvO45DTpWUNqZ52rd2CW60bFGg2hqOSWyVrJrTMog6JkdS/rTgIzp31SbWXIlFATy43Z0VCPethTlXFh905L3+2b9xVORUqp9GmuJcjnnOn5FR+nboz2jkyTQ010SnLeupCYVl1dZmpyzePkv3/S4Nyc3MDwIMHDwD42Z/9WXLO/ME/+AfPX/Nd3/VdfOxjH+Onf/qn+d7v/V5++qd/mt/xO37Hh0Y+f+gP/SF+8Ad/kM9+9rP87t/9u3/D95nnmXl+CXe5vb0F4OHD13nt9cc8uHqNePUm6swS6MeNCbcoeG+2uEaihYZzkehNzBfqBi4e4vqiZ2K73rJqlTJd9zniA9QVok9GinQCrlJZcAQ7CSJomXC14cOOMd1jGi4Jj38rMm5ou3eQ6Y4y7VkO1voy7UYyGJpETphoIeK9I8qIX28YH3wbTSP18NTSfFeXLKVRd+8bc2L8CFkyshxxx53xXeKGWmZEwK0eEtwjTts30WzjIzxVHKoR7xJl3pHrAcG0CoKgEqjNU1qDUmh1ptZiIj1xBD+wiPLOs6d84VefEfLE7/u+N3ny+AkuRbPESaS5SkyBGF5njFfc5u/i5t/+f0i6J2ztfNDu0FaYjwd8HAlpRMVEiHl6jk8Jp2axdQJSF8rdjNSCjgWJK4Iz15aWRm0WzCitEMdHxLDCeUeePUUyUjNyvAUccdhyef9j7LSQ989wfiQMA37zBno8QlULWQmJNF4wXL1pIjUXUUysaE0W0xG4sEJdwosxKqQ5xM20esS5NYTA6upNDtUxXH+Nx4ef4xH/ho2/Q/Tb0P01uv8VvAp66yxdu3bSaRXiClz5Ksf4EWR4kza/wDVH9QWCGqelL3xhhpagFdtteXVwbNQdPUukoh5aFvwitAAyAtnyceSm4q4qgQNejwiQ2pGVJHL5gKm9zvEwUv2G4gdyzQRgzg1tDhcaNYs5zZYdrVgo4P7m1oqNuhj3Iy9UVUpxyNA7B0BZ5u54sg6j9ipaehIwKjjsZ60KuIgDYoi92GjnbkprNnYo5QTwEmo1yzCcFlzwzhOCjYdLXWzGX1/uLM/6M2fhhJaDYnwTGrRudTbxpJ7/nBcTx4ZgjpDgI+IsHqKqEJ0nV6NFh7ShDWNneEBKoUPoTDeRXCCFgRRHhmHEB8tA8d4zRBNd7p+/w3uf/Vccbp6hzuIUBLFiqZmgWgm4tIbVmmE14mIvTr0g3eZvEwwDgS1LJufK/tB6kaWMopYsXXs2jQqqAa0NZbaHvA/47FB3wDmoYYWPG9zqdVI5sJSCy88o+Q7vR9sQtoNpzGhIGa0b6QdiWBFdQPwaSVfEC7iY4SNvTrx4/xnvv/8BOVfyYpogVdugCqDTLXdf/iXavCcfb9nlyuu1sc2V7cU9g2KmSk0DyQveSdcdCl6UJlZ3OTXqlReoZ8WoXRcn0exLS3KzlO5OI27dBSrO7kfXAW1yKlLUE9X0K4KFc3qp4MSy56TSeqejoUYLr9Xe5752995PL6xPzjN/phm3V4qtV0c450NOOip3/p1Ov2ZPbnils3hWeiGtsZTCXJXohKRKFQ8SkNYM9vdNHv/FBUprjT/1p/4U3/d938dv/+2/HYB3332XlBL37t370Ne+9tprvPvuu+evebU4OX3+9LlvdPy1v/bX+It/8S/+ho9/7FO/i2G7JaQNmgzqA4BP1hJTRVNE/YjD49TamEk8dLCPiqAuEaA/TMxWVksh9oqvTDfU5YZFC/g1HocTC2bSjqO2LlsXCNUDHiX6e8h4ycIbzNWi5GkLrdlOzdc9yGSdA2+2SieOcVjj0gouHhM2b1Bw1OU5yY0wXFDzRDnc4UQIaUupFZle0MoRdd6AqW3Ci8V7a6dgnsv82tCQcMOKUpQUlLK/pk4HSyB2zgLX1NEkoZgup5ZGKzNlmSjLkTaZy6fuZn7xV9/j5p3n/Pe/85Lt658gr+8zbj/G/sUNV+mai0cfw68fUhq88+67vP1T/0/qB79KuLykhUj0CVnW4BQJCfUrJKyJ44Dg8F7J0x6dZwgb4ngPHzcEdyTvnqPTgThsqWHExxFCoFIM798sQDFunpCGC4TIsuzRekRbZT7cMSDEtGF79UluXnyRdnifYf2I8eIxu2lHnp6S5jt8XFFCJazuIccXOPGoixQUaWriSOdNYHvadakSvIIOeL+i1AMhw3tf/0WGw5f57hd/j3X7AppAVusOEywM8S3ysaHcWtZLdTAX2l5wc6FevY9Xo/RWL7S7jHiheiFppMSZECvZN0J2RA9VK1ps991KI0ZHPRi7oVbFNd/trWLMlFKJKsRJIAjHduICTTjX8LpntXydlThK3jLJBVnuc8yVpitKjXiE3GxBzscjTYS6aBesN8ZhjVJpSzUNVp+nK9XGkU2Zl4V5zhhAzfe5uNE7xXIMcE5IPiIhWgdVnD2AzwnF9byLPE4TOdt1gc+UZQbUWvsASHcA6VmXctoNnzUodna7oFR74WQL8GkXapZma80754jRqNGtR3i17kAMYSSGy25XhqrCEAdCWhHSirRa41M05pcTfIzEcc2wWp8dPDEk+529J/hAy5UPfuVnee9rnyfEgGAWdbCcH4JDQ7C8nHFlXKkYccH3MVYv/KpSSmXJFZrt1g/7vRFPawRNCAUc+Ci22ZDQ27AGZKBWasksWQjiiBJomC7FhRUyPGa9rbSSOe735Ok9nAS0LDYq9dBKQJcGy4zGCfFj19kEZBgZLh9w+dpbvPXtv4V3n36Vp1+/tod48Ba2GASnkHNhOV7z/J3CXGcWCj4faK993MISayattrYGpoHgPamj7U0bK+ei02zmp9HhSz2HjQ9tvANi17za2LGduhBiLRdpmMhdq3FqpBLo36M1S1B3Zs5w0ohqhWEzFgbaCmghd+kBSL8G+5+77srGRdK1S3LWqAgvXWicPvZKd+XVwkVfqUbkla99tbRR7flIJVN8tGBZiRbgGBKV/Td8zn+j47+4QPnMZz7DL/zCL/BTP/VT/6Uv8U0ff/bP/ll+5Ed+5Pz329tb3nrrLXbjQ2b3gLFdc3H5iBrW1HCy0EZia7YbxFsLuOcutP4GqgJacXVGMHuaKQcDftygAgOg9aPsj9ccb77GkCdaWnWxU985nX0DBVEstK/scFGQYY1Pl4T1G+TSaPMz63ogaMtUzeab7z3hcXWF6ga/eohsH1FxtGVnlXG6oOSJPO1soU4jtc6U3QcwX+PSBvzKLp5lsZ1A26F1RtT1WfdCa3vrGPkNEtfU5FiWI44NYbhP9YPFbovHESxHpCy0NtFaps6Fdjig0w6f98xlIrrGaw+Fe1eXbO5/lPDwt5HHLYe754QPfo2Lh6+h6vjau1/ns//i/8Hy9k+yvtwgMjC4iMaArB/jxRFTx18LFoQmQi4zUg4WACgO8h1u+wi3eYAPzkZyQRBnlj4EhICrC/Vwg9blHE0ehivSuGWZBWn2/i/zHsQjYU3YPKbsvkqdn+PSY1YPPsL8IpNLRspCTGtk/Yhp/z60gnMB37riXR1NTLwInqZmj3Y4hot7VJfQD94mq2NzfMEnr/8RY/kCum4E8bTpgNd3IGygRJx/jLgKZUc72n6nOUUraL3GvXeDXqyI9wrtuUMXoVAoNRNcRUYFL9QZE/B6oNgcPZzmx7bBQ5qQl4JLgi6Kv7giPH1h4xEvSAikScmuWPeiZCxzJVABp3tGPRL1a/gcqe2S2/kxpXqCCLU4VCZqdRyPyjTPxDBymOyh4FzoOgcHnZzcGiylMi023lEq/jQf9647LnyHmHmz9wd7QNdcaKXvVqVbeTs2II0bSlPmeeF4vEN1B3qyodoIt9XGkGJ359SzpdjkJi8x6bWvzif3T9PaBZXtHKLmvdGgXd9Tl6K2PqAmavUR50ecj50Ku2ZYXxDjyvQYIZgQ1duox6zFI8OwZhhWeB/PxckYE148+9v3+cq//ymkZZyL1OWk2TNHI95BSMRxTVqtSdE6t4bXd4ZlyBNaFpZpYl4yrSrLdDSBqVbK7HCyIQaLkPAu9nvW0/CIRNso1korM1ngZDuuEhEZcD4hwyNCy6zLTJ0O7O6e4vH4eGnjebxRvRWkZrTMlOUIeHxamyh43DJsH3P/0Sf42Ftf5vbZL7GfFxCHw0Mo5/FaiErWhXr3DruvOp6z4MpCyQu5FsZSaUVpY2MYBqQpMXj6ZAd7mZcofCs4XhYpZwfM+allRayNy6uxjLRnLfnA0DvZ0ZluyKu3OClOTQsFKr0+tSwsURu96UxpE7lOtOZoeOsTzm0AAQAASURBVOPBSMC8QQ7nIfT3z3tLeEZPWqpvLKTtv+VvKFy+EVH2Q0WKNsMT5IU5OELogu20xq8ukeG/coHyQz/0Q/zET/wE//yf/3PefPPN88dff/11lmXh+vr6Q12Up0+f8vrrr5+/5l//63/9odd7+vTp+XPf6Bg6eO3XH+9ff43xvZ/nY7/F4T/xHdT4CFcryIKlWVfyPOO0IXW2Nm9bUM2IKtTe3vV206iPSFwhYYVzK2JYWzssJtzmPuLuUZ7/wllkdz5ZnUJp4r2C1IK03IV4De/WDOM9yEeWuqfVHbRC0WrivlJQzYTVJX59D3/vTWT7BMRR5hvL0oiXzBTafEcTcDHS8pHl9n38codLK9tZznsoEyXfIU6JPtjiQKXQehJrMjR/2qL+gioDfm122OISikHhamtoydTlaHqPemQ53FGOe2uPzgfasodS+d/+rz5O2e1IIXK8e8FqvLHU45w5pA3vPv0S7x2/zHv//h+wOnyNi7c+hRvvsfJC6SMtYTGNAtb215LPAsYyXwNCHNastk8I28esLl/DjfcJ49bsjTicd51c0cwllWdoB2qb4XhjN71m3HBFCmYvhwXVwrLcEWNms7rgmK+o83Noz4lpQ7r/CZb5mtoyqSVaGIhXr7Hc3UBdzJ7oHeV8lxqx0nAcxuRQEtoMIz9K4a3d/8R2/mXa4PFYSq/3sByeE5eC+guajoQUWSZFYmVoA6CUfUZWVmSoVuaDYy4Vr+AT+HtCcKYRIDlqaXgX0NpHEE3QquCsOG61kmKiZsUVoVWlHu8Ik5KDIlXIh4bmhgsYmj95E3HPFYoQBqH0wadPjXvzC6pOvHt4g0Urc7GHY4pKbYr3ialk8mIjFMTs+GjF+5WNIEszSnRerEjq768ZMCGECGoiQue8UWRLI9fcabDm7mm1gAg+DjgfCXFA3Ir1dk0atxzjLYf9C4uHUEOmhxhorZnWBbW8Gi0f2kW+tCO/XNxr3426E/rcux5Z0RCsqyJY2Jtg50GSpX83FRN+pzU+jVZsDQkfg3WWFLwLlrkTV6Q4mF4iREIIJB9JIVBz5tnnf5EP3n/bYu+jB9la9pIfzQ0UEjGtSOOaOAymN1ATmNc8G+RxOVCPR+bpyDzPaE86zktmLoWjA08meusahJh6GKMzK7Q6RE2rR600Fpr3NJ8pbu5JzBEfV0h7QNpMDMfn7G4qebmjxQtivETi0LfsC3naWWchzehYzH3kB+tEsMKHJ6w3r3Nx8XWmfEvRBZcC3kVwEM8aDaVqg91T9u/YSKUsmVIqm/uZss3UzSW1bqhpoKZECsHoyf3cnQxPXUJkERHn0UcXpdA7cbzswnU1Y9etJ7uOSfg2mDW3RRPpqjnvXh6nn7yXLp3z1epEWW6ZS6OoIBLxbkAkmkC5eaIkkm8kSbYZ4xSx8vIbvFqEnDos36Ae4dcrZ1++TuvjIItkWXIk9VwlCRtknQnr3Td6wW94/KYKFFXlh3/4h/m7f/fv8k//6T/lk5/85Ic+/3t+z+8hxsg//sf/mB/4gR8A4Fd+5Vf48pe/zKc//WkAPv3pT/NX/spf4b333uPJkycA/I//4//I5eUl3/3d3/2b+XFYfu3fk/Saxu8iTwdquyM3QY4fUOqBeX6Bx/ILAtne7Lqj5jtqPpr1Uyu1ZiCQhnvE8ZIwXBI3D/Gbj+AvH1oSsYvk9T3m6Qo53vW2sVihA9CMeTAfbpFyTQoeWrLd56A0n9BxheZL8nzos3ShLDPSGqvNfTYP3iLeexO3uk9pSi0HnLOiqZSFMt2iYgsR5Ui5fR+mPYQBENrxmnx8H9E9Ma4QjRQC4hISN7i4RdIlPl0Z1dQltCOJ6aI/F4U6F6QVpBRoC+IWnPfkbBCeilKdtYcdIy5UiGsuHr7JdHfDTXPo9BzfhKaOaWrsvvoF5vd/iQ0H4r3XaOqRtjDVBQWDrs0vELGHgj1MRsZhwziucU9+JyGuzMobRvw4mnBQG/W4s1m494add8Hi3LFF0sloi0heKOU9tBxJqvjhyqx+Pays1cXsq34ipK1pBo4vrF2a1vjVFSwT2iZc8IzjA8iZdtgjdaKptd69G22nh+2bWjGMeb5+GyWTZMNHbv4HLnf/kjZ27VMzYW0+ePz930u7/mV825FwlHxLjI5lqkxlwg22k5drDxslfzAhR8fmHtTo8UGpF428b4Yh94okMS1GN4+IdApqbSAO6Yh4slILNpIYM7JOuLagEzYaFHDRUUoj32YaCT8UmuuEkaYELA8LpzxeV1x9n6/cXXI4mGXYuZHL1cDd0RwgiIljQ4jENFKbUpsj18JSiwlX5aX247QzFe2sE+3diqbkks9aE+0OHUrrQsiKOLveh3GN85lxc0lKK/yV4el3N8+JUs4iQYBxHAze5iDG0MkDL0WD54yT2l6OAE6SBDV3X32lg6tqeoNaGyFYjEVeMi4ujOtAaUpaXxBWa4ZxJK7SuWuCTzg/kOKaYVwxDitD8DtPiomUbKR2uHnGu7/6s7ghkraXONeo1eOimDW/25V9h7qJ8yjZwGn7a+bdHXmeWPJEPRbyYrZcQWgn4WyplJp5rrm7fiIhjTjv8ZrtuqkOC3X0tO68qkvGOSPmeh/NYek9kq5stHlxx+bekd31uyxNSWGF+ETJBwtjZaIsO4q/pewPME644YoYRmqN3LYNU3qDsnqEjBOJQPBK8KbTcTYfwaBmzcTryx27p2+z3+/Y7+64fPKCzeOPstx/zKo8YFhvWemKQQeG4IkY2O1UMDvhFVcPXTT9kiPinCP4SAqRUROVhsVbKk4HRCxfSySazlAilZ6O3SrFQP00V1GHOcUElELTQq4Lc5m5Ww4cyoK4gHejsZ3E431iCGvWXJjr0jn8Kw2TD3NRrPhQPTmE4CS0fbWjctKgnF+j/6/2sXarxfAPJeK90PwAwxVp+18J1PaZz3yGH//xH+fv/b2/x8XFxVkzcnV1xWq14urqij/5J/8kP/IjP8KDBw+4vLzkh3/4h/n0pz/N937v9wLw/d///Xz3d383f/SP/lH++l//67z77rv8+T//5/nMZz7zDbsk/8kfPinbR59gGR7gph3NeQ53M/7Zv4G2Z7l9xiopYXMf7dHorrVu1yq0igHIyhHvAyKZZX6fLJ5YnzCurnDhDQgjpYscfRN7s9sph8NORikzuS6U+QWyXKN+REIklIKvgoxrHJ4QtjQ/UvNCLsUEhxf3WD/8OOnyTRgubJZYMsEN+DCQ85G67AjNDPKaF/LxjlYmnLOFN08ZKbek6NHwOqzv44cLI7zGLaGLNhFvcS4CXqTrdhy1FIO0NRB/gj0NaNzY66xnYnmNTSuUPFPKDHlGykTNZnEVcWzfSHZRxwG8R104q7v91cfxtSu5y4R3Ee/vW/Cf94h7E+cTISV8GE1ciiA4tM0s5YBO1ybydaY5COMaF1aEtEFiMjJTm6nOaJsmSOukS602B2aiuReQJ0RCv6Hos3OhlQUXBnzaMsR1dzZZRySOa0Qyqp62eky6etOup2r+/6YFV2e0WhGhulhMSVmo5YiocvXez3Dv2T/GD8UC1ypobdTtp5DDc+Sdn0Ey6ADVj3jZotyxWgm5dN32Wmizor7v0u4CZZPR1HBbRZ3DrUGXRnMNCfZ9XKi0bCJvqkUm4Dy5VAyqQM9RcrBdQ7gPt1+GeSSE2SBaLhGkUOZK9OZM0mBMIJGAtoIUwXllCoEHr3072T2naWBfq43UqrmRPMH+XbMk8VxtZCN94O/UWsQnHYgPZmOVMyKzZ+moxb2f7slTcaJALRWRhveQs4m8Wy14P9NaZVyDT4nN5T1qXbi7vj07GGotLNV22lIaroOvTjCzs2iwC0PRExyr9uLExL9nOZKD0otR5w2aFr091IJfM45XLPOBYbyg8VIPQxeZEhIujoRxZbDEk27EOWIMpBhpNXP9ztss+6dcXT2kbEZiszBKGbY4P9jYBXuctFoo82Td5bzjePuc+e6OVhp5sQIRNQ6IFdOnzZlB3HZ3GRcicRxJqwEfPYN3vQh2pkfpmgwBCgWVpbuCbAwh44D4hKSHhM3E9r7dY9NULP6jWmq75j0lzyzZs+iaY4SyGpB1Io0BquNQLjmsP467umZcKlJviDGwHjzedY4JZjGveWHylWVamG7fZ7675W53y+7uOReHHfNxx8WDidX2AXV7RVutaIN1U4bgOy+lr1Jia1V1Dandst6ajegAdWrcqtZo6lG83S81IjrgGay9oqdrqV9nzToluVWKmP5EsMgDMLp5aXBslbt84Ga+pYoQ/YoopwyngXEoNJcIfiBosDGRnsIx/cti+9wSwq6TLhz/9YyfV1srH8okesXeX8rCNDuGVcS5iIuOMG6/+Wf8N/2VwN/8m38TgN//+3//hz7+Yz/2Y/yJP/EnAPgbf+Nv4JzjB37gBz4Eajsd3nt+4id+gh/8wR/k05/+NJvNhj/+x/84f+kv/aXfzI8CwNWjRzz66Fvk0vB3X++kVaW0QmAixII6bE7OGnFQOQABL56qRyrZ2pLONAzORYK/ZFy/RlhtII20aujkuUzE5dbsfu4kvLMRD63i28RSFyweaELzkbTMhFKIPMDHC7I4JAy2IDlh2D5hfPBx0tXruLCitILScK7f1PlI2z9F2wLDQyimQcnTNbocsNwgJaZE2r7FsLqPxgtk2CInxT+ebH23HmS4gFaWk867ZhOEVeyBrtl2fNiOoOZMDA538QYOZzChGKmlmoj4cEOtd/YeEE0sGoyMGZynqrLaXtFctFRmLbS6mN5CjIrZWrGdZpnQYu1lLQecFoRG8CPDOOLGNS4OqB+IybDX+Agnyq3YaMfuMgtrdGKQJHGpJ47ajrE1jOUiAu4VK6p4S4wWKOot2kCbFbVd9OakC9QEs6IHRwinp1CjVcFi3OzvtMpcI+H2Pe49/Uf4INQQcW6NtAx3R5ybITnkqLRDI8wB154i9y/JNZK2iVozjYKFISuhwfjEkyWT7wQ/C3pZ7YGNR2IlDUIugl8aTUAG6zgQoSxqv0uwMY/zGFAqVZhnSnmOFEd1GZeFsIJyO9NmTxBrl+/3gmw8ftVo+0pziotQF6W5RHr0HTy6+2n2q8Y0bYmukam0RQghdZqyN7R6H4+0syLRdcZJ7Q0xfw7oA+xeO6cXa19QuzakmiDROqS1pwL3sYxWcj5Sqrkh1mKIgnF9yXS85XC4peaZzWplDkK1tcscIZwBcH0bC+hLO6aqCUO7TgBp9vOLQ1vDOXMEpegJ3qIefLokbq6sW5Q2+NWKMA4QDMpm13zEh5Fh2LAaN8brCJbVk1IiRQsTnG/vuP7ar9FcZdheEGLAnd4HVRvb1mrnG4NnzfORoI0yHzjujuQ5m3bBeWIPp1NV6iu7ZhVH63ySea7sD4XtVBgmy0DzknE+9SwpuisHqlo6tfOB6jLZLUgJ+KiIH/Cr1xi6/qg8f8p095QY1zgyu7s9NzeZZzeV5zKxbNe49Z64WjGsA9txQOuGHY8YH3wc0QMcv84qKCk2vOu0VW0cp4m5QV4Wcq4sS+MwHyi7yXhOd0eW3cRyXNg+PECZacsly2ZLKivqMDDGYN3bLqIVAdfdPhXMCn66RrShboBoa5GXFR4oKjSNKLE/+M0wKJ1t0pxDq2MupyKhMXYImjihYk7C2pS5zhzrkaVBcI3oTYsyRHNBzbWStVFaI/VOjD0j2ksxuJOzAJZXXTr9Ou9/6x/7BiTdl94AKIUqC8ficMmTXDCd5Dd5/KZHPP+5YxxHfvRHf5Qf/dEf/Y9+zcc//nH+wT/4B7+Zb/0ND1eO7N/7ItFV5t1XGK5ew8ct4+oKL/fJfkKYwVVaOyBaaW2h9VhraqBVyE4Z0oC4DWn4KOPqHm61RjavITLQvGM53OFuP4ur18w8tJboy2Gi1SgNQhgNRd0V1KXO5GVvM8Oxx2qr2aHXV/cZ778J60e0MFI1U1s2/gIDdcnk6Tk6zaxWW2rL1OWW5XhNmw+2axofMKzv49KAhEiVaDdGO+IWzJXTlIb55ymFthygZUqZaeWIzBM4I/7l4w1VRnCNJS89xjwQ0gp2e6hHWjlSa2UYRkJIgCelLT6usFDFiESQ1rh7/hxpC3G1Zri433eAW/CRUivL/pZy+w6xLRzuPsCVCVwjrUbWV0+sgFs/7Du/iITUd88vI++VU1ZK7XPVaiK/ZrZo281Wqg2pca0gtRcOfcYr0sWiJyeY9FmsU0o7GeNeEkZbV7oImBahd4lqbUjVs1vCnrFCaA6XZx7+4v+JTf6CWdXvIi45iPdom29DPvhlZKMQFT8KrWaKrEnpIWH+KsfjhMuBlFawrkYyy7C4Ag8hrR1172mLEjeR3DI+KEuBFgqldWmAwzDkrhIvBsqyEJ0Y1wJHlEaZrJXbjjM+NUIN1NLQg1AWwQcTq077hvPQlkK5hlpA1oCruObZyo4PPv8PKMcF1y6JzVF1jTYhem+6KLrDodbuhlG0ud6REtPJ9LXnHPLXj6ZWJP56ZLddD7ban3J1TkXMSfTaVGx2f3eN9wPO2fp1cXGPnGeWOTMdj2bzDb4X19r5KK6LARvBGdTLHBFiagOtdj02EzTmbD+37USx6ASvBv5qie3lQ1Mq1Mbq4sIs+jGatd5bUKn4SIojq3HLkFakOJK8te9jiCQvaG3cPfs6uxdfYxxXOOeoZSaXSskZ1SMiRsbNs7FzRCu1LizLQivZqL9dTGzaPLvXSuk0XedIg7OuWc83UvHMx4VlyuTFxibeZdRllIwEj/en3XmzDUo+UnoyswvRBFje4dKIX91nqBOrfMvx+mvsj3fM2fPBs8KX3sv80tOJd3IhPljxibcuefJwIUWlRQU/MFw8RMoR5zLsAsPyHslXtM3UpbIsmWlemObMPBWmKVOrkufCUmemw567acd0e+Rud82j6U3qcWFzf2ZVbD1uZUNOiRQ9wymOQDl3VRCjONsFaXlt0SpdxEU8GS/CIkppnlxtw3vimFj2j13jrWFQvGZu0+paF7oH60z41Dsl0t09jiaFuRTrxLt8tr6jev65XIcOfijB+MPykg8dr3ZKXi1STp87uYW0K4m1WYduWQpRvLkFT2vsN3F8S2fx+OAYUkRLpcwz4zSZX3y8RNJ9/PiAkAJac/eGQ3Im5Kl1wdXKoNX0Fm7AuTU+OmpoxMuHEDZ4IjONPN8g7dZYVCFTVU3wlxKtVkStOEjjQ0JIqHN95LMw3zxjPt4yoDiJUCtxuCJefBS3ekjzg7lkaj7Prku9IR/uyMst43hJaTBNz2mHG5oqcX2PcXNpUDkXIToU2824lpE6ozWTa7YuzGI7AK0zuewRLI9HtI8femdHqNRypE4L8+FIrQu5LMzLxDTdUKaMquPy/htcPXnDhMVLxifTtByPR5sPLzNFrc0fTy1VQteUQxosNCxtVrgkXD74JPce/jbS1Wt4HwhxRYijFQLirFuh9jqgaM0ohdJ309pdH1qLCcfKDC2bBY+Kakab6RNEexfFByumunPCckPMvaSv3GTBWcue8wOoPzDbS3EcKCrVOmsK0UGuplFquZHzzMNf+78w7v4JZeOQu0b9jj9BGb8X/x/+95AucEfr5HhfyQipRPxmYXnvbbxXwgbq0qiXj5D2jFI6X6IBUZDHFcZKO0CWjFtDqYIGtdHE1qiUeS4wCK00SqgQAtPSkMHjXUELNKcQMkkCc7HGvA5iGO5RyHOjTZZ86gdo6pnnChoJdWbO3kSxy8KQC3H9mLp7QeQFX5jvcZc3IO3sgmhqLjrTN1SKmn5H3EsGw2k+/qqToIl1M2rVVz5Xcd4Toj/rV4x/UniJre+v6QLRO0o5UhZv7W+f8C4wpLVtCk46tdbQ9tJ2fO7UnFLGOWHJu5A1JHPq1dzdMZ0aLXSHneIIrFYPaC3YNRgGhtXGROwh4mPqcDZDJ6RxzTiuSMlyd0L0hBhZpwEvMB32fPDlXyQfXoBWG4csE2VeWGYjePqY7L7voy45uUmcN/hlMD2JF09ToWg1Mm1Ixk8ScN4Rm/bi3EYE4gPLXMjHzBQmvBuRUPBaz5sBnOvC4EqZj1biOyOt2nsjSEj48YJIYbU8Z3Nxj69+6Yu8+3zgvdvEL7wLP/eVxtPdHY/f2hDH54zDCnHCfh5YrR8wjiPu8jWc8xZo+mLieHiXViaWuTDNC0uemUshZ9tYtNrsvaiNvBxo84F8d2C3/4DD7pr81sS96S220wOm7SXT5h7jess4DKyGRkoJ741l4052Y7GxnjSHF29/1oZ3SoiBIMIgSq7KJJCplFapNLJax9PEHpyL+Jh6AVQb4j3RJbI3btBquGBdJuaaURXTPYkV/pzGUQq+pxSfef58uNB49Tj99cPBiPLyPnzpv/+Qc8k+Z6OeuixMCBCNGP5NHt/SBcp+d0dshRSFED3H4x2hbRncgpMjpEBjQNLGHAA9DwEUJ7bblgbSDIDjxRGCY1xd4ofXrQpFcLWwSpH54W9F715AMxFpa7ZLKm0hRIjpPueq0gXGsGaqdywoebqh6QJhiw+B1cVD/HhprfjpjlZzp/Z5AyHtn8OyI/oBLRNz3SFFGYZ7SFohw5oqBrzybY/zhlt3fd4rzm6MitKWagLbfEAkgLf3g5rJy4y4yLC+RMVTph2aZ5ZamPLMdLgx4mpboDRcSNx79DGevPVdxNU9667ExqM3fxvx4i1yqcx3T3n65V/mcPM1Xjx9m2l/Y+mkzkOzFvO4Cbz22id59PATrF77CHHzcdIw0rrqHMyF1XRGqUju6bZ4fIhM0x5tGbDzoM2yjWgFUfv/5zm7gsG8omlRfLSbtY/R7OaSs7BLNXcA2EthGL9uPqtdCde62I5eKAkOxaM6I9nap2jgwZd+ks2zf4Ff9YLCNcqLf4PLnyXESN6+hlZBZ8WNtoiUUHFV8dEj0qA4xDXK4auECLK1ubZkh3Q2vLuwHfyiDe8cLloHQqpSaEiz38ENoNlRXCGGAYlKkMpy43CuoeuASKVJQRq0tSNmIe9MPKrFOk5+BG2BZc7dnpzJBwixUiUgk8OJUurE1AopVN7U5/zS0ZOGLXfHwxkVL76AWrdBQ9/NuZeWTXvAvyxWxIF0sLeNTTqMrbaXRUjXqdjXdHFx3yl676lNWJaj8TIEhuQRnDmeWrYU37J0a2kvTlRtFFq1x2pYKnLrwnfvuzYBNcv7SSvTyvly8t4Tg6WY44IxTrzdhy4OViAEY7pI7MLYYc04rLqDZ+gp6ya8jM6x5MLt07f54Mu/wHy8I88zy3SwTtw8m4VcnHVmvEewIuVk224UpLWXImCx0VtcrbsmzRnZVE65Rq7vthtNhKVUWoXd/kiTjv93EZFslOqOvD/pwVqr6DIZtt8HmijeOU4cFYlbxvXrDBd3pPABHzy/5nPX8LlnmZtjI6WIk8rd/pqvveuYtTHWCzYtcU+8RQAMj3Bjo7Ydu+MH7A8vyFOm5sZcC7U2Ss1nTZORoAu0Rs4H8lwpvYjyVanzTJnfYH35kOkys1oWVusNOVfWq0aIgRqU5MMrdvTu6LJLyEg5zrZsPjSKqKUTi5K9I+fKJNaFlVOHF8HHzjURxUvDu9qtyEp0ibXbUMMMo3LIB6aSe5aV4Lv2JLpooaanCIf/TLHwH+WhcNow9FHmuZsiZ2Htq2JabY28LKhA/q+VxfO/tOPi4gGrzaanZSpKoDihTkc4zqSwJ44bXBhpacAPa3y0mXeDc+onPtCkEn0ihA0aL9G4Bp/IZYHlOWhlTA/wjx/1XV5BW0FbZp73HA/X0K47oVFQrHCpzDSXkHDFOF4QVvdYrbfE7X2yDuBMA+KcpZVWBHJmTCuigvcGAWuqltsT7YHa1B6KgokSXS19d3OKvjaQXPAOP27RuOkslNDhVkaylRBsIRzvUfC05YCnUgrkw5G751/jxbu/xov3vsiFFN74+Hdx/yPfiWweQwjQFtIwWNHkGnFIbK++k/tvfqfNdg83lOmOk4VU1bpdQxLS+grcyqBS6mhhQYvNg2s27Ym2Ym3DsDYnQ9ex1DzhyOBCLwoUkYDzZmtUl2zj4XowW6vUPJvdtBzNhUAXiJ0scmEwJoWc/rPdXu034En7cBawlYJq6efBChYQagNXDmipZBwX15/jwTv/d1o4nlHowQmrFz+HlgrZkcr/QLuaaXvQRXA14NfFmCW+Wlrs+iTEa9QAYXA2/oiNWgQWcKPgLiLaRzNNHLU6WlGonjkb2bZQ0eqImwirSyTDwjNkK6hvuOpoc2Pu9unx/uss7z9FUkMnxQdHc6BVOS6ZEB2tYgqv2UYicmy0oBQHy+2eFECLsPaNtX/B7bGiarTW1kWc9AyfVmpP/vUvxza10zjVvr/NdhqituDWZmLWU06OhQG2c+KsiBV6p9Th1vUUpRTEmXA+LwuCM0dYtl2198Es65ju4iSG9c6/Uv5YAWQIekx4Wu36qHXBuZd9c+eE1WoDssE5K0yOubBej6T1Fkk2Jg4xGVZeAiEkxs48MShbIASzCo+Dgdfy7R3v/PL/zM3Tt01ncLQsLa21z6BPLA4IYx9TOW8bgVqp2vAnmywOiYEQBtJgQLUQrBvh/Km972jFcm2WXthPcyZnR1UbVXtvUR7eG9rdRmRGXtaWbaS0zLRgEDp1QhOPD4OJeccr4uUTrp48Z/WVI1/71YXj4rjYDEgcWKeAa5U6z7z39H22rVGaJ6WRzbgipRXiH7NMn8QfP2D/7DnlsDtvMrR1aGHHsjcBvMcrSBO0NVqZKIcbji/e5UYry/GOi4evsT7umQ73mS+uKNt75HljzqrVSIvJ9B89lsA2xa4Lo/sYR8yo4Kg96RpSFbIImk3g7U4YgGY5Tc1HtBzQWnu8hq2pAbiIW7wqST1rGTnIwlQz1QkpXLGNF6x8IjrjAf3HkoxfPT4cgvhhzYld+9ZF+/Bo6NXXsX9rTKBGW2Ce/ytl8fwv7VhdPmZ9cQ/cyt4bCYgZTfsueCHXHaI73LwwXy/4kBhWD3B+g6pQg6M4Rxq2aFOqCwxDZCHQ5pn5eE3MbxPkkurXZLHsFe8jEkfAk1ZPWF0sLMc90+F9nEzEOCBuRCShn+h8PxEKzlrOarPE0B+Q0vHV7jSnPZ1tVatKGyj1fIE4PWUA2ddpRzCfum2iHlVvi7d0nQZG30QNzNyctVkcmSZGH4yrC6pAUkUfKBcf/ThvfPd/R80ZqYdOhjRQs7SKqwYnox3IixDCBcfbI3GIBJdw6w1ptTKVfxrIuxv8hWGWT0LCUg/UkmFWHA6VZsLYMKCMeDdYd8AnnETmfCQQoFVqm03o1Qx8pKduRrXWesuZlztYe69dp+U6Hyy0zPkOCYumZxWlUbr4suKXl/Cufj+ei6K+PTjjolsTs2EXpR4PbA5f4eGz/xuVu96RCcQJsiuEA5CF+uiT+OuvI05IFyNtnmhjoVYgCiSFbG6bsglW0PZA7aaKDKBjFzIGkAJhArJjLopUm3oZkK4RkyBVkVSZ7xRZPoCsuNGdC9syFaiBYQu1CaXdWlEj4IJDvLBMSpuEBOSsTJPy4Dt/P7v3/i112rMaHbL+OEddoC3Mt3tbXGPjExv4V08b69QYou+779qBhdKZJt2W2no+jvfnHZ8VDlakCPSCFpxypmK2Htp3ctKcOyC9aLEU9EiM/gwoa81EoyE4QgjM/Zw7ceR+/Vh7250FJSHEDszq91g1OufJzSoNXqLybZ2a58K48aS0Jo5bWrggjRf40e6VkAZitI6ouMSQRlbjinEYSSkRQiCGPtpxypwn3vvcf+Crv/QzzMc7WoVlyYgaoEtCOD8/RISWC6UX3vU8rgJcsMLce8KJVDskQvDEYNZa7RuHuhRyKcyHiaVmcilMhyNVG3NeAWLFVEy4MNBcsMJXDLqG9HFsnSk5UHxFnHWlqtj96dKW9b03WT5a+fad480v/zK7JpBWqLPiti6ZXdnhV43sHfMETRLDGHkQRoZ4Qd28hrv8dtLFM/LuiKixd5yIEbMBnCMOER9t8zI2j+pEFbGxeb5muqvkeUfeXXO8e8F4/yHzvQeU4xOWzRXD+oLjsmU9rhnTwJAiPvgOvnyZ63NyEmkf+Tm1eIrmhGAXDMHB3CBGTykNrULzgTwpdT6QlxnXXZK9scWaEUexgickVh7wiRgvWMdLLvzIcKK7nktrO74xqM1ugF+vP311zGreJTnXJcpp1HMqZGzEp0ArC3mZ/lOP9Q8d39IFSp73zF7AHxFnoVl+WCNhYxW/88SYrAvVzN3RaLhoeGTTCSlJhOQGm6sOG6omuwmXCV/3tLpAu2XxBYkDiTXazEKrfWbeWkF8M12IXPVWMHSQMPVUTMhpcSxWTQPnCrUXoTTbZWjXuRiIqGsjNJtwVQQTQdioystJAS7dDth3/a9+hwZIO/MYHM0opYop9sUZcdWZNqWvZrZrDAO1rlAKvls4W5mt4GnZGAd6TclHCBfk3IhRSKFbGvvvoyEwl8l+9tIw8FW3DRPADwRnkes2Rik0jjY2MR4YuEbYbJiPUA+3cIJnKTQMEGRFS0HEOhxgv6P4HufeQUItL6C2e7f/sy6I9AVSxLgqp4LGh8EeRk0NZd/6IquGKKfN+GYx71sRHu1+ilV7Fxf7bsgXA58tpgGRCu7qe3C7O+p8gE13WoweUaFtCi47asRICLKg3lGL4iyTHYJSm+AGoR6hLjMBT86K8w49NPxKyBnr2DmhFGUQyFPDF3P5tFkIKFqsexBTY/YP0e0Wfe/LxCFQXEFXCrkRspARXHBQGsk7yt3XCPEBQsb7yO752+wLTFWpWcmGwOTq8j4Pd5kXu53Br7xnLq3DthqoLaCvgqK0qf38csoIMUXQS1jaaWHUM0jROXdG3MOva1O3Uz6J6VxKqQhW0JZSXoa+KZZFJc66Jl0oe+r80Of5J7Q92qhl7sWeQRyhEZPHn/zGzuzKTR3iVqThinF9jxCS6T1iH/GIdQPG1ZaYxo61N0FmiIEYBCrcvP+Ut//Dv+Dm2ddJoVGLiY2Nc+I7o6V11LrtZn3wveNj7kUfo+m/ggl0h9WG1XprJFUtXdNlAMVlOrJMC8f9kXlayDlbYnUpzPNku+RWGFIgRuv0BOdozptIXUC7HuIkcK/V4UpB6gJ+IYjgZQVJWd17izc+Vvne3/UBu5+75bpEcnMcSmE6HNDQTNsnSpWE3tyQQqI04eEYaW1klkfEq4/jXzyj7t6xTVJTwBNTMuFyjzppmFbNYbiD3DqErC7U6Ya7ac/hsGO4fcH67iHLgz3z1SOG7X3SfMlxfclmXLMeLScpxUjzHi8gzln3xJn2CnE4BVebWZQd4K3wcKWQnMMapJlCpUYouVHyYsGnYgGZrZrYWeuMKxVfGyIB71Ykt2J0iejsd7RNrK3xvz5A8DxCfUUQexqtnu+dX9d1OY14zvoTTiyg0xjIinm6juabPb6lCxTVRIhbw8kPG+LqshcfA2DiSJwVKK0Vgg8dnoNhp73dkKY0gThukXhhFf6SacsNUo9Ud0nVii+L7S6lgVjyr9aCqtl2xYVuaXVUXs0ocAZEcz05WPSsfj8h5U+io/6b2Yl2pwelZQcZVyD1FnOj9ofjyTZ7OuQ8F7TK/De+caCnGfCpiGm5t3Y7Q6KdoFJ9DGIrOHrK82hmna2dyutooI0gjtJu0ZLQNpPbipCMyGswMttVOueR4BEZTQjJaSZaOKXKlmojNIchyF0LLNJIXhjGj7DbvYDWXqaNihF+tSqaF7Qu9rug4D0ueAidbtmC3bwG4bAio9tcvbdiw3gcdr5acefxjNbQ2Se9MG2K1ELRO8gZqZVWbri8/jk2/BItRJxmNAY0K6Ejq0tS3KDoV/5f1II5ehRqBBcUifb9GKRjmtR60B68U0pRS/7VAjmQ50IQYZ4j4hqyuk9joZQdblFwxoFQPKWCHOzhr8GhuSJZaN6gaTF53OoS4R7LBFogbCqaBQZbdJpTSqjEMaB3FZ+U8uLXaC7gY2KeD8xTo862a64KwSmlNo63H/CJtTIdlMNRIAaGdMEyNSrWTfHBU+vL5OFGw6k9KGw8Yavsq4vlKecEeielj2OcONso9MVRuhZtWRaawsDa3Dm+25ObPQC0jwl7eXu+Nuw1Xrq6jJlir+1E+mjNrtvgHSU3RGwENQwBnwaGYYv4NU0Cq80GP46EMODTYK4WZ/biOBjtdRjXhJhMc+IDKSZwSluU55/79zz9/L+xUVQxDg4NKxrqy9/b+b4WebFgwRBxIRhgbVxberIYkdkcRInaLMOl5IV8vEVzZj4eWZbCPC9Mh5mcLTKiaqaVwnyc0FaIQ8SlgEuRC98jIFpDnaH7EbOxlqq4YhYzLQqu4cHQD2nDWJX64DW+4zs/ztdefI6ff3umFevSlKrUciRU6yKboHjNC7nhMC/sHl7x0CWqv0fbfpL45I7aFmTZMzrjy6jDUs19fxj394qmCI6cG7spc5gnSmnUJshh5rA/cNztWQ4T8+HA+t6OYbrPtJnI6w3LesV6fUVIiaE7flJIfb2y13bS4Waud6MFqjZCL2QbQlalusLCQvYVHSyktpZi455S0bZQ8p5pWVhyQXF4vwLJEApE6yg2bXjrgXPW6b3SEfl1j4pedLwsUn69tfgMeQO7H/vrng4ntqHW7qo7IwK+ieNbukBZ3f8Ew9UVhIj6QHODPTj7rsVJQKvtWCwzo3bHjd1wogutZYJECIGCw9N6yzHjlgOqR06CoEY19oTaeKI1wxc70X4CivE4xPJ8VEJ/+Br96lSVmgvElPNKF3ieLhSrHl6qwJ0tKKdGmorlZJyqUpHIGYjUv17EIuaRk3WMlyIwTh73ZvNVrdSWu9C0dGEhaK8kTmMR1Hb/Tj1Oe4S9NNBkQlw5Is1cNNKZE60pLIvtSsOWmDb41QUuW1FoOhBzQLRiO+KlVqTOUA60fETrYtqR+cixLLRxhOGKZXG06TnenRqV0sPaOhVzXOHcBTGNiIsmwjNoibXpteetnNqQZ5dGMTGjVgND1YaUcraoVy24mqDemiCXjJaJnCegULQheeb+4es8cf8M2XyCPH8NCTub8atQJo9PENTjyKhXSnbn6wlnzggfHDFYMdFc54D0IEcXQCc4HiopYi37RSiqiK8si+LSCp8+RtXPolOhuIZ4xTWlZqiLx6dGvmvEIEzHigvmNtGqVLllfnaDNiF5Syd2UZAWmctsOoYqTMdCiEJbbPeXgnKcjuTcCC7ixBD53jXaIixFcVvPax/7Tq4PP8fbuRcP8y3i1zi1LsSpRWyWXjXtVSsf2t2duiCnj7Vz18W6KCFYFyyXzEkMbdqU2neyQgqRFON5kZ7nue/o9aVGpX+/49Ggji/vtVODW872ZVtv6F2LSvSxc1OUGDsvo1ViCBA3uLTGDyt88rjoLSAzJQjxZXESB4YwEF0g+sAqDYwx0urM8fZdvvLv/gnT7oYxJpZ5IXuQYsXmMBgyX1XwKeK96WBCCAZlTANpGBk2j/A+9O6U8ZjmZUJqIbhGPu457na0ZSZPmZwL85KRBkE81dWeZmzry83NHdV7wmrFOA5EJ6zVDAn4iMQ1hDXSz1GrRkSlOqT6HrxZgBUuDKT1BZf3nvBdn7jl+d1TPv9exqlH4tDfa4FSyS+uua0e1xx+X9jPjunBhvVqjedNxnawzcX+y0QWgilWGZJnlSJ9SIuqMC+VZc7WDa+NuswWNrhUKnvitGc67Jh2e5a7A/P+hu2DO9rlAZ025GnDPB1Ig+lThnHNOFQbLXqPpb2furZgzC5wVEtbbxXBgxZEKpVCcRVJgeCTjX6XGakTpRXycmDaH8it2donEVcWZDnSHGStEEYkhd/w9P8NXZGT9uubfSCrrb/nQ+TUjz5tuc/35jd7fEsXKEUaSzkiyw7LaFRacL1je2ofgsN2UNBQ1yz3pJmVMkShphEfHuPjfXuA5YyUA8v+y/YwDxfgkrX1vUI99kLBgzhD+DpLtTUgWEBctEJCzFZrrIHSY9+7VqIvZEg/rXryqTfURUAo7dSF8V1c4jgFo1mnpO8k+1hC5TyR7EWP9oXeAEWnYsW5aBZR5whYR+Wk5ai981BbwTUTMKg2Wlt3YXDDSe06jWy23crZwpbGB2hYwbIHzcZawdF8JIQtbrgkLxNVb2l1MVdLK5Rlh5RMma7RfEdejudL24UVIXqqGylaKfN7DHHEgpTARF+hz2JrH635c/u4tUZrS/8dm2Ul5WxjnlYsNbUUhELNswlwq6nom4+g2X6Xmu39KRmvSs5H3Po+GleM+YBcf5nRPeXJ/Fl4UqnzV0jckcfX8NOEtkxg6s4VpZRIm6zl7aOnSsMPDryNBguN1o0NPnqWpdnY/iCUpUF1FhCmCl7xajqYII7j3Vdx6T1CBJNgKD458rFBU2Jy7KfCMATrqEhBHJTSKHMl1N61C5UyKaSIjgWtCzE62s40FTVa5HxbHGWx9jhZqLM5S5aqoCZIrwKehhtG5MGnuD98lv0FvHNXiJpxQU04rNaypot0W1O7j/1LXoOjB0r2ouPDPIZeMJxHq9KLBPvaU15MzpUUI8FHBKHkQj2NCF/ZLZ6Kl1MgoAUA+v79lJwbiK1BuWZE7fyKc/Z6CN4FqkLyDpGJ95+/x/3XHvHg/muk4QrvNrhkBQNhIA4bxvUFq9WWcVzhQziPgMZxwKHkufHeF3+Zr37u39kuezpgb3JD1fgiEgJpHEAcKQbU2SgqRgMcijNNnd2GtsmobYEeZErO5FqYj3vm/R4ttskTFZIPdq12y3Euub+H1oU9HjK3twe2m5FVhMDeCqS4MmdLWKOSjNek9LGrUmnMviAl4p2tMWZtT7g6sEoBzTukjTgXCD6wLIVWhJzvOO73PJ+V1fiQq13hdla+7c0HPLoYqO3j1l31C3G5Zbtq+EBfD4VaM0suVojMmcNhYckWR9Dy3B1RmWUpTAcLKZwPE3nJ7Kcd8zRRp4V6uWXaj0yrO9bre8zjhmF9wTKuGMcVqzj0gtF/iEXixK6RWrGcJsyV2FrBaSWICXvHsCYMHpmOaEgcjpU574w1pdrBlNFcqmViOVa0gawiUhvNyfn7/qeOl/fVf9lzWruUoiv2PjSS/c8d39IFipaFWpydDM0IGZcrhhcvPSdhprbJFrNacb4iDrwXQhoI7hFOPob3A1rVAqrqkbrscSRccEby04rTYt2TsEFcD0ByFiWtLvSux2lEUvrP0CharEvC6cRory4FVQ9Uav+4tclcT0nuXAVxJh4Ta5E6F88dEVWgKiq57+b8eRDYezXWbKG32URw4ql9ZOTotuRe6HgxIVxpFS0mUCytmK6jmmWRWpHWTA9Tl85XqbgYkBRYpbc4toSKiaFsl1uNEitCTBu0VZblDikzlEYre5bpBQONdtyZpTGtwQ/WjQoJGAkh2gKr8ZzG26CXp2bDswThBW2T8VFKseK12EimlQkp1oqmZVwzYF2rR1qdoRmKv8wzoIRuy/RxbTvS9IgqM3naWXLxPJPLjulwzeb4AffDr7CJR/ziyMcj/srTdoW6DwzjR2j8Gto1ARKzBfhpj1UfLExQnXVSCIL3Si3CcrAis0xGmvTiLJF3atTikCCElbWOl8Ugbt5VWvTWvUHIRfEJIwRrwTnBj45ypKcwN0oJBueia7dwlOhJF5+C/ZcsNNJBejAw7xaYlHlRc/WIWJaPKMG7DmgSltoI0ZOcVeOX68h7n/1ngOPNq4Gb447jJPhyJIUNFU8pldqXNSu5Xb+ntRfMtTvDPjwb/1CHpXdL7M/0nnVvo/csnBBipxh3EBvCnEtv2tiO8JRKPI4jcoL4dS2Kdh1FrYXgrWAppdK04ZoQxEa0q/V91psNh90trcHm4gFpc4GPIz4aIj6OK/ADLoykYc0wbLpzp1NjY2QYTINS8pGbu+dcv/NlDocdfuiBoUMknkY4cbQOzGokeEcKDu8AGq11l1OeKcuMq4BagGopEzQb30qDsszkeabmiijEblNG1YCLuVixGCJpXBHGfk+6QCuwTDYO8izEZBRiCXZfikA59XZztRGpGGJgaZYBtvhIrcrtB5m3nzvev4VpaUzTgegtRNT7wJJnDvuJm2PhePtF0nbP9uoJFzjCNnLvySXDcJ+yesJ8fN5FvHu8L7QyM82FJWfmaWKZF+alME+ZXBplKcaMyga0a7VSShfih5ljqaxyptRGy43luGHcrKjriXzYE1cXpGnHsF6zWl8wDxvGYSREO6/BO0M79s1WCOlMvG7NUcW0QwUBNaeTl2DjSu8JFLxmBr9hqZUgkShj7/5aqKBqz/+h9mfPN3iuvlqY83L805uY/9njQyOffsv14Q4vXUHf3PEtXaCYhc3sDKJqeoxsDyCLpha0HdC272MSy2YpyxFSIrpLJFzi0wXqAvNytBZ/XRCfqH5E1BJRvRvw6ZLqVzaGORUJXQin2XDaJ4U7HdGuWs6jFgDbB2I6EJM29a5GD6yjr6GNc8fEWnz93xcLi+qDmr4YF9u9SR9hIOfv7e3Fu2PGtuIn94zRNfsF3n+GhoCGzmSJ+JAItaClUMuWVmdanQz+VjK0BdGOixffx022E1E/WEHQk1y1ZoI4mk9dNLdGUZyvNNlYlyp4wqb0tn48F2Pl3Pq0xOAAHcKGff9arMvRLNa9NUtllXyHzndIy1aoeEPxt7Kn5V0vZDKUxVgtUtEyg2Ykm1DS6wWERpVAWr/G6vI7ud0/xTdwbkvLGQ+E1WMe65e5OtyiTsg7s99qFiKR/H3/R5bP/RR+fhu/t3PsN9lIqt60JSrW5WjSUGmod8yzElyApuS5WtyQKnGFKfsriG94icyLdY9KC1Qt1AxhbIR1RNRR5pnazA2gGXCWCQWh7/Ytt2a1Tcz7hbkpnkjcLky3XyV014wMjqMuuLVy4nC5wfqCtQittp5FZJdkdLCmMDlHLcLNO8/s1hEHTLw+eL4wW+ei1YhPGytCqp4LAe9P+hN5pQjvi5/q6RaxLuUr64TdI840ErUh3b4cguBd7HHw1hkppTDPM6XYCGaa83lm7r0n58xpTAS8AmyjU2G7pVcbWhWXvI1SXIcPhgsu7l2Rm8BwxfriIXG1QYZAWo/4aOOMmDasVpeshg1DHAxnHwMpmAal1czx7o7d86+Rd09ZbS7xwazBklZ45xmGRBi3JrCNwcYGbaHlAzln8jzRSrWHXq74eEBcQBFqPnZ9lRVpraPxrcFrKbylmgTfxQEfEuKsQzNiehuj7wp4C78rRZi1d0R9L+xqRsu+i2c9rRSmpbKfCte7maeHwleewzu7gUyg6IrbZwP7Z5795Ngfd4xOWDMSQmCabtjtZm72hbtyh19NvLeb2ByP5AT3kmdTG7t5C/qER5KJ8xGpC1qOzNNMqY2yzAZ060nay5It4bvaeu2dIwbpouAjy2FiWUongVd8U+q8YtmvKJsjYVwT15fEw5q02TCtL5nGe6xXK+K4ZhhGYhpIHd4mEvAecBWP4LUXMXUg5sX0XGodez+YbdvrgifjvJJUGXxkFJM9lGWmqeLj2EnlJqCF3zjaOR+v6LlefujDoMQPH128w2k0dNokYBttO/P8N1OglDyRpwI1QzmCHkEXGntrD2fwTIgsxJS6ar4RHMS4wacnSHqN4ta2oFKsGyMBVPBaET/i4hZ1kSJqI5yauwCz4NpsGotWaF3XcAqdE3rr/XQupXMT1GBFJxCVDaG6W0ZMwGeVq51Mewlb/JzYjlu1gegrtrVoVmFxHQrUdSPNyKbtVMx4C4+yp6FpHUT7gq/dlulrxyZ0ia9zECMaPdSI1pGYtlCvkLagKog3JwtaOFZnAHMJRvqM1qKnVpwUct6TxgtWq/sca0WZUQ24cNXhRV0v1AmyJnbs1X8rxqEpi135rdJ0QTVDLbS8kOcd1AntWhZpBcHjXEJxlhWTLvDhgtoWWp2QOuPyHrRQdEcrjqU2HMp+vkMWjxxf4I4Hjs/ftffcr4CG10CVSGw71vuvGkckQdmCz42yKEFWTONrpMMzooPqG+KcYfRXBVm6fEpPD1sbZ+BMt6BLpZbGvED0glNYJmhNTEdSMPYEDqmVlBqzE5o0vN9SyhqNGR8bVQc8AXxkmd9HF8VLplZz5IjAPE+0RajOWBflFga/0NYLzjfyrdCqY7127JYFpx7voEq1HB3MVluyoM70SrtFWJpHpKCzIwSzb88HGFziyRpeTLAsR0KX8bk+vnPu1J3U8xpo0DXsOj9ruF7aiKWnzQqngqmd5/21NoY0GvQsRVrTc3Giahh/I602QnCYWUdYlgXvQxfuWseu7wFw3lOWhZoz4zgYLFEbrc7UuhDmgWFcsSzgV49ZXz0mrC8gDoRhhcQB5xMxrRjHblUdRlJMDDERfWQcknXO5gO3N9fcfv3zfP3zn+Xi3hUuOdw4EN2IuAGfAiFdWPGgDa1WmNTjkdzpslorNVfmZaG0O+OGOIuJsEaLCcwNWW64fBO4A91CPK7WXdRLh8A5LCSpWPHdrJe72y1osrDVOHYnXd5T60SSLbU5bvcz7zy/4fPvXvO5d274lXeUt282XLdL5hjI6yvuu8jHeECMSpJqkRyHI9475qUxF2UqhcNckfICOS7spsYUtvgpcOU9S/EkvcfsdjR9AXPGl9lGz71TkqsJpVszjIAEc/Jp7puyZuPzMURiEBqZNt+x7ISjCL6uafOKMs1EPzBsdqTtlrpdU1Y78rBn2mxIq/7fsGY1WrdsDLG7rGxsHURwzRP9wDoUSrONQVVsI+JNvyQlIihJAqOPxBoIEtA49kDQSPCB4N1ZgwUf1p2cOUL2gW/w5NXzdX8a38jpL68UOaZlstc0ZINyDtn8Jo9v6QLl9ukXmH1Fy4K2hdomggphNC5CPtywWQfwGJPDFdabC1b3PkW4/Bht8xFIVwY30y6MxMYdThz4lYFxJNiDvi04LaCLPfS02M5caz+bNkIRqdCha6f21qm6NOvhaeSCjYrCgPjVeScozVrvJ6TwaWFGzXEj0p0I6k31Dv1iMyKjCwEw69pJ0yJt6uMZW4DNoWK5OacRj3WE1FwwWs6C0Xp2Qdiu37XSPz8zOBgvX2fBUXNGa7a0VlcQMRcAZEvNFTWLbp6o4gjDJWG8ohw/wLfZ3EGqlD6asnCydq639aTRadYGrjlbtkY9WMcDb4VOWgMrC4QEnItYh8DGAV4ACeYUaRmXzRKqJaN5Iiy35OMzhnRDW+6o89FgUvm5hdm5NS6u8HHF4AKLNooI28NXGPLXTMAqiuw8pSqyh7I+svmf/w+0d/8FkiaIW0J6wHJ8h0CjinX8WtVOhbWikupMfKgCVQgeanH4oZFnNfKnCt4pSxZKdXhttGpdmBCUJV7i19+OzF9mynfQFpp662bhDM5lsmVatt2Ots5ocA5/sQa9Y5oyvjTyzjFNle1VZJ6ricKHZh2ZCOIrZfIoBR8C4gpaIaaBex/7Lq6/+mvcTZM9w/zJ1tnYRnNKvb+v1DqdQVRWHSi1Ezh7+Y2NKSqcNFdNew5J7waeAwTlPOevaiF11kExgFjOxVwoXR/mcF3c6nHDSCmLhV7Sej6NnMWkcNJ0CSUfrAPlHKUU0piQZs6iNDhqvmP37MjSIhebxwzbe4RhSxpH4jDghhUSR+K4YbXeMKTOPEmGmU+rgSF4Wq3c3nxgDqTj+8QBhvECH023UnEvN0OtUmcbXbaypy4H8t0dZSmUXM5jntqkx17kM7hsqSb4JXemjFj3ZDWM+HEgrlcmsI2DYduDt+Rp502Ls+zJ05FcMnOuHKeJPDgInjRW/HyH1gPIilkP3OyFr30w8Utf/oCf+8Itv/pO5ek+MMeFISlBtuTjHr284tGbb/GdDz5OWN7lS196m89/5ZYpm8boOFuMgDih4aAW3N17HN9xfOG4J42X1Lgxzkh4nUVecJUPxHxjwvecDVfQxf5GiDIOztLdUBZBAH606lR879BLtI52fkGdJuYyshz3uLQmzHvStGYzrVltLyirW/J8ybLaMKy2LGlFXm1J44ZjSgyjjbSj8wTvcRJsdOeiaauiFUlLdWRRVDfk2lAmMyVW8BhKiTiSUvereoP0KScoXz9ObptXC4iXVcgrf4ffWKT0z/FyQ34yE5/CPC0Pjv92CpTd+29ToiJYW1UpVNcoO4jJUfKem9sjg9uQwpoHb3yUex//3yCXn6LEsQ9bxB54wBmD7gdUEk2CKRzaAnWCOtHagpRsBU13XDgXwSWaG9ATylk8ghUBDhs/0V0zYsIRANQZYdH88J3r0KtzutDV/Or9hlPbeYD25F7rMtRWOzsl9t1dxPloSWXOIS1RykKpE6XNiE4EQFwCF1ExpxEuQHCIRgPK1UwtO1qbDf6G6TgMSFVQKqldIsPrHFhAJrwaSRcRXDMejDgDxJ2q6TzdYk/DRJlukXxN9WtOeRxWrNl5duKNNNtb94JZA6MEGhuCXvb301rK6l62GOWUk6LQ2kJw0YoA8R06p/gh2EIdgNjQ4QFu9QRZ9rTDDhmfwXQD8zXMt+Tjjnn/AuqRKY54sTyXMP17WhSQQC2Z5LUDloDphqb/DhcnagYpC8t8ZxqhJL2oadZ86w9tsIaoFbKJnPsDOenZ8tykJ5+2Dg/zChnK4mheCQq6HKh6R9l/BacNl16jxifI/d9P/eX/Mz5WE41X10eFQDA7Ms3hfWGa+vc7BnRpUIX5UAlOzPHToIZA1oqvikrDhS7Pbp6VU65zYffFr1OXjJaKc5FcGtVXJALaE75dYz9PhEHtfuyR8g7Xi/wuej+LV+1smyq+81H6Qiqn9gb01jadZ2MkVnPTCTnPKBaQOYTYF14TWIcQqNWyfLwPXeD80m58Cq1s2vqYxwrsWgopesqiREloWZhqZfP4Y1w8eoNxc4ELnjAkwrDCxZFh3LBeb1j1vB0DsiWGFFkNA1obh8OO59fXhDZTjzdsL7aGxBdzIlHMcbbk/PI9qJmajyzzkfk4dUwAJjCVnjWFdZFa1xucHiQiQkx97CumkxnWW9J6RRpWDMNASNFsuWCE5TzTsuV5HY4TU64c54V5CRCFOEKpO8Zxg0rmeHB89Vr4ha9M/PyvXvPFdzPP94FFKrM4Kgvqj2gYuXi85fd+z5v8r18PuMMV776e+Mn55/nXv3xg0l44Oo/v50Fbpi1Cvr7m2aTIRaGsGuNqzToFxD0EveNSE1EPFtSoSptthKxyKojF2DNxzcmVeboGfXB4cefkdNVGzTPH6WAMFT8QVhvS+oLjYcPmOLO+zKxWhbRM5OOONAyUacs0rIjpknm1Ig4jKQ6kGO2aPRXI4hEJNm6SSBBPcI7gE+twJC8LdEijqDmcxJlDD9c79Sc5iLwUnp9Kh2/WafMb04xfKk9OehMzKBjB+NTt/GaPb+kCRXUyCqhYoJUTR3OVpVRqVlLYMFzc4/LqTTaPvoPVo2/DrV8n033gmlHNBu8Sj/gBOUPCwNWuUaizjTJOdmDx55A+60J40w50WNppJ1YptljWvnY6D5IszfJkCxbpuxjjtoh8GE5lMDa1gqVBJKDdBtd6V0acIZNVhNadF+o91Vmst/gBL4NlMYTRhF1tTy07pB5xbTIEPpbQrDXgwwaRgTTep+WBZbqjze8hbSFg9EXVxqwzz9/7PJdPRsZwxRIzuTakJmyA1IPEzrPHCio0FebpSOEW7ytSollqlfNIS+RENrUHgBM11w49mbaDjpReDOJR37kt1QpIa22brmg57gyRf7px9MSzNSKp4cg9rSN5JUTCeouvK1y6Tzleo/Ea566J4Ug+OKbdNbXesNIDQ7g1UacuUBy5cx1aUHzNtOMzA4e5SpCC0+cojmVS4spuYEtD7S4eaYhvpOiYjgWJpnlIgzDdOrQ1fBTEKdPRimCH0LyDbUGbUBy06Ya6+w94l8kemG/wMsI7/2/cRSIfjjhRqjRCFEq1Gbs0O1fTdU/ADR6ZDGQWUkTJLEWQ5miuoTmT1jZ68iGiRZEZ6vyY4/SCcigo70OwLtZRC4p1NzKO6VjBC1draBOoqybmdoqot/+cfGhBbK2dBXjQbfndHu/dS8ujjeRsufPeOB/zMiEIpdiY4NSRmecT2E3P+rAYI0Xrmclir9OBat4zzXvrdari/akwU+alsb54gHMbdsfnkNbce/272Vw+JqYVYRzxacCnFX7YsF5fdKT9YAVKTMQwMKYBaY2ajzx7/12WZUKnd5mu3yF6mOaF0hacTGcxeCnZKLE9ikFrY5mO1GJp376D50qpll/Ux1wqEJLZYHFiOP312oqpmAhhYFxfkMaBYRiIIdC0oHWmLAt5WsjTjuPhjuP+yOEwM5dGbo3DAdPGqXK1DVCESuCDuwu+/lx599ZxtyQkOMaVsPT8sCqKWzm+57vv8wd+533+++96xEevBtxyxeOLxG7/gl979+f54ruZ4CNDGro12PJonGRYbsjSO17N03zk1kc89xllR0ozQ15wOoMKWU8dN/u9vQihnxPnHMFb5yx4hw+hx1B4clEOhyO7srDMO3b7IzkH/LBm2Nxnvb1imSolC3Uzc1GuIAWYI2W6xcWRmO7w45owrklpJKUVcRiIKRGjxZMEFwid3RMl4N2a4AY2cUMr2aJG8mRJ1q31rKjatVq9sOr3Uusd+tO98vIZezJufFj4+irQ7Tdk9NBf6lS4t/aKVkvO9883c3xLFyid0Wr6gg5ekupZJWWzuWJzdY/tw0+R7n8KLh6Dj6jOOF2sc4EtLuoSGhLNGRWVfDCrRJupHWbmXMD5FeIS1UlHl9mcVRdLzs09PE7VCJ44Ex6qC1acuC56FXMh1BMzof/Xz6r9Xet5nGcPbOukVJ1QCqrN6INqX9u8N6eJd1YwaeexyAQ4shjzwPkB5wbDxwcHtY/HagZm+/lqtBRkCTg/IjLg0iOKVua7d3HLjQnufLLFo8zsPvgVhvUT0vo+1Q0m8lWlSTtf+Kdq/RQ9LqJQlOZXSMJcT248X+RAb1M3Kz6hw35etu6dB1XXDVL275o21FlrtlZQHCEMrLae22d7JN8BBu87EUlTHGnqWA53eC2It1gC46okBCEMV0hY4cKGlo/EzevE269x3A9weEqTnWmTum6iZHChWn5MU0JoNGeE47xX4srjU0WycT+cKLUpOdcz3AgPFMHVhg7KEIVyEEtoVVhmR6kNJ564qpRGzyNSQlO0RiQuhJSZD2I/ezRY3Sw3xHBJlb3ByPq6IU3RJlQNSIy0RfBhYHzyFsd3f9maiUsmDHRUuBKDR10jjpAj1GmBIyzjPR7+d9/PBz/3r9HDLxGccKge9Y6yCNUteHW88dobtPfe42a34IBVEPZLtiJNBrt3tKDN9FnqnEGtTpfVKU+lM1G8O+m3XhYup+5GrdUSiBXmZTmPi0p5eT2cWtjjONiDvpbeNalmeXYv3T2tGVq8tAJYborSCMHjg5BbYbtecxU36HiB3z4gDVvSsCattoRxix/WjKttJ7euSGEghEDqOPsQPVoLzz94j+vnH7DdRO6efoHD8/doCEtWcqk4PVllK6UaKuBkiXZiImjUbMGFRsnZ4IYihDQShobzQkjeHrrB4+KGYbVBYiTGkeDj2dmGCDkvlHln46PpyHKcmaeZ6bhnnmemyTQTuVVqrdzmCZfB10umaWYqjXcm5fnO8rrefP0xm83EzZTZHUdqUcoYWL95wf/ud77G933qPq9fJMZxDSmynV7n4x/9Tn7Xp55yc/d1dpMj0Bi9w7qq5tgSFlye8X6P5sRyFCa5ZLdZ8358k01as54a9fAVG7PHkeAN1jiMqReMkegdQ/TEYOGVJ3jfUpQ5qyXYLwtzNuF9mY/MU6Ee7jgedizHHXk5Uucjev8eUhvjmGhjwkXThs3+Dhdt7JfGLcOwJY4rwmDgvjiMpGAdNh/MLCIuEHBmLQ6JmhIlj5RazGlVK7k1iloHVjllAvX76NRp7PfMuQDhw7JWa+jrh4qUV7/eOnC27mt7ySVSmxj/t9NBMX+LLcat2fhgPUQePH7M9vIT+M0jwv2Pweq+ZY80w4SLi0Z79RENK7xai1OzuVOkTibK7A916VkttvNXZN4boaLZje16gdS8Qz22IxfrrDQJaNe1CK7DxMym66z/ajkPLJ3mauA2UTGRXZ+vmw3Y9fjYkyYlo3WiLHvURUJIqDfrswsjznvU2ehLWajVLKPiPE4GYMCJjVBO80gbIXZYknSGCHvrZqSBdPGIdnQsh/eRfMA1E3LVeqRMz1nXb2O8+jiLGBHWvfKQsBvBFrXaMtIawV+Sq4CbDenfZivqOLXuO/rZ3oWu3znNP+3v7ZXrXdWydhp0KJ+N1GptRuEct5RyRFW6rsYWzZwXihaEGW2ZsszGDDjdoL2l6sTjnYcwgCjh8hE+bFC3okxPO8DYLOBBHZLNOlgDuCYWeBcgDEKbW89rUXRWvBN8FHt4Ivb+q4XwmQ22C56bsBRbAKrajtePjoJnroUxVLRZOmzTQp0d5ooXwuZ1lv1TtC2Iph6wB8KASx4kko+3DE5xFKpzZAWWmf2zr5PWNrIShNVWunnKg6849SwVWCq6h7kJ7nbP7T/5v6IHc3NMtUdA5MYY4FCENikfvPMlqIEoSsseh9mTc62UOhFTAg82vZPzPWB/PY1zrLV+YueYNsvunzPs7WzfV0ou/eLRV/J77HwEH2it9u9vD3kLRzq9nn2HWs2t12q3+feV3H4WpZWC95nd8Y7LB2+xvv8Gq4sL/GqFW61xw5YwXJDGLetxwzCMjGkkhYEYTMA7RIeg7G5vePbee3gHvizcff1z7G/3Np7rD+HWGjF465yICZ6dGnG0qJGWTw6rEA2bH0Yjm4YQCNEbRTjYfz5EQtwQ0hqfVtaurUrJM3nunKC6UOYDZdqxHA4ss7FQSpkt9FHcOUCxtcrxUNDicExUgetp4MWwosqK9TgQB08YPNulcDPN7G8nslMutiNXVysuxxWhGXPFiSJpJMUnPNy+xhsPrnn3RcE5papDslBVaLlho8KCTjui80zec5MCQ4207SPSdkvK4G4ref8OYxDLaAqO1ZhIKTDEyNCR+KqNWqxgnebKfs4sRSlzZSmZUq1g9WJ2/1oydSosFJzOtLxnmY/sDwcuLi65uNiQUuoaKTGS7zTQDjvKsMKPI35YE4a1FS1pRQyD4TKS5SUF78/jT987/LENNBqpVkqr5NILxlptA97aOYH4NJb5j/lsXi1aXvZBulvvNE7kZVFyijI41SStO+m+2eNbukBpeHwcaKXgvOPq6pKLxx9lHO8h4xbdfhJdrcxCJr7rPUy/gLvs88mZ2jCNSTkgteGcjTi0jxgUpWVzhWgthtJ3Ae/HbsvryZMqfYfV8equIt52t655rCQxK6ZThS6ybXVG85HeI+hZBaWLllwX3ga8j1SnSAg4SUjYWES92+DKHq2FJc92ccYJF1dIWFlF7VzHfjekzShHa28jpkM52bA7rdYKgU7tbPagcxGEFXHzCfxwjzK9jywTrjayGv661s9zOVxBugdo14C8UmSpPaBcqzitLLJYFkgJlnnTFqSFLv7tXBdetvDRznY5Tb20wGknfW672CwYJzYhEyUkc/DgQ9f4WGemlqULdwtSLGWzqu02TDDcTHPUx3s2CrKRlW9C6XeqCxFXTRiqB0VDI0slRUOkS6ksO2d4/2i25IyjFCV5pboPq+kt/RmcNgpKaSDNU3MlDEIYHMedw4kFLEpsCCO+NuZFwVekOqpXQnS2gwx65tWQrhjiW0yHL+PcHU0vwUeiGzkuM7kcDbq2LDaGRHHLHa0ZLySkQK3GCjke/3/k/VmsbWt6lgk+fzvGmHOubvenj84OO9J2QlVSlEGyKFEyKRB5wx0SmBtAVoAE3FggI9EILLjiDu7gAnxpipKFELgEpixIUFFlnOA2Iuzw6Xa/VzPnHGP83VcX37/WsYEkwyllSqFc0pbO2mfvvdaazRjf/37v+7yFMKK+owYteYrU3ilSKMWQjUckY6v6k2bAr/0pqoA1eFt7dLsSsBQRgtU2aSXLtt7q/dlr4Tcb826HTlXYbF8HSYOc812Cp7V697kAtYoi9RHGcUNatI/KWse6znd+FNtVmRB0fVmKKqC3pYTG0C/4dKCcHlZMEdwYsf6Ek9O3GMctbjtihwE3bnDDhs3mhO2kptgYIt55hhCZxgFn4LjOPP/0KUtKbGNk3b/i8umH7Pd9PdtZL7ffo6BNvKX3BeVSwKBdQd3U6YaBOE2EIWKsYQgW7y0hemw/mGFUbWtSlUtTkqblykIuSYedUsjLgbQcaalA67UWOHwMNEHXDDWzSiPXhs2Bw1GYW+LFwfKJXDIOQpg82VV25yPnwMM68clm4XouDC3z5uUbPj0Zaa0ytgHrvXrLdp6zh29x//4rTDhQi2UpwAwEhy9CTolUF8Su1FXw08TjOPDovuH9d055K0RO17cw4ZqjrEw+s5kCwVtCcESv/TnSoJRMWjvQrRqWtXFIGlG2RchrxxY0wRvHZtwxiHSFvZIPB/Ja2S8rw/HIvCwsyynTNDKNI8E5wpBxfqX5I3nRFZCPA85PhHFLGs6IYYOPE34YsUGHKO1oioQOCzVe8RrGObwIMTRqa5T+utDnRrk9dIvC7cX0Vj3RRnAdXW7XPu0/9YDphat7t/paUczdcHJ33/4/Cuo+OIWKVSOcbDfce/wF3LDBTQ/wp09ww057B3wA59UL4QbEBYSiSZy89oiw7sNtjFijnSySj9SqLai4gHGjSt4mYJF+c0y0cnsjr90Q0ns0nIXaaNLNapIRW3Xfl2dsOqoBVwRjnbZmdjc4YYv1QRMaiF4Q5mtgD/R+njiB30IIuPiE2haoCUomr0dYFMvthy1uOAM3aSWAUeOv6Yke6oHuxNTHyKoqIuK008MIlYpPDWMTTTIh7FSh8TfUmhhQ34K0zHr9TcJ9RzV64jI4JcxzO1wotr00g5V+svFbKK5P202rCFDAGJKo9damBtJqHwnV2a59LEq2VNGl3a3B9MfSm0VeD4QwIdu3kao4c9MqUhdM0wtKKxkrGY+FVimtYFwfiGoHHtVKS4nSBGRR82bJurITsMEgTmiroXmN+hoDOEHmjC+OapuqDtZSrICHKoITixsDVSqmiN7BV4FV1YpmNe4oVSitYaPDOqFmgzUHjLPa9yNCMdLTBadUrvEFSvk12jgxiFC4IVy8Q108dVnI8yXH/YeMMZCqJZ4HypywTVdpOTXaamnOMExb9ocrjFjyWimrxQWjQ6wR/eVE1TWxmFlf56VpBcuph0NV/HsRwRRDphGsZW0N7zyOTHCAsfgwgCjKvzWNaKsZ0vZkTR/+Owpf+s0AoyCxz2BTeoKrvcNJpMega29DbpXtGMlVGIaBJoVSE3GIOOtYloWcE8bYO9VlGEYslbTu9UARtJE29pqKpQUenr3L7tET7MkZbnvKOJ0xbFS232w2bDYb4jgxhFHR++PAtBkJ1pHzyutnn/L02YecXtwn5syrFz/H9dUeaRqbp6+1bE8QNZG+4lHUnfooAiFGnI93xFplb1jGUQ2Y1pg7nksrhZJn1tLXuXYll8o8H7ECOa/UouyamlZq0oK/6BzG+e6Hg5yUSDvEDeICw7bimPDB4KsQnCHPhbTc4CvIBG893vDo7AQxhvPpik+uZhYS3/wP3+T1qwPf9eX3+J4n9zkZNAywOTnngy++T/ErV28+4sWnr7jJA++GU4w0rq+OfPp8zydXldygGeHhw8jv/m93fPejcx6/dcoZG168sFzbI2a5ZvQr51uPkaU3NBdanskps6xZAW5Z1QhjtJQyJa1Jaa12mrBl2IyADm3ag6WqpVBp+YjMjdUkju1AXTfkccsYJuKg5ZB+iLiijC8bCsYv1PVACjfUuIUwEMcN3m9IccBGhfoNYVDGT/iMVuuswxlHsJqckxBUIWyNVAulFFJrXY27hbP1KoeOo/iM022haSXFnR3B/CdR4j702LvDwW9Y+X8LH9/WA4pznrxmTjeee4+f4OKI3T4mnDzEDDtaT3twu2LxA2IGXc3kI76uiGStkHcBxHdQlJb/SVcXdH+tSZuSC6ZeU3vk1dxeIDEUH7mNpHU9tVNOhSazeluWS2y6prWZ2tMZxg5YHEa8VqC7isuB6jyCw4XQy9GCElBFgUAtH3DuiPOeMk56kw87jBdcPKNmJb+mtUB6iQsRG7fYeIpxZ1QHtFW/r7r2o2ymGN1Tu07HNdYxOE8h9OTNJa3u1UfiA05mxVNXXTsFL5AzLuz0BYvcJXBsj0VLu6VwqhSiHh0PEpB6oJVGw1Jb4a5QqxdpaalW69P8Z2+k3/hh0KO57XHTVJUAGUKglcQ6X+F6KV0ridYSrVVKWqEWvDXQGs46qhnARHwQTXy1Hq0tSghWIq1QCpQGJUHcaPRXOomVHocVZyleuRA2oOpGqhCE4K0ajFvTd6aFmtXTgNPHzBhHTpVa9HQew0hKmZRXnOtqm7dUNGZsfQDjcUZLJa24Xnl/SS0F5x8QhnOCrd1s/gZMJRgIcQOl6g9lu8pQNBGQV8irwzk11tYMxrS+Y77F0zeCc7gYWUXj6jhDqWp89V7IRQ3eulYxOoAVXQc4CzE4ajOknHCuJ824tTP1aodbMNtvOK21po+9sWqkdc5Riq5qBEPo/S8pZ+WqiWLsc8kciqoS6PVY+2+sMn3039a0jlZOGG439E06mwV9jbYmlCrE7Tnb0ydsT+8zbJUbEoYNIW4Yxy3DsCWEiRBG/b68YxwGBuuorXBz+ZqPv/lruHFkCp5y3LM+f0qrlRgHVZX6+6rWerf6ct5pz5g1WO+Jw+06YMTHkTCOhBDwVo3dVipSFfGekw4faU2sa0PE6+EOQ0qrroxKucMAGPkMylelexSsw3rLEBRlYJ1jh6bsnBkw0tiJlk7ed1BbYLWFexcX3NuNnJ1s8Mbgq+GYLS/2haurGz5584ZDESYT+d73LlRF9IF333qb07OB/eUFn579Ks/eHLm4uGBygWdPr/hZ+ZCrtOflUhl84517jt/xwQnf/eSc7XbCGwjBs9kY5q0nHj5mOTynpUwpK8syk9eFmnOPpte7qLaYW/7ObSJUFTwxBppFUHSF7w3V9DVMaR0h0FbSoteXkhKrX7QfaZyIacDFSBwipjbwVhEQKeNTwvgA6w3VDZg49A6uidQ5P94POO8JIWKcwzp9LhzqoQnG4hFCC7SgCpd6VrShunbVR7/Prlqa36iEdCOCMXcrHX6jf8XQVcbfbHD/Vj6+rQcU6zxTEE62E2Y6w4738JsHehO2ndJq9cVh3EjDUtoR0+a+EqEneIx2QJRCKwnTqnaM9N11Xltfu2jUFxcBe9c42ozXfa/oAqdQESm03Jtu00xdnmPbG0wVmtEmTxGNvlrboDRqvgErNBMoBowJOD9S3YT1E9aGO3Ofpm1m2nqkmgWznmL8CYSRFgLNb8CPEEaMaCuw0KjpQEmzDithQtwGiWdU1ztz2lH7cHJSWR+DMY7Wb3TNVLwfKfNL7P5XsO2I2d7HukdIPEE4wVAgv8KECTGDPm76bfcLuP6bv/ExQzRqi7EYydRyhXWB4Kfuw1CTIua25l5hSbe+g7uBXfT0TtOv5TqSW5poQVcYka1Q84wpcz+Nm14giSoO1qvBt6dBpGkjbKsVqSpx2477t61BSrS86ErEWOUG5g4OM4aW9H3to6i4ZlBfR4OWhGqFaDXZ5Ty4aGhO1FybBCkG73W1klNTlc01nBMdrETjkLmK0idFQW86ozWCrZTiMeMO7ESenwED8fT/wvHml3F+g60LwkIxGdt3yVeX11jTNMJ868cxWvW+P9xQskFLhaP2zayBWtU8K91InpdEWbOmucTdXajSIneDJqbXPnQ/iMXgLRhv+g1QB9RbmVluL4II0p9nkdv3uj6wteprSo+AaEWCSE+16PNaq+7jnffaSXI35Kr3ap5njGlEr0NR7UrMbRKh1qo3gGBYDkdEBO8+83OJdQy7R5w//g5OHz5iODklbjb4cYsbtvhhIgwbYhz0tdl/jZsN0QeMCOvhhk8+/FWWlLj/+F3scaGROb54jrP6M0kVmlMlyHjPEAPWafeRcWqWvy0EdCFgfcC5iA+jyvBNse6y7pUse/v+b411TeQk1GoQfD9UdOR61XJMPZmb3mZs8M4Thoh1lnGatAzQOazT79dHizSLoRcyusoyNOZkaG7g/m7LmXM4A7G/7pv0AV2EljIff/gpP7fdcOoMD3eRMG04P9lwehowFxOPtoFf++RDfBzYxR2jN7x4/ZJvvjjw+lhxNRNNYQhwsrHsBkttwv3zDafbifV0Q73Z8OnXF16/fA7lQEoraVm05bo3REs3mbruG6lN04Gt9gSa89jg8J3gGjst9nawNajSVWrpRSiJsmQSM/u9wbnIMOpAO223nGy3TJuBwejjQRPlShWn3C0bcMNGacJxxMeB4Cd8jOQwgve6jvaB6Mf+3KmyYqzWIASn5tmxxe5Z0R65Uls3hctdcAPuLGCqlHD7BuUWat7f17fvq1tW+rf28W09oOR1Ztx43Okj/Ok7+OltbJwQ6+6Q8d54sIHSiY6UI5RZBxLTL5ClQpkxXUmoplK7JIUdMW6LDSPGB8Ta7nKWvuet3ELFbC+RQzKUIza9xpYbpM7qQ3EnVGMVACQGxBOMRdKRtVxifMS5kYAWVmnDbga7UvOBanyXxxzGKl2VlmntBtZLLIbod0g8pfgR4zcwnCI+In4kN43d2lZpy0yZ96pc+AHcgDFB/Sze4UxQoFeruk5YEyI3ekHzKy43OFwSNidgH4JxlHaJcwXvNpSqF05cx4R7h+smPWOVN6ONwrcY7awnVItWhNcFKz2aLHpTUSJ/NyliP3tjcLvvN3ojpEPwTFO3eqfuCkJKM40CspLzUaVYIKUV130MIlbhXM7qqqmgfiBrdPjsFeeSVup60McxX1OT0CIK+ipNTdYNEO0vQSzNVSwee2U1gbQVnNchWPmbjZrARmWcSAZTTd//Sy9pgxAgi75O9LRm7040rWN0drsnHNZL5kWftzwfgZuOxhloy2tCuMDICrInLR8R/Zb56kBNFWN1BZISvf0bjT0bQ1otPlRKFmirnqFMxgfpBFl9LFvVn8sYi+JHV6wVPKL+HaM+IT1lqfRtvcX3oj1HY3CBQiTlWxpsfxv0tFTHpUH3f916TUS6gmUs8zzfrXduhxPvle2BCKbpjcIYHY5EdA2AEYo0aslYa1WBa5pO8M4RrNN4bV8ZOmO028epWrm993nO3/kC4WKH327x0wVuPGGYtkybHeOoA0ocovbCxEjsPqk1Lbz4+CNevHrB9uIhwQRYbmhl4fWbV+TSFH+/0cGDThN1zn7WMeQDxt16F0L3yqn3oKRjJ9/O+ms5UEtRdWQt/bnPfdBWBlKpSRNCrXXFqpfOeYezlmEc2Gy3jNOEWMM4bnAh4oKa971TVbS1Ri0rdVkYxsb20GgOchSMaSzHlUOuFCwfPrvk45cHsnimTYQQSOmKb/7813jz6Q2P3rnH5995zJff8jy5GPHmlJPNfXbTS9bUCBKYgmccA4ODjTPsBsvWV1I5cJ1OgKC0cLE6uI07ijxiOn+H9uknHK5f08pMSupDdM7hbAA+U4VviymNsfgYcEbjyWEY71JPQ4zapWTVAxecbkFyyczzzP6wsixatZBKoTaLHTb4uGEYtxxOdpyc7jg9O2UaNpioouLt5qQ1g10WbPDYMFBipPSYcvYDJkzYOGJ8ZPWLrlK9I3g15zpn9fu2Vl/frTG41lext56VfDeg1tbuhhOa6D319vP/NA3UvSv/h1nxTKPn4q33cfe/E7d7Fxt3OKfOf7EOH7Y6rIj2wNh0TVteUUvCuAAi1HyAcoO0I4aKMSO4U7A7iDswKm1ac5v00RruJr2zh3InjbZ8hHrEVFVpoKi6b0DwrCWhBSrnpMMrDK9JUrE24jfvQHwANqBIe6cSIaIrqFvDX1n7CTIDYPC0eoqpV9T0nCW9glkHEgW1nWDjGXa4gLBToJzxNOd0hZVXWPfADYLyU4oNWCuaijaas9dIkcc1g5iFVPcM9YbClhomwnCuJ/YOcnM2IIa7AUFhRgrHsi7qz2R0ItGTSVNUdCsggrMntFapbeU2NSG3pVfS0OIrjdqau3cIPW6sEqS5BUehEqXcyt8l4TDgXCcHC2bY9DhiwUrRrgqp1JRwXY7FWqyP6ieyvddpFqwUjCm6hmqVnIQhdpd/vW3OhVYNsirtsib12YTBwqCqgaLb+666dehTV29qASMGH63GNbOQm2BjoGWF1FlTaGKxHow0cltptjJsB3L1BGA+3uBON/i4RcKIG+9Rrj8kLyu2VJY001KPdVePjRAGtJgzgzSnXpgmRO+JEZZZDc+5GurR9ACbKmKtWHIyavr1GSqUTB9OPjPd1SqI1chyqzpU1KJeo9oKRRRSqGuEPoQY7pgOt8ngW4Pe7WOpH5VUi5avGXcnOdd6m9xpelPxt7Hl1gcudADq/x/k7u84o8malBZKXVRV6cZY7wdsPGNz8S5nTz7HdO8+cXPCMJ0QpzPitGMz7dhMWwWdxY6zj4HNOOKAWhKvXzzj2dNPGbY7zu9dwH6l1sJy+ZTDccF4hW7FcSBst7geg71VNJy/7cbqSbtW1byZ107OVYWtpJVWEnlZqKXo49hhhtK5S7eU6VIqa83kCrGj7kMHtTnnGIeRYbMhThM+BvVQBMWrq/rbqGshLQvH4579/kBpILmR1sZ1XineUhehSWCf4dUhcSgQvLA7GTghcnN95OrmwNdef4Of+dpHbB+/z//w330n/7evPODMVK72hacvVw7HK66nA7kk7ODYnY88CQNvv33O9749cl5fsX8l3MhLViaOdWI4fcSDkx0RmC7eZnvvHa5ePqeVo4LSnMe70A1xfa3VX2/BuTuFzRiHHwZ8GBiGQAhB+TYh9AFFu5GWJWkDctI0FDVT1qSNyUXguNeBYpzI85blsGE+nLHbnXOyOyMMgdAHU2s9UlZay5BXWB0SPM1HrB9xcaO1Cn7AxhGcpzpHcQEbtCnb+6grIB+wfVgBizfCEAK1DjTRAUXTSlUHW7qi8xsGk1tzee0bCFXayrd8j/+2HlC224Fw8hZmfAfrzxCTyPOMCQPOa4S2plmbavM1srzClhusjdRqMelaFQ7jMH6HhDOIp1g/YazvKZHWZf2MtIyhQjlQ0hHKAeqBlg6d6npE+3F0mKlVf9nhjOnsA4QtaX0B6RVSjjjvsPEMM9yHeAH04j4f1LdiLGKtnnxugwp+2yX30tUbQzVH7fH1mcol2ID3J5T1CqmvNXbmt9jhAhMfYOM9TNhgvIdhQ4tNV1s1KSNmPVBue3msw/a4mnG3zZkn2HHow1Gl5YVqriHsiHGn5tawoYhg+3CiN2vF0Rvbv2/p5MraiNHjWiRLodUjjaUPMYFboqdBlYjaSo9r3+n93GHwBW5NxK2jz7UKXjqV1eCGkVoirnfatFJxTnkQ3gKlkuaVOh/wRrkBcRixTkmprWn9uTiLWL0wtKrsEBFoRQghkCTrTVg8UjOOTpX1lWFnqbl/v6WLC9JzS1bXEmsq3bysr3djwI+GWtU65IN2K9WmRlxbtUqAqmbjVG/AFrKoJFME4nBGS1dkeYOxN+qFkpnaMk4EbL37GuRCq5aUNCLtnOLedYet4Ke0CDV7Xct4IeeGqeA2FoxyWaoYWn8NYA02CNE5bvZKMCxNh3jrXf+8/yxGVTPpz6MqZJ/pyWq47oY8Y+6UzNsP29koADFENcSqHoAWZd56SVxf32hnVOvARGMsUpv2lmDxIVJsJ0DXdifTOxx5bhgnCJZ1rWymHacX73L+4CFxMxLjfca4JQ4jm80Zm0lR9oqzHxVlHwc1xdbE4eoNzz/5mGIN5xf3mJxjORwoFN58/KsEH/CTI7iIC57NdosfpzvJHpp61bI2E2ufZiWlfuPLixqFceqjqIVaGlJaB5DZHr6TnlrpMDdpWhEQPcOkJs7QByMXPOMwMQwjwzDio++ohEYuiZy06yYd1c9xdbVnXoseJARCEcySudxnqhmZq0PsllQNzgV224HoHdF7qMK6rmyNcO9y5pNf+Qb/ICW+efgCv/3ehvVq4Rd+tVLSwsX2hovdKbvNlvffER4nePLkhO+4f859P7HMe+Zq+PjNG16tG8wFfPfnH/L+6Y6zR+8iac989ZTXH+8xklXFvWWHOIcPyquxXv0dvvNhrHW6VvOe6B0hxM5MUXhkrZW0HJmPK8fDkbQmSlp/M2CvFZooybzUA0vek5eB9XjDYXPFYXvBtOtrn6Gvg0LAdvqySFenZcHHhMmqmPlhxJQREyKm4ylqdjTnyS52Bo6qXs5pJYSydAzRaTChBcfYArmHB3JWllDp9z3g7v2nfhQ1p7ffyIX4X/j4th5QhpNzbDhD3JaSrwnzNYQNLmxpYinzDMdPacszaAtiEuKCChu16JPi3wO3A+cxYQNuREzRXo1lxawvMOUVbb2mzJfYdMDsNsj+ipSfMp1/gbpa6vGb1HpJMwPOb8jLgWG7Y7j4b/DTV9jvX1LyL+EZCHGHObkPNmLshPUnulISgbpg7qrMHZhRS+6URa4xY2kY06hGn0B/6zHxAWPfx/uBygDxMZKuEUkgibIeMOsB61+C22HjCXY4oYWp814CIgZCwdaVuhzISSvSaRVCxNuAiZUgp6ybM2IMCnc1TSVQIGwuaDb01Uy+W1IaEUrNtGzVCd6MqktNFzPjuMHu7lHXgfXgoB7UxNwE2wpNHILr1F+hVeFW4BdrUFMHUAslax+SFTVc0nef6lCwGDyut08Xus8k3ZCWPS2v+ndMI8SoxWHpSIgDm2liqRmqrpNCMJRimURv8J0jBz2tIgKNjDcGKfTTPbgpYEJBTKXN4KPFDZZqtK36tvDR0MDpiiWvhnRQz0wrEE71eTbzDKvBTGjU2VaMhDsTalOQCeItw/YJ6ZDxScC96fvhooReZ3A00uqxUmGEvNfXGr4j7UXVHO81VbTOGicsUrWd2Rpd761qnrXW9EIzp34WNHEVnFYfGGeIzpBaw0XYTQM3x4VWu/mygbMaLW6oWqYvp1vFqScN6P6UppFyVUjks1VQpzVb5xjiQFrXvja8Pe2q+lGKEnhrrdCE4HqbOLCuiTAELQ2sSq62RjkgztGVNoeb7nPy+HOcPHmL4fScuL0gbnf4zSnDdPrZTTxOxDAwhsg4RKJT8+Px+opnn/468/Ga6eIB47CFVMhvbmhTYX/1jGkaGLeqwMRJ/x3rFX6Y80LLqTfyqipSsvZvLWuiFUhp6Wqkhg1KbXeKVqt9hS29vRiH85YQFfVOcNgwEKYRFyJxOMVZbW12oasm1uColGVPXo/UmlmPM8uysK6Z47xynDO5KjbBe8+pz4gpvFgKs/Ec5hmCYKYTXKtYH1lLZp0XanU47zh1lnAu2IPwtW8+4//5MvEzZxvCsGW4OeEsX3N1SATfeHI68D3TGfsqmOBIS+K1K4Q4si6N0Aob71jzDTk/ZDNOTBvL1n0B5iv+p6tL3lw9wwfLNg74wWODZ/AbfHT6yyhO3nm95sU49oBDL1xvRpNgaSHlmTwvzIejIhpKoaRMLRkaxGAZgr+jsOacyMeV5QCLv2Y/bNnvLtmcnrM92XGy3bKbVqZJPSc+DphuC7DGUsuiV0AjehDrHVJWFKVgOuqgukDx/i6NalwEHzEmEEMk3ql0uuIMVlXlEj6LK5eiXU9rT8Gqh096uORbN8p+Ww8o8eSJGjHzc5BEs5EQt9CS+j6sh/MnWPsEvxbS5S9Q56fY8JAyniFuQ/Bn/SCuJ7+2XNLW11Be4Q8f09qBAjR/it3ex2zPMTjC+UMcn4fxBBNEQWntEmmzMgV2byNxh8QL5uNLpM5sNg8Qpl4xb5CWcVYl1FYFazzGRESsmsqMntBB9+HGNKSV3n1StIdFDFIyViq1WJqs5Lrg3QYjhmjPaF4www4hUNMrarqGohO5XZ6rOuJH8Ft83IHbICbCFLsylGlpwZhGqjM+D9RyQ7TvkJrFrpmA1RuZEWgjhrHL952tIo3S1ITc9EdCU9l62sulULMOZ04qpsz6HIpKzgmjUCapajzG4hpkmg4bYrUgrxWaVQOe1FWTI00fN91RFFrL2HSg5qOmFWrWlYYZCMMON25Jy6LUmpwxphCc7u4lzeSbV7DMOjTW0omSc+/H0Wh5XoGg73Gp0CkeClICyo1gvSGMjmQr1QndA6wkWHO7IrkF3QneCinzWU1TXkmlk1C9Rklb6UUIJuNEjYrLnBliRCST56fUcgBbcO0V0hws15Aza7XkKnhbqV4oN7qKGk93QCTdvEGy1+crVUqB1iw5F6bRYAaVjMt+T8s6YOQkGFwnSjqsa0gx7G8S3huMVZaMswbJwlxm/e++s9bYvRqpFXnSU0224azHh4iIsKxJGR+2kVNC6c/qxShFo/26nnGsywLQPTYqzTvnSCn196UqWbaXtEHfoyPUUjX54j2lZPWrVE2XZTFYd87Fww+4/84HbM7vM+wuCJsT/LQhTlvGacM0jR1l7wnBMU4DQwi0Wri+ueL10w+5uXqDPzllszkFZ0iXN0BmPbxm42G8f6q8JucwVPJ6oKUjtRXSulJz0rRXVk9Jzlk9I3TOkvkNZYqopyZ3pok1luCVA+NjYBg9cejY/ThgXI+uhgnvB60IMd0rZrTxOZVMKwtpvmE93lBz0u+rNtacqbUPdE7Jt6M1mGnA4LheF/brNTUDYsg95jxvR+JGB5fLy4VmAtvTMzbTyIMAeypPj3te5kIdCveGHYb7uFR5ddzz+YcbduOG/PrA0zcLr17ccHYWeXQxsZlOefLohPPmWL3lYtSizcGPrGFCNk8w5+8Q1wODd4zjwHYXCWPEW0/wBhd1lXWrNNTWqEUVvvWYyalQc2Oej6Q8U3IizQe9BiWNa2stAWxiwHtdI5WclXhsW1+TCVIylZkFjdZLTdhasDXjTcUzqXqStRoE63WACt1fKE6VMt1r6wx/6yfpqVdxUTue/EjzA8155mRJfcXlnL9b391aEgYX9doeGjVWNjLSSiWXfKeurGH+lu/x39YDivE7al2hXCqSfThjpcF8SbAVP91jzUfq1TfIaa+xt80HNH8Gtq9iTNGJLy+0lDD5gORLnC3I7vN45/AukkWr1b01ZHEKGzMT4jJh9JTte8jhKXV5Sssf0dbXyPIcsySMPUeAZb9oKV8/kYXgseNjjVb2i69Y081s7k7ap58OsQbXDBjBOY+zTk/7WMLp+5TjwrL/BoFIKzfUBs5dYgA3O10ZGe0z0ck5QFWzGjkjZibZK3We2wEbJnVjuoCLp5roiSq1VzmQTVA/QNb2We8MccjU2WEIWLelmnCnbhgKxiol93YdU0tWab4JVTJSCrXL9dgIEqitYuqqJYvStzq3zcudvUJP2yAVyf0iLKL1BX0toHFwT6kOBosJI3HsMLbaqFQdQlphsKOalH2iLvocqAyk0v6aDkhJTOOGYA2YQhbhsFqmqGpJd4cquKonYJqB2sBGJVCuq3qUxfXHRA1Lanh1gDO6CtLtCMEbijhqq5RZCB6qE8SptyJ3vkernsVXpFS8G3DT+7T6mjxfqupQVpb9K42Si8UMI22eabnoT2o8DcHZSjnOlLJQk1BaQUr3/RilHTsHuQjjdmC32zFf7fFRPTrWmN4CrCfzlPrKR/S0flsaYYwlFe3iEX1zYcVQ6bvsJprMRIcawVBL6R4pjzP6+a1gQl8paf2D5zjPXR34DUCzviPPOev7yXlKKd2nYu5WPnTLvVZQWPKaCEFjyLVmHE6j0/GE8ydf4uF738XJ/bcIm3PCeErYnDFuL5imHdMwMYRuiA2BsXsSDEJeVl69+ITD62eYEBimU6x31P0RefmGZhvMb9gGSyaQcmE5rtqzgr7Gai3K4iiVTt7vnrZbvN1nUc/WRIFjKSl3p/sqQowM09iHkxEXXG9cnnBu6BA3202y6nOppZDWGayllaJclLxQlgPrfNTra60463FYvDOEMOjqwHtaSewXXW14KdhqaC1oweKykPHcANOT+xh0kH5++YYLPPfOtmyD4R1Rz9uHWVlFtQnJbjnKGdeL47AmBiuk6z2ffHzDx9czJ2dbvus94UvvTGzDEWFkGA1jWyFlUtVB/OoovMnnyPAOMSaGrWezDdih02WD0fcDQK2UnFhTJlVDSmr6TeuqBtOiseWcMkY0PSrt9rXae8a6UlhbL131TodYtTmDdRq7t06LOSkEI2yCxdUVWQo1OfWp2UCxFqYNxlZC0Pi5rUWxCWo6ovXklnUOYwPWF2yLqs7XGXpli7hAcZFsrPaVdQXMeY00qy2gJ4MExN9WLjRSyaR+QPhWPr6tBxRaoaUrPXH6UVcBhzfIuietz8iScFbwbaaFEeseYGznozQ1tpaqJzHnHG7cYnYXYD7QYrEeYbXGYRrKR2kZX47UetQbWAXrPcP2MW28j12/SJ2f0vZfh/zr1PWA+Ax2i5MTfTE40Te6NLwRso00yVrc1PeXRjqSGuWQ3DZB6v5TI8rWOAUu+R0SIthEdFtIR/UUhAnJhbRcghRNFriIM1prX42Cg4y5dVfr7rlaNWmKd9ATQNZvcH6CGDEm4vyO1rRwLHhHWo/UlFXlweJtxI2AGSitt0LXpDdHHGC5yx6jEqjUhnFaJW7jTmPFfV/r/YZ0eEZdL7vfxGGaxZaitQVotFZ6jJSmu3I7TOC8/ltNKLniGxhJlLLq66AVJM34NmPE0sptgkPNX1jXkyMK5xs2G1w9USheFeq8x60Fg+GYDGsSNq30AUxv1OKNpslKxlgIg9ehbFVFoc6COMEbq783qC/FRU2LtWRVtWiCmIo4hzUVmq5UTGjUZHoKRfDhhOpOWfInYCC1E3LbI6VqmsI2VTl8opkIacHVjBivEK610QpEbzEeijScgxCsEnD7EBii65Fex/HNNfn6hslpHqlUQ7UOMRVoxBhJSW8czquCliusTYsKCTqu1P6ysK4bWHuygN6/o2j+bpBtjbWsQI8wWoezyvzIWTkeTUQhZnCnHNSqBlzTvQLaUGx/U1z9NqJ8mxbTfpGqA3pJ+voFasuEeMru0Rd49Lkvcvr4bcaT+8TphGF7zjCdMo0nTOOGIcTes/NZrJjWyGnhzatnvH72EU5gOLlg2kyYlsnPL/Fzom0c+eYZKSdySexToqbbIji9o5VSyXmlNT2A2S7b38asQaPWy5J6vLXpmssF4jThXGAYJsZpYhgD4xixtymg0FcG6CCU1yPLkjrNW1uWteBTKOsMLetJPxek0kv8JkJwyorq66RaGzkXUhF8CFycbWB0yNFykz3H/ZHDmql2YL13wWgt4ziy1ms+fPYSFzwPTidOR8PnfMDPlmwnarPk5nnNyFmBZ1eXHN+85vmzGy4vHZ++qbw5zgRjoGZej4YaTjh54gghcEbik2Pjw6vMv//I8uHhIU+CsNvcMJwI253VtIwxWNOouep1cE2klFjWRG6WJRXKmjUMkFb1iaHPjzMWFyPi9YXXqoL1MDoAW9HkUzDKsjEWjHV9paYKqQDbzY4pDjgqtlZyqv2Qpn/eGIPkCaEwBE8YI9H4fpjJ1J5AbbXSiHjr8KZhWurqOQoftR5jI80FPfxZT/WeZD0ujHg/6evFu47eV/+i9x4vQmgDNeVv+Rb/bT2g6AogYu2ZGjPX15h0SSk3wAFrnEZzwxnBntJEyPl5B16BmA3iT2EKRD/03hzFOxvjqKZgTdRYIQXrRpoEfHVYo43AJi2U5QrW1zQ/4rcjNn6OMt2jHJ8gh4+o+9dIu6L6BePjnX/EUbBeKMMTNZ5ZSzN6wW/lqNdHO8BtpFa0b0OoBD90CT3piagUzDAS/Rcoyx6Trsjza0qdqWmG5RNa2SNODcQ6+oyIWHwIOhQZQ4hDH4Sko64jxiqps8YzWvQ4fwZ2i3iDcyO5mc4ogZwTyIyzV5rICFuMHzXpJBljAgXteem4MgSl7mqstqeTgppzpVV8gNIS1IQruk6rzdDqojKmWCXumkZLR1peGDYPmE4uMGHUmzrKNbG2aLNwAVN6kl9ufQyGVrVD6LbfyRh6nFuoJZPzTGi53zyF0VaaLFjRtFiyjdqcwqMGTaxEx53prLamw0hW8mQRwe2MGmWDgtRKaYjT1l1xDXznsWSrXhbbiJv77PcvMWhKSEF5DWOFYiy5XCH5GvEDRSqDvCDIAQmwLhkzGvzmBCNLfxwhG0epjYWJ7Xf+95iv/4/U9SnrviLN4rHk0qg0mlhdKbaq9hVrSEXIooTKaDrZd654A4sYjovGS51VAx+mUaS3VtPTcQhZAbEdfoXOsuhr37oe8zcGG/T9qnF1bVA2Vj0jrQhyG/gSwQZdWSzriqB8nGAtOWdKzvq6R1Hwd1h8Y/prQFdNzjlapafLtNVZxFIJnJ29w/13v4uzt95jOL1g2J4ydM/JMGyZhpExDgwhMgzanTLEAW8MJR25fPGUpx9+HZpgz+5zsr3ARMPNNz/Fvb6iehBWDpcvOMxHaiocih6QcifH6loMjDisBe9vDbOG1srdz6XlgALOMgxKHQ29mdjHSIwjwzgqNt3ddoI1Wlk0YpwyyzKzHo8cjivOD6o+pZWcNc2G1N7u28sHXSSEyDhM+ly2RimN1BNbw+YCu9ErwmlLXDTD6X7lo5cwHwz1kDheH7mcj7x7MnB/G7i5OOGjNzdc31xxbzcwxoCPnnG3obmBWh0vj55XyXDtRyQKdSm8WWY+eZ5Y24gT+PTTA9f7Gd8ywlPs5mPe/9yX+PLje7zeO/7j08LPXzqyv8/3ec+ZfYkZFqJTb16uK2mZmY+Z2jLrcWFZFpY1k0p/XTftvdF4u65DnDPYpgM3XRuhlxPaMPRosmL2vVczbrAe3823weu9SRVAp6+BkilWyzzzutKq9rUPIeJsIIplNDDSCKb21Xvua27BBG21t8Zqzx0CJekaCYN0T4rtnW9i1YYgflCDr5u1S8iHHrcf8EEj7855rHPEOH7L9/jf0oDyYz/2Y/zET/wEv/iLv8g0Tfyu3/W7+Bt/42/w5S9/+e7P/J7f83v46Z/+6d/09/7kn/yT/J2/83fuPv/1X/91fviHf5h//s//Obvdjh/6oR/ix37sx3SH/Fv4aMYiblA5t1xBvqKWG6gNaxWzbSRgZKW2l9SmxE9jIybcw4xnGL/Biqf1grfaFjC3sK+eDrgtCfMR6wY92TJiw5YWdrjpAikZmQ+0dqMeB9/w41uIOcG4K+bDhyzXH+LKjDNHhmjARezJfUJwSDWUUsBqQ7IxPeYVNtg46iqkGxExTb0WJVFL0kjvIeGjEkQZthg/4eIOSQdsuAfjjvX4nLbMcHhFWV8QhjNMvE9uTnP0NbFwg/MCbQ+tYP0G7weyUS6GCRFvI2Y4h+0jsj/Bha3WAFjX1QZDTgsYj/MRI9rEagRMHPUGk/YYwJuR2nuAtFTR3MnHrWm6SGSF9QD7jyjHZwpScxNSKs3ooGa7F6OJZ9zdZ3v/bcSNlJy6WVdjxoiQ15WyHJCmceJaVkpaVMES5bJY55FsAEcVh5GGMz3RVBW9n68/wqSFnK5wKROKwp7WVcjJsI1OmSugHUlNVTJxUGrTjiBv1FDrrcLZutfCiKGt0JzHxdq9PZaS9DQ/z68oBTXjoQNWs56WK94L4KAVvcjZUygJyUcaFh+UTWJtoa1CPnYWiNFiMz89IIzfRQo/Tz5+yjBOHK8zRYquXqwhNf0a3gnWdqWpGKQazKiKVhEg3Co/epI3t2OpbVS5XWdVnNULda3qL6q1aUWABY0nWoz064PR3pzbpIBzrlch9IZj0/kwt2sZa2ml4qMqFrlmvUFYQ+utxNI7o9SHcpcg5fa7liakoisKay3GenIu1OY5uXiPRx98Nxdvv8909jbD7owwnRCmE8bNhmmcmAaNEvsYsCEQg8dbS14Xrl8/59OPvkZerxjO32XcnRGCZ00rxxdXbGVFxh3L4VOWec/SibhG+oBnDd1Wzx3iv/93TgUw1FaxVgsBwzhgna4clVEy4v3AOI3qa+krKIt275Rl1TVQzuScqamQUmaZF9ZUaEZpscEZbP/6zlmsd7qWRW+/KSeU4WQxzuK3W4KP4DwuTkwdAne72b6XC2ebA0le8PqwcJxXEoX7F/fxTRjHyPZ0R6mNlldSbTTXsHHDvdMJSQu1NPaz4fXuHqdPHvMen2Ji4Zc++jqyF5KbeHMFz64X9fblQnVv+OUP9/yb8/sYt2O/eq6GM9yDt7ny97g0jlfra8z8Kd4mQGtFjnt9nOZl1UEu61pDRDBeCePmdjUvfWC8vbcYo76OqMqJ8yPDEJmGwBAd0xj1MXWR1od6I4aUSjeUq0dP+lCtfg/tlZvGyOAHpjhyupmYvMXWhbK2riAaQvDUnlTzfsD6CRdiPwQfmef5zsNn+rpZbtezBLQSrdBaUmhc9lSjNQQleI0wBx2Gl7x+y/f439JE8NM//dN89atf5Xf8jt9BKYW/8Bf+Aj/4gz/Iz//8z7Pdbu/+3B//43+cv/JX/srd55vN5u6/a638gT/wB3jy5An/6l/9Kz799FP+6B/9o4QQ+Ot//a//Vr4djJswbtcjvzeIzLS8YkOgWU2cWANSF72h54oNE248pYYLrYQpl9S2YqQoshxNhDTjsG5QEFHZYyhgB5w7pVqDC2eYqHFk70cIO/w4QrmgLAslX4I5YhC8H9luzhl2b5NunlGXj0jpBTJfMZYV13scvCQqBUPFxROwTgsIU2JZjnh182KMoeSMiBCCluCVdU/JffXhVAESMTh/ipgJE7aIu08b90i6hPQ2lYYJp4TpHt4FlsMVh6vnhPlKf+ZyQwgD1SuFdzM5yI4qhTo/x6bnhOkCNzxCxgeIG5CWsWFE7Ih3E5KOkDNGRGVkyQSjfUZNZkzdE1hpTg2WzYx6VyhV43VWQLRLKd77AjKcQ17UA+SKlj0ue1o6EIcd29O3iRePMcOOmhekJaRUatEiO+lpptaqRvFqwfY9rXUO6ydu9i8Zyox3nmK9Dqp9V5tz1j8/bBBrWI4vOs/AcqxCEnh5hLPLxrAR7A4cvqewtCen0mCsONGbcckO0//9Mhu9Qa8GOwht0Is+Ecxq8Gj3lLhCiF7Nn9kjwROGh5T1FXk56BpIoORLjF0BHZ6w3cHfLLIq26Rym7KC5irp8BHDx/8vhrd/F4fnv8IwNsJUKLMjeouYyrhRObpkoPmO+DaaVqj0IR9yVniUdAT87Y0xVyhVfSLWOXLVgk1NnEo/tUsfJJyuWFrRiztaXmftiPeOs7jDu8CcKmIazcBhyeQya69M3wnlku40O2lVPS/djCu16MnPGC0PxXT0jTbgtv5caSN5oUmlYtmevcW9D76Le+9/kd2DJ8TNOUMHsQ2bDcM0EcPU/RaR4EJPQajaeNhf8+LTrzNfPcdePMRtTgg+Umi8unyNHxy2OJgGjh9/RMEhfkNrCn/0RrpM1Dp+PWt6CRiC8puc1UoF5y1hHPDd62BDwLiobIwwAuqFkFoorZLSzJoW8ppYj5oy0XWNUFJWZVIE67qiZTsIjs47ycqasdboGtB67ACb7RY3KnzMxoEwDIhVH4OxFtP0PrGpDckvuP/mhrPtxHUqjEbjzRfREsZG3O1YVh2iXr654c0xUYc9uMAUIt7DQOXGwr3Hb/GeHzndBl48veKf/fuXHHKAYMhVb/a1VFpuzOs1+6tK2Rzw4ZQWNrw1Ou7tJph2XLsdY17g+Ou0ulDTjByO5JS6yt0pzt5jur+sipJ2cync+pqsC31t4vFDZNpEghNinBgHzxAcIXpiUPNpLUIqrSMsNHV227d066MSLGHQ1/C02XL/3hmhiq5vHT127AjhpLdWW6yNYAdM8Lgw4PyICQPBeWo6Mi3XpOMNy7LQ0CoLrdVQTyOiOAZ1NqvXUROXkZw9uFUJxiHosPMtfvyWBpR/8k/+yW/6/O/9vb/Ho0eP+Hf/7t/xAz/wA3e/v9lsePLkyX/x3/in//Sf8vM///P81E/9FI8fP+a3/bbfxl/9q3+VH/mRH+Ev/aW/RIzxW/+GjD4ZdCczoG8CZ6hSaNKNecse6x0h7nBug5QZKV9DaqKKpdGLBE3EurG/dhKkS6QJrq00WRDTWGqlpCNOqjIlth9gTt5BwkhzJ1gbcdMGv93QUiGv17T1DWbe411ks72P1M+T0wGTrwibx6ylYSST06z4+So0t9DwtHwk5xVxDvyIlAPLfFC5Lg7kZaEU6emUgveBltodUlgNUI1mDPiAcydIHCCd0dKRnGduXn7Yse0Z35JGpeM93OaC4Cds2GHsiAkG01acVad4SitSXiE5Erq87MKW1W+xNkCpWElYU3TFYnWdYP2gfhgbu4xeaPkKmRPORnw8J1lDa7bvrgU3nmDClmOpxBhoq0Vs0Zu8u8BfeEwcCOMJ1jlqmqk5UZOmdhRvjnpxrEOpuRZbLM064mairSs4x9nF+yyvvom0Gee32Lwoe+WWiIjgg+P04j1ev/h1bLPkJFhvqXPlahWWxbK8bsQAqy1YLG3YMtx/Qrv6Bi7p0FNXvQH6akk0vG+YtZfuWdTU1jwrlhZrp53a3hujFymR2g2iTxGpmv6pBuMUu+2skMoR4wSL51ggugIZijFEA1n01mJxyCq8+NWfw06/DL4x35heAWBIc1XPT62cnk8sSybNhTgY1oWOtNRW4Vpb72eyKusXVSpKAYyl9q6cXCrYW/qvUeBd90WJGGppZEkY4yg5kxwMDLw9CkO6hqs9MWyoY+QQT7kulTFUgh11KLFCbsqvMcYSvMIQtYhS1z1w6ztBFU1RA6mmE3R915rG0ptAro5h94CHn/tuHn7uS2zvv0XYnBE3uw5l2zCOE2OPEvvgiTEohl6EWoRlPvDsk1/h6sXH2GkgbO6xGye8t+xv9qTjwoOgQMUsR9JyZDp9xGA8y/Vz0vEGyQrKSimRcmHNiZwrweupexxjbye2mGAZx0nLAq1Ro7n1YD1FGpJWUgeEIaLMlJxYlwPrvEDVs4PrfBhrdT08jkOHcenNMq2LKjlG1a0QAiZ4/DDitjvi2T3ieEIYt7i+yvChF7C2qqkWKbSqK9aLGHiwNTQbmepCXhbMuCNGuLfZsC4LN1I04t4qZT3w5s1T6ulDygrGC8PxwCeHxve9fUFYj3zhvbd475NX/MKnOpgHGxCHDlZJy0ybLMhqyRLBC4+2IxebAReEOW84yD18fk6br5CUkVz6wcn1CLrek1qpmqzr/0+x8jp0h2FiiJqQisPAdjMwDpboA842glNVKSflNOVUWFOhtl5BIppwa61hqQyjcmmC2ZCPK24cmMYRkyrZRcL2jHF7it9EwnSBD0qPVR+gwtucV8uDhIhp4J3Bu4qvK7YWslhcdBgXVYHsHVjBKlyx5kRtPbnojrQSaM7Tiqdlzzr/b6Sg/KcfV1dXANy7d+83/f4/+Af/gL//9/8+T5484Q/+wT/IX/yLf/FORfnX//pf873f+708fvz47s//vt/3+/jhH/5h/uN//I/89t/+2/+zr7OuK+v62Q91fX3d/6tR64ppBYxFxHcn+y2aN5NTxrkBF/XJNDWhQIoVh8OFE5rfIW6ionxxyQeoi3IxskaHrXGEsMHiiMOWlvbk5QVteYU5fIgdH2kJ33CfGgY19rkTzHiKGbbYccEs10jeI21h2N7HAWInnNcpVWxjGD3LzUJLleJrNwiC9ZFUVtbLp7Ra9JS1WFKZ1YjUuyBaqjT6CqhW3e23oifXVijrjG3qrYnTOXgP3ndypNWEhNViKWs1+26smkN9awhHMkkbNHdDj2PqHj34gA8TNZ5hvYEyU8oRwajpyjuaBFpe8C4iwwROByAbGm29gv1zPfmOD5SPkg/UtZLzkVwzssy6M3VGYXPDhmAuENNIdcGsC8ie1lasOe2JkaLQIpQF4YL6jFIuYCPVRqbTB+xffIis1wzDBc07qAGpK855oh3IS8YFj7OeNF/rSWWzo17f4JwhDOCa5VANl3PjdPSUq4I/sTTfEJ9YyxVtgXIoak40gvFCckLLFpOV7OJio2TBRk0BeCv4UUhimI+VtIDyOar6qkWoOVMw+thLo6M88AF8gzVDKRZvMhKDxr5XSzEeUJWrlY56d0eWw5GTwbEGQ1lEsTwGDPp4vnwxa0ttMH1QahhHJ0/qQGiswYmjikbppSeQrFNDrHJi1PDaRImc2imizcW5NapFYYNSMQ6GFnkUPLuXN8jrgjFC44g58UwP0UFWGkdZCd6CUcfTmps2aKPx9nIrv/fTrenwrVqyVjO4bvgsegPw3vbTamCYznn4/nfz6Avfzfb+E8aze/hxx7A5ZRp33cMxEMJIDKGj7NV70EohHWZevvg6r5/+T1izJZy/xW5zSoiRkjKfvnzJQ28xn76i3H/AevwUHze4EDR1F0f212847m90QClauYFxxMETncUHx7idCJO2Fqv3QdeoCtbKlHWliXqL1mVmmVfWNWkzeakaEc0ztRa88Qw+spkmjKC4e1DzeS2kvJJqVg+K1T6ecZrwUU/lw+YEvznHbXYM2zOGYaOJEUX76so6zSyHPcfjnly002w3wr2NoVoLspLWI/vVMeeG6RH1EANh8JztJgge2kqrMyFuOHeW01r4V1/7NR76D/hS2BLH+7z74IJPnr3iJk3a7NyNxMVWmlRKS9hqGP0JYYJgdG09nkwM+YS0PqLll8j+JT5VbAg4FD5ZSiGX2/4wQ8XhoifGiDcGF5yStYeRMUbGYWTabBnGiLOCqYU0H1jXhZTU0FxLVY5MyjSxhGEiRh1AQxiYYmSKAes9tjYkNzXUxpF4smEznTLce9QH6IkQJ5zTYdXqphnjFU9xWzui9G1dwUbvkRCwTf1xxqtSb5wa2KU1xFRKq8x5IYuox8Z4bPXgPGIDdvnfwSTbWuPP/Jk/w+/+3b+b7/me77n7/T/8h/8wH3zwAW+//TY/93M/x4/8yI/wS7/0S/zET/wEAE+fPv1Nwwlw9/nTp0//i1/rx37sx/jLf/kv/2e/L7VQU6WVWXkdGESC5v7X3LsPdvrglQwmU+yKIWD8KWIHjTHmGVNWKBqXbflGT/1uwgaPt2eA73HZgjETbnyAhFOsGWjLgTq/ZL76FBs9fjjHx0cUvwc/KlLYOsz2gianmJaxdVXZPwSkiKLH8WRZIAjO7hS4VmYES8uNcniNM3piLmkBaeR8QFzAGDX83VauG9c3uQZaWSiHK8gzy+GgxXmbEwb7kFKqqgQxYP2gqoLR06Oii1dMLVjTKNZj3SkhTJ3SWXTYq0fF6A87qp1wFtJxD7IqudUGpFk1j3qV+au5zdwueFswwwlu+w5m8wBpnrQcMW0hH9/Q0htM0WIyZzz4Cb+5wMZzQPuIyrIn1IzJL1nnD1XFGr8MDL0DJNCXBtRWiT5QWWnr0nkPEzaMtHUhyYx1A85uqfJGWzxFO3OkWmquGPrNbrjQAkoLy1oIzRK9cEzCfl/ZGkP0lrYRsDOyzFpqF1FvTAGKg6nhp/795QazQAUZDCb04kCnu2pb1HjbTKElVR6kOawTZfkUg3daMihiSMuCNENJ2ofEo/871STq8/831njG03vk5ZKWMs4CXlF2QxGWBvGkEiO8eFEJzhL7BS14x216pq6mF8JpFLs2Ne86a/R1jq5ZpMeTc5f+c9VNdmsddd+09NB6wTiL1a1R/+tCYMP9jWPaz3DTyK0TdY2QbgyH6+ds3jrDmBWkMGzua90C2oxsmmiDbNWLrvAZvv6uXZuge32jCpSG62w3WgfG7UMevfsFHn7hO9k+fEKczhimE8K0I44bhu45Cf42ThwZoraiS62sy8KbVx9z9ewXoewZ7n+eafuEcZgAz+ubK67yyntzYX52YPrSFzh89AtYHLk17dDpLJFaG9J/FmsdwXu880Tv2UwbNtMWO3hiCAiiJYApsa6JtGZdj1QwxjGnrFHYnHWYEc3bGRE1Z7qgN0QXOtBOSDmDVXMzzhGCU1aKj8SNrrm8j4Q4EkdVl7xXL4KuzxUyWdaFumo/2HG/Z3+cKU1j5tE07m8jzUDuicHjHNRvJwlrtQdoHAf1NXlVSEtaCfdO+dzpfeqa+PQ487O/9prpy+dszz/g8f1r7u1e8Pz5DVYq3g+qanhPKgVrPHFw3J8g1j15fsk+NLanjicPTnk4bRnnyNU3C5ff+P8xr1c4sZ+tc4zVx2EYGZwnDIHNGBnCrb8wKPnVWXwnhldpLLOWj6b5yHzck1Omdep26RgAazwemEJkGiIhquRaiiqmxjrsoKtxs9kST+8zntwnbM+Jw8QYR0IMODv0SpNESqWvwQslryABLyg6v6xdNXMMfT2oZZ+oX9OKrj5b0sNkOlCNw/ut+uSMrmhrbfj0v4OC8tWvfpX/8B/+Az/zMz/zm37/T/yJP3H339/7vd/LW2+9xe/9vb+Xr3/963zxi1/8X/W1/vyf//P8uT/35+4+v76+5r333kPKQjPas1PoTcMi1JzASN9390A5Hmt877RxGBRqVhBqM/1GrEj71haaESwBQ+u44KS0TYtixdsMLeJOn8DmbUp7F3+4oR0/pR4ucWmG8Zxit8gyYV3UFtM4Qtxg2GGapgMijZoSrWWMqfhB20NposbMONFyQq5uFL4WPOuaqGnBmUpZViRrsd9aKs5rP4ezjpZm1v0rpM2KXXYBf3KGmbbYYUMMrkt0hVoWai09vaOnTGs9zk4468BPWh9QF1J6ozn6DN4ubB4/QaYH2gBtBONGaEEvnE5R77W0HjnN1FKxOYMLFOfwTYhaftHVF1huXmHXV9jjM6pxhO3b2M0j3HBBcwEpM3nNtKQ1BpL3BEmYukAyiL/BBg/m9rnXU0xKidoUuKWSvVFPQ3Dg+zpDoq6H+kXGtlsaZ+udF9oozHarb070zd1MIzqFk2WEugjrVSYGTzbamRO2Gl2vszJhQgSHkllLEezYwILbDZT7/x3m9b+ieYdJFZrDjWAU9XCXJsKJ7oCNpZVGTeoBUbx/J/daoVbLyZd+gP2Lj7FPf4YWMpRLbd62GkskacW9tcKawO4M8fx93n73/8SrX/wZ2vqaZoScGiH0m7ptOK8rGjG3CRr93kpuOG/IRU3APTF/J4e3UnsqSH8GjVneMkjUQ0GrBAOnxnERAvN8jRHLeC5Yr14MuWqUN5V8NuPOLG0WbM2EOFJrItVMa1BF0evBD50V5JSA2Wpf83QjrzfUUnUFZMCYyObkERdvfZHHX/wSu8dv4XdnTJtzhvF2ONkQfST6QAjqqwjeK5SsCXlZuHr5nMtX3+C4/4gwvI09ecR23DCGgcNyxcdXezZ4zLMj/t13ae0N++tnyqGxIEmvG+M24u0ptRRag+A8wVq8d3pSj76D5DJzOqrCsa6kteqAkor6Lho9Mdej2k1dkOY2aSKe1no8OeljKK2RisIiQ9RkkvOeOEWcj7hhQ9zscGFSv4v1eBfVCGoMrd/0apkpaSbPR/K6UNeFdZ6Zj4lSBGMDQ3Dc2xSct7yeF2oK1DXRbFB/kIJvmIZILhq5zrXiyDhZ2PjGIVUenm4R67mcDR/ce8xb76y89+wZv/LiV0jpQDNG/SA4nDQQS3COyVXCeoU9bFn8wJJ2nJ8MfNe7Z8S84XBu+YXjCw7fuCaVjA+BMKp/Y4gj2+1GXwvRMw2RMWiZpPHqP6FmPRCmRkqZtC6UvJB6UqpkBbRZo4Tp4CNjnNhtThiiGlmlaEoSDDZ6wjgybTZU63HbM9zJfcaTe4ybnXYCWY8ExVkglZpn7WliJViP5ETtSVIrlZoXBSA2IY6aRgMoeaUuWgBbckaqqm9DHPRrT1tCHBU61wo5aVP2t/rxv2pA+VN/6k/xkz/5k/zLf/kveffdd/+rf/Z3/s7fCcDXvvY1vvjFL/LkyRP+7b/9t7/pzzx79gzgf9a3MgwagftPP+qq64PWip4ii+C6HCXWdJSzGhitGxRj7wZoRUsCpWBomFL6k9ugNkxVg1wt1xrjKpnD/oo4BEIcce4KCadIBJteYswFMW6R84k6nVFunnI8fIS5eUnYBtxmpLSZfFwwacSGkRAmnB+UaWIjfqv7Poun0Ii2GwhFORfkPaeP36MVIecZcqWmlZIOFLNQy5Wal4LtoKYMUmnpiHeF6fSCcXtBYcCESCuZdtxTxVM7sdMHT7QTzXuc9yBOb7pSKWXFrtfknDpPQrAWwuAIw9tIvIcNW8q60Jaqsl9LarhLq3ojaErddA5Lo0lSGJYbafEUmRa8VOxwQtxOxOHzpKsddfsI77a04QQbNxp1TQfsco3Mb5D0GsmvCW7DML5N4RzTDogcsOYEYwb1oRjtFJFO0sQqaCj6SBg3pLCljplSwBOxQ8bECfIN5eaItKQk2540qgX8ZoOJkXIUHJWUDRsLrSqZczXa9+NSRTyKxl9FG31PIpI1kSEJ1tUTdw+wPiPtFeVVhhf/FnnosPe+QE7fIGQ1piGemtY+MEFd9TU+jAPLcmDORedy0/BWVUWLwXnD4Ws/Tqg71iaYasgHVVisE4w4UtY6hdWCHQx51a6ik//rn+T16yPy8T8HSYSgMr/F91OYmhR8sJQqLGsH8NVulFXbjTI7eoJHIWyGKgp0E9R7I6IHDml68R2cZXKW0eyRsiPsIuKqeseCoTaLO4VtNNysMyfmlLOhkZY3SNtokV9r6s1BExWlg95E9IYrfTjyXtdTghBDxBhdHQ+7B9x7+wvcf/872b31PtPZhULYphNCnBiGDWOcOllU1ZM4qOJAa+TlyOH6JTevP2S++jWKm9je/zK73RkxWJwRrq9fc7y+4VE2ZLNj+NID9i/+v4gUTR85zzRNyCiE04DJlZIzJjdCl6hK1gE8p8Syzn2wWEh5JZfKnJuuf5t6bqzR96O9Jfhaq2ZhEeak3p1ahZwLznq2ux3jMBKnETdG4hAZN9tOmo04p/0ubjzB+wFnjBYStsRyWDQy3xqtLuTlhrweWW+O1KJJoVb0eR3jwBgm8KIE4n3hZqkaZz0eaD5gBtEqCBfZbqJeq5eCt1ZZQ4drPs4FvznnbNxCCEqdHgPjyUMev/M+7//6M77x6StSNYRpq3DO3vdTS2GpCZcs8+sXJDFszk/JzjINlouT++xCY/me7+ewT7y+fEqIjnEMjGNgt5nYbjaE6BkGz+A9rQhr0UG5LjNpWZjXmYqh5UzNmgKqaaXkpt1eMbLbbAnBk0tWNa0WahLiFJX8i6Z8zHiC25wxbU4ww4jfXrC9eMQ0njBEB6bo6SYV5Z/URFszLamfSYYdXRu9W9Maq7yrnDJSKlkWpCbSuue4rITpjDBExEascUzbgBsmxA9axxAihkbNCYnX/9m9/H/u47c0oIgIf/pP/2n+4T/8h/yLf/Ev+PznP/+/+Hd+9md/FoC33noLgO///u/nr/21v8bz58959OgRAP/sn/0zTk9P+cpXvvJb+XbIeWH0obMwtOclt0oMUaE2zgMR4ycdTvrEXauqDdKKgipaQUyjqotPd9JFY3TeGjWwtoKpQlm0LMr7a9x4o2CoGjEJXTtMEeNHzHBKvv6I5XiFaxY/nuLjRj0yJdFK1kZUFzAh6orFRMR6rFecNC5gmsHXgg0DDCdqhG25Y9ZV8ak5U/OBmvXfLVkbS1ureNcwVk2qFYvkokAeaRBGgh/wva1Xy9SMdtmkA4bbyGLD2kYzAR8nog+aaHAeFxwl3MeYSRWH0nAWWklQZ53QOwwLabSqSSNrPaZVfH0JLcH4COu/SOWAqQkJJ/i4Yzx/m1wf0cqKaQ5JN9j5mrb/lDp/hKkFZxzN7CBsSSwYsXh/ihhLa0lPKg4gY9BMvvRItDpnDWldMFZPB77Belgw6M8h6w1SMzFEiiR93YhSVmu5ZtwoIdWh1NfawNH0pown50asgisgvmGd3gQla1x5sI48NpyphC9/P+2YMZ/8Y8rQENuQg2H69BuYqXVgmsPYwtj7ebSTpuoJtK66S7aGnqC9k+OxQMvYV79IE0uMjqVmSuoAtCqkY9OBykOwMFjDYYHD8pLj/+PP0K5e4mi4oDUGdWnqoZLls6+XoXQGSa06lNTcaM1ROlsGo508uXxGzVR/hKL6Ae30ESEYw2ANrmqrYMVg431sdGQBCRZCwLZK2H/CNjfSzTUuWiRVSrtBvMfZ2BnOooklUZUEUJaM6FAirXaeisKxanOM23tcvPUFHn/+Ozh58i5hd48wnTMMW2LcMAzT3UkxeK84fOeVrVPU+3W8ueZw/YLl8Ouk9UB88BU2Zw+YgscaS0rXvHn5mns+cj5b2lsTza7U9UYLBW1Qw6cfcHHSx7FUalrIhwP5oLDEJSXmZWHNhZQzVaB0A2trSld2OKLTXFL0niFEqlTWNbOsmbkWUtWTu3PK/DXW4seBsBnZ7E4JY8R4x7RRZTjGEe890qChnoSSNZ6e5oN6WUpRnlPOlLxS1pmSF8pxVaPouIOmK8YQPAbLIc3kKuTS8F7VDS0PdUhJCFoM6gb1Y+T9ghghiqPVzHqsvDouXKfMvd0FMRgeng48efcB0/jd5P0bjscDn1zp9+TthFEDBmvOvLk5YophKBYXbji+fsUvfGPic/cGtucTxo0Mp+9w/73vZkYYBzibHCfbkWns8WvvaLWyLitpLSxrIVWhFX2MUslKaa5VPZVidJ06TQqXDCPRayebFBBvsSEQppHN6QlhCGpeNpGwu2Dc3WfanuK8xw0DIU79GixIzeR0pC2L+vPKQlkSTfTdUcVB8Op/tAry1Nk3k5cbiks4N2ryVQSxjuK9JrNCR/TbnhALt5HlgG2CC5Upu2/5Hv9bGlC++tWv8uM//uP8o3/0jzg5ObnzjJydnTFNE1//+tf58R//cX7/7//93L9/n5/7uZ/jz/7ZP8sP/MAP8H3f930A/OAP/iBf+cpX+CN/5I/wN//m3+Tp06f86I/+KF/96lf/iyrJf+2j1kKtltaU8y9SNRbYJT/ng/JA/FbfMLXQ8pFSEtwWYXVFs/U9uetxzOOs6F/jFN603V30qGPFkAnBYdpCu/kmrlUID8lk4uY+JgyY3X1MmMj7l5TDJTUvtM2F4q/DFkzTJ7iu1GXGJEUXG9fBSdMOZFBojrU4t6GxxbdGbbknUzIWiLc4cWPvmkdtR31LK3ekQHNLdmwJSkVKumuXlKZ0QyO1Q+nVPKgEZ6vYeWeRUnSXmFdNA8QNLniMZKQDtmrOtKqnrSbKl/BhQ5OEbVlZCeWA1IR3Edur62u5RGzDGmWqlCXj/EAIO10FlUTFs9405puPMceP8H6LCVtMDKqCicHaCWO3WA+wahLIn9CsV9XEepAOsLo1d6Wj9lK4QPBKynQ4ClD3mUZB6orPe0p6oya8suLKG5pf8IOjJY3FlioEb0hVmJeKdZZ1X/FYPA03aqIlz4D3VA/llfooll/71yADpgp2dfitodRKbhWTlRViQ1Oughdc0bSMNZYxOkoDY1RRtN3kmRcltzpTSauuUpyvSBWCtYhVxWA6i5TcIClEyzlYUiMdRWXgm1/RdmgBh6pA6m9MjKPD2EYpQlpQEJ6A1I6u7ywR27uGSjWsq0Ks7rhDKIXYWE+VqmZCrxXtMYwMzuDbASlCDQPZnDCdP6COg6La968x6zPG2FiOhmXRFEVNTaOyUqneYUZ9fTmRbvoF4zyltF5PoBfyWiu5eYaTC87e/gKPvvBlzp68Rzy514v/ThiGDdO4UW9GvzjfYuyttVCFUhaW4zXrsifNV8z71/jdu5ze+wK7MeCtrrOWeeF8e8rWnOFlJl1YTL7CkNluJzUfW9uJrtpv1PJMK5W1FJZ5YTks7I9H1pp7B1FVNkZv+/be9cdSVRKhg/5Ko9TKsiYOy8zaqbzWWawPbKaNppC2G8btjql3CoWofAtj/d1zrAWFlSoWoeP31yPrcoAeJ2+53K2TatHnfdyccLI9VYUkrfr38txfy57tqcc1z7F4rpti3pecWZMFG5Als1ZDXjL7unC2O1UOTmnYw4HVRtaw5c2yZ+GE73jrHh88/CIbN3N9eMPVv/tVbspCawFnI2I1fn48HqE2drVxEkd4/oxvlsY/jY3L957wwfmGhVP8xec4zwVTX3JxOjAFByZR10ROakAuKWtFwZpYU8KKV56PkbuuLmfVTBudxdTKvi6U2tSb5Tx+E7FDIG63bHYnjNutlk/6qIj66ZzN6QMtDZRGFm1FzmtWe4KstP1L8mFWNbMu1Npw0wU+bAlhAO/UuVX1ep/TTD684eb6FcQdu9O38WHE+omt9Zhhwk07wjDguw9SU7a3tSpCqYsmmtr/RibZv/23/zagMLbf+PF3/+7f5Y/9sT9GjJGf+qmf4m/9rb/F4XDgvffe4w/9oT/Ej/7oj979WeccP/mTP8kP//AP8/3f//1st1t+6Id+6DdxU77Vj1obpeQuQZZO51P0r3ER68e+y65KQSwrrcwdNdz3dk1R4ojCylJayEmHF+dHXK/Q1mx7w1st8ltzwhvB2iN5/yE+vsbI55Vg4zcYHNN4Dz9dUHfXyLKnrNekq+cYP+K3G/yo6x6HrlKUp1F0TVIO2LjprcBgrce6QJMIRj8Xo+1yDXWyKxbc4YOhNiW1Ygym6epLgWX9WFsLphW8aX2AqbfRfFpNeFF6oFayQ0sLLs2spYEP6sswFspKKSuln0CtNR0mB6ZoCZX1llquEQLObtXYRsP5oHFr67EtYGsgFqfDjlk+c5EXjf9aP2CmM7ZPNmzuPeBw+Yzl5S+R9s8w+zcMflQmzeAhDmALRhLO7zDxMdafIcbRivotxFpwA3E4Vbx6PiJGV10jEyUtDJsN6/4E5JpyuMYWAEvIn9KoOFuoTjCxYMUoJ6ZVKpDFQmkM4rHJ4LPQvMYeXTDYDdTLylIs/vEGc5OR8gybnCoFpuHQqLVUoa4VEzV2WGvFBYdxjSq9lM8VyIaarXYeqR6As55pMuRcySj0jKam05r1+Wq5cHjTmB6ccP26YI5HWtWor/eWWppC5VClI2c1zFlXCEGxZq0ZLUdrRn0cTb/vVkxf8zRloJTGmg21Gnw03RdiEDTt43uJH9Zycf6I7cU7ECImJ5JY/O4eftz2FE5VFa02cjvSEtgAIVbWJNq+bdCLc+nU1XLUGghrNNnn1GSpKR11ybQG1k5sTx9z9v4Xuf+FL3L2+PMMJ+f4zQnD5pRx3DJGjYmGTs689ZxYY/ppN7Ecr0hppeY3XL95jvCAiyffze7sHrb7AMqacWHLgyfn1Oc31HsD1gvMN4QQ8dOJDvLd+15KIucDdVnY3+y5ut6zHI6sx1WL5dCyRGeM1k+I3jCij2yGEW89ay4c14V11e6elJOaO60jxoE4jozjSBgGNuPIOG2I09SjsaOu0ltFSqO0oxqA14WaEsu8kLM2ECNQysqyHBWCaN1dVN5bh3EK3bQ48pJJKeOGAEPENRjjgIuhM1cic2q8nBuH1CiXC2lZUUe5vi6XJXF5yJyer1yMjlJWooGbyze8wPK5+59nMwQmb7l3esJXvusrrPs9l6+P/Juvfcw6H/CTJbiAR8gtsZ+PHFMiGxh9YEiZnz3e8LPffMMP/Ldf5PMCV+uOah9yaufON8qkekO+XlmXhXnRwa1Jo5XKsqz6/uoDyWYaiTHecWNaqXp67td5P22J2wE3DprgGUamact2d6rPjR/V2G11WFZKbKWVRBHwpvNpypFy/YYyJwWoGfR9YgMujlorIY2U9qT5BimK7peSwBoqlmwtdtrq6yEErI96L3N93YuuTWteyC3jjOt+oyNLT/9+Kx+/5RXPf+3jvffe+88osv+ljw8++IB//I//8W/lS/8XP2rJylC4RZUbdbJbo+aj1sBQKXlPqys0jeBWFPdspWKtniBqbZSa1BwbHNZ2nK8zyktwokmWkpA805qhWTCtEGRGZMXUG/L6HMI9fNzS4n3s9hFmcwrTBl/uUddFoWr7K+qyR8LAECd8GGjO6NCBKhbWDZhuULqFyBXzWT18E+3QAEsTbc0E9OiKIrlb0zQCoiY3g7oTb/+fXkRXbNUTixj1AwzDhmAcV6+falqkJnIT3LjDoEAlafWuAdYYjTGrAK/KjTGCJ+opJjTCeEpzC6VocZsjk9YrnAuE5nEuUdyKd1Hheay0VBEiPgrWn2J90D152BLf/k7a4+9k3V+Srj8lHz6hpStlO+AJfoMPJ4ifsMOmt/KpIbiVhVazDmp5JtDAVEy+hmzwwwk1DEogPX+IXGdCmdldvEvKK4dvXNNufpmIIcaAIzOnblgVxbX7bjQ+LJr3Dc7gR4s3llTVrxEunpCuZ+SwqIqwePV1RFV41jnjJ6NofFGuimL96QmsLm6ZrlIkHaLFGpwTDgdAhOOhIGKIsas3CeLgdD1aHBTBYtlt3sPbI1ff+Eg9TFRCNISgq5pSDNY1bNOivBDBuaYG347VFhHtWElQlAZ+tz7MWcgVqoAYR0qV5rT1GScETI/jQnBw9vgREi+QXOHeW4wXTzRthWClcrx6hW8Fublkv39KSbOuLHtRY84FPF3R0vWtRVRNqgbpVM9CpmNQEDH4eI/x7DH33v2Ah1/8MidP3mfY3CdudwzTVg2xQSOiIQx3qonrJWm6Jk3k5ciaC7TEergmV8fu3S9zcv6E4HxPSahfaNyeIOtK21lMNTgqJV0z+Y3GsGvuuPTCOh+Yj5fkJTEviVSF6iImCtHqgcUYsEZUpWiNXCu5FGZWRPTv3SwzuRTGYcJOG2LoaZPdjjgMOqgMG11rB4+PAecDNOmHuaUj1VVxLjmR15W0LKy5KikWVclKLXcN0hhHbYJY0dVThXVo2NHrGmkaMN7j44j1I6533hjgtAqbJXF5eUleE0/fXFEkcLI9Q9aElAbF8Onra3ZvXWCBnI8sh0IbNly/nvn409c8OfGc+okYRt57532+97u+xIcvr/jw1YGSF/2aIeJKo66V1jKvjzf4YeLhdIJceWaX+OVffUU8v6C2LZfLxH2zQZbXDGamlWtkgcN+z3E+korWDUTn+4ofxmFgnDb4GBGppJKg2t4yH3BOD8vx7JztyU57kiYdjqdxgx86pdUGWk3q9/GD+v1qhnykmBl8pyenVe8L1uHGM4ZBe+wKWrw6z3uaJEo+kNY9tRVlhTll9YybM8LunvZMjaqY3CqRmuqsSMu0krDzkVQEsRFHoa1HzM3Lb/ke/23dxUP3SEir3SmuXRk+aDxRWqW2pnE2DD5uiW5UOJsUJB+oZdYLsVjA9VOh/rrt8TDqKNBs+7pCOeobzVi8hev9JSE4psFja6EuH4MfkPgWLl/CeI4N2x6PPcGPE2U9QlkhrwomCk7R/DbqSsXNrHmvp4aWMa2nG1oB72nG0UzEBa8pjXy74tI+mXU56ukJxbyrnKqlbVLVw6KybqbmBYwlbs8UvrPuuXn+y9h0pXezcUsVixsv1NdRFn2Rl3yX9tGvpRFTMfq8VKmklvAYAhuwjWoOGuuuiSyJOG4w/gQTtzQrSjmsK7BH0jXl8CnOF+Ti+7CxYoLpz/UWaQqE25ycMp6eUfN3UNeFfLym5UtKOdIAb1U+xQjiIzbs8GIxTdQwXWaGEWKIzNeJut7QmtJ83XSC2Z5hdw9pV/dZr55S5leE+JA6XFLWZ9jOg9ieGOYspGLU3oIOE2uqRNCY72JJpsBGORP+na/ghz3tw39D8Ra7V4KsHUXx8AUkW1pVDkdbAQzGCqboYBo8eAe1WZCGxTGvWkpojcEFSIulVFW4aKqqlK4gWiomaAT2zUe/iIjF+qqkZUFVNUyX2rWheIi3nqXW+0AA9HWAFVJrfb3D3eBcqqEU5Z9gOlzRGAQotzHiCNZklsVQomFz+Rw/FOy9d7AP3sYM2uNRUkXEMZw+QsqKy4lWV33cRUAcYrsaUgSsR0Sfp1ulR380fc3iDGB0hx83jBcPOX/vCzz6/Hdz8eRd4u6MYTpls9kRhlF7ZWJgCNrG673vhYTq4cppYU2zpoVwHJaF62Nh8/Bz7O4/YQhW0QjFkAuEcUAd6UBw1Dxj2dMOL0E8QiatM6UUUqmkNbHOqn6IdQybU+LGIXmlzlfYDiesVd+Dy7pyPC4sOeNCJIaBhsWEgXEY2W63euPbdMT6ZoPripA1I63pAa6UwpoW6pooaWZdFvKStAW7Vm0yrt34LYZK1jSgUz9J6wWtrWUEq94Eq+pJ8w53umPc7PQxdQEfR/ywUb+bgVYU7e5CJu2PbIOmYl4fZg6zrg5drdSUWfeG43rC2RCx5oY1Z47znqurK375V48cyg3P337Md947ZQ3nXLz3Jd794DWH/DWulsbgGzF6ioscpZJqoS2Jy/01Ox8ZbCQsN9w8c/zqYhimiN8+IFvH/qYwH260lXlVaGTpWPtxnNhNE6bBmpQkXrMakW8BhThPnDaEODBYjx0nppN7nJycsN1MxDhowe0tZ6VkkJWWVFFqKRPGjR6Sqh4iq6B1Ld4Txh3ZFv3cGHI+knLBDVFvEwhiB9zQhw+/xQw74nCqyv8wEaadGoq59XMu2oVWkvo8c0LmlTUnxU1gsA0kt2/5Dv9tPaDUUhGnq4UQukOv72m1raOBCTivqGllYRhMVdZBtWr+qXkleMcQNmDQJx01loloDLmV0hHbWq5kqzaQDk5v/DlnTIvY9ZowRJpbSPsr3OFX8OEcP74D01swbJU10jkRzbsOlmtY0zCivQoGOjjNItbrhdwFvKha0TAY48FvECzYRujpmLQe8U36JKtqSa0JUwu0TE1HZSnsX1GWG8a3v4Px8ZfYv3zK+vG/R+qscLZ8xNSEXa+VMlgrqVMDaY1WFQ0tYgg+aBzTGsJGJUiXFvJ6YJFGi1tGPBN7pnhDdIK1yjYgNJLc4GpBctMG5rxSl+csVx/iXGM6XuF37xF371N8wPoFGytEjxFNDrgQYTgjbE+hvEVZrlgOz0jpiKuVUAvWrZgy0KzGt/EDbjzRXemU2WzvkQ83pP0b8rKnlcR49pBxPEV2/w1p9xDzeoMxI81tObz5WWp+ATJjTMVNBjmiNwfQgcFpd82SKm0tjIPH1aI385f/H1WJIjDpiq4ewJzfx6cbyk1l3TfiaFXudQ68kPYKRWsd5iYdjua0ykMNuc0QB9PpsoZWYT6oUmJ96/hxowhqqVQRZFbIX6oQzK0/S2mrglJqKbom0sHDsaaGZEi5u/4bPWasqo4x9s4MqzwE9GsZjUCLaTSpDF5XtNz6JSzI8oJCIvi3YbTUvPQuEI/xUU+C1WoaoaeDnDcUNIEkRQ+RpVVq762Rhq4fQtBrhnQTrx1xwwnbx+/x4IPPc+/97+T00fsMm3OmzYZhPGEcJsIwEENUAFuPijqr9FCphZxXyrpousboiuEwr9jtfXb3nrALHm8szTRqTVinnVXUREH66T2QX3zK8eVzxGmbec5JY9vowIgbsNHg/MSwOcf6ASkLy+sPObx6yjovHJaZ47wyL7r6qU3wkyFMOzabHS4E4uDZjCPTNBGGqMWWrh/YeqP7fFST65IWasmaNlk1elqTIvD1qvWZ0j6OA8NmwlhLypl1TSx57fCwAR90fTSMA2MYsNMWFwfidBvXnnDB4UPUNvdayCWRykKrjegc5Ey0iix0ArlVLAUrGZkNr65nLt65z9lm4upy5dmrp1z5EZN3vFmOfLQ08tzw4nDhAU8efoHj1YHt5WuIjt1GyarXy8TzN5csx0S9ueEjazkfZkxamcsVN3Pl3ltv8X9++5SpQisPKdfPMGtjpPTWeb3Wn2xPCD6wzDMihlIEZwQfPcOgjxc+EKZb8/VEmDaMu3uc7E6YBo8xhZZX6nrUtCqGUgttudEyR3PJtDll3Kgq5p0jWKOH7x5QqFkb3QuOkmZKE2p1qlq5AasESKwPWvg6nBDjTr1QrpfJ1kopRwWbLtfk+Ub9RVXTkqXXXgSrhnExhubst3yP//YeUOrtiUjVDWcdLgZwuku2Do3zhi04R61ZPShNi/ekiaJNbK/MRn0cOuBAdFtlptgBQfdyplVSnWlppsyXlPkKZ5K60VsmzTM703Sadg5fG3X5lDy/xMwfYjfvY8YnmHiCiZHgtgwuamLHeQW62YA1AenIcNtBSLqgsRgqXjQeIT272WrTlVfVXeHQ/Rv5VpFY96TlhpJmJX5ax/D4S5w9+iLL5Usuf+nfYeoRF9VtXdcjlkCqMKdGbUfMzZEYI4IhDBvG88cQT7GSSMuRsq5IKZTLPdkeNYrrDH66xy6espNv8sD9ArtwQ6PQcsGZE1o5B5moNw3TbmiSqEbfuIyQq4GbI3L8VeL1I1I7IOGc4fRzmPEtxG0gjIg/QeKOcdhSQ8T5e4TNfWreU+bXyHqNybPKDW5Asfe3XRiB5re4YPH3HhPOHtLSwnr9kuXyFdZYwvYMf3aB3fx28unbuKf3tONCvka5+VXWdcEFy7hT5L3ue4XWUeJrbrgbS3XgLdTRQ7mhNS3VkrWSayVER/n0NXZU9c5RKXPtDceKWsc5OvuRPIsyWeytKgA+GspqGEZNy4yTYQe8fqZrGh8qVoQwRPbXhdggiaiJtAoDmniL0VCkYl0F0SHHYMm59pUP6t+qBim62kEEaegFSRq5iKZ6kN5sq+bRJg3jNDEjWEoWRCrj4NmNDecbtYExI1PY4quw/v/J+5NfW7MzvRP7rfZrdnOa29+4EYwgg0kymcmsLEmWykYVXKgCEoLhkSeeaayxBp5pqv9AOfDAExuQAc+MQgGGUTJcKilLkEpNKjOZyWQyGIzudqfbe3/Naj141zmkJiVOCjChC1wk42bcuOfus/e33vW8z/N7UGStpA1cF/I6o5Yb1sNb5jnjsgYKyqrG6dAoJ2weTZU1pdUY36GtvJfXGFFKM2wfsX/8LS6/9ynPPvwuw8VzaSUetvTdlr6XuKQYAWUgN9a1pIOipEhcF1JYxCQurTTcpZViPOf7S8aup9OKgpHVscp0/UgsFZsrId6S8kznR24//wvCaaFaTSWRWomlshu6/gLrBkiz+JG6HQVBBtSquDvcMR2OHNcgypqxdKO04/b9hv3+jLPLR3SbEWPle2LbbbxUiSjnJFUFMays88I8TyxrWzdnUWJzTqgi9QTedXSDb89jiYynGIgpk0pBWUPvt3J5cR1+HOiHXnD4vqcbdvT9lq4bsG6QqLKqlCihgBJn5uMdx8OBiqES2A2VnS+crADDRm+Z18DQadKUeX994OJ85PnoefZow7uvJt5dvaPfn/Gy23OZRv7yyys2/ZagesZn3+JFPDH4zM008fhyw367ZzkFNtryi/qe0/FEvb3hVh1RbqVcXfHleOJptfzgomc/eI7dY5S/pOaZc69xiIk5NhZIXKI827sB2zn6oafbjPh+Q0UUJz9sGcYdfdeDMrhhh1aWFBI1T9T5QJ5PpLBKj1lJUg6rFeie2vcYBpSSlJimUmMklghhZjlcEW2Es0vsZi+GeSON0NZvpFFZG7QV20HWHm88qiTWMEMSH2MNB1ReSIcrwulOQJNVLkzKOuyww/V7Sc7lGfhfyIPy/28/TCM/amXQykJLsSg0xkpZUc7S4ZFbVY80Agd0bZKa6CKyz2/KCUke8lgFxpLD/e5UYDmmShxVD4+oZScJmFqhJiEZYtHKywpm2GPsiPEbtNuCG1FugAd5zlCVaw2X+cE/ou5JVm2QKlWqsSWZI79O6wbJOULzk1AzqmaWMMl+OKyU9YQKd5SSMN2O/uJb2P6Mogzx9jXh+nPxEWDJy9qaah1KOWJ1lJpQSnDfSwClK8flFnVzAJR0P/TyUPG9MCeMqljlKEq6KHa3/28+2P1LdnpiPYLJVdYHhYc4rmshUN3WdqkUjPaAo9QIq4GTpihFUQ59u6GaLao6rDVUM5K6SxifUPwLaveCNDyhmD3Vb4ndjphn8npHzRGmA1Cp1mD8BlUyxSjQAwaF6wbck1fk3SPCdCTevheE/jiyOX9JHs5g+4j1F5eQFab8pdyI+yytvlFRbPMkN3dOjBW7ZvSgIEeqVigTKB3UVd5GalDEpWCDwo2ZEhQlyXomrQNwhq5vKVmLUdc0EqulmSFliNFOfl1jSCGjtGW314Ra6Pcj09uZeMoMRlOGQpfFnyN2p0CYkc9RY5hQC1rL97P+ign2PkorA34lpfZxKPqheK8iHpX7iLGs3JDCMSUPNK0KfS9fQ9VtwMmKvlPCopmPKO3AbZoHKlLWI/XmC8Ltz1FUgqqEWRFKarWbAr9L8mVgjQLjBWkeE9UYlB3ohwsuX32HR598ytmH32U8uxAc+7Cn70cZTLzHWY83DquF2Kq1bgNZkv6nMMmhrawQVnNB49ntRzGcGiNAMArEhGlGfl0ysa1mfTfC4T1Xbz4TZILuhUNhJBpv+w1++0Sgj3HmdLpinq+gatbpxHGeSdpQvBwwXgkB1jvH2Eurcr/d0m0G/NjKOYEUIjEIJGyepgbVEuUrhCDrnBBQzQwPtcXXRQ01pnGTSmENkVihR5I+ve/QncSRrbX4cUA7QbxrJYWq1vc4P6Kso+QM6UQuUbAMcSatR06HG6aDqAZKeTa68rRbyB3cZFAOdr3mMGWmnFhD4XoJfLjfscuZV0fDX7675sDE480junTgZil89uWE3mz5+PElF/3v8KVfWD/7qUA/a2boFRfbgWkaySEwh0DSFcqBwgjqyJvPfsY/edrzf/jhKz551BH2kcObSv7mC1CCq4+lCF3W93Te0RmL6SxDP7Dd7Oj7AYyojrrb0vd7jJIhseZITCtrXKAs1OmGdLphXRaUaX1GSlTNbnNG31/IcKMVJa+sWTAQtRZSWik5UtWRGDeY3YVg9l2HMwOqF1q4RbJ1KgndNhaLiitpuSZh5LiKR0iBfDgRlpmqHV03SgWH7jFuEMN+kr9DSv/zXtZf/fEbPaDEkrGpirRaJcKoSiHH2KBDQlSIuYjkV6SRtKaFsJ5QtUhkz8ltEWBdFow2pBQx5hqlLbUIVAolNeIFYYhge5SVZk5tPMUKsEm7EaV7tB0pD451+yB91tp2sK3iHZYGTpKViTBJZG9Izq2yXGrIS02CiL83uNYqg5OBHILUzKs2eBmN8x3Ynry5RNcmjWdLmo7My4G83EgPUBtySlHkGEHLLbnqDuNGGexaOgCtUTVCFS4BaJbDHTm8lsGkk5VUKgmWiZc+8+KTH9O7Eykp1CL8kVoqNVZqqijkRq/JEOV1EsT5AlUeipLsaKqXVxBvAHkJqypiylUapRxa95R+T+oHlOnAfkDY/E3C/lNK94jInlhzizuvMEeUPbWG7IAynlo9yvT4YYfuN/TjOfP1l0zHNxSOmO0e9/TbqM5Tx5HweYe6/TG6zqShkI30ydSSMEVhMa0ltqJXhevkIa8skIXAqq0ihJbAyYq8IMkwbdBrIe1e4L77X7H86f8Dp67FPGkryiqklE9ur8ppXOcJJRKCpNZKyGhVcEqz3kV0MeSSxJNVWuS2/aSCa0ZdGtOEek+GhZKqqCZFEdYEKBn4Ze4Q9kkpraNH4tca6dqR7yMPiZSSJdkkKn7z71TovKx78s0VenhN1j1m8wTldqiiqGFFhQPL3Vccbt+1Zlctr4mW7qWqKkXLu0lb6ePKtbbPniZWQ7d/wuXL7/Dyuz/k4tW36PaX+GGD7WTVcK+c2LbSsaatdKR6WXwVMUj6rrR0k5aSPygyHHgvbb1K8OC5CLvJGiuHcYmsywldPN703Fz9FO8Guu0OZ7yYgQqUklBZ4JJZRdJyYj7ecDjd4F0vpOl+w/nTl6TW5G1KwaFxDfJWnGDVlamUsJJLIsbAMk2si3gYQojEmJtKnRtQr/zSx4O0TKsKORVSDcQExspzLlUpIrTDjnG7keRJL+sKZSy230hZoNKoRrGGSk4Cn6QEclrJ81HWyDkRl5npeCDMgUJB247NOPB0q8nLzHqIHBbLaZEIe7GJx48GPr7c8uEHZ6RDpYSROxs48ytnPnB7fcXP3i58fRyoTzQfvzzjo+EJZv6Q67dvePf+Het8Ynd2gbKesRu4GBIp3jLdF9QmjTIeliMTmQ9e7vnhuSPMe96ed3yx3HL77kRVFuU93bijHzf0Q4+2Hj/KELcZNnTOyfokRNaYqCYQa2SZDuj1hFWaFBeokbqcSIusEp12DJtz+r6XS0k3YowiLCeUhpQWYlwoVaGs8Gv05lISpeNj3HjJsBla8qtQjUI9MDgW0jKxLolqFqlCmQ+Sdi1SCNucMyjj0N0Wt3uESZmUZ3KO5NiASNpgu/HXPuN/owcUVZHeB6rw/pGHX4lR9vDGSLqhFihBmBx2wNpBpCrV+lUwOC+3ROt3aG1IOYHRGNOhzAi6ewDPKLVBOS/Ze+sEaqMVVneSpkGMiqpWdENoCxwuCKsjJ2oW577SSlInFTQCzYlpxWPR2koKAahabqFaO5HKmhlNI9TPXITdILT/Km3CxuL3e3R/RlKWXCqEI+vtN6x376mna+LhLcsy47tODGuuxyor0roV5ammKlXeoxh4UyoYFR+iZMpouqFDdSMlLC2mWei6kfH8OU8eXVJHTV7/CDMnasioqJBiRumP0bm0dJEYwanqgShaS8Hk+/i4omjxWqBkCEMVMkYOOV3RukB/gOWAW8XEBT+mc/892WtK9wG5/xusm28Ru8cU+5isd2Q1UMmUeI0qDlU0sKd2Pdb00Hs2L76DPT1m+fovyO9+hnI9dtiRPvwbuOEc9cUF8e2foPQbXClUa+T1WBSxJIn4hoqZK7ZTKAdYWX8UA1Yp0iozCSjyBNkXjK2suhK++Dn2+I/Q4YQe5KAoyYDNFJ2pRQ6OmirrFHggl1uFtYqwFmKQQdRULcOuKm0gan9mFBXE2NIO4EoMYJSoJnkFVa0g6pvfRCtDiblx72RVWpp6IiEOGVIekmYIqdRoJQbfX/lQlwzeyU8pQx5w/VPoB2KsVDtJkux0IN5+xnT1FXFOVKPFGKsFFJazlhoDLW3eRpv2uYloazB2w+7yORcf/4AX3/4dds8+oDs/Zxh3+G7Auv6Xw4mxzQwraqcYYqs8fENs3A5ZnUpBnOzZjbEYo7DONcN9S7SEFWVlkC8pksJMjivDdkcOt8TTNRdPX0lKLydynkhpJcx3HOc3mMOR7uwRtUEmrdUYq+i7DebsrNUcFEgz8SRdM/rBU5SI00SMmZAzIUVCjMzhRE6FkqtwoYoooSjhmxRAGf2wUl9SaN/nQsoF11XOhkE8JdZIPHkrh7HxHt/3OGupyoLpwUiTbl4DOS6iBLSUncozYT0QpwNpTZQo3KsUA9Y4etfTbzbUWnBkBrWQT4HDSXOcFNOUGS7P+E9/65IfffKIpxc7lsmz3Xd8t8DZuKGjcntz4vT2jpvDxF1U/NnTC159vOfJB9/j42liXf+U28Mdd+E9Z/tX7PaXpJS5XReWVXxPNSWqnsk28+0Lzwc7y/aip9sObNzHzIcfcgyVvC7sxh7f94y7HeNupPeSADNOIte6SKghzUfmKbZGZ/EDxjkRkdWos/L+813feo42bLbnDF3HNB1kYHaWkkMbJLMMtsZj7Ijb7NDWoF1HPz6m35zjvGqpxom8LC0NVSnxxHo6sa4zsQ1ZlgqhCMnaaqrx4Dx11iQqJa3oZSWHQB0UetygfI/RFmt/fd7Zb/SA4pzHei8Hr2l0UCM3G+Mlkx1TxjuHcgPGb7F+327HSTge2oFyaGOlwtrIqqg3pkHGmvqhlMS+2u/R1kEVo6BGIn2FKsMSUHImrQdIB1QVA5lRcsBWbcB7FFoMZ3UlRUm3WAy9dqQqDwXTTHg5Z3LKqHB4IN5SAilHZCclD8RaMilllLLiVbj6BrQh54aIXo/E6Za0HCGfpCOj25DWlenmPbuzS0w/UAvyJmxdLzElrB5bjbjBKkVOa3PFGMbdBeP+Ebbf0PWD9FE4h3UeVTvupg/o5s/Q02tSTKhUICtIckMvLZqqElJyl1v1kVLiV8iy09ZOunJKgkoh6dzYKwVlSjvwK2RBzKOk+biogrUzzlaK+QnW/jmdd2j3nGJekMyGuPkepf8+k9+Qyw6iB07ASLHnYhS2nm57ifv495i/+Tnru39LPXwF/Tlq/xT74d8CvyF+8yfo8DnOZaIypBCpSeiuFU0MBbtI03BRSFmdE96IDB3Sw1RRqFigaNAR3xfyegdJuLjVZtKiJGrcKUJSZCX8EzGMWigi7WYFORh0AUphjVkUxfbn51ybYVRYJyWIkVZVMXWnJMNIDhIVzYnGPWk3ewy1VkKsRBFVHhqK7+n2LegutFhFG+a1pIMMUCvGQN9pjFYYe0568XuEy+9SqsIWEdjyek26/ozD15+x3B0xxsjbSebc1scivi3lHUbbpgZogf8NZ2yefsrFx5/y6Nu/zdlTSeq4caDrxAfhnH8gwlpjMO3/aqXlUpAjMbRYZ7kvHBQYoOyvakMfaIzSD7C0kiV+3G12DyDFdV2w3lGNJUzXaLMh2wN1nQnhwHI6Mh9PLOuRNUYYChfjgO9Gtv0lw3aLdY6+26CME46T0hBOTCjSfCSFhWWeWedV/BAps8RIaFUhJacWw5f1a+87nBVw3hoC8zqzBBlmYsoUpfCdp+sHvNLYfmQ42zFuBvzQ0/Vi8rS+b/gHWZOHEOQ9bzOhRPI6sR6vmwfcSj3GOhHXibRO1KJwRpJH3ntqrnhjUCURYiDGiHOK/cZwtcRmsi9cnj/lW08GvvN8y+A7bl3l0fmOohWjMxxPM2/eyMG9hoR9/55/9tmO33nccakMH7z8hMPVLUYpXl/fsMwH9uMe5yyus+gQpTrBKjYj7B8rXm0zuZ7QuUMbTdf3vHj526RiuXr9JYODs/2OcTvgvcNpUY1UiKwhSIoxBdbDLYfjij2d6IdeqhCKPPe9NVij6foNzljhwBhPqjDPMzWuLVixQY8SyqBIG7rtRsxwjh8vcFZIScaKMTfFFZVW9HJHnK7E/JwyeT3K+7woYRowgvVYJ4ogukqNRFqp65FYFcp1DP2AG0eK9dD3aO8EQqrdr33G/0YPKMP2knG/B+OwtsNacaArJckXZT2d3+L68aGHRyn5K+v7kKGSuDCI4nLPE5HdOKJ20K6hLSlQ9dLgWYmaVnKK1BwpNVLyIu2POWFKQoUJjEjOwi+xIoNZj1I9xm2oyuLsllStoNUVqCzGMEUixYUcJrQqlDDLQ66WdiMTMl9tt5mKRO1KUazzSsqZXJLED8MqnSzaYJWhG3e48+cUZcjLgUqk1olwWNDKtSpuRzYyxJmYUVZh+xF3vhW0dNczDGey2rJdS4TISi2jZLAhU8wZS+7xBcpSUAkIFYJ6AMQBmAyktiow8gEiycGjjZg0q5G1iPEKnRQlNs+OVuAreI3SpblFJZKryRStqFaacrUDlkhVn2P0L9Ba4W/+BaV7gu8+pvhX0D0h64E8b8junGwvyf1TGJ5i/ED96HsoNzB/+c8pNz/H+z04j33yHRyWQCRefQkloS3kpFlKRlVNLAqzVuhkpaGNbn4iGS5qlTiuGitlzpiqMSiKqayrECYVyHCcKjk1daPwUPJWSmVdAqaT9pkwV2IUY61Toi7WUh+SNaoYQiikrLDWkkuQITpDnGvznShypFUWiNcE5FZdqqgm90OIlDBCRUimuTHtK7JOsvctp1VjbMEa8Z10Xnp4gtnjL39Affxdkt9i1kpiAhw6R8Ltlyy3t2KY1L/y3y+yWsM2LpKVPp0QI1UP+P1jLl5+zOW3f4/zVx+yffoC1+3xg0SIu655TpzDOy8QN2MaHVMJviBH8rqSYnwYTu5N7rQVmVLq4ef9D6Wk36WEGbPZUHJgOh5w3mK8p4aVMge08pyON6TTLfN8JMdKXAs5a6wd6Mcz+n7LsDuj8x3o8pAkoimYpSTSKgnD492B5XTHuqxMIRBiJJXSng9CmB1d1/AKoopoIwNyDIl5WTjNC3MIFK0wztMNI9txZDcOmK7D9gN910tcue/aUCeqa82RFBdqCSzLyrwW+nFLpZLDwno4CBsIJciCpiQoDEorfD8wDj0hyO9fY6TmVRJVmzP0uOXbO1DDgapnbt5Fht6JkTolkqs4o+mMhdEzKIVKib3PeLuQayblFXV7wy/eXGD2HWe7S7r9U9zdDba8Zbp5Iz6QkiS5ZSxZZfbnmr/+g8f88NOnfHoOZr7mdKs5+g6tHP3FEy5CQCtHWm7px57OWzSZsE6k+URtPJkYA2ldWdeFdU3oEKlpiz/b0RmLtQbr5BJurUMrw1JnoaQrRTUdvveAgX6L7Yc2oDf0hrbY/oyuP8eXREqzlPfVSo4nVFxQx3ecbr+SIbRqCVloh+0fMfRCi01VuDc5RSGz53q/B6aajuy2lP0FxlaJSWupRVHWSujh1/zxGz2gjPsnbC4uUbpDuwGQeIRGJFWlDFhP0a6BrAIgjY8A5R6KlgUqVlOGkrCqmWlzpuSIqjLZ1mZU1RVyCcQ0Q54xtQ0IMaJVafwVUR9qntqqgnaDQP58pSlF0fVbUoWUIs4g0q/SEokubbcdF6iJUgtrmNCmw1hPLvqhFl6VhVwqyo4oHDlV1jUSw0qY7kjh1FDwyOS9OcftxL0djzd4VejOH0uU8fiGeU0Sz96cMVxccL57gh33uE7ASdYP8vDR9j5cKFyUHEhpEUtonjFZUepMKJVSPybqz9FFOlzqDHWVAQQllfapQoltuDBKUiEZYSk0A17VCho4TCUhlSorN2+jETJnkR4VrYU2eu8/VkZTvWD8q7ZUcovMKcgTqvwVPr2mLP8juB5lepQdKfo5yT9n2fyImf+MNDyiMwM8/Qj8huln/5R8+xcY15Oromy3mEffxeWFur4W2m9XIBkiha5qYsjoteK81KwrizQq10qIihAz3f0KRxXMplLXigqiW1VEhZI5ulKrIQcZgJSVKG/MoIom50xZWzRYQUiV7GXIjcIwxBgxvTqrW2qCVvMuhX+qNpWltD+zeUxQMoSkUh/UCwnzKFLzWQluXXxizcYgnzmt5HshXxbeV7xTGG2pl5+Snv8AYzdS6KmFqpliQYdIOJ3QNRG0DEKghBVTitCT2xq2VCkv7IY93f4pu2ef8Py7v0P/6hOGi3P8sKXvdjg3ilzeOaGIWovV5pft1YgyWnIkrYJjp5QWy7US9RY+wL3v9N8bTtrHpA0oK3GduDtObFvRXjWOPL1hPh5ZT9cst7ccDteYavGjo98OsuZE0+8eMe7OxaPi22q5eVlSnMjrTA4L0901d3fXnI63LPNEilFous27ZrSiM4K/HzvfVK7K3fHEcVokUaKkFFI5z9j34ifxA92wZbsZ2Q09ru+xvRBqrW2G/5zI5USIiRJncphZ18B0nAixMvd7rPeUkijrLPBIVcEaXFNctJU197pK07oMxRq/3UnE2ndYv6HqzL4Wxu0ZvrviUG/xgKqRvJ6Yakcik7uIi5G+G6hozrZnfPzBwlHD6xt4f7rixzcX6Lhns0a6Z9/ikaocT3e8//xL3p0CvR/F8G0txsIHH+353/zoGT96dc5+WNDzLe9DZbEjw7Bjkw3782c4DddvM9N8oCZpdE55ps5H1uONrApzJYVIrhXvBsZ+YLvZMPQdRlWMkgEup8Qq0ThyChjfwG79DjV4vBPT8eg8tuuaN0+Kb6GiYiDliXW9ZVlWjO0paYFlIh9esx7eo92I7c6xeqSaHXb3BGcNtiZiOhKmAzlMQAHbofwe1V3itZSvOmsY+hFtLNxvPZUkvn7dH7/RA4rpNhg7yppGGxQanaHqlRQWUB4VZzHM3j88moQpW/8q5tgmvUoJXCJXsfzUtAofw5iHvbrSUJRrHI0tVe3hIcbVCsYAW2VQEaLeURb3JaDySk2z/HNeWI+TKA+q8VCS7GOLEnl4XQPaCOnW+x5jelFsUhQcclFYP6D0gO1HlN9TdYdKCdXP5BCZpz3r6UBeDsTlREVIq2k6UcOK1oY1JnJeGbc7di9/h6e7R/TnLzCbR2gv3SXtcia349wSRjlIAViO6PZ3rCW2dJSmmtKKBG9Z8zO6U4GpUmZNDYWyQokKa0QdqEWRUytvC8hhqKqwMjIPJkNVK0QZ3JV4ESXFIjERVCzUbYRewbWihorpFWUu4GUNoKwQXpVryotKqGBQeUL7AvVIofWf2K9R6dtYPIkLYvk+YTyjM2eYy6co91+w/NSR3v0byuE9OI9znnr2KWUJzMstJkMowgTpnKhEzOC6CJ0c3NoUqUEfCqwVeoXvDOsxkGdLiplUJRKvIu19Iod8WeS11KY8xLStMcRjIRdFCBXQaJPJWVODpepIWUXVSEaamtMioD1RT4r4UbAsSyLXxvtTilykd6i24r9UmopV26erGVRLEa+JbkNKNferH4XTGusEu+udkuoFFMp0OC0G52St9BCRqQqMWom3VyzrieRA1/vVmPBNUktzZKUkWaY9qfZsH33IxcffZfetT9k8/xC/u6DzA1034N2I65pqYgS+ZoxGG41SWnqWUxH5fT2R4iqf+Qc8gPz9lFbyjGmjRClFmnFp16KqcNazpIXrz/8C9+QjXCdR0FoCy82XzNMVRlvwI/0Qcd7RbR/Rb8+k4qJmnOvpxj3a9WIsb2mXsF4RDjfEJTOHSFgnQg6k5n/SRhI9OWehaWuDkAoyawyUWplj4DQvTEug6wY2m47t5hzlJC3X9z3OdfhB2or7XujOylhq1aSWaFqnA2WdCMtMWAM5VUiZELKocseFbuyxnZOSPAUOTQmZuaEgSi6EkBnHXeO+OFzXY8eBcbvDurGlVwQaOGwl6n99c+LLsnI6LhwPkaxeU60jYrh8vKPXlaI1j5485r/8Gx0/vL7jT346809+Wvjqs9fYi5Vd1/Hhs0u2m5c8e/qar9+946sv36P7lb7boo3Bb7Y8f/aUj54/4vH2AqsUaymE9cT72yMn7thdPuKjsxHrBkx3zvXNe9ZyQNeESjNxkfRLiFn8NeOIswKw22+3eG8xVQbHdZnbWkXwE1opqfXwG7AddhzpNm3dZ+2DP9JUQ40LJp6Y14zqMiWuEGfy8YoY5P0sfqgTUff48SX92SsMoRHbEzEurHFpnW6K0m3BWvAD1m2x3Yj1HXaQyLS1rRuvNDZNiJRw+rXP+N/oAQVkAJDTKbT22gJKuAaGCKyUuraDSzwaIkQZMZIiIUijvdwSjEa1NsbanYtHw3oqRsijSsqPjFIUcbkKR0PpFgsG2n6d2m7qIE/1kil5lRVQCfgcUTlIXwIC6yk5ijJTpVtiYzv5eovCWo+3HTFG7rncVUmHUAmTGKGQK611Fj8+JddKn6K4vec7wnTXyLsa5zx+PKM/e4oezjH9Drc9AztglJjZcq3Srlliuz1LG3Jtv0ZaoST5uyGJI6WUrIfQktSpCVsrKq2ko6ZOCoLc/nNEBhGD0FGVYOpSrC11pe4FJ4QrVsUYW3hozwUavE7Wc2K61ai53fKP0jQcm/FPlYpThqpauqXn3ssJ9/6NAgrp/6n1gqI3QIdKd/T5S2J4QciZ0lXMcM54/hj9we8zh5nl9o8oV29wvSgBZvsEf3dLWQquK8RZMYfK4BUmV9ZZzIjaQs6i5iijMb6yBsWakvQrkchFkYus+IyRL7pkiTLXkkErYlDUFWjrGaU1484R1sgyFayXJFlFUyoyWIAkY6oMfaCJuZByGyaopNL+f7WVBNYqgTLEzJzFXCJqSfOfKKXE0Hz/0RD3ZsOwK4yuWANVabQuaN+hNs+x25dEeyHqZl2pVYt3q3WJrOEapcTbFWIhVRl+UhGPj5SGdtQ6YP0FZ88+Yv/Rdzn/6Nv0T57hN7KW7HyP94O0ervWQqyNqCJaCvWohZIiNa7EZSat0nStuw5l7uNIqq2v5PkgZs/SBoD2+ur2fDKG4eIZbntOd3ZJzqBdJZ/umI93kvgZenznSfMBo6R6wQ87oFLT0pgWEymvlCqN5jGtxHDHejqyLisZ6Rlywxm1GspyQpdMSZnUvGrzGpmXlWVZAbDegzIYP3I27hmGgf1upBs32K6XFYOVXjDj5GKlaiHGlbzOlFQkCRQmltOdrC7CQgitGLKKIqqb6VipSilB/EIlk1MhhIQCUWKQttxqDW4zMGx3+M7jupFxs29QMSsrihTQtXK22/L00ZbDTSIusuZeTieupsCNtnzbWmoEZy2b7cDmcuDDJ4946t4RT1/x//3sDZ+dFtx+4MoqfvfxGf7i2zy9eM3Xb25Yl5VFe7yVAkWNZZ0mTlvPqDoZBtaZu+sD//LrFZ58zF/79EM+HkZMQ00crt/i00ynC7oNrVrBZtxycbanlkJcV3QtxGVmXU4YayhZ/ELKeawd0J28f023xZ1f0u8vGYYBTW404ZUaKyFVVDoy371hOi0Yu8NYJ+dRWAUdoKsUwJZLvKtYN0oqMBwJYSbbDu2skLy9w46DCAPGY/2I7bby+5v51rgOqIJvCIEcxQi+Hg+/9gn/Gz2gCGxN3szq/rTSWlgQ5b6v5p5RIhOnag58dIfWvWS5rUGZDoNqO2QDxmKrQisjsVqgxRxkPwo8YORrlnp7ZOCR+kF5YCllxPditAwzTQJuz/nWVyO/SzbwrWEULZTNKp4CpaTe/t6soZSWdIYR2T7WjC5J0PQli+SOkl+rmVKVRCsb6rPeD1bGYowHpdvaoKJqEhR+Xqg5SAw3R2pK6Cq02woPbaQyj6mG6pcBoKRCqUdxjpMp5UhMt9g1oUPzjWRJbFA1tShKlmZhYzWgm9dGbt/QOl5QDysCmQVbwE3rNhAVSKLwFCrdYFCtPDOvWdg5WdYfSvFL6bFxz5SF6uR/1yIG3WrOKNVgQ6bkG6z9Cwb3isrHxPAeVRN2+xRz+QH6+H22yzVz+pcst1d4a6jWoUdRhAyK0lXmtUU1rWYNBW/EAFp0xXhJoymrsAj6vmZFXKRBWAZkS0oJlCZFUdJKqTincBaWRfwKCk0KkWORJE7NYiCuyGtc7n0rgC6KXJDOGiXlcjnlxi+R73OtcgBXxPxakJtdlrcvhUoqoqjcS1v1/v2u1EO7tTb35ZZKeoNsxVqP3X6EevbblN1jtBlIulLXmRINxo2kWgjzFcvyjhgl3pibMlqKeMewVuKT/px+/4Lty2+x/+h7nH30Kd3+AtePDMMG6zoxwlphhBhjxDehZDDRSj4zpfm3YlyYpxmtNN53SKW8Q5NbnYR8njC//Jze/9BtOBEkgEb5Db4/ky4eo6glUI7X9F2P6QXtrkqmrBMlrBjtsb6Xht8wEWbpSEEbMYunQEiZVBIlG3I1FBTdcEG36dhtA+vda5a7d8S8sC6B47JwOC2sUSKtRhs2tmd/ds6429ENHt+1Eru+F2BX6/O6f57EEkmLqCRrWNBVEdaVdZUVVolrS3sJr6o9rKWHyDqMUizTzCkmUkptVadxTlDurpN1kh+22HGL22wZxp7Oj0KPNlpwElkI2fPxQM6Ri53nedb0RhOytLd/9dVbPlsrK57f+uAxF/sB4xzn/QbVWR5fHNjaGUthnSbmGLkKCoPlY3/G2cV3efXoLZ+//oowFaLzqCVx/c0df/bTBGHmW2fnoB3zWojTzM3VkZv5LU8vL3jx6hFnF5eU8pJjnlivf4F2WgjmtbKuiW4QLk5qBY4YKykZralWPBxGK0y/wfQ7uvGMoRsFqrZ9RNftMFRMjajlSEonYgzENVHWE+vxPesaSPWKbvusGbg9rhMKeKVifZB14OkGY4xQZ1vHmFYa0/VY38nZYT0oi7KCozC2PTyb0h1DgBxRcSLNd6RlZj3c/dpn/G/0gKJUGyaUBuUa9VWc+hWh4qH2AnGrcjPWVgBkVVtyO1Clll4/7MZKEeaIqlU6e2q7cZbcDH764adWHq0sWlmKbnLW/R5a6Za+uI9AF6j3OPuCRrVRprSvj4efpZFrxbibkMZlTWlFgSiJ4aLljdCpDujbLQVQBXsPkEO+lqpMI01KaZpECSUFVNOClOjFNnnLQ7fWJCpVLtQseHIxRMpgoGuRw6EmSk6oElElonNGqSyEwShtx772eI2oPBVKkYVYRirXtZbY67rIKu1hjw+AKCeFIn4ScYhKTDFLS3Nj3KGUwqKpS4u9xioeD90GjlSIFdxGow3UKAC+YjTK8zC4SDV8lKGgbKAsGDVTlhsG/VfEixeEuqEs13IL2j2he/YJ63JLN/+MdLwjTVdYq8FfUOwVeZGagn4PYa5c3yl2Y6U5qTG+EOaE8+qh3LFWJaj47FDBUEuQ90A10hJcZbDIpSWXiqgrOUGIUf4+UVQOpWjrB2ETu84Q5izQtbYWyhnx5rQBhsYZqm1IzPWXvub7Qbw2AMr996yoyn1nX6mVVH/53lbNj2VsM5FSsUZh3IjdPSLvL2A4w9oB0kIIhX6/k/bi4wLTNXG9QWnpAEqlxesVGO3Qdocdz9k/+zZnH36X7UefsH36EXZzju9GKcFzHt+NEhk2cis0RtI2ul1ISkyUIkj3dZmZTkdyrYJv91a8ZWHhtJ6IIWBcz7g7E1z//ef/35tTRCFMYRV1pmhJEw6WNE3YGtlcXAiMTjc+itHELKbaNU6tZ+vEfLql5CCf6yLf06Qsym+wmwHdSQLDDTu866nxSJrec5qP3N4emZeVeY2kWrC+xw8ynA3jjt35Obvzc4axw3WueUoqGqm3WE9HYkykLIbfsEhEeAmrxIgb5lwQD3IpdFZeZ2+trAUzhDWR50DMgTVFjJP1jXED3dDTD1JW2A8b3LijGzZCvLUGpSopTaRYIYuHZT7dcXdzy7RmnCk82mi6zhDWhdvrG968O/FuyWwuDnz75RM6Z/Fjh1MwzTO3pyPvl5VDgJAMrniUWvn5F9dsP3rK8OQF33K/hx8mPv/5O07HjlUZvvg88d/f3PLzz6753qsbXn14zpPLCza94+MnZ6z7LR9sDGNX2e03bIZX3OnClVXo6TXOKuHizgvH40HWt8aitKV4j+mFk2KcEaVFa4HajRf02wsG07rYsJSYWdOMLXeU42um4zXrEiR8kSbiGtDdI7rdc4ZxL5aFFFF5hZwbKfjAfDqQzIDuHePmjG7coIzBeo/peozrWr1I187MSmkrzpwSKQestpAiOa3otBKWE3FdBX73a/74jR5QjO2wTm7/SsuQIE9JJwqB0YAQB6tcEWWoQQ75iqDrhYxpkINcJN2qDLmRG5WyDf4lBMpyny6RnQCZ0tYFDUHbphQpzQOTMqo0l2alwdUAaos6y21Wygpt079FRyko+XNRgsDXWmTk+wegbs09pYgjvvULqSp/T1kPJVnJIApILVGizw2PD/JrVBkuSO1nkZ+qqTLqfi2AQ9lOzHNhpsQ7qAlVxJNCDhhxuUqNgHYy0FVFDgpi29PLbkCGnioH7f1LkdtOwMhc1qKw9cFsqbQoS6UdpW3bIUNKqbKqKYolZvEEWY0rmhSTvNQKSHKC1izFdtqL+lNbLFaVgnKVpD5DqVdQAjotmPwRhR/j/GP8+PuEagnHt3Smox/28PJ3mG++xp1NLMe3hDxjXY/qoKZMjprjJBj3sirCCv2Q6DtN18nf33q5hTtXMWMlt9eo1vyQWMlZlLbSVi8ZiBlikPdfKpkQwXtHUamtVhyVQlG1DT0yKMYs/71Sq7yGSjWTrYwz2vzy10Jpw6kWD0ouslpR969rU/JLhazVg8pRqe3fqVQyzmhUrdihx529RA+PiG4vDBqjWUtsJvCOlGGdT4Tbr5ne/kLed/cxfyVqVFKazl+yvfyI7Qcfsv/kB+xffszm/BFuc47tNjgvvB/nPF47tJHIptTE/9LAV3NqbJJIjCvH04m8rozbDX0nDKWwrNwdDkJuzZntptBtNnjVNXX0vo5AfoigVKg502nHcnvAnA2CDUgzylmcEr8ZOWFKJoSZsJ6I80kItRXWdWFZZhTlgd6KqRjj8P0ON5xTcmSZ7ohxouaZ9XTLNN0x50DUFr/16E5QC33X0XUdfd9ju45uGOk3kgbRRkvXS0qsi/glpuOB0/FESEXw+EXMl6UWEllWUkbUNmOFDl2rJtdCjJEQixjJlXj7/Lils0pWSMMW7QZc3+G8o7cGYzv8IAWCNQfWaWLNQUyzKUOUioHj4Y51CcxrwfiOXmvUfEeuPXNMZKXoraNXFW8qndH02hPjynr7jqurW2pRDMzcKc1UJZJ/vHnLnw+O73/yjP/ko9/hB08V/1P3z/nXf/GOebIc2JFOmi+vEj8/Vf7g0ZYXjzMXF4bv9ZY8Vl5t4WxQeF/p9JY4XhDMW2p4S4kJRyLFTNWGrAzdsMV5h3YdfhikU8dLStVQZYXoNzg3oMoqg3wKZNuJxzHeEm/fcbh9R01a+nzcBW7jMMMZ46MXDKqS8koIR9bpmhyidK0Zg+3PUMYRlHpgzljvMd4/FPOipB2ZkqixQUKLp+RAbgWB5EQpGV2zJIFcxff/vrr4P/fjN3pAUS3PV6qsNqq2srpR8oSscYF6lKelbsjfZooVp72TW6HSaNNLP4sW9LxSFmPkYK3QbimtEefBKSjKh8SSdRtA5Gqv2oEqB/A9VZKHm5W6V0JQ7be0eDTSEqvuGRj3sriWPg5VmsrTtJd6fyJAU2FENaktTVFrRRFl94x4A2qWYYJSZX1TRPmoWcyu5X5VlKOoKkW8JTmvKNNhuxFtHXE9UuNRUMe1tL+3RVkxzdmiUHag2ICaAvnulnSo1NRMlNJ6JoTR5tmBgnbyGtY2aNzHje8NoWhFUTLYiBXn3pwonh+a4qWrEgOlKtQkh7pGSXdIqJRjFsOsAu2lw4XSlAJ5a0Gu6LpQNwqGv0mdP4P0Bn36mg2ebB8Th+9TykI6fYOpCj+ekT76z4jv/hh39oj17q0UD2oLNqJtJQVFKgrj5Xt5ioqQK35V7W0iSle/UWwl5Q0pU5RGezE7xlMiT5LayQ3nXnIRnDtIQsZWssqoIkNfqYmS2nqmii9H/poypDw8dHIhK01C/t37lyMKu6lxNu7VPoHnVVoMvMptKhVJEVEFpKi1AlUR7570v7juHHfxEer5d8ndDqOVMIAOM/hO3nvaMIcohuZwjQ4HqJUlFmoR5dPUjmFzxv7F9zj/6AfsP/qI/vkruu1jfL9D+06aYZ2XllzjsVbUEq21rH3b5yVnKWIrUfwTx2nmNC90w4juR1CW07RydThxO68U47CuY7BeaKHt+XP/mgk4UZSOHAPOWPL7GXO7oC+2kKKoIVXSfCUGSpCem3U+si5HYozUGNGI18gaJwqV0Y1MG8hZkkXGRnJeCcvEabqRyHnMaDeyf/ySXRVsfg4Bg8Ib+WdjbfssGLTKxCBldnmdCcvMfDoK5n2auTtN5ALeWrxzaKPorJWvTesH6mzMiRASuUgM2jdelTYO3/fS+9I7KXP1PbbfYv2meUsUpIVUCjEG1nWGvJDnAynOxGUmr/Icy7kyzauozhg649iOnnUtnE5iDHW+8mS35dXZwNZk8XfczhyPJ75+856v7ir9sOX7H1u2V4pfHCvTulK9Ynr7hp/tPX/zO6/4zsXvocuBr97+D3z27o5kOxbvUbFwQJG1o9eO7CvPrSLYACqQU8CrjdC2bUdMlpup0BPZOFBuwA5b1Oacbn9ON/RY7+m8x1mDtxLV1TUSppUQVoKaiPFEna5Z1hVte7nEhBP1tFCCwnR7+vNn9OM5WuuH9fYyL8Q4keNKylJNQL/DjjspYnQG1W/pt48YNmMz5Yrur5HUWAkS905rICRNdj1GV1Qr2pUhWi6p3jp5f8bjr33G/0YPKKUG8S1UUQgKFWO3UhCmFdQV0iR4dAe1WFlDKIV2A8qOVO2k10NJd889IUXVgkqr7FqbtE2rwpayPY3CCqSG1kCsfsUQ17LFWsvAoY1k+2U9V8g1Iu4T+fcrrQ1UZYqSQ8c006vItw7nN9zX18uH3AP1V4zCSvaBSgvUTcG9lP+A9661TVvCSqAtm9pOg/shI9dCc0wAwkVQtVLDSljfU5DXXKmKtjsqgnvW9+jumtuwE6QYK0xE49CZh+hNqY130hQwaUaWvhRtoOoqUK4KygiX5p7EqTTgKgR5hRQKfe/MBPFDNC/E/e3NNVVGa/l3chClyRgpnlQFdN9ItcrIHzwo4giu3K/BCrmMmHxNjf8dg39F6F+Rhj35cIsaAm7s2bx8xenr/zXm7husOTHFG0wdQAUwYIxqFF5AG6YTxDWzHR2KhDPyOiyloo5ajK0UUZYi9DrDWaWOBrVAPkAKpRHRpbvnHp2f71UhDalKO3du5tjc9i4VGiVU/MtShi0qT9WaWmWth9aY2n69Nm9Flikzlab0FEhVEaoipopSlfNNz9BtiPmEqgtWd2D3mKe/jXnyAWV7KeV9SqGq3CaHi8fkUki3NyglcfGcKktaWbMklXQF7Dnd/hlnH37M2Xd+l82LT9hcPG2qydhuj06M4w1Vb7VFGd3aXX/pz6o5EdOKjpEUA6c5cDuvKGUZvEdrxe1p4eow8W6aWCsMSqFrpnc9Tvu2fmyXFWXIBHmRQyQnMGsgfP6O8vyM3mbKYaKe7khhIeeFGCohrOR8ar9m5AKkvdQmuMZMyVDTTM4rNUXisjLfzYSLmaw0Oc1yYGvPsNsItE/TOn00OSzUcELlJKu4KNC5eJoptbJmIc3GJbDOCyEm8eQkWeFaDJaKUzKsQiXVImV4MbGuQVSTVFHK4r2lHwf80KGNpet7+k6GFLpRgg2uF1+PFuUmx8DpeGjP1iJ1HnEhzkfSGljW1LrY5ILhrGO/P2MYN6LqxoW7U6CmyuNHW/Zjz4ePBkyYOV0nvjpm/urza/78myNL1uw353zgRy66yLPTys9uKl9OGR8XXl/d8WOr+S8++ha77sQ3X3zJ7eEzbtZbjD9DmY5qPalqikqitGnLdJy5ff85SSVGb+hqIaRCqDuO5inoO7wKDNuOYfuY4eyccb9h7LS0AGsrZaBKVuElnAh3V8wR1HCO0Za0LIR1Quc7vLVoY1DjjqHboP05/eYRQ+fEh7JMhNJhGy0dNUjaJhvMcI4bRoauA6PQXU/Xn+GcbRfuRA0zKSVqzuQQiGkhxUJMiuoXqvMY61H6vlJBKhGssVJFk/4jWfGIHGDkcDIOUKiqHwYNbTzabkErcluHSCLZobQjK1FQNM2A2vws6t7U11QNhdzM2922KTe6rWmkbFB281KYBjTVoVAo5JJQ6R50lZvqW1BlQuUFnSdUuW2gsRcY8xxdJ2leJqFMD2qg5kp0gzSaGg+67QNUboqQrABKKWirmm+m/LLHpuZ2s8kC1coCcxLXaKQkMcPmhuIni//kPpZdakKrDqU9Co+2QpXFOKR8bcU0ubGEidzgdjmdYJ3J4ZqU23qnKR9y6xZksrYC2YLaHPxtVYAMerLjKC0Bwv3eh2oqUYmP5P6nhJwLphaUwB1+GTVHDJq1yC1AG4XWFYNuJlJZxVQHDAa3+VuwfA3Lf4Pa/wHq6f+RSWnq1/9Putf/HwZ3SX70X5H9I9ThSLG3aN/x6NO/xbsv/xg1H2E4ocMJFR1ZJ/ACnksR1jVjrMF5TdcnvKkMncJ7he8rrlMoU+VbjSLlQiHhekvJleqrRJPvFHmVwaQoUWl+NfJaayVkKc9TKMq9OqhkxMu5tBQMoCAq8Qfx8H6qorhUMdZmJSmpohQhyTyXogw9ucq/562i86a1/3bgDJQB7XbY/TPM7gmq34I25JjEiKkNzmjiHFBWk2Ik1ZU83fH+6hdMx0lURt3Rby8Yn3/C+cc/ZP/qO4zPv4XbPcL1G2nNtQ5vrTBNrJBg70v+tLq/6dcGXRT4VI0rcwqEEHh3nMm5cLYfKFZzdXvkm9sjvzjcsZRMbxy1eLrRYF1BtydqrRpTkVWqs5RlZV1m9KyoX5woU8A/GskpUG9/zu31l6QcUbGScaRaRBVJGecGXPcYox15vSUc35DXGXIiLEfCspBSYjqdWBOc247d5VPscAFKiRnVtoSScU2ZLUIIPVbC8UBcg6gk08TpNLGE0MzOEOPaYqYykDprcN2AtwbvPShYwsIaAmsMMpgk6UHzXc+w6fDdgPc9/UYgbt3Qo62TNYZSyFGkyWsi1Bl0ELXm9J7j4SjGaqUkAJDFK1dzldfZevq+l88+FaM167rImisEas1Yo+jGjkeXWzadZzd4DtfXvPnFG/6nP73i82tLrIWLy4XHFzs2neflZuB8p3l8nLkxnu9+csnvdwbbOV5+8Al//a/9Pp+9u+Xf/PQd6+Tp6Vjfr3z5+R1fn3V861KI2/H6mj/+7Kechq/47Y+/xVnf8c1V5atbTciPiW5g068Me8P24oz92TM2mwFTTqh4gnBooLtMTjNxnZjujkxzRPt39Jsd1hq8Mpi+x3SDnH26oqOoaiGeUNURjlescaWWAb05w7oOa4SR0mHQfoMZdvRukPeIQkpli0KXAHkmTbek+SSR5HWVBBce7XforhNPl9WSJEJRs/TO5UqrfFl+7SP+N3tAoVBUkelZGdHBlUTY0JZqjMjq2vCATtL3txGDxrSDXRI2VZnmhZC9sfA2CrW2evF2wEPgnqdSS0IrkYZ1iQ/AKEWhpBlNW6fUgCtQ0g2URoVNX2DyFVV50OfgPwQD1UEwG+g2KN1j3RatDcVUXG1+mnveR6tsp8gtsJYAJVIehhDJoFaJpZBTkp1hqaiaG8OkqSE5AS0JURRaOVAS9QNQ9gxre3JRUBNGy1BSlolaE3mdKCVAOgnvpSyQIqpM5PnEOn+DqvnfX80Y0E7YDqX1fiQlnToCZZO/as3ijWgLNFSRw1CDHJ6qkvXDRQNdhIOBq5hUG422CgVTiW9BNbWBXOWTsMrvL8jrqzrQnaP470A8SDqoviH7Di5+iFKPqT/+PzG8/sek7lOu99+B6zt4c8B0W+YlIzucgquGZZqIIZFTlbWEUWz8/deSGTpL78B7MFrKD7WXhAtKFBCjKx4LXSapTE6abAtRGcpJdLhKlXRSp1pTtEi6KQkTTxRBHoyvtPVD1Q1L/2B2+6V6WHIlo1lTWwmhiLl5V4A1yeonZTmclQKrqwx/zYtVtUXbTkBy4yVl95zipNyMUgTNrjQpZkpZyYeKsYa722s0gXT9nvV4i64G7fd0Zx/w5KPvsP3ou4wvP6W/eIbdnuPdIMOI9zjrcO2AvqfBmpb4U7U2M3iSnXkzd5ZSOK0r16eJwxw52+/xzjEdAz97847Pbw+cSmHjPd4rOlUZjaNTDictXKLqqkIthrKurNMtenLom4V1mXDPL7G9YO1vv/wxU1zRpVKMkbZmv8XQQwxY2+HGRw9r3npbWW9vpAAuBYnfG4frRwwGZwxd19Nv9+iWTpKyxgbYq0o+n1V6y+bTiel45HA6cjzNnNZFuEvW4dq6xmmNs9LgbJR8T13D/s9xZV7k96UqPj/fd/hOvAv9uKHrN79SfyHeL+tsW0XLSiLFyOG4sMaK7Xqp7YiCvM85S49L+/5ZZ8la1JthEG6IRK0DdZrQvmuVJ55dpzEJooVN3xNLBePp+pGSK0uurNFQq+HduzsOx4mzyx2Pz3ds9zt+9PwDtvuR5y/2PPeFJQW863n56jt8/ztf8PXrI1/e3LFYAzeaf/1XnvOtR+fITit+8dMv+ZM/v+Kvbr/gXz15w26/4bB23HUXfPydF/zu9ozhHPpuZdhuGXuPXlcB3a0H5sMb4pwoMVFrJgSpKkgZeiXmY9dt2iDq2zmWIAV0OLLcXrHEGc6eYPqRftxSnMNsznBevC2iUCqqMpIQ0h5VFkpa5c9VippXCT1MB+LxrhUVBjIVO5zhuo6+H3FOKLk5LZTsBFxaM7QqjPwfC+oe2oFlLeCktVZp6cnREplFS9rmXl0R5UO1VYvE3x4OYJA+ipKBLIbGIlFb1W7vwv84iQmuFiBKVr9UYJWDrSoxVNYFOdUWar6loqHeoeoB01I6yr+E/nuw+R51eEzpRqrpsGaD0q3WHi03hweibQGyqMi1SBNxzeLUrlI9m4owELQxZHJTdJo3p4qaVJA1kVHStUOFlBLWGBSt0AypAIgxksNJShRLaWbYhZJkIFI1U+NEziu6igxb40yNUh6oS8JgpRivCBk15oLWYIwMeNaq5q1BzL7330OUmDDvv+cF7ltVtap4LV002hgxvKYqP00VAJrAE9Gxyq3LgKlNbRFhiQokIx0kNUFZxZJUY0GnvyQd36OTRuc/Qbn/Bmc8peuoZ7+PPvyP+Nt/S799RrAj4d1PqHWhJk8NE7kqajpilBAiAXqnGLymdxlvaQeIeH28ly9Ma4XuhPCqjG6qhER+tQK8wXYd5ImhNyxd5fBGMx0LMRWMLSi0+EKoVCtrtXsDcq2VLNOYDGVakRDTaymV3PqdCgJ5W3MRE12p5CrEnVolRXW/ipOqhUbm1UqMlMaCcWhrqUqM08oPFO9k9RiDfAOcI0cIzayLSqiUcArmm/cs717jUoc7O2d89in7D3/Axbc+wj36ALe/wPcj1o14K4eyc04e2qYd0kb/CqMkU5Ikz2pO5IasL7VwnFeuDoGrKbIbOsbBMa+RX7x+x0/evCWbnrEfGDrDi82Op9ue/XbPrtvL6/1gJldQTyy37zCpQ72dCHHFOEd9vqeiWN9+xulwQ/U9ptU4WDfgt08wticvd4T5jhpPxAolzNKevKyCZreOzfk54+6CnEQVtMPIZtzgxx3GWbkQNA5TzRlSZF0OLIdrbm+vOdzesp5OzIsg8NEabzWDd1gFVjtqLlgjyPQUYuvTkR6nmBO1KvHhdHLwOy+qiet6fCdDijYGSHKJKZm0BFKQVdo6T4R15TQthFSk5bdzsj6vBU3FalklaWR9phFycWiVA+LmVuAdbjNivcMBW2vZo5iWgm63+VIy3lvOzjacDwe+sZmYFapalilzXK55f5h4+bHmh4+f8OGTDc83G07rwpvrQt1t2Fw84we/9SO++vI9p/WnXMeJefV8+bXnn3WKOh/piHz5l6/5ydcr12vHV4d36O6EMltOw8zy6Anfe7Tlbp3ZdZnpbmXQBqYraoyE6T2H23ess4A5re0wQEoFO+7ZPPmQ3fkzrNGkOFMbGiI30yo50GoeKban2+4xzovS0fxY3olxnpxJQRI/mQxlJa9HYSkZJVT1LPC83Lx6btzS+x7rt3S98FfqGoi1ENIKSIqwKt1alDvs+Evj+H/ox2/0gGLMgLEbjHEULNp4qrGiiJSWrClFioxKFCm7eTEqcjLdL2xKFulQ1IWIKolcZnKeJTZbNbVKN05JE9aIQ7/kFWqS2vTSWOsUVJ5FEtMGpQ01T+SSUQaSfULpfxu1/ZS0eYXuLlC2l/SNFvUilywFNClIdDcLGK0itNtaEqm2FQ2ZWm0z3orRVlVRGapqxNKqW2JINW6LFUZMyQ+JEOs8NWbSdEBTSWGi1ozRLc8eT1BpaR1JB6haRL1IgZpmynKCFGXdozoxe1qDVhuqeky1XxHmiDbgRv2wfqpoUTyUrBFIEpVVLREVU4s3NyUFRFlRVcyfXitsQ+HUKoOL7qFsNWkBE++tN02GqRJ1bPMPSksPUA0V0ynq0l43lcj5X6BjRKPBLHTv/lvSckt4/Dep+x9Qwxe4+a/YXP0lYfwBdI9Jr/85Kt7g+JrqZ4w6MnpZG1kFnQPTVoLOWFnhKBnYtG0RXStfr7qHW4GwCkqhnBxGSxfSuszkEHDOcnap2e4r82xY18LSunVy1GQqERl00JqCZi1Z1lqI+hGrImZB4adSKErehiHBmlVrHdbS6dMw9SBJIHMPzmtG5ZgqTht8v6cb9ihlxRGlasMBSBwRCiUlUrIS/y8K4waMUazHowy684FSIpvHH3Px4ffYffw9+hcf4M6e4MczbO/prMdoJ2RR3VYbRm7dunWRyJU9tTTV2i4kMhCFXJjWhXe3J66OM9th5Hzfs85Hvnp35CdX76lKM4wdm97x/cdPePXknKEzDG6gakVEqINGafJyw+1XP8b6J6jJoP7qSzh7SjnzUuy4XJPefU2/uxBEQYqoKEZ4haRzckkcjt9glMP5LUpl3Lhj+0RR44LyHW5zht+eiRejyArUdT3W3LOEhICbGtU1rhPrfGSeDtwdTyzrQi4Zr5WsXowMzLbFrXOprCmR0sra+p1Szhjr6Pue7W7DaESVdp3H+g7XjXR+QFmHwqBM85CkQFhW1tNR1jnLKtyUmMgpiueoCA3ZKYW2YuItWobsQiFlUVfXNZJSZTNu8cbjBo91DuM9bjMybLbSLm1E0RtOgffXN4QQONYZnSNOV56PHVf7ldd3K0vsBK4YCndfX3GTDeP5jv5g2Lqe22kh20TnHTvd8/Tlp/zoRzfcrQv/7mdfMS0HYrW8uen5aWfpwpHX7xJv5kwyCZvmRt11bHLm3Rdv+IuN5WACV5srnupA/OASX++oIRJPR9ZjIswBZSy+H7nYb+QS3Z+xO3sqSlZZieHEOl0TUqAqi+236O4Mt7Xo8Ry7f0K33eCMxnm5nNYiDkRJagXKdMeCxrhRLqkxEWNCGS0DojbYfoNGkX3AdxuM64hrJqWEKkl6urR8lquSHrCqPdUPGOux8f6q+R/+8Rs9oChrUI2kii6UGsVD0aAyhdadk6G0g11Uj/ufYjxUyFRYW1skOVFyoNYIRVSE0hIvSkc0irhGaolonchFbgGyd61QV1Q8oqj4cY+xPVWdUYdnuP2n+N23KcNTlO1wSsk3skiTJLFVbDNDKqh0osb3qBpaB5AcsFUZCh5UB8rIhuv+sK+FkjKU6eG2UKscNqUUck5iLqyZFGZUFTJssVZc2SWJakREVSmDyjFgylHWRSVR6iodGhU5XFbpJRGvnMaY1i+TZD0wrzNr+IZOZfCKkAumgrOGGDMxykPHWBkYrWlDSSoCqm0391rbikfdl+MpYswtfZFBgSnNs9EhnT1zlXRO81wYMV4IzK1WrFbiD66KbFriRCH7kFNF14ByhmoVea3Y7ho9/TvcO0O15/IeCldw+Dlq7anTAXX8OfX6n+KnW/RywlqBsql875Up4k8zikIRiqpWLUZOiz5XoY82hs+9zlcLaBLxupCYMW2VElZh9iij8L0kqFzvMbayzoE5LNRcSErWXWtMhHtLVFNJYtakqlhCphZF1TKsSDuxap8b1YB5zZV1nxpra0atJIVkjGWz2dNvLrC+l1WiURRjhUK7BqoudONIzollOeCcFFQuxzvwClMUOUSUHXj08XN2H/4WZ68+xV8+w22lCNT7EWtsw4MbgUsZJbtwJesN1VTOmttBGEOL38uvh5y4nRfe39xyNy2M48jlmVRlfP32DZ9frzgMyhtebDZ8/PgxHz59hO8Npkq6IVdBx5MLy+EzDm9/Qsw7Nt0l+V//mPUQqT4wfmuDNZX5/efNnGzEK7AslOXE9enImCN+uCCFGTDEWuk7aQm3+3PUZaLMR0gJO+4w4x6jDTUskMTMmhbpryklEtPCMh37o65PAAEAAElEQVRZ5lNTHOaG4feMmx2mD/J9o0gJXMnEWFhCZFkWQozyWjoPvsc7Szd09M1Pct9HZJ2wMawbxaieZSUhSkkkzBPrOnG6u4Usn90Ugryfsqi3vbcSBzeioKaUZYBMiRAlEeSMYB+01RKL3WwYdiPGWrzv6PqBvt+0wahSU6KoBZUir9++45vXjlw0h0UTlOK8hzRrvlgLyVa81xg90M2Jq2+u+CwVpsOM9j3n52fklFlUgN7w4pPv8L2bG66PK599c0OKt0zLyKmcsVG9IDDKStJIRLgEQjmia8f69c/5twTuLi/5xhg+GFeO8+d8sLUMPeiq8F2P0tAPZ1w+ecXZfktKK8sayOFISEZAdSmSlSHbHtXtMLvH+F7sAXrYMGzP6PsBZ6oU0ZZKzYGYhVVCmFmPtywh4PozocEa6IxFWSMXKaUwtoCF5XAP2gzEeaaywfYOrJThatcuylWBHdD9KENzyL/2Gf8bPaDUKlHf+1tmTrL+aIZyGU6UElmz5lYJ39z6NQsnpdaHHgmlrXhRqiKn+EsVpERqXUk1kELBUqUBkox3wquIYaJmHjD41ezQ/hy9fQWbJ7jxW6jhOdUPAgRDlIhSV5HU4oLOK2q9Q4Wv0XmCFICFWhe5rSoL6oKsHFqPaJXJLJQKOgrYSw7dhKoJijBJVMmkvAASZ5QBpCDoffHJUIrEVGOUgS3FZqqVtZJSlZQjOS6iPBkLSiR5iqybVDUo7YmxkmKkWo/qzljefkOcvibNb9k42Drxi0QqMdb2Bm7MGC1rh9ww+ClU8ZRQhZaKrDxIhZogFY3RoFfF0Myu1QJOSZNxrdQIMYKhPPhYqKpBy9oBm8WbobVCxUrR4g3RSaGiJLBUNdSgKB2gR7lx5J9j0i+o6YAefhulPPr1v6W8/id09Q21QN+B1aCKorQED9QHaLFQd5Ebc6IdpPLPRrfhpKlr9ykxlJQKlixLsdqLlyZH4RgkewHPfo+he4qdj8S7Lxlvfs5puSLnyvVR0jbWaI5RC6CtZpLWlFiJyIujq0Vcw/eDR1vhVBlChHDcXlQFaIkpG23Z7UbOLi6xbgBVKbqgjEc5T9WKnCNrjAQUN8cjJQW2Y0/vHSUk6qxQtmfcX+CffcT+xUf0zz/E7s9xww7ne5zV4jEx4jXRRt4PqnGQVPMriX8sSqQyyDqntovcElZup4l3N9dM88y26znbWmw5cXj3jpubG6rdwlr55OkZ3/7gKefbMzadQym54tiqSApMqaTjN9y++SPmRXH5/HfxV5m720DcDhDuMJevKOst8fobcpwI04H1eMc8L0zzgUl5zrTjkRvxXU83vARt6AchdWpr0aWQph4VJPpv/IACUhSuRQjLQxIrlkyOs0RJi6x7jHV4NzLsBogTaXpPXQ/EWAg5My8zyxpYlkDNMiRYrxicZ9hu6Ye++Uw6rBePz/0StpRCCpOYZ2cB3M3TRKW2hM/a1mvt/aMVXmuhoRol3iHnSTmzxshpPhFrJTeDuOt6TC+pMOc63DDQbQZ857HW4qzHWt9Sim11sUwcr69YpgPz8cS728jNonh3Uux2G/rOsR8Lu1g5lErVFm89hUg5Lsx24hdzRO227C8ucVrhVSZ38OTpY777Wz/k9ubAMv0xXx8WlvmOJb/Ebc/Zbo8MdyunJRKtR+kMRNZSKavl9Zu3eK0J2x2+P+fRdMelTewHy7C1OLOnrjNZK5y3pBjJWTqhsoJhe47yHUZt6Wpq9Okt/Xgur5VBLsO9oOmpEZUCy7pQYxCza4qUOLNMd4RFqiV6JQZk7SXSblQLVqSKKkIvjiULQG8zkq0D26Gdl6Zl23yepZDuvTJaMf8KG+g/9OM3ekChyocBbSlVIyRZeSiJKTSjlECpaqqi4ytJpOQk+F+FwmpHCU1Z0EZc80aTq+MeK2pbYkXnHlKg6ywx3JBjxDmHMhu5XZgtZniK2z7Hbp6jukswI8o6ihI2gAqRmmdKTOh4QocrKLfS4BqP1PAX1Bzl4Wp6rN5QcS1uuEjBW5opSRITqiTWlkbJcZU3YJkhLW0neR9Drqgsa62SIzlP5BLx3lNyY1gAMTfmhLq/ySdyM9jmtEh00TlQmRQKuUjOvaREzgHVPWX74j/FPf0twumOX/z5/5V0e8USC8MOPjmTVYFwTQTzHkOVAVFDypVlzdwHjIzSEvtuCoqkoTSlKozKrS26YpuxxA0dpovgC6kKsK00kNi/l9yozSirEIhckahxzeqXB2/R1KhAyRCmTTMXc0TFOxRvqPUtKR5Q5Q6z3aGGxxS9otZCTUrWgVXej85IKgyNlOfFgi2asMoHv9Tahj35mgoSVsoVjKVxYCopZwq2HbxiLJXeHUXdf4J98rewr/5LOHtJufk5+Rf/kt2TTzFf/ytsvebsrBLDnnfffIV3K7dKUjkqSQxbNYx9LklgZrQeGa2a36oxaagPDBqtVYO9KbbjwMXlJZvtjhQL03xHSCd8t8VpR9arrJGU5fXNG04hst122BhJOeJ0z7jds3/0lP2LD+kunjGcPxG1oGvMDONxRmOtXC6ECHvPF2rPh3sMfQoQAzlEQcQrTVWFJQSubm55+/Yt0+GW/W7DvtsxGIOjysokBHQ9shs2vDjrudj0dN6jm+kPVYlFYSucltfMb/4px6vXdM//t/T2jPzuPe7ZU9LNLfZbj+iGwuFn/46bL/+Su/fvOU2rqAgl02/3PHr6AZtHLxi2e7quQ3stySMj5GSqhVwxZElYrIEyH6gls55umA7viOEkPo+qSWgkHNWh3Eg3nKFVxbiRrttRw4FDmTndvuM0LZzmSUBwStF7j+835CjPo74f6LcbxvMtznmJjoJETlMgxsA8Cz6fqliWhXUNLMtCpd3Ga8EpjTIKayzVyepAkAxS+riuohAsIbDkhOl6+n5E2x7Xd/SdZ9P3dN2AG7a4rsMoxNSZA7FxWFKK1BgIpxPv377lbl5xyuJ1JcwTd1eB28PE8+dP2W0GXjnLl7czcyjSt1YV05I5hkSnrbjBS8Xqwn7jKafCyVQunzzh2z/4LabpNcef/IKb+cD16RZ9/ohh0zN0Dr3OpBIxuSmOgIozZbW8efcOtlue1Z6bmpiYpEOr63BGnoN3a2AJM8rLKstv9tBtYNhJQZ/3mCYGK+3phh2+GyBHSWQlGVZrWanrRDq9Y51m6fLSuqmMGtdt8cMZw2aLtWI3EH8jpCRBEVUyRRswHdl3GOseTLrGS5JHoyS0kgM1zuRVEYwjzP+RdPGgtbATdIdqPBOqPMSVFoJhKVV2+j7LCqjKSkfnKCaitpbQNUCcKHklpjt0Mc10a1HKUqs8KBzt1l0CJs4Y7dHdGd4OmP4M5bYovyMbL+VmqjbF4hadK6wrNZwg31LCN6jwDaQrFIFqBkruMEqSFQqoKVBIpPuEAZWYE3m9Q1eNsTtKXluaB4nglUStEdMYLDEFadbNlRyzeAFqIhVxhccYm+mpUFOCNTY4lyGXlqxIrdStyMCWo6KShYJZMgWP9hfU6jj74LcpF9/m+vXXvP03/x3T9V/iETn5kCKpID6PRiGNQARyUKw1NQaeKFGhCMdFV9MOY9ANtmGM+B68BW/F3a+Hit5FtMuoTlMWWNsDQSwI9SGB1cIu1KpoZ24LR1WBTVUBn6moqQ5qTeisZDDEUbWjLlfAHSiNWl9j/Q774vcp738L1n9ODYoUKrpotBIKbIVGtm/KTUWCYVkkXeHViZKkTJMmaiXHSm1JJ9XaE4PoxjL/GAePPyGd/w3cxY8wjz5EP/4ATm/RwxPU0/8cPU2sP/tv6T7863Tf/98R/uj/xnjzM/wpcbVU0lJJQeOdlhLLIq+NNFkLOVa3FExtYDNQD3K81oau6zg72zEOWypGordWiSweZpIxIs27nlIK3luUdwx9T98PbMcNj568or98jL14gruQJt/Ob2SHfZ/IUWCVdGtp2y4n2rSVbCNElyjDeEqQZNVZtRZex3TicPOOm9dfst7dsh02PNr0nJ+d4bZbMYjrjuS3LFVxttvy4vIJoxubZ0rWXbUq0Jnl8AW3V/+CcPWXLPwujy++i3m/Emohe0d3vqf/zhOmq8/54l/9U67efsWcKn53Tn8+oEpiu3/J2dMPGR49w292eO8wRr4BpaRWvyHJwpoLIays0x1lPZFrYV4nltMtJa1Ya+T1UhqrLbYb8eNTnNtRqxjqS86kuBJTYoqZjMb7sRX5KVmVjHtZ6pWMcQ7bSweOIrMuCzkmcoiUEJhPBw7TzJySEKRpnjjEv2oU4hPRLT2Z64OSt4TItEgSqLQBWHeOjRM/ybDZ47otxo9YA4oo+Px+i7E9uizEeGBZDqQ1EWMkLkFUghA5TkdCLHi34cVlJyyiZPjsZuL6cGKzGTkbDUVp7oJimQ3HBeKSSCnzfLchp8gXX/+CF2cfM1qwpXB7FaHA/uIDPv7O97m+ueXHXx25PVxzu+7o+p7z85GgE2sVhfYUQ0vzJUqAGcXrEvlP7BlXVH4eFE/niZEjaaiwnKB4kjaUcY/dbNgYD05gbr7rsVYGlJKzmN91M2ynQIqByv3KP5HXE/nuPfM0g+lx2wu6ricbTabDDVsxs6cF0koOM7G0S1FLd+puh3YbVD/gOovTomAqbcQvl7NckNcT5XTNUjao7ow4/0fSxSOTQvsAKOGf3DeQKi3RY2Vb340WYJSi9WNUhVe/lMxrLbLKkVpYqM3T0hzj4si0IqfrRk9RRm4lxqGsAM4EgV8xJUp6J0VUjBDvUOFEWd9Aeo+qjX1STu1QcsT0npJPaLMniTwkRVs1E8OKKlESCfoCrbbQn6G3H5Gx6LCQU5SHWc6UnGTPn1aqDVRr5ebrZKVltIMsrBatNCoG6R1xK8lIxl3VKLyFKpj+WmuDRUnPS8qFFGV9lvMtzkbiCsfX3xDi/4vj6ZYaT834qtAmc0yKr06ap64CmqJkgEBrQkkPykFOAr/TjTezpsy9Jr/rFPtR09uKqIoaYwu2B31e0O3WXspCCpW4gjFWlLQqiovR8nDMuT4YPe89lPcDSwMBi2el8WZUtQLLUmfE/kPK/DN0foNBU45/hp5eo7szsK/I+l9CV4nHgpoqXlswSYaOBp7TKHIsYoEp7bCrLZZbQEfpEKq0X2v7S6NL41RIGWLRUM62lP33qMNL6M+I4Yi5/YL5dI2zI3V6jx5foi9+i+X6hh2R/aNPSf1If/slj63m869u+MW6tKSVFltiSYImb2kfVYocHm1QEXXKUGpBGcHIOy+fhZhkFZqa6lbQVJ3JNZJLoFRwxuKMZ3/2iP3FY84fPWHz+Dn92RPM9gLTb/Bdh1MW7V3rzKmY2mLmqrGMAGpLGDSfCVk+07mktvIV49+6Tkw3N5yuX8PpHWdWcXa+ZX92jncSAcf19L7nw+05yhi89XS2lzSKzm2jIdPhOr3h9upfsd78GVfhMU8/+VuM2qGmI5WC7Qzmo2foLnP7s8+4nReqH9htR86fvmLYbNGq0rkOv93QDR7rbTMeF0qW172kSIytnXydmO6uCac7aphaYtHQ+R6c1ICY5mHSbdg2FGqN4kFJzbcQVowb2D172dZ6hRRXail45xiHDbVK0ibnjMqJcjiylsK8LCzLyjTN0geTMzFn1hSxtjD0A9YolJVBUoz8LfmTKyEIadYqgQeuOaOdpRsGhqEHqzDeM4xbfD9i3AbT7bHakOPEPB9Z1wWdCmW5ZT6853B7Q1gDMeS2zhUkpq6KTTcwDiO+E/Ntmiu3a+Tt8Q5353l5ccajc88YFG8rhCDQwDDNnG7eo0PgF59/xlXQ/MEPP2brEmGa+OIE+7MzLp59h4+ffs3rdz/h+uYd31zveHl+wbc+fc5v+xfUmrn56h0/++aOL24SS1JUVrTPDFo4ITEX/mLSPFUOReB8HBnGDqvES2K3F5jtHuuktkF6rUyr/1TonMlxIYWV4ry0XodFUlyIYkyNZO2oLqOdp99s2Qw9aSmcppWaZtYk6IyqKmuMlFylbdz1cr5aj+l34gNrfUL1vuE+J7lcxxNlksTYHCxmOLKE9dc+4n+jB5QUTqRZg5aJDppB6P6D0Hgo99RXIVUKNfUexma0tMbUkuXXFNzvUs09dZXmU1H3DTNWIpn3tbqtg0arWdYnpcrBvt6i4y3EG3Q+CpAtv6eWA0aZZvKsxBLIeSLFhRxuyNxRiyXngnZeTLRVpPOsNN1wie8eU7s9dfsKa/ZU06GLDGGdsbLmqrVxTppyVKU0r1RwriPGREkrRhXUeiQG+Rrt4R15PUE+oc01ZZWStFLEXBlDZplWwhpYwyLU2gIUKYUrpWKyFPvFxgApWaLREfhqUiyl8Hin6VwrS0zyZjwlMV7Flft2InLKoBUexd7Dk7PKZihyC9MG7RXVVZTXKF9RrseoSlln4qJY1io907riNQ99PvdE2trYJ1a3/qQKNRWhY2qoOlMiaCegvRIUJX4JegflPRRDsQXWG8q7P6V2r7DOENA4F7ED5Ovma3GGmgpFV6wz5FLJqQ1DuT4oO/c/y72qIy8vIGpGKpZQKkklnDGozUjaf5fSPcPaLVV31OMJHRLD5pJgRJnxT34A4UvCn/0Tbv/o/4zLkLUU0PnO01vN+f4JX19dE2J9+HMlaSU+HWPMQ2cNaIwTAJ4uwgAxilaNILAzRcUoJaTNIopQCoEcDbYfGDd79k8+YP/sFe7iEcPlI/z+HN/tcX4jLcfG4KyV7/d93LQNKAJXRC4VWRJBOQVZdxZZGZQqKThSJJ9mwulImo90vmd8/i3ISdgbVOJ0EO/QudRfWDtKGaFWFK3QWlbHVIdCkcORw+2/Yz7+OVcHRz3/azx+dIkJK+FwRK8J83KPeuQo8YZcC09+6/chLfhSGLcX2H4vEd54Jx6Tht7P9/ylKPHPsMxM04G4zpQYiGGR+5PrUUrTWYvRg6yxSkZRia3qfg4JFRa6/SNySmJKVxrjHd6d0xk5+E0t5DCR1kn+G1qhkqLkTJxW5vVAyEkUj2khtBZi6TOSGPnYC7G3cwZjZRVYihi/17ASYiTmSoyZEDNdNzBuRnadw3pPP/R0fY/1Dut88xRJm690e8lnZj4euY23OOuJyx1hPrKcZlLMpFwEUGcd2jpG2zMMG8ahx+iIKnDaB57cdVzdBG6PC4/PzhiNwVrFtY6gIefK8ThRw0xZAu9vJv705t9Rh4H/+tWFNJKXlVPNXOzPGC5e8eT8a+rxjry8RfmnfPzpU37wfM9A4M1nA//6T39BCTf87Cjv5a2tPNOFkmdAuuB+fOixW8OgBrYX51inscOeYXdJP24ak6Yg3RVRVMNSKOtEPB5Yc0V3m4fqCqqoUtY5qRrYWow7AhqvDaSMqZUw3VJCkNi+NVTtoGvgQT9ieklHqdZs7H2HIzelZaKEkyh660xeZ9IyMc0zIVZ8riTV/dpn/G/0gBJPb5jSexQa04heguXWrQq6Q5sO40cwEn+rpgPjhTyrFNhfvtkLGqXEgay08B3uEz8N7QRViHopZagJXYp0/qS19Zws1HKCdESHa0y5oeajPDTriqqZUgMhL+22rCg5NDy8wOZijYKTz5XOXIjXwrS68xpI82vS8a2sepTF6b7xLAzaOowdqNrJr2WBMZUUQImkKyqPFt5Hjq2FuKLIpLTKbTlLSqkkAVjlXFhOJ3KVdVNchD1QSC3+e692FGHL1EzNBoUm1oTqLTlbGUJCRFfY9ZmapeAuB/ASmCeE2lp0K0ZlvK10BnpVuRxlSCml3QSykcSRUeiqIEAx76lONc+GIcbCWhK9Be3FhFqKKCNGKYpq2HloKVShykqpXUVZZADLYtLTGOryNSq/R61BDiltyBHi6z+mdO/RLJA9yxQwk8ImKC6Ts2mdNPJnpVxA0t4UoagB8ver7YFS2qCitZT1VSRabTeFHDTJavTZt1Hj98HtGu9GvCS5yHvH2HPW659QD2+Yv/gpZT2w/Eyk1ug6Yk4c1cLLH/znfPK7/3v+2f/9/8JPfvonGHOfmgKnLRaNMbqlwSR5ZJyllIp3jt5rhs5QU2JORwoZYwSWOOfCEkFXjbaa3fk5568+ZvPkJZtnH9JfPMZvzzBdj/ajGCCNk8FEKYmxatP8O/d2MknhSPNwoqZEbuvbUmQlKgf8Sp4n8rKIOqcU+2fPsd0OrTTr8YZaEt73DaU+0W1WzNBJIZ5GPj+qfY+yplgo4YbT7Z8xn37CTfQc7Q/43ssf0OlCOpyI7+/wjx/Bo4GqVuI8c/7iQ0qy5HBCL7d451B+gJIwupeOk+MJ5QKhJmKUHpcUAmldWcNCyvIZ0s6j3AXW9Zi0osItKk/UHChxJsTENC+E+cS8rNjzDOOGzo94pcXQbORCh7HtMIukSbOklbSsTGFlmU/cXN8wHRcOyyypOiWXMWrFWyk67DtHZx1970FXcsmE1MzQIVJSIaZENRqMR3Ud48YzbDaM48AweLpehhTrfEtFiqE6hYX1NGF7+YzMh2tO19fcHW6FSAvt+y84ZG9l0NlutjhlWOYJVCHGRMjii8l5Yd8lLkdFsqKqWqPoO8uTmKjKkNRIjIGbq/ec1sIxKtKb1/zRH/+Ul+OP+J3LLT942fNuSvTe0V2+oL94xhOrGHxh2y3sNwPn24ELv+GR0zBHXr898TZFzOB5NBq6uzcsdY8dR3bnW5y3xNESek/dbtmMG7puoBt24vdQGRUEphZjFHUtzOTplrTMZOVQKaP6Ddbq1t9k22pcIJ85wrrOrEtPUBpbM7YbWeipfofyDmUdVgmVXPsR1++wRktnVK2UuBKzWCTifE2cbgmnW+LpSI7SlRRLAb9DuQ2DvucR/Id//EYPKMvpCp1te7r/6rohcg+3UNrguw7lBrQbsJ3sK60T30qyA9WOUgyoDcpYoc8qI4dTK/66VyBAEUugJCmuUmWm5KOcLukE8YiuR0DivrEmKHLbKWWFasgZctUtMgsK0xqLNaUYjNng1EzJAesdayzEKEmWXAaKKo1Om8llFcgHhZQSxjiWXFFKSzRayc1FFVBoacBVllyhKHkY1ZIf9vYxrGIMrZmwLsSwkmMmxSQ3Ut28KkWYMeIqBZCHkaaVl1Ua9lsiZbVkgfxUaRE9GxTWFNZVsSbxddihsh9gM2hZMcSEB7zW9LUKRdVUQhDSaS0VpSslIS3ILmKyhgDoTG3GX7lHStoiZvlatdYPoDKhsChikZu+KbV9gFVLhxVs0vItDmC7Ij4Ym6hAWuRrLZ0ihIkavkSFCcfAOi/Md5ldlSr0nArGCi4nBHElF5l1JcFTZWVRmxZbyj1GEEnnZOkrwktMuShg2KGHFzJGF2mVLTVTT7fU2x8TliPaesrdFxRnMAwk64gqMPozLn70N/n6L/41h6vXHH/xZ1y/fY86/pTtoEjNXJ6rpHy0Qv65SATadx7rnaw7tWYzOPrOSDt0Ed5OrJWbZeL6lKA758njFzx68ZKnrz5hePIB46NnmN2FmPNcJ0AwJ82tWmusFeCgVhoDMp1UxHfVWEAl5WbSXsk1NTUqU0qkhIU83ZFCQNme7uwCP47QbdHWYnKWVEyKuGGH7nvSvEgEXUnUumpRBbVu0WqtqHVmmj9nmf+UOa+8nz7k+csfcr7rKSEQvrrBjRt4vid3FZUXnLdYcwaI1yeZiM60gRiWeWKeZ1Qb/EJJxJwaDBJQRkBb1kl6zvWY8TF+OEMvt9TbI9PdDXGZmeeJOUQhxxZB5o/jZQOn7XDWigql5VJRJRdMSYE4n7i9uma6u+P69shxOnE3TzIoK4XXBqsN2hs6a5uZUzefiRYvRK3MIXA3z5yWtTFJLLrf0PUD3Sggt2GzxzuPtQrvNM7J+z5XSRPmGCkpscwTVzcnjL+jGwbCdCKtMzWuxJKwre3dOQ1a1JOhH+idJ8XEnAJ385HBj+gKIUO36XnRVRYbuckKq7JwbTrLkywX3bk4YjLofEa8OaFyoGaYf/45/8OLF3zn8cBQM75GUAPPP/iAr6++h/8m0dsAp/e8+2bDN11l+/SSYbPlycWex9uO7irge83GVPJ8y6lmhs7TX1zwv/rkkp1JuNFhB0fvHcZZ8afFgKqBPN2yzLeEJUp4IAeW6YoSAq4/pzcGZ8VgbyyolshLMQkVNk6cphNOj4ybPdl0uGEH1eO6Aess1ndohLacG7G9VrmYp5hJRlPjKmmw5URaZsI0EU8zVVlcf0avM9Vu0b6nrv8LlQX+4R/+IX/4h3/IZ599BsAPf/hD/v7f//v87b/9t2VgWBb+3t/7e/yjf/SPWNeVP/iDP+Af/sN/yLNnzx7+G59//jl/9+/+Xf7xP/7HbLdb/s7f+Tv8g3/wD7D/P/L+LNa69T7rBX9vN7rZreZrd+/tbW/HdjpCChsdShSiKYQ4VBGVKJVEcoHqIgIuyA2KxAWNAogbxEXEVYS4iUoKJaQqoBQCR1AQHNKQ2IkTx3Zs7+7rVzeb0bxtXfzHXHvnnFDHLomqis6Utva39vr2XM2cY7z/5nl+j/3OayXvk9g3S7iFLcXo0QjCWsLnClMvUxClLcY6IFNXDuMcddOhjMO6RvJbTIWyFVpVgNislFLEOFDShNYZqy0ZRwwjOW7RecLa9bwemAOd0oRiIsYDVhtxGenFXAh4tKpmIa9QLdFzDkwWsZ92S5SeiFns0Gm2lGbETeTTiMmSZuPTQC5GrIBGo+dCKxUJ+RNL7SxIK0LLRIm9Nfhxnh5kxnEieI/OiZikEy3H7IskpDDhjYiduxiRoh/H/dKtiiXWzFlASkOOihJlpaaUECFXnRQuHkNRiWWrObubWayAUCBC6gsmCEDNJD2LHmfpkWJ2v0b5WWyRj1NGZygTDDeFm4tECDN10hSSqEWk2yrSLWmNuLyKaEAKwlSRNWBBeaHM4go6iVDUJFllqSRYaZ0MsfsM9uxPMO738PhXicERg4DJelNogjiXilaUIFbYkiR1Oc/W4pRBWXHDSHTBMX20oKIQW+UHlpUfC4XpNvL9ItC1nDOp31LGLarfEsMEh2um/Q5dZVifQ35A2b5HtXyNxdt/Fvet99A85+LRC3qeYZTl/kZzmBRDNAxe0km1kqJNz465lDM2F+pFRWVr6spK9IAFPa9PQwioaHGrhtXDj/Pg45/k7iuvsLp7F7c8o14sUa7FuUZQ9EbhjvTXW8iauX09ZJEuOqujADClCDGTsyeqCEGTfS9Ou/2eqb/ALs5pTu9Tre5TVXaeosriSjuLMQZT1+jkUKmIswSJyVBHHIFOkET87ocbhsPXSbnn+XZDvXmbl+6fY7UmbQd0sbiPnZLPLToeJBaj0qhsySVgtCErRRp3xJiIaeJw84x+6NFOEpKzmrtNpbFVi3Idul7LNK2/wfsDrgRUKcSUCd5zfXmFH3qKsejFitq2qJKxVcfi5B7VYoNrGym2UkKlacaSR6G67i+5fPaY9x895bDv8SHIe904aqNprWFRV1gtrBlrxU1ltOYwTvTTiCKTUuIwTUw5gTUslkvqdoVzS5p2QdU6bF1R10usqwWbEHpS7AVE6T1xkqDC5CP9MDD2AzH3NHWNVhBTwjojr5U6uu/kLTJMnqJHYkZyfYaATwlTFxZdS6c1VmXuqIJrBr7xzFOqwDjssXoBMeJvBnytyLam6VqWU6IP4pwLuy3vfP13+J2XVry1ari5OXBZBu6ennHn3gPC1Qf0/TtoJt77nfexleN8vUKbQixyj80xE6eJ7Gpy1ozxiuAc7faczp7z0rphiHLtDcOOKhmyFV6XCgNpuKS/fkwKGduuMMph0Bgn7qumXVI7I262EOdz0RNTEPt1TKAc0dRQN2jXoq2hVlYwHFphtEGVhFGBcdqTiiGrDOMV434U9x8FnTwpFdA1xi0oVSHrhurkIXXaM46enCYR5n6bj++oKnjllVf4+3//7/OJT3yCUgr/9J/+U/7cn/tz/Oqv/iqf+cxn+Gt/7a/xL//lv+RnfuZn2Gw2/JW/8lf483/+z/PzP//zAKSU+DN/5s/w4MED/tN/+k88fvyYH/7hH8Y5x9/9u3/3O/lWAHHrpFRQSXDvMYmlEKMklCjJKFeR0WaurFWeqaeRjCcrSfKM88w4qyLrIV1JO2tBG2GpxDjIDap9g8VLnyNUd8jhgE4RlBOapkno8ZLp+jdQ4/uopInHac5sZdW2lgN/jhYUHoehaIWrBTynjNwQg/ciLmMOvbKaEDw5RJRCNCYg6x8UMeYZpaVnZ8osAs5BmBmzbqaUwjgehB2TJcElBQkGjFnWNnKx59uio8yduTbydfTcXeY4O6fglity/Lq6CPQsGAVYdPG0TZGiKwpELpdCvSycPZDcmTQIXE2JvlKw9bN4Nc37DsPcxao5KTpC9lKECJJBM+wzu20mB82HCPJEMVBUwarZnYKSA4m56MhybiVd0CJBEjfYrLNhFvTlVNClkCx4lUj2LvrkTeq7G6bzj6Hf/Th2+lnC4WukKlCsxWQJdkw5ySETFXrW9zBLPYVKLv9NFQnxQ+l5CgZJSTEeRqjWmqw0IQrGX1eaEif89XNMiWJVNhalJacpXu7Q7QLTbEA/Rt//JIPa4LcB7xN6ttTnEtEZ6ZI5CpZFP2JQYCTTyhlL13a0naTlGq1nUJqlGEuxFZ1pWLQbzNlDuodvcHL/JZYnp5LPUnfYyt5OL5WSaAWjxKlztAyrWRxc0rFgjjM/aCJFyZ6ScEs56Ms4Mh2eM/RPSIdrYkys2nl9VDdoZ2QlqCCpSNFmntRUlDxKSKh1MxlaJqdZaZwuAj5MgWH725R0zfNdy85/ku9+6y2WrcMMA2Ub0XfXcMdileznldEzamCU+884crh+wXD1DD2nTafkRfOhxEXojMM6mR6FmCjaYtoN1lboFOmv3yMOe6qNwBRDzCTXkROYqmZ19jJNdzbHIyRc1WGrhRQnJaDyhB/3TMOW2B/YjxNjv+dqe6D3iaI068WCqhIirzMaq4U3RBIwYCoSMjnFxE1/4GZ3wDhD7TR1V9FUjqptabslXbPG1Rtcu8JUTgimGGxVQ/biaNnt8TP6PoxpXiUXhmmUiWIR3o8yBmvMbbZS8p5MwccEWTNMmcEr1muDNdAsapxp6FYruq6TospqSo4suwUlXPDBvudwrTkcPDkmpsFztY205/ewbUWdarowylq2NAyPHvMffvuUxaff4nLb86xPZLdguVxQ3fsk/qJw2H6DcPMU2y64u1yRl5ZLPzFq0Hlk2GoOWLq2JWuLSZ706H3+wwb+1Hd9jNYf+ODKc9JCrT3t8oRWaTnzppHU96Kp69a45ZrFVDPECdssaYpGJ9EhTqOInEOKZKXQVYdpKupWU7pTdN1JwKYxQCHP/socBmwYKMMVcXdB1C9Q9VpSx8c9sUTQhsqIi8fqBapY+jGIRTx4mAamKaJcR7HVt33Gf0cFyp/9s3/2d338Ez/xE/zjf/yP+YVf+AVeeeUVfuqnfoqf/umf5o/9sT8GwD/5J/+E7/qu7+IXfuEX+NznPse//tf/mt/8zd/k3/ybf8P9+/f5vu/7Pv7O3/k7/PW//tf5m3/zb0oy5u/xmKaJafpQ+bvdyu5c3wpBZf1i9DGvRO7kSs0puLN2JAYJH7O6EqS4Ehtt0XruCss8a09i4ytR+BYkrO0wOFRxGH9B/8EX0O05FEfRlmwUqkiqaJx2ZP9cFM3GzbbeWWRYZi85etZ9HIuGIi4hJX9Ws9PGuTmwT3kyk9BSUWhjyCkSQpL9sZZwP0mMzFBkF6uU9IApzUTZLI4GCqQg0xVdpLQwxknXruQGlFJCK9lB5pyJyc9dbJIb6LzL0YbZXiKuGK2lk0lzTktjhUcxTZmqFEzR9PuCQTJ27MzRUFq4AykLhE0yk2TKULwckmLLnTUktqCqo+lKRuDqVstRCKMiJ0VihrwljQqzItaJtjpSyHEWW5YyO6pmvkdB8PYq3/JHxH0jNFcRDctUQxnINx8Qn36BdPcHqO+8Tn/yGtXpJxh+4Z+xfe8L+DzQIFoaZxVZZ7ITiJxCck0yHxYnt5OCWYeilYaSCGX+HpRh7wNVNNDMBaKxqBLR41aSmrWmtgofPMQoB8L2KdPhOTFU2Ld+kHDxDv6whaCIWnKfjLY4E4lFqLfWWMSxwpwKbYQZ1FS41mEqWcMoZVCuxdYLzGJNe/qA6uQu9uQu9dk97PqUqltQVQ1aC3RMXDkyKVEzNNHAh+rgctSYZEoUPH3OYR7/ixC2lEQKkRwC03jDtH0fP77HlC8p2QMVsYyQMzpnbIaiROgXs1xvbk7mLSlJjs98P5rnaJicRbyuA/3ud5jGrzDkBZfxFV577RM83HSQEuGml+v+Tg2mMPkBnQdMQtx4aSfpz4Nn7Ht6P+C0AMbc4pxKa5QWpoS84cVerMJAzonkOiZVkcc9h/2BKezZVCfU3ZLa3kXbmhI8GkW12tCt72BUIU57VNLo0KMyTNMeP+4ZDzv6sSdME4MvUDT14pT71QoTB2pnsFrfCjGD94S5ATwcRnZDEFqpUSijqVcdVVOxbCsWiw43c2uMqyXIse5Ai87PxyhhgGEi+p5hf8Vhe83UD4JumifjhYKzjsZU871RGjW5l8l9Z5wmQgzyWul6niRrlCk0ywrXGmrX0tYLKlej5mlziJ6KgQebjucXL9geMtdxgkryakxMJDXx2qt3eFuf8vhpzVffueTxZWIaJ77+zW/x6LXXuVNbXoTEi+srGltTn5yyXPwgw6PI4/e/wvvf+gani5bNJ1+nXp3zmbcLYyx85d0tF5fPGbsF1fqE8zZwePKIL+53XD6/5tMP77JMinc7z6uLwp2UMV0ra7F6iVrfpYSAdkuqZkFbVUy7RIqBId6QbaQgoM1SClEpSTxuzkSPogq5WWGbFuOMcEvSgI4TJM/U74jJE/pLts/eZxpBL1+mWp5gTKGyDqvqOXJmokTJaYtpx3DwgtuvFlgDURvRH32bj/+PNSgpJX7mZ36Gw+HA5z//eX7lV36FEAJ//I//8du/86lPfYrXXnuNL3zhC3zuc5/jC1/4At/93d/9u1Y+f+pP/Sl+9Ed/lC9/+ct8//d//+/5tf7e3/t7/K2/9bd+j88UnK2IOcshVvLtiE+Q4fPIvkTBl2vp+JUV0R7W3XZt8k9GIRV5KQi9VTvIeqa4KopKhNKSx2eU4SlQhGSarQgXtcOUIGmwqsU44X0YfbRmJFJKOGsJPswH3UzD1WX+tyKHhLYO6xZEHLpqyXhcmsg5E+JBGBJtJ910NOR0nJbMkw+iHPQIQ+GIvc8zW8SYedKREbYGkBLoeceotbpdPwjErhZdjfcoLTqVfPTiquOJyuyOkU7bupraKPxhxOnMphZ7cMmiB8lFJh51xbzWKOQwr1wSUsDNUwVdZsy7UrPQVck6x8yk1TlDhFkEmyKinUCcOSRFOLIYssLMQtzj5Of4E8xpSvJz54L9CDel5DID1eAYy5yjIoyKPnyAH38JNyni2WcwZw8or3+KxeL/TP7Fu/DVnyXHa3R1zBY6Elk1QXGrNxHt9LzOUJZMpmQIKmJXwm6ZJpkCZW2IuqLEgiuSIErKlP6GPMlUUei4A3EaCSlhfM/Uj5x+4vPY89e5+Ll/wm5/QWMVk5LJnGhhwMxMGK00BaGzVlU1/9nRtjWVq9Fa1qSuWlJv7lKf3qE+v0d99hCzOsMs11TNkqpuhWFiLcrMTgCtJfdFiW1KLPzz+7JI8SGWWIlgiCFwDMXMMYilOAVymLNcwsg4XDFNL8hmlImPSqL1KXouskX4q7KI/2RV4UQfljJN04gebfabK+Sgy6kwTY8Z+l9jjJnH/Qnr9cd47f4J2mRSyBRXY+4YitWyPhluSMM1MSSG4UAIO1DS7KhmgVMKpy1tZUBLflVOgRwmchwhT6g0Me2u2feeagzUJy+hdcX63htEFO3yHFM3OHuK3tyFMJL9gLFSFIhebUsYdhxuejKZYeyJwROzAB/BUTW18DRcLYX6dEMebygpEJKEBA7DwH7omabIOAV8SCyXC7quo+laVGWpWseybamclUJLia4vU/BTj9IRgH635bDfoZQhhokUJ6ZhIgQJJ1Qm0ziL0Rbm9XZdN0DkZneNHyZ8lLDCrBQRhdWGum1Y1R2ubXC1Y7GoaJtawHJagU5I4rxH+QP9zTW5TLTaMww9N6GmURUv3V2g6wHazEmT+Pj5GR+7u+L1sxO++M4T3nt0yfPrS7747hP+zHc95BNNTfCJZ4cDxlR0qyXm/A2q6+c8v3jGt959wup0xadeu8+nP72m1pEc3+FL799wtbuhSoVKG6YKhqs9Hzy54cufnPhjD14ivDgw3K1Yrhy2bqmrGq0t3lbEwyXWGWyYSPFA6rf0eUtabTBuKe/vWgr+ksA2S1yzwVoNcSRqKfrIkZIOlP6KsLsmDD0+BklJ94NQpsMIYaJGyMLH92v0nhAGko+o5EkJjO1ISpFrhzUVRmly+W9YoPz6r/86n//85xnHkeVyyT//5/+cT3/60/zar/0aVVVxcnLyu/7+/fv3efLkCQBPnjz5XcXJ8fPHz/3XHj/+4z/Oj/3Yj91+vN1uefXVV+eMmYIxhlgsMUySQVPkpiyTACPVslZYW6Fsg3M12ji0rSVhdRZJGq1IeRSNRsmYNKL0GjV3MQXRuhQtE5VShImSk58PSEPRRkBjytxaH4ueoRaANgJJk2JC8jtygZzU7QmpjdwQc8oEPLpqQDUUU5FLpGDBJHIUjQxIFHYI0+yoiSiVsVqRUrpd76R5JKv1TAfM4bb7FNGoiOWMygK/0gY1t/LGmNlZIiFsOatbtXw51l7Mk5MsN2pjBCLW+0gOmWWtOFnIyiBMoqHIOdO2sFppYsgQQHnRaJSoIM7Tldk1Iuts0bOUVChRNBtZpvUoDMSCD4VpnNdPRgTGx8IlJkU0CpUyaHHyoI5U3nm1YubnK1JMqaxkrBuZizI1C3PlPTn0mcPuGel6T95eUj24okzfhzp/mereA05/8H/PdYb49D8xhCeo4mVlVYrg+YtkAKmiCZMEpsnETazPFEVuFNX9hrybhLJrC7peCqnYOEoJhMMWUwoljPggTjGjLNMk3AunNL3PYM85+97PMTz+LS6+9p/BBuLRQQQYExHUvhJ9WJiFz1pcPM7WcwFdo6sF1eqU9vQOzeYe3d2XcadnmPUGuzzFVgvpoG2F0wY7u0aUNrd0X3U7pyi3xQdJUril+Jh1JjmTcppzdBLZe3IIMuVyBqNrnEnYvmKY8mxPtagsEx+jHUVpUtHowi2B17qajJ6nm0aQBLNIWn14aeKnJwz7L+HTNRfTGcV8kpfPzljXDaHIBNauWlLx2JKJ0w3D098h9QcSdi58M0p5bNPg2nPc6h46TOi4neFWHvxAHA+EsSeEgRx2+BCYcoWKHpczdbukWW0ke6muUcrgXCXagjCRRoPyE3k8kEtg2F+xvXgqGHzFvF6xc2ZTh6mF1GptjXYtSkHcO8aw52Z7xX6/ZRgG/CTrQKU1dd1Q14q2bVivlnSbFXXXYa2EYOZjc1CKrORiZLs7UBDX5TgO+LGXdF5jkUDBeYJlLZVztHUrOr4sU92CAOpCCIQYiblgK3H/VMh7dtF11N1SxMBVLe6kMgFJSNBjT4wRfGQaBy4ubrg8BJxVrGvLQTvUQnPvTsVmKjy92vH4/XdxxvHK+T0++fpDmnXF/ZMVX3n/Ge/1j7k4LLBesfUJn8GZmvqs4e1XP83dZeILv/AfeXF1xQfPn/DSww3375zx6Y+/zPNne969nHh/2BL7PZeuZrlscKaisY7SB55de0KKPN8PLE9WnJ9ZWiXMr8paEhE/XBPHCl2S2M4L5KYW141rcZWFkrApwxw0ao1C5VFAbGQxmIQt/vox4+Uz+t1OprLVmsrWmPoEpw/otqF2DkMi+Qnvd4QQiVlTkty7slthuzW6WVGcxjkZBsTw37BAefvtt/m1X/s1bm5u+Gf/7J/xIz/yI/z7f//vv9On+Y4edV1T1/9T73TOmZhnrUkBax0+imhKGyU7cVvRNA05R7HiuYVcfLYG3cjNyDj0PGUwJQgxtcRb/oPSSYICs/A+VN7PgtARFSK1WxFtpqSRTJLnB8wR+KVnUelRP2Es3g+yhjKyf8/HgxFF9AljNVZJl519wNYtzrVkjPALqoacDzJdKJJPYxzolJiidNwxiYgszYmtotGRQinNkdnHoDdjJG9DzZZbM681TMkSWJcS2gh1VkBiSVYxQGF2y8zhe3kOkSOJliGlwHppWbtM8gEfNSGA1YWug5MTQ1UXSnTEEFBRCo8SjtMUsEXNeh0p7HJOR0GGYO9zJoWCzomSC/seDjtIWbgZSonuJZdCKGBFl0xSsuZRcItqN3Br7c0Fgpc1lvbALTFVJjR+VBA17DRpyuRywF/9BqFconRPk74PtfkYZdOw+ME/g7v+g1x/8f+Ce/5fMCHPeP8oQ6x5+uOLTG6UUuLQ0nJC1ucb3NmCy8snZDLWga5a0T8VCMMo3UxO5BRg1nPYbsHkR/K4JylR4q9fe5PD5TPe/w//guS3NEvR45igGfO8WZirT23AWkNK87rUKFRtce2Sdn3G8s5LLO+9THXnIXZ9h2ZzB9126MrhXEdlq3ncbii3olcra4HZyp1mholkZUXUvJJMUbJC8izYPhbBpWRxd+QsP2O1RDcVMnjLmMqhbAGnRdyMwjUaa4toaopi9IUwBtGnmRofEuMUWXQNpqooysyOtUxWiRJ2HPZfJIZLrvoTej7Fy+dn3D9ZkmJCq8wsx54ddxP7i/c4PH8HbRylWeOqJY1aklVCVzWuW1NXS8rhBn/9An94RvbiohkOexFSlkyeMm654fTsJeqzV4U+2zqZMijJeiqz2zAD2jimDNPuQiynKdHvb+gPe9AFM8PQtKpRtqHpNuiTV3BVAykyjSPR70lh5NDvuLy6IASPQngki0VDU1c0dYMzBucs7WqB6TpcI89R5vvPFLyso8cJP0xsdztiKqI7mdPVC1IwWeNoVh0UaT41RuztWjNMPf3QM2bPnFInK3vnaBcrjGuwdYPWUBmNto62W9MtTijRMw2PGK6uyF4sxoMPTFOkeIG1pWSpnOaV0wYzVtizjgdnFXVpaHTFOy9u+NX/8jWevDHw5npDAc42G77PdXyySaix57IfeHo9cHr6Ei+fbnjr5RO+++WGdF9Tlxv+n1/6DfqbZ1y82PCgbgiHgRwjLitUMYQQudjdYJqGqoVWRaqbLddFkY2iV47feDLy5llPszZUGuwx5C9k2vOXJcXYtaxshXVKij3XiDasBEqMhBjJ2csqedzjx4nc1+gcUHFi3O+JQSztGcPCndIsTym+wccIITL1B4IOAvabRsYwgurQrgVrxWHWrGkXZyydpkx7Jr9lGG++7bP/Oy5QqqrirbfeAuAHfuAH+KVf+iX+0T/6R/yFv/AX8N5zfX39u6YoT58+5cGDBwA8ePCAX/zFX/xdz/f06dPbz32nj2LP0BzQxhCJZC9jw5QklKxgqapGigXnRAVvGylOjEMrS1FmtiPrWYOhKSqi0FglZMaiapQ2pDhiSiTHNcxJxyULC4Qs2RUlRUrxxKIwxkkRUkR0JBBbC8VgnBIwWikoVUuceEkk76UDSpZSGWxVoVRDzmJxNM0dmZBwgBAJ00FcRqEIZyDLvrHkQswSTS72M0UM85hbSd5Nmi9ylSMEKVasEWvnFIQeK+DwKCsexYzAz7OWZe7s5WmY8+2ksMugjSFMI5WGSiesKkwe8cQXmYScn1jaNhNDFnx+AjyooCBA8VKshFTm9Q4UlWckvUwz0pyYqWbtTkiFsQcfBEeeZ7dOyplSxH7tMtiMEFgVJD1LHrKSm2YW4Z9Gy4QjFHBGDu6Zi1JmN0+IilgiKRVikWmA2T6iKr/M5LUA59TraOdIyw16dZ/wTBE1FJ1xWqY6w5ShaDFmm9kdVRQ+gmoVy9fO8EPApyKaE6dkr0uc3S5O2BQJfJKcKaUgTAMpjCQi0Ufc5j6mXfD81/89sX8sgt8BjNO3MLjZ9wZF0VpLddrSj4ZCw3J9xuL0Dov7L7O49yrN3ZfQ61PcakXVdlS1NAD6aN1XMnXR85+FQnucmYhbSTzccvPMcWbwpOOk5EP0OczMmlLQ1qGrGm0U1rUSWJcSUw5E9pQmYF2DMgmdKpxboFQLwOgTh1EgeauNw0fYbcUxZ6qWZDQmSXJ5UoqSevrdFxmmdxlTy8F/nHunr/Ly+RqrRBek07w2ULMVdHfJdP1Mdvu2xVZrbHeGalfoNBLCIPwmFMUoxhS4efYeZVLEOFG0pl7dQdcNJmeUtXSbO9TLM0xbYa2sK0WTNRcncSLFjPeefveC3fVz8jShlcVYy2K9EuFtZVGqRhlxeGiVJMYjB/ywx+9e0A9XjIcbDjHRrE5YGYOzjpITRkHlKglqpJDJGGfFveNHYpSRf99PDKMnhCjOnJAIs9NM54IzVuBwRglaXWlSjCjAzyGqMY1Ya5jGyDhOaKdomgplNAurMc5gXY2tTtHthqZaoNLI6G9ErJ0MfugZLg9cPH2OHw5MoeCT3OdKEc1VXVd0lWPVOvyVZ188ta54cLIg+8TTJ4nrPvHkW494bl+QiqZtHN2yojVL7pwtub+21C7zbHfg4nDFx9KKVbWkfvkhf/QP/yBD7PnWk3d5/v4H/Pplz2438dWnEzfTKO+DUijTwG77HBOWuGLp1Z7tvsctNizWa77+Ozt+2WX0S5aTdc1aJ5JuoTbgltjFGXVdYc1MmFWarCX3zCQoccDvL5jsGUlZ8nDA99eEorGmkftGvaFSDRSDnxLV4gRlDM4pCBO70eNNTVtXVLqQnaySsnaYakNVNUAi2QZdu9tMsaINRv03Esn+Xo+cM9M08QM/8AM45/i3//bf8kM/9EMA/PZv/zbvvvsun//85wH4/Oc/z0/8xE/w7Nkz7t27B8DP/dzPsV6v+fSnP/0df+3FG38aO3xA2P02anohkDLjZEQao9zgc6RYh9Yzhj5FUJ6SApkJoySzJ87+fhFqCjFLu0o6BguKBmdqSnEYu5K1iCqQIzH0lDxh8l7sempmhGgz7/JldJmR3UEpBqUqVDbCcEDL9zRrUXKBytVU7QpTr6RAKYmQBmwYMdZRbIU3FSEdIHtSmFkGWRw1R4FhKTLZSFFgayVLtxpjvP05KUmKMa3nw8TITnf+figyKk+5kJKW4icdYWKisTmi2NP8PYj7Q5OTIubCFMBKHIqsiRQsFoXVSrQdMVhJKE6K4sXBU2a7cUmzxEU0veQ5F4Z5khFSEYpuOep54MOt02wbzmVeI8jaxqeCq5RMUFSZU69BOw1BOCmKTCoZndXs9pFBf0F+zpwheObf6+wIyhCCrGxSeRera6q6JZsFVEv81bvsH3+DNicR1vrMkOdVkpZQwyRn1q1te7IKZQo3T54zHkaZnmhFxuDTJAe9qdE4gS/Zlvr0AdP1BaF/IdOoFEjTiHZL1g9fZ/f0m/QX32JI4irKWaGiaDEE9R8oCjSGXCzObji5e4d6dYfFw9dZ3H9Ac/ch1eYc261F/1BVs123Ai0rFZQIYD/UmNzeOObfY5rZG4Hkx3laIvEOlA91ZUpJJpTRFqUhpYDTIsrVRmNMRSkRXTwp3BDzNbaS6VTJCqUqtF6ijGOKmf0eximxWIpT6OZ6wk+FzamjMg6QFRzGkINn3L/Htv8NMh3X08ts1q/xxtmCrjIyhcXP66lMKRPJj0zbpxitcd19KIlsNNiatlpSYo0f9hymp9TtCSWNhGQpzSmm0ujoQWm69X2q5QmVNoQ44OqGuhLnGkoJgXi2XAc/iVPDB2LKpKSwtiOljHMG41qMrcUyqtNchUZCGPHjHh8NqjaMw5Z+d0nKkdpZmrMzjDFUrhLraUxz0SOFZgqecegFrR4nfEwMoydO4621txTmtbGmdkIG1k6aoaIKKitiFIhlChLTEVJgGDy+NCwWHaa1LLsK7aC2FbW1iNEqE7MhV45quaGuTsnjFfv9c7bbD6jtJf3uBf3VNReXNwyTn0XIbtYqisV81a3ZrFdYIpprvnF9zcVlg1Yw+Iy2hWWjGf3A5c2e3WAoxlIvHcvTgWwMn354xvevOl7sNVch8M133+P1O463TztOz8757pc/zqMPnvD+Ozc8ag4otSDZjjtn51h9w+XhwIDG9yO7KBOkmCH0B9TywDoMtLsl/w+/p49r/vDbhuViAeuORjdUq1OaZUddWShR1tcpQhwhB0oIxP0N081T+nRANyuUynL9a43RZWYOLUUSEbZ4f8M09pA8RUeKrcWUoDS4jqwVtkVE1KVGLU5kzedH+jk6JmuNrluqylKlb7/s+I4KlB//8R/nT//pP81rr73Gbrfjp3/6p/l3/+7f8bM/+7NsNhv+0l/6S/zYj/0YZ2dnrNdr/upf/at8/vOf53Of+xwAf/JP/kk+/elP8xf/4l/kH/yDf8CTJ0/4G3/jb/CX//Jf/j1XOP9zj80rH0fr19hdfgxevEu+/ho271Eq4v2erBIxF1zOZGS6oNWIigLpUkpQ48As+rOyNihJuBPBYbUFO1BMi3ENMWWUMaIx0c3cKbZygiaPyTNAJ/bIiarlxZ9BY2V22WidMbRyA04Rm6WwKaalpIixBkxDMUuU6VAklNfkEMRWjcHYDmNH/Hgc5wv7hPIhsr8g1txSZKKRSOQwZ6lg5CBAxKqqqFkxP9sclUKpMgt5IzErUlazkFCRklgwj3oUyTXSMkJBihBjBO3uvRLtSM7oAm0Hp2cGYxPBg0qaErOsiT2oWCDKDZh5BSN7GMmEYe6oM/JrzrHI1AFhieQy/xtuHTcocWpV5lhwqFmQirBJkIlQ0eIKMLNYNM62Y6tEJ1K0TKByFr1I8FnuARH8XLhoAyoOpP37lKvfIbf3yOuXyFrw0NNOyQ3eQogySclFEQpkrebx9mxvNYZi4ObFXtZZaBqbRRsVdtj0AVndBaUlD2Uu1CGTxgM+T6g8j4gamLYvuH72LtM4Uaaaqp6wrcJnmFIkFkVRFmsX2PYMvXpIff8N6oev407v0Z7fw63XqHaJqRe4qhIiqXIyJdHy3tEzEl/ycjS3DJNSRIeVE2WGcKUwCbV4FpEfhe5aa9TcWRcUaIuxUkxrpYUGqNRMP/aEYYv3H4A6CMQvVOgARTfSSZbIrlfs9obl0tItKvohMAyJprG03TFwDVCWlAJhfMZ+98sUPNfhdZrFJ3j97hmNreUAiZL0W5S8N02ZCLunpP4GrWtU0pg84acDuWiCUoQ8ctg/Y+ivWaxfomkaquUppltiS4bpQPETTbumWpyKjmoU5LtJCRU9MQd8DNIwBAnHm/xAHEfJB3MLuvMV2d+g8gGDRquKlEbRg0ye7AfG4cD25oCvL+lOz3DWSVqza2jrWrRD1qGMCOOZRqIqlCiIgNEH9vv97KLJ+JwoRZZdBUU120rFQWmorNBmsyoM00AsiewjY/Cy0krI/cc6uq5lsehYLjtB3zt5/YXDM+c/lYQOApkMw4GSNH5/yc3FBU+ePMeYmhQ9/f5A8h5rNNY62sWGum7JMRB9j6scq8WKPNzQmojpb3jyXuJqF3C2xVhHFRNhDJTeE31Ncg1hD9fbF1xfbbm+esD3vPmAt+6d8/zyhl/61nv8m2Hk5mMvcXpnQXP+KqfnH+fp5ZcZnm2xXWK1eZmz0xPG0w1Pnj/n0dWOfspMcw6XwaBinEnemnSnxsQlv/nM8/ZrLa+cruhasfVXbYurF1ijJPzWj+Dl3yn0xGHPtLth2m2JZU9Z38O2S5zrMK6ikLDZk30iJU/yE+PQY80eW50RbIfddKxLQduaylkBNRpNEyPDIO+NqCsMAW2RYtRYiXehxiXzbZ/x31GB8uzZM374h3+Yx48fs9ls+J7v+R5+9md/lj/xJ/4EAP/wH/5DtNb80A/90O8CtR0fxhj+xb/4F/zoj/4on//851ksFvzIj/wIf/tv/+3v5Nu4fSzWa9q2YbG8w3D6Ov32LYYnX0P5Zxj1ATpcofI0j5CNOGsI82Enau+Ystz0TDWLRGesZ46oshd/uTZkVWPrFm3dPDq3JNeizUJOI6NR1qJoIAtSGKAUCzkK32Cm+AncQ0FxsyZEoyupdIttZIVTssTcW3leSoNSNbFc39qHtXEoU8neeFZaMyf+5pznjEM1dwnMgtZZ8FoyOZVZ12GFbaD0nEapaVxNSgk/TTM2XDQqKcnBEmOSiU1BnivP9NMy6zlmF5HR8utxRlYlykBba1brjHWJ6IGi0UZ0Ad4XbuONZ/4J5SjQVB/CupQUKLHIasogz3MsyHwohFzmI/44UZqnK0VEyCEIy8VambCIYymhZ4bLcQwjqv9yK8QtzAVQhJJkBTMFWdFMScbW3oj4WY9b8vYx9eoSjCPHgWIsQymoo8gXjc+KkPKsWZqrJiU4alKkBAjzdEyrQvKipzAmE7aPKdzFLF6SSWA4MF30xMM109STCVhliQTy/imHi3eY9jfEnMHAUAw2FECKblevqNd3aO+8RXX/ZfT9l9Gn96g3d6gWG/ScnmpsNWevKASEauaPRQQnyy75jeV5GnIE/5U0FyUhyHsqHqeOan5t5+dRwvdBSUGiZhiXMW7uyq28/0hMU89h/1Wm8A6oiLMvU9VvYVjhbIfr7hBiJkdN11lOTmRXd9hG6tpxcqqpK03WYicuFKIfOBy+xBjf55DeQDWf5dXzcxauvhV5UwJZu3m1mSiHK9LFN0iHGyl0ph0xjvT9gV63dC9/kqZbYq2laRZUzlI3Fc45tGpQBPLY4w83aG2wjayMNJkw7Bj8HmyNjxM+htmCaCi6EheQkwPYLc8x1YI4rUi79ynDDSUMBH9g7Hf4YRKtSfJoXbNcrmkWK6q6wVl3C0ArMIvpA8zaoBwC/WHPoR/Y73pudjt8jHPatMEYcE6mFK5q5XssGUqhNg5rNMM0kGJkd9iTfKIoBVq+96ppaBYd7XJBU9dUlaNyetbazOvwMgMkQ8D3E5fXBzwvWCxP8cOOq4sXDLsdKR9wVYWzhq5aklPCVTXrtuXenXuM/cDjy4HLwyWVczAM9NGw2qwowXKz3ZFtwPeZkI7J55mYepJWGNehqbh5PvKL1+/w/rMbPve5T/HZZcv9uuI/f/Gr/PI3n/K57/kUb3Udi5M3OL97w5P+a9y8eMYwKU7uPODB/Xvcvb/gpRcv+ObjFzy9HplioihHTg4aR3tywltv3Of18wXjNPHVF4k339hwp1ZElbFYTNak6FFxYNo+IR2u8EMkpkCMB9GX9B61OKN2Nc46XEkUfyDHkRR7pslLltY4kbUjGk20FdqJM0spwXoopeX97ws6eMruKZMfqG2DqRzNrJFClTnYEEYfvu0z/jsqUH7qp37q/+3nm6bhJ3/yJ/nJn/zJ/+rfef311/lX/+pffSdf9r/6MNpQuwaLpaoc9XrB4vQ+w/UTpotvEa/fIU+PwV+jVZp39Ek0J8fpiVJoLErPab8ZCXNLojEhJ5TSZDUQxj3WWpSu5eborFj4jBZPvW3RpkPpSgKtjEVnS9FBXkgyagafCZhVFKA5R5RxssMuYpk2pcy75YBTgDKkosEtUYyU5KXLNoYczNyRSlFS+LAwMUZe4hwTMUbhPTgnY/Qj6E5Jh2u0BLIJjtthjuC7nJhiFLvn0aY8Vw45ZzlI1bw/zbIEKUoSS3UqNAYqnWmrQlvDemloG02JgZhFo1FyBi+1oTqKNI+BsQBlnoIw08o0c0K1rEKOuSw5QAgZH4QvglZYPUe3zxMhlKDHYgY7Wyx1kSBEPa+S9PHnkQpnXv2Jxfg4zokhi/4ka6ZYiLNdO6QCXtZFhYif9thhi6lq9Lgn+gM+c4vbn3IhxKN7aIZO5UIOx8wgTWUMfUxi+S2ZEAuVm0XXSmN4B13fp2gLKcwRBQOpJIoCnwZCEEx3SAOohHaKMQdccSg6utP7tHfeRJ28TH33Lubeq5iTM8xqg24WctDMwX0SDKdvk4SVVrfvg4+8asL4yVKc5BRn0au/Fb+WKK9nnq8LM1+b4vD5UO0vZ5een/PDqAJUFt3EeMN+9y36/iukdMC6uyyXf4Bu+SaqWojjwDhCyrQKmrqmqTL9EKhrxXJZsWjFVi2rQU1OPcP+Wxz6b9LHewT9vbx8cs6qsmQVcKYWWjUacpY0V79levo10vUj4jiy320J4w0qRzKGsq5IOWF1Rbu6IwXyjDCXUD25fo1y6FCThj3p5pIA9Lsr+t1z0ApXn1GUFj2W1piqw1TCn9GlCHzR1bi6w+lEv9Mcdlvi0DP0B7wfMbbFNAt0aWiaJe36LvVmQ11XojcDVFYi/s+TIOXDyP7miqvnz+m3e2KcdW5K4VxFVTuq2t3en52raboOrTR+muinnmmc0CozToH92DOFgKsUdbOkapY07Zqm6ai6jmq5xFqNLh7ySMlhLm4DfhqJPpCmif3+wPX1wGHK3NSXUsTHQFsZjHUCtisi4h8mGH2kDYFpGumHgcMwsQ+Jphk56RY0yyX37xrOQ+b59YHHFweevOjZBkvdNRRTUARimshJ3lvKVsTJ881vXKIeXvL293+cNx+e84FPPJng+fXAcoIXQTOau1TuKUN/waOrd3DbntzUfPZjG16533DvTsPvvHvBxTYyhMKoLPZkwYNXV3zPaxseLGp6X/N0u+WrT3fce+2MKvcEv0elIPb0cM1w9Yz+8jHTmFG2xtbyfWqXUO2Grl7gSMTphsP+Rl7jFCi6QVlDcUusM+TuFNWscbXBzuEhzGtFgaOKQB0FafuM0XbY03vUiw4nqmWZpJaMyv7bPuN/X2fxCH9DDoLaWVzVkOqOerFiuvMqw/WnyDfvE158i3j4OiZeo4w4J44aBTlUR0qJgBYyZRhlfD4jtmNKKG0oOTL5iax7FBk1ZFLVUNWNHJRaY+sN2q7Jxoq1Bjs3DmLlTfNqyVQVuiwEX58ztsypwznMtsmEUgmVhYmCK2AVlAUqF0jhw8yWIjjiJEjaec0jU4DjgXEUJqZ0/O8yelOAmeFjap5SWGuxxsp0Yp6qGKNR6ahrKbcvgVIzpr0UyDNbYj7cYyzYAlWlqExhvTKcnBSMCrO+xECSG5xOhjxldCyoomXag9iKJYOEedqjZhs28nPpYwMpXXZKsm5JCYkXmKFuShVJB1QStFeSiFOFk8PtmZqL6FFSkqTjW1toYZ6czXDAeZXjQ8HHwpQyymkqpZhCmvH+BVUpSpMI0xWxbwiHHYdp5CZqulJwqnAoYmNWyOsn7wlFjAofhY/TkUimYEvCuEzW4lDqU6ZSUA8HcrhB1WfkIpMcKT4VMQtTAyIhi6CTrLF2yXp1j3Z1l+reQ6qHb2PPX0atN7jVEtetKE2LriTq3WjhBxktHuyij9MeRPyqpUvO8/tDWDWZkiRoKEcvKIB5nZOz8CuYk8f17Gi7fb7jWohj4cOs8fiwCMop4n3PYfs+w+E3ieEKZzcsl9/D+uxtmnYt+UpaUUqNtYWqijgr4ZaVsZyfFez8sVWQiiLFRPA9N9tflY/197Dc3OOsa9HakTPk4qHI4WxVIYw98cU7bL/1RYofGELCJ8BtyNMBW3V06/usVyfUqxrrRDRcVBH9EAViJOZA8oHpsGN/84GsT40ljJHkR0xVk1NGO4W1R32OEutou8EaI2nEOeP7LYw3TLs925stJQfiFDF1x2J9TtN1lCLumXbZolyFLm5ezwiaPE97fL9n7HdMfuTQjxz8hM8F52oWy4qFePnEyWRkMp2CQN2M8Rht6fueFzeXTOM4Rw1YXN3QLJdUnaVbnFK3a5pmJUGBRmPtmqpymNIzHTz+sCf0A3nybPcDe1/wIUpuWMrivow9xla3qyTIhJiICH01h55dHxhCJNqKylm61RlOVVQnJ9TrhmUt+AanMndOWlwqPH4m2UZ116IL2KwlXsHI19VWUynDNAYeP9/xzvMXfGy94Qff/Bi7w8DFuOXx456rrBhPHrL2nrP9NZePnnJ1ccFvff0dNs1D2pOG89aRzzecNZkxRUrrWN8/5cFZzb0usDArlktDpQq//c1n3NWB15sRSFRNK/pLPzAdJO8shERVNSyWL6HzwCG+J5PM/RYfD0xxy/Vuh8qFpA2u7ajqDoWIl6vNA7rTO7RqIu9f4Pdb4jSQh72cp8v7VMs1TsGyPMUfnhOWJ7R6JbiLLERgnSNx7L/tI/73dYESYyBEsQlqNRMpjUO7FW27YNGt8OcPGe+/zXT9B4jbb5Jvfgvln6NzFACWmg+pGIlZkn61EpqnShGP0DurtqVYw7i/JKeCsY4YPCVKMVG3a5Sx+Nij0jRTMTUFI7C3olG2EteLcqALWjn4ULo5d/aarBQmR2L2qJJwc+R5MksUkaIqChUp74BEVoYwBzcJjK0QU8SK0lKYMOIlEU5JiqLBKAVnRUdSkIIsAYfJYxlRc7dCEnhXbTQqmTkqR8b28jTq1i6qtRPeCmBRrJvMqnbULrG6E+mWmnErThqQFGMVNSFnnJ+pqjlj5gP7KBomyZpFG1l/6CIQNsUsQ8ACIlwWu57A6LTSqDnsLcoSAgCLjMyzKmQttFmthGqbg7weMResnicsIDbmICuLFBUSYm0JCcaoKDGjK0NWM10X+Zo6FdIwktIzDtsXYrPLsEvzwVsySam5uzCEpEgZYjzaXBU7iTPBGFDZYHIhFpi8JpIZY8S/+CrV4i7Grm6t90kb4nGiWhpcdUpXr3AvnaA3D7F3X0fffUCzPkcfNRBVjbWWYhspTrWaCwZZk+rjO/Z2aiIanXzLxYlyXeRIDpEUIzEGpmkQe32ZV2vGoYwSnVJW4pVFocy84TJ6pkUfpyeybhUyciSawtBvKf0HjNvfJKcXGHdGt/oeVpu3WS7OBCiYs0xkAHLBGofW0k1XlZGD1eS5gRABSkqem8sv0YdrYv056u5NHq6XOGswKs//vwaVhD+TFBwuefHNLxGur8SiXHWsz+9T2g6dPDYG2tWZrHfq6vb9rSiUPIkWZ3fJMN4QhonDzRXjeINSDlN12Lqmbpe4qhVOizGUklBFYhySv6JMLVk3TOM1w+4ZskAsRKNZ3HsohNkwoYyh6e7QLNeQJnQMFNdIYRL3hNgTp54cPIdRAuWi9yhdo9t7rKs7+Gmg1hlTxD1ZgmR5TSESvMcPnjFEyvU1VV0RQ5BCvnK4tmK16qjrirZtaeoK7Wp03WHqU7SpBeVOIgdPGK7ZX19y9fwpYfSkmJhi4TAEjNFYY9h0S4oxoCT2YByDiMZzYBg9qSRImpAjWCUr+crQnd9hYx3G1rTLlq6G2liMsvg0sepacvA8v94KKyTLZDvmhGlq7r+8Ynm2QgfFzfMrnuYtej/y5PkV09U1m7NXWbSKgy/81vaGKVlO795ndfYq3fP3WV7f4PuJ7c2OX/m65sWioTGRdJjwKQoQ0Z2wNLBwET31HFISvpCx+BeX/MI0sH+geK0ZqJcLatOgbY3tOmp1hj0cMO051fIM619wiCNPdo85P4lYrSg0VG1N1pkKRezW6PVdllVFPjzn4BZo21D8KLlxGgqJGAax2LtT7NrRnZ5jrUUfthi00I+1OIfCzK4Z9odv+4z/fV2g+GkghQaymV0DctwbJGNBG4OrLHXT4lcb4vgSfvtJ/M1T4u49yuFblHBByQFrKpyxxCzcjkykKE0uAasK4zQyhUL2GWcypQhjpW4brHOzEFXWHUVFyBptLQlNTgGlteTkFEUhoIwAho5TnDwDmvKcjRNDlu8hB7JJVLrGtTVJC/AkaUsuWvbgUdJbU0y3XJOSlIxo5+fnIxMApfRMiY0zOv/DlUacPOMUMEQqI3ZCSsbqJBMFU5C5ggjgshJ1fs5J3CdE1OziWdhMZwtOR1ZLqJzA2IyzGAXxkLHZyigrRkqeHQJJBkAGNWuAJCW1GHEBgxxgxswTHCUMBSVOVXZ9ls5VyRpIzCRq5rSkORZgfqLj8wGxKMgKM+P20TO0bOZhKDVbqBOkWAhBMYTAvihi0oQkguxowFYFY6o5yt6IS2Lc4w8XlNSj9FHqi1ikkwEioSj6AD4VcszirmLOM0rCOPGz3kcEgvPvIBTCdo89DKAbqtV9MAtKbqjbJVXboboTzPoe1ek97OYEtTrBrE6xzRJbtxRbo518z3pOApfC5DjRmKdxt7+02zkkOc0BnUnevymIwC6lQJgD3/rhABRc5WiqGm01yojbJ8/rIaU1xpp5sllmZ5m8UFqLACErEZrnfku8+Rr74ev4eI2r7tEtvovV6hO03Z3ZkZRn/dGH+pUPf47jNHEW4M6k4qgm/P6b7Pbv0Js/wKZ7nburlqaqbic5SkmGCyWjYkbHnsPNU6hautfeJsWAztAtTrHrU1SJlLHHzFknKhUR04cBkvBexqFnf/OEwU/kUlG3Z9RuidFGIFlVhbFOVs4pSi5R8ZJIPg4cpoFxe0WzvEPKPdHfSJ5PvaTa3MFqAzlR/J4UAtYtqeqOHLXk3ewviSXhw8Toe3wU7onSFUqvMHURkNv6Idp1lGlHPjwh9i9Ic1EyjgO7cWKcAn0/sh9HjLWcbtasupZlY6ibmrp2NG2DcVa0drpB4kgySk/olDjsb+iniRSC2J1vLri5ueKotba2ZrFYSpyjyrSrJav1CSYEbq5eMISew7QjJI8vipIddd2wbE447U5pT+6wOd+wWixwJlFZi3ZG+Dk4irG4BGEaaZ3ltKs4aScu/Cj3z1rz8BNr/vj33udjd8847Ca++Ms7fnWvuRgCNxPUu4HfefTbvPTmxzkzHY058MHFDSEURlPo1QLVbHDjNaHf4rcdz7zgGYZxEGprLtT7AXdacX5eY0xEFZi2iUl3rFcbnt3s+FLOuIeOTywbqpnwil3QYRjGEZ8GpmmHHw5E01GIBDSu6zDGYnBzrpnHuxrbndA2jpBHpjKvZpXB1hucqjDNgFI10+FGgJe2oWoaqlwIw47sd/jek40mhoFp9MRpoB8+nMD/zz1+Xxco4zTQTi3OOonxNkWU/bNmQCuFmh0Ntq5J7YJqsSSePSQMH2e6fkrYPSbv3if2Tyn+ipJ6JGsGlHI4XUOBYZK9vtWZqrLU3YJcNLaqsE5G3yXPAK8UJasChzIFVDXnq3i5CSIBTjkl2c0XyXQp0ZODl4KjwDGHo+gIpsJpC2YtI3FbgWmAg6w1Upwth3N2xXwRC21XJh0aIetSiuQEkbFWz6P4gkC5Mk0tjiQ9d0ZHYm+cUaMFYQfATAEtSVwcc7py22naKlNrBZPGlExTK0qQ6UBVJZo7S7zyhJtIH8R2UyWZ85T5UD6u8IwRF1Sa1wr5I4JXp5BCKWd0BD8pbvqC/8g6KhXBw8uhLvqOpBXGfPjxcefOET8/pxrHgtBlY5FiQzTNME84QsqkpBlTolp0WLfG+2dk5bBuI+8LagHU+QNx3MvrO0/uJCMoo5ECMPjMoQefxKJrdUSXQu0UtRhWpGia06uLkimPczXnDz9BjoGLF5eobOg257jVferNXarNGSw36OUd1HKDazp0NXetthKt0pFXcjyE9YerlQ+FxjO95FiQlyyskvmQPbpxQphk7D4OTOPE2I+EHKnbDuvkepUgvlkAK93FrQpZzZMurc2HBYUSF0mIPX1/ib9+n+nwPrE42sVnWKxeo1u+QrPYYFwzM10KcHTtzeuQ2y8ja8+cy4xSF8HrtL/g+eWXGfUrtN3HOenWrNtmDu08iqzVvD7Mou86bDHWcPLgTSkgpgNMA65pcIslJSZikslcCXKdj+M107RHRUVMmSmMTL2n2BrTnlGt70mDkCZc8igkqyqnhEqe6PfE6AnDiO8H9n6UFWnVUtcNq9V9jHO4uhFHlVHCWDoowniQCWuciNOB3e4Kv7ugIIGpkUzWFtvUqPqc5mQljVbOuG5F1W4wecnAgavtIw67PeNhZLu7YTuOxDRDD41hsViw3qw5PzvFNZrKVbcN5LHATXEip8R+P1LyJVpbdtsrLm52kGUlG8NIShlrHU1V0bZLVqszFDBNBxGlzyAmHyKHKeCjxdYL2nZJu+hompqu66jqNe3qlGbdUpuMUfp23auLFh1PmCBODNc37LdbmtZwsrGUWBOL4+VK89rH7/HpeyecrZYcrOH9TYO2juQTF0Pg5Uazf/yEL/02/MHPvM0b9zaE3YEvP3sBVYcpS4K9jy574uGGwXVUypEC7FOWptYXhn1g308kH2BuZK9eTNzoFavT+5yfn+KHPe/uFPdPE7pEmmLJaYLxwNRfsVeeql1TmRZ18jLnXRDtUtdincNph1OQhhvyuCPnIk2RbahIlDJQjCJrh3IVjVrTtB3j9QumoGfcRZHJuxL7udeJpIWvFIsVs6tZfNtn/O/rAqU/7KmrClcJFMpYKyseo9BFRJ/SfUqwmnaKyhpy3RG6NfXyDt6/SRpvCNdP8Jfv4Lfvw/iMHLYYHci6EIrBl0Qu0yxEbeSm5qxYexNAEUS01qAzdmaRJISFMkNDyEmCtY4prEIxl1Y9xSCHTkgyppzXDkUJTj/4g+yIbS3Cr7ojTgeKMsQZAU5K5JKJKeE+yu6Yb6bWiP33OHUQwaGZx8wJowq6CIk050luyLOYWGkJONQiG5gv6HJ7rihEJrOqYdVmSAYfFFWrMTaTJ9GXxJTwdmIcAqEv4DVEiMmRtZdViZbz61h0CQpqbtqV6EO0EWrnrdMnKIZR432eBbWQyzz2uNVtKlIseF2oZtFsLqJjUuT56yErsHnNUJgty1kEvDoJ+C7FxBQg5ERVLdh87I/gww36vQvZ46uFXJi+4NOOw3BN8Mf9q54PeOnuvcqkDNuQOaQ5c6hEiilShM1Fk0KJe0hnkaplDUWhqpbQntGt73N2X9FsHrI4fxmzuUNZLnHLJdo0mKaBSooS0S4IyMnM65SPFgPMCH6Yp3A53xYmoi0JkOJ8uERimPDjwDQHz41jzzSNsgrNiqptsc7g6gpTVygjYkrB3uvbwqQUdVuYHF1XHwZQJqbhiqm/ImtLe/ZJlu4ObXdH7JX1hzbk25C9WVBbCrcaKfmZsjQLWrKdKIEwbbl+9jvswhlq+QbniwV3N+1sdxdSbMqipC45UaKnxD1FDVTLGlUMWgEe/EEs0rqAKplx3DLsr2WlpQqH6RofAsYusc0a3ZzSWBG/2m5DtbpL1dQUvyVfPyaNPTp60jSw77ccxl7W3OOErZZ0Jw9pzu6y2JzhrIDcmK3atx2LBmsyIewZek9OiRB6dleXjNHjXEXdiFMD68A2VKsz6uV9NIVx/2K2AidUTqSYuLm54eLFFWGIjNMAzrBZLejqhhITi9WSk/Mz2s1SXIJKfh8qF2KcZF3nB4bDxMXFDbuDx5iaUgqHYcBoR+VEU9LWglKotKa2FevlGhSEMHHx/IJhjFgHg8pUp6e0rqFZrKgXZywXLbaCymUMjrpdUXWt6PzSFpU9JSWmw57kI2WKTMPIzc0lh3GiW3a8VrfcDZbcLagrx53Tjv31Fmcsw9UNu30vuWExcNiOhNMz7nUr/ss73+ILtuZ/+5k3efuVM16EC75y4anNgrx8iXi4Qh12+N0FN6bQKIcy4KwjZbC2AQ+H64Gng4BAnz+fuNAjTYBPvfkqnbN85YNrGjPxB152VEoR4gEfNWPKjCVxGCbMsqWpa/RSYWwn0yBVIHpKHmC4oOyuiM0dVP2QarlCJY/WZV6nSQCu/FlBHIg3O1QcKVOGlNF1TYiZmAQ1YIzBkSnjROj/F6JB2W9fYCtwoaVyDc5WaCsx7VpL+JfRounQs+hOzS+8sg7nKtq0IC5P8Cf3GO+/hdtdk66fEreP8Pv3yMNzctzh1Nxl6YqqFmWz0pmcvPhojweYdhgnBUQpWrI/ykRJk3Q7UawpWSnCHINtbDXfiOOsMUmz3Rm0sxL7rg2khFIHlMoYXVG5htSsMM1IGXpQUWi1KcrYPEuomkJh59NeUPGC5S5kUXsbqWx19oLMV5pkRKSrrXR9KUMIkZwFeR6CFA4KJRoNU7BaURmDKYkUNEZl1qeK5bJgHaiQKSGTo6K/iuhRNAD21YeMe0X/1ccwu22ws0MnFyG2GuYEYum0Z+yd1B1JUqyHCDdjkXRaLdOJKE5auQj1ESAn65GUs0xWDGQ0tTVkH+fireDU7E4xgBFxrcpaxIdjYQxCuVesOPm+/x3NW/8d+//4U6LFqBQhpXlkHhmnyJT2EqdwK8gVqq6PmilVxMlQcBijmOJI1mHuOBQ+FVIWx4bCUNWObrmk6e7RnDykPntAc34Pe/KQ7uQubrVCNR2uXaG1TEpwcniqj9h3lZY00+PGqxyLIc28LpynJbM9OM9QsBzDPO2TDCjvR6KfGIae8dCTQiCEiZgiWivqpmOxWNAultR1izH2w0Lh+Dp+ZAUJ3E49jo8YI9M0UrJi0d3BVGu0W0rui7UUFFbPycgqE+fiDY5FVp4LH/mqstack7lLJPo9z598g6uDx6ze4rQ74c7JYqY9z+u9kubVShYnXewxcaSx9Rz2KQd3RDKwymHEsydOO55+8A12uwsWyxWu21CMRtkOWy+plnfQ3SnOGNKwBzUfBnZByQXPE3bXzyjTDWE4ME6F4lZY7cgm0TQVq805i5M71ItOrn+ExKuK2LiT9+QU8PsrdjfP6W8OaOtwTsuq2m5wrsZah7bqFryoQgbEDVNby+HFB/Q3z9BK0V89Z/ABZRx1a3AWmq7j9GRzW6C4usJVFlXE/VFSIQaZtk3DME/aEofDRN9P9MOEUoGmaugamZBbowXoVxLEyD57QlFUzRJVFNv9gV2cCLnifHHC3TtnZFuJcLWqMPWaRXMmWWhpJyuuaoGtTtAqMm2vmW5eMOwPjKNnGBOkgJ96xnFEW6HM3ttsuD54tspQUDx/8oxE4brfM257nu97DuOI0obhpudKnXBnaTnTma/81tdoTk74Y6/c5w+EyH54zuNDQVUd5uRN8tSTp0uyP0B9jjONOJacx1WWofe898ELih9Znt+hlAqTEiYEttd77rcavxv4j791wOaGjy+hrg26RHRzwtIu0N0KtT6hMmBTT4mBMvZzQ1qIaYJhT7+/hvopU7dmebqhqTqs0lj0PDXNlJTIMZGSIsfE0O+JdY3OCWUdrZ41a6WQ/SDO2LAjXTz6ts/439cFyvXlEzCJulnSzBHazlVzOKAj2oh1btYhCKPBKBl1lrmzstqijcNaTV01hMWGeH6fML5JPByI2+f0N89I+yfY4RFGBVnpIPu0VDJVu5R01hyQ1GOh7B3TX4tagO1QLmEr0X3kXFC6JYdeRIRBMoVmk/08+VSz+LeeVzOKHHrRHtgsVWzV4BYr6qln2ovrRVEEHjUD54TtIQeRWIpmzkv2aKKIM5UCHTA6sqxEZ2PndOBcND5kYtKMfSJ4yYXQSombI2esFTeHCIzB2cKyNnQdqDl4rsQiHX8qEAEHVadwZxV6sWT6nUeQCtoqIURWAkkTMqvsVbQqGCMsAp8zZkbOT7Gwi7CdCkYptC6k2cw0QzdRSnDPImnIR6MTJYP3kTLNNV4Cq+fpTE7kouYDSl6XlIXXEqKA69q3/hDrP/x/Ynr+nPHqOSjNlCD4EZ+85CFlSCXOv38JXgwJfDb45LDVfTZ3X6c5u0cqiqtHT7h+/C2IV5SiUU6Ehd1iSdetWZw8oL3zOvbsJex6I/8sz7B1h2kqqBzKSoKompNgbw/7WTAqrgvkc0VAYxRmbpC4cfIc2JdTpASxB6c0ESdP9IMcMtPAMBwYx9n26eNsx5eE1KZpWK1WLBZLbNXIeqeIpuQolj5OSz7UunA7vbl1TqWEVoqmOcU6B7aC4qidXGchBnHsUMnvmUC5TUeer/m5ODm+9kfxbU6e58/e5/LiEersE9w5O+d01VErM6ev5nmlMzcPSIFCmiSrizJrSSZKKuS+53D5Pv31tax1VcGXiO426O6EerlGuWZeTRuxCDcbbNOirWV//QHj9hm6aJI/cNhfst1eY1IgetBuyfLkLk23IfhrjEp0dU1TVcDMc0qFHEdKHAnTSJh6OYRCFPhdVaOrFl1XVFiUAWPc7MqLlBJkHRXfY6QQ6o5p95hnT7+Oz4XV8gRn4e7DlyFnbPLE3Q1aV3SrNba24iSbf88lIem4PuBHzziM7A97YgjEJMnzmIqmg8pZHKItjNHjI3O8RsL7yBgnLm52TBhOz85xmxV3T9c03YKTrqVrW5StOGIdcTW6bWnqE0pq2V8/IYw7lHaksaffXrG7uGDYD4QQpWsxAq+stKOqW5ZtzWrpcCaxv7rmxVXgvWcHQrPk42+1vLzqePUk8vhF4vHg8X3g2c3AG8uG00WNvd7ypS/+OifLH+D779zle/eBwzeuuM4au7hLffI66cbjp5GRPZWrsK4mKGlq+j5wEQsEz2R7ah3QZaLfjXz9es92s+Lq+Q3v2prV0wbjAw/vLTlbthLcWAp6ucCuTuhKIO52jIcr0nAhDpvqTNZO9QpVjajoKVm0gEY79PyeT6GnzOGthIlp2BNT4lAy6/VS0Pf+gI0HfPD4kMFVqGqBWtyjW+y/7TP+93WBcvX0G9i0JXYb/GKNa5a4qqOqF1h7hA1ZrJNQLa002Vh0EkukNkrEjxqsdlgczhZyJXqVuPLEs3u048fJ45Y0XpH9Hnd4QegfU8L7oPYoJrLPKF2RlQYnu2JjGrEEayjKYIwB3aBnt4KpTog5QZjQfQ/TnhL3qCQjMJvBKoPSNQlNwZNSwuSELQVtO5y11G1DWa1QMTB6L84dzHwDzzg9oUgy3UkTmZFsFKSJriroMlEoNLngVGRzAlUjRYDTMp1QRjHsoTfgJ6Gf5iw25MqKjcNPoHLEVlA3im4OZssRSgAmTQ5wFCjmpAh7mL7yHr5XEqBmpJCgCLdLaahquV+EKARXbee0zKzIvpBK4RBgP8gUS1soWZFKhTOebqmZfCEFWQuhIlZ9qOcoyO46BjCioZWpwuzuIc1OIa3Q6qgZUvhcqLqXOPsj/0eq+6/x6Of+71wfHtHZClUqotKMIRPzJG/YrFCqRbUvYajxw4irW1anDzi5/zrLl96kuvuQqmoZtyOP33mP+ME7GJ2xtaFeNrSnp9TrNWZ9B7s8wbVLbC3i1uOBp4wGM5OAj6JWdSttFSH37CA5irRFW3FE8MpBkEogBImNSGHORQk9MUUOfY+fxBLvx5FhGMh+whqNqyvsDOqypqGqW5p2ga5rcdwBv8sGP4tTP1pEHFknx0cpUv1aO09fjq4esanJ9WIcKCMamXld9JEBzGyn53ZtpJSETFprOPSBKdUs77zN6Z1X6NoaZ8zcKMR5RVQkkwpNygGVRmwc0QzomInjjjCNpBQY+yt220v2/Z6qXlJVa9Z376KtpapbeW60OMiKJ6eenAZ0bChJsdu+YLu/4vzOAVxFpKa797rcsMOEKpHF6pSqOyHFhuJ7bCWNTMqeHA7kyeOnQIwT0Y/EJBdUtiv05oy2G3F5xKhMVhqdA6UEkp8ELjlNxGnian9gevKYk7NzCkEmLq7mdL2mcYbKzhTd4Ek3F0Tvsd0aZQrTYU8cRsatZEjtxxHvPSllppgYxwmlFE476qamaRqMgmkamMJI9hPRB2KMjD6QjZXVoFvQLjrM4oRqc0rTOuqqpq4sTeVm4bWs9sI0UtKETQmUg6wZD9dcvHjOenMflQPTeGAMMpctOVI5S7tYonPC+4lcFItuQd00UBL3K3h0c8X1dcEvOlLRrO8u+ayB7c0lV+9rBt9zsVOkU0NXtVS6J14+5Vd+/Wu8+ge/l9df2vDosOXLlxM5aezqVZTf4fuvMk0TydasmgWNrSlK3HoxWbrawmHH822hT5mQYdANX+1OMdZif/BzvPTGBh0e0WfH6d232CwtcfuEEaEjxziStTjBfEZS0Kcac+8uVbtmZRrGGKjUJPrF6MnJk31gHC5IvpCyBhIxDuQ0oddv0K7vU8drYhoZw0Qcr/D7A7T3aLqHVKu7rMr/F7N4/n/5+NbXvsW43bNZn9KsVtjFkqpZ0Cw2VPWCum5wrsG4GuecQMicJKtqbTAm3XZsxujZ3SL/WKT7LBWkLlHSKTk8IMeAjwO2P1DvLsnTBSpdEaYrwnCFJWPLhDKJUmUSLSo7jth4tBO4mlIYY7E02HqBrU9I3s9roAk1bWWnp6SroBRB3PtEBkIcwGSUa3FuQ+kM46Qp2ZD6K3RIVFZTiqdMhcpCVgPORFzRpDhhbEGbgEoTtWgyqTtwblbTU0hasON+gsoWqGdiQ6XJiGalMYVcDMHNZNksxNhIFrdCAiZgzKjZ8loKkISeGsYMUaHVzNdQmSSbqPmgAqUz1mmKUsQsvwNhfCl8gjEwpzqrW9cSNuEWhvWbnyKoO1z+5i+T0l4OrfnnVccNwLF714qUElMp2CJroZyLBLNVWsbeM5wrKsPZxz6DfeWz7L7+Ho+/8j9gSERVUYqewxSlWCw5Y/SG7v530b35h3DnL1P8RBp2tIuO+t4dFncfolenoC0rLPc++1mm3otdNwWZnBmLnt/Lxlq0dRStUVqEpyIh+VDcCh8VuDJPRtJ8gMtEQHKUROzJTHktOeGnXtg/4zDbRg8EP4AqjH1Pv9uDD2QfiCHKCqWusc7Sdi1t3cjKtaplkqnMjK2fXUhKpLAfddccr0f10Z9hdhEJ9dd85GebuTQ5C+V3XlvNZdet2+gIFVRz7tZHJylKIZMZY7l3/2UR8B7JynwIkIMsKP6MFNLJo8dnlPEKHyPTOLHfviBMPUbXKN3SrN6gXkaUMdi6xjkrlGilSVm0YCoGlD8wjk/ZXT2lOXsNlSOxRCE+50zjapZnD1F25rlMA7G/wVULqnZD9o6UI2k6MF5nlIFp2jP5XtxVWJRZo6uKXBS2XVK1K1SMxP1jQn8hmIUo95/oR/zYE8aJcRjoMXORJHqS8/v3UMrIWl2LYD3lIrEFOZJ2W4qf8DmzvdpzfXkjrh7v8cWjlcYaS1VVnKzXkpSuFE3bsFos0SiuU2LoD9wcDsSUhUekLHW9ZLE5oTm5w3JzzqJrWXaOxh2bD337viZ5HKCK5/rymhs7sDydyH7P9uoZ11dPGfsDi7aDAk3bUZyFVgJWm+U5y8axvbxgHMROu99uCX7Cp8DpyZKTaeIqR4qX16laBLpagQ8kDI/3Az1nVE2iq6DfWvbvfcD/sKr5oy+/zPf+wGf5Qybw9IOn/OI39hz8K0zTnnL5HmF/QXAVbb0me4+tFbbKrFc1u8trLnYDL4ZAUgZllpShoFYrPrFu+P7XXsbuKp6OA8mtqR2Yask0TcRQk0yFahRWO1rXYYwmBkfVLlicntOMNeb6GWXaEneOYhQlicMzTQfGqSeoDnP6BifdQ6arJxxMRUThTAPtBouFak0uT/FjT0k9pllTn5x922f87+sC5fnjF4R+4np5xfrkhMVmRbtcMS2vqboldTtPU6oFVd1iK1n/WFthtAhcjTEYa0hpzv2YEdsirtWgQRtFtpZStUJOzSNmcUY5uT+D3Q4w9pjxiuK3xOkC/A4bEiZ5MHEWeSbJyNDNrAHQFNOgi6EyQOsopSPiCFEuJJKXiy1HZAchaaUlTdgGqmqNqs6wi4e4xctMw45p7BmvH5Gv35F0PqNxeqLkEUvC6ExVWwgNRU0409Kwk1XQoUCW0KiclUC9VMHUCu8NpWSshZgjTWUEaAUyikBsMiY5lJebhFJCQiVpSVD2CZ3nlJki+TtagW2kg+8Pc4ExC0yKmoWpM0VWMk+kOIlFEXNmnCBkJTjYuShIRkSXLimqdsXqpY8Rnn2T8fGBoo/duwh9U5LwQ11mi06WyUlRBTsflD5KgCEOfFEMWYolTj9G/+IDnv7n/xuteYHSC4p1xKSIPkp2UXZkDNX6VU7f/AG6Nz8F61OsUYRhwBhDvepwyw11u8JVjdhetaY5tbJaSHPAY/4wPO/ossnzlESrj0QBHB+zk0UyliQW4BhKKZbgQMqzoyzKGif6kRQjOYz0w55pHMlJQkEP+x1aW4lY3/eoYSIOg0winHSxjXNUzgnaWluKUvicMClhnaQaF0Abg8rlf1SQSLFkxEM+/wgiJJbGQt1OM4QObG8FsHoWhR8nJPIaf2SEAlKI5Xy72pE1k3w9O9uNdREx7PHXKFqoTMkeaGSisn2fm0e/IZqUbMm6JalaEAPKYBYnuO4+hkJKI7oI08gUJYVOivg4kL0nDAd2+xt6bVm5mkV7xub0NbQ2NO0CW4suLCuBKBTtUClJiGWKqDRx2L3Ajz2mWggjZb52tIJsa1R7gm1WgCabGt1WuKIpcc9w8xi/3xNmXs3QH0gxkJOszDarDavTOyxOTqnnyZJYsxM5B/Ixh6skpmGi3+447A7sxoltP7Lb9yL0z5nK2hl/72i7jnW3pORCf9gSvWcynso6QkzsxpGdj1R1R7da0yzXrNandIslzXpDt1xTW4tTEVUm8ky6VkU4BSoEpsnjDwdePHrEdXrKyb09rVPEEERXYRVOF4yrqZuWHCN+HJlGT90uqGpH0w5c39zgLy+pmgZrDfXpGa+uHFN1QbkY2O32fPDEES8uefRiSxyhGEdIA0FVdG3DxlqeqUIeB9772tf5d3XLX/3vvovvv98y3dzlM994zM//8hO+9M3AuIDD80eMw828SrdcHQq1c3if2Q+RwxjwKaOdmzUkAdNVnNYjWk8sl0se7TIX+5H7jcW5Dqfy3Ng6illQNRv08pS6NhyurzFW41TGaEO0NTElgh8JWprdrCSN2LIXps3yHm5psWTGBLoMt2GD2tXUyzParqV//h7J95AzlflvlMXz/2+PYRyBzNQfOOx2NBcti9WC7mRNvexoFwuWyw1Vs8LUC2yzoHYtrmpnMZh0NcZqjLLzVEWKluMNU2s1W1TUrHY2OLMEkyiuJZZITkvqFIjxnoCppgGmPYRrsr8WtG8ehX+RAyVuUSVRlEWZBehK+j6lQLcYXaFNQ3GSGhmCp2RPcQPOOmJIQuIskHFU9Qrb3WF1IvTQ3mfG7Quuv+6Ynn+JSgVqK0nBxmgsnm61Qa8/S0oT+vJbpKsb2tMWc/ZZnJFutORCDh6UJ5UJnSKH3YXsF8WAQY6FOIHKGZUlME+VgMXMoV+znTYW4pgoXm6ahUJJkJMUgkVpYooY++GBlW41ImJ3ozDricSGlJIIY6c0Cy310V0k4F2lMmNSXHz1S3SP3xWlvOH2IBQsv5BDDbLWOYpyFcyWZsTNIlYMQFZKMSe0WZCrDf1Xfpmrr34BYo/WLTlIQaKVw7aNiPGaDcsHr1O99AbV2bnYflVGt7VMiIwc5DkFdBGRta6cuFqUIZeC91GE1eXDZGZJHJ5FpkU6P1GXHLVMUrQdpyQly7g8J/lziZ4UJ1LwtwXKOBwYx4GcIrv9lv2wF2GuqfBjwFlxYTAFlA+YLPlDxjmqpsa1DRgB+uUUb6FxrZ11P0p/6PwyWooLNXNOjp9Rc30ggqEP//4cGZFzvrUiw/+0EDk+bguZ2cmTj/auj/w/UtxIoSOp1pmijxQh2QnllOaiEcL1Bbt3fpPxcI0ynay0mjOqqkbHnhJ6TLvErpY4Y0m7a9L+BoK4RIZxz3TYSqeMaLNM3bE5vc9qcZ9usURbscFb5wCZTql5BadIpDAwHfb06jlKRYZ+hw9BrKLW4upOXFk5EbSjmArdLHGukeIzR2LOhBDY7bf0L16QZkJyXdfUXY01Gq0Ki805y9NzTLvAVhKTIdu1iEqR4r1YSseey+sLXjx7zmE/kBGdXFM7jJ3vPaqaAZkaq4wQbLsGP468uLzictfTdgtiydjVmrONYb05Z7E+oe7WdN0KVzc4a6iqBle1lDgQvVhvVU7EaeLQ90zDSJiOWpeJRGK4uaRadsKVWjQ4p0RboQopeZxWxBLx45797pI8OfqhJ2kNdUt1co6tHI2zmNmGPZUPeDaOPLsZ6Z/1PLkcGYM4NlW/JaBYtyuqdos7eHpfKPsD7z665INDz+fsgtN7d/ijbc399ozP/uCn0cNn+a2f//d84Ve+zNX1Abt4yD47oLDfZ/q9Z4ySAp29COeyW/DSd73Kf/+JeyzSwGE/8ezykidT5GzxgNc7R+M2KFOoTQ1GsnFU1tj2FH8YyOFAHJ242kxNjBPFj7frUQE1ZuqSOAzjrMfSuHbBOgWM8hhVoys7M4wUyq2xfsXV9kA+3FDC/0JcPD4F8BBjZpgCZr+nur6mevGcdtmx3ixYLpes1me4dk3VLqiaFbZuqeoFrqpxleQ0OFdhbYU1leQqaLEpGyOuEcmqEYBVMXG2RjpscWBaVMlUORFyJC2DxNWHQPYevKekgRL2lHAgxwFij8oSgU0WxXgpkaJH0AParSnUKFdjbE0ukRhqdH2HlKVASSGCXaPshsq1aKksWBZYb5asFgu2j95gePJF1MWv0KmDHFRxS1tPOF+zv/Hky/dwY6FdTbjKEU9ewdiGQi1prNoQckTFidr3xBSwVYeplxwOHrO7JhwuSYedpMnun5HGAZOk8xxCpkwJneSoSZpZgBkpCYxSlJKJglaZi5cyD1HkwogJUGIRTr5gnSVlCQs8Qk6UnjUzSYICj7TPw7an3x7QKFoF2mq5Kc2I/ly0PA8yhFEZ1CzEVYByAgcrSTpYckLlwuaT/2uG0zd4/kv/jpIM9eJtfLfE1huadoVdbFCLDbrdYLoN3eaE+mSNWW/Q9YocA6bSFAIZ8ClC8BgTsCgRKRsR+olgWmGPhYk+UnbL7e/sWIiULPO6Yzhfnickt0LXJHTXFEfBzoeJsR8Io0Cx+ulAP/TkrNgPe7yf6NoFprEs25ZaFUwBuopsCiXPuVOrNXrZzd0WkqyMwjpNrWWaomZnlBBzxOJLkeJcCgZ9u6LTMyhPzdffrMmWYvSIwj+qXefH8eOP5vYcC5RjcfLRYuaj9uOjldqYDwsXmQqKUslqx+S3jE+/QgqZbvkquSR00+AWJ9jFBl084/VzUszUWaGdgNUO+2v8/poYPPvxBlUaXNtJLlbwdMsT6pO7rE9arPuIxTtPMvVKog3KuQjsqr9k2L1A43D1gmZ5hyorsA5T1wIjPPqKU0L5ER0TymjSdMPk9+QpMO0uxcbadoJGKIrlckNdN1TOiCi9WVIvVhStSTlRirx/CBPTYcdud42fRgowFUt2C1wFlcnzKlI0BzF6xhgkR8dHsfLGQl4WtoeRq34kW0t13rJeLTitDHVV4+qapllRtytstSAXi5qjScocqpmDZ3/xhGkYmXym7wd8EDp2TIW6W7FuKlRV0bo5wlIbEcflOeYjJ1JJpBzZDz378IiXHr5EvVpxvj6luJp2tcFVNfVs/6ut5TN+JD26Ytgf2O0Gel8IEodNtTtwMyXuWGhdYWMUua0JRbE8tdzsb3hxrTmxDQrLK+cnPFx33Kvv8I3VSBt3/Ouf/21uLp5TTs641hU3hwl8lGR6xHhglMWYzFnx3O2vmCbHhWo46Zb8xrMDv/6tF9z5+H3O1iuUEXaTSwM5T8LRmjzJjwx9T0qeuq4FwmktVilKiOicCflA8lfE3XN0WqK2T8ndPZRraZ1FkTHKcISCUsAqRSITD1fs84cC9W/n8fu6QNG6ImUt3aATUmuMmX6Y2G0P7C+vxEGwvmKx7liul9SLDbpeUHcrXCPTlMq11G2HszXGVTgrRYq1Zl4FOQnem9kMEvWeUVpQ+bcrfg0GKVpKhmwipY4UIiWLNZOYxKKZJlSeyGEgxREVBlQYKKGXjJKYKKWHOAsskVDDrCzFGGzVURUHpsG0kqGBEqeOcwZV1TTdhs39j9Ff/EEuv/w68dG/wAzfxHnwvcc/+ipxFMeGqTTbRwnz9BdwtULVieQU2AXKrTHNglgsWhW0rQh2QarX1LamrE5wi9cpCaHnFkhTgP4gnbkf8U8fkS6eYZgtmhmiTyQfiWFOSRX4B8fUUykCAS1rjVDAZ0U/QfaSSl2UFkCfnqcKRVY9tZk744KkFOuCNUVGDrKPklVA1rOjRqBsDnBzPkrRmmw0KjuoLKWpia6imJrmbIP7rv8N9tU3CdeXlI+/ie1Oid0SVTWixWg7StWgbYMyohmxzmEqgdtpbSjVkljk4kcXkjL4mChqjjcoR+qtEj2EEr0FeeagzkVKnhkl5Cw6gBTn8XuQcX2ULJzsp5l9MeKHA8kPTD7S7w/0/UBKiTFO9NOIVpaUI01Ts+wWLKuaxlpaqwnTSC6JoGT6Q9XAYkmwRl6DLOsTbSuqbjGn9ta3E0lVBIgnxcGREvuR9c3tCEUmV0pLtSgOkw9ptv9jANvvXu3o2Ub8e09XlFJEqXxlzXM7poGiPvz+ShYmUS4Rnr9DHvbUqzPM7BKRtW/Eug5rVujouXj0FXLcY5sz4rDjxf4KJo8pcpjU3YLu5CVMvUCnA84omm4pdvoSUUlLcZn8TOWNhBgoGAoa3ALbFTRa6K51J5qq4zpvDvpMOZKnHr99xnS4pN48IPkdftpScsIazebOA5wBnTK5GKxraZtu1j5FXL2W92qZSNMB328ZDntSLkw+sN/tGCePa9ZUJ69xZ62IuwuUv0InWQ+O48Q0Hpj8RD+MHIaJIWSa9QknMdK0DXfXr2HajrPzE9rKUlcWY82MjXDkkjCAqwV8uN89JyUR+R9uXnD57DkhJGkwgbpqQGsWxlFVjqbtUKYih4No+DCQMylk0bXljLWWpCzt6V2ytuSqwS7W1JX8jqvKSWK3yuRiafWSe3vDg+z55sXIixc3bMcwv3czzTCx2w3YVtFWFcoNdMni3n6Z/8Mf+Tj/q/uG6WrLl/sn5DHy/BJG1/LWWy/z8LVP80f+0AXvvfucX/rGlqLWrDcnnK/uYOLAzeUl1zd7pgiKBLsbHv/Gl/jnhws2r7/Opz/xFiebJW9pzdWN590+s+gGXJZzKPge70eZnA2jZPJMitDeo6rWOL+jpC1p3ON9oFQrdH2CrjJR78mhx9w8Ylo67GpFddT14SGKAywGj/cjg4eoHCpMRPLveT3+Xo/f1wVKTGCNJpckgsmcEYBoRoWMH8GZA4ftQL2wLFcLusWKuu1olivqxZKmm+PF607Eqq6hajqJK7cW64Sy6ayTYkU7ihEmitb2du+vZ9GddG6CZldGbHKlVGLdtB8SOFOaGSVJ7L7qiLoPPSpKbkeMk5A5/UCJEZUTxUBRiqIVyjlM0+LaDu060uTJ00hxUC9bTFWjlKJad9TL/57x6/cZv/5/JT76pRm7L/wSgcuKRTlNhbFPt5OLXPagJDshk2cR6qwN0YJnQXdo3UjcQNOi6hbjBMTl6iWuXVK/dB/18isot0Tii6IcrjEwDTKiFS2iJg0T6eY54+ExqAFnBHkfMyQEbR+zdNFGZbEwz9P+Mh9owmYFe9QnzNOQTJEa6Lg2UGJBN7bG6ArrWkzVousat1zy/yLvz2Jl2/O8PvDzH9daMe3xTHfMe28OVTcrawAXRYJtcGPABY15QOqWkKCeAJV4gheEBJZtBFh+sp/KVssPluySWljgtmnbBbQxkpuhqtyVZNaQw72Zeccz7LOnmNbwn/rh9499ThZUk6lWt7rUkcM5e5/YO1ZErFj/3/87uuWCOH8VfIvulph2RfELlF9Sju+RDSy/8CMUb9FuBsagnQcjVQfKSDMvIO4ZZUQsjVgYDzt0abmWD27WhgSMMWGyLEIH7U3K8r7lu8C0VFOKJZOnpHg3jKQUarrrgEqZMA0M+x05RYahZ9jvidPElBPr7ZbtIG6FlDJTGFkuFqxWS05WSxZNx9K3qCSlXylFoaS8JzlPPIhftcZoizdWduFtS9M0kkWhDWjpjdE1FZZyyENRh//eUTsvRwAeNCG5vKAA4aA90XfuHHmZVUVLXuS7pJTufu4QMZCSvN5aHX6wUjyH48iRnOU8yTFI8ejmGuvFCpxVvtOsxOGG1B+h7IwUAjdXTygolmcRax2L89fwxmGyIg0bnMrMjs8pzYI4eHSccKbB2SUpD0x9T4wjOSfiFAhTL91ceoGZn6D8gs736NQDEVSS1OM0ocJEyZmYpLZj3K/ZDTtyv2GlE97PaH2LtRrvnby6WqL745Rxdo61HaSJaX9F2l8TUiCmge3mivXNlbgJZ0tUs8AfPSTvdxil6ZbnuMU5efec3dPfYLi8Ydzt6fc9m/Wa3TAyTIWhQG46uvmc5nTBvZMTXNNhm462bTBKKNeU5f1RcYC0Zxj2+PaIzeaSZ08/JYaIc4YUJkIqaONwztK2Dd1sgUITpgGlFNY1LGfHhMlwux6rYLT2BmWFcprVbIlRHSerOa6b0biWtvWSn3LootKaUgwW0ROlfo+OI+Nmzc12yz5AVh5TIE0bri+fsr3foAo47YmzjnffuM+PPzzmobN8+nzLGoUbC+99dM239pZvseD/+Dtf5+133uVHPvsb/PNnv0pp4NH5CT/xudeZqYmnjz/h48cXXN703OwHdlNg++kz/uH1Lc31RJ4f8wd/eMXnFys+Xo98sB45NSOnPqDzlrDdMo6BqKwkyjYrdBgoi0c08xatE+N0zdRfM2x2pKVjsVzhuvsY38LFB4T9YyifoVEaU4JkucSeGCJp2pPGxDj0DKMlzh7RNA7bX33fa/xv6wElFWqHjXTfWCsIwlg7QKp0BLMfsDtNu94y8xu61tHO57TdnOXREX7R0M0X2HYmuSJNS1vr5Y1dYN0M5yzWiPrcWoM2DqXNnetHa1N5dIFF7y6i5eBSMHe7M61AW19zNV5EhuccMbFSPQmIIzoI9FbGQeyeRHTRWGtxTYPvFth2QXENCs04TeRD2quVwDrvHOq+x6rfgWPH+vYSffNt6bqJqQoo6y5dgSoKgwhVc10UpA1WkAaoDcBZEjhVGillqLtfSXHVWmGVFMKhNdkomtWCbvUAZeYUJc4A3XQ0xyu8bdG+RZ2cE9wRZbql/fQJN9/9NtcXX2GcJqYQJCbmAMlqweaLEXwpIUiJUgadwSuDbjzaNHLxcx5jPNbOMHaO9i3aN5hmifFLVDfHtAuUn1F8h5nNUN0M37RgHMU6svMU4yXcrkbqqlNL0Ra0WHxV1VjI1Poy1QC8tPMXoXCRXp6aNPzi3JZ+m1RKFf2pypPXnXHKkt4aJ0qqDoo0krLstmP9DEzTQNhvUbkwxcBms2aaBnIWTUs/jIw500dpd00501gvZZvLJScnJxwv5th65NMYGGMPU0AVTXCW4K0kjjqNcRqvDa02tI3HNY2kmmoj70vVdJQq1nXayvcytaKh1H4nof30wdWjq4A2vQhvK5Q75x31O8Cd3kQ2DC+cOy/TPYfPnVICfx42GFBdZ6qInbUkyrSn7C7J10/kMbWEhqFqt01/y8XlU7i94PjsDXSJdMcP0EqzWMzwdWA31srOtZ+jYqRZnKD9nKEEQhyI40YCCEti6LcyoBQloli/QscRpT3aLjCzJbrdkNbPyPsepSRrh9iT9luxqaZMiYmsEcRnfkQzm9N1kouhtWh/CrLjKEGhTIA8kpMMUtuLj7m5vpSANJWl9HGaZPjG0M7v4ZYP6MYtqb/FNA3NfIH1wE3H9c0t19c3bIbAdhiIdsbsaMWRd3TzFffuPeD+wzO6ZYc1FqWkbFMqQFLtOypMw56b61uub7csViekFOk3a2KO5LbFO8dyOccaiXOwrqFpZjS+ZRq2bCq9FsIoXUPDwHa3w/qG0pyJy7NzGO/let8t8LMF3nYYIuSAKaL5UGkgjD3DuCOsL1mvN/jW8PB0xtVN4GLq2QyVr16t5BiVgxJIKKyG6WbN9UdPGcrATjke3HvIBHzm7WN2l4m2hagLq1ff4Pf9vt/H159d86tPe8q4Zbdf8/pbJzw4f523Hhzx/rc+4RsfX/LxsGeIioHI/ruf8MuLU87GkftHR5zcf428DjybEixHjpRsgMO+J87v4U8+wyrvSOm73JYdiRnKH6NWYJOG+DElbMnjNfr4Hbr56ywaw83lE0kXT3ty3DH1G6bdjt1UiMoICh7ECajO7mGOzrC77vte439bDyimkYUooSVPJCqUlWEhxcAYJmLOspsdwe4GGtPjnaNrNrRNx3x+g19YZss5s+VMFv2mo52JVsW4Ba5ZYn0rJ3HNVLFWrJPGygdCK1Gnm4MTqF4I1Ut/At/ztVaKrDSqSCeF1k7EsZQa6jWHlND1QlOiNIeqLAODdwbnRT+TtEEpK6FiJJK2GIygGFjwI/HkDP3aT2GeX1K6X6ZMG8q0RU09uiRMEZFojJCyxISnIHJLW+OQZWcq8sQYY+VRpOsnZw1ZC0KQIepqV86KnCPD7RVrdQ1KZCO5KHAW5Tq0b8F1mNWr+Pkp+egI//ANzk5eQX9rxbP3voqzima2oJmtiBX+L8qjjFhZjfNY78TS6iSEyjRzTLNANzMJbrIS4a2tDCfKe7At2jRILlTVUxhDTaoDW0vitK6Ima7w0QtHTamMf2X9v2fYONAYVCfN4XZYkNUB3VH1ezmLzuWgL6nOE2msHSFFSILChSAx82UaSUGEreM40u92tdtnYHNzLY8HkldSkajdMLCfIlEZQs5gLMbCbDnnZLXkbLXiaLmksU4g9Ul2nTllknaUxqN8g2sbnHW0GowuEAd0NpjsMXqOvNvS9XOw7Yot2EJJmCgJueWAmlTe9G5sLqK/kCHlxYAHSnJVXhLAfo+9um4SDtqTA/1jjLkryXz5JgWA0suSyQxTL029wzXT1UeoJMhEiUJ9hBgpw5a0e06/vUW7hnhyj1k75+zBa1itadwh/8iIzkZriI0M6EUJ5VMU1zfP2eTHzJf3UL6Vor6cSNpj2hXar1BpJOeNuLtmx+iyIISJ7c0z0lh7ecY9/XZDRFKdNZr58Smzk3u41Rm2meHMIb9irLkfMviq1DNtbxhv16SYmaae25vnbDZrXHW5aOdoFp0M6FY+Z355Rnv8gOHmE9aba4y6IMaR282WT67W7HYjWTvak4csX/kM90+OacuIK5pmfkQ7n+N9I/RUlOTbkhMhRNJ4K46gdc/tesd+CuymiWXXMZ+1FKVo2lYadLVYjMehZxwGvG4xXqzpQ9/z9OqGo+UZSiWy0jTLI5z3lNl9XCe6G62kg0wZjXIds8U5pIFh8ylxv4aQGPYbwtQz7Lds12uGIeBtx5uPzmhx9B9e8bh3GNMwf+WEf+NHX+OtI8fcDFyMa67WA59881P+9uOPscArP/wOX+SWs4f3+dETx8lpz66xmBDxxys+/6Uv8tPf/Q4Xf/8XuXn6mPfKxOmx4bWjFh8T0zCwHQNTjqRiUBlKv+WDX/s1/ruPPkS3R5y/+Xn+9R96i+8MO155mPnJVywL52jsQJ8UxR9j0MzPHzKqgaTANTOcV/juBDdfsnv2HeJ4g/GOdtahU8/68mP6Zx+wnZaQ94zDQBx6xpCZFo+w3YKuWdDyjG0K4Ba4lf4XPnu/1e239YDSth6NImdHmCZKyUzVOlKsFX4xT4RJwjeUiux0xGqL7x2N3dNtd7S3HudvWBzNJYm2m9M0t8wWHU3X4GeCVBjTynTdeqGCnAwp1nqsln4RrU0V15qKptTmYP1ipycDir7bvcmxmbuvVQGLoVTXUDHljh5CCWphlOSDYA1ZO7mmW42ZaXRJUoddLaiUhAaM71D37uN+9A+g9v8aOY6o0MOwQ8dAjgkVIy5O5GmglIhNws2qnDBZ3ASlRNGijHvZeY69LCdZ3RlHDrtRQZBMpYtAcgnFYaOUkVp64ynWU4wjGcuIw9sF0whlfkz3Q/8Wr7/5E3KB9x22XZCUqT0jRi4mxqONk2HU1TAnI0OjtlZoljpkUHNwtNYSaqar+NUoqs3nDgUrta/mgH6oAw9xeIYHzUO++zZVqloX2vq9OpwcaJyDwDXXXhey8OApC0JCSuLaqI6LnBMxBkoU4XWJI1MITEH6VOLYM+33Uk0fJoaxRxsp7NrtdjJIW8nhGGIhFsWQElN1JCmt6dqGeddwcrxkuZgzb2aUXBiGkRgCQ5hIWlGsIFKmkQG9MQZbMrYWtYV+kFDEbibCZgqqBEpEdm0xSLokBts6vLWSKKuUUKvGAJ3UEhiB1XO1nqs6tIhQ9kVWysudQS8PKgdb8eH28sByZ2+u75pQPoWkEjmE6roZSOvnlN0NMfTEGInDlhCCJBUPt8Rx4PjkId35aywWK1rfYJCBSoZTSWY9eH9LTvTrZwzrK2y3IsWeGIO42MZB0n+NE4G+bsC1tMsVCui3hinscP01HkMKe24314TdDeRMCQNow9HROU3jIUaa2YJFt8T5pja0a3KeUFnqv9M4UeJE399w8/w5NxcXxGmiaVqwhqNKvxjfSIU4MjSP44Zw+V3S7Ii0OEeFnuuPvknQLW3TcNuP2Puvc885Om1ZWMvy7AHz4xVajaS+BwqGiRKV2IRr+WG/69n1PX2/IUyREAVNW83ntO2MrpVsKxRYa6peJ0DJ6BQZ+omL7Zb9Yimv9zAwxomQI6ujOQUwrsV4g2o8zfwc2y2xemK6fU7sb8luRRzls7VZX7O/+oS429PvR0qSktUQCyEUOuM4Pb1P13hugVVv8cs5r7y64He+1nK6WnGm9jx5uuGXt7Df9ry/gWgU+6Nr3jo54tUwEfs9DxYrFqsFR60mDDum9ZZxWmDsimF7y/5qw2985Ts8W7QMtxu++fSWpztBRdEBO1sImhQLz6eAOXGQ1vzDX/86ZbAc3aw47jw/caIq7RwoaUtpPHZ5zlka0DpW3aUG39KYV2lL5OZWks/zlCQ5Pffsbz6llFOcl2RnbRI2bAkhoe0Z7dGCxjekp58wbU5Q5v9PNCitr15XpdBW1wsTkvw3JIq1slNXUVpnc4IiEdAmZWxQtDHQjdJHsauQn3UbfNMymznazjKbtzRdh2vlw+GbDudabNNgfItpWqxrcLbDGsl/sMahjQiqjNaSt2Iq5KX0XSeIqjyUdJMcshdqpoVWVVxRUSADuhQwNfRNiRI966prKKWWwNXUFSX6HAVkrUnaQbPAP/iMWEyTaB+EQhCzQi4vDyMS6CWdC0GQnipg1QqalKvjJtafr74bdRhOZJeqtLlbViS7VwNVs6AKymjyAaZXioLDEClagesky4H6eihVo9slCVgdIPf6GEUp0X1gJMCs7lrVnUao7s6VRKDz0iKnakWBDCI1T0N+Uz3yF7rNA0UjOgh5dlR04JBkykHoWSDXNNKXh5LDRTUXQchSSqLtqO3AlAjTSA6BlGTwSCEQx5E4jQzDxDCN4oAKE8O+RxfRouyHHmMUti7wxhpiSowxErKmj4ndMIqV2xiWbcPxasasbZg1HgOMIchxZEQkaayIBNuOxjk8mRJGyrCXc7qZo5wj6SCtuXHChImQRaQ7TIHNdsO+7+mHIGnLrafzLUprYgzMvOV8tWR+ep/lfI6tFutce4gEnRIB1G8eQg4Uzl2OSv23w9ByiA8oqW5i6hB5QFRKzvVzlMkh1mGrJ/W3rC8/ZNxt0UkCwHS3wC7voRqH3W2ZzY5YLBc0jUYR5Ww/UKdJRLYpF1KCcRjY3jwjTCPN8gzfzmjnx6At1ntB7DQSVZ8mVNiTcsFbj83w/PE3mLZPmM3PGfsbcIbZ8Zlkrgx7tLKsTl+h7ebi0lIa7WcUbUXoGwep2AgDYb+n367rkKSZkiJ6obp922CsdH6hRaStUoCpJ/Y7Sr/ndvch232ie/AZ1P4ZV7dPGO2MVxav8eDNt3n4mc9hNDRpRE97tDJ471EYQqh28ShW5W2/Z7fbMvYjwzgxhIgqGmc9XatpGi+tu9ZX+79sCmK1b6MycZJBeJwmnl9dcxQGVkfHHJ2ds7SGppnRdQ3aWIxrSSWiKoFp/Zy2W+Fy4MnFt9nd3DIdSYng+uaC9dUNcT8whYT3LV07k7DNPGAbx/JowdHCs15v2V9Cc9Jy3lpKKIQQOTk+4v7SYNREaTs6a9jt1tw8/ZSv5S03H8y4nAqrz/4If+hLHp8Dtx9s+OWvfMwvfGDZmPtotWW/3/DhJxMfWUsMifVOPkuFAq7j9c8/5Pe+cYzKhuc7uP+5d/mRV+/z+IOn/I9fec7/9nHHg2Xi/juFe6dHLI9OsCaQtcfbBoWEPzpt0aqpw5ynOEcJG4brxzQzB9ME7oR2JlUTyhg0QuMaHYm3j0mr18na0c0azPpDvvHkGSf3Hn7fa/xv6wHFOXtXDW+0unM9KK3BKHSKhHEij5PY2WIAZCHICUqWi1kYA95qBqtpm4hxAd337HYGZxSNa3De084aZouGpunwvqXpWlzTYLtGslXsDOs9xkvhljINxkiBoTYGY5yc0EacQS+nZ5oafqQqjYCiulgkJ+TwPHNFJF5YI3MdJA7VeXXXrrSErKXDHWWvYr3Gelsv3NUJggwdpg4U5eB0KUUcA9V+malainLQAYjgUFdNymFAPGgByiFlTUlaqSoGjZPjVaneS4nStg5nBkUxSf4skFTBogUlqrqCUpTof+4AikoHKMmqEXFsPQz9Qq9wdzsMJfmgC6mDRanN05VqkS9kgSzq8JwOegYRDb9AUWrj8kv0TMmHVNtyh4LknEi5upZSIEdJio21LrnEIK6NNEHJhHFg6gdKDAxjzzgJmhFjYhwmwjhK8WOJhJBFV1QyIU1o76U3SmtBW2JhTIqb3Y7dGDHG0rQN88WMWetonMJZOadSLsQ0Saih1QLvGyle88aJ1XTYkbZrylTwx2fodo5rW3JKDDeX7C4u0LueXYDrPnG7H7jZ7xlSFMEhhm7W0vhA27SUPOFjz+31JffGHvPoVaxaQIEUJVzNu4ailIRBWQO53A0hh9vLFmN5uyutCiJIrkLZg5srxYP+R35PGEd0HCH05HHDbhjoiwe/kATXArP5Me7sdcn0WT9D6RZvu/pZkAEqxgmNIowjU5jkPUZEyHp2ijO9ZOQ0S6zRd11IKdfBMI4QJ6bttYjlZ8f0tx+xvvwuw3gGytO2c85ffauG3mXYriEEmvkxuJaUC05rQWRDIAy3hN1T9sOeEBMpGdGrxADtKf7kmNNjsHGHjjsgSRbSOME4kqeBMO7Y7bfcrPc8WY+w1bxSYLWc8eidd/HzJavlis5J3YgiUcKO4fYSNU7ymunCmEeG9Z7YT2xCz3rsiaXQaodWCm8tCkXjPYvZjKZp7oTiYxjJUdrYpylIgWcIlFJwrsMu7nHcndG2jtlizqxr0d7VElnIWWzujfHEfWDaf4pzlmnqGG4v2N5ccXF9S39zjTOavt8Qh0jBYJzC+IbV+UMs8PSTD1nvdizXN6hxwLYOpyf6dc+Fzox94e23Iq+Egg+J5qjlh959i+OS+fZHT9lt13zr4+f889sdmzyj2Zxxb9bw2O14/zee8U+/NfCtfYOdv04Z16xvPkF7mC0b2m7OonjSsGXMCrPoePOVBb/7HcmsefZsxCwDrxxpPvfWEZcf3/D00z3/7L2GR13DH5w3PDRzOudwhpoM7AW9p0AM5DQQxzVh2DONPf3VY3RcYJRseBeNhxIgTZgwEKcrdNxSLp7wyW4kNF9Glx29mqE9/Ivk6m99+209oFgrzaVUFbqtKYU7q9GjePaD81gfmMaEmkamOIjALQoPWVIBZdAFvLEVzdASjBUyISTGIYiDZ61xVuNmHuccbeNpm4Z21tG2Ha4Tmsf5Bm1dRWOaSj94zJ192WNsIwNIbV4WHYu6+7tRNY6/VtFL1HeliVR1H2glAkOS2Mzugq6KDDyINkJRUDlX2idSsChl0UbsvignVAyVQkJJkByHwUZu5iUhIXU4Et5eVvWX8wFlADh8UceDUl7YNA620MoJ6Rf+DZIqNSFUqCCLItbH1S8d0wGTUYffk8UJo4FY0ZQXB3F4HLGzKiU5J+UltFHQHbmfIlVKLYn9uT7God03l1wd0RUVqS6cQ7EeRdJfS5Juj1TqolOr6sX6O5LSSEmJGIXSSdPINPTEaaBoGIbAsNvJfVKUi3GMGA1hkkwJozMacTygDb7xzGYiGEQpduPEEBL9lNmFQEjgjMG3nqZt8E6SPb09dFZVTVXjaJsOazRtO8NagzWaFDJ5tyFeXZOmCbs8wS1PMN0C7zypHbmZnvD0yWO2ObNJhpvSMWhLcB7dLGS4dx6aFlsDwLpG4+JIuLni5uY5rkS2rcQAdPMly+VKaBMl55epuRz5btB8ce59D5WD6E64o3/qoE3VvlSB8h2NlhMq7rh99oHYf+dHNO0xEMnrZ5TtlWgnZkcorZjSCMVi9RyNI8Q9/XhLSEmQgilIH0zO4gBqW9rmNYiT2OCtETtwjqggA2uJIyns6fdrbm6uUTePObn3FtoUHrz6eZrZMc1iQeM6lKmfgwxZGcJ2izJa0qLHxHB7QVw/oyjY7m5Zry8YQsS0c7rVq7ijhxD3WDOnO32Imy1ge8nw+BtM1xeEcU2ZAv12ZLcbGOPIPkZGO2P11jscPXqDhw/us1wsa0SDwjktNm1qVk8yWOuEHlvvGcOOT58/Y7Me5PPrGrKxeGOYuZbWSdt8IpJCEuoGMMYSCYwp0Pc9IUbpKdOWpAyua2jmK3x3zrGbYUyh82BIIr5WipQicuVMpGkirK94vrnGXDzjaPWQ/fYx4yBmhHG3pViD04Zm5hinnn6YCEmhaFBFUMwnl2uUmbOaG4p3vHKmeXoz8fjxhstuyWrlsLnnNg48evUB//a7ZyyV4XOffUBIsPnoE37xF7/J155sWX/wbX7x0YKfOJ7x3Wcj39nsKFYRVYedvwnbDfv9FVp5VudHHD88Zc5AqzKmc9xzlvU6YH1k6iOffPPrXPeBL53fY9kG5jbydO/4he9Ejtsd/9ZxYpUUpsnVhDChYibHPSH0hD4w7m/oNzck3WG6E4rVFCI67lFxS04T5InSb4i7W6Zx4ObxY7479myOz5k/eo3Za1/i8zOHCi99WP9Va/z3fc//H7wZc4DfZQF3RuO9AZrawluYQsC6iaYpNGnGdr9jsiM5REpIsoCUgtEG7x1N6/Cth6KJaaKkTIqBEKpDgILZiSLf1YHIty2ukThj1zh8hSJ912J9i3UG7zzaik5CW0FZjHUUbTHG462TnBWJj8RqizMS7a2MRWkrNfJ1Jyj9Qe5ObCuGEQVKEvxSQXbWgNAusphW06R8r+ZOKGrk62/STXxvaLq6s+cCdZYwtYKe7xEnHigPpbhDWgR1+d5grTsERVWNwYFGyZC0vC+6QOTQrlJeDE13moMXA0i5e/zEC2VBpWcOQtSX5vcD8nGYeHLJddCRUeUwdAieLEMIRWrGQdp+U67nUErkEmVXV/MjSi6kIHHySmdpuo2BHAuhpm/GNEqA1RBksMmZYdgTJ6EFlFKMQy/0A+K6iiGQitQIECeUlUHEO4f3nqZpEEohM2Xo+8R+CgwpUYyhnTdy/raeputom5ZZdUI0vhHE0DlM09F4j1Ea7zxGa2IYKbsd/c01cRpw3YLZyX26xQk4J9ZpY5lS5Onzaz7t9wQ3I87PMPMjVCPuKeM87bxl3syYdw2tVhhnRORsHGr9CTdXz1HLY2b3V7TdAt/MKrsjKNFdxDovzr+DAP174/DVXdhaKYeBpp7vWejNkkWgSYqYEgnjgLWW+UwybLS2ZCAZQ7QO7RrapgOjSfuWYXvN/vYxtp0zpJ7d7oaCRds5xneY1kEYsLahNHNcMxeHzeYZZbiVKPgwkcdb+u2eMvXk2DMWJGTy+BHtYknbzWQTY41QeEqTiMQcMZiqpwrkfs007ulvL/j4g29I+NZsTkYTs6B7WivU7Jju+E1meU8aBlw3Y768R1aG9Pw91rcXbDZrxjFxO2mC7ehmMxpjOT57yNlrb7O6/ypt19DaWgIKkAMlT1X0LU6o/c0zbi6vmPYDUwoizmzmzFonm0tnMRU58dahtUdrxTjs2O/W5DThjGMce653ezb7Htd0NIsl7fwE287ROtF5jfcdfn4sYvfUk6dd/ZxHShRKaNxtydPI7nbL7XpD0j372+eQC2OIWGfuNl1TmDBaMpPWu8iwvkRrx7JxJG3AN+ynkbaVLqrzpafRms1HW9bjNRePW5b3W+69cs6ynTNPGazmzYfH3Ju1DM2eJ7/R8I2nPdPNBb/01V/nwZd/itfeeIUfSo/51ccTGktpT1Cr1yn9ms3FBSVM3H/rs7z9+bf5oTPwU2IzPOfJRxvGlBk2hY+D4Vm+oGs6Xv/ht/n98yv+1/d7Lq8tv/BU8867HW9Yj01J8nHGPcO4h2nHfsoyGJWCQRFVQLWnmKMlLRvUpxeE3VNxg46Bi2fPWe8DQ1FMecHp+Ssczxd0i6U0bTeWYTP8v1rWv+f223pAubm+Zd61HFIldSloZ2mcRXb5mtZaYtNIUmdO+MbR9wMxZKZ+qMLQRFKQlBIR2CEroSRyGkjTKJ0ESXhprVwVwSqUqmFCzuHsjKZp75wkrvE472laQ9MYfCNUkbYGU7lUZbyUGdqmOkcsWC9hTq6p7bTSG6SVlqh3bYRD1RLapK3Bavl3jJUK9wNdxMtFbJU6qkPDQVDxm4OsDhf2w+2FEPGFQFS+URf5GlfOS7qMw+7p4F5RL/2M/F9+kYJ6971DvP2LYeag987qBdRxWHzq0b309Us0Tq5uGF7c7+7xSxG9Yh1AXoSDHfQK9aKaM6REUKrqRWI9JyK6iAU4pViTgA9290hKEzlOaGXF9ZIjSsM47IhTLxRNSEzTRAiiTQkhSEdMjfynuoKcqo3QJRHHoVr6kuTgoKrYWMk54BTWiVWzaBHkTlNgnCSp1rYeugY3E9fErA4zXdMwa8XmOe/mgqJoIyJVW0sIcyENA7HfMt08Y9pvaRdLFucPcatjTCv9QTlK9kZOmagdsXiZAcaBrBVOFUyZ0DiUGjFxhNiQlKZYTSDRKWhNy+n9Bzw8v898JaFoWUkCsNWGTJFSzbsl5MWJedCkiAD2QAuWl6idKpalSEBeQWy1KWJKjw4DncnMVsu7ckKhGzXMFpQ0oVLApIFSHFO/48mn38G3DcvjRxKaqMUGb4zDtkcUZyjTQOhvaJTGz04gJ8Jwxf7qAsJADpHt9pYhBLRK6JKZHT2gu/cZmpMHtLMWY2uLc0GcXHFE64xGEcMAccPu6kOG7R7jZwzTxHaqdB6atvEU3WJtIWmLtp5mccq8fcj2+ccMQ4/Va/J+w9VmzUc3O2K0GN3QnBxz/vAN5osWR8TblsXqiHbWiaOSVGP0M8QgeSxhSxg37G5vefz4KVf7SNt2NH7JqdNoq4UeN4hGkEQKEzlHtHGCSpbMdr/n9vaWpu1kA6YMs+MTmqZltjxmtjjHd0dy/7hBqYyxDa5bUaYNOewY+1vGcct+c0vfj4xDoGSYhkCMBUwm9WO9ZskmbjokL6eIrlECSVn6OPJsfU1ezNHWcXqywjuLNroO9YVV27DeKL766ch3Lq9ZHj/gM288JE+Rm2cf8zw53pzPiJPh6nbD7TRI0FkopI+f8Q+/9s/5d3/H7+JLP6QY+IhvPBvQZo4+ehPX7wi7r7F+/AmDb3jw9iucnyxpQs/+uxPffnLB4+cT+zETmyX3ujkXVy333jrm93/pAfP+CX/nqeGZOuL9jeLHdzfM48A0XtPv1oQcSGMgZkVYdizn9/B+Trr8daYywOwt2mhQRrG7/ZjNbc/NRvE8ruCVdzk5nnOye4Zqlqy6FY1t8U0jDdc/wNTx23pAud7s6McJhcIoxaxpsBT8vK0BbgLtOiMfhIxCl8LMO0LIxPlcYPIYpYdESzJiDHVXlSKxH8gpMMUgVuZS0PR3x2CsDA8ogzVrnJdoeHMQy1rZ1Yod2Eo4UtPgG4/zVuxx1uMbKaHSTjQrzhpG7YQacr5SQVqGD21AyyCitPwOUx0rylhQVfNiD7RRxRP0oWRO4FddxBKs9MvuFHWARIAXDhzgrmTvoHU5DC4myA70MJCoqoPId3XBLyMeVCLlkP8Cd0NEQRws5RByX+CutO0lPKfU4YJc0ZF0mHLISRwpd70rqtIy5aUSuTtdiDyW2HiF0kpZNEqUhM4JlUWsmnKu+SP1+Gpia8mZFIWyUXWwmaJkkGjl2O12hBBx3jLs++8J4EopUlKp1uU6gCmN802V5his8cSaJFoyULJYQmMkHqi8orE18RhtiUoRCwylsFegFnPmbYNtHKbr8E1L4xpmTUfrPP6A8CmLd2IZvrNFK8m7KHFH3N0w3F4xbq/xiyPmZ49oju6j/AytjQwlORCmkb4UhuWK3B5hSu1P0hpLxKcRN2WszlASUxnRrkVrhy2JeeN5dP8Nzk9OaXyHa9qqM0m1iKzm7xwovPK97ccv552IWDW9OB/qTWzGL86pQsGqiA47KUtzGqV8RdXEOl6IaJ2Zwp7+8hOG66fo+Qn7caDYBkwD2mCbDu0bKFroU6NxqzNMLoSrxLh9jnNLsJph2HBx8TFx6DHF4G3H/OxVfLdChcism9EtTvBNQxVH1c9IudOppNhDLsQpsN8859nFNdN+xM8DTdNx7/4DnJdNlfeeVKDEiWkcyNvnpOGGwZ4whVuePn6PRbOi9Z6pmbH4wu9i0cyxYcAoxfLkIc18iYk7KRNsHJpECTJE65wp00CeNtzeXrDfbQhZE5LGHr/K+VGicYVWN3inQCUoRj4LoW4KSmK72zNOtxjlGIY9u34kYSm2Y3F8RNN6mtZRAOsdrim4xmDdijx5pt0F4/5SNjrDlt3VU26vnxDHgf12xxQyqYjjUmFpm4amaclJcl6mkhinSIxRgiRzRBlH066YdTPa5ZJCAlXwtjCrVnKjDCpGKZctkdeOWj76ZMsnN3u+fTXy2v0FJ17xK083XIwG/dFHmHv36ecLHv7Q67xpL/jwoxvWw8iz9z/if/Rz/ve/40f4Pe++jlOP+ebziaId0/3Po6YR9fxXGZ885qPvfsj6tS/wznnDG+dn7PaRJ7fPub7eS/hk8ZiSGLZPmLcLFnlgee+Y3/H6GWkb+Ponidfdx+g4kYq666hT4y191qTVO5wt4MQmvvnRr3LVdLRWs9nu+c6VIaYjvE0szh5w9Pa7nJ+d4scnpP2EdQZnFcYW2eAfSta+j9tv6wFlHCPaGImrToVpCBgyx95I3kURblqh8Eag39w0hJTJjXDUgoxIaVaaJkiBnKUcLIyj2ApDEG66SBx2VvGOwiCJQBE0KQ7E0IN2ONfiUkfUiWmcUOZFoJsECbk6sIiV0juHc/KndY62kSIt1zSCitRBRFkjtrqDRsV6+d1KKCRjXR1WrIgIlTye0QcHkfx5MGwqXe6cLodQMZQgGPK15i4HS9W230oZHf7U3wOr1NtLs8wBoTj875D/Id4cKvJShadSeiO/oLpH5C4Hi67siHOudMvdoHMQOgp8nXLizlmTX0ZK8t1gIj5RgflzFqFxzELfHIr0qLRLTjIYiJZVqB3uhK/I+ROEPppSJOWCtprdbs80DjjrGIdBBLB5hBzQJVd6zmDqgto0HU6B9YLkxWlAxYjOgZIl9bLkgjVCXRjjUdqR8aBbAkaKFxXQeNpWYxtJc7VWuk0639I6yfXR2mCNqpUOHoo05yplwBRiLuSwZ9pcMVw/Y9xvsd2CxfkjmtU5rlnIuZOyCINjYBwHNrnQL49AS4AgWIzRdE3L3Dkaa2mdY9F6dCNhbSYXjtuWt87PmXetDNa6hvIh2T9aq7sMk0Pq64EA/M0U4h3J99K5d6B8SkHyVbQ4rFSeMHGPzUEE90oLkqZV7TGqSMuYGPuB7WbNaCYaO0e1c04fvYWzHtfMwCpy0VA0Oe5Ro8byCOc82s94/MEv0vc3dMt7hJBwy0fMjjSkiUZruvPXcN0RZQooCrrtpHspi4ZJLNsjceoJ/YZpf0vCEJkRssMtz2jaiGscxjXV0RSlZmLaQxpk4N0P3Dy7ZD9Flo/eJmyuGYcR5yKz9ogHy3d4aGZidx42MO4kcblryQFsjFjTogtSFjjtmPbSpj7FxDhlUmlQ1jFbnGCbI3TZo8ItOkUSilKiNDOXRAqBcdgTw8TtzZqb2z1N27JYdpyen2B8g2s7mtkMb00tGa0f4SmiWiRLylr2w4brxx+gmyNUifS3T9ns9pSQyFFhTEtrDJBJ2eCcYz6bUVJk3/ds9xLlr7TFtnNyDNLGXsBaR3d6hnEtLvSM2+dM0ySoFKMgoq6hz4k0bXnQTuz7hqEvjENhTSQGYIqsvGY573h4/5QH9x7wmUcf8k//ydf55feeczP1fPrNb/ELyvHTP/4FfvJHPOYbT/jWsy3ZLdCn78B4Q77+kKfvfZNfOnbYL5zDWDi5d493rUXZjilpCD27MGO8veJXfuM9/tlVyyufuceraqQrC77+VDEtFa93hoXN0r5tnDz/3SXELYojrF3B42/zSa9Rj94gdg9ZfPF1Gq3Q60/IIbPwiuVsjp0/YmjWoBusm2EoZC0o8/d7+209oBgjO6GMhPHkUojjSJoC1traeCqwbkoaawzeOihR/p1qxy2ZITimwVCCo+TMMEkUPRqKtmiXxFKZAhRJDJULWamx3aru5AzONWJDdg5jnPSnlCzpnzkT65R+sEKi5GLrjL5rhW29kcbOtqFUB5C3Dm2UDCpK32VbaK3Ryr1wCtXhRFJNpaFZgoxcRV9qAFldAISqMi+smwpQuja8IlkhFFm4qCFld1RR/fMOZnmhBVEl3+1RS35BpRxi2l/OtDhYmmOuC0dJ1WVTIfba4SO/vCJcJcmmshbi1aORZNWM2K1joMR4d2y5FoKVuiMvuaBqK2CKw10kesmJcRoEXUvUVukoIWc5kaqw8nD/lDIxyjHEnJjSBBTGsSeGSeL6UyATa4ZNkaC6Yir61tB4Lw6HGIlFzktGGY5TFE2L1hplNco1EntvHNo6mm5OtBblROPkjBJ9lJcQK+88jROUrbFeUjuFh8Aoef8PgWdFSby4vNSBPOyY1pfst2uabk539pDm6B6umdfF/kCbFEK/Y1hviNrQLFdQJApAG48xhnnTsGobZs7SWIdXlphHZkpzdrTg3tERJ22LcpKLYRSyU9dy3pWivgcJUVpJhD6lWrmR0mlEpHyYlF/QnOXOwp+T+LDyuMdNO2yZaniinBMlVft239fYakXGodtjurM38Mbj5iuU8xgNOot9XZxZAVUUatoQ9leMtiXPzuh3awqFELY0ecXs6JT50T2x0ve3qGmgXZygfcekdlAzh6zSpDQx9TfEacc09qQYmIaB3eY5SXv80RFmfkLnV5hhg81D1er0MpjEkRIH4tQz7Hr2+8JFMDTdBX55Qjdf8sbyR2i96OmU0qiqKwleE7YKg8ZYEaTm0EsS6TYzxpHdsGUc9xTANEe4xRmdVZQY8c0MPztGm3PC7hnT5jGEEVUyMQyM2w3r9YbdrieFQEiFxXLJ8njJYjnDtx7feAlb1IoSQSkr14I8Mo5rbp9taecnlDBx/fwZz55+irZSNqhzxOR6PXcW5+d03RylFLe3V+z6PdY6UhCL/m7fY51n3nW0i2OU9ey2G4b9jimONDjO7r+FzzsuP+25ePKpIJkq4rXCzlZkrchYFnPNaZq43l3yje8Ejk8WKKugjNxe3bA/WjFvPfeXLat3X8XeXvPkyYbr9RYzDHz89W/w35XIv/PjP8RPvvs6bfeUX/l4Q3+0IuUvkIcNw8U1v/xLv87Fh8esHjzgd/zY67zeBkav2O4T3meaBmZdR9c27IPiZNjy5L33+Wp3yh/40hfYcZ+b4Qmd26FNIBFpjKN5/gHP31c0b7+L320J9z9LM7/HbHmKaea0jTSys18yXF6gG4X1jkatIO2IIaJTwJXIGLZM6+ff9xr/23pAOVrMUdYwxsSwG6EUwiQCRN91OG3qRTZLRLaVkDCtZHDx1pDzJAmkDkZjSFF2xCoEmvmCGBNhigzDUKPnR1RJKApG10h5re5C1IwxtG2Hb8WKrK29QwxSlIWtcNDsyaKb6g59KDWmXdWwNwva6ReDh7KSYmvE3eOsEWGhtRgS2miB+ZVCWzlJrH0xdIgTSGL6Bf0QikCbatvVMuwcskCMMeQilk6RrEiUvzqgMVWKqrW5G0DuckYOg0qlCnJ+yZJbF/dDh5EqhVR3uuQoC0h5oSMAeX0PmhKhiMXKG6uDxlaEbBonckiYxlOAMPTEaUQXoX9E35ElSr6KXKkajhCDfJiUpZTCMEyEGGWhqaLMGGTYKSWDtfWYheJJaaoDWBCBdYkoleTfVam8+ME2LkOmVdKibbQhA2EMJFNfIlXIU6rvnaNYjbKSUZCUrcGAGts2Mph4g7Ue7zpB4oyVzpXaxG2cpWhpbhYnkMVqXWPPDRldm4arxCFG4tCzuXhGf3uDb2fMzx4yP7uP72bVKRNEN1NEwzHuB/I44W3HqlsxBaH0sNIQ3jpLYyxGKyiBKY3MveHN02POlgvpetKBQnOHYkjVu4L69aEYMOeMRp5bqYNqyQVj5DyP6QX1d0D8Sili4625KsSAHnY0aY9xhr5k8jiRpgQpE9LIOA7yPmiw3bEksi5XmCiIbUqCHKUcBb2LIyXuKCkS+pGb2wv0fsPpKz+Mt4r7j76EbeY0rcc6RTaWEuW8TmaBcUc08yU5R8brS9L2mmI6prBlu33OftyQssXNztDNA9yiQ407rJnTrl7DeUtaPyFcvkfaPMPmntALHX57uxdEt0TU6hXufe7HmD98ndPFinkr6JsyUTILiwaVMCmSrSYaRxkD4eaGEnsun32Hze4GY1pMOycbj21Fi+Fth54d03RzchwZhwFtNK49orGK9fCU8faS0I/sx57tds9+mHCupVusKErjmo7uSAYUY2sXkzokWCdiGskxEvst2+s1F9dbfNvRNJYwBfQh7C5lQLSCpUjseh4Ti65h4Rc8D4GLyys2ux2NsxQK3jnQCmMbutkR3eqYew9f5ebiU54//Zj1zSUPXvu8hJmZjCJSoiZp2IdEU3oWS8nKianwUA3k3ZrHH0WCnfNDp0tubjZ856PnqMZC26JPTvGd58Fxx8Nly3s3O/axYMuWDz/4Dv9T1/Lv/uQP84d/97sc/9pjvvLBLdfmFabxc/QXv87w/Bm/cdtzZO/xedXy5pHh892c2O8wruP8geXN5Yxje8THm1tujAc18vGwwb7q+Wy6z9NPN1wPG+7NFEYlwrihv3zKx9eJ6eScV2crzj7/e3jFw7ydiczAgs0FpR5Ks7nraJym0Qm7mXj+7BOurx7TWtjtdlxc33zfa/xv6wGlU4bWNaROsTGGoY8kVYgpoVRCaUsIGVUUOhZCmshAHCdU5ZhL1XN4VVDxoOFQxOQIKRErQtNPDSkkwjRBHCu1IaFmqu7EtZLdnPZSMGiUrh0k1QjbyMCUSyHFTEoGCoRxIqQofHeI0jSLCCYPehB1sBwbhXOOUkQQKWiJxVld0Q5Rwxey5AhYI4ODUhglNlGtBdfQWr0QAVL7c5xFW+G6NZLsWbKqiIqqqAsV9REh5YHG0eVFBkUqUHLEGUcqqqIfLxaQQ2aINpLxEg8hb9TOojo8HHQGaUq1EE8GoVRezrBAFnnvGMaBOI5Y7ygoxv0guRNKBpSc013c+uF2kMGoGmxnrdAt0zQSQ8YZKZYrVSMjsfNSrRCnKoJFgslyTtVlIpk7VI2PZL3I8GmN9NKUXIe/ooTaQiBk6dmRY9PaoZwBY8naYJtGEkGNIGzOWeGLnUDbbTPDUCPW0TWVVESlCRj7SN9LsJNvCrNOdCFYEbwpsoSjZcW439PfPGXaXeGaGcsHrzE7Pcc3cxnalKLUnBhyJEexOw9pQFvF0mmCUzjlUMahKTirsGXCBOic5XzuOTk65mS5onW+Lj4HXK0OzsZWhCtRVMYd6NsiSBQvSqNqkJugXNKbo2r+kcYaJ/kXWcIJcz+it0+IF+8RTUOZnTKmiWHYE9KAVh7bHqGbFqN3Yk9V4NojjG9R28eM26s751aJPXnYMYwjQ79H5wCmpTt9E3dyn7b1NNbjlyuKEipNws9qsJvz4jYJW8ouoYcN6yfvsb25pplJgWlUipRBWYvqTmhOHtIqz3j7MUlltDO0yxOKtezWHzPcPGG97hnHkSFlpvaM7nSJM4nF2SscP3qF2fER3sswInG9hwiATEmFiKZMW8L6KTfPPiWliZgKt/1AVorjxZzZXCgY7a0M5SVLN1q7whuDUk+J+wvAooYtU7/n8fNLcpCNmNKexcIzX57Szo/I04DJgPVoZDAFEYrnGEnTyH47iN5pv2ezi+xCZow7SvIoq2mcrSLqQilSpLqPhX4nkfmbPnE8XzDESAJ2Y08xM5y1LLUm6SJt3/0Nzdk95g/eZjHrGHeXfHr1CU+f/Ab3juc4Yzg5OSPEiE6R/bYX+tw2WCe4bswjXZrYZc/V+or96ozjozkozUwr7H7LWCYuHz/j//H1J1zsB5RuMVqugc29h7x6/5zh9paNhtePPP3rx3x3f0T/YE7+TsPz936F7W7PZn3Dp9vAl19Z8pDEJ9+55L2na0J/QlzesL9NhKsBt1zzQ+9+kd+xWvK5ORwN8P60YD2s6KYP0HHDMM7ZPfhxXnn0CufH95h3Hd46nJGgTMkVEl1j8QrfQA4D03pNawf2zz/m09/4RfZjZv76F7CzJenO+vCvvv1AA8rP/dzP8XM/93N897vfBeCLX/wi/96/9+/x0z/90wD8/t//+/lH/+gffc/P/Lk/9+f4z/6z/+zu6w8//JCf/dmf5R/+w3/IYrHgZ37mZ/ibf/NvYu0PPitJvXyiUZb2eMHQyAfdekNMBd+5uqBEYoqiIwmFYbvFmULTtujOo5ZKLvq23L3gWimclf6MXMA7R86ZFBJjlk6OfBBMFtndq9opkpViDEnipOMkBYPGyGJsJGDMuEwyhRAjxhliFItqjpEUImpKhLogAxyi8ZVSjEZOCOOrU6j1TN5JiqqahMYpmX2pKauIo0mGEBk0Uk6yq9bmTjiqjMZ5hzIALwYUCYYrGGUkvbGGn6mClBZaU3UdLwLcqO2oWimKtpVOCVBeQPIxxkov6TuERXFI2RXahBo5n7JkioQpkFK6+7mpUh/OyYAyTSPTMEjiqdZMY6xUnwwocBClSo5GfXFlaFOy6MVcpMlayUCILrVrKNcQtSiumhhqmZnokHKM4gDLBYiin1BKMkX0C0HzwQqbi+xSlbZk7ajqF1AG5SW8LzqHb0RHYJXGOUlztdpKS7C1WC9R/sY6vGkqQkOlAR0pK8YxMPaJ7S4xDNJKvVhmGl9ISqFKpDViG481Or+//IT97TXWeRb3HtIcneKamZxT3+P8queDBt14TDODMeCHHfN2RuMM3kNOMhg4o1nM5pwcHdF5sThrVbUlStVwvXJHaRmjX4iniwylSpU66MniJedEfV1fdvHUdmVBzeIdChj3A/TPGZ5/yPrDb3GrLLPzVxm9o2CgpjFr61DtHNW0pPVTEdM6j50dg4bp9hl5/UzQo2FLKoqkW3kxSsJ6y+rknObkPq6VBUerRCpZqE6K9G3lSJl64tXHrHd7TNMSpp7tZsN+jOALrpFW9cZ6im7IrsF0x3TdjKb1bJ59BOtrUjMjDbds1hs+vdyjise5GW7ZcfLobWarJboUnHbMnaU1RZqZD9qwmj5NjOQQGceBfn3J86cfcnF5geuWzOenHK8eYJ2j60R0ralRTFqTYiaMG1x3itZzvILri+8y8pi2admPI251RqNbnBUqU2tF2y1xfkHqGnJIHBKyQ5gYxz3jbsu037PZ7Nnto9DEWjq0ZsZiDbJpqruOFAemKUgoXYj0KTPGzHYq7G8HEp7Oa46PFqAU1jlJjsoZmzJjGFhffMqkHbPVOSZlFqsTlmMk9pcMfkJbRbdaMCuZNOwI00iMAaOpyHlmu+uJRdOqwM3lFd8YJ7JtefWNM46IXHz7O3zt2YbvPon8xvPATe/QFlSZyFnRjCMPwoS6Hvjqsy36+JzTkzlqobGnb+DePOXb/pZvfOWbrJ8/5dP3H/P0vuaEzNNP9/zqNx/zFfWYeQNhyFwOBrNNtMsFv++d12gvFM/GwLcuRj6+baEtPPQF20W684fcO39IN+/wxmAPVv4MGYsUmWayzjS5Z/vJN7neR27aOft+x6Z7HU5mtMcPWXQL+vb/Qzbj1157jf/oP/qP+NznPkcphf/yv/wv+eN//I/zK7/yK3zxi18E4M/8mT/Df/gf/od3PzObze7+nlLij/7RP8rDhw/5x//4H/P48WP+9J/+0zjn+Bt/42/8IIcCUGmASJgSM2tYti2hFLQ5XAwzChE3FiUX6XDbM9ysYRrx3tAtZ3RnK8z5MdnKBdoWSeREiQuoVK1JyQaco0P4/1Ik6CwmWWxTlK6aGKuAsmoXVBgrumJrgFrd9SlJJEmhukEmEYzFYURF4dDrcg9KaAlKkW4Z+SSiSkRnjVOeF4OBzKg5ZYipojbUvhPRfaSqy1Cq5puoyuc7J8iNSGpqZ41EgassaZ7OV5QpBKw22LuagSpiLEWQlyJaEGMdKYmWwlpbh4VqM67r3CHK/JCcZoy9oxpE0CsIRq62XF8H2lC7gpwRzU1KURpLrUTpx1jHHiWVAUqJZFOpQjHyftjqDFFG1UTfuv5mkQTHLENOnCZiGGrGSaREoXJySjX994WTydT+H23sXUCfUQaj5LWrSzCpGHLWknVjLbpRFKPRzmEbjymKWdfd9Tt5KzZ6jQhejRYUxlorTi8lw0sumVQEAQxTYrcdWW8C+31AKc984bHe4BuLtU7stkqRsyb0O8br5+yfP0HbhsXJOe3xmbQ/a8tBPA3cuWNkkFC08xX3X32TRd9TMHTLI9q6eOWS0dZKGq33NN7TWGkFL1kWSdE7AZQ7S20K4x0KmIsIXI1W3zOMvNzFcxBNp5TJKsnmAlXpu0KeBtLmirK9YLffcYvCWUdBM2u6qjGjtklDNzvCN3MZ+i+fkpvnFN8RY2DXr1lfPEGnCHmkPXlEd+8t+fxsn2IoNEYz0xmVJ3KJkmFTKyZMLqRxJIae/e6Gi0+/zuXFLd38iOXxKfOjY2bHBmMaoWKd0HEFAySIEWNbjMvs+ms+ef+Xmb/2RVzbEdpjFj/8b2BnR8wImHFH282ZHZ+ig+TuKOdJ2oJKlKQoMUAKhGnHfn1Nv98QgCk7QnfG6pUTVrOOhW+xzgttrrh7D2OaJDxaRcy4Zv1kg5+fM/S3rPcbUl7j7D0WJ2ec3HsFXTSUCb3vCWHCmA7XzIhTNS6Me/r1wLbfcb1es+9HSioSjpc13jpa4+naJY3xpDIyhoFhmuiHgRACISS2w0hB4bs5i6Mj5ieeUsCXQQIIdaU5U3pRrZATmkQYBsann3B5fMbZ0tMtLI/MEY3z6JykZkMHcg4M08BuvyVMUjuilaIfBkrtY/Jqhx0Vn6wn9s2Co0fHLM8aLr77nP/7157yycYzqhnOelCZVreMcaJ8/AFf9Zm3jk/ZJ2jmx7xzr6NcPGWcOj7/5ue5xx8k7ff88+9+yIff/BZ/d/uck9by/OktH1wHNtOIqWh+0Jpm9yHPtwOffOcz/Ohbb/Dg9CGfPkt8LXe0fIafnN3wziKy1IVZM6MzDqqp4mBqEA9DRseCG3r2Vxd88N33uO4t56+9Qbd8wKOjFt9YZp3o4aIK3/ca/wMNKH/sj/2x7/n6r//1v87P/dzP8U//6T+9G1BmsxkPH/7Ls/b/3t/7e/z6r/86/+Af/AMePHjAj//4j/PX/tpf4y/9pb/Ev//v//t473+Qw0FZWcCctsRJuOWQRrz3aNWIXsBo4fKzurMnxn4ibHf0OTPc7BnXe7rdiD9boFtHcU4g7yQ7OIzGUQhRMhjaemE8WH/HEAiTCBtDitIAnDLqIKiMEylnYiyoIEMMSkSilCJOoFxREsAoQyagDYJyHJazAllpuXBbjXWKrnO0XYtvxMGTSyZTI/xDIEWhpcSFkilBrLM5pZpeWYcgIxoF31SYtg5SOUnQmdhiqagEIlKtqZzO2Jr7IJ03uShCTegsCGpUciblwIgMRakGZhUglVKZLE3KL3pXDlpIrQ2oiiaVgtEaq1WFfWu8e7XFQUHFUKkWjeHgdtIvEnu1Eh1SdStpLcnBB0GuoDOiM5FmZnF1SVaK6GamKnhWmDuxszrsLJSStlfn5SS1FoylaCOhTrr2BdWhCuRr77wgbMZgG4fznoU22OriMsaJdsp5eV3vKDqx0Su0UF9GRIQ5JuLUM02FaZ+IMdcwwgbfyFmVY5Z0W2UYI6QxsLt8zv7yYxSG+dl9mrMH+GZR81Fe2HlfFqtqLR0cqjFYP+P8HlAy2jZVOyL3bZtOzuZcaK3khcjrzJ0Lp5QiyJxzdYGJdwhrQZq1jdbVkSWDiTGmDkuiDxPHTsFaJZqTAjplCHsYrtk+eR8zjjS+4fyVz0n5p23xVpHQ5BygBMKwwxqNa+bMjs95/u2vsLv+hOM39hhrSMbjzl5DDxvceC3uo24GvkNZSP0WZSSeP+eAKhOmREqYCGHHuLli2G5JxhGUxxy/ylFzRucds66lICiuqWWjCUE0TeoZbj6gX18ShzdRSnPbb9jGiTYMzE7OmC/nGDdDG4cNA2l3DSSa5gicIoy3qDThxoZSIpvtE3a7vaC5BfowEZPCNXP80TmzdoEvo8TgK1dF1WImKKEm4eZISiNx2LO+veJ2GJid39L6Bef33sY4jW9bvK9dXLnWQBTRCBo9R2vDfthz9fRjbm82bIeRocS7ZN7Weo5mjVQupEzRolPyjSOGyO1m4NnVDVNKQoXZFj2fozV0szmnJw9oFqcYZxi3nzLeXhGnSQbS6taLKdZYgYIuGpMzaXtBaWe0jcKbBmIkxEAYJdU5hIFxGikIsvv86hqnRZOkDLhc2IcNaZ9IYYHqlqzHhGoMD446VPL040Rq5HVxShO0w9tMSiOfXK5ZdEvK5paLD1u+dP4lXj9a8c+fPOa7jeOtt3+Ef/33XLJJ/zOPn275+oeGYsHExJiyuNWKIhax5g/FMnx6wddSy8Wt543XNMNmx/Ojt7k9f8iT2ZLXui3nfilrna10VUmUVHCAGgaG/oY4bomjYj167Gs/wj3Tcrw8p209zkSMcdUoMmLS/xfKAlNK/K2/9bfY7XZ8+ctfvvv+f/1f/9f8V//Vf8XDhw/5Y3/sj/FX/+pfvUNR/sk/+Sd86Utf4sGDB3f3/8N/+A/zsz/7s/zar/0aP/ETP/EvfaxxlJr3w229XgO8KP+quyfnPSVlxnEihkjKgflijnOWcQjCSZhCM/fkyZKmyDhF8vWOGDLLIdCczClHc/JMUgRzyrKgaYMg27pC96CNlmCsyse5UmiyUEFFcscJKRInJwt9EgqnpFgTWDOqCJ+cUyY7RSkBvJdujpwxWmGrmBRVw6WMkdRFZ+mcp7EC+ysjOSk5F2KSAWjKiVASJUZUyORpIkdJx03V7qW1QjuD0mLRJRvh9quFOOeEKYVCQmV1t5DklKRETOSViBlUeMmiDSXXQcEJuqMO+owIFIH0p5hFd1HdPanmTtxRINWtpJScc7qiWjpVB1DOeGNRUaze1iiM1fWCLpH+qjYfKy3DkrNSWaAPEGyGMGUmEc5ga1S3VlUJcaA0lBQVFqXR3kBydwm4MqEJPSHtyRbjReipD9SZ1mJ/r8ciWQMy3Dhr8KbBKY2zttrOLRYRzFrbYLQllZp9om1d9OU1zwmck1bXkhKhH5hCTwgBkMydmbGkLAjE0AfIhcY6kktop8jDjvH2mvXFp5Q4sbz/Ct3ZfWy3EGE04lgTNC7VQcLVQU9eH6sFjfLevXB0lYJVVVuksiBLTtcezBcITJVhC6iWS3VQ5TtqsxShdawxlEqxHYo3DzRryS+lIlNzcQoiVB57GDdMN88g7PHLBdbNafWhk0oR8ghJAwEddgzbHZvGwT0Y97ckJUjP1K85Wp1ycv4a6TSRNs9JV0IFtiqhrSJZS7BejllpYugZd5eEYS1UGor9dsuwW2ObI2YnD2lPPss87FD9BSYNwtenQlAOHSZUHghTT+7XXF1dcz1qVsOO44evYk8e8fr9t1gsl3RthzNJzlWTCcGSyxzCIOJP3zINj1k//oA9DWNRXG6fk7LC+AXN8pzm+BGdn2FDj+6WNKszjM7k/XPy/pacJkpJ5LCXzpYwCuIyDeSkUH7JyYOHuPmMRbvC64ZiwFZXYM4BpUTzkl1H6jeE3RXTFLi8veHjJ5dMMeGdYe5nd8in0grfdqxmx+Qw8Pz6gtv1DbENhDBys+vZD7I5nbUzZqsz/OyYnCNp6skl4do58+NTZrOW62HHdrMlhIjRplr5I03T0i0acoBUNE2Z0FE2ECkEYuiZxol+SPQDDNMkMf/NkmVj2Ox37PYb0rhjioE0JfbTROwTWoMfNuye3/J0rphuBpKaiCmiR0dsMo1zZBJJW1KfYDewGXactS3h+pZf/sq3+aFXTphpw2Y3sT+d8YUf+91c7W74Z7/8y/RZYZoT6eJa33K7viXFJGtYBur1PFw+5tYvuTw+YuUaTs7mvHlk+O515r73nN+3nGiDVx059+RxT5x6xpSYxh2bzSX73RXJnmCPPsvpI4svAz5mMBNkU+MjNEY7kvr+23h+4AHla1/7Gl/+8pcZhoHFYsHf+Tt/h3fffReAP/kn/yRvvvkmr7zyCl/96lf5S3/pL/GNb3yDv/23/zYAT548+Z7hBLj7+smTJ7/lY/7Nv/k3+Q/+g//gX/h+jhHrLSlMKMB3La5bsM07wjQRNztKTnjXksNETBMxDygPzcKTRsu4j5RsGHcB8oYwBZqQ8UeF0nlBNFIBb1HV4ihQvQxHRiGLTB02tNbEKqS0xhBx9IOXASVHQpjIh+baXC/4WX62xAQ+QRMhTEIllCy0DUJrm8NCaWXRDTljc2aME6YYdKo73ASqRrCnMJHGSJkScQwVPUkiHtYKo7Q8J1NFr6RqfqgIEjIYWE2Ney712OsbURKqvEiTVXBnFbYoymHIUwpFdZOowpQUikjIhRLrcJIPOgwAicBPU0S0syL0zEVanQ8FikVJQSA51yVOjl1pEVnmosXFVWpgGLqG6Smh51IixMIwHDQvmcYbEflpOa6sNMo4kGeEcQVTkTQpV6y6ICf0m9YG48X6rTQ0jUXXvBGrxaXVeCd8binyfePQpeqUjEZbVcW0RbqblBb0SyusKhUVktdI/pNlcYiFOE4UEsaAdo4ywTRM0lg7CQXi688XpQlhZLy+YHj+mGF9w8mDV+jO72HbhSQnO1fFxepOyyODA/WRE0ZbEVJn0T6lA+JV7cwlZ3TROCUlcCpDukvvzQhYebA7C/UJL9CRGnNDKTXkrrrGDsWO6gDN14LNUhK5aKzS5DgSxy06DmilWa7OKL6DoillFAF3LJRUEYHUk3dX7J4/Y8gTSlu8tpy++gWwmq6d09bIglIyQ+wI8yOUcrhmRnYdxewI05580WM2lnHccLV9zph6jF/gjt7CHz1A+1tKSTi/wi/vQZozxT1l6IFAiQMlXhP6Nfv1mn4cycoSmyWL1z7D0fnrrObHeGOxjcJ4JW3kymGru8kaT3GFaX9D6J8Qsuby2bf46LvfwnVntCdn5Oa0trXP8N0RzfwY281IYUOcEl5pmtkp2Tv245rUb4jTQBz27Ddr+qGXoZ+McR3dYsX85IymlW4ySeeWcIdUPzMlT+RpImyec/H4IzZXt6RiSBoWizlGS2u90YqconSjTUPV7Yjovh8HPnl+gzYd3go93cwWOG/p5nOWyxXH54/QtmF7/QGbqyfsbz2zxuELQlsajTMNs6Ylh8gw7HHKsZzNRMycC95rylQYwi37YV/LHzUxJaZUmGJCB4VvWmarU5rlETcXn3C5vmK7l0ZqjafxGtc6jM5cPXnGL20nxu2G290aXebkHCEFkpKaiWIKXsE49VzfbnjlrUfEi1ve//Zv8M1PDctHr/LZV5fsY2T2xgN++stfJq8/4mvf/pT29DUenZ+zv1nx3oeFJ8+vKRRMVmQCJSpi3nLz9H2UVcw/9+P871495vOzif/p48J/ux7p3CcsbaRZHVHChu3Vx2x2PdEuMUf3Mcu3mJkVfYh0i2OWRyf4fAPbTwm9qkaPINfootG8ABv+VbcfeED5whe+wFe+8hVub2/5b/6b/4af+Zmf4R/9o3/Eu+++y5/9s3/27n5f+tKXePToEX/gD/wB3n//fd55550f9KHubn/5L/9l/uJf/It3X6/Xa15//XWG2w1lJhx6VppxGJmtPM3ckLXFFMc0JYZpw7QfSf1ImUbhFr2IfIxDtB/FMA2BXArjGGh3A361QmPk4ukMyipsY4mzilZoSfGUHBJLIgufmWTR9M7TKIVTkRgiSnlybhlDZIqBuwjtSoWEWhon1lWhg4iBlEttqZXeHH1H9xQisB6HGjqmsFpX908ihIkURaNTggh8S04UxNVitEZZ0N5W9T3SB2ME7bHWEqaxBiJVmNlIWWEuqSoRDqFtIGJc2YumWAu+tMaUVHtChG5TRu7T1EAwlzLWWcYUSUkRUyYrKPlF0qfSL1EKWfo+lFI4owhxqlXfmlIHA1et2eIyEeqo5EyJSUoTI4RUyBn6KTFMkTDlO3dTIeOQUDwDKGukCkGL00drBUZhiqQYa4SHd67m0VjphnLeSkyZlV00WdF1cxlgbb3w5kQpRoS0SlWrtzx3owz12cpgqhS6iME7liIlikWEljkE9vttRW3ETq6NIYdCHCL7TSRkh/GW5cJzejyjcZo07NnffMTm8jH9zQ7bzXBnx8zmx4IQ1sE0xizR/vW1P4Se5YwMJ3VotwZKjHXHW/U2RYbUg4NL3OZC7RzyVw45JUJS1qZsbcQRoZUIMHMi1EA8GVzkXJTIIkHQUtZVnxVQUTMNtzBeo6YMzRx33JI2l+S8x6UsSECSELOhv2Xa7bA5Uaxl9uht/MlruMbQ2UKnZ1UNfHBqZTKRtllQ7AhGobDYac9u/SmffvurbG5uOD9/gJstiNqg3ArljjD+jPb0ISr2jJefElSgaxc4e45NA5fP3iNt1uQU2e4mbsdID8ycZ950PHjjR5g/epPZUqgppQ5lbwL9KWACUJEcCmlzxfWHv8bFs2vcbE5uW+z9d1it7rM4Osc3Bm8NUVkR+NuEb1aY2ZLt7TOmaY1tOgiFPO64ufiQYbcn5UgMGes9s8WRDODG4xppfje+RZUMJZKxmFLLMIcNYb9mNwb63cBmCAzK0jSOReuwylGMre/5QMyFPvay2F+ueWYuQSvWmz1jLBgdaVxL13ha3+C0JuXMsF8zpTOOj85p7ev02w0ff/QNxrDl5OQMM2s49w8wxtA0XsSxNzeEUdE1M9AwTD3EyDgEtrs1fUxkCs4VlOmYHZ3hS2F/9YT17ROKsqxOpabh8uIZeMdqZqUxPlPR3ZFhD9fbwDBkhtwRisLkET1GQgF8h8+OYJMIgf2MOGbaruF85knzU7x39Fpx0hnGKXB+/xW+8CO/m4+v/j7r6wum1uE7z6P795nCxPN1T1KCe7scyUoTp55nH75Hv7vmub3l7d//e/g3fuwhv+4XNKcj+8tfYvrgQ3bqjE1Cog4aw+LBfR6c3mORHzJdf4sbpTBdR6c1iUSaPqIME0k1lNyTtxfsHr//fa/9P/CA4r3ns5/9LAC/83f+Tn7pl36J//Q//U/5z//z//xfuO9P/dRPAfDee+/xzjvv8PDhQ37xF3/xe+7z9OlTgN9StwLQNE0tQPve27SbaDQkL4WBaM+4y/i2vSubU1WDkLvC0PfS76sUiYJuDE5bylgoUSx10xhRMRLGhN+nmpIoWRTGG3zn8Ms5i5MFThu0KVijiUahYkFnSQbNcMelt9owVb5VWStFgtHdoQQFoWRQsjs9JGWGEKB0dbCoVEOUbWRJ9cKsajtnhbdjrtqW2rJbUqEUCffSWZGpKIwWikoZXbuBarfQHZshPUPOSuaJ1hpXg91SzlgjIW65gD3oAerCU5KU9RVlkIVVnCpKK4pWInBE1lUOSJS2dKolZ4g5E3OWoQKq1Vv0PJkXPUEiIK5OG6UQ+5EMI4pcnQly0Y4RYswEI+jPHQKkNVFrsjHQiS4DDcUWioNsMtmI4BWtsdbgEKrBGQkssVpoB1udNLru4LXR1eGUBaXSloLCmkaoOq1rDk0iR3HWHJCHA+WhraWQyGSstpSUmUIhDBMhJ6zTleceJKgsZdrZAtM4rDZMw8A4CsffNLBoLfPlgsZrvMmE/obh9oJ+c8H2dk2OgePVAxarE+mNqo6rlCTcTlnpvQKqBiTfuWcOFnOpDXgxeFTDsLxndbBJVdx8eJ4HdxNQkRChsnIWC7G18nk50JIvjqGI3lxBKgMlehQSt25UI06yNDFurlHK0h4/wLczhjTC08dM044cMnnaE6aeoDtUVsSxp+vuMzt6hWaxxLlC0k7ek/KiYFClgEKKIslrdk8+ZHr6AcpaLm8/ZTcl1PFDyuoebjZn5ueiSSGBBdXNaPw90IXtk99gv33GYn7ENGy5uHjOzfNrvPO0ds7Jo89w/96rGFvw4w3LhaF1Ga8rQoUVwq9IR1RWo7S2TwOb3Zrrqws2fWacnbI4PqdbHkvtgXfQGEw2EgpXRBwcQk8pE9Yc01nN1cX7DJtPUMqzudmwC4p2dkRTkS3bzmmWRyLYjgFjG5piKAnCIcF53BLGgf1+y259wdBvUG6Och0nD99hGnaY2EtukUIav8eRsB8JMTDuA7vNwOV2S7aGxXxB03keHC9pnRT0Ga3IWvDWFEf21xu5DqJpVEI7SzfvJCpAFZbLI5TKGKMxGlSccGVOvx0wNrPd7bnZrUVCkKUDqiD0chxAW8ts0bE8f8Clzjy7eMzN/gMeqIJ3mtN79zgGulahUmTcTUxjgWJYTpH9sKfYGYtmZD9MjCmBypQ4yOfFib4QMjoH4tQza2c88B3qqGV5ds6SgXF7y6gKH9slZ1/4Mv9Os+a//zv/N77+7TVdc8zpySMentxn3z9mlyfJuNORjEPFTFGB7dhzcXnDJ13m//D2Q/7I8pRVY0i3lqtvOsKmsDy6T9saub4SpG/OH+Hsm6TbXiQBTYexHdFo+vGavL5hffOci+c3fHJz/Vuu9b/59v92DkrO+Xv0IS/fvvKVrwDw6NEjAL785S/z1//6X+fZs2fcv38fgL//9/8+q9Xqjib6QW5hPxFUwc09WQW0L8QpA1NFGQraO7IutPMWZy27yxvyGKvyXDFftZRkiH1g2o9M+17okZCYNjtpHJ3kRDZGkbwjrUdUPzK7txLNipbeG6LA27JLFO5b3B3iOshIv8pdpQ1VdGmMcI+yRZRFnkJOjdBFCVIVA0rkQ66i26ohiVZ2nqlSRfnFTlf7BlwQmiWXqmkQmFXoh2rN1IfkiYIuYrXOyt4Fd6EUEYkdR2m0BZCWYoFqM+qQ8IoRrljkulXnkapiH8lFoXalaINTIv4tteROFy2dHsiwoo2ubg1d6Z8XqI0qYE11sxwGK2vIVS+S0TKgVfRHa/n3YuQ5K6MxaJwzuGrPrbpopLtR3fUfaSMN1rq6SIw1kAQ1M1pDUnddMQpVRay+8r7ydwGFDcrUxZVa+lft20UhFQVVtIs24krLMoz2Q2K37tntIilHWgedV+TY42cOZ1uargNvSfs9sZc6+6ZpmM8t7dwD0tI97nv260vSfk2exD6tDfjW4/xL7rsqONbmUMx44JDl3BBaphYq5gPiVUeSKtE5DCsHt5egI/K+Oufuhk44ICvmbuhRSujccujFqfUHOb2IrwdBcdSBbsOiSgQK2jlUSaRhjZ4egGlRGW4un7Db3WIK6DiwOLnH7OwtlEmUm09pmhWNc1g1kotB53quKyWISxoh9OQwkPsNF0/e54MPPqRzx5yfn2PaEx68+gaqaaUsVPhgsrLkaaTsrmGYMPMjnF/SP7/k6cf/M/de+xz7fsutnuNeOWXWdcy1YXn6gPm9VzBWkTaNhNbV0a8ofZc1lGNg3G7o+1vGIaL1jFED8xOOmlPue8vcN/XzIJqfVKibj4KKkhWU108Zhi1x+Sqhf8rNs49AO+arB7SrY5qjE6GVYoRxRCmDnS0xzhKnHn3QE009aX/Nbn/F0Pci1EQzKUM2LRaFnp/h5g9pYk98/j7TzRNCmkghMfQjt9ue/RgIOYO2rOaST3NytKQzWqIAdEGlzDgGUsiEMBFCII4T18MnGN9w/94pJ6sFR0czvPN457G61G1iIieJKdjvd6zXO5z1DFNgGiU2wpqapp0mTC6M/cSQtgy6w8znoGC2WBGibF4XS8+8Oak5WZLfYorF6UyYMnq6YVxvmdqHUtxpJ6acAYdKk1QxGAXaoVNhd33F87FleRRoXY8OkTd/5LP8mIl85Zvf4Xl3zufe/iyvvnHGFxf/Or/6T36V97/6Pvshk2zHvXnLovP0U6SogEGTU0A3npOzBW988R6/91+7x799VFg0iSNXaDoD7g0a09E8+4RQDN5pFIF92DGRKc0SbxPL/XvcXn/MXp3SDVeET7/Jp9/6Btc3N/TaYx9+gdX5DwN/+/ta43+gAeUv/+W/zE//9E/zxhtvsNls+Pmf/3n+l//lf+EXfuEXeP/99/n5n/95/sgf+SOcnZ3x1a9+lb/wF/4C/+a/+W/yoz/6owD8oT/0h3j33Xf5U3/qT/Ef/8f/MU+ePOGv/JW/wp//83/+X4qQ/Ktu2liZchuDSZkwTjg/E3dCiYwx4rXGeom5Xxwv0MBwK10Uxils5ynFoOcON7XojSGPAYNYTkuQXXSJwp+GMBLGQEwjqQQyiUaBnXVoJ0m0EhdenTcxSdOsMUxZ9B+67rgPF+vGe2x6UcqnqyC0mJp0eljIChijCaUwJcl2scZJ+i1adC0hCNJQXSdFG0zl6A+CQ8k6eFGydrBm6sPAUcQCXbThcFC5ulNk0SloVceZVEC/lBxbXRg5CZ1Ut5oVJVF1MKzkkJKsE2Us2kok+p3Utjo7TA1ZIxXZUVVBtL5Li0NKrUoRGuXgqKlR/bpqYlBCkZlDcqqhdrvIcZhKx+SUa6Cd0AfWaKyW3fvBYaOybBld0xBDkPelIkTwknibgrFSJClznIaKkB2qFgQIkNwdtISJUeQ1yimhXrYlZ812M3J1taPvM9pkaAsmg1YBZxvmyzkYRUwTw25D2u9wbUfXLCTfZH8ri3rWjPutCEeNJNQqa8hpxL2ktyql1NBBVTUo3L0WRrsXA0QVzcr9xW10KPST4UxEvYdqgMPtN5f8Hb6XUoDqkFIVWSwHgbCiBue9SBqm2kRNjnJeotjeXLBsl3TtKXl2zIcf/jqbzZqzh+8wjFuG+bG4OaYNev2crlkxO7rHpCdK7tEBnJJhPaKgNuuSA2q8ZVo/Zb+5kVBCs2IyZywftPhuQbM6koI0J6/FwfZc4oBWe9R0y3h7xTRdU9JPMW2e8t6n32EcB9TqmOOzB7z1pd+Fcx5XCvQbnPe0rUXbhpiOSeOIsQu0mUtNQ78mDhumceB2s2Hdb8UmfrSgWdxn2SwwYY8KN+gSURYh04rGZCillyEwjEzjyO3Vx6x3PYsHN6yWHSfnr2BMi206rBf6UyuDKRD2e3IsdO0pRRfS2DPcPiGUgSlNbPqeYZKNrG5aTHdOe/QZCUK7/QSVE65d0XWvMZWBZ1cfsrm6JQQYw8R2HME2rFbHeJVxOIzztG2DyiND3DOGgTAlxjAxDoGcatBkmmicxqZA4zTtfEkuESEPI2nsSbkwhZEwDYx9YH27Z78f8S7jrJHQNy1BhnkciXFgHEamIdCnictQ0O2c82XHm6+/SirQzhqhf50jpoFhP7Lt9/S7kTBm4hjZ725JQ6Ykjeru03ae7TQSU8boTAmZiMZ2jkNe1MXNLbfXa4wpWNNwMe/4yX/7p7jXOv7WV36Vb+w2/LvHv4s3793nx37vT/P++r/nw0+v2E2Be4sFM9/idc+YHYWEXzY8eHPJ7/7hI37vZ495+yyyjE8o0RNzSzNqvJ/hTs45Z8tuva6p545ZTqT9BtUugYZmvGXz/v/K7vTzzPOeT693PGnOcecndO2Mh298Cd8tv+81/gcaUJ49e8af/tN/msePH3N0dMSP/uiP8gu/8Av8wT/4B/noo4/4B//gH/Cf/Cf/Cbvdjtdff50/8Sf+BH/lr/yVu583xvB3/+7f5Wd/9mf58pe/zHw+52d+5me+JzflB7kdPThhf3XD2Ec6nQlhLwE5y4520aC0ph8GDB6Mopt3qNWCFCHmXi6CCrRTJJVF/ObmlHFiGkfRI3iD9sBUYXilCCGzW28I0yjWtJDpThN23lbaRjQHpVrWdA1E0zqJe6cefykHa64W/rguXKaGsqUc8a6pYWAFrQzOedBK7Gil0LYt0zQRlYQLTdNURYyVAlGHx5FhIJdD8JsS294h6CpGpIU4vYDmS6nziTyplOX5U0QVoUu1eh+QC0DYJRHninZA31mZRb8ig9thwNGHHbc1FTkS/YFV8kK5usMrdTh5QRjIwuasaGNkfa9R/IjTSJpK5bGNtdUBpO9yXqyx8r4gGgt9WOxyEfFkzljjq1Op3Lm1qEiCxuCdtPhqpV9akDVZCU2hq9hYFvZyh5xoK/fXdRAS+7KCSu/kmqyrtZEunpTIcWLX9/TDSCxZ+mwaDWUgx4k4etIsUCZQORP7PdOwI4ZAQZGKom29nFtREBPnW1CF7XTJfr/BWUFIUg7k6HDO310Y77JGUKgs1KDIXyoidzd4vCju09UBdfj5lx05OedaYmiJUbKADgOJrkNjKbkGsx1yT2RgF4eXujtPGxVJsTCWiC0J9s+Znvw6cXmf6egB/bijHwup7JkPO9rZnFfmP4zWltw/JzoJQFN5T2NaRtuR4l6GMLOCtCH1l4T9hhInwrDn8vkzbrdbVDtjcf8hyzd+mOMywfZjtBopOIn1qW6xEifKuCEOa7a3az55/JSr8hFv94bVbMb5Z7/Iojvm/PyMedPK62sMxEi8bUSH4+do12LSyLh+TnpyhW9bdiGw2a4FiW3m6O6Uo+VDVEk0zmObGe3JOSWuSTcDZRwE7SqFnCdynoi7W+KwIQ4DwxSJxjC/9yar1TmzeYdxItZ3Vkr2QFGKrlRzxKSBvJuIIXB78SFPnnyI1uDmMzlm1+Gsl2BA5XHzB8yOPI3zrB9/g53y6JPXCDFytQ883wRm3tF4z6P5UrJotME4QVG7xQpvLJubC4Z9z67fQ4IQIlNMzJYnnB6fwzTR79aijSKC9vKa5kCIgbzt6ceRYRyltNK0dMtzsh0h7SlpwOhAjIlxHBjHnikkxlDIMVOU5Wh2JPTpTHN6dF67SKU8dupHxt2W7W7HdrdnuxvZ7UfCMBBTYuETKV/R5xlN1zIbPbsh1oj+go4jJVi0aSg1vXs37SijojQj5n/7Kv+nk3P+zI++xR/te/7PX3uf//bkVd79Az/E7/vJfw19/TE//3/5v/Lh9TOexMTUTxjlaOea0zPL2286fs8XZvzwuaOdD5iyx9xcMrUtk8o03S2khXRn+ZbkRsooA7eJW7aP/zeuyy0LFVl/+C3e/85jmvKIR69+huUX3+ZMaco0oaYdbbME9wKd/VfdfqAB5b/4L/6L3/LfXn/99X8hRfZfdnvzzTf5H/6H/+EHedjf8tasZmht2V9dE8cIuhC2O+J+zTKu6I5PKI0iDCO6JIITAaXtGuyYSH1k2Ozp5nOctUzTWBXiAXRGoau4r1C0QN3GGFzjZbLdBvqyIU+JNIzMHx4xWy3rxViOMSMhbtZaWYyiDC2HPA4pyUsYq4lJ0A9tJTRMFYH80bpGi2tyTnjbMGtbYk51UVZ01qNRTFRr5UEXUEWf2pjqQz9EgGvGGDgoOQ5oTqpDy0EHc9jdxhjJqDvnjiw2QtnouhirKuSMOWFLvrMEHxY2U/UVSsNdfxFyCPpgDT3sonWlSahoiBLty4EuoAi1ILH0NeOGUoWa0qfhjITQSTrtYcGsItQimhWl9cEcfYdWpBxxzgllpkSno6r2RYIyRblfkOcnRbeSRAtZ+kzqgqoEErp7H1LWlf7RpIMItChSDEKFhRp7roVjMnmSksEcpZ/Ea7qZJ4YJaxJGZ3TOhDixX2/IBZxvUTmTQxDNj3c0jScpQzdbMk57pmmH9iLMHbbXbDeXhHGDpSWEiXJXglnu3u/D+WAq5aWUkeMq4uMxtZvorjX4Ltm1kCotdBi+Dv9+yJDJh2Tm+pqamp1zoDEPOSg5xTvkTx/KNg+fNC1pznr9lP1HX+Xio/fZ33uNEwRVee3tH8W2M5rW0rkal6gUUa0os2Ni6HEUtG8wfka6/ZjhYovtHzDunvH86hP6fY9ujnCnb6LOH9CaJ5Swx9oOvzrGNS25VfTPv4slibMsBuLuku3lM3a3O8aQ2U6ZS72ke/Qmy+MVx0cnnDav0hiD9waHoRhLKFHyjbpEGQbUMKAipM0Nzz/9FvvbG7rje+TZEbgW266wzRK/OKWdrdBxy7i/JOU9Rmva1asEZegvPqIMV6QwEvs9+37DsNlR4g5tGrxrOTq+T3f+Or5rhIbTBenK0sQqxAUkR6ffcP34Q4b9HoynDyM4j/EttpnVjB8EDUyBNNyg4w6/WKHmZ4zjjmdf/2XOH3yCzRmtZxzd8xx1DqelqNT7OUpZwtTLRlSJO28KiSlllGuZLxfkGPC7kXm35Hh1RNsY1tcapT02J+JuyzTsidNEiIEUM8MkoYaqgG81s6Mzlg+PGDZPuPzkW/SbNcN4SCSPYDqOz05pnGHsB2zb0pqMcwKUhmkihR1Tv2a76bnZ7Nn2kVAa5qtTdBO4ub3GhivaMpJyZNx8Smwe0jrPNAVxBWWNYpRNR2PAGoryWCexFPa1B7y+gMff/Bp/b9Xw0z/2WTYB/seLx7x3/ZDhas1HmxMwR2wuP2K3WUPX0D68z49/puWLDzvePsm8uvQsrUGlBOGGyWaa588pO0s5eptw+g52foRyC9xsIoyX5P0tcX/J9sOvs794ysnpQ9b+FRY//jmOT+9xvlzgnXxudc7k3SUhGkbTft9r/G/rLp7MyPJ8jnGKYdPjrCwW/e1Imm6IMdEsFqgyEaeRtM60xyu6hSOHhillaV/dbLGNleHFaEo00pDsrdh0kaAnUxymSBiWtoZxH+jzlpgn0jSicsHiKTNJ3HPaoqptNqlyx+NPIYiughdWVSkBdJL9UD+QmZrDokQHkWOhqFQHE0EIQggYpeiaVha2FAilCDJQg9+cc9WOmSlWdj6iN5DE06b1kpIaU12U7Z1dUArXNNkWGdqUuCWkAiDxYoygUmtwF3BGrr8HpFVZE+Mk9FYpHGLvCyLozSnjtCGV+lop5PWLmUQh5kncOdZVhEQQkTsaoKJM2tjKUtVFloJGNCiSFCkWcK2FZjHGMoXxBRJTs0mM/c2DmoTW3dle63MWROngtDqk/6oquqv9QhU8KgBWSRBgKShtxB0TI6HfMe3WhGnAOMds3knjsTGgHE3jODvtaBpD6hVxGChTICvJcskxoMdRmoqNpT06YeY82nrmiyVhGgmhZxx2lKSYzedMU8/19WM2N08Y4yhZKjUQSwTT0sgd4ogxUkAouT3pbrjm7hyQNNlDVQFwR+kcEK/DAOOcFB2GaTy8it8ztKQUqsj9hfX4UA4YCxikoE9Zi8qBMRacjcQn3+Hmg68w7i9Znb5Cd/Iazs3pvBcXkKrWegq5DOQ0YZQh/T+p+5Nly7YzOxP7ZrXKXZ3Sy+u3BgJFFAwGyEhGGpOZDaWKjpRSqiM1ZGqqrY6eQS8g6QFksjRLkyklmcykJEWmUaQYZAQZwUAAuLi4tRenPrtY1SzVmOscQK1EF27mBsCvX8fxffZe85/jH+Mb9QIbJpZzGiiEkdfffcX2+oL66ClJl/RuQgpNpSWFWdAcf0S1PGW6+wpNRGlDe3REbBqiGwnf/TumoWPqet5cXvHmrichOWoKjp684MmLj1m/94Kj1YpG1SgpCCpiyAWjUYbckCwFCsXh7g1Dd49Qmq2duLYJsXxBvT5jsVxjigptMtZfKjBlg2xXpOCww5Y49qRygRGKw7jl9u0XDMNI8gEVs6lVt6u8wtEFTdtSFoJCF3gVIc3KbooQPckdCCHihoHd/QW3uzu8TZS1pGxb2vUGITOxNXc1ZdqqciOu77ibHHbck7xjdJFDd8Dc3bBeLDhaL/JhLwwxTBAjVb2iqhbs7t6xvbmh23eUbYuuW04WLeWipq4bpA90N5ckB02pqZsK4hJ7GIhdz+Ad3TjkZ6AQKKMo2xVVsSbFkX57hbE99fF7GBm5v/iO/XCJUgV1086r8oqT4xNOjlvsaNnve5TriENgjNmsPo49fT+y7zy7fiCMuZi13RzRnLRUixNu5efsrkZcGHH9HWGQhOYcZSQyibz6iRGVLMFPoBoKwGuFefke/53/+Hv8w1PDLy/3vN6+5bVd85PvP+HLuwqbPK9j4OfxlPv1HyDVBeP2nqp+ykcfLvhP/2jDy0Jk8CiZNG5SwPgO3n2O9Xu2Ry8RxfvUocfHBmVKqqpF6F/QX/wtb96OvE0bitNz1ounnJ+2PDEaU6j8k8xccSphyhoXJkLqf+sz/nd6QFmtWpwTLDarDMIiUrQVt2/uGLqJlLaM3UAQ2Z9QVxEtoN4siU2JHwM6RPr7LaobMMsqpzDqCjtzFnSZD4boEkaWTKOHfiI4S5yHjbiPyAh7IVFVRZEWWfnQBlOYjKhHYIo8uBRlBsrpOYlgrSOESFVVmZ3iHEpLiqLEe5cVmVn+DvEh/aBz8+tDkmLeyz+sBQR57//gCUkp33xTCrPJVBKTo6mb/PuCgOjyTZis4CRkpjWmPGA4nw+Tqsyo/4fSvYekx0M/Q14Ahey8I87lggIlC1TRZPhQ8EjU4+BEEohCEWdwV0oBKSPT0OEnTxA53WFMA0kglcw9NDq/JlLIbMoTAm1KgvPZRyKzf+EhRSJVZtjEOPsldB6QjCl+vZKAHPOelRAlxOOKSczG0DSbnx8NmrP5OXty5tSUlNnk9uCDQebXIs31AnEG3Pm546nv2N9cMQ09ZVWSbIuUFbpeICkQKdHUBcZofKNxo8kepzkJo5RCG4OpSnRRZTKnekDTg7MjQ3fASElRGGy/5eLiG64uvyH5PUkpQnTY6YCII6QW73uMaTJHg+z7UjLTkwP+N1Yvv7myFFlxe6hGMLk9O844fTND6KYpryPVTNOtqnyzCiHg3FzMp3MjbYgPiaFMAU7R/7pELkSKaOlef8H9L/8Nor9h9fR92vd+hGwWuaBQkP0D2OwhmqsqZMrqZAoD/fZbbq+/oVw84+r2kq/e7egnxXmTOGlK9GKN0hXITAAumxXF5pSprjm8+SX+9jW+LjEJpu0dn//iM/bbA4UQiGrBk09+n835M4owsq4aNufvU7W5BDAJQVAg0cQ0G9J9ADvi3cTu5pKvvv4Frp9Yn7ykPn7Kk9MlVVHRVIaZL59TYDZg3Tj7qlrq9Zpp+x03X/x/WT39BG977vZbJt1QPnmaTcJTl+GAzSqrCAmMakAV2fAePSII8CPeT0xjz9Rt8TaRVIGVLfXqKVpAUelHNdbaXO5HtPhpINgD4+jY7yZupxvWd7ecHq9pmgUfvCxoCo1REiX07DnzhJSwkyO6G8J4wMaJtKwRZUm5PqZdrCmKimK+pIk40RiB7y0xaaZpYHfYsdsdspIsIEWfoY4iL5W1Llkdv6I0kjfjgTff/ILd0HN2dopZnnDyfsOqbVg3ijD27HZ9NsfritU6Y+Cdnxj3B4ZpYBgs4zSBNFTtKcvilG57S3e4Ju0Ei9MPefriFY2Cz/dbru527PpA7yMpGIrlMc7l9J6QuUw0uQEhC7wqoCz5+ONz/nsfHPHMOD58siKJgtuD41eh5n/0k0/4yYuW5ffO+PR7z/hXPz7jv/w/bvnVz/4lJ23ko1Jwqg2LRkNyBCWY7A69u6M7XKGSxq++x6r5QV5tT7f4cg1NjSkF7a7k9d0l190px3/nTzl9dsxRW89k63w5VkJkdVhKVEgYXVDbW7r9/rc+43+nB5S6NJTGcDgcMFWOkupCsjxfsHsHrnc4e0DWhiF4rOyw3YHoJ8zymLptOEyRqQ+Ibo+YAqEskIVBV+UMysqFLKY0KF3TrjW+d8hDh2wKwmjBR1CK4CKHmx3VlPs2jNGYusyDitaIZY1qCkxpmA5jVgj0g6lTPA4iKT2Un83fnlldEOrXxlI5r6AefqSHEwIefSAxxoyFn6X34P2jFK/N3HWTBNb6DDKbVyEi+bkqSFAUOXmSVytV9mWIzLIQQqELTYw83niVygRRa908VD0kk/JQYcoSO00UWiOFJHg3p2I0UhZYPCQ/0ygtru9xk8s9LkrBfPhKpR/9HlqpxwciM3Au+rzmSg+pH8Sj2fiBQfKQThK/8fo/3vxn5spj8mR+7YXMDAyRsjKS5m4YKbL6ouDx60kpkYL4tXQimNdauQjvoV3Ze4+dJoZ+YBwHYvDYCQ4pIXnH4vgUXR9nRUhKggiYGVn/UKgnVeYBaZWtf0nnqLtIkJxlHHrsMOR8ixJ0+1turr/h+uo7gt8j8MQI1k0cdjfE8Q5ZHechGNDSkERO0oQwM2Ueza1kRURlX034DUPrg2KSvSjMvU0BO42PX3uazd8PvToZwjb7hWSOFns/ex5mHo5RszfJjchpz7S7AjuwOD5HlpJls6I0GpRFpjjH7snR0jSX88WAIBKGyPbmO77+6mt2d1vOz++RSnF2+oRisaJaLCnKAqNLEDBOPa47IIOnXpyjRGD71V/x1V//v1jef4/j9XPe3tyzL8/RT05phefs5ISjFx9RLI9JPg9mxaJBVxVJzO+7lLJHIiWCnfDdHt8fGEJgexjQ62e5u2V9nqGURiEIyBTxMV9CUvDI5ElTj7v8mmLzFDsNDP2Bu7u3WCFoV0dszp4g9SukrmAaoLsHFGWzIEVwNiCrmlJXECzTuMMOe+ywwycItLgpEK1FLVaURy8R7UjovsK5HcILgvMEN+CmkbEfGMcR4RxjSrhQsF42LApBkSKiNIjaZGp2io/dXTFYog0Mh4FtgPXZUxbHT2ifVagye1qUzpRjLfN6m5DQ0nCYdgyjnb0mHqkK9IxTUKpEkLJy3E3sxrcEXXN0coYoW7ww7PYDp2eCs2dnnAgotKIUEeFXNO0B102IyTIyMQ4H9ruB3g0M04h1+bmjZijo8ZP3WJ084eqbv+H23VvsCPVHDavNiqKomSZwPpH5wh0qLWhMQR/H3PSeFDIG1HjAVQtkqvOQHTzJVByu9oSy4PnmiD4ZXq0dR7EjOvjofMVH//D7rK/+E/4P/bfU/i2iv+TipmJZHVEbgzq8Zbz6FW+2O4r6CUfPP8K8+CHFyRGk1+DWRD6hKE4o5AHWL1l9+HscNy0nT19ysiwQIs4/xUyqzeBLlQJB5ueddN9y+Owvf+sz/nd6QNne3iClZugPFEWJlDXOWharApFW3L3N8UmNQ0oY05QJgDFwFDW62qCqCl019Fe3yHCgbBqS9jg9US8rTGNASVwMxGBpqgK5rlCtpo0Lkg2EyeFGRxw8h9s9436AmFcDpiwo6gqlFX7TUp+t0es239xTejwchRDYaQIiZlY6ctHdg49jXp1keEceYPj1jfU3S9NmN8e8K83wsCybM9MPmVMqsyEyPCRdMqE18WvvQJYJmG+vijRDqkTMlFYpdX4oitxo/BBxniZLiA6t8zCTzYYSFSAmi9EtUus5pQQg8d7hoyM5jxvHHN90c7uzzP08D+uBOEeCs1Evv07GFNnvEBJCqRxJ5tcqR/bM5KjowzrhgUfycNt/NKhKmbty5oNDzbdm5i6ah6RKnJuVgfz7VO6kiT57fZSSORoeMgQuBY9QiuBE7keKDyV9kcllvkJZ53ZfHzzDYYc0hlq1KFPnCLRSQG4zDt5jyuLxz8gguYT3Id9myM3EduyzIlFqpmHH1fU33N58jbO7eXjIfpRIZLe7ZXv5DSfrp2h9NHt68sDp46/boB9+PNQRpJjLO5lNyg+v5YMnRau5UM77x9daqYf1TcLa6fHfUVI9tk0/AN2IiYyjFHgb0TIiujvC1efIYFmcvY+tjxgvci+0mUmywbvHZJhMkegd47AjjAcCEGLJXecYxJry5IhyuaSuW3RRocqKNK8Cvc9qoIqefveaYfEZVQXD/o43V5d89+4tz9oNbXvG2av3efrhR6jgiNtL6mSpqxJVFlgZcFEhywZVbEjRktwOMe2x0ZEidN3I4e6CGCyyPac5+YDFKRRKUBqNkgmYZox4VjjygeUgRuzNO672P2Xz4vuU9QmmOeekOaFentJUBj33UAmlQHomZ4hUFM1JHpTct0x3b0m7CpsC+2GLmyZ88IiiRC2O0OtXRLsnCIFZbdCiwMUd0+EKYSeijxz2ew6HA4fB44OkrQtUrdgUS+q2pWwLtCgR6gGd4BAxENzEOPWkKClNSbtpKcslyycvWdYV5YwMSClXmqRgczx6Gpj294y7nt32nihB6YpFfURbzzHsaBEpMtkBN/ZMh46rrkeOgaA19fqIV99vMKZksaipzQxLTLnINaVAoRIxTPSd5bq/4/rujmnwmOIh9DA3aLue3faK45OXnD75GBMtt29e8/a7z+l85Nl6STdZJm0wRy1nixWLdsHgAveTYfAK70f0nDma0ohyCWULtq/v+em3msWzM2KIfP7Vd1wVrwlDxS++u+RnmzXnr57w+68ajrTih++/5PTVPyBe/DP6bcf15SXnZgI5sL38jmny1EcfUj9/QfPkCWWrUGJCJp+tBvaADwNRF6T2JetXP+GluUPWJVVZQfSE2M2lmBJkmHf+AZwj9lt2333B3/z057/1Gf87PaDs7u8pimxgDDaiKgPeYuPIYrMCsebuIk/x2kikMoQAd9db3OBZnQXQLYujJbFfM+x2xMFSGEgiMjqPGwuqTYuuDW6a6J1Dlw+lboJYkivZTRZShq4jTBOFyk2GY28Zbg9IKZh2DW60tDZgjpZz0mOO/sYIc6dNmm/v3nuKmRGRFZCASIrFsiE88GdS9pjE6PHWEf0D/Co/2IMQswckQ9YU+dCd7DT/+WX2u4g8XLgQMlRMqdlHkn0dPKZN8s2A2QCagVsPcdkcV87DVMINOfrno8VohS8rXGUwVU2QipTKR66Jm1dq0Wd3uxuH/LAlUpRm/logOIdIIatHMn9ND0CvGANx9mJAHugeGBsxRorfMOzm/0yPr+3Dn/HrG7/KcLb4cJOX84AR5/zCr03LcjYjxzminSX6CDED9pL3jzH16KaMjZc2r4uEQMRAciN4j5DycdUx9gPTNKLHkTLNjc0yq3qSTG4NM28nxoDzLjceF0VGto+WwY6M3QGtDVVdMnU73l58yc3tt3i/R5qM/xdoHor7rO+5v33Dpn+DKhYgNM7ZuQYh4efo8aOP53H19bBOzN4jKc3j8A1ZwZNKYIx+fI+oR/XLPb5njTEYlZM9zgeEyB03iYQWuZ5AiUQYJYIpUgAA84xJREFUD/j9Jen2K5pmhY4ObUpiWTBGx6Jo0WVD8DfY7o7gLSlGxqHj7uot/nCPqNYszj6lPf8hL5snyOmKsqrRusrPi+iJrkPMn9NoBck6dtdv+eryNR/2e0zRUJ1/wA9e/RGnp8dslkdI43LXUEjYQhD3O4RpKYoVUpbQ3+PuLyj9QIwj+/0V3WFLxKDLDdSn6E2NH+8omw3t0QlKgB+3yJD7gjwgk82KjB0J1pKSZxwOdEOHrRqcESybkmqZ/SlKxkeYG8ITcESVoK5J/YA93GJSZPfuSy7efo7RFaZdILRBFSXKFISUCElQLJ4hOWM8XEPy1KtzaveK/vIL3l18zTA5pnEiuASmZXX2hMWyBjyNalgfn5GEx00H/DTk4T14vLXYkBCmplodI8oaXbWYdkXTtDSSjMxPnuRGxLTHjXvGoeOw3dLd70hB53V7Wc5RaIVPI9F6CJa+2zH0B/qhZ9tB2RxzdPY+p8fPWDYGKdzjM5kUiX4kTD3YkbG/47DdcfF2Sz8GBj/hfG7NLooZzRAEMTistXSHN3haVuWSdbum0Ibddsdd/y2Hsw0UJc/ff58kJKeNZl0abrcOuQtYKrRWtFVFDLAbhvw9KwynL45p1YqvLi4xvWd3sefffHnB7UHw7eKc9u/9Kf+b9w1dd2C8fcc//9kNw/IPaacD08U/515/wy+3EpEim9OnvPjwCcfHC0pVU4WIDndEV+FEiylL8B3RD8TyCFRLtXnOenL0wiNUiTYVjJYYpnwx8gOy6/HdHX7Ys993vN0Ktkc/Bv75b3XG/04PKErlYrWHhIsddhhj8C5w2F5RNEvWTzYcbgTCW1QCrZYkmRiGPenmiqKa0Epx9GTNvQa3HXIte5kJiHHwdHZLu2mRlaS3HYU3aJ2bl2NWsTLrQyeaZYkbpmy+kpnt4UaHc4797R5cQthAQ0KuaiICIx+4Gb82Cz6sLB7kca31o+HQWjejvyUxepxzs/dintr//5gRgmEYZi/AnOUXgvlCgK4MSmmGoc/xvGkkhbyfNVpBVSNVmUmwApjjtA+FiKYo5+GERxVCFwodDFOv6IeByV6jpGMqKrSqqOs1iw2U1TJbS2cPRYZMBew4EZ1FiEhRanRREF3E25EwHRj2hqJZkESOXj4QewFC8KjHkqC8PnsYSh4SIQ+39pBPycd//pu/90GZEjKbYnkcPvIDPsSIiOnR96OMgZlQK0i5hj1FhPcka4mTJVr/mNLRWmVfy6LNB984ILwjWIsvi7lIUBJs9q0olTHwebEkYK5vf/h+Z9KvJs1JI9d1+GlCSEFZt5RVhZsmbm4uuLn6Dmu3eU2oFEJlpopUKZOFY+B++47br/4G8UpQrT/AB40qs6fmwUyclaRcnOac4+Ed/NAO/ZtptRBc/t6oYh4G8yDj3chvvPz576FnNW1mCuUiwgz+CzHhp54YJpQIVHVNWGwQUaEkeBUgOvzuknD/GumPsHcXXL77islOmOYIVS3xZsOkRpT3+DBRbF5RLdfYe0XqrnDe589v8qTg8C7gpgkhYehHpqBpT55QLFeslsecPntBqQ1KZyKwkmaOxUeoGrxPmDI39WoJbvuOr7/5jKaqUe2CSSqSXFBUNUkoqmbJsn6O278l2AFVNlRVg9Pgdpck73INRhiZ9nd0uyvs2KNNiVQFy80TTk9eUbcNUnuUzCh4iIiYvQF5RM8pMOEch3dfsL3P5NquG+msplIyJ2h0mYmmUoHrCP0WeeRZHJ9jROLu6hvENBGHHfed46aXIGua5YJSJqq65ejsCVJVONtT6IJyeUJRKA7XX3C42WNTRBcVRXtKXbSYZk293swAQ40sN5SlAX9H6K7AdkyHe7rtHYddR0pmvrBnsm5ZL5GmyuwZ3xGHjq7rGIaBfW/R5QK9OmEh76jbNc+fnLFZV2ghiCFXK8Qw4qd7Qr9j6Hv2h5HJefZ72DmJjy77gEoNKT+fnU/4KWKn3G48WegPv8D7wHpZsQ8KmjW6ammPjzhtshprgyfZESKsG0HnHBOaqtywbApKHG8vJt4FWP/wnP/87z3jB5s1r//2iv/687cMA3x3OfBVLCiPCj5oNEapPNQ3a9zRHdwK3NP/kDq+5fLtv+JW9zx7ccyHR4aTokO5SAo9fWyRzTmYkqow+CQBj+vusbqmNBWxWWJWDeXuLVEqRNOg0gC2J4wjvr/k/uvPeXtxhzcStfoA8+Gf8SevRuB/91ud8b/TA0qOvkF0c8U6ATs5SlMhhILoWB8dZ3l98ng7ZXOqKSkKTQgOd9jjkkAXhlXbMniYuj4XjMVEcgmmQO92mKMKtdQ4HyCOEEWW633M3SNRYEqTDas2IBToQlPUBWM/MRx6pkOPMZLyeEG5arEkfAzzkJKLBhVijtH+ZiQz02rTDKh6GH5y2Vr2oxRFgSQyxphTADAzPmbscwKNJoXc8SKrbBAdpxE75ZZj7z1hHBjcRFUaiAGlbUaNa/OY10nz+khrjZtvvw8wr+A9CHIHh1F5zxsO+UERqrwPlgatKqTJt3JERtp773DWQgxUhaIocwLJA8LLPMAMh/w6FyUp5UqANDNSpNaUVcU0WtSjZye3lKZ5cMsrBkVI/pESG2PIIDehssdHAWkuoJuHk4zzD9hpwE9T/vPmwkfVtHPMmpwYCw4ZAnG0uG5k2neEKWa/z/x9kUpQtRWq1HidH2rBJaZ+JJUaFywSgdQGafKKDOZiPQUhzGrWHDWPMeL7gYhgHAaMMVRtmyPyIdIf9uzu7/G2R4vcRC2kRBcJoSLKpFxtkMDbjrdvPsOlxPlLiWmf4efGZWWyGZqUULPhMMSZEZMAkQdqNbcOBzdTi2feiZzXNy6kOU2mADVbdVKGlUWVPVpSZU9VzDF3ES1aRkodMVrjO4lXOitZSGRSJA+vv/qSy9eXbE6fsbMD9/sdpMTqxLA5fp/1+hm2LrF3FwQ7IMuWanOEWJwy/vKfknZXxNkYbO3Ivp+wo0ULS1M3vPf+91i8/wn1ekOpFXI2M6IkKTlizDH2JBT6oTn46luqsmIMjteX3/Lu8o71UcXZ+pRm0WLqJdKY3A4cLaZqMOqU7uo7vAvoxQJVTozua4brq5wcxGMnh0+aojnJn3cdaOuC0igqLfAkhBcImQg684tyQ7IjOp99LjdXXF3c0I07qrpl2S5oFivUXHKZICcavSNOB8axxwZBIX6fqbvlu69+Rll/zWa5RJU1T56/oq3qvJqNAzIp6npNVa/oDpfs7y9JKlLVLS5E5OKY9XKDaZZIVYMsUWWFbhq06wl2QipFUR0RDlv67p7Q7bDbLbuba7qup1gcUy+PMMqQSNSLE4qi4nD1NTfX14yTY/KOODmErNk8/x4nT54S7n/FtB3Q7kCyS4Qp8/cwjLjxjumw4/5my+1uhwuSZnFKe/oSveoZr7/Cdt0jwG90iXEMeBtwbmKYLL2NTL7jOknOX76gOTvn07NziqZhXRpKkWtY0pSYhMSHvEZdN5rDNDH2kVurOWoUhXGoasGnr455v4K43/J6kqw++oAPDh2uixw6R1UJ/miZiMLiKTk6Pec/+4c1wX3NP3+3wZ78x9iLLzH+HTolxOEt2yQweoFePaPZrPHonFqNBp8UyTn0dEuwC7w6RpolVb0gvPsl4+EOFifIact+e0E3WIaguXA1XWNYyJRtEWfvUf0mHeC/5cfv9IASYkCGNBvfND5k+W7se3TRUIgCUuDobEm/H8GXuGkkORBSU6ka5x3eR+wsh2uhqZsWKRKTjxn0FhJ6TESZWDQbRFHixgERYu7fEQrrPDHInJzQGolGxPzAl3pG0bv84ZiGAbs7YBY1clETZ9OkLotc7DczRtKDppLmYkOp5mEgPXoaHkyfUkriDPbRWhOshfw7UXOTaG4R9ZnC+NhX8aC4RJJIvzbOTpbobEb3m9mQKnX2kUiF1AatGkLwj63MubAvzqyKXKee4lxuKLL3JASfke39QFGOlIvi0UCZYDZIepQAUxjM3EGRhJxhdNk3I+ehzTqH1jrDvmL2fZB+zWkxRs+gM5d9DSrHYbOx5iHdk9knD426pIQPLpfyRUBnlcU5zzT1DLst/X6HdxMkSV1ViKMjCmUy8C0B3uWBdDsw3vf02w6Q+AhSl1n1SCBkh6k0olHEQuOV4uBHzJQdt+u2pijrxxUb8BjRfvRxCHDTwNgdsONE3TRIpTB1gypKYsqpDpREFxVl1WQZVidMVSG0RZmENiDlLP/LROe2hItfEa3g6fuJsvqAKEpUym3E0Qe8sEDmxaSY37GLukFKyTiOxODmOHUeFvPKDdw0YowhSUUIiaIwaKMYxyHzhgjZU0SOvtdFSbQdpJ5ag4wur17cgB86+ts36DQQVc1XX3/NZ9eWMvW8L6BcLDk6PUMLCDHTj5vTc3TdIKKn31+xZKSpl9R6wVW54O31z/FRUhhFszqmOTmlkBXycEmjHeujNc16PXsOct9Vyru2WQfNihp+wvY7vv36M66+/oLlcsPyyTmhOuXZ91+yPjqnWSxQOhdMRp+LMq3bI7SiaV7C2HF38xmNDCTfc3f1lu3FWySCtikp6zWL1RFltUB4x7S7QEfQcWJeUiCVwadIsAGcxe/vmbwlxIooE12EsilQy1Pqeo0pFDEKVBpBFARvUdERpw7b7bm5vOe7z37Jy6t3nJ+cIELADQdEU7NcrjlarzDK4KJHeIMfxkxVLUv0oJmGPYGALCvMyXucGY2p1igjITpcMMjqGN2s0eM128PP6fpb4vSE0N9g91NOHhY1ul1SJUm5WFEfnxOHA93NJa470BjDGCI3qaR58orjzRFVf4/f3lI1mlVrEMvv4etropKZ32Q70rSluz+w6+5BaKLaQKmI3Q5iYHl8QqufMirH1Te/ZHufV7TOW5wF5z2HYcD5iJA1oV6xOTrm6emG0zazXQKScbAMkyXNnq3cqj1h3Uj0CulG4qQZ1ZoBQ7s65Sw5nrk9RWhJvYOjY/50XfHdZwNPP32Gud5z1e94++Yr+GCDPjlGR3hSwY/bA//cBnbHH7H+4H+Aefdfcejf8foWzn2iqRLr+pxSC1JwRDfgpCRVa4oA0R0I4xanSkxRY4oK4295/e//Kbp5xnb0XA4WVZcs3/szTn/vjPfiQHX3HYexx0eJXGx+6zP+d3pASSFmWYxEjIJxHCA4ynJ2xuuIDT1l2VItNeNhpJAVxVLPtMFI1SwplMSNltvrLXYKSJtYtQ3VpqZ0nsPuQPKOGEB7RbleMamScX9AepGl/JQ7GoY5KWFERpb74NFG4n1Oa0TnkT0cLm8JMbB4fo5oK+xj8VqW7oU0zA2BM4k1PAKsAIL3FGWBUIphhgdpndcwUj501aRsapybhUPwOaZZ5UiuEpkmmyRIIzM7Q9YMKWJig+sPDH1HVZUkkX5t/NSKul0hiyIngxL4FIgpA8Wcnei7A313zzTcE6NH6fz1hOiQwuK9Z/ITIo6YpLP3xnv85CBElJ4l1hjzMDVZwtwAbUyJlDqTDOcYa0oJPZt4vfezUmIJwRF9eETgh7nbJ2+rsulVKkVIWYVIInfsZABZhryFWeL3zrG733N/dcm02+HsRFGWsFplDk0RQGgEMO4PbC/39Dc9425EaVCNwSxL6qrCz50h027Cj45iLIiLCAuJjZ4QoapKTL2iaJbZjJx+bWZGZh5PHEfCNBKmaS7zM5h2gU4SXRQIKVEYUiHQK8Vwcs5+9y0x3VOWEl2lvPaTIGVmXShRUFQlUSuELzncd9zyDTJoFk+ekcqjrA6kiHV54CSJzKeRefdOFKT466hxTk7lx417iBfLuUhRqczq8DbXI8TEGOyjkVmESHA3yPGCqpBIFwjeQhLY7Y67i9d888ufUtbfsj57ThAF73/8fUpjOF/NZXhSE1Lg0E8E2yHKJcvzT6jaE/b/+v/MxWd/TqFAU3B9v+NerimbCh16qnrB8ul71IsN3eEIf7ikaRqM0pkyPBu9Y260hOAI0x12srgQubm55LKb4PgV1fkz1ufnHJVLTGHyQPiwdp29PTLWDN0Wd/+GePQBSQjeffmXDLcX1ItjRFFz8t4nuUsneDSCulqg5sEwxogyBaY+AikIvsfbPVPXI1EMU8/+9g3jeKBePqc4fkV5fkpVbwjT3ZwEi+gIKVZg+7w2322ZDlv2h5HtFKFcMviewJLzszO0grpq8ucUT/QWjUdEx/XtJW9u7nn6/vdomobTT/4AVTbUixXGVDMSwGRvmRuIvie5e2RcZO/W1DHdvSPurjPWYDZmB1Ggl0cIaSh0zfnihL21fHZzxbib+HSxpnn5fT75qGLZrNB1A8M14epb3JgVSlNu0OVAt7vA+YHBBa7vr+nHAaFrdFXTnjzn/Lhguv+G7v4t+9sLTj76e6w/PMKPBy7uL5mGgLcTg3W4CSZZszp7wouzJ+gSFquG9apBP6ww7YgIuTcu35c8KVrcdGC/79ntR3ZdYHI1ctky+cSirXmmYP/zL/n/3N8QgkH9/o+Q+z2XuuT7n6wZGsN3B89QSL67ekcZei5ut7z+1Tv+6S8cr6cl9v2a5pOfsNb3HF7/l1y86SjCCnVi8K5j2L7FuBGWx8g4UlcLimqJn0aEvsmXVXFKYVbIxTHfXN2yD5r1i2esnjzHEKBesj56xbqGuDTEt68Z7C0+1r/1Gf87PaB03YhUMR/U2lA1SxZ1TV1p2lWBUJIoDF0fEKVAWYHtXP5Aryp0gGmyeAKr0yXlsuH+Zo89jPTO0siCk+cnLM83jP2AlgLVlOiyRBYFKQrGmx0MU5bck0ch8dYyTgOlzpFil/L6IJB/Su+x+7mpsjDU5gwfAodpoih1rvw2c2JA5ThwTMxdPAqSeEy/SD37KYJ/9CJkBorBB49zHjdaovN0fY82ks0qUlc1RWlyFE7kJEaSIIwhNdWMXQ/gR2Lw+XaX4vzwyZJ68A5hyjz8+IHJ9dgpO+OHfo91O3zsECL7LmKcjbs4YrKozC8jhYhzkbEfiM5BiiiVMesImCaHd54YE7ooUKbMLAop83AIs/IzG1yVmPP4+X0SfwMQhniIYGe1oNAapXNXhhAZ5exsNvVGnyFkpJwg8giCAGkK3BztrLWhrCqKwiCFJHqPjYFxtPTdxNA7YiJTW9c1VIpUCsqyoYiaeuUYbw5gPWVRIhuJG3uSjJRNRbE5R9YrkA8Fgtkg6PrDTKu0OWKpDWXTIkyFLmoe4HgPEDSpcp9Q0y4p6gUpNSAtRJBJI5JEigKlWoxZY9QCWW2olxuqckFrSqJUDIeeUjQUVYNSCufz7a8qK6qmYRgOTEOPFLlkIFNn5WyMzSvErH6Bd1Om3goIwc4dRQVC56ZopWSuYLA7lLulTB4xWuxwIEx7fEgcJsWbSXPXvuJkdYRu16xXDbJQVCZ3CHlAk2sHSh0Z7t/ghzsWq1Moa7op8fabX5KqltOzZ2yef8jRe9/LyuPt11TDlpqQ3xubc5IU6GpFoWuSDBlZ7i0heqyd2N5dM+6ukbKkWDynXD/j+adH1KVmWbdZyRMuf6AFufco+Py+FBHhHe7+NW/v3zC+uCEFR9McYcqadrnEFCeZE5ISbhgIwwGjGqRuiRp0s2Tqb5CHC5QsGPsdd9vLXGVQNhTNE/TmA8xwlVuYk6JcPkE2x8T7L3C7C0SYCHZimjyH3T1X9x3OWUywaFFydn7K+2cvadcNZVHNkftpZjJZwthlf1awjKNlKleoxTFyuaZYrmiLbELWUqKIyBgIccqG8uEOv7/kMET04jVmtoC19SZ/nkl4nZMyJuWSwPtx5HK3RZQ5tnv66R+SFidsTp/SGImey0CDEITlEcSAu79DpEDsr7m7fctXX/+KpAx13YASCA1SBEQc8L5n9eR9VosNaRy5uPiG9vn3aTYnGUg3Be5uJ3xVslyfs5BLis2KZ882vGwN1o344Eh2xFPhoyChCdLNRuwpq1OHA/tdz+XOMRwmQFJrENKRfDb3nqxrtrf3/OOvL7lXS/7+5ozNacWrj8553+74bF3x40+PqRctz5dLVLfln/03P+f/9PN7bo+PWb73jD/83or/4NMznvM/xf2N5Kf/+L9gN8C51OhC5SGxEzhj0EXBNByQaotcbcDuUaok6gZbVqinf4cP/gxu7gY2ywW1LsAN9OESnz4iVUcUxScs/AhvfsalHX7rM/53ekCRpmK5LqnaBkmBlIJlJakrg9LZVBiFoKoM211HLCR+CgzjAWEE5WKJUOCGgX23p6obTp4d0+/7nKwAZClZn67QfZU7XHJqFFNVlCEQB8ehG2FyaJN9IkVREG3Ajpl1UjYlddtQrSVTP5EmmwcJkyFuzjmElLgQ2N3sKYymbrNPpixLqrpFG5VXMSFmWqqUOJf9Gg+GWO8zOMsUBXVR0Q8D3g1Mg2MaRu7u7tBaMA0Tm+WKxXJB0RTwwAiZfS/aGLzPyO/gRry1CIpMNRXiASeCkAnrJ0LXMY73DLOT3k09IQxERiJuJsjOOPOYiCL7QHK5YyK5gB0cUz+BtzkBomQ2RMbINNqsZhQlRbtE1y1CZbUoJR5jqw8x5IckSYbW/SZ2XhBmxouWJneIpIzpTxGUMWhtcNbliZBM8CV5jBQkUZCONtngmhLDoUOVBlUUmLKkkIpgHdGSX6CQuTZSKaTOcLQuWIZuoIwVVdVQbRpijNidRVQlRWWopUMpWG1OKJolQurMF3E9pMRkJ3AzFl4rTNXmVc2sLCHEXKYo58Hw0X5N06x48fIH3F0t6Lp7ssG3oKwbmsWGqj6irDYY06KrhmaxyrC0lIjePgLqYrAIqedBMPdHpRBm03EkzHHwsq7yr8WAd/m9KpTOyo6UM6gvEB8Q4gmqqiIES/ICM9xSje8Q7oBLArd/x8W7Sw7DhKwXbJ7+Hs8//UNOn+9pFBRlmd/DIr+GKYKODtwOv98TdgPv3r3lttvy8Y//LA9Xm1M+OHnJ2ZOnNMujPGxK8EnjdcR9e8A5S1ktqUVADrfE/QW6gAnY3V6w3d3gAVWtGC1MYkGpDIUxLM9fshCSONxmRSHroXk4DClzh9xA8hPYiX1/x35/TY+mPt6ybFY8/+BHaFPkIkeZlb1ZYCXIBMKghMGISAojF9/8HD+NVItTnDIc3JjrMwBhCurVc6rNE/z2G5K9QckXFEfvEeLE/Vf/nt3NW26HQPKJ6CKj0Byfn2UOiPUs2oZFU9M0i3xZcRMiDNhxwI4H+qEj6JqqXSKPzjhbHtOsjlgURU5/5fpuZAqIYAmux07XxP0d0/2WvrdMsiLaEcqCPC0YIJdzFiHippHgJtw0MgRw61P61THN8QkfGINWJQUCqTyoiE8CkQLSTwh/4Ob6a253e4zKQYIkDUVZUxhNoUHKFqLE+4HDzXeUxx9zcv4Rz2PH/e3/m4uf/gvi+Usu311wS4V68YJn5ye8OlpSW08yUC0VCpu9jAiSD4Q05DVtDAjrsIc93W7P7nBgbwVx/ZzKTOjyHjl7r4LocEDvGg69IkyW6/2Bew3vLi/5+y9/j5eF4M3PL/l2J/jDZcVZlWh7z3d/9Sv+/GbL9k9/n//ln5zxo/ff4w9P17zXHqGrc+IP/xd8f7Pnn/xX/w/2/Y4TX1HURwijifZA6Au8bnHmgPN5jeinXe7UUccU7Rkff/JDTr79it5GohIkXdCGgml3hVitaYxBlzXj4Ut2X33zW5/xv9MDyunTc47PW6QSTH3CjQ5jIkpFtMgHeogenzylApdG6kIwRMl+3+FCoqxqTJkTDvv9jqKoqJdVXiukXPqGSDRtg7X5oDRaIkVkebwiIXAJhqt7+u5AVZcYbUgh4aaASBYhoFy2rJ5uQOusBvjsFdGLGgqZjYOxYOw77q/v0DtN05S0bU0zepq6piiy1yKmgFL6MVqbT+8HaNaD8TA//IWSTMGy6zt6axEuw7iGYWA5dDSrBW3bUBgzqxC5EFAXJb70+OGAtxNiNpWSwCVP8A7nR/phR397T9/d4uwB7ywhWpA2P09E9tdkc2/ODQTpsG7P/d0l05gVAO8m/Axtq8qKEKAbpqzwqAKhNbosKeoGpcsZdz/j7edUzq/ZLfMElV+QjGWfb+0Zf8+Mas9GywwjTdS64CFQkqOy80D2GxC9zXrNcrFEIGkWI24asS5w2O+pTJE9K3PfUdlUxDFhe4ufEv39gNceJ+wMH7OIBah17mkStcnqz0OoNgnS1BNSZPKOaeqp64YYc/lhUWRoHSo/vJU22SiaX/aMJBcPyH2DMRWmrCnKlsXyCdY6irpGSEVZlpiiyGwbZUAK2qbCCI2fQXm6qNCqwLohv05pboCOkXE8ICbxqOBBNpBDXl16ax9j3IFAWRRZ9fMOAXjnKE2mxrpxQIwdwm0R+y8YD9/Se01qn7MbCi66/KBvZCBawfGL90mnkPZv0OFAVBoZBaK/hGnL1N/RHw6Eg2e7j1weoG0mut0Ni5NXvPfxjymKkqqEZApEFMgUMELSt8fYxQlBpAy50xXd5Hjz7/8Fjfa45pR3kyJIwWpzynp5wun5UyDghi1p7iBqFyc4DeFwgQoeHwNilvSHbstwf4cdeoyUJFOyPv+I4+UJdbOmrkqklrlgc55KhJAgI1JWBGcZb79itB5lau62V1zcW1LSnCwLmuWaU2Py+ypFrEgUylBvnjJJzcWv/iUHl3j+6X9ASprPryZ+8dkVdV3yYl2zrjXH7ZLT8ycURcFhf4dMgqJuEbLBj1eE7orxsMUHkKaF+oxqcYJZtIhyQdke0RaKIo1EMipfJkvq73C7azp7IHqF7ToOt5cECsrNkqpoM7dEhtmA3jOOFjuO2KjRRYOoFjTHRzw5e5/NsqFQeS0s0uypwxC8RUdLmrZMhzu2V695d/OObedZLlqKpuGsrSm1mX15HmwkOMd42DGGO67Lv6apNNoN7Hc9P794w8tu4GTT8r2/84e0VctJKSmlIBwGpsESnWMSCSMDSuVLXjfusd5hneOw77nbj1zc7Bh9pD15wfMPfoRJI9df/5zd7SXTNDDZLc57hJAc9pFDb+mnhA6Wb3/5DX8xWX4WE59fbHkzCT7/2WccLQqaowVdVfPxP/w9/ld/8vv86FyzURVyIdFlpNCg9Xv83f/of4a7/hV/8a9/SnebAyOFLpEE/P4KIQ3RlAz9MleViANxvCQpQdDnlOuXNK5jurxBpYakHGmy6NufcV8kODmj6u/wlzdc/vzitz7jf6cHlKqpqdoiS+gIkAbrDsg0EaQnBM84RfZdjx1dBmMFKKs1RVkxukAcR9ScTTHKYKcJay1VXWWfBfnhWZiKUhdYa3HWElOgqCuqVQtCURjDcGPodlum0SEipJgRzdpIQnQEEak3FbUp8D73iihjHkv8VGlo12v2h4Hu0DNYxzA6tOpY1DUn6zWLzRLE/PulzgwUn/t5HsBWIcS8b9YKoSCkwH4csDlkQPCB0PdMMWDGnuWinUu1ihxnRaAk6MLgixJrJ+yhB5Ue4WjjkJM+u/FAd7dj7O+JcUQQEQqE9iifY7pS5QuQkVnmD8Ex+jtCclh3h4BclhcEVXGUmR0yr160MSitCUiKqsyFdHMkO84rraIoHn0n2aMjHn04cS4YzOZY8MExL5bwITBNEzGJnFwJjuQiIdicCpF5ZSNUlmoEMT/0fGDZLlk2K/rDnrHbMgwTw77L3SkhEm1gsi5LynIGt00BhKFatlRtiS401WqNqSrq1cgwdHR9jx0PIBPbO/B2ZLFYzPHuiGpaVFmAmrkURUWamSwq5fUVUue/8/zrVVXjnJ1ju6CKklYbWgRFWWYlKpKHGa0zXC8lRJr/3iIXIYbkUSmbk02RW45D8LnFmYAQea3WLJaEGLDWYsfcoxJTZscoI3OjbYi5MTzmtaiSGqMNIljc1TeE139NKjRXt9dcXV9R1i1P339Ge/pDXi4+wg8X2P6eSUQWVUGxPCMUBvvm35D2r2G4J97fcn+34+5+YHCStlC0x8d8/4d/yNGHH7NeHFNUFYV0oHJnjE4KLyMpZvOrKQpCu2L39jPkl3+JLmvevXnDL68nlpsFRyfPWJ0/oypLSp3QZZufG1oxEBn7O6IdqMoFZVWx67e47VuS67H2gBs7DvsDkw1zJ5Bks3xCuT6nPt7k3iuVU3pZvMq9TCAIYSD5RL+95rvP/i39Yc/y6Dl6/YSzT35MURSUBrTKnTYkCNMB0V0zJU25WaIXx3T7nr/58/8rfxANG5NIVc2Tj3/Ae0+WNFpQDD0iOYwpUUWLUdsciw8WIyT7oWPX71HlCrPI6Y5FsaRaLNFSELShaNdUusAP17jDO9J4TxoPDPc3HK4vCNFQHz9DNSuKlF//ZnmCahrSeE0Y9oyjx81gQ4oV9fH7tOtTVHQkWVI3FYWaIYAiXzJidGAHwu6efv8O1+/pO89ucpSLE57XEaOAIvegiZAI1jINHS55/BSwY88wDrz+q3/J7u6Guq651y1PfviC5y/OedGWqDSRYiAGl4fuQqGSQQSBS4nRO5Lbk6aB3faW28GxdeCaE9bP3ufpkeNw+w4t4Uh6Fusj4v0xdr8jOMcUcxePYIulYucCNkWEn9hf3PGPr/YkqXOiTZZc7h2lk/z41ZJ/9HfP+ZMnLWt1QIcVwkSwDiH7zHSSUB5/xB//o/8J12+/5e5my3IRadMJVX2Gkhrf32BNiZI1U1GiZQHjFqkqkmqgWaKPP2BtJ7bX93ibMO6AuPg5F5ffET75OxRvf85f32h+3rz6rc/43+kBJZFlZSNLTFugm4r9O8v1zTWSfLPtxxztTD6gpcaOjvEwcHz+lOWipR9cjhzHbJw1RTmjxx0p7inraoawWYqyIkrN2OV43jTuKKsGqQTFpkJXp9AYpu0OMToqVYLSmGWFaRoQmuATuoCqLPINOUW0zAeuEZqqlpTLJdZZpgjDYURLyTA63OQo9x3rVZVv0npuAX70psxcDiFRSuNDwFk3t8TmhFHSkuAc1nvGfqIKYF3PNCXWy4bNqskxbJWIRuGaFSoprL8luhE1w9u6Q4f1jn7sGIc9IUy5PFAkCjX34iDQSiJNBCVy3Dnk5FAMDs8WHx/kaoORDaYoUGWJrCoW7YayqrOxtTAokVt/M7E2zBFshfe/0d1iZmQ6CW1MTkdpQ1EU2GlCoNDa5MFGZJ8SIVCWRR5Gvc/lXLOBU4pcHhfFbKolx7bbtiJFiGFCppowTAzbEbfPBM2MtFfIWtMuDElGdGWQRUGxaFmdHpG0ZHF0DAiGu2umcWTqHdYFIODdDjdM+KnLapsp6ETAlNW8hmvRUiCkQShJUrmAMKVcQqiNwVqLt47gfVbbZnaJmAdbM5uwk8hVBGWZ+T7OWpILeBlIAlJIj0NfUZakFJjGEa01ZVkxRkuM6bG8MYSQo+gwR6HVXC9gcbaHmBH3Iuel882+27L94q/47F//N0g38Ozle9iioD5+ghGK0XnauqVaP2PsDPVWMI09ceqpzxZEAePPbrj/6ufEEAhO8MYZ4vEzqnpN4fecHGk2J2uK5QJVlzN/yCBTfITNCRRCZLJp6Hv219f84rPPKPXXnL/3MaFa8/6f/COWqzVNkdUNbYpMMXUHvJ9oF8/y6zjd4bo3jMMTTNkS7YGLb/8WN1lE8lRImtUJi2dPELKE/o6iXlCWJSLknkiEzARnYv7suAPJZY/I2Pfc3lywU0eIp+9Rbk5YNMscDwaijMjgIYacyNhfsb15y233GU9cYnN8Qi8L9lKzffs59dkxHz45QStFVdWQIq7YEYYdplyxPHqPNE18/u1fso3w5FlALzZsjs4x7QnaFBAcUZTo5QmVKrHTHSFMiHKFCJ79/TvSNJBSQaTFU+NDoCyW6GpFS2Loe0x7QtvUXO9u2PYWWR9Rrp6gpSQhqVcb6qZBhI5IhajPUGZC2S1xuCfZMQ82+1u621u6rscBBEllFGiNJBBTwCVLmjrCkBiHA6PtmKZA31sGa7mdPJ1eU4qK49On/PHzF7SFQsuEJBCcxAU3U33lY82HjJGx6+gPt9xvt9zedxyGxJAUVBXHJzWvXn3Cql5w8fVPefvdT7m7/AJd/YDFyXOa7S3DYUsi5L+nHRhiRWKNSpnSPflc7pmEI5UKUVS0H7zgP/yTE/7TVyueLstcMDjscUUiKomSHjG4TDFWBUKXbJ7+iH/wpz/k//5/+WfcX29pzIRLmmr5FMKEPVxTasNUNmh5RiEcwt6TVI3WEqEbiuUR7d13HO46wnjNzbev+aq/w9en1MUz1N/9AX9XG/jf/u9/qzP+d3pAaTbHhOkeP+7piIwOrq+37G/v0CI7tJ2P1JVBmhzD9SFAFGzv71htEpUpgMBqvcL5QHcYUFrnW17yjENH4QtkWZKMxJQKZzVSJUyKTG4kRkl0AS0TR8/WhJMaf+jRwiDqCmGy8U+XJQSINmCakigEwUdkSpRFkeFkyVGWhr6sCeR4cTeO9FNPPznawbM77Dk5WnO82iCCQ+uSsqpy9DjNxYN1hQxhNtjOzcgPsWWdSZ6Dc/jgcIWknw6M1hFiZLMQFIWmKqsMaENgJ0foQ+7ciYnkRybfM/ZbQsxrLPOAhp8JrQ+3b6kESUaCyGwYJWZMdYx4n9uSJZqyNCwWNe3xBt0uMGU1d7Ck2ZeQu2AePCBK5TJE7yyklPk0c58QwNTnVURVlhAiIkFZlHNKZ46Dxl8D7UQkR2JTvuVbm6PJpsio8xDjnLjI5k1vO8LQkfYOvx3pLg7EAEVtqFcluipybFKR47IpIHSiXBhWmw1TDNRVTdcP+Cngeke0isqsSDgmLOM4IUTE6KwCDXuJUgWVUeiiZFquaVZH1KtTZJyR9yHXwosUSSEwxTxcyJmW+9AV5GVWl4oim6V9CNhpmuPLIjNMfB70jClQOv++4DNKPkMDbSa8mmL2vST2u/uMwydzeow2FEWJswE3OYg+G76loWwahOuI/Q23n/8F333zLVO74unJRyyPjhEiIgqFC5CyrYfm7Bn18RnddKB/8+f0SbBuNoxTxxdfvebtr67ZLCqePDnjg+9/j8WzTzGmZrr5hnT4DiESKmWCppoTgEnNQ4kbIUSsy8mVq3fvePPuHWn5Ae3ZMc2TF5hmkZU9ST74w4TyEaELYugZ+htWZy9Ar6nua26+/RvG3Zb1kw/Zd3dMizWmLTG2R41b2rKkWp6QqiW2MmihKHVLVCVC+zygB0cIFmcH+rs3bO+/Q+slqjmnOvqYFyuH1ILaKISwJOaC0RCI3pHsQHd7zd3VBZfbe952gmHxCz5J73G03vAnf/x3OK7WFFo+8o0yZVmCsAyHLaTvKLQhmYLqxaeUmxX65DlNvUHq+XMtBDEUWGRO6dQn4O+5fvcL5OJAcHtSapBFjdQlVJYoPHFQVGffY7VaMX534NubL+jVkqfmfdTxK9bPvke1WFMaAzZD/FQhkWWT1YBpQquCqlrgpnt2l18wXr4jusDoHKMLM1xToIuGo8UxNkXc9A57t0dMDuyeYeoZhpG+j1zcjHRCs16fcXZ8yocfvs+rZ0ecNAVKgZ9GvBuzkqs0WntisIw4rOvwXYfbDdzdH/j2fsfNvsOngkW7ZFMV1JXCuAPh9i3NJz/i/Pn77K6/4u76LbvdS05PnnBysmF3/RXxEHCTZxwkI4pYZAVYiIRMOr/+KTFgOf7REf/zP32Pv/espE0RESLR9UQjcGOJVQfqMCCShX4g6KeY9XtgWp59/z/ixYuf8sUXb9mUe470ljAUUB0hXI8dbpHlikE1pKJGTD1abPFSoVuDXrygOnrN7S/+nO9+9ppf+hO2n/4BL59/yvH6hDMN49j91mf87/SAMgw9t7c32O0elywxggsyrwaExo0T0TlUkxuJnUvESkKSlKbAjh3aTEhVcthONIsNRWkYB08SkrJuIHjsOOCDI6ZAWdfUTYG1mX8idMI7j9CK7nBgHDsKozDrGl0VLNcrirLlfnfAEzASIh7nBtRcjx6tJwaHFrCoC0Jo8EFwt9vnThhTEoVi5zzD1FEVCTd5cJHj4yUgEVah5wcLRJxz+Bgpy5KyzhHkyU65bZccqw1J4JwjSoPWivthwsVE148cr1qWq4aiKFDrFikEfSmww0Bw+TVxPl9+meXUFLOB0oWATgmlBFKnzBOZh5IYyFRQmea1RCSGB0qrx4UR73oMdXbQS4WQBucc4zhklsJseM34eUBopIS6rPGRuSso5gMzJbx1OPKgk3zE+exPeXwIh9y1E+eddVEWjxwarXNqSsS5YG70xNHS7w5Ml7cMdx2289jRE0VgcbqhWreIQuKTZYwWN1qST6QQckJ4KriZ3mQ1a98xTSNhGEguobpM96UtEToQpnuSzx07pi5/7RERihhh3N1g+zvs/o7V8TNUe0QUCqE03tvcGfTr2pyZTJx/oSzLxwRYTJmz88CZQUCUioc24qau2e122NlH0rZtXp8xNw7rrN5Mo80GwFnRE1JmkKGbEDNiPSVFjA4tO8Khx96+Y/vVXzJ6x+nzp7wsG0qpwRiEzt1JZV3RH+6Y9m85Tu+jyw22qHn39htuf/Yrbm+3LM6eEY4/5Pz8U1p7w5G0HJ2eU26OEIVh0q/oRUSaBUW9BGXwYSDgCdMAPjLst9y++wUIiWnOidWKk48+5ZkuWTQ1ZVnn1Fh0s0IkECL7iaQoMTHRX33BWNXI+oT9/S3vrm+pg6HcHGjbpyyOniMLg+j2uNtvSNZjhEAVhpAWJHvI/ihZEsaOqbuj290wjR2RgqQKhrRBBUNjaprVMXVK+O4a4XsEM2F62oK1bPcd3f4Gu7vHOihXZ/zw+69YtgtKJWmqFSdlRRAzBh9Bio5oJ4IfCd2B0cMYI0UpWR6/5NOXH6FNhVZ5BSVJeVWeILmBeLjG9zeY9Svo97jbd2z311TtMXVhELjMSUoJYyp2d1eIfst6tUK0Zyw+/GPYPCWenLBWBSpFlIgZ2VAY7ARRLVisnpDGivvbv+Jw8SXu5BnT/SX3F5eEfiRYjyprmnqJjpbRBpzW6KPnrJZHDO8Cby+/5XA/0vV77saemDRbW7J/+jGvnh7z3kKwkIn12RLdVCiTOSdBSERRgbcQHC5kSGXsd+yvr3j75prb7cAYBKNuqDfHbNqaWguUCAjhCclx9fZLxOqY8+cf8uLV77G7veH69c9RfsAOO/rJcrOz9GPCRQnCUaYuWxqizKWXMRCl4tXHS/7zv1vyp88UZSQb+1NE+oCwE7if4e+vmUyLPv8TjLRYpRBxhSo2qGf/Gf/wf/wU/1/8r7l8d48q3rIuS2SQRLWE/gavlyi9wBag2hWFHXBynwtKqxOKp3/E4pMvufnskvrkFb/3gz/i1cmCsiqoYqRzvz2p7Xd6QHn75ecENyBjRBtFoUuM9khhMiExZWhYjPngKcuCxaIlJoW1lsl1HPoDWiqKuiGlRFGvKKsC6yJpciwXNWVdcnd/h9vtCYOnPVtS1wV979C6oFCKmBxlc8bd3R3b/T2qz7tx5Sxp4SiEJsrsj4iBXD4YJ4qqzB6KMEEMFEqxakqsz5HeXTcRs+xASpLJDbjeE21Ap0ihE6uNRHiFIK96ytLgY8BPlrIoOFpLpnHC3Tg65xAyU1uzGcwxTD2lbHA+G4mHaWIcR54mz9HxKje5Hi8pW0233eOGCVPW6Dr3cnh3IIYJN6sdQuauRCFC/t8IZBKPZlmA3Hmek0NSZNKtC5Z9d4e1A6thQKzOM/fBqDxoAlplzkleJ6Q5QpwPW11UEEJe5wx99quQ5gNaPsLbQvw13j6EkCsESkF4MHHGQCQgRUKKRPSZ6RDtSOxG7L6nv75jf7FnuB+y30hAfbakPa5xOPpDx2HYzxyWhI4FMiq0MPTdgWk7Yhaafn9DDBadFMJr5OBIPlGu12xOFvSDYRh2eLIRujD5+yvm91IKkugmDvdXpGBZKYOq1nnYEA+m6TBD6wq0VrM6wmOTcoxZ5cix4ezVqquSanXE2HfZp+Mc0bsMaEuJvu8py4K2bZBCEObkU/QP2H1FVVWMdiL6fPsv20U2xI6BhCKkA/HuO+7f/AphKo7Wx8Qk0IUmJY9WmV8itUKnhIyW7Zf/gkpGFsev+OLrn/H1TqNWz0mbc9ZPn3L83sdEpRH33yGuv8zgRFMQFVCVpLLFxoiKEBnxMyrdjROYmvth5H6UGBJHlWZ99BJRSrTbIWJAxLmjJfnHlZCfZrJ06gn9nqtvf0a3vePJB79P0JKnP/gJy8URi6pGq8zZSQjC4ohRSOxhC6bAVDUVsL34guniW+rmlM713PVb+smhRaBqT6k3r9icLgjdBQIPylBuzlHtGnv1Jd3VlxASwXbYw57toc8rz6KiVInVsmXRVBRaEfyIZVYT3YSzPcmNuRsLjSkrxOo5509PMZtj2mVLpVVe1wqZe7EiEH0uDhzvsP0BO454r2AcAEldLREyIL0nykSMI8lHkgskP7JPgmHasRCO+sl7PAmeUhUUWqNlVmKjKIhSoWLAuHsOu0u80qgwMG3fcfXtl0y7C0TKze6qrHEMyKJiefoKJSR6/477+xvuLn9FrV5hfM/99ZYvbwduoiRVz3h5fMyHiyXHL97n5GSDdDum60uEzbgGpEJJhVeRaC2MPXE64LoDfTdwfXHNF6+vuT5YqmrJ0brheVOgpESJXBjqE9iQSMkyui3vvv2c9XrNclHSVpKr777BHUa8H7ndBu5HBUqzWC84WtRIKbnuEpf7iPUOhOD0gyP+7NOWH+pE7PbY+ohCCVJSEK7g8hfE4S0HXWPO/4QYHGutkcrh6EnFOVXRc/T++/yDPzji//Zmy353oFy8ZiFbYpMggN1d5gSP1BhVMkqJ9D1pKNCixpc1x9/7H/IT/ZSbq45Vo1iYKveuSYX/zW6L/5Yfv9MDCnagKhVaGZSQQG5UFSIitGSxaCgKPdMrFcpkFcE62O57pjChC0mtNYXMaYSh7zBFQ11XjMNItz+wPllzdHrK9m7L0I+Em8Dq5JiqKrCDyx9vKShLzfJkjZVwuLnF3+2YDgfqrqNZrhG6JJITGKbI/z2QkGWdm4Jdpr+2VZlvFiLfOG/3A0EAEoTOgLLeBfbdyHa7pWpqkjZzeoJHdUErmQsNtWDRlHRDRX83zrXeeeWVnMMHjzIGoTU2Rmxw4Cy1kdS1mc23GZGfG5QzZ0SKFjaJw0EzTgeiHxEEtJYoLVA6ZLOaB+9z2eFD5DUlMceE54dPDAQ3ME2OXmiS84ggEEJTLASQjchmptpaazFaEoXA+YzIjylSmDKrJj4TTHMvzNxUrB6ou3lFpGf0+kO6BFLGtidBsBNaCMZpmKsMHGly2Puew9WOw+2e7X1PdB4tJFJLwuTo7js617PrtnMza0Ing5H5ay+KimQjLo5QGJydsLanMQ1VbFAiIWKgKTVHT45YiCPu766ReJKfiFIQhUGmfAsRSqJljoVbbxhdoqllJvfGzIhQSoHIA0OMAWsniCmXXM7yip+TX2r+KaUizeqhSAknmP0a2ZBel2VmmwB2nAgh5M9Z4jFdNY4Ddsr/TEhJ6SxaaeqFJk0j/pDok0AvlxQxQMp4e1VWxMLk3qoUMGGE/p7h4lv++pe/4uhiyyc//vvE+pxP/sF/n9PjI042x5Slz6kjIYnijL6/BikpzBJfaVzYY0zJ/vVPEbc/hfqcd33P3jqUMKw2T1mdvKA++5DY36KCRRtNtX5C6BTucJVJsSnX3D+s0OLulmF/gRSB0SZMvaHYPKcsVywXa1IhCMLPn51IiJn+a+qKkAT0B8LuHRARh2vefP4zLm9vePryU+rVEcnUrNoVpVBEaUBCs3mGXKzodq8J0WcYpVmxvXvHxc0FKUl0SjCN1GVJtTxGKkW/vc6JpcVJLiu9/5ZkD8QRBpHXrgmJWT2lXD8BU1EphapaynZBoRMSCyhEigh3IA73hKlnOlwwXHzL2Fvk0QvK1RNwAwIwusifUT9miKLtSWkkSkMqT3j6o59QL0qaUlPIRGkkSsY5Dzb7qiJoZ4nTHnf3hrdf/pzbzdesqobpsKdq1gif8C5zk7TQlLrCuqx0rY6fsy4ju29+wc9+9gverteUMvD5WNG9+ICn733Ay80RJ3ZPFSfKxqCqGqqCODm8kLRVJn9PtsOPe6TdE3ZbLi/ecHG1Y+9bwtEzmo9f8Wx/Sx06FoVEJEkSuU8tiJjLJ2PMtRrO0b/7Fd/iqQvPYbvn5mYk3l+hC4mjZHlU07Y1x4uG47aE4Cn0iJsslx6K4yP++EcL/vSZoGLEjyEbWWODHr9kuPl3hPsbyvY5zbOPqRZPkdO3JFVirCe0P4bqDBV3SPUzSukYnETsFOtmi9U3iKLEmBXB9bjDNUrXTFqCOqHUmuQcbtxhRISy4fz9n9BubohyQakXSGlxIc1+qt/ux+/0gFIW5jHyK0LE4xFCZyqqDigTKUzBMI30Y4/3u0z6lBWmbmjaY9rVgroqED7S7/YsFjXOR2KYaNuaqTtwuL+nXi9ZHW3YpR0SydAdqBcNZSkZeofAMI2Zunp2coLSiu31Ff1+ix7uabtbmmZJWVZ4qZlMiSkqYlHhfMyY6bmOXsbE8arJO24hkcZwfX8gxJA9EEnhXWLXdawXkmmaqJYrHluP58PCFAWJgEyBtio5O9ngUuJ2O+DcXIUdIykk7Dgg6yanYyZLK6rcYpwE45Sd6fbQ0+/3mT8Q8hqgLktk8RS5v8W7DnCZkSESAomfItMQcjdJyCsXZMxflyT7AfRseCZB8iQZOUwHCruk9jbvl3VJ0VQ5pQJz14tGKyiMIob892YmkoaZjcK80ohxLvqTmgizr8LMcDCNVpqUAhGBTPPKx06k4Ene4SdPGBz7uz13F1vcIWCixLoIMg9E097gPAzDAD5RFCVIgSkr2uWGtq5gsnTdnuACyXjGNJJEYowTDJEQEsu6Zd1WVOuSUrfUmw1VWbHfHWZGh8RO+3nI0xiV+5+EEjRtps7GGBFSU1QVWhnGccrcHDfNKy1JVZaM0whk5c2Y3O/knWPoO6axn70iuRogJlCmoK4qpMxVE846gstJHiUkVV3iY2C0Y+6FSYlCglQxm2OHgd3+5+xuLnj/gz+jfvJRrhfob0jSEIRG4SjGHYz3uOGe/X7H3dbxs3u4XX6fFx/9Pstnr3harTCFQqucEEopob1CKIEzLXpxih07fLfHyDV23HN//Za//dkXtLri9MMSuXnCarOkiglpcgnlpllz0IHd7bdY37Fuj4hVSehv8OOBFBzB9vh+z7g/cLPb0h22nK5q2sURRy8/oT1/gS4zIVn5SJQaERIyGDAiF0uGCSMs2/tvefOLv6RuNoSi4noEef4p8vwVy2aBFgkpAzJqXLI4f4eSlvr8FVorXn/x5xAnqmKJ7W9ZLZ8iygKjJNP2CuU8zeoUXZS4YQci0VZnaA3fvvmMm3cXqGZDtdpQrY+Q7THN5pS2bZExwx5DvaFaPqXAEvZfkLor0rDF7r6h216TzBFBVhwGid17FgsJVUO0I3LIHBkPmUitS6iOMaZEGIMwa+rNMW0l0KRchJhyLF0lh0+O6CfEOBGGPV235bA9EImkwyW2z1BKpSQhWETKfKXoAyoF+rtLvrj+jg9ffYQe93z11Rv+8j7yrD3nD773B/zo7z/lZLngWAqkKZmGe/ztNVJESma+UmmYrKNIGhl7+rs3dLtr+n3H5fWezy/uuB0dy/NTfv97P+HVk3P23/17Lr7+Kf04IEW+BIRgCf7XkfsYLON4YOgs97db2tWS27vA/WRQteDp8ZqjpxXSJAoZKZKklJJpFKxN5Gkj2AfJ6brmJydLjgtHcIEyeZJ6SzhccP/d39LtE+sXf4B+dkTZSpjeEHXBpJ4h1IBI12h/jtAFoTzFrP6Qsrjguvc020jRXFOZBWqhSWZBGPf44ZZBa6QxGFHjqh3BBIKdUGmirhcsyxdMXpJ0BXGPCgMig6J+qx+/0wNK5l3kJIdQkmJmP8QQ8NNIGCe8i/RJEJVivTmibZY0dUPVGEwt0SoRU2ToBCGsECpwtKkZs4hAUTeMXUe83bI5OWJzvGTsJ1KC/jCybJcUBqx1mZdhA3UteXKywiC51QsO95eM91uGsaNtG1aLJTF6QiLDvLqeqTOYyqB1yTCNFEHTmgK5SJRK0BjJ7XbP/QHCrDgIm0g23y5MYSh0wTiO2cSYsqkyoJDSUBSClRSkE0gB7rYHJudIszdDptzREUNCpkhRKlZHC1Rp8IMlDJ6pG7DDhB06EiEzSYqStlqiTY2fJqwdmKYdwe9mOqbMJuKYAWlCJMRseMxpI/XIbNHaIKLCR0fEMdievt+jdUmQkmHoszckJaq6yfk4woz3l/MBK+bIcXhc4zgXHpt3o8tDbLVYkKLHO5fx8PUSe9iSYsKHXGGPteB9fsFiIMUcUw+BzPjQAm2aXKgo8oEdR4sSCl211MuKalFRtxWL9ZIUEzdvRpzLxYMYm2nB0iB8ZLITJTq/N5sSjUJIRdIFTbMhBYGUAaTCmKNHvl5Z1YSQB0JlMksl21UlxpS4ccqHKh6FJopccuhdNuBWVTmrMCHnRLxFZdhKNsXGzJpZHx3hyTH1cehJ0VEYg9TZgVDUde79mXqIiaZZ5Xbx4AgE4uQI05b9xRUyCpwuWD39lOb4Jfe//Cfo2++I/UQ/XOKGiXe3O/Z7Ry2gWZZ875Pf4+nv/X3WR2vKQs5+JDnzmRNaGCJxhjRW2GrN9Rd/wd3X/57Ns4+5t5Krrufo0x9zcvSK1WZFUQqkahDJMwxb/LBHHr9kUTT0hyu6m284Pf8EUxridODqi78hDh3IkM3F5QnLFz9gZffo4TVt07AqFxhJNqoiQKsMZ9OZJY2V+NgT7I7u8g3fvHnH9bjguDA0qyPe/3hD1a4wVYnCIbyDaWQiIH3Eb6/ZiiVGKdLUsbu7od++5cnxCxSK49PzbEwXglJIhvtr1HLF0bMfkFTFN3/zX2OKluX5e4Sj59TH77NYPaU2EZlUpjg3NUVlEAGII8n1FEJipMC6Hf32FjftmS4PdJc9+qiiOtlQLs+IsqY6/YSj00/o3v0198OeoBpkUWLKBbI4Rh+taXXCWotIJaZqESaQ3ABxQrhDRuzbjnF0dLcXDP1ETBOSGmFa1vUawkhI+VYuREIRCMkTRos7jIT+wOXlDT97e+BXFwfee/YS98M/44+fPePjp095WbUYmYtmTRQ4PGVdIJoCgkKVS6TwwMTh28/Zu79BCM937y75+r4nrZ5z9uFPePU+nN29Jg49xm4pyg85efE9+v07hu9e44PDR4VIjhQc0+QIY8e227HbTdyOBqsVJwKK9RnvnSoWreGkLdA+kJwjYpmsw/mE0ppFW2Nt5EkKnG8CZZFIPqGZCHIi3vwtdxff0Psz1ufvUS4SDFeMriA0PUI9RRYSHTS6/wpXHSGrc7Q8o/7kv8uPfvAr/uW/+oy7g6DdHTD6HV4VpGVDiiNh7EnFhO0PIBsqoamwxKICExiLEWXWoCqMHJExZbL47vVvfcL/Tg8oSoISed84OZelZACp6CePVAVV27BuGsrKsFxWlKWeH2BiLrDL7Z7tqmB1dsrQTwR/oDYemQTDOGE0ODdye3Obu3tM8UhtPRwOVHVNWRfYyeGcJYq8WthsVpmaqSPb68Ch3xLClskONM2KloBQ+XCPMdF3I1JkUuLQi2xwrSoaLRGLgsosUWpgt93joqDAoIxGG800ZTldq4KmaTOrxQWkFJQi3zIliWVTcLJp8HbCDzzGc2NwSAmlUhxVJaerBW1ZM+177DCBj3hvMWWBKTZIo6jbBc1yiSlbtod9/v/f7ZHBIwsD5LWOTyEbMWOue08zy0AXCinJSZeZHisQFMFDiLhx5LC/oawkq/q9HKOOmbfhnZsNsjnamsmUeW2kZ5pvCDFHW5MgRj+TZCMpWawdcvoncxqBkLHfOTuVWQZhHjwSCCkoFzVLJEkKRJAI5qhzSvNARC5W1AphJOvjDUlEfHAUC4UfHUKEvIKMgjRAXdf40ZMsaKWolyXtukYV5E6mmUY7HLZIYgaWxLzCCzHmdI1U+bPwEDeOEUHMt7MuDyNKaoRQ+TAXIUewk6AqW3x0DGNPSpF2sSCpEjflIkRdFvgw03BTAOvyKtL5PJAJM9cgeKZuj0yJ5NwcKR9xaUIkj4yJ5eIFoV4Cjt3bz3HDFQZHWTTc7Pd8/fN/x/3OMww9m3qJbReUHzyhRbKK9zw7OaJd5EoBvCSJSFJ5/560BlmTvCcy4eyB4XDL128OhGh5f9lTLV7w9MUJZVVTmBols8dIEkgym6+H3TXLV4nF6iknR8/44t/+P3ntLeunH/L64jtuJ4sp1xRxogwTq7bFnD4hcUZ/k9dfUilSksgoiUrhyUhbYSdsd83Y3SIw2LTgvguYpuW9j89ZNIvM+pnrAXA9hI7o9ySXG6OHbuT+cMv25hI3XLGqV5yuVmilMapGykTEMm+dMRqGacv+9S9ZNsfUiyXi2Q/YNcdU63NOTt+jUIpCC1SAwQ1EqSmWT9FVBd13uP0Nh+FLkt/S1ivGYcQLgTBLzOoYNYzo9pjq5H2m7bf422853H1Fc3IGi2fU7RNkvcx9W9NIMoai3mC0gniLTRFdrijbFnf3BePd1ww3V3TbO8ARU8N0OOT4vYpUZUO9KBG0eLsnxCkzivyIPfSM99dM+wN974nlim7xivRxRfW97/P8o+f8eNFSGoORAp8CIjhSClipwJOLTwvDeHmB8BN1kRDXX/D1L3/Bt1cdoii4nkAul5xULU+ev8ezJ68YLr/k4pu/wN7/iv39KYvTF6xf/B77mysO+xGRRsLQMw49d13H9r6n6yK7WNHVJ6yeHPPkxZonbU1lBCSXQXF9YvSZbxWjxY0BpRakJGgrzTMZeN46GpG5S0lM6Okd3dVrdq5lfXJGWTimfsdEianPaasW7UEH0MmQoia5gDQBoY7gyT/gwx/9NZ///DveHfbs9pG6uKIxNbJaItWC4AIqOKZxhHJADPm5qG1A6UgwgqLYYVRBqs5JwmKm13Rf/evf+oz/nR5QgrcoIRlHRz/kJE2zaKiKlrKWrI8XtKtqjr86BJlPIUUum5JCoIXIVEMpUVrTRcf1uztcd8cUEtHmh+2iXSGip9ttadqWuqqZpgkQDONA0zS0i5pxHOckRETKwHpVE2RGf/fXCeducP0BHx3eHfD+mNXmKVW7QacZPoYgBZgGj5sGApGQAnhPYwSuUOBnVLgAoif4CaUKpM5QLZ98NnfGvP8typLkwKVIWRfUleEgIlPwGGNojaJtCtqmYl0XtKVme3OHC9lHUSjFYp0NsyH4/JCvSoqqBKFQxYZD1xOtw3YlhcxRvKTzMCIUc+w0kya0LtAmY7ulUvnvSCKGbKr0kyU4R98PlNt7ymJN2SwQMsddc5tzbmdWKq9+BKDmPS8IyrLKSZ8EIQiUMlRlSUy5CVpJMR9MYMc+k2zJO++irPCJvCt2El2KTG4tC3ShMaogREv04dFLE+fUlDAKMX8fpskjAoTkUIWkXS/BGrq7A9HFPCj4bCxuljXrJyva81VmdKAQUmRfCpnLkQhIpUBqqlI/RoZTghAd3maeR2Ek2YoZiULgsxGIqm5o6qMcD445Ru+zYzYzX1LEBZ/XjoVkvd4w2Ylut2O/zTJ6jCF7rqo6gwKtJfQZGqZKTdFUGF3AeCB1lwz3b1FlzdGPX6A2H4K7Y/fZn/PlX/wTSqnRy1N+9uU3XA4LqrMzqjSy6G85OV/TfPApXqyx179AKUFwI1EJUBqtTEb7a4GInjTssIdbJrslyRqbNJuP/pC2adisW4qyQqgCKQL4fBFIQhPmN2ehFf3VG7pv/4r2xR8SnOfy5pqdE7zXnrA4+5TT598HVRD6PenyK0RRU2iBNC2xOc7lb80S1SxyusN2uc9qTBzu33J98TlT11OuTlk8/SOa8z+gaN8i3C2V1HgpidEhgkO5PWHY4aYO13tcAhc17eoZqlojUwIfWS6Os0KFQ2IRziGRWN8R+y2D7dlZKPqeo7NT3v/x36MyZV6Rq6xq5k6AAiMSdtqRplWGSPY949BBGBjuXiP6DpxDBwFoRNFmczqatj7D6IbDAL5e4cuG+vicVgskCWkDU1ViQ0C3ZzSVwUlLvPyS7u2B5un3iPstv/ybf892t8P3B6r1mhdPn1K2ikJapvFAlAHTnFCvP2LovmL/3b8jHg6M/cA4WLZdz1fvOuzynFcf/Qnf/+j3+OPjDUsDK+URKac0Q4iIEIAajSUmTxIJ53qE7zi8+znfvDtgTMUXV9/wb9/0rFZPeHG25JOqYFMpTFnA/h36yXucP/+AOL3j7befs7t5hz55weL4CetWcHPb4fcT3b7j7aHjqgv4qWBx9IyXT46oVzUvTxqeLotcrxEn9geXL3bBI0REEVEiYQxYv4eYqGVWo4fLe/bP1hw/qRFhy3D7lmGQLI6fYWpyv1cMiHKJUS1RapyvmaxC+RKZGlIUxCSI0qBWZxx/+kc8O/tnXPZfc9sH6sKjy5GitZRLiTQCZ+/RaSB1jjBJ+uJAqTRmusHEb0hCM61+gnpxgl6tEesfU37gf+sz/nd6QDl0I4u6Qpslx21Fu6rRbQs2IpPnaNNgypwXz3wM9ZApgbnDZRgnps7hR0s3vWE3WPbbjkIItE5UlUYryTjuMypcF4y9pdLQVCW9tdgA4zDR1vkW7OZOkZjyrX1VGsTxce61uT8g44jzjn1yjM4z+cCJ0pTNKg8ZUuRkUVHifWR3OFApg/QSnyBVJTImwuDwztJtsyGzqhpCP9Ih0Drj02OKjNOAHRVSCYwEExLSW4rkWRpN01Ys6oqm0NRGUimVExvasFytcoQUKEyZPTAPQPgUic4hdKDWJbJuYOPxwRK7PieTkpuleIXUisrU2dhZarQx86FfzvRTmV3i0THYiXF3YNzvcJNgsgdUqVFSIjAz0SXHlyErQVIogohzA7KiKAzTOJFSoq6rGYOfBzsfIkYXGKWRMqe6ZMqvm9YVQ9+j2gVGKQxyVm4SRarQTZ0pwWOO0mqd/SuCDDGLKTIOA/0wYLSmKkq0yLfieCJYLJfcXxnGYaCuyv8feX8Wa2ua32WCzzt885r2Xns+8zlxYo4cnelMY1cZcOEqWWq1cEutFgKL5soyyMJcWJa4YDbiouECIyGEuEOWULdaVUAXNjTG2M7E6ZwiMjIyzhBxxj3vvcZvfoe+eHcGZUF3p4umJavW3Vn7ROxpnfW93///+z0PQiQkiSYf5eSbI/SoQOQFKh2FQ4kLq4EoTkEGnH0xHAbCbxdCqFqqMCm6OoToKKapK6yxROmQNI6wXXX1/SmWiwpne/I8I09yqrLEmo6mqlE6IssywNJWK/rOoJzHK38lS0yJlaJr1tRNCTawRGQUTMa687C+QPYLXDdDUbG+XJC9/IBxnNPXDc9PT3l8ckqTfYVXXv881z/1Re7nYwZRRtPV9I++StFV5F7R5ikmT+mtZZxu4/KUzjUY19PXZXgtGEO5uGA5P6XrKgajPUZ7r5JNryP7msj1CDzOB9aPljEOg7fBvOuNgXbN/OKM5y+O6T7T02vF/ls/wmQyZTwYhQA2LULF2ERRVhPQEh1lyME22vV0Rx9ijh+S1FPquuFy9pT16oxocAM9vIYY3cXYIzAK3TVM9l5DDcdUs0f0qyNi22JsiTUl9WzFatFQW4c1jix2bGzskoy2mGzsh0mcteAMkTO4vsb0c5zpEF5jpKZPdxm+/TbjfMiwmJDGnkIG4m/g1oTJkXMW160Qq1Pa82Oq+Bg7vYZr1jipyaIJSqgrwFqL9z19F5gxpcxYrBbk9YzBdI/djR9BRyl5rJA+8I8sHpEkxL7G1heIJkHld+i7NY++/RWOL+e8+tqnyETIMm3u7CLaDdrOolVBsrWPjz16dsjy7JCqmrNxI2cQDVl/cM7Txy857RTF9Cabb/4IN74wZjCOuT5IGSZj9GiHmBL6MqgoHFj4RPLa9wbRt/TtChbnzI8+4jsPvsujI83Gxj6quMEbb2fsjzXjVJDqwG5yUtO2FywWh8Q332J6403qiyPmhw9ZK1DDhPVqzuXhGYdnPSsDfjCl2Ntia1Rw7WCbe+MIaWvWLqxcpbN0FhARTjhUrHEYrPfESJQLgsq6bHGdZFH2LC482WjE9qAhbZ+yWNbEg32yLMZ34FyEE+rqva4nrsGlkt4YOuNQnUG2K0xSoIRGxBHp7me4/9kf53z928xXj7hYWaLYsDGusbbGohCyQJgeu1qCUlhXYpvn+MVjIrmB3voxosE2iZCkYgjDCcPbX/iBr/F/qA8ok+kuw+EA7xRJGrMxzemEYDlf0xuPMZY01eG0HO7PAyW26ek7T1MaZosFZdVgWoOQBhnFpIkmiSLSSKBUmIYoGWNMh7c9UVpQrxfkWcZgMGSxbuibmsr2QQqYhCyIEgpnHJGEcRagWvN6jnIeoUyoGnpLVS+Q50dMNiEvxggv8PQIFZFEipHIMNaQphmDIqfJe+brmtVSUi2XNKczsrQmT0sGaUYUR9RXkwp7dYhwLkxmhJY0vcPXLZFUyEgQyau7cAIbpTWeJJEMhwOKwQCpI+ouUFjF9+3B3iNlTBSnWNvS1i1CKYbDIVJCE68oF3Pa1lwRX0F5Ee4OvcSKsEZRSOIoJY5jhFDIJAqZA2tp4iFdPkZhURqcC3yTQJIN5NNBMQqHCxEYMPbKlut9aGR93/Bsr0akRgTDrhCCvuuCgVi6q4NGRJLEWGuJk5QoDqFRiw/1QO9DfTH12K5HS0ESB/Jq2zZkaYpzjmoxpyzXxElCluXhgOYF3lqSTCIzxUYywPsCZDCtyigiyQriooA4gig4dpQMLiMVpRSDMcv1LLBmupamrq/w2o5ilGFd4DM4a+iNCmA1ayjyhDwfMD9vqMslbb0MazYEWkWIq4lfmqRkSQxX4squacPPEwUetBDEaYLtLF25xjQVeEsUh1Wqt6BMBcunqHaG6EpsV9GuK549OePJs5d81kkWzZL42j0+fe9HuHmwy2hrk2mcEwuNlILWRSy2biLOH5PECWJrh0512OOn9BfPyMU+XdewWpxQrWck6YB4uEenRkSjCGFKus4igXy4Sbc6xjclkgghekBcqRXChMHaDm17ynWFzCbk0zEiVmwUY6bjbXSsiCOPoA03CCKQjZONKdX8DG9qlDfEwvH86QM+fPGIzf19jB5TdiELlauGze0Rk/E1knyCmR8juhVeO7KNGyRZwurxOf3Zc9ZVQ9OUmKZEZmPSnbtoKaA8wgkf1nmuA7tGOk/flYHs23YsyyUyycnyQcDuD/bJJlPSRKCERGmFEhHW9yBcoL52a/p6hr34mPnpBUaPiKYjmuVFuFglKQoLXYMzNV3XhdW4TrBxQXHzDoM0Ih1vIlPNIE1QQqEs4AyYBmwDtsMtTliePON8/U1ufep/wFdzLsuWOE6p1xfEqWZzYxAEmXFM1xvabslg7x7Z5h6DOOPi5SOevPc1xOqSLO747vNLHrRj0tt32Lx5k2u3X2Vj44BYgqgv6fsGb0r0cAuvI2x1GsL2Dnxn8b4KQd7ZIevVEU+enfL+yyVzt8vW/S1emaYMokC4Vjqse5UEKxTCGaK2YnV5zPjgdQbpiCyOeXbxIacXx4xHBc+enPHw3FHrEZOdEfdvXGN/c0KRdKSxIlOWplW4MgrZLBX8TVoIEh1o6UaGf6+mNawXFZfzmstljRIaLyGKYk7PF7x4UTLRFTKeUBRDrG3wLkGoPJRHRIBbdp1BNQ2oFbIbIprgzpJJGtZ3eoDM9tj73Jf4bKt48sDx8YsPqduKdHWBjDU6kcg4QekM266wsxeY9cd06xrLBsn+50nzVzEoRFcS2RXaFmj5v5EWz+71bZSKWS3bT4RuiZaMJhtYJ4EW7x3GNfR9T1W1rOYlTRXYFKbvqesS0MRJHNwVKgmsBtcgUVdBy1DFjKM4BCaNp+9alm1N7DryYsy6r+lbi9IKncZESURd1kQ6uE6k7dkYjenbKd067OUhrDhc31ItZ0QqQ6uENC3wVtKYliSJyZIU6xx936EUZHGKSBNQgoumY7laM6tXDNKWSdExHhYkaQ5CYXuLddA1hqZah7CW9XRXVF0voOl7Om+pbIQ2oZ6XJi29NZiuQ0cJaTEkTlNM1wTcvJCMNncQKmZ5/oLehYpulsSM84JEavJiRFNX1OtLbFdjnUMJiK+qws55uqbEOROcLHGCFpJIawSQDgaoyYTetPRdfzVxiVEinPxlHFgfxlmyNEVAyMFozSc0W75fZ3ZXGRiBujJBh6pxjDMhBCuVwtieru2CtK4PIVQwKKWvUvcuNIh0kCvaqwmOiBOMECzLNReXF0RKMRpkSK1CpReJcTYEX5UkyVO8VKR5jkg0XmuiKCPKirB2UkEW4BGYFnCBPeKsBd/ROkcQH4a/iwt5H6VkaG8pjRQOhaWrVvRtTd/V4a7ZhvWm8Yb1esYgH5PnaWiRWUff1ngRpngoRZLm4XfedXSroHkw5opwKmCYxWjX0TUX2PIUuziinp1Rlw2d1aw6wYktSLIRXduzubHLxnSTIhoRvIShdutEhxMS6SEZDbF1DlHKYDBC2D0ev/s1nrz/FfZfeQubDun6wCHp3JrBwDKY3g7MivKUZjmn7SqmG58izkcsX7yL7NsAzDMlbblkeX5Eu7zACs8kiUmTIfvX3qDYuUmcBOOyUBJve7AaRIIimJysjvDpANbvc/HsXSZb1zhcLXjv6QllnXGrKdieThkNIpQKBTbb1SSbt9CDCSslcYsX2NkpqhihswG9SDnteixJ8ACJniKJyYcpUbHDet5inSTauk2WDliffJPy7JjWGExSIAd7JPu3SIsxcZLQdSVxlqOjCKlF2AUIcL6BrsFXF5hV+Bn4rmcxW3J2eES6dZudTYF2PUZrEh/TVZfU5Rlt10A8QsQDdDQiKjYYD0dkaUwUJQGiaBvwDm8cpjqhvDikXi6wpuXy4pjDswWz1uOjgp0i4fbOJlJalI6DbNKBcAqfxWR5xNn5MfriY0bDgoQWX61474OPObqYc+uNt0je+e/48t4+B5tDhlIjrCDTILMBqBazbLC9QaUTkiyibi4x5WVYVTYr7PkTnj95yqPjE+Jih3x8h5v34RUt2UwVsbZo8f2WISDiIGq0Dc6ZMEk5e8Lpd34dPxkyP3rGo8cvOGkkw+kElewwfn2f+1tjbgxSDoqMWCi6JmDqexd4TGks6U2L8Tr8PJRDdm0IC69r6kVDbQTrRjNvodcxWRwzTnRwZTUVj55orl+bcGs4xvWE9ZBOEVISKY0WEd4JrIC2tgjZotOeKOpwqsI2DU6tQEY4nRPvvMHumx1aJyzLC8rFOcOqxOQlWTZEygbfzTDn7zN/ckJDxmDzLvn0Bna8RWOXRKtDkkLiIgG5pLHVD3yN/0N9QMkSBcpRbBR0TairJkmEinMcMcuTE+rLOV27ZFXVLOcVdePwUpJoAX1gaQyKBJkkgYDpXVhg+GBQExa8tFjf0bbgjaJrVwHB7gVFZShGhiQPo/q+auj7jjiOyZMQpnVGYpwj1oK96RYXWMpyicAgsMGUGuUBiiQEbduSxMHs2vUNQgZ1vYgS2rZFSUsuJXYwZFGU+K5FGMuiMZRdybqxjAvD1taE0STHCENnYnSZU61aunVJV5WBJOtMYB+4GGFBtB4lW2RtuFhbErGmiBSTYcF0axygbUmCkBFtvaDrWzrbk6RDRsMJq9WMputQUjKeZGxsDlgsctq2oW9KhOlwzgSUfpZhe0NbVmBhYycniuIrN0xo63hrkV6SxhlploXOkfckaYzzHttZ0jgNDRt3Zc41YR0jROAPSBnsxUIosmJI33dI4fHCh4OAkogrR5A1IRdivUXiMd9Hw+sUZftAENVJYGRo/UmrSEpB37aU6zXleo3WimiusaanSDK2dvaxMgLvifMBXkickOTjaSBS+jbA1bzHG0PfhZC2E1EIx5oGY5sQ4CPkpkK1FoSQOOHCa1+noXTkHK1toA2HEhklKHllF/YO5xVxlJFd4fi9FXRVTdfWpGmK1hGdb0miDCUdXbkiEhFWK+LBEBqDkg7pKuqTB4j5IaZesZrNmS1KZvOK1vTc3tpk8/p19m+9TbF3g3ESE+kUHymED3kth8E5ibQCESu0UKTFBnNvOX/0a+y2L1mtPA/PzkhszsjGDLItks0RSknadoXtDDofo+IBOIOpFlTNMfvOIYfbtHFKc/IB7bok8jVV07H0BX7vHtnqHFvP0eOYSV6gU4VTV2A/QjXdEySViApjE2xX0s+f88HDD3n5+AW71y+IJlOm12/z6sYmeZ6TJXkgB3tB16/pyzO8vMN051XySHC4OuPswVfpl6cMxiOc84y2X8cnGqoZ7uwJQkWoKCOKNJHTVLNDzOQSkoyydVyKDeLtKXEckcQD8sk2STFAS0/XKrAQJWPUoEB1M/r5IbY19M2Mrl1RHh9RHh2FdWexQT6+QTIcIwa7SHqYv8QZQ+saalEgtl4jnm6QuR5Xleh4SJRNUKlGC4OoV3T1Bd16jqlXdF3LbD7j5PnT8BpKCrIkZ7w5IvcNfdOTF4NAmnZXROokojc90juM8kS+4+E3/w2rww9ItOe7i5ju0z/C1qc+xSs3XqFQglh7An3PY6oWbDB1k+0gvWFxckZXTonzDO0axMUTqvMn1Jc1z5Ydj2YdvZhyb7DNa6+8SZoVrFYndPUZlhTpFFK5q7acwfZtyF31Da4vqc7nPP7wAaNBzovDCz7sNtGbG9y8tcWt3THjFIaRRHpLUze03uGkwrUVjZA0TRsM61i8DJiErl1StQ3zWcvx6YyFi9i88w7XJxOKl49oZidoHL21KOGDs60VrNYx/aaho8OSEeeKBInzAislwrWYJrRfm0aimyUyEgiVIOs5SnuUjPF6A51vk+/dx/uM186+xX9497dYrnuiTYNzJWJ2wvr4Y85nHUbeotjaI9oc4lKwpkabiL47oro0FBwj1xVpefQDX+P/UB9QIqEpigg/2ma9TqgvjyiXCxozp+4Ml6enmGpBrKBrO2xnSLIUnaQIC3UdPCK99UT2ijjq/heCLu8wfQgMtsZQNR14SZqMSEcRSRZaNlGc0HQG4Vu0UkgCSMxYR5JmtG33CUU1Ugk7WwdU6ZC2bZFCoVRoneRZRhxHeP99664ljhSmNxjjUEqTJAl91yKcI1aSPEuZLRREEqWhbTsuyorWGErTMR4UjDaGZIOMPB+wvRuxqEpOLi5ZzGr6svmkHWNpAzgIjZKazoam1KLvOW078sWCzXHB/tYGG+MJmBDEFUAWx2gRFOeRlGEK1Qc9QBRrBoNNcCPK1ZKmXNP3PVIauq4N3hjbkec5KE1d1yG0fOV2CSsbS9tUVxcJgvDvanrgvb3yUYfWjRKhERRcOjqMi104eMqryY2xhsFwAEC5XqMQKMVVK0eA8EELD6R5DiqiWlYIqUjShHbdgPOhSeLDuqrtgzunbVts57noO/q+pR6OGE6nZMMhXmry4Zi27zHWoVON6TpMXdMjESLkYLq6C9OzLKZrqmBxlQqcA2/RSUGa5kGWGcmrQ6ajrQLnJMmHaClxUqLl1fTHtFhgY3sPIXSoUruOvjF4B8b0eDzOA9YjieiWKzohENKxrOZkwzFxHKGyKVF9Sfng3zN/8Yjleo3peoSKOVZj/L1XSZWA6oStoWa4OUQPikC7kUHtEDlH7xxegIoStBJYEWr//XrG06NLTp8ecmMdE42mvPLKK+TDXUaTTaIry7UUoOho+jWR7Sim9yitpy8vWZ19zOmD32Kyf4/Z2UuOnj7H1h0D3bA9nXB3a4d4+1X6dk399F1UNEbGaQi4W4+QGkcUqviuC1XfbgUOmtWKJ0/e56L3bNz/FLv7e2SDBCkTYqmRUYKPM5y0qK5Gy4Ty4pTF0/cYJAm+KxE6JUpa2vIc5dbksWaYKrxUOLXNcjWn61uyyQH55g3Ozh7x8MWMtX7O/fGE5PrnOIgkSRLhTYdtGmQikcUYrXOkjihnL6kWLxmqXbp6TruYhTC4UKhoTDbuMa3DiQx5+zMU1Qy3PmOQ7+DaC87aFUYk5Okeg2xKOtmkKDTCOSoh6P0SxBZpsYOoj6nWh/StoOsd9fKCalVTlRUYE4LgWcbGzvWQy5ICf2Xe9randw3eG2TrUTYY2oXvWV2c8+i0YRHDK2++zZtvHvDHRhOmSYryHd53CCnwRiKQ2Kina2tioxhkW/RpxXz57zn85vfYmN5idfQe733zPc7LmunGlOs3X+FH39jFdRX14pyyumC0ecBGnLA4t3S2RXiLMw7cGtusUI3BLy5ZrOacnK1493DNGRk37gzYeOUmP/LpCfujgt2cK04P9M7TNBZUHA743tE5B77HdGvK5RKcoLGOqm9YrWp8sgWDa+AuSRZzCmHZ3RozSu7z4nFHMz9FakksJBoNQrOu4WxhsbkjinpULzBCoVWClIHthIywXYKQkmp1Gd5zVEQUpci4QKs1XoNUI6LxhKJ8wfY4J/cJrdNIXyIvP+b0qOPMXSeb7DPZ2SQrcq5cEOh+ideCuFuQzx4ja0s7uU+d/egPfI3/Q31AcdLSdI7u9IzFHE5PjmnLC7wJiHV71XiwXtA1NRpLnkl0IukriJUKPfBIIbAh1e8dTdOHA0ndYXsXRpdRRj7IGY2GjEc5SRahorAu8F7Sd5qmqmnbDuVStE5o2hb/iejO0feBzxFFEZubmxhjadv+yrYbhWyFJ+QanKPrOnpACg/y+/ZXQRRpwEBriCJNXgxo+nDYirWmb3tWvaUua5Ztx7iq2N2cMN6cECWCSZGSqG3qTc/lbM3ZbIlv+3DoER5jQh2465swXVASJYPx2S6r8HV1hu3tbXSiiKzEG8vs8py+bwM8zxMEhdahhMAbE2iixRAvNNVyQbkqiWJFPhqTF8Pw86hWQSsv9CcEWE+YUHh8UKJLCTqi61qkCrkF6yym70nihCiK6PowhYriBC8ECo33gr7viJREJTHO2pDZcKGOrLWmuQrV5kUgRoaCiw9hXx0hpQr+Ih9YJlmS4Zyj61qiKGE4GtE2NV1dhVBbPiAbTUCqUFf2IdBrOoPwlna9CLtwFwR8g+EI54NNWCpN33fUbY+KI/JsEOrPzhIlGaPxBuvFjKauwDuiKA1cF+9JI431grJuMN4TDXKEg8j35DrA/eqmpm0qlNLkeY7WSeCzAJGWNOUCb2Ew2aL2LakqSFyJv3zI5it/lGV9zsOHTzlfdNhkwtCW7Mmez9+9yei1z2NVxurR+wh/QdSU+LbCR1GA9l39XhWhJu76HmMbDIpV2TE7fEqvInZf/SE2p5uko03iJEXpFKEk34dRejxRkoVV4vI5o3abLI6Z1WsWz59weXrG7a6jlxHDO58m8eAvH5MlljiJSLRHpVP6yRTvI+JsG5+lGNNhTIs3Dd60mGrJ5YtHLFaXFBt7pKNbpLtvcG98jWGWkeoIhQ/MGAKQTADaeaRt6KpLunLGyyclkfbEUUyuYTSa4pUHLfBonPcIwsFRCM/Rs0fIbMjtLCPfuMGtL+4z2jogHxeMopCvQwpc5Om9xTiB0hlZNqCtT7HVDNOdo/oSLX3ILImgPhBEROmIdFzTNpbt7bvQnPD44gGX62cMRlOGt78IGsR6hVYJKkmRcYxwlmwUEZdntIsP8KNxINWuLhGNol8dUp58hFMjsnhIcW1Ia2t81xKpnGy8i/VNoPP29qoRF0CNvVmzWq/pbUyx9wbc3eLOqzl3ru1xbTgIDUFhQYQMVmjQCRBXigokfv2E9ZNT2HoVsXrC/PF7fOujSzau36ftS+bRBpPbtyiwJNmQ6/uvg59z9NKzqla0pmI03QNzzvnhDG8szjVQz5HVnLPjOQ+fXXBUe9ZiRL21w+6tDV67u8v9YkQiI6yvsX1Nb4Muw7ZtCGMLgSM0FUXbYPqaejnj7PiMdQvLBhbeg4qZ7me8/upbpLLn8MNvsJgdMj/LGO3cYPf6LS7MiqoKxnvhXbAUG8HlmaWMLFtbMToyKNmirMShkCiEFwjXYrtwqGmb1RXCP0GkQxKt8Br6OCFKRqhiiFApTsb4vqQ7XvJy1jBPbzC9/xqTzQ0cHR6LEhHCS1A9untMdrWp6LN7mPyzuOndH/ga/190QPnbf/tv80u/9Ev8/M//PH/v7/09AJqm4S/9pb/Er/7qr9K2LT/5kz/JP/gH/4Dd3d1P/rtnz57xsz/7s/zbf/tvGQwG/MzP/Ay//Mu/fJUd+MEfJ8cLTNuwXAVxXds2ICCPI5JIgpYBltpaampa05E7hwKyImMyGiKUoOs71k1J3xu6tkNGMa0BR8JgI6fIR0FpLz3b0yE6CeRNj/tERqfwpGmGpKTvFjgrGA23qGrDerkmK/JgdDV98Fz4oLZXkaRte5y/CjUS2CtFnpKIhLbr6E1HnKZAAMJpJYkizUAGBX2V53SLjt4LvNKoRGKajkC0j1g0Dn88o+0M091NBklKnqTE0wG70w2GJ+dczNaU6xZnPZ3ucfYqo6FUqAlf3d17FbPuPc9OZ1R9z8H+lCIZ4a/4IVJKjOlDs8X5UDd1LgDArKO1HuKUqOgQ3jGajImLgjQvMG1ghCgVVh29Cc2c7zNruAq45sUg8FVcqOAqFUiS1nl65+nbjr5tGRRFoK6aAGpDBHid9wQrq/OhARHFpHlGWzdIFTEoMqz/vghO4ZxFSRkySChM1yK9ICmGQcpoDPHVwchYy4aMgv/JNMRJymRziyTJQgZGCto+XHwgmJi9B+stzgs6Y2naJiD5rSXWwfib5AXjySazy3NcazFtTbm8ZLWaY0xHUQzIiwLrepqqYr1aBJS5dURZwuZ0SlU1lLNTFpdn9BaMC78vfXXwqqoS2zXBGKt72rrFSEuBYzzexPuCkwff4Du/9X/jx//7lpmLaW7e4+7GDmk6xl2+hI+/zpb2FMJgBiPEjduY0xYvM3w0xso+hMSdpHUdNCvq9ZxyNseZmsHGAWSbxFu3uDveJcoypPLoKL2yIIcDlFAxznPldpJIb3j67r9F9A1KD5gtL9HX7jGaXCOabLKVhDt2aT2LXNMuTxhm24h0E4FADcf0ixO68ow83sXWc6rZU1azIwwxarDNmU1ZtRKzWJCOJNvX36Cvl7jyCOFNeG1LhexW6PaEpippqpK4a3E4hoNthoM9RAfWd0HMpyVehVWS6HuctFjb0JUrzlc1y2yPLNpgimC8e4/NuEcqRSQTHGG074QgkhpnO9bLE+gakvEWTXVChEQpQVcvsCpk0qTvMdoh+xW6uqS+PObh03P8ZJ/9g5tMX/8R9GCLKI3InMDi6Lyk71uINGJwjbhf0K3O8W3N8ePfplxZxpMtDp89wzcObEW1btk+mDDZvE7fVxhb01YdvmuINw4Qgy3qo3eZf/zN8B7hgzH8+GzJpY0YXr/DcO8ud6f7vB5rYtkiaBGmo/ce71zQBxA0185ZnO2DpXf+iCcPn5HtvEp/8YL/+bvHtMUGny80N0e3uKNylPKYtmddrunpGA5GTDcPsN1j6rZjM9+lGB1y+eIDmvWM9XrJ+nLBy8Ml371wnMkJO9MRb94+4Ma1HUYbBaNUgzW01mB7T9cTxK7eYm1YoxjT0PcrFqcvadaKRV1xNJtzPl/hhCZOCsZJQl7EaLvEzc/ZvHUPbtxlvbrk5OSIwc5d7t57lay94MnHz8K/iSuJ4XLecdQZrHdcqxJeY8xk7DCqJI5SIqlRkcPbFiEjsB3Veg0yRsg1xOfEShMJBW6OSAvU5BU2P/XjHHz0jAcPv8uzi45ZH/PKZw/YnuaoWNNZgSNBCYupHtOtFxgk0XQLmUzQjInNHFGe/cDX+P/VB5Svfe1r/MN/+A/51Kc+9fue/4t/8S/yL/7Fv+Cf/bN/xng85s//+T/Pn/yTf5Lf/u3fBkIY6Kd+6qfY29vjd37ndzg6OuLP/Jk/QxRF/K2/9bf+QF/Do8dhB+eEI4kT0iT4ZAQ+jAdtqPeDpRjmeBejVELVWoSpUF5gvWddNqyvKphpWpDFA+IIBlnCZKPAIakqS9c2zBdL8kkCLuDCrbFY42jblrZq8MbQNGVoWPhjtrb2UVHGom7QcYpQKmiypaCsWrSMUUqzrpswAYnDOL5pGuI4Dr4Yb2jX6wDLEgrrgilYqUB2TKTAG0tv7JWrx+NFuAt3SIzStH3P8viMqmvY3dsh1jGyCt3/aazJpwOWecx8XbGuHJ33GOuwViCvIFZYR+M6lFJ01tOcXNB2DVujijTLiHREmuU0fU9ZLoPZ9BNZn0BIRToYgTfUHkwU0ziBbQ1ls0RLRXFVB67bNhxQZMgCOC/+o0VXtCznc+I4ZjAacTlb4F2PFNC2fQi2ek/TXATGh5fESQq4qwmMIopi+rZF+EBlrJuWtm7QOqJuaowzeB94G5EE33dYFDpKcH2DdwZZ1oHoi0BcNa+ttWSjTeIkpl7OQShkUlB2He16RaQDayCMTUDoCIfEmwalI7reBrqulFR1TdW04D11XdPUNfPZDGdChkarMHWzztI2HeW6oaxLhLO0JrRrtBesz8+pqhW2bXAelipFRSkISyQ99bpkdlW779sK4S1ZnBAVYzQxL7/7W9QXT9m5+2U+vlhylF7no+OSyUbK7Rtvk2uFjjS10lyeH3F0viIbnkOf09YtzbqhExdM0l3qfkm7PqOuWtAJVW+5vDgjcS3OtqjLmu1bA8RwC+cuKVdLlFRI0WK8QfL90YkLVXhnMM2Ki7Mzji7Ombv32Ll+D7l5g6FKiNOMrgnTIqkkzjj6XmBKgyk78lRiu4rV0nDy4UMe/e5XGW8csCTioutoTUuajdjZz8gmB4yiCeXlC14cHzLOdlBqTNee0V+cEHlPb2qa8pL1cknjNQhPvJ6RFjkDl5HIJbPWoaLQbHO+CXh2s0a0FaZbI/ItejXEbd3n+r0NsnRI1TS0XYWvPFJemaJdj/AG21eYaompalZND/qILBvgZbB3S69BWgQmTDabJdbVuNawKA1rPSF67U3m0Qi5tugkwa1X9GuF5SoE2vd0l4+pP3qX6b0/itYR5dF3mb18j2fPXzI8+wob4z3qpkFFKVIN6WLLyazBpB6lBnRVxXp5zno+59LA/q3XcKsFh89ecLJYIIs9Nu59BvXOHvuxYiANprxgbh3FcJvKrbHdAisVkfXYq4O9NzXStoiuw7Yt69mHPP/O9/jd54Yb9xxboy227n2BG6MBg0jTWY0mxvaO3nrq1vLkbM6e2kYmG/S958F3vka9mlP0Zxw/+piXL17yvaM1cx+RDXfI717jS7d22U0cw8gziFLEWjIvazo6mr7CtU3IQ10Fyp3tcG1Ds1xxeXHJy5dHrJeeNZLKK+JsykauGSYCLQHX0a7Pefjhu1Qy5trOHlvTLR4/esLTRx8yeuVauClykrJqsULSWEFrJaiCOFGUNuV8HkSouu4Z5gIlDbKRSNHiVYdKDCpuma9qsqIkmS2Yb67IBtuksYasQKoJcvAme5/7MZ68OObByxdEI2jqGWeXGSKzCBEjzXPE/Ij1/AKndsg2d1gn10jFJhkp0dmCevbeD3yN/191QFmv1/ypP/Wn+Ef/6B/xN/7G3/jk+cViwT/+x/+Yf/pP/yl/7I/9MQD+yT/5J7zxxht89atf5Utf+hK/9mu/xne/+13+9b/+1+zu7vKZz3yGv/7X/zq/+Iu/yF/5K3+F+Kq2+QN98dKRZwmRDNkCZGh/WG+ClM+FgJqXkBcRwsZUnWNVN+ADDE3oCJ0WTKcT8jwlzzKshaZuyKOINFX0QhChg6dFy6t2XoM1XRjBV4ay7unalr4L9cy2aZFC0LfPGQwK8sEQ4zucUKgoQYkQmLS2C/VZJYl0qLU6F4ByXR+qyEpHICRt16IDw57eOGxv0HHEIAvQJSsDuVJqj/UOnCSKNU6ogJBvK8pVRTOsEIUI0w2vwEGqI5KRZpwpqi58P+u6ozM+5BKcBaHRSiCFJ9YxymmWqx7VzxhPDMVgGLgjzl9Vh6OwDjJBRR5HEYNiQLleImXMYByovLYJb9JoRdNKjA3fdzEYAoLehjdWT1i3NFVIgUshaJsmrDUwCCkwzoQwqJDhgOEhSXPiKKGu13gESVZgrz6HFOFQ0Tt7xVQRdL3BOoNQEVmS0TWBnRDnBVJF2L5GqO8zda7U4eFT4YUnS9Kwy3EOqRXWGKwxeMAgieMY5x2dsQxG0/C6rRdhdUU4vDgbFATWGGzfQQVNubp63XMlPAxgOucczhi8NmRpRhxFeBXcOV3XY42hqgNNFm+JpCCLNU1dsyoXaCGIkgJvHXEU+CcqUkTVKadHL3j47BlWKn7oWsX1gwOu3ThgFGfEAU+MkgIng+Qv29jHXp4R5RuoYorAs+wcT7/7O3SrI3y6wcVqTlktGY/3yHfus3VtC2HWmG5NtTih72omw01sJLBXcjnrDfL73B1b0y4v6cpV0Dk4i8rG3H77NYYbG2RZcCBpIa7q4QKFAOODqysbYaWmPXtI4nssnuXpM56cVcwWPTcjyCcjRoM03DA4h/Am0FInO3jbsVocM+4biuE+Mis4nZ1hy8ugWbCGOM2YTnaRaUF3eYpplyidogcb9CLDtWu8r6Fdg2npvKW2CpHsIoUmGU4ZDrZIYoiFQ2iQPhz2bbdGt6FC3xhDv76kPPoY23v05jWidBS0Et7hfA8CRFdj60ts39N7BRqsHKMPdjnY3iPXOVp7lFAgPd5rpDdIKRCmRNan1C+/y4cPH3JgM67feo1qNWdZwWDzBoNigFY1o8EA5yQISSQmVKsVFhhs3SJOC0x5wbJZ8+D3foPy6fuIJOWJSbHXb7O3ucNk54DBZIdIgeyX9JUJaxKlSNJNlPDYZnllfwdlGnxzTrs4CaLOyvL47Jxn1ZDilX12ru+wmyfsa40KqM4rB1iPEwKlJcNBhuzWUGXoCFIaLj7+FuXTb5FIxQdHLY/qjH5rh1u7+7yxu8l0lDMejRDOUTZLRKwQKLrOYqwhIsJqSd/XGF/jmgpTVSznS05mK16crblcSZIIsjRlI9YUcbjhtDZMZZ0P+lfZXjI7+oi96WfZ2djgRDzi/OlDnssV9AvO1xXzykE0YHtzyo3NDBUJYiWITBNusqIhUjXU7ZJYdyiVEYvABVKRAe+wfUvfNWgZ0ZQNQs3QDFA6R/ggNx3e+6O8/aNnHF/+jyzaFlMu6bucIvGY5UsWJ08o1waZ7JBvjLFqcIX4EOE92bX4+r+yzfjnfu7n+Kmf+il+4id+4vcdUL7+9a/T9z0/8RM/8clzr7/+Ojdv3uQrX/kKX/rSl/jKV77CO++88/tWPj/5kz/Jz/7sz/L+++/z2c9+9j/5fG3bXlFbw2O5XAKQpzFKhnCj9xZnry4S9FcrCYv3krpxrGwHpsN0Ai8j4lHMcDQmSwuyXAebr3QI4UP9q9H01uJMj0wkg80NhuMdmsWS1fwU060wfU1TW9Zlh7UhvOicpG0tnbEkcVhVLMs166oiywvSLEO5Au9SRBIDEmNtaDBICULjrLsiB16FV6UgSVOUjmlrg7PuytIrqNvwubdGAy7KNY0QeBTCWJIoB50AHtO2IMIkQapwx6+QWOECZt67kFGIMooYRpmlNd+vwRnavsX0/zE47K/Cjb2xXKwaDOCsJEsdXnjyPHzeJM2QRpG4UCetlgvKcokUkuFwTNPUWB/qqs456iszbpzo4FOyodEjtAqNmavMQprGKCEwXYMH4jgHDF1dARKtvg9x80HUZZrQUMkKhAfTVIE4qyKctSglEF7gjMVicFiyKAsG5K5BSEkcSZpmjTXN1RuwobMeoSOUDxdqtMaYHnCkWRZcOjYoB6IkRsQZ+XhKt7rAS8tovEFdruhNOKg6FMa1CNczGG8SpynN/CxAua7Cvs5YOuFJshDyTWNNmkQggunaNTXW9Xil8cbipWK8uYvzjnZ+jqnmrOsFDoHwFnwEDrLRCNVWVMcPKBcXVEJSxxNuvfMlNja32RgPSJREeRBxGP1rAdKJ0H7ygqQY0Zw/xZfnRMWI5eVzHh+dcnne0A1aJtsxeniNLNmg6lpy6RlvHOCqc/qVpYtTunJOrCPE9ADXNdjLQ6StsN0a13bMlyfMag1tT0HJ9v4tJtMbxBsb+CxF4K7IACo0tYTCOgEaJJLYOGZNxYP3fo/h8EOGW3usrGKwd5vJ7ZTpqCCLk/AegqLva9p6gWwX5Dtvo5Tk7OVDnn7z17n7xg9TLY6p2g5dXENiEdVpADlqh9A90cYGq7MalW0y2HkHaxbMHz5ivZ6D0iTpBIoDhqMhcZzQdk34NxFFqGwDKInMGmNqfLfCrWesLw/xPsLle1hijMvxzSIYrHWC9CGIrLoFGIftPcveYvMt4nwT5VukhOFkShELYmHDe6cXeKfwksD8aRbY5RHV2SMujg4pm46zF99jEoHre0aDCUJIVJTgpUNiA6LfSXxraJo5/uh9JgmkZo1bXXJ2UfFwCd21fW7dusv90ZjNvED4hrauEc6h8hwhLYltWNcdfd8wGByAs7jqkr4rEV1Pc/GQJx8+4KMXC3xccO+VOxzcfo0b0YgkE0Q6oNzBIZ3DShUs597ilQqqhq6jevHbvDzKmeYJs8ePePrknKVT7F+7hrv/Gp/Z3eZgY8KWVsT1MpQSVIKRDqEipLdIPSLJJL5dYW2PtRW+vsTNnnNxvuL4omNhYtz0BtGeYZovmKiKSAc4G95grcF6iXQCd6VxEZ1jfviMk0HBlq4R3rKenfERHiJJnYwpRinjJGJ/krM7zIiSLMAaV7PQXM32GE+GVOsH0B/j2hYbqzCB60pUpJEyxnQlnfSINkXWIkQXVBR8bi7Bppvsf/7/wB+pLb/xa/+c07MFybAjXnXMnpactpDEkMfB9G29wLuIrnNEaYAKhgnyD/b4Ax9QfvVXf5VvfOMbfO1r/ylP//j4mDiOmUwmv+/53d1djo+PP/k7/8vDyfc//v2P/ecev/zLv8xf/at/9T/9gLNhWmAJlWDp8MaSpgkb0+vsHFxHp0NklNF3DavVnPnlCd42pKkkRD6uwqfKIqXCmlBV3tzO6J3A2AY/X+GjwFs4Oz2lnc+QIlgp+9bTtz1JEqHzBOdcGB22DkEwz3oZhC7VeklVLUnjlDgbkA6HxDoNhmPjaOqSSEfEUUTvoTM9sdJ4L6mqlihN0FGYIpirXIUUEik9gyLHa8npfAF4tJbfHzlgbEuuYTgYsrMxZHM8Ck2hvsM5TxInaBWBCKsEiSBRksF4SJLmlFVJ09ZBfeYd3gmqxtI2Pd4Y2rqmXIW688ZkQpYXtFWDUT1ego50gERJcDjGowF931Mu55+EOgOzRCJxVw5AexX8VUR5gZKKdV2B92RZhncO41qU1GyMxiGNXpWMRhOkjK6cO+4TAJ1xjsFoA+sctu9REKZTSiFEuOgLpYiVRFwFaoW1mKYNeRWpMb3H9g6tMuIkxSGIbDBzFsUwvCSdxXkbDjZCfnKoQjjwhlRLhO9p6xJnLRdHTzFthTUdeTpkMBoyX7RXpFyNdwbnXaA2El9NiiRZMWK6tUO5XgXVfVmRphneesr1AqQkKUZEMeAlZr2kbdZ0XY1OUpJ0gGtL8BqEpRAd85ff5vEHT5kt1uwWhvsbKbs375Beu4VMhgjXo7B4ldLTIZTAehkOOkpwFdvhyeFzzo6fs3PzVRZRwWDnOru332RzvEEUJ8goBt8xvzjCN3Pi6FXsdkptWobGUC3OaC+fMrz2Kkp5Tp5/m/pyhrGeQgvUYMTBwX1EpGkvHhInEWmaIBWhtiyuLkJXDSsvFMIanOmwVtCslrw4OueITUSyz6jYYawtW1FEpnKccBh7dQD3PShJZ+Hk+XeC6kBHNJ1ndvmCOI8Z5kN2r90ijsKEqlonNBdHoIck+59DN0sW8xecnT1mvLGNEx1zlRHt7FPkm+BXoe2QDYjyAaovsfUcZWuyeA8hBOXiKd2yxDZL6sUlzdEpQlpGNzTJ+BXsnqJbvGCw/Trxzh3M2bdoTh5T9hKSEdFon3iwQzYchQCkWdEuz9EqhmSLHodoLxFeoRwot6ZbntEvLlldvGB+eYJOtrh95w5qMqXtQ0MuygqQ4Psa0VS4tsdZQ+stSZySOsNH3/xtLj58j/2tTZ6ZCe2dt/nCzfvc2b5GoUBKA17S+xTR92BmCDEiHmxhfEfUvKA6fUgiezQeM3tO9ew9Vhc9F6bng5mk27jBQZGihGRn6yZZMaIuz/Bdi/ctQkeB6ePBaRUcoE0N5pJ6fc7xwwecLRsmccqjVnK4/wZ37rzKOzevMZqMyLRBCei9pFWevl6RJRMiPL5v6JoLVL0mjnK68oKuPKNazLhYXHJ82XN4bmmNIJuMeP31t9jbKDj/8F0unn+EdQbjBM6EKYN00Lsr11Xn8AZM1fL0owcsi4STRnIRD2GwwbXNAbcTQRYF95nsa6TrUKIgTTMSDbrp8MWY0d0fZrea8OLxr2NMj7U9URTUD11bkag8ZBAbkNEKHScYpejqJcgkNAJVgiom3PrxP8Mfywd89V/+c86enXLWwryMGEw64skI4iGdENA6utYik56mXiJdh63d//eDxtXjD3RAef78OT//8z/Pr//6r5NehTb///H4pV/6JX7hF37hkz8vl0tu3LgRbLkWvBXhkKJgc/c29+6/QRIPWJYXXJ69QKHZ2trk/qt3GIw/x2p2zssn36Ms51xVRnDehrtnG8BskYoxTnJ6vsQ3S9rmGGMFTd/gHATJnMJ6GxgnQoCXSOEZDDKGxQhrBZYwbQg0VYkxgqqqYGkYNJbJqPjEbeOdo21LIhcRJTlogelrpNAIoenKsNJJkoRIK3oTRHCxlPTGEXlBLsUn9VopFVpIinzCxmRMJBXedvRNj44j8iJFOrDW05seYy06jsBcSfCspVmuENYw1BoZDXF9jbUdWSFhEJMlCeuypVytccZS1z3GrBkNw6oAr/CRojMd3jqkjtBKYZ1F64giLfDOUZaBVIu40gTYK9JpkjIYTgJDRUgG4xFSSNaLGTiDFz2VsxjTY5zH+RQlLYbgGImkwDnQMsJ7i5ZXzSKpKIoBHmg7SPIcoVXgacg4SORcOMSQfB8THcLRgyxHak3TB1R6kQd+TdO24VhxdeD5xNjmHNY0WNtTmlBPxAdoHe06rO0kKGnx9Hhn8KajrcvwMwR0krFzcJN6PaerK6RpKS9PaNpAlFVcUW51zHAyJIoLHBFdM6Ot1qgk1Ihdb4iKjHwwocVRrz7Cnx9yuKpZTvbZeucLHGhB89GHtN0RcVehuxU+SRFK0xMhlScWeYjRXAnMrO+p5hc8f/qQMz/E52Om2Tabo222o5BREUKAUnhCNT1NM8r1jLFvGA72YbSgrSvm5QWPfvdfcPdzFednZzydW9Jij6xeYZoZ470R4/GIPt9AaElbnpNojYjHYaVxRfx1IuTDTHNBNT+lqpZk4328HrJx4zU2r9fkOiKJInyIWNFhP2mKCeGJcXjbY6qSh0fPMNawM91hujFhZ3tKkcYkKky+nBThdTUY0C9T2nrNdhpBOuG5jFmUhmyxYHP7gJ2be4jUE5Nim4y2XiLlADW8TurmVM2M+uJZ8GAlMd26orVd4I6MM+gdzXqG00NEMSCxc5bzGRf9A+5sbbNINqk230QO9kmlQdiKLM9JkzSI3LTGNxLfztDxLXSa0J4eo5o17eqEbn1JV9W0naa1BT7dQSYDtnduIuICZxvwHbZr8N0a1zZ0TUdnA0DQOgdeU+tNjpIDqs2b7L76Fnc3d4iVJ4skWnUIFSbBeML6OM5ZV3P6dEIa76OiHNksaR79Hs+e7TPePOC73/5dPnh2yGi4y+c/8yl+7I2tsOqrz5ldntC4mM1iiLcr1rZByujKCqswzkJX4tqSvlrQVysOn7/kW88bymiTT915nVu3bvG5rT32Bgmjq/cN4wXWGaTp0UJjjMN1Jcl4wsDlnJ89YH5xjrCWqmk4Or/kZN3C5Bobr77CrYML6rOniM4TzS+JxkM2NneZnb2kWVXh/sWFlmffOWzf4XCY3tL2sDIxdm3wgzGT67fYylI2RxmbmSZXYVXd9h19HW6kIhkxGm1Bv8R0T+gunlAfjRkma2Qb0ZrlVSOxuaoFS2xfoUSGlBGmrWjLBQqBkBFSL0AadDTGRgKdjLn95T+FzjRf/3/833ny8TlKWophgvAFeb6BSBK8bbHdgr5s8Y3H64iu+6+04vn617/O6ekpn/vc5z55zlrLb/7mb/L3//7f51/9q39F13XM5/PfN0U5OTlhb28PgL29PX73d3/39/1/T05OPvnYf+6RJAlJkvwnz1dNB114ExIi5tXX3+Gtz/4oL18+4ONnj8Jdf28YxBLpZyxXHzMYbXL79qd481M/zHvvfpW6nOOdR6uYMIjpaZuOZr1mMV+xrmryIkamA3IUBQVtVyF1YIU462nqBoQgy2O0liQqJstyLmZzms5ifdh7Sh3jRYLEhVpnmiOdoS5XRDohzQYoHYN3mLaiNxalJVI4uq5E6RhvBW1tUUoTRzrQWDvDres3Ob04JVJgjGNQDLlx4waXF5ehvSIlSRwQ/p3xdMagJEjRISWgI6RPcR42NsdEkaZpGuaXl2ANMtLhgipC1TnQ7CRZVrC1vY/tDU1bs1ytaZs6ZE6kYjzeJB0UVGWJMT1ZklPXNX0bZHXSO6zpQytpvIHUmr63YA191+ClQgqJMZ5iMGY0HAesfFaEizKWtq2ItAyV3zCbJtEpcaRo1guatkdFEEeCtqsxJmD/v89/CVOklLZu8KbDoBFSoXFIqUiKImwMvEMrQdOWiP4/yhq9B9NWNFVNMZ6Gho7tcR6GowlaauaXpxR5hjOGrm1IiwFCBsqsdwblLN4L1qsSpTRZEoWLuk4DhRdHt57RrhdheuYtfVejdMx0OqUYDPHO0/aGvg9iMNO2lOsFUkVsbGwFkKhpKIoB1lqevXzC2ZNHTJozDrYn3H/tNZLtmxgFZ+mQ5tHv0HWGBPBKIaxDS66+b43tFtSzQ+pqhSp2qTsF6YTX3tpjNBpS5AWIMFWQPlBlhBfhYCMkcTyiXlzSnX4HPxjS9w2HLz7k6PicPpJMTg9J0hH33v5CmJrVJdWzb4V8gmmIXYMphvTNEuE9eTagNmv68gJfL+j6DisHrMsVq4szmvUlAyPYOJiwsXcTvzhG2gbft1gEQkd4LImQ1M2atlmEjIMP68Lr198gTwZI69gcFYFQ7MBehZ5lQAsjbU/frTg6fEY6PmB4sM/07meYqjzYv4VEKwlSorzDptDVDb6fU0R3kTaidB7T1qxnL4OvyUMWD5FeBW3VZkNXzYmiEaPJFvPFIRc+Bx0zbmqyyRZ7G7tIEYOt6GuPER02SVGxQnY9Opaszx6TDjbR6ibN8oTTk0O6ZkW5rIhURD7ZYTDYIm4W1OtLRJyTjfcw1TnV5QPa1RrXOpwMgdNqWXN+fk4pNRt3Ntl//ct88a0fZ5RqNmKHkgTqsQg3gtKGI3gwqhucivDtBZdPDzEHn2Zg1sw/+i7f+vZzmnTN7bsddrjNa5+7SU5YUYxHG6RpQTPv6aoL2maOi/YYTA8QUtOsK/A1pq+gLrHlBecnR1ysOuR4j9n+58mua17d3uftvSlFmhFLH4Bm1uB8i7VcrUMNKo4RylKff4Ryu7jmkuXRQx48PqbsYOELOhETpTkb+Qa3rt2gYJNjOePsxSkXLx8TjSdMpztMJgXr1TmmswjvsX2L6QxNA5UJfJpGF/jtMTvXNri9O2QcySDZ9BaBxXhweBKtiNKYsquv5KSQRprqcMXxsydcHj1jUgw5LHsGo4jr2pKlAXEgvcX2VYgOiIA+Np2mlQlCpUjdIHD0tiUyI4hTRBxx8zP/J5Lhbbrf/C0Ov/d1Fs0pw7ZHeIuMQpC7ry7wnULmCa3WNP0Pfub4Ax1Q/vgf/+O8997vT+D+2T/7Z3n99df5xV/8RW7cuEEURfybf/Nv+Omf/mkAPvzwQ549e8aXv/xlAL785S/zN//m3+T09JSdnR0Afv3Xf53RaMSbb775B/lymC1LklST5Rm3777BF3/kJ3j+9EMuTh8zHGQMRyPieMzu5g7FKKGuV8wujvjgw6/z5ptf5P4r7/D1r/072mqBFJKdvbt86p3PcHZ6xu999Tfp6zWxgK3NKZ/7wo+RJgXf+sbvcHZyymtvvsN0uk3XNKTZkHQ0ZXnxlHJZkWcDJhtj/qd/8T/yoz/2Y8gkoekqRpMpo/E2s9PnJCkMJlOqdc3XvvLbjDe2+OwPfREBLC7OGQ1yvIp48PABhy+fBHy5kHhrcHj6riG2KVGaUhQ5B9cOODx8Rq4VxCk3b94k0prt7SmvvfEpPJ6Tw+eBcJpk4e51teLh976JlILxdJebt14hjlI+fvqYtqnZ2d3j3r1X+Pijx8xnZ/Tt2dXFSTEcT7l771X6puH8/IQsS7l99zZV2/LkyRNMXaGVIs1y7t59Ha0V1Xr5ia05yxIW6xVPHj/EmBYhWrqmZO/GPW7cvIOzhgffexfTd0RK8OYXv4yQMLu4oFwv2RwNyZKY0WSLr/72b6IixcZkh3uvvoUxHU3vMH2HoifOh1zO1xy+eETf1sRaIqXG2j4wTlTErdv3uDg74ejwY4giRoMJbTWj61q2rt3lxvUbSBwfPXif5fwSawzj7W3uvPIGq8WKl88eo6xltLXPaDhiczRkVa1RStN3HcOioBgUrJZLPn70AYmHG3deZTrdYTU7o+s7JqMxxBlHL16wvDzCeo9t23CX6iWLrkF6i1IxavOAjfGIg50pdblmvV6jlSIvIqSaMjs7oqtmoZEz2OX6jdu01YyTJ8e0tmL3xj2GX/6jvNi/Q/nga6jmjCLTjG7dJk8HxKrgePkSmWbEg+v0+Q5meYIqLxhdu00+vsazp9/h2fOX1MsL9l4ZU0xukeVDsC3q6oJtvEVj6H2QdYZclUFIjVIK21V89K2vcsfnLPqOhYXNu2+zOd1hOB4S64iJDObpJkpQWzex9QKyMXq8S9KtWZ98yOUHv0landLFCaezM+rVDOEFg81bpKM9PAk6PsT1Fu8k8XAXJ1PK8++RiB5pNfQt1iyZzS9Y1Q7TdMTtmvFkxGRrl93RAC9jvJchB6Y8jg5hQpPLC401lr5Z44c7DKb3MaMhVnm2Nw+IpKCTlkiGw6hzBkPAwkvvWJ+9T64EKIvvO9J8hJMauLJjW4n1HbZf4FYnHL4847h5j09vTvE7d7m9fZcizYl1dJXBCjFuIXOivmOxPkcNdyiK63gElThlcXHMavk7HNyqKVcr+l4jsym5HNGWC4gnDCYHuDbHtXOqds0gHxFlCc3ZA6qLcwSaXnjE+IDLyTbH6S4bO5tsjbaY5inpYIQXHk2F7w1eCdSVQsQiwFiUb4O0r1uRLF7y8XffZX14SRELfuO9j7ksdrh35w6be1OuxxmRTJCm4/T8MVU9p5hsk0x2GXQLFlVH31ry0QiRVZjlC2bLU2xZY3vP45czPrzssIMxn9q7z6ff+DzDOELbkki0aOFDJsx6Oh8mmAIBV6RnZyyyPeP40Qec+ohV0/Dw9JyTpWIwGrM5yNjMNRKo7QX1yQsGt15jvP8mZ2crmqbm/PKCyd4OW9NdLg4PWbcV1jgWdUdlJJXLaQdDtncOuDso2JhodjYSBpEISAchwyTfCZQ1obkoNWns6H1NffGMM2oG2nF4NOPRRYvuB2wLgR5NifMG5+ZXGwOLkGFV7WWEEVHge4kaxOoqn0kwgLuaPneoHlIHymcc3P8h/nfX3qA8/QLv/V//Ci8uWtJsySCSWCsh0agkxaIRRtDW/5VsxsPhkLfffvv3PVcUBdPp9JPn/9yf+3P8wi/8Apubm4xGI/7CX/gLfPnLX+ZLX/oSAH/iT/wJ3nzzTf70n/7T/J2/83c4Pj7mL//lv8zP/dzP/WenJP+fHvvX9phsDhiNN/jcD/0RWtfw5OkT4khw/fptbtx9g7aPWC1nRHHPZHSTYTLg29/+Lb61/A0+/dn/Fq02+PjZI3CGxRwOrr3JyfEhF7NTcBaN4+LkhLPTSw4Ock5OXmKahvX8hAff+T36pubW3df43Jd+gq8//oj3vvMuUiq+9OUfIZ+M6FXDk4++i9KeHXeXa9dv8x9++31Md0k+3ODGzbvEg5y1h15FlGdHfO3f/zpSa37oR36cL3zhh/iaaTh8+RyURGl9hUQGYzv6ynLzxh0WyzlxrOnb4IeYTrf48LvvsVivuHX7VeI44cnTj7FdixCCJMu4dnAthBGV5vq1W2RJzLe/8TWM7bHeMbu85Nr+dd566x0efPg9qnJBW3dkxYjPfu4LvHj+hGcfP0ThmCM5P7vk7U9/jvv3X+fxh9/BmZZyvaJvO6Io49GjD1nOLwEYTTZ4/c13+OE/8t/y8IPvcHrygvVyzuHTx2xv7bIqy+C0EQ4la3priAQ8f/qIplpTFAO63nBwS6LjCOs6Ts5Oufva2yyWc549e8Z6Pceajk9/7su88sqrzE6f0/mwOrLWEjKtgtHGlJ2D6wiluLg4JBmMKYoR9eoirMVC7BJnDLPZJc4EzH0xmnB+esLTjx5j+hrjPNXinK5csj2+z+LymOPDQ7yzWNMw3txlenCXYrJFXy9ROmK0OeXjB9+hqpacHke8+uZnuHZwjWp2Qm8asD0giYsRILHtmmI44tqd+wxTzcP3v8Hp0SF5UVxxhBz3XnuHYriBaWqKSPPmZ36Is+NDHr7/e8TZkHg4ZV037B3cZHt7l0fCcfbgd1gfPmO49xbR7W02rx1w8XjM+vgReQTJ/l2Oj5/TNzXpxpR4AE6NGey8gfMfUy3OyLeukxUbNGdPca7DyBjhBE4qpLTQt7i+QpkGKQXz5ZLzwxMuSdjqa4psxCtvfpEiDhdYhcMKj3EC4RRx7LAbezTdEt/NyPQBdDGLdc3D97/HwfmMnWt3IS2I8m3asqRczdma3ibdHIO3dPNTuq5hNBwTb27jzYLLx1/DV0ukM0RaIOWIwcYYj8BfnqClJZYGZ1qMBOUt0ns6IxGuD9j7vkEpjc63UNuvsJMPyfMEJQ1aSTwGLyB2IK3DmCXKGLrVEXa9QDAh9gXL8xeoNEZ4QawUDovrWsRVtd0agxeatRyiPv3fkO3ewQ9GTLRiKAMBGRmyV+GqVeKEwCXAsmL2+DeI1E8Q07M8PcHYAkHP4vwJXgqSJMcSqM6ir3EyIh5PSdyA6uJDTh58Fbk6ZzoZUp8c8+z5nEsUyfYmd2/d5s2br/JOEpEIh2/mdM0lRmQkyQaqNxhhg1QSgfcG71qMqenaCtk0rI8f8uDb3+V3n5XcMGe8ce8Gb33xv2FjuMEgIqzcUFhp0EnEYDCk6xt6FTPcuYvyNfWjb3H+wQVuZxtZzTh+/D0eHp6zcAnbt95m8Pkf5gtJzu4wJXMdxRUsEiR2FVZV3gsQjsgrBAl4Q2dWuGaGn51w8vFDvvnBMzqXQJLh8zF37o7YKTS5BusMTe8xdU95+pj84A6b+/c5OHvGRw8fsn75lNnmBoO+p6w7jpcdrY1wyQbRcJOt3U12toZcy2PGNoTuA1lbXUH2wirNi9AkdDic71CmxjQ1z59f8OzlnCRJuHAZYjriYGvA7jhloGPG+Yiyh7afEzsTSgICvOkRGrAe3zVY4elagcQhTQrEJF1HrJ8CFX32Bs5fo8hGFHuv8tYP/+85+3/+JqcXF8h4xGhnGMB+Osb5ADWV8Q9+7Pj/OUn27/7dv4uUkp/+6Z/+faC27z+UUvzzf/7P+dmf/Vm+/OUvUxQFP/MzP8Nf+2t/7Q/8ua7f2mVYFGSDMVu7+7z77jfx0rCxdZ07t9/i9PQlZxdzLlYd5cVTZB/qqcv5jK45YTjcZ7wxxVpNU/bEWcDLC2lQ0od1gRDB/CoczhuUh8Yanj9/wXy+wFvD8dkFddeg0ojNnQlZltCaJds7A7r2gmERKKh5DNGV/bGpNa7vuDg9JNGwsbWJFpJVWXJ5eUrb9Aj1OwwGYybDEU/rBTpJ8CoBJLGUCKVpjSXNEo6OXmBdj45iBsNNurZluVyiVKCs9l2H6zpiLdBaUVcrDp8/pesdezt77O7t8/63v0FXLdFxTJokxNKwnJ9j7U3uvfom3/wP/44oirn/xlsgJC9fPMGYDiEEQkLbrDl8+YRPffYLLC4ueP7kAVEaUPRKSJztSBMNQlCtV7z3rW/whS/+EW7euc9ytUD5JZGSeCBKwve5Wq+JtaXrO7yFvq9DWdB24D0nhx9TDBKEizBIsuyqGmrWJNJhI8n52RFbWztMpxNODx8TRxE4gcUiVMTuzgGX52cMRhPSYoi1PfPZOW3TksYRTTmjLEckWiKlgChma28P1/ecnpwhsURakqoEuoooS9HZgGExYl3MAUHfRmipMM0KTIcSjrZe0qzXNOWCxeUxRZozPz1m//othqMJ5xdHCBkjhaHIc0zX0lRd+B2nmqOnD5idHaFUcHiAxtue0xePyYcTFvML7r35ObROKas1O9dfIctitHRo17K8POb67TfZe/UtyvUJPs2RaUxvOrJ4iE8LvvXoKVvHZxyUDX2UsXntFcqyZmJaimJIv3MNbStmZx8iOku+ewfTdTSrIyIvUKbHYBCuoZzPmC8uMOsLBpEkzSfsXrvN7b1bFOMxuQ56eelDdsd4i/aB1uyEQgiPTmOqqmT57N+xc/4Ck4zpjGLjlR9ivLVFmuUIXKiQIimrNY3zjLev4ZRleXHI4vl3KCYFyeYB9eKC89ka7QXSWDYyzXQrwg3GWCmoaOmWC1R2m3TnHl13zurwXURX4uIxKs4w8Qg1uoYWwROl04wkHZJoF1xbzhAh8NT4rqFrrrIFXUV18hLOTtGTbaKtawidh1WR60Ldvq3pm5Ku7zFRQhwXiHSH4XSL3Y0JsVZEdgWRwntJJEKIXqhQQ3deBvNxXWFWFaeHT3DydxlkBabv2Jjs4qXHCQ/W42SH9B5HBFLQLV/Qr0bEg00S0/Hxu9/hw/c+5I3X77KItmle/wy7+zfYLmLGUpCIniTOQfTgIkxn6RpLNB6SZgVu+RJRzzCuRXclfjmjOn3J7GKOiQwnS8MTdcDeD+1y79o2e5MBkYpBeIxpscJcCQnB0KPTFFsu6C+eIeI7pJFGrk756IOHXE4mdFbzvtmgObjL7nTIxvY17t1+nUEc4ewSV83oXIUTOxTRlE73oCq87zBGIpzH9UsoT3GrMy5OTnhyeM6T0wV1usF0Y4PNImeQWLRSONeHyac1OONQUtK1DeuXjxjeeoXxsABbsT4vefEdS0LHYSNZDg4YjbfYG+dMhymjTDIexGhvMI2lMwLnBHGITILzVyun0GBVVzLPo/M5z49KTmtFnqVcK4ZcyzOGccQ40+RxBFKxMj1KjpC+QYg6TIawV01UGw7hKJzt6fsVSuUkWPzlezh3ilMJbnALF/egFT7JiJP7bH35/8yPmJJ//xtfIZtMSEbDoNHoe5SOcVh8df4DX+P/iw8ov/Ebv/H7/pymKb/yK7/Cr/zKr/y//W9u3brFv/yX//K/9FMTJ8H5srG5EZo4tuHanXvce/VTXK6WfPy9b9GsS2Y1nJ8cIb2lt12wOTaejx495nNf+GEmkxGXXQPCYK1DIEi1pnHm+xnaTwJMOE9vPKvZAu8FWTpktLnNaHOTazf3cCyJtUCIGrwIFxQh6LVEJzAYZuzeusnLFx22WnN6fELb9FTLmq2NXS4XS2arEuccq6qi6WGxLjk5OWL32m2+8M4XefTwfcrlCUK0jEZ7WJtQrkqMaRDE3LqzzfnFMVGicG0XTtveYNqaet0wKAq2t7dpmpa0GLKzd411WbNYzIKwzjnK1RLw6DilbRuu336V8cYmOMHGdIuzw+fQdSgncFJgrEVJz2I+R+qE6c4+L59/HOBffUfXmcBtUTJYS4VB2J755Rk37rxKXgw4W8yQ1iCVRDuN7w1SQD4eksRpmA754Bvq+5bBaJO2XOEiHYizOgrsABkjpEDHCUpqsiwnjWOEl0w2NtE6pilrjOvZ3rtOlCScnx/x1v7n2b92m+MXT/DCMplMUBKEa1BSkCY5061tRpNNTN9ycvSMQbFBn0Q43+Fd4JgoHFIonDV0bXnVVALylIuXjxFXIWRTVdC19N6i4wylE6J8iE5zkiwhTVP6ZgmdoLo8pzcrdJSwsXMbqTTzyzOkFOTJgPEwp5qfM3vxjGW1ZnL/TTb2b7F/6y5aGLLY46L4Kkuj8SqhNRZjLdOtXS43tigfvYfbnjJbnhMPJgy3D9i491mGwwy9scl4OGW8vUdVzWj7iiiOSEYTynkKUtC0p+wMMwp3B7c4Jm7mdKsLmmaF8Yp5K6nTffBD7Pwpk1FEsb2HHsU4rXHShTcwoQOQTcRYIRAq5IRs1+PXcxaV4WxhqbOSwWbEdJSxG2lUHAddBBYcaCzC9tjqlEF8l3j3FrPH7/Hs0beo6pL7d1/DNSWbO9fRSY4p51TzY4atB90FxLtwrLqaUXnGlrxOIzvOOgs2J5YKkU4YD3dRaYJ0HV29xgqJHk4hTumbYyiPMU1LX13QLg7xlUNu3sdFBW6wg1tbRFyQTu4iXYUrj3F9Sd+VOJ9Tqglya4t8MMA7ixCaokjJ4/iqQTXAEfQBve1ASIQzKNtAu8ZUl5SLC8rFirwYQ72m7UqM92jhiJDYzgRHkhBEFHg3R7dnHD87ZP3029zY2+XZ0zMec8Dk9ftEb7/D7fEubw4jYimQKJpqQdMsiIsNongLS0TaXtLMn+NHE5LBEGRHV8+x9YLzkw94+OEzvv7wjBbFZ1+9w/17n+b2GwN0rEmkxHmD86EtiAQnwiFAonEyQjPBdk95/u6v0R1to9fnfPfbj3h3rXjr5j32r93jM1u7bI8HjLSmbzuUtOhiAkbRdgtE2eLyJTLeQQ6nONPg2x5rSuT8JeXpI54/e8G8SoiGe7TpDvvXJkwGmkx6NJbWXYXvEXQevNAIEWrX0nVcPP+AVPao5ZLlouVw7ln5msH+mOm9DV4ZDdjLEwpnEH34fqWJsCoC5UmlJElSiCVNvUZ0LSG946gvZ1yeL1iJlEoX1APNpHBMs5idsSbSAqUdHkNn5dWQLUKmI1LlsP1zurYjIsdJiySY54UTSJ8SGUvUPEV2H9Aun+CGn6bdeJMkfhMtU7RXKBfkgyrb5sYP/R95++VDWnOKFgOMTFBJhO2WuPlHnH30n2/r/ucef6hdPEqERkGRpJhqRbec42VMFqV8+9HXefDBA9ZlTe8UEk+kZOBSOEvfNpyfnpJmOUlSEMWSpMjxcU6UbZCkY1RmwXu01CidYnyQLkVpTjLQDMc5o0HKzv6UfJCSZzFppFDqqt7qJXiBwIWJh/f0rgZb06xKLo9P6buKtqnY2OgwpmNUFGxOpzgEr772Fl3X8ux5sJo6pykbuDifs1rOUOKcUTrEladEtgsn3xjyLObRoydgQiVXJwnCS4Yb23gsG6MNoiji/OwBSeHY3tpmuVzS1CXNumG8MSCKUuq6pipLZrMZe9d7tvcO8H1HlqSslzParscLhfQOHWm08JTrJcv5JVGkGQ6HGGcCgO6KvFuVa4RoMaYLAa5yiVaCvYMDysUlaZahhWBRLjB9HUiGfU/XdcRxwmRjC5sPUZFma/cazx59l7oqAzsmjsKkRYT6q9YxG9Mtrl27xuzigqePH6K1o+nB2g6PYDTc4OLylNVqxvz8jN2DW8xOD7Ftib1yo+BdyP4Y2Nu/CXjef/g+3vYhDIvDOUEUD/C+oynntHVFWozZ3N6hXpfkxZh8OOTw4XsY59HjbeLRFBGnbG5fw41rhsMxk+mU42ePuDx8Qm8sfdti4hHC1di6IhvuM71+HS0E9AbVLjHrmvcft7w4nZGVJ7y2PWI3S5hcv0WSptimxAtNJDVeBueLkAIpPbY3xHFG2cE3Hh/T5E/Y2u/Jtgw7B3f57OcEzfoM6S2DPMeXS2h6TFdhvKKY3qaqz5AnD5g9+Q5bWwfIZMLi6ClnL58z7xyJq7i9MeL63g3U/iuIeMD86QauPCJSCunC68PhUELhrwirwlpo1nTdDB8NcXLAoinJJkPujd8hS0ehseVtWKH0FVIG3LcgQqsMJZY8+va/IzI9w53raCm4fusN4mRI39UkcUySarxUeDXgfKW4nC/Yu/4WarBD/9HX8M7x7sN3KUXPZOsug1ufQYsU2c3pbYdOMlSW4q1Au4q+naPsiiIuaGrPerFk3fU0q5rFswsGsSLNz0mS6+TFmHVxBmnG8OAAmpqXR9+jalwQQg63GE9vkg63iFVEYyraaobUCTYeIlWP6y2iNQjfoGyHqNZ01TmLxRnlaoGtK9LRlNH4RvBrYRDEpAIgEKiVlGhrMWZBzzG+KlkeP+M7D49o1BZfHh7Ap3+M/+4n9tgZ5oyEJhE9CEtHUGEM0gLKJX1XMdjcRzrFqkpoLr7J7OIJ/f0fI5p/j8P3/gNnF2vS0R6z9BU2vvQ214oBg/ocgWNneoCIoF4eXU0iwkrNO4mXClB4Gfg+qW8x5pxnD77Jyceb1MUmD1/5I7zy6mt89voWkygjEhYpIqw3RMJi6hl+MCZLB8h8H+tOaRcvWUhJQYdenVAeP6KZf8zR4ZzvHtVctIrRMOYLu1Pu5zlNs6Jen4UNrJLEvccKSX8lIXPWIZxBmgZTzrhcWmbzCuUlF3KMur1FfnOft6eOQZKSKImwFtcGSrcTLVhDJBO8inHWIKVD9i1+PadqK/oezuctz04vKa1ltD3l9XtvsTtOaM+eUy3P8TIKh35LWPlJj7B9QGxIg4w36ewpyjZ4D53rSE2K0BbvOmRbYRcfMZt/zMoOkDtvk07+BPFgkyiOccLQ2znODbAyWJP96C7v/NE/xdf+p/8L1SwhHQ7RbcXqyTd58jThUO4BH/1A1/g/1AcU0xuEN1Rli1Rrnjx7QVdW3L77WcrlglVZ4aUg1hDJ8K1KKXHKQx6HtYPzeOFJI8X+1haT0Qbr8ZJrt+5igDSOAcFkc4e0KJjs7VDO1wjnmA6HJCOJSkDJQGGMohglghXW9BacwHU9ddOx9mtWF0uefu8DTp4/o2874lRT5CmDYUKaKJJ4wmv3X0FHBVp6vvqbv87xy49IYrg8e8FX/93/TFeXzNYB2DOZVpyefov1ckY+GnLt1n0uzl7i2hZvoQW6pmEynrC9s4Xpe9I4pSobqrKmM5au6ZBChDfqXrNaBZCaEII8C6E7nKOrG+SVK0fFmtH2FCMUZjmnbRosFq3TILOTEtv31E1N17UkcXqlrweBD3JE75kv5rRtR9V0GH9VE5IQR4rRaAi2pzcG27UkWcawKOikCq0ipQhD9NAs0lpT5Dmu69ja2KIoRownE5x1lOWKKFbU5RwlE7zryIsxru+5PDukLksODw955Z09tE6YnR2iszFSCGxf4Y1FKZA6RaqIyeYO85MzmqrDegvEDIqcrq9wNsgWdZKgooThKGFr+yY9DuKMSEgmkxFaCrSAQguKyTWGG5t8+J1vcPHyEUkSUfeGbLTHZO829eKSXsZkwxxRLlhVC777u7+Bnx0z2R6z/fqPsn3vLRYffYPEVWjXoroVsVLUTqJlBEKhpEXYFleXtLanH++gtcDgmN57leGt++gsYbmes6PC6vGkOsU5SIqc5fkRUZZjrWV9+ZJROmacDDhveh4+/Agvf5Ot2++wSKZE9w+4kY7h9CGqeUGeSIhabFqQbW1hmgtksUE03sPZObazKGewymLblmZ2RLOcUa0WSJWxee0N8uIA7TyiXSMocS7U+IWIEFLhnELqiEgE2eGiLLmsJB89+Yjrdk2ROLJoSqQVV3ZQcD3CtuENWQsOZ5dkZx9zrZiwGu0zzqYUac5ktEmWRWS6QXhFXw3p6wuscmSjPbwpaeslrlwyf/EAt55jbUuHQkQx6VDjdzt81ZCO7yIGY+zlQ0zd8uLxt0Mj5/YrxPuvIKMpUQqUF8RaoKIOEWmyWCNdCrYliSVRNKFZnWIun9NUS7pqAWVJ37d0IkKqDOMcxsaMsgFJpOj6JrifMOANkgbXrbHVnG4+xzgJo10+4g7mjTe4/fqbXLtzk1EEMQYvPN6HnIYjQkmPwOJ1hBQt1cm3SfOMQZogy2Pmzx7wvZdr7rYR/eKU33tWMYwHfG4y5Ys7d8kHA+rmgsVlQ91XOK2JB2Ni27Cen4Ow9LaHK1Ch9g5Rl3TGsF4teffBIR+J27z1xud56/5tPp+PGMcJsRY4AV4IjO2QHqRQ+K6kXM1Ik13iNEGvetZH36E8fEKfOE4//g4fPviYTg7Yu36bG69HXOtbVF8jnSDJp8g0pakWCGHpXI/0KihGcEhX01aXVKXhfN5xvm44dxnjNObm7jZvvFIw2Biwl2ky6TCNoe8N9ooUjLAYEZhM1je0Tcl6PkedCZR3HM6WnFU9RmYUB6+yt/8a/ewQ2Tlcec7w4A6T6AaHbUVdVvTCIoXGOYEUmigO4DVpJapf4tY9TgOJI0KBs9huiXcl1C9Yzi9p3SbJ3qfYmN7D+R5r1jSlJRcJuDO0OSUSLcieuC/xWcWr+z3vP3mMujQcPes5KrdgZ58bB1vwr/83cEBZXC6Cprv7mNv3h8wWFX01Y11eMJqMyYsRzrbEIjhhvAgIdJAMBgXXbtzC+/Di7Y1lNV9Qr5Y8+fgR7337PzDIMpI0IVIRWzs7XLu9zeY0p697fNvjjcN5QRxrtAz0UmcN3hn6zlKuOqp1i+1q1lWNM0NebQMjI8s1wyIGIdAKYiVR0jObzzg+fo6z0DQ1rnMM8wFtH1wx9bLCCcm8tmTFiHTzGs8/+DaRMaxPztnZucHp6hnNeoaOI4QpsKZncXnOg/e/DXiyLGc42MD2Fi9a1usFcRxjraXrG7TWRFGEtcEQLK/WXG1boYSg61rSOEFZi5aCPs7QUXZlgA1J8Koqsc4xHA7QOogVvff0XYcQIdjqhSfJClSUoLVGRZqqbWmbnq61VGWFxGG8DAZiCRdnx6zmS7IixfuAkkdJTNOSCkFTVZyfHvPs4wfkV2j6yeaUV155iyz9DO+/93tXIdeUyXSPyXSKivz/q703j7Usu+96P2utPZ75nDvfqltzdfXc7iHdbjsh6LlDEiICCEV5kZHMIJCDIxIpQgQQmH/AkXjiPUA88wCRIIGwmJyEkMnYsRMHT93u6u7qoeaqe+vO98xnz3ut9f7Y5XqvsZM4fnrdXeF8pCNVnb10a51f7XvWb6/1+32/mMLQ7PXwPZ+V9Q12d24TuA5CG0xZ+abkRcz1a1c4e/5hLlx4nJvyDY76uygLruujTYywJY16mzAIGe/u0N/dRhhNkcRY5VBkCa6QTLZv4GpN1mpz59YVwmab0+ETdHo9Roc1jFB4rqXRXiSY7hDtXEXrkkhYthuLhO0epr2BMxuxFvos1lPyznG84iHsnTewCLRwKBHIuyJ8GE2WpUyO7lBGEybRjF7vOKq5RKe3ii8s7Va9UrTNhkwHh3R6y4xrLVBOpWCbJpggoGkkh1degyQlD9rsCIs8/SSyu4oftjh1dgnlVLuIsZeT3J7gB0sYURX7ekET7YTIMqHhulAIov4ucTrGqy9RqJAotuTGA7dLNB2iJn1a60s4zSXyPEPo9G6RoKg0aowmnoyJ0wxP5yiT0w5qdB98AN8P8KXCyMogURiLLgsk9q63kaHIC1ABpb/MQe7TLFNqq0u0gEC6VA01BlM6KOUj6y5FPqGYHeG0V5FSkZQCx21jUcTRCGvy6sjPVAJhXs0nmQ1RjqC5tMr+4XUmbpf8/Glm7VWark93eQNjPbRryU2ONhbfX7y7G9YHz2Wy/SpFvMfyxtPMhrvs3XoTYyRCF8g0Iay3aXQX7+4MQZZPyMoEv7VMYDXx/lV0MQCTI7OUfDTjxu0tdhKBs7TIEw88xiOPnOLJMKDlVrtgyloKY0CJSonVKqx7V+vIViKFni7ob13kzmCf9dVHuHzxd/jC148Y14/TKlKWl0/wwePnqRcx+egQa8B3HHBDrN+kn6VEWhCGDRruOWSeMpoV4AUoXaCiPke3b7B7eEDRPUXr9HfRfOpBPtBtcLrXouVaBBLPgrZZtcPFXXd1q8FKKBOSg+vEUhBKgzO5xvD617i9n+E3l5jEGbFcwm81WFpZZ7HdJZsOGQ63SfKETATUFtdoZwWHd96iJK/axXVJFo+JhlMODvqMco+J04XFk6wdX+bs8UWON1xcYXC0QZYFidF3SwcKsJWPmAZMUSKoEoFoMmZ3b0g0KxHKYS+3iLBOt+az3O5w/sID2HGLvVtvMR1vs7tbY3XjYbonBembXyXPy0qI9O4JgtQlNkrJ0iFHwyNuDwXdtZAzgUW6BSUFXj4hG26SjDJE7Rje8qO47WOUWuJkI7QLheOioz5OtANOiQ6W0Y1H0WJKkHyJbE8zvDnleq4Ys8Txxy+wvFJHp9nvtay/jfs6QdnZuoNCMpsZHnzoGZaWFjnYjNm5eZ1HH3+ao5077O9fw+qSsrRIBNZUqqK+77Bx6hT9wYDdw0PytIQoJbeG1kKLjXMnqfsexlrSSYKjXHzlE/ohbiMikYZMQl04kJeUuiCLYyb9EXE0YzKNiWYl1kh0WWCymOPHS5zQoVELSSKnUllF3XXwdaouAKXI0pgiz9BGEKiQWZJzMJ5R5iWu7xLWAlY7dTZOnmOh02C/6WKyJoGUaCQH+wek8QDle/ihJMsNrudAqcnLmDybkaYxtdAHRzDoH3B84zRFUZkUYiqzwSAI8H1Fo1nH2pKjgx1cJySKY1qtLulsWimyKoUXNuh2F0G4tDpNhsN98iwiijTLqwn1eoNOt4XncLedMccqSbfbI09T+gd7FMkM3w3xAo92sIBwXfLZGImkKAqm0zHRbEaZJ5SOZjo6wlGK1eVViiwDAV7g4Ycu9UZAWebkWcxuPOPUqfM0Gl1cr8YsnuH5dRqNDleuvU4aRZgCgsM9kkyzsrrM4tpxZuMhJk0IPZcwrOE4kmh4wPYNyUOPPskjTzzN1euv0e8fILRAlxnWZOR0KIxkNhoSjQYEnssgvU1mJIHrITCkZY5wFcLx8RoLROMJB7ducObCw3iiZOfWm5QYxnu3+J03r6I9nwfOn6LdaNJqBRxb7fH0B7+bN397SJLsYiYj6isudnGJ0VETz+/g+Q1KKxAiJR3fQREQly7jaYoyLsJpYazBFhmu3yL1J5RFjCoNqswZDA9oLSxTXz6OR8bhravobISfesRuwn4mKAZjeqttzj/wLKFbPaE7qhLFsrJyxfZbDcpWGxu28RdPoWxGnsyIoj6bX/1FumvLZLLJ3mBGWqTU63usnn6asLmCm/RxvBRJRpaMcd0m/vJJJtIl23kdQYktLWWRMJpNmZQuNmhhoyFhOmDt5CmUL9COxBYFUlWJCVojyoxcGyygvDpOa43uUofeQ3V83yN0ClwlMVJR9c0JsAVCZFAUuEWJyhJmsxlD6eB7NYxOUZ7CGAPmrtif1ZQ6vtuFk3Fzd4d9+ypPNDrI9QdZP6Y5E3qE+LiypBABDg6BKPDCNrNpH0xCLVwmK8aY2SFH1y8zy68gc0uU9vGaqyg3xCsTpnu7aOsQhh0KBF7DkuxeJ5+Oaa4+hCcL8u0Z0c4m2SzFNFfI1j7A9WbCSrfNyVZAWyrCsEFQr+OYESarlDAlJSCRMgDhYvOoWliFQmR9kp03uPzKW+yVt3nqiYJt1ln84AXev7bIiUYN11AVAXuK/uyAKBvTddeohQ1MERMWBTKZIGeVIKX2PNwioZgekuUJW1PNS32Jap7j1PI6x849wMOtFk4xhjKtklYFKTlSp3hCQmHJhax2Q40Fqyh3XmH78DoNobn5xqu8dhThuh3ONNucX28gpEMeRRSzEWV3haC3gZtNmezvMBvv0Vw5zvL6SaaH10hHR6SzGbNZzm4/5eZEkzgLLCyt8MjxNZbbPqurq/jNGoVOKfIIipzclFibVU9/0iLKElEWiCIhHvUZJzlRVLAzjjlKNX6jSbfd5GTg0fIVriwRyTbRYJGVxQ3yZEqxdYXD/Ts4qw9y9vTjmIPrbB4O8F2F7wryOOJoNGM6ixEOeH4Lb2WDzqpE2j1MaZD5mMlsk0pAdhW/vYFb76JUic4UpSpx8y3c/GWsKSlrHUytiVQnEbUNXK8kt09j1Wd4/U5JJAOefH+LM0sZwlskyv9/lLp/L2GNRReaaDxgNu7zwPkHSMYH3Nl8g4XFJd7//j/CzRtL9I8GaFvSarYRAl7++lfJtKDR6XDx5ZdRApaPLbN6/Dhr6z2sWCLL9yhsZZUtSot1fQpTPY22u116K010njPu7yJszGSccbA7YmfrgCSa3u2ECZHKIR6VREWBkBbXVr4vUlgkzl3PkLsS3dpSFJo4zpjEMUUGjomRvkOtUafd7LKysgAmQ5icRx86wY1rl3GkpfQk7c4S9WaHQm/j1xpobSnzHKEzBod9tjdvEvhQr/lE8QzXdVFByNbtK6ytrnPu3ENcfOkrGJshgTyrjkEcpbj06iuUyQyv5TGZDllZWqbRbTM8PETkJaZMMWXEmXMPk4wH9HdvE/gufhBQq9dQShBFMcPhEFcoAr/G0rF1VtfWuHn9Cgc7mxRZjLcQYHVJNJmh0wTPcaqiP6UQCBzHxbgORZGhx32U32B55Tg7W1tYW5Ln/486paTazu4t9aiFHmmekKURjpAcW1unEbhQxnieQgY+mIJxf4/jJ05xbOM0N8YDoixB+A66LMEohNb09zZ5OY0488ATrG9cwHEXuHPrDWaTPpSC5kKAG3pIP6S2cAwdjcnTKb2TD7GycY7B1hX0aIAxltCXtEMJkzH717arJ24vZGt7n3ZvCRO0eOTZ97O+vIgqZ4CmmPbJFtc4+8BDDLevoW/MaKw8iemeRbCFbHQY7V2hmG6zv3Wa7rHzjAdDdJrTXX+UzvJJMIZWq4UViv7RFm53DSebIlWKVXWsk6LjGdPDHVq9FQ6vXWP7jZeQJsdZndDuLXL+iWeRVuG6goaqg9AYaXDKyqCv0BLXVr4umVejf+cSa75BKpfZ0T5b/SkHBxPWspCltR5BbwG3NBSTAVm0T235UXA9ivE2Xr1NPpqQjjdprq1TbywwGCcMRwPSeEZLFARKsriygbe0QhI6TLYiSgPKSLS2yHKMyQ1C+BQWEu1RCkvND7ECHNfHqzep1yWOLDHVunF3UdZYk0Jh0XlGPjlgdvgGhelSW3y02vGMpwhdPQm7WLjrDK21rDQ+tCEJVqg/s0Gnt04Zduk4OcIISqqHFSMlSimQElNYkD42PmT41hXCh36ALBlzcOcqkawTLDVIZwcEVlFza2hKCpPgt5vM4gyjatTaq4TFlPHNV3nrq7/KI/mAli+59uprvDW10F7gXO8kJ8+e5Ufrvao+JRuQjg4p8z506piyhfFKdB7jWgdjNKXIUMUUlU3I4hk2L9i59RZffu06O/kia2dOE3bWefJEG9erdpBskVXqqEojSoUTNskKKFQdv9UksDnebJfhW79CdM2l3V6mmA54c3Obawdj3LWHefKP/Ajf/90N6ipGzA5QSuOFTZxaQDk9wKQZlCVKGIT1MTrHSou2BZQGlYzIB9e59eaXOcjbrHYX6DvLnD7fYL0ZUAsUpa3qQdyWy3ScMM1h7cQJunbGcHeLg803EY5h3deY0T47m7vcGeTMCh+3s87ChQXWz5xgpRmyaMd48ZBCzyi1wrm7Q1I6VeG3LASmyBFlik0jZqMpB/0xO3t9ZhkYr0MZLNJZ8FjruHRDH09YrAQrPfJ8ynD/KmG7Q+vUE2SzAfGNW0xuvEnWabG0tsLh4Q7pMGGcGIYpjAtJ7jbp+orV3iJnlpo06hDFNYQZoMc7JImlDFqErVVqjV61Q1RKpJpgJhcpRyNyL8DWT0F9Bd9ZIJQpKhthbRtVf5bOk99D8KXPkjOjwyGy9EnL0V1T1G+P+zpB8RyXIs/J05grVy7y7PP/C8dPnufq1dd45etf5GD9NItLK5w608NayziacevGDRy/xfs+8L0YR+I6OQ88fBa/7rC8skq9HtLt9Ai8AFmkeJ5Ha63LyrFj1OtNPNdFjvqk04zJLKJ/tIt0axRFievXSdISqy2tWoDnhxTakrsOfhDSavZwlUur22PY36UoLWWZY3VOUO/ek//OjERbj7Ad0KjXaAYeQRhy/MQpHnjwCd64+GX6gyGu5zGZjJBKEjqGY2urOEGDzvIKR/ubxNMRUmlmUUSuYZQJhptbuFjqoU+j6RI2faajHV4uZzz45Hdz5vyDHG7fwBhLs7PMyvpJ7tzZ5vrVS8giJy015SsvIh54mNMPPIrjXWd0dIQnFCc2TuL7TS5dfIkkmaAchUBSD0N832NhcZkwCGg0uzQ6i4S1Gre37rB3cAdtLK4bEARNBIJ66FNvNIgmYzzPIajV8JVgobdIkgSURuN6HsePnWI4Syh1SegrfE8S+HXqYQt0QaezyOqxE0RJyu3rb1IkMxqNNr3lFY6OjkgiTXelxyzNSAYDlPIRjk+90aXeqFNkM3B9pBR4vk/YapPMhswmE+5cfYPHvvtDnDx7AUHGzq0Mk5W4IicZ91laW0UFAdH+HjbcYG31BA3fMEkPaZUTQtNiMhgwvP4Wd65cYqQdrk1Knv/QD/Ps9/8YRXyAiWPqzTaLqyc5OLhFNq38i/oHt1laOcWjjz/LTj4kUCkeMcbx2RQOl97c4tyxVfzsDtSXWTn9XRxtXUYJQ3P5BIEqaYcu1956FakEp545j1AP40Wb2HhEHB+gjeWgGJJkZ3j96g1mVuFOc5ZX/SrZDwRREqFLjZUGDLjWUqpKq0FaXbnHipzCwpuvvMid66/SXT3FVASErQ7nllbpNisJdildpBREjiBOYtphB2/pOOMyhfE2WR5z6cX/xmNWY3TOyGpk9zh+fUo+uIObjXFmhwS9RbTnQuBja12aJ5/G2Iz9y79NnmVI3+DVlqkvruHXG0hXUs6GlGlcyQl4XbTQqGIMtkBbgUpGlNMd0tInFw3yfszo1hhPJXScJo5oYaxCGo0RJblWmHRMluWUbgPpBgTNDt3eCVbDoHKCxlRO1o7AMRYFGCkRRlZdg7pExCOSgwNu3byCctZwAw/hhhxfrVHph1q0UpXjOGClxGOCE/cZ70uWQwgpKSZ9Xr66SxL9DqcfepSjjadYXzvDqaUOdZvhZROcxiLC8xC6jvLHZOkUIQKCZkAal5UJXpkCGeQps53b7GzeYhglrKxsMDE9Og+tc+HYCqvNOrW7FiK2sGgUVgqkEXi5oDA5SoFI+ujJFpY2fjagZhJu3brG4Sjj+MoJDoMOg6WnWXmgRSeExUDSXlrGFxGJLInSERTLuPUFVBiTpYO796PEmqJqnS0jnNGAIplxsHuHzb0jtvUyC8tLLCy2OeYFCGkRsmrRtoWh8j4AyojpwS0Wlpap+Q71WsHRzR2uHd3gyG9wZWfC1aKOWTvHqXaH84t1lmqK1lIPx2+QRbrq4EMgixyrzF0NGFOZPyYpOpkwiYb0hwn7h4btcU4sayx1W6w2G9QDBy+QBK6sDEnN3QdaI1BGko0OGOze4uSjz3FsdZmDzVsMb17kpjOlm+5x6/Yee7Mc7TQ4tnKMs50WnV4dr5yh84wkm9Jwj1FXh4wGu8yiAusE1BpLBEG9emA1BpUekUc3ybIxabBIo9tEEOAaDTrFlopSRwjZpFAtgnP/K+9//xaf/e9vsrk1I/CPCJdCCjrf9hp/XycoQiqCuk9ZhBzub3P98qs89NBTlKXm5o0rXH7zNd584+s4no/nBZUDrdfghR/4YdZPrvPmm1+i1XOwQhGEAa4juXHzTbTRdBd6TEd9hFa0OwtMBofsb95mcDBm9/Ye49kEpKDdXsBTDtevvEqazSpjxM3blbuxrIqT6u0ma2sbaGv5+sUvkqURtbDB7sEuSZziKB+/I7n4+iUC1+HYxkmcLCFo1XFcB61LpFIURcxsOiLPUzqdBof7O4Seizaiaq/UGdeuvMJ0OmV/fx9rFEuLAZcufh2rPLq9HvFsxGw0JskixjPwhw6+47K3Oeba1U02zp2j1WxTr3sIAft33mQyHuGSkwmXNIkxecqrL4+o9RZZW1ph8cIDOEgmsylbV99kMtjDcyCstWnWaxzt796tgSio10J0nnJw5yZ5URDWAxa6DQJPIIyl1ahxtLdLmmUox0O5Pq32AoODPUyRY6zGC30CqXCkQzQdcHRnC0dIQr/BtUuvUWiJ7zco8hiD5uaty8wmE2ya4giLUoKdvX3SJCEMHJLRAdPJFGEMYaC4c/lF4iTB8QL8RgfPa3B0uEctDGiEdVShSfIEVI3ta69QWo8yy1lcPEaGpHR9+rMcmc9I44h6d4HFlkO0d4U3rr3FjWtXaLa6XL89YSwvU4y3aRnBqU6dxXPrBOkhoVcldhECozU6zXCMQAuJkZBMjtiJhtgcYrnEweGIevkK+4mhKDVnHnuabrOF6/voaEpWWhB1/Jqgd/IM4yhmd/NVkjgln25zYniVVusB9m/vMb2zSX//KqqI6NbauEaxfvoBMvEgerBPPj0gunUZsXwa6UBhQBiDsAJhHbQEdIljLXmZEx0dsrO9z17ZRLotmqpFPZT0ug1QBt9xq/oJqRAKpKOIxkeY6DbtxcfIwi7X3/oa/SQmTgsOd67QanVZW17HUwpNl1mgmO5eR6+cwaw/Qj2PGPX3ONq9QTsM8ReOIVoncII2yhWoeETN1TiuQtR8PLrMyikiCAk6Z8AmZHsTsvEIbSzpwWUYbKEai7i905TNJURrpVIc7R6HRkBxuIUyGXlpsE6N3OsiFlcI603E9BDpKKSv8BxRueFaVSmqal0ZFGLxSktZHGKmR5h0RjTcq0wwN85DmUOSEfoBWteQGDAJpszQzFBlhJNmRJM+s+mMq9ff4tzmyzTCHhf3ckZnP4R96iG6ZzZYrdWpOQprC4yGcjKlzGLC5irg4pgpcrBHOtkl9Dbw0STxNuVsgp4OmMSWly7vcmsYEQZ1Gmstzpw7x5mwCybClnmldCoV2AypBFlRIE3lxm4Bz2mR5dfYvPgZ1joBaX/Ate0JL42btM4+zoWHnmBloYsb9mgpRV6OyfQEZIbjLeA1U/L4kDgZE9YcAr9J4TQoilllhJdN0MMtDm5dZ3vviNrCKjJcJFxb5MlQ0HQEruehTYmxDqW2lWdWmWK1BiMx6ZSd7Ysom7BWm5Lvb7GzOWbfuixtrFNeeJBHjx3jwvIyPZvBdL/aORNV96aQIU7Lp+bVMbYkyoaUeoZJhpjhiOFun62jiMNpRh6s4K2fo3cyYyMd0HUELV8CJUZIrLHkusAaDUiEUEhTUiYJu7cuE7aaHNMJsowYHY0Z5xpfGnZMHWdxhcVGjbWmy4JfshA44K8wHO8zTVNCU2fB80nijMS6NGvLeE4NXc4QhYF8SDLdJppGuH6bTj1AG4k2DnleINIU6fooN8Zxc7RbQ7TO8uj3/Xm2pr/M9dGI8GiL42IP261922v8fZ2gKCnotWp4ToP+dMpXv/YlDg8nPPuB51jfOMNbl68xjYfU2g3qtQ4LrSUeOH8WNyi4+vp/p4ynlcU4liwpOcp2gR0MFqMNaZySzjKuvnmDNC+IooQyK/EDBz9UhIGPyVP629sc3bnFrMyYDmdVoVNR4MjwrieBwVcJhzs3eP2NAVmcossS6SqE8JDSJU9nDMeGorSYRNPpOPi1ajtVlwVWG5LZmDcvfRnKnDQ3HPV3MVlBmmdMJynXr2+htUbb8q7ZnsN4uMNktF21lgJC59QDgTUOwvFBCYqyxFGGbNrnla8dcdifIG1Bs+YSBj6Neo12q06z00A5ISgfk8+YbE0Ybt9CBSGUOUJ5eH6d0HGx1jCLpmRlSn+8R1kUeK6HsAopQEqDlZCM7woauR6O7zPUM8TYQzguQlnavTpFGbN96y3yTJNXv5uVSLbJkary6gHBaDSi3qjRXVggzzLSWYQxGikE3VYL06gznU6ZTCZMZxEKKlnrQuPLAOFaRocHbG/dph6G1MOAIskYHu6ykC0w0BqjIS9KhOeRdzrsj0fYwS20LfFW1iGKafsho/EWInfoPPS9DIZ9fu1//9+YJhNiqwjIaNaHBM0lauefIjbrxDdvUwugKQu0lgwGfYQSSAekMOztb6GLGE8nYHIEMJhZtjavsTWe0vIbnKZHUGty9tgG0r9r5mlKsmJGaTOGk0NGo5jvOvEsgd9iYgUin7G3eQv/tz5N7/STvPLGi5S2Tdi5gLzzCnU9hviQ9vIxcqdOZCyHN9+g5bu49ZPkrqk8egRoKymKBPKIbLRHVmpEY51pUhKEDs89+2RlsyAsyg2o+jFNJRRmDVZnOBr0JGZ/c4fx8Nd4TJdE0wmSkrXeEvWNBmHjrgaJ0VglcYyFRsDUb7O8dIpuZ5ksHbNVW2YSZ+yagGWvztKpJax0sEZQmJQkT2mudPFrLXSxi+co9GQb2V1CoRnNRsTxDOUGpKJGHkNDpehojOgsU19YYLJ/G9/zWV5+mFuDAw6TFOE0aIUBtc4GjeYSyvGIrcDkCaqUqHav6j3Lx+gyx6Aq3ZJ8xniwzWD3NvFkik4yFhaX6PZWkPUVHJNhlIOxGiHjKrnIJ4gsokinpFl1TCWtT5nuc/OwxHhtLjz1DE88fozv6/boqKo13rUxVjtY5eI6PqUXM0r3aLmnIAgwZozcvUL/lU+TnXqaZhmz9erneeutPQrV4tH3/1Geev/7eJ+UmGiAiQ5QQY364ir58DZxfITRklI6iDJBUh2XCUdiXRCiDvmUYrrLa6/eYrR+Ht1bZfd9z/Bd6yc51/Lpei6e1BQiB6MIpI+NImwU43gtVNjFOA6jg+sMlKbjhahihB7eIh4fkQxHvHH7iDuRxXc7nFVLnNk4Sxj4pJNdTBGTGINTZJVCrzGIsqzMUjHk2QRTTpgcjXntYJvteoMrSZPd9Qc59tA5njp/gsXQI5CgTY7UAi1apBOFU1vA9xVKJyQHN0iVhyMtMjkiOjpk//CQ8dRwbQaHkSUIuywvLfDEIyc51goZ7lxldLhX+bIZUdU0lTlCWGypyXX1oCVNCiYmOppy45Jl6qZc3ovYK+t0/Q4PHmuy7ClayqET+iRxwnQSUY6nrJ86zYqj2LpzkyTKUCeexD3YQ2UZrhtghEZYgZ7cZhL3iWYpylU4YY08j9Ezi3BXsX6JNC5x5lP3BSYZ4uojrDV4x5/mj/3kC5iyILr2n9j84r8nuasm/u1wXycoURxhsxlSThgmmqgU3Ni8TSENp0+d58mnnmJxuYvwFWlhGB9MuHbldbJij0ok1qkM86QLWpAnKUk8pihKsrRkMklJk4TS6KqFNXCRtRDfqbbahJDkIqOUFiEkvqyhWg6EdZSyZKmmKA1xktDP+xRaMsktrqdotFoEtTqOtbRbTXpLDQqpiSNNogtsBroGQlp8z630VASYLKPICqZxxHgyI42yykiuKMAKPNfFsw7W0UhZqbJiKwVfYyzSusi74mVha4F+/4jpeECSlyRFyTS1RIlBOIpMKrxCM0pm7PRnhO4R9cDB9RVeUMPzG0jXRcYRgSNBuqTJFN9VgFO1HFf6UTgWhKFyZi1yrAGpLaW2ePUmnh8wm0yxowTH9ak3G5TWUNgSVykcV2OxtJptgkaLyXCETlO8erfqnpj18VyJ6yji0fDudrdACBfP9yun2jJDKYEKmjjtJXQ6wUZ9XC/A665T5DmqmNKq1fGEQRcpQatNu9umLFOMKQhcl4bnkuscu/kys9kYEfZYOPt+ys4JNn/7P3Dj6otsDcYsLy7yJJatacHQq+P2Fqi5DdyjTYKyz0OtkvXzK2yPj7E93CIv+4i8QHkOUgsslaO0FCVlEZNOthkO9qsuhZUHiYMlwvaEh3vL1Oodar5CCltpihRV0bASAk95SDegvbjC4Z03md54kcb6BdLpgEF/mwEtnFhSz0vOP/J+Qr9JiWDoFOQHVwCFKC2um+HXQ7xei0zedXWWOclgnywbIRyPyEgG2zcZ7lxHWs2JBxwanVVqKsXTEwrpVfVEpkSIHJtF5FmClALlOOjCkMUjJoVLOisYbF7F9RVLy8t4qobrOuRCYHSG1DnaSNA5SoAVmiuv/Can40OcxVOsnH+CE2FI4CmkrHYuBHm1aDYb6NjQDDt4jQXSdMpkzzIebQKS0PUwpaksBozF7xxHZzOMhs6FPwrKJ927xc3DlOmV16h1FwiOPUgPhas1ZryLrwTSlQhHEPZ6FONDbBnj1xYQUpP2R5jpLkl/jzxJEVjiKKH0mvhrq8SjIalOWGgcx2vWyYd3KLMClMBGQ0w0IotiwKJESUkTt3WS/nTCfqvOyR84zQPnLnAidJBOhqJ6eFHVjYGxBbLa5kB5ddz+JrPDK/R6pzB5Sjm8wu1Xr9Lcn+E4Li/tS5yNp1lSFteFxbVjWCmY9RPGsWUWpXSOB4jGEmkSoaN9FD5IiabS3XG0IZuMmQyvoIXPNX2M2YVj1B95kBPLyzzpKuqOUx0NYhDGre4TKv8WxxMk0SFB6BK6Pq7yMftfZ7h/E9PpMNp6nWs3b3IwMpy58D42LpzhtAuUI7KoBKnwa0tYWzDp38ZkOUVZoG1WPQhmJbbUFGVGnhTsHmiuRT55d52Hz53m3NpJvnt5mZV6jbqtTPG0sSRIPAJKt07uJPiOIghqFKllONrkcG+K0dCfxVw7iugbQ3PlOCeeeYzzZYSdHODZHI420eF5Wt1VJuMxyXRUSVV8Q4/JaITWOEis1uRpymgccTA1mLzPdKGDXD/DhV6LjW6djXp1rFYYF9f38FyJKTKSNEWHCyyuLjAbbdLfucxy4wGOdZcxo6rBQtgcsogkmhInJY4TIn0XIz3y0sNaB5mXuHmldox0SKcxjhBo30cEi4hWQD0E1wb0Hv0+/NGbfPnzv/Ntr/H3dYLSn6bMpEsQQHOhzWqnS6/TQfkOW9vX2du+yfHji7ihzyxNORrGlGlKp+HihQHWlhRJgi6q/4BBf0QSpTjSJU1ysJpa00c4Ia7rVj3qiEq1Viqs1riuqp7+So01Ba7SSGnISs3B4ZhMF2SliysVvW6HjXaHRtvF8yVRXGCTAkdYsFVXQehI6scXSVULnQwJzAxrCrTWRNGM8SRjOsmI0oQ4muFah7AVUqv5WGNRonJYdpRb3dCAqxx0UaLvtlsbYzB5go37TGYzZkbeLeYzeKGi0a3jKIknQQGlqRwzi8IQlZUnjVeU1KUP1DDDfTw7wACFVXhO1f1Tr/vMLASewvFcvKCGcR2U62OtQitZfcmYAkdDw5eUToAUkqJMScsSz3UpS40uCpQQ6MmI6XSERRB4Pm45JM8yWn4dZINSF+S2QElBu9GmKKv2ZiEsHooCSyN0CJ2YmVtQ1EJC18ErjzB5Qs1VzPKi0t4wOaQTJtGUaJaQl0UlAockKyx5qomSKc1um9OnH6PecHizhKuHI+LCoT+Y4F2+SLOzxPseP0+j20O5C+zf6jG+/DVEobGTfbrNUwxWlvFKl87iQ8ycOjY/QhVj9OQA7StS2+DOfkQ6lWR5yql2Ru/UCg2hIZugpQBt0BiUo1COi3AU6CoRR0gCmRNPZnzpN3+Rh555P5Hx8Jce4vFzHTrNgIZXw0gFjkCVmubSCfJyhmyt4DRPkBMj8xg8lzvXXiaQGWXYYHN3xCQ6ZHlpheb6w3jNFfx2xnj/Dgf7e5xafZBECeKDq3jFFCuBoqDQKelswjRKmcU5Qgk8KWiFIY+cWMDrrRE4PsKVGBNW5noahC5QlHc1dSTGbVJIQW2jhXVD8oUlgmabRcepOvcwqEo9626tjAK/iRldZ7r3Mj31JEncx6Jo9o6hC02qU6TyAYujDI4vobHAzuXXWR5sUTt2ljvd8xx7/gFanR6551ETLjUqZ+PEdCnLGF85uLUOJouwomB6dJUkEDRbK4xuvsLu5lUK46LzgoW6S2PxHO1aC4FhWusx3XuL0le0GgsYK4h3rmKiI4osQwE6S5jORuwlCtFSnDu7QO/cU6y4Lk0nxxcFooiqnT9h8BBoZbHWglBYIUFrvLIgGe1wtH0Ve+Y54oMbfPGlW9yetnji1DFOLC3wgbM1WqGPiCZMRwfkZcHC4iomGTIND4jKKSUGv9kjTAbk8RCkWxUZFxkmSrl+/So3dmeMXcPDT/8RHnzue3jKKekyQBmqJMpklNKtjrBEgrHgWAdspc2k+5fZHdyis3gOd3CVoyuvcnMqOblxmjtRiemeZ6lZ4vkh68fP43uC5OAtpsWANJ5i1h6kWfNJR9tk6T4WD6stjrVE8YDd/SH7wwzRWUOe/aNsPN3g+EKT872Qpt+orBhEWbkvYxFG4wswpsB4ElcUxHdehcAl7e9y8/oOV+9MMKpN7AeYxTbrDYfFoMG5lVV6dYf9rZJZf4dkdMig0WVxdYOFlZidaEwaT4Bq59kKKPKUIi9JCsssyTlKArJam9XVBU6stmnUDE3fxRNepc8iDFIayjLHU4qaXyMZ9RkNdjnRXWQ1NFx67Qr9oyNWFnt4NR9XgGNLkrhPlJRIt4N2PWQQIlSAwgOl0NkUnUgynSPylMTrIMIWnqpREy4qj9GOrBTK3QWWnvgR2i+/+G2v8fd1gtJaOsbySpPV1SXCQBCELgJNkkOaKIpM0x8e0aSOi8vaiWPE1sMcHhIf7jGNxqRxRlkUTGcJGvC8EI2iLA3C5DiOhxN4GF25g2JBKIkwpupaKA3aGHRekiYFcTxClyWF9UmMorGwRLdVY6Ed0m4GCCUJHEWW5MiaRyIkWaGxuUW6CiNLek0Pt73OweaQwcGAZDIlS0uiJCHKcwQB9htmYKbAky5K1hEKECA8MPobZyESbSylNWTGEheGNEnBCLxIUzounWZAy9d4jkOgFFI6977+la0E8UpjUI0e0q2TFzPCos+pxRbZ6iNc+u//jehwQFlYskJQpmABP6y0SzxhqXkujTCg2aojlMXzPaRQBPUQpRTCQD0MUI4LVqNciRcE1FVAWaRkaYaVHl6rA1ZgdYGJp0xLgU+OkLpyzC1iKEq0CMi1Jo6PsDrDDVrUOifIk4yDay+hk3HlJVNKtkcR48GIoshQjovnBxQItIW81Bht8PxKq8UKS+B5uK6H5xgiJNs7e7z2W/+JpQvX2B8d4a1f4MTyAt2Oz3qzgV+rUfMDhOvjuA0c9wRmdB2jcjAaL/Dptjz8ROE5OWE0YHh0ndFgxOFoQq3Zobu+Tnu5S29J0x8eko8HOMJBt3roYR+fKvErrUBZB+UoymKGSGegM6yAKE5IpaS5dBqtQhbbLfzeMiiJdnKEMUhh8YRAuw6m24NxDakzagqU8shGd4hGEy5eOWA3UrRX15DaQYuAvb1d6p11Gp0lpClwyhmT6QRtNZ0zzzKwkuzKZ3FMQZokxCWUpWCYQaraeI4inxzS1paGHyA9sGisLnEsWMrKM6bMKPIJVmtEYwGvvoCqLXIybOLcLSR0KBGmuKt/pBHWwQoPaQrceEwcT8jihCzeR2dfAaFwvBpIUxWrorE2RwlJliWYYkoUDdnxejSynI0yZv3CBaTOcZEIZSqVaQRSKeguEe3dRo/3CDsrFKIky1K2Xr3I9pXrnHr8A0xTS235HL5fp4wnxIMtmq4kDEPieB8R78KkT7K7j1g4TW1hjXx4h+nRhFIXFL2z7DvH2Faa1afPstwIcG3Kci1ABB2cYghJhkEipEHqHKF8pJFAjjXVIovOKKYDxltv8rXbU04XFl82aFz4I/zRXoe1VgvHFAhTYvII3BCUJJlNKJaO0Vw9ThnvMNy9TbLTIegsE1Iyc6A82mTnxiWOaNF8+EPcONHEvyD4YFjHUx4roaTVWkLHJTqdYa3Fz12EA8LmCOuQC4nGVruJRYE83GZw6yrF2hWuX73OxaHP8qkNvKU1Hl2XSAUyjzgazpilEfXuSYKFFaaDTY42rxIunSNcatNqd5kMdiGLMGXOYBRz8fqALVPDOfEgZ463efqRx1lut1BljrVVbUr1GACiFFhVmYlaYTFKo/IU0j7XX/saSWLYH2RcnFlE/ThnVhc40/Vp+A6+AlMa8uktyuA89YUTTCZ9siTj4GAHv7PCwvIqo91r5JOqCzUrS5I4oz+I6OcCWeuyvnKSh0+3qbcDljuShlS4jlPVTYkSVxdoU3XVKSfAKge/XoIcs/vGZ+kMexztTThKFyBcpNE+SaersbMbJLNtJvEIIepYGeCJAA8fz69TCoH0oNQR0UDjN1oY4eOGHVwpcKRPXgp8FSEzl9KVBBh0fZ33fd8Pw7/4v76tNf6+TlCefN8ZWq0ArK2k5Q0gBQpotJpYqWjUQnQyoyxTyvEN8sxwcDShPzhEaI2iysqLKKVZD6k1Q3ItSSYRZVZisgzh+Ug8pLZoZRFlQqYlJquEuUZpySTKyazEaEW73WOx06LbaxA2XAJHVfMTpjK6shbPUTgOdE4+ysi2yA+vYeMDiixns/8GSm5x0B8yGk3AatDVLoInJX5Toq2CrF6pnAqH0Hcrx11TmX6Juw68udYUmabIcjTV/By/TqPhUvM8/EDhe9VoKdS93SF1r/8ZHE9RZDntjkd95RQ3b9/h6GAbffVV3J0tDvYOiCOBMhYPg6MgqAco5ZAWJXFaEMUlWT4jMhblONRcg++7TGeTu8cvEmsN6LudTUIRpwVC5zQ8AZ5TJQ9eQBj4uJ7Ec78h0iVRTlVElsxyQtdw+oM/RNr5Ll7/3P9JuncFmRvOnbjASLq88ebr2GyGgwdu9YozjSJlpVGjpg2ZG+Iri+M5ldCW66KkwLUWypQ4iSnKnMJpMNENrk8lziTisfNnadZrNOu1Sr3YapQoETrB6hRbjOmIkoPOIg2VsHTqfeROm60o4+XLV4mjKc21B8jCFeisUG9CNNymrRTNhRPYyT69vMbB7iHrtqC2doYk7ePmKUiFKAukzilGEcP+bYZ725gsp9cI6B07w9OPPI1qL1fHbhKsNQhp8HCRjgSqonEFeMoyJeDK1z7L2cO3yBYvsHdwwO3BlNbpCywcW6TdCBGeiy0F+3duMz3aZunk+yjr6wTJhPH0MuM7r7F+6gJm7TSvfjUlGc8Y55XnyPHQsNEM8dfWMAun6O/cYHSwRcdfxjo9pIjQeUFhClSZYetdrKwzcxr49TaqTJA6oy5KXM/gKTAmo7AKaQVSWVQpMUVKkvYxsz3yzTfISkNt6SRO9zSlAceYyiXWanSeo/IhBkFWSkpHUTpt5KlzPPtYQCMIcaUAk1fCx46DkgpHCGxpEFbjSBclS/Yv/QqqSPAXTrC/t8m4dZzFpTXQhm5zGWvjKoFoL2DSAdPDTZpLJ/Gaj5Frh2J2hWs7n8WOXmF5YYWDW7tcOioplOKx1Q4PPPwkj9YauOUEWaRMpxHT2ZCF1jLIJmU+QuoMQ4CQshJa0wkyn6GTQXU8nMCdKzd5ba+BPHWKevcUq7UOG55BYNDptJJBx1afUWRIIUmmR5hsgh/WqLWPs//W17n46z/P2dVjWCQ3dvd5pW+YeHWWFmt8aP0UDy8fQ5gpIjkkmo0x0SF0Vwmb68TmFmUyobjbhmqUoMDFKWfoUmNGQyabb3Dx9be4NpM8HJS4xx7ggw+3WA8DQmlJESiTI4Uk8ByKZEKuJPXFc9QPbrK/8yo3X/7P1DbOUaQ75Ht73LxzSF8GtM89h/89Kzyy1OHxtSZLuiSzJX6jgTQWnRxRlgaMgzAlSIMtSqBAZTPs6IDJ7jUuv3aNl+6UeO0aYafH2VMtjrcDFgOXuiPQukQj0apkNpkyUnv0Vk+x1DtBOrlEMthi9yqojSVsFpMkMf3xlDhVRLQw4XHkkstKp86FbpfluoN0wAoPoaDQBWVZ7bprqRAYfFnDUZURoIwikuGUG/2SYexSBk06D7U5vbTAUijw/AWsnXLUv0aaUx3pi0pYs0SDMEgUIqla8Uu/gRJhpW+TJAinS1GUKNuHLEKIFJcBpXMeGXQQfvhtr/H3dYLie1XhpxV31SFFpVgQypxa6KCai0wTw+6da+TxlEk0qSrLcXEk1GoNXOWSpxlZVImHlWWGEAGNRo3UUUjXx+AgbIHVebVLEM8YRyV5mhHWA4xXx6nX6C3UaDUaVQ2DUnhKACWSqv3PUEm9W60R1qCMoJvvUWtZrs8Sku2Dqvc9j3DUhFwbHCUQUkEOoqy2ZZXy8B0f17ro0sPzAoRyEMag71qyF4UmLQqyUqOUi+tVXUoBkjBwaLU8pFQIKnnl6vRKIpAI6VatcdZgdEFmMjId0d+LiJOcWT9if5RwEGe4boQ1lkbNA20wWY6SFs9VeG6A66YUssB3Q5aXVshQqHKIVLJy/TTVccl4VjCLE9IyJwgUGihKQagcpAzwhIN0XITnYhRYBVpWN7CUAqMLpHQodNVWfWbvJh1ZYzSKGfYh0Zqt7DqBkKRaYK1HTQianiRs1elPC3RqcI1Hc/k4bm0ZcXgLKXNc6VZfKlqyHxVM44TSQDdUrJ/ssPHQcRYXlum1F7DW4Ioct4wwOgU0VnkI5eM4AVY6uKak11ti+8rL+C/+Mk73GGPjo489xXRllfriUtUmLQVNozmIFY6E9tIqU1PilQnCCxHTHZbOPMxO9zjZ5d/ApAnj6RSnTNFWMnXb6JXHKUcDJrM7rApR/c44Als5CoD0qvvLGoQFcbcFUhtLEW2zPz7izV3DuBhyvLyDkQGnN44Rhk18x6LQGAHKFTSbDfb6IzZcRa27QnGwyuTS13n5q1+i3eiiaz2SoImsLbDoeGTRgGxvl3Q0hLpHrd2mXfc4kB7+2hq1008RH17j6I2vknkOroBu3eJ0V6k3Wyi3hkkHJEe3MXiocAVMgU2OcCgh12T5BD3aJuvvYoIepddiPFWQxmh2aXgdRLtb1bTgoE1BGU2YFjm23sP3Aww5tWaHemcR5VqEqR4AhFIgqlq0byiAKiFQOkNPD5jtbXFzZ0Tmv8WGVQStdR5preMCWkqMLVDGgi0wZYxnMnZ39llYvM3C2ffh9VbZ0SXXtw7Z7fd59FzIbOkpeg+usdapUc8n+CaiHiyh0xhrNPXQZzybocsUL2xB3iGb7oCe4OocnReUw02G20fc3t2mtbjGyvGH8M48wROnEpZq7cpBVxaIIkdYW9kJUPlRaWuwSqA8QXx4mQM9obe0jExj9Djh2o0jihh07xg3V59g7YmTfHCpQ1OPcbNtHPcEvtsmLycEfkihM4p0TNBexPMbmDSuqk2sRuQlbjJA9jcZDQZs7mzzlb7Ga53iwXOLnFzq0VAGXInOEwrtIISDLguKokChSYd7pPuLtLsdXNehIRRXL10i27xNkQteSXyi1SfYaPsce+hRnt04S8stMEWELAt0kmDKglqzh3EtyWiPosyQRoA1OOUR4miLw5073Nwf8Np2Rl8u0HmgzemFBst1qEmF4zgINIUtKJGUWYHJC/I0Iz7cptZq0+vW2N8WxHHJaGuHNDkk2tvl1nbCPnW6rS4ba8uc6DRpBCDcEkcYEq2rBwxlK40anYM1SFk9vEkEAo0pXbLhEZubW1zv1yg7LRq9DivtGmFN0XQFrvAwNidoHKfWfYxpcZXSZASug+v6WAxlHmGNubtL3kN4DXIkJikJxBTjHyHKa8jpy+T0SNsP4zUfxQvPImo1SrsM/B/f1hp/XyYo1lqg6qZwVOXDYnRJWRp0EZNnhskkpSx36A9GjKcRvpJ4jqBRryEdF+7uGGgNhRFoDWle4KRVkWK9UaPW6mKNJo5mZPGQMovJC0FmPKJM4Ht1OvU2TuDheQ4LnQDfd7CislhPSglC4IlqftqWleRyllPkGUmcc+P6Dti3GI4LsjJDigLqPg3r4vkCJQVlmVcSzVqQaTBGgZE0Gi1q9YDCSsbjQ9IsqTxt8hLHqWy1vbqi0WjiOVUtR5mWeFLgVKb01fmucqpjE1v5VlS7NSllkZKmGbO4ZDRLSNMC/ygBFE7o4jk+vrJ4dUWpIUtLsqKonDAB5YLAQZsaxzsdHn34ApcGKcOtIWUUkSQ5VgqmieFwmKEt1FyXuuuy1PaRSqEc8H0IHB/fdfC9SiMCa5A5aNdFInAcj2iWMElyDo8mxF96iU7zGqNxgnYlTdfH93w8oXA9GI8t2mi8ICBsdjF5iskyfN/wvtOL9BvHeevoOvFkRjKbYK0mWFxBt4/hrNSpOR5isIljNSsutGxBNtwlNVNcFL7fQvo+XlBHOj7GSMgE2kRM0332D/e5eiQY2RmrocfysdOsKggVCGtIohGmSBBWkuQl0WTIEoqysUze30JLzc1LnyfonWA0SnnrlTeZJJo0y1hUORvLSywc38BbP8mw3WZy5YjJeEqtNUEIF8fzwEq0FSB09YVzt2PMGIuxlp2ta/T7Y1YeeIhazUGjcJwARxqUtSRxtSgKpxKwS5MJd25d49hL/43VRz5InBccpQ7W73H56i16nSFr7Q6O8sD6JHWf/emYdJqx1n2Gon6C2eArjKYjXv/ar3M2mZKpDrPWBnJxA/KIYbRJ6MTUBSgVUeqYLI8xRYHrNxFFxmhyjdlwExvFZEnMaGcXOTugXffwTz2Os7DCZD/D7T5IsfI+dH6H8uAOZamRfou0tobXaeC3eqhkRDLcoUxyHC9BubbqRhGCkhyjbXX0aAtEMiAe7VMkQ7JCEKcRi6ceJQx6ZHsHGFeR2gzlBJBnYKbIIsYWKXkywUwitu7cZrizw0OPXSRPR7y4dcTt3gO874lnaZ4+QbveoO5mFLmhKB36B7fB7yCsoEgMooB0NuLgzlV6a2cxSUY07KPzATIekU4yLt844FIkcIOA8+OIZi9mabmLKRWzaIomxUhJaIHCkNkCDGhdIowEz0OpBpOjq2y9+RZnF2v0BzG/sT1jtv4+Tj7+BMeXuzzQaNKUglxoTNkmGfUZDA7pdbpoI0jSkiSJiMubFEbiF4ZZVJKVEU46IN+5wVtvvcnN7QN6a8dYXTvLE8s1mgHUhIvOI8ZS4ucuMi+I9ISizJFCk5cCx+Qc7t9md+8as43jqIMb3Llyma/cMSydO8PSgw9wammBjeUei0pQGIExERYfkxvyPMWWJdPRECtr1HyfosiJxxOEnsHgOke3rnPxxgGDwqPZW6VxYo3TXZ+uJ2i67t2Wak1m8krgL83QBopCQ1lSljn90Yy0NBxrVz5oR4cThqMchm2saBIt99hYCTgZuCw5ljYRupBo6ZCUmiKNadUaeG4NbSxZUSLsXcNaMoimDIYZEyXwmmtMVx6g1Y5ZVJKu7+BKQZqVIAtcFLIoyPwaXu9BVsMFDna+zjhOaQYujiMql+2yxAsboF10qhF5hhAlWXqVYHeTIooYxiBXvg/XD/HrJWE5wEniylLi/7WO/14I++2Meo9x48YNzp49+25PY86cOXPmzJnzHbC1tcXx48d/zzH35Q5Kr9cDYHNzk3a7/S7P5v5hMpmwsbHB1tYWrVbr3Z7OfcE8Zt8Z87j9wZnH7DtjHrc/OO9mzKy1TKdT1tfXf9+x92WCIu8WUbXb7fkN+R3QarXmcfsDMo/Zd8Y8bn9w5jH7zpjH7Q/OuxWzb3dj4dt37ZkzZ86cOXPmzHmHmCcoc+bMmTNnzpz3HPdlguL7Ph//+Mfxff/dnsp9xTxuf3DmMfvOmMftD848Zt8Z87j9wblfYnZfdvHMmTNnzpw5c/5wc1/uoMyZM2fOnDlz/nAzT1DmzJkzZ86cOe855gnKnDlz5syZM+c9xzxBmTNnzpw5c+a855gnKHPmzJkzZ86c9xz3ZYLyT//pP+XUqVMEQcBzzz3HV7/61Xd7Su8av/Vbv8Wf+BN/gvX1dYQQ/MIv/MLbrltr+Tt/5++wtrZGGIa88MILXL169W1jBoMBH/7wh2m1WnQ6Hf7iX/yLzGazd/BTvLN84hOf4Lu+67toNpssLy/zp/7Un+Ly5ctvG5OmKR/72MdYWFig0WjwZ/7Mn2F/f/9tYzY3N/mhH/oharUay8vL/LW/9tcoy/Kd/CjvKJ/85Cd5/PHH76lPPv/88/zqr/7qvevzmP3+/OzP/ixCCH7qp37q3nvzuH0zf/fv/l2EEG97Pfjgg/euz2P2rdne3ubP/tk/y8LCAmEY8thjj/Hiiy/eu37frQf2PuNTn/qU9TzP/qt/9a/s66+/bv/SX/pLttPp2P39/Xd7au8Kv/Irv2L/1t/6W/Y//+f/bAH76U9/+m3Xf/Znf9a22237C7/wC/aVV16xP/zDP2xPnz5tkyS5N+YHfuAH7BNPPGG//OUv29/+7d+2586dsz/2Yz/2Dn+Sd47v//7vtz/3cz9nL126ZC9evGj/+B//4/bEiRN2NpvdG/PRj37Ubmxs2M9+9rP2xRdftO9///vtBz7wgXvXy7K0jz76qH3hhRfsyy+/bH/lV37FLi4u2r/xN/7Gu/GR3hF+6Zd+yf7X//pf7ZUrV+zly5ft3/ybf9O6rmsvXbpkrZ3H7Pfjq1/9qj116pR9/PHH7U/+5E/ee38et2/m4x//uH3kkUfs7u7uvdfh4eG96/OYfTODwcCePHnS/rk/9+fsV77yFXvjxg3767/+6/batWv3xtxv68F9l6A8++yz9mMf+9i9v2ut7fr6uv3EJz7xLs7qvcH/mKAYY+zq6qr9B//gH9x7bzQaWd/37b/7d//OWmvtG2+8YQH7ta997d6YX/3VX7VCCLu9vf2Ozf3d5ODgwAL2C1/4grW2ipHruvY//If/cG/Mm2++aQH7pS99yVpbJYZSSru3t3dvzCc/+UnbarVslmXv7Ad4F+l2u/Zf/st/OY/Z78N0OrXnz5+3n/nMZ+z3fu/33ktQ5nH71nz84x+3TzzxxLe8No/Zt+av//W/br/7u7/7d71+P64H99URT57nvPTSS7zwwgv33pNS8sILL/ClL33pXZzZe5ObN2+yt7f3tni1222ee+65e/H60pe+RKfT4Zlnnrk35oUXXkBKyVe+8pV3fM7vBuPxGPh/XLJfeukliqJ4W9wefPBBTpw48ba4PfbYY6ysrNwb8/3f//1MJhNef/31d3D27w5aaz71qU8RRRHPP//8PGa/Dx/72Mf4oR/6obfFB+b32u/F1atXWV9f58yZM3z4wx9mc3MTmMfsd+OXfumXeOaZZ/iRH/kRlpeXefLJJ/kX/+Jf3Lt+P64H91WCcnR0hNb6bTcdwMrKCnt7e+/SrN67fCMmv1e89vb2WF5eftt1x3Ho9Xr/U8TUGMNP/dRP8cEPfpBHH30UqGLieR6dTudtY//HuH2ruH7j2h9WXnvtNRqNBr7v89GPfpRPf/rTPPzww/OY/R586lOf4utf/zqf+MQnvunaPG7fmueee46f//mf59d+7df45Cc/yc2bN/me7/keptPpPGa/Czdu3OCTn/wk58+f59d//df58R//cf7qX/2r/Ot//a+B+3M9cN7xf3HOnPcQH/vYx7h06RJf/OIX3+2p3BdcuHCBixcvMh6P+Y//8T/ykY98hC984Qvv9rTes2xtbfGTP/mTfOYznyEIgnd7OvcNP/iDP3jvz48//jjPPfccJ0+e5N//+39PGIbv4szeuxhjeOaZZ/j7f//vA/Dkk09y6dIl/tk/+2d85CMfeZdn951xX+2gLC4uopT6pmrt/f19VldX36VZvXf5Rkx+r3itrq5ycHDwtutlWTIYDP7Qx/QnfuIn+OVf/mV+8zd/k+PHj997f3V1lTzPGY1Gbxv/P8btW8X1G9f+sOJ5HufOnePpp5/mE5/4BE888QT/6B/9o3nMfhdeeuklDg4OeOqpp3AcB8dx+MIXvsA//sf/GMdxWFlZmcft26DT6fDAAw9w7dq1+b32u7C2tsbDDz/8tvceeuihe0dj9+N6cF8lKJ7n8fTTT/PZz3723nvGGD772c/y/PPPv4sze29y+vRpVldX3xavyWTCV77ylXvxev755xmNRrz00kv3xnzuc5/DGMNzzz33js/5ncBay0/8xE/w6U9/ms997nOcPn36bdeffvppXNd9W9wuX77M5ubm2+L22muvve2X+TOf+QytVuubviT+MGOMIcuyecx+Fz70oQ/x2muvcfHixXuvZ555hg9/+MP3/jyP2+/PbDbj+vXrrK2tze+134UPfvCD3ySXcOXKFU6ePAncp+vBO16W+/+RT33qU9b3ffvzP//z9o033rB/+S//ZdvpdN5Wrf0/E9Pp1L788sv25ZdftoD9h//wH9qXX37Z3r5921pbtZV1Oh37i7/4i/bVV1+1f/JP/slv2Vb25JNP2q985Sv2i1/8oj1//vwf6jbjH//xH7ftdtt+/vOff1sbYxzH98Z89KMftSdOnLCf+9zn7Isvvmiff/55+/zzz9+7/o02xj/2x/6YvXjxov21X/s1u7S09Ie6jfFnfuZn7Be+8AV78+ZN++qrr9qf+ZmfsUII+xu/8RvW2nnMvl3+31081s7j9q346Z/+afv5z3/e3rx50/7O7/yOfeGFF+zi4qI9ODiw1s5j9q346le/ah3HsX/v7/09e/XqVftv/+2/tbVazf6bf/Nv7o2539aD+y5Bsdbaf/JP/ok9ceKE9TzPPvvss/bLX/7yuz2ld43f/M3ftMA3vT7ykY9Ya6vWsr/9t/+2XVlZsb7v2w996EP28uXLb/sZ/X7f/tiP/ZhtNBq21WrZP//n/7ydTqfvwqd5Z/hW8QLsz/3cz90bkySJ/St/5a/Ybrdra7Wa/dN/+k/b3d3dt/2cW7du2R/8wR+0YRjaxcVF+9M//dO2KIp3+NO8c/yFv/AX7MmTJ63neXZpacl+6EMfupecWDuP2bfL/5igzOP2zfzoj/6oXVtbs57n2WPHjtkf/dEffZuexzxm35r/8l/+i3300Uet7/v2wQcftP/8n//zt12/39YDYa217/y+zZw5c+bMmTNnzu/OfVWDMmfOnDlz5sz5n4N5gjJnzpw5c+bMec8xT1DmzJkzZ86cOe855gnKnDlz5syZM+c9xzxBmTNnzpw5c+a855gnKHPmzJkzZ86c9xzzBGXOnDlz5syZ855jnqDMmTNnzpw5c95zzBOUOXPmzJkzZ857jnmCMmfOnDlz5sx5zzFPUObMmTNnzpw57zn+b/v7TcuzXTh+AAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Is this rice noodle soup?\n", + "true answers: ['yes', 'yes', 'yes', 'yes', 'yes', 'yes', 'yes', 'yes', 'yes', 'yes']\n", + "pred answers: ['yes']\n" + ] + } + ], + "source": [ + "show_results(5, data, vqav2_dataset)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "c4701b24-c336-4a88-aa1e-8c28ddc0b3ac", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "What is the man doing in the street?\n", + "true answers: ['crossing it', 'walking', 'walking', 'crossing', 'crossing road', 'walking', 'crossing', 'walking', 'crossing', 'walking']\n", + "pred answers: ['crossing the street']\n" + ] + } + ], + "source": [ + "show_results(7, data, vqav2_dataset)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "2fb32a53-2f94-42a9-b31c-cb352f9cc5c9", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "What does the truck on the left sell?\n", + "true answers: ['ice cream', 'ice cream', 'ice cream', 'ice cream', 'ice cream', 'ice cream', 'ice cream', 'ice cream', 'ice cream', 'ice cream']\n", + "pred answers: ['ice cream']\n" + ] + } + ], + "source": [ + "show_results(9, data, vqav2_dataset)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "425b2e3f-6b2d-4722-ab05-f615797055c3", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/download_gqa.py b/download_gqa.py new file mode 100644 index 0000000..4b740f7 --- /dev/null +++ b/download_gqa.py @@ -0,0 +1,53 @@ +import os +import requests +import zipfile +from tqdm import tqdm + + +def download_and_unzip(url, target_dir): + # Create target directory if it doesn't exist + os.makedirs(target_dir, exist_ok=True) + + # Get the filename from the URL + filename = url.split("/")[-1] + filepath = os.path.join(target_dir, filename) + + # Download the file + print(f"Downloading {filename}...") + response = requests.get(url, stream=True) + total_size = int(response.headers.get("content-length", 0)) + + with open(filepath, "wb") as file, tqdm( + desc=filename, + total=total_size, + unit="iB", + unit_scale=True, + unit_divisor=1024, + ) as progress_bar: + for data in response.iter_content(chunk_size=1024): + size = file.write(data) + progress_bar.update(size) + + # Unzip the file + print(f"Unzipping {filename}...") + with zipfile.ZipFile(filepath, "r") as zip_ref: + zip_ref.extractall(target_dir) + + # Remove the zip file + os.remove(filepath) + print(f"Removed {filename}") + + +def main(): + target_dir = "/fs/cfar-projects/low-bit-vision/datasets/gqa" + images = "https://downloads.cs.stanford.edu/nlp/data/gqa/images.zip" + questions = "https://downloads.cs.stanford.edu/nlp/data/gqa/questions1.2.zip" + + download_and_unzip(images, target_dir) + download_and_unzip(questions, target_dir+"/questions") + + print("Download and unzip process completed.") + + +if __name__ == "__main__": + main() diff --git a/download_vqa2.py b/download_vqa2.py index 2085864..5c9e80c 100644 --- a/download_vqa2.py +++ b/download_vqa2.py @@ -39,8 +39,8 @@ def download_and_unzip(url, target_dir): def main(): - target_dir = "./data/vqa2" - images = "http://images.cocodataset.org/zips/val2017.zip" + target_dir = "./data/vqav2" + images = "http://images.cocodataset.org/zips/val2014.zip" annotations = "https://s3.amazonaws.com/cvmlp/vqa/mscoco/vqa/v2_Annotations_Val_mscoco.zip" questions = "https://s3.amazonaws.com/cvmlp/vqa/mscoco/vqa/v2_Questions_Val_mscoco.zip" diff --git a/final_results_postprocessing.ipynb b/final_results_postprocessing.ipynb new file mode 100644 index 0000000..b0226ce --- /dev/null +++ b/final_results_postprocessing.ipynb @@ -0,0 +1,9795 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 19, + "id": "46510845", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "from transformers import Blip2Processor, Blip2ForConditionalGeneration, AutoProcessor, Blip2ForImageTextRetrieval\n", + "from operator import attrgetter\n", + "\n", + "import torch.nn as nn\n", + "import os\n", + "import re\n", + "\n", + "from transformers import LlavaForConditionalGeneration\n", + "import torch" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "a558483b", + "metadata": {}, + "outputs": [], + "source": [ + "from collections import OrderedDict\n", + "\n", + "def get_leaf_modules(model: nn.Module) -> OrderedDict[str, nn.Module]:\n", + " \"\"\"\n", + " Returns an ordered dictionary containing only the leaf modules of a PyTorch model.\n", + " Leaf modules are those that do not have any children.\n", + " \"\"\"\n", + " leaf_modules = OrderedDict()\n", + " for name, module in model.named_modules():\n", + " if not list(module.children()): # Check if the module has no children\n", + " leaf_modules[name] = module\n", + " return leaf_modules" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "a4220344", + "metadata": {}, + "outputs": [], + "source": [ + "def compute_bpw(leaves, quantized_mods, total_params, vision_bits = None, qformer_bits =None, llm_bits=None, fp_size = 16):\n", + "\n", + " total_bits = 0\n", + " vision_params = 0\n", + " qformer_params = 0\n", + " llm_params = 0\n", + "\n", + " for key, module in leaves.items():\n", + "\n", + " fp_mod_flag = True\n", + "\n", + " # check if parameters in module should be quantized\n", + " for q_mod in quantized_mods:\n", + " \n", + " # add quantized linear bit sizes\n", + " if q_mod in key and isinstance(module, nn.Linear):\n", + " num_el = module.weight.numel()\n", + "\n", + " if \"vision\" in q_mod:\n", + " total_bits += vision_bits*num_el\n", + " vision_params += num_el\n", + " elif \"qformer\" in q_mod:\n", + " total_bits += qformer_bits*num_el\n", + " qformer_params += num_el\n", + " elif \"language\" in q_mod:\n", + " total_bits += llm_bits*num_el\n", + " llm_params += num_el\n", + " else:\n", + " raise Exception()\n", + " \n", + " fp_mod_flag = False\n", + " \n", + " # full_precision module\n", + " if fp_mod_flag:\n", + " # print(key)\n", + " for param in module.parameters():\n", + " total_bits += fp_size*param.numel()\n", + "\n", + "\n", + " print(f'vision q params: {vision_params}')\n", + " print(f'qformer q params: {qformer_params}')\n", + " print(f'llm_params: {llm_params}')\n", + "\n", + " return total_bits / total_params" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e146c044", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Loading checkpoint shards: 100%|██████████| 3/3 [00:01<00:00, 1.99it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "7063427072\n", + "vision q params: 301989888\n", + "qformer q params: 0\n", + "llm_params: 6476005376\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "data": { + "text/plain": [ + "4.484414482250918" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# model_name = \"Salesforce/blip2-itm-vit-g-coco\"\n", + "# model = Blip2ForImageTextRetrieval.from_pretrained(model_name)\n", + "\n", + "# leaves = get_leaf_modules(model)\n", + "# total_params = sum(p.numel() for p in model.parameters())\n", + "# print(total_params)\n", + "# quantized_mods = [\n", + "# \"vision_model.encoder.layers\",\n", + "# \"qformer.encoder.layer\",\n", + "# ]\n", + "\n", + "\n", + "\n", + "\n", + "# model_name = \"Salesforce/blip2-opt-2.7b\"\n", + "# model = Blip2ForConditionalGeneration.from_pretrained(model_name)\n", + "# model.to('cpu')\n", + "\n", + "# leaves = get_leaf_modules(model)\n", + "# total_params = sum(p.numel() for p in model.parameters())\n", + "# quantized_mods = [\n", + "# \"vision_model.encoder.layers\",\n", + "# \"qformer.encoder.layer\",\n", + "# \"language_model.model.decoder.layers\"\n", + "# ]\n", + "\n", + "\n", + "\n", + "# Load the model\n", + "# model = LlavaForConditionalGeneration.from_pretrained(\"llava-hf/llava-1.5-7b-hf\", torch_dtype=torch.float16)\n", + "# # offload model to cpu for now\n", + "# model.to('cpu')\n", + "\n", + "\n", + "# quantized_mods = [\n", + "# \"vision_tower.vision_model.encoder.layers\",\n", + "# \"language_model.model.layers\",\n", + "# ]\n", + "\n", + "# leaves = get_leaf_modules(model)\n", + "# total_params = sum(p.numel() for p in model.parameters())\n", + "# print(total_params)\n", + "\n", + "# compute_bpw(leaves, quantized_mods, total_params,\n", + "# vision_bits=4,\n", + "# qformer_bits=4,\n", + "# llm_bits=4)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "0fcd4835", + "metadata": {}, + "outputs": [], + "source": [ + "def compute_bpw_llava(vision_bits, llm_bits, fp_bits = 16):\n", + "\n", + " total_params = 7063427072\n", + "\n", + " vision_q_params = 301989888\n", + " llm_q_params = 6476005376\n", + "\n", + " non_q_params = total_params - vision_q_params - llm_q_params\n", + "\n", + " bpw = (vision_bits*vision_q_params + \\\n", + " llm_bits*llm_q_params + \\\n", + " fp_bits*non_q_params) / total_params\n", + " \n", + " return bpw\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "a1bce57f", + "metadata": {}, + "outputs": [], + "source": [ + "def compute_bpw_blip_full(vision_bits, qformer_bits, llm_bits, fp_bits = 16):\n", + "\n", + " total_params = 3744761856\n", + "\n", + " vision_q_params = 984023040\n", + " qformer_q_params = 104988672\n", + " llm_q_params = 2516582400\n", + "\n", + " non_q_params = total_params - vision_q_params - qformer_q_params - llm_q_params\n", + "\n", + " bpw = (vision_bits*vision_q_params + \\\n", + " qformer_bits*qformer_q_params + \\\n", + " llm_bits*llm_q_params + \\\n", + " fp_bits*non_q_params) / total_params\n", + " \n", + " return bpw\n" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "dcaff34a", + "metadata": {}, + "outputs": [], + "source": [ + "def compute_bpw_blip_retrieval(vision_bits, qformer_bits, fp_bits = 16):\n", + " \n", + " total_params = 1172623618\n", + "\n", + " vision_q_params = 984023040\n", + " qformer_q_params = 161611776\n", + "\n", + " non_q_params = total_params - vision_q_params - qformer_q_params\n", + "\n", + " bpw = (vision_bits*vision_q_params + \\\n", + " qformer_bits*qformer_q_params + \\\n", + " fp_bits*non_q_params) / total_params\n", + " \n", + " return bpw" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "89debfa4", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.microsoft.datawrangler.viewer.v0+json": { + "columns": [ + { + "name": "index", + "rawType": "int64", + "type": "integer" + }, + { + "name": "vit_bits", + "rawType": "int64", + "type": "integer" + }, + { + "name": "qformer_bits", + "rawType": "int64", + "type": "integer" + }, + { + "name": "llm_bits", + "rawType": "int64", + "type": "integer" + }, + { + "name": "METEOR", + "rawType": "float64", + "type": "float" + }, + { + "name": "CIDEr", + "rawType": "float64", + "type": "float" + } + ], + "conversionMethod": "pd.DataFrame", + "ref": "96ee60dd-ea70-4424-b1de-dceaf177e96b", + "rows": [ + [ + "0", + "2", + "2", + "2", + "0.0298844811343493", + "0.0007902168927993" + ], + [ + "1", + "2", + "2", + "3", + "0.1498566138247552", + "0.3892738924855776" + ], + [ + "2", + "2", + "2", + "4", + "0.1837345249958186", + "0.5443518230890101" + ], + [ + "3", + "2", + "2", + "5", + "0.1886596236812807", + "0.577806426477473" + ], + [ + "4", + "2", + "2", + "6", + "0.1921587687164518", + "0.5940621735445684" + ], + [ + "5", + "2", + "2", + "8", + "0.1935281864323662", + "0.6015116071758554" + ], + [ + "6", + "2", + "2", + "16", + "0.1931800073082082", + "0.6016201760679405" + ], + [ + "7", + "2", + "3", + "2", + "0.0248888129640967", + "0.0017409187992352" + ], + [ + "8", + "2", + "3", + "3", + "0.1536184976125826", + "0.4199558355788407" + ], + [ + "9", + "2", + "3", + "4", + "0.192547252681466", + "0.6413107364012529" + ], + [ + "10", + "2", + "3", + "5", + "0.1971479719875085", + "0.6644740132743288" + ], + [ + "11", + "2", + "3", + "6", + "0.2053801049843051", + "0.7005174822474439" + ], + [ + "12", + "2", + "3", + "8", + "0.2062730261379436", + "0.7060009487248382" + ], + [ + "13", + "2", + "3", + "16", + "0.2082902403750201", + "0.7114414564035497" + ], + [ + "14", + "2", + "4", + "2", + "0.029512816604721", + "0.0015242110097923" + ], + [ + "15", + "2", + "4", + "3", + "0.1537266011841024", + "0.4225848027587028" + ], + [ + "16", + "2", + "4", + "4", + "0.1964836864850164", + "0.6677658754857135" + ], + [ + "17", + "2", + "4", + "5", + "0.1998364948737692", + "0.6794140389055991" + ], + [ + "18", + "2", + "4", + "6", + "0.2067698934744963", + "0.7158724879010914" + ], + [ + "19", + "2", + "4", + "8", + "0.2085390794369036", + "0.7239213616454379" + ], + [ + "20", + "2", + "4", + "16", + "0.2096374717583629", + "0.7267086690055766" + ], + [ + "21", + "2", + "5", + "2", + "0.0287472582273225", + "0.0015383744706068" + ], + [ + "22", + "2", + "5", + "3", + "0.156545753956854", + "0.4342188097083193" + ], + [ + "23", + "2", + "5", + "4", + "0.196610560416409", + "0.6643003856622003" + ], + [ + "24", + "2", + "5", + "5", + "0.201813096512405", + "0.6880985619516059" + ], + [ + "25", + "2", + "5", + "6", + "0.208814901329159", + "0.7235377941797142" + ], + [ + "26", + "2", + "5", + "8", + "0.2111062432632992", + "0.7375435379366277" + ], + [ + "27", + "2", + "5", + "16", + "0.2119571593229862", + "0.7375809567131982" + ], + [ + "28", + "2", + "6", + "2", + "0.030631103913381", + "0.0020275794823421" + ], + [ + "29", + "2", + "6", + "3", + "0.1535624775179599", + "0.424523049292371" + ], + [ + "30", + "2", + "6", + "4", + "0.1975830578073438", + "0.6760507495669246" + ], + [ + "31", + "2", + "6", + "5", + "0.2007745793399482", + "0.6885736800597454" + ], + [ + "32", + "2", + "6", + "6", + "0.2084986314559541", + "0.7263028445719076" + ], + [ + "33", + "2", + "6", + "8", + "0.2098694607988712", + "0.7315217945479447" + ], + [ + "34", + "2", + "6", + "16", + "0.2104895431279897", + "0.7339511837416856" + ], + [ + "35", + "2", + "8", + "2", + "0.0294354848117307", + "0.0016666316965743" + ], + [ + "36", + "2", + "8", + "3", + "0.1568363211152181", + "0.4377116832400026" + ], + [ + "37", + "2", + "8", + "4", + "0.1971530070668118", + "0.6719258108942063" + ], + [ + "38", + "2", + "8", + "5", + "0.2010881485961166", + "0.6872950083608445" + ], + [ + "39", + "2", + "8", + "6", + "0.2085898952116036", + "0.7246423668329751" + ], + [ + "40", + "2", + "8", + "8", + "0.210553933277872", + "0.7350918293335355" + ], + [ + "41", + "2", + "8", + "16", + "0.2111149150856989", + "0.7362575171829725" + ], + [ + "42", + "2", + "16", + "2", + "0.0295098771216461", + "0.0016837862661927" + ], + [ + "43", + "2", + "16", + "3", + "0.1569115889624636", + "0.4362796051124896" + ], + [ + "44", + "2", + "16", + "4", + "0.1972734524909803", + "0.6727059028707632" + ], + [ + "45", + "2", + "16", + "5", + "0.2010642391090116", + "0.6882361624647443" + ], + [ + "46", + "2", + "16", + "6", + "0.2086024048502772", + "0.7246993426668064" + ], + [ + "47", + "2", + "16", + "8", + "0.2105720489139707", + "0.734900136126526" + ], + [ + "48", + "2", + "16", + "16", + "0.2109831741881737", + "0.7357684637386981" + ], + [ + "49", + "3", + "2", + "2", + "0.0318570369375779", + "0.0022093327972838" + ] + ], + "shape": { + "columns": 5, + "rows": 343 + } + }, + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
vit_bitsqformer_bitsllm_bitsMETEORCIDEr
02220.0298840.000790
12230.1498570.389274
22240.1837350.544352
32250.1886600.577806
42260.1921590.594062
..................
338161640.2664131.163837
339161650.2708661.195060
340161660.2789891.245283
341161680.2801471.249383
3421616160.2812821.254198
\n", + "

343 rows × 5 columns

\n", + "
" + ], + "text/plain": [ + " vit_bits qformer_bits llm_bits METEOR CIDEr\n", + "0 2 2 2 0.029884 0.000790\n", + "1 2 2 3 0.149857 0.389274\n", + "2 2 2 4 0.183735 0.544352\n", + "3 2 2 5 0.188660 0.577806\n", + "4 2 2 6 0.192159 0.594062\n", + ".. ... ... ... ... ...\n", + "338 16 16 4 0.266413 1.163837\n", + "339 16 16 5 0.270866 1.195060\n", + "340 16 16 6 0.278989 1.245283\n", + "341 16 16 8 0.280147 1.249383\n", + "342 16 16 16 0.281282 1.254198\n", + "\n", + "[343 rows x 5 columns]" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "path = '/fs/cfar-projects/low-bit-vision/final_results/blip2/awq/image_captioning/awq_image_captioning.csv'\n", + "df_awq_coco = pd.read_csv(path)\n", + "df_awq_coco = df_awq_coco.drop(['model_size'], axis = 1)\n", + "df_awq_coco" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "b3359474", + "metadata": {}, + "outputs": [], + "source": [ + "# # compute bpw\n", + "# model_name = \"Salesforce/blip2-opt-2.7b\"\n", + "# model = Blip2ForConditionalGeneration.from_pretrained(model_name)\n", + "# model.to('cpu')\n", + "\n", + "# leaves = get_leaf_modules(model)\n", + "# total_params = sum(p.numel() for p in model.parameters())\n", + "# quantized_mods = [\n", + "# \"vision_model.encoder.layers\",\n", + "# \"qformer.encoder.layer\",\n", + "# \"language_model.model.decoder.layers\"\n", + "# ]\n", + "\n", + "# df_awq_coco['bpw'] = [compute_bpw(leaves, quantized_mods, total_params,\n", + "# vision_bits=x['vit_bits'],\n", + "# qformer_bits=x['qformer_bits'],\n", + "# llm_bits=x['llm_bits']) for x in df_awq_coco.to_dict(orient='records')]\n", + "\n", + "df_awq_coco['bpw'] = [compute_bpw_blip_full(vision_bits=x['vit_bits'],\n", + " qformer_bits=x['qformer_bits'],\n", + " llm_bits=x['llm_bits']) for x in df_awq_coco.to_dict(orient='records')]\n", + "\n", + "df_awq_coco['quant_method'] = 'awq'" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "cc1ba8b7", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.microsoft.datawrangler.viewer.v0+json": { + "columns": [ + { + "name": "index", + "rawType": "int64", + "type": "integer" + }, + { + "name": "vit_bits", + "rawType": "int64", + "type": "integer" + }, + { + "name": "qformer_bits", + "rawType": "int64", + "type": "integer" + }, + { + "name": "llm_bits", + "rawType": "int64", + "type": "integer" + }, + { + "name": "METEOR", + "rawType": "float64", + "type": "float" + }, + { + "name": "CIDEr", + "rawType": "float64", + "type": "float" + }, + { + "name": "bpw", + "rawType": "float64", + "type": "float" + }, + { + "name": "quant_method", + "rawType": "object", + "type": "string" + } + ], + "conversionMethod": "pd.DataFrame", + "ref": "3966847d-75c3-42af-aad9-65d790c7e989", + "rows": [ + [ + "0", + "2", + "2", + "2", + "0.0298844811343493", + "0.0007902168927993", + "2.5202863335296692", + "awq" + ], + [ + "1", + "2", + "2", + "3", + "0.1498566138247552", + "0.3892738924855776", + "3.192313687143047", + "awq" + ], + [ + "2", + "2", + "2", + "4", + "0.1837345249958186", + "0.5443518230890101", + "3.8643410407564245", + "awq" + ], + [ + "3", + "2", + "2", + "5", + "0.1886596236812807", + "0.577806426477473", + "4.536368394369802", + "awq" + ], + [ + "4", + "2", + "2", + "6", + "0.1921587687164518", + "0.5940621735445684", + "5.20839574798318", + "awq" + ], + [ + "5", + "2", + "2", + "8", + "0.1935281864323662", + "0.6015116071758554", + "6.552450455209935", + "awq" + ], + [ + "6", + "2", + "2", + "16", + "0.1931800073082082", + "0.6016201760679405", + "11.928669284116955", + "awq" + ], + [ + "7", + "2", + "3", + "2", + "0.0248888129640967", + "0.0017409187992352", + "2.5483224746882276", + "awq" + ], + [ + "8", + "2", + "3", + "3", + "0.1536184976125826", + "0.4199558355788407", + "3.220349828301605", + "awq" + ], + [ + "9", + "2", + "3", + "4", + "0.192547252681466", + "0.6413107364012529", + "3.892377181914983", + "awq" + ], + [ + "10", + "2", + "3", + "5", + "0.1971479719875085", + "0.6644740132743288", + "4.56440453552836", + "awq" + ], + [ + "11", + "2", + "3", + "6", + "0.2053801049843051", + "0.7005174822474439", + "5.236431889141738", + "awq" + ], + [ + "12", + "2", + "3", + "8", + "0.2062730261379436", + "0.7060009487248382", + "6.580486596368493", + "awq" + ], + [ + "13", + "2", + "3", + "16", + "0.2082902403750201", + "0.7114414564035497", + "11.956705425275514", + "awq" + ], + [ + "14", + "2", + "4", + "2", + "0.029512816604721", + "0.0015242110097923", + "2.5763586158467855", + "awq" + ], + [ + "15", + "2", + "4", + "3", + "0.1537266011841024", + "0.4225848027587028", + "3.248385969460163", + "awq" + ], + [ + "16", + "2", + "4", + "4", + "0.1964836864850164", + "0.6677658754857135", + "3.9204133230735407", + "awq" + ], + [ + "17", + "2", + "4", + "5", + "0.1998364948737692", + "0.6794140389055991", + "4.592440676686918", + "awq" + ], + [ + "18", + "2", + "4", + "6", + "0.2067698934744963", + "0.7158724879010914", + "5.2644680303002955", + "awq" + ], + [ + "19", + "2", + "4", + "8", + "0.2085390794369036", + "0.7239213616454379", + "6.608522737527051", + "awq" + ], + [ + "20", + "2", + "4", + "16", + "0.2096374717583629", + "0.7267086690055766", + "11.98474156643407", + "awq" + ], + [ + "21", + "2", + "5", + "2", + "0.0287472582273225", + "0.0015383744706068", + "2.604394757005344", + "awq" + ], + [ + "22", + "2", + "5", + "3", + "0.156545753956854", + "0.4342188097083193", + "3.2764221106187215", + "awq" + ], + [ + "23", + "2", + "5", + "4", + "0.196610560416409", + "0.6643003856622003", + "3.9484494642320986", + "awq" + ], + [ + "24", + "2", + "5", + "5", + "0.201813096512405", + "0.6880985619516059", + "4.620476817845477", + "awq" + ], + [ + "25", + "2", + "5", + "6", + "0.208814901329159", + "0.7235377941797142", + "5.292504171458854", + "awq" + ], + [ + "26", + "2", + "5", + "8", + "0.2111062432632992", + "0.7375435379366277", + "6.636558878685609", + "awq" + ], + [ + "27", + "2", + "5", + "16", + "0.2119571593229862", + "0.7375809567131982", + "12.01277770759263", + "awq" + ], + [ + "28", + "2", + "6", + "2", + "0.030631103913381", + "0.0020275794823421", + "2.6324308981639017", + "awq" + ], + [ + "29", + "2", + "6", + "3", + "0.1535624775179599", + "0.424523049292371", + "3.3044582517772794", + "awq" + ], + [ + "30", + "2", + "6", + "4", + "0.1975830578073438", + "0.6760507495669246", + "3.976485605390657", + "awq" + ], + [ + "31", + "2", + "6", + "5", + "0.2007745793399482", + "0.6885736800597454", + "4.648512959004035", + "awq" + ], + [ + "32", + "2", + "6", + "6", + "0.2084986314559541", + "0.7263028445719076", + "5.320540312617412", + "awq" + ], + [ + "33", + "2", + "6", + "8", + "0.2098694607988712", + "0.7315217945479447", + "6.6645950198441675", + "awq" + ], + [ + "34", + "2", + "6", + "16", + "0.2104895431279897", + "0.7339511837416856", + "12.040813848751187", + "awq" + ], + [ + "35", + "2", + "8", + "2", + "0.0294354848117307", + "0.0016666316965743", + "2.688503180481018", + "awq" + ], + [ + "36", + "2", + "8", + "3", + "0.1568363211152181", + "0.4377116832400026", + "3.3605305340943956", + "awq" + ], + [ + "37", + "2", + "8", + "4", + "0.1971530070668118", + "0.6719258108942063", + "4.032557887707773", + "awq" + ], + [ + "38", + "2", + "8", + "5", + "0.2010881485961166", + "0.6872950083608445", + "4.70458524132115", + "awq" + ], + [ + "39", + "2", + "8", + "6", + "0.2085898952116036", + "0.7246423668329751", + "5.376612594934528", + "awq" + ], + [ + "40", + "2", + "8", + "8", + "0.210553933277872", + "0.7350918293335355", + "6.720667302161283", + "awq" + ], + [ + "41", + "2", + "8", + "16", + "0.2111149150856989", + "0.7362575171829725", + "12.096886131068304", + "awq" + ], + [ + "42", + "2", + "16", + "2", + "0.0295098771216461", + "0.0016837862661927", + "2.9127923097494826", + "awq" + ], + [ + "43", + "2", + "16", + "3", + "0.1569115889624636", + "0.4362796051124896", + "3.58481966336286", + "awq" + ], + [ + "44", + "2", + "16", + "4", + "0.1972734524909803", + "0.6727059028707632", + "4.256847016976238", + "awq" + ], + [ + "45", + "2", + "16", + "5", + "0.2010642391090116", + "0.6882361624647443", + "4.928874370589615", + "awq" + ], + [ + "46", + "2", + "16", + "6", + "0.2086024048502772", + "0.7246993426668064", + "5.600901724202993", + "awq" + ], + [ + "47", + "2", + "16", + "8", + "0.2105720489139707", + "0.734900136126526", + "6.944956431429748", + "awq" + ], + [ + "48", + "2", + "16", + "16", + "0.2109831741881737", + "0.7357684637386981", + "12.32117526033677", + "awq" + ], + [ + "49", + "3", + "2", + "2", + "0.0318570369375779", + "0.0022093327972838", + "2.7830595292199", + "awq" + ] + ], + "shape": { + "columns": 7, + "rows": 343 + } + }, + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
vit_bitsqformer_bitsllm_bitsMETEORCIDErbpwquant_method
02220.0298840.0007902.520286awq
12230.1498570.3892743.192314awq
22240.1837350.5443523.864341awq
32250.1886600.5778064.536368awq
42260.1921590.5940625.208396awq
........................
338161640.2664131.1638377.935672awq
339161650.2708661.1950608.607699awq
340161660.2789891.2452839.279726awq
341161680.2801471.24938310.623781awq
3421616160.2812821.25419816.000000awq
\n", + "

343 rows × 7 columns

\n", + "
" + ], + "text/plain": [ + " vit_bits qformer_bits llm_bits METEOR CIDEr bpw \\\n", + "0 2 2 2 0.029884 0.000790 2.520286 \n", + "1 2 2 3 0.149857 0.389274 3.192314 \n", + "2 2 2 4 0.183735 0.544352 3.864341 \n", + "3 2 2 5 0.188660 0.577806 4.536368 \n", + "4 2 2 6 0.192159 0.594062 5.208396 \n", + ".. ... ... ... ... ... ... \n", + "338 16 16 4 0.266413 1.163837 7.935672 \n", + "339 16 16 5 0.270866 1.195060 8.607699 \n", + "340 16 16 6 0.278989 1.245283 9.279726 \n", + "341 16 16 8 0.280147 1.249383 10.623781 \n", + "342 16 16 16 0.281282 1.254198 16.000000 \n", + "\n", + " quant_method \n", + "0 awq \n", + "1 awq \n", + "2 awq \n", + "3 awq \n", + "4 awq \n", + ".. ... \n", + "338 awq \n", + "339 awq \n", + "340 awq \n", + "341 awq \n", + "342 awq \n", + "\n", + "[343 rows x 7 columns]" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_awq_coco" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "19e85627", + "metadata": {}, + "outputs": [], + "source": [ + "df_awq_coco.to_csv(os.path.join('/fs/cfar-projects/low-bit-vision/final_results/all_results','blip2_awq_coco.csv'), index=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "ce72e303", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.microsoft.datawrangler.viewer.v0+json": { + "columns": [ + { + "name": "index", + "rawType": "int64", + "type": "integer" + }, + { + "name": "vit_bits", + "rawType": "int64", + "type": "integer" + }, + { + "name": "qformer_bits", + "rawType": "int64", + "type": "integer" + }, + { + "name": "txt_r1", + "rawType": "float64", + "type": "float" + }, + { + "name": "txt_r5", + "rawType": "float64", + "type": "float" + }, + { + "name": "txt_r10", + "rawType": "float64", + "type": "float" + }, + { + "name": "txt_r_mean", + "rawType": "float64", + "type": "float" + }, + { + "name": "img_r1", + "rawType": "float64", + "type": "float" + }, + { + "name": "img_r5", + "rawType": "float64", + "type": "float" + }, + { + "name": "img_r10", + "rawType": "float64", + "type": "float" + }, + { + "name": "img_r_mean", + "rawType": "float64", + "type": "float" + }, + { + "name": "r_mean", + "rawType": "float64", + "type": "float" + }, + { + "name": "agg_metrics", + "rawType": "float64", + "type": "float" + }, + { + "name": "model_size", + "rawType": "int64", + "type": "integer" + } + ], + "conversionMethod": "pd.DataFrame", + "ref": "b2b75c7d-92ad-417a-b6ec-8476ca0e5b8f", + "rows": [ + [ + "0", + "2", + "2", + "67.5", + "83.0", + "88.1", + "79.53333333333333", + "61.32", + "81.88", + "86.72", + "76.64", + "78.08666666666667", + "79.53333333333333", + "3103760704" + ], + [ + "1", + "2", + "3", + "83.8", + "95.7", + "97.6", + "92.36666666666667", + "70.5", + "89.62", + "93.62", + "84.58", + "88.47333333333333", + "92.36666666666667", + "3265519936" + ], + [ + "2", + "2", + "4", + "84.5", + "95.4", + "97.4", + "92.43333333333334", + "71.22", + "89.9", + "93.62", + "84.91333333333334", + "88.67333333333335", + "92.43333333333334", + "3427279168" + ], + [ + "3", + "2", + "5", + "83.9", + "95.6", + "97.5", + "92.33333333333331", + "71.42", + "89.74", + "93.86", + "85.00666666666666", + "88.66999999999999", + "92.33333333333331", + "3589038400" + ], + [ + "4", + "2", + "6", + "83.7", + "95.3", + "97.4", + "92.13333333333333", + "71.1", + "89.82", + "93.7", + "84.87333333333333", + "88.50333333333333", + "92.13333333333333", + "3750797632" + ], + [ + "5", + "2", + "8", + "84.0", + "95.1", + "97.3", + "92.13333333333333", + "71.2", + "89.94", + "93.66", + "84.93333333333332", + "88.53333333333333", + "92.13333333333333", + "4074316096" + ], + [ + "6", + "2", + "16", + "84.1", + "95.1", + "97.4", + "92.2", + "71.24", + "89.98", + "93.68", + "84.96666666666667", + "88.58333333333334", + "92.2", + "5368389952" + ], + [ + "7", + "3", + "2", + "87.8", + "94.2", + "95.5", + "92.5", + "82.1", + "94.94", + "96.64", + "91.22666666666667", + "91.86333333333334", + "92.5", + "4088297920" + ], + [ + "8", + "3", + "3", + "97.2", + "100.0", + "100.0", + "99.06666666666666", + "88.54", + "98.18", + "99.02", + "95.24666666666668", + "97.15666666666668", + "99.06666666666666", + "4250057152" + ], + [ + "9", + "3", + "4", + "97.5", + "100.0", + "100.0", + "99.16666666666669", + "88.52", + "97.88", + "99.06", + "95.15333333333332", + "97.16", + "99.16666666666669", + "4411816384" + ], + [ + "10", + "3", + "5", + "97.1", + "100.0", + "100.0", + "99.03333333333336", + "88.76", + "97.8", + "98.98", + "95.18", + "97.10666666666668", + "99.03333333333336", + "4573575616" + ], + [ + "11", + "3", + "6", + "97.3", + "100.0", + "100.0", + "99.1", + "88.82", + "97.88", + "98.92", + "95.20666666666666", + "97.15333333333334", + "99.1", + "4735334848" + ], + [ + "12", + "3", + "8", + "97.4", + "100.0", + "100.0", + "99.13333333333333", + "88.62", + "97.84", + "98.9", + "95.12", + "97.12666666666668", + "99.13333333333333", + "5058853312" + ], + [ + "13", + "3", + "16", + "97.4", + "100.0", + "100.0", + "99.13333333333333", + "88.68", + "97.86", + "98.92", + "95.15333333333336", + "97.14333333333336", + "99.13333333333333", + "6352927168" + ], + [ + "14", + "4", + "2", + "87.4", + "94.7", + "95.5", + "92.53333333333336", + "83.32", + "95.46", + "96.88", + "91.88666666666666", + "92.21", + "92.53333333333336", + "5072835136" + ], + [ + "15", + "4", + "3", + "97.6", + "100.0", + "100.0", + "99.2", + "89.3", + "98.28", + "99.06", + "95.54666666666668", + "97.37333333333332", + "99.2", + "5234594368" + ], + [ + "16", + "4", + "4", + "97.6", + "100.0", + "100.0", + "99.2", + "89.68", + "98.22", + "99.08", + "95.66", + "97.43", + "99.2", + "5396353600" + ], + [ + "17", + "4", + "5", + "97.3", + "100.0", + "100.0", + "99.1", + "89.5", + "98.22", + "98.98", + "95.56666666666666", + "97.33333333333334", + "99.1", + "5558112832" + ], + [ + "18", + "4", + "6", + "97.4", + "100.0", + "100.0", + "99.13333333333333", + "89.6", + "98.26", + "99.04", + "95.63333333333334", + "97.38333333333333", + "99.13333333333333", + "5719872064" + ], + [ + "19", + "4", + "8", + "97.4", + "100.0", + "100.0", + "99.13333333333333", + "89.64", + "98.2", + "99.04", + "95.62666666666668", + "97.38", + "99.13333333333333", + "6043390528" + ], + [ + "20", + "4", + "16", + "97.4", + "100.0", + "100.0", + "99.13333333333333", + "89.66", + "98.2", + "99.02", + "95.62666666666668", + "97.38", + "99.13333333333333", + "7337464384" + ], + [ + "21", + "5", + "2", + "88.1", + "94.6", + "95.3", + "92.66666666666669", + "83.18", + "95.54", + "96.88", + "91.86666666666667", + "92.26666666666668", + "92.66666666666669", + "6057372352" + ], + [ + "22", + "5", + "3", + "98.2", + "100.0", + "100.0", + "99.4", + "89.44", + "98.22", + "99.06", + "95.57333333333334", + "97.48666666666666", + "99.4", + "6219131584" + ], + [ + "23", + "5", + "4", + "97.9", + "100.0", + "100.0", + "99.3", + "89.58", + "98.18", + "99.12", + "95.62666666666668", + "97.46333333333334", + "99.3", + "6380890816" + ], + [ + "24", + "5", + "5", + "97.6", + "100.0", + "100.0", + "99.2", + "89.44", + "98.18", + "99.08", + "95.56666666666666", + "97.38333333333333", + "99.2", + "6542650048" + ], + [ + "25", + "5", + "6", + "97.9", + "100.0", + "100.0", + "99.3", + "89.46", + "98.2", + "99.04", + "95.56666666666666", + "97.43333333333334", + "99.3", + "6704409280" + ], + [ + "26", + "5", + "8", + "98.0", + "100.0", + "100.0", + "99.33333333333331", + "89.4", + "98.18", + "99.08", + "95.55333333333334", + "97.44333333333331", + "99.33333333333331", + "7027927744" + ], + [ + "27", + "5", + "16", + "97.8", + "100.0", + "100.0", + "99.26666666666668", + "89.36", + "98.18", + "99.08", + "95.54", + "97.40333333333334", + "99.26666666666668", + "8322001600" + ], + [ + "28", + "6", + "2", + "87.8", + "94.9", + "95.4", + "92.7", + "83.22", + "95.76", + "97.04", + "92.00666666666667", + "92.35333333333334", + "92.7", + "7041909568" + ], + [ + "29", + "6", + "3", + "98.2", + "100.0", + "100.0", + "99.4", + "89.46", + "98.3", + "99.1", + "95.62", + "97.51", + "99.4", + "7203668800" + ], + [ + "30", + "6", + "4", + "97.9", + "100.0", + "100.0", + "99.3", + "89.68", + "98.26", + "99.06", + "95.66666666666669", + "97.48333333333332", + "99.3", + "7365428032" + ], + [ + "31", + "6", + "5", + "97.8", + "100.0", + "100.0", + "99.26666666666668", + "89.58", + "98.14", + "99.06", + "95.59333333333332", + "97.43", + "99.26666666666668", + "7527187264" + ], + [ + "32", + "6", + "6", + "97.9", + "100.0", + "100.0", + "99.3", + "89.54", + "98.2", + "99.04", + "95.59333333333336", + "97.44666666666669", + "99.3", + "7688946496" + ], + [ + "33", + "6", + "8", + "97.9", + "100.0", + "100.0", + "99.3", + "89.42", + "98.2", + "99.06", + "95.56", + "97.43", + "99.3", + "8012464960" + ], + [ + "34", + "6", + "16", + "97.9", + "100.0", + "100.0", + "99.3", + "89.4", + "98.2", + "99.06", + "95.55333333333334", + "97.42666666666668", + "99.3", + "9306538816" + ], + [ + "35", + "8", + "2", + "87.8", + "94.9", + "95.5", + "92.73333333333332", + "83.7", + "95.62", + "97.06", + "92.12666666666668", + "92.43", + "92.73333333333332", + "9010984000" + ], + [ + "36", + "8", + "3", + "98.2", + "100.0", + "100.0", + "99.4", + "89.5", + "98.3", + "99.1", + "95.63333333333333", + "97.51666666666664", + "99.4", + "9172743232" + ], + [ + "37", + "8", + "4", + "97.9", + "100.0", + "100.0", + "99.3", + "89.66", + "98.2", + "99.1", + "95.65333333333336", + "97.47666666666667", + "99.3", + "9334502464" + ], + [ + "38", + "8", + "5", + "97.9", + "100.0", + "100.0", + "99.3", + "89.68", + "98.2", + "99.08", + "95.65333333333332", + "97.47666666666666", + "99.3", + "9496261696" + ], + [ + "39", + "8", + "6", + "97.9", + "100.0", + "100.0", + "99.3", + "89.48", + "98.16", + "99.04", + "95.56", + "97.43", + "99.3", + "9658020928" + ], + [ + "40", + "8", + "8", + "97.9", + "100.0", + "100.0", + "99.3", + "89.44", + "98.2", + "99.04", + "95.56", + "97.43", + "99.3", + "9981539392" + ], + [ + "41", + "8", + "16", + "97.9", + "100.0", + "100.0", + "99.3", + "89.44", + "98.2", + "99.04", + "95.56", + "97.43", + "99.3", + "11275613248" + ], + [ + "42", + "16", + "2", + "88.0", + "94.8", + "95.4", + "92.73333333333336", + "83.28", + "95.78", + "97.08", + "92.04666666666668", + "92.39", + "92.73333333333336", + "16887281728" + ], + [ + "43", + "16", + "3", + "98.2", + "100.0", + "100.0", + "99.4", + "89.5", + "98.28", + "99.08", + "95.62", + "97.51", + "99.4", + "17049040960" + ], + [ + "44", + "16", + "4", + "97.9", + "100.0", + "100.0", + "99.3", + "89.64", + "98.22", + "99.1", + "95.65333333333336", + "97.47666666666667", + "99.3", + "17210800192" + ], + [ + "45", + "16", + "5", + "97.8", + "100.0", + "100.0", + "99.26666666666668", + "89.66", + "98.18", + "99.06", + "95.63333333333333", + "97.45", + "99.26666666666668", + "17372559424" + ], + [ + "46", + "16", + "6", + "97.9", + "100.0", + "100.0", + "99.3", + "89.5", + "98.16", + "99.04", + "95.56666666666666", + "97.43333333333334", + "99.3", + "17534318656" + ], + [ + "47", + "16", + "8", + "97.9", + "100.0", + "100.0", + "99.3", + "89.46", + "98.2", + "99.04", + "95.56666666666666", + "97.43333333333334", + "99.3", + "17857837120" + ], + [ + "48", + "16", + "16", + "97.9", + "100.0", + "100.0", + "99.3", + "89.46", + "98.22", + "99.04", + "95.57333333333334", + "97.43666666666668", + "99.3", + "19151910976" + ] + ], + "shape": { + "columns": 13, + "rows": 49 + } + }, + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
vit_bitsqformer_bitstxt_r1txt_r5txt_r10txt_r_meanimg_r1img_r5img_r10img_r_meanr_meanagg_metricsmodel_size
02267.583.088.179.53333361.3281.8886.7276.64000078.08666779.5333333103760704
12383.895.797.692.36666770.5089.6293.6284.58000088.47333392.3666673265519936
22484.595.497.492.43333371.2289.9093.6284.91333388.67333392.4333333427279168
32583.995.697.592.33333371.4289.7493.8685.00666788.67000092.3333333589038400
42683.795.397.492.13333371.1089.8293.7084.87333388.50333392.1333333750797632
52884.095.197.392.13333371.2089.9493.6684.93333388.53333392.1333334074316096
621684.195.197.492.20000071.2489.9893.6884.96666788.58333392.2000005368389952
73287.894.295.592.50000082.1094.9496.6491.22666791.86333392.5000004088297920
83397.2100.0100.099.06666788.5498.1899.0295.24666797.15666799.0666674250057152
93497.5100.0100.099.16666788.5297.8899.0695.15333397.16000099.1666674411816384
103597.1100.0100.099.03333388.7697.8098.9895.18000097.10666799.0333334573575616
113697.3100.0100.099.10000088.8297.8898.9295.20666797.15333399.1000004735334848
123897.4100.0100.099.13333388.6297.8498.9095.12000097.12666799.1333335058853312
1331697.4100.0100.099.13333388.6897.8698.9295.15333397.14333399.1333336352927168
144287.494.795.592.53333383.3295.4696.8891.88666792.21000092.5333335072835136
154397.6100.0100.099.20000089.3098.2899.0695.54666797.37333399.2000005234594368
164497.6100.0100.099.20000089.6898.2299.0895.66000097.43000099.2000005396353600
174597.3100.0100.099.10000089.5098.2298.9895.56666797.33333399.1000005558112832
184697.4100.0100.099.13333389.6098.2699.0495.63333397.38333399.1333335719872064
194897.4100.0100.099.13333389.6498.2099.0495.62666797.38000099.1333336043390528
2041697.4100.0100.099.13333389.6698.2099.0295.62666797.38000099.1333337337464384
215288.194.695.392.66666783.1895.5496.8891.86666792.26666792.6666676057372352
225398.2100.0100.099.40000089.4498.2299.0695.57333397.48666799.4000006219131584
235497.9100.0100.099.30000089.5898.1899.1295.62666797.46333399.3000006380890816
245597.6100.0100.099.20000089.4498.1899.0895.56666797.38333399.2000006542650048
255697.9100.0100.099.30000089.4698.2099.0495.56666797.43333399.3000006704409280
265898.0100.0100.099.33333389.4098.1899.0895.55333397.44333399.3333337027927744
2751697.8100.0100.099.26666789.3698.1899.0895.54000097.40333399.2666678322001600
286287.894.995.492.70000083.2295.7697.0492.00666792.35333392.7000007041909568
296398.2100.0100.099.40000089.4698.3099.1095.62000097.51000099.4000007203668800
306497.9100.0100.099.30000089.6898.2699.0695.66666797.48333399.3000007365428032
316597.8100.0100.099.26666789.5898.1499.0695.59333397.43000099.2666677527187264
326697.9100.0100.099.30000089.5498.2099.0495.59333397.44666799.3000007688946496
336897.9100.0100.099.30000089.4298.2099.0695.56000097.43000099.3000008012464960
3461697.9100.0100.099.30000089.4098.2099.0695.55333397.42666799.3000009306538816
358287.894.995.592.73333383.7095.6297.0692.12666792.43000092.7333339010984000
368398.2100.0100.099.40000089.5098.3099.1095.63333397.51666799.4000009172743232
378497.9100.0100.099.30000089.6698.2099.1095.65333397.47666799.3000009334502464
388597.9100.0100.099.30000089.6898.2099.0895.65333397.47666799.3000009496261696
398697.9100.0100.099.30000089.4898.1699.0495.56000097.43000099.3000009658020928
408897.9100.0100.099.30000089.4498.2099.0495.56000097.43000099.3000009981539392
4181697.9100.0100.099.30000089.4498.2099.0495.56000097.43000099.30000011275613248
4216288.094.895.492.73333383.2895.7897.0892.04666792.39000092.73333316887281728
4316398.2100.0100.099.40000089.5098.2899.0895.62000097.51000099.40000017049040960
4416497.9100.0100.099.30000089.6498.2299.1095.65333397.47666799.30000017210800192
4516597.8100.0100.099.26666789.6698.1899.0695.63333397.45000099.26666717372559424
4616697.9100.0100.099.30000089.5098.1699.0495.56666797.43333399.30000017534318656
4716897.9100.0100.099.30000089.4698.2099.0495.56666797.43333399.30000017857837120
48161697.9100.0100.099.30000089.4698.2299.0495.57333397.43666799.30000019151910976
\n", + "
" + ], + "text/plain": [ + " vit_bits qformer_bits txt_r1 txt_r5 txt_r10 txt_r_mean img_r1 \\\n", + "0 2 2 67.5 83.0 88.1 79.533333 61.32 \n", + "1 2 3 83.8 95.7 97.6 92.366667 70.50 \n", + "2 2 4 84.5 95.4 97.4 92.433333 71.22 \n", + "3 2 5 83.9 95.6 97.5 92.333333 71.42 \n", + "4 2 6 83.7 95.3 97.4 92.133333 71.10 \n", + "5 2 8 84.0 95.1 97.3 92.133333 71.20 \n", + "6 2 16 84.1 95.1 97.4 92.200000 71.24 \n", + "7 3 2 87.8 94.2 95.5 92.500000 82.10 \n", + "8 3 3 97.2 100.0 100.0 99.066667 88.54 \n", + "9 3 4 97.5 100.0 100.0 99.166667 88.52 \n", + "10 3 5 97.1 100.0 100.0 99.033333 88.76 \n", + "11 3 6 97.3 100.0 100.0 99.100000 88.82 \n", + "12 3 8 97.4 100.0 100.0 99.133333 88.62 \n", + "13 3 16 97.4 100.0 100.0 99.133333 88.68 \n", + "14 4 2 87.4 94.7 95.5 92.533333 83.32 \n", + "15 4 3 97.6 100.0 100.0 99.200000 89.30 \n", + "16 4 4 97.6 100.0 100.0 99.200000 89.68 \n", + "17 4 5 97.3 100.0 100.0 99.100000 89.50 \n", + "18 4 6 97.4 100.0 100.0 99.133333 89.60 \n", + "19 4 8 97.4 100.0 100.0 99.133333 89.64 \n", + "20 4 16 97.4 100.0 100.0 99.133333 89.66 \n", + "21 5 2 88.1 94.6 95.3 92.666667 83.18 \n", + "22 5 3 98.2 100.0 100.0 99.400000 89.44 \n", + "23 5 4 97.9 100.0 100.0 99.300000 89.58 \n", + "24 5 5 97.6 100.0 100.0 99.200000 89.44 \n", + "25 5 6 97.9 100.0 100.0 99.300000 89.46 \n", + "26 5 8 98.0 100.0 100.0 99.333333 89.40 \n", + "27 5 16 97.8 100.0 100.0 99.266667 89.36 \n", + "28 6 2 87.8 94.9 95.4 92.700000 83.22 \n", + "29 6 3 98.2 100.0 100.0 99.400000 89.46 \n", + "30 6 4 97.9 100.0 100.0 99.300000 89.68 \n", + "31 6 5 97.8 100.0 100.0 99.266667 89.58 \n", + "32 6 6 97.9 100.0 100.0 99.300000 89.54 \n", + "33 6 8 97.9 100.0 100.0 99.300000 89.42 \n", + "34 6 16 97.9 100.0 100.0 99.300000 89.40 \n", + "35 8 2 87.8 94.9 95.5 92.733333 83.70 \n", + "36 8 3 98.2 100.0 100.0 99.400000 89.50 \n", + "37 8 4 97.9 100.0 100.0 99.300000 89.66 \n", + "38 8 5 97.9 100.0 100.0 99.300000 89.68 \n", + "39 8 6 97.9 100.0 100.0 99.300000 89.48 \n", + "40 8 8 97.9 100.0 100.0 99.300000 89.44 \n", + "41 8 16 97.9 100.0 100.0 99.300000 89.44 \n", + "42 16 2 88.0 94.8 95.4 92.733333 83.28 \n", + "43 16 3 98.2 100.0 100.0 99.400000 89.50 \n", + "44 16 4 97.9 100.0 100.0 99.300000 89.64 \n", + "45 16 5 97.8 100.0 100.0 99.266667 89.66 \n", + "46 16 6 97.9 100.0 100.0 99.300000 89.50 \n", + "47 16 8 97.9 100.0 100.0 99.300000 89.46 \n", + "48 16 16 97.9 100.0 100.0 99.300000 89.46 \n", + "\n", + " img_r5 img_r10 img_r_mean r_mean agg_metrics model_size \n", + "0 81.88 86.72 76.640000 78.086667 79.533333 3103760704 \n", + "1 89.62 93.62 84.580000 88.473333 92.366667 3265519936 \n", + "2 89.90 93.62 84.913333 88.673333 92.433333 3427279168 \n", + "3 89.74 93.86 85.006667 88.670000 92.333333 3589038400 \n", + "4 89.82 93.70 84.873333 88.503333 92.133333 3750797632 \n", + "5 89.94 93.66 84.933333 88.533333 92.133333 4074316096 \n", + "6 89.98 93.68 84.966667 88.583333 92.200000 5368389952 \n", + "7 94.94 96.64 91.226667 91.863333 92.500000 4088297920 \n", + "8 98.18 99.02 95.246667 97.156667 99.066667 4250057152 \n", + "9 97.88 99.06 95.153333 97.160000 99.166667 4411816384 \n", + "10 97.80 98.98 95.180000 97.106667 99.033333 4573575616 \n", + "11 97.88 98.92 95.206667 97.153333 99.100000 4735334848 \n", + "12 97.84 98.90 95.120000 97.126667 99.133333 5058853312 \n", + "13 97.86 98.92 95.153333 97.143333 99.133333 6352927168 \n", + "14 95.46 96.88 91.886667 92.210000 92.533333 5072835136 \n", + "15 98.28 99.06 95.546667 97.373333 99.200000 5234594368 \n", + "16 98.22 99.08 95.660000 97.430000 99.200000 5396353600 \n", + "17 98.22 98.98 95.566667 97.333333 99.100000 5558112832 \n", + "18 98.26 99.04 95.633333 97.383333 99.133333 5719872064 \n", + "19 98.20 99.04 95.626667 97.380000 99.133333 6043390528 \n", + "20 98.20 99.02 95.626667 97.380000 99.133333 7337464384 \n", + "21 95.54 96.88 91.866667 92.266667 92.666667 6057372352 \n", + "22 98.22 99.06 95.573333 97.486667 99.400000 6219131584 \n", + "23 98.18 99.12 95.626667 97.463333 99.300000 6380890816 \n", + "24 98.18 99.08 95.566667 97.383333 99.200000 6542650048 \n", + "25 98.20 99.04 95.566667 97.433333 99.300000 6704409280 \n", + "26 98.18 99.08 95.553333 97.443333 99.333333 7027927744 \n", + "27 98.18 99.08 95.540000 97.403333 99.266667 8322001600 \n", + "28 95.76 97.04 92.006667 92.353333 92.700000 7041909568 \n", + "29 98.30 99.10 95.620000 97.510000 99.400000 7203668800 \n", + "30 98.26 99.06 95.666667 97.483333 99.300000 7365428032 \n", + "31 98.14 99.06 95.593333 97.430000 99.266667 7527187264 \n", + "32 98.20 99.04 95.593333 97.446667 99.300000 7688946496 \n", + "33 98.20 99.06 95.560000 97.430000 99.300000 8012464960 \n", + "34 98.20 99.06 95.553333 97.426667 99.300000 9306538816 \n", + "35 95.62 97.06 92.126667 92.430000 92.733333 9010984000 \n", + "36 98.30 99.10 95.633333 97.516667 99.400000 9172743232 \n", + "37 98.20 99.10 95.653333 97.476667 99.300000 9334502464 \n", + "38 98.20 99.08 95.653333 97.476667 99.300000 9496261696 \n", + "39 98.16 99.04 95.560000 97.430000 99.300000 9658020928 \n", + "40 98.20 99.04 95.560000 97.430000 99.300000 9981539392 \n", + "41 98.20 99.04 95.560000 97.430000 99.300000 11275613248 \n", + "42 95.78 97.08 92.046667 92.390000 92.733333 16887281728 \n", + "43 98.28 99.08 95.620000 97.510000 99.400000 17049040960 \n", + "44 98.22 99.10 95.653333 97.476667 99.300000 17210800192 \n", + "45 98.18 99.06 95.633333 97.450000 99.266667 17372559424 \n", + "46 98.16 99.04 95.566667 97.433333 99.300000 17534318656 \n", + "47 98.20 99.04 95.566667 97.433333 99.300000 17857837120 \n", + "48 98.22 99.04 95.573333 97.436667 99.300000 19151910976 " + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "path = '/fs/cfar-projects/low-bit-vision/final_results/blip2/awq/image_text_retrieval/awq_image_text_retrieval.csv'\n", + "df_awq_flickr = pd.read_csv(path)\n", + "df_awq_flickr" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8fa2f81d", + "metadata": {}, + "outputs": [], + "source": [ + "df_awq_flickr['bpw'] = [compute_bpw_blip_retrieval(vision_bits=x['vit_bits'],\n", + " qformer_bits=x['qformer_bits'],) for x in df_awq_flickr.to_dict(orient='records')]\n", + "\n", + "df_awq_flickr['quant_method'] = 'awq'\n", + "df_awq_flickr = df_awq_flickr.drop(['model_size'], axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "9f123399", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.microsoft.datawrangler.viewer.v0+json": { + "columns": [ + { + "name": "index", + "rawType": "int64", + "type": "integer" + }, + { + "name": "vit_bits", + "rawType": "int64", + "type": "integer" + }, + { + "name": "qformer_bits", + "rawType": "int64", + "type": "integer" + }, + { + "name": "txt_r1", + "rawType": "float64", + "type": "float" + }, + { + "name": "txt_r5", + "rawType": "float64", + "type": "float" + }, + { + "name": "txt_r10", + "rawType": "float64", + "type": "float" + }, + { + "name": "txt_r_mean", + "rawType": "float64", + "type": "float" + }, + { + "name": "img_r1", + "rawType": "float64", + "type": "float" + }, + { + "name": "img_r5", + "rawType": "float64", + "type": "float" + }, + { + "name": "img_r10", + "rawType": "float64", + "type": "float" + }, + { + "name": "img_r_mean", + "rawType": "float64", + "type": "float" + }, + { + "name": "r_mean", + "rawType": "float64", + "type": "float" + }, + { + "name": "agg_metrics", + "rawType": "float64", + "type": "float" + }, + { + "name": "bpw", + "rawType": "float64", + "type": "float" + }, + { + "name": "quant_method", + "rawType": "object", + "type": "string" + } + ], + "conversionMethod": "pd.DataFrame", + "ref": "bf2e37cc-1279-4f2b-82f3-d29487d4f5a3", + "rows": [ + [ + "0", + "2", + "2", + "67.5", + "83.0", + "88.1", + "79.53333333333333", + "61.32", + "81.88", + "86.72", + "76.64", + "78.08666666666667", + "79.53333333333333", + "2.322220380179994", + "awq" + ], + [ + "1", + "2", + "3", + "83.8", + "95.7", + "97.6", + "92.36666666666667", + "70.5", + "89.62", + "93.62", + "84.58", + "88.47333333333333", + "92.36666666666667", + "2.460041053002226", + "awq" + ], + [ + "2", + "2", + "4", + "84.5", + "95.4", + "97.4", + "92.43333333333334", + "71.22", + "89.9", + "93.62", + "84.91333333333334", + "88.67333333333335", + "92.43333333333334", + "2.597861725824458", + "awq" + ], + [ + "3", + "2", + "5", + "83.9", + "95.6", + "97.5", + "92.33333333333331", + "71.42", + "89.74", + "93.86", + "85.00666666666666", + "88.66999999999999", + "92.33333333333331", + "2.73568239864669", + "awq" + ], + [ + "4", + "2", + "6", + "83.7", + "95.3", + "97.4", + "92.13333333333333", + "71.1", + "89.82", + "93.7", + "84.87333333333333", + "88.50333333333333", + "92.13333333333333", + "2.8735030714689223", + "awq" + ], + [ + "5", + "2", + "8", + "84.0", + "95.1", + "97.3", + "92.13333333333333", + "71.2", + "89.94", + "93.66", + "84.93333333333332", + "88.53333333333333", + "92.13333333333333", + "3.149144417113386", + "awq" + ], + [ + "6", + "2", + "16", + "84.1", + "95.1", + "97.4", + "92.2", + "71.24", + "89.98", + "93.68", + "84.96666666666667", + "88.58333333333334", + "92.2", + "4.251709799691242", + "awq" + ], + [ + "7", + "3", + "2", + "87.8", + "94.2", + "95.5", + "92.5", + "82.1", + "94.94", + "96.64", + "91.22666666666667", + "91.86333333333334", + "92.5", + "3.161383965916334", + "awq" + ], + [ + "8", + "3", + "3", + "97.2", + "100.0", + "100.0", + "99.06666666666666", + "88.54", + "98.18", + "99.02", + "95.24666666666668", + "97.15666666666668", + "99.06666666666666", + "3.2992046387385656", + "awq" + ], + [ + "9", + "3", + "4", + "97.5", + "100.0", + "100.0", + "99.16666666666669", + "88.52", + "97.88", + "99.06", + "95.15333333333332", + "97.16", + "99.16666666666669", + "3.437025311560798", + "awq" + ], + [ + "10", + "3", + "5", + "97.1", + "100.0", + "100.0", + "99.03333333333336", + "88.76", + "97.8", + "98.98", + "95.18", + "97.10666666666668", + "99.03333333333336", + "3.57484598438303", + "awq" + ], + [ + "11", + "3", + "6", + "97.3", + "100.0", + "100.0", + "99.1", + "88.82", + "97.88", + "98.92", + "95.20666666666666", + "97.15333333333334", + "99.1", + "3.712666657205262", + "awq" + ], + [ + "12", + "3", + "8", + "97.4", + "100.0", + "100.0", + "99.13333333333333", + "88.62", + "97.84", + "98.9", + "95.12", + "97.12666666666668", + "99.13333333333333", + "3.988308002849726", + "awq" + ], + [ + "13", + "3", + "16", + "97.4", + "100.0", + "100.0", + "99.13333333333333", + "88.68", + "97.86", + "98.92", + "95.15333333333336", + "97.14333333333336", + "99.13333333333333", + "5.0908733854275825", + "awq" + ], + [ + "14", + "4", + "2", + "87.4", + "94.7", + "95.5", + "92.53333333333336", + "83.32", + "95.46", + "96.88", + "91.88666666666666", + "92.21", + "92.53333333333336", + "4.000547551652674", + "awq" + ], + [ + "15", + "4", + "3", + "97.6", + "100.0", + "100.0", + "99.2", + "89.3", + "98.28", + "99.06", + "95.54666666666668", + "97.37333333333332", + "99.2", + "4.138368224474906", + "awq" + ], + [ + "16", + "4", + "4", + "97.6", + "100.0", + "100.0", + "99.2", + "89.68", + "98.22", + "99.08", + "95.66", + "97.43", + "99.2", + "4.276188897297137", + "awq" + ], + [ + "17", + "4", + "5", + "97.3", + "100.0", + "100.0", + "99.1", + "89.5", + "98.22", + "98.98", + "95.56666666666666", + "97.33333333333334", + "99.1", + "4.41400957011937", + "awq" + ], + [ + "18", + "4", + "6", + "97.4", + "100.0", + "100.0", + "99.13333333333333", + "89.6", + "98.26", + "99.04", + "95.63333333333334", + "97.38333333333333", + "99.13333333333333", + "4.551830242941602", + "awq" + ], + [ + "19", + "4", + "8", + "97.4", + "100.0", + "100.0", + "99.13333333333333", + "89.64", + "98.2", + "99.04", + "95.62666666666668", + "97.38", + "99.13333333333333", + "4.827471588586066", + "awq" + ], + [ + "20", + "4", + "16", + "97.4", + "100.0", + "100.0", + "99.13333333333333", + "89.66", + "98.2", + "99.02", + "95.62666666666668", + "97.38", + "99.13333333333333", + "5.930036971163922", + "awq" + ], + [ + "21", + "5", + "2", + "88.1", + "94.6", + "95.3", + "92.66666666666669", + "83.18", + "95.54", + "96.88", + "91.86666666666667", + "92.26666666666668", + "92.66666666666669", + "4.839711137389013", + "awq" + ], + [ + "22", + "5", + "3", + "98.2", + "100.0", + "100.0", + "99.4", + "89.44", + "98.22", + "99.06", + "95.57333333333334", + "97.48666666666666", + "99.4", + "4.977531810211246", + "awq" + ], + [ + "23", + "5", + "4", + "97.9", + "100.0", + "100.0", + "99.3", + "89.58", + "98.18", + "99.12", + "95.62666666666668", + "97.46333333333334", + "99.3", + "5.115352483033478", + "awq" + ], + [ + "24", + "5", + "5", + "97.6", + "100.0", + "100.0", + "99.2", + "89.44", + "98.18", + "99.08", + "95.56666666666666", + "97.38333333333333", + "99.2", + "5.253173155855709", + "awq" + ], + [ + "25", + "5", + "6", + "97.9", + "100.0", + "100.0", + "99.3", + "89.46", + "98.2", + "99.04", + "95.56666666666666", + "97.43333333333334", + "99.3", + "5.390993828677941", + "awq" + ], + [ + "26", + "5", + "8", + "98.0", + "100.0", + "100.0", + "99.33333333333331", + "89.4", + "98.18", + "99.08", + "95.55333333333334", + "97.44333333333331", + "99.33333333333331", + "5.666635174322406", + "awq" + ], + [ + "27", + "5", + "16", + "97.8", + "100.0", + "100.0", + "99.26666666666668", + "89.36", + "98.18", + "99.08", + "95.54", + "97.40333333333334", + "99.26666666666668", + "6.769200556900262", + "awq" + ], + [ + "28", + "6", + "2", + "87.8", + "94.9", + "95.4", + "92.7", + "83.22", + "95.76", + "97.04", + "92.00666666666667", + "92.35333333333334", + "92.7", + "5.678874723125353", + "awq" + ], + [ + "29", + "6", + "3", + "98.2", + "100.0", + "100.0", + "99.4", + "89.46", + "98.3", + "99.1", + "95.62", + "97.51", + "99.4", + "5.816695395947585", + "awq" + ], + [ + "30", + "6", + "4", + "97.9", + "100.0", + "100.0", + "99.3", + "89.68", + "98.26", + "99.06", + "95.66666666666669", + "97.48333333333332", + "99.3", + "5.954516068769817", + "awq" + ], + [ + "31", + "6", + "5", + "97.8", + "100.0", + "100.0", + "99.26666666666668", + "89.58", + "98.14", + "99.06", + "95.59333333333332", + "97.43", + "99.26666666666668", + "6.0923367415920495", + "awq" + ], + [ + "32", + "6", + "6", + "97.9", + "100.0", + "100.0", + "99.3", + "89.54", + "98.2", + "99.04", + "95.59333333333336", + "97.44666666666669", + "99.3", + "6.230157414414282", + "awq" + ], + [ + "33", + "6", + "8", + "97.9", + "100.0", + "100.0", + "99.3", + "89.42", + "98.2", + "99.06", + "95.56", + "97.43", + "99.3", + "6.505798760058745", + "awq" + ], + [ + "34", + "6", + "16", + "97.9", + "100.0", + "100.0", + "99.3", + "89.4", + "98.2", + "99.06", + "95.55333333333334", + "97.42666666666668", + "99.3", + "7.608364142636602", + "awq" + ], + [ + "35", + "8", + "2", + "87.8", + "94.9", + "95.5", + "92.73333333333332", + "83.7", + "95.62", + "97.06", + "92.12666666666668", + "92.43", + "92.73333333333332", + "7.357201894598033", + "awq" + ], + [ + "36", + "8", + "3", + "98.2", + "100.0", + "100.0", + "99.4", + "89.5", + "98.3", + "99.1", + "95.63333333333333", + "97.51666666666664", + "99.4", + "7.495022567420265", + "awq" + ], + [ + "37", + "8", + "4", + "97.9", + "100.0", + "100.0", + "99.3", + "89.66", + "98.2", + "99.1", + "95.65333333333336", + "97.47666666666667", + "99.3", + "7.632843240242497", + "awq" + ], + [ + "38", + "8", + "5", + "97.9", + "100.0", + "100.0", + "99.3", + "89.68", + "98.2", + "99.08", + "95.65333333333332", + "97.47666666666666", + "99.3", + "7.770663913064729", + "awq" + ], + [ + "39", + "8", + "6", + "97.9", + "100.0", + "100.0", + "99.3", + "89.48", + "98.16", + "99.04", + "95.56", + "97.43", + "99.3", + "7.908484585886961", + "awq" + ], + [ + "40", + "8", + "8", + "97.9", + "100.0", + "100.0", + "99.3", + "89.44", + "98.2", + "99.04", + "95.56", + "97.43", + "99.3", + "8.184125931531424", + "awq" + ], + [ + "41", + "8", + "16", + "97.9", + "100.0", + "100.0", + "99.3", + "89.44", + "98.2", + "99.04", + "95.56", + "97.43", + "99.3", + "9.286691314109282", + "awq" + ], + [ + "42", + "16", + "2", + "88.0", + "94.8", + "95.4", + "92.73333333333336", + "83.28", + "95.78", + "97.08", + "92.04666666666668", + "92.39", + "92.73333333333336", + "14.070510580488751", + "awq" + ], + [ + "43", + "16", + "3", + "98.2", + "100.0", + "100.0", + "99.4", + "89.5", + "98.28", + "99.08", + "95.62", + "97.51", + "99.4", + "14.208331253310984", + "awq" + ], + [ + "44", + "16", + "4", + "97.9", + "100.0", + "100.0", + "99.3", + "89.64", + "98.22", + "99.1", + "95.65333333333336", + "97.47666666666667", + "99.3", + "14.346151926133215", + "awq" + ], + [ + "45", + "16", + "5", + "97.8", + "100.0", + "100.0", + "99.26666666666668", + "89.66", + "98.18", + "99.06", + "95.63333333333333", + "97.45", + "99.26666666666668", + "14.483972598955448", + "awq" + ], + [ + "46", + "16", + "6", + "97.9", + "100.0", + "100.0", + "99.3", + "89.5", + "98.16", + "99.04", + "95.56666666666666", + "97.43333333333334", + "99.3", + "14.62179327177768", + "awq" + ], + [ + "47", + "16", + "8", + "97.9", + "100.0", + "100.0", + "99.3", + "89.46", + "98.2", + "99.04", + "95.56666666666666", + "97.43333333333334", + "99.3", + "14.897434617422144", + "awq" + ], + [ + "48", + "16", + "16", + "97.9", + "100.0", + "100.0", + "99.3", + "89.46", + "98.22", + "99.04", + "95.57333333333334", + "97.43666666666668", + "99.3", + "16.0", + "awq" + ] + ], + "shape": { + "columns": 14, + "rows": 49 + } + }, + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
vit_bitsqformer_bitstxt_r1txt_r5txt_r10txt_r_meanimg_r1img_r5img_r10img_r_meanr_meanagg_metricsbpwquant_method
02267.583.088.179.53333361.3281.8886.7276.64000078.08666779.5333332.322220awq
12383.895.797.692.36666770.5089.6293.6284.58000088.47333392.3666672.460041awq
22484.595.497.492.43333371.2289.9093.6284.91333388.67333392.4333332.597862awq
32583.995.697.592.33333371.4289.7493.8685.00666788.67000092.3333332.735682awq
42683.795.397.492.13333371.1089.8293.7084.87333388.50333392.1333332.873503awq
52884.095.197.392.13333371.2089.9493.6684.93333388.53333392.1333333.149144awq
621684.195.197.492.20000071.2489.9893.6884.96666788.58333392.2000004.251710awq
73287.894.295.592.50000082.1094.9496.6491.22666791.86333392.5000003.161384awq
83397.2100.0100.099.06666788.5498.1899.0295.24666797.15666799.0666673.299205awq
93497.5100.0100.099.16666788.5297.8899.0695.15333397.16000099.1666673.437025awq
103597.1100.0100.099.03333388.7697.8098.9895.18000097.10666799.0333333.574846awq
113697.3100.0100.099.10000088.8297.8898.9295.20666797.15333399.1000003.712667awq
123897.4100.0100.099.13333388.6297.8498.9095.12000097.12666799.1333333.988308awq
1331697.4100.0100.099.13333388.6897.8698.9295.15333397.14333399.1333335.090873awq
144287.494.795.592.53333383.3295.4696.8891.88666792.21000092.5333334.000548awq
154397.6100.0100.099.20000089.3098.2899.0695.54666797.37333399.2000004.138368awq
164497.6100.0100.099.20000089.6898.2299.0895.66000097.43000099.2000004.276189awq
174597.3100.0100.099.10000089.5098.2298.9895.56666797.33333399.1000004.414010awq
184697.4100.0100.099.13333389.6098.2699.0495.63333397.38333399.1333334.551830awq
194897.4100.0100.099.13333389.6498.2099.0495.62666797.38000099.1333334.827472awq
2041697.4100.0100.099.13333389.6698.2099.0295.62666797.38000099.1333335.930037awq
215288.194.695.392.66666783.1895.5496.8891.86666792.26666792.6666674.839711awq
225398.2100.0100.099.40000089.4498.2299.0695.57333397.48666799.4000004.977532awq
235497.9100.0100.099.30000089.5898.1899.1295.62666797.46333399.3000005.115352awq
245597.6100.0100.099.20000089.4498.1899.0895.56666797.38333399.2000005.253173awq
255697.9100.0100.099.30000089.4698.2099.0495.56666797.43333399.3000005.390994awq
265898.0100.0100.099.33333389.4098.1899.0895.55333397.44333399.3333335.666635awq
2751697.8100.0100.099.26666789.3698.1899.0895.54000097.40333399.2666676.769201awq
286287.894.995.492.70000083.2295.7697.0492.00666792.35333392.7000005.678875awq
296398.2100.0100.099.40000089.4698.3099.1095.62000097.51000099.4000005.816695awq
306497.9100.0100.099.30000089.6898.2699.0695.66666797.48333399.3000005.954516awq
316597.8100.0100.099.26666789.5898.1499.0695.59333397.43000099.2666676.092337awq
326697.9100.0100.099.30000089.5498.2099.0495.59333397.44666799.3000006.230157awq
336897.9100.0100.099.30000089.4298.2099.0695.56000097.43000099.3000006.505799awq
3461697.9100.0100.099.30000089.4098.2099.0695.55333397.42666799.3000007.608364awq
358287.894.995.592.73333383.7095.6297.0692.12666792.43000092.7333337.357202awq
368398.2100.0100.099.40000089.5098.3099.1095.63333397.51666799.4000007.495023awq
378497.9100.0100.099.30000089.6698.2099.1095.65333397.47666799.3000007.632843awq
388597.9100.0100.099.30000089.6898.2099.0895.65333397.47666799.3000007.770664awq
398697.9100.0100.099.30000089.4898.1699.0495.56000097.43000099.3000007.908485awq
408897.9100.0100.099.30000089.4498.2099.0495.56000097.43000099.3000008.184126awq
4181697.9100.0100.099.30000089.4498.2099.0495.56000097.43000099.3000009.286691awq
4216288.094.895.492.73333383.2895.7897.0892.04666792.39000092.73333314.070511awq
4316398.2100.0100.099.40000089.5098.2899.0895.62000097.51000099.40000014.208331awq
4416497.9100.0100.099.30000089.6498.2299.1095.65333397.47666799.30000014.346152awq
4516597.8100.0100.099.26666789.6698.1899.0695.63333397.45000099.26666714.483973awq
4616697.9100.0100.099.30000089.5098.1699.0495.56666797.43333399.30000014.621793awq
4716897.9100.0100.099.30000089.4698.2099.0495.56666797.43333399.30000014.897435awq
48161697.9100.0100.099.30000089.4698.2299.0495.57333397.43666799.30000016.000000awq
\n", + "
" + ], + "text/plain": [ + " vit_bits qformer_bits txt_r1 txt_r5 txt_r10 txt_r_mean img_r1 \\\n", + "0 2 2 67.5 83.0 88.1 79.533333 61.32 \n", + "1 2 3 83.8 95.7 97.6 92.366667 70.50 \n", + "2 2 4 84.5 95.4 97.4 92.433333 71.22 \n", + "3 2 5 83.9 95.6 97.5 92.333333 71.42 \n", + "4 2 6 83.7 95.3 97.4 92.133333 71.10 \n", + "5 2 8 84.0 95.1 97.3 92.133333 71.20 \n", + "6 2 16 84.1 95.1 97.4 92.200000 71.24 \n", + "7 3 2 87.8 94.2 95.5 92.500000 82.10 \n", + "8 3 3 97.2 100.0 100.0 99.066667 88.54 \n", + "9 3 4 97.5 100.0 100.0 99.166667 88.52 \n", + "10 3 5 97.1 100.0 100.0 99.033333 88.76 \n", + "11 3 6 97.3 100.0 100.0 99.100000 88.82 \n", + "12 3 8 97.4 100.0 100.0 99.133333 88.62 \n", + "13 3 16 97.4 100.0 100.0 99.133333 88.68 \n", + "14 4 2 87.4 94.7 95.5 92.533333 83.32 \n", + "15 4 3 97.6 100.0 100.0 99.200000 89.30 \n", + "16 4 4 97.6 100.0 100.0 99.200000 89.68 \n", + "17 4 5 97.3 100.0 100.0 99.100000 89.50 \n", + "18 4 6 97.4 100.0 100.0 99.133333 89.60 \n", + "19 4 8 97.4 100.0 100.0 99.133333 89.64 \n", + "20 4 16 97.4 100.0 100.0 99.133333 89.66 \n", + "21 5 2 88.1 94.6 95.3 92.666667 83.18 \n", + "22 5 3 98.2 100.0 100.0 99.400000 89.44 \n", + "23 5 4 97.9 100.0 100.0 99.300000 89.58 \n", + "24 5 5 97.6 100.0 100.0 99.200000 89.44 \n", + "25 5 6 97.9 100.0 100.0 99.300000 89.46 \n", + "26 5 8 98.0 100.0 100.0 99.333333 89.40 \n", + "27 5 16 97.8 100.0 100.0 99.266667 89.36 \n", + "28 6 2 87.8 94.9 95.4 92.700000 83.22 \n", + "29 6 3 98.2 100.0 100.0 99.400000 89.46 \n", + "30 6 4 97.9 100.0 100.0 99.300000 89.68 \n", + "31 6 5 97.8 100.0 100.0 99.266667 89.58 \n", + "32 6 6 97.9 100.0 100.0 99.300000 89.54 \n", + "33 6 8 97.9 100.0 100.0 99.300000 89.42 \n", + "34 6 16 97.9 100.0 100.0 99.300000 89.40 \n", + "35 8 2 87.8 94.9 95.5 92.733333 83.70 \n", + "36 8 3 98.2 100.0 100.0 99.400000 89.50 \n", + "37 8 4 97.9 100.0 100.0 99.300000 89.66 \n", + "38 8 5 97.9 100.0 100.0 99.300000 89.68 \n", + "39 8 6 97.9 100.0 100.0 99.300000 89.48 \n", + "40 8 8 97.9 100.0 100.0 99.300000 89.44 \n", + "41 8 16 97.9 100.0 100.0 99.300000 89.44 \n", + "42 16 2 88.0 94.8 95.4 92.733333 83.28 \n", + "43 16 3 98.2 100.0 100.0 99.400000 89.50 \n", + "44 16 4 97.9 100.0 100.0 99.300000 89.64 \n", + "45 16 5 97.8 100.0 100.0 99.266667 89.66 \n", + "46 16 6 97.9 100.0 100.0 99.300000 89.50 \n", + "47 16 8 97.9 100.0 100.0 99.300000 89.46 \n", + "48 16 16 97.9 100.0 100.0 99.300000 89.46 \n", + "\n", + " img_r5 img_r10 img_r_mean r_mean agg_metrics bpw \\\n", + "0 81.88 86.72 76.640000 78.086667 79.533333 2.322220 \n", + "1 89.62 93.62 84.580000 88.473333 92.366667 2.460041 \n", + "2 89.90 93.62 84.913333 88.673333 92.433333 2.597862 \n", + "3 89.74 93.86 85.006667 88.670000 92.333333 2.735682 \n", + "4 89.82 93.70 84.873333 88.503333 92.133333 2.873503 \n", + "5 89.94 93.66 84.933333 88.533333 92.133333 3.149144 \n", + "6 89.98 93.68 84.966667 88.583333 92.200000 4.251710 \n", + "7 94.94 96.64 91.226667 91.863333 92.500000 3.161384 \n", + "8 98.18 99.02 95.246667 97.156667 99.066667 3.299205 \n", + "9 97.88 99.06 95.153333 97.160000 99.166667 3.437025 \n", + "10 97.80 98.98 95.180000 97.106667 99.033333 3.574846 \n", + "11 97.88 98.92 95.206667 97.153333 99.100000 3.712667 \n", + "12 97.84 98.90 95.120000 97.126667 99.133333 3.988308 \n", + "13 97.86 98.92 95.153333 97.143333 99.133333 5.090873 \n", + "14 95.46 96.88 91.886667 92.210000 92.533333 4.000548 \n", + "15 98.28 99.06 95.546667 97.373333 99.200000 4.138368 \n", + "16 98.22 99.08 95.660000 97.430000 99.200000 4.276189 \n", + "17 98.22 98.98 95.566667 97.333333 99.100000 4.414010 \n", + "18 98.26 99.04 95.633333 97.383333 99.133333 4.551830 \n", + "19 98.20 99.04 95.626667 97.380000 99.133333 4.827472 \n", + "20 98.20 99.02 95.626667 97.380000 99.133333 5.930037 \n", + "21 95.54 96.88 91.866667 92.266667 92.666667 4.839711 \n", + "22 98.22 99.06 95.573333 97.486667 99.400000 4.977532 \n", + "23 98.18 99.12 95.626667 97.463333 99.300000 5.115352 \n", + "24 98.18 99.08 95.566667 97.383333 99.200000 5.253173 \n", + "25 98.20 99.04 95.566667 97.433333 99.300000 5.390994 \n", + "26 98.18 99.08 95.553333 97.443333 99.333333 5.666635 \n", + "27 98.18 99.08 95.540000 97.403333 99.266667 6.769201 \n", + "28 95.76 97.04 92.006667 92.353333 92.700000 5.678875 \n", + "29 98.30 99.10 95.620000 97.510000 99.400000 5.816695 \n", + "30 98.26 99.06 95.666667 97.483333 99.300000 5.954516 \n", + "31 98.14 99.06 95.593333 97.430000 99.266667 6.092337 \n", + "32 98.20 99.04 95.593333 97.446667 99.300000 6.230157 \n", + "33 98.20 99.06 95.560000 97.430000 99.300000 6.505799 \n", + "34 98.20 99.06 95.553333 97.426667 99.300000 7.608364 \n", + "35 95.62 97.06 92.126667 92.430000 92.733333 7.357202 \n", + "36 98.30 99.10 95.633333 97.516667 99.400000 7.495023 \n", + "37 98.20 99.10 95.653333 97.476667 99.300000 7.632843 \n", + "38 98.20 99.08 95.653333 97.476667 99.300000 7.770664 \n", + "39 98.16 99.04 95.560000 97.430000 99.300000 7.908485 \n", + "40 98.20 99.04 95.560000 97.430000 99.300000 8.184126 \n", + "41 98.20 99.04 95.560000 97.430000 99.300000 9.286691 \n", + "42 95.78 97.08 92.046667 92.390000 92.733333 14.070511 \n", + "43 98.28 99.08 95.620000 97.510000 99.400000 14.208331 \n", + "44 98.22 99.10 95.653333 97.476667 99.300000 14.346152 \n", + "45 98.18 99.06 95.633333 97.450000 99.266667 14.483973 \n", + "46 98.16 99.04 95.566667 97.433333 99.300000 14.621793 \n", + "47 98.20 99.04 95.566667 97.433333 99.300000 14.897435 \n", + "48 98.22 99.04 95.573333 97.436667 99.300000 16.000000 \n", + "\n", + " quant_method \n", + "0 awq \n", + "1 awq \n", + "2 awq \n", + "3 awq \n", + "4 awq \n", + "5 awq \n", + "6 awq \n", + "7 awq \n", + "8 awq \n", + "9 awq \n", + "10 awq \n", + "11 awq \n", + "12 awq \n", + "13 awq \n", + "14 awq \n", + "15 awq \n", + "16 awq \n", + "17 awq \n", + "18 awq \n", + "19 awq \n", + "20 awq \n", + "21 awq \n", + "22 awq \n", + "23 awq \n", + "24 awq \n", + "25 awq \n", + "26 awq \n", + "27 awq \n", + "28 awq \n", + "29 awq \n", + "30 awq \n", + "31 awq \n", + "32 awq \n", + "33 awq \n", + "34 awq \n", + "35 awq \n", + "36 awq \n", + "37 awq \n", + "38 awq \n", + "39 awq \n", + "40 awq \n", + "41 awq \n", + "42 awq \n", + "43 awq \n", + "44 awq \n", + "45 awq \n", + "46 awq \n", + "47 awq \n", + "48 awq " + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_awq_flickr" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "fa5edc44", + "metadata": {}, + "outputs": [], + "source": [ + "df_awq_flickr.to_csv(os.path.join('/fs/cfar-projects/low-bit-vision/final_results/all_results','blip2_awq_flickr.csv'), index=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "cb1e8e82", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.microsoft.datawrangler.viewer.v0+json": { + "columns": [ + { + "name": "index", + "rawType": "int64", + "type": "integer" + }, + { + "name": "vision_bits", + "rawType": "int64", + "type": "integer" + }, + { + "name": "language_bits", + "rawType": "int64", + "type": "integer" + }, + { + "name": "acc", + "rawType": "float64", + "type": "float" + } + ], + "conversionMethod": "pd.DataFrame", + "ref": "48fb5457-59b3-405d-94ca-79758ace0ca5", + "rows": [ + [ + "0", + "6", + "5", + "61.34" + ], + [ + "1", + "8", + "4", + "60.79" + ], + [ + "2", + "3", + "5", + "59.59" + ], + [ + "3", + "5", + "6", + "61.19" + ], + [ + "4", + "6", + "6", + "61.33" + ] + ], + "shape": { + "columns": 3, + "rows": 5 + } + }, + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
vision_bitslanguage_bitsacc
06561.34
18460.79
23559.59
35661.19
46661.33
\n", + "
" + ], + "text/plain": [ + " vision_bits language_bits acc\n", + "0 6 5 61.34\n", + "1 8 4 60.79\n", + "2 3 5 59.59\n", + "3 5 6 61.19\n", + "4 6 6 61.33" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# GQA\n", + "df_gptq_gqa = pd.read_csv('/fs/cfar-projects/low-bit-vision/final_results/llava/llava_gptq_gqa_results.csv')\n", + "df_gptq_gqa.head(5)" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "id": "efd43af7", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.microsoft.datawrangler.viewer.v0+json": { + "columns": [ + { + "name": "index", + "rawType": "int64", + "type": "integer" + }, + { + "name": "vision_bits", + "rawType": "int64", + "type": "integer" + }, + { + "name": "language_bits", + "rawType": "int64", + "type": "integer" + }, + { + "name": "acc", + "rawType": "float64", + "type": "float" + }, + { + "name": "bpw", + "rawType": "float64", + "type": "float" + }, + { + "name": "quant_method", + "rawType": "object", + "type": "string" + } + ], + "conversionMethod": "pd.DataFrame", + "ref": "9e378b9d-f1d1-4848-a5df-f670f469c64e", + "rows": [ + [ + "0", + "6", + "5", + "61.34", + "5.487262024640041", + "gptq" + ], + [ + "1", + "8", + "4", + "60.79", + "4.6559338973522", + "gptq" + ], + [ + "2", + "3", + "5", + "59.59", + "5.358999970715631", + "gptq" + ], + [ + "3", + "5", + "6", + "61.19", + "6.361344169902686", + "gptq" + ], + [ + "4", + "6", + "6", + "61.33", + "6.404098187877489", + "gptq" + ] + ], + "shape": { + "columns": 5, + "rows": 5 + } + }, + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
vision_bitslanguage_bitsaccbpwquant_method
06561.345.487262gptq
18460.794.655934gptq
23559.595.359000gptq
35661.196.361344gptq
46661.336.404098gptq
\n", + "
" + ], + "text/plain": [ + " vision_bits language_bits acc bpw quant_method\n", + "0 6 5 61.34 5.487262 gptq\n", + "1 8 4 60.79 4.655934 gptq\n", + "2 3 5 59.59 5.359000 gptq\n", + "3 5 6 61.19 6.361344 gptq\n", + "4 6 6 61.33 6.404098 gptq" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_gptq_gqa['bpw'] =[compute_bpw_llava(vision_bits=x['vision_bits'],\n", + " llm_bits=x['language_bits'],) for x in df_gptq_gqa.to_dict(orient='records')]\n", + "\n", + "df_gptq_gqa['quant_method'] = 'gptq'\n", + "\n", + "df_gptq_gqa.head(5)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "id": "f90530e2", + "metadata": {}, + "outputs": [], + "source": [ + "df_gptq_gqa.to_csv(os.path.join('/fs/cfar-projects/low-bit-vision/final_results/all_results','llava_gptq_gqa.csv'), index=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "id": "f645535d", + "metadata": {}, + "outputs": [], + "source": [ + "df_gptq_vqav2 = pd.read_csv('/fs/cfar-projects/low-bit-vision/final_results/llava/llava_gptq_vqav2.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "id": "325cec5d", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.microsoft.datawrangler.viewer.v0+json": { + "columns": [ + { + "name": "index", + "rawType": "int64", + "type": "integer" + }, + { + "name": "vision_bits", + "rawType": "int64", + "type": "integer" + }, + { + "name": "language_bits", + "rawType": "int64", + "type": "integer" + }, + { + "name": "acc", + "rawType": "float64", + "type": "float" + }, + { + "name": "other", + "rawType": "float64", + "type": "float" + }, + { + "name": "yes/no", + "rawType": "float64", + "type": "float" + }, + { + "name": "number", + "rawType": "float64", + "type": "float" + }, + { + "name": "bpw", + "rawType": "float64", + "type": "float" + }, + { + "name": "quant_method", + "rawType": "object", + "type": "string" + } + ], + "conversionMethod": "pd.DataFrame", + "ref": "c24a7bec-6b96-4038-8e4f-baf5d8fdbdfd", + "rows": [ + [ + "0", + "6", + "5", + "75.61", + "68.83", + "90.91", + "57.29", + "5.487262024640041", + "gptq" + ], + [ + "1", + "8", + "4", + "74.95", + "68.11", + "90.2", + "56.98", + "4.6559338973522", + "gptq" + ] + ], + "shape": { + "columns": 8, + "rows": 2 + } + }, + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
vision_bitslanguage_bitsaccotheryes/nonumberbpwquant_method
06575.6168.8390.9157.295.487262gptq
18474.9568.1190.2056.984.655934gptq
\n", + "
" + ], + "text/plain": [ + " vision_bits language_bits acc other yes/no number bpw \\\n", + "0 6 5 75.61 68.83 90.91 57.29 5.487262 \n", + "1 8 4 74.95 68.11 90.20 56.98 4.655934 \n", + "\n", + " quant_method \n", + "0 gptq \n", + "1 gptq " + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_gptq_vqav2['bpw'] = [compute_bpw_llava(vision_bits=x['vision_bits'],\n", + " llm_bits=x['language_bits'],) for x in df_gptq_vqav2.to_dict(orient='records')]\n", + "\n", + "df_gptq_vqav2['quant_method'] = 'gptq'\n", + "df_gptq_vqav2 = df_gptq_vqav2.rename({'agg_metrics': 'acc'}, axis = 1)\n", + "\n", + "df_gptq_vqav2.head(2)" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "id": "2b48362c", + "metadata": {}, + "outputs": [], + "source": [ + "df_gptq_vqav2.to_csv('/fs/cfar-projects/low-bit-vision/final_results/all_results/llava_gptq_vqav2.csv', index = None)" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "id": "ea7eb884", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.microsoft.datawrangler.viewer.v0+json": { + "columns": [ + { + "name": "index", + "rawType": "int64", + "type": "integer" + }, + { + "name": "vision_bits", + "rawType": "int64", + "type": "integer" + }, + { + "name": "language_bits", + "rawType": "int64", + "type": "integer" + }, + { + "name": "acc", + "rawType": "float64", + "type": "float" + } + ], + "conversionMethod": "pd.DataFrame", + "ref": "38549c87-d3fc-40b8-a36c-5d8e57efdc5a", + "rows": [ + [ + "0", + "6", + "5", + "0.0" + ], + [ + "1", + "8", + "4", + "0.0" + ], + [ + "2", + "3", + "5", + "0.0" + ], + [ + "3", + "5", + "6", + "60.68" + ], + [ + "4", + "6", + "6", + "61.08" + ] + ], + "shape": { + "columns": 3, + "rows": 5 + } + }, + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
vision_bitslanguage_bitsacc
0650.00
1840.00
2350.00
35660.68
46661.08
\n", + "
" + ], + "text/plain": [ + " vision_bits language_bits acc\n", + "0 6 5 0.00\n", + "1 8 4 0.00\n", + "2 3 5 0.00\n", + "3 5 6 60.68\n", + "4 6 6 61.08" + ] + }, + "execution_count": 49, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_awq_gqa = pd.read_csv('/fs/cfar-projects/low-bit-vision/final_results/llava/llava_awq_gqa.csv')\n", + "df_awq_gqa.head(5)" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "id": "97754e59", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.microsoft.datawrangler.viewer.v0+json": { + "columns": [ + { + "name": "index", + "rawType": "int64", + "type": "integer" + }, + { + "name": "vision_bits", + "rawType": "int64", + "type": "integer" + }, + { + "name": "language_bits", + "rawType": "int64", + "type": "integer" + }, + { + "name": "acc", + "rawType": "float64", + "type": "float" + }, + { + "name": "bpw", + "rawType": "float64", + "type": "float" + }, + { + "name": "quant_method", + "rawType": "object", + "type": "string" + } + ], + "conversionMethod": "pd.DataFrame", + "ref": "876dc2aa-cd44-46cc-82f1-907e12a45644", + "rows": [ + [ + "0", + "6", + "5", + "0.0", + "5.487262024640041", + "awq" + ], + [ + "1", + "8", + "4", + "0.0", + "4.6559338973522", + "awq" + ] + ], + "shape": { + "columns": 5, + "rows": 2 + } + }, + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
vision_bitslanguage_bitsaccbpwquant_method
0650.05.487262awq
1840.04.655934awq
\n", + "
" + ], + "text/plain": [ + " vision_bits language_bits acc bpw quant_method\n", + "0 6 5 0.0 5.487262 awq\n", + "1 8 4 0.0 4.655934 awq" + ] + }, + "execution_count": 50, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "df_awq_gqa['bpw'] = [compute_bpw_llava(vision_bits=x['vision_bits'],\n", + " llm_bits=x['language_bits'],) for x in df_awq_gqa.to_dict(orient='records')]\n", + "\n", + "df_awq_gqa['quant_method'] = 'awq'\n", + "\n", + "df_awq_gqa.head(2)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "id": "08a980d5", + "metadata": {}, + "outputs": [], + "source": [ + "df_awq_gqa.to_csv('/fs/cfar-projects/low-bit-vision/final_results/all_results/llava_awq_gqa.csv', index = None)" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "id": "e1cee347", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.microsoft.datawrangler.viewer.v0+json": { + "columns": [ + { + "name": "index", + "rawType": "int64", + "type": "integer" + }, + { + "name": "vision_bits", + "rawType": "int64", + "type": "integer" + }, + { + "name": "language_bits", + "rawType": "int64", + "type": "integer" + }, + { + "name": "agg_metrics", + "rawType": "float64", + "type": "float" + }, + { + "name": "other", + "rawType": "float64", + "type": "float" + }, + { + "name": "yes/no", + "rawType": "float64", + "type": "float" + }, + { + "name": "number", + "rawType": "float64", + "type": "float" + } + ], + "conversionMethod": "pd.DataFrame", + "ref": "f7836e6e-7594-4c5f-99d4-47a391143659", + "rows": [ + [ + "0", + "6", + "5", + "0.0", + "0.0", + "0.0", + "0.0" + ], + [ + "1", + "8", + "4", + "0.0", + "0.0", + "0.0", + "0.0" + ], + [ + "2", + "3", + "5", + "0.0", + "0.0", + "0.0", + "0.0" + ], + [ + "3", + "5", + "6", + "73.04", + "66.47", + "90.41", + "48.7" + ], + [ + "4", + "6", + "6", + "73.08", + "66.53", + "90.34", + "49.04" + ] + ], + "shape": { + "columns": 6, + "rows": 5 + } + }, + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
vision_bitslanguage_bitsagg_metricsotheryes/nonumber
0650.000.000.000.00
1840.000.000.000.00
2350.000.000.000.00
35673.0466.4790.4148.70
46673.0866.5390.3449.04
\n", + "
" + ], + "text/plain": [ + " vision_bits language_bits agg_metrics other yes/no number\n", + "0 6 5 0.00 0.00 0.00 0.00\n", + "1 8 4 0.00 0.00 0.00 0.00\n", + "2 3 5 0.00 0.00 0.00 0.00\n", + "3 5 6 73.04 66.47 90.41 48.70\n", + "4 6 6 73.08 66.53 90.34 49.04" + ] + }, + "execution_count": 52, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_awq_vqav2 = pd.read_csv('/fs/cfar-projects/low-bit-vision/final_results/llava/llava_awq_vqav2.csv')\n", + "df_awq_vqav2.head(5)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "698cb332", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.microsoft.datawrangler.viewer.v0+json": { + "columns": [ + { + "name": "index", + "rawType": "int64", + "type": "integer" + }, + { + "name": "vision_bits", + "rawType": "int64", + "type": "integer" + }, + { + "name": "language_bits", + "rawType": "int64", + "type": "integer" + }, + { + "name": "acc", + "rawType": "float64", + "type": "float" + }, + { + "name": "other", + "rawType": "float64", + "type": "float" + }, + { + "name": "yes/no", + "rawType": "float64", + "type": "float" + }, + { + "name": "number", + "rawType": "float64", + "type": "float" + }, + { + "name": "bpw", + "rawType": "float64", + "type": "float" + }, + { + "name": "quant_method", + "rawType": "object", + "type": "string" + } + ], + "conversionMethod": "pd.DataFrame", + "ref": "bb147621-d048-401e-8303-cee506ea6b71", + "rows": [ + [ + "0", + "6", + "5", + "0.0", + "0.0", + "0.0", + "0.0", + "5.487262024640041", + "awq" + ], + [ + "1", + "8", + "4", + "0.0", + "0.0", + "0.0", + "0.0", + "4.6559338973522", + "awq" + ] + ], + "shape": { + "columns": 8, + "rows": 2 + } + }, + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
vision_bitslanguage_bitsaccotheryes/nonumberbpwquant_method
0650.00.00.00.05.487262awq
1840.00.00.00.04.655934awq
\n", + "
" + ], + "text/plain": [ + " vision_bits language_bits acc other yes/no number bpw \\\n", + "0 6 5 0.0 0.0 0.0 0.0 5.487262 \n", + "1 8 4 0.0 0.0 0.0 0.0 4.655934 \n", + "\n", + " quant_method \n", + "0 awq \n", + "1 awq " + ] + }, + "execution_count": 53, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "df_awq_vqav2['bpw'] = [compute_bpw_llava(vision_bits=x['vision_bits'],\n", + " llm_bits=x['language_bits'],) for x in df_awq_vqav2.to_dict(orient='records')]\n", + "\n", + "df_awq_vqav2['quant_method'] = 'awq'\n", + "df_awq_vqav2 = df_awq_vqav2.rename({'agg_metrics': 'acc'}, axis = 1)\n", + "\n", + "df_awq_vqav2.head(2)" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "id": "d20bc38b", + "metadata": {}, + "outputs": [], + "source": [ + "df_awq_vqav2.to_csv('/fs/cfar-projects/low-bit-vision/final_results/all_results/llava_awq_vqav2.csv', index = None)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "40da30dc", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.microsoft.datawrangler.viewer.v0+json": { + "columns": [ + { + "name": "index", + "rawType": "int64", + "type": "integer" + }, + { + "name": "txt_r1", + "rawType": "float64", + "type": "float" + }, + { + "name": "txt_r5", + "rawType": "float64", + "type": "float" + }, + { + "name": "txt_r10", + "rawType": "float64", + "type": "float" + }, + { + "name": "txt_r_mean", + "rawType": "float64", + "type": "float" + }, + { + "name": "img_r1", + "rawType": "float64", + "type": "float" + }, + { + "name": "img_r5", + "rawType": "float64", + "type": "float" + }, + { + "name": "img_r10", + "rawType": "float64", + "type": "float" + }, + { + "name": "img_r_mean", + "rawType": "float64", + "type": "float" + }, + { + "name": "r_mean", + "rawType": "float64", + "type": "float" + }, + { + "name": "agg_metrics", + "rawType": "float64", + "type": "float" + }, + { + "name": "model_size", + "rawType": "float64", + "type": "float" + }, + { + "name": "visual_encoder_block_modules", + "rawType": "object", + "type": "unknown" + }, + { + "name": "visual_encoder_block_indices", + "rawType": "object", + "type": "unknown" + }, + { + "name": "visual_encoder_block_weight_bits", + "rawType": "float64", + "type": "float" + }, + { + "name": "qformer_layer_indices", + "rawType": "object", + "type": "string" + }, + { + "name": "qformer_self_attention_modules", + "rawType": "object", + "type": "unknown" + }, + { + "name": "qformer_self_attention_weight_bits", + "rawType": "float64", + "type": "float" + }, + { + "name": "qformer_cross_attention_modules", + "rawType": "object", + "type": "unknown" + }, + { + "name": "qformer_cross_attention_weight_bits", + "rawType": "float64", + "type": "float" + }, + { + "name": "qformer_text_ff_modules", + "rawType": "object", + "type": "string" + }, + { + "name": "qformer_text_ff_weight_bits", + "rawType": "float64", + "type": "float" + }, + { + "name": "qformer_img_ff_modules", + "rawType": "object", + "type": "unknown" + }, + { + "name": "qformer_img_ff_weight_bits", + "rawType": "float64", + "type": "float" + }, + { + "name": "job_batch", + "rawType": "object", + "type": "string" + }, + { + "name": "vit_attn", + "rawType": "bool", + "type": "boolean" + }, + { + "name": "vit_ff", + "rawType": "bool", + "type": "boolean" + }, + { + "name": "vit_front_blocks", + "rawType": "bool", + "type": "boolean" + }, + { + "name": "vit_middle_blocks", + "rawType": "bool", + "type": "boolean" + }, + { + "name": "vit_end_blocks", + "rawType": "bool", + "type": "boolean" + }, + { + "name": "vit_weight_bits", + "rawType": "float64", + "type": "float" + }, + { + "name": "qformer_front_blocks", + "rawType": "bool", + "type": "boolean" + }, + { + "name": "qformer_middle_blocks", + "rawType": "bool", + "type": "boolean" + }, + { + "name": "qformer_end_blocks", + "rawType": "bool", + "type": "boolean" + }, + { + "name": "qformer_self_attn", + "rawType": "bool", + "type": "boolean" + }, + { + "name": "qformer_cross_attn", + "rawType": "bool", + "type": "boolean" + }, + { + "name": "qformer_text_ff", + "rawType": "bool", + "type": "boolean" + }, + { + "name": "qformer_img_ff", + "rawType": "bool", + "type": "boolean" + }, + { + "name": "qformer_weight_bits", + "rawType": "float64", + "type": "float" + }, + { + "name": "Quantized Portion", + "rawType": "object", + "type": "string" + }, + { + "name": "weight_bits", + "rawType": "float64", + "type": "float" + } + ], + "conversionMethod": "pd.DataFrame", + "ref": "19cc01fb-1dd8-4404-927b-b2f67ee32db7", + "rows": [ + [ + "0", + "0.0", + "0.0", + "0.4", + "0.1333333333333333", + "0.1", + "0.34", + "0.72", + "0.3866666666666667", + "0.26", + "0.1333333333333333", + "4324.694484", + "['qkv', 'proj']", + "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]", + "2.0", + "[0, 1, 2, 3]", + null, + "2.0", + null, + "2.0", + "['intermediate', 'output']", + "2.0", + null, + "2.0", + "nbit_flickr_4_2", + "True", + "False", + "True", + "False", + "False", + "2.0", + "True", + "False", + "False", + "False", + "False", + "True", + "False", + "2.0", + "ViT + Q-Former", + "2.0" + ], + [ + "1", + "0.0", + "0.1", + "0.3", + "0.1333333333333333", + "0.06", + "0.22", + "0.56", + "0.28", + "0.2066666666666666", + "0.1333333333333333", + "4711.343604", + null, + null, + null, + "[0, 1, 2, 3]", + null, + "2.0", + null, + "2.0", + "['intermediate', 'output']", + "2.0", + null, + "2.0", + "nbit_qformer", + "False", + "False", + "False", + "False", + "False", + null, + "True", + "False", + "False", + "False", + "False", + "True", + "False", + "2.0", + "Q-Former", + "2.0" + ], + [ + "2", + "0.0", + "0.2", + "0.2", + "0.1333333333333333", + "0.14", + "0.3", + "0.72", + "0.3866666666666666", + "0.26", + "0.1333333333333333", + "4640.507124", + null, + null, + null, + "[0, 1, 2, 3, 8, 9, 10, 11]", + null, + "2.0", + null, + "2.0", + "['intermediate', 'output']", + "2.0", + null, + "2.0", + "nbit_qformer", + "False", + "False", + "False", + "False", + "False", + null, + "True", + "False", + "True", + "False", + "False", + "True", + "False", + "2.0", + "Q-Former", + "2.0" + ], + [ + "3", + "0.0", + "0.3", + "0.4", + "0.2333333333333333", + "0.14", + "0.42", + "0.62", + "0.3933333333333333", + "0.3133333333333333", + "0.2333333333333333", + "4264.168404", + "['qkv', 'proj']", + "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]", + "2.0", + "[0, 1, 2, 3]", + "['query', 'key', 'value', 'dense']", + "2.0", + "['query', 'key', 'value', 'dense']", + "2.0", + "['intermediate', 'output']", + "2.0", + null, + "2.0", + "nbit_flickr_4_2", + "True", + "False", + "True", + "False", + "False", + "2.0", + "True", + "False", + "False", + "True", + "True", + "True", + "False", + "2.0", + "ViT + Q-Former", + "2.0" + ], + [ + "4", + "0.0", + "0.3", + "0.7", + "0.3333333333333333", + "0.16", + "0.6", + "1.08", + "0.6133333333333334", + "0.4733333333333334", + "0.3333333333333333", + "3807.001044", + "['fc1', 'fc2']", + "[13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25]", + "2.0", + "[4, 5, 6, 7]", + "['query', 'key', 'value', 'dense']", + "2.0", + "['query', 'key', 'value', 'dense']", + "2.0", + "['intermediate', 'output']", + "2.0", + null, + "2.0", + "nbit_flickr_4_2", + "False", + "True", + "False", + "True", + "False", + "2.0", + "False", + "True", + "False", + "True", + "True", + "True", + "False", + "2.0", + "ViT + Q-Former", + "2.0" + ] + ], + "shape": { + "columns": 40, + "rows": 5 + } + }, + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
txt_r1txt_r5txt_r10txt_r_meanimg_r1img_r5img_r10img_r_meanr_meanagg_metrics...qformer_front_blocksqformer_middle_blocksqformer_end_blocksqformer_self_attnqformer_cross_attnqformer_text_ffqformer_img_ffqformer_weight_bitsQuantized Portionweight_bits
00.00.00.40.1333330.100.340.720.3866670.2600000.133333...TrueFalseFalseFalseFalseTrueFalse2.0ViT + Q-Former2.0
10.00.10.30.1333330.060.220.560.2800000.2066670.133333...TrueFalseFalseFalseFalseTrueFalse2.0Q-Former2.0
20.00.20.20.1333330.140.300.720.3866670.2600000.133333...TrueFalseTrueFalseFalseTrueFalse2.0Q-Former2.0
30.00.30.40.2333330.140.420.620.3933330.3133330.233333...TrueFalseFalseTrueTrueTrueFalse2.0ViT + Q-Former2.0
40.00.30.70.3333330.160.601.080.6133330.4733330.333333...FalseTrueFalseTrueTrueTrueFalse2.0ViT + Q-Former2.0
\n", + "

5 rows × 40 columns

\n", + "
" + ], + "text/plain": [ + " txt_r1 txt_r5 txt_r10 txt_r_mean img_r1 img_r5 img_r10 img_r_mean \\\n", + "0 0.0 0.0 0.4 0.133333 0.10 0.34 0.72 0.386667 \n", + "1 0.0 0.1 0.3 0.133333 0.06 0.22 0.56 0.280000 \n", + "2 0.0 0.2 0.2 0.133333 0.14 0.30 0.72 0.386667 \n", + "3 0.0 0.3 0.4 0.233333 0.14 0.42 0.62 0.393333 \n", + "4 0.0 0.3 0.7 0.333333 0.16 0.60 1.08 0.613333 \n", + "\n", + " r_mean agg_metrics ... qformer_front_blocks qformer_middle_blocks \\\n", + "0 0.260000 0.133333 ... True False \n", + "1 0.206667 0.133333 ... True False \n", + "2 0.260000 0.133333 ... True False \n", + "3 0.313333 0.233333 ... True False \n", + "4 0.473333 0.333333 ... False True \n", + "\n", + " qformer_end_blocks qformer_self_attn qformer_cross_attn qformer_text_ff \\\n", + "0 False False False True \n", + "1 False False False True \n", + "2 True False False True \n", + "3 False True True True \n", + "4 False True True True \n", + "\n", + " qformer_img_ff qformer_weight_bits Quantized Portion weight_bits \n", + "0 False 2.0 ViT + Q-Former 2.0 \n", + "1 False 2.0 Q-Former 2.0 \n", + "2 False 2.0 Q-Former 2.0 \n", + "3 False 2.0 ViT + Q-Former 2.0 \n", + "4 False 2.0 ViT + Q-Former 2.0 \n", + "\n", + "[5 rows x 40 columns]" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# uniform flickr\n", + "df_uniform_flickr = pd.read_csv('/fs/cfar-projects/low-bit-vision/final_results/blip2/uniform/blip2_flickr_results.csv')\n", + "df_uniform_flickr.head(5)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "250936a9", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "952" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(df_uniform_flickr)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "b395b730", + "metadata": {}, + "outputs": [], + "source": [ + "model_name = \"Salesforce/blip2-itm-vit-g-coco\"\n", + "model = Blip2ForImageTextRetrieval.from_pretrained(model_name)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "ee784b4f", + "metadata": {}, + "outputs": [], + "source": [ + "def compute_bpw_uniform(leaves, quantized_mods, total_params, row_dict, fp_size = 16):\n", + "\n", + " total_bits = 0\n", + "\n", + " for key, module in leaves.items():\n", + "\n", + " fp_mod_flag = True\n", + "\n", + " # check if parameters in module should be quantized\n", + " for q_mod in quantized_mods:\n", + " \n", + " # add quantized linear bit sizes\n", + " if q_mod in key and isinstance(module, nn.Linear):\n", + " num_el = module.weight.numel()\n", + "\n", + " # parse out layer index and module name\n", + " layer_idx = int(re.findall(r'layer[s]*.(\\d*)', key)[-1])\n", + " mod_name = key.split('.')[-1]\n", + "\n", + " if mod_name == 'projection':\n", + " mod_name = 'proj'\n", + "\n", + " # quantized vision module and layer idx included and mod_name included\n", + " if \"vision\" in q_mod: \n", + " \n", + " # sanity check for nan values \n", + " if row_dict['visual_encoder_block_indices'] == row_dict['visual_encoder_block_indices'] and \\\n", + " layer_idx in eval(row_dict['visual_encoder_block_indices']) and \\\n", + " mod_name in eval(row_dict['visual_encoder_block_modules']):\n", + " \n", + " # print(layer_idx)\n", + " # print(mod_name)\n", + "\n", + " total_bits += int(row_dict['visual_encoder_block_weight_bits']) * num_el\n", + " fp_mod_flag = False\n", + "\n", + "\n", + " # total_bits += vision_bits*num_el\n", + "\n", + " elif \"qformer\" in q_mod: #and \\\n", + " \n", + " # sanity check for nan values \n", + " if row_dict['qformer_layer_indices'] == row_dict['qformer_layer_indices'] and \\\n", + " layer_idx in eval(row_dict['qformer_layer_indices']):\n", + " \n", + " qformer_weight_bits = int(row_dict['qformer_weight_bits'])\n", + " \n", + " # NOTE: same quantized mods for self/cross-attn\n", + " if 'attention' in key:\n", + " if row_dict['qformer_self_attention_modules'] == row_dict['qformer_self_attention_modules'] and \\\n", + " mod_name in eval(row_dict['qformer_self_attention_modules']):\n", + " total_bits += qformer_weight_bits * num_el\n", + " fp_mod_flag = False\n", + " # img_ff\n", + " elif 'query' in key:\n", + " \n", + " if row_dict['qformer_img_ff_modules'] == row_dict['qformer_img_ff_modules'] and \\\n", + " any(x in key for x in eval(row_dict['qformer_img_ff_modules'])):\n", + " total_bits += qformer_weight_bits * num_el\n", + " fp_mod_flag = False\n", + "\n", + " \n", + " # text_ff\n", + " else:\n", + " if row_dict['qformer_text_ff_modules'] == row_dict['qformer_text_ff_modules'] and \\\n", + " any(x in key for x in eval(row_dict['qformer_text_ff_modules'])): \n", + " total_bits += qformer_weight_bits * num_el\n", + " fp_mod_flag = False\n", + "\n", + " \n", + " # full_precision module\n", + " if fp_mod_flag:\n", + " # print(key)\n", + " for param in module.parameters():\n", + " total_bits += fp_size*param.numel()\n", + "\n", + " return total_bits / total_params" + ] + }, + { + "cell_type": "code", + "execution_count": 476, + "id": "d6e9f246", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "visual_encoder_block_modules\n", + "['qkv', 'proj'] 252\n", + "['fc1', 'fc2'] 252\n", + "['qkv', 'proj', 'fc1', 'fc2'] 252\n", + "Name: count, dtype: int64" + ] + }, + "execution_count": 476, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_uniform_flickr['visual_encoder_block_modules'].value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 477, + "id": "538f3e3a", + "metadata": {}, + "outputs": [], + "source": [ + "row_dict = df_uniform_flickr.to_dict(orient='records')[202]" + ] + }, + { + "cell_type": "code", + "execution_count": 478, + "id": "80afaaa2", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "dict_keys(['txt_r1', 'txt_r5', 'txt_r10', 'txt_r_mean', 'img_r1', 'img_r5', 'img_r10', 'img_r_mean', 'r_mean', 'agg_metrics', 'model_size', 'visual_encoder_block_modules', 'visual_encoder_block_indices', 'visual_encoder_block_weight_bits', 'qformer_layer_indices', 'qformer_self_attention_modules', 'qformer_self_attention_weight_bits', 'qformer_cross_attention_modules', 'qformer_cross_attention_weight_bits', 'qformer_text_ff_modules', 'qformer_text_ff_weight_bits', 'qformer_img_ff_modules', 'qformer_img_ff_weight_bits', 'job_batch', 'vit_attn', 'vit_ff', 'vit_front_blocks', 'vit_middle_blocks', 'vit_end_blocks', 'vit_weight_bits', 'qformer_front_blocks', 'qformer_middle_blocks', 'qformer_end_blocks', 'qformer_self_attn', 'qformer_cross_attn', 'qformer_text_ff', 'qformer_img_ff', 'qformer_weight_bits', 'Quantized Portion', 'weight_bits'])" + ] + }, + "execution_count": 478, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "row_dict.keys()" + ] + }, + { + "cell_type": "code", + "execution_count": 479, + "id": "f7ff9b82", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 479, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "row_dict['qformer_layer_indices'] == row_dict['qformer_layer_indices']" + ] + }, + { + "cell_type": "code", + "execution_count": 493, + "id": "bfc38a56", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\"['query', 'key', 'value', 'dense']\"" + ] + }, + "execution_count": 493, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "row_dict['qformer_self_attention_modules']" + ] + }, + { + "cell_type": "code", + "execution_count": 492, + "id": "18291857", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\"['query', 'key', 'value', 'dense']\"" + ] + }, + "execution_count": 492, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "row_dict['qformer_cross_attention_modules']" + ] + }, + { + "cell_type": "code", + "execution_count": 489, + "id": "235e8411", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "nan" + ] + }, + "execution_count": 489, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "row_dict['qformer_img_ff_modules']" + ] + }, + { + "cell_type": "code", + "execution_count": 488, + "id": "17c29f0f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\"['intermediate', 'output']\"" + ] + }, + "execution_count": 488, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "row_dict['qformer_text_ff_modules']" + ] + }, + { + "cell_type": "code", + "execution_count": 484, + "id": "2c699c06", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'[13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38]'" + ] + }, + "execution_count": 484, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "row_dict['visual_encoder_block_indices']" + ] + }, + { + "cell_type": "code", + "execution_count": 490, + "id": "a45d731b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "4.0" + ] + }, + "execution_count": 490, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "row_dict['qformer_weight_bits']" + ] + }, + { + "cell_type": "code", + "execution_count": 491, + "id": "20a4f5fd", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "4.0" + ] + }, + "execution_count": 491, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "row_dict['visual_encoder_block_weight_bits']" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "e11b9a2a", + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "leaves = get_leaf_modules(model)\n", + "total_params = sum(p.numel() for p in model.parameters())\n", + "\n", + "quantized_mods = [\n", + " \"vision_model.encoder.layers\",\n", + " \"qformer.encoder.layer\",\n", + "]\n", + "\n", + "\n", + "df_uniform_flickr['bpw'] = [compute_bpw_uniform(leaves, quantized_mods, total_params, row_dict)\n", + " for row_dict in df_uniform_flickr.to_dict(orient='records')]\n", + "\n", + "\n", + "df_uniform_flickr['quant_method'] = 'uniform'" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "a8b0dafc", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "min 2.299832\n", + "max 15.876399\n", + "Name: bpw, dtype: float64" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_uniform_flickr.bpw.agg(['min', \n", + " 'max'])" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "1ec4ce8e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['txt_r1', 'txt_r5', 'txt_r10', 'txt_r_mean', 'img_r1', 'img_r5',\n", + " 'img_r10', 'img_r_mean', 'r_mean', 'agg_metrics', 'model_size',\n", + " 'visual_encoder_block_modules', 'visual_encoder_block_indices',\n", + " 'visual_encoder_block_weight_bits', 'qformer_layer_indices',\n", + " 'qformer_self_attention_modules', 'qformer_self_attention_weight_bits',\n", + " 'qformer_cross_attention_modules',\n", + " 'qformer_cross_attention_weight_bits', 'qformer_text_ff_modules',\n", + " 'qformer_text_ff_weight_bits', 'qformer_img_ff_modules',\n", + " 'qformer_img_ff_weight_bits', 'job_batch', 'vit_attn', 'vit_ff',\n", + " 'vit_front_blocks', 'vit_middle_blocks', 'vit_end_blocks',\n", + " 'vit_weight_bits', 'qformer_front_blocks', 'qformer_middle_blocks',\n", + " 'qformer_end_blocks', 'qformer_self_attn', 'qformer_cross_attn',\n", + " 'qformer_text_ff', 'qformer_img_ff', 'qformer_weight_bits',\n", + " 'Quantized Portion', 'weight_bits', 'bpw', 'quant_method'],\n", + " dtype='object')" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_uniform_flickr.columns" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "fd60bf9e", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.microsoft.datawrangler.viewer.v0+json": { + "columns": [ + { + "name": "index", + "rawType": "int64", + "type": "integer" + }, + { + "name": "txt_r1", + "rawType": "float64", + "type": "float" + }, + { + "name": "txt_r5", + "rawType": "float64", + "type": "float" + }, + { + "name": "txt_r10", + "rawType": "float64", + "type": "float" + }, + { + "name": "txt_r_mean", + "rawType": "float64", + "type": "float" + }, + { + "name": "img_r1", + "rawType": "float64", + "type": "float" + }, + { + "name": "img_r5", + "rawType": "float64", + "type": "float" + }, + { + "name": "img_r10", + "rawType": "float64", + "type": "float" + }, + { + "name": "img_r_mean", + "rawType": "float64", + "type": "float" + }, + { + "name": "r_mean", + "rawType": "float64", + "type": "float" + }, + { + "name": "vit_attn", + "rawType": "bool", + "type": "boolean" + }, + { + "name": "vit_ff", + "rawType": "bool", + "type": "boolean" + }, + { + "name": "vit_front_blocks", + "rawType": "bool", + "type": "boolean" + }, + { + "name": "vit_middle_blocks", + "rawType": "bool", + "type": "boolean" + }, + { + "name": "vit_end_blocks", + "rawType": "bool", + "type": "boolean" + }, + { + "name": "vit_weight_bits", + "rawType": "float64", + "type": "float" + }, + { + "name": "qformer_front_blocks", + "rawType": "bool", + "type": "boolean" + }, + { + "name": "qformer_middle_blocks", + "rawType": "bool", + "type": "boolean" + }, + { + "name": "qformer_end_blocks", + "rawType": "bool", + "type": "boolean" + }, + { + "name": "qformer_self_attn", + "rawType": "bool", + "type": "boolean" + }, + { + "name": "qformer_cross_attn", + "rawType": "bool", + "type": "boolean" + }, + { + "name": "qformer_text_ff", + "rawType": "bool", + "type": "boolean" + }, + { + "name": "qformer_img_ff", + "rawType": "bool", + "type": "boolean" + }, + { + "name": "qformer_weight_bits", + "rawType": "float64", + "type": "float" + }, + { + "name": "Quantized Portion", + "rawType": "object", + "type": "string" + }, + { + "name": "weight_bits", + "rawType": "float64", + "type": "float" + }, + { + "name": "bpw", + "rawType": "float64", + "type": "float" + }, + { + "name": "quant_method", + "rawType": "object", + "type": "string" + } + ], + "conversionMethod": "pd.DataFrame", + "ref": "97dfc4d3-611d-4e29-ab5f-f40c20a78a02", + "rows": [ + [ + "0", + "0.0", + "0.0", + "0.4", + "0.1333333333333333", + "0.1", + "0.34", + "0.72", + "0.3866666666666667", + "0.26", + "True", + "False", + "True", + "False", + "False", + "2.0", + "True", + "False", + "False", + "False", + "False", + "True", + "False", + "2.0", + "ViT + Q-Former", + "2.0", + "14.529315516481436", + "uniform" + ], + [ + "1", + "0.0", + "0.1", + "0.3", + "0.1333333333333333", + "0.06", + "0.22", + "0.56", + "0.28", + "0.2066666666666666", + "False", + "False", + "False", + "False", + "False", + null, + "True", + "False", + "False", + "False", + "False", + "True", + "False", + "2.0", + "Q-Former", + "2.0", + "15.761087779830135", + "uniform" + ], + [ + "2", + "0.0", + "0.2", + "0.2", + "0.1333333333333333", + "0.14", + "0.3", + "0.72", + "0.3866666666666666", + "0.26", + "False", + "False", + "False", + "False", + "False", + null, + "True", + "False", + "True", + "False", + "False", + "True", + "False", + "2.0", + "Q-Former", + "2.0", + "15.535536368499104", + "uniform" + ], + [ + "3", + "0.0", + "0.3", + "0.4", + "0.2333333333333333", + "0.14", + "0.42", + "0.62", + "0.3933333333333333", + "0.3133333333333333", + "True", + "False", + "True", + "False", + "False", + "2.0", + "True", + "False", + "False", + "True", + "True", + "True", + "False", + "2.0", + "ViT + Q-Former", + "2.0", + "14.336584539098034", + "uniform" + ], + [ + "4", + "0.0", + "0.3", + "0.7", + "0.3333333333333333", + "0.16", + "0.6", + "1.08", + "0.6133333333333334", + "0.4733333333333334", + "False", + "True", + "False", + "True", + "False", + "2.0", + "False", + "True", + "False", + "True", + "True", + "True", + "False", + "2.0", + "ViT + Q-Former", + "2.0", + "12.881693754184644", + "uniform" + ], + [ + "5", + "0.0", + "0.3", + "0.8", + "0.3666666666666667", + "0.18", + "0.54", + "0.92", + "0.5466666666666667", + "0.4566666666666667", + "True", + "True", + "True", + "False", + "True", + "2.0", + "True", + "False", + "True", + "False", + "False", + "True", + "False", + "2.0", + "ViT + Q-Former", + "2.0", + "7.698665745277527", + "uniform" + ], + [ + "6", + "0.0", + "0.3", + "0.9", + "0.3999999999999999", + "0.1", + "0.42", + "1.0", + "0.5066666666666667", + "0.4533333333333333", + "True", + "True", + "True", + "False", + "True", + "2.0", + "True", + "False", + "True", + "True", + "True", + "True", + "True", + "2.0", + "ViT + Q-Former", + "2.0", + "6.862100967848662", + "uniform" + ], + [ + "7", + "0.0", + "0.3", + "1.0", + "0.4333333333333333", + "0.12", + "0.5", + "1.02", + "0.5466666666666667", + "0.49", + "False", + "True", + "False", + "True", + "True", + "2.0", + "False", + "True", + "True", + "False", + "False", + "True", + "False", + "2.0", + "ViT + Q-Former", + "2.0", + "10.162210271974924", + "uniform" + ], + [ + "8", + "0.0", + "0.3", + "1.3", + "0.5333333333333333", + "0.2", + "0.9", + "1.68", + "0.9266666666666667", + "0.73", + "False", + "True", + "False", + "True", + "False", + "2.0", + "False", + "True", + "False", + "True", + "True", + "False", + "True", + "2.0", + "ViT + Q-Former", + "2.0", + "12.881693754184644", + "uniform" + ], + [ + "9", + "0.0", + "0.4", + "0.6", + "0.3333333333333333", + "0.02", + "0.26", + "0.56", + "0.28", + "0.3066666666666666", + "False", + "False", + "False", + "False", + "False", + null, + "True", + "False", + "False", + "True", + "True", + "True", + "False", + "2.0", + "Q-Former", + "2.0", + "15.568356802446734", + "uniform" + ], + [ + "10", + "0.0", + "0.4", + "0.8", + "0.4000000000000001", + "0.1", + "0.52", + "0.86", + "0.4933333333333333", + "0.4466666666666667", + "False", + "True", + "True", + "False", + "True", + "2.0", + "True", + "False", + "True", + "True", + "True", + "False", + "True", + "2.0", + "ViT + Q-Former", + "2.0", + "9.776748317208122", + "uniform" + ], + [ + "11", + "0.0", + "0.4", + "0.8", + "0.4000000000000001", + "0.14", + "0.46", + "0.88", + "0.4933333333333333", + "0.4466666666666667", + "True", + "False", + "True", + "False", + "True", + "2.0", + "True", + "False", + "True", + "True", + "True", + "True", + "False", + "2.0", + "ViT + Q-Former", + "2.0", + "12.686529887034904", + "uniform" + ], + [ + "12", + "0.0", + "0.4", + "0.8", + "0.4000000000000001", + "0.14", + "0.52", + "0.86", + "0.5066666666666667", + "0.4533333333333333", + "True", + "True", + "True", + "True", + "True", + "2.0", + "True", + "True", + "True", + "False", + "False", + "True", + "True", + "2.0", + "ViT + Q-Former", + "2.0", + "2.8780247883426138", + "uniform" + ], + [ + "13", + "0.0", + "0.4", + "0.9", + "0.4333333333333333", + "0.08", + "0.46", + "0.98", + "0.5066666666666667", + "0.47", + "True", + "True", + "False", + "True", + "True", + "2.0", + "False", + "True", + "True", + "True", + "True", + "False", + "False", + "2.0", + "ViT + Q-Former", + "2.0", + "7.764306613172787", + "uniform" + ], + [ + "14", + "0.0", + "0.5", + "0.8", + "0.4333333333333333", + "0.12", + "0.48", + "1.16", + "0.5866666666666666", + "0.51", + "True", + "True", + "True", + "False", + "False", + "2.0", + "True", + "False", + "False", + "False", + "False", + "False", + "True", + "2.0", + "ViT + Q-Former", + "2.0", + "11.842652468219347", + "uniform" + ], + [ + "15", + "0.0", + "0.5", + "0.8", + "0.4333333333333333", + "0.12", + "0.56", + "1.24", + "0.64", + "0.5366666666666666", + "True", + "True", + "False", + "True", + "False", + "2.0", + "False", + "True", + "False", + "True", + "True", + "True", + "False", + "2.0", + "ViT + Q-Former", + "2.0", + "11.649921490835945", + "uniform" + ], + [ + "16", + "0.0", + "0.5", + "0.9", + "0.4666666666666666", + "0.16", + "0.5", + "0.98", + "0.5466666666666667", + "0.5066666666666667", + "True", + "True", + "True", + "False", + "False", + "2.0", + "True", + "False", + "False", + "False", + "False", + "False", + "False", + "2.0", + "ViT + Q-Former", + "2.0", + "12.068203879550378", + "uniform" + ], + [ + "17", + "0.0", + "0.5", + "0.9", + "0.4666666666666666", + "0.16", + "0.5", + "0.98", + "0.5466666666666667", + "0.5066666666666667", + "True", + "True", + "True", + "False", + "False", + "2.0", + "False", + "False", + "False", + "False", + "False", + "False", + "False", + null, + "ViT", + "2.0", + "12.068203879550378", + "uniform" + ], + [ + "18", + "0.0", + "0.5", + "1.0", + "0.5", + "0.02", + "0.54", + "1.02", + "0.5266666666666667", + "0.5133333333333334", + "False", + "True", + "False", + "True", + "True", + "2.0", + "False", + "True", + "True", + "True", + "True", + "True", + "False", + "2.0", + "ViT + Q-Former", + "2.0", + "9.776748317208122", + "uniform" + ], + [ + "19", + "0.0", + "0.5", + "1.0", + "0.5", + "0.26", + "0.84", + "1.44", + "0.8466666666666667", + "0.6733333333333333", + "True", + "True", + "False", + "False", + "True", + "2.0", + "False", + "False", + "True", + "False", + "False", + "True", + "False", + "2.0", + "ViT + Q-Former", + "2.0", + "11.842652468219347", + "uniform" + ], + [ + "20", + "0.0", + "0.6", + "0.9", + "0.5", + "0.2", + "0.68", + "1.1", + "0.66", + "0.5800000000000001", + "True", + "True", + "True", + "False", + "False", + "2.0", + "True", + "False", + "False", + "False", + "False", + "True", + "False", + "2.0", + "ViT + Q-Former", + "2.0", + "11.842652468219347", + "uniform" + ], + [ + "21", + "0.0", + "0.6", + "1.0", + "0.5333333333333333", + "0.06", + "0.56", + "1.0", + "0.54", + "0.5366666666666666", + "True", + "True", + "False", + "True", + "True", + "2.0", + "False", + "True", + "True", + "True", + "True", + "True", + "True", + "2.0", + "ViT + Q-Former", + "2.0", + "6.862100967848662", + "uniform" + ], + [ + "22", + "0.0", + "0.6", + "1.0", + "0.5333333333333333", + "0.1", + "0.44", + "0.84", + "0.4599999999999999", + "0.4966666666666666", + "True", + "False", + "True", + "True", + "True", + "2.0", + "True", + "True", + "True", + "False", + "False", + "True", + "False", + "2.0", + "ViT + Q-Former", + "2.0", + "11.614668167121977", + "uniform" + ], + [ + "23", + "0.0", + "0.6", + "1.1", + "0.5666666666666668", + "0.06", + "0.44", + "0.94", + "0.48", + "0.5233333333333334", + "True", + "True", + "False", + "True", + "True", + "2.0", + "False", + "True", + "True", + "True", + "True", + "True", + "False", + "2.0", + "ViT + Q-Former", + "2.0", + "7.313203790510725", + "uniform" + ], + [ + "24", + "0.0", + "0.6", + "1.1", + "0.5666666666666668", + "0.12", + "0.48", + "1.0", + "0.5333333333333333", + "0.55", + "True", + "True", + "True", + "True", + "True", + "2.0", + "True", + "True", + "True", + "False", + "False", + "True", + "False", + "2.0", + "ViT + Q-Former", + "2.0", + "3.554679022335707", + "uniform" + ], + [ + "25", + "0.1", + "0.3", + "0.6", + "0.3333333333333333", + "0.14", + "0.76", + "1.54", + "0.8133333333333334", + "0.5733333333333334", + "False", + "True", + "True", + "True", + "True", + "2.0", + "True", + "True", + "True", + "False", + "False", + "False", + "False", + "2.0", + "ViT + Q-Former", + "2.0", + "7.926650046374897", + "uniform" + ], + [ + "26", + "0.1", + "0.3", + "0.6", + "0.3333333333333333", + "0.14", + "0.76", + "1.54", + "0.8133333333333334", + "0.5733333333333334", + "False", + "True", + "True", + "True", + "True", + "2.0", + "False", + "False", + "False", + "False", + "False", + "False", + "False", + null, + "ViT", + "2.0", + "7.926650046374897", + "uniform" + ], + [ + "27", + "0.1", + "0.3", + "0.7", + "0.3666666666666667", + "0.18", + "0.9", + "1.44", + "0.84", + "0.6033333333333333", + "True", + "True", + "False", + "True", + "True", + "2.0", + "False", + "True", + "True", + "False", + "False", + "False", + "True", + "2.0", + "ViT + Q-Former", + "2.0", + "7.698665745277527", + "uniform" + ], + [ + "28", + "0.1", + "0.3", + "0.8", + "0.4000000000000001", + "0.06", + "0.48", + "0.98", + "0.5066666666666667", + "0.4533333333333333", + "False", + "True", + "False", + "True", + "True", + "2.0", + "False", + "True", + "True", + "True", + "True", + "True", + "True", + "2.0", + "ViT + Q-Former", + "2.0", + "9.32564549454606", + "uniform" + ], + [ + "29", + "0.1", + "0.3", + "0.8", + "0.4000000000000001", + "0.32", + "0.84", + "1.18", + "0.7799999999999999", + "0.59", + "True", + "True", + "True", + "False", + "True", + "2.0", + "True", + "False", + "True", + "False", + "False", + "False", + "False", + "2.0", + "ViT + Q-Former", + "2.0", + "8.149768567939589", + "uniform" + ], + [ + "30", + "0.1", + "0.3", + "0.8", + "0.4000000000000001", + "0.32", + "0.84", + "1.18", + "0.7799999999999999", + "0.59", + "True", + "True", + "True", + "False", + "True", + "2.0", + "False", + "False", + "False", + "False", + "False", + "False", + "False", + null, + "ViT", + "2.0", + "8.149768567939589", + "uniform" + ], + [ + "31", + "0.1", + "0.3", + "0.9", + "0.4333333333333333", + "0.06", + "0.4", + "1.08", + "0.5133333333333333", + "0.4733333333333333", + "False", + "True", + "False", + "True", + "False", + "2.0", + "False", + "True", + "False", + "False", + "False", + "False", + "False", + "2.0", + "ViT + Q-Former", + "2.0", + "13.299976142899077", + "uniform" + ], + [ + "32", + "0.1", + "0.3", + "0.9", + "0.4333333333333333", + "0.06", + "0.4", + "1.08", + "0.5133333333333333", + "0.4733333333333333", + "False", + "True", + "False", + "True", + "False", + "2.0", + "False", + "False", + "False", + "False", + "False", + "False", + "False", + null, + "ViT", + "2.0", + "13.299976142899077", + "uniform" + ], + [ + "33", + "0.1", + "0.3", + "1.0", + "0.4666666666666666", + "0.06", + "0.4", + "0.92", + "0.46", + "0.4633333333333333", + "True", + "True", + "True", + "True", + "False", + "2.0", + "True", + "True", + "False", + "False", + "False", + "True", + "False", + "2.0", + "ViT + Q-Former", + "2.0", + "7.698665745277527", + "uniform" + ], + [ + "34", + "0.1", + "0.3", + "1.0", + "0.4666666666666666", + "0.12", + "0.5", + "1.5", + "0.7066666666666667", + "0.5866666666666667", + "False", + "True", + "False", + "True", + "True", + "2.0", + "False", + "True", + "True", + "False", + "False", + "True", + "True", + "2.0", + "ViT + Q-Former", + "2.0", + "9.711107449312863", + "uniform" + ], + [ + "35", + "0.1", + "0.4", + "0.6", + "0.3666666666666667", + "0.16", + "0.46", + "1.02", + "0.5466666666666667", + "0.4566666666666667", + "True", + "True", + "True", + "False", + "False", + "2.0", + "True", + "False", + "False", + "True", + "True", + "False", + "True", + "2.0", + "ViT + Q-Former", + "2.0", + "11.649921490835945", + "uniform" + ], + [ + "36", + "0.1", + "0.4", + "0.6", + "0.3666666666666667", + "0.18", + "0.84", + "1.84", + "0.9533333333333336", + "0.6600000000000001", + "True", + "True", + "False", + "True", + "False", + "2.0", + "False", + "True", + "False", + "True", + "True", + "False", + "False", + "2.0", + "ViT + Q-Former", + "2.0", + "11.875472902166976", + "uniform" + ], + [ + "37", + "0.1", + "0.4", + "0.8", + "0.4333333333333333", + "0.04", + "0.4", + "1.0", + "0.48", + "0.4566666666666666", + "True", + "True", + "True", + "False", + "False", + "2.0", + "True", + "False", + "False", + "True", + "True", + "False", + "False", + "2.0", + "ViT + Q-Former", + "2.0", + "11.875472902166976", + "uniform" + ], + [ + "38", + "0.1", + "0.4", + "0.8", + "0.4333333333333333", + "0.06", + "0.44", + "0.96", + "0.4866666666666666", + "0.4599999999999999", + "True", + "True", + "True", + "True", + "False", + "2.0", + "True", + "True", + "False", + "False", + "False", + "True", + "True", + "2.0", + "ViT + Q-Former", + "2.0", + "7.247562922615464", + "uniform" + ], + [ + "39", + "0.1", + "0.4", + "0.8", + "0.4333333333333333", + "0.1", + "0.52", + "1.0", + "0.54", + "0.4866666666666667", + "True", + "True", + "True", + "False", + "False", + "2.0", + "True", + "False", + "False", + "False", + "False", + "True", + "True", + "2.0", + "ViT + Q-Former", + "2.0", + "11.617101056888314", + "uniform" + ], + [ + "40", + "0.1", + "0.4", + "0.8", + "0.4333333333333333", + "0.1", + "0.68", + "1.1", + "0.6266666666666667", + "0.53", + "False", + "True", + "True", + "False", + "True", + "2.0", + "True", + "False", + "True", + "False", + "False", + "False", + "True", + "2.0", + "ViT + Q-Former", + "2.0", + "10.162210271974924", + "uniform" + ], + [ + "41", + "0.1", + "0.4", + "0.8", + "0.4333333333333333", + "0.18", + "0.9", + "1.54", + "0.8733333333333334", + "0.6533333333333333", + "False", + "True", + "False", + "False", + "True", + "2.0", + "False", + "False", + "True", + "True", + "True", + "False", + "False", + "2.0", + "ViT + Q-Former", + "2.0", + "13.107245165515675", + "uniform" + ], + [ + "42", + "0.1", + "0.4", + "0.9", + "0.4666666666666666", + "0.08", + "0.54", + "1.0", + "0.54", + "0.5033333333333333", + "True", + "True", + "True", + "True", + "True", + "2.0", + "True", + "True", + "True", + "True", + "True", + "True", + "True", + "2.0", + "ViT + Q-Former", + "2.0", + "2.2998318561924105", + "uniform" + ], + [ + "43", + "0.1", + "0.4", + "0.9", + "0.4666666666666666", + "0.18", + "0.6", + "1.02", + "0.6", + "0.5333333333333333", + "True", + "True", + "True", + "True", + "True", + "2.0", + "True", + "True", + "True", + "False", + "False", + "False", + "True", + "2.0", + "ViT + Q-Former", + "2.0", + "3.554679022335707", + "uniform" + ], + [ + "44", + "0.1", + "0.4", + "1.0", + "0.5", + "0.04", + "0.54", + "1.12", + "0.5666666666666668", + "0.5333333333333334", + "True", + "True", + "False", + "True", + "True", + "2.0", + "False", + "True", + "True", + "False", + "False", + "True", + "False", + "2.0", + "ViT + Q-Former", + "2.0", + "7.698665745277527", + "uniform" + ], + [ + "45", + "0.1", + "0.4", + "1.0", + "0.5", + "0.06", + "0.76", + "1.2", + "0.6733333333333333", + "0.5866666666666667", + "False", + "True", + "True", + "False", + "True", + "2.0", + "True", + "False", + "True", + "True", + "True", + "True", + "True", + "2.0", + "ViT + Q-Former", + "2.0", + "9.32564549454606", + "uniform" + ], + [ + "46", + "0.1", + "0.4", + "1.0", + "0.5", + "0.08", + "0.44", + "0.92", + "0.48", + "0.49", + "True", + "True", + "True", + "True", + "False", + "2.0", + "True", + "True", + "False", + "True", + "True", + "True", + "False", + "2.0", + "ViT + Q-Former", + "2.0", + "7.313203790510725", + "uniform" + ], + [ + "47", + "0.1", + "0.4", + "1.0", + "0.5", + "0.1", + "0.64", + "1.18", + "0.64", + "0.5700000000000001", + "False", + "True", + "False", + "True", + "False", + "2.0", + "False", + "True", + "False", + "False", + "False", + "True", + "True", + "2.0", + "ViT + Q-Former", + "2.0", + "12.848873320237015", + "uniform" + ], + [ + "48", + "0.1", + "0.4", + "1.0", + "0.5", + "0.2", + "0.92", + "1.92", + "1.0133333333333334", + "0.7566666666666667", + "False", + "True", + "False", + "False", + "True", + "2.0", + "False", + "False", + "True", + "False", + "False", + "False", + "True", + "2.0", + "ViT + Q-Former", + "2.0", + "13.074424731568046", + "uniform" + ], + [ + "49", + "0.1", + "0.4", + "1.1", + "0.5333333333333333", + "0.14", + "0.5", + "1.0", + "0.5466666666666667", + "0.54", + "True", + "True", + "True", + "True", + "False", + "2.0", + "True", + "True", + "False", + "True", + "True", + "False", + "False", + "2.0", + "ViT + Q-Former", + "2.0", + "7.764306613172787", + "uniform" + ] + ], + "shape": { + "columns": 27, + "rows": 952 + } + }, + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
txt_r1txt_r5txt_r10txt_r_meanimg_r1img_r5img_r10img_r_meanr_meanvit_attn...qformer_end_blocksqformer_self_attnqformer_cross_attnqformer_text_ffqformer_img_ffqformer_weight_bitsQuantized Portionweight_bitsbpwquant_method
00.00.00.40.1333330.100.340.720.3866670.260000True...FalseFalseFalseTrueFalse2.0ViT + Q-Former2.014.529316uniform
10.00.10.30.1333330.060.220.560.2800000.206667False...FalseFalseFalseTrueFalse2.0Q-Former2.015.761088uniform
20.00.20.20.1333330.140.300.720.3866670.260000False...TrueFalseFalseTrueFalse2.0Q-Former2.015.535536uniform
30.00.30.40.2333330.140.420.620.3933330.313333True...FalseTrueTrueTrueFalse2.0ViT + Q-Former2.014.336585uniform
40.00.30.70.3333330.160.601.080.6133330.473333False...FalseTrueTrueTrueFalse2.0ViT + Q-Former2.012.881694uniform
..................................................................
94798.0100.0100.099.33333388.1297.8898.8294.94000097.136667True...FalseFalseFalseFalseFalse2.0ViT + Q-Former2.014.754867uniform
94898.0100.0100.099.33333388.1297.8898.8294.94000097.136667True...FalseFalseFalseFalseFalseNaNViT2.014.754867uniform
94998.0100.0100.099.33333389.6098.1098.9695.55333397.443333False...TrueTrueTrueFalseTrue4.0Q-Former4.014.910858uniform
95098.0100.0100.099.33333389.6698.1098.9295.56000097.446667False...TrueTrueTrueFalseTrue4.0Q-Former4.015.269452uniform
95198.0100.0100.099.33333389.7698.1698.9695.62666797.480000True...FalseTrueTrueFalseTrue4.0ViT + Q-Former4.014.572098uniform
\n", + "

952 rows × 27 columns

\n", + "
" + ], + "text/plain": [ + " txt_r1 txt_r5 txt_r10 txt_r_mean img_r1 img_r5 img_r10 img_r_mean \\\n", + "0 0.0 0.0 0.4 0.133333 0.10 0.34 0.72 0.386667 \n", + "1 0.0 0.1 0.3 0.133333 0.06 0.22 0.56 0.280000 \n", + "2 0.0 0.2 0.2 0.133333 0.14 0.30 0.72 0.386667 \n", + "3 0.0 0.3 0.4 0.233333 0.14 0.42 0.62 0.393333 \n", + "4 0.0 0.3 0.7 0.333333 0.16 0.60 1.08 0.613333 \n", + ".. ... ... ... ... ... ... ... ... \n", + "947 98.0 100.0 100.0 99.333333 88.12 97.88 98.82 94.940000 \n", + "948 98.0 100.0 100.0 99.333333 88.12 97.88 98.82 94.940000 \n", + "949 98.0 100.0 100.0 99.333333 89.60 98.10 98.96 95.553333 \n", + "950 98.0 100.0 100.0 99.333333 89.66 98.10 98.92 95.560000 \n", + "951 98.0 100.0 100.0 99.333333 89.76 98.16 98.96 95.626667 \n", + "\n", + " r_mean vit_attn ... qformer_end_blocks qformer_self_attn \\\n", + "0 0.260000 True ... False False \n", + "1 0.206667 False ... False False \n", + "2 0.260000 False ... True False \n", + "3 0.313333 True ... False True \n", + "4 0.473333 False ... False True \n", + ".. ... ... ... ... ... \n", + "947 97.136667 True ... False False \n", + "948 97.136667 True ... False False \n", + "949 97.443333 False ... True True \n", + "950 97.446667 False ... True True \n", + "951 97.480000 True ... False True \n", + "\n", + " qformer_cross_attn qformer_text_ff qformer_img_ff qformer_weight_bits \\\n", + "0 False True False 2.0 \n", + "1 False True False 2.0 \n", + "2 False True False 2.0 \n", + "3 True True False 2.0 \n", + "4 True True False 2.0 \n", + ".. ... ... ... ... \n", + "947 False False False 2.0 \n", + "948 False False False NaN \n", + "949 True False True 4.0 \n", + "950 True False True 4.0 \n", + "951 True False True 4.0 \n", + "\n", + " Quantized Portion weight_bits bpw quant_method \n", + "0 ViT + Q-Former 2.0 14.529316 uniform \n", + "1 Q-Former 2.0 15.761088 uniform \n", + "2 Q-Former 2.0 15.535536 uniform \n", + "3 ViT + Q-Former 2.0 14.336585 uniform \n", + "4 ViT + Q-Former 2.0 12.881694 uniform \n", + ".. ... ... ... ... \n", + "947 ViT + Q-Former 2.0 14.754867 uniform \n", + "948 ViT 2.0 14.754867 uniform \n", + "949 Q-Former 4.0 14.910858 uniform \n", + "950 Q-Former 4.0 15.269452 uniform \n", + "951 ViT + Q-Former 4.0 14.572098 uniform \n", + "\n", + "[952 rows x 27 columns]" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_export = df_uniform_flickr[['txt_r1', 'txt_r5', 'txt_r10', 'txt_r_mean', 'img_r1', 'img_r5',\n", + " 'img_r10', 'img_r_mean', 'r_mean', 'vit_attn', 'vit_ff',\n", + " 'vit_front_blocks', 'vit_middle_blocks', 'vit_end_blocks',\n", + " 'vit_weight_bits', 'qformer_front_blocks', 'qformer_middle_blocks',\n", + " 'qformer_end_blocks', 'qformer_self_attn', 'qformer_cross_attn',\n", + " 'qformer_text_ff', 'qformer_img_ff', 'qformer_weight_bits',\n", + " 'Quantized Portion', 'weight_bits', 'bpw', 'quant_method']]\n", + "\n", + "df_export" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "ec4a9407", + "metadata": {}, + "outputs": [], + "source": [ + "df_export.to_csv(os.path.join('/fs/cfar-projects/low-bit-vision/final_results/all_results', 'blip2_uniform_flickr.csv'), index=None)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/gptq_llava.ipynb b/gptq_llava.ipynb new file mode 100644 index 0000000..fef2c03 --- /dev/null +++ b/gptq_llava.ipynb @@ -0,0 +1,2402 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "eeebc607", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/fs/nexus-scratch/vla/micromamba/envs/MMQ_LLAVA/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + " from .autonotebook import tqdm as notebook_tqdm\n" + ] + } + ], + "source": [ + "import math\n", + "import time\n", + "from typing import List, Dict, Any, Optional\n", + "import argparse\n", + "import random\n", + "import os\n", + "import json\n", + "\n", + "import torch\n", + "import torch.nn as nn\n", + "from tqdm import tqdm\n", + "import torch\n", + "from torch.utils.data import DataLoader\n", + "from transformers import AutoProcessor, LlavaForConditionalGeneration\n", + "from transformers.models.llava.image_processing_llava import LlavaImageProcessor\n", + "\n", + "from dataset import VQAv2Eval\n", + "import transformers" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "2c285b2c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "4.51.3\n" + ] + } + ], + "source": [ + "print(transformers.__version__)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "2362434c", + "metadata": {}, + "outputs": [], + "source": [ + "DEBUG = False\n", + "\n", + "torch.backends.cuda.matmul.allow_tf32 = False\n", + "torch.backends.cudnn.allow_tf32 = False" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "cab5e4a0", + "metadata": {}, + "outputs": [], + "source": [ + "#\n", + "\n", + "# ====================================================\n", + "# Quantization Classes and Functions\n", + "# ====================================================\n", + "\n", + "def quantize(x, scale, zero, maxq):\n", + " if maxq < 0:\n", + " return (x > scale / 2).float() * scale + (x < zero / 2).float() * zero\n", + " q = torch.clamp(torch.round(x / scale) + zero, 0, maxq)\n", + " return scale * (q - zero)\n", + "\n", + "\n", + "class Quantizer(nn.Module):\n", + " def __init__(self, shape=1):\n", + " super(Quantizer, self).__init__()\n", + " self.register_buffer(\"maxq\", torch.tensor(0))\n", + " self.register_buffer(\"scale\", torch.zeros(shape))\n", + " self.register_buffer(\"zero\", torch.zeros(shape))\n", + "\n", + " def configure(\n", + " self,\n", + " bits,\n", + " perchannel=False,\n", + " sym=True,\n", + " mse=False,\n", + " norm=2.4,\n", + " grid=100,\n", + " maxshrink=0.8,\n", + " trits=False,\n", + " ):\n", + " device = self.maxq.device\n", + " self.maxq = torch.tensor(2**bits - 1, device=device)\n", + " self.perchannel = perchannel\n", + " self.sym = sym\n", + " self.mse = mse\n", + " self.norm = norm\n", + " self.grid = grid\n", + " self.maxshrink = maxshrink\n", + " if trits:\n", + " self.maxq = torch.tensor(-1, device=device)\n", + "\n", + " def find_params(self, x, weight=False):\n", + " dev = x.device\n", + " self.maxq = self.maxq.to(dev)\n", + "\n", + " shape = x.shape\n", + " if self.perchannel:\n", + " if weight:\n", + " x = x.flatten(1)\n", + " else:\n", + " if len(shape) == 4:\n", + " x = x.permute([1, 0, 2, 3])\n", + " x = x.flatten(1)\n", + " if len(shape) == 3:\n", + " x = x.reshape((-1, shape[-1])).t()\n", + " if len(shape) == 2:\n", + " x = x.t()\n", + " else:\n", + " x = x.flatten().unsqueeze(0)\n", + "\n", + " tmp = torch.zeros(x.shape[0], device=dev)\n", + " xmin = torch.minimum(x.min(1)[0], tmp)\n", + " xmax = torch.maximum(x.max(1)[0], tmp)\n", + "\n", + " if self.sym:\n", + " xmax = torch.maximum(torch.abs(xmin), xmax)\n", + " tmp = xmin < 0\n", + " if torch.any(tmp):\n", + " xmin[tmp] = -xmax[tmp]\n", + " tmp = (xmin == 0) & (xmax == 0)\n", + " xmin[tmp] = -1\n", + " xmax[tmp] = +1\n", + "\n", + " if self.maxq < 0:\n", + " self.scale = xmax\n", + " self.zero = xmin\n", + " else:\n", + " self.scale = (xmax - xmin) / self.maxq\n", + " if self.sym:\n", + " self.zero = torch.full_like(self.scale, (self.maxq + 1) / 2)\n", + " else:\n", + " self.zero = torch.round(-xmin / self.scale)\n", + "\n", + " if self.mse:\n", + " best = torch.full([x.shape[0]], float(\"inf\"), device=dev)\n", + " for i in range(int(self.maxshrink * self.grid)):\n", + " p = 1 - i / self.grid\n", + " xmin1 = p * xmin\n", + " xmax1 = p * xmax\n", + " scale1 = (xmax1 - xmin1) / self.maxq\n", + " zero1 = torch.round(-xmin1 / scale1) if not self.sym else self.zero\n", + " q = quantize(x, scale1.unsqueeze(1), zero1.unsqueeze(1), self.maxq)\n", + " q -= x\n", + " q.abs_()\n", + " q.pow_(self.norm)\n", + " err = torch.sum(q, 1)\n", + " tmp = err < best\n", + " if torch.any(tmp):\n", + " best[tmp] = err[tmp]\n", + " self.scale[tmp] = scale1[tmp]\n", + " self.zero[tmp] = zero1[tmp]\n", + " if not self.perchannel:\n", + " if weight:\n", + " tmp = shape[0]\n", + " else:\n", + " tmp = shape[1] if len(shape) != 3 else shape[2]\n", + " self.scale = self.scale.repeat(tmp)\n", + " self.zero = self.zero.repeat(tmp)\n", + "\n", + " if weight:\n", + " shape = [-1] + [1] * (len(shape) - 1)\n", + " self.scale = self.scale.reshape(shape)\n", + " self.zero = self.zero.reshape(shape)\n", + " return\n", + " if len(shape) == 4:\n", + " self.scale = self.scale.reshape((1, -1, 1, 1))\n", + " self.zero = self.zero.reshape((1, -1, 1, 1))\n", + " if len(shape) == 3:\n", + " self.scale = self.scale.reshape((1, 1, -1))\n", + " self.zero = self.zero.reshape((1, 1, -1))\n", + " if len(shape) == 2:\n", + " self.scale = self.scale.unsqueeze(0)\n", + " self.zero = self.zero.unsqueeze(0)\n", + "\n", + " # Ensure buffers are on the same device as input x\n", + " self.scale = self.scale.to(dev)\n", + " self.zero = self.zero.to(dev)\n", + "\n", + " def quantize(self, x):\n", + " if self.ready():\n", + " # Ensure buffers are on the same device as x\n", + " self.scale = self.scale.to(x.device)\n", + " self.zero = self.zero.to(x.device)\n", + " self.maxq = self.maxq.to(x.device)\n", + " return quantize(x, self.scale, self.zero, self.maxq)\n", + " return x\n", + "\n", + " def enabled(self):\n", + " return self.maxq > 0\n", + "\n", + " def ready(self):\n", + " return torch.all(self.scale != 0)\n", + "\n", + "\n", + "class GPTQ:\n", + " def __init__(self, layer):\n", + " self.layer = layer\n", + " self.dev = self.layer.weight.device\n", + " W = layer.weight.data.clone()\n", + " if isinstance(self.layer, nn.Conv2d):\n", + " W = W.flatten(1)\n", + " if isinstance(self.layer, transformers.Conv1D):\n", + " W = W.t()\n", + " self.rows = W.shape[0]\n", + " self.columns = W.shape[1]\n", + " self.H = torch.zeros((self.columns, self.columns), device=self.dev)\n", + " self.nsamples = 0\n", + " self.quantizer = Quantizer()\n", + " self.quantizer.to(self.dev)\n", + "\n", + " def add_batch(self, inp, out):\n", + " if DEBUG:\n", + " self.inp1 = inp\n", + " self.out1 = out\n", + " if len(inp.shape) == 2:\n", + " inp = inp.unsqueeze(0)\n", + " tmp = inp.shape[0]\n", + " if isinstance(self.layer, nn.Linear) or isinstance(\n", + " self.layer, transformers.Conv1D\n", + " ):\n", + " if len(inp.shape) == 3:\n", + " inp = inp.reshape((-1, inp.shape[-1]))\n", + " inp = inp.t()\n", + " if isinstance(self.layer, nn.Conv2d):\n", + " unfold = nn.Unfold(\n", + " self.layer.kernel_size,\n", + " dilation=self.layer.dilation,\n", + " padding=self.layer.padding,\n", + " stride=self.layer.stride,\n", + " )\n", + " inp = unfold(inp)\n", + " inp = inp.permute([1, 0, 2])\n", + " inp = inp.flatten(1)\n", + "\n", + " self.H *= self.nsamples / (self.nsamples + tmp)\n", + " self.nsamples += tmp\n", + " inp = math.sqrt(2 / self.nsamples) * inp.float()\n", + " self.H += inp.matmul(inp.t())\n", + "\n", + " def fasterquant(\n", + " self,\n", + " blocksize=128,\n", + " percdamp=0.01,\n", + " groupsize=-1,\n", + " actorder=False,\n", + " static_groups=False,\n", + " ):\n", + " W = self.layer.weight.data.clone()\n", + " if isinstance(self.layer, nn.Conv2d):\n", + " W = W.flatten(1)\n", + " if isinstance(self.layer, transformers.Conv1D):\n", + " W = W.t()\n", + " W = W.float()\n", + "\n", + " tick = time.time()\n", + "\n", + " if not self.quantizer.ready():\n", + " self.quantizer.find_params(W, weight=True)\n", + "\n", + " H = self.H\n", + " del self.H\n", + " dead = torch.diag(H) == 0\n", + " H[dead, dead] = 1\n", + " W[:, dead] = 0\n", + "\n", + " if static_groups:\n", + " import copy\n", + "\n", + " groups = []\n", + " for i in range(0, self.columns, groupsize):\n", + " quantizer = copy.deepcopy(self.quantizer)\n", + " quantizer.find_params(W[:, i : (i + groupsize)], weight=True)\n", + " groups.append(quantizer)\n", + "\n", + " if actorder:\n", + " perm = torch.argsort(torch.diag(H), descending=True)\n", + " W = W[:, perm]\n", + " H = H[perm][:, perm]\n", + " invperm = torch.argsort(perm)\n", + "\n", + " Losses = torch.zeros_like(W)\n", + " Q = torch.zeros_like(W)\n", + "\n", + " damp = percdamp * torch.mean(torch.diag(H))\n", + " diag = torch.arange(self.columns, device=self.dev)\n", + " H[diag, diag] += damp\n", + " H = torch.linalg.cholesky(H)\n", + " H = torch.cholesky_inverse(H)\n", + " H = torch.linalg.cholesky(H, upper=True)\n", + " Hinv = H\n", + "\n", + " for i1 in range(0, self.columns, blocksize):\n", + " i2 = min(i1 + blocksize, self.columns)\n", + " count = i2 - i1\n", + "\n", + " W1 = W[:, i1:i2].clone()\n", + " Q1 = torch.zeros_like(W1)\n", + " Err1 = torch.zeros_like(W1)\n", + " Losses1 = torch.zeros_like(W1)\n", + " Hinv1 = Hinv[i1:i2, i1:i2]\n", + "\n", + " for i in range(count):\n", + " w = W1[:, i]\n", + " d = Hinv1[i, i]\n", + "\n", + " if groupsize != -1:\n", + " if not static_groups:\n", + " if (i1 + i) % groupsize == 0:\n", + " self.quantizer.find_params(\n", + " W[:, (i1 + i) : (i1 + i + groupsize)], weight=True\n", + " )\n", + " else:\n", + " idx = i1 + i\n", + " if actorder:\n", + " idx = perm[idx]\n", + " self.quantizer = groups[idx // groupsize]\n", + "\n", + " q = quantize(\n", + " w.unsqueeze(1),\n", + " self.quantizer.scale,\n", + " self.quantizer.zero,\n", + " self.quantizer.maxq,\n", + " ).flatten()\n", + " Q1[:, i] = q\n", + " Losses1[:, i] = (w - q) ** 2 / d**2\n", + "\n", + " err1 = (w - q) / d\n", + " W1[:, i:] -= err1.unsqueeze(1).matmul(Hinv1[i, i:].unsqueeze(0))\n", + " Err1[:, i] = err1\n", + "\n", + " Q[:, i1:i2] = Q1\n", + " Losses[:, i1:i2] = Losses1 / 2\n", + "\n", + " W[:, i2:] -= Err1.matmul(Hinv[i1:i2, i2:])\n", + "\n", + " if DEBUG:\n", + " self.layer.weight.data[:, :i2] = Q[:, :i2]\n", + " self.layer.weight.data[:, i2:] = W[:, i2:]\n", + " print(torch.sum((self.layer(self.inp1) - self.out1) ** 2))\n", + " print(torch.sum(Losses))\n", + "\n", + " torch.cuda.synchronize()\n", + " print(\"Time for quantization: %.2f seconds\" % (time.time() - tick))\n", + " print(\"Total quantization error:\", torch.sum(Losses).item())\n", + "\n", + " if actorder:\n", + " Q = Q[:, invperm]\n", + "\n", + " if isinstance(self.layer, transformers.Conv1D):\n", + " Q = Q.t()\n", + " self.layer.weight.data = Q.reshape(self.layer.weight.shape).to(\n", + " self.layer.weight.data.dtype\n", + " )\n", + " if DEBUG:\n", + " print(torch.sum((self.layer(self.inp1) - self.out1) ** 2))\n", + "\n", + " def free(self):\n", + " if DEBUG:\n", + " self.inp1 = None\n", + " self.out1 = None\n", + " self.H = None\n", + " self.Losses = None\n", + " self.Trace = None\n", + " torch.cuda.empty_cache()\n", + "\n", + "\n", + "def find_linear_layers_in_model(model):\n", + " layers = {}\n", + "\n", + " def recurse(module, prefix=\"\"):\n", + " if isinstance(module, nn.Linear):\n", + " layers[prefix.rstrip(\".\")] = module\n", + " for name, child in module.named_children():\n", + " recurse(child, prefix + name + \".\")\n", + "\n", + " recurse(model)\n", + " return layers\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "09fb58db", + "metadata": {}, + "outputs": [], + "source": [ + "class LlavaQuantizer:\n", + " def __init__(self, model, processor, device, chunk_size=32, task = 'vqav2'):\n", + " self.model = model\n", + " self.processor = processor\n", + " self.device = device\n", + " self.chunk_size = chunk_size\n", + " self.task = task\n", + "\n", + " # Component-specific configuration parameters\n", + " self.config = {\n", + " \"vision\": {\n", + " \"bits\": 4,\n", + " \"percent_dampening\": 0.01,\n", + " \"group_size\": -1,\n", + " \"use_symmetric\": True,\n", + " \"use_act_order\": False,\n", + " \"use_static_groups\": False,\n", + " },\n", + " \"language\": {\n", + " \"bits\": 4,\n", + " \"percent_dampening\": 0.01,\n", + " \"group_size\": -1,\n", + " \"use_symmetric\": True,\n", + " \"use_act_order\": False,\n", + " \"use_static_groups\": False,\n", + " },\n", + " }\n", + "\n", + "\n", + " def _prepare_quantizers(self, layers, component_type):\n", + " \"\"\"Initialize GPTQ quantizers for given layers with component-specific settings\"\"\"\n", + " config = self.config[component_type]\n", + " quantizers = {}\n", + " for name, layer in layers.items():\n", + " quantizers[name] = GPTQ(layer)\n", + " quantizers[name].quantizer.configure(\n", + " bits=config[\"bits\"],\n", + " perchannel=True,\n", + " sym=config[\"use_symmetric\"],\n", + " mse=False,\n", + " )\n", + " return quantizers\n", + " \n", + " def _process_chunk(\n", + " self, layers, start_idx, end_idx, forward_func, desc, component_type\n", + " ):\n", + " \"\"\"Process a chunk of layers with component-specific quantization settings\"\"\"\n", + " current_layers = dict(list(layers.items())[start_idx:end_idx])\n", + " print(\n", + " f\"\\nProcessing {desc} layers {start_idx} to {end_idx-1} with {self.config[component_type]['bits']}-bit precision\"\n", + " )\n", + "\n", + " # Initialize quantizers for current chunk\n", + " quantizers = self._prepare_quantizers(current_layers, component_type)\n", + " hooks = []\n", + "\n", + " def get_hook(name):\n", + " def hook(module, inp, out):\n", + " if name in quantizers:\n", + " quantizers[name].add_batch(inp[0].detach(), out.detach())\n", + "\n", + " return hook\n", + "\n", + " for name, layer in current_layers.items():\n", + " hooks.append(layer.register_forward_hook(get_hook(name)))\n", + "\n", + " forward_func()\n", + "\n", + " for hook in hooks:\n", + " hook.remove()\n", + "\n", + " config = self.config[component_type]\n", + " for name, layer in current_layers.items():\n", + " print(f\"Quantizing layer {name}...\")\n", + " quantizer = quantizers[name]\n", + " quantizer.fasterquant(\n", + " blocksize=32,\n", + " percdamp=config[\"percent_dampening\"],\n", + " groupsize=config[\"group_size\"],\n", + " actorder=config[\"use_act_order\"],\n", + " static_groups=config[\"use_static_groups\"],\n", + " )\n", + "\n", + " layer.weight.data = quantizer.quantizer.quantize(layer.weight.data).to(\n", + " layer.weight.data.dtype\n", + " )\n", + " quantizer.free()\n", + "\n", + " torch.cuda.empty_cache()\n", + "\n", + "\n", + " def quantize_vision_model(self, calibration_set):\n", + " \"\"\"Quantize vision model with 8-bit precision\"\"\"\n", + " print(\n", + " f\"Quantizing Vision Model with {self.config['vision']['bits']}-bit precision...\"\n", + " )\n", + "\n", + " # some extra components need to be on device for vision model forward pass\n", + " # self.model.vision_tower.to(self.device)\n", + " self.model.to(self.device)\n", + " self.model.language_model.to('cpu')\n", + "\n", + " layers = find_linear_layers_in_model(self.model.vision_tower.vision_model)\n", + " total_layers = len(layers)\n", + "\n", + " print(f'total_layers: {total_layers}')\n", + " print(layers)\n", + "\n", + " def forward_pass():\n", + " \n", + " vision_feature_layer = self.model.config.vision_feature_layer\n", + " vision_feature_select_strategy = self.model.config.vision_feature_select_strategy\n", + " image_sizes = None\n", + " \n", + " # TODO: adjust for GQA if needed\n", + " if self.task == 'vqav2':\n", + " \n", + " for img, prompt in tqdm(calibration_set, desc='Processing vision model batch'):\n", + "\n", + " inputs = self.processor(images = [img],\n", + " text= [prompt],\n", + " return_tensors='pt',\n", + " padding=True).to(self.device)\n", + " \n", + " # runs forward pass through vision_tower\n", + " self.model.get_image_features(\n", + " pixel_values = inputs['pixel_values'],\n", + " vision_feature_layer=vision_feature_layer,\n", + " vision_feature_select_strategy=vision_feature_select_strategy,\n", + " image_sizes=image_sizes\n", + " )\n", + "\n", + "\n", + " for start_idx in range(0, total_layers, self.chunk_size):\n", + " end_idx = min(start_idx + self.chunk_size, total_layers)\n", + " self._process_chunk(\n", + " layers, start_idx, end_idx, forward_pass, \"vision model\", \"vision\"\n", + " )\n", + "\n", + " self.model.vision_tower.vision_model.cpu()\n", + " print(\"Vision Model quantization complete.\\n\")\n", + "\n", + "\n", + " def quantize_language_model(self, calibration_set):\n", + " \"\"\"Quantize language model with 4-bit precision\"\"\"\n", + " print(\n", + " f\"Quantizing Language Model with {self.config['language']['bits']}-bit precision...\"\n", + " )\n", + " self.model.to(self.device)\n", + "\n", + " layers = find_linear_layers_in_model(self.model.language_model.model)\n", + " # layers[\"language_projection\"] = self.model.language_projection\n", + " total_layers = len(layers)\n", + "\n", + " def forward_pass():\n", + " # TODO: adjust for GQA if needed\n", + " if self.task == 'vqav2':\n", + " \n", + " for img, prompt in tqdm(calibration_set, desc='Processing language model batch'):\n", + "\n", + " inputs = self.processor(images = [img],\n", + " text= [prompt],\n", + " return_tensors='pt',\n", + " padding=True).to(self.device)\n", + " \n", + " self.model.generate(**inputs)\n", + " \n", + "\n", + " for start_idx in range(0, total_layers, self.chunk_size):\n", + " end_idx = min(start_idx + self.chunk_size, total_layers)\n", + " self._process_chunk(\n", + " layers, start_idx, end_idx, forward_pass, \"language model\", \"language\"\n", + " )\n", + "\n", + " self.model.cpu()\n", + " print(\"Language Model quantization complete.\\n\")\n", + "\n", + " def quantize(self, calibration_set):\n", + " \"\"\"Quantize all LLAVA components\"\"\"\n", + " print(\"Starting LLAVA model quantization...\")\n", + " self.quantize_vision_model(calibration_set)\n", + " self.quantize_language_model(calibration_set)\n", + " print(\"LLAVA model quantization complete.\")\n", + "\n", + "\n", + " # TODO:\n", + " def prepare_for_inference(self):\n", + " self.model.to(self.device)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "50d8aef8", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "cuda\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Loading checkpoint shards: 100%|██████████| 3/3 [00:00<00:00, 18.58it/s]\n" + ] + } + ], + "source": [ + "if torch.backends.mps.is_available():\n", + " device = torch.device(\"mps\")\n", + "else:\n", + " device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", + " \n", + "print(device)\n", + "\n", + "# Load the model\n", + "model = LlavaForConditionalGeneration.from_pretrained(\"llava-hf/llava-1.5-7b-hf\", torch_dtype=torch.float16)\n", + "model.to('cpu')\n", + "# processor = AutoProcessor.from_pretrained(\"llava-hf/llava-1.5-7b-hf\", pad_token = '')\n", + "processor = AutoProcessor.from_pretrained(\"llava-hf/llava-1.5-7b-hf\", pad_token = '', use_fast = False)\n", + "\n", + "\n", + "image_processor = LlavaImageProcessor.from_pretrained(\"llava-hf/llava-1.5-7b-hf\",\n", + " do_pad=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "125a09a8", + "metadata": {}, + "outputs": [], + "source": [ + "processor.image_processor = image_processor" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "abec4170", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "LlavaProcessor:\n", + "- image_processor: LlavaImageProcessor {\n", + " \"crop_size\": {\n", + " \"height\": 336,\n", + " \"width\": 336\n", + " },\n", + " \"do_center_crop\": true,\n", + " \"do_convert_rgb\": true,\n", + " \"do_normalize\": true,\n", + " \"do_pad\": true,\n", + " \"do_rescale\": true,\n", + " \"do_resize\": true,\n", + " \"image_mean\": [\n", + " 0.48145466,\n", + " 0.4578275,\n", + " 0.40821073\n", + " ],\n", + " \"image_processor_type\": \"LlavaImageProcessor\",\n", + " \"image_std\": [\n", + " 0.26862954,\n", + " 0.26130258,\n", + " 0.27577711\n", + " ],\n", + " \"processor_class\": \"LlavaProcessor\",\n", + " \"resample\": 3,\n", + " \"rescale_factor\": 0.00392156862745098,\n", + " \"size\": {\n", + " \"shortest_edge\": 336\n", + " }\n", + "}\n", + "\n", + "- tokenizer: LlamaTokenizer(name_or_path='llava-hf/llava-1.5-7b-hf', vocab_size=32000, model_max_length=1000000000000000019884624838656, is_fast=False, padding_side='left', truncation_side='right', special_tokens={'bos_token': '', 'eos_token': '', 'unk_token': '', 'pad_token': '', 'image_token': ''}, clean_up_tokenization_spaces=False, added_tokens_decoder={\n", + "\t0: AddedToken(\"\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n", + "\t1: AddedToken(\"\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n", + "\t2: AddedToken(\"\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n", + "\t32000: AddedToken(\"\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n", + "\t32001: AddedToken(\"\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n", + "}\n", + ")\n", + "\n", + "{\n", + " \"image_token\": \"\",\n", + " \"num_additional_image_tokens\": 1,\n", + " \"patch_size\": 14,\n", + " \"processor_class\": \"LlavaProcessor\",\n", + " \"vision_feature_select_strategy\": \"default\"\n", + "}" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "processor" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "16da1586", + "metadata": {}, + "outputs": [], + "source": [ + "# # VQAv2 dataset paths\n", + "# ann_root = '/fs/cfar-projects/low-bit-vision/datasets/vqav2/annotations'\n", + "# q_root = '/fs/cfar-projects/low-bit-vision/datasets/vqav2/questions'\n", + "# image_root = '/fs/cfar-projects/low-bit-vision/datasets/vqav2/val2014'\n", + "\n", + "# # short answer prompting according to: https://github.com/haotian-liu/LLaVA/blob/main/docs/Evaluation.md\n", + "llava_prompt = 'USER: \\n{}\\nAnswer the question using a single word or phrase. ASSISTANT:'\n", + "\n", + "# dataset = VQAv2Eval(image_root=image_root,\n", + "# ann_root=ann_root,\n", + "# q_root=q_root,\n", + "# prompt=llava_prompt)\n", + "\n", + "\n", + "from dataset import GQAEval\n", + "\n", + "image_root = '/fs/cfar-projects/low-bit-vision/datasets/gqa/images'\n", + "q_root = '/fs/cfar-projects/low-bit-vision/datasets/gqa/questions'\n", + "\n", + "dataset = GQAEval(\n", + " image_root,\n", + " q_root,\n", + " prompt=llava_prompt\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "9c4d889f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'question_id': '20300425',\n", + " 'image': ,\n", + " 'text_input': 'USER: \\nWhich kind of vehicle is waiting for the traffic light?\\nAnswer the question using a single word or phrase. ASSISTANT:',\n", + " 'gt_answer': 'cars'}" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dataset[110]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "59bc1787", + "metadata": {}, + "outputs": [], + "source": [ + "dataloader = DataLoader(dataset,\n", + " batch_size=16,\n", + " num_workers=1,\n", + " pin_memory=False,\n", + " shuffle=False,\n", + " collate_fn = dataset.collater)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "cc0bf40e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "LlavaForConditionalGeneration(\n", + " (vision_tower): CLIPVisionModel(\n", + " (vision_model): CLIPVisionTransformer(\n", + " (embeddings): CLIPVisionEmbeddings(\n", + " (patch_embedding): Conv2d(3, 1024, kernel_size=(14, 14), stride=(14, 14), bias=False)\n", + " (position_embedding): Embedding(577, 1024)\n", + " )\n", + " (pre_layrnorm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n", + " (encoder): CLIPEncoder(\n", + " (layers): ModuleList(\n", + " (0-23): 24 x CLIPEncoderLayer(\n", + " (self_attn): CLIPSdpaAttention(\n", + " (k_proj): Linear(in_features=1024, out_features=1024, bias=True)\n", + " (v_proj): Linear(in_features=1024, out_features=1024, bias=True)\n", + " (q_proj): Linear(in_features=1024, out_features=1024, bias=True)\n", + " (out_proj): Linear(in_features=1024, out_features=1024, bias=True)\n", + " )\n", + " (layer_norm1): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n", + " (mlp): CLIPMLP(\n", + " (activation_fn): QuickGELUActivation()\n", + " (fc1): Linear(in_features=1024, out_features=4096, bias=True)\n", + " (fc2): Linear(in_features=4096, out_features=1024, bias=True)\n", + " )\n", + " (layer_norm2): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n", + " )\n", + " )\n", + " )\n", + " (post_layernorm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n", + " )\n", + " )\n", + " (multi_modal_projector): LlavaMultiModalProjector(\n", + " (linear_1): Linear(in_features=1024, out_features=4096, bias=True)\n", + " (act): GELUActivation()\n", + " (linear_2): Linear(in_features=4096, out_features=4096, bias=True)\n", + " )\n", + " (language_model): LlamaForCausalLM(\n", + " (model): LlamaModel(\n", + " (embed_tokens): Embedding(32064, 4096)\n", + " (layers): ModuleList(\n", + " (0-31): 32 x LlamaDecoderLayer(\n", + " (self_attn): LlamaAttention(\n", + " (q_proj): Linear(in_features=4096, out_features=4096, bias=False)\n", + " (k_proj): Linear(in_features=4096, out_features=4096, bias=False)\n", + " (v_proj): Linear(in_features=4096, out_features=4096, bias=False)\n", + " (o_proj): Linear(in_features=4096, out_features=4096, bias=False)\n", + " )\n", + " (mlp): LlamaMLP(\n", + " (gate_proj): Linear(in_features=4096, out_features=11008, bias=False)\n", + " (up_proj): Linear(in_features=4096, out_features=11008, bias=False)\n", + " (down_proj): Linear(in_features=11008, out_features=4096, bias=False)\n", + " (act_fn): SiLU()\n", + " )\n", + " (input_layernorm): LlamaRMSNorm((4096,), eps=1e-05)\n", + " (post_attention_layernorm): LlamaRMSNorm((4096,), eps=1e-05)\n", + " )\n", + " )\n", + " (norm): LlamaRMSNorm((4096,), eps=1e-05)\n", + " (rotary_emb): LlamaRotaryEmbedding()\n", + " )\n", + " (lm_head): Linear(in_features=4096, out_features=32064, bias=False)\n", + " )\n", + ")" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from inference_pipeline import InferencePipeline\n", + "inferencer = InferencePipeline(model, device, processor)\n", + "processor_kwargs = dict(padding=True)\n", + "model.to(device)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "4a04b464", + "metadata": {}, + "outputs": [], + "source": [ + "# results = inferencer.run_inference(\n", + "# dataloader,\n", + "# task = 'vqav2',\n", + "# processor_kwargs = processor_kwargs,\n", + "# generate_kwargs = None\n", + "# )\n", + "\n", + "# results = inferencer.run_inference(\n", + "# dataloader,\n", + "# task = 'gqa',\n", + "# processor_kwargs = processor_kwargs,\n", + "# generate_kwargs = None\n", + "# )" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "6505e84c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "range(0, 214354)" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "total_indices = range(len(dataset))\n", + "total_indices" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "c273e0e1", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "128" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "CALIBRATION_SIZE = 128\n", + "calibration_indices = random.sample(total_indices, CALIBRATION_SIZE)\n", + "\n", + "calibration_set = [(dataset[i]['image'], dataset[i]['text_input']) for i in calibration_indices]\n", + "len(calibration_set)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "fc3cf219", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Starting LLAVA model quantization...\n", + "Quantizing Vision Model with 2-bit precision...\n", + "total_layers: 144\n", + "{'encoder.layers.0.self_attn.k_proj': Linear(in_features=1024, out_features=1024, bias=True), 'encoder.layers.0.self_attn.v_proj': Linear(in_features=1024, out_features=1024, bias=True), 'encoder.layers.0.self_attn.q_proj': Linear(in_features=1024, out_features=1024, bias=True), 'encoder.layers.0.self_attn.out_proj': Linear(in_features=1024, out_features=1024, bias=True), 'encoder.layers.0.mlp.fc1': Linear(in_features=1024, out_features=4096, bias=True), 'encoder.layers.0.mlp.fc2': Linear(in_features=4096, out_features=1024, bias=True), 'encoder.layers.1.self_attn.k_proj': Linear(in_features=1024, out_features=1024, bias=True), 'encoder.layers.1.self_attn.v_proj': Linear(in_features=1024, out_features=1024, bias=True), 'encoder.layers.1.self_attn.q_proj': Linear(in_features=1024, out_features=1024, bias=True), 'encoder.layers.1.self_attn.out_proj': Linear(in_features=1024, out_features=1024, bias=True), 'encoder.layers.1.mlp.fc1': Linear(in_features=1024, out_features=4096, bias=True), 'encoder.layers.1.mlp.fc2': Linear(in_features=4096, out_features=1024, bias=True), 'encoder.layers.2.self_attn.k_proj': Linear(in_features=1024, out_features=1024, bias=True), 'encoder.layers.2.self_attn.v_proj': Linear(in_features=1024, out_features=1024, bias=True), 'encoder.layers.2.self_attn.q_proj': Linear(in_features=1024, out_features=1024, bias=True), 'encoder.layers.2.self_attn.out_proj': Linear(in_features=1024, out_features=1024, bias=True), 'encoder.layers.2.mlp.fc1': Linear(in_features=1024, out_features=4096, bias=True), 'encoder.layers.2.mlp.fc2': Linear(in_features=4096, out_features=1024, bias=True), 'encoder.layers.3.self_attn.k_proj': Linear(in_features=1024, out_features=1024, bias=True), 'encoder.layers.3.self_attn.v_proj': Linear(in_features=1024, out_features=1024, bias=True), 'encoder.layers.3.self_attn.q_proj': Linear(in_features=1024, out_features=1024, bias=True), 'encoder.layers.3.self_attn.out_proj': Linear(in_features=1024, out_features=1024, bias=True), 'encoder.layers.3.mlp.fc1': Linear(in_features=1024, out_features=4096, bias=True), 'encoder.layers.3.mlp.fc2': Linear(in_features=4096, out_features=1024, bias=True), 'encoder.layers.4.self_attn.k_proj': Linear(in_features=1024, out_features=1024, bias=True), 'encoder.layers.4.self_attn.v_proj': Linear(in_features=1024, out_features=1024, bias=True), 'encoder.layers.4.self_attn.q_proj': Linear(in_features=1024, out_features=1024, bias=True), 'encoder.layers.4.self_attn.out_proj': Linear(in_features=1024, out_features=1024, bias=True), 'encoder.layers.4.mlp.fc1': Linear(in_features=1024, out_features=4096, bias=True), 'encoder.layers.4.mlp.fc2': Linear(in_features=4096, out_features=1024, bias=True), 'encoder.layers.5.self_attn.k_proj': Linear(in_features=1024, out_features=1024, bias=True), 'encoder.layers.5.self_attn.v_proj': Linear(in_features=1024, out_features=1024, bias=True), 'encoder.layers.5.self_attn.q_proj': Linear(in_features=1024, out_features=1024, bias=True), 'encoder.layers.5.self_attn.out_proj': Linear(in_features=1024, out_features=1024, bias=True), 'encoder.layers.5.mlp.fc1': Linear(in_features=1024, out_features=4096, bias=True), 'encoder.layers.5.mlp.fc2': Linear(in_features=4096, out_features=1024, bias=True), 'encoder.layers.6.self_attn.k_proj': Linear(in_features=1024, out_features=1024, bias=True), 'encoder.layers.6.self_attn.v_proj': Linear(in_features=1024, out_features=1024, bias=True), 'encoder.layers.6.self_attn.q_proj': Linear(in_features=1024, out_features=1024, bias=True), 'encoder.layers.6.self_attn.out_proj': Linear(in_features=1024, out_features=1024, bias=True), 'encoder.layers.6.mlp.fc1': Linear(in_features=1024, out_features=4096, bias=True), 'encoder.layers.6.mlp.fc2': Linear(in_features=4096, out_features=1024, bias=True), 'encoder.layers.7.self_attn.k_proj': Linear(in_features=1024, out_features=1024, bias=True), 'encoder.layers.7.self_attn.v_proj': Linear(in_features=1024, out_features=1024, bias=True), 'encoder.layers.7.self_attn.q_proj': Linear(in_features=1024, out_features=1024, bias=True), 'encoder.layers.7.self_attn.out_proj': Linear(in_features=1024, out_features=1024, bias=True), 'encoder.layers.7.mlp.fc1': Linear(in_features=1024, out_features=4096, bias=True), 'encoder.layers.7.mlp.fc2': Linear(in_features=4096, out_features=1024, bias=True), 'encoder.layers.8.self_attn.k_proj': Linear(in_features=1024, out_features=1024, bias=True), 'encoder.layers.8.self_attn.v_proj': Linear(in_features=1024, out_features=1024, bias=True), 'encoder.layers.8.self_attn.q_proj': Linear(in_features=1024, out_features=1024, bias=True), 'encoder.layers.8.self_attn.out_proj': Linear(in_features=1024, out_features=1024, bias=True), 'encoder.layers.8.mlp.fc1': Linear(in_features=1024, out_features=4096, bias=True), 'encoder.layers.8.mlp.fc2': Linear(in_features=4096, out_features=1024, bias=True), 'encoder.layers.9.self_attn.k_proj': Linear(in_features=1024, out_features=1024, bias=True), 'encoder.layers.9.self_attn.v_proj': Linear(in_features=1024, out_features=1024, bias=True), 'encoder.layers.9.self_attn.q_proj': Linear(in_features=1024, out_features=1024, bias=True), 'encoder.layers.9.self_attn.out_proj': Linear(in_features=1024, out_features=1024, bias=True), 'encoder.layers.9.mlp.fc1': Linear(in_features=1024, out_features=4096, bias=True), 'encoder.layers.9.mlp.fc2': Linear(in_features=4096, out_features=1024, bias=True), 'encoder.layers.10.self_attn.k_proj': Linear(in_features=1024, out_features=1024, bias=True), 'encoder.layers.10.self_attn.v_proj': Linear(in_features=1024, out_features=1024, bias=True), 'encoder.layers.10.self_attn.q_proj': Linear(in_features=1024, out_features=1024, bias=True), 'encoder.layers.10.self_attn.out_proj': Linear(in_features=1024, out_features=1024, bias=True), 'encoder.layers.10.mlp.fc1': Linear(in_features=1024, out_features=4096, bias=True), 'encoder.layers.10.mlp.fc2': Linear(in_features=4096, out_features=1024, bias=True), 'encoder.layers.11.self_attn.k_proj': Linear(in_features=1024, out_features=1024, bias=True), 'encoder.layers.11.self_attn.v_proj': Linear(in_features=1024, out_features=1024, bias=True), 'encoder.layers.11.self_attn.q_proj': Linear(in_features=1024, out_features=1024, bias=True), 'encoder.layers.11.self_attn.out_proj': Linear(in_features=1024, out_features=1024, bias=True), 'encoder.layers.11.mlp.fc1': Linear(in_features=1024, out_features=4096, bias=True), 'encoder.layers.11.mlp.fc2': Linear(in_features=4096, out_features=1024, bias=True), 'encoder.layers.12.self_attn.k_proj': Linear(in_features=1024, out_features=1024, bias=True), 'encoder.layers.12.self_attn.v_proj': Linear(in_features=1024, out_features=1024, bias=True), 'encoder.layers.12.self_attn.q_proj': Linear(in_features=1024, out_features=1024, bias=True), 'encoder.layers.12.self_attn.out_proj': Linear(in_features=1024, out_features=1024, bias=True), 'encoder.layers.12.mlp.fc1': Linear(in_features=1024, out_features=4096, bias=True), 'encoder.layers.12.mlp.fc2': Linear(in_features=4096, out_features=1024, bias=True), 'encoder.layers.13.self_attn.k_proj': Linear(in_features=1024, out_features=1024, bias=True), 'encoder.layers.13.self_attn.v_proj': Linear(in_features=1024, out_features=1024, bias=True), 'encoder.layers.13.self_attn.q_proj': Linear(in_features=1024, out_features=1024, bias=True), 'encoder.layers.13.self_attn.out_proj': Linear(in_features=1024, out_features=1024, bias=True), 'encoder.layers.13.mlp.fc1': Linear(in_features=1024, out_features=4096, bias=True), 'encoder.layers.13.mlp.fc2': Linear(in_features=4096, out_features=1024, bias=True), 'encoder.layers.14.self_attn.k_proj': Linear(in_features=1024, out_features=1024, bias=True), 'encoder.layers.14.self_attn.v_proj': Linear(in_features=1024, out_features=1024, bias=True), 'encoder.layers.14.self_attn.q_proj': Linear(in_features=1024, out_features=1024, bias=True), 'encoder.layers.14.self_attn.out_proj': Linear(in_features=1024, out_features=1024, bias=True), 'encoder.layers.14.mlp.fc1': Linear(in_features=1024, out_features=4096, bias=True), 'encoder.layers.14.mlp.fc2': Linear(in_features=4096, out_features=1024, bias=True), 'encoder.layers.15.self_attn.k_proj': Linear(in_features=1024, out_features=1024, bias=True), 'encoder.layers.15.self_attn.v_proj': Linear(in_features=1024, out_features=1024, bias=True), 'encoder.layers.15.self_attn.q_proj': Linear(in_features=1024, out_features=1024, bias=True), 'encoder.layers.15.self_attn.out_proj': Linear(in_features=1024, out_features=1024, bias=True), 'encoder.layers.15.mlp.fc1': Linear(in_features=1024, out_features=4096, bias=True), 'encoder.layers.15.mlp.fc2': Linear(in_features=4096, out_features=1024, bias=True), 'encoder.layers.16.self_attn.k_proj': Linear(in_features=1024, out_features=1024, bias=True), 'encoder.layers.16.self_attn.v_proj': Linear(in_features=1024, out_features=1024, bias=True), 'encoder.layers.16.self_attn.q_proj': Linear(in_features=1024, out_features=1024, bias=True), 'encoder.layers.16.self_attn.out_proj': Linear(in_features=1024, out_features=1024, bias=True), 'encoder.layers.16.mlp.fc1': Linear(in_features=1024, out_features=4096, bias=True), 'encoder.layers.16.mlp.fc2': Linear(in_features=4096, out_features=1024, bias=True), 'encoder.layers.17.self_attn.k_proj': Linear(in_features=1024, out_features=1024, bias=True), 'encoder.layers.17.self_attn.v_proj': Linear(in_features=1024, out_features=1024, bias=True), 'encoder.layers.17.self_attn.q_proj': Linear(in_features=1024, out_features=1024, bias=True), 'encoder.layers.17.self_attn.out_proj': Linear(in_features=1024, out_features=1024, bias=True), 'encoder.layers.17.mlp.fc1': Linear(in_features=1024, out_features=4096, bias=True), 'encoder.layers.17.mlp.fc2': Linear(in_features=4096, out_features=1024, bias=True), 'encoder.layers.18.self_attn.k_proj': Linear(in_features=1024, out_features=1024, bias=True), 'encoder.layers.18.self_attn.v_proj': Linear(in_features=1024, out_features=1024, bias=True), 'encoder.layers.18.self_attn.q_proj': Linear(in_features=1024, out_features=1024, bias=True), 'encoder.layers.18.self_attn.out_proj': Linear(in_features=1024, out_features=1024, bias=True), 'encoder.layers.18.mlp.fc1': Linear(in_features=1024, out_features=4096, bias=True), 'encoder.layers.18.mlp.fc2': Linear(in_features=4096, out_features=1024, bias=True), 'encoder.layers.19.self_attn.k_proj': Linear(in_features=1024, out_features=1024, bias=True), 'encoder.layers.19.self_attn.v_proj': Linear(in_features=1024, out_features=1024, bias=True), 'encoder.layers.19.self_attn.q_proj': Linear(in_features=1024, out_features=1024, bias=True), 'encoder.layers.19.self_attn.out_proj': Linear(in_features=1024, out_features=1024, bias=True), 'encoder.layers.19.mlp.fc1': Linear(in_features=1024, out_features=4096, bias=True), 'encoder.layers.19.mlp.fc2': Linear(in_features=4096, out_features=1024, bias=True), 'encoder.layers.20.self_attn.k_proj': Linear(in_features=1024, out_features=1024, bias=True), 'encoder.layers.20.self_attn.v_proj': Linear(in_features=1024, out_features=1024, bias=True), 'encoder.layers.20.self_attn.q_proj': Linear(in_features=1024, out_features=1024, bias=True), 'encoder.layers.20.self_attn.out_proj': Linear(in_features=1024, out_features=1024, bias=True), 'encoder.layers.20.mlp.fc1': Linear(in_features=1024, out_features=4096, bias=True), 'encoder.layers.20.mlp.fc2': Linear(in_features=4096, out_features=1024, bias=True), 'encoder.layers.21.self_attn.k_proj': Linear(in_features=1024, out_features=1024, bias=True), 'encoder.layers.21.self_attn.v_proj': Linear(in_features=1024, out_features=1024, bias=True), 'encoder.layers.21.self_attn.q_proj': Linear(in_features=1024, out_features=1024, bias=True), 'encoder.layers.21.self_attn.out_proj': Linear(in_features=1024, out_features=1024, bias=True), 'encoder.layers.21.mlp.fc1': Linear(in_features=1024, out_features=4096, bias=True), 'encoder.layers.21.mlp.fc2': Linear(in_features=4096, out_features=1024, bias=True), 'encoder.layers.22.self_attn.k_proj': Linear(in_features=1024, out_features=1024, bias=True), 'encoder.layers.22.self_attn.v_proj': Linear(in_features=1024, out_features=1024, bias=True), 'encoder.layers.22.self_attn.q_proj': Linear(in_features=1024, out_features=1024, bias=True), 'encoder.layers.22.self_attn.out_proj': Linear(in_features=1024, out_features=1024, bias=True), 'encoder.layers.22.mlp.fc1': Linear(in_features=1024, out_features=4096, bias=True), 'encoder.layers.22.mlp.fc2': Linear(in_features=4096, out_features=1024, bias=True), 'encoder.layers.23.self_attn.k_proj': Linear(in_features=1024, out_features=1024, bias=True), 'encoder.layers.23.self_attn.v_proj': Linear(in_features=1024, out_features=1024, bias=True), 'encoder.layers.23.self_attn.q_proj': Linear(in_features=1024, out_features=1024, bias=True), 'encoder.layers.23.self_attn.out_proj': Linear(in_features=1024, out_features=1024, bias=True), 'encoder.layers.23.mlp.fc1': Linear(in_features=1024, out_features=4096, bias=True), 'encoder.layers.23.mlp.fc2': Linear(in_features=4096, out_features=1024, bias=True)}\n", + "\n", + "Processing vision model layers 0 to 31 with 2-bit precision\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Processing vision model batch: 100%|██████████| 128/128 [00:08<00:00, 15.59it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Quantizing layer encoder.layers.0.self_attn.k_proj...\n", + "Time for quantization: 1.72 seconds\n", + "Total quantization error: 7536.826171875\n", + "Quantizing layer encoder.layers.0.self_attn.v_proj...\n", + "Time for quantization: 0.28 seconds\n", + "Total quantization error: 5068.3720703125\n", + "Quantizing layer encoder.layers.0.self_attn.q_proj...\n", + "Time for quantization: 0.27 seconds\n", + "Total quantization error: 8809.1796875\n", + "Quantizing layer encoder.layers.0.self_attn.out_proj...\n", + "Time for quantization: 0.28 seconds\n", + "Total quantization error: 36.56419372558594\n", + "Quantizing layer encoder.layers.0.mlp.fc1...\n", + "Time for quantization: 0.30 seconds\n", + "Total quantization error: 138271.296875\n", + "Quantizing layer encoder.layers.0.mlp.fc2...\n", + "Time for quantization: 1.14 seconds\n", + "Total quantization error: 801.8564453125\n", + "Quantizing layer encoder.layers.1.self_attn.k_proj...\n", + "Time for quantization: 0.27 seconds\n", + "Total quantization error: 7883.9990234375\n", + "Quantizing layer encoder.layers.1.self_attn.v_proj...\n", + "Time for quantization: 0.28 seconds\n", + "Total quantization error: 5601.65673828125\n", + "Quantizing layer encoder.layers.1.self_attn.q_proj...\n", + "Time for quantization: 0.27 seconds\n", + "Total quantization error: 7485.513671875\n", + "Quantizing layer encoder.layers.1.self_attn.out_proj...\n", + "Time for quantization: 0.28 seconds\n", + "Total quantization error: 25.986534118652344\n", + "Quantizing layer encoder.layers.1.mlp.fc1...\n", + "Time for quantization: 0.29 seconds\n", + "Total quantization error: 62316.61328125\n", + "Quantizing layer encoder.layers.1.mlp.fc2...\n", + "Time for quantization: 1.11 seconds\n", + "Total quantization error: 755.3704223632812\n", + "Quantizing layer encoder.layers.2.self_attn.k_proj...\n", + "Time for quantization: 0.27 seconds\n", + "Total quantization error: 15435.603515625\n", + "Quantizing layer encoder.layers.2.self_attn.v_proj...\n", + "Time for quantization: 0.27 seconds\n", + "Total quantization error: 7931.7978515625\n", + "Quantizing layer encoder.layers.2.self_attn.q_proj...\n", + "Time for quantization: 0.28 seconds\n", + "Total quantization error: 14603.431640625\n", + "Quantizing layer encoder.layers.2.self_attn.out_proj...\n", + "Time for quantization: 0.27 seconds\n", + "Total quantization error: 45.67170333862305\n", + "Quantizing layer encoder.layers.2.mlp.fc1...\n", + "Time for quantization: 0.27 seconds\n", + "Total quantization error: 88615.875\n", + "Quantizing layer encoder.layers.2.mlp.fc2...\n", + "Time for quantization: 1.10 seconds\n", + "Total quantization error: 1087.5069580078125\n", + "Quantizing layer encoder.layers.3.self_attn.k_proj...\n", + "Time for quantization: 0.27 seconds\n", + "Total quantization error: 44980.125\n", + "Quantizing layer encoder.layers.3.self_attn.v_proj...\n", + "Time for quantization: 0.27 seconds\n", + "Total quantization error: 10103.92578125\n", + "Quantizing layer encoder.layers.3.self_attn.q_proj...\n", + "Time for quantization: 0.27 seconds\n", + "Total quantization error: 39181.890625\n", + "Quantizing layer encoder.layers.3.self_attn.out_proj...\n", + "Time for quantization: 0.28 seconds\n", + "Total quantization error: 593.7203369140625\n", + "Quantizing layer encoder.layers.3.mlp.fc1...\n", + "Time for quantization: 0.29 seconds\n", + "Total quantization error: 84676.21875\n", + "Quantizing layer encoder.layers.3.mlp.fc2...\n", + "Time for quantization: 1.10 seconds\n", + "Total quantization error: 1164.66064453125\n", + "Quantizing layer encoder.layers.4.self_attn.k_proj...\n", + "Time for quantization: 0.28 seconds\n", + "Total quantization error: 29548.58203125\n", + "Quantizing layer encoder.layers.4.self_attn.v_proj...\n", + "Time for quantization: 0.27 seconds\n", + "Total quantization error: 9609.4208984375\n", + "Quantizing layer encoder.layers.4.self_attn.q_proj...\n", + "Time for quantization: 0.27 seconds\n", + "Total quantization error: 24242.59765625\n", + "Quantizing layer encoder.layers.4.self_attn.out_proj...\n", + "Time for quantization: 0.28 seconds\n", + "Total quantization error: 182.055419921875\n", + "Quantizing layer encoder.layers.4.mlp.fc1...\n", + "Time for quantization: 0.28 seconds\n", + "Total quantization error: 94816.0234375\n", + "Quantizing layer encoder.layers.4.mlp.fc2...\n", + "Time for quantization: 1.10 seconds\n", + "Total quantization error: 1488.71044921875\n", + "Quantizing layer encoder.layers.5.self_attn.k_proj...\n", + "Time for quantization: 0.27 seconds\n", + "Total quantization error: 34231.47265625\n", + "Quantizing layer encoder.layers.5.self_attn.v_proj...\n", + "Time for quantization: 0.28 seconds\n", + "Total quantization error: 12763.234375\n", + "\n", + "Processing vision model layers 32 to 63 with 2-bit precision\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Processing vision model batch: 100%|██████████| 128/128 [00:03<00:00, 42.29it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Quantizing layer encoder.layers.5.self_attn.q_proj...\n", + "Time for quantization: 0.28 seconds\n", + "Total quantization error: 30146.412109375\n", + "Quantizing layer encoder.layers.5.self_attn.out_proj...\n", + "Time for quantization: 0.27 seconds\n", + "Total quantization error: 209.6897430419922\n", + "Quantizing layer encoder.layers.5.mlp.fc1...\n", + "Time for quantization: 0.27 seconds\n", + "Total quantization error: 137523.125\n", + "Quantizing layer encoder.layers.5.mlp.fc2...\n", + "Time for quantization: 1.10 seconds\n", + "Total quantization error: 1430.869873046875\n", + "Quantizing layer encoder.layers.6.self_attn.k_proj...\n", + "Time for quantization: 0.27 seconds\n", + "Total quantization error: 79953.078125\n", + "Quantizing layer encoder.layers.6.self_attn.v_proj...\n", + "Time for quantization: 0.28 seconds\n", + "Total quantization error: 22287.55859375\n", + "Quantizing layer encoder.layers.6.self_attn.q_proj...\n", + "Time for quantization: 0.31 seconds\n", + "Total quantization error: 58968.96875\n", + "Quantizing layer encoder.layers.6.self_attn.out_proj...\n", + "Time for quantization: 0.30 seconds\n", + "Total quantization error: 850.9054565429688\n", + "Quantizing layer encoder.layers.6.mlp.fc1...\n", + "Time for quantization: 0.28 seconds\n", + "Total quantization error: 130215.1953125\n", + "Quantizing layer encoder.layers.6.mlp.fc2...\n", + "Time for quantization: 1.10 seconds\n", + "Total quantization error: 1656.4346923828125\n", + "Quantizing layer encoder.layers.7.self_attn.k_proj...\n", + "Time for quantization: 0.27 seconds\n", + "Total quantization error: 59021.7421875\n", + "Quantizing layer encoder.layers.7.self_attn.v_proj...\n", + "Time for quantization: 0.27 seconds\n", + "Total quantization error: 28601.73046875\n", + "Quantizing layer encoder.layers.7.self_attn.q_proj...\n", + "Time for quantization: 0.28 seconds\n", + "Total quantization error: 48874.6484375\n", + "Quantizing layer encoder.layers.7.self_attn.out_proj...\n", + "Time for quantization: 0.27 seconds\n", + "Total quantization error: 515.337158203125\n", + "Quantizing layer encoder.layers.7.mlp.fc1...\n", + "Time for quantization: 0.28 seconds\n", + "Total quantization error: 148671.59375\n", + "Quantizing layer encoder.layers.7.mlp.fc2...\n", + "Time for quantization: 1.10 seconds\n", + "Total quantization error: 2141.197265625\n", + "Quantizing layer encoder.layers.8.self_attn.k_proj...\n", + "Time for quantization: 0.27 seconds\n", + "Total quantization error: 62288.75\n", + "Quantizing layer encoder.layers.8.self_attn.v_proj...\n", + "Time for quantization: 0.27 seconds\n", + "Total quantization error: 30358.32421875\n", + "Quantizing layer encoder.layers.8.self_attn.q_proj...\n", + "Time for quantization: 0.27 seconds\n", + "Total quantization error: 53317.671875\n", + "Quantizing layer encoder.layers.8.self_attn.out_proj...\n", + "Time for quantization: 0.27 seconds\n", + "Total quantization error: 765.0619506835938\n", + "Quantizing layer encoder.layers.8.mlp.fc1...\n", + "Time for quantization: 0.28 seconds\n", + "Total quantization error: 164230.6875\n", + "Quantizing layer encoder.layers.8.mlp.fc2...\n", + "Time for quantization: 1.13 seconds\n", + "Total quantization error: 2816.334228515625\n", + "Quantizing layer encoder.layers.9.self_attn.k_proj...\n", + "Time for quantization: 0.28 seconds\n", + "Total quantization error: 60985.80859375\n", + "Quantizing layer encoder.layers.9.self_attn.v_proj...\n", + "Time for quantization: 0.28 seconds\n", + "Total quantization error: 29847.2421875\n", + "Quantizing layer encoder.layers.9.self_attn.q_proj...\n", + "Time for quantization: 0.27 seconds\n", + "Total quantization error: 51286.03125\n", + "Quantizing layer encoder.layers.9.self_attn.out_proj...\n", + "Time for quantization: 0.27 seconds\n", + "Total quantization error: 846.2679443359375\n", + "Quantizing layer encoder.layers.9.mlp.fc1...\n", + "Time for quantization: 0.28 seconds\n", + "Total quantization error: 170620.34375\n", + "Quantizing layer encoder.layers.9.mlp.fc2...\n", + "Time for quantization: 1.11 seconds\n", + "Total quantization error: 2736.060791015625\n", + "Quantizing layer encoder.layers.10.self_attn.k_proj...\n", + "Time for quantization: 0.28 seconds\n", + "Total quantization error: 62666.9375\n", + "Quantizing layer encoder.layers.10.self_attn.v_proj...\n", + "Time for quantization: 0.28 seconds\n", + "Total quantization error: 31524.046875\n", + "Quantizing layer encoder.layers.10.self_attn.q_proj...\n", + "Time for quantization: 0.28 seconds\n", + "Total quantization error: 55102.5390625\n", + "Quantizing layer encoder.layers.10.self_attn.out_proj...\n", + "Time for quantization: 0.30 seconds\n", + "Total quantization error: 901.84130859375\n", + "\n", + "Processing vision model layers 64 to 95 with 2-bit precision\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Processing vision model batch: 100%|██████████| 128/128 [00:03<00:00, 40.21it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Quantizing layer encoder.layers.10.mlp.fc1...\n", + "Time for quantization: 0.28 seconds\n", + "Total quantization error: 215141.921875\n", + "Quantizing layer encoder.layers.10.mlp.fc2...\n", + "Time for quantization: 1.15 seconds\n", + "Total quantization error: 2340.267578125\n", + "Quantizing layer encoder.layers.11.self_attn.k_proj...\n", + "Time for quantization: 0.28 seconds\n", + "Total quantization error: 72329.453125\n", + "Quantizing layer encoder.layers.11.self_attn.v_proj...\n", + "Time for quantization: 0.28 seconds\n", + "Total quantization error: 35899.09765625\n", + "Quantizing layer encoder.layers.11.self_attn.q_proj...\n", + "Time for quantization: 0.27 seconds\n", + "Total quantization error: 65553.28125\n", + "Quantizing layer encoder.layers.11.self_attn.out_proj...\n", + "Time for quantization: 0.29 seconds\n", + "Total quantization error: 406.91229248046875\n", + "Quantizing layer encoder.layers.11.mlp.fc1...\n", + "Time for quantization: 0.28 seconds\n", + "Total quantization error: 225343.96875\n", + "Quantizing layer encoder.layers.11.mlp.fc2...\n", + "Time for quantization: 1.14 seconds\n", + "Total quantization error: 2778.89697265625\n", + "Quantizing layer encoder.layers.12.self_attn.k_proj...\n", + "Time for quantization: 0.27 seconds\n", + "Total quantization error: 68561.578125\n", + "Quantizing layer encoder.layers.12.self_attn.v_proj...\n", + "Time for quantization: 0.27 seconds\n", + "Total quantization error: 33404.51171875\n", + "Quantizing layer encoder.layers.12.self_attn.q_proj...\n", + "Time for quantization: 0.28 seconds\n", + "Total quantization error: 63146.9375\n", + "Quantizing layer encoder.layers.12.self_attn.out_proj...\n", + "Time for quantization: 0.27 seconds\n", + "Total quantization error: 278.0452880859375\n", + "Quantizing layer encoder.layers.12.mlp.fc1...\n", + "Time for quantization: 0.28 seconds\n", + "Total quantization error: 222461.15625\n", + "Quantizing layer encoder.layers.12.mlp.fc2...\n", + "Time for quantization: 1.12 seconds\n", + "Total quantization error: 10091.5458984375\n", + "Quantizing layer encoder.layers.13.self_attn.k_proj...\n", + "Time for quantization: 0.27 seconds\n", + "Total quantization error: 71382.625\n", + "Quantizing layer encoder.layers.13.self_attn.v_proj...\n", + "Time for quantization: 0.27 seconds\n", + "Total quantization error: 36280.6796875\n", + "Quantizing layer encoder.layers.13.self_attn.q_proj...\n", + "Time for quantization: 0.27 seconds\n", + "Total quantization error: 67001.9375\n", + "Quantizing layer encoder.layers.13.self_attn.out_proj...\n", + "Time for quantization: 0.28 seconds\n", + "Total quantization error: 310.2740478515625\n", + "Quantizing layer encoder.layers.13.mlp.fc1...\n", + "Time for quantization: 0.32 seconds\n", + "Total quantization error: 205684.90625\n", + "Quantizing layer encoder.layers.13.mlp.fc2...\n", + "Time for quantization: 1.26 seconds\n", + "Total quantization error: 1846.7236328125\n", + "Quantizing layer encoder.layers.14.self_attn.k_proj...\n", + "Time for quantization: 0.29 seconds\n", + "Total quantization error: 60569.984375\n", + "Quantizing layer encoder.layers.14.self_attn.v_proj...\n", + "Time for quantization: 0.31 seconds\n", + "Total quantization error: 31290.86328125\n", + "Quantizing layer encoder.layers.14.self_attn.q_proj...\n", + "Time for quantization: 0.30 seconds\n", + "Total quantization error: 57308.58984375\n", + "Quantizing layer encoder.layers.14.self_attn.out_proj...\n", + "Time for quantization: 0.28 seconds\n", + "Total quantization error: 230.7410888671875\n", + "Quantizing layer encoder.layers.14.mlp.fc1...\n", + "Time for quantization: 0.28 seconds\n", + "Total quantization error: 222883.46875\n", + "Quantizing layer encoder.layers.14.mlp.fc2...\n", + "Time for quantization: 1.12 seconds\n", + "Total quantization error: 1922.51123046875\n", + "Quantizing layer encoder.layers.15.self_attn.k_proj...\n", + "Time for quantization: 0.28 seconds\n", + "Total quantization error: 53570.953125\n", + "Quantizing layer encoder.layers.15.self_attn.v_proj...\n", + "Time for quantization: 0.28 seconds\n", + "Total quantization error: 31346.1015625\n", + "Quantizing layer encoder.layers.15.self_attn.q_proj...\n", + "Time for quantization: 0.28 seconds\n", + "Total quantization error: 51951.453125\n", + "Quantizing layer encoder.layers.15.self_attn.out_proj...\n", + "Time for quantization: 0.28 seconds\n", + "Total quantization error: 181.08407592773438\n", + "Quantizing layer encoder.layers.15.mlp.fc1...\n", + "Time for quantization: 0.28 seconds\n", + "Total quantization error: 259875.578125\n", + "Quantizing layer encoder.layers.15.mlp.fc2...\n", + "Time for quantization: 1.13 seconds\n", + "Total quantization error: 2083.7099609375\n", + "\n", + "Processing vision model layers 96 to 127 with 2-bit precision\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Processing vision model batch: 100%|██████████| 128/128 [00:03<00:00, 42.19it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Quantizing layer encoder.layers.16.self_attn.k_proj...\n", + "Time for quantization: 0.28 seconds\n", + "Total quantization error: 96940.3515625\n", + "Quantizing layer encoder.layers.16.self_attn.v_proj...\n", + "Time for quantization: 0.28 seconds\n", + "Total quantization error: 61425.203125\n", + "Quantizing layer encoder.layers.16.self_attn.q_proj...\n", + "Time for quantization: 0.29 seconds\n", + "Total quantization error: 93793.9375\n", + "Quantizing layer encoder.layers.16.self_attn.out_proj...\n", + "Time for quantization: 0.28 seconds\n", + "Total quantization error: 114.7514877319336\n", + "Quantizing layer encoder.layers.16.mlp.fc1...\n", + "Time for quantization: 0.28 seconds\n", + "Total quantization error: 562054.25\n", + "Quantizing layer encoder.layers.16.mlp.fc2...\n", + "Time for quantization: 1.11 seconds\n", + "Total quantization error: 2076.9423828125\n", + "Quantizing layer encoder.layers.17.self_attn.k_proj...\n", + "Time for quantization: 0.28 seconds\n", + "Total quantization error: 98655.75\n", + "Quantizing layer encoder.layers.17.self_attn.v_proj...\n", + "Time for quantization: 0.28 seconds\n", + "Total quantization error: 63209.3671875\n", + "Quantizing layer encoder.layers.17.self_attn.q_proj...\n", + "Time for quantization: 0.31 seconds\n", + "Total quantization error: 96483.15625\n", + "Quantizing layer encoder.layers.17.self_attn.out_proj...\n", + "Time for quantization: 0.29 seconds\n", + "Total quantization error: 77.26315307617188\n", + "Quantizing layer encoder.layers.17.mlp.fc1...\n", + "Time for quantization: 0.29 seconds\n", + "Total quantization error: 781201.5\n", + "Quantizing layer encoder.layers.17.mlp.fc2...\n", + "Time for quantization: 1.21 seconds\n", + "Total quantization error: 3663.663330078125\n", + "Quantizing layer encoder.layers.18.self_attn.k_proj...\n", + "Time for quantization: 0.31 seconds\n", + "Total quantization error: 110853.6953125\n", + "Quantizing layer encoder.layers.18.self_attn.v_proj...\n", + "Time for quantization: 0.32 seconds\n", + "Total quantization error: 76915.140625\n", + "Quantizing layer encoder.layers.18.self_attn.q_proj...\n", + "Time for quantization: 0.32 seconds\n", + "Total quantization error: 112623.234375\n", + "Quantizing layer encoder.layers.18.self_attn.out_proj...\n", + "Time for quantization: 0.29 seconds\n", + "Total quantization error: 97.75102996826172\n", + "Quantizing layer encoder.layers.18.mlp.fc1...\n", + "Time for quantization: 0.29 seconds\n", + "Total quantization error: 964601.375\n", + "Quantizing layer encoder.layers.18.mlp.fc2...\n", + "Time for quantization: 1.16 seconds\n", + "Total quantization error: 4506.630859375\n", + "Quantizing layer encoder.layers.19.self_attn.k_proj...\n", + "Time for quantization: 0.28 seconds\n", + "Total quantization error: 115003.609375\n", + "Quantizing layer encoder.layers.19.self_attn.v_proj...\n", + "Time for quantization: 0.28 seconds\n", + "Total quantization error: 81111.65625\n", + "Quantizing layer encoder.layers.19.self_attn.q_proj...\n", + "Time for quantization: 0.28 seconds\n", + "Total quantization error: 113607.96875\n", + "Quantizing layer encoder.layers.19.self_attn.out_proj...\n", + "Time for quantization: 0.28 seconds\n", + "Total quantization error: 86.44048309326172\n", + "Quantizing layer encoder.layers.19.mlp.fc1...\n", + "Time for quantization: 0.28 seconds\n", + "Total quantization error: 1041932.25\n", + "Quantizing layer encoder.layers.19.mlp.fc2...\n", + "Time for quantization: 1.13 seconds\n", + "Total quantization error: 5255.044921875\n", + "Quantizing layer encoder.layers.20.self_attn.k_proj...\n", + "Time for quantization: 0.29 seconds\n", + "Total quantization error: 110170.0546875\n", + "Quantizing layer encoder.layers.20.self_attn.v_proj...\n", + "Time for quantization: 0.30 seconds\n", + "Total quantization error: 87566.390625\n", + "Quantizing layer encoder.layers.20.self_attn.q_proj...\n", + "Time for quantization: 0.29 seconds\n", + "Total quantization error: 111702.796875\n", + "Quantizing layer encoder.layers.20.self_attn.out_proj...\n", + "Time for quantization: 0.28 seconds\n", + "Total quantization error: 121.17045593261719\n", + "Quantizing layer encoder.layers.20.mlp.fc1...\n", + "Time for quantization: 0.28 seconds\n", + "Total quantization error: 1364214.75\n", + "Quantizing layer encoder.layers.20.mlp.fc2...\n", + "Time for quantization: 1.16 seconds\n", + "Total quantization error: 5751.2216796875\n", + "Quantizing layer encoder.layers.21.self_attn.k_proj...\n", + "Time for quantization: 0.29 seconds\n", + "Total quantization error: 96696.6875\n", + "Quantizing layer encoder.layers.21.self_attn.v_proj...\n", + "Time for quantization: 0.29 seconds\n", + "Total quantization error: 87815.703125\n", + "\n", + "Processing vision model layers 128 to 143 with 2-bit precision\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Processing vision model batch: 100%|██████████| 128/128 [00:03<00:00, 39.67it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Quantizing layer encoder.layers.21.self_attn.q_proj...\n", + "Time for quantization: 0.28 seconds\n", + "Total quantization error: 142811.8125\n", + "Quantizing layer encoder.layers.21.self_attn.out_proj...\n", + "Time for quantization: 0.27 seconds\n", + "Total quantization error: 400.9639892578125\n", + "Quantizing layer encoder.layers.21.mlp.fc1...\n", + "Time for quantization: 0.28 seconds\n", + "Total quantization error: 1575822.375\n", + "Quantizing layer encoder.layers.21.mlp.fc2...\n", + "Time for quantization: 1.18 seconds\n", + "Total quantization error: 9809.55859375\n", + "Quantizing layer encoder.layers.22.self_attn.k_proj...\n", + "Time for quantization: 0.28 seconds\n", + "Total quantization error: 110969.3125\n", + "Quantizing layer encoder.layers.22.self_attn.v_proj...\n", + "Time for quantization: 0.28 seconds\n", + "Total quantization error: 115392.4375\n", + "Quantizing layer encoder.layers.22.self_attn.q_proj...\n", + "Time for quantization: 0.28 seconds\n", + "Total quantization error: 987890.5\n", + "Quantizing layer encoder.layers.22.self_attn.out_proj...\n", + "Time for quantization: 0.28 seconds\n", + "Total quantization error: 301.11627197265625\n", + "Quantizing layer encoder.layers.22.mlp.fc1...\n", + "Time for quantization: 0.28 seconds\n", + "Total quantization error: 1003122.0625\n", + "Quantizing layer encoder.layers.22.mlp.fc2...\n", + "Time for quantization: 1.13 seconds\n", + "Total quantization error: 7096.63720703125\n", + "Quantizing layer encoder.layers.23.self_attn.k_proj...\n", + "Time for quantization: 0.28 seconds\n", + "Total quantization error: 87967.21875\n", + "Quantizing layer encoder.layers.23.self_attn.v_proj...\n", + "Time for quantization: 0.28 seconds\n", + "Total quantization error: 133565.421875\n", + "Quantizing layer encoder.layers.23.self_attn.q_proj...\n", + "Time for quantization: 0.30 seconds\n", + "Total quantization error: 84975.1640625\n", + "Quantizing layer encoder.layers.23.self_attn.out_proj...\n", + "Time for quantization: 0.30 seconds\n", + "Total quantization error: 249.34539794921875\n", + "Quantizing layer encoder.layers.23.mlp.fc1...\n", + "Time for quantization: 0.29 seconds\n", + "Total quantization error: 422525.9375\n", + "Quantizing layer encoder.layers.23.mlp.fc2...\n", + "Time for quantization: 1.15 seconds\n", + "Total quantization error: 4236.0546875\n", + "Vision Model quantization complete.\n", + "\n", + "Quantizing Language Model with 6-bit precision...\n", + "\n", + "Processing language model layers 0 to 31 with 6-bit precision\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Processing language model batch: 100%|██████████| 128/128 [00:43<00:00, 2.93it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Quantizing layer layers.0.self_attn.q_proj...\n", + "Time for quantization: 1.32 seconds\n", + "Total quantization error: 2.989701747894287\n", + "Quantizing layer layers.0.self_attn.k_proj...\n", + "Time for quantization: 1.99 seconds\n", + "Total quantization error: 2.831799030303955\n", + "Quantizing layer layers.0.self_attn.v_proj...\n", + "Time for quantization: 1.14 seconds\n", + "Total quantization error: 0.2066064178943634\n", + "Quantizing layer layers.0.self_attn.o_proj...\n", + "Time for quantization: 1.14 seconds\n", + "Total quantization error: 0.004312355071306229\n", + "Quantizing layer layers.0.mlp.gate_proj...\n", + "Time for quantization: 1.22 seconds\n", + "Total quantization error: 1.2334898710250854\n", + "Quantizing layer layers.0.mlp.up_proj...\n", + "Time for quantization: 1.87 seconds\n", + "Total quantization error: 1.1072502136230469\n", + "Quantizing layer layers.0.mlp.down_proj...\n", + "Time for quantization: 4.05 seconds\n", + "Total quantization error: 0.009842751547694206\n", + "Quantizing layer layers.1.self_attn.q_proj...\n", + "Time for quantization: 2.10 seconds\n", + "Total quantization error: 7.370405197143555\n", + "Quantizing layer layers.1.self_attn.k_proj...\n", + "Time for quantization: 1.74 seconds\n", + "Total quantization error: 7.984486103057861\n", + "Quantizing layer layers.1.self_attn.v_proj...\n", + "Time for quantization: 1.23 seconds\n", + "Total quantization error: 0.5757219791412354\n", + "Quantizing layer layers.1.self_attn.o_proj...\n", + "Time for quantization: 1.59 seconds\n", + "Total quantization error: 0.013945920392870903\n", + "Quantizing layer layers.1.mlp.gate_proj...\n", + "Time for quantization: 1.22 seconds\n", + "Total quantization error: 4.589108467102051\n", + "Quantizing layer layers.1.mlp.up_proj...\n", + "Time for quantization: 1.23 seconds\n", + "Total quantization error: 3.8899872303009033\n", + "Quantizing layer layers.1.mlp.down_proj...\n", + "Time for quantization: 4.11 seconds\n", + "Total quantization error: 42.539634704589844\n", + "Quantizing layer layers.2.self_attn.q_proj...\n", + "Time for quantization: 1.23 seconds\n", + "Total quantization error: 26.90134048461914\n", + "Quantizing layer layers.2.self_attn.k_proj...\n", + "Time for quantization: 1.26 seconds\n", + "Total quantization error: 19.149137496948242\n", + "Quantizing layer layers.2.self_attn.v_proj...\n", + "Time for quantization: 1.21 seconds\n", + "Total quantization error: 3.2028112411499023\n", + "Quantizing layer layers.2.self_attn.o_proj...\n", + "Time for quantization: 1.23 seconds\n", + "Total quantization error: 0.09774317592382431\n", + "Quantizing layer layers.2.mlp.gate_proj...\n", + "Time for quantization: 1.29 seconds\n", + "Total quantization error: 8.7083740234375\n", + "Quantizing layer layers.2.mlp.up_proj...\n", + "Time for quantization: 1.19 seconds\n", + "Total quantization error: 7.3677239418029785\n", + "Quantizing layer layers.2.mlp.down_proj...\n", + "Time for quantization: 3.44 seconds\n", + "Total quantization error: 0.32798123359680176\n", + "Quantizing layer layers.3.self_attn.q_proj...\n", + "Time for quantization: 1.14 seconds\n", + "Total quantization error: 74.21306610107422\n", + "Quantizing layer layers.3.self_attn.k_proj...\n", + "Time for quantization: 1.13 seconds\n", + "Total quantization error: 39.04096603393555\n", + "Quantizing layer layers.3.self_attn.v_proj...\n", + "Time for quantization: 1.21 seconds\n", + "Total quantization error: 8.094205856323242\n", + "Quantizing layer layers.3.self_attn.o_proj...\n", + "Time for quantization: 1.39 seconds\n", + "Total quantization error: 0.048147402703762054\n", + "Quantizing layer layers.3.mlp.gate_proj...\n", + "Time for quantization: 1.23 seconds\n", + "Total quantization error: 15.363450050354004\n", + "Quantizing layer layers.3.mlp.up_proj...\n", + "Time for quantization: 1.19 seconds\n", + "Total quantization error: 12.84113597869873\n", + "Quantizing layer layers.3.mlp.down_proj...\n", + "Time for quantization: 3.77 seconds\n", + "Total quantization error: 0.8056259155273438\n", + "Quantizing layer layers.4.self_attn.q_proj...\n", + "Time for quantization: 1.17 seconds\n", + "Total quantization error: 69.60504913330078\n", + "Quantizing layer layers.4.self_attn.k_proj...\n", + "Time for quantization: 1.14 seconds\n", + "Total quantization error: 39.72223663330078\n", + "Quantizing layer layers.4.self_attn.v_proj...\n", + "Time for quantization: 1.13 seconds\n", + "Total quantization error: 8.469852447509766\n", + "Quantizing layer layers.4.self_attn.o_proj...\n", + "Time for quantization: 1.13 seconds\n", + "Total quantization error: 0.08602683991193771\n", + "\n", + "Processing language model layers 32 to 63 with 6-bit precision\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Processing language model batch: 100%|██████████| 128/128 [00:48<00:00, 2.66it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Quantizing layer layers.4.mlp.gate_proj...\n", + "Time for quantization: 2.92 seconds\n", + "Total quantization error: 21.84815216064453\n", + "Quantizing layer layers.4.mlp.up_proj...\n", + "Time for quantization: 3.22 seconds\n", + "Total quantization error: 17.30970001220703\n", + "Quantizing layer layers.4.mlp.down_proj...\n", + "Time for quantization: 8.32 seconds\n", + "Total quantization error: 1.3419699668884277\n", + "Quantizing layer layers.5.self_attn.q_proj...\n", + "Time for quantization: 3.32 seconds\n", + "Total quantization error: 58.1400260925293\n", + "Quantizing layer layers.5.self_attn.k_proj...\n", + "Time for quantization: 2.93 seconds\n", + "Total quantization error: 47.555625915527344\n", + "Quantizing layer layers.5.self_attn.v_proj...\n", + "Time for quantization: 3.22 seconds\n", + "Total quantization error: 10.853681564331055\n", + "Quantizing layer layers.5.self_attn.o_proj...\n", + "Time for quantization: 1.13 seconds\n", + "Total quantization error: 0.20798379182815552\n", + "Quantizing layer layers.5.mlp.gate_proj...\n", + "Time for quantization: 1.22 seconds\n", + "Total quantization error: 27.885303497314453\n", + "Quantizing layer layers.5.mlp.up_proj...\n", + "Time for quantization: 1.18 seconds\n", + "Total quantization error: 22.130115509033203\n", + "Quantizing layer layers.5.mlp.down_proj...\n", + "Time for quantization: 3.67 seconds\n", + "Total quantization error: 1.5060265064239502\n", + "Quantizing layer layers.6.self_attn.q_proj...\n", + "Time for quantization: 1.53 seconds\n", + "Total quantization error: 90.01118469238281\n", + "Quantizing layer layers.6.self_attn.k_proj...\n", + "Time for quantization: 1.29 seconds\n", + "Total quantization error: 64.48128509521484\n", + "Quantizing layer layers.6.self_attn.v_proj...\n", + "Time for quantization: 1.20 seconds\n", + "Total quantization error: 16.861934661865234\n", + "Quantizing layer layers.6.self_attn.o_proj...\n", + "Time for quantization: 1.13 seconds\n", + "Total quantization error: 0.268207311630249\n", + "Quantizing layer layers.6.mlp.gate_proj...\n", + "Time for quantization: 1.18 seconds\n", + "Total quantization error: 36.16478729248047\n", + "Quantizing layer layers.6.mlp.up_proj...\n", + "Time for quantization: 1.17 seconds\n", + "Total quantization error: 27.540023803710938\n", + "Quantizing layer layers.6.mlp.down_proj...\n", + "Time for quantization: 3.41 seconds\n", + "Total quantization error: 1.9666942358016968\n", + "Quantizing layer layers.7.self_attn.q_proj...\n", + "Time for quantization: 1.13 seconds\n", + "Total quantization error: 89.46115112304688\n", + "Quantizing layer layers.7.self_attn.k_proj...\n", + "Time for quantization: 1.12 seconds\n", + "Total quantization error: 70.85684204101562\n", + "Quantizing layer layers.7.self_attn.v_proj...\n", + "Time for quantization: 1.13 seconds\n", + "Total quantization error: 20.639238357543945\n", + "Quantizing layer layers.7.self_attn.o_proj...\n", + "Time for quantization: 1.15 seconds\n", + "Total quantization error: 0.4250693917274475\n", + "Quantizing layer layers.7.mlp.gate_proj...\n", + "Time for quantization: 1.20 seconds\n", + "Total quantization error: 44.569618225097656\n", + "Quantizing layer layers.7.mlp.up_proj...\n", + "Time for quantization: 1.18 seconds\n", + "Total quantization error: 34.13462829589844\n", + "Quantizing layer layers.7.mlp.down_proj...\n", + "Time for quantization: 3.40 seconds\n", + "Total quantization error: 2.4605703353881836\n", + "Quantizing layer layers.8.self_attn.q_proj...\n", + "Time for quantization: 1.13 seconds\n", + "Total quantization error: 86.86235809326172\n", + "Quantizing layer layers.8.self_attn.k_proj...\n", + "Time for quantization: 1.12 seconds\n", + "Total quantization error: 76.52308654785156\n", + "Quantizing layer layers.8.self_attn.v_proj...\n", + "Time for quantization: 1.13 seconds\n", + "Total quantization error: 21.796897888183594\n", + "Quantizing layer layers.8.self_attn.o_proj...\n", + "Time for quantization: 1.14 seconds\n", + "Total quantization error: 0.7182276248931885\n", + "Quantizing layer layers.8.mlp.gate_proj...\n", + "Time for quantization: 1.17 seconds\n", + "Total quantization error: 47.52708435058594\n", + "Quantizing layer layers.8.mlp.up_proj...\n", + "Time for quantization: 1.18 seconds\n", + "Total quantization error: 39.065330505371094\n", + "Quantizing layer layers.8.mlp.down_proj...\n", + "Time for quantization: 3.39 seconds\n", + "Total quantization error: 2.9835262298583984\n", + "Quantizing layer layers.9.self_attn.q_proj...\n", + "Time for quantization: 1.13 seconds\n", + "Total quantization error: 90.47483825683594\n", + "\n", + "Processing language model layers 64 to 95 with 6-bit precision\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Processing language model batch: 100%|██████████| 128/128 [00:45<00:00, 2.81it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Quantizing layer layers.9.self_attn.k_proj...\n", + "Time for quantization: 1.33 seconds\n", + "Total quantization error: 85.71088409423828\n", + "Quantizing layer layers.9.self_attn.v_proj...\n", + "Time for quantization: 1.21 seconds\n", + "Total quantization error: 24.97563934326172\n", + "Quantizing layer layers.9.self_attn.o_proj...\n", + "Time for quantization: 1.20 seconds\n", + "Total quantization error: 1.2394875288009644\n", + "Quantizing layer layers.9.mlp.gate_proj...\n", + "Time for quantization: 1.18 seconds\n", + "Total quantization error: 51.35231018066406\n", + "Quantizing layer layers.9.mlp.up_proj...\n", + "Time for quantization: 1.20 seconds\n", + "Total quantization error: 43.4822998046875\n", + "Quantizing layer layers.9.mlp.down_proj...\n", + "Time for quantization: 3.40 seconds\n", + "Total quantization error: 3.4020423889160156\n", + "Quantizing layer layers.10.self_attn.q_proj...\n", + "Time for quantization: 1.13 seconds\n", + "Total quantization error: 88.2155532836914\n", + "Quantizing layer layers.10.self_attn.k_proj...\n", + "Time for quantization: 1.14 seconds\n", + "Total quantization error: 91.2552490234375\n", + "Quantizing layer layers.10.self_attn.v_proj...\n", + "Time for quantization: 1.13 seconds\n", + "Total quantization error: 27.669450759887695\n", + "Quantizing layer layers.10.self_attn.o_proj...\n", + "Time for quantization: 1.14 seconds\n", + "Total quantization error: 1.6878772974014282\n", + "Quantizing layer layers.10.mlp.gate_proj...\n", + "Time for quantization: 1.34 seconds\n", + "Total quantization error: 51.82904815673828\n", + "Quantizing layer layers.10.mlp.up_proj...\n", + "Time for quantization: 2.86 seconds\n", + "Total quantization error: 44.39678955078125\n", + "Quantizing layer layers.10.mlp.down_proj...\n", + "Time for quantization: 4.08 seconds\n", + "Total quantization error: 3.891646385192871\n", + "Quantizing layer layers.11.self_attn.q_proj...\n", + "Time for quantization: 1.45 seconds\n", + "Total quantization error: 99.66990661621094\n", + "Quantizing layer layers.11.self_attn.k_proj...\n", + "Time for quantization: 1.49 seconds\n", + "Total quantization error: 91.78538513183594\n", + "Quantizing layer layers.11.self_attn.v_proj...\n", + "Time for quantization: 1.17 seconds\n", + "Total quantization error: 36.9306755065918\n", + "Quantizing layer layers.11.self_attn.o_proj...\n", + "Time for quantization: 1.14 seconds\n", + "Total quantization error: 1.4064407348632812\n", + "Quantizing layer layers.11.mlp.gate_proj...\n", + "Time for quantization: 1.18 seconds\n", + "Total quantization error: 56.678279876708984\n", + "Quantizing layer layers.11.mlp.up_proj...\n", + "Time for quantization: 1.28 seconds\n", + "Total quantization error: 50.15105438232422\n", + "Quantizing layer layers.11.mlp.down_proj...\n", + "Time for quantization: 3.45 seconds\n", + "Total quantization error: 3.8708343505859375\n", + "Quantizing layer layers.12.self_attn.q_proj...\n", + "Time for quantization: 1.13 seconds\n", + "Total quantization error: 106.29507446289062\n", + "Quantizing layer layers.12.self_attn.k_proj...\n", + "Time for quantization: 1.13 seconds\n", + "Total quantization error: 106.59021759033203\n", + "Quantizing layer layers.12.self_attn.v_proj...\n", + "Time for quantization: 1.12 seconds\n", + "Total quantization error: 37.61466979980469\n", + "Quantizing layer layers.12.self_attn.o_proj...\n", + "Time for quantization: 1.13 seconds\n", + "Total quantization error: 0.9477210640907288\n", + "Quantizing layer layers.12.mlp.gate_proj...\n", + "Time for quantization: 1.17 seconds\n", + "Total quantization error: 58.96004104614258\n", + "Quantizing layer layers.12.mlp.up_proj...\n", + "Time for quantization: 1.17 seconds\n", + "Total quantization error: 53.08171463012695\n", + "Quantizing layer layers.12.mlp.down_proj...\n", + "Time for quantization: 3.45 seconds\n", + "Total quantization error: 4.247705936431885\n", + "Quantizing layer layers.13.self_attn.q_proj...\n", + "Time for quantization: 1.13 seconds\n", + "Total quantization error: 116.15562438964844\n", + "Quantizing layer layers.13.self_attn.k_proj...\n", + "Time for quantization: 1.16 seconds\n", + "Total quantization error: 104.52471923828125\n", + "Quantizing layer layers.13.self_attn.v_proj...\n", + "Time for quantization: 1.61 seconds\n", + "Total quantization error: 38.8678092956543\n", + "Quantizing layer layers.13.self_attn.o_proj...\n", + "Time for quantization: 1.62 seconds\n", + "Total quantization error: 1.1996291875839233\n", + "Quantizing layer layers.13.mlp.gate_proj...\n", + "Time for quantization: 1.50 seconds\n", + "Total quantization error: 60.3423957824707\n", + "\n", + "Processing language model layers 96 to 127 with 6-bit precision\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Processing language model batch: 100%|██████████| 128/128 [00:46<00:00, 2.76it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Quantizing layer layers.13.mlp.up_proj...\n", + "Time for quantization: 1.73 seconds\n", + "Total quantization error: 55.68977737426758\n", + "Quantizing layer layers.13.mlp.down_proj...\n", + "Time for quantization: 3.51 seconds\n", + "Total quantization error: 5.318157196044922\n", + "Quantizing layer layers.14.self_attn.q_proj...\n", + "Time for quantization: 1.27 seconds\n", + "Total quantization error: 106.26704406738281\n", + "Quantizing layer layers.14.self_attn.k_proj...\n", + "Time for quantization: 1.15 seconds\n", + "Total quantization error: 109.53216552734375\n", + "Quantizing layer layers.14.self_attn.v_proj...\n", + "Time for quantization: 1.13 seconds\n", + "Total quantization error: 38.33792495727539\n", + "Quantizing layer layers.14.self_attn.o_proj...\n", + "Time for quantization: 1.15 seconds\n", + "Total quantization error: 1.5643620491027832\n", + "Quantizing layer layers.14.mlp.gate_proj...\n", + "Time for quantization: 1.62 seconds\n", + "Total quantization error: 68.84748840332031\n", + "Quantizing layer layers.14.mlp.up_proj...\n", + "Time for quantization: 2.19 seconds\n", + "Total quantization error: 64.00037384033203\n", + "Quantizing layer layers.14.mlp.down_proj...\n", + "Time for quantization: 6.13 seconds\n", + "Total quantization error: 5.932445049285889\n", + "Quantizing layer layers.15.self_attn.q_proj...\n", + "Time for quantization: 3.07 seconds\n", + "Total quantization error: 124.31282806396484\n", + "Quantizing layer layers.15.self_attn.k_proj...\n", + "Time for quantization: 1.18 seconds\n", + "Total quantization error: 113.63148498535156\n", + "Quantizing layer layers.15.self_attn.v_proj...\n", + "Time for quantization: 1.18 seconds\n", + "Total quantization error: 42.92724609375\n", + "Quantizing layer layers.15.self_attn.o_proj...\n", + "Time for quantization: 1.18 seconds\n", + "Total quantization error: 1.751847505569458\n", + "Quantizing layer layers.15.mlp.gate_proj...\n", + "Time for quantization: 1.18 seconds\n", + "Total quantization error: 77.96691131591797\n", + "Quantizing layer layers.15.mlp.up_proj...\n", + "Time for quantization: 1.18 seconds\n", + "Total quantization error: 73.45317077636719\n", + "Quantizing layer layers.15.mlp.down_proj...\n", + "Time for quantization: 3.43 seconds\n", + "Total quantization error: 8.775169372558594\n", + "Quantizing layer layers.16.self_attn.q_proj...\n", + "Time for quantization: 1.13 seconds\n", + "Total quantization error: 144.11874389648438\n", + "Quantizing layer layers.16.self_attn.k_proj...\n", + "Time for quantization: 1.22 seconds\n", + "Total quantization error: 126.98933410644531\n", + "Quantizing layer layers.16.self_attn.v_proj...\n", + "Time for quantization: 1.15 seconds\n", + "Total quantization error: 50.07670593261719\n", + "Quantizing layer layers.16.self_attn.o_proj...\n", + "Time for quantization: 1.14 seconds\n", + "Total quantization error: 0.8928433656692505\n", + "Quantizing layer layers.16.mlp.gate_proj...\n", + "Time for quantization: 1.41 seconds\n", + "Total quantization error: 91.5534896850586\n", + "Quantizing layer layers.16.mlp.up_proj...\n", + "Time for quantization: 1.69 seconds\n", + "Total quantization error: 84.37324523925781\n", + "Quantizing layer layers.16.mlp.down_proj...\n", + "Time for quantization: 4.18 seconds\n", + "Total quantization error: 11.662395477294922\n", + "Quantizing layer layers.17.self_attn.q_proj...\n", + "Time for quantization: 1.15 seconds\n", + "Total quantization error: 149.36753845214844\n", + "Quantizing layer layers.17.self_attn.k_proj...\n", + "Time for quantization: 1.24 seconds\n", + "Total quantization error: 130.962158203125\n", + "Quantizing layer layers.17.self_attn.v_proj...\n", + "Time for quantization: 1.19 seconds\n", + "Total quantization error: 54.16375732421875\n", + "Quantizing layer layers.17.self_attn.o_proj...\n", + "Time for quantization: 1.19 seconds\n", + "Total quantization error: 1.1655478477478027\n", + "Quantizing layer layers.17.mlp.gate_proj...\n", + "Time for quantization: 1.71 seconds\n", + "Total quantization error: 108.42695617675781\n", + "Quantizing layer layers.17.mlp.up_proj...\n", + "Time for quantization: 1.67 seconds\n", + "Total quantization error: 97.18403625488281\n", + "Quantizing layer layers.17.mlp.down_proj...\n", + "Time for quantization: 5.13 seconds\n", + "Total quantization error: 13.064023971557617\n", + "Quantizing layer layers.18.self_attn.q_proj...\n", + "Time for quantization: 1.70 seconds\n", + "Total quantization error: 179.85853576660156\n", + "Quantizing layer layers.18.self_attn.k_proj...\n", + "Time for quantization: 1.72 seconds\n", + "Total quantization error: 145.8607940673828\n", + "\n", + "Processing language model layers 128 to 159 with 6-bit precision\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Processing language model batch: 100%|██████████| 128/128 [00:46<00:00, 2.78it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Quantizing layer layers.18.self_attn.v_proj...\n", + "Time for quantization: 1.14 seconds\n", + "Total quantization error: 67.752197265625\n", + "Quantizing layer layers.18.self_attn.o_proj...\n", + "Time for quantization: 1.13 seconds\n", + "Total quantization error: 1.2422922849655151\n", + "Quantizing layer layers.18.mlp.gate_proj...\n", + "Time for quantization: 1.23 seconds\n", + "Total quantization error: 127.36318969726562\n", + "Quantizing layer layers.18.mlp.up_proj...\n", + "Time for quantization: 1.17 seconds\n", + "Total quantization error: 111.65200805664062\n", + "Quantizing layer layers.18.mlp.down_proj...\n", + "Time for quantization: 3.40 seconds\n", + "Total quantization error: 16.19788360595703\n", + "Quantizing layer layers.19.self_attn.q_proj...\n", + "Time for quantization: 1.79 seconds\n", + "Total quantization error: 192.74815368652344\n", + "Quantizing layer layers.19.self_attn.k_proj...\n", + "Time for quantization: 1.12 seconds\n", + "Total quantization error: 153.8065185546875\n", + "Quantizing layer layers.19.self_attn.v_proj...\n", + "Time for quantization: 1.13 seconds\n", + "Total quantization error: 68.77992248535156\n", + "Quantizing layer layers.19.self_attn.o_proj...\n", + "Time for quantization: 1.14 seconds\n", + "Total quantization error: 1.6653181314468384\n", + "Quantizing layer layers.19.mlp.gate_proj...\n", + "Time for quantization: 1.27 seconds\n", + "Total quantization error: 138.4702911376953\n", + "Quantizing layer layers.19.mlp.up_proj...\n", + "Time for quantization: 1.24 seconds\n", + "Total quantization error: 121.71914672851562\n", + "Quantizing layer layers.19.mlp.down_proj...\n", + "Time for quantization: 3.63 seconds\n", + "Total quantization error: 17.684993743896484\n", + "Quantizing layer layers.20.self_attn.q_proj...\n", + "Time for quantization: 1.14 seconds\n", + "Total quantization error: 192.4244384765625\n", + "Quantizing layer layers.20.self_attn.k_proj...\n", + "Time for quantization: 1.14 seconds\n", + "Total quantization error: 158.15936279296875\n", + "Quantizing layer layers.20.self_attn.v_proj...\n", + "Time for quantization: 1.13 seconds\n", + "Total quantization error: 70.11392211914062\n", + "Quantizing layer layers.20.self_attn.o_proj...\n", + "Time for quantization: 1.13 seconds\n", + "Total quantization error: 1.5406880378723145\n", + "Quantizing layer layers.20.mlp.gate_proj...\n", + "Time for quantization: 1.18 seconds\n", + "Total quantization error: 149.1056671142578\n", + "Quantizing layer layers.20.mlp.up_proj...\n", + "Time for quantization: 1.90 seconds\n", + "Total quantization error: 129.33721923828125\n", + "Quantizing layer layers.20.mlp.down_proj...\n", + "Time for quantization: 3.47 seconds\n", + "Total quantization error: 18.94611930847168\n", + "Quantizing layer layers.21.self_attn.q_proj...\n", + "Time for quantization: 1.37 seconds\n", + "Total quantization error: 238.35638427734375\n", + "Quantizing layer layers.21.self_attn.k_proj...\n", + "Time for quantization: 1.26 seconds\n", + "Total quantization error: 176.2716064453125\n", + "Quantizing layer layers.21.self_attn.v_proj...\n", + "Time for quantization: 1.15 seconds\n", + "Total quantization error: 84.16791534423828\n", + "Quantizing layer layers.21.self_attn.o_proj...\n", + "Time for quantization: 1.25 seconds\n", + "Total quantization error: 0.8847126960754395\n", + "Quantizing layer layers.21.mlp.gate_proj...\n", + "Time for quantization: 1.25 seconds\n", + "Total quantization error: 163.19677734375\n", + "Quantizing layer layers.21.mlp.up_proj...\n", + "Time for quantization: 1.22 seconds\n", + "Total quantization error: 139.49661254882812\n", + "Quantizing layer layers.21.mlp.down_proj...\n", + "Time for quantization: 3.44 seconds\n", + "Total quantization error: 18.52564239501953\n", + "Quantizing layer layers.22.self_attn.q_proj...\n", + "Time for quantization: 1.13 seconds\n", + "Total quantization error: 240.757080078125\n", + "Quantizing layer layers.22.self_attn.k_proj...\n", + "Time for quantization: 1.13 seconds\n", + "Total quantization error: 193.0281219482422\n", + "Quantizing layer layers.22.self_attn.v_proj...\n", + "Time for quantization: 1.13 seconds\n", + "Total quantization error: 90.3445816040039\n", + "Quantizing layer layers.22.self_attn.o_proj...\n", + "Time for quantization: 1.13 seconds\n", + "Total quantization error: 1.543156623840332\n", + "Quantizing layer layers.22.mlp.gate_proj...\n", + "Time for quantization: 1.17 seconds\n", + "Total quantization error: 168.78958129882812\n", + "Quantizing layer layers.22.mlp.up_proj...\n", + "Time for quantization: 1.17 seconds\n", + "Total quantization error: 142.2694854736328\n", + "\n", + "Processing language model layers 160 to 191 with 6-bit precision\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Processing language model batch: 100%|██████████| 128/128 [00:44<00:00, 2.89it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Quantizing layer layers.22.mlp.down_proj...\n", + "Time for quantization: 3.46 seconds\n", + "Total quantization error: 17.65645980834961\n", + "Quantizing layer layers.23.self_attn.q_proj...\n", + "Time for quantization: 1.13 seconds\n", + "Total quantization error: 260.3820495605469\n", + "Quantizing layer layers.23.self_attn.k_proj...\n", + "Time for quantization: 1.12 seconds\n", + "Total quantization error: 196.86280822753906\n", + "Quantizing layer layers.23.self_attn.v_proj...\n", + "Time for quantization: 1.12 seconds\n", + "Total quantization error: 104.8172836303711\n", + "Quantizing layer layers.23.self_attn.o_proj...\n", + "Time for quantization: 1.13 seconds\n", + "Total quantization error: 1.3783042430877686\n", + "Quantizing layer layers.23.mlp.gate_proj...\n", + "Time for quantization: 1.17 seconds\n", + "Total quantization error: 180.47747802734375\n", + "Quantizing layer layers.23.mlp.up_proj...\n", + "Time for quantization: 1.17 seconds\n", + "Total quantization error: 154.5054473876953\n", + "Quantizing layer layers.23.mlp.down_proj...\n", + "Time for quantization: 3.38 seconds\n", + "Total quantization error: 20.91553497314453\n", + "Quantizing layer layers.24.self_attn.q_proj...\n", + "Time for quantization: 1.12 seconds\n", + "Total quantization error: 230.61013793945312\n", + "Quantizing layer layers.24.self_attn.k_proj...\n", + "Time for quantization: 1.12 seconds\n", + "Total quantization error: 191.51058959960938\n", + "Quantizing layer layers.24.self_attn.v_proj...\n", + "Time for quantization: 1.12 seconds\n", + "Total quantization error: 100.37843322753906\n", + "Quantizing layer layers.24.self_attn.o_proj...\n", + "Time for quantization: 1.13 seconds\n", + "Total quantization error: 1.539262294769287\n", + "Quantizing layer layers.24.mlp.gate_proj...\n", + "Time for quantization: 1.18 seconds\n", + "Total quantization error: 176.47439575195312\n", + "Quantizing layer layers.24.mlp.up_proj...\n", + "Time for quantization: 1.19 seconds\n", + "Total quantization error: 150.14842224121094\n", + "Quantizing layer layers.24.mlp.down_proj...\n", + "Time for quantization: 3.71 seconds\n", + "Total quantization error: 18.425758361816406\n", + "Quantizing layer layers.25.self_attn.q_proj...\n", + "Time for quantization: 1.18 seconds\n", + "Total quantization error: 249.8153533935547\n", + "Quantizing layer layers.25.self_attn.k_proj...\n", + "Time for quantization: 1.18 seconds\n", + "Total quantization error: 195.68783569335938\n", + "Quantizing layer layers.25.self_attn.v_proj...\n", + "Time for quantization: 1.18 seconds\n", + "Total quantization error: 114.73066711425781\n", + "Quantizing layer layers.25.self_attn.o_proj...\n", + "Time for quantization: 1.15 seconds\n", + "Total quantization error: 0.985366702079773\n", + "Quantizing layer layers.25.mlp.gate_proj...\n", + "Time for quantization: 1.20 seconds\n", + "Total quantization error: 188.82435607910156\n", + "Quantizing layer layers.25.mlp.up_proj...\n", + "Time for quantization: 1.17 seconds\n", + "Total quantization error: 160.66192626953125\n", + "Quantizing layer layers.25.mlp.down_proj...\n", + "Time for quantization: 3.69 seconds\n", + "Total quantization error: 19.751079559326172\n", + "Quantizing layer layers.26.self_attn.q_proj...\n", + "Time for quantization: 1.19 seconds\n", + "Total quantization error: 277.21923828125\n", + "Quantizing layer layers.26.self_attn.k_proj...\n", + "Time for quantization: 1.14 seconds\n", + "Total quantization error: 199.06280517578125\n", + "Quantizing layer layers.26.self_attn.v_proj...\n", + "Time for quantization: 1.14 seconds\n", + "Total quantization error: 115.04022216796875\n", + "Quantizing layer layers.26.self_attn.o_proj...\n", + "Time for quantization: 1.13 seconds\n", + "Total quantization error: 3.0137643814086914\n", + "Quantizing layer layers.26.mlp.gate_proj...\n", + "Time for quantization: 1.18 seconds\n", + "Total quantization error: 181.84329223632812\n", + "Quantizing layer layers.26.mlp.up_proj...\n", + "Time for quantization: 1.18 seconds\n", + "Total quantization error: 156.48687744140625\n", + "Quantizing layer layers.26.mlp.down_proj...\n", + "Time for quantization: 3.39 seconds\n", + "Total quantization error: 17.526222229003906\n", + "Quantizing layer layers.27.self_attn.q_proj...\n", + "Time for quantization: 1.14 seconds\n", + "Total quantization error: 292.4416809082031\n", + "Quantizing layer layers.27.self_attn.k_proj...\n", + "Time for quantization: 1.20 seconds\n", + "Total quantization error: 183.27902221679688\n", + "Quantizing layer layers.27.self_attn.v_proj...\n", + "Time for quantization: 1.24 seconds\n", + "Total quantization error: 108.62908935546875\n", + "\n", + "Processing language model layers 192 to 223 with 6-bit precision\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Processing language model batch: 100%|██████████| 128/128 [00:44<00:00, 2.88it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Quantizing layer layers.27.self_attn.o_proj...\n", + "Time for quantization: 1.13 seconds\n", + "Total quantization error: 1.2542890310287476\n", + "Quantizing layer layers.27.mlp.gate_proj...\n", + "Time for quantization: 1.17 seconds\n", + "Total quantization error: 191.4755096435547\n", + "Quantizing layer layers.27.mlp.up_proj...\n", + "Time for quantization: 1.23 seconds\n", + "Total quantization error: 166.24130249023438\n", + "Quantizing layer layers.27.mlp.down_proj...\n", + "Time for quantization: 3.39 seconds\n", + "Total quantization error: 19.493118286132812\n", + "Quantizing layer layers.28.self_attn.q_proj...\n", + "Time for quantization: 1.14 seconds\n", + "Total quantization error: 332.40203857421875\n", + "Quantizing layer layers.28.self_attn.k_proj...\n", + "Time for quantization: 1.14 seconds\n", + "Total quantization error: 185.15736389160156\n", + "Quantizing layer layers.28.self_attn.v_proj...\n", + "Time for quantization: 1.13 seconds\n", + "Total quantization error: 117.61782836914062\n", + "Quantizing layer layers.28.self_attn.o_proj...\n", + "Time for quantization: 1.12 seconds\n", + "Total quantization error: 1.7196297645568848\n", + "Quantizing layer layers.28.mlp.gate_proj...\n", + "Time for quantization: 1.17 seconds\n", + "Total quantization error: 190.54931640625\n", + "Quantizing layer layers.28.mlp.up_proj...\n", + "Time for quantization: 1.19 seconds\n", + "Total quantization error: 170.57394409179688\n", + "Quantizing layer layers.28.mlp.down_proj...\n", + "Time for quantization: 3.39 seconds\n", + "Total quantization error: 21.602191925048828\n", + "Quantizing layer layers.29.self_attn.q_proj...\n", + "Time for quantization: 1.12 seconds\n", + "Total quantization error: 310.69635009765625\n", + "Quantizing layer layers.29.self_attn.k_proj...\n", + "Time for quantization: 1.14 seconds\n", + "Total quantization error: 171.73776245117188\n", + "Quantizing layer layers.29.self_attn.v_proj...\n", + "Time for quantization: 1.13 seconds\n", + "Total quantization error: 105.93821716308594\n", + "Quantizing layer layers.29.self_attn.o_proj...\n", + "Time for quantization: 1.13 seconds\n", + "Total quantization error: 2.1055049896240234\n", + "Quantizing layer layers.29.mlp.gate_proj...\n", + "Time for quantization: 1.17 seconds\n", + "Total quantization error: 191.65054321289062\n", + "Quantizing layer layers.29.mlp.up_proj...\n", + "Time for quantization: 1.17 seconds\n", + "Total quantization error: 173.7242431640625\n", + "Quantizing layer layers.29.mlp.down_proj...\n", + "Time for quantization: 3.45 seconds\n", + "Total quantization error: 27.428131103515625\n", + "Quantizing layer layers.30.self_attn.q_proj...\n", + "Time for quantization: 1.13 seconds\n", + "Total quantization error: 405.4654235839844\n", + "Quantizing layer layers.30.self_attn.k_proj...\n", + "Time for quantization: 1.13 seconds\n", + "Total quantization error: 173.12855529785156\n", + "Quantizing layer layers.30.self_attn.v_proj...\n", + "Time for quantization: 1.14 seconds\n", + "Total quantization error: 119.32344055175781\n", + "Quantizing layer layers.30.self_attn.o_proj...\n", + "Time for quantization: 1.13 seconds\n", + "Total quantization error: 2.7953596115112305\n", + "Quantizing layer layers.30.mlp.gate_proj...\n", + "Time for quantization: 1.23 seconds\n", + "Total quantization error: 201.66592407226562\n", + "Quantizing layer layers.30.mlp.up_proj...\n", + "Time for quantization: 1.17 seconds\n", + "Total quantization error: 176.90870666503906\n", + "Quantizing layer layers.30.mlp.down_proj...\n", + "Time for quantization: 3.66 seconds\n", + "Total quantization error: 53.23955535888672\n", + "Quantizing layer layers.31.self_attn.q_proj...\n", + "Time for quantization: 1.15 seconds\n", + "Total quantization error: 168.50360107421875\n", + "Quantizing layer layers.31.self_attn.k_proj...\n", + "Time for quantization: 1.19 seconds\n", + "Total quantization error: 144.22125244140625\n", + "Quantizing layer layers.31.self_attn.v_proj...\n", + "Time for quantization: 1.14 seconds\n", + "Total quantization error: 71.99061584472656\n", + "Quantizing layer layers.31.self_attn.o_proj...\n", + "Time for quantization: 1.21 seconds\n", + "Total quantization error: 7.958558082580566\n", + "Quantizing layer layers.31.mlp.gate_proj...\n", + "Time for quantization: 1.25 seconds\n", + "Total quantization error: 181.67929077148438\n", + "Quantizing layer layers.31.mlp.up_proj...\n", + "Time for quantization: 1.52 seconds\n", + "Total quantization error: 163.659912109375\n", + "Quantizing layer layers.31.mlp.down_proj...\n", + "Time for quantization: 3.43 seconds\n", + "Total quantization error: 156.51048278808594\n", + "Language Model quantization complete.\n", + "\n", + "LLAVA model quantization complete.\n", + "Elapsed time: 827.1064443588257\n" + ] + } + ], + "source": [ + "quantizer = LlavaQuantizer(model, processor, device)\n", + "\n", + "quantizer.config = {\n", + " \"vision\": {\n", + " \"bits\": 2,\n", + " \"percent_dampening\": 0.01,\n", + " \"group_size\": -1,\n", + " \"use_symmetric\": True,\n", + " \"use_act_order\": False,\n", + " \"use_static_groups\": False,\n", + " },\n", + " \"language\": {\n", + " \"bits\": 6,\n", + " \"percent_dampening\": 0.01,\n", + " \"group_size\": -1,\n", + " \"use_symmetric\": True,\n", + " \"use_act_order\": False,\n", + " \"use_static_groups\": False,\n", + " },\n", + "}\n", + "\n", + "# quantizer.quantize_vision_model(calibration_set)\n", + "# quantizer.quantize_language_model(calibration_set)\n", + "\n", + "start_time = time.time()\n", + "quantizer.quantize(calibration_set)\n", + "\n", + "print(f'Elapsed time: {time.time() - start_time}')" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "77f17f06", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "USER: \n", + "How is the clothing item that is pink called?\n", + "Answer the question using a single word or phrase. ASSISTANT:\n" + ] + }, + { + "data": { + "text/plain": [ + "['USER: \\nHow is the clothing item that is pink called?\\nAnswer the question using a single word or phrase. ASSISTANT: Tank top']" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "img = dataset[42]['image']\n", + "# prompt = 'USER: \\n' + dataset.qa_pairs[42]['question'] + '\\nAnswer the question using a single word or phrase. ASSISTANT:'\n", + "\n", + "prompt = dataset[42]['text_input']\n", + "print(prompt)\n", + "\n", + "model.to('cuda')\n", + "\n", + "# set this according to huggingface usage tips: https://huggingface.co/docs/transformers/en/model_doc/llava\n", + "processor.tokenizer.padding_side = \"left\"\n", + "samples = processor(images = [img],\n", + " text=[prompt],\n", + " return_tensors='pt',\n", + " padding=True).to(model.device)\n", + "\n", + "# Generate\n", + "# generate_ids = model.generate(**inputs, max_new_tokens=30)\n", + "generate_ids = model.generate(**samples, max_new_tokens=30)\n", + "output = processor.batch_decode(generate_ids, skip_special_tokens=True)\n", + "output" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "de6c18ea", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'Tank top'" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "output[0].split('ASSISTANT: ')[-1]" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "68090ec2", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'question_id': '201974971',\n", + " 'image': ,\n", + " 'text_input': 'USER: \\nHow is the clothing item that is pink called?\\nAnswer the question using a single word or phrase. ASSISTANT:',\n", + " 'gt_answer': 'tank top'}" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dataset[42]" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/gqa.py b/gqa.py new file mode 100644 index 0000000..c63ee51 --- /dev/null +++ b/gqa.py @@ -0,0 +1,154 @@ +import argparse +import os +import json +import builtins as __builtin__ + +import torch +import torch.distributed as dist +from torch.utils.data import DistributedSampler, DataLoader +from transformers import Blip2ForConditionalGeneration, Blip2Processor + +from dataset import GQAEval +from inference_pipeline import InferencePipeline +from scoring_pipeline import ScoringPipeline + +def init_distributed(): + rank = int(os.environ["RANK"]) + world_size = int(os.environ["WORLD_SIZE"]) + gpu = int(os.environ["LOCAL_RANK"]) + dist.init_process_group(backend="nccl", init_method="env://", rank=rank, world_size=world_size) + torch.cuda.set_device(gpu) + + builtin_print = __builtin__.print + def print(*args, **kwargs): + if rank == 0: + builtin_print(*args, **kwargs) + __builtin__.print = print + + return rank, world_size, gpu + +def compute_gqa_results(results, scorer, save_path=None): + gqa_results = scorer.compute_scores(results, "gqa") + print(gqa_results) + if save_path: + with open(save_path, "w") as f: + json.dump(gqa_results, f) + +if __name__ == "__main__": + parser = argparse.ArgumentParser( + prog='GQA Balanced-Testdev Eval', + description='Performs VQA evaluation using BLIP2 on GQA', + ) + + parser.add_argument("--distributed", action="store_true") + parser.add_argument("--batch_size", default=64, type=int) + parser.add_argument("--num_workers", default=1, type=int) + parser.add_argument("--output_dir", default="./output", type=str) + parser.add_argument("--dataset_dir", default="./data/gqa", type=str) + + args = parser.parse_args() + os.makedirs(args.output_dir, exist_ok=True) + + processor = Blip2Processor.from_pretrained("salesforce/blip2-opt-2.7b", padding_side="left") + gqa = GQAEval( + os.path.join(args.dataset_dir, "images"), + os.path.join(args.dataset_dir, "questions"), + ) + + if args.distributed: + rank, world_size, gpu = init_distributed() + dist.barrier() + + try: + sampler = DistributedSampler( + gqa, + shuffle=False, + num_replicas=world_size, + rank=rank + ) + + dataloader = DataLoader( + gqa, + batch_size=args.batch_size, + num_workers=args.num_workers, + pin_memory=False, + shuffle=False, + sampler=sampler, + collate_fn=gqa.collater, + ) + model = Blip2ForConditionalGeneration.from_pretrained("Salesforce/blip2-opt-2.7b", device_map=gpu) + + inferencer = InferencePipeline(model, gpu, processor) + scorer = ScoringPipeline() + + # T5 kwargs + # processor_kwargs={"padding": "longest", "max_length": 32, "truncation": True} + # generate_kwargs={"num_beams": 5, "max_new_tokens": 10, "min_length": 1, "length_penalty": -1, "do_sample": False} + # OPT kwargs + processor_kwargs={"padding": "longest", "max_length": 32, "truncation": True} + generate_kwargs={"num_beams": 5, "max_new_tokens": 10, "min_length": 1, "length_penalty": 0, "do_sample": False} + + results = inferencer.run_inference( + dataloader, + task="gqa", + processor_kwargs=processor_kwargs, + generate_kwargs=generate_kwargs + ) + + with open(os.path.join(args.output_dir, f"{rank}_results.json"), 'w') as f: + json.dump(results, f) + dist.barrier() + + if rank == 0: + results = [] + + question_ids = set() + for rank_id in range(world_size): + with open(os.path.join(args.output_dir, f"{rank_id}_results.json"), 'r') as f: + rank_results = json.load(f) + for answer in rank_results: + question_id = answer["question_id"] + if question_id not in question_ids: + results.append(answer) + question_ids.add(question_id) + + compute_gqa_results(results, scorer, os.path.join(args.output_dir, "results.json")) + finally: + dist.destroy_process_group() + else: + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + + model = Blip2ForConditionalGeneration.from_pretrained("Salesforce/blip2-opt-2.7b", device_map=device) + + inferencer = InferencePipeline(model, device, processor) + scorer = ScoringPipeline() + + # T5 kwargs + #processor_kwargs={"padding": "longest", "max_length": 32, "truncation": True} + #generate_kwargs={"num_beams": 5, "max_new_tokens": 10, "min_length": 1, "length_penalty": -1, "do_sample": False} + # OPT kwargs + processor_kwargs={"padding": "longest", "max_length": 32, "truncation": True} + generate_kwargs={"num_beams": 5, "max_new_tokens": 10, "min_length": 1, "length_penalty": 0, "do_sample": False} + + dataloader = DataLoader( + gqa, + batch_size=args.batch_size, + num_workers=args.num_workers, + pin_memory=False, + shuffle=False, + collate_fn=gqa.collater, + ) + + results = inferencer.run_inference( + dataloader, + task="gqa", + processor_kwargs=processor_kwargs, + generate_kwargs=generate_kwargs + ) + + with open(os.path.join(args.output_dir, "answers.json"), 'w') as f: + json.dump(results, f) + + compute_gqa_results(results, scorer, os.path.join(args.output_dir, "results.json")) + + diff --git a/inference_pipeline.py b/inference_pipeline.py index 931f08f..97441c4 100644 --- a/inference_pipeline.py +++ b/inference_pipeline.py @@ -1,9 +1,12 @@ import torch import torch.nn.functional as F +import numpy as np from tqdm import tqdm import json -from transformers import BertTokenizer, AutoProcessor +from transformers import BertTokenizer, AutoProcessor, OPTForCausalLM import numpy as np +from torch.utils.data import Dataset, DataLoader +import torch.distributed as dist class InferencePipeline: def __init__(self, model, device, processor=None): @@ -16,10 +19,14 @@ def run_inference(self, dataset, task, **kwargs): return self._run_image_captioning(dataset, **kwargs) elif task == 'image_text_retrieval': return self._run_retrieval(dataset, **kwargs) + elif task == "vqav2": + return self._run_vqav2(dataset, **kwargs) + elif task == "gqa": + return self._run_gqa(dataset, **kwargs) else: raise ValueError(f"Unsupported task: {task}") - def _run_image_captioning(self, dataset, max_samples=None): + def _run_image_captioning(self, dataset, max_samples=None, processor_kwargs={}, generate_kwargs={}): results = [] references = [] @@ -27,9 +34,9 @@ def _run_image_captioning(self, dataset, max_samples=None): image = dataset[i][0] captions = dataset[i][1] img_id = dataset.ids[i] - inputs = self.processor(images=image, return_tensors="pt").to(self.device) + inputs = self.processor(images=image, return_tensors="pt", **processor_kwargs).to(self.device) with torch.no_grad(): - out = self.model.generate(**inputs) + out = self.model.generate(**inputs, **generate_kwargs) caption = self.processor.decode(out[0], skip_special_tokens=True).strip() @@ -41,6 +48,72 @@ def _run_image_captioning(self, dataset, max_samples=None): 'references': references } + def _prepare_data(self, data, max_samples=None): + if max_samples is not None: + data.set_max_samples(max_samples) + + if isinstance(data, torch.utils.data.Dataset): + data = DataLoader( + data, + batch_size=1, + shuffle=False, + collate_fn=data.collater + ) + + return data + + def _predict_answers(self, images, questions, processor_kwargs=None, generate_kwargs=None): + processor_kwargs = processor_kwargs or {} + generate_kwargs = generate_kwargs or {} + inputs = self.processor(images=images, text=questions, return_tensors="pt", **processor_kwargs).to(self.device) + + with torch.no_grad(): + out = self.model.generate(**inputs, **generate_kwargs) + + answers = self.processor.batch_decode(out, skip_special_tokens=True) + return answers + + def _run_gqa(self, data, distributed=False, max_samples=None, processor_kwargs=None, generate_kwargs=None): + processor_kwargs = processor_kwargs or {} + generate_kwargs = generate_kwargs or {} + results = [] + data = self._prepare_data(data, max_samples=max_samples) + + for samples in tqdm(data): + question_ids = samples["question_id"] + images = samples["image"] + questions = samples["text_input"] + gt_answers = samples["gt_answer"] + + answers = self._predict_answers(images, questions, processor_kwargs, generate_kwargs) + + for question_id, question, answer, gt_answer in zip(question_ids, questions, answers, gt_answers): + if answer.startswith(question): + answer = answer[len(question):] + results.append({"question_id": question_id, "answer": answer.strip(), "gt_answer": gt_answer}) + + return results + + def _run_vqav2(self, data, distributed=False, max_samples=None, processor_kwargs=None, generate_kwargs=None): + processor_kwargs = processor_kwargs or {} + generate_kwargs = generate_kwargs or {} + results = [] + data = self._prepare_data(data, max_samples=max_samples) + + for samples in tqdm(data): + images = samples["image"] + questions = samples["text_input"] + question_ids = samples["question_id"] + + answers = self._predict_answers(images, questions, processor_kwargs, generate_kwargs) + + for answer, question, question_id in zip(answers, questions, question_ids): + if answer.startswith(question): + answer = answer[len(question):] + results.append({"question_id": question_id, "answer": answer.strip()}) + + return results + def _compute_itm(self, image_inputs, text_ids, text_atts): image_atts = torch.ones(image_inputs.size()[:-1], dtype=torch.long).to(image_inputs.device) query_tokens = self.model.query_tokens.expand(image_inputs.shape[0], -1, -1) @@ -71,8 +144,15 @@ def _compute_itm(self, image_inputs, text_ids, text_atts): itm_logit = itm_logit[:, :, 1].mean(dim=1).float() return itm_logit - def _run_retrieval(self, dataset, k_test=128, max_samples=None, text_bs=4): + def _run_retrieval(self, dataset, max_samples=None, k_test=128, text_bs=4, tokenizer_kwargs=None): with torch.no_grad(): + if not tokenizer_kwargs: + tokenizer_kwargs = { + "padding": "max_length", + "truncation": True, + "max_length": 35 + } + tokenizer = BertTokenizer.from_pretrained("bert-base-uncased", truncation_side="right") tokenizer.add_special_tokens({"bos_token": "[DEC]"}) @@ -82,17 +162,14 @@ def _run_retrieval(self, dataset, k_test=128, max_samples=None, text_bs=4): text_ids = [] text_embeds = [] text_atts = [] - model = self.model print("Getting text embeddings") for i in tqdm(range(0, num_text, text_bs)): text = texts[i : min(num_text, i + text_bs)] text_input = tokenizer( text, - padding="max_length", - truncation=True, - max_length=35, - return_tensors="pt" + return_tensors="pt", + **tokenizer_kwargs ).to(self.model.device) query_embeds = self.model.embeddings(text_input.input_ids) diff --git a/llava_runs/awq_gqa_multi_sbatch_submit.sh b/llava_runs/awq_gqa_multi_sbatch_submit.sh new file mode 100755 index 0000000..20a5fea --- /dev/null +++ b/llava_runs/awq_gqa_multi_sbatch_submit.sh @@ -0,0 +1,12 @@ +python multi_sbatch_awq_gqa.py --env slurm_files \ + --nhrs 4 \ + --qos scav \ + --partition vulcan \ + --gpu 1 \ + --gpu-type a5000 a6000 \ + --cores 1 \ + --mem 48 \ + --output-dirname gqa_awq_output \ + # --dryrun + + diff --git a/llava_runs/awq_llava.py b/llava_runs/awq_llava.py new file mode 100755 index 0000000..a64fde5 --- /dev/null +++ b/llava_runs/awq_llava.py @@ -0,0 +1,251 @@ +import sys +sys.path.append('..') +# import math +# import time +# from typing import List, Dict, Any, Optional +import argparse +import random +import os +import json + +import torch +# import torch.nn as nn +from torch.utils.data import DataLoader +# from tqdm import tqdm +import torch +from transformers import AutoProcessor, LlavaForConditionalGeneration +from transformers.models.llava.image_processing_llava import LlavaImageProcessor +# import transformers + +from dataset import VQAv2Eval, GQAEval +from awq.llava_quantizer import LlavaAWQQuantizer +from inference_pipeline import InferencePipeline +# from scoring_pipeline import ScoringPipeline + + +def get_args(): + + parser = argparse.ArgumentParser(description="LLAVA AWQ Quantization Script") + + parser.add_argument( + '--task', + type=str, + choices=['vqav2', 'gqa'], + required=True, + help='task to evaluate AWQ-quantized LLAVA on' + ) + + # Add arguments for bit sizes + parser.add_argument( + "--vision-bits", + type=int, + default=8, + choices=[2, 3, 4, 5, 6, 7, 8, 16], + help="Bit size for vision component", + ) + + parser.add_argument( + "--language-bits", + type=int, + default=4, + choices=[2, 3, 4, 5, 6, 7, 8, 16], + help="Bit size for language component", + ) + + parser.add_argument( + "--calibration-size", type=int, default=128, help="Size of calibration dataset" + ) + + parser.add_argument( + "--seed", + type=int, + default=None, + help="Random seed for reproducibility. If not provided, a random seed will be generated.", + ) + + + parser.add_argument( + "--device", + type=str, + default="cuda:0", + help="Device to use (cuda:0, cuda:1, cpu, etc.)", + ) + + parser.add_argument( + "--output_dir", + type=str, + default="awq_results", + help="Directory to save results", + ) + + parser.add_argument( + '--no_quant', + default=False, + action='store_true', + help="Set to true to apply no quantization (full-precision run)" + ) + + parser.add_argument( + '--batch_size', + type = int, + default = 16, + help = 'batch size for task evaulation' + ) + + return parser.parse_args() + +def main(): + args = get_args() + + # Generate random seed if not provided + if args.seed is None: + args.seed = random.randint(0, 2**32 - 1) + print(f"Generated random seed: {args.seed}") + + + # Set random seed + random.seed(args.seed) + torch.manual_seed(args.seed) + + # Setup device + device = torch.device( + args.device if torch.cuda.is_available() and "cuda" in args.device else "cpu" + ) + + print("Loading LLAVA model...") + # Load the model + model = LlavaForConditionalGeneration.from_pretrained("llava-hf/llava-1.5-7b-hf", torch_dtype=torch.float16) + model.to(device) + + processor = AutoProcessor.from_pretrained("llava-hf/llava-1.5-7b-hf", pad_token = '', use_fast = False) + + # need to use this image processor w/ do_pad=True according to "Note regarding reproducing original implementation" + # https://huggingface.co/docs/transformers/en/model_doc/llava + image_processor = LlavaImageProcessor.from_pretrained("llava-hf/llava-1.5-7b-hf", + do_pad=True) + + processor.image_processor = image_processor + + # short answer prompting according to: https://github.com/haotian-liu/LLaVA/blob/main/docs/Evaluation.md + llava_prompt = 'USER: \n{}\nAnswer the question using a single word or phrase. ASSISTANT:' + + if args.task == 'vqav2': + # VQAv2 dataset paths + ann_root = '/fs/cfar-projects/low-bit-vision/datasets/vqav2/annotations' + q_root = '/fs/cfar-projects/low-bit-vision/datasets/vqav2/questions' + image_root = '/fs/cfar-projects/low-bit-vision/datasets/vqav2/val2014' + + dataset = VQAv2Eval(image_root=image_root, + ann_root=ann_root, + q_root=q_root, + prompt = llava_prompt) + + dataset.set_max_samples(21435) + + elif args.task == 'gqa': + # GQA dataset paths + image_root = '/fs/cfar-projects/low-bit-vision/datasets/gqa/images' + q_root = '/fs/cfar-projects/low-bit-vision/datasets/gqa/questions' + + dataset = GQAEval( + image_root, + q_root, + prompt=llava_prompt + ) + + + if args.vision_bits == 16 and args.language_bits == 16: + args.no_quant = True + + if not args.no_quant: + # Update quantizer config with specified bit sizes + config = {} + + if args.vision_bits != 16: + config['vision_layers'] = { + 'self_attn': args.vision_bits, + 'mlp': args.vision_bits + } + + if args.language_bits != 16: + config['llm_layers'] = { + 'self_attn': args.language_bits, + 'mlp': args.language_bits + } + + # Print configuration + print("\nQuantization Configuration:") + print(f"Vision bits: {args.vision_bits}") + print(f"Language bits: {args.language_bits}") + print(f"Calibration size: {args.calibration_size}") + print(f"Device: {device}\n") + + print(f'config: {config}') + + + quantizer = LlavaAWQQuantizer(model, device, processor, dataset, config) + quantizer.n_samples = args.calibration_size + quantizer.seed = args.seed + + # Quantize model + quantizer.quantize() + print(model) + model.to(device) + + # Evaluate on task + gpu_name = torch.cuda.get_device_name() + print(gpu_name) + + # adjust batch sizes depending on available gpu memory + if "A5000" in gpu_name.replace(" ", ""): + args.batch_size = 16 + elif "A6000" in gpu_name.replace(" ", ""): + args.batch_size = 56 + + print(f'Evaluating on task: {args.task}') + print(f'batch_size: {args.batch_size}') + + dataloader = DataLoader(dataset, + batch_size=args.batch_size, + num_workers=1, + pin_memory=False, + shuffle=False, + collate_fn = dataset.collater) + + inferencer = InferencePipeline(model, device, processor) + + # set this according to huggingface usage tips: https://huggingface.co/docs/transformers/en/model_doc/llava + processor.tokenizer.padding_side = "left" + processor_kwargs = dict(padding=True) + + # greedy decoding + generate_kwargs = { + 'num_beams': 1, + 'do_sample': False + } + + results = inferencer.run_inference( + dataloader, + task = args.task, + processor_kwargs = processor_kwargs, + generate_kwargs = generate_kwargs + ) + + + json_out = { + "answers": results, + "vision_bits": args.vision_bits, + "language_bits": args.language_bits + } + + os.makedirs(args.output_dir, exist_ok=True) + json_path = os.path.join(args.output_dir, f"results_v{args.vision_bits}_l{args.language_bits}.json") + with open(json_path, 'w') as f: + json.dump(json_out, f) + + + print(f"Output results to {json_path}") + + +if __name__ == '__main__': + main() diff --git a/llava_runs/awq_vqav2_multi_sbatch_submit.sh b/llava_runs/awq_vqav2_multi_sbatch_submit.sh new file mode 100755 index 0000000..6758342 --- /dev/null +++ b/llava_runs/awq_vqav2_multi_sbatch_submit.sh @@ -0,0 +1,12 @@ +python multi_sbatch_awq_vqav2.py --env slurm_files \ + --nhrs 4 \ + --qos scav \ + --partition vulcan \ + --gpu 1 \ + --gpu-type a5000 a6000 \ + --cores 1 \ + --mem 48 \ + --output-dirname vqav2_awq_output \ + # --dryrun + + diff --git a/llava_runs/gptq_gqa_multi_sbatch_submit.sh b/llava_runs/gptq_gqa_multi_sbatch_submit.sh new file mode 100755 index 0000000..e468d2c --- /dev/null +++ b/llava_runs/gptq_gqa_multi_sbatch_submit.sh @@ -0,0 +1,12 @@ +python multi_sbatch_gptq_gqa.py --env slurm_files \ + --nhrs 4 \ + --qos scav \ + --partition vulcan \ + --gpu 1 \ + --gpu-type a5000 a6000 \ + --cores 1 \ + --mem 48 \ + --output-dirname gpa_gptq_output \ + # --dryrun + + diff --git a/llava_runs/gptq_llava.py b/llava_runs/gptq_llava.py new file mode 100755 index 0000000..5d4f58f --- /dev/null +++ b/llava_runs/gptq_llava.py @@ -0,0 +1,776 @@ +import sys +sys.path.append('..') +import math +import time +from typing import List, Dict, Any, Optional +import argparse +import random +import os +import json + +import torch +import torch.nn as nn +from torch.utils.data import DataLoader +from tqdm import tqdm +import torch +from transformers import AutoProcessor, LlavaForConditionalGeneration +from transformers.models.llava.image_processing_llava import LlavaImageProcessor +import transformers + +from dataset import VQAv2Eval, GQAEval +from inference_pipeline import InferencePipeline +from scoring_pipeline import ScoringPipeline + +DEBUG = False + +torch.backends.cuda.matmul.allow_tf32 = False +torch.backends.cudnn.allow_tf32 = False + +# + +# ==================================================== +# Quantization Classes and Functions +# ==================================================== + +def quantize(x, scale, zero, maxq): + if maxq < 0: + return (x > scale / 2).float() * scale + (x < zero / 2).float() * zero + q = torch.clamp(torch.round(x / scale) + zero, 0, maxq) + return scale * (q - zero) + +def find_linear_layers_in_model(model): + layers = {} + + def recurse(module, prefix=""): + if isinstance(module, nn.Linear): + layers[prefix.rstrip(".")] = module + for name, child in module.named_children(): + recurse(child, prefix + name + ".") + + recurse(model) + return layers + +class Quantizer(nn.Module): + def __init__(self, shape=1): + super(Quantizer, self).__init__() + self.register_buffer("maxq", torch.tensor(0)) + self.register_buffer("scale", torch.zeros(shape)) + self.register_buffer("zero", torch.zeros(shape)) + + def configure( + self, + bits, + perchannel=False, + sym=True, + mse=False, + norm=2.4, + grid=100, + maxshrink=0.8, + trits=False, + ): + device = self.maxq.device + self.maxq = torch.tensor(2**bits - 1, device=device) + self.perchannel = perchannel + self.sym = sym + self.mse = mse + self.norm = norm + self.grid = grid + self.maxshrink = maxshrink + if trits: + self.maxq = torch.tensor(-1, device=device) + + def find_params(self, x, weight=False): + dev = x.device + self.maxq = self.maxq.to(dev) + + shape = x.shape + if self.perchannel: + if weight: + x = x.flatten(1) + else: + if len(shape) == 4: + x = x.permute([1, 0, 2, 3]) + x = x.flatten(1) + if len(shape) == 3: + x = x.reshape((-1, shape[-1])).t() + if len(shape) == 2: + x = x.t() + else: + x = x.flatten().unsqueeze(0) + + tmp = torch.zeros(x.shape[0], device=dev) + xmin = torch.minimum(x.min(1)[0], tmp) + xmax = torch.maximum(x.max(1)[0], tmp) + + if self.sym: + xmax = torch.maximum(torch.abs(xmin), xmax) + tmp = xmin < 0 + if torch.any(tmp): + xmin[tmp] = -xmax[tmp] + tmp = (xmin == 0) & (xmax == 0) + xmin[tmp] = -1 + xmax[tmp] = +1 + + if self.maxq < 0: + self.scale = xmax + self.zero = xmin + else: + self.scale = (xmax - xmin) / self.maxq + if self.sym: + self.zero = torch.full_like(self.scale, (self.maxq + 1) / 2) + else: + self.zero = torch.round(-xmin / self.scale) + + if self.mse: + best = torch.full([x.shape[0]], float("inf"), device=dev) + for i in range(int(self.maxshrink * self.grid)): + p = 1 - i / self.grid + xmin1 = p * xmin + xmax1 = p * xmax + scale1 = (xmax1 - xmin1) / self.maxq + zero1 = torch.round(-xmin1 / scale1) if not self.sym else self.zero + q = quantize(x, scale1.unsqueeze(1), zero1.unsqueeze(1), self.maxq) + q -= x + q.abs_() + q.pow_(self.norm) + err = torch.sum(q, 1) + tmp = err < best + if torch.any(tmp): + best[tmp] = err[tmp] + self.scale[tmp] = scale1[tmp] + self.zero[tmp] = zero1[tmp] + if not self.perchannel: + if weight: + tmp = shape[0] + else: + tmp = shape[1] if len(shape) != 3 else shape[2] + self.scale = self.scale.repeat(tmp) + self.zero = self.zero.repeat(tmp) + + if weight: + shape = [-1] + [1] * (len(shape) - 1) + self.scale = self.scale.reshape(shape) + self.zero = self.zero.reshape(shape) + return + if len(shape) == 4: + self.scale = self.scale.reshape((1, -1, 1, 1)) + self.zero = self.zero.reshape((1, -1, 1, 1)) + if len(shape) == 3: + self.scale = self.scale.reshape((1, 1, -1)) + self.zero = self.zero.reshape((1, 1, -1)) + if len(shape) == 2: + self.scale = self.scale.unsqueeze(0) + self.zero = self.zero.unsqueeze(0) + + # Ensure buffers are on the same device as input x + self.scale = self.scale.to(dev) + self.zero = self.zero.to(dev) + + def quantize(self, x): + if self.ready(): + # Ensure buffers are on the same device as x + self.scale = self.scale.to(x.device) + self.zero = self.zero.to(x.device) + self.maxq = self.maxq.to(x.device) + return quantize(x, self.scale, self.zero, self.maxq) + return x + + def enabled(self): + return self.maxq > 0 + + def ready(self): + return torch.all(self.scale != 0) + +class GPTQ: + def __init__(self, layer): + self.layer = layer + self.dev = self.layer.weight.device + W = layer.weight.data.clone() + if isinstance(self.layer, nn.Conv2d): + W = W.flatten(1) + if isinstance(self.layer, transformers.Conv1D): + W = W.t() + self.rows = W.shape[0] + self.columns = W.shape[1] + self.H = torch.zeros((self.columns, self.columns), device=self.dev) + self.nsamples = 0 + self.quantizer = Quantizer() + self.quantizer.to(self.dev) + + def add_batch(self, inp, out): + if DEBUG: + self.inp1 = inp + self.out1 = out + if len(inp.shape) == 2: + inp = inp.unsqueeze(0) + tmp = inp.shape[0] + if isinstance(self.layer, nn.Linear) or isinstance( + self.layer, transformers.Conv1D + ): + if len(inp.shape) == 3: + inp = inp.reshape((-1, inp.shape[-1])) + inp = inp.t() + if isinstance(self.layer, nn.Conv2d): + unfold = nn.Unfold( + self.layer.kernel_size, + dilation=self.layer.dilation, + padding=self.layer.padding, + stride=self.layer.stride, + ) + inp = unfold(inp) + inp = inp.permute([1, 0, 2]) + inp = inp.flatten(1) + + self.H *= self.nsamples / (self.nsamples + tmp) + self.nsamples += tmp + inp = math.sqrt(2 / self.nsamples) * inp.float() + self.H += inp.matmul(inp.t()) + + def fasterquant( + self, + blocksize=128, + percdamp=0.01, + groupsize=-1, + actorder=False, + static_groups=False, + ): + W = self.layer.weight.data.clone() + if isinstance(self.layer, nn.Conv2d): + W = W.flatten(1) + if isinstance(self.layer, transformers.Conv1D): + W = W.t() + W = W.float() + + tick = time.time() + + if not self.quantizer.ready(): + self.quantizer.find_params(W, weight=True) + + H = self.H + del self.H + dead = torch.diag(H) == 0 + H[dead, dead] = 1 + W[:, dead] = 0 + + if static_groups: + import copy + + groups = [] + for i in range(0, self.columns, groupsize): + quantizer = copy.deepcopy(self.quantizer) + quantizer.find_params(W[:, i : (i + groupsize)], weight=True) + groups.append(quantizer) + + if actorder: + perm = torch.argsort(torch.diag(H), descending=True) + W = W[:, perm] + H = H[perm][:, perm] + invperm = torch.argsort(perm) + + Losses = torch.zeros_like(W) + Q = torch.zeros_like(W) + + damp = percdamp * torch.mean(torch.diag(H)) + diag = torch.arange(self.columns, device=self.dev) + H[diag, diag] += damp + H = torch.linalg.cholesky(H) + H = torch.cholesky_inverse(H) + H = torch.linalg.cholesky(H, upper=True) + Hinv = H + + for i1 in range(0, self.columns, blocksize): + i2 = min(i1 + blocksize, self.columns) + count = i2 - i1 + + W1 = W[:, i1:i2].clone() + Q1 = torch.zeros_like(W1) + Err1 = torch.zeros_like(W1) + Losses1 = torch.zeros_like(W1) + Hinv1 = Hinv[i1:i2, i1:i2] + + for i in range(count): + w = W1[:, i] + d = Hinv1[i, i] + + if groupsize != -1: + if not static_groups: + if (i1 + i) % groupsize == 0: + self.quantizer.find_params( + W[:, (i1 + i) : (i1 + i + groupsize)], weight=True + ) + else: + idx = i1 + i + if actorder: + idx = perm[idx] + self.quantizer = groups[idx // groupsize] + + q = quantize( + w.unsqueeze(1), + self.quantizer.scale, + self.quantizer.zero, + self.quantizer.maxq, + ).flatten() + Q1[:, i] = q + Losses1[:, i] = (w - q) ** 2 / d**2 + + err1 = (w - q) / d + W1[:, i:] -= err1.unsqueeze(1).matmul(Hinv1[i, i:].unsqueeze(0)) + Err1[:, i] = err1 + + Q[:, i1:i2] = Q1 + Losses[:, i1:i2] = Losses1 / 2 + + W[:, i2:] -= Err1.matmul(Hinv[i1:i2, i2:]) + + if DEBUG: + self.layer.weight.data[:, :i2] = Q[:, :i2] + self.layer.weight.data[:, i2:] = W[:, i2:] + print(torch.sum((self.layer(self.inp1) - self.out1) ** 2)) + print(torch.sum(Losses)) + + torch.cuda.synchronize() + print("Time for quantization: %.2f seconds" % (time.time() - tick)) + print("Total quantization error:", torch.sum(Losses).item()) + + if actorder: + Q = Q[:, invperm] + + if isinstance(self.layer, transformers.Conv1D): + Q = Q.t() + self.layer.weight.data = Q.reshape(self.layer.weight.shape).to( + self.layer.weight.data.dtype + ) + if DEBUG: + print(torch.sum((self.layer(self.inp1) - self.out1) ** 2)) + + def free(self): + if DEBUG: + self.inp1 = None + self.out1 = None + self.H = None + self.Losses = None + self.Trace = None + torch.cuda.empty_cache() + +class LlavaQuantizer: + def __init__(self, model, processor, device, chunk_size=32, task = 'vqav2'): + self.model = model + self.processor = processor + self.device = device + self.chunk_size = chunk_size + self.task = task + + # Component-specific configuration parameters + self.config = { + "vision": { + "bits": 4, + "percent_dampening": 0.01, + "group_size": -1, + "use_symmetric": True, + "use_act_order": False, + "use_static_groups": False, + }, + "language": { + "bits": 4, + "percent_dampening": 0.01, + "group_size": -1, + "use_symmetric": True, + "use_act_order": False, + "use_static_groups": False, + }, + } + + + def _prepare_quantizers(self, layers, component_type): + """Initialize GPTQ quantizers for given layers with component-specific settings""" + config = self.config[component_type] + quantizers = {} + for name, layer in layers.items(): + quantizers[name] = GPTQ(layer) + quantizers[name].quantizer.configure( + bits=config["bits"], + perchannel=True, + sym=config["use_symmetric"], + mse=False, + ) + return quantizers + + def _process_chunk( + self, layers, start_idx, end_idx, forward_func, desc, component_type + ): + """Process a chunk of layers with component-specific quantization settings""" + current_layers = dict(list(layers.items())[start_idx:end_idx]) + print( + f"\nProcessing {desc} layers {start_idx} to {end_idx-1} with {self.config[component_type]['bits']}-bit precision" + ) + + # Initialize quantizers for current chunk + quantizers = self._prepare_quantizers(current_layers, component_type) + hooks = [] + + def get_hook(name): + def hook(module, inp, out): + if name in quantizers: + quantizers[name].add_batch(inp[0].detach(), out.detach()) + + return hook + + for name, layer in current_layers.items(): + hooks.append(layer.register_forward_hook(get_hook(name))) + + forward_func() + + for hook in hooks: + hook.remove() + + config = self.config[component_type] + for name, layer in current_layers.items(): + print(f"Quantizing layer {name}...") + quantizer = quantizers[name] + quantizer.fasterquant( + blocksize=32, + percdamp=config["percent_dampening"], + groupsize=config["group_size"], + actorder=config["use_act_order"], + static_groups=config["use_static_groups"], + ) + + layer.weight.data = quantizer.quantizer.quantize(layer.weight.data).to( + layer.weight.data.dtype + ) + quantizer.free() + + torch.cuda.empty_cache() + + + def quantize_vision_model(self, calibration_set): + """Quantize vision model with 8-bit precision""" + print( + f"Quantizing Vision Model with {self.config['vision']['bits']}-bit precision..." + ) + + # some extra components need to be on device for vision model forward pass + # self.model.vision_tower.to(self.device) + self.model.to(self.device) + self.model.language_model.to('cpu') + + layers = find_linear_layers_in_model(self.model.vision_tower.vision_model) + total_layers = len(layers) + + print(f'total_layers: {total_layers}') + print(layers) + + def forward_pass(): + + vision_feature_layer = self.model.config.vision_feature_layer + vision_feature_select_strategy = self.model.config.vision_feature_select_strategy + image_sizes = None + + # TODO: adjust for GQA if needed + if self.task == 'vqav2': + + for img, prompt in tqdm(calibration_set, desc='Processing vision model batch'): + + inputs = self.processor(images = [img], + text= [prompt], + return_tensors='pt', + padding=True).to(self.device) + + # runs forward pass through vision_tower + self.model.get_image_features( + pixel_values = inputs['pixel_values'], + vision_feature_layer=vision_feature_layer, + vision_feature_select_strategy=vision_feature_select_strategy, + image_sizes=image_sizes + ) + + + for start_idx in range(0, total_layers, self.chunk_size): + end_idx = min(start_idx + self.chunk_size, total_layers) + self._process_chunk( + layers, start_idx, end_idx, forward_pass, "vision model", "vision" + ) + + self.model.vision_tower.vision_model.cpu() + print("Vision Model quantization complete.\n") + + + def quantize_language_model(self, calibration_set): + """Quantize language model with 4-bit precision""" + print( + f"Quantizing Language Model with {self.config['language']['bits']}-bit precision..." + ) + self.model.to(self.device) + + layers = find_linear_layers_in_model(self.model.language_model.model) + # layers["language_projection"] = self.model.language_projection + total_layers = len(layers) + + def forward_pass(): + # TODO: adjust for GQA if needed + if self.task == 'vqav2': + + for img, prompt in tqdm(calibration_set, desc='Processing language model batch'): + + inputs = self.processor(images = [img], + text= [prompt], + return_tensors='pt', + padding=True).to(self.device) + + self.model.generate(**inputs) + + + for start_idx in range(0, total_layers, self.chunk_size): + end_idx = min(start_idx + self.chunk_size, total_layers) + self._process_chunk( + layers, start_idx, end_idx, forward_pass, "language model", "language" + ) + + self.model.cpu() + print("Language Model quantization complete.\n") + + def quantize(self, calibration_set): + """Quantize all LLAVA components""" + print("Starting LLAVA model quantization...") + self.quantize_vision_model(calibration_set) + self.quantize_language_model(calibration_set) + print("LLAVA model quantization complete.") + + def prepare_for_inference(self): + self.model.to(self.device) + + +def get_args(): + + parser = argparse.ArgumentParser(description="LLAVA GPTQ Quantization Script") + + parser.add_argument( + '--task', + type=str, + choices=['vqav2', 'gqa'], + required=True, + help='task to evaluate GPTQ-quantized LLAVA on' + ) + + # Add arguments for bit sizes + parser.add_argument( + "--vision-bits", + type=int, + default=8, + choices=[2, 3, 4, 5, 6, 7, 8, 16], + help="Bit size for vision component", + ) + + parser.add_argument( + "--language-bits", + type=int, + default=4, + choices=[2, 3, 4, 5, 6, 7, 8, 16], + help="Bit size for language component", + ) + + parser.add_argument( + "--calibration-size", type=int, default=128, help="Size of calibration dataset" + ) + + parser.add_argument( + "--seed", + type=int, + default=None, + help="Random seed for reproducibility. If not provided, a random seed will be generated.", + ) + + + parser.add_argument( + "--device", + type=str, + default="cuda:0", + help="Device to use (cuda:0, cuda:1, cpu, etc.)", + ) + + parser.add_argument( + "--output_dir", + type=str, + default="gptq_results", + help="Directory to save results", + ) + + parser.add_argument( + '--no_quant', + default=False, + action='store_true', + help="Set to true to apply no quantization (full-precision run)" + ) + + parser.add_argument( + '--batch_size', + type = int, + default = 16, + help = 'batch size for task evaulation' + ) + + return parser.parse_args() + +def main(): + args = get_args() + + # Generate random seed if not provided + if args.seed is None: + args.seed = random.randint(0, 2**32 - 1) + print(f"Generated random seed: {args.seed}") + + + # Set random seed + random.seed(args.seed) + torch.manual_seed(args.seed) + + # Setup device + device = torch.device( + args.device if torch.cuda.is_available() and "cuda" in args.device else "cpu" + ) + + print("Loading LLAVA model...") + # Load the model + model = LlavaForConditionalGeneration.from_pretrained("llava-hf/llava-1.5-7b-hf", torch_dtype=torch.float16) + # offload model to cpu for now + model.to('cpu') + # Free up memory + torch.cuda.empty_cache() + + processor = AutoProcessor.from_pretrained("llava-hf/llava-1.5-7b-hf", pad_token = '', use_fast = False) + + # need to use this image processor w/ do_pad=True according to "Note regarding reproducing original implementation" + # https://huggingface.co/docs/transformers/en/model_doc/llava + image_processor = LlavaImageProcessor.from_pretrained("llava-hf/llava-1.5-7b-hf", + do_pad=True) + + processor.image_processor = image_processor + + # short answer prompting according to: https://github.com/haotian-liu/LLaVA/blob/main/docs/Evaluation.md + llava_prompt = 'USER: \n{}\nAnswer the question using a single word or phrase. ASSISTANT:' + + if args.task == 'vqav2': + # VQAv2 dataset paths + ann_root = '/fs/cfar-projects/low-bit-vision/datasets/vqav2/annotations' + q_root = '/fs/cfar-projects/low-bit-vision/datasets/vqav2/questions' + image_root = '/fs/cfar-projects/low-bit-vision/datasets/vqav2/val2014' + + dataset = VQAv2Eval(image_root=image_root, + ann_root=ann_root, + q_root=q_root, + prompt = llava_prompt) + + dataset.set_max_samples(21435) + + elif args.task == 'gqa': + # GQA dataset paths + image_root = '/fs/cfar-projects/low-bit-vision/datasets/gqa/images' + q_root = '/fs/cfar-projects/low-bit-vision/datasets/gqa/questions' + + dataset = GQAEval( + image_root, + q_root, + prompt=llava_prompt + ) + + + # Get random calibration indices + total_indices = list(range(len(dataset))) # Total dataset size + calibration_indices = random.sample(total_indices, args.calibration_size) + calibration_set = [(dataset[i]['image'], dataset[i]['text_input']) for i in calibration_indices] + + # Create quantizer + quantizer = LlavaQuantizer(model, processor, device) + + if not args.no_quant: + # Update quantizer config with specified bit sizes + quantizer.config["vision"]["bits"] = args.vision_bits + quantizer.config["language"]["bits"] = args.language_bits + + # Print configuration + print("\nQuantization Configuration:") + print(f"Vision bits: {args.vision_bits}") + print(f"Language bits: {args.language_bits}") + print(f"Calibration size: {args.calibration_size}") + print(f"Device: {device}\n") + + + # Quantize model + quantizer.quantize(calibration_set) + + # Evaluate on task + gpu_name = torch.cuda.get_device_name() + print(gpu_name) + + # adjust batch sizes depending on available gpu memory + if "A5000" in gpu_name.replace(" ", ""): + args.batch_size = 16 + elif "A6000" in gpu_name.replace(" ", ""): + args.batch_size = 56 + + print(f'Evaluating on task: {args.task}') + print(f'batch_size: {args.batch_size}') + quantizer.prepare_for_inference() + + dataloader = DataLoader(dataset, + batch_size=args.batch_size, + num_workers=1, + pin_memory=False, + shuffle=False, + collate_fn = dataset.collater) + + + inferencer = InferencePipeline(model, device, processor) + + # set this according to huggingface usage tips: https://huggingface.co/docs/transformers/en/model_doc/llava + processor.tokenizer.padding_side = "left" + processor_kwargs = dict(padding=True) + + # greedy decoding + generate_kwargs = { + 'num_beams': 1, + 'do_sample': False + } + + results = inferencer.run_inference( + dataloader, + task = args.task, + processor_kwargs = processor_kwargs, + generate_kwargs = generate_kwargs + ) + + + json_out = { + "answers": results, + "vision_bits": args.vision_bits, + "language_bits": args.language_bits + } + + os.makedirs(args.output_dir, exist_ok=True) + json_path = os.path.join(args.output_dir, f"results_v{args.vision_bits}_l{args.language_bits}.json") + with open(json_path, 'w') as f: + json.dump(json_out, f) + + + print(f"Output results to {json_path}") + + + # if args.task == 'vqav2': + # results["annotations"] = os.path.join(ann_root, "v2_mscoco_val2014_annotations.json") + # results["questions"] = os.path.join(q_root, "v2_OpenEnded_mscoco_val2014_questions.json") + + + # scorer = ScoringPipeline() + + # def compute_vqa_results(results, scorer, save_path=None): + # vqa_results = scorer.compute_scores(results, "vqav2") + # print(vqa_results) + # if save_path: + # with open(save_path, "w") as f: + # json.dump(vqa_results, f) + + # compute_vqa_results(results, scorer, os.path.join(args.output_dir, "results.json")) + + +if __name__ == '__main__': + main() diff --git a/llava_runs/gptq_vqav2_multi_sbatch_submit.sh b/llava_runs/gptq_vqav2_multi_sbatch_submit.sh new file mode 100755 index 0000000..d3282d5 --- /dev/null +++ b/llava_runs/gptq_vqav2_multi_sbatch_submit.sh @@ -0,0 +1,12 @@ +python multi_sbatch_gptq_vqav2.py --env slurm_files \ + --nhrs 4 \ + --qos scav \ + --partition vulcan \ + --gpu 1 \ + --gpu-type a5000 a6000 \ + --cores 1 \ + --mem 48 \ + --output-dirname vqav2_subset_gptq_output \ + # --dryrun + + diff --git a/llava_runs/inference_test.ipynb b/llava_runs/inference_test.ipynb new file mode 100644 index 0000000..9f6296e --- /dev/null +++ b/llava_runs/inference_test.ipynb @@ -0,0 +1,3236 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "id": "6d8777b1", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/fs/nexus-scratch/vla/micromamba/envs/MMQ_LLAVA/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + " from .autonotebook import tqdm as notebook_tqdm\n" + ] + } + ], + "source": [ + "import sys\n", + "sys.path.append('..')\n", + "\n", + "from transformers import AutoProcessor, LlavaForConditionalGeneration\n", + "from transformers.models.llava.image_processing_llava import LlavaImageProcessor\n", + "import transformers\n", + "\n", + "from dataset import VQAv2Eval, GQAEval\n", + "from inference_pipeline import InferencePipeline\n", + "\n", + "import torch\n", + "from torch.utils.data import DataLoader" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "a122f95c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "214354" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ann_root = '/fs/cfar-projects/low-bit-vision/datasets/vqav2/annotations'\n", + "q_root = '/fs/cfar-projects/low-bit-vision/datasets/vqav2/questions'\n", + "image_root = '/fs/cfar-projects/low-bit-vision/datasets/vqav2/val2014'\n", + "# short answer prompting according to: https://github.com/haotian-liu/LLaVA/blob/main/docs/Evaluation.md\n", + "llava_prompt = 'USER: \\n{}\\nAnswer the question using a single word or phrase. ASSISTANT:'\n", + "\n", + "dataset = VQAv2Eval(image_root=image_root,\n", + " ann_root=ann_root,\n", + " q_root=q_root,\n", + " prompt = llava_prompt)\n", + "\n", + "# dataset.set_max_samples(21435)\n", + "\n", + "len(dataset)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "fe5bd2bf", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Loading checkpoint shards: 100%|██████████| 3/3 [00:00<00:00, 13.18it/s]\n" + ] + } + ], + "source": [ + "if torch.backends.mps.is_available():\n", + " device = torch.device(\"mps\")\n", + "else:\n", + " device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", + "\n", + "model = LlavaForConditionalGeneration.from_pretrained(\"llava-hf/llava-1.5-7b-hf\", torch_dtype=torch.float16)\n", + "model.to('cuda')\n", + "processor = AutoProcessor.from_pretrained(\"llava-hf/llava-1.5-7b-hf\", pad_token = '', use_fast = False)\n", + "\n", + "# need to use this image processor w/ do_pad=True according to \"Note regarding reproducing original implementation\"\n", + "# https://huggingface.co/docs/transformers/en/model_doc/llava\n", + "image_processor = LlavaImageProcessor.from_pretrained(\"llava-hf/llava-1.5-7b-hf\",\n", + " do_pad=True)\n", + "\n", + "processor.image_processor = image_processor" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "395ef9e2", + "metadata": {}, + "outputs": [], + "source": [ + "# short answer prompting according to: https://github.com/haotian-liu/LLaVA/blob/main/docs/Evaluation.md\n", + "llava_prompt = 'USER: \\n{}\\nAnswer the question using a single word or phrase. ASSISTANT:'\n", + "\n", + "# GQA dataset paths\n", + "image_root = '/fs/cfar-projects/low-bit-vision/datasets/gqa/images'\n", + "q_root = '/fs/cfar-projects/low-bit-vision/datasets/gqa/questions'\n", + "\n", + "dataset = GQAEval(\n", + " image_root,\n", + " q_root,\n", + " prompt=llava_prompt\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "bf43f9a4", + "metadata": {}, + "outputs": [], + "source": [ + "dataloader = DataLoader(dataset,\n", + " batch_size=16,\n", + " num_workers=1,\n", + " pin_memory=False,\n", + " shuffle=False,\n", + " collate_fn = dataset.collater)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "ce9d1701", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 787/787 [33:25<00:00, 2.55s/it]\n" + ] + }, + { + "data": { + "text/plain": [ + "[{'question_id': '201307251',\n", + " 'answer': 'USER: \\nIs it overcast?\\nAnswer the question using a single word or phrase. ASSISTANT: No',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '201640614',\n", + " 'answer': 'USER: \\nWho is wearing the dress?\\nAnswer the question using a single word or phrase. ASSISTANT: Lady',\n", + " 'gt_answer': 'women'},\n", + " {'question_id': '202225914',\n", + " 'answer': 'USER: \\nDoes the utensil on top of the table look clean and black?\\nAnswer the question using a single word or phrase. ASSISTANT: No',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '2062325',\n", + " 'answer': 'USER: \\nIs the surfer that looks wet wearing a wetsuit?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '201303229',\n", + " 'answer': 'USER: \\nHow tall is the chair in the bottom of the photo?\\nAnswer the question using a single word or phrase. ASSISTANT: Tall',\n", + " 'gt_answer': 'short'},\n", + " {'question_id': '201902997',\n", + " 'answer': 'USER: \\nWhat kind of device is on top of the desk?\\nAnswer the question using a single word or phrase. ASSISTANT: Computer',\n", + " 'gt_answer': 'keyboard'},\n", + " {'question_id': '20567512',\n", + " 'answer': 'USER: \\nWhat is the airplane flying above?\\nAnswer the question using a single word or phrase. ASSISTANT: Beach',\n", + " 'gt_answer': 'ocean'},\n", + " {'question_id': '20136592',\n", + " 'answer': 'USER: \\nWhat color are the pants?\\nAnswer the question using a single word or phrase. ASSISTANT: Red',\n", + " 'gt_answer': 'red'},\n", + " {'question_id': '20602803',\n", + " 'answer': 'USER: \\nIs the ground blue or brown?\\nAnswer the question using a single word or phrase. ASSISTANT: Brown',\n", + " 'gt_answer': 'brown'},\n", + " {'question_id': '201079951',\n", + " 'answer': 'USER: \\nWhat is around the open window?\\nAnswer the question using a single word or phrase. ASSISTANT: Curtain',\n", + " 'gt_answer': 'drapes'},\n", + " {'question_id': '201079952',\n", + " 'answer': \"USER: \\nWhat's around the window?\\nAnswer the question using a single word or phrase. ASSISTANT: Curtain\",\n", + " 'gt_answer': 'drapes'},\n", + " {'question_id': '20982537',\n", + " 'answer': 'USER: \\nWho is standing at the table?\\nAnswer the question using a single word or phrase. ASSISTANT: Man',\n", + " 'gt_answer': 'woman'},\n", + " {'question_id': '201079958',\n", + " 'answer': 'USER: \\nAre there drapes to the right of the bed?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '202218649',\n", + " 'answer': 'USER: \\nWhat is hanging above the chalkboard?\\nAnswer the question using a single word or phrase. ASSISTANT: Picture',\n", + " 'gt_answer': 'picture'},\n", + " {'question_id': '20609782',\n", + " 'answer': 'USER: \\nIs the cake on a platter?\\nAnswer the question using a single word or phrase. ASSISTANT: No',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '201757757',\n", + " 'answer': 'USER: \\nIs the person to the right of the cup wearing jeans?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '201902993',\n", + " 'answer': 'USER: \\nWhat device is sitting next to the mouse pad?\\nAnswer the question using a single word or phrase. ASSISTANT: Keyboard',\n", + " 'gt_answer': 'keyboard'},\n", + " {'question_id': '20306193',\n", + " 'answer': 'USER: \\nDoes the sweater look open and blue?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '20183468',\n", + " 'answer': 'USER: \\nIs the jacket long sleeved and black?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '20753400',\n", + " 'answer': 'USER: \\nAre there beds next to the small outlet?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '20151723',\n", + " 'answer': 'USER: \\nOn which side of the picture is the leather bag?\\nAnswer the question using a single word or phrase. ASSISTANT: Left',\n", + " 'gt_answer': 'right'},\n", + " {'question_id': '201030735',\n", + " 'answer': 'USER: \\nIs the blue pillow square and large?\\nAnswer the question using a single word or phrase. ASSISTANT: No',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '201637161',\n", + " 'answer': 'USER: \\nWhich color is the cake?\\nAnswer the question using a single word or phrase. ASSISTANT: White',\n", + " 'gt_answer': 'white'},\n", + " {'question_id': '202218839',\n", + " 'answer': 'USER: \\nWhat is the name of the cooking utensil that is hang from the hook?\\nAnswer the question using a single word or phrase. ASSISTANT: Kettle',\n", + " 'gt_answer': 'pan'},\n", + " {'question_id': '20982539',\n", + " 'answer': 'USER: \\nWhere is the skinny person standing?\\nAnswer the question using a single word or phrase. ASSISTANT: Left',\n", + " 'gt_answer': 'table'},\n", + " {'question_id': '201110833',\n", + " 'answer': 'USER: \\nAre the plates on top of an ottoman?\\nAnswer the question using a single word or phrase. ASSISTANT: No',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '20866249',\n", + " 'answer': 'USER: \\nIs the freezer near the wall small or large?\\nAnswer the question using a single word or phrase. ASSISTANT: Small',\n", + " 'gt_answer': 'large'},\n", + " {'question_id': '201110525',\n", + " 'answer': 'USER: \\nWhat type of food is to the left of the baby that is sitting atop the woman?\\nAnswer the question using a single word or phrase. ASSISTANT: Dessert',\n", + " 'gt_answer': 'marshmallow'},\n", + " {'question_id': '20120533',\n", + " 'answer': 'USER: \\nIs the fence made of cement or aluminum?\\nAnswer the question using a single word or phrase. ASSISTANT: Aluminum',\n", + " 'gt_answer': 'aluminum'},\n", + " {'question_id': '201952977',\n", + " 'answer': 'USER: \\nWhich side are the white houses on?\\nAnswer the question using a single word or phrase. ASSISTANT: Left',\n", + " 'gt_answer': 'left'},\n", + " {'question_id': '201497576',\n", + " 'answer': 'USER: \\nAre both the phone and the coffee cup the same color?\\nAnswer the question using a single word or phrase. ASSISTANT: No',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '20866242',\n", + " 'answer': 'USER: \\nAre there either any small refrigerators or microwaves in the picture?\\nAnswer the question using a single word or phrase. ASSISTANT: No',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '201455911',\n", + " 'answer': 'USER: \\nHow does that car look like, orange or maybe white?\\nAnswer the question using a single word or phrase. ASSISTANT: White',\n", + " 'gt_answer': 'white'},\n", + " {'question_id': '20856965',\n", + " 'answer': 'USER: \\nWhat color is the book?\\nAnswer the question using a single word or phrase. ASSISTANT: Black',\n", + " 'gt_answer': 'white'},\n", + " {'question_id': '2059565',\n", + " 'answer': 'USER: \\nWhat color is the dirt?\\nAnswer the question using a single word or phrase. ASSISTANT: Brown',\n", + " 'gt_answer': 'red'},\n", + " {'question_id': '20856960',\n", + " 'answer': 'USER: \\nIs the bag made of leather lying on top of a sofa?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '201079954',\n", + " 'answer': 'USER: \\nWhat are the drapes around of?\\nAnswer the question using a single word or phrase. ASSISTANT: Window',\n", + " 'gt_answer': 'window'},\n", + " {'question_id': '201548894',\n", + " 'answer': 'USER: \\nOn which side is the picture?\\nAnswer the question using a single word or phrase. ASSISTANT: Left',\n", + " 'gt_answer': 'left'},\n", + " {'question_id': '201573912',\n", + " 'answer': 'USER: \\nWhat material is the crosswalk in front of the stores?\\nAnswer the question using a single word or phrase. ASSISTANT: Brick',\n", + " 'gt_answer': 'concrete'},\n", + " {'question_id': '202243820',\n", + " 'answer': 'USER: \\nHow large are the sprinkles that are sprinkled on the cupcakes?\\nAnswer the question using a single word or phrase. ASSISTANT: Large',\n", + " 'gt_answer': 'small'},\n", + " {'question_id': '201573918',\n", + " 'answer': 'USER: \\nWhat type of material is the crosswalk near the street lamp made of?\\nAnswer the question using a single word or phrase. ASSISTANT: Concrete',\n", + " 'gt_answer': 'concrete'},\n", + " {'question_id': '201974972',\n", + " 'answer': 'USER: \\nWhich kind of clothing is pink?\\nAnswer the question using a single word or phrase. ASSISTANT: Tank top',\n", + " 'gt_answer': 'tank top'},\n", + " {'question_id': '201974971',\n", + " 'answer': 'USER: \\nHow is the clothing item that is pink called?\\nAnswer the question using a single word or phrase. ASSISTANT: Tank top',\n", + " 'gt_answer': 'tank top'},\n", + " {'question_id': '201974976',\n", + " 'answer': 'USER: \\nWhich kind of clothing is not pink?\\nAnswer the question using a single word or phrase. ASSISTANT: Cap',\n", + " 'gt_answer': 'hat'},\n", + " {'question_id': '201996743',\n", + " 'answer': 'USER: \\nIs this helicopter on or off?\\nAnswer the question using a single word or phrase. ASSISTANT: Off',\n", + " 'gt_answer': 'off'},\n", + " {'question_id': '20797666',\n", + " 'answer': 'USER: \\nDo you see any cats?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '20797665',\n", + " 'answer': 'USER: \\nIs that shoe behind a dog?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '201974979',\n", + " 'answer': 'USER: \\nWhat kind of clothing is sleeveless?\\nAnswer the question using a single word or phrase. ASSISTANT: Tank top',\n", + " 'gt_answer': 'tank top'},\n", + " {'question_id': '201156138',\n", + " 'answer': 'USER: \\nIs the field soft and snowy?\\nAnswer the question using a single word or phrase. ASSISTANT: No',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '20442334',\n", + " 'answer': 'USER: \\nWhat fruits are beneath the microwave?\\nAnswer the question using a single word or phrase. ASSISTANT: Bananas',\n", + " 'gt_answer': 'bananas'},\n", + " {'question_id': '201765651',\n", + " 'answer': 'USER: \\nWhich color is the shirt?\\nAnswer the question using a single word or phrase. ASSISTANT: White',\n", + " 'gt_answer': 'white'},\n", + " {'question_id': '20442331',\n", + " 'answer': 'USER: \\nWhat is beneath the microwave?\\nAnswer the question using a single word or phrase. ASSISTANT: Counter',\n", + " 'gt_answer': 'bananas'},\n", + " {'question_id': '20508243',\n", + " 'answer': 'USER: \\nOn which side of the picture is the chair?\\nAnswer the question using a single word or phrase. ASSISTANT: Right',\n", + " 'gt_answer': 'right'},\n", + " {'question_id': '2046473',\n", + " 'answer': 'USER: \\nIs the happy man to the left or to the right of the woman in the center?\\nAnswer the question using a single word or phrase. ASSISTANT: Right',\n", + " 'gt_answer': 'right'},\n", + " {'question_id': '20618932',\n", + " 'answer': 'USER: \\nWho is wearing a wristband?\\nAnswer the question using a single word or phrase. ASSISTANT: Girl',\n", + " 'gt_answer': 'woman'},\n", + " {'question_id': '20442338',\n", + " 'answer': 'USER: \\nIs there a pear beneath the appliance that looks silver and black?\\nAnswer the question using a single word or phrase. ASSISTANT: No',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '202005788',\n", + " 'answer': 'USER: \\nIs this a bed or a cabinet?\\nAnswer the question using a single word or phrase. ASSISTANT: Cabinet',\n", + " 'gt_answer': 'cabinet'},\n", + " {'question_id': '201902515',\n", + " 'answer': 'USER: \\nOn which side is the router?\\nAnswer the question using a single word or phrase. ASSISTANT: Left',\n", + " 'gt_answer': 'left'},\n", + " {'question_id': '201303404',\n", + " 'answer': 'USER: \\nWhat is the color of the pants?\\nAnswer the question using a single word or phrase. ASSISTANT: Gray',\n", + " 'gt_answer': 'gray'},\n", + " {'question_id': '20942157',\n", + " 'answer': 'USER: \\nWho is wearing the shirt?\\nAnswer the question using a single word or phrase. ASSISTANT: Woman',\n", + " 'gt_answer': 'girl'},\n", + " {'question_id': '20942156',\n", + " 'answer': 'USER: \\nWho is wearing a shirt?\\nAnswer the question using a single word or phrase. ASSISTANT: Woman',\n", + " 'gt_answer': 'girl'},\n", + " {'question_id': '20898685',\n", + " 'answer': \"USER: \\nWhat's the man doing?\\nAnswer the question using a single word or phrase. ASSISTANT: Waiting\",\n", + " 'gt_answer': 'standing'},\n", + " {'question_id': '202116974',\n", + " 'answer': 'USER: \\nAre there rivers or oceans that are not calm?\\nAnswer the question using a single word or phrase. ASSISTANT: No',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '201621328',\n", + " 'answer': 'USER: \\nWhat is the picture frame hanging from?\\nAnswer the question using a single word or phrase. ASSISTANT: Wall',\n", + " 'gt_answer': 'wall'},\n", + " {'question_id': '2076819',\n", + " 'answer': 'USER: \\nHow do the cars look like, dense or sparse?\\nAnswer the question using a single word or phrase. ASSISTANT: Sparse',\n", + " 'gt_answer': 'dense'},\n", + " {'question_id': '202244099',\n", + " 'answer': \"USER: \\nWhat food isn't baked?\\nAnswer the question using a single word or phrase. ASSISTANT: Carrot\",\n", + " 'gt_answer': 'cookies'},\n", + " {'question_id': '201951771',\n", + " 'answer': 'USER: \\nWhich kind of vehicle is in front of the flag?\\nAnswer the question using a single word or phrase. ASSISTANT: Van',\n", + " 'gt_answer': 'van'},\n", + " {'question_id': '201951770',\n", + " 'answer': 'USER: \\nWhat is the vehicle that is in front of the flag?\\nAnswer the question using a single word or phrase. ASSISTANT: Van',\n", + " 'gt_answer': 'van'},\n", + " {'question_id': '201621326',\n", + " 'answer': 'USER: \\nWhat is hanging from the wall?\\nAnswer the question using a single word or phrase. ASSISTANT: Picture',\n", + " 'gt_answer': 'picture frame'},\n", + " {'question_id': '201233862',\n", + " 'answer': \"USER: \\nWhat's the skateboarder jumping off of?\\nAnswer the question using a single word or phrase. ASSISTANT: Ramp\",\n", + " 'gt_answer': 'pavement'},\n", + " {'question_id': '201951776',\n", + " 'answer': 'USER: \\nIs the van in front of a balloon?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '20489632',\n", + " 'answer': 'USER: \\nWhat is the color of this bench?\\nAnswer the question using a single word or phrase. ASSISTANT: Brown',\n", + " 'gt_answer': 'beige'},\n", + " {'question_id': '201623784',\n", + " 'answer': 'USER: \\nAre the cabinets below the stove wooden and open?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '202023424',\n", + " 'answer': 'USER: \\nHow is the item of furniture that is plaid called?\\nAnswer the question using a single word or phrase. ASSISTANT: Bed',\n", + " 'gt_answer': 'bed'},\n", + " {'question_id': '20182936',\n", + " 'answer': 'USER: \\nAre the boxes to the right of the man full and square?\\nAnswer the question using a single word or phrase. ASSISTANT: No',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '201654344',\n", + " 'answer': 'USER: \\nIs the horse next to the other horse both baby and brown?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '20746468',\n", + " 'answer': 'USER: \\nIs the river wide or is it narrow?\\nAnswer the question using a single word or phrase. ASSISTANT: Narrow',\n", + " 'gt_answer': 'narrow'},\n", + " {'question_id': '201428996',\n", + " 'answer': 'USER: \\nWhat appliance is the refrigerator larger than?\\nAnswer the question using a single word or phrase. ASSISTANT: Stove',\n", + " 'gt_answer': 'stove'},\n", + " {'question_id': '20899362',\n", + " 'answer': 'USER: \\nIs the umbrella in the bottom part of the picture?\\nAnswer the question using a single word or phrase. ASSISTANT: No',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '202244009',\n", + " 'answer': 'USER: \\nWhat is inside the bowl to the right of the beans?\\nAnswer the question using a single word or phrase. ASSISTANT: Cookie',\n", + " 'gt_answer': 'cookies'},\n", + " {'question_id': '20287556',\n", + " 'answer': 'USER: \\nHow clean do you think is the face mask the catcher is wearing?\\nAnswer the question using a single word or phrase. ASSISTANT: Dirty',\n", + " 'gt_answer': 'clean'},\n", + " {'question_id': '20631973',\n", + " 'answer': 'USER: \\nWhere is the catcher standing on?\\nAnswer the question using a single word or phrase. ASSISTANT: Home plate',\n", + " 'gt_answer': 'field'},\n", + " {'question_id': '20287551',\n", + " 'answer': 'USER: \\nWhat is the color of the glove?\\nAnswer the question using a single word or phrase. ASSISTANT: Black',\n", + " 'gt_answer': 'black'},\n", + " {'question_id': '201481824',\n", + " 'answer': 'USER: \\nDoes the blanket look soft and white?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '201663656',\n", + " 'answer': 'USER: \\nWhat color are the drawers?\\nAnswer the question using a single word or phrase. ASSISTANT: Brown',\n", + " 'gt_answer': 'light brown'},\n", + " {'question_id': '20308576',\n", + " 'answer': 'USER: \\nAre there refrigerators to the left of the stove?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '201065067',\n", + " 'answer': 'USER: \\nWhich kind of clothing is bright?\\nAnswer the question using a single word or phrase. ASSISTANT: Dress',\n", + " 'gt_answer': 'gown'},\n", + " {'question_id': '20462070',\n", + " 'answer': 'USER: \\nIs there an elephant near the person that is wearing a coat?\\nAnswer the question using a single word or phrase. ASSISTANT: No',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '20462076',\n", + " 'answer': 'USER: \\nWhat is the woman wearing?\\nAnswer the question using a single word or phrase. ASSISTANT: Hat',\n", + " 'gt_answer': 'gloves'},\n", + " {'question_id': '20462075',\n", + " 'answer': 'USER: \\nWhat do you think is the standing person near the man wearing?\\nAnswer the question using a single word or phrase. ASSISTANT: Skis',\n", + " 'gt_answer': 'gloves'},\n", + " {'question_id': '201065062',\n", + " 'answer': 'USER: \\nWhich type of clothing is pink?\\nAnswer the question using a single word or phrase. ASSISTANT: Dress',\n", + " 'gt_answer': 'gown'},\n", + " {'question_id': '20754631',\n", + " 'answer': 'USER: \\nWhat is the person that is sitting down sitting atop?\\nAnswer the question using a single word or phrase. ASSISTANT: Steps',\n", + " 'gt_answer': 'stairs'},\n", + " {'question_id': '201935960',\n", + " 'answer': \"USER: \\nWhat's standing on the floor?\\nAnswer the question using a single word or phrase. ASSISTANT: Bookshelf\",\n", + " 'gt_answer': 'shelf'},\n", + " {'question_id': '20412222',\n", + " 'answer': 'USER: \\nWhat items of furniture are to the left of the boy?\\nAnswer the question using a single word or phrase. ASSISTANT: Chairs',\n", + " 'gt_answer': 'tables'},\n", + " {'question_id': '201935966',\n", + " 'answer': 'USER: \\nWhat is in front of the wall that is not short?\\nAnswer the question using a single word or phrase. ASSISTANT: Bookshelf',\n", + " 'gt_answer': 'shelf'},\n", + " {'question_id': '20878946',\n", + " 'answer': 'USER: \\nHow wide is the parking lot made of cement?\\nAnswer the question using a single word or phrase. ASSISTANT: Wide',\n", + " 'gt_answer': 'wide'},\n", + " {'question_id': '201947446',\n", + " 'answer': 'USER: \\nOn which side of the picture is the clean mirror?\\nAnswer the question using a single word or phrase. ASSISTANT: Left',\n", + " 'gt_answer': 'left'},\n", + " {'question_id': '201498767',\n", + " 'answer': 'USER: \\nWhat is the device in front of the flat computer?\\nAnswer the question using a single word or phrase. ASSISTANT: Keyboard',\n", + " 'gt_answer': 'phone'},\n", + " {'question_id': '20306764',\n", + " 'answer': 'USER: \\nWhat is sitting on the floor?\\nAnswer the question using a single word or phrase. ASSISTANT: Snowboard',\n", + " 'gt_answer': 'gift'},\n", + " {'question_id': '202144708',\n", + " 'answer': 'USER: \\nIs there a blender to the right of the yellow drink?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '20306767',\n", + " 'answer': 'USER: \\nIs the gift sitting on the floor?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '201996815',\n", + " 'answer': 'USER: \\nWhich material makes up the round glasses, glass or wire?\\nAnswer the question using a single word or phrase. ASSISTANT: Glass',\n", + " 'gt_answer': 'glass'},\n", + " {'question_id': '201996813',\n", + " 'answer': 'USER: \\nWhat are the glasses made of?\\nAnswer the question using a single word or phrase. ASSISTANT: Plastic',\n", + " 'gt_answer': 'glass'},\n", + " {'question_id': '202060122',\n", + " 'answer': 'USER: \\nWhat animal is the couch behind of?\\nAnswer the question using a single word or phrase. ASSISTANT: Dog',\n", + " 'gt_answer': 'dog'},\n", + " {'question_id': '201067797',\n", + " 'answer': 'USER: \\nWhat is the color of the device that is on the left of the photo?\\nAnswer the question using a single word or phrase. ASSISTANT: Black',\n", + " 'gt_answer': 'black'},\n", + " {'question_id': '20394919',\n", + " 'answer': 'USER: \\nIs the knife to the right of a man?\\nAnswer the question using a single word or phrase. ASSISTANT: No',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '201497916',\n", + " 'answer': 'USER: \\nWhat device is above the keyboard?\\nAnswer the question using a single word or phrase. ASSISTANT: Monitor',\n", + " 'gt_answer': 'monitor'},\n", + " {'question_id': '20303081',\n", + " 'answer': 'USER: \\nWhat is the man to the left of the glasses doing?\\nAnswer the question using a single word or phrase. ASSISTANT: Sitting',\n", + " 'gt_answer': 'resting'},\n", + " {'question_id': '201498727',\n", + " 'answer': 'USER: \\nWhat is the phone made of?\\nAnswer the question using a single word or phrase. ASSISTANT: Plastic',\n", + " 'gt_answer': 'plastic'},\n", + " {'question_id': '201873473',\n", + " 'answer': 'USER: \\nAre there any red fire trucks?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '20300425',\n", + " 'answer': 'USER: \\nWhich kind of vehicle is waiting for the traffic light?\\nAnswer the question using a single word or phrase. ASSISTANT: Car',\n", + " 'gt_answer': 'cars'},\n", + " {'question_id': '20300424',\n", + " 'answer': 'USER: \\nWhat kind of vehicle is waiting for the traffic light?\\nAnswer the question using a single word or phrase. ASSISTANT: Car',\n", + " 'gt_answer': 'cars'},\n", + " {'question_id': '20899558',\n", + " 'answer': 'USER: \\nThe electronic device to the left of the notebook has what color?\\nAnswer the question using a single word or phrase. ASSISTANT: Silver',\n", + " 'gt_answer': 'blue'},\n", + " {'question_id': '20300420',\n", + " 'answer': 'USER: \\nWhat are the vehicles above the road near the side walk?\\nAnswer the question using a single word or phrase. ASSISTANT: Cars',\n", + " 'gt_answer': 'cars'},\n", + " {'question_id': '20300423',\n", + " 'answer': 'USER: \\nWhat is waiting for the traffic light?\\nAnswer the question using a single word or phrase. ASSISTANT: Car',\n", + " 'gt_answer': 'cars'},\n", + " {'question_id': '20836565',\n", + " 'answer': 'USER: \\nWhat is sitting in front of the table that looks yellow and black?\\nAnswer the question using a single word or phrase. ASSISTANT: Suitcase',\n", + " 'gt_answer': 'luggage'},\n", + " {'question_id': '201947624',\n", + " 'answer': 'USER: \\nAre there both toothbrushes and mats in this picture?\\nAnswer the question using a single word or phrase. ASSISTANT: No',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '201947620',\n", + " 'answer': 'USER: \\nIs the soap dish to the right of the soap dispenser?\\nAnswer the question using a single word or phrase. ASSISTANT: No',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '20300428',\n", + " 'answer': 'USER: \\nThe parked vehicles are waiting for what?\\nAnswer the question using a single word or phrase. ASSISTANT: Traffic light',\n", + " 'gt_answer': 'traffic light'},\n", + " {'question_id': '201504960',\n", + " 'answer': 'USER: \\nAre the shorts large and blue?\\nAnswer the question using a single word or phrase. ASSISTANT: No',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '201947591',\n", + " 'answer': 'USER: \\nThe soap dispenser made of chrome is sitting on what?\\nAnswer the question using a single word or phrase. ASSISTANT: Counter',\n", + " 'gt_answer': 'countertop'},\n", + " {'question_id': '20177575',\n", + " 'answer': 'USER: \\nWhat color is the serving tray that looks rectangular?\\nAnswer the question using a single word or phrase. ASSISTANT: Silver',\n", + " 'gt_answer': 'white'},\n", + " {'question_id': '20381557',\n", + " 'answer': 'USER: \\nDoes the device under the picture frame look black?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '201982068',\n", + " 'answer': 'USER: \\nWhich material are the trousers made of, cloth or leather?\\nAnswer the question using a single word or phrase. ASSISTANT: Cloth',\n", + " 'gt_answer': 'cloth'},\n", + " {'question_id': '201370428',\n", + " 'answer': 'USER: \\nHow do the pens look, colorful or black and white?\\nAnswer the question using a single word or phrase. ASSISTANT: Black and white',\n", + " 'gt_answer': 'black and white'},\n", + " {'question_id': '201878325',\n", + " 'answer': 'USER: \\nWho is the jacket worn around?\\nAnswer the question using a single word or phrase. ASSISTANT: Woman',\n", + " 'gt_answer': 'man'},\n", + " {'question_id': '201370422',\n", + " 'answer': 'USER: \\nOn which side of the picture are the pens?\\nAnswer the question using a single word or phrase. ASSISTANT: Right',\n", + " 'gt_answer': 'right'},\n", + " {'question_id': '2075709',\n", + " 'answer': 'USER: \\nDo you see any skis?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '201896034',\n", + " 'answer': 'USER: \\nWhat is the item of furniture to the right of the lady that is looking down at the cake called?\\nAnswer the question using a single word or phrase. ASSISTANT: Chair',\n", + " 'gt_answer': 'table'},\n", + " {'question_id': '201065497',\n", + " 'answer': 'USER: \\nIs the man to the left of the performer brunette or blond?\\nAnswer the question using a single word or phrase. ASSISTANT: Brunette',\n", + " 'gt_answer': 'blond'},\n", + " {'question_id': '20857175',\n", + " 'answer': 'USER: \\nIs the cell phone lying on top of a desk?\\nAnswer the question using a single word or phrase. ASSISTANT: No',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '20648122',\n", + " 'answer': 'USER: \\nIs the plastic helmet to the left of a woman?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '20636999',\n", + " 'answer': 'USER: \\nDoes the utensil beside the pan have black color and small size?\\nAnswer the question using a single word or phrase. ASSISTANT: No',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '20258752',\n", + " 'answer': 'USER: \\nWho is wearing jeans?\\nAnswer the question using a single word or phrase. ASSISTANT: Boy',\n", + " 'gt_answer': 'child'},\n", + " {'question_id': '201156466',\n", + " 'answer': 'USER: \\nDo the balls to the left of the other ball look light?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '201885557',\n", + " 'answer': 'USER: \\nWhat gender is the swimsuit?\\nAnswer the question using a single word or phrase. ASSISTANT: Male',\n", + " 'gt_answer': 'male'},\n", + " {'question_id': '202081210',\n", + " 'answer': 'USER: \\nIs the toaster to the right of a refrigerator?\\nAnswer the question using a single word or phrase. ASSISTANT: No',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '20117834',\n", + " 'answer': 'USER: \\nAre the trees on the field bare or lush?\\nAnswer the question using a single word or phrase. ASSISTANT: Bare',\n", + " 'gt_answer': 'lush'},\n", + " {'question_id': '201438286',\n", + " 'answer': 'USER: \\nIs the net in front of the man?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '20117781',\n", + " 'answer': 'USER: \\nIs the weather cloudy?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '201756501',\n", + " 'answer': 'USER: \\nWhat is common to the door and the kitten?\\nAnswer the question using a single word or phrase. ASSISTANT: Color',\n", + " 'gt_answer': 'color'},\n", + " {'question_id': '20716925',\n", + " 'answer': 'USER: \\nIs the jacket made of cotton large or small?\\nAnswer the question using a single word or phrase. ASSISTANT: Small',\n", + " 'gt_answer': 'small'},\n", + " {'question_id': '20541270',\n", + " 'answer': 'USER: \\nIs the gray chair to the left or to the right of the couch in the picture?\\nAnswer the question using a single word or phrase. ASSISTANT: Right',\n", + " 'gt_answer': 'right'},\n", + " {'question_id': '201056079',\n", + " 'answer': 'USER: \\nIs the soccer player that is to the left of the ball female or male?\\nAnswer the question using a single word or phrase. ASSISTANT: Female',\n", + " 'gt_answer': 'male'},\n", + " {'question_id': '20468617',\n", + " 'answer': 'USER: \\nWas iron used to make the fence?\\nAnswer the question using a single word or phrase. ASSISTANT: No',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '2017235',\n", + " 'answer': 'USER: \\nWhat color is the hair?\\nAnswer the question using a single word or phrase. ASSISTANT: Black',\n", + " 'gt_answer': 'black'},\n", + " {'question_id': '20427913',\n", + " 'answer': 'USER: \\nWhat is the picture hanging above?\\nAnswer the question using a single word or phrase. ASSISTANT: Chair',\n", + " 'gt_answer': 'chair'},\n", + " {'question_id': '20427912',\n", + " 'answer': 'USER: \\nThe framed picture is hanging above what?\\nAnswer the question using a single word or phrase. ASSISTANT: Chair',\n", + " 'gt_answer': 'chair'},\n", + " {'question_id': '201480278',\n", + " 'answer': 'USER: \\nIs the hat wet?\\nAnswer the question using a single word or phrase. ASSISTANT: No',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '201056072',\n", + " 'answer': 'USER: \\nWho is running?\\nAnswer the question using a single word or phrase. ASSISTANT: Boy',\n", + " 'gt_answer': 'soccer player'},\n", + " {'question_id': '20887449',\n", + " 'answer': 'USER: \\nWhat is the large device called?\\nAnswer the question using a single word or phrase. ASSISTANT: Computer',\n", + " 'gt_answer': 'keyboard'},\n", + " {'question_id': '20648218',\n", + " 'answer': 'USER: \\nWho is wearing a helmet?\\nAnswer the question using a single word or phrase. ASSISTANT: Man',\n", + " 'gt_answer': 'policeman'},\n", + " {'question_id': '202102931',\n", + " 'answer': 'USER: \\nWhat is beneath the microwave?\\nAnswer the question using a single word or phrase. ASSISTANT: Cabinet',\n", + " 'gt_answer': 'dishwasher'},\n", + " {'question_id': '201047479',\n", + " 'answer': 'USER: \\nIs the dress shirt gray or teal?\\nAnswer the question using a single word or phrase. ASSISTANT: Teal',\n", + " 'gt_answer': 'teal'},\n", + " {'question_id': '201370398',\n", + " 'answer': 'USER: \\nOf what color are the scissors?\\nAnswer the question using a single word or phrase. ASSISTANT: Black',\n", + " 'gt_answer': 'gray'},\n", + " {'question_id': '20672944',\n", + " 'answer': 'USER: \\nOn which side of the photo is the toilet brush?\\nAnswer the question using a single word or phrase. ASSISTANT: Left',\n", + " 'gt_answer': 'left'},\n", + " {'question_id': '201752690',\n", + " 'answer': 'USER: \\nWhat is the boy on?\\nAnswer the question using a single word or phrase. ASSISTANT: Bike',\n", + " 'gt_answer': 'bike'},\n", + " {'question_id': '20672940',\n", + " 'answer': 'USER: \\nDo you see either any containers or dream catchers?\\nAnswer the question using a single word or phrase. ASSISTANT: No',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '201752694',\n", + " 'answer': 'USER: \\nIs the young person on the bike?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '20699276',\n", + " 'answer': 'USER: \\nWhat is the name of the clothing item that is navy?\\nAnswer the question using a single word or phrase. ASSISTANT: Jacket',\n", + " 'gt_answer': 'jacket'},\n", + " {'question_id': '2097681',\n", + " 'answer': 'USER: \\nWhat is in front of the poster?\\nAnswer the question using a single word or phrase. ASSISTANT: Monitor',\n", + " 'gt_answer': 'monitor'},\n", + " {'question_id': '201760591',\n", + " 'answer': 'USER: \\nAre there both girls and soccer balls in this image?\\nAnswer the question using a single word or phrase. ASSISTANT: No',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '201110526',\n", + " 'answer': 'USER: \\nWhat kind of food is to the left of the baby?\\nAnswer the question using a single word or phrase. ASSISTANT: Dessert',\n", + " 'gt_answer': 'marshmallow'},\n", + " {'question_id': '20673099',\n", + " 'answer': 'USER: \\nWhat item of furniture is the toilet paper to the right of the toilet resting on?\\nAnswer the question using a single word or phrase. ASSISTANT: Chair',\n", + " 'gt_answer': 'chair'},\n", + " {'question_id': '20673098',\n", + " 'answer': 'USER: \\nThe toilet paper to the right of the toilet is resting on what?\\nAnswer the question using a single word or phrase. ASSISTANT: Chair',\n", + " 'gt_answer': 'chair'},\n", + " {'question_id': '20361249',\n", + " 'answer': 'USER: \\nAre there either any skateboarders or snowboarders that are jumping?\\nAnswer the question using a single word or phrase. ASSISTANT: No',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '201480696',\n", + " 'answer': 'USER: \\nWhat shape is the bench?\\nAnswer the question using a single word or phrase. ASSISTANT: Rectangle',\n", + " 'gt_answer': 'rectangular'},\n", + " {'question_id': '201879167',\n", + " 'answer': 'USER: \\nWhat is the person below the crowd bigger than?\\nAnswer the question using a single word or phrase. ASSISTANT: Window',\n", + " 'gt_answer': 'sneakers'},\n", + " {'question_id': '201438759',\n", + " 'answer': 'USER: \\nDoes the brown field appear to be large and dirty?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '20295599',\n", + " 'answer': 'USER: \\nDoes the picture frame made of plastic look black and small?\\nAnswer the question using a single word or phrase. ASSISTANT: No',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '20204868',\n", + " 'answer': 'USER: \\nIs there a silver laptop or DVD player?\\nAnswer the question using a single word or phrase. ASSISTANT: No',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '20361243',\n", + " 'answer': 'USER: \\nWho is standing?\\nAnswer the question using a single word or phrase. ASSISTANT: Woman',\n", + " 'gt_answer': 'snowboarder'},\n", + " {'question_id': '20667494',\n", + " 'answer': 'USER: \\nWhat piece of furniture is made of wood?\\nAnswer the question using a single word or phrase. ASSISTANT: Coffee table',\n", + " 'gt_answer': 'coffee table'},\n", + " {'question_id': '20667492',\n", + " 'answer': 'USER: \\nWhat piece of furniture is wooden?\\nAnswer the question using a single word or phrase. ASSISTANT: Coffee table',\n", + " 'gt_answer': 'coffee table'},\n", + " {'question_id': '20667493',\n", + " 'answer': 'USER: \\nHow the piece of furniture that is made of wood is called?\\nAnswer the question using a single word or phrase. ASSISTANT: Coffee table',\n", + " 'gt_answer': 'coffee table'},\n", + " {'question_id': '201056254',\n", + " 'answer': 'USER: \\nWho is looking up?\\nAnswer the question using a single word or phrase. ASSISTANT: Boy',\n", + " 'gt_answer': 'spectator'},\n", + " {'question_id': '201064816',\n", + " 'answer': 'USER: \\nWhich kind of furniture is blue?\\nAnswer the question using a single word or phrase. ASSISTANT: Table',\n", + " 'gt_answer': 'sofa'},\n", + " {'question_id': '2097684',\n", + " 'answer': 'USER: \\nWhat is that monitor in front of?\\nAnswer the question using a single word or phrase. ASSISTANT: Speaker',\n", + " 'gt_answer': 'poster'},\n", + " {'question_id': '201064812',\n", + " 'answer': 'USER: \\nWhat type of furniture is this, a cabinet or a sofa?\\nAnswer the question using a single word or phrase. ASSISTANT: Sofa',\n", + " 'gt_answer': 'sofa'},\n", + " {'question_id': '201056252',\n", + " 'answer': 'USER: \\nWhat do you think is that spectator doing?\\nAnswer the question using a single word or phrase. ASSISTANT: Watching',\n", + " 'gt_answer': 'looking up'},\n", + " {'question_id': '201064810',\n", + " 'answer': 'USER: \\nWhat piece of furniture is it?\\nAnswer the question using a single word or phrase. ASSISTANT: Bed',\n", + " 'gt_answer': 'sofa'},\n", + " {'question_id': '201935799',\n", + " 'answer': 'USER: \\nWhat is the container made of glass sitting on top of?\\nAnswer the question using a single word or phrase. ASSISTANT: Shelf',\n", + " 'gt_answer': 'shelf'},\n", + " {'question_id': '20756897',\n", + " 'answer': 'USER: \\nWhat is the name of the smooth piece of clothing?\\nAnswer the question using a single word or phrase. ASSISTANT: Shirt',\n", + " 'gt_answer': 'robe'},\n", + " {'question_id': '201065430',\n", + " 'answer': 'USER: \\nOn which side of the photo are the chairs?\\nAnswer the question using a single word or phrase. ASSISTANT: Right',\n", + " 'gt_answer': 'right'},\n", + " {'question_id': '202243368',\n", + " 'answer': 'USER: \\nWhat color is the shirt the woman wears?\\nAnswer the question using a single word or phrase. ASSISTANT: White',\n", + " 'gt_answer': 'white'},\n", + " {'question_id': '202121334',\n", + " 'answer': 'USER: \\nDo the soap bottle and the clock have the same color?\\nAnswer the question using a single word or phrase. ASSISTANT: No',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '201935797',\n", + " 'answer': 'USER: \\nWhat is sitting on top of the shelf?\\nAnswer the question using a single word or phrase. ASSISTANT: Jar',\n", + " 'gt_answer': 'jar'},\n", + " {'question_id': '201639189',\n", + " 'answer': 'USER: \\nDoes the vehicle behind the zebras look black?\\nAnswer the question using a single word or phrase. ASSISTANT: No',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '20741279',\n", + " 'answer': 'USER: \\nHow tall do you think is the person?\\nAnswer the question using a single word or phrase. ASSISTANT: Tall',\n", + " 'gt_answer': 'tall'},\n", + " {'question_id': '201143145',\n", + " 'answer': 'USER: \\nThe wood floor is what color?\\nAnswer the question using a single word or phrase. ASSISTANT: Brown',\n", + " 'gt_answer': 'dark brown'},\n", + " {'question_id': '201669504',\n", + " 'answer': 'USER: \\nAre the brown cookies on the right of the picture?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '201763810',\n", + " 'answer': 'USER: \\nAre there any televisions or curtains in the picture?\\nAnswer the question using a single word or phrase. ASSISTANT: No',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '202119900',\n", + " 'answer': 'USER: \\nWhat appliance is in front of the wall?\\nAnswer the question using a single word or phrase. ASSISTANT: Refrigerator',\n", + " 'gt_answer': 'refrigerator'},\n", + " {'question_id': '202119903',\n", + " 'answer': 'USER: \\nIs there a refrigerator in front of the wall made of wood?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '20340771',\n", + " 'answer': 'USER: \\nWhat is the name of the piece of furniture in front of the fence?\\nAnswer the question using a single word or phrase. ASSISTANT: Table',\n", + " 'gt_answer': 'chair'},\n", + " {'question_id': '20340770',\n", + " 'answer': \"USER: \\nWhat's in front of the fence?\\nAnswer the question using a single word or phrase. ASSISTANT: Chair\",\n", + " 'gt_answer': 'chair'},\n", + " {'question_id': '20285405',\n", + " 'answer': 'USER: \\nHow clean are the walls the window is on?\\nAnswer the question using a single word or phrase. ASSISTANT: Clean',\n", + " 'gt_answer': 'clean'},\n", + " {'question_id': '20340772',\n", + " 'answer': 'USER: \\nWhich kind of furniture is in front of the fence?\\nAnswer the question using a single word or phrase. ASSISTANT: Chair',\n", + " 'gt_answer': 'chair'},\n", + " {'question_id': '201593445',\n", + " 'answer': 'USER: \\nWhat animal is standing against the grass?\\nAnswer the question using a single word or phrase. ASSISTANT: Cow',\n", + " 'gt_answer': 'cow'},\n", + " {'question_id': '201347404',\n", + " 'answer': 'USER: \\nWho is wearing shorts?\\nAnswer the question using a single word or phrase. ASSISTANT: Boy',\n", + " 'gt_answer': 'skateboarder'},\n", + " {'question_id': '202100755',\n", + " 'answer': 'USER: \\nAre the mugs to the right of the plastic utensils?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '201462472',\n", + " 'answer': 'USER: \\nOn which side of the photo is the catcher?\\nAnswer the question using a single word or phrase. ASSISTANT: Left',\n", + " 'gt_answer': 'left'},\n", + " {'question_id': '201887286',\n", + " 'answer': 'USER: \\nWhat is in the basket?\\nAnswer the question using a single word or phrase. ASSISTANT: Broccoli',\n", + " 'gt_answer': 'broccoli'},\n", + " {'question_id': '20518589',\n", + " 'answer': 'USER: \\nWhat is the sink on?\\nAnswer the question using a single word or phrase. ASSISTANT: Counter',\n", + " 'gt_answer': 'countertop'},\n", + " {'question_id': '201590142',\n", + " 'answer': 'USER: \\nIs the player to the right of the frisbee that looks white?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '20341130',\n", + " 'answer': 'USER: \\nDo you see a fence in front of the tree that is in front of the school?\\nAnswer the question using a single word or phrase. ASSISTANT: No',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '201795286',\n", + " 'answer': 'USER: \\nDo the baskets that are not empty look colorful?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '201832545',\n", + " 'answer': 'USER: \\nIs there any bed or table that is not dark brown?\\nAnswer the question using a single word or phrase. ASSISTANT: No',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '202082102',\n", + " 'answer': 'USER: \\nWhich kind of device is to the left of the lamp?\\nAnswer the question using a single word or phrase. ASSISTANT: Computer',\n", + " 'gt_answer': 'laptop'},\n", + " {'question_id': '20645705',\n", + " 'answer': 'USER: \\nDoes the heater next to the toilet look white and large?\\nAnswer the question using a single word or phrase. ASSISTANT: No',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '201795846',\n", + " 'answer': 'USER: \\nDoes the man appear to be sitting?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '201879789',\n", + " 'answer': 'USER: \\nHow large is the bike below the sky?\\nAnswer the question using a single word or phrase. ASSISTANT: Small',\n", + " 'gt_answer': 'large'},\n", + " {'question_id': '201143364',\n", + " 'answer': 'USER: \\nWhat is located on top of the table?\\nAnswer the question using a single word or phrase. ASSISTANT: Flowers',\n", + " 'gt_answer': 'flowers'},\n", + " {'question_id': '20827171',\n", + " 'answer': 'USER: \\nDoes the side table that is not big look wooden and long?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '20940166',\n", + " 'answer': 'USER: \\nDoes the porcelain sink have round shape?\\nAnswer the question using a single word or phrase. ASSISTANT: No',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '201859351',\n", + " 'answer': 'USER: \\nWhat is the lid made of?\\nAnswer the question using a single word or phrase. ASSISTANT: Plastic',\n", + " 'gt_answer': 'plastic'},\n", + " {'question_id': '201595841',\n", + " 'answer': 'USER: \\nIs the person near the grass sitting on a bench?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '20923252',\n", + " 'answer': 'USER: \\nWhat is parked alongside the barn?\\nAnswer the question using a single word or phrase. ASSISTANT: Truck',\n", + " 'gt_answer': 'ambulance'},\n", + " {'question_id': '202243438',\n", + " 'answer': 'USER: \\nWhich kind of vehicle is metallic?\\nAnswer the question using a single word or phrase. ASSISTANT: Truck',\n", + " 'gt_answer': 'truck'},\n", + " {'question_id': '20923257',\n", + " 'answer': 'USER: \\nIs the ambulance that is to the left of the workers parked alongside the barn?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '20923256',\n", + " 'answer': 'USER: \\nWhat vehicle is parked alongside the barn?\\nAnswer the question using a single word or phrase. ASSISTANT: Truck',\n", + " 'gt_answer': 'ambulance'},\n", + " {'question_id': '201976414',\n", + " 'answer': 'USER: \\nAre the flags triangular and red?\\nAnswer the question using a single word or phrase. ASSISTANT: No',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '20865499',\n", + " 'answer': 'USER: \\nDoes the calf have brown color and large size?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '20600137',\n", + " 'answer': 'USER: \\nWhat is beneath the zebras the rock sits beside?\\nAnswer the question using a single word or phrase. ASSISTANT: Grass',\n", + " 'gt_answer': 'grass'},\n", + " {'question_id': '20600132',\n", + " 'answer': 'USER: \\nWhat is beneath the zebra that is not large?\\nAnswer the question using a single word or phrase. ASSISTANT: Grass',\n", + " 'gt_answer': 'grass'},\n", + " {'question_id': '20836758',\n", + " 'answer': 'USER: \\nDoes the blue bag look small?\\nAnswer the question using a single word or phrase. ASSISTANT: No',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '20632010',\n", + " 'answer': 'USER: \\nIs the baseball mitt bright?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '201640606',\n", + " 'answer': 'USER: \\nWho is wearing the watch?\\nAnswer the question using a single word or phrase. ASSISTANT: Woman',\n", + " 'gt_answer': 'women'},\n", + " {'question_id': '201640605',\n", + " 'answer': 'USER: \\nWho is wearing a watch?\\nAnswer the question using a single word or phrase. ASSISTANT: Woman',\n", + " 'gt_answer': 'women'},\n", + " {'question_id': '201640602',\n", + " 'answer': 'USER: \\nWhere are the people to the left of the bulb sitting?\\nAnswer the question using a single word or phrase. ASSISTANT: Restaurant',\n", + " 'gt_answer': 'restaurant'},\n", + " {'question_id': '20306515',\n", + " 'answer': 'USER: \\nWhat is the name of the device that the young man near the gift is holding?\\nAnswer the question using a single word or phrase. ASSISTANT: Camera',\n", + " 'gt_answer': 'cell phone'},\n", + " {'question_id': '202228132',\n", + " 'answer': \"USER: \\nWhich kind of device isn't illuminated?\\nAnswer the question using a single word or phrase. ASSISTANT: Speaker\",\n", + " 'gt_answer': 'speaker'},\n", + " {'question_id': '20692296',\n", + " 'answer': 'USER: \\nWhat is resting on the marble counter?\\nAnswer the question using a single word or phrase. ASSISTANT: Book',\n", + " 'gt_answer': 'books'},\n", + " {'question_id': '20692294',\n", + " 'answer': 'USER: \\nAre the books to the left or to the right of the wood cabinet?\\nAnswer the question using a single word or phrase. ASSISTANT: Left',\n", + " 'gt_answer': 'left'},\n", + " {'question_id': '20710151',\n", + " 'answer': 'USER: \\nIs the sky above a train?\\nAnswer the question using a single word or phrase. ASSISTANT: No',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '202262373',\n", + " 'answer': 'USER: \\nIs the mug in front of the cup green and small?\\nAnswer the question using a single word or phrase. ASSISTANT: No',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '20679393',\n", + " 'answer': 'USER: \\nAre there any seals or bunnies in the picture?\\nAnswer the question using a single word or phrase. ASSISTANT: No',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '20710154',\n", + " 'answer': 'USER: \\nIs the sky above an airplane?\\nAnswer the question using a single word or phrase. ASSISTANT: No',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '202243431',\n", + " 'answer': 'USER: \\nWhat is the large vehicle?\\nAnswer the question using a single word or phrase. ASSISTANT: Truck',\n", + " 'gt_answer': 'truck'},\n", + " {'question_id': '201556497',\n", + " 'answer': 'USER: \\nWhat is the piece of furniture that is not small called?\\nAnswer the question using a single word or phrase. ASSISTANT: Chair',\n", + " 'gt_answer': 'shelf'},\n", + " {'question_id': '201556499',\n", + " 'answer': \"USER: \\nWhat kind of furniture isn't small?\\nAnswer the question using a single word or phrase. ASSISTANT: Chair\",\n", + " 'gt_answer': 'shelf'},\n", + " {'question_id': '20177492',\n", + " 'answer': 'USER: \\nDo you see any forks?\\nAnswer the question using a single word or phrase. ASSISTANT: No',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '20711540',\n", + " 'answer': 'USER: \\nWhich kind of toy is soft?\\nAnswer the question using a single word or phrase. ASSISTANT: Stuffed bear',\n", + " 'gt_answer': 'stuffed bear'},\n", + " {'question_id': '20899315',\n", + " 'answer': \"USER: \\nWhat's the bottle made of?\\nAnswer the question using a single word or phrase. ASSISTANT: Plastic\",\n", + " 'gt_answer': 'plastic'},\n", + " {'question_id': '20711546',\n", + " 'answer': 'USER: \\nWhat is the colorful toy in the picture?\\nAnswer the question using a single word or phrase. ASSISTANT: Teddy bear',\n", + " 'gt_answer': 'stuffed bear'},\n", + " {'question_id': '201624174',\n", + " 'answer': 'USER: \\nWhat is on the pan?\\nAnswer the question using a single word or phrase. ASSISTANT: Pizza',\n", + " 'gt_answer': 'pizza'},\n", + " {'question_id': '201997192',\n", + " 'answer': 'USER: \\nDoes the chair look large?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '20866524',\n", + " 'answer': 'USER: \\nIs the container made of plastic light and blue?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '20866526',\n", + " 'answer': 'USER: \\nWhat tone does the container made of plastic have?\\nAnswer the question using a single word or phrase. ASSISTANT: Light',\n", + " 'gt_answer': 'light'},\n", + " {'question_id': '20866521',\n", + " 'answer': 'USER: \\nWhich side of the picture is the plastic container on, the right or the left?\\nAnswer the question using a single word or phrase. ASSISTANT: Right',\n", + " 'gt_answer': 'right'},\n", + " {'question_id': '201510327',\n", + " 'answer': 'USER: \\nWhich kind of fruit is it?\\nAnswer the question using a single word or phrase. ASSISTANT: Apple',\n", + " 'gt_answer': 'pear'},\n", + " {'question_id': '201492240',\n", + " 'answer': 'USER: \\nWhat clothing item is not long sleeved?\\nAnswer the question using a single word or phrase. ASSISTANT: Glove',\n", + " 'gt_answer': 'baseball mitt'},\n", + " {'question_id': '20691652',\n", + " 'answer': 'USER: \\nWhat color are the towels made of cloth, black or white?\\nAnswer the question using a single word or phrase. ASSISTANT: Black',\n", + " 'gt_answer': 'black'},\n", + " {'question_id': '202144423',\n", + " 'answer': 'USER: \\nWhat color are the shorts that the man is wearing?\\nAnswer the question using a single word or phrase. ASSISTANT: Black',\n", + " 'gt_answer': 'brown'},\n", + " {'question_id': '20836578',\n", + " 'answer': 'USER: \\nWhat is the piece of furniture that the luggage that is brown and black is sitting in front of?\\nAnswer the question using a single word or phrase. ASSISTANT: Table',\n", + " 'gt_answer': 'table'},\n", + " {'question_id': '20349798',\n", + " 'answer': 'USER: \\nWho is wearing the shirt?\\nAnswer the question using a single word or phrase. ASSISTANT: Girl',\n", + " 'gt_answer': 'woman'},\n", + " {'question_id': '201663481',\n", + " 'answer': 'USER: \\nAre there any new dishwashers?\\nAnswer the question using a single word or phrase. ASSISTANT: No',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '20692079',\n", + " 'answer': 'USER: \\nHow big is the sink?\\nAnswer the question using a single word or phrase. ASSISTANT: Small',\n", + " 'gt_answer': 'small'},\n", + " {'question_id': '201030507',\n", + " 'answer': 'USER: \\nDoes the shirt seem to be sleeveless or long sleeved?\\nAnswer the question using a single word or phrase. ASSISTANT: Long sleeved',\n", + " 'gt_answer': 'long sleeved'},\n", + " {'question_id': '201997611',\n", + " 'answer': 'USER: \\nDoes the ceiling above the table look blue and clean?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '201153290',\n", + " 'answer': 'USER: \\nWho is staring at the giraffe?\\nAnswer the question using a single word or phrase. ASSISTANT: Man',\n", + " 'gt_answer': 'woman'},\n", + " {'question_id': '201983816',\n", + " 'answer': 'USER: \\nIs there any bag that is black?\\nAnswer the question using a single word or phrase. ASSISTANT: No',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '201153292',\n", + " 'answer': 'USER: \\nWhat animal is the person in front of the post staring at?\\nAnswer the question using a single word or phrase. ASSISTANT: Giraffe',\n", + " 'gt_answer': 'giraffe'},\n", + " {'question_id': '201153293',\n", + " 'answer': 'USER: \\nWhat animal is the woman staring at?\\nAnswer the question using a single word or phrase. ASSISTANT: Giraffe',\n", + " 'gt_answer': 'giraffe'},\n", + " {'question_id': '201153297',\n", + " 'answer': 'USER: \\nWhich kind of animal is the woman staring at?\\nAnswer the question using a single word or phrase. ASSISTANT: Giraffe',\n", + " 'gt_answer': 'giraffe'},\n", + " {'question_id': '20645858',\n", + " 'answer': 'USER: \\nWhat color is the small bathroom?\\nAnswer the question using a single word or phrase. ASSISTANT: Green',\n", + " 'gt_answer': 'white'},\n", + " {'question_id': '20441903',\n", + " 'answer': 'USER: \\nDoes the table look brown and rectangular?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '201570581',\n", + " 'answer': 'USER: \\nIs the plate different in color than the purse?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '201535714',\n", + " 'answer': 'USER: \\nWhich kind of clothing is yellow?\\nAnswer the question using a single word or phrase. ASSISTANT: Jacket',\n", + " 'gt_answer': 'coat'},\n", + " {'question_id': '20652278',\n", + " 'answer': 'USER: \\nWhat is common to the soccer ball and the backpack?\\nAnswer the question using a single word or phrase. ASSISTANT: Color',\n", + " 'gt_answer': 'shape'},\n", + " {'question_id': '201535713',\n", + " 'answer': 'USER: \\nWhat kind of clothing is yellow?\\nAnswer the question using a single word or phrase. ASSISTANT: Jacket',\n", + " 'gt_answer': 'coat'},\n", + " {'question_id': '201111170',\n", + " 'answer': 'USER: \\nThe table has which color?\\nAnswer the question using a single word or phrase. ASSISTANT: Brown',\n", + " 'gt_answer': 'brown'},\n", + " {'question_id': '20891561',\n", + " 'answer': 'USER: \\nIs the kid wearing shorts?\\nAnswer the question using a single word or phrase. ASSISTANT: No',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '20891560',\n", + " 'answer': \"USER: \\nWhat's the child wearing?\\nAnswer the question using a single word or phrase. ASSISTANT: Shirt\",\n", + " 'gt_answer': 'shorts'},\n", + " {'question_id': '20503737',\n", + " 'answer': 'USER: \\nIs the sticker both white and rectangular?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '20883191',\n", + " 'answer': 'USER: \\nDo you think this boy is real?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '20503730',\n", + " 'answer': 'USER: \\nWhat color is the sticker that looks rectangular?\\nAnswer the question using a single word or phrase. ASSISTANT: White',\n", + " 'gt_answer': 'purple'},\n", + " {'question_id': '201974600',\n", + " 'answer': 'USER: \\nWhat is the color of the shirt?\\nAnswer the question using a single word or phrase. ASSISTANT: Pink',\n", + " 'gt_answer': 'dark blue'},\n", + " {'question_id': '201972712',\n", + " 'answer': 'USER: \\nWhat color is the sky?\\nAnswer the question using a single word or phrase. ASSISTANT: Blue',\n", + " 'gt_answer': 'blue'},\n", + " {'question_id': '20783517',\n", + " 'answer': 'USER: \\nDo you see any chairs that are not red?\\nAnswer the question using a single word or phrase. ASSISTANT: No',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '20797833',\n", + " 'answer': 'USER: \\nThe man is in front of what?\\nAnswer the question using a single word or phrase. ASSISTANT: Plant',\n", + " 'gt_answer': 'tree'},\n", + " {'question_id': '20797830',\n", + " 'answer': 'USER: \\nWho is in front of the tree that is in front of the sky?\\nAnswer the question using a single word or phrase. ASSISTANT: People',\n", + " 'gt_answer': 'man'},\n", + " {'question_id': '20342305',\n", + " 'answer': 'USER: \\nDoes the person in front of the other person appear to be sitting?\\nAnswer the question using a single word or phrase. ASSISTANT: No',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '20783519',\n", + " 'answer': 'USER: \\nWhat item of furniture is in front of the people that are sitting around the table?\\nAnswer the question using a single word or phrase. ASSISTANT: Chair',\n", + " 'gt_answer': 'chair'},\n", + " {'question_id': '20797834',\n", + " 'answer': 'USER: \\nWhat is the man in front of?\\nAnswer the question using a single word or phrase. ASSISTANT: Plant',\n", + " 'gt_answer': 'tree'},\n", + " {'question_id': '2053782',\n", + " 'answer': 'USER: \\nWhat is the sidewalk made of?\\nAnswer the question using a single word or phrase. ASSISTANT: Concrete',\n", + " 'gt_answer': 'concrete'},\n", + " {'question_id': '202106445',\n", + " 'answer': 'USER: \\nAre there green snowboards or rackets?\\nAnswer the question using a single word or phrase. ASSISTANT: No',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '201401744',\n", + " 'answer': 'USER: \\nDoes the blue sky look bright and clear?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '20536241',\n", + " 'answer': 'USER: \\nHow heavy is the bison?\\nAnswer the question using a single word or phrase. ASSISTANT: Heavy',\n", + " 'gt_answer': 'heavy'},\n", + " {'question_id': '2053786',\n", + " 'answer': 'USER: \\nDoes the concrete sidewalk look rough and paved?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '20899760',\n", + " 'answer': 'USER: \\nWhat kind of device is to the left of the tomatoes?\\nAnswer the question using a single word or phrase. ASSISTANT: Laptop',\n", + " 'gt_answer': 'laptop'},\n", + " {'question_id': '20899763',\n", + " 'answer': 'USER: \\nAre there any phones to the left of the tomatoes that are being in the bowl?\\nAnswer the question using a single word or phrase. ASSISTANT: No',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '201621812',\n", + " 'answer': 'USER: \\nWhat is the device to the right of the couch?\\nAnswer the question using a single word or phrase. ASSISTANT: Speaker',\n", + " 'gt_answer': 'speaker'},\n", + " {'question_id': '20536249',\n", + " 'answer': 'USER: \\nWhat is the bison doing?\\nAnswer the question using a single word or phrase. ASSISTANT: Eating',\n", + " 'gt_answer': 'looking down'},\n", + " {'question_id': '20899769',\n", + " 'answer': 'USER: \\nWhat type of device is sitting on the square table?\\nAnswer the question using a single word or phrase. ASSISTANT: Laptop',\n", + " 'gt_answer': 'laptop'},\n", + " {'question_id': '20306372',\n", + " 'answer': 'USER: \\nWhat device is the woman holding?\\nAnswer the question using a single word or phrase. ASSISTANT: Camera',\n", + " 'gt_answer': 'camera'},\n", + " {'question_id': '20306370',\n", + " 'answer': 'USER: \\nWhat is the device that the young woman is holding?\\nAnswer the question using a single word or phrase. ASSISTANT: Camera',\n", + " 'gt_answer': 'camera'},\n", + " {'question_id': '20866380',\n", + " 'answer': 'USER: \\nWhich color is the floor?\\nAnswer the question using a single word or phrase. ASSISTANT: Gray',\n", + " 'gt_answer': 'gray'},\n", + " {'question_id': '20473110',\n", + " 'answer': 'USER: \\nAre there glasses or women?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '201467402',\n", + " 'answer': 'USER: \\nWhat is common to the door and the room?\\nAnswer the question using a single word or phrase. ASSISTANT: Color',\n", + " 'gt_answer': 'color'},\n", + " {'question_id': '20518336',\n", + " 'answer': 'USER: \\nWhat type of appliance is under the sink that is shown in the picture?\\nAnswer the question using a single word or phrase. ASSISTANT: Heater',\n", + " 'gt_answer': 'radiator'},\n", + " {'question_id': '20518337',\n", + " 'answer': 'USER: \\nWhich kind of appliance is under the sink?\\nAnswer the question using a single word or phrase. ASSISTANT: Heater',\n", + " 'gt_answer': 'radiator'},\n", + " {'question_id': '20518334',\n", + " 'answer': 'USER: \\nWhat is under the sink on the counter top?\\nAnswer the question using a single word or phrase. ASSISTANT: Trash can',\n", + " 'gt_answer': 'radiator'},\n", + " {'question_id': '20518335',\n", + " 'answer': \"USER: \\nWhat's under the sink?\\nAnswer the question using a single word or phrase. ASSISTANT: Trash can\",\n", + " 'gt_answer': 'radiator'},\n", + " {'question_id': '201759317',\n", + " 'answer': 'USER: \\nWhat is the window made of, glass or plastic?\\nAnswer the question using a single word or phrase. ASSISTANT: Glass',\n", + " 'gt_answer': 'glass'},\n", + " {'question_id': '20518339',\n", + " 'answer': 'USER: \\nDo you see any drawers under the sink?\\nAnswer the question using a single word or phrase. ASSISTANT: No',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '201952898',\n", + " 'answer': 'USER: \\nWhat is the vehicle that is parked near the houses called?\\nAnswer the question using a single word or phrase. ASSISTANT: Train',\n", + " 'gt_answer': 'car'},\n", + " {'question_id': '20480525',\n", + " 'answer': 'USER: \\nDo the tall books look colorful and thick?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '202053173',\n", + " 'answer': 'USER: \\nWho in this photograph is staring?\\nAnswer the question using a single word or phrase. ASSISTANT: Batter',\n", + " 'gt_answer': 'umpire'},\n", + " {'question_id': '20183255',\n", + " 'answer': 'USER: \\nWhat is the woman that is not young sitting on top of?\\nAnswer the question using a single word or phrase. ASSISTANT: Bench',\n", + " 'gt_answer': 'steps'},\n", + " {'question_id': '20797661',\n", + " 'answer': 'USER: \\nWhat animal is the shoe behind of?\\nAnswer the question using a single word or phrase. ASSISTANT: Cat',\n", + " 'gt_answer': 'cat'},\n", + " {'question_id': '201548930',\n", + " 'answer': 'USER: \\nWhat is the room holding?\\nAnswer the question using a single word or phrase. ASSISTANT: Blender',\n", + " 'gt_answer': 'picture'},\n", + " {'question_id': '20157379',\n", + " 'answer': 'USER: \\nWhat kind of furniture is wooden?\\nAnswer the question using a single word or phrase. ASSISTANT: Table',\n", + " 'gt_answer': 'table'},\n", + " {'question_id': '20257105',\n", + " 'answer': 'USER: \\nAre the life vest and the shirt the same color?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '20489405',\n", + " 'answer': 'USER: \\nDoes the table lamp have the same color as the pillow?\\nAnswer the question using a single word or phrase. ASSISTANT: No',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '20709846',\n", + " 'answer': 'USER: \\nIn which part is the open umbrella?\\nAnswer the question using a single word or phrase. ASSISTANT: Right',\n", + " 'gt_answer': 'left'},\n", + " {'question_id': '20754796',\n", + " 'answer': 'USER: \\nDoes the skateboard have brown color and large size?\\nAnswer the question using a single word or phrase. ASSISTANT: No',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '202169340',\n", + " 'answer': 'USER: \\nIs the bench in front of the woman the man is to the left of?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '20963807',\n", + " 'answer': 'USER: \\nWhat is beneath the mirror?\\nAnswer the question using a single word or phrase. ASSISTANT: Sink',\n", + " 'gt_answer': 'faucet'},\n", + " {'question_id': '2053569',\n", + " 'answer': 'USER: \\nIs plastic used to make the bottle to the right of the cow?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '20941978',\n", + " 'answer': 'USER: \\nIs the building in front of the trees that are not short?\\nAnswer the question using a single word or phrase. ASSISTANT: No',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '20667405',\n", + " 'answer': 'USER: \\nWhat device is to the right of the man that is sitting in front of the pillow?\\nAnswer the question using a single word or phrase. ASSISTANT: Remote control',\n", + " 'gt_answer': 'wii controller'},\n", + " {'question_id': '202156967',\n", + " 'answer': 'USER: \\nWhat color is the dirt the elephants are on?\\nAnswer the question using a single word or phrase. ASSISTANT: Brown',\n", + " 'gt_answer': 'brown'},\n", + " {'question_id': '20757114',\n", + " 'answer': 'USER: \\nDo you think that lady is looking down?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '201571188',\n", + " 'answer': 'USER: \\nIs the suitcase to the right of the other suitcase tall and brown?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '20757119',\n", + " 'answer': 'USER: \\nWhat is the lady doing?\\nAnswer the question using a single word or phrase. ASSISTANT: Cooking',\n", + " 'gt_answer': 'looking down'},\n", + " {'question_id': '20394761',\n", + " 'answer': 'USER: \\nWhat is the woman wearing?\\nAnswer the question using a single word or phrase. ASSISTANT: Dress',\n", + " 'gt_answer': 'dress'},\n", + " {'question_id': '20394760',\n", + " 'answer': 'USER: \\nWhat is the happy woman wearing?\\nAnswer the question using a single word or phrase. ASSISTANT: Dress',\n", + " 'gt_answer': 'dress'},\n", + " {'question_id': '20508714',\n", + " 'answer': 'USER: \\nWho is wearing a shirt?\\nAnswer the question using a single word or phrase. ASSISTANT: Woman',\n", + " 'gt_answer': 'woman'},\n", + " {'question_id': '202053318',\n", + " 'answer': 'USER: \\nIs the athletic person in front of the umpire young and female?\\nAnswer the question using a single word or phrase. ASSISTANT: No',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '202174529',\n", + " 'answer': 'USER: \\nIs the window rectangular and white?\\nAnswer the question using a single word or phrase. ASSISTANT: No',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '201908788',\n", + " 'answer': 'USER: \\nDo the silver forks look hard?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '20403340',\n", + " 'answer': 'USER: \\nWhat shape is the large mirror?\\nAnswer the question using a single word or phrase. ASSISTANT: Square',\n", + " 'gt_answer': 'square'},\n", + " {'question_id': '20306592',\n", + " 'answer': 'USER: \\nIs the camera on the right side of the picture?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '20403344',\n", + " 'answer': 'USER: \\nIs there a mirror near the white lamp?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '20435303',\n", + " 'answer': 'USER: \\nWhat makes up the napkin, paper or cloth?\\nAnswer the question using a single word or phrase. ASSISTANT: Paper',\n", + " 'gt_answer': 'paper'},\n", + " {'question_id': '20939909',\n", + " 'answer': 'USER: \\nIs the garbage can behind a mat?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '20435309',\n", + " 'answer': 'USER: \\nHow clean is that napkin?\\nAnswer the question using a single word or phrase. ASSISTANT: Dirty',\n", + " 'gt_answer': 'dirty'},\n", + " {'question_id': '20939906',\n", + " 'answer': 'USER: \\nIs the small trash can underneath the sink?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '201887219',\n", + " 'answer': 'USER: \\nOn which side of the picture is the eggplant?\\nAnswer the question using a single word or phrase. ASSISTANT: Right',\n", + " 'gt_answer': 'right'},\n", + " {'question_id': '20939902',\n", + " 'answer': 'USER: \\nIs the garbage bin below a sink?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '20901821',\n", + " 'answer': 'USER: \\nIs the chubby man to the left of the umbrella wearing shorts?\\nAnswer the question using a single word or phrase. ASSISTANT: No',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '20901822',\n", + " 'answer': 'USER: \\nWhat does the chubby man hold?\\nAnswer the question using a single word or phrase. ASSISTANT: Umbrella',\n", + " 'gt_answer': 'umbrella'},\n", + " {'question_id': '201984046',\n", + " 'answer': 'USER: \\nWhat is the woman doing?\\nAnswer the question using a single word or phrase. ASSISTANT: Texting',\n", + " 'gt_answer': 'looking down'},\n", + " {'question_id': '201902722',\n", + " 'answer': 'USER: \\nThe monitor to the right of the other monitor has which color?\\nAnswer the question using a single word or phrase. ASSISTANT: Red',\n", + " 'gt_answer': 'black'},\n", + " {'question_id': '20492039',\n", + " 'answer': 'USER: \\nWhat animal is walking on the ground?\\nAnswer the question using a single word or phrase. ASSISTANT: Bear',\n", + " 'gt_answer': 'birds'},\n", + " {'question_id': '201902726',\n", + " 'answer': 'USER: \\nWhat sits on top of the desk?\\nAnswer the question using a single word or phrase. ASSISTANT: Computer',\n", + " 'gt_answer': 'monitor'},\n", + " {'question_id': '202100478',\n", + " 'answer': 'USER: \\nDoes the sky look bright and blue?\\nAnswer the question using a single word or phrase. ASSISTANT: No',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '20287967',\n", + " 'answer': 'USER: \\nAre both the spectators to the left of the batter and the spectators that are to the right of the batter sitting?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '20896252',\n", + " 'answer': 'USER: \\nIs the plastic container on the left side of the picture?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '201510942',\n", + " 'answer': 'USER: \\nDoes the suitcase to the right of the rug have small size?\\nAnswer the question using a single word or phrase. ASSISTANT: No',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '201621467',\n", + " 'answer': 'USER: \\nWhat is the name of the wooden piece of furniture?\\nAnswer the question using a single word or phrase. ASSISTANT: Table',\n", + " 'gt_answer': 'tv stand'},\n", + " {'question_id': '201621466',\n", + " 'answer': 'USER: \\nWhich kind of furniture is brown?\\nAnswer the question using a single word or phrase. ASSISTANT: Entertainment center',\n", + " 'gt_answer': 'tv stand'},\n", + " {'question_id': '20427613',\n", + " 'answer': 'USER: \\nDo both the smiling gentleman in front of the picture and the Caucasian person look young?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '201342263',\n", + " 'answer': 'USER: \\nHow big is the plane?\\nAnswer the question using a single word or phrase. ASSISTANT: Large',\n", + " 'gt_answer': 'large'},\n", + " {'question_id': '20618704',\n", + " 'answer': 'USER: \\nWhat color does the wrist watch the woman is wearing have?\\nAnswer the question using a single word or phrase. ASSISTANT: Pink',\n", + " 'gt_answer': 'pink'},\n", + " {'question_id': '20427618',\n", + " 'answer': 'USER: \\nHow old is the gentleman?\\nAnswer the question using a single word or phrase. ASSISTANT: 50',\n", + " 'gt_answer': 'young'},\n", + " {'question_id': '202231873',\n", + " 'answer': 'USER: \\nWhich color do you think the wood floor is?\\nAnswer the question using a single word or phrase. ASSISTANT: Brown',\n", + " 'gt_answer': 'dark brown'},\n", + " {'question_id': '201536434',\n", + " 'answer': 'USER: \\nOn which side of the image are the baseball players?\\nAnswer the question using a single word or phrase. ASSISTANT: Right',\n", + " 'gt_answer': 'right'},\n", + " {'question_id': '201975054',\n", + " 'answer': 'USER: \\nIs there a colorful hat or scarf?\\nAnswer the question using a single word or phrase. ASSISTANT: No',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '201640551',\n", + " 'answer': 'USER: \\nHow fat are the old people who are wearing a dress?\\nAnswer the question using a single word or phrase. ASSISTANT: Skinny',\n", + " 'gt_answer': 'fat'},\n", + " {'question_id': '201885430',\n", + " 'answer': \"USER: \\nWhat's the man doing?\\nAnswer the question using a single word or phrase. ASSISTANT: Swimming\",\n", + " 'gt_answer': 'jumping'},\n", + " {'question_id': '201654400',\n", + " 'answer': 'USER: \\nAre there both a horse and a fence in the image?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '201434287',\n", + " 'answer': 'USER: \\nAre there both trucks and airplanes?\\nAnswer the question using a single word or phrase. ASSISTANT: No',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '201770899',\n", + " 'answer': 'USER: \\nWhat is this, a cup or a bottle?\\nAnswer the question using a single word or phrase. ASSISTANT: Bottle',\n", + " 'gt_answer': 'bottle'},\n", + " {'question_id': '202100782',\n", + " 'answer': 'USER: \\nWhat are the jars sitting on top of?\\nAnswer the question using a single word or phrase. ASSISTANT: Stove',\n", + " 'gt_answer': 'stove'},\n", + " {'question_id': '201713599',\n", + " 'answer': 'USER: \\nAre there any curtains or trash cans in the photo?\\nAnswer the question using a single word or phrase. ASSISTANT: No',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '201068686',\n", + " 'answer': 'USER: \\nWhat is the Asian person near the water bottle wearing?\\nAnswer the question using a single word or phrase. ASSISTANT: Shirt',\n", + " 'gt_answer': 'dress shirt'},\n", + " {'question_id': '201068687',\n", + " 'answer': 'USER: \\nWhat is the man wearing?\\nAnswer the question using a single word or phrase. ASSISTANT: Shirt',\n", + " 'gt_answer': 'dress shirt'},\n", + " {'question_id': '20717125',\n", + " 'answer': 'USER: \\nWhich kind of furniture is fluffy?\\nAnswer the question using a single word or phrase. ASSISTANT: Bed',\n", + " 'gt_answer': 'bed'},\n", + " {'question_id': '201556938',\n", + " 'answer': 'USER: \\nWhat is the pen made of?\\nAnswer the question using a single word or phrase. ASSISTANT: Plastic',\n", + " 'gt_answer': 'plastic'},\n", + " {'question_id': '201556939',\n", + " 'answer': 'USER: \\nWhat is located on top of the white paper?\\nAnswer the question using a single word or phrase. ASSISTANT: Pen',\n", + " 'gt_answer': 'pen'},\n", + " {'question_id': '20756792',\n", + " 'answer': 'USER: \\nWhat color is the hair, gray or red?\\nAnswer the question using a single word or phrase. ASSISTANT: Gray',\n", + " 'gt_answer': 'gray'},\n", + " {'question_id': '201556937',\n", + " 'answer': 'USER: \\nWhat material is the pen?\\nAnswer the question using a single word or phrase. ASSISTANT: Plastic',\n", + " 'gt_answer': 'plastic'},\n", + " {'question_id': '202285527',\n", + " 'answer': 'USER: \\nDo you see any waffles to the left of the fork?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '201879573',\n", + " 'answer': 'USER: \\nIs the truck in front of the basket?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '201795103',\n", + " 'answer': 'USER: \\nWhat color is the large animal?\\nAnswer the question using a single word or phrase. ASSISTANT: Gray',\n", + " 'gt_answer': 'dark brown'},\n", + " {'question_id': '20248178',\n", + " 'answer': 'USER: \\nAre the sweater and the black dress shirt both long sleeved?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '201156172',\n", + " 'answer': 'USER: \\nWhat makes up the jacket, cloth or leather?\\nAnswer the question using a single word or phrase. ASSISTANT: Cloth',\n", + " 'gt_answer': 'cloth'},\n", + " {'question_id': '20923001',\n", + " 'answer': 'USER: \\nIs the car to the left or to the right of the vehicle that is parked along the street?\\nAnswer the question using a single word or phrase. ASSISTANT: Right',\n", + " 'gt_answer': 'right'},\n", + " {'question_id': '20245902',\n", + " 'answer': 'USER: \\nWho wears a knee pad?\\nAnswer the question using a single word or phrase. ASSISTANT: Man',\n", + " 'gt_answer': 'skateboarder'},\n", + " {'question_id': '20245900',\n", + " 'answer': 'USER: \\nWho is wearing a helmet?\\nAnswer the question using a single word or phrase. ASSISTANT: Skateboarder',\n", + " 'gt_answer': 'skateboarder'},\n", + " {'question_id': '20245901',\n", + " 'answer': 'USER: \\nWho is wearing the helmet?\\nAnswer the question using a single word or phrase. ASSISTANT: Skateboarder',\n", + " 'gt_answer': 'skateboarder'},\n", + " {'question_id': '20245906',\n", + " 'answer': 'USER: \\nWho is skating on the skateboard?\\nAnswer the question using a single word or phrase. ASSISTANT: Man',\n", + " 'gt_answer': 'skateboarder'},\n", + " {'question_id': '20245907',\n", + " 'answer': 'USER: \\nWho is wearing a knee pad?\\nAnswer the question using a single word or phrase. ASSISTANT: Skateboarder',\n", + " 'gt_answer': 'skateboarder'},\n", + " {'question_id': '20248177',\n", + " 'answer': 'USER: \\nDoes the striped sweater look long sleeved and black?\\nAnswer the question using a single word or phrase. ASSISTANT: No',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '201987480',\n", + " 'answer': 'USER: \\nIs the driver in the photo wearing a helmet?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '201795359',\n", + " 'answer': 'USER: \\nIs the fat woman to the right of an elephant?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '201735541',\n", + " 'answer': 'USER: \\nWhich kind of furniture are the shelves sitting on top of?\\nAnswer the question using a single word or phrase. ASSISTANT: Desk',\n", + " 'gt_answer': 'desk'},\n", + " {'question_id': '201735547',\n", + " 'answer': 'USER: \\nWhat type of furniture is above the newspaper that looks red and white?\\nAnswer the question using a single word or phrase. ASSISTANT: Shelf',\n", + " 'gt_answer': 'shelves'},\n", + " {'question_id': '20492150',\n", + " 'answer': 'USER: \\nIs the bear that is to the left of the other bear long and white?\\nAnswer the question using a single word or phrase. ASSISTANT: No',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '20416826',\n", + " 'answer': 'USER: \\nWhat kind of food is not tasty?\\nAnswer the question using a single word or phrase. ASSISTANT: Pepper',\n", + " 'gt_answer': 'sausage'},\n", + " {'question_id': '20416825',\n", + " 'answer': 'USER: \\nWhich type of food is not tasty?\\nAnswer the question using a single word or phrase. ASSISTANT: Pepper',\n", + " 'gt_answer': 'sausage'},\n", + " {'question_id': '20119166',\n", + " 'answer': 'USER: \\nWhere in the photograph is the umbrella, in the top or in the bottom?\\nAnswer the question using a single word or phrase. ASSISTANT: Top',\n", + " 'gt_answer': 'top'},\n", + " {'question_id': '20300360',\n", + " 'answer': 'USER: \\nIs the weather cloudy?\\nAnswer the question using a single word or phrase. ASSISTANT: No',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '20244509',\n", + " 'answer': 'USER: \\nWhich place is this?\\nAnswer the question using a single word or phrase. ASSISTANT: Street',\n", + " 'gt_answer': 'sidewalk'},\n", + " {'question_id': '201935164',\n", + " 'answer': 'USER: \\nDoes the smooth table have brown color?\\nAnswer the question using a single word or phrase. ASSISTANT: No',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '202036880',\n", + " 'answer': 'USER: \\nWhat is the name of the food that is on the food with the spinach?\\nAnswer the question using a single word or phrase. ASSISTANT: Pepperoni',\n", + " 'gt_answer': 'sausage'},\n", + " {'question_id': '202036881',\n", + " 'answer': 'USER: \\nWhat food is on the pizza?\\nAnswer the question using a single word or phrase. ASSISTANT: Pepperoni',\n", + " 'gt_answer': 'sausage'},\n", + " {'question_id': '202106209',\n", + " 'answer': 'USER: \\nIs the man to the left of a ball?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '20541727',\n", + " 'answer': 'USER: \\nIs she to the right of the couch that is to the left of the television?\\nAnswer the question using a single word or phrase. ASSISTANT: No',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '201037055',\n", + " 'answer': 'USER: \\nIs the traffic sign behind the girl octagonal and red?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '20894256',\n", + " 'answer': 'USER: \\nWhat is the color of the long pants?\\nAnswer the question using a single word or phrase. ASSISTANT: Gray',\n", + " 'gt_answer': 'brown'},\n", + " {'question_id': '201795818',\n", + " 'answer': 'USER: \\nHow hard are the brown sandals?\\nAnswer the question using a single word or phrase. ASSISTANT: Soft',\n", + " 'gt_answer': 'hard'},\n", + " {'question_id': '201621321',\n", + " 'answer': 'USER: \\nWhat is the color of the picture frame which is hanging from the wall?\\nAnswer the question using a single word or phrase. ASSISTANT: Black',\n", + " 'gt_answer': 'black'},\n", + " {'question_id': '201319547',\n", + " 'answer': 'USER: \\nWho is in front of the window frame that looks light brown?\\nAnswer the question using a single word or phrase. ASSISTANT: Woman',\n", + " 'gt_answer': 'women'},\n", + " {'question_id': '201439730',\n", + " 'answer': 'USER: \\nWhat is the color of the shorts made of cloth?\\nAnswer the question using a single word or phrase. ASSISTANT: White',\n", + " 'gt_answer': 'dark'},\n", + " {'question_id': '201319540',\n", + " 'answer': 'USER: \\nWho is wearing a shirt?\\nAnswer the question using a single word or phrase. ASSISTANT: Woman',\n", + " 'gt_answer': 'women'},\n", + " {'question_id': '201392138',\n", + " 'answer': 'USER: \\nIs the shirt made of cotton short sleeved and gray?\\nAnswer the question using a single word or phrase. ASSISTANT: No',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '201439735',\n", + " 'answer': 'USER: \\nAre both the shorts and the black leggings made of cloth?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '202073305',\n", + " 'answer': 'USER: \\nWhat kind of animal is beautiful?\\nAnswer the question using a single word or phrase. ASSISTANT: Zebra',\n", + " 'gt_answer': 'deer'},\n", + " {'question_id': '202218780',\n", + " 'answer': 'USER: \\nIs the cooking utensil in front of the window blue and metallic?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '2094004',\n", + " 'answer': 'USER: \\nWhat is the height of the green grass near the mud?\\nAnswer the question using a single word or phrase. ASSISTANT: Short',\n", + " 'gt_answer': 'short'},\n", + " {'question_id': '201407351',\n", + " 'answer': 'USER: \\nWhat does the man hold?\\nAnswer the question using a single word or phrase. ASSISTANT: Racket',\n", + " 'gt_answer': 'racket'},\n", + " {'question_id': '20169624',\n", + " 'answer': 'USER: \\nIs the water wavy?\\nAnswer the question using a single word or phrase. ASSISTANT: No',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '201527694',\n", + " 'answer': 'USER: \\nIs the round cake to the right of the young girl?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '20902594',\n", + " 'answer': 'USER: \\nDoes the backpack appear to be clean and blue?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '201407359',\n", + " 'answer': 'USER: \\nWhat is the person behind the net playing with?\\nAnswer the question using a single word or phrase. ASSISTANT: Tennis ball',\n", + " 'gt_answer': 'tennis ball'},\n", + " {'question_id': '201982219',\n", + " 'answer': 'USER: \\nIs there a lamp in this picture that is large?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '2065884',\n", + " 'answer': 'USER: \\nIs the girl to the right of the man happy and old?\\nAnswer the question using a single word or phrase. ASSISTANT: No',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '201935304',\n", + " 'answer': 'USER: \\nIs the woman near the man standing on the bricks?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '201935303',\n", + " 'answer': 'USER: \\nThe woman near the man is standing on what?\\nAnswer the question using a single word or phrase. ASSISTANT: Platform',\n", + " 'gt_answer': 'bricks'},\n", + " {'question_id': '20902848',\n", + " 'answer': 'USER: \\nHow is the animal that is in the backpack called?\\nAnswer the question using a single word or phrase. ASSISTANT: Dog',\n", + " 'gt_answer': 'dog'},\n", + " {'question_id': '202231418',\n", + " 'answer': 'USER: \\nWhat material is the stop sign on top of the pole made of?\\nAnswer the question using a single word or phrase. ASSISTANT: Metal',\n", + " 'gt_answer': 'metal'},\n", + " {'question_id': '20247773',\n", + " 'answer': 'USER: \\nIs the person to the left of the glasses wearing jeans?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '201462312',\n", + " 'answer': 'USER: \\nWhat is the person on top of the field holding?\\nAnswer the question using a single word or phrase. ASSISTANT: Bat',\n", + " 'gt_answer': 'bat'},\n", + " {'question_id': '20340435',\n", + " 'answer': 'USER: \\nWhat is the yard in front of?\\nAnswer the question using a single word or phrase. ASSISTANT: Trees',\n", + " 'gt_answer': 'tree'},\n", + " {'question_id': '201462314',\n", + " 'answer': 'USER: \\nWhat is the man holding?\\nAnswer the question using a single word or phrase. ASSISTANT: Bat',\n", + " 'gt_answer': 'bat'},\n", + " {'question_id': '20247778',\n", + " 'answer': \"USER: \\nWhat's the man sitting on?\\nAnswer the question using a single word or phrase. ASSISTANT: Bench\",\n", + " 'gt_answer': 'bench'},\n", + " {'question_id': '201987813',\n", + " 'answer': 'USER: \\nOn which side of the picture is the small bottle?\\nAnswer the question using a single word or phrase. ASSISTANT: Right',\n", + " 'gt_answer': 'right'},\n", + " {'question_id': '201887171',\n", + " 'answer': 'USER: \\nWhat kind of food is not round?\\nAnswer the question using a single word or phrase. ASSISTANT: Broccoli',\n", + " 'gt_answer': 'broccoli'},\n", + " {'question_id': '201438693',\n", + " 'answer': 'USER: \\nWhat is the batter standing beside of?\\nAnswer the question using a single word or phrase. ASSISTANT: Home plate',\n", + " 'gt_answer': 'home plate'},\n", + " {'question_id': '20491789',\n", + " 'answer': 'USER: \\nWhat is filled with bird?\\nAnswer the question using a single word or phrase. ASSISTANT: Sky',\n", + " 'gt_answer': 'sky'},\n", + " {'question_id': '20655012',\n", + " 'answer': 'USER: \\nIs the coat comfortable?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '20756930',\n", + " 'answer': 'USER: \\nIs the robe red and smooth?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '20330524',\n", + " 'answer': 'USER: \\nIs that car silver?\\nAnswer the question using a single word or phrase. ASSISTANT: No',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '20609412',\n", + " 'answer': 'USER: \\nDoes the plate look white?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '201527960',\n", + " 'answer': 'USER: \\nWho is sitting?\\nAnswer the question using a single word or phrase. ASSISTANT: Woman',\n", + " 'gt_answer': 'girl'},\n", + " {'question_id': '201482397',\n", + " 'answer': 'USER: \\nDo the striped pants appear to be black and white?\\nAnswer the question using a single word or phrase. ASSISTANT: No',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '201446971',\n", + " 'answer': 'USER: \\nWhich side of the image is the cup on?\\nAnswer the question using a single word or phrase. ASSISTANT: Right',\n", + " 'gt_answer': 'right'},\n", + " {'question_id': '20963696',\n", + " 'answer': 'USER: \\nDoes the toilet look brown?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '201207480',\n", + " 'answer': 'USER: \\nWhat does the mug sit on?\\nAnswer the question using a single word or phrase. ASSISTANT: Table',\n", + " 'gt_answer': 'mat'},\n", + " {'question_id': '20752230',\n", + " 'answer': 'USER: \\nDoes the door look black?\\nAnswer the question using a single word or phrase. ASSISTANT: No',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '201482055',\n", + " 'answer': 'USER: \\nIs the jacket gray and bright?\\nAnswer the question using a single word or phrase. ASSISTANT: No',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '20567532',\n", + " 'answer': 'USER: \\nDoes the sunny beach look bright?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '201599785',\n", + " 'answer': 'USER: \\nWhat kind of furniture is wooden?\\nAnswer the question using a single word or phrase. ASSISTANT: Chair',\n", + " 'gt_answer': 'chair'},\n", + " {'question_id': '201599787',\n", + " 'answer': 'USER: \\nWhat kind of furniture is made of wood?\\nAnswer the question using a single word or phrase. ASSISTANT: Chair',\n", + " 'gt_answer': 'chair'},\n", + " {'question_id': '20567537',\n", + " 'answer': 'USER: \\nDoes the man that is to the left of the other man seem to be standing?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '202162618',\n", + " 'answer': 'USER: \\nWhich kind of furniture is black?\\nAnswer the question using a single word or phrase. ASSISTANT: Bed',\n", + " 'gt_answer': 'bookcase'},\n", + " {'question_id': '202162615',\n", + " 'answer': 'USER: \\nWhat is the black item of furniture?\\nAnswer the question using a single word or phrase. ASSISTANT: Bed',\n", + " 'gt_answer': 'bookcase'},\n", + " {'question_id': '201599788',\n", + " 'answer': 'USER: \\nWhat is the old piece of furniture in the photo?\\nAnswer the question using a single word or phrase. ASSISTANT: Chair',\n", + " 'gt_answer': 'chair'},\n", + " {'question_id': '20550578',\n", + " 'answer': 'USER: \\nWhere are the grouped people standing on?\\nAnswer the question using a single word or phrase. ASSISTANT: Field',\n", + " 'gt_answer': 'grass'},\n", + " {'question_id': '20340484',\n", + " 'answer': 'USER: \\nIs that a small fence?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '20151749',\n", + " 'answer': 'USER: \\nWhich color is the bag that is made of leather, tan or purple?\\nAnswer the question using a single word or phrase. ASSISTANT: Tan',\n", + " 'gt_answer': 'tan'},\n", + " {'question_id': '202246141',\n", + " 'answer': 'USER: \\nIs the mouse pad to the right of her both clean and large?\\nAnswer the question using a single word or phrase. ASSISTANT: No',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '20654941',\n", + " 'answer': 'USER: \\nDoes the hat have a different color than the jacket?\\nAnswer the question using a single word or phrase. ASSISTANT: No',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '20309040',\n", + " 'answer': 'USER: \\nDoes the knife in the knife block have black color and short length?\\nAnswer the question using a single word or phrase. ASSISTANT: No',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '20654949',\n", + " 'answer': 'USER: \\nIs the color of the sidewalk different than the street sign?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '202004006',\n", + " 'answer': 'USER: \\nWhat are the doors made of?\\nAnswer the question using a single word or phrase. ASSISTANT: Wood',\n", + " 'gt_answer': 'wood'},\n", + " {'question_id': '20120514',\n", + " 'answer': 'USER: \\nDoes the fence in front of the bench look tall and gray?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '2059544',\n", + " 'answer': 'USER: \\nWhat color is the girl that is sitting down?\\nAnswer the question using a single word or phrase. ASSISTANT: Blond',\n", + " 'gt_answer': 'blond'},\n", + " {'question_id': '20866265',\n", + " 'answer': 'USER: \\nDoes this fridge look wide and new?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '202218911',\n", + " 'answer': 'USER: \\nWhat is the color of the flower pot?\\nAnswer the question using a single word or phrase. ASSISTANT: Green',\n", + " 'gt_answer': 'light blue'},\n", + " {'question_id': '201574236',\n", + " 'answer': 'USER: \\nWho do you think is in front of the door made of wood?\\nAnswer the question using a single word or phrase. ASSISTANT: Man',\n", + " 'gt_answer': 'man'},\n", + " {'question_id': '201637286',\n", + " 'answer': 'USER: \\nIs there a tomato in this photo?\\nAnswer the question using a single word or phrase. ASSISTANT: No',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '201885232',\n", + " 'answer': 'USER: \\nIs the frisbee above the pool both round and white?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '202121678',\n", + " 'answer': 'USER: \\nIs the sink dirty and gray?\\nAnswer the question using a single word or phrase. ASSISTANT: No',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '20856903',\n", + " 'answer': 'USER: \\nWhich kind of bag is it?\\nAnswer the question using a single word or phrase. ASSISTANT: Purse',\n", + " 'gt_answer': 'purse'},\n", + " {'question_id': '201346563',\n", + " 'answer': 'USER: \\nAre there both a helmet and a motorcycle in the image?\\nAnswer the question using a single word or phrase. ASSISTANT: No',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '201346560',\n", + " 'answer': 'USER: \\nIs the large vehicle to the right or to the left of the vehicle that is parked along the street?\\nAnswer the question using a single word or phrase. ASSISTANT: Left',\n", + " 'gt_answer': 'left'},\n", + " {'question_id': '20856909',\n", + " 'answer': 'USER: \\nWhich kind of bag is green?\\nAnswer the question using a single word or phrase. ASSISTANT: Purse',\n", + " 'gt_answer': 'purse'},\n", + " {'question_id': '201479185',\n", + " 'answer': 'USER: \\nHow does the fruit in front of the cup look, unpeeled or peeled?\\nAnswer the question using a single word or phrase. ASSISTANT: Peeled',\n", + " 'gt_answer': 'unpeeled'},\n", + " {'question_id': '201974958',\n", + " 'answer': 'USER: \\nWhat is the color of the shorts?\\nAnswer the question using a single word or phrase. ASSISTANT: Black',\n", + " 'gt_answer': 'black'},\n", + " {'question_id': '20295332',\n", + " 'answer': 'USER: \\nWhat is the color of the television?\\nAnswer the question using a single word or phrase. ASSISTANT: Silver',\n", + " 'gt_answer': 'gray'},\n", + " {'question_id': '20258542',\n", + " 'answer': 'USER: \\nDoes the shirt have the same color as the chair?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '201996765',\n", + " 'answer': 'USER: \\nAre the words large and black?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '201156113',\n", + " 'answer': 'USER: \\nDoes the bag to the left of the boy look black?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '201153193',\n", + " 'answer': 'USER: \\nIs there a woman that is not young?\\nAnswer the question using a single word or phrase. ASSISTANT: No',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '20797647',\n", + " 'answer': 'USER: \\nWhat is in front of the animal that is in front of the man?\\nAnswer the question using a single word or phrase. ASSISTANT: Shoe',\n", + " 'gt_answer': 'shoe'},\n", + " {'question_id': '201207118',\n", + " 'answer': 'USER: \\nWhat vegetable is sitting on the plate?\\nAnswer the question using a single word or phrase. ASSISTANT: Broccoli',\n", + " 'gt_answer': 'broccoli'},\n", + " {'question_id': '201878450',\n", + " 'answer': 'USER: \\nHow old is the person that the jacket is worn around?\\nAnswer the question using a single word or phrase. ASSISTANT: Young',\n", + " 'gt_answer': 'old'},\n", + " {'question_id': '20385288',\n", + " 'answer': 'USER: \\nWhat material is the calculator that is to the right of the charger made of?\\nAnswer the question using a single word or phrase. ASSISTANT: Plastic',\n", + " 'gt_answer': 'plastic'},\n", + " {'question_id': '20385537',\n", + " 'answer': 'USER: \\nAre both the device next to the book and the calculator made of plastic?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '201303200',\n", + " 'answer': 'USER: \\nAre the cups made of plastic or porcelain?\\nAnswer the question using a single word or phrase. ASSISTANT: Porcelain',\n", + " 'gt_answer': 'porcelain'},\n", + " {'question_id': '201303202',\n", + " 'answer': 'USER: \\nWhat is located on top of the table?\\nAnswer the question using a single word or phrase. ASSISTANT: Flowers',\n", + " 'gt_answer': 'cups'},\n", + " {'question_id': '20797648',\n", + " 'answer': \"USER: \\nWhat's in front of the cat?\\nAnswer the question using a single word or phrase. ASSISTANT: Shoe\",\n", + " 'gt_answer': 'shoe'},\n", + " {'question_id': '201976886',\n", + " 'answer': 'USER: \\nWhat is in front of the building the sky is above?\\nAnswer the question using a single word or phrase. ASSISTANT: Fence',\n", + " 'gt_answer': 'street sign'},\n", + " {'question_id': '201976887',\n", + " 'answer': 'USER: \\nWhat is in front of the building?\\nAnswer the question using a single word or phrase. ASSISTANT: Fence',\n", + " 'gt_answer': 'street sign'},\n", + " {'question_id': '201497854',\n", + " 'answer': 'USER: \\nWhich kind of device is reflective?\\nAnswer the question using a single word or phrase. ASSISTANT: Screen',\n", + " 'gt_answer': 'monitor'},\n", + " {'question_id': '202133541',\n", + " 'answer': 'USER: \\nIs the shirt short sleeved or long sleeved?\\nAnswer the question using a single word or phrase. ASSISTANT: Short sleeved',\n", + " 'gt_answer': 'short sleeved'},\n", + " {'question_id': '20171188',\n", + " 'answer': 'USER: \\nWhat kind of cooking utensil is on top of the table?\\nAnswer the question using a single word or phrase. ASSISTANT: Pan',\n", + " 'gt_answer': 'baking pan'},\n", + " {'question_id': '201902537',\n", + " 'answer': 'USER: \\nDoes the router that is to the left of the chair look rectangular and black?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '201738047',\n", + " 'answer': 'USER: \\nDoes the helmet have blue color?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '201713385',\n", + " 'answer': 'USER: \\nIs the mug to the left of a plate?\\nAnswer the question using a single word or phrase. ASSISTANT: No',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '202244248',\n", + " 'answer': 'USER: \\nWhat shape does the food the cupcakes are to the left of have?\\nAnswer the question using a single word or phrase. ASSISTANT: Round',\n", + " 'gt_answer': 'triangular'},\n", + " {'question_id': '202158849',\n", + " 'answer': 'USER: \\nIs the bus near the people black and small?\\nAnswer the question using a single word or phrase. ASSISTANT: No',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '20818677',\n", + " 'answer': 'USER: \\nDo the shoes look black or pink?\\nAnswer the question using a single word or phrase. ASSISTANT: Black',\n", + " 'gt_answer': 'black'},\n", + " {'question_id': '201879394',\n", + " 'answer': 'USER: \\nWhat is the mirror made of?\\nAnswer the question using a single word or phrase. ASSISTANT: Metal',\n", + " 'gt_answer': 'metal'},\n", + " {'question_id': '201621693',\n", + " 'answer': 'USER: \\nWhat type of furniture is to the right of the device that is on top of the TV stand?\\nAnswer the question using a single word or phrase. ASSISTANT: Couch',\n", + " 'gt_answer': 'couch'},\n", + " {'question_id': '202122091',\n", + " 'answer': 'USER: \\nIs the chandelier to the left of the clock made of metal or glass?\\nAnswer the question using a single word or phrase. ASSISTANT: Metal',\n", + " 'gt_answer': 'metal'},\n", + " {'question_id': '201887315',\n", + " 'answer': 'USER: \\nAre there any fruits or toilets?\\nAnswer the question using a single word or phrase. ASSISTANT: No',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '202004237',\n", + " 'answer': 'USER: \\nThe black backpack is hanging on what?\\nAnswer the question using a single word or phrase. ASSISTANT: Chair',\n", + " 'gt_answer': 'doors'},\n", + " {'question_id': '201982149',\n", + " 'answer': 'USER: \\nIs the man that is looking down wearing a uniform?\\nAnswer the question using a single word or phrase. ASSISTANT: No',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '201737851',\n", + " 'answer': 'USER: \\nWhat color is the wood bench?\\nAnswer the question using a single word or phrase. ASSISTANT: Blue',\n", + " 'gt_answer': 'gray'},\n", + " {'question_id': '201896540',\n", + " 'answer': 'USER: \\nWhat do you think is the old lady wearing?\\nAnswer the question using a single word or phrase. ASSISTANT: Jacket',\n", + " 'gt_answer': 'coat'},\n", + " {'question_id': '201654361',\n", + " 'answer': 'USER: \\nAre there any horses in front of the white fence?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '202023443',\n", + " 'answer': 'USER: \\nWhich color is the bed that the closet is behind of?\\nAnswer the question using a single word or phrase. ASSISTANT: Blue',\n", + " 'gt_answer': 'yellow'},\n", + " {'question_id': '20182918',\n", + " 'answer': 'USER: \\nTo what is the barrier made of metal mounted?\\nAnswer the question using a single word or phrase. ASSISTANT: Roof',\n", + " 'gt_answer': 'shop'},\n", + " {'question_id': '201480491',\n", + " 'answer': \"USER: \\nWhat's the mound in front of?\\nAnswer the question using a single word or phrase. ASSISTANT: Tree\",\n", + " 'gt_answer': 'grass'},\n", + " {'question_id': '20978368',\n", + " 'answer': 'USER: \\nWho is walking next to the boy on the left of the picture?\\nAnswer the question using a single word or phrase. ASSISTANT: Girl',\n", + " 'gt_answer': 'girl'},\n", + " {'question_id': '201713366',\n", + " 'answer': 'USER: \\nIs there any mug in the bathroom?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '20818654',\n", + " 'answer': 'USER: \\nAre both the helmet and the bat made of the same material?\\nAnswer the question using a single word or phrase. ASSISTANT: No',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '20308803',\n", + " 'answer': 'USER: \\nWhat appliance is to the left of the light brown object on the counter?\\nAnswer the question using a single word or phrase. ASSISTANT: Refrigerator',\n", + " 'gt_answer': 'stove'},\n", + " {'question_id': '20308802',\n", + " 'answer': 'USER: \\nIs the stove to the left of a drawer?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '201663676',\n", + " 'answer': 'USER: \\nAre the drawers underneath the countertop?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '20631953',\n", + " 'answer': 'USER: \\nWho is in front of the person that is crouching?\\nAnswer the question using a single word or phrase. ASSISTANT: Player',\n", + " 'gt_answer': 'catcher'},\n", + " {'question_id': '201663673',\n", + " 'answer': 'USER: \\nWhat kind of furniture is underneath the countertop?\\nAnswer the question using a single word or phrase. ASSISTANT: Drawer',\n", + " 'gt_answer': 'drawers'},\n", + " {'question_id': '20515082',\n", + " 'answer': 'USER: \\nDoes the brown grass look tall?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '202262632',\n", + " 'answer': 'USER: \\nWhich color is the frisbee, red or yellow?\\nAnswer the question using a single word or phrase. ASSISTANT: Red',\n", + " 'gt_answer': 'red'},\n", + " {'question_id': '202262633',\n", + " 'answer': 'USER: \\nWhat is about to hit the green grass?\\nAnswer the question using a single word or phrase. ASSISTANT: Frisbee',\n", + " 'gt_answer': 'frisbee'},\n", + " {'question_id': '201065063',\n", + " 'answer': 'USER: \\nWhich kind of clothing is pink?\\nAnswer the question using a single word or phrase. ASSISTANT: Dress',\n", + " 'gt_answer': 'gown'},\n", + " {'question_id': '202262636',\n", + " 'answer': 'USER: \\nWhat is the frisbee about to hit?\\nAnswer the question using a single word or phrase. ASSISTANT: Ground',\n", + " 'gt_answer': 'grass'},\n", + " {'question_id': '202286783',\n", + " 'answer': 'USER: \\nThe short sleeved shirt is what color?\\nAnswer the question using a single word or phrase. ASSISTANT: Pink',\n", + " 'gt_answer': 'pink'},\n", + " {'question_id': '20412245',\n", + " 'answer': 'USER: \\nAre the trees behind the young boy?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '20515088',\n", + " 'answer': 'USER: \\nAre there any bicycles?\\nAnswer the question using a single word or phrase. ASSISTANT: No',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '20456346',\n", + " 'answer': 'USER: \\nWhat are the shelves made of?\\nAnswer the question using a single word or phrase. ASSISTANT: Wood',\n", + " 'gt_answer': 'metal'},\n", + " {'question_id': '20306747',\n", + " 'answer': 'USER: \\nWhat device is to the left of the television?\\nAnswer the question using a single word or phrase. ASSISTANT: Camera',\n", + " 'gt_answer': 'cell phone'},\n", + " {'question_id': '201185307',\n", + " 'answer': 'USER: \\nWhat is the ground made of?\\nAnswer the question using a single word or phrase. ASSISTANT: Concrete',\n", + " 'gt_answer': 'concrete'},\n", + " {'question_id': '202144720',\n", + " 'answer': 'USER: \\nWhat is the blender full of?\\nAnswer the question using a single word or phrase. ASSISTANT: Water',\n", + " 'gt_answer': 'ice'},\n", + " {'question_id': '202144727',\n", + " 'answer': 'USER: \\nWhat is that blender sitting atop?\\nAnswer the question using a single word or phrase. ASSISTANT: Crate',\n", + " 'gt_answer': 'crate'},\n", + " {'question_id': '201996835',\n", + " 'answer': 'USER: \\nWhich kind of clothing is dark colored?\\nAnswer the question using a single word or phrase. ASSISTANT: Shirt',\n", + " 'gt_answer': 'sweater'},\n", + " {'question_id': '202144724',\n", + " 'answer': 'USER: \\nWhat is sitting atop the crate?\\nAnswer the question using a single word or phrase. ASSISTANT: Bottle',\n", + " 'gt_answer': 'blender'},\n", + " {'question_id': '201676234',\n", + " 'answer': 'USER: \\nDoes she seem to be sitting?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '20435152',\n", + " 'answer': 'USER: \\nInside what is the pizza?\\nAnswer the question using a single word or phrase. ASSISTANT: Box',\n", + " 'gt_answer': 'pizza box'},\n", + " {'question_id': '20456349',\n", + " 'answer': 'USER: \\nAre the shelves made of wood?\\nAnswer the question using a single word or phrase. ASSISTANT: No',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '201682212',\n", + " 'answer': 'USER: \\nIs the tennis racket round and red?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '202053434',\n", + " 'answer': 'USER: \\nThe pitcher stands where?\\nAnswer the question using a single word or phrase. ASSISTANT: Mound',\n", + " 'gt_answer': 'field'},\n", + " {'question_id': '202053437',\n", + " 'answer': 'USER: \\nWhat is throwing the baseball?\\nAnswer the question using a single word or phrase. ASSISTANT: Pitcher',\n", + " 'gt_answer': 'pitcher'},\n", + " {'question_id': '20785809',\n", + " 'answer': 'USER: \\nHow large is the concrete sidewalk?\\nAnswer the question using a single word or phrase. ASSISTANT: Large',\n", + " 'gt_answer': 'large'},\n", + " {'question_id': '201935967',\n", + " 'answer': 'USER: \\nWhat is in front of the wall?\\nAnswer the question using a single word or phrase. ASSISTANT: Bookshelf',\n", + " 'gt_answer': 'shelf'},\n", + " {'question_id': '20811359',\n", + " 'answer': 'USER: \\nWhat type of furniture is to the left of the shelf that looks light brown?\\nAnswer the question using a single word or phrase. ASSISTANT: Chair',\n", + " 'gt_answer': 'chair'},\n", + " {'question_id': '201879243',\n", + " 'answer': 'USER: \\nWho is wearing the skirt?\\nAnswer the question using a single word or phrase. ASSISTANT: Lady',\n", + " 'gt_answer': 'athlete'},\n", + " {'question_id': '20756653',\n", + " 'answer': 'USER: \\nWhich kind of furniture is made of wood?\\nAnswer the question using a single word or phrase. ASSISTANT: Shelf',\n", + " 'gt_answer': 'shelf'},\n", + " {'question_id': '201873454',\n", + " 'answer': 'USER: \\nDoes the fire truck on the street look dirty and white?\\nAnswer the question using a single word or phrase. ASSISTANT: No',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '20661400',\n", + " 'answer': 'USER: \\nDo the trees look tall and green?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '202012452',\n", + " 'answer': 'USER: \\nDoes the person in front of the cabinets have brunette color?\\nAnswer the question using a single word or phrase. ASSISTANT: No',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '20756658',\n", + " 'answer': 'USER: \\nDoes the shelf that is made of wood look high and brown?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '20536038',\n", + " 'answer': 'USER: \\nHow is the weather?\\nAnswer the question using a single word or phrase. ASSISTANT: Cloudless',\n", + " 'gt_answer': 'cloudless'},\n", + " {'question_id': '201110773',\n", + " 'answer': 'USER: \\nIs the small cup made of glass?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '202053361',\n", + " 'answer': 'USER: \\nWho is in front of the umpire that is staring?\\nAnswer the question using a single word or phrase. ASSISTANT: Batter',\n", + " 'gt_answer': 'batter'},\n", + " {'question_id': '20536035',\n", + " 'answer': 'USER: \\nWhich place is it?\\nAnswer the question using a single word or phrase. ASSISTANT: Field',\n", + " 'gt_answer': 'plain'},\n", + " {'question_id': '20786092',\n", + " 'answer': 'USER: \\nAre there either women or men that are eating?\\nAnswer the question using a single word or phrase. ASSISTANT: No',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '201504947',\n", + " 'answer': 'USER: \\nWhere do you think is the happy person standing on?\\nAnswer the question using a single word or phrase. ASSISTANT: Beach',\n", + " 'gt_answer': 'beach'},\n", + " {'question_id': '201504940',\n", + " 'answer': 'USER: \\nWho do you think wears a shirt?\\nAnswer the question using a single word or phrase. ASSISTANT: Girl',\n", + " 'gt_answer': 'woman'},\n", + " {'question_id': '201482310',\n", + " 'answer': 'USER: \\nIs the brown bag behind the bright fruits?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '20511412',\n", + " 'answer': 'USER: \\nWhat aircraft is military?\\nAnswer the question using a single word or phrase. ASSISTANT: Helicopter',\n", + " 'gt_answer': 'helicopter'},\n", + " {'question_id': '202053363',\n", + " 'answer': 'USER: \\nWho is the batter in front of?\\nAnswer the question using a single word or phrase. ASSISTANT: Catcher',\n", + " 'gt_answer': 'umpire'},\n", + " {'question_id': '20511415',\n", + " 'answer': 'USER: \\nWhat aircraft is heavy?\\nAnswer the question using a single word or phrase. ASSISTANT: Helicopter',\n", + " 'gt_answer': 'helicopter'},\n", + " {'question_id': '20879007',\n", + " 'answer': 'USER: \\nDo you see any skaters in the parking lot?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '20511418',\n", + " 'answer': 'USER: \\nIn which part is the large helicopter, the bottom or the top?\\nAnswer the question using a single word or phrase. ASSISTANT: Top',\n", + " 'gt_answer': 'top'},\n", + " {'question_id': '201759431',\n", + " 'answer': 'USER: \\nIs the door behind the mat open and rectangular?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '20518455',\n", + " 'answer': 'USER: \\nIs the countertop brown?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '201030789',\n", + " 'answer': 'USER: \\nWhich kind of clothing is striped?\\nAnswer the question using a single word or phrase. ASSISTANT: Pants',\n", + " 'gt_answer': 'pants'},\n", + " {'question_id': '201982508',\n", + " 'answer': 'USER: \\nIs the small side table to the right or to the left of the couch?\\nAnswer the question using a single word or phrase. ASSISTANT: Left',\n", + " 'gt_answer': 'left'},\n", + " {'question_id': '201987565',\n", + " 'answer': 'USER: \\nIs the helmet made of plastic or metal?\\nAnswer the question using a single word or phrase. ASSISTANT: Plastic',\n", + " 'gt_answer': 'plastic'},\n", + " {'question_id': '201987569',\n", + " 'answer': 'USER: \\nIs the street next to the sidewalk hard and paved?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '201370409',\n", + " 'answer': 'USER: \\nWhat are the scissors on?\\nAnswer the question using a single word or phrase. ASSISTANT: Carpet',\n", + " 'gt_answer': 'paper'},\n", + " {'question_id': '202180269',\n", + " 'answer': 'USER: \\nIs the soccer ball on the right side or on the left?\\nAnswer the question using a single word or phrase. ASSISTANT: Right',\n", + " 'gt_answer': 'right'},\n", + " {'question_id': '201770690',\n", + " 'answer': 'USER: \\nDoes the toothbrush to the left of the faucet seem to be small and blue?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '20551696',\n", + " 'answer': 'USER: \\nWhat is on the pole that is not short?\\nAnswer the question using a single word or phrase. ASSISTANT: Traffic light',\n", + " 'gt_answer': 'traffic light'},\n", + " {'question_id': '20551697',\n", + " 'answer': 'USER: \\nWhat is on the pole?\\nAnswer the question using a single word or phrase. ASSISTANT: Traffic light',\n", + " 'gt_answer': 'traffic light'},\n", + " {'question_id': '20551694',\n", + " 'answer': 'USER: \\nIs the traffic light red or black?\\nAnswer the question using a single word or phrase. ASSISTANT: Red',\n", + " 'gt_answer': 'black'},\n", + " {'question_id': '20870471',\n", + " 'answer': 'USER: \\nIs the player next to the other player female or male?\\nAnswer the question using a single word or phrase. ASSISTANT: Male',\n", + " 'gt_answer': 'male'},\n", + " {'question_id': '20870472',\n", + " 'answer': 'USER: \\nIs the player next to the other player Caucasian and male?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '201498423',\n", + " 'answer': 'USER: \\nDoes the tape that is made of plastic look small and yellow?\\nAnswer the question using a single word or phrase. ASSISTANT: No',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '202285209',\n", + " 'answer': 'USER: \\nHow long is the cooked sausage?\\nAnswer the question using a single word or phrase. ASSISTANT: Long',\n", + " 'gt_answer': 'short'},\n", + " {'question_id': '20887464',\n", + " 'answer': 'USER: \\nWhat device is not black?\\nAnswer the question using a single word or phrase. ASSISTANT: Computer',\n", + " 'gt_answer': 'computer mouse'},\n", + " {'question_id': '20887460',\n", + " 'answer': 'USER: \\nWhat device is black?\\nAnswer the question using a single word or phrase. ASSISTANT: Keyboard',\n", + " 'gt_answer': 'keyboard'},\n", + " {'question_id': '20468367',\n", + " 'answer': 'USER: \\nDo you see any small cars or windows?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '2017250',\n", + " 'answer': 'USER: \\nIs the shirt bright and black and white?\\nAnswer the question using a single word or phrase. ASSISTANT: No',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '202144703',\n", + " 'answer': 'USER: \\nWhat appliance is right of the liquor?\\nAnswer the question using a single word or phrase. ASSISTANT: Blender',\n", + " 'gt_answer': 'blender'},\n", + " {'question_id': '201347393',\n", + " 'answer': 'USER: \\nWho is wearing the sneakers?\\nAnswer the question using a single word or phrase. ASSISTANT: Boy',\n", + " 'gt_answer': 'skateboarder'},\n", + " {'question_id': '20721787',\n", + " 'answer': 'USER: \\nWho is posing?\\nAnswer the question using a single word or phrase. ASSISTANT: Girl',\n", + " 'gt_answer': 'girl'},\n", + " {'question_id': '201056015',\n", + " 'answer': 'USER: \\nWhat is the vehicle to the right of the soccer player that is wearing a jersey?\\nAnswer the question using a single word or phrase. ASSISTANT: Car',\n", + " 'gt_answer': 'car'},\n", + " {'question_id': '20183437',\n", + " 'answer': 'USER: \\nWhat is the vegetable inside of?\\nAnswer the question using a single word or phrase. ASSISTANT: Bag',\n", + " 'gt_answer': 'boxes'},\n", + " {'question_id': '202246793',\n", + " 'answer': 'USER: \\nIs the shirt green?\\nAnswer the question using a single word or phrase. ASSISTANT: No',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '201951989',\n", + " 'answer': 'USER: \\nWhat is in front of the tree leaves?\\nAnswer the question using a single word or phrase. ASSISTANT: Van',\n", + " 'gt_answer': 'pole'},\n", + " {'question_id': '201047183',\n", + " 'answer': 'USER: \\nDo the telephone and the suit have a different colors?\\nAnswer the question using a single word or phrase. ASSISTANT: No',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '202257504',\n", + " 'answer': 'USER: \\nDoes the sand on the beach look wet and rough?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '201430751',\n", + " 'answer': 'USER: \\nIs the person pulling a tie?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '202257501',\n", + " 'answer': 'USER: \\nDoes the sand that looks wet look smooth or rough?\\nAnswer the question using a single word or phrase. ASSISTANT: Rough',\n", + " 'gt_answer': 'rough'},\n", + " {'question_id': '20936036',\n", + " 'answer': 'USER: \\nAre there any benches in front of the green trees?\\nAnswer the question using a single word or phrase. ASSISTANT: No',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '20551476',\n", + " 'answer': 'USER: \\nWhich kind of vehicle are the buildings behind of?\\nAnswer the question using a single word or phrase. ASSISTANT: Train',\n", + " 'gt_answer': 'train'},\n", + " {'question_id': '20827523',\n", + " 'answer': 'USER: \\nAre the chairs square and black?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '201247296',\n", + " 'answer': \"USER: \\nWhat's inside the flower pot?\\nAnswer the question using a single word or phrase. ASSISTANT: Plant\",\n", + " 'gt_answer': 'plant'},\n", + " {'question_id': '20827527',\n", + " 'answer': 'USER: \\nWhat shape are the tall chairs?\\nAnswer the question using a single word or phrase. ASSISTANT: Square',\n", + " 'gt_answer': 'square'},\n", + " {'question_id': '201247292',\n", + " 'answer': 'USER: \\nWhat is located on top of the side table?\\nAnswer the question using a single word or phrase. ASSISTANT: Plant',\n", + " 'gt_answer': 'plant'},\n", + " {'question_id': '201247293',\n", + " 'answer': \"USER: \\nWhat's located on top of the side table?\\nAnswer the question using a single word or phrase. ASSISTANT: Plant\",\n", + " 'gt_answer': 'plant'},\n", + " {'question_id': '201957203',\n", + " 'answer': 'USER: \\nDoes the Wii remotes look white and small?\\nAnswer the question using a single word or phrase. ASSISTANT: No',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '20361266',\n", + " 'answer': 'USER: \\nWho is riding?\\nAnswer the question using a single word or phrase. ASSISTANT: Woman',\n", + " 'gt_answer': 'snowboarder'},\n", + " {'question_id': '20349947',\n", + " 'answer': 'USER: \\nIs the black hair long or short?\\nAnswer the question using a single word or phrase. ASSISTANT: Long',\n", + " 'gt_answer': 'long'},\n", + " {'question_id': '201492116',\n", + " 'answer': 'USER: \\nIs the shape of the log different than the tree?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '201879148',\n", + " 'answer': 'USER: \\nWhich is bigger, the racket or the wristband?\\nAnswer the question using a single word or phrase. ASSISTANT: Wristband',\n", + " 'gt_answer': 'racket'},\n", + " {'question_id': '201064875',\n", + " 'answer': 'USER: \\nWhat type of furniture is the girl to the left of the hamburger sitting in?\\nAnswer the question using a single word or phrase. ASSISTANT: Chair',\n", + " 'gt_answer': 'sofa'},\n", + " {'question_id': '201498043',\n", + " 'answer': \"USER: \\nWhat's underneath the keyboard?\\nAnswer the question using a single word or phrase. ASSISTANT: Mouse\",\n", + " 'gt_answer': 'desk'},\n", + " {'question_id': '201064873',\n", + " 'answer': 'USER: \\nWhat is the girl to the left of the plate sitting in?\\nAnswer the question using a single word or phrase. ASSISTANT: Bench',\n", + " 'gt_answer': 'sofa'},\n", + " {'question_id': '20856756',\n", + " 'answer': 'USER: \\nWhat is the item of furniture called?\\nAnswer the question using a single word or phrase. ASSISTANT: Bed',\n", + " 'gt_answer': 'bed'},\n", + " {'question_id': '20856758',\n", + " 'answer': 'USER: \\nWhich kind of furniture is it?\\nAnswer the question using a single word or phrase. ASSISTANT: Bed',\n", + " 'gt_answer': 'bed'},\n", + " {'question_id': '20241036',\n", + " 'answer': 'USER: \\nWhat is the man eating?\\nAnswer the question using a single word or phrase. ASSISTANT: Sandwich',\n", + " 'gt_answer': 'sandwich'},\n", + " {'question_id': '201760719',\n", + " 'answer': 'USER: \\nWho is wearing the shirt?\\nAnswer the question using a single word or phrase. ASSISTANT: Man',\n", + " 'gt_answer': 'man'},\n", + " {'question_id': '201760718',\n", + " 'answer': 'USER: \\nWho is wearing a shirt?\\nAnswer the question using a single word or phrase. ASSISTANT: Man',\n", + " 'gt_answer': 'man'},\n", + " {'question_id': '202041969',\n", + " 'answer': 'USER: \\nAre the doors open or closed?\\nAnswer the question using a single word or phrase. ASSISTANT: Closed',\n", + " 'gt_answer': 'closed'},\n", + " {'question_id': '20637135',\n", + " 'answer': 'USER: \\nAre there pots?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '20645492',\n", + " 'answer': 'USER: \\nOn which side of the photo is the small faucet?\\nAnswer the question using a single word or phrase. ASSISTANT: Left',\n", + " 'gt_answer': 'left'},\n", + " {'question_id': '20645496',\n", + " 'answer': 'USER: \\nDoes the tap that is not dirty look small and silver?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '201498046',\n", + " 'answer': 'USER: \\nWhat kind of furniture is underneath the device that looks long?\\nAnswer the question using a single word or phrase. ASSISTANT: Desk',\n", + " 'gt_answer': 'desk'},\n", + " {'question_id': '2076580',\n", + " 'answer': \"USER: \\nWhat's full of the stone?\\nAnswer the question using a single word or phrase. ASSISTANT: Building\",\n", + " 'gt_answer': 'entrance'},\n", + " {'question_id': '201410997',\n", + " 'answer': 'USER: \\nWhat is the gender of the person near the toilet?\\nAnswer the question using a single word or phrase. ASSISTANT: Female',\n", + " 'gt_answer': 'female'},\n", + " {'question_id': '2076582',\n", + " 'answer': \"USER: \\nWhat's the entrance full of?\\nAnswer the question using a single word or phrase. ASSISTANT: Signs\",\n", + " 'gt_answer': 'stone'},\n", + " {'question_id': '201822292',\n", + " 'answer': 'USER: \\nDoes that toilet seat seem to be white and small?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '2076589',\n", + " 'answer': 'USER: \\nDo you see both doors and windows?\\nAnswer the question using a single word or phrase. ASSISTANT: No',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '20790005',\n", + " 'answer': 'USER: \\nWho is riding?\\nAnswer the question using a single word or phrase. ASSISTANT: People',\n", + " 'gt_answer': 'people'},\n", + " {'question_id': '201438619',\n", + " 'answer': 'USER: \\nIs the batter in front of the catcher?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '201143169',\n", + " 'answer': 'USER: \\nWhat items of furniture are wooden?\\nAnswer the question using a single word or phrase. ASSISTANT: Chairs',\n", + " 'gt_answer': 'chairs'},\n", + " {'question_id': '201080313',\n", + " 'answer': 'USER: \\nIs the fireplace near the couch white and closed?\\nAnswer the question using a single word or phrase. ASSISTANT: No',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '201037196',\n", + " 'answer': 'USER: \\nIs the young girl wearing a skirt?\\nAnswer the question using a single word or phrase. ASSISTANT: No',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '201037194',\n", + " 'answer': 'USER: \\nWho is wearing a skirt?\\nAnswer the question using a single word or phrase. ASSISTANT: Woman',\n", + " 'gt_answer': 'girl'},\n", + " {'question_id': '201037195',\n", + " 'answer': 'USER: \\nWho is wearing the skirt?\\nAnswer the question using a single word or phrase. ASSISTANT: Woman',\n", + " 'gt_answer': 'girl'},\n", + " {'question_id': '20285424',\n", + " 'answer': 'USER: \\nIs the carpet yellow and soft?\\nAnswer the question using a single word or phrase. ASSISTANT: No',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '20381280',\n", + " 'answer': 'USER: \\nAre there metal chairs or lamps?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '201047450',\n", + " 'answer': 'USER: \\nDoes the dress shirt seem to be ugly?\\nAnswer the question using a single word or phrase. ASSISTANT: No',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '20308247',\n", + " 'answer': 'USER: \\nWhat are the items of furniture that are to the right of the refrigerator made of stainless steel?\\nAnswer the question using a single word or phrase. ASSISTANT: Cabinets',\n", + " 'gt_answer': 'cabinets'},\n", + " {'question_id': '201935444',\n", + " 'answer': 'USER: \\nDoes the skatepark look hard and smooth?\\nAnswer the question using a single word or phrase. ASSISTANT: No',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '201143349',\n", + " 'answer': 'USER: \\nIs the window clear and high?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '201576511',\n", + " 'answer': 'USER: \\nIs the jacket white and open?\\nAnswer the question using a single word or phrase. ASSISTANT: No',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '201576517',\n", + " 'answer': 'USER: \\nDoes the jacket appear to be red?\\nAnswer the question using a single word or phrase. ASSISTANT: No',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '201400101',\n", + " 'answer': 'USER: \\nWhat color is the round mirror?\\nAnswer the question using a single word or phrase. ASSISTANT: Brown',\n", + " 'gt_answer': 'brown'},\n", + " {'question_id': '201832568',\n", + " 'answer': 'USER: \\nWhat is the bed made of?\\nAnswer the question using a single word or phrase. ASSISTANT: Wood',\n", + " 'gt_answer': 'wood'},\n", + " {'question_id': '201319754',\n", + " 'answer': 'USER: \\nWhat is the color of the coffee cup which is on the table?\\nAnswer the question using a single word or phrase. ASSISTANT: White',\n", + " 'gt_answer': 'white'},\n", + " {'question_id': '20891582',\n", + " 'answer': 'USER: \\nDo the shorts look small and blue?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '201360694',\n", + " 'answer': 'USER: \\nWho is in front of the table that is made of wood?\\nAnswer the question using a single word or phrase. ASSISTANT: Girl',\n", + " 'gt_answer': 'boy'},\n", + " {'question_id': '201360695',\n", + " 'answer': 'USER: \\nWho is in front of the table?\\nAnswer the question using a single word or phrase. ASSISTANT: Girl',\n", + " 'gt_answer': 'boy'},\n", + " {'question_id': '201883195',\n", + " 'answer': 'USER: \\nWhat kind of furniture is to the right of the lamp above the table?\\nAnswer the question using a single word or phrase. ASSISTANT: Chair',\n", + " 'gt_answer': 'bed'},\n", + " {'question_id': '20836778',\n", + " 'answer': \"USER: \\nWhat's the purse hanging from?\\nAnswer the question using a single word or phrase. ASSISTANT: Cart\",\n", + " 'gt_answer': 'table'},\n", + " {'question_id': '201886951',\n", + " 'answer': 'USER: \\nDoes the container that is not large look white or brown?\\nAnswer the question using a single word or phrase. ASSISTANT: Brown',\n", + " 'gt_answer': 'brown'},\n", + " {'question_id': '20836773',\n", + " 'answer': 'USER: \\nWhat is hanging from the table that is to the left of the guitar?\\nAnswer the question using a single word or phrase. ASSISTANT: Bag',\n", + " 'gt_answer': 'purse'},\n", + " {'question_id': '2046358',\n", + " 'answer': 'USER: \\nHow tall is the person that is not old?\\nAnswer the question using a single word or phrase. ASSISTANT: Tall',\n", + " 'gt_answer': 'short'},\n", + " {'question_id': '201711276',\n", + " 'answer': 'USER: \\nDoes the fireplace have white color?\\nAnswer the question using a single word or phrase. ASSISTANT: No',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '202040317',\n", + " 'answer': 'USER: \\nDoes the train that looks orange and gray look old or new?\\nAnswer the question using a single word or phrase. ASSISTANT: New',\n", + " 'gt_answer': 'new'},\n", + " {'question_id': '20600114',\n", + " 'answer': 'USER: \\nIs the grass tall or short?\\nAnswer the question using a single word or phrase. ASSISTANT: Short',\n", + " 'gt_answer': 'short'},\n", + " {'question_id': '20600115',\n", + " 'answer': 'USER: \\nDoes the grass below the zebra appear to be green and short?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '201735202',\n", + " 'answer': 'USER: \\nIs there a towel that is gray?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '201047238',\n", + " 'answer': 'USER: \\nIs the person in front of the frame healthy and adult?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '201878237',\n", + " 'answer': 'USER: \\nIs the color of the hat the same as the coat?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '201428730',\n", + " 'answer': 'USER: \\nIs the bed sheet the same material as the pipe?\\nAnswer the question using a single word or phrase. ASSISTANT: No',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '20308230',\n", + " 'answer': 'USER: \\nWhich kind of furniture is white?\\nAnswer the question using a single word or phrase. ASSISTANT: Cabinets',\n", + " 'gt_answer': 'cabinets'},\n", + " {'question_id': '20416581',\n", + " 'answer': 'USER: \\nAre there chairs that are not short?\\nAnswer the question using a single word or phrase. ASSISTANT: No',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '201882662',\n", + " 'answer': 'USER: \\nAre the paintings on the right or on the left side?\\nAnswer the question using a single word or phrase. ASSISTANT: Left',\n", + " 'gt_answer': 'left'},\n", + " {'question_id': '20308237',\n", + " 'answer': 'USER: \\nWhat are the cabinets made of?\\nAnswer the question using a single word or phrase. ASSISTANT: Wood',\n", + " 'gt_answer': 'glass'},\n", + " {'question_id': '20542972',\n", + " 'answer': 'USER: \\nThe elephant is in front of what?\\nAnswer the question using a single word or phrase. ASSISTANT: Fence',\n", + " 'gt_answer': 'fence'},\n", + " {'question_id': '20205041',\n", + " 'answer': 'USER: \\nWhat kind of furniture is the smiling person sitting on?\\nAnswer the question using a single word or phrase. ASSISTANT: Chair',\n", + " 'gt_answer': 'chair'},\n", + " {'question_id': '202060013',\n", + " 'answer': 'USER: \\nWhat animal is wearing the hat?\\nAnswer the question using a single word or phrase. ASSISTANT: Dog',\n", + " 'gt_answer': 'dog'},\n", + " {'question_id': '201873218',\n", + " 'answer': 'USER: \\nIs the pedestrian to the right of the fire truck female and young?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '20929331',\n", + " 'answer': 'USER: \\nWhich side is the mirror on?\\nAnswer the question using a single word or phrase. ASSISTANT: Right',\n", + " 'gt_answer': 'right'},\n", + " {'question_id': '20151689',\n", + " 'answer': 'USER: \\nIs the black sweatshirt off or on?\\nAnswer the question using a single word or phrase. ASSISTANT: On',\n", + " 'gt_answer': 'on'},\n", + " {'question_id': '201765995',\n", + " 'answer': 'USER: \\nWhat are the trees growing on?\\nAnswer the question using a single word or phrase. ASSISTANT: Sand',\n", + " 'gt_answer': 'dirt'},\n", + " {'question_id': '201873216',\n", + " 'answer': 'USER: \\nIs the pedestrian old or young?\\nAnswer the question using a single word or phrase. ASSISTANT: Young',\n", + " 'gt_answer': 'young'},\n", + " {'question_id': '201765990',\n", + " 'answer': 'USER: \\nAre the trees sparse or dense?\\nAnswer the question using a single word or phrase. ASSISTANT: Dense',\n", + " 'gt_answer': 'sparse'},\n", + " {'question_id': '201765991',\n", + " 'answer': 'USER: \\nWhat is growing on the dirt the beach is in front of?\\nAnswer the question using a single word or phrase. ASSISTANT: Trees',\n", + " 'gt_answer': 'trees'},\n", + " {'question_id': '201951566',\n", + " 'answer': 'USER: \\nWho is standing?\\nAnswer the question using a single word or phrase. ASSISTANT: Girl',\n", + " 'gt_answer': 'girl'},\n", + " {'question_id': '201951567',\n", + " 'answer': 'USER: \\nDoes the girl that is to the right of the other girl seem to be walking?\\nAnswer the question using a single word or phrase. ASSISTANT: No',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '20661240',\n", + " 'answer': 'USER: \\nDoes the bus in front of the trees look red?\\nAnswer the question using a single word or phrase. ASSISTANT: No',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '2058558',\n", + " 'answer': 'USER: \\nIs the door white and open?\\nAnswer the question using a single word or phrase. ASSISTANT: No',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '20247344',\n", + " 'answer': 'USER: \\nAre there any umbrellas or folding chairs in the photograph?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '20247340',\n", + " 'answer': 'USER: \\nIs she wearing a skirt?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '20899335',\n", + " 'answer': 'USER: \\nIs the bottle to the left of the purse?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '20411752',\n", + " 'answer': 'USER: \\nAre there both bowls and carrots in this photo?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '202024715',\n", + " 'answer': 'USER: \\nWhat kind of place is pictured, a beach or a park?\\nAnswer the question using a single word or phrase. ASSISTANT: Park',\n", + " 'gt_answer': 'park'},\n", + " {'question_id': '20836551',\n", + " 'answer': 'USER: \\nIs the luggage that is tan and black sitting in front of a bookcase?\\nAnswer the question using a single word or phrase. ASSISTANT: No',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '201510305',\n", + " 'answer': 'USER: \\nWhat is the fork on?\\nAnswer the question using a single word or phrase. ASSISTANT: Plate',\n", + " 'gt_answer': 'tray'},\n", + " {'question_id': '20631436',\n", + " 'answer': 'USER: \\nWho is wearing a shirt?\\nAnswer the question using a single word or phrase. ASSISTANT: Umpire',\n", + " 'gt_answer': 'umpire'},\n", + " {'question_id': '201490842',\n", + " 'answer': 'USER: \\nAre these animals of the same species?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '20341110',\n", + " 'answer': 'USER: \\nOn which side of the photo is the sculpture?\\nAnswer the question using a single word or phrase. ASSISTANT: Right',\n", + " 'gt_answer': 'right'},\n", + " {'question_id': '20341116',\n", + " 'answer': 'USER: \\nDoes the sculpture made of metal look curved?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '20341117',\n", + " 'answer': 'USER: \\nDoes the sculpture look tall and curved?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '201826530',\n", + " 'answer': 'USER: \\nWho is on top of the elephant?\\nAnswer the question using a single word or phrase. ASSISTANT: Man',\n", + " 'gt_answer': 'man'},\n", + " {'question_id': '20151976',\n", + " 'answer': 'USER: \\nDoes the table that is made of wood look square or round?\\nAnswer the question using a single word or phrase. ASSISTANT: Square',\n", + " 'gt_answer': 'square'},\n", + " {'question_id': '202262102',\n", + " 'answer': 'USER: \\nAre the white flowers behind the menu?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '20710289',\n", + " 'answer': 'USER: \\nIs the parent that is to the right of the other parent male and happy?\\nAnswer the question using a single word or phrase. ASSISTANT: No',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '20285569',\n", + " 'answer': 'USER: \\nDoes the curtain to the left of the other curtain look soft?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '20797581',\n", + " 'answer': 'USER: \\nIs the shirt soft and white?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '201889233',\n", + " 'answer': 'USER: \\nDo the mountain side and the pole have the same color?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '201951690',\n", + " 'answer': 'USER: \\nIs the flag green?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '202262134',\n", + " 'answer': 'USER: \\nWhat is the fork in front of?\\nAnswer the question using a single word or phrase. ASSISTANT: Glass',\n", + " 'gt_answer': 'mug'},\n", + " {'question_id': '202081474',\n", + " 'answer': 'USER: \\nIs the color of the plant different than the mouse?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '201185178',\n", + " 'answer': 'USER: \\nIs the person wearing a hat?\\nAnswer the question using a single word or phrase. ASSISTANT: No',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '201185172',\n", + " 'answer': 'USER: \\nWhat is the person near the wall walking on?\\nAnswer the question using a single word or phrase. ASSISTANT: Ground',\n", + " 'gt_answer': 'ground'},\n", + " {'question_id': '201185173',\n", + " 'answer': 'USER: \\nWhat is the person walking on?\\nAnswer the question using a single word or phrase. ASSISTANT: Pavement',\n", + " 'gt_answer': 'ground'},\n", + " {'question_id': '20891232',\n", + " 'answer': 'USER: \\nWhich place is it?\\nAnswer the question using a single word or phrase. ASSISTANT: Store',\n", + " 'gt_answer': 'street'},\n", + " {'question_id': '20891231',\n", + " 'answer': 'USER: \\nWhich place is this?\\nAnswer the question using a single word or phrase. ASSISTANT: Store',\n", + " 'gt_answer': 'street'},\n", + " {'question_id': '20891541',\n", + " 'answer': 'USER: \\nIs the young child to the right or to the left of the person that is wearing a shirt?\\nAnswer the question using a single word or phrase. ASSISTANT: Left',\n", + " 'gt_answer': 'left'},\n", + " {'question_id': '20652527',\n", + " 'answer': 'USER: \\nOn which side is the soccer ball?\\nAnswer the question using a single word or phrase. ASSISTANT: Left',\n", + " 'gt_answer': 'left'},\n", + " {'question_id': '201455887',\n", + " 'answer': 'USER: \\nDoes the car in front of the hill look white and large?\\nAnswer the question using a single word or phrase. ASSISTANT: No',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '20724222',\n", + " 'answer': 'USER: \\nWho is wearing a jacket?\\nAnswer the question using a single word or phrase. ASSISTANT: Man',\n", + " 'gt_answer': 'snowboarder'},\n", + " {'question_id': '20810927',\n", + " 'answer': 'USER: \\nWhat is hanging from the large tree?\\nAnswer the question using a single word or phrase. ASSISTANT: Ornaments',\n", + " 'gt_answer': 'ornament'},\n", + " {'question_id': '201346485',\n", + " 'answer': 'USER: \\nIs the van blue?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '20954197',\n", + " 'answer': 'USER: \\nAre there women in front of the person that is standing?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '20954194',\n", + " 'answer': 'USER: \\nWho is the woman in front of?\\nAnswer the question using a single word or phrase. ASSISTANT: Man',\n", + " 'gt_answer': 'man'},\n", + " {'question_id': '20162099',\n", + " 'answer': 'USER: \\nIs the hat the same color as the uniform?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '202228116',\n", + " 'answer': \"USER: \\nWhich kind of device isn't on?\\nAnswer the question using a single word or phrase. ASSISTANT: Speaker\",\n", + " 'gt_answer': 'dvd player'},\n", + " {'question_id': '20982562',\n", + " 'answer': 'USER: \\nWho is wearing glasses?\\nAnswer the question using a single word or phrase. ASSISTANT: Woman',\n", + " 'gt_answer': 'woman'},\n", + " {'question_id': '20954191',\n", + " 'answer': 'USER: \\nWho is in front of the man?\\nAnswer the question using a single word or phrase. ASSISTANT: Woman',\n", + " 'gt_answer': 'woman'},\n", + " {'question_id': '202012734',\n", + " 'answer': \"USER: \\nWhat's located on top of the television?\\nAnswer the question using a single word or phrase. ASSISTANT: Flowers\",\n", + " 'gt_answer': 'remote control'},\n", + " {'question_id': '20911295',\n", + " 'answer': 'USER: \\nOn which side of the picture is the traffic cone?\\nAnswer the question using a single word or phrase. ASSISTANT: Right',\n", + " 'gt_answer': 'right'},\n", + " {'question_id': '201401768',\n", + " 'answer': 'USER: \\nWhat is the color of the rocky mountains?\\nAnswer the question using a single word or phrase. ASSISTANT: Gray',\n", + " 'gt_answer': 'dark'},\n", + " {'question_id': '201882482',\n", + " 'answer': 'USER: \\nDoes the pillow to the left of the bed seem to be red?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '202012733',\n", + " 'answer': 'USER: \\nWhat is located on top of the device the man is looking at?\\nAnswer the question using a single word or phrase. ASSISTANT: Flowers',\n", + " 'gt_answer': 'remote control'},\n", + " {'question_id': '20724226',\n", + " 'answer': 'USER: \\nIs the snowboarder wearing a hat?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '201401762',\n", + " 'answer': 'USER: \\nAre the mountains dark and rocky?\\nAnswer the question using a single word or phrase. ASSISTANT: No',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '20342499',\n", + " 'answer': 'USER: \\nDoes the shirt look yellow?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '20306355',\n", + " 'answer': 'USER: \\nWho wears the sweater?\\nAnswer the question using a single word or phrase. ASSISTANT: Man',\n", + " 'gt_answer': 'woman'},\n", + " {'question_id': '20306354',\n", + " 'answer': 'USER: \\nWho wears a sweater?\\nAnswer the question using a single word or phrase. ASSISTANT: Man',\n", + " 'gt_answer': 'woman'},\n", + " {'question_id': '20306357',\n", + " 'answer': 'USER: \\nWhat does the person to the left of the man wear?\\nAnswer the question using a single word or phrase. ASSISTANT: Pants',\n", + " 'gt_answer': 'sweater'},\n", + " {'question_id': '201859542',\n", + " 'answer': 'USER: \\nIs the cup on the left side or on the right?\\nAnswer the question using a single word or phrase. ASSISTANT: Right',\n", + " 'gt_answer': 'right'},\n", + " {'question_id': '201110489',\n", + " 'answer': 'USER: \\nIn which part is the white marshmallow, the bottom or the top?\\nAnswer the question using a single word or phrase. ASSISTANT: Bottom',\n", + " 'gt_answer': 'bottom'},\n", + " {'question_id': '20149668',\n", + " 'answer': \"USER: \\nWhat's the pipe made of?\\nAnswer the question using a single word or phrase. ASSISTANT: Metal\",\n", + " 'gt_answer': 'plastic'},\n", + " {'question_id': '201832652',\n", + " 'answer': 'USER: \\nWhat kind of furniture are the books sitting atop?\\nAnswer the question using a single word or phrase. ASSISTANT: Nightstand',\n", + " 'gt_answer': 'nightstand'},\n", + " {'question_id': '20306358',\n", + " 'answer': 'USER: \\nWhat does the woman wear?\\nAnswer the question using a single word or phrase. ASSISTANT: Jacket',\n", + " 'gt_answer': 'sweater'},\n", + " {'question_id': '20120167',\n", + " 'answer': 'USER: \\nWhich part of the picture is the athlete in, the left or the right?\\nAnswer the question using a single word or phrase. ASSISTANT: Right',\n", + " 'gt_answer': 'right'},\n", + " {'question_id': '202257089',\n", + " 'answer': 'USER: \\nIs this photo showing a park?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '201467424',\n", + " 'answer': 'USER: \\nAre the couch and the sky the same color?\\nAnswer the question using a single word or phrase. ASSISTANT: No',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '201467422',\n", + " 'answer': 'USER: \\nAre both the rose and the couch the same color?\\nAnswer the question using a single word or phrase. ASSISTANT: No',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '202257086',\n", + " 'answer': 'USER: \\nWhich place is it?\\nAnswer the question using a single word or phrase. ASSISTANT: Beach',\n", + " 'gt_answer': 'beach'},\n", + " {'question_id': '20317099',\n", + " 'answer': 'USER: \\nDo you see any towels there?\\nAnswer the question using a single word or phrase. ASSISTANT: No',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '20183235',\n", + " 'answer': 'USER: \\nHow old is the person in the bottom part of the image?\\nAnswer the question using a single word or phrase. ASSISTANT: Young',\n", + " 'gt_answer': 'old'},\n", + " {'question_id': '202053154',\n", + " 'answer': 'USER: \\nIs the shirt that is made of cloth black and long sleeved?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '202053153',\n", + " 'answer': 'USER: \\nIs the shirt long sleeved or short sleeved?\\nAnswer the question using a single word or phrase. ASSISTANT: Short sleeved',\n", + " 'gt_answer': 'long sleeved'},\n", + " {'question_id': '201920535',\n", + " 'answer': 'USER: \\nDo the shoes look black and clean?\\nAnswer the question using a single word or phrase. ASSISTANT: No',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '202241158',\n", + " 'answer': 'USER: \\nWhat color is the chair that the woman is to the right of?\\nAnswer the question using a single word or phrase. ASSISTANT: Brown',\n", + " 'gt_answer': 'black'},\n", + " {'question_id': '201548912',\n", + " 'answer': 'USER: \\nWhat color is the picture?\\nAnswer the question using a single word or phrase. ASSISTANT: Green',\n", + " 'gt_answer': 'gray'},\n", + " {'question_id': '20709866',\n", + " 'answer': 'USER: \\nIs the umbrella green?\\nAnswer the question using a single word or phrase. ASSISTANT: No',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '20489464',\n", + " 'answer': 'USER: \\nWhat is he doing?\\nAnswer the question using a single word or phrase. ASSISTANT: Sleeping',\n", + " 'gt_answer': 'sleeping'},\n", + " {'question_id': '201758426',\n", + " 'answer': 'USER: \\nWhat kind of toy is hanging from the stroller?\\nAnswer the question using a single word or phrase. ASSISTANT: Teddy bear',\n", + " 'gt_answer': 'stuffed dog'},\n", + " {'question_id': '202012841',\n", + " 'answer': 'USER: \\nWho is playing at the television?\\nAnswer the question using a single word or phrase. ASSISTANT: Man',\n", + " 'gt_answer': 'man'},\n", + " {'question_id': '201758429',\n", + " 'answer': 'USER: \\nWhat is the stuffed dog hanging from?\\nAnswer the question using a single word or phrase. ASSISTANT: Stroller',\n", + " 'gt_answer': 'stroller'},\n", + " {'question_id': '20403586',\n", + " 'answer': 'USER: \\nWhat is the item of furniture that is below the folded towel near the sink?\\nAnswer the question using a single word or phrase. ASSISTANT: Shelf',\n", + " 'gt_answer': 'table'},\n", + " {'question_id': '202012848',\n", + " 'answer': 'USER: \\nWhich kind of device is the man playing at?\\nAnswer the question using a single word or phrase. ASSISTANT: Television',\n", + " 'gt_answer': 'television'},\n", + " {'question_id': '201498211',\n", + " 'answer': 'USER: \\nWhat device is the computer behind of?\\nAnswer the question using a single word or phrase. ASSISTANT: Monitor',\n", + " 'gt_answer': 'phone'},\n", + " {'question_id': '2053509',\n", + " 'answer': 'USER: \\nIs the bottle made of the same material as the motorcycle?\\nAnswer the question using a single word or phrase. ASSISTANT: No',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '201110662',\n", + " 'answer': 'USER: \\nIs the white cake short or tall?\\nAnswer the question using a single word or phrase. ASSISTANT: Tall',\n", + " 'gt_answer': 'short'},\n", + " {'question_id': '20668033',\n", + " 'answer': 'USER: \\nIs the soft pillow to the left or to the right of the man that is wearing shorts?\\nAnswer the question using a single word or phrase. ASSISTANT: Left',\n", + " 'gt_answer': 'left'},\n", + " {'question_id': '2046539',\n", + " 'answer': 'USER: \\nAre there women that are not young?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '2053501',\n", + " 'answer': 'USER: \\nIs the motorcycle made of the same material as the streetlight?\\nAnswer the question using a single word or phrase. ASSISTANT: No',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '2053505',\n", + " 'answer': 'USER: \\nAre the post and the motorbike made of the same material?\\nAnswer the question using a single word or phrase. ASSISTANT: No',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '2046530',\n", + " 'answer': 'USER: \\nIs there a woman in this picture?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '202174058',\n", + " 'answer': 'USER: \\nWhat appliance is closed?\\nAnswer the question using a single word or phrase. ASSISTANT: Stove',\n", + " 'gt_answer': 'oven'},\n", + " {'question_id': '201804455',\n", + " 'answer': 'USER: \\nWhat is the device to the right of the keyboard?\\nAnswer the question using a single word or phrase. ASSISTANT: Computer mouse',\n", + " 'gt_answer': 'computer monitor'},\n", + " {'question_id': '20157537',\n", + " 'answer': 'USER: \\nDoes the coffee mug look blue?\\nAnswer the question using a single word or phrase. ASSISTANT: No',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '20982385',\n", + " 'answer': 'USER: \\nWhich kind of clothing is thick?\\nAnswer the question using a single word or phrase. ASSISTANT: Sweater',\n", + " 'gt_answer': 'shirt'},\n", + " {'question_id': '20320230',\n", + " 'answer': 'USER: \\nHow clean is the sidewalk that the sign is in front of?\\nAnswer the question using a single word or phrase. ASSISTANT: Dirty',\n", + " 'gt_answer': 'dirty'},\n", + " {'question_id': '202240953',\n", + " 'answer': 'USER: \\nWhat color do you think the shirt is?\\nAnswer the question using a single word or phrase. ASSISTANT: Brown',\n", + " 'gt_answer': 'black'},\n", + " {'question_id': '2093835',\n", + " 'answer': 'USER: \\nAre there both giraffes and camels in the photo?\\nAnswer the question using a single word or phrase. ASSISTANT: No',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '201156303',\n", + " 'answer': 'USER: \\nWho is wearing a jacket?\\nAnswer the question using a single word or phrase. ASSISTANT: Boy',\n", + " 'gt_answer': 'boy'},\n", + " {'question_id': '20631894',\n", + " 'answer': 'USER: \\nIs the catcher playing or waiting?\\nAnswer the question using a single word or phrase. ASSISTANT: Waiting',\n", + " 'gt_answer': 'waiting'},\n", + " {'question_id': '20978280',\n", + " 'answer': 'USER: \\nIs the young boy to the right or to the left of the man?\\nAnswer the question using a single word or phrase. ASSISTANT: Left',\n", + " 'gt_answer': 'left'},\n", + " {'question_id': '201156304',\n", + " 'answer': 'USER: \\nWho is wearing the jacket?\\nAnswer the question using a single word or phrase. ASSISTANT: Boy',\n", + " 'gt_answer': 'boy'},\n", + " {'question_id': '201570788',\n", + " 'answer': 'USER: \\nIs the plate round and orange?\\nAnswer the question using a single word or phrase. ASSISTANT: No',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '202021477',\n", + " 'answer': 'USER: \\nWhat is on the sign post?\\nAnswer the question using a single word or phrase. ASSISTANT: Sign',\n", + " 'gt_answer': 'artwork'},\n", + " {'question_id': '201491071',\n", + " 'answer': 'USER: \\nAre these goats or a sheep?\\nAnswer the question using a single word or phrase. ASSISTANT: Goat',\n", + " 'gt_answer': 'goats'},\n", + " {'question_id': '201491070',\n", + " 'answer': 'USER: \\nWhat animals are white?\\nAnswer the question using a single word or phrase. ASSISTANT: Cows',\n", + " 'gt_answer': 'goats'},\n", + " {'question_id': '201445018',\n", + " 'answer': 'USER: \\nHow hard is the street sign that is standing in the lawn?\\nAnswer the question using a single word or phrase. ASSISTANT: Very',\n", + " 'gt_answer': 'hard'},\n", + " {'question_id': '201623420',\n", + " 'answer': 'USER: \\nDoes the microwave above the oven look silver or black?\\nAnswer the question using a single word or phrase. ASSISTANT: Silver',\n", + " 'gt_answer': 'silver'},\n", + " {'question_id': '20442165',\n", + " 'answer': 'USER: \\nWhat kind of furniture is above the countertop?\\nAnswer the question using a single word or phrase. ASSISTANT: Cabinet',\n", + " 'gt_answer': 'cabinet'},\n", + " {'question_id': '20442164',\n", + " 'answer': 'USER: \\nWhat is the item of furniture above the countertop?\\nAnswer the question using a single word or phrase. ASSISTANT: Cabinet',\n", + " 'gt_answer': 'cabinet'},\n", + " {'question_id': '202100414',\n", + " 'answer': 'USER: \\nDoes the flying airplane above the sailboat look clean and white?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '20245693',\n", + " 'answer': 'USER: \\nAre the fence and the skateboard the same color?\\nAnswer the question using a single word or phrase. ASSISTANT: No',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '20287908',\n", + " 'answer': 'USER: \\nAre the aluminum bleachers long and gray?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '20492010',\n", + " 'answer': 'USER: \\nIs the tall person looking at the picture frame?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '201624139',\n", + " 'answer': \"USER: \\nWhat cooking utensil isn't warm?\\nAnswer the question using a single word or phrase. ASSISTANT: Spatula\",\n", + " 'gt_answer': 'spatula'},\n", + " {'question_id': '202125899',\n", + " 'answer': 'USER: \\nWho is sitting on the chairs?\\nAnswer the question using a single word or phrase. ASSISTANT: People',\n", + " 'gt_answer': 'audience'},\n", + " {'question_id': '20227104',\n", + " 'answer': 'USER: \\nWhat is the young woman looking at?\\nAnswer the question using a single word or phrase. ASSISTANT: Man',\n", + " 'gt_answer': 'menu'},\n", + " {'question_id': '20227105',\n", + " 'answer': 'USER: \\nIs the woman that is sitting looking at the menu?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '202012599',\n", + " 'answer': 'USER: \\nOn which side of the photo are the shelves?\\nAnswer the question using a single word or phrase. ASSISTANT: Right',\n", + " 'gt_answer': 'left'},\n", + " {'question_id': '201621484',\n", + " 'answer': \"USER: \\nWhat kind of furniture isn't short?\\nAnswer the question using a single word or phrase. ASSISTANT: Table\",\n", + " 'gt_answer': 'desk'},\n", + " {'question_id': '201621489',\n", + " 'answer': 'USER: \\nOn which side of the picture is the TV stand?\\nAnswer the question using a single word or phrase. ASSISTANT: Left',\n", + " 'gt_answer': 'left'},\n", + " {'question_id': '201624134',\n", + " 'answer': 'USER: \\nWhich kind of cooking utensil is warm?\\nAnswer the question using a single word or phrase. ASSISTANT: Pan',\n", + " 'gt_answer': 'pan'},\n", + " {'question_id': '201536418',\n", + " 'answer': 'USER: \\nAre there umpires to the right of the batter?\\nAnswer the question using a single word or phrase. ASSISTANT: No',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '20511621',\n", + " 'answer': 'USER: \\nIs the canopy dark and dry?\\nAnswer the question using a single word or phrase. ASSISTANT: No',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '202218593',\n", + " 'answer': 'USER: \\nAre the white paper towels in the bottom part or in the top of the photo?\\nAnswer the question using a single word or phrase. ASSISTANT: Bottom',\n", + " 'gt_answer': 'bottom'},\n", + " {'question_id': '201975071',\n", + " 'answer': 'USER: \\nWhich company is the hat from?\\nAnswer the question using a single word or phrase. ASSISTANT: Adidas',\n", + " 'gt_answer': 'adidas'},\n", + " {'question_id': '201434265',\n", + " 'answer': 'USER: \\nWhat are the windows made of?\\nAnswer the question using a single word or phrase. ASSISTANT: Glass',\n", + " 'gt_answer': 'glass'},\n", + " {'question_id': '201654426',\n", + " 'answer': 'USER: \\nWhat are the animals to the left of the jockey that is sitting in the cart?\\nAnswer the question using a single word or phrase. ASSISTANT: Horses',\n", + " 'gt_answer': 'horses'},\n", + " {'question_id': '201654424',\n", + " 'answer': 'USER: \\nAre there any cows to the left of the fence?\\nAnswer the question using a single word or phrase. ASSISTANT: No',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '20211274',\n", + " 'answer': 'USER: \\nIs the woman behind the plate near the glass?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '20543092',\n", + " 'answer': 'USER: \\nWhat kind of animal is the fence behind of?\\nAnswer the question using a single word or phrase. ASSISTANT: Elephant',\n", + " 'gt_answer': 'elephant'},\n", + " {'question_id': '201412341',\n", + " 'answer': 'USER: \\nDo the snowpants that are not short look wet?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '201976777',\n", + " 'answer': 'USER: \\nDoes the backpack look small?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '201641288',\n", + " 'answer': 'USER: \\nHow is the sign that is hanging above the tall man called?\\nAnswer the question using a single word or phrase. ASSISTANT: Street sign',\n", + " 'gt_answer': 'street sign'},\n", + " {'question_id': '20717109',\n", + " 'answer': 'USER: \\nWhat piece of furniture is it?\\nAnswer the question using a single word or phrase. ASSISTANT: Bed',\n", + " 'gt_answer': 'bed'},\n", + " {'question_id': '201975049',\n", + " 'answer': 'USER: \\nDoes the hat look black and white?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '201641287',\n", + " 'answer': 'USER: \\nWhat is hanging above the man?\\nAnswer the question using a single word or phrase. ASSISTANT: Traffic light',\n", + " 'gt_answer': 'street sign'},\n", + " {'question_id': '201641286',\n", + " 'answer': 'USER: \\nWhat is hanging above the tall person?\\nAnswer the question using a single word or phrase. ASSISTANT: Traffic light',\n", + " 'gt_answer': 'street sign'},\n", + " {'question_id': '201641282',\n", + " 'answer': 'USER: \\nWhat is the street sign that looks black and white hanging from?\\nAnswer the question using a single word or phrase. ASSISTANT: Pole',\n", + " 'gt_answer': 'traffic light'},\n", + " {'question_id': '201794876',\n", + " 'answer': 'USER: \\nWhat is common to the tree and the bag?\\nAnswer the question using a single word or phrase. ASSISTANT: Color',\n", + " 'gt_answer': 'material'},\n", + " {'question_id': '20412052',\n", + " 'answer': 'USER: \\nIs the dessert below tomatoes?\\nAnswer the question using a single word or phrase. ASSISTANT: No',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '20412053',\n", + " 'answer': 'USER: \\nWhat type of food is below the small lid?\\nAnswer the question using a single word or phrase. ASSISTANT: Carrot',\n", + " 'gt_answer': 'dessert'},\n", + " {'question_id': '20673114',\n", + " 'answer': 'USER: \\nAre both the white thing to the right of the chair and the toilet paper to the right of the toilet made of paper?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '2044674',\n", + " 'answer': 'USER: \\nIs the woman still and sad?\\nAnswer the question using a single word or phrase. ASSISTANT: No',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '20673117',\n", + " 'answer': 'USER: \\nWhat is resting on the table?\\nAnswer the question using a single word or phrase. ASSISTANT: Toilet paper',\n", + " 'gt_answer': 'toilet paper'},\n", + " {'question_id': '20923068',\n", + " 'answer': 'USER: \\nWhat vehicle is not red?\\nAnswer the question using a single word or phrase. ASSISTANT: Truck',\n", + " 'gt_answer': 'ambulance'},\n", + " {'question_id': '2017111',\n", + " 'answer': 'USER: \\nDoes the goat to the right of the horse look white?\\nAnswer the question using a single word or phrase. ASSISTANT: No',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '20705812',\n", + " 'answer': 'USER: \\nWhat item of furniture is it?\\nAnswer the question using a single word or phrase. ASSISTANT: Desk',\n", + " 'gt_answer': 'desk'},\n", + " {'question_id': '20550406',\n", + " 'answer': 'USER: \\nWhat is the animal that is standing on the grass called?\\nAnswer the question using a single word or phrase. ASSISTANT: Horse',\n", + " 'gt_answer': 'horse'},\n", + " {'question_id': '20550407',\n", + " 'answer': 'USER: \\nWhat kind of animal is standing on the grass?\\nAnswer the question using a single word or phrase. ASSISTANT: Horse',\n", + " 'gt_answer': 'horse'},\n", + " {'question_id': '20673118',\n", + " 'answer': 'USER: \\nThe toilet paper that is to the right of the chair is resting on what?\\nAnswer the question using a single word or phrase. ASSISTANT: Table',\n", + " 'gt_answer': 'table'},\n", + " {'question_id': '20705816',\n", + " 'answer': 'USER: \\nWhat is the large item of furniture in the scene?\\nAnswer the question using a single word or phrase. ASSISTANT: Desk',\n", + " 'gt_answer': 'desk'},\n", + " {'question_id': '20468429',\n", + " 'answer': 'USER: \\nHow big is the pipe that is not short?\\nAnswer the question using a single word or phrase. ASSISTANT: Large',\n", + " 'gt_answer': 'small'},\n", + " {'question_id': '201739230',\n", + " 'answer': 'USER: \\nIs there a hat that is not blue?\\nAnswer the question using a single word or phrase. ASSISTANT: No',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '20248159',\n", + " 'answer': 'USER: \\nWhat kind of clothing is white?\\nAnswer the question using a single word or phrase. ASSISTANT: Shirt',\n", + " 'gt_answer': 'sweater'},\n", + " {'question_id': '202021472',\n", + " 'answer': 'USER: \\nWhat color does the artwork have?\\nAnswer the question using a single word or phrase. ASSISTANT: Red',\n", + " 'gt_answer': 'red'},\n", + " {'question_id': '201068695',\n", + " 'answer': 'USER: \\nHow is the gray clothing item called?\\nAnswer the question using a single word or phrase. ASSISTANT: Shirt',\n", + " 'gt_answer': 'dress shirt'},\n", + " {'question_id': '20248154',\n", + " 'answer': 'USER: \\nWhat is the clothing item that is striped?\\nAnswer the question using a single word or phrase. ASSISTANT: Shirt',\n", + " 'gt_answer': 'sweater'},\n", + " {'question_id': '20144639',\n", + " 'answer': 'USER: \\nDoes the bus behind the other bus look white and rectangular?\\nAnswer the question using a single word or phrase. ASSISTANT: No',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '201735564',\n", + " 'answer': 'USER: \\nOn which side is the wood tray?\\nAnswer the question using a single word or phrase. ASSISTANT: Right',\n", + " 'gt_answer': 'right'},\n", + " {'question_id': '201391831',\n", + " 'answer': 'USER: \\nWhat does the happy man hold?\\nAnswer the question using a single word or phrase. ASSISTANT: Wii controller',\n", + " 'gt_answer': 'wii controller'},\n", + " {'question_id': '201391832',\n", + " 'answer': 'USER: \\nWhat is the device that the man that is not sad holds?\\nAnswer the question using a single word or phrase. ASSISTANT: Controller',\n", + " 'gt_answer': 'wii controller'},\n", + " {'question_id': '201065519',\n", + " 'answer': 'USER: \\nIs the man that is to the left of the performer standing near the chairs?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '20204532',\n", + " 'answer': 'USER: \\nWhat is the item of furniture that the rug is in front of?\\nAnswer the question using a single word or phrase. ASSISTANT: Chair',\n", + " 'gt_answer': 'chair'},\n", + " {'question_id': '202101231',\n", + " 'answer': 'USER: \\nIs the lamp to the right or to the left of the car?\\nAnswer the question using a single word or phrase. ASSISTANT: Right',\n", + " 'gt_answer': 'left'},\n", + " {'question_id': '20258759',\n", + " 'answer': 'USER: \\nWho is talking on the device that looks black and silver?\\nAnswer the question using a single word or phrase. ASSISTANT: Boy',\n", + " 'gt_answer': 'child'},\n", + " {'question_id': '20262704',\n", + " 'answer': 'USER: \\nWho is wearing a skirt?\\nAnswer the question using a single word or phrase. ASSISTANT: Girl',\n", + " 'gt_answer': 'girl'},\n", + " {'question_id': '202265747',\n", + " 'answer': 'USER: \\nWho is wearing a shirt?\\nAnswer the question using a single word or phrase. ASSISTANT: Woman',\n", + " 'gt_answer': 'woman'},\n", + " {'question_id': '202024849',\n", + " 'answer': 'USER: \\nWhere is the tree?\\nAnswer the question using a single word or phrase. ASSISTANT: Park',\n", + " 'gt_answer': 'park'},\n", + " {'question_id': '20340632',\n", + " 'answer': 'USER: \\nWhat is the bird doing, resting or playing?\\nAnswer the question using a single word or phrase. ASSISTANT: Resting',\n", + " 'gt_answer': 'playing'},\n", + " {'question_id': '201498444',\n", + " 'answer': 'USER: \\nIs the tape behind the coffee cup?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '201037030',\n", + " 'answer': 'USER: \\nAre the sidewalk and the traffic sign made of the same material?\\nAnswer the question using a single word or phrase. ASSISTANT: No',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '2044903',\n", + " 'answer': 'USER: \\nIs the bag to the left or to the right of the person covered by the umbrella?\\nAnswer the question using a single word or phrase. ASSISTANT: Right',\n", + " 'gt_answer': 'right'},\n", + " {'question_id': '201593873',\n", + " 'answer': 'USER: \\nWhat is she hitting?\\nAnswer the question using a single word or phrase. ASSISTANT: Tennis ball',\n", + " 'gt_answer': 'tennis ball'},\n", + " {'question_id': '201593875',\n", + " 'answer': 'USER: \\nWhat is the girl hitting?\\nAnswer the question using a single word or phrase. ASSISTANT: Tennis ball',\n", + " 'gt_answer': 'tennis ball'},\n", + " {'question_id': '201438282',\n", + " 'answer': 'USER: \\nWhat is in front of the man?\\nAnswer the question using a single word or phrase. ASSISTANT: Home plate',\n", + " 'gt_answer': 'net'},\n", + " {'question_id': '2075243',\n", + " 'answer': 'USER: \\nIs there any zebra or deer in this scene?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '20691469',\n", + " 'answer': 'USER: \\nWhat are the folded towels sitting inside of?\\nAnswer the question using a single word or phrase. ASSISTANT: Basket',\n", + " 'gt_answer': 'shelves'},\n", + " {'question_id': '201407334',\n", + " 'answer': 'USER: \\nAre there men behind the fence that is presented in this photograph?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '20691466',\n", + " 'answer': 'USER: \\nWhat color are the towels that are sitting inside the shelves?\\nAnswer the question using a single word or phrase. ASSISTANT: Green',\n", + " 'gt_answer': 'black'},\n", + " {'question_id': '201407331',\n", + " 'answer': 'USER: \\nWhat is this man in front of?\\nAnswer the question using a single word or phrase. ASSISTANT: Fence',\n", + " 'gt_answer': 'fence'},\n", + " {'question_id': '20177899',\n", + " 'answer': 'USER: \\nHow big are the pickles that are on top of the cutting board?\\nAnswer the question using a single word or phrase. ASSISTANT: Small',\n", + " 'gt_answer': 'small'},\n", + " {'question_id': '201735690',\n", + " 'answer': 'USER: \\nIs the newspaper under the shelves?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '201987605',\n", + " 'answer': 'USER: \\nIs the color of the bottle the same as the paper?\\nAnswer the question using a single word or phrase. ASSISTANT: No',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '20169603',\n", + " 'answer': 'USER: \\nIs the color of the road the same as that of the flag?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '201983045',\n", + " 'answer': 'USER: \\nAre there any phones to the left of the man below the frame?\\nAnswer the question using a single word or phrase. ASSISTANT: No',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '201864553',\n", + " 'answer': 'USER: \\nIs the bat black and metallic?\\nAnswer the question using a single word or phrase. ASSISTANT: No',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '20706289',\n", + " 'answer': 'USER: \\nIs the small device on the left?\\nAnswer the question using a single word or phrase. ASSISTANT: No',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '202003684',\n", + " 'answer': 'USER: \\nAre the devices on top of the desk black and open?\\nAnswer the question using a single word or phrase. ASSISTANT: No',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '201481487',\n", + " 'answer': 'USER: \\nThe sitting down woman to the right of the glasses is holding what?\\nAnswer the question using a single word or phrase. ASSISTANT: Umbrella',\n", + " 'gt_answer': 'umbrella'},\n", + " {'question_id': '201756642',\n", + " 'answer': 'USER: \\nIs the animal that looks small black and young?\\nAnswer the question using a single word or phrase. ASSISTANT: No',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '202285540',\n", + " 'answer': 'USER: \\nDo you see any apples to the right of the fruit that is not thick?\\nAnswer the question using a single word or phrase. ASSISTANT: No',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '202119928',\n", + " 'answer': 'USER: \\nIs the freezer to the right or to the left of the short table near the door?\\nAnswer the question using a single word or phrase. ASSISTANT: Right',\n", + " 'gt_answer': 'right'},\n", + " {'question_id': '20984434',\n", + " 'answer': 'USER: \\nHow old is the skater in the middle of the photo?\\nAnswer the question using a single word or phrase. ASSISTANT: 16',\n", + " 'gt_answer': 'young'},\n", + " {'question_id': '20340983',\n", + " 'answer': 'USER: \\nAre the high windows both wide and closed?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '20984387',\n", + " 'answer': 'USER: \\nWho is the black hat worn on?\\nAnswer the question using a single word or phrase. ASSISTANT: Boy',\n", + " 'gt_answer': 'skater'},\n", + " {'question_id': '201347368',\n", + " 'answer': 'USER: \\nWhat is the person that is looking up doing, skating or riding?\\nAnswer the question using a single word or phrase. ASSISTANT: Skating',\n", + " 'gt_answer': 'skating'},\n", + " {'question_id': '20541514',\n", + " 'answer': 'USER: \\nWhich side of the picture is the black device on?\\nAnswer the question using a single word or phrase. ASSISTANT: Right',\n", + " 'gt_answer': 'right'},\n", + " {'question_id': '20340988',\n", + " 'answer': 'USER: \\nHow wide are the windows the tree is next to?\\nAnswer the question using a single word or phrase. ASSISTANT: Wide',\n", + " 'gt_answer': 'wide'},\n", + " {'question_id': '202006219',\n", + " 'answer': 'USER: \\nWhat is the color of the basket above the other basket?\\nAnswer the question using a single word or phrase. ASSISTANT: Brown',\n", + " 'gt_answer': 'tan'},\n", + " {'question_id': '20705745',\n", + " 'answer': 'USER: \\nWhat device is on top of the desk?\\nAnswer the question using a single word or phrase. ASSISTANT: Computer',\n", + " 'gt_answer': 'monitor'},\n", + " {'question_id': '201556920',\n", + " 'answer': 'USER: \\nWhat device is above the keyboard?\\nAnswer the question using a single word or phrase. ASSISTANT: Laptop',\n", + " 'gt_answer': 'laptop'},\n", + " {'question_id': '201956961',\n", + " 'answer': 'USER: \\nAre the DVDs inside the TV stand open or closed?\\nAnswer the question using a single word or phrase. ASSISTANT: Closed',\n", + " 'gt_answer': 'closed'},\n", + " {'question_id': '202006213',\n", + " 'answer': 'USER: \\nIs the woven basket above the other basket both tan and large?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '20756914',\n", + " 'answer': 'USER: \\nIs the robe smooth?\\nAnswer the question using a single word or phrase. ASSISTANT: No',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '202243791',\n", + " 'answer': 'USER: \\nWhat color does this table have?\\nAnswer the question using a single word or phrase. ASSISTANT: Brown',\n", + " 'gt_answer': 'red'},\n", + " {'question_id': '20330509',\n", + " 'answer': 'USER: \\nIs the long fence behind or in front of the tree made of wood?\\nAnswer the question using a single word or phrase. ASSISTANT: Behind',\n", + " 'gt_answer': 'behind'},\n", + " {'question_id': '20247860',\n", + " 'answer': 'USER: \\nIs the sitting down person to the right of the man old and happy?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '20596312',\n", + " 'answer': 'USER: \\nWhat is he doing?\\nAnswer the question using a single word or phrase. ASSISTANT: Standing',\n", + " 'gt_answer': 'standing'},\n", + " {'question_id': '201247081',\n", + " 'answer': 'USER: \\nWhat is the chair in front of?\\nAnswer the question using a single word or phrase. ASSISTANT: Plant',\n", + " 'gt_answer': 'plant'},\n", + " {'question_id': '202245872',\n", + " 'answer': 'USER: \\nDoes the computer desk have a different color than the cable?\\nAnswer the question using a single word or phrase. ASSISTANT: No',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '20551315',\n", + " 'answer': 'USER: \\nIs the traffic signal different in color than the platform?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '202162658',\n", + " 'answer': 'USER: \\nWhich color is the bookcase?\\nAnswer the question using a single word or phrase. ASSISTANT: Black',\n", + " 'gt_answer': 'black'},\n", + " {'question_id': '20667821',\n", + " 'answer': 'USER: \\nWhat is the woman wearing?\\nAnswer the question using a single word or phrase. ASSISTANT: Tank top',\n", + " 'gt_answer': 'shirt'},\n", + " {'question_id': '202158779',\n", + " 'answer': 'USER: \\nWhat is the sidewalk made of?\\nAnswer the question using a single word or phrase. ASSISTANT: Concrete',\n", + " 'gt_answer': 'concrete'},\n", + " {'question_id': '201795384',\n", + " 'answer': 'USER: \\nThe woman that to the right of the elephant sits on what?\\nAnswer the question using a single word or phrase. ASSISTANT: Bench',\n", + " 'gt_answer': 'bench'},\n", + " {'question_id': '201795385',\n", + " 'answer': 'USER: \\nDoes the woman that is not thin sit on a bench?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '201795382',\n", + " 'answer': 'USER: \\nIs the woman that is not thin looking at a cell phone?\\nAnswer the question using a single word or phrase. ASSISTANT: No',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '202158778',\n", + " 'answer': 'USER: \\nWhat is the side walk made of?\\nAnswer the question using a single word or phrase. ASSISTANT: Concrete',\n", + " 'gt_answer': 'concrete'},\n", + " {'question_id': '201766528',\n", + " 'answer': 'USER: \\nIs the girl on the left side or on the right?\\nAnswer the question using a single word or phrase. ASSISTANT: Left',\n", + " 'gt_answer': 'left'},\n", + " {'question_id': '202241056',\n", + " 'answer': 'USER: \\nWhat color is the pizza in the bottom of the image?\\nAnswer the question using a single word or phrase. ASSISTANT: White',\n", + " 'gt_answer': 'white'},\n", + " {'question_id': '202000663',\n", + " 'answer': 'USER: \\nIs the sidewalk beside the street clean and hard?\\nAnswer the question using a single word or phrase. ASSISTANT: No',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '2056075',\n", + " 'answer': 'USER: \\nIs the black vehicle parked behind a truck?\\nAnswer the question using a single word or phrase. ASSISTANT: No',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '20968332',\n", + " 'answer': 'USER: \\nWhat is the man leaning against?\\nAnswer the question using a single word or phrase. ASSISTANT: Sign',\n", + " 'gt_answer': 'pole'},\n", + " {'question_id': '20541546',\n", + " 'answer': 'USER: \\nIs the black device to the left or to the right of the couch?\\nAnswer the question using a single word or phrase. ASSISTANT: Right',\n", + " 'gt_answer': 'right'},\n", + " {'question_id': '201393608',\n", + " 'answer': 'USER: \\nWhat is the color of the shirt?\\nAnswer the question using a single word or phrase. ASSISTANT: Gray',\n", + " 'gt_answer': 'dark'},\n", + " {'question_id': '20954058',\n", + " 'answer': 'USER: \\nWhat is located on top of the luggage that looks large?\\nAnswer the question using a single word or phrase. ASSISTANT: Jacket',\n", + " 'gt_answer': 'receipt'},\n", + " {'question_id': '20637305',\n", + " 'answer': 'USER: \\nWhat appliance is white?\\nAnswer the question using a single word or phrase. ASSISTANT: Stove',\n", + " 'gt_answer': 'stove'},\n", + " {'question_id': '20516049',\n", + " 'answer': 'USER: \\nIs the large vehicle to the left or to the right of the people?\\nAnswer the question using a single word or phrase. ASSISTANT: Left',\n", + " 'gt_answer': 'left'},\n", + " {'question_id': '201393601',\n", + " 'answer': 'USER: \\nWhat type of clothing is not dark, the shirt or the sock?\\nAnswer the question using a single word or phrase. ASSISTANT: Sock',\n", + " 'gt_answer': 'sock'},\n", + " {'question_id': '201393603',\n", + " 'answer': 'USER: \\nWhich kind of clothing is not dark?\\nAnswer the question using a single word or phrase. ASSISTANT: Socks',\n", + " 'gt_answer': 'sock'},\n", + " {'question_id': '201795116',\n", + " 'answer': 'USER: \\nWhat type of animal is to the left of the woman that the child is sitting next to?\\nAnswer the question using a single word or phrase. ASSISTANT: Elephant',\n", + " 'gt_answer': 'elephant'},\n", + " {'question_id': '20782987',\n", + " 'answer': 'USER: \\nWhat is the man sitting on?\\nAnswer the question using a single word or phrase. ASSISTANT: Chair',\n", + " 'gt_answer': 'chair'},\n", + " {'question_id': '20151769',\n", + " 'answer': 'USER: \\nDo the trousers appear to be off?\\nAnswer the question using a single word or phrase. ASSISTANT: No',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '202285048',\n", + " 'answer': 'USER: \\nIs the fruit on top of the tray yellow and thick?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '201882905',\n", + " 'answer': 'USER: \\nWhat device is on the right of the curtains?\\nAnswer the question using a single word or phrase. ASSISTANT: Phone',\n", + " 'gt_answer': 'television'},\n", + " {'question_id': '202262837',\n", + " 'answer': 'USER: \\nIs the black car to the left of a woman?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '201879568',\n", + " 'answer': 'USER: \\nWhat is the vehicle that is in front of the basket?\\nAnswer the question using a single word or phrase. ASSISTANT: Truck',\n", + " 'gt_answer': 'truck'},\n", + " {'question_id': '20596524',\n", + " 'answer': 'USER: \\nWhat is the sidewalk made of?\\nAnswer the question using a single word or phrase. ASSISTANT: Concrete',\n", + " 'gt_answer': 'concrete'},\n", + " {'question_id': '20611554',\n", + " 'answer': 'USER: \\nIs this container large or small?\\nAnswer the question using a single word or phrase. ASSISTANT: Large',\n", + " 'gt_answer': 'large'},\n", + " {'question_id': '201669332',\n", + " 'answer': 'USER: \\nAre there both candles and cakes in this image?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '201624192',\n", + " 'answer': 'USER: \\nIs the pizza on the cooking utensil the plate is beside of?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '201804274',\n", + " 'answer': 'USER: \\nAre there either any small desks or boxes?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '201972699',\n", + " 'answer': 'USER: \\nDoes the scarf have a different color than the bush?\\nAnswer the question using a single word or phrase. ASSISTANT: No',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '201770658',\n", + " 'answer': 'USER: \\nAre there toothbrushes in this image?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '201574214',\n", + " 'answer': 'USER: \\nIs there an umbrella above the man that is in front of the door?\\nAnswer the question using a single word or phrase. ASSISTANT: No',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '202156687',\n", + " 'answer': 'USER: \\nAre the clouds above the trees both thin and gray?\\nAnswer the question using a single word or phrase. ASSISTANT: No',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '201885214',\n", + " 'answer': 'USER: \\nOn which side of the picture is the frisbee?\\nAnswer the question using a single word or phrase. ASSISTANT: Right',\n", + " 'gt_answer': 'right'},\n", + " {'question_id': '201896270',\n", + " 'answer': 'USER: \\nHow big is the utensil that is slicing the cake?\\nAnswer the question using a single word or phrase. ASSISTANT: Large',\n", + " 'gt_answer': 'large'},\n", + " {'question_id': '20982174',\n", + " 'answer': 'USER: \\nIs the blouse long sleeved or short sleeved?\\nAnswer the question using a single word or phrase. ASSISTANT: Long sleeved',\n", + " 'gt_answer': 'long sleeved'},\n", + " {'question_id': '20982179',\n", + " 'answer': 'USER: \\nWhat color is the blouse?\\nAnswer the question using a single word or phrase. ASSISTANT: Black',\n", + " 'gt_answer': 'black'},\n", + " {'question_id': '201896318',\n", + " 'answer': 'USER: \\nWhat is the knife slicing?\\nAnswer the question using a single word or phrase. ASSISTANT: Cake',\n", + " 'gt_answer': 'cake'},\n", + " {'question_id': '20618861',\n", + " 'answer': 'USER: \\nIs the shirt orange and short sleeved?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '202147831',\n", + " 'answer': 'USER: \\nWho is the person to the right of the chair sitting in front of?\\nAnswer the question using a single word or phrase. ASSISTANT: Spectator',\n", + " 'gt_answer': 'athlete'},\n", + " {'question_id': '201996785',\n", + " 'answer': 'USER: \\nWhat color are the words?\\nAnswer the question using a single word or phrase. ASSISTANT: Black',\n", + " 'gt_answer': 'gold'},\n", + " {'question_id': '20434808',\n", + " 'answer': 'USER: \\nIs the boy on the left side of the picture?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '20618869',\n", + " 'answer': 'USER: \\nDoes the short sleeved shirt have blue color?\\nAnswer the question using a single word or phrase. ASSISTANT: No',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '201974935',\n", + " 'answer': 'USER: \\nAre the shorts that are not long soft and green?\\nAnswer the question using a single word or phrase. ASSISTANT: No',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '202258138',\n", + " 'answer': 'USER: \\nIs the bag near the fence made of plastic or cloth?\\nAnswer the question using a single word or phrase. ASSISTANT: Cloth',\n", + " 'gt_answer': 'cloth'},\n", + " {'question_id': '202258139',\n", + " 'answer': 'USER: \\nIs there any bag near the fence?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '20385517',\n", + " 'answer': 'USER: \\nAre there both remote controls and computer mice in this photograph?\\nAnswer the question using a single word or phrase. ASSISTANT: No',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '2062362',\n", + " 'answer': 'USER: \\nDoes that surf board look hard and blue?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '201735165',\n", + " 'answer': 'USER: \\nIs the wall different in color than the keyboard?\\nAnswer the question using a single word or phrase. ASSISTANT: No',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '20699279',\n", + " 'answer': 'USER: \\nWhat kind of clothing is navy, the jacket or the hat?\\nAnswer the question using a single word or phrase. ASSISTANT: Jacket',\n", + " 'gt_answer': 'jacket'},\n", + " {'question_id': '201959852',\n", + " 'answer': 'USER: \\nIs the airplane near the sign small or large?\\nAnswer the question using a single word or phrase. ASSISTANT: Large',\n", + " 'gt_answer': 'large'},\n", + " {'question_id': '201439380',\n", + " 'answer': 'USER: \\nAre the two people the same gender?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '201757688',\n", + " 'answer': 'USER: \\nAre there any cups in the scene?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '201429017',\n", + " 'answer': 'USER: \\nWhat appliance is to the left of the refrigerator?\\nAnswer the question using a single word or phrase. ASSISTANT: Stove',\n", + " 'gt_answer': 'stove'},\n", + " {'question_id': '202133566',\n", + " 'answer': 'USER: \\nIs the skateboarder above the skatepark wearing knee pads?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '201902557',\n", + " 'answer': 'USER: \\nWhat device is on top of the computer?\\nAnswer the question using a single word or phrase. ASSISTANT: Monitor',\n", + " 'gt_answer': 'router'},\n", + " {'question_id': '202133564',\n", + " 'answer': 'USER: \\nWho is wearing the jeans?\\nAnswer the question using a single word or phrase. ASSISTANT: Man',\n", + " 'gt_answer': 'skateboarder'},\n", + " {'question_id': '202133563',\n", + " 'answer': 'USER: \\nWho is wearing jeans?\\nAnswer the question using a single word or phrase. ASSISTANT: Man',\n", + " 'gt_answer': 'skateboarder'},\n", + " {'question_id': '201902552',\n", + " 'answer': 'USER: \\nWhat device is on top of the computer underneath the desk?\\nAnswer the question using a single word or phrase. ASSISTANT: Speaker',\n", + " 'gt_answer': 'router'},\n", + " {'question_id': '202244266',\n", + " 'answer': 'USER: \\nWhat is the food that is to the right of the cupcakes near the carrots called?\\nAnswer the question using a single word or phrase. ASSISTANT: Rice',\n", + " 'gt_answer': 'rice'},\n", + " {'question_id': '201188320',\n", + " 'answer': 'USER: \\nWho is wearing a jacket?\\nAnswer the question using a single word or phrase. ASSISTANT: Man',\n", + " 'gt_answer': 'man'},\n", + " {'question_id': '201951734',\n", + " 'answer': 'USER: \\nWhich kind of vehicle is old?\\nAnswer the question using a single word or phrase. ASSISTANT: Van',\n", + " 'gt_answer': 'van'},\n", + " {'question_id': '20385778',\n", + " 'answer': 'USER: \\nAre there both umbrellas and bags in the picture?\\nAnswer the question using a single word or phrase. ASSISTANT: No',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '201188325',\n", + " 'answer': 'USER: \\nIs the old person wearing a hat?\\nAnswer the question using a single word or phrase. ASSISTANT: No',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '20462152',\n", + " 'answer': 'USER: \\nWho is wearing trousers?\\nAnswer the question using a single word or phrase. ASSISTANT: Woman',\n", + " 'gt_answer': 'man'},\n", + " {'question_id': '20462153',\n", + " 'answer': 'USER: \\nWho is wearing the pants?\\nAnswer the question using a single word or phrase. ASSISTANT: Woman',\n", + " 'gt_answer': 'man'},\n", + " {'question_id': '20462158',\n", + " 'answer': 'USER: \\nWho is wearing a coat?\\nAnswer the question using a single word or phrase. ASSISTANT: Woman',\n", + " 'gt_answer': 'man'},\n", + " {'question_id': '201804491',\n", + " 'answer': 'USER: \\nIs the fireplace square and black?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '20953087',\n", + " 'answer': 'USER: \\nWho is wearing a polo shirt?\\nAnswer the question using a single word or phrase. ASSISTANT: Man',\n", + " 'gt_answer': 'player'},\n", + " {'question_id': '20891689',\n", + " 'answer': 'USER: \\nThe person that is sitting-down is looking at who?\\nAnswer the question using a single word or phrase. ASSISTANT: Child',\n", + " 'gt_answer': 'child'},\n", + " {'question_id': '20953081',\n", + " 'answer': 'USER: \\nWho is wearing shorts?\\nAnswer the question using a single word or phrase. ASSISTANT: Man',\n", + " 'gt_answer': 'player'},\n", + " {'question_id': '201737879',\n", + " 'answer': 'USER: \\nWhere in the picture is the tall player, on the left or on the right?\\nAnswer the question using a single word or phrase. ASSISTANT: Left',\n", + " 'gt_answer': 'left'},\n", + " {'question_id': '20953088',\n", + " 'answer': 'USER: \\nWho is wearing the polo shirt?\\nAnswer the question using a single word or phrase. ASSISTANT: Man',\n", + " 'gt_answer': 'player'},\n", + " {'question_id': '20827504',\n", + " 'answer': 'USER: \\nWhich kind of furniture is cushioned?\\nAnswer the question using a single word or phrase. ASSISTANT: Couch',\n", + " 'gt_answer': 'chairs'},\n", + " {'question_id': '201322631',\n", + " 'answer': 'USER: \\nOn which side of the picture is the car, the right or the left?\\nAnswer the question using a single word or phrase. ASSISTANT: Left',\n", + " 'gt_answer': 'left'},\n", + " {'question_id': '202174096',\n", + " 'answer': 'USER: \\nWhat color does the appliance the stove is on top of have?\\nAnswer the question using a single word or phrase. ASSISTANT: Black',\n", + " 'gt_answer': 'black'},\n", + " {'question_id': '201935943',\n", + " 'answer': 'USER: \\nIs that a brown shelf?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '20978612',\n", + " 'answer': 'USER: \\nWhat is the fence made of?\\nAnswer the question using a single word or phrase. ASSISTANT: Wood',\n", + " 'gt_answer': 'wood'},\n", + " {'question_id': '20434779',\n", + " 'answer': \"USER: \\nWhat's the chair made of?\\nAnswer the question using a single word or phrase. ASSISTANT: Plastic\",\n", + " 'gt_answer': 'wood'},\n", + " {'question_id': '201556748',\n", + " 'answer': 'USER: \\nWhat device is below the laptop that is not closed?\\nAnswer the question using a single word or phrase. ASSISTANT: Keyboard',\n", + " 'gt_answer': 'keyboard'},\n", + " {'question_id': '201030339',\n", + " 'answer': 'USER: \\nHow big are the stars?\\nAnswer the question using a single word or phrase. ASSISTANT: Small',\n", + " 'gt_answer': 'small'},\n", + " {'question_id': '20434770',\n", + " 'answer': 'USER: \\nIs the chair white and wooden?\\nAnswer the question using a single word or phrase. ASSISTANT: No',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '20827501',\n", + " 'answer': 'USER: \\nWhich kind of furniture is black?\\nAnswer the question using a single word or phrase. ASSISTANT: Bar stool',\n", + " 'gt_answer': 'chairs'},\n", + " {'question_id': '20636816',\n", + " 'answer': 'USER: \\nIs the appliance that is to the left of the cutting board closed or maybe open?\\nAnswer the question using a single word or phrase. ASSISTANT: Open',\n", + " 'gt_answer': 'closed'},\n", + " {'question_id': '201935924',\n", + " 'answer': 'USER: \\nHow does the wood shelf look, large or small?\\nAnswer the question using a single word or phrase. ASSISTANT: Small',\n", + " 'gt_answer': 'small'},\n", + " {'question_id': '20262487',\n", + " 'answer': 'USER: \\nAre there either green boots or skis?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '201185896',\n", + " 'answer': 'USER: \\nWhat is flying above the ground?\\nAnswer the question using a single word or phrase. ASSISTANT: Frisbee',\n", + " 'gt_answer': 'frisbee'},\n", + " {'question_id': '201185893',\n", + " 'answer': 'USER: \\nIs the frisbee in front of the trees?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '201935929',\n", + " 'answer': 'USER: \\nDoes the shelf have brown color and small size?\\nAnswer the question using a single word or phrase. ASSISTANT: No',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '20246006',\n", + " 'answer': 'USER: \\nIs the helmet to the right of a motorcycle?\\nAnswer the question using a single word or phrase. ASSISTANT: No',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '201735422',\n", + " 'answer': 'USER: \\nThe black chicken is standing on top of what?\\nAnswer the question using a single word or phrase. ASSISTANT: Desk',\n", + " 'gt_answer': 'desk'},\n", + " {'question_id': '201982950',\n", + " 'answer': 'USER: \\nIs the coffee table that looks little brown or white?\\nAnswer the question using a single word or phrase. ASSISTANT: Brown',\n", + " 'gt_answer': 'brown'},\n", + " {'question_id': '202023602',\n", + " 'answer': 'USER: \\nWhat is the material of the chair?\\nAnswer the question using a single word or phrase. ASSISTANT: Metal',\n", + " 'gt_answer': 'metal'},\n", + " {'question_id': '202156925',\n", + " 'answer': 'USER: \\nWhat kind of animal is in front of the trees?\\nAnswer the question using a single word or phrase. ASSISTANT: Elephant',\n", + " 'gt_answer': 'elephants'},\n", + " {'question_id': '202156922',\n", + " 'answer': 'USER: \\nWhat animal is in front of the trees?\\nAnswer the question using a single word or phrase. ASSISTANT: Elephant',\n", + " 'gt_answer': 'elephants'},\n", + " {'question_id': '201676219',\n", + " 'answer': 'USER: \\nWhat device is to the right of the shelf?\\nAnswer the question using a single word or phrase. ASSISTANT: Laptop',\n", + " 'gt_answer': 'computer'},\n", + " {'question_id': '202156920',\n", + " 'answer': 'USER: \\nWhat animal is in front of the trees the clouds are above?\\nAnswer the question using a single word or phrase. ASSISTANT: Elephant',\n", + " 'gt_answer': 'elephants'},\n", + " {'question_id': '20785827',\n", + " 'answer': 'USER: \\nWhat is the color of the concrete sidewalk?\\nAnswer the question using a single word or phrase. ASSISTANT: Gray',\n", + " 'gt_answer': 'white'},\n", + " {'question_id': '201804660',\n", + " 'answer': 'USER: \\nWhat is the device below the white artwork called?\\nAnswer the question using a single word or phrase. ASSISTANT: Laptop',\n", + " 'gt_answer': 'television'},\n", + " {'question_id': '201804661',\n", + " 'answer': 'USER: \\nWhat device is below the artwork?\\nAnswer the question using a single word or phrase. ASSISTANT: Speaker',\n", + " 'gt_answer': 'television'},\n", + " {'question_id': '202244695',\n", + " 'answer': 'USER: \\nDoes the bowl that is to the left of the other bowl look white and empty?\\nAnswer the question using a single word or phrase. ASSISTANT: No',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '202179615',\n", + " 'answer': 'USER: \\nIs the man that is not short wearing jeans?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '20827509',\n", + " 'answer': 'USER: \\nWhich side of the image are the chairs on?\\nAnswer the question using a single word or phrase. ASSISTANT: Left',\n", + " 'gt_answer': 'left'},\n", + " {'question_id': '20734057',\n", + " 'answer': 'USER: \\nOn which side are the people?\\nAnswer the question using a single word or phrase. ASSISTANT: Left',\n", + " 'gt_answer': 'left'},\n", + " {'question_id': '20783255',\n", + " 'answer': 'USER: \\nWhich kind of device is to the right of the man?\\nAnswer the question using a single word or phrase. ASSISTANT: Laptop',\n", + " 'gt_answer': 'screen'},\n", + " {'question_id': '20929612',\n", + " 'answer': 'USER: \\nIs the cowboy hat above the jacket near the motorcycle?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '20349965',\n", + " 'answer': 'USER: \\nIs the shirt sleeveless or short sleeved?\\nAnswer the question using a single word or phrase. ASSISTANT: Sleeveless',\n", + " 'gt_answer': 'sleeveless'},\n", + " {'question_id': '20783250',\n", + " 'answer': 'USER: \\nIs the screen in front of a laptop?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'yes'},\n", + " {'question_id': '202012477',\n", + " 'answer': 'USER: \\nWhat do you think is the woman in front of?\\nAnswer the question using a single word or phrase. ASSISTANT: Television',\n", + " 'gt_answer': 'cabinets'},\n", + " {'question_id': '202012476',\n", + " 'answer': 'USER: \\nWho is in front of the cabinets?\\nAnswer the question using a single word or phrase. ASSISTANT: Man',\n", + " 'gt_answer': 'woman'},\n", + " {'question_id': '20862758',\n", + " 'answer': 'USER: \\nAre the people of the same gender?\\nAnswer the question using a single word or phrase. ASSISTANT: No',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '20661463',\n", + " 'answer': 'USER: \\nAre there any bicycles?\\nAnswer the question using a single word or phrase. ASSISTANT: No',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '20262625',\n", + " 'answer': 'USER: \\nWhat is the height of the girl that is holding the kite?\\nAnswer the question using a single word or phrase. ASSISTANT: Tall',\n", + " 'gt_answer': 'tall'},\n", + " {'question_id': '20661461',\n", + " 'answer': 'USER: \\nWhat is located on the edge of the bus that is not narrow?\\nAnswer the question using a single word or phrase. ASSISTANT: Window',\n", + " 'gt_answer': 'door'},\n", + " {'question_id': '201663548',\n", + " 'answer': 'USER: \\nHow clean is the appliance underneath the countertop?\\nAnswer the question using a single word or phrase. ASSISTANT: Clean',\n", + " 'gt_answer': 'clean'},\n", + " {'question_id': '202223163',\n", + " 'answer': 'USER: \\nWho is playing?\\nAnswer the question using a single word or phrase. ASSISTANT: Man',\n", + " 'gt_answer': 'man'},\n", + " {'question_id': '201061187',\n", + " 'answer': 'USER: \\nDoes the orange shirt look sleeveless?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '20226469',\n", + " 'answer': 'USER: \\nThe person near the menu wears what?\\nAnswer the question using a single word or phrase. ASSISTANT: Shirt',\n", + " 'gt_answer': 'shirt'},\n", + " {'question_id': '20518474',\n", + " 'answer': 'USER: \\nIs the shower near the mirror dirty and large?\\nAnswer the question using a single word or phrase. ASSISTANT: No',\n", + " 'gt_answer': 'no'},\n", + " {'question_id': '202023469',\n", + " 'answer': 'USER: \\nWhich kind of furniture is in front of the bookshelf?\\nAnswer the question using a single word or phrase. ASSISTANT: Bed',\n", + " 'gt_answer': 'bed'},\n", + " {'question_id': '202007027',\n", + " 'answer': 'USER: \\nWhat kind of furniture is the kitchen decorated by?\\nAnswer the question using a single word or phrase. ASSISTANT: Cabinets',\n", + " 'gt_answer': 'cabinet'},\n", + " {'question_id': '202007024',\n", + " 'answer': \"USER: \\nWhat's the kitchen decorated by?\\nAnswer the question using a single word or phrase. ASSISTANT: Tiles\",\n", + " 'gt_answer': 'cabinet'},\n", + " ...]" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "inferencer = InferencePipeline(model, device, processor)\n", + "\n", + "# set this according to huggingface usage tips: https://huggingface.co/docs/transformers/en/model_doc/llava\n", + "processor.tokenizer.padding_side = \"left\"\n", + "processor_kwargs = dict(padding=True)\n", + "\n", + "# greedy decoding\n", + "# generate_kwargs = {\n", + "# 'num_beams': 1,\n", + "# 'do_sample': False\n", + "# }\n", + "\n", + "results = inferencer.run_inference(\n", + " dataloader,\n", + " task = 'gqa',\n", + " processor_kwargs = processor_kwargs,\n", + " generate_kwargs = None\n", + ")\n", + "\n", + "results" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "072a5290", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Adding current path to python system paths\n", + "{'agg_metrics': 61.47, 'acc': 61.47}\n" + ] + } + ], + "source": [ + "from scoring_pipeline import ScoringPipeline\n", + "\n", + "for res in results:\n", + " res['answer'] = res['answer'].split('ASSISTANT: ')[-1]\n", + "\n", + "def compute_gqa_results(results, scorer, save_path=None):\n", + " gqa_results = scorer.compute_scores(results, \"gqa\")\n", + " print(gqa_results)\n", + "# if save_path:\n", + "# with open(save_path, \"w\") as f:\n", + "# json.dump(gqa_results, f)\n", + "\n", + "scorer = ScoringPipeline()\n", + "compute_gqa_results(results, scorer)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/llava_runs/llava_scoring.ipynb b/llava_runs/llava_scoring.ipynb new file mode 100644 index 0000000..fec5ebd --- /dev/null +++ b/llava_runs/llava_scoring.ipynb @@ -0,0 +1,3202 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 8, + "id": "b9b04667", + "metadata": {}, + "outputs": [], + "source": [ + "import sys\n", + "sys.path.append('..')\n", + "from scoring_pipeline import ScoringPipeline\n", + "import json\n", + "import os\n", + "import torch\n", + "import pandas as pd\n", + "from tqdm import tqdm" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "7f611e0c", + "metadata": {}, + "outputs": [], + "source": [ + "def compute_scores(results_dir, task):\n", + "\n", + " scorer = ScoringPipeline()\n", + "\n", + " gather = []\n", + " for results_file in tqdm(os.listdir(results_dir)):\n", + "\n", + " results_path = os.path.join(results_dir, results_file)\n", + "\n", + " with open(results_path, 'r') as f:\n", + " results = json.load(f)\n", + " \n", + " # post-processing llava output\n", + " answers = results['answers']\n", + " for ans in answers:\n", + " ans['answer'] = ans['answer'].split('ASSISTANT: ')[-1]\n", + "\n", + " \n", + " if task == 'vqav2':\n", + " ann_root = '/fs/cfar-projects/low-bit-vision/datasets/vqav2/annotations'\n", + " q_root = '/fs/cfar-projects/low-bit-vision/datasets/vqav2/questions'\n", + "\n", + " # results[\"answers\"] = answers\n", + " results[\"annotations\"] = os.path.join(ann_root, \"v2_mscoco_val2014_annotations.json\")\n", + " results[\"questions\"] = os.path.join(q_root, \"v2_OpenEnded_mscoco_val2014_questions.json\")\n", + "\n", + " score = scorer.compute_scores(results, task)\n", + " # print(score)\n", + "\n", + " record = dict(\n", + " vision_bits = results['vision_bits'],\n", + " language_bits = results['language_bits'],\n", + " )\n", + "\n", + " record.update(score)\n", + "\n", + " print(record)\n", + " break\n", + "\n", + " elif task == 'gqa':\n", + " score = scorer.compute_scores(answers, task)['acc']\n", + " \n", + " record = dict(\n", + " vision_bits = results['vision_bits'],\n", + " language_bits = results['language_bits'],\n", + " acc = score\n", + " )\n", + "\n", + " gather.append(record)\n", + " \n", + " return pd.DataFrame(gather)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "0dd4d8be", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[{'question_id': '201307251', 'answer': 'No', 'gt_answer': 'no'},\n", + " {'question_id': '201640614', 'answer': 'Lady', 'gt_answer': 'women'},\n", + " {'question_id': '202225914', 'answer': 'No', 'gt_answer': 'no'},\n", + " {'question_id': '2062325', 'answer': 'Yes', 'gt_answer': 'yes'},\n", + " {'question_id': '201303229', 'answer': 'Tall', 'gt_answer': 'short'},\n", + " {'question_id': '201902997', 'answer': 'Computer', 'gt_answer': 'keyboard'},\n", + " {'question_id': '20567512', 'answer': 'Beach', 'gt_answer': 'ocean'},\n", + " {'question_id': '20136592', 'answer': 'Red', 'gt_answer': 'red'},\n", + " {'question_id': '20602803', 'answer': 'Brown', 'gt_answer': 'brown'},\n", + " {'question_id': '201079951', 'answer': 'Curtain', 'gt_answer': 'drapes'},\n", + " {'question_id': '201079952', 'answer': 'Curtains', 'gt_answer': 'drapes'},\n", + " {'question_id': '20982537', 'answer': 'People', 'gt_answer': 'woman'},\n", + " {'question_id': '201079958', 'answer': 'Yes', 'gt_answer': 'yes'},\n", + " {'question_id': '202218649', 'answer': 'Picture', 'gt_answer': 'picture'},\n", + " {'question_id': '20609782', 'answer': 'Yes', 'gt_answer': 'no'},\n", + " {'question_id': '201757757', 'answer': 'Yes', 'gt_answer': 'yes'},\n", + " {'question_id': '201902993', 'answer': 'Keyboard', 'gt_answer': 'keyboard'},\n", + " {'question_id': '20306193', 'answer': 'Yes', 'gt_answer': 'yes'},\n", + " {'question_id': '20183468', 'answer': 'Yes', 'gt_answer': 'yes'},\n", + " {'question_id': '20753400', 'answer': 'Yes', 'gt_answer': 'yes'},\n", + " {'question_id': '20151723', 'answer': 'Left', 'gt_answer': 'right'},\n", + " {'question_id': '201030735', 'answer': 'No', 'gt_answer': 'no'},\n", + " {'question_id': '201637161', 'answer': 'White', 'gt_answer': 'white'},\n", + " {'question_id': '202218839', 'answer': 'Pan', 'gt_answer': 'pan'},\n", + " {'question_id': '20982539', 'answer': 'Left', 'gt_answer': 'table'},\n", + " {'question_id': '201110833', 'answer': 'No', 'gt_answer': 'no'},\n", + " {'question_id': '20866249', 'answer': 'Large', 'gt_answer': 'large'},\n", + " {'question_id': '201110525', 'answer': 'Cake', 'gt_answer': 'marshmallow'},\n", + " {'question_id': '20120533', 'answer': 'Aluminum', 'gt_answer': 'aluminum'},\n", + " {'question_id': '201952977', 'answer': 'Left', 'gt_answer': 'left'},\n", + " {'question_id': '201497576', 'answer': 'No', 'gt_answer': 'yes'},\n", + " {'question_id': '20866242', 'answer': 'No', 'gt_answer': 'no'},\n", + " {'question_id': '201455911', 'answer': 'White', 'gt_answer': 'white'},\n", + " {'question_id': '20856965', 'answer': 'Black', 'gt_answer': 'white'},\n", + " {'question_id': '2059565', 'answer': 'Red', 'gt_answer': 'red'},\n", + " {'question_id': '20856960', 'answer': 'No', 'gt_answer': 'no'},\n", + " {'question_id': '201079954', 'answer': 'Window', 'gt_answer': 'window'},\n", + " {'question_id': '201548894', 'answer': 'Left', 'gt_answer': 'left'},\n", + " {'question_id': '201573912', 'answer': 'Concrete', 'gt_answer': 'concrete'},\n", + " {'question_id': '202243820', 'answer': 'Small', 'gt_answer': 'small'},\n", + " {'question_id': '201573918', 'answer': 'Concrete', 'gt_answer': 'concrete'},\n", + " {'question_id': '201974972', 'answer': 'Shirt', 'gt_answer': 'tank top'},\n", + " {'question_id': '201974971', 'answer': 'Shirt', 'gt_answer': 'tank top'},\n", + " {'question_id': '201974976', 'answer': 'Cap', 'gt_answer': 'hat'},\n", + " {'question_id': '201996743', 'answer': 'Off', 'gt_answer': 'off'},\n", + " {'question_id': '20797666', 'answer': 'Yes', 'gt_answer': 'yes'},\n", + " {'question_id': '20797665', 'answer': 'No', 'gt_answer': 'no'},\n", + " {'question_id': '201974979', 'answer': 'Tank top', 'gt_answer': 'tank top'},\n", + " {'question_id': '201156138', 'answer': 'No', 'gt_answer': 'no'},\n", + " {'question_id': '20442334', 'answer': 'Bananas', 'gt_answer': 'bananas'},\n", + " {'question_id': '201765651', 'answer': 'White', 'gt_answer': 'white'},\n", + " {'question_id': '20442331', 'answer': 'Counter', 'gt_answer': 'bananas'},\n", + " {'question_id': '20508243', 'answer': 'Right', 'gt_answer': 'right'},\n", + " {'question_id': '2046473', 'answer': 'Left', 'gt_answer': 'right'},\n", + " {'question_id': '20618932', 'answer': 'Girl', 'gt_answer': 'woman'},\n", + " {'question_id': '20442338', 'answer': 'No', 'gt_answer': 'no'},\n", + " {'question_id': '202005788', 'answer': 'Cabinet', 'gt_answer': 'cabinet'},\n", + " {'question_id': '201902515', 'answer': 'Left', 'gt_answer': 'left'},\n", + " {'question_id': '201303404', 'answer': 'Gray', 'gt_answer': 'gray'},\n", + " {'question_id': '20942157', 'answer': 'Woman', 'gt_answer': 'girl'},\n", + " {'question_id': '20942156', 'answer': 'Woman', 'gt_answer': 'girl'},\n", + " {'question_id': '20898685', 'answer': 'Waiting', 'gt_answer': 'standing'},\n", + " {'question_id': '202116974', 'answer': 'No', 'gt_answer': 'no'},\n", + " {'question_id': '201621328', 'answer': 'Wall', 'gt_answer': 'wall'},\n", + " {'question_id': '2076819', 'answer': 'Dense', 'gt_answer': 'dense'},\n", + " {'question_id': '202244099', 'answer': 'Beans', 'gt_answer': 'cookies'},\n", + " {'question_id': '201951771', 'answer': 'Truck', 'gt_answer': 'van'},\n", + " {'question_id': '201951770', 'answer': 'Van', 'gt_answer': 'van'},\n", + " {'question_id': '201621326',\n", + " 'answer': 'Picture',\n", + " 'gt_answer': 'picture frame'},\n", + " {'question_id': '201233862', 'answer': 'Ramp', 'gt_answer': 'pavement'},\n", + " {'question_id': '201951776', 'answer': 'Yes', 'gt_answer': 'no'},\n", + " {'question_id': '20489632', 'answer': 'Brown', 'gt_answer': 'beige'},\n", + " {'question_id': '201623784', 'answer': 'Yes', 'gt_answer': 'no'},\n", + " {'question_id': '202023424', 'answer': 'Bed', 'gt_answer': 'bed'},\n", + " {'question_id': '20182936', 'answer': 'No', 'gt_answer': 'yes'},\n", + " {'question_id': '201654344', 'answer': 'Yes', 'gt_answer': 'no'},\n", + " {'question_id': '20746468', 'answer': 'Narrow', 'gt_answer': 'narrow'},\n", + " {'question_id': '201428996', 'answer': 'Stove', 'gt_answer': 'stove'},\n", + " {'question_id': '20899362', 'answer': 'No', 'gt_answer': 'no'},\n", + " {'question_id': '202244009', 'answer': 'Cookie', 'gt_answer': 'cookies'},\n", + " {'question_id': '20287556', 'answer': 'Dirty', 'gt_answer': 'clean'},\n", + " {'question_id': '20631973', 'answer': 'Home plate', 'gt_answer': 'field'},\n", + " {'question_id': '20287551', 'answer': 'Black', 'gt_answer': 'black'},\n", + " {'question_id': '201481824', 'answer': 'Yes', 'gt_answer': 'yes'},\n", + " {'question_id': '201663656', 'answer': 'Brown', 'gt_answer': 'light brown'},\n", + " {'question_id': '20308576', 'answer': 'Yes', 'gt_answer': 'yes'},\n", + " {'question_id': '201065067', 'answer': 'Dress', 'gt_answer': 'gown'},\n", + " {'question_id': '20462070', 'answer': 'No', 'gt_answer': 'no'},\n", + " {'question_id': '20462076', 'answer': 'Pants', 'gt_answer': 'gloves'},\n", + " {'question_id': '20462075', 'answer': 'Skis', 'gt_answer': 'gloves'},\n", + " {'question_id': '201065062', 'answer': 'Dress', 'gt_answer': 'gown'},\n", + " {'question_id': '20754631', 'answer': 'Stairs', 'gt_answer': 'stairs'},\n", + " {'question_id': '201935960', 'answer': 'Shelf', 'gt_answer': 'shelf'},\n", + " {'question_id': '20412222', 'answer': 'Tables', 'gt_answer': 'tables'},\n", + " {'question_id': '201935966', 'answer': 'Bookshelf', 'gt_answer': 'shelf'},\n", + " {'question_id': '20878946', 'answer': 'Narrow', 'gt_answer': 'wide'},\n", + " {'question_id': '201947446', 'answer': 'Left', 'gt_answer': 'left'},\n", + " {'question_id': '201498767', 'answer': 'Keyboard', 'gt_answer': 'phone'},\n", + " {'question_id': '20306764', 'answer': 'Snowboard', 'gt_answer': 'gift'},\n", + " {'question_id': '202144708', 'answer': 'Yes', 'gt_answer': 'yes'},\n", + " {'question_id': '20306767', 'answer': 'Yes', 'gt_answer': 'yes'},\n", + " {'question_id': '201996815', 'answer': 'Glass', 'gt_answer': 'glass'},\n", + " {'question_id': '201996813', 'answer': 'Plastic', 'gt_answer': 'glass'},\n", + " {'question_id': '202060122', 'answer': 'Dog', 'gt_answer': 'dog'},\n", + " {'question_id': '201067797', 'answer': 'Silver', 'gt_answer': 'black'},\n", + " {'question_id': '20394919', 'answer': 'No', 'gt_answer': 'no'},\n", + " {'question_id': '201497916', 'answer': 'Monitor', 'gt_answer': 'monitor'},\n", + " {'question_id': '20303081', 'answer': 'Sitting', 'gt_answer': 'resting'},\n", + " {'question_id': '201498727', 'answer': 'Plastic', 'gt_answer': 'plastic'},\n", + " {'question_id': '201873473', 'answer': 'Yes', 'gt_answer': 'no'},\n", + " {'question_id': '20300425', 'answer': 'Car', 'gt_answer': 'cars'},\n", + " {'question_id': '20300424', 'answer': 'Car', 'gt_answer': 'cars'},\n", + " {'question_id': '20899558', 'answer': 'Silver', 'gt_answer': 'blue'},\n", + " {'question_id': '20300420', 'answer': 'Cars', 'gt_answer': 'cars'},\n", + " {'question_id': '20300423', 'answer': 'Car', 'gt_answer': 'cars'},\n", + " {'question_id': '20836565', 'answer': 'Luggage', 'gt_answer': 'luggage'},\n", + " {'question_id': '201947624', 'answer': 'No', 'gt_answer': 'no'},\n", + " {'question_id': '201947620', 'answer': 'No', 'gt_answer': 'yes'},\n", + " {'question_id': '20300428',\n", + " 'answer': 'Traffic light',\n", + " 'gt_answer': 'traffic light'},\n", + " {'question_id': '201504960', 'answer': 'No', 'gt_answer': 'no'},\n", + " {'question_id': '201947591', 'answer': 'Counter', 'gt_answer': 'countertop'},\n", + " {'question_id': '20177575', 'answer': 'Silver', 'gt_answer': 'white'},\n", + " {'question_id': '20381557', 'answer': 'No', 'gt_answer': 'no'},\n", + " {'question_id': '201982068', 'answer': 'Cloth', 'gt_answer': 'cloth'},\n", + " {'question_id': '201370428',\n", + " 'answer': 'Colorful',\n", + " 'gt_answer': 'black and white'},\n", + " {'question_id': '201878325', 'answer': 'Woman', 'gt_answer': 'man'},\n", + " {'question_id': '201370422', 'answer': 'Right', 'gt_answer': 'right'},\n", + " {'question_id': '2075709', 'answer': 'No', 'gt_answer': 'yes'},\n", + " {'question_id': '201896034', 'answer': 'Chair', 'gt_answer': 'table'},\n", + " {'question_id': '201065497', 'answer': 'Brunette', 'gt_answer': 'blond'},\n", + " {'question_id': '20857175', 'answer': 'No', 'gt_answer': 'no'},\n", + " {'question_id': '20648122', 'answer': 'Yes', 'gt_answer': 'yes'},\n", + " {'question_id': '20636999', 'answer': 'No', 'gt_answer': 'no'},\n", + " {'question_id': '20258752', 'answer': 'Boy', 'gt_answer': 'child'},\n", + " {'question_id': '201156466', 'answer': 'Yes', 'gt_answer': 'yes'},\n", + " {'question_id': '201885557', 'answer': 'Male', 'gt_answer': 'male'},\n", + " {'question_id': '202081210', 'answer': 'No', 'gt_answer': 'no'},\n", + " {'question_id': '20117834', 'answer': 'Bare', 'gt_answer': 'lush'},\n", + " {'question_id': '201438286', 'answer': 'Yes', 'gt_answer': 'yes'},\n", + " {'question_id': '20117781', 'answer': 'Yes', 'gt_answer': 'yes'},\n", + " {'question_id': '201756501', 'answer': 'Color', 'gt_answer': 'color'},\n", + " {'question_id': '20716925', 'answer': 'Small', 'gt_answer': 'small'},\n", + " {'question_id': '20541270', 'answer': 'Left', 'gt_answer': 'right'},\n", + " {'question_id': '201056079', 'answer': 'Female', 'gt_answer': 'male'},\n", + " {'question_id': '20468617', 'answer': 'No', 'gt_answer': 'no'},\n", + " {'question_id': '2017235', 'answer': 'Black', 'gt_answer': 'black'},\n", + " {'question_id': '20427913', 'answer': 'Chair', 'gt_answer': 'chair'},\n", + " {'question_id': '20427912', 'answer': 'Chair', 'gt_answer': 'chair'},\n", + " {'question_id': '201480278', 'answer': 'No', 'gt_answer': 'no'},\n", + " {'question_id': '201056072', 'answer': 'Boy', 'gt_answer': 'soccer player'},\n", + " {'question_id': '20887449', 'answer': 'Computer', 'gt_answer': 'keyboard'},\n", + " {'question_id': '20648218', 'answer': 'Man', 'gt_answer': 'policeman'},\n", + " {'question_id': '202102931', 'answer': 'Cabinet', 'gt_answer': 'dishwasher'},\n", + " {'question_id': '201047479', 'answer': 'Teal', 'gt_answer': 'teal'},\n", + " {'question_id': '201370398', 'answer': 'Black', 'gt_answer': 'gray'},\n", + " {'question_id': '20672944', 'answer': 'Left', 'gt_answer': 'left'},\n", + " {'question_id': '201752690', 'answer': 'Bike', 'gt_answer': 'bike'},\n", + " {'question_id': '20672940', 'answer': 'No', 'gt_answer': 'no'},\n", + " {'question_id': '201752694', 'answer': 'Yes', 'gt_answer': 'yes'},\n", + " {'question_id': '20699276', 'answer': 'Jacket', 'gt_answer': 'jacket'},\n", + " {'question_id': '2097681', 'answer': 'Monitor', 'gt_answer': 'monitor'},\n", + " {'question_id': '201760591', 'answer': 'No', 'gt_answer': 'no'},\n", + " {'question_id': '201110526', 'answer': 'Dessert', 'gt_answer': 'marshmallow'},\n", + " {'question_id': '20673099', 'answer': 'Chair', 'gt_answer': 'chair'},\n", + " {'question_id': '20673098', 'answer': 'Chair', 'gt_answer': 'chair'},\n", + " {'question_id': '20361249', 'answer': 'No', 'gt_answer': 'no'},\n", + " {'question_id': '201480696',\n", + " 'answer': 'Rectangle',\n", + " 'gt_answer': 'rectangular'},\n", + " {'question_id': '201879167', 'answer': 'Boat', 'gt_answer': 'sneakers'},\n", + " {'question_id': '201438759', 'answer': 'Yes', 'gt_answer': 'yes'},\n", + " {'question_id': '20295599', 'answer': 'No', 'gt_answer': 'yes'},\n", + " {'question_id': '20204868', 'answer': 'No', 'gt_answer': 'no'},\n", + " {'question_id': '20361243', 'answer': 'Woman', 'gt_answer': 'snowboarder'},\n", + " {'question_id': '20667494', 'answer': 'Table', 'gt_answer': 'coffee table'},\n", + " {'question_id': '20667492',\n", + " 'answer': 'Coffee table',\n", + " 'gt_answer': 'coffee table'},\n", + " {'question_id': '20667493',\n", + " 'answer': 'Coffee table',\n", + " 'gt_answer': 'coffee table'},\n", + " {'question_id': '201056254', 'answer': 'Man', 'gt_answer': 'spectator'},\n", + " {'question_id': '201064816', 'answer': 'Chair', 'gt_answer': 'sofa'},\n", + " {'question_id': '2097684', 'answer': 'Speaker', 'gt_answer': 'poster'},\n", + " {'question_id': '201064812', 'answer': 'Sofa', 'gt_answer': 'sofa'},\n", + " {'question_id': '201056252', 'answer': 'Watching', 'gt_answer': 'looking up'},\n", + " {'question_id': '201064810', 'answer': 'Chair', 'gt_answer': 'sofa'},\n", + " {'question_id': '201935799', 'answer': 'Shelf', 'gt_answer': 'shelf'},\n", + " {'question_id': '20756897', 'answer': 'Dress', 'gt_answer': 'robe'},\n", + " {'question_id': '201065430', 'answer': 'Right', 'gt_answer': 'right'},\n", + " {'question_id': '202243368', 'answer': 'White', 'gt_answer': 'white'},\n", + " {'question_id': '202121334', 'answer': 'No', 'gt_answer': 'yes'},\n", + " {'question_id': '201935797', 'answer': 'Jar', 'gt_answer': 'jar'},\n", + " {'question_id': '201639189', 'answer': 'No', 'gt_answer': 'no'},\n", + " {'question_id': '20741279', 'answer': '6 feet', 'gt_answer': 'tall'},\n", + " {'question_id': '201143145', 'answer': 'Brown', 'gt_answer': 'dark brown'},\n", + " {'question_id': '201669504', 'answer': 'Yes', 'gt_answer': 'yes'},\n", + " {'question_id': '201763810', 'answer': 'No', 'gt_answer': 'no'},\n", + " {'question_id': '202119900',\n", + " 'answer': 'Refrigerator',\n", + " 'gt_answer': 'refrigerator'},\n", + " {'question_id': '202119903', 'answer': 'Yes', 'gt_answer': 'yes'},\n", + " {'question_id': '20340771', 'answer': 'Table', 'gt_answer': 'chair'},\n", + " {'question_id': '20340770', 'answer': 'Table', 'gt_answer': 'chair'},\n", + " {'question_id': '20285405', 'answer': 'Clean', 'gt_answer': 'clean'},\n", + " {'question_id': '20340772', 'answer': 'Table', 'gt_answer': 'chair'},\n", + " {'question_id': '201593445', 'answer': 'Cow', 'gt_answer': 'cow'},\n", + " {'question_id': '201347404', 'answer': 'Boy', 'gt_answer': 'skateboarder'},\n", + " {'question_id': '202100755', 'answer': 'Yes', 'gt_answer': 'yes'},\n", + " {'question_id': '201462472', 'answer': 'Left', 'gt_answer': 'left'},\n", + " {'question_id': '201887286', 'answer': 'Broccoli', 'gt_answer': 'broccoli'},\n", + " {'question_id': '20518589', 'answer': 'Counter', 'gt_answer': 'countertop'},\n", + " {'question_id': '201590142', 'answer': 'Yes', 'gt_answer': 'yes'},\n", + " {'question_id': '20341130', 'answer': 'No', 'gt_answer': 'no'},\n", + " {'question_id': '201795286', 'answer': 'Yes', 'gt_answer': 'yes'},\n", + " {'question_id': '201832545', 'answer': 'No', 'gt_answer': 'no'},\n", + " {'question_id': '202082102',\n", + " 'answer': 'Computer mouse',\n", + " 'gt_answer': 'laptop'},\n", + " {'question_id': '20645705', 'answer': 'No', 'gt_answer': 'no'},\n", + " {'question_id': '201795846', 'answer': 'Yes', 'gt_answer': 'no'},\n", + " {'question_id': '201879789', 'answer': 'Large', 'gt_answer': 'large'},\n", + " {'question_id': '201143364', 'answer': 'Flowers', 'gt_answer': 'flowers'},\n", + " {'question_id': '20827171', 'answer': 'Yes', 'gt_answer': 'no'},\n", + " {'question_id': '20940166', 'answer': 'No', 'gt_answer': 'no'},\n", + " {'question_id': '201859351', 'answer': 'Plastic', 'gt_answer': 'plastic'},\n", + " {'question_id': '201595841', 'answer': 'Yes', 'gt_answer': 'yes'},\n", + " {'question_id': '20923252', 'answer': 'Truck', 'gt_answer': 'ambulance'},\n", + " {'question_id': '202243438', 'answer': 'Truck', 'gt_answer': 'truck'},\n", + " {'question_id': '20923257', 'answer': 'Yes', 'gt_answer': 'yes'},\n", + " {'question_id': '20923256', 'answer': 'Truck', 'gt_answer': 'ambulance'},\n", + " {'question_id': '201976414', 'answer': 'No', 'gt_answer': 'no'},\n", + " {'question_id': '20865499', 'answer': 'Yes', 'gt_answer': 'no'},\n", + " {'question_id': '20600137', 'answer': 'Grass', 'gt_answer': 'grass'},\n", + " {'question_id': '20600132', 'answer': 'Grass', 'gt_answer': 'grass'},\n", + " {'question_id': '20836758', 'answer': 'No', 'gt_answer': 'yes'},\n", + " {'question_id': '20632010', 'answer': 'Yes', 'gt_answer': 'yes'},\n", + " {'question_id': '201640606', 'answer': 'Woman', 'gt_answer': 'women'},\n", + " {'question_id': '201640605', 'answer': 'Woman', 'gt_answer': 'women'},\n", + " {'question_id': '201640602',\n", + " 'answer': 'Restaurant',\n", + " 'gt_answer': 'restaurant'},\n", + " {'question_id': '20306515', 'answer': 'Camera', 'gt_answer': 'cell phone'},\n", + " {'question_id': '202228132', 'answer': 'Speaker', 'gt_answer': 'speaker'},\n", + " {'question_id': '20692296', 'answer': 'Book', 'gt_answer': 'books'},\n", + " {'question_id': '20692294', 'answer': 'Left', 'gt_answer': 'left'},\n", + " {'question_id': '20710151', 'answer': 'No', 'gt_answer': 'no'},\n", + " {'question_id': '202262373', 'answer': 'No', 'gt_answer': 'no'},\n", + " {'question_id': '20679393', 'answer': 'No', 'gt_answer': 'no'},\n", + " {'question_id': '20710154', 'answer': 'No', 'gt_answer': 'no'},\n", + " {'question_id': '202243431', 'answer': 'Truck', 'gt_answer': 'truck'},\n", + " {'question_id': '201556497', 'answer': 'Chair', 'gt_answer': 'shelf'},\n", + " {'question_id': '201556499', 'answer': 'Chair', 'gt_answer': 'shelf'},\n", + " {'question_id': '20177492', 'answer': 'No', 'gt_answer': 'no'},\n", + " {'question_id': '20711540',\n", + " 'answer': 'Teddy bear',\n", + " 'gt_answer': 'stuffed bear'},\n", + " {'question_id': '20899315', 'answer': 'Plastic', 'gt_answer': 'plastic'},\n", + " {'question_id': '20711546',\n", + " 'answer': 'Teddy bear',\n", + " 'gt_answer': 'stuffed bear'},\n", + " {'question_id': '201624174', 'answer': 'Pizza', 'gt_answer': 'pizza'},\n", + " {'question_id': '201997192', 'answer': 'Yes', 'gt_answer': 'yes'},\n", + " {'question_id': '20866524', 'answer': 'Yes', 'gt_answer': 'no'},\n", + " {'question_id': '20866526', 'answer': 'Light', 'gt_answer': 'light'},\n", + " {'question_id': '20866521', 'answer': 'Right', 'gt_answer': 'right'},\n", + " {'question_id': '201510327', 'answer': 'Apple', 'gt_answer': 'pear'},\n", + " {'question_id': '201492240', 'answer': 'Glove', 'gt_answer': 'baseball mitt'},\n", + " {'question_id': '20691652', 'answer': 'Black', 'gt_answer': 'black'},\n", + " {'question_id': '202144423', 'answer': 'Brown', 'gt_answer': 'brown'},\n", + " {'question_id': '20836578', 'answer': 'Table', 'gt_answer': 'table'},\n", + " {'question_id': '20349798', 'answer': 'Girl', 'gt_answer': 'woman'},\n", + " {'question_id': '201663481', 'answer': 'Yes', 'gt_answer': 'yes'},\n", + " {'question_id': '20692079', 'answer': 'Large', 'gt_answer': 'small'},\n", + " {'question_id': '201030507',\n", + " 'answer': 'Long sleeved',\n", + " 'gt_answer': 'long sleeved'},\n", + " {'question_id': '201997611', 'answer': 'Yes', 'gt_answer': 'no'},\n", + " {'question_id': '201153290', 'answer': 'Man', 'gt_answer': 'woman'},\n", + " {'question_id': '201983816', 'answer': 'Yes', 'gt_answer': 'no'},\n", + " {'question_id': '201153292', 'answer': 'Giraffe', 'gt_answer': 'giraffe'},\n", + " {'question_id': '201153293', 'answer': 'Giraffe', 'gt_answer': 'giraffe'},\n", + " {'question_id': '201153297', 'answer': 'Giraffe', 'gt_answer': 'giraffe'},\n", + " {'question_id': '20645858', 'answer': 'Green', 'gt_answer': 'white'},\n", + " {'question_id': '20441903', 'answer': 'Yes', 'gt_answer': 'yes'},\n", + " {'question_id': '201570581', 'answer': 'Yes', 'gt_answer': 'yes'},\n", + " {'question_id': '201535714', 'answer': 'Jacket', 'gt_answer': 'coat'},\n", + " {'question_id': '20652278', 'answer': 'Color', 'gt_answer': 'shape'},\n", + " {'question_id': '201535713', 'answer': 'Jacket', 'gt_answer': 'coat'},\n", + " {'question_id': '201111170', 'answer': 'Brown', 'gt_answer': 'brown'},\n", + " {'question_id': '20891561', 'answer': 'No', 'gt_answer': 'yes'},\n", + " {'question_id': '20891560', 'answer': 'Shirt', 'gt_answer': 'shorts'},\n", + " {'question_id': '20503737', 'answer': 'Yes', 'gt_answer': 'no'},\n", + " {'question_id': '20883191', 'answer': 'Yes', 'gt_answer': 'yes'},\n", + " {'question_id': '20503730', 'answer': 'White', 'gt_answer': 'purple'},\n", + " {'question_id': '201974600', 'answer': 'Pink', 'gt_answer': 'dark blue'},\n", + " {'question_id': '201972712', 'answer': 'Blue', 'gt_answer': 'blue'},\n", + " {'question_id': '20783517', 'answer': 'No', 'gt_answer': 'no'},\n", + " {'question_id': '20797833', 'answer': 'Plant', 'gt_answer': 'tree'},\n", + " {'question_id': '20797830', 'answer': 'People', 'gt_answer': 'man'},\n", + " {'question_id': '20342305', 'answer': 'No', 'gt_answer': 'no'},\n", + " {'question_id': '20783519', 'answer': 'Chair', 'gt_answer': 'chair'},\n", + " {'question_id': '20797834', 'answer': 'Plant', 'gt_answer': 'tree'},\n", + " {'question_id': '2053782', 'answer': 'Concrete', 'gt_answer': 'concrete'},\n", + " {'question_id': '202106445', 'answer': 'No', 'gt_answer': 'yes'},\n", + " {'question_id': '201401744', 'answer': 'No', 'gt_answer': 'no'},\n", + " {'question_id': '20536241', 'answer': 'Heavy', 'gt_answer': 'heavy'},\n", + " {'question_id': '2053786', 'answer': 'Yes', 'gt_answer': 'yes'},\n", + " {'question_id': '20899760', 'answer': 'Laptop', 'gt_answer': 'laptop'},\n", + " {'question_id': '20899763', 'answer': 'No', 'gt_answer': 'no'},\n", + " {'question_id': '201621812', 'answer': 'Speaker', 'gt_answer': 'speaker'},\n", + " {'question_id': '20536249', 'answer': 'Eating', 'gt_answer': 'looking down'},\n", + " {'question_id': '20899769', 'answer': 'Laptop', 'gt_answer': 'laptop'},\n", + " {'question_id': '20306372', 'answer': 'Camera', 'gt_answer': 'camera'},\n", + " {'question_id': '20306370', 'answer': 'Camera', 'gt_answer': 'camera'},\n", + " {'question_id': '20866380', 'answer': 'Gray', 'gt_answer': 'gray'},\n", + " {'question_id': '20473110', 'answer': 'Yes', 'gt_answer': 'yes'},\n", + " {'question_id': '201467402', 'answer': 'Color', 'gt_answer': 'color'},\n", + " {'question_id': '20518336', 'answer': 'Heater', 'gt_answer': 'radiator'},\n", + " {'question_id': '20518337', 'answer': 'Heater', 'gt_answer': 'radiator'},\n", + " {'question_id': '20518334', 'answer': 'Trash can', 'gt_answer': 'radiator'},\n", + " {'question_id': '20518335', 'answer': 'Trash can', 'gt_answer': 'radiator'},\n", + " {'question_id': '201759317', 'answer': 'Glass', 'gt_answer': 'glass'},\n", + " {'question_id': '20518339', 'answer': 'No', 'gt_answer': 'no'},\n", + " {'question_id': '201952898', 'answer': 'Train', 'gt_answer': 'car'},\n", + " {'question_id': '20480525', 'answer': 'Yes', 'gt_answer': 'no'},\n", + " {'question_id': '202053173', 'answer': 'Batter', 'gt_answer': 'umpire'},\n", + " {'question_id': '20183255', 'answer': 'Bench', 'gt_answer': 'steps'},\n", + " {'question_id': '20797661', 'answer': 'Cat', 'gt_answer': 'cat'},\n", + " {'question_id': '201548930', 'answer': 'Blender', 'gt_answer': 'picture'},\n", + " {'question_id': '20157379', 'answer': 'Table', 'gt_answer': 'table'},\n", + " {'question_id': '20257105', 'answer': 'Yes', 'gt_answer': 'no'},\n", + " {'question_id': '20489405', 'answer': 'No', 'gt_answer': 'no'},\n", + " {'question_id': '20709846', 'answer': 'Left', 'gt_answer': 'left'},\n", + " {'question_id': '20754796', 'answer': 'No', 'gt_answer': 'no'},\n", + " {'question_id': '202169340', 'answer': 'Yes', 'gt_answer': 'yes'},\n", + " {'question_id': '20963807', 'answer': 'Sink', 'gt_answer': 'faucet'},\n", + " {'question_id': '2053569', 'answer': 'Yes', 'gt_answer': 'yes'},\n", + " {'question_id': '20941978', 'answer': 'Yes', 'gt_answer': 'yes'},\n", + " {'question_id': '20667405',\n", + " 'answer': 'Remote control',\n", + " 'gt_answer': 'wii controller'},\n", + " {'question_id': '202156967', 'answer': 'Brown', 'gt_answer': 'brown'},\n", + " {'question_id': '20757114', 'answer': 'Yes', 'gt_answer': 'yes'},\n", + " {'question_id': '201571188', 'answer': 'Yes', 'gt_answer': 'yes'},\n", + " {'question_id': '20757119', 'answer': 'Cooking', 'gt_answer': 'looking down'},\n", + " {'question_id': '20394761', 'answer': 'Dress', 'gt_answer': 'dress'},\n", + " {'question_id': '20394760', 'answer': 'Dress', 'gt_answer': 'dress'},\n", + " {'question_id': '20508714', 'answer': 'Woman', 'gt_answer': 'woman'},\n", + " {'question_id': '202053318', 'answer': 'No', 'gt_answer': 'no'},\n", + " {'question_id': '202174529', 'answer': 'Yes', 'gt_answer': 'yes'},\n", + " {'question_id': '201908788', 'answer': 'No', 'gt_answer': 'yes'},\n", + " {'question_id': '20403340', 'answer': 'Rectangle', 'gt_answer': 'square'},\n", + " {'question_id': '20306592', 'answer': 'Yes', 'gt_answer': 'no'},\n", + " {'question_id': '20403344', 'answer': 'Yes', 'gt_answer': 'yes'},\n", + " {'question_id': '20435303', 'answer': 'Paper', 'gt_answer': 'paper'},\n", + " {'question_id': '20939909', 'answer': 'Yes', 'gt_answer': 'yes'},\n", + " {'question_id': '20435309', 'answer': 'Dirty', 'gt_answer': 'dirty'},\n", + " {'question_id': '20939906', 'answer': 'Yes', 'gt_answer': 'yes'},\n", + " {'question_id': '201887219', 'answer': 'Right', 'gt_answer': 'right'},\n", + " {'question_id': '20939902', 'answer': 'Yes', 'gt_answer': 'yes'},\n", + " {'question_id': '20901821', 'answer': 'No', 'gt_answer': 'no'},\n", + " {'question_id': '20901822', 'answer': 'Umbrella', 'gt_answer': 'umbrella'},\n", + " {'question_id': '201984046',\n", + " 'answer': 'Texting',\n", + " 'gt_answer': 'looking down'},\n", + " {'question_id': '201902722', 'answer': 'Red', 'gt_answer': 'black'},\n", + " {'question_id': '20492039', 'answer': 'Bear', 'gt_answer': 'birds'},\n", + " {'question_id': '201902726', 'answer': 'Computer', 'gt_answer': 'monitor'},\n", + " {'question_id': '202100478', 'answer': 'No', 'gt_answer': 'no'},\n", + " {'question_id': '20287967', 'answer': 'Yes', 'gt_answer': 'yes'},\n", + " {'question_id': '20896252', 'answer': 'Yes', 'gt_answer': 'yes'},\n", + " {'question_id': '201510942', 'answer': 'No', 'gt_answer': 'no'},\n", + " {'question_id': '201621467', 'answer': 'Shelf', 'gt_answer': 'tv stand'},\n", + " {'question_id': '201621466', 'answer': 'Couch', 'gt_answer': 'tv stand'},\n", + " {'question_id': '20427613', 'answer': 'Yes', 'gt_answer': 'yes'},\n", + " {'question_id': '201342263', 'answer': 'Large', 'gt_answer': 'large'},\n", + " {'question_id': '20618704', 'answer': 'Pink', 'gt_answer': 'pink'},\n", + " {'question_id': '20427618', 'answer': '40', 'gt_answer': 'young'},\n", + " {'question_id': '202231873', 'answer': 'Brown', 'gt_answer': 'dark brown'},\n", + " {'question_id': '201536434', 'answer': 'Right', 'gt_answer': 'right'},\n", + " {'question_id': '201975054', 'answer': 'No', 'gt_answer': 'no'},\n", + " {'question_id': '201640551', 'answer': 'Skinny', 'gt_answer': 'fat'},\n", + " {'question_id': '201885430', 'answer': 'Swimming', 'gt_answer': 'jumping'},\n", + " {'question_id': '201654400', 'answer': 'Yes', 'gt_answer': 'yes'},\n", + " {'question_id': '201434287', 'answer': 'No', 'gt_answer': 'no'},\n", + " {'question_id': '201770899', 'answer': 'Bottle', 'gt_answer': 'bottle'},\n", + " {'question_id': '202100782', 'answer': 'Counter', 'gt_answer': 'stove'},\n", + " {'question_id': '201713599', 'answer': 'No', 'gt_answer': 'no'},\n", + " {'question_id': '201068686', 'answer': 'Shirt', 'gt_answer': 'dress shirt'},\n", + " {'question_id': '201068687', 'answer': 'Shirt', 'gt_answer': 'dress shirt'},\n", + " {'question_id': '20717125', 'answer': 'Bed', 'gt_answer': 'bed'},\n", + " {'question_id': '201556938', 'answer': 'Plastic', 'gt_answer': 'plastic'},\n", + " {'question_id': '201556939', 'answer': 'Pen', 'gt_answer': 'pen'},\n", + " {'question_id': '20756792', 'answer': 'Gray', 'gt_answer': 'gray'},\n", + " {'question_id': '201556937', 'answer': 'Plastic', 'gt_answer': 'plastic'},\n", + " {'question_id': '202285527', 'answer': 'No', 'gt_answer': 'yes'},\n", + " {'question_id': '201879573', 'answer': 'Yes', 'gt_answer': 'yes'},\n", + " {'question_id': '201795103', 'answer': 'Gray', 'gt_answer': 'dark brown'},\n", + " {'question_id': '20248178', 'answer': 'Yes', 'gt_answer': 'yes'},\n", + " {'question_id': '201156172', 'answer': 'Leather', 'gt_answer': 'cloth'},\n", + " {'question_id': '20923001', 'answer': 'Right', 'gt_answer': 'right'},\n", + " {'question_id': '20245902', 'answer': 'Man', 'gt_answer': 'skateboarder'},\n", + " {'question_id': '20245900',\n", + " 'answer': 'Skateboarder',\n", + " 'gt_answer': 'skateboarder'},\n", + " {'question_id': '20245901',\n", + " 'answer': 'Skateboarder',\n", + " 'gt_answer': 'skateboarder'},\n", + " {'question_id': '20245906', 'answer': 'Man', 'gt_answer': 'skateboarder'},\n", + " {'question_id': '20245907',\n", + " 'answer': 'Skateboarder',\n", + " 'gt_answer': 'skateboarder'},\n", + " {'question_id': '20248177', 'answer': 'No', 'gt_answer': 'no'},\n", + " {'question_id': '201987480', 'answer': 'No', 'gt_answer': 'yes'},\n", + " {'question_id': '201795359', 'answer': 'Yes', 'gt_answer': 'yes'},\n", + " {'question_id': '201735541', 'answer': 'Desk', 'gt_answer': 'desk'},\n", + " {'question_id': '201735547', 'answer': 'Desk', 'gt_answer': 'shelves'},\n", + " {'question_id': '20492150', 'answer': 'No', 'gt_answer': 'no'},\n", + " {'question_id': '20416826', 'answer': 'Pepper', 'gt_answer': 'sausage'},\n", + " {'question_id': '20416825', 'answer': 'Pepper', 'gt_answer': 'sausage'},\n", + " {'question_id': '20119166', 'answer': 'Top', 'gt_answer': 'top'},\n", + " {'question_id': '20300360', 'answer': 'Yes', 'gt_answer': 'yes'},\n", + " {'question_id': '20244509', 'answer': 'Street', 'gt_answer': 'sidewalk'},\n", + " {'question_id': '201935164', 'answer': 'No', 'gt_answer': 'no'},\n", + " {'question_id': '202036880', 'answer': 'Mushroom', 'gt_answer': 'sausage'},\n", + " {'question_id': '202036881', 'answer': 'Pepperoni', 'gt_answer': 'sausage'},\n", + " {'question_id': '202106209', 'answer': 'Yes', 'gt_answer': 'yes'},\n", + " {'question_id': '20541727', 'answer': 'No', 'gt_answer': 'no'},\n", + " {'question_id': '201037055', 'answer': 'Yes', 'gt_answer': 'yes'},\n", + " {'question_id': '20894256', 'answer': 'Gray', 'gt_answer': 'brown'},\n", + " {'question_id': '201795818', 'answer': 'Soft', 'gt_answer': 'hard'},\n", + " {'question_id': '201621321', 'answer': 'Black', 'gt_answer': 'black'},\n", + " {'question_id': '201319547', 'answer': 'Woman', 'gt_answer': 'women'},\n", + " {'question_id': '201439730', 'answer': 'Black', 'gt_answer': 'dark'},\n", + " {'question_id': '201319540', 'answer': 'Woman', 'gt_answer': 'women'},\n", + " {'question_id': '201392138', 'answer': 'No', 'gt_answer': 'no'},\n", + " {'question_id': '201439735', 'answer': 'Yes', 'gt_answer': 'yes'},\n", + " {'question_id': '202073305', 'answer': 'Zebra', 'gt_answer': 'deer'},\n", + " {'question_id': '202218780', 'answer': 'Yes', 'gt_answer': 'yes'},\n", + " {'question_id': '2094004', 'answer': 'Short', 'gt_answer': 'short'},\n", + " {'question_id': '201407351', 'answer': 'Racket', 'gt_answer': 'racket'},\n", + " {'question_id': '20169624', 'answer': 'No', 'gt_answer': 'no'},\n", + " {'question_id': '201527694', 'answer': 'Yes', 'gt_answer': 'yes'},\n", + " {'question_id': '20902594', 'answer': 'Yes', 'gt_answer': 'yes'},\n", + " {'question_id': '201407359',\n", + " 'answer': 'Tennis ball',\n", + " 'gt_answer': 'tennis ball'},\n", + " {'question_id': '201982219', 'answer': 'Yes', 'gt_answer': 'yes'},\n", + " {'question_id': '2065884', 'answer': 'No', 'gt_answer': 'no'},\n", + " {'question_id': '201935304', 'answer': 'Yes', 'gt_answer': 'yes'},\n", + " {'question_id': '201935303', 'answer': 'Platform', 'gt_answer': 'bricks'},\n", + " {'question_id': '20902848', 'answer': 'Dog', 'gt_answer': 'dog'},\n", + " {'question_id': '202231418', 'answer': 'Metal', 'gt_answer': 'metal'},\n", + " {'question_id': '20247773', 'answer': 'No', 'gt_answer': 'yes'},\n", + " {'question_id': '201462312', 'answer': 'Bat', 'gt_answer': 'bat'},\n", + " {'question_id': '20340435', 'answer': 'Trees', 'gt_answer': 'tree'},\n", + " {'question_id': '201462314', 'answer': 'Bat', 'gt_answer': 'bat'},\n", + " {'question_id': '20247778', 'answer': 'Bench', 'gt_answer': 'bench'},\n", + " {'question_id': '201987813', 'answer': 'Right', 'gt_answer': 'right'},\n", + " {'question_id': '201887171', 'answer': 'Broccoli', 'gt_answer': 'broccoli'},\n", + " {'question_id': '201438693',\n", + " 'answer': 'Home plate',\n", + " 'gt_answer': 'home plate'},\n", + " {'question_id': '20491789', 'answer': 'Sky', 'gt_answer': 'sky'},\n", + " {'question_id': '20655012', 'answer': 'Yes', 'gt_answer': 'yes'},\n", + " {'question_id': '20756930', 'answer': 'Yes', 'gt_answer': 'yes'},\n", + " {'question_id': '20330524', 'answer': 'No', 'gt_answer': 'no'},\n", + " {'question_id': '20609412', 'answer': 'Yes', 'gt_answer': 'yes'},\n", + " {'question_id': '201527960', 'answer': 'Woman', 'gt_answer': 'girl'},\n", + " {'question_id': '201482397', 'answer': 'No', 'gt_answer': 'yes'},\n", + " {'question_id': '201446971', 'answer': 'Right', 'gt_answer': 'right'},\n", + " {'question_id': '20963696', 'answer': 'Yes', 'gt_answer': 'yes'},\n", + " {'question_id': '201207480', 'answer': 'Table', 'gt_answer': 'mat'},\n", + " {'question_id': '20752230', 'answer': 'No', 'gt_answer': 'no'},\n", + " {'question_id': '201482055', 'answer': 'No', 'gt_answer': 'no'},\n", + " {'question_id': '20567532', 'answer': 'Yes', 'gt_answer': 'yes'},\n", + " {'question_id': '201599785', 'answer': 'Chair', 'gt_answer': 'chair'},\n", + " {'question_id': '201599787', 'answer': 'Chair', 'gt_answer': 'chair'},\n", + " {'question_id': '20567537', 'answer': 'Yes', 'gt_answer': 'yes'},\n", + " {'question_id': '202162618', 'answer': 'Bed', 'gt_answer': 'bookcase'},\n", + " {'question_id': '202162615', 'answer': 'Bed', 'gt_answer': 'bookcase'},\n", + " {'question_id': '201599788', 'answer': 'Chair', 'gt_answer': 'chair'},\n", + " {'question_id': '20550578', 'answer': 'Grass', 'gt_answer': 'grass'},\n", + " {'question_id': '20340484', 'answer': 'Yes', 'gt_answer': 'yes'},\n", + " {'question_id': '20151749', 'answer': 'Tan', 'gt_answer': 'tan'},\n", + " {'question_id': '202246141', 'answer': 'No', 'gt_answer': 'no'},\n", + " {'question_id': '20654941', 'answer': 'No', 'gt_answer': 'no'},\n", + " {'question_id': '20309040', 'answer': 'No', 'gt_answer': 'no'},\n", + " {'question_id': '20654949', 'answer': 'No', 'gt_answer': 'yes'},\n", + " {'question_id': '202004006', 'answer': 'Wood', 'gt_answer': 'wood'},\n", + " {'question_id': '20120514', 'answer': 'Yes', 'gt_answer': 'yes'},\n", + " {'question_id': '2059544', 'answer': 'Blond', 'gt_answer': 'blond'},\n", + " {'question_id': '20866265', 'answer': 'Yes', 'gt_answer': 'yes'},\n", + " {'question_id': '202218911', 'answer': 'Green', 'gt_answer': 'light blue'},\n", + " {'question_id': '201574236', 'answer': 'Man', 'gt_answer': 'man'},\n", + " {'question_id': '201637286', 'answer': 'No', 'gt_answer': 'no'},\n", + " {'question_id': '201885232', 'answer': 'Yes', 'gt_answer': 'no'},\n", + " {'question_id': '202121678', 'answer': 'No', 'gt_answer': 'no'},\n", + " {'question_id': '20856903', 'answer': 'Purse', 'gt_answer': 'purse'},\n", + " {'question_id': '201346563', 'answer': 'No', 'gt_answer': 'no'},\n", + " {'question_id': '201346560', 'answer': 'Left', 'gt_answer': 'left'},\n", + " {'question_id': '20856909', 'answer': 'Purse', 'gt_answer': 'purse'},\n", + " {'question_id': '201479185', 'answer': 'Peeled', 'gt_answer': 'unpeeled'},\n", + " {'question_id': '201974958', 'answer': 'Black', 'gt_answer': 'black'},\n", + " {'question_id': '20295332', 'answer': 'Silver', 'gt_answer': 'gray'},\n", + " {'question_id': '20258542', 'answer': 'No', 'gt_answer': 'yes'},\n", + " {'question_id': '201996765', 'answer': 'No', 'gt_answer': 'no'},\n", + " {'question_id': '201156113', 'answer': 'Yes', 'gt_answer': 'yes'},\n", + " {'question_id': '201153193', 'answer': 'No', 'gt_answer': 'yes'},\n", + " {'question_id': '20797647', 'answer': 'Shoe', 'gt_answer': 'shoe'},\n", + " {'question_id': '201207118', 'answer': 'Broccoli', 'gt_answer': 'broccoli'},\n", + " {'question_id': '201878450', 'answer': 'Young', 'gt_answer': 'old'},\n", + " {'question_id': '20385288', 'answer': 'Plastic', 'gt_answer': 'plastic'},\n", + " {'question_id': '20385537', 'answer': 'Yes', 'gt_answer': 'yes'},\n", + " {'question_id': '201303200', 'answer': 'Porcelain', 'gt_answer': 'porcelain'},\n", + " {'question_id': '201303202', 'answer': 'Chair', 'gt_answer': 'cups'},\n", + " {'question_id': '20797648', 'answer': 'Shoe', 'gt_answer': 'shoe'},\n", + " {'question_id': '201976886', 'answer': 'Fence', 'gt_answer': 'street sign'},\n", + " {'question_id': '201976887', 'answer': 'Fence', 'gt_answer': 'street sign'},\n", + " {'question_id': '201497854', 'answer': 'Screen', 'gt_answer': 'monitor'},\n", + " {'question_id': '202133541',\n", + " 'answer': 'Short sleeved',\n", + " 'gt_answer': 'short sleeved'},\n", + " {'question_id': '20171188', 'answer': 'Pan', 'gt_answer': 'baking pan'},\n", + " {'question_id': '201902537', 'answer': 'Yes', 'gt_answer': 'no'},\n", + " {'question_id': '201738047', 'answer': 'Yes', 'gt_answer': 'yes'},\n", + " {'question_id': '201713385', 'answer': 'No', 'gt_answer': 'no'},\n", + " {'question_id': '202244248', 'answer': 'Round', 'gt_answer': 'triangular'},\n", + " {'question_id': '202158849', 'answer': 'No', 'gt_answer': 'no'},\n", + " {'question_id': '20818677', 'answer': 'Black', 'gt_answer': 'black'},\n", + " {'question_id': '201879394', 'answer': 'Glass', 'gt_answer': 'metal'},\n", + " {'question_id': '201621693', 'answer': 'Couch', 'gt_answer': 'couch'},\n", + " {'question_id': '202122091', 'answer': 'Metal', 'gt_answer': 'metal'},\n", + " {'question_id': '201887315', 'answer': 'No', 'gt_answer': 'no'},\n", + " {'question_id': '202004237', 'answer': 'Chair', 'gt_answer': 'doors'},\n", + " {'question_id': '201982149', 'answer': 'No', 'gt_answer': 'no'},\n", + " {'question_id': '201737851', 'answer': 'Blue', 'gt_answer': 'gray'},\n", + " {'question_id': '201896540', 'answer': 'Glasses', 'gt_answer': 'coat'},\n", + " {'question_id': '201654361', 'answer': 'Yes', 'gt_answer': 'no'},\n", + " {'question_id': '202023443', 'answer': 'Blue', 'gt_answer': 'yellow'},\n", + " {'question_id': '20182918', 'answer': 'Roof', 'gt_answer': 'shop'},\n", + " {'question_id': '201480491', 'answer': 'Bench', 'gt_answer': 'grass'},\n", + " {'question_id': '20978368', 'answer': 'Girl', 'gt_answer': 'girl'},\n", + " {'question_id': '201713366', 'answer': 'Yes', 'gt_answer': 'yes'},\n", + " {'question_id': '20818654', 'answer': 'No', 'gt_answer': 'no'},\n", + " {'question_id': '20308803', 'answer': 'Refrigerator', 'gt_answer': 'stove'},\n", + " {'question_id': '20308802', 'answer': 'Yes', 'gt_answer': 'no'},\n", + " {'question_id': '201663676', 'answer': 'Yes', 'gt_answer': 'yes'},\n", + " {'question_id': '20631953', 'answer': 'Player', 'gt_answer': 'catcher'},\n", + " {'question_id': '201663673', 'answer': 'Drawer', 'gt_answer': 'drawers'},\n", + " {'question_id': '20515082', 'answer': 'Yes', 'gt_answer': 'yes'},\n", + " {'question_id': '202262632', 'answer': 'Red', 'gt_answer': 'red'},\n", + " {'question_id': '202262633', 'answer': 'Frisbee', 'gt_answer': 'frisbee'},\n", + " {'question_id': '201065063', 'answer': 'Dress', 'gt_answer': 'gown'},\n", + " {'question_id': '202262636', 'answer': 'Ground', 'gt_answer': 'grass'},\n", + " {'question_id': '202286783', 'answer': 'Pink', 'gt_answer': 'pink'},\n", + " {'question_id': '20412245', 'answer': 'Yes', 'gt_answer': 'yes'},\n", + " {'question_id': '20515088', 'answer': 'No', 'gt_answer': 'no'},\n", + " {'question_id': '20456346', 'answer': 'Wood', 'gt_answer': 'metal'},\n", + " {'question_id': '20306747', 'answer': 'Camera', 'gt_answer': 'cell phone'},\n", + " {'question_id': '201185307', 'answer': 'Concrete', 'gt_answer': 'concrete'},\n", + " {'question_id': '202144720', 'answer': 'Water', 'gt_answer': 'ice'},\n", + " {'question_id': '202144727', 'answer': 'Crate', 'gt_answer': 'crate'},\n", + " {'question_id': '201996835', 'answer': 'Shirt', 'gt_answer': 'sweater'},\n", + " {'question_id': '202144724', 'answer': 'Bottle', 'gt_answer': 'blender'},\n", + " {'question_id': '201676234', 'answer': 'Yes', 'gt_answer': 'yes'},\n", + " {'question_id': '20435152', 'answer': 'Pizza', 'gt_answer': 'pizza box'},\n", + " {'question_id': '20456349', 'answer': 'No', 'gt_answer': 'no'},\n", + " {'question_id': '201682212', 'answer': 'Yes', 'gt_answer': 'yes'},\n", + " {'question_id': '202053434', 'answer': 'Mound', 'gt_answer': 'field'},\n", + " {'question_id': '202053437', 'answer': 'Pitcher', 'gt_answer': 'pitcher'},\n", + " {'question_id': '20785809', 'answer': 'Large', 'gt_answer': 'large'},\n", + " {'question_id': '201935967', 'answer': 'Shelf', 'gt_answer': 'shelf'},\n", + " {'question_id': '20811359', 'answer': 'Chair', 'gt_answer': 'chair'},\n", + " {'question_id': '201879243', 'answer': 'Lady', 'gt_answer': 'athlete'},\n", + " {'question_id': '20756653', 'answer': 'Shelf', 'gt_answer': 'shelf'},\n", + " {'question_id': '201873454', 'answer': 'No', 'gt_answer': 'no'},\n", + " {'question_id': '20661400', 'answer': 'Yes', 'gt_answer': 'yes'},\n", + " {'question_id': '202012452', 'answer': 'No', 'gt_answer': 'yes'},\n", + " {'question_id': '20756658', 'answer': 'Yes', 'gt_answer': 'yes'},\n", + " {'question_id': '20536038', 'answer': 'Cloudless', 'gt_answer': 'cloudless'},\n", + " {'question_id': '201110773', 'answer': 'Yes', 'gt_answer': 'yes'},\n", + " {'question_id': '202053361', 'answer': 'Batter', 'gt_answer': 'batter'},\n", + " {'question_id': '20536035', 'answer': 'Field', 'gt_answer': 'plain'},\n", + " {'question_id': '20786092', 'answer': 'No', 'gt_answer': 'no'},\n", + " {'question_id': '201504947', 'answer': 'Beach', 'gt_answer': 'beach'},\n", + " {'question_id': '201504940', 'answer': 'Girl', 'gt_answer': 'woman'},\n", + " {'question_id': '201482310', 'answer': 'Yes', 'gt_answer': 'yes'},\n", + " {'question_id': '20511412',\n", + " 'answer': 'Helicopter',\n", + " 'gt_answer': 'helicopter'},\n", + " {'question_id': '202053363', 'answer': 'Catcher', 'gt_answer': 'umpire'},\n", + " {'question_id': '20511415',\n", + " 'answer': 'Helicopter',\n", + " 'gt_answer': 'helicopter'},\n", + " {'question_id': '20879007', 'answer': 'Yes', 'gt_answer': 'yes'},\n", + " {'question_id': '20511418', 'answer': 'Top', 'gt_answer': 'top'},\n", + " {'question_id': '201759431', 'answer': 'No', 'gt_answer': 'no'},\n", + " {'question_id': '20518455', 'answer': 'No', 'gt_answer': 'yes'},\n", + " {'question_id': '201030789', 'answer': 'Pants', 'gt_answer': 'pants'},\n", + " {'question_id': '201982508', 'answer': 'Left', 'gt_answer': 'left'},\n", + " {'question_id': '201987565', 'answer': 'Plastic', 'gt_answer': 'plastic'},\n", + " {'question_id': '201987569', 'answer': 'Yes', 'gt_answer': 'yes'},\n", + " {'question_id': '201370409', 'answer': 'Carpet', 'gt_answer': 'paper'},\n", + " {'question_id': '202180269', 'answer': 'Right', 'gt_answer': 'right'},\n", + " {'question_id': '201770690', 'answer': 'Yes', 'gt_answer': 'yes'},\n", + " {'question_id': '20551696',\n", + " 'answer': 'Traffic light',\n", + " 'gt_answer': 'traffic light'},\n", + " {'question_id': '20551697',\n", + " 'answer': 'Traffic light',\n", + " 'gt_answer': 'traffic light'},\n", + " {'question_id': '20551694', 'answer': 'Red', 'gt_answer': 'black'},\n", + " {'question_id': '20870471', 'answer': 'Male', 'gt_answer': 'male'},\n", + " {'question_id': '20870472', 'answer': 'No', 'gt_answer': 'yes'},\n", + " {'question_id': '201498423', 'answer': 'No', 'gt_answer': 'no'},\n", + " {'question_id': '202285209', 'answer': 'Long', 'gt_answer': 'short'},\n", + " {'question_id': '20887464',\n", + " 'answer': 'Computer mouse',\n", + " 'gt_answer': 'computer mouse'},\n", + " {'question_id': '20887460', 'answer': 'Keyboard', 'gt_answer': 'keyboard'},\n", + " {'question_id': '20468367', 'answer': 'Yes', 'gt_answer': 'yes'},\n", + " {'question_id': '2017250', 'answer': 'No', 'gt_answer': 'no'},\n", + " {'question_id': '202144703', 'answer': 'Blender', 'gt_answer': 'blender'},\n", + " {'question_id': '201347393', 'answer': 'Boy', 'gt_answer': 'skateboarder'},\n", + " {'question_id': '20721787', 'answer': 'Girl', 'gt_answer': 'girl'},\n", + " {'question_id': '201056015', 'answer': 'Car', 'gt_answer': 'car'},\n", + " {'question_id': '20183437', 'answer': 'Basket', 'gt_answer': 'boxes'},\n", + " {'question_id': '202246793', 'answer': 'No', 'gt_answer': 'no'},\n", + " {'question_id': '201951989', 'answer': 'Truck', 'gt_answer': 'pole'},\n", + " {'question_id': '201047183', 'answer': 'No', 'gt_answer': 'no'},\n", + " {'question_id': '202257504', 'answer': 'No', 'gt_answer': 'yes'},\n", + " {'question_id': '201430751', 'answer': 'Yes', 'gt_answer': 'yes'},\n", + " {'question_id': '202257501', 'answer': 'Rough', 'gt_answer': 'rough'},\n", + " {'question_id': '20936036', 'answer': 'No', 'gt_answer': 'no'},\n", + " {'question_id': '20551476', 'answer': 'Train', 'gt_answer': 'train'},\n", + " {'question_id': '20827523', 'answer': 'Yes', 'gt_answer': 'yes'},\n", + " {'question_id': '201247296', 'answer': 'Plant', 'gt_answer': 'plant'},\n", + " {'question_id': '20827527', 'answer': 'Rectangle', 'gt_answer': 'square'},\n", + " {'question_id': '201247292', 'answer': 'Plant', 'gt_answer': 'plant'},\n", + " {'question_id': '201247293', 'answer': 'Plant', 'gt_answer': 'plant'},\n", + " {'question_id': '201957203', 'answer': 'No', 'gt_answer': 'yes'},\n", + " {'question_id': '20361266', 'answer': 'Woman', 'gt_answer': 'snowboarder'},\n", + " {'question_id': '20349947', 'answer': 'Long', 'gt_answer': 'long'},\n", + " {'question_id': '201492116', 'answer': 'No', 'gt_answer': 'no'},\n", + " {'question_id': '201879148', 'answer': 'Wristband', 'gt_answer': 'racket'},\n", + " {'question_id': '201064875', 'answer': 'Chair', 'gt_answer': 'sofa'},\n", + " {'question_id': '201498043', 'answer': 'Paper', 'gt_answer': 'desk'},\n", + " {'question_id': '201064873', 'answer': 'Bench', 'gt_answer': 'sofa'},\n", + " {'question_id': '20856756', 'answer': 'Bed', 'gt_answer': 'bed'},\n", + " {'question_id': '20856758', 'answer': 'Bed', 'gt_answer': 'bed'},\n", + " {'question_id': '20241036', 'answer': 'Sandwich', 'gt_answer': 'sandwich'},\n", + " {'question_id': '201760719', 'answer': 'Man', 'gt_answer': 'man'},\n", + " {'question_id': '201760718', 'answer': 'Man', 'gt_answer': 'man'},\n", + " {'question_id': '202041969', 'answer': 'Closed', 'gt_answer': 'closed'},\n", + " {'question_id': '20637135', 'answer': 'Yes', 'gt_answer': 'yes'},\n", + " {'question_id': '20645492', 'answer': 'Left', 'gt_answer': 'left'},\n", + " {'question_id': '20645496', 'answer': 'Yes', 'gt_answer': 'yes'},\n", + " {'question_id': '201498046', 'answer': 'Desk', 'gt_answer': 'desk'},\n", + " {'question_id': '2076580', 'answer': 'Building', 'gt_answer': 'entrance'},\n", + " {'question_id': '201410997', 'answer': 'Female', 'gt_answer': 'female'},\n", + " {'question_id': '2076582', 'answer': 'People', 'gt_answer': 'stone'},\n", + " {'question_id': '201822292', 'answer': 'Yes', 'gt_answer': 'yes'},\n", + " {'question_id': '2076589', 'answer': 'No', 'gt_answer': 'no'},\n", + " {'question_id': '20790005', 'answer': 'People', 'gt_answer': 'people'},\n", + " {'question_id': '201438619', 'answer': 'Yes', 'gt_answer': 'yes'},\n", + " {'question_id': '201143169', 'answer': 'Chairs', 'gt_answer': 'chairs'},\n", + " {'question_id': '201080313', 'answer': 'No', 'gt_answer': 'no'},\n", + " {'question_id': '201037196', 'answer': 'Yes', 'gt_answer': 'yes'},\n", + " {'question_id': '201037194', 'answer': 'Woman', 'gt_answer': 'girl'},\n", + " {'question_id': '201037195', 'answer': 'Woman', 'gt_answer': 'girl'},\n", + " {'question_id': '20285424', 'answer': 'No', 'gt_answer': 'no'},\n", + " {'question_id': '20381280', 'answer': 'Yes', 'gt_answer': 'yes'},\n", + " {'question_id': '201047450', 'answer': 'No', 'gt_answer': 'yes'},\n", + " {'question_id': '20308247', 'answer': 'Cabinets', 'gt_answer': 'cabinets'},\n", + " {'question_id': '201935444', 'answer': 'No', 'gt_answer': 'yes'},\n", + " {'question_id': '201143349', 'answer': 'Yes', 'gt_answer': 'yes'},\n", + " {'question_id': '201576511', 'answer': 'No', 'gt_answer': 'yes'},\n", + " {'question_id': '201576517', 'answer': 'No', 'gt_answer': 'no'},\n", + " {'question_id': '201400101', 'answer': 'Black', 'gt_answer': 'brown'},\n", + " {'question_id': '201832568', 'answer': 'Wood', 'gt_answer': 'wood'},\n", + " {'question_id': '201319754', 'answer': 'White', 'gt_answer': 'white'},\n", + " {'question_id': '20891582', 'answer': 'Yes', 'gt_answer': 'yes'},\n", + " {'question_id': '201360694', 'answer': 'Girl', 'gt_answer': 'boy'},\n", + " {'question_id': '201360695', 'answer': 'Girl', 'gt_answer': 'boy'},\n", + " {'question_id': '201883195', 'answer': 'Chair', 'gt_answer': 'bed'},\n", + " {'question_id': '20836778', 'answer': 'Luggage', 'gt_answer': 'table'},\n", + " {'question_id': '201886951', 'answer': 'Brown', 'gt_answer': 'brown'},\n", + " {'question_id': '20836773', 'answer': 'Bag', 'gt_answer': 'purse'},\n", + " {'question_id': '2046358', 'answer': 'Tall', 'gt_answer': 'short'},\n", + " {'question_id': '201711276', 'answer': 'No', 'gt_answer': 'no'},\n", + " {'question_id': '202040317', 'answer': 'New', 'gt_answer': 'new'},\n", + " {'question_id': '20600114', 'answer': 'Short', 'gt_answer': 'short'},\n", + " {'question_id': '20600115', 'answer': 'Yes', 'gt_answer': 'yes'},\n", + " {'question_id': '201735202', 'answer': 'Yes', 'gt_answer': 'yes'},\n", + " {'question_id': '201047238', 'answer': 'Yes', 'gt_answer': 'no'},\n", + " {'question_id': '201878237', 'answer': 'Yes', 'gt_answer': 'no'},\n", + " {'question_id': '201428730', 'answer': 'No', 'gt_answer': 'yes'},\n", + " {'question_id': '20308230', 'answer': 'Cabinets', 'gt_answer': 'cabinets'},\n", + " {'question_id': '20416581', 'answer': 'No', 'gt_answer': 'yes'},\n", + " {'question_id': '201882662', 'answer': 'Left', 'gt_answer': 'left'},\n", + " {'question_id': '20308237', 'answer': 'Wood', 'gt_answer': 'glass'},\n", + " {'question_id': '20542972', 'answer': 'Fence', 'gt_answer': 'fence'},\n", + " {'question_id': '20205041', 'answer': 'Chair', 'gt_answer': 'chair'},\n", + " {'question_id': '202060013', 'answer': 'Dog', 'gt_answer': 'dog'},\n", + " {'question_id': '201873218', 'answer': 'Yes', 'gt_answer': 'yes'},\n", + " {'question_id': '20929331', 'answer': 'Right', 'gt_answer': 'right'},\n", + " {'question_id': '20151689', 'answer': 'On', 'gt_answer': 'on'},\n", + " {'question_id': '201765995', 'answer': 'Beach', 'gt_answer': 'dirt'},\n", + " {'question_id': '201873216', 'answer': 'Old', 'gt_answer': 'young'},\n", + " {'question_id': '201765990', 'answer': 'Dense', 'gt_answer': 'sparse'},\n", + " {'question_id': '201765991', 'answer': 'Tree', 'gt_answer': 'trees'},\n", + " {'question_id': '201951566', 'answer': 'People', 'gt_answer': 'girl'},\n", + " {'question_id': '201951567', 'answer': 'No', 'gt_answer': 'no'},\n", + " {'question_id': '20661240', 'answer': 'No', 'gt_answer': 'no'},\n", + " {'question_id': '2058558', 'answer': 'No', 'gt_answer': 'no'},\n", + " {'question_id': '20247344', 'answer': 'Yes', 'gt_answer': 'yes'},\n", + " {'question_id': '20247340', 'answer': 'Yes', 'gt_answer': 'yes'},\n", + " {'question_id': '20899335', 'answer': 'Yes', 'gt_answer': 'yes'},\n", + " {'question_id': '20411752', 'answer': 'Yes', 'gt_answer': 'no'},\n", + " {'question_id': '202024715', 'answer': 'Park', 'gt_answer': 'park'},\n", + " {'question_id': '20836551', 'answer': 'No', 'gt_answer': 'no'},\n", + " {'question_id': '201510305', 'answer': 'Tray', 'gt_answer': 'tray'},\n", + " {'question_id': '20631436', 'answer': 'Player', 'gt_answer': 'umpire'},\n", + " {'question_id': '201490842', 'answer': 'Yes', 'gt_answer': 'yes'},\n", + " {'question_id': '20341110', 'answer': 'Right', 'gt_answer': 'right'},\n", + " {'question_id': '20341116', 'answer': 'Yes', 'gt_answer': 'yes'},\n", + " {'question_id': '20341117', 'answer': 'Yes', 'gt_answer': 'yes'},\n", + " {'question_id': '201826530', 'answer': 'Men', 'gt_answer': 'man'},\n", + " {'question_id': '20151976', 'answer': 'Square', 'gt_answer': 'square'},\n", + " {'question_id': '202262102', 'answer': 'Yes', 'gt_answer': 'yes'},\n", + " {'question_id': '20710289', 'answer': 'Yes', 'gt_answer': 'yes'},\n", + " {'question_id': '20285569', 'answer': 'Yes', 'gt_answer': 'yes'},\n", + " {'question_id': '20797581', 'answer': 'Yes', 'gt_answer': 'yes'},\n", + " {'question_id': '201889233', 'answer': 'Yes', 'gt_answer': 'no'},\n", + " {'question_id': '201951690', 'answer': 'No', 'gt_answer': 'yes'},\n", + " {'question_id': '202262134', 'answer': 'Napkin', 'gt_answer': 'mug'},\n", + " {'question_id': '202081474', 'answer': 'Yes', 'gt_answer': 'yes'},\n", + " {'question_id': '201185178', 'answer': 'No', 'gt_answer': 'no'},\n", + " {'question_id': '201185172', 'answer': 'Ground', 'gt_answer': 'ground'},\n", + " {'question_id': '201185173', 'answer': 'Pavement', 'gt_answer': 'ground'},\n", + " {'question_id': '20891232', 'answer': 'Store', 'gt_answer': 'street'},\n", + " {'question_id': '20891231', 'answer': 'Store', 'gt_answer': 'street'},\n", + " {'question_id': '20891541', 'answer': 'Left', 'gt_answer': 'left'},\n", + " {'question_id': '20652527', 'answer': 'Left', 'gt_answer': 'left'},\n", + " {'question_id': '201455887', 'answer': 'No', 'gt_answer': 'yes'},\n", + " {'question_id': '20724222', 'answer': 'Man', 'gt_answer': 'snowboarder'},\n", + " {'question_id': '20810927', 'answer': 'Ornaments', 'gt_answer': 'ornament'},\n", + " {'question_id': '201346485', 'answer': 'Yes', 'gt_answer': 'yes'},\n", + " {'question_id': '20954197', 'answer': 'Yes', 'gt_answer': 'yes'},\n", + " {'question_id': '20954194', 'answer': 'Man', 'gt_answer': 'man'},\n", + " {'question_id': '20162099', 'answer': 'Yes', 'gt_answer': 'yes'},\n", + " {'question_id': '202228116', 'answer': 'Speaker', 'gt_answer': 'dvd player'},\n", + " {'question_id': '20982562', 'answer': 'Woman', 'gt_answer': 'woman'},\n", + " {'question_id': '20954191', 'answer': 'Woman', 'gt_answer': 'woman'},\n", + " {'question_id': '202012734',\n", + " 'answer': 'Plant',\n", + " 'gt_answer': 'remote control'},\n", + " {'question_id': '20911295', 'answer': 'Right', 'gt_answer': 'right'},\n", + " {'question_id': '201401768', 'answer': 'Gray', 'gt_answer': 'dark'},\n", + " {'question_id': '201882482', 'answer': 'Yes', 'gt_answer': 'yes'},\n", + " {'question_id': '202012733',\n", + " 'answer': 'Wii controller',\n", + " 'gt_answer': 'remote control'},\n", + " {'question_id': '20724226', 'answer': 'No', 'gt_answer': 'no'},\n", + " {'question_id': '201401762', 'answer': 'No', 'gt_answer': 'yes'},\n", + " {'question_id': '20342499', 'answer': 'Yes', 'gt_answer': 'no'},\n", + " {'question_id': '20306355', 'answer': 'Girl', 'gt_answer': 'woman'},\n", + " {'question_id': '20306354', 'answer': 'Man', 'gt_answer': 'woman'},\n", + " {'question_id': '20306357', 'answer': 'Pants', 'gt_answer': 'sweater'},\n", + " {'question_id': '201859542', 'answer': 'Right', 'gt_answer': 'right'},\n", + " {'question_id': '201110489', 'answer': 'Bottom', 'gt_answer': 'bottom'},\n", + " {'question_id': '20149668', 'answer': 'Metal', 'gt_answer': 'plastic'},\n", + " {'question_id': '201832652',\n", + " 'answer': 'Nightstand',\n", + " 'gt_answer': 'nightstand'},\n", + " {'question_id': '20306358', 'answer': 'Jacket', 'gt_answer': 'sweater'},\n", + " {'question_id': '20120167', 'answer': 'Right', 'gt_answer': 'right'},\n", + " {'question_id': '202257089', 'answer': 'Yes', 'gt_answer': 'no'},\n", + " {'question_id': '201467424', 'answer': 'No', 'gt_answer': 'no'},\n", + " {'question_id': '201467422', 'answer': 'No', 'gt_answer': 'no'},\n", + " {'question_id': '202257086', 'answer': 'Beach', 'gt_answer': 'beach'},\n", + " {'question_id': '20317099', 'answer': 'No', 'gt_answer': 'no'},\n", + " {'question_id': '20183235', 'answer': 'Young', 'gt_answer': 'old'},\n", + " {'question_id': '202053154', 'answer': 'Yes', 'gt_answer': 'yes'},\n", + " {'question_id': '202053153',\n", + " 'answer': 'Long sleeved',\n", + " 'gt_answer': 'long sleeved'},\n", + " {'question_id': '201920535', 'answer': 'No', 'gt_answer': 'yes'},\n", + " {'question_id': '202241158', 'answer': 'Brown', 'gt_answer': 'black'},\n", + " {'question_id': '201548912', 'answer': 'Green', 'gt_answer': 'gray'},\n", + " {'question_id': '20709866', 'answer': 'No', 'gt_answer': 'no'},\n", + " {'question_id': '20489464', 'answer': 'Sleeping', 'gt_answer': 'sleeping'},\n", + " {'question_id': '201758426',\n", + " 'answer': 'Teddy bear',\n", + " 'gt_answer': 'stuffed dog'},\n", + " {'question_id': '202012841', 'answer': 'Man', 'gt_answer': 'man'},\n", + " {'question_id': '201758429', 'answer': 'Stroller', 'gt_answer': 'stroller'},\n", + " {'question_id': '20403586', 'answer': 'Chair', 'gt_answer': 'table'},\n", + " {'question_id': '202012848', 'answer': 'Wii', 'gt_answer': 'television'},\n", + " {'question_id': '201498211', 'answer': 'Monitor', 'gt_answer': 'phone'},\n", + " {'question_id': '2053509', 'answer': 'No', 'gt_answer': 'no'},\n", + " {'question_id': '201110662', 'answer': 'Tall', 'gt_answer': 'short'},\n", + " {'question_id': '20668033', 'answer': 'Left', 'gt_answer': 'left'},\n", + " {'question_id': '2046539', 'answer': 'Yes', 'gt_answer': 'yes'},\n", + " {'question_id': '2053501', 'answer': 'No', 'gt_answer': 'yes'},\n", + " {'question_id': '2053505', 'answer': 'No', 'gt_answer': 'no'},\n", + " {'question_id': '2046530', 'answer': 'Yes', 'gt_answer': 'yes'},\n", + " {'question_id': '202174058', 'answer': 'Stove', 'gt_answer': 'oven'},\n", + " {'question_id': '201804455',\n", + " 'answer': 'Computer mouse',\n", + " 'gt_answer': 'computer monitor'},\n", + " {'question_id': '20157537', 'answer': 'No', 'gt_answer': 'no'},\n", + " {'question_id': '20982385', 'answer': 'Sweater', 'gt_answer': 'shirt'},\n", + " {'question_id': '20320230', 'answer': 'Dirty', 'gt_answer': 'dirty'},\n", + " {'question_id': '202240953', 'answer': 'Brown', 'gt_answer': 'black'},\n", + " {'question_id': '2093835', 'answer': 'No', 'gt_answer': 'no'},\n", + " {'question_id': '201156303', 'answer': 'Boy', 'gt_answer': 'boy'},\n", + " {'question_id': '20631894', 'answer': 'Waiting', 'gt_answer': 'waiting'},\n", + " {'question_id': '20978280', 'answer': 'Left', 'gt_answer': 'left'},\n", + " {'question_id': '201156304', 'answer': 'Boy', 'gt_answer': 'boy'},\n", + " {'question_id': '201570788', 'answer': 'No', 'gt_answer': 'no'},\n", + " {'question_id': '202021477', 'answer': 'Sign', 'gt_answer': 'artwork'},\n", + " {'question_id': '201491071', 'answer': 'Goats', 'gt_answer': 'goats'},\n", + " {'question_id': '201491070', 'answer': 'Cows', 'gt_answer': 'goats'},\n", + " {'question_id': '201445018', 'answer': 'Very', 'gt_answer': 'hard'},\n", + " {'question_id': '201623420', 'answer': 'Silver', 'gt_answer': 'silver'},\n", + " {'question_id': '20442165', 'answer': 'Cabinet', 'gt_answer': 'cabinet'},\n", + " {'question_id': '20442164', 'answer': 'Cabinet', 'gt_answer': 'cabinet'},\n", + " {'question_id': '202100414', 'answer': 'Yes', 'gt_answer': 'yes'},\n", + " {'question_id': '20245693', 'answer': 'No', 'gt_answer': 'yes'},\n", + " {'question_id': '20287908', 'answer': 'Yes', 'gt_answer': 'no'},\n", + " {'question_id': '20492010', 'answer': 'Yes', 'gt_answer': 'yes'},\n", + " {'question_id': '201624139', 'answer': 'Spatula', 'gt_answer': 'spatula'},\n", + " {'question_id': '202125899', 'answer': 'People', 'gt_answer': 'audience'},\n", + " {'question_id': '20227104', 'answer': 'Menu', 'gt_answer': 'menu'},\n", + " {'question_id': '20227105', 'answer': 'Yes', 'gt_answer': 'yes'},\n", + " {'question_id': '202012599', 'answer': 'Right', 'gt_answer': 'left'},\n", + " {'question_id': '201621484', 'answer': 'Couch', 'gt_answer': 'desk'},\n", + " {'question_id': '201621489', 'answer': 'Left', 'gt_answer': 'left'},\n", + " {'question_id': '201624134', 'answer': 'Pan', 'gt_answer': 'pan'},\n", + " {'question_id': '201536418', 'answer': 'No', 'gt_answer': 'no'},\n", + " {'question_id': '20511621', 'answer': 'No', 'gt_answer': 'yes'},\n", + " {'question_id': '202218593', 'answer': 'Bottom', 'gt_answer': 'bottom'},\n", + " {'question_id': '201975071', 'answer': 'Adidas', 'gt_answer': 'adidas'},\n", + " {'question_id': '201434265', 'answer': 'Glass', 'gt_answer': 'glass'},\n", + " {'question_id': '201654426', 'answer': 'Horses', 'gt_answer': 'horses'},\n", + " {'question_id': '201654424', 'answer': 'No', 'gt_answer': 'no'},\n", + " {'question_id': '20211274', 'answer': 'Yes', 'gt_answer': 'yes'},\n", + " {'question_id': '20543092', 'answer': 'Elephant', 'gt_answer': 'elephant'},\n", + " {'question_id': '201412341', 'answer': 'Yes', 'gt_answer': 'yes'},\n", + " {'question_id': '201976777', 'answer': 'Yes', 'gt_answer': 'yes'},\n", + " {'question_id': '201641288',\n", + " 'answer': 'Street sign',\n", + " 'gt_answer': 'street sign'},\n", + " {'question_id': '20717109', 'answer': 'Bed', 'gt_answer': 'bed'},\n", + " {'question_id': '201975049', 'answer': 'Yes', 'gt_answer': 'yes'},\n", + " {'question_id': '201641287',\n", + " 'answer': 'Traffic light',\n", + " 'gt_answer': 'street sign'},\n", + " {'question_id': '201641286',\n", + " 'answer': 'Traffic light',\n", + " 'gt_answer': 'street sign'},\n", + " {'question_id': '201641282', 'answer': 'Pole', 'gt_answer': 'traffic light'},\n", + " {'question_id': '201794876', 'answer': 'Color', 'gt_answer': 'material'},\n", + " {'question_id': '20412052', 'answer': 'Yes', 'gt_answer': 'no'},\n", + " {'question_id': '20412053', 'answer': 'Carrots', 'gt_answer': 'dessert'},\n", + " {'question_id': '20673114', 'answer': 'Yes', 'gt_answer': 'yes'},\n", + " {'question_id': '2044674', 'answer': 'No', 'gt_answer': 'no'},\n", + " {'question_id': '20673117',\n", + " 'answer': 'Toilet paper',\n", + " 'gt_answer': 'toilet paper'},\n", + " {'question_id': '20923068', 'answer': 'Truck', 'gt_answer': 'ambulance'},\n", + " {'question_id': '2017111', 'answer': 'No', 'gt_answer': 'no'},\n", + " {'question_id': '20705812', 'answer': 'Desk', 'gt_answer': 'desk'},\n", + " {'question_id': '20550406', 'answer': 'Horse', 'gt_answer': 'horse'},\n", + " {'question_id': '20550407', 'answer': 'Horse', 'gt_answer': 'horse'},\n", + " {'question_id': '20673118', 'answer': 'Table', 'gt_answer': 'table'},\n", + " {'question_id': '20705816', 'answer': 'Desk', 'gt_answer': 'desk'},\n", + " {'question_id': '20468429', 'answer': 'Large', 'gt_answer': 'small'},\n", + " {'question_id': '201739230', 'answer': 'No', 'gt_answer': 'yes'},\n", + " {'question_id': '20248159', 'answer': 'Outfit', 'gt_answer': 'sweater'},\n", + " {'question_id': '202021472', 'answer': 'Red', 'gt_answer': 'red'},\n", + " {'question_id': '201068695', 'answer': 'Shirt', 'gt_answer': 'dress shirt'},\n", + " {'question_id': '20248154', 'answer': 'Shirt', 'gt_answer': 'sweater'},\n", + " {'question_id': '20144639', 'answer': 'No', 'gt_answer': 'no'},\n", + " {'question_id': '201735564', 'answer': 'Right', 'gt_answer': 'right'},\n", + " {'question_id': '201391831',\n", + " 'answer': 'Wii controller',\n", + " 'gt_answer': 'wii controller'},\n", + " {'question_id': '201391832',\n", + " 'answer': 'Controller',\n", + " 'gt_answer': 'wii controller'},\n", + " {'question_id': '201065519', 'answer': 'Yes', 'gt_answer': 'yes'},\n", + " {'question_id': '20204532', 'answer': 'Chair', 'gt_answer': 'chair'},\n", + " {'question_id': '202101231', 'answer': 'Right', 'gt_answer': 'left'},\n", + " {'question_id': '20258759', 'answer': 'Boy', 'gt_answer': 'child'},\n", + " {'question_id': '20262704', 'answer': 'Girl', 'gt_answer': 'girl'},\n", + " {'question_id': '202265747', 'answer': 'Woman', 'gt_answer': 'woman'},\n", + " {'question_id': '202024849', 'answer': 'Park', 'gt_answer': 'park'},\n", + " {'question_id': '20340632', 'answer': 'Resting', 'gt_answer': 'playing'},\n", + " {'question_id': '201498444', 'answer': 'Yes', 'gt_answer': 'yes'},\n", + " {'question_id': '201037030', 'answer': 'No', 'gt_answer': 'no'},\n", + " {'question_id': '2044903', 'answer': 'Right', 'gt_answer': 'right'},\n", + " {'question_id': '201593873',\n", + " 'answer': 'Tennis ball',\n", + " 'gt_answer': 'tennis ball'},\n", + " {'question_id': '201593875',\n", + " 'answer': 'Tennis ball',\n", + " 'gt_answer': 'tennis ball'},\n", + " {'question_id': '201438282', 'answer': 'Home plate', 'gt_answer': 'net'},\n", + " {'question_id': '2075243', 'answer': 'Yes', 'gt_answer': 'yes'},\n", + " {'question_id': '20691469', 'answer': 'Dryer', 'gt_answer': 'shelves'},\n", + " {'question_id': '201407334', 'answer': 'Yes', 'gt_answer': 'no'},\n", + " {'question_id': '20691466', 'answer': 'Green', 'gt_answer': 'black'},\n", + " {'question_id': '201407331', 'answer': 'Fence', 'gt_answer': 'fence'},\n", + " {'question_id': '20177899', 'answer': 'Small', 'gt_answer': 'small'},\n", + " {'question_id': '201735690', 'answer': 'Yes', 'gt_answer': 'yes'},\n", + " {'question_id': '201987605', 'answer': 'No', 'gt_answer': 'yes'},\n", + " {'question_id': '20169603', 'answer': 'Yes', 'gt_answer': 'no'},\n", + " {'question_id': '201983045', 'answer': 'No', 'gt_answer': 'yes'},\n", + " {'question_id': '201864553', 'answer': 'Yes', 'gt_answer': 'no'},\n", + " {'question_id': '20706289', 'answer': 'No', 'gt_answer': 'no'},\n", + " {'question_id': '202003684', 'answer': 'Yes', 'gt_answer': 'no'},\n", + " {'question_id': '201481487', 'answer': 'Umbrella', 'gt_answer': 'umbrella'},\n", + " {'question_id': '201756642', 'answer': 'No', 'gt_answer': 'no'},\n", + " {'question_id': '202285540', 'answer': 'No', 'gt_answer': 'no'},\n", + " {'question_id': '202119928', 'answer': 'Right', 'gt_answer': 'right'},\n", + " {'question_id': '20984434', 'answer': '16', 'gt_answer': 'young'},\n", + " {'question_id': '20340983', 'answer': 'No', 'gt_answer': 'yes'},\n", + " {'question_id': '20984387', 'answer': 'Boy', 'gt_answer': 'skater'},\n", + " {'question_id': '201347368', 'answer': 'Skating', 'gt_answer': 'skating'},\n", + " {'question_id': '20541514', 'answer': 'Right', 'gt_answer': 'right'},\n", + " {'question_id': '20340988', 'answer': 'Wide', 'gt_answer': 'wide'},\n", + " {'question_id': '202006219', 'answer': 'Brown', 'gt_answer': 'tan'},\n", + " {'question_id': '20705745', 'answer': 'Computer', 'gt_answer': 'monitor'},\n", + " {'question_id': '201556920', 'answer': 'Laptop', 'gt_answer': 'laptop'},\n", + " {'question_id': '201956961', 'answer': 'Open', 'gt_answer': 'closed'},\n", + " {'question_id': '202006213', 'answer': 'Yes', 'gt_answer': 'yes'},\n", + " {'question_id': '20756914', 'answer': 'Yes', 'gt_answer': 'yes'},\n", + " {'question_id': '202243791', 'answer': 'Brown', 'gt_answer': 'red'},\n", + " {'question_id': '20330509', 'answer': 'Front', 'gt_answer': 'behind'},\n", + " {'question_id': '20247860', 'answer': 'Yes', 'gt_answer': 'yes'},\n", + " {'question_id': '20596312', 'answer': 'Standing', 'gt_answer': 'standing'},\n", + " {'question_id': '201247081', 'answer': 'Plant', 'gt_answer': 'plant'},\n", + " {'question_id': '202245872', 'answer': 'No', 'gt_answer': 'no'},\n", + " {'question_id': '20551315', 'answer': 'Yes', 'gt_answer': 'yes'},\n", + " {'question_id': '202162658', 'answer': 'Black', 'gt_answer': 'black'},\n", + " {'question_id': '20667821', 'answer': 'Tank top', 'gt_answer': 'shirt'},\n", + " {'question_id': '202158779', 'answer': 'Concrete', 'gt_answer': 'concrete'},\n", + " {'question_id': '201795384', 'answer': 'Bench', 'gt_answer': 'bench'},\n", + " {'question_id': '201795385', 'answer': 'Yes', 'gt_answer': 'yes'},\n", + " {'question_id': '201795382', 'answer': 'No', 'gt_answer': 'no'},\n", + " {'question_id': '202158778', 'answer': 'Concrete', 'gt_answer': 'concrete'},\n", + " {'question_id': '201766528', 'answer': 'Left', 'gt_answer': 'left'},\n", + " {'question_id': '202241056', 'answer': 'White', 'gt_answer': 'white'},\n", + " {'question_id': '202000663', 'answer': 'No', 'gt_answer': 'no'},\n", + " {'question_id': '2056075', 'answer': 'No', 'gt_answer': 'no'},\n", + " {'question_id': '20968332', 'answer': 'Sign', 'gt_answer': 'pole'},\n", + " {'question_id': '20541546', 'answer': 'Right', 'gt_answer': 'right'},\n", + " {'question_id': '201393608', 'answer': 'Gray', 'gt_answer': 'dark'},\n", + " {'question_id': '20954058', 'answer': 'Jacket', 'gt_answer': 'receipt'},\n", + " {'question_id': '20637305', 'answer': 'Stove', 'gt_answer': 'stove'},\n", + " {'question_id': '20516049', 'answer': 'Left', 'gt_answer': 'left'},\n", + " {'question_id': '201393601', 'answer': 'Sock', 'gt_answer': 'sock'},\n", + " {'question_id': '201393603', 'answer': 'Sock', 'gt_answer': 'sock'},\n", + " {'question_id': '201795116', 'answer': 'Elephant', 'gt_answer': 'elephant'},\n", + " {'question_id': '20782987', 'answer': 'Chair', 'gt_answer': 'chair'},\n", + " {'question_id': '20151769', 'answer': 'No', 'gt_answer': 'no'},\n", + " {'question_id': '202285048', 'answer': 'Yes', 'gt_answer': 'no'},\n", + " {'question_id': '201882905', 'answer': 'Phone', 'gt_answer': 'television'},\n", + " {'question_id': '202262837', 'answer': 'Yes', 'gt_answer': 'no'},\n", + " {'question_id': '201879568', 'answer': 'Truck', 'gt_answer': 'truck'},\n", + " {'question_id': '20596524', 'answer': 'Concrete', 'gt_answer': 'concrete'},\n", + " {'question_id': '20611554', 'answer': 'Large', 'gt_answer': 'large'},\n", + " {'question_id': '201669332', 'answer': 'Yes', 'gt_answer': 'yes'},\n", + " {'question_id': '201624192', 'answer': 'Yes', 'gt_answer': 'yes'},\n", + " {'question_id': '201804274', 'answer': 'Yes', 'gt_answer': 'yes'},\n", + " {'question_id': '201972699', 'answer': 'No', 'gt_answer': 'yes'},\n", + " {'question_id': '201770658', 'answer': 'Yes', 'gt_answer': 'yes'},\n", + " {'question_id': '201574214', 'answer': 'No', 'gt_answer': 'no'},\n", + " {'question_id': '202156687', 'answer': 'No', 'gt_answer': 'no'},\n", + " {'question_id': '201885214', 'answer': 'Right', 'gt_answer': 'right'},\n", + " {'question_id': '201896270', 'answer': 'Large', 'gt_answer': 'large'},\n", + " {'question_id': '20982174',\n", + " 'answer': 'Long sleeved',\n", + " 'gt_answer': 'long sleeved'},\n", + " {'question_id': '20982179', 'answer': 'Black', 'gt_answer': 'black'},\n", + " {'question_id': '201896318', 'answer': 'Cake', 'gt_answer': 'cake'},\n", + " {'question_id': '20618861', 'answer': 'Yes', 'gt_answer': 'yes'},\n", + " {'question_id': '202147831', 'answer': 'Spectator', 'gt_answer': 'athlete'},\n", + " {'question_id': '201996785', 'answer': 'Black', 'gt_answer': 'gold'},\n", + " {'question_id': '20434808', 'answer': 'Yes', 'gt_answer': 'yes'},\n", + " {'question_id': '20618869', 'answer': 'No', 'gt_answer': 'no'},\n", + " {'question_id': '201974935', 'answer': 'No', 'gt_answer': 'no'},\n", + " {'question_id': '202258138', 'answer': 'Plastic', 'gt_answer': 'cloth'},\n", + " {'question_id': '202258139', 'answer': 'Yes', 'gt_answer': 'yes'},\n", + " {'question_id': '20385517', 'answer': 'No', 'gt_answer': 'yes'},\n", + " {'question_id': '2062362', 'answer': 'Yes', 'gt_answer': 'no'},\n", + " {'question_id': '201735165', 'answer': 'No', 'gt_answer': 'no'},\n", + " {'question_id': '20699279', 'answer': 'Jacket', 'gt_answer': 'jacket'},\n", + " {'question_id': '201959852', 'answer': 'Large', 'gt_answer': 'large'},\n", + " {'question_id': '201439380', 'answer': 'Yes', 'gt_answer': 'yes'},\n", + " {'question_id': '201757688', 'answer': 'Yes', 'gt_answer': 'yes'},\n", + " {'question_id': '201429017', 'answer': 'Stove', 'gt_answer': 'stove'},\n", + " {'question_id': '202133566', 'answer': 'Yes', 'gt_answer': 'no'},\n", + " {'question_id': '201902557', 'answer': 'Monitor', 'gt_answer': 'router'},\n", + " {'question_id': '202133564',\n", + " 'answer': 'Skateboarder',\n", + " 'gt_answer': 'skateboarder'},\n", + " {'question_id': '202133563',\n", + " 'answer': 'Skateboarder',\n", + " 'gt_answer': 'skateboarder'},\n", + " {'question_id': '201902552', 'answer': 'Keyboard', 'gt_answer': 'router'},\n", + " {'question_id': '202244266', 'answer': 'Rice', 'gt_answer': 'rice'},\n", + " {'question_id': '201188320', 'answer': 'Man', 'gt_answer': 'man'},\n", + " {'question_id': '201951734', 'answer': 'Bus', 'gt_answer': 'van'},\n", + " {'question_id': '20385778', 'answer': 'No', 'gt_answer': 'no'},\n", + " {'question_id': '201188325', 'answer': 'No', 'gt_answer': 'no'},\n", + " {'question_id': '20462152', 'answer': 'Woman', 'gt_answer': 'man'},\n", + " {'question_id': '20462153', 'answer': 'Woman', 'gt_answer': 'man'},\n", + " {'question_id': '20462158', 'answer': 'Woman', 'gt_answer': 'man'},\n", + " {'question_id': '201804491', 'answer': 'No', 'gt_answer': 'yes'},\n", + " {'question_id': '20953087', 'answer': 'Man', 'gt_answer': 'player'},\n", + " {'question_id': '20891689', 'answer': 'Child', 'gt_answer': 'child'},\n", + " {'question_id': '20953081', 'answer': 'Man', 'gt_answer': 'player'},\n", + " {'question_id': '201737879', 'answer': 'Left', 'gt_answer': 'left'},\n", + " {'question_id': '20953088', 'answer': 'Man', 'gt_answer': 'player'},\n", + " {'question_id': '20827504', 'answer': 'Couch', 'gt_answer': 'chairs'},\n", + " {'question_id': '201322631', 'answer': 'Left', 'gt_answer': 'left'},\n", + " {'question_id': '202174096', 'answer': 'Black', 'gt_answer': 'black'},\n", + " {'question_id': '201935943', 'answer': 'Yes', 'gt_answer': 'yes'},\n", + " {'question_id': '20978612', 'answer': 'Wood', 'gt_answer': 'wood'},\n", + " {'question_id': '20434779', 'answer': 'Plastic', 'gt_answer': 'wood'},\n", + " {'question_id': '201556748', 'answer': 'Keyboard', 'gt_answer': 'keyboard'},\n", + " {'question_id': '201030339', 'answer': 'Small', 'gt_answer': 'small'},\n", + " {'question_id': '20434770', 'answer': 'No', 'gt_answer': 'no'},\n", + " {'question_id': '20827501', 'answer': 'Bar stool', 'gt_answer': 'chairs'},\n", + " {'question_id': '20636816', 'answer': 'Open', 'gt_answer': 'closed'},\n", + " {'question_id': '201935924', 'answer': 'Small', 'gt_answer': 'small'},\n", + " {'question_id': '20262487', 'answer': 'Yes', 'gt_answer': 'yes'},\n", + " {'question_id': '201185896', 'answer': 'Frisbee', 'gt_answer': 'frisbee'},\n", + " {'question_id': '201185893', 'answer': 'Yes', 'gt_answer': 'yes'},\n", + " {'question_id': '201935929', 'answer': 'No', 'gt_answer': 'yes'},\n", + " {'question_id': '20246006', 'answer': 'No', 'gt_answer': 'no'},\n", + " {'question_id': '201735422', 'answer': 'Desk', 'gt_answer': 'desk'},\n", + " {'question_id': '201982950', 'answer': 'Brown', 'gt_answer': 'brown'},\n", + " {'question_id': '202023602', 'answer': 'Plastic', 'gt_answer': 'metal'},\n", + " {'question_id': '202156925', 'answer': 'Elephant', 'gt_answer': 'elephants'},\n", + " {'question_id': '202156922', 'answer': 'Elephant', 'gt_answer': 'elephants'},\n", + " {'question_id': '201676219', 'answer': 'Laptop', 'gt_answer': 'computer'},\n", + " {'question_id': '202156920', 'answer': 'Elephant', 'gt_answer': 'elephants'},\n", + " {'question_id': '20785827', 'answer': 'Gray', 'gt_answer': 'white'},\n", + " {'question_id': '201804660', 'answer': 'Speaker', 'gt_answer': 'television'},\n", + " {'question_id': '201804661', 'answer': 'Speaker', 'gt_answer': 'television'},\n", + " {'question_id': '202244695', 'answer': 'No', 'gt_answer': 'no'},\n", + " {'question_id': '202179615', 'answer': 'Yes', 'gt_answer': 'no'},\n", + " {'question_id': '20827509', 'answer': 'Left', 'gt_answer': 'left'},\n", + " {'question_id': '20734057', 'answer': 'Left', 'gt_answer': 'left'},\n", + " {'question_id': '20783255', 'answer': 'Laptop', 'gt_answer': 'screen'},\n", + " {'question_id': '20929612', 'answer': 'Yes', 'gt_answer': 'yes'},\n", + " {'question_id': '20349965',\n", + " 'answer': 'Sleeveless',\n", + " 'gt_answer': 'sleeveless'},\n", + " {'question_id': '20783250', 'answer': 'Yes', 'gt_answer': 'yes'},\n", + " {'question_id': '202012477', 'answer': 'Tv', 'gt_answer': 'cabinets'},\n", + " {'question_id': '202012476', 'answer': 'Man', 'gt_answer': 'woman'},\n", + " {'question_id': '20862758', 'answer': 'No', 'gt_answer': 'no'},\n", + " {'question_id': '20661463', 'answer': 'No', 'gt_answer': 'no'},\n", + " {'question_id': '20262625', 'answer': 'Tall', 'gt_answer': 'tall'},\n", + " {'question_id': '20661461', 'answer': 'Grill', 'gt_answer': 'door'},\n", + " {'question_id': '201663548', 'answer': 'Dirty', 'gt_answer': 'clean'},\n", + " {'question_id': '202223163', 'answer': 'Man', 'gt_answer': 'man'},\n", + " {'question_id': '201061187', 'answer': 'No', 'gt_answer': 'no'},\n", + " {'question_id': '20226469', 'answer': 'Shirt', 'gt_answer': 'shirt'},\n", + " {'question_id': '20518474', 'answer': 'No', 'gt_answer': 'no'},\n", + " {'question_id': '202023469', 'answer': 'Bed', 'gt_answer': 'bed'},\n", + " {'question_id': '202007027', 'answer': 'Cabinet', 'gt_answer': 'cabinet'},\n", + " {'question_id': '202007024', 'answer': 'Tiles', 'gt_answer': 'cabinet'},\n", + " ...]" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "results_path = '/fs/cfar-projects/low-bit-vision/llava/gptq/gqa' + '/results_v4_l4.json'\n", + "\n", + "\n", + "with open(results_path, 'r') as f:\n", + " results = json.load(f)\n", + " \n", + " # post-processing llava output\n", + " answers = results['answers']\n", + " for ans in answers:\n", + " ans['answer'] = ans['answer'].split('ASSISTANT: ')[-1]\n", + "\n", + "answers" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "7d3d9420", + "metadata": {}, + "outputs": [], + "source": [ + "# TODO:\n", + "results_dir = '/fs/cfar-projects/low-bit-vision/llava/awq/gqa'\n", + "df_gqa_awq = compute_scores(results_dir, 'gqa')" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "56c33e22", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.microsoft.datawrangler.viewer.v0+json": { + "columns": [ + { + "name": "index", + "rawType": "int64", + "type": "integer" + }, + { + "name": "vision_bits", + "rawType": "int64", + "type": "integer" + }, + { + "name": "language_bits", + "rawType": "int64", + "type": "integer" + }, + { + "name": "acc", + "rawType": "float64", + "type": "float" + } + ], + "conversionMethod": "pd.DataFrame", + "ref": "470dd1cf-9baf-4570-bfbd-4c85f4a79214", + "rows": [ + [ + "0", + "6", + "5", + "0.0" + ], + [ + "1", + "8", + "4", + "0.0" + ], + [ + "2", + "3", + "5", + "0.0" + ], + [ + "3", + "5", + "6", + "60.68" + ], + [ + "4", + "6", + "6", + "61.08" + ], + [ + "5", + "2", + "4", + "0.0" + ], + [ + "6", + "6", + "16", + "61.13" + ], + [ + "7", + "16", + "3", + "0.0" + ], + [ + "8", + "3", + "2", + "0.0" + ], + [ + "9", + "4", + "5", + "0.0" + ], + [ + "10", + "16", + "16", + "61.47" + ], + [ + "11", + "8", + "8", + "61.39" + ], + [ + "12", + "6", + "4", + "0.0" + ], + [ + "13", + "5", + "2", + "0.0" + ], + [ + "14", + "8", + "6", + "61.28" + ], + [ + "15", + "3", + "3", + "0.0" + ], + [ + "16", + "6", + "3", + "0.0" + ], + [ + "17", + "6", + "8", + "61.16" + ], + [ + "18", + "5", + "3", + "0.0" + ], + [ + "19", + "2", + "5", + "0.0" + ], + [ + "20", + "8", + "2", + "0.0" + ], + [ + "21", + "16", + "2", + "0.0" + ], + [ + "22", + "2", + "16", + "37.12" + ], + [ + "23", + "4", + "16", + "60.2" + ], + [ + "24", + "8", + "16", + "61.39" + ], + [ + "25", + "6", + "2", + "0.0" + ], + [ + "26", + "2", + "2", + "0.0" + ], + [ + "27", + "8", + "3", + "0.0" + ], + [ + "28", + "3", + "6", + "56.69" + ], + [ + "29", + "16", + "6", + "61.34" + ], + [ + "30", + "4", + "8", + "60.13" + ], + [ + "31", + "2", + "8", + "37.16" + ], + [ + "32", + "8", + "5", + "0.0" + ], + [ + "33", + "16", + "4", + "0.0" + ], + [ + "34", + "5", + "4", + "0.0" + ], + [ + "35", + "16", + "8", + "61.39" + ], + [ + "36", + "3", + "8", + "56.52" + ], + [ + "37", + "4", + "2", + "0.0" + ], + [ + "38", + "5", + "5", + "0.0" + ], + [ + "39", + "3", + "16", + "56.54" + ], + [ + "40", + "2", + "6", + "36.73" + ], + [ + "41", + "4", + "6", + "60.09" + ], + [ + "42", + "16", + "5", + "0.0" + ], + [ + "43", + "5", + "8", + "60.72" + ], + [ + "44", + "5", + "16", + "60.76" + ], + [ + "45", + "3", + "4", + "0.0" + ], + [ + "46", + "4", + "4", + "0.0" + ], + [ + "47", + "4", + "3", + "0.0" + ], + [ + "48", + "2", + "3", + "0.0" + ] + ], + "shape": { + "columns": 3, + "rows": 49 + } + }, + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
vision_bitslanguage_bitsacc
0650.00
1840.00
2350.00
35660.68
46661.08
5240.00
661661.13
71630.00
8320.00
9450.00
10161661.47
118861.39
12640.00
13520.00
148661.28
15330.00
16630.00
176861.16
18530.00
19250.00
20820.00
211620.00
2221637.12
2341660.20
2481661.39
25620.00
26220.00
27830.00
283656.69
2916661.34
304860.13
312837.16
32850.00
331640.00
34540.00
3516861.39
363856.52
37420.00
38550.00
3931656.54
402636.73
414660.09
421650.00
435860.72
4451660.76
45340.00
46440.00
47430.00
48230.00
\n", + "
" + ], + "text/plain": [ + " vision_bits language_bits acc\n", + "0 6 5 0.00\n", + "1 8 4 0.00\n", + "2 3 5 0.00\n", + "3 5 6 60.68\n", + "4 6 6 61.08\n", + "5 2 4 0.00\n", + "6 6 16 61.13\n", + "7 16 3 0.00\n", + "8 3 2 0.00\n", + "9 4 5 0.00\n", + "10 16 16 61.47\n", + "11 8 8 61.39\n", + "12 6 4 0.00\n", + "13 5 2 0.00\n", + "14 8 6 61.28\n", + "15 3 3 0.00\n", + "16 6 3 0.00\n", + "17 6 8 61.16\n", + "18 5 3 0.00\n", + "19 2 5 0.00\n", + "20 8 2 0.00\n", + "21 16 2 0.00\n", + "22 2 16 37.12\n", + "23 4 16 60.20\n", + "24 8 16 61.39\n", + "25 6 2 0.00\n", + "26 2 2 0.00\n", + "27 8 3 0.00\n", + "28 3 6 56.69\n", + "29 16 6 61.34\n", + "30 4 8 60.13\n", + "31 2 8 37.16\n", + "32 8 5 0.00\n", + "33 16 4 0.00\n", + "34 5 4 0.00\n", + "35 16 8 61.39\n", + "36 3 8 56.52\n", + "37 4 2 0.00\n", + "38 5 5 0.00\n", + "39 3 16 56.54\n", + "40 2 6 36.73\n", + "41 4 6 60.09\n", + "42 16 5 0.00\n", + "43 5 8 60.72\n", + "44 5 16 60.76\n", + "45 3 4 0.00\n", + "46 4 4 0.00\n", + "47 4 3 0.00\n", + "48 2 3 0.00" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_gqa_awq" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "021226cb", + "metadata": {}, + "outputs": [], + "source": [ + "df_gqa_awq.to_csv('/fs/cfar-projects/low-bit-vision/final_results/llava/llava_awq_gqa.csv', index=None)" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "a1118157", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "loading VQA annotations and questions into memory...\n", + "0:00:06.638954\n", + "creating index...\n", + "index created!\n", + "Loading and preparing results... \n", + "DONE (t=0.01s)\n", + "creating index...\n", + "index created!\n", + "computing accuracy\n", + "Finshed Percent: [####################] 99% Done computing accuracy\n", + "{'agg_metrics': 0.0, 'other': 0.0, 'yes/no': 0.0, 'number': 0.0}\n" + ] + } + ], + "source": [ + "# TODO:\n", + "results_dir = '/fs/cfar-projects/low-bit-vision/llava/awq/vqav2'\n", + "df_vqav2_awq = compute_scores(results_dir, 'vqav2')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "60bda52d", + "metadata": {}, + "outputs": [], + "source": [ + "df_vqav2_awq.head(5)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d8c4e742", + "metadata": {}, + "outputs": [], + "source": [ + "df_vqav2_awq.to_csv('/fs/cfar-projects/low-bit-vision/final_results/llava/awq_vqav2.csv', index=None)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "a46c57cf", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 0%| | 0/46 [00:00>\n", + "Traceback (most recent call last):\n", + " File \"/fs/nexus-scratch/vla/micromamba/envs/MMQ_LLAVA/lib/python3.10/site-packages/ipykernel/ipkernel.py\", line 775, in _clean_thread_parent_frames\n", + " def _clean_thread_parent_frames(\n", + " File \"_pydevd_bundle\\\\pydevd_cython.pyx\", line 1697, in _pydevd_bundle.pydevd_cython.SafeCallWrapper.__call__\n", + " File \"_pydevd_bundle\\\\pydevd_cython.pyx\", line 2017, in _pydevd_bundle.pydevd_cython.ThreadTracer.__call__\n", + " File \"/fs/nexus-scratch/vla/micromamba/envs/MMQ_LLAVA/lib/python3.10/site-packages/debugpy/_vendored/pydevd/_pydev_bundle/pydev_is_thread_alive.py\", line 16, in is_thread_alive\n", + " def is_thread_alive(t):\n", + "KeyboardInterrupt: \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0:00:05.758219\n", + "creating index...\n", + "index created!\n", + "Loading and preparing results... \n", + "DONE (t=0.30s)\n", + "creating index...\n", + "index created!\n", + "computing accuracy\n", + "Finshed Percent: [##------------------] 12% " + ] + }, + { + "ename": "", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[1;31mCannot execute code, session has been disposed. Please try restarting the Kernel." + ] + }, + { + "ename": "", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[1;31mCannot execute code, session has been disposed. Please try restarting the Kernel. \n", + "\u001b[1;31mView Jupyter log for further details." + ] + } + ], + "source": [ + "# TODO:\n", + "results_dir = '/fs/cfar-projects/low-bit-vision/llava/gptq/vqav2'\n", + "df_vqav2_gptq = compute_scores(results_dir, 'vqav2')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fbb0860b", + "metadata": {}, + "outputs": [], + "source": [ + "df_vqav2_gptq.to_csv('/fs/cfar-projects/low-bit-vision/final_results/llava/gptq_vqav2.csv', index=None)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "216a3325", + "metadata": {}, + "outputs": [], + "source": [ + "# compute scores across \n", + "\n", + "results_dir = '/fs/cfar-projects/low-bit-vision/llava/gptq/gqa'\n", + "scorer = ScoringPipeline()\n", + "\n", + "gather = []\n", + "for results_file in os.listdir(results_dir):\n", + "\n", + " results_path = os.path.join(results_dir, results_file)\n", + "\n", + " with open(results_path, 'r') as f:\n", + " results = json.load(f)\n", + " \n", + " # post-processing llava output\n", + " answers = results['answers']\n", + " for ans in answers:\n", + " ans['answer'] = ans['answer'].split('ASSISTANT: ')[-1]\n", + "\n", + "\n", + " # print(scorer.compute_scores(answers, \"gqa\"))\n", + " score = scorer.compute_scores(answers, \"gqa\")['acc']\n", + " \n", + " record = dict(\n", + " vision_bits = results['vision_bits'],\n", + " language_bits = results['language_bits'],\n", + " acc = score\n", + " )\n", + "\n", + " gather.append(record)\n", + " \n" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "97e75355", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[{'vision_bits': 6, 'language_bits': 5, 'acc': 61.34},\n", + " {'vision_bits': 8, 'language_bits': 4, 'acc': 60.79},\n", + " {'vision_bits': 3, 'language_bits': 5, 'acc': 59.59},\n", + " {'vision_bits': 5, 'language_bits': 6, 'acc': 61.19},\n", + " {'vision_bits': 6, 'language_bits': 6, 'acc': 61.33},\n", + " {'vision_bits': 2, 'language_bits': 4, 'acc': 35.78},\n", + " {'vision_bits': 6, 'language_bits': 16, 'acc': 61.27},\n", + " {'vision_bits': 16, 'language_bits': 3, 'acc': 55.82},\n", + " {'vision_bits': 3, 'language_bits': 2, 'acc': 0.0},\n", + " {'vision_bits': 4, 'language_bits': 5, 'acc': 60.81},\n", + " {'vision_bits': 16, 'language_bits': 16, 'acc': 61.46},\n", + " {'vision_bits': 8, 'language_bits': 8, 'acc': 61.47},\n", + " {'vision_bits': 6, 'language_bits': 4, 'acc': 60.8},\n", + " {'vision_bits': 5, 'language_bits': 2, 'acc': 0.0},\n", + " {'vision_bits': 8, 'language_bits': 6, 'acc': 61.35},\n", + " {'vision_bits': 3, 'language_bits': 3, 'acc': 54.71},\n", + " {'vision_bits': 6, 'language_bits': 3, 'acc': 56.26},\n", + " {'vision_bits': 6, 'language_bits': 8, 'acc': 61.29},\n", + " {'vision_bits': 5, 'language_bits': 3, 'acc': 56.54},\n", + " {'vision_bits': 2, 'language_bits': 5, 'acc': 37.09},\n", + " {'vision_bits': 8, 'language_bits': 2, 'acc': 0.0},\n", + " {'vision_bits': 16, 'language_bits': 2, 'acc': 0.0},\n", + " {'vision_bits': 2, 'language_bits': 16, 'acc': 37.2},\n", + " {'vision_bits': 4, 'language_bits': 16, 'acc': 60.88},\n", + " {'vision_bits': 8, 'language_bits': 16, 'acc': 61.42},\n", + " {'vision_bits': 6, 'language_bits': 2, 'acc': 0.01},\n", + " {'vision_bits': 2, 'language_bits': 2, 'acc': 0.0},\n", + " {'vision_bits': 8, 'language_bits': 3, 'acc': 56.5},\n", + " {'vision_bits': 3, 'language_bits': 6, 'acc': 59.45},\n", + " {'vision_bits': 16, 'language_bits': 6, 'acc': 61.3},\n", + " {'vision_bits': 4, 'language_bits': 8, 'acc': 60.9},\n", + " {'vision_bits': 2, 'language_bits': 8, 'acc': 37.22},\n", + " {'vision_bits': 8, 'language_bits': 5, 'acc': 61.42},\n", + " {'vision_bits': 16, 'language_bits': 4, 'acc': 60.66},\n", + " {'vision_bits': 5, 'language_bits': 4, 'acc': 60.53},\n", + " {'vision_bits': 16, 'language_bits': 8, 'acc': 61.45},\n", + " {'vision_bits': 3, 'language_bits': 8, 'acc': 59.53},\n", + " {'vision_bits': 4, 'language_bits': 2, 'acc': 0.0},\n", + " {'vision_bits': 5, 'language_bits': 5, 'acc': 61.13},\n", + " {'vision_bits': 3, 'language_bits': 16, 'acc': 59.48},\n", + " {'vision_bits': 2, 'language_bits': 6, 'acc': 37.08},\n", + " {'vision_bits': 4, 'language_bits': 6, 'acc': 60.95},\n", + " {'vision_bits': 16, 'language_bits': 5, 'acc': 61.32},\n", + " {'vision_bits': 5, 'language_bits': 8, 'acc': 61.3},\n", + " {'vision_bits': 5, 'language_bits': 16, 'acc': 61.23},\n", + " {'vision_bits': 3, 'language_bits': 4, 'acc': 58.69},\n", + " {'vision_bits': 4, 'language_bits': 4, 'acc': 60.45},\n", + " {'vision_bits': 4, 'language_bits': 3, 'acc': 56.65},\n", + " {'vision_bits': 2, 'language_bits': 3, 'acc': 33.13}]" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "gather" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cd929674", + "metadata": {}, + "outputs": [], + "source": [ + "df_gqa = pd.DataFrame(gather)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "8476abfc", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.microsoft.datawrangler.viewer.v0+json": { + "columns": [ + { + "name": "index", + "rawType": "int64", + "type": "integer" + }, + { + "name": "vision_bits", + "rawType": "int64", + "type": "integer" + }, + { + "name": "language_bits", + "rawType": "int64", + "type": "integer" + }, + { + "name": "acc", + "rawType": "float64", + "type": "float" + } + ], + "conversionMethod": "pd.DataFrame", + "ref": "328a8e49-1755-4fc0-9717-f2b89c16b2ed", + "rows": [ + [ + "0", + "6", + "5", + "61.34" + ], + [ + "1", + "8", + "4", + "60.79" + ], + [ + "2", + "3", + "5", + "59.59" + ], + [ + "3", + "5", + "6", + "61.19" + ], + [ + "4", + "6", + "6", + "61.33" + ], + [ + "5", + "2", + "4", + "35.78" + ], + [ + "6", + "6", + "16", + "61.27" + ], + [ + "7", + "16", + "3", + "55.82" + ], + [ + "8", + "3", + "2", + "0.0" + ], + [ + "9", + "4", + "5", + "60.81" + ], + [ + "10", + "16", + "16", + "61.46" + ], + [ + "11", + "8", + "8", + "61.47" + ], + [ + "12", + "6", + "4", + "60.8" + ], + [ + "13", + "5", + "2", + "0.0" + ], + [ + "14", + "8", + "6", + "61.35" + ], + [ + "15", + "3", + "3", + "54.71" + ], + [ + "16", + "6", + "3", + "56.26" + ], + [ + "17", + "6", + "8", + "61.29" + ], + [ + "18", + "5", + "3", + "56.54" + ], + [ + "19", + "2", + "5", + "37.09" + ], + [ + "20", + "8", + "2", + "0.0" + ], + [ + "21", + "16", + "2", + "0.0" + ], + [ + "22", + "2", + "16", + "37.2" + ], + [ + "23", + "4", + "16", + "60.88" + ], + [ + "24", + "8", + "16", + "61.42" + ], + [ + "25", + "6", + "2", + "0.01" + ], + [ + "26", + "2", + "2", + "0.0" + ], + [ + "27", + "8", + "3", + "56.5" + ], + [ + "28", + "3", + "6", + "59.45" + ], + [ + "29", + "16", + "6", + "61.3" + ], + [ + "30", + "4", + "8", + "60.9" + ], + [ + "31", + "2", + "8", + "37.22" + ], + [ + "32", + "8", + "5", + "61.42" + ], + [ + "33", + "16", + "4", + "60.66" + ], + [ + "34", + "5", + "4", + "60.53" + ], + [ + "35", + "16", + "8", + "61.45" + ], + [ + "36", + "3", + "8", + "59.53" + ], + [ + "37", + "4", + "2", + "0.0" + ], + [ + "38", + "5", + "5", + "61.13" + ], + [ + "39", + "3", + "16", + "59.48" + ], + [ + "40", + "2", + "6", + "37.08" + ], + [ + "41", + "4", + "6", + "60.95" + ], + [ + "42", + "16", + "5", + "61.32" + ], + [ + "43", + "5", + "8", + "61.3" + ], + [ + "44", + "5", + "16", + "61.23" + ], + [ + "45", + "3", + "4", + "58.69" + ], + [ + "46", + "4", + "4", + "60.45" + ], + [ + "47", + "4", + "3", + "56.65" + ], + [ + "48", + "2", + "3", + "33.13" + ] + ], + "shape": { + "columns": 3, + "rows": 49 + } + }, + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
vision_bitslanguage_bitsacc
06561.34
18460.79
23559.59
35661.19
46661.33
52435.78
661661.27
716355.82
8320.00
94560.81
10161661.46
118861.47
126460.80
13520.00
148661.35
153354.71
166356.26
176861.29
185356.54
192537.09
20820.00
211620.00
2221637.20
2341660.88
2481661.42
25620.01
26220.00
278356.50
283659.45
2916661.30
304860.90
312837.22
328561.42
3316460.66
345460.53
3516861.45
363859.53
37420.00
385561.13
3931659.48
402637.08
414660.95
4216561.32
435861.30
4451661.23
453458.69
464460.45
474356.65
482333.13
\n", + "
" + ], + "text/plain": [ + " vision_bits language_bits acc\n", + "0 6 5 61.34\n", + "1 8 4 60.79\n", + "2 3 5 59.59\n", + "3 5 6 61.19\n", + "4 6 6 61.33\n", + "5 2 4 35.78\n", + "6 6 16 61.27\n", + "7 16 3 55.82\n", + "8 3 2 0.00\n", + "9 4 5 60.81\n", + "10 16 16 61.46\n", + "11 8 8 61.47\n", + "12 6 4 60.80\n", + "13 5 2 0.00\n", + "14 8 6 61.35\n", + "15 3 3 54.71\n", + "16 6 3 56.26\n", + "17 6 8 61.29\n", + "18 5 3 56.54\n", + "19 2 5 37.09\n", + "20 8 2 0.00\n", + "21 16 2 0.00\n", + "22 2 16 37.20\n", + "23 4 16 60.88\n", + "24 8 16 61.42\n", + "25 6 2 0.01\n", + "26 2 2 0.00\n", + "27 8 3 56.50\n", + "28 3 6 59.45\n", + "29 16 6 61.30\n", + "30 4 8 60.90\n", + "31 2 8 37.22\n", + "32 8 5 61.42\n", + "33 16 4 60.66\n", + "34 5 4 60.53\n", + "35 16 8 61.45\n", + "36 3 8 59.53\n", + "37 4 2 0.00\n", + "38 5 5 61.13\n", + "39 3 16 59.48\n", + "40 2 6 37.08\n", + "41 4 6 60.95\n", + "42 16 5 61.32\n", + "43 5 8 61.30\n", + "44 5 16 61.23\n", + "45 3 4 58.69\n", + "46 4 4 60.45\n", + "47 4 3 56.65\n", + "48 2 3 33.13" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_gqa" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "74f4593e", + "metadata": {}, + "outputs": [], + "source": [ + "df_gqa.to_csv('/fs/cfar-projects/low-bit-vision/final_results/llava/gptq_gqa.csv', index=None)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d5ff6b3d", + "metadata": {}, + "outputs": [], + "source": [ + "results_dir = '/fs/cfar-projects/low-bit-vision/llava/gptq/gqa'" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "694c8669", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'answers': [{'question_id': '201307251', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201640614', 'answer': 'Lady', 'gt_answer': 'women'}, {'question_id': '202225914', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '2062325', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201303229', 'answer': 'Tall', 'gt_answer': 'short'}, {'question_id': '201902997', 'answer': 'Computer', 'gt_answer': 'keyboard'}, {'question_id': '20567512', 'answer': 'Beach', 'gt_answer': 'ocean'}, {'question_id': '20136592', 'answer': 'Red', 'gt_answer': 'red'}, {'question_id': '20602803', 'answer': 'Brown', 'gt_answer': 'brown'}, {'question_id': '201079951', 'answer': 'Curtain', 'gt_answer': 'drapes'}, {'question_id': '201079952', 'answer': 'Curtain', 'gt_answer': 'drapes'}, {'question_id': '20982537', 'answer': 'Man', 'gt_answer': 'woman'}, {'question_id': '201079958', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202218649', 'answer': 'Picture', 'gt_answer': 'picture'}, {'question_id': '20609782', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201757757', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201902993', 'answer': 'Keyboard', 'gt_answer': 'keyboard'}, {'question_id': '20306193', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20183468', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20753400', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20151723', 'answer': 'Left', 'gt_answer': 'right'}, {'question_id': '201030735', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201637161', 'answer': 'White', 'gt_answer': 'white'}, {'question_id': '202218839', 'answer': 'Kettle', 'gt_answer': 'pan'}, {'question_id': '20982539', 'answer': 'Left', 'gt_answer': 'table'}, {'question_id': '201110833', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20866249', 'answer': 'Small', 'gt_answer': 'large'}, {'question_id': '201110525', 'answer': 'Dessert', 'gt_answer': 'marshmallow'}, {'question_id': '20120533', 'answer': 'Aluminum', 'gt_answer': 'aluminum'}, {'question_id': '201952977', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '201497576', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20866242', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201455911', 'answer': 'White', 'gt_answer': 'white'}, {'question_id': '20856965', 'answer': 'Black', 'gt_answer': 'white'}, {'question_id': '2059565', 'answer': 'Brown', 'gt_answer': 'red'}, {'question_id': '20856960', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201079954', 'answer': 'Window', 'gt_answer': 'window'}, {'question_id': '201548894', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '201573912', 'answer': 'Concrete', 'gt_answer': 'concrete'}, {'question_id': '202243820', 'answer': 'Large', 'gt_answer': 'small'}, {'question_id': '201573918', 'answer': 'Concrete', 'gt_answer': 'concrete'}, {'question_id': '201974972', 'answer': 'Tank top', 'gt_answer': 'tank top'}, {'question_id': '201974971', 'answer': 'Tank top', 'gt_answer': 'tank top'}, {'question_id': '201974976', 'answer': 'Cap', 'gt_answer': 'hat'}, {'question_id': '201996743', 'answer': 'Off', 'gt_answer': 'off'}, {'question_id': '20797666', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20797665', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201974979', 'answer': 'Tank top', 'gt_answer': 'tank top'}, {'question_id': '201156138', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20442334', 'answer': 'Bananas', 'gt_answer': 'bananas'}, {'question_id': '201765651', 'answer': 'White', 'gt_answer': 'white'}, {'question_id': '20442331', 'answer': 'Counter', 'gt_answer': 'bananas'}, {'question_id': '20508243', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '2046473', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '20618932', 'answer': 'Girl', 'gt_answer': 'woman'}, {'question_id': '20442338', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202005788', 'answer': 'Cabinet', 'gt_answer': 'cabinet'}, {'question_id': '201902515', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '201303404', 'answer': 'Gray', 'gt_answer': 'gray'}, {'question_id': '20942157', 'answer': 'Woman', 'gt_answer': 'girl'}, {'question_id': '20942156', 'answer': 'Woman', 'gt_answer': 'girl'}, {'question_id': '20898685', 'answer': 'Waiting', 'gt_answer': 'standing'}, {'question_id': '202116974', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201621328', 'answer': 'Wall', 'gt_answer': 'wall'}, {'question_id': '2076819', 'answer': 'Sparse', 'gt_answer': 'dense'}, {'question_id': '202244099', 'answer': 'Carrot', 'gt_answer': 'cookies'}, {'question_id': '201951771', 'answer': 'Van', 'gt_answer': 'van'}, {'question_id': '201951770', 'answer': 'Van', 'gt_answer': 'van'}, {'question_id': '201621326', 'answer': 'Picture', 'gt_answer': 'picture frame'}, {'question_id': '201233862', 'answer': 'Ramp', 'gt_answer': 'pavement'}, {'question_id': '201951776', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20489632', 'answer': 'Brown', 'gt_answer': 'beige'}, {'question_id': '201623784', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '202023424', 'answer': 'Bed', 'gt_answer': 'bed'}, {'question_id': '20182936', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201654344', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20746468', 'answer': 'Narrow', 'gt_answer': 'narrow'}, {'question_id': '201428996', 'answer': 'Stove', 'gt_answer': 'stove'}, {'question_id': '20899362', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202244009', 'answer': 'Cookie', 'gt_answer': 'cookies'}, {'question_id': '20287556', 'answer': 'Dirty', 'gt_answer': 'clean'}, {'question_id': '20631973', 'answer': 'Home plate', 'gt_answer': 'field'}, {'question_id': '20287551', 'answer': 'Black', 'gt_answer': 'black'}, {'question_id': '201481824', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201663656', 'answer': 'Brown', 'gt_answer': 'light brown'}, {'question_id': '20308576', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201065067', 'answer': 'Dress', 'gt_answer': 'gown'}, {'question_id': '20462070', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20462076', 'answer': 'Hat', 'gt_answer': 'gloves'}, {'question_id': '20462075', 'answer': 'Skis', 'gt_answer': 'gloves'}, {'question_id': '201065062', 'answer': 'Dress', 'gt_answer': 'gown'}, {'question_id': '20754631', 'answer': 'Steps', 'gt_answer': 'stairs'}, {'question_id': '201935960', 'answer': 'Bookshelf', 'gt_answer': 'shelf'}, {'question_id': '20412222', 'answer': 'Chairs', 'gt_answer': 'tables'}, {'question_id': '201935966', 'answer': 'Bookshelf', 'gt_answer': 'shelf'}, {'question_id': '20878946', 'answer': 'Wide', 'gt_answer': 'wide'}, {'question_id': '201947446', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '201498767', 'answer': 'Keyboard', 'gt_answer': 'phone'}, {'question_id': '20306764', 'answer': 'Snowboard', 'gt_answer': 'gift'}, {'question_id': '202144708', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20306767', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201996815', 'answer': 'Glass', 'gt_answer': 'glass'}, {'question_id': '201996813', 'answer': 'Plastic', 'gt_answer': 'glass'}, {'question_id': '202060122', 'answer': 'Dog', 'gt_answer': 'dog'}, {'question_id': '201067797', 'answer': 'Black', 'gt_answer': 'black'}, {'question_id': '20394919', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201497916', 'answer': 'Monitor', 'gt_answer': 'monitor'}, {'question_id': '20303081', 'answer': 'Sitting', 'gt_answer': 'resting'}, {'question_id': '201498727', 'answer': 'Plastic', 'gt_answer': 'plastic'}, {'question_id': '201873473', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20300425', 'answer': 'Car', 'gt_answer': 'cars'}, {'question_id': '20300424', 'answer': 'Car', 'gt_answer': 'cars'}, {'question_id': '20899558', 'answer': 'Silver', 'gt_answer': 'blue'}, {'question_id': '20300420', 'answer': 'Cars', 'gt_answer': 'cars'}, {'question_id': '20300423', 'answer': 'Car', 'gt_answer': 'cars'}, {'question_id': '20836565', 'answer': 'Suitcase', 'gt_answer': 'luggage'}, {'question_id': '201947624', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201947620', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20300428', 'answer': 'Traffic light', 'gt_answer': 'traffic light'}, {'question_id': '201504960', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201947591', 'answer': 'Counter', 'gt_answer': 'countertop'}, {'question_id': '20177575', 'answer': 'Silver', 'gt_answer': 'white'}, {'question_id': '20381557', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201982068', 'answer': 'Cloth', 'gt_answer': 'cloth'}, {'question_id': '201370428', 'answer': 'Black and white', 'gt_answer': 'black and white'}, {'question_id': '201878325', 'answer': 'Woman', 'gt_answer': 'man'}, {'question_id': '201370422', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '2075709', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201896034', 'answer': 'Chair', 'gt_answer': 'table'}, {'question_id': '201065497', 'answer': 'Brunette', 'gt_answer': 'blond'}, {'question_id': '20857175', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20648122', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20636999', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20258752', 'answer': 'Boy', 'gt_answer': 'child'}, {'question_id': '201156466', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201885557', 'answer': 'Male', 'gt_answer': 'male'}, {'question_id': '202081210', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20117834', 'answer': 'Bare', 'gt_answer': 'lush'}, {'question_id': '201438286', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20117781', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201756501', 'answer': 'Color', 'gt_answer': 'color'}, {'question_id': '20716925', 'answer': 'Small', 'gt_answer': 'small'}, {'question_id': '20541270', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '201056079', 'answer': 'Female', 'gt_answer': 'male'}, {'question_id': '20468617', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '2017235', 'answer': 'Black', 'gt_answer': 'black'}, {'question_id': '20427913', 'answer': 'Chair', 'gt_answer': 'chair'}, {'question_id': '20427912', 'answer': 'Chair', 'gt_answer': 'chair'}, {'question_id': '201480278', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201056072', 'answer': 'Boy', 'gt_answer': 'soccer player'}, {'question_id': '20887449', 'answer': 'Computer', 'gt_answer': 'keyboard'}, {'question_id': '20648218', 'answer': 'Man', 'gt_answer': 'policeman'}, {'question_id': '202102931', 'answer': 'Cabinet', 'gt_answer': 'dishwasher'}, {'question_id': '201047479', 'answer': 'Teal', 'gt_answer': 'teal'}, {'question_id': '201370398', 'answer': 'Black', 'gt_answer': 'gray'}, {'question_id': '20672944', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '201752690', 'answer': 'Bike', 'gt_answer': 'bike'}, {'question_id': '20672940', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201752694', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20699276', 'answer': 'Jacket', 'gt_answer': 'jacket'}, {'question_id': '2097681', 'answer': 'Monitor', 'gt_answer': 'monitor'}, {'question_id': '201760591', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201110526', 'answer': 'Dessert', 'gt_answer': 'marshmallow'}, {'question_id': '20673099', 'answer': 'Chair', 'gt_answer': 'chair'}, {'question_id': '20673098', 'answer': 'Chair', 'gt_answer': 'chair'}, {'question_id': '20361249', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201480696', 'answer': 'Rectangle', 'gt_answer': 'rectangular'}, {'question_id': '201879167', 'answer': 'Window', 'gt_answer': 'sneakers'}, {'question_id': '201438759', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20295599', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20204868', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20361243', 'answer': 'Woman', 'gt_answer': 'snowboarder'}, {'question_id': '20667494', 'answer': 'Coffee table', 'gt_answer': 'coffee table'}, {'question_id': '20667492', 'answer': 'Coffee table', 'gt_answer': 'coffee table'}, {'question_id': '20667493', 'answer': 'Coffee table', 'gt_answer': 'coffee table'}, {'question_id': '201056254', 'answer': 'Boy', 'gt_answer': 'spectator'}, {'question_id': '201064816', 'answer': 'Table', 'gt_answer': 'sofa'}, {'question_id': '2097684', 'answer': 'Speaker', 'gt_answer': 'poster'}, {'question_id': '201064812', 'answer': 'Sofa', 'gt_answer': 'sofa'}, {'question_id': '201056252', 'answer': 'Watching', 'gt_answer': 'looking up'}, {'question_id': '201064810', 'answer': 'Bed', 'gt_answer': 'sofa'}, {'question_id': '201935799', 'answer': 'Shelf', 'gt_answer': 'shelf'}, {'question_id': '20756897', 'answer': 'Shirt', 'gt_answer': 'robe'}, {'question_id': '201065430', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '202243368', 'answer': 'White', 'gt_answer': 'white'}, {'question_id': '202121334', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201935797', 'answer': 'Jar', 'gt_answer': 'jar'}, {'question_id': '201639189', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20741279', 'answer': 'Tall', 'gt_answer': 'tall'}, {'question_id': '201143145', 'answer': 'Brown', 'gt_answer': 'dark brown'}, {'question_id': '201669504', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201763810', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202119900', 'answer': 'Refrigerator', 'gt_answer': 'refrigerator'}, {'question_id': '202119903', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20340771', 'answer': 'Table', 'gt_answer': 'chair'}, {'question_id': '20340770', 'answer': 'Chair', 'gt_answer': 'chair'}, {'question_id': '20285405', 'answer': 'Clean', 'gt_answer': 'clean'}, {'question_id': '20340772', 'answer': 'Chair', 'gt_answer': 'chair'}, {'question_id': '201593445', 'answer': 'Cow', 'gt_answer': 'cow'}, {'question_id': '201347404', 'answer': 'Boy', 'gt_answer': 'skateboarder'}, {'question_id': '202100755', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201462472', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '201887286', 'answer': 'Broccoli', 'gt_answer': 'broccoli'}, {'question_id': '20518589', 'answer': 'Counter', 'gt_answer': 'countertop'}, {'question_id': '201590142', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20341130', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201795286', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201832545', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202082102', 'answer': 'Computer', 'gt_answer': 'laptop'}, {'question_id': '20645705', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201795846', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201879789', 'answer': 'Small', 'gt_answer': 'large'}, {'question_id': '201143364', 'answer': 'Flowers', 'gt_answer': 'flowers'}, {'question_id': '20827171', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20940166', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201859351', 'answer': 'Plastic', 'gt_answer': 'plastic'}, {'question_id': '201595841', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20923252', 'answer': 'Truck', 'gt_answer': 'ambulance'}, {'question_id': '202243438', 'answer': 'Truck', 'gt_answer': 'truck'}, {'question_id': '20923257', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20923256', 'answer': 'Truck', 'gt_answer': 'ambulance'}, {'question_id': '201976414', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20865499', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20600137', 'answer': 'Grass', 'gt_answer': 'grass'}, {'question_id': '20600132', 'answer': 'Grass', 'gt_answer': 'grass'}, {'question_id': '20836758', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20632010', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201640606', 'answer': 'Woman', 'gt_answer': 'women'}, {'question_id': '201640605', 'answer': 'Woman', 'gt_answer': 'women'}, {'question_id': '201640602', 'answer': 'Restaurant', 'gt_answer': 'restaurant'}, {'question_id': '20306515', 'answer': 'Camera', 'gt_answer': 'cell phone'}, {'question_id': '202228132', 'answer': 'Speaker', 'gt_answer': 'speaker'}, {'question_id': '20692296', 'answer': 'Book', 'gt_answer': 'books'}, {'question_id': '20692294', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '20710151', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202262373', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20679393', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20710154', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202243431', 'answer': 'Truck', 'gt_answer': 'truck'}, {'question_id': '201556497', 'answer': 'Chair', 'gt_answer': 'shelf'}, {'question_id': '201556499', 'answer': 'Chair', 'gt_answer': 'shelf'}, {'question_id': '20177492', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20711540', 'answer': 'Stuffed bear', 'gt_answer': 'stuffed bear'}, {'question_id': '20899315', 'answer': 'Plastic', 'gt_answer': 'plastic'}, {'question_id': '20711546', 'answer': 'Teddy bear', 'gt_answer': 'stuffed bear'}, {'question_id': '201624174', 'answer': 'Pizza', 'gt_answer': 'pizza'}, {'question_id': '201997192', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20866524', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20866526', 'answer': 'Light', 'gt_answer': 'light'}, {'question_id': '20866521', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '201510327', 'answer': 'Apple', 'gt_answer': 'pear'}, {'question_id': '201492240', 'answer': 'Glove', 'gt_answer': 'baseball mitt'}, {'question_id': '20691652', 'answer': 'Black', 'gt_answer': 'black'}, {'question_id': '202144423', 'answer': 'Black', 'gt_answer': 'brown'}, {'question_id': '20836578', 'answer': 'Table', 'gt_answer': 'table'}, {'question_id': '20349798', 'answer': 'Girl', 'gt_answer': 'woman'}, {'question_id': '201663481', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20692079', 'answer': 'Small', 'gt_answer': 'small'}, {'question_id': '201030507', 'answer': 'Long sleeved', 'gt_answer': 'long sleeved'}, {'question_id': '201997611', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201153290', 'answer': 'Man', 'gt_answer': 'woman'}, {'question_id': '201983816', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201153292', 'answer': 'Giraffe', 'gt_answer': 'giraffe'}, {'question_id': '201153293', 'answer': 'Giraffe', 'gt_answer': 'giraffe'}, {'question_id': '201153297', 'answer': 'Giraffe', 'gt_answer': 'giraffe'}, {'question_id': '20645858', 'answer': 'Green', 'gt_answer': 'white'}, {'question_id': '20441903', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201570581', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201535714', 'answer': 'Jacket', 'gt_answer': 'coat'}, {'question_id': '20652278', 'answer': 'Color', 'gt_answer': 'shape'}, {'question_id': '201535713', 'answer': 'Jacket', 'gt_answer': 'coat'}, {'question_id': '201111170', 'answer': 'Brown', 'gt_answer': 'brown'}, {'question_id': '20891561', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20891560', 'answer': 'Shirt', 'gt_answer': 'shorts'}, {'question_id': '20503737', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20883191', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20503730', 'answer': 'White', 'gt_answer': 'purple'}, {'question_id': '201974600', 'answer': 'Pink', 'gt_answer': 'dark blue'}, {'question_id': '201972712', 'answer': 'Blue', 'gt_answer': 'blue'}, {'question_id': '20783517', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20797833', 'answer': 'Plant', 'gt_answer': 'tree'}, {'question_id': '20797830', 'answer': 'People', 'gt_answer': 'man'}, {'question_id': '20342305', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20783519', 'answer': 'Chair', 'gt_answer': 'chair'}, {'question_id': '20797834', 'answer': 'Plant', 'gt_answer': 'tree'}, {'question_id': '2053782', 'answer': 'Concrete', 'gt_answer': 'concrete'}, {'question_id': '202106445', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201401744', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20536241', 'answer': 'Heavy', 'gt_answer': 'heavy'}, {'question_id': '2053786', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20899760', 'answer': 'Laptop', 'gt_answer': 'laptop'}, {'question_id': '20899763', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201621812', 'answer': 'Speaker', 'gt_answer': 'speaker'}, {'question_id': '20536249', 'answer': 'Eating', 'gt_answer': 'looking down'}, {'question_id': '20899769', 'answer': 'Laptop', 'gt_answer': 'laptop'}, {'question_id': '20306372', 'answer': 'Camera', 'gt_answer': 'camera'}, {'question_id': '20306370', 'answer': 'Camera', 'gt_answer': 'camera'}, {'question_id': '20866380', 'answer': 'Gray', 'gt_answer': 'gray'}, {'question_id': '20473110', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201467402', 'answer': 'Color', 'gt_answer': 'color'}, {'question_id': '20518336', 'answer': 'Heater', 'gt_answer': 'radiator'}, {'question_id': '20518337', 'answer': 'Heater', 'gt_answer': 'radiator'}, {'question_id': '20518334', 'answer': 'Trash can', 'gt_answer': 'radiator'}, {'question_id': '20518335', 'answer': 'Trash can', 'gt_answer': 'radiator'}, {'question_id': '201759317', 'answer': 'Glass', 'gt_answer': 'glass'}, {'question_id': '20518339', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201952898', 'answer': 'Train', 'gt_answer': 'car'}, {'question_id': '20480525', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '202053173', 'answer': 'Batter', 'gt_answer': 'umpire'}, {'question_id': '20183255', 'answer': 'Bench', 'gt_answer': 'steps'}, {'question_id': '20797661', 'answer': 'Cat', 'gt_answer': 'cat'}, {'question_id': '201548930', 'answer': 'Blender', 'gt_answer': 'picture'}, {'question_id': '20157379', 'answer': 'Table', 'gt_answer': 'table'}, {'question_id': '20257105', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20489405', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20709846', 'answer': 'Right', 'gt_answer': 'left'}, {'question_id': '20754796', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202169340', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20963807', 'answer': 'Sink', 'gt_answer': 'faucet'}, {'question_id': '2053569', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20941978', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20667405', 'answer': 'Remote control', 'gt_answer': 'wii controller'}, {'question_id': '202156967', 'answer': 'Brown', 'gt_answer': 'brown'}, {'question_id': '20757114', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201571188', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20757119', 'answer': 'Cooking', 'gt_answer': 'looking down'}, {'question_id': '20394761', 'answer': 'Dress', 'gt_answer': 'dress'}, {'question_id': '20394760', 'answer': 'Dress', 'gt_answer': 'dress'}, {'question_id': '20508714', 'answer': 'Woman', 'gt_answer': 'woman'}, {'question_id': '202053318', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202174529', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201908788', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20403340', 'answer': 'Square', 'gt_answer': 'square'}, {'question_id': '20306592', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20403344', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20435303', 'answer': 'Paper', 'gt_answer': 'paper'}, {'question_id': '20939909', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20435309', 'answer': 'Dirty', 'gt_answer': 'dirty'}, {'question_id': '20939906', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201887219', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '20939902', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20901821', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20901822', 'answer': 'Umbrella', 'gt_answer': 'umbrella'}, {'question_id': '201984046', 'answer': 'Texting', 'gt_answer': 'looking down'}, {'question_id': '201902722', 'answer': 'Red', 'gt_answer': 'black'}, {'question_id': '20492039', 'answer': 'Bear', 'gt_answer': 'birds'}, {'question_id': '201902726', 'answer': 'Computer', 'gt_answer': 'monitor'}, {'question_id': '202100478', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20287967', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20896252', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201510942', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201621467', 'answer': 'Table', 'gt_answer': 'tv stand'}, {'question_id': '201621466', 'answer': 'Entertainment center', 'gt_answer': 'tv stand'}, {'question_id': '20427613', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201342263', 'answer': 'Large', 'gt_answer': 'large'}, {'question_id': '20618704', 'answer': 'Pink', 'gt_answer': 'pink'}, {'question_id': '20427618', 'answer': '50', 'gt_answer': 'young'}, {'question_id': '202231873', 'answer': 'Brown', 'gt_answer': 'dark brown'}, {'question_id': '201536434', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '201975054', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201640551', 'answer': 'Skinny', 'gt_answer': 'fat'}, {'question_id': '201885430', 'answer': 'Swimming', 'gt_answer': 'jumping'}, {'question_id': '201654400', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201434287', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201770899', 'answer': 'Bottle', 'gt_answer': 'bottle'}, {'question_id': '202100782', 'answer': 'Stove', 'gt_answer': 'stove'}, {'question_id': '201713599', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201068686', 'answer': 'Shirt', 'gt_answer': 'dress shirt'}, {'question_id': '201068687', 'answer': 'Shirt', 'gt_answer': 'dress shirt'}, {'question_id': '20717125', 'answer': 'Bed', 'gt_answer': 'bed'}, {'question_id': '201556938', 'answer': 'Plastic', 'gt_answer': 'plastic'}, {'question_id': '201556939', 'answer': 'Pen', 'gt_answer': 'pen'}, {'question_id': '20756792', 'answer': 'Gray', 'gt_answer': 'gray'}, {'question_id': '201556937', 'answer': 'Plastic', 'gt_answer': 'plastic'}, {'question_id': '202285527', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201879573', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201795103', 'answer': 'Gray', 'gt_answer': 'dark brown'}, {'question_id': '20248178', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201156172', 'answer': 'Cloth', 'gt_answer': 'cloth'}, {'question_id': '20923001', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '20245902', 'answer': 'Man', 'gt_answer': 'skateboarder'}, {'question_id': '20245900', 'answer': 'Skateboarder', 'gt_answer': 'skateboarder'}, {'question_id': '20245901', 'answer': 'Skateboarder', 'gt_answer': 'skateboarder'}, {'question_id': '20245906', 'answer': 'Man', 'gt_answer': 'skateboarder'}, {'question_id': '20245907', 'answer': 'Skateboarder', 'gt_answer': 'skateboarder'}, {'question_id': '20248177', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201987480', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201795359', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201735541', 'answer': 'Desk', 'gt_answer': 'desk'}, {'question_id': '201735547', 'answer': 'Shelf', 'gt_answer': 'shelves'}, {'question_id': '20492150', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20416826', 'answer': 'Pepper', 'gt_answer': 'sausage'}, {'question_id': '20416825', 'answer': 'Pepper', 'gt_answer': 'sausage'}, {'question_id': '20119166', 'answer': 'Top', 'gt_answer': 'top'}, {'question_id': '20300360', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20244509', 'answer': 'Street', 'gt_answer': 'sidewalk'}, {'question_id': '201935164', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202036880', 'answer': 'Pepperoni', 'gt_answer': 'sausage'}, {'question_id': '202036881', 'answer': 'Pepperoni', 'gt_answer': 'sausage'}, {'question_id': '202106209', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20541727', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201037055', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20894256', 'answer': 'Gray', 'gt_answer': 'brown'}, {'question_id': '201795818', 'answer': 'Soft', 'gt_answer': 'hard'}, {'question_id': '201621321', 'answer': 'Black', 'gt_answer': 'black'}, {'question_id': '201319547', 'answer': 'Woman', 'gt_answer': 'women'}, {'question_id': '201439730', 'answer': 'White', 'gt_answer': 'dark'}, {'question_id': '201319540', 'answer': 'Woman', 'gt_answer': 'women'}, {'question_id': '201392138', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201439735', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202073305', 'answer': 'Zebra', 'gt_answer': 'deer'}, {'question_id': '202218780', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '2094004', 'answer': 'Short', 'gt_answer': 'short'}, {'question_id': '201407351', 'answer': 'Racket', 'gt_answer': 'racket'}, {'question_id': '20169624', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201527694', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20902594', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201407359', 'answer': 'Tennis ball', 'gt_answer': 'tennis ball'}, {'question_id': '201982219', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '2065884', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201935304', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201935303', 'answer': 'Platform', 'gt_answer': 'bricks'}, {'question_id': '20902848', 'answer': 'Dog', 'gt_answer': 'dog'}, {'question_id': '202231418', 'answer': 'Metal', 'gt_answer': 'metal'}, {'question_id': '20247773', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201462312', 'answer': 'Bat', 'gt_answer': 'bat'}, {'question_id': '20340435', 'answer': 'Trees', 'gt_answer': 'tree'}, {'question_id': '201462314', 'answer': 'Bat', 'gt_answer': 'bat'}, {'question_id': '20247778', 'answer': 'Bench', 'gt_answer': 'bench'}, {'question_id': '201987813', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '201887171', 'answer': 'Broccoli', 'gt_answer': 'broccoli'}, {'question_id': '201438693', 'answer': 'Home plate', 'gt_answer': 'home plate'}, {'question_id': '20491789', 'answer': 'Sky', 'gt_answer': 'sky'}, {'question_id': '20655012', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20756930', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20330524', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20609412', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201527960', 'answer': 'Woman', 'gt_answer': 'girl'}, {'question_id': '201482397', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201446971', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '20963696', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201207480', 'answer': 'Table', 'gt_answer': 'mat'}, {'question_id': '20752230', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201482055', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20567532', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201599785', 'answer': 'Chair', 'gt_answer': 'chair'}, {'question_id': '201599787', 'answer': 'Chair', 'gt_answer': 'chair'}, {'question_id': '20567537', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202162618', 'answer': 'Bed', 'gt_answer': 'bookcase'}, {'question_id': '202162615', 'answer': 'Bed', 'gt_answer': 'bookcase'}, {'question_id': '201599788', 'answer': 'Chair', 'gt_answer': 'chair'}, {'question_id': '20550578', 'answer': 'Field', 'gt_answer': 'grass'}, {'question_id': '20340484', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20151749', 'answer': 'Tan', 'gt_answer': 'tan'}, {'question_id': '202246141', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20654941', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20309040', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20654949', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202004006', 'answer': 'Wood', 'gt_answer': 'wood'}, {'question_id': '20120514', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '2059544', 'answer': 'Blond', 'gt_answer': 'blond'}, {'question_id': '20866265', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202218911', 'answer': 'Green', 'gt_answer': 'light blue'}, {'question_id': '201574236', 'answer': 'Man', 'gt_answer': 'man'}, {'question_id': '201637286', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201885232', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '202121678', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20856903', 'answer': 'Purse', 'gt_answer': 'purse'}, {'question_id': '201346563', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201346560', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '20856909', 'answer': 'Purse', 'gt_answer': 'purse'}, {'question_id': '201479185', 'answer': 'Peeled', 'gt_answer': 'unpeeled'}, {'question_id': '201974958', 'answer': 'Black', 'gt_answer': 'black'}, {'question_id': '20295332', 'answer': 'Silver', 'gt_answer': 'gray'}, {'question_id': '20258542', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201996765', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201156113', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201153193', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20797647', 'answer': 'Shoe', 'gt_answer': 'shoe'}, {'question_id': '201207118', 'answer': 'Broccoli', 'gt_answer': 'broccoli'}, {'question_id': '201878450', 'answer': 'Young', 'gt_answer': 'old'}, {'question_id': '20385288', 'answer': 'Plastic', 'gt_answer': 'plastic'}, {'question_id': '20385537', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201303200', 'answer': 'Porcelain', 'gt_answer': 'porcelain'}, {'question_id': '201303202', 'answer': 'Flowers', 'gt_answer': 'cups'}, {'question_id': '20797648', 'answer': 'Shoe', 'gt_answer': 'shoe'}, {'question_id': '201976886', 'answer': 'Fence', 'gt_answer': 'street sign'}, {'question_id': '201976887', 'answer': 'Fence', 'gt_answer': 'street sign'}, {'question_id': '201497854', 'answer': 'Screen', 'gt_answer': 'monitor'}, {'question_id': '202133541', 'answer': 'Short sleeved', 'gt_answer': 'short sleeved'}, {'question_id': '20171188', 'answer': 'Pan', 'gt_answer': 'baking pan'}, {'question_id': '201902537', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201738047', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201713385', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202244248', 'answer': 'Round', 'gt_answer': 'triangular'}, {'question_id': '202158849', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20818677', 'answer': 'Black', 'gt_answer': 'black'}, {'question_id': '201879394', 'answer': 'Metal', 'gt_answer': 'metal'}, {'question_id': '201621693', 'answer': 'Couch', 'gt_answer': 'couch'}, {'question_id': '202122091', 'answer': 'Metal', 'gt_answer': 'metal'}, {'question_id': '201887315', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202004237', 'answer': 'Chair', 'gt_answer': 'doors'}, {'question_id': '201982149', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201737851', 'answer': 'Blue', 'gt_answer': 'gray'}, {'question_id': '201896540', 'answer': 'Jacket', 'gt_answer': 'coat'}, {'question_id': '201654361', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '202023443', 'answer': 'Blue', 'gt_answer': 'yellow'}, {'question_id': '20182918', 'answer': 'Roof', 'gt_answer': 'shop'}, {'question_id': '201480491', 'answer': 'Tree', 'gt_answer': 'grass'}, {'question_id': '20978368', 'answer': 'Girl', 'gt_answer': 'girl'}, {'question_id': '201713366', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20818654', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20308803', 'answer': 'Refrigerator', 'gt_answer': 'stove'}, {'question_id': '20308802', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201663676', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20631953', 'answer': 'Player', 'gt_answer': 'catcher'}, {'question_id': '201663673', 'answer': 'Drawer', 'gt_answer': 'drawers'}, {'question_id': '20515082', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202262632', 'answer': 'Red', 'gt_answer': 'red'}, {'question_id': '202262633', 'answer': 'Frisbee', 'gt_answer': 'frisbee'}, {'question_id': '201065063', 'answer': 'Dress', 'gt_answer': 'gown'}, {'question_id': '202262636', 'answer': 'Ground', 'gt_answer': 'grass'}, {'question_id': '202286783', 'answer': 'Pink', 'gt_answer': 'pink'}, {'question_id': '20412245', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20515088', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20456346', 'answer': 'Wood', 'gt_answer': 'metal'}, {'question_id': '20306747', 'answer': 'Camera', 'gt_answer': 'cell phone'}, {'question_id': '201185307', 'answer': 'Concrete', 'gt_answer': 'concrete'}, {'question_id': '202144720', 'answer': 'Water', 'gt_answer': 'ice'}, {'question_id': '202144727', 'answer': 'Crate', 'gt_answer': 'crate'}, {'question_id': '201996835', 'answer': 'Shirt', 'gt_answer': 'sweater'}, {'question_id': '202144724', 'answer': 'Bottle', 'gt_answer': 'blender'}, {'question_id': '201676234', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20435152', 'answer': 'Box', 'gt_answer': 'pizza box'}, {'question_id': '20456349', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201682212', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202053434', 'answer': 'Mound', 'gt_answer': 'field'}, {'question_id': '202053437', 'answer': 'Pitcher', 'gt_answer': 'pitcher'}, {'question_id': '20785809', 'answer': 'Large', 'gt_answer': 'large'}, {'question_id': '201935967', 'answer': 'Bookshelf', 'gt_answer': 'shelf'}, {'question_id': '20811359', 'answer': 'Chair', 'gt_answer': 'chair'}, {'question_id': '201879243', 'answer': 'Lady', 'gt_answer': 'athlete'}, {'question_id': '20756653', 'answer': 'Shelf', 'gt_answer': 'shelf'}, {'question_id': '201873454', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20661400', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202012452', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20756658', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20536038', 'answer': 'Cloudless', 'gt_answer': 'cloudless'}, {'question_id': '201110773', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202053361', 'answer': 'Batter', 'gt_answer': 'batter'}, {'question_id': '20536035', 'answer': 'Field', 'gt_answer': 'plain'}, {'question_id': '20786092', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201504947', 'answer': 'Beach', 'gt_answer': 'beach'}, {'question_id': '201504940', 'answer': 'Girl', 'gt_answer': 'woman'}, {'question_id': '201482310', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20511412', 'answer': 'Helicopter', 'gt_answer': 'helicopter'}, {'question_id': '202053363', 'answer': 'Catcher', 'gt_answer': 'umpire'}, {'question_id': '20511415', 'answer': 'Helicopter', 'gt_answer': 'helicopter'}, {'question_id': '20879007', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20511418', 'answer': 'Top', 'gt_answer': 'top'}, {'question_id': '201759431', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20518455', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201030789', 'answer': 'Pants', 'gt_answer': 'pants'}, {'question_id': '201982508', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '201987565', 'answer': 'Plastic', 'gt_answer': 'plastic'}, {'question_id': '201987569', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201370409', 'answer': 'Carpet', 'gt_answer': 'paper'}, {'question_id': '202180269', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '201770690', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20551696', 'answer': 'Traffic light', 'gt_answer': 'traffic light'}, {'question_id': '20551697', 'answer': 'Traffic light', 'gt_answer': 'traffic light'}, {'question_id': '20551694', 'answer': 'Red', 'gt_answer': 'black'}, {'question_id': '20870471', 'answer': 'Male', 'gt_answer': 'male'}, {'question_id': '20870472', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201498423', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202285209', 'answer': 'Long', 'gt_answer': 'short'}, {'question_id': '20887464', 'answer': 'Computer', 'gt_answer': 'computer mouse'}, {'question_id': '20887460', 'answer': 'Keyboard', 'gt_answer': 'keyboard'}, {'question_id': '20468367', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '2017250', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202144703', 'answer': 'Blender', 'gt_answer': 'blender'}, {'question_id': '201347393', 'answer': 'Boy', 'gt_answer': 'skateboarder'}, {'question_id': '20721787', 'answer': 'Girl', 'gt_answer': 'girl'}, {'question_id': '201056015', 'answer': 'Car', 'gt_answer': 'car'}, {'question_id': '20183437', 'answer': 'Bag', 'gt_answer': 'boxes'}, {'question_id': '202246793', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201951989', 'answer': 'Van', 'gt_answer': 'pole'}, {'question_id': '201047183', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202257504', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201430751', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202257501', 'answer': 'Rough', 'gt_answer': 'rough'}, {'question_id': '20936036', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20551476', 'answer': 'Train', 'gt_answer': 'train'}, {'question_id': '20827523', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201247296', 'answer': 'Plant', 'gt_answer': 'plant'}, {'question_id': '20827527', 'answer': 'Square', 'gt_answer': 'square'}, {'question_id': '201247292', 'answer': 'Plant', 'gt_answer': 'plant'}, {'question_id': '201247293', 'answer': 'Plant', 'gt_answer': 'plant'}, {'question_id': '201957203', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20361266', 'answer': 'Woman', 'gt_answer': 'snowboarder'}, {'question_id': '20349947', 'answer': 'Long', 'gt_answer': 'long'}, {'question_id': '201492116', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201879148', 'answer': 'Wristband', 'gt_answer': 'racket'}, {'question_id': '201064875', 'answer': 'Chair', 'gt_answer': 'sofa'}, {'question_id': '201498043', 'answer': 'Mouse', 'gt_answer': 'desk'}, {'question_id': '201064873', 'answer': 'Bench', 'gt_answer': 'sofa'}, {'question_id': '20856756', 'answer': 'Bed', 'gt_answer': 'bed'}, {'question_id': '20856758', 'answer': 'Bed', 'gt_answer': 'bed'}, {'question_id': '20241036', 'answer': 'Sandwich', 'gt_answer': 'sandwich'}, {'question_id': '201760719', 'answer': 'Man', 'gt_answer': 'man'}, {'question_id': '201760718', 'answer': 'Man', 'gt_answer': 'man'}, {'question_id': '202041969', 'answer': 'Closed', 'gt_answer': 'closed'}, {'question_id': '20637135', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20645492', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '20645496', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201498046', 'answer': 'Desk', 'gt_answer': 'desk'}, {'question_id': '2076580', 'answer': 'Building', 'gt_answer': 'entrance'}, {'question_id': '201410997', 'answer': 'Female', 'gt_answer': 'female'}, {'question_id': '2076582', 'answer': 'Signs', 'gt_answer': 'stone'}, {'question_id': '201822292', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '2076589', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20790005', 'answer': 'People', 'gt_answer': 'people'}, {'question_id': '201438619', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201143169', 'answer': 'Chairs', 'gt_answer': 'chairs'}, {'question_id': '201080313', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201037196', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201037194', 'answer': 'Woman', 'gt_answer': 'girl'}, {'question_id': '201037195', 'answer': 'Woman', 'gt_answer': 'girl'}, {'question_id': '20285424', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20381280', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201047450', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20308247', 'answer': 'Cabinets', 'gt_answer': 'cabinets'}, {'question_id': '201935444', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201143349', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201576511', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201576517', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201400101', 'answer': 'Brown', 'gt_answer': 'brown'}, {'question_id': '201832568', 'answer': 'Wood', 'gt_answer': 'wood'}, {'question_id': '201319754', 'answer': 'White', 'gt_answer': 'white'}, {'question_id': '20891582', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201360694', 'answer': 'Girl', 'gt_answer': 'boy'}, {'question_id': '201360695', 'answer': 'Girl', 'gt_answer': 'boy'}, {'question_id': '201883195', 'answer': 'Chair', 'gt_answer': 'bed'}, {'question_id': '20836778', 'answer': 'Cart', 'gt_answer': 'table'}, {'question_id': '201886951', 'answer': 'Brown', 'gt_answer': 'brown'}, {'question_id': '20836773', 'answer': 'Bag', 'gt_answer': 'purse'}, {'question_id': '2046358', 'answer': 'Tall', 'gt_answer': 'short'}, {'question_id': '201711276', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202040317', 'answer': 'New', 'gt_answer': 'new'}, {'question_id': '20600114', 'answer': 'Short', 'gt_answer': 'short'}, {'question_id': '20600115', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201735202', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201047238', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201878237', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201428730', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20308230', 'answer': 'Cabinets', 'gt_answer': 'cabinets'}, {'question_id': '20416581', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201882662', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '20308237', 'answer': 'Wood', 'gt_answer': 'glass'}, {'question_id': '20542972', 'answer': 'Fence', 'gt_answer': 'fence'}, {'question_id': '20205041', 'answer': 'Chair', 'gt_answer': 'chair'}, {'question_id': '202060013', 'answer': 'Dog', 'gt_answer': 'dog'}, {'question_id': '201873218', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20929331', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '20151689', 'answer': 'On', 'gt_answer': 'on'}, {'question_id': '201765995', 'answer': 'Sand', 'gt_answer': 'dirt'}, {'question_id': '201873216', 'answer': 'Young', 'gt_answer': 'young'}, {'question_id': '201765990', 'answer': 'Dense', 'gt_answer': 'sparse'}, {'question_id': '201765991', 'answer': 'Trees', 'gt_answer': 'trees'}, {'question_id': '201951566', 'answer': 'Girl', 'gt_answer': 'girl'}, {'question_id': '201951567', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20661240', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '2058558', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20247344', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20247340', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20899335', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20411752', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '202024715', 'answer': 'Park', 'gt_answer': 'park'}, {'question_id': '20836551', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201510305', 'answer': 'Plate', 'gt_answer': 'tray'}, {'question_id': '20631436', 'answer': 'Umpire', 'gt_answer': 'umpire'}, {'question_id': '201490842', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20341110', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '20341116', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20341117', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201826530', 'answer': 'Man', 'gt_answer': 'man'}, {'question_id': '20151976', 'answer': 'Square', 'gt_answer': 'square'}, {'question_id': '202262102', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20710289', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20285569', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20797581', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201889233', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201951690', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202262134', 'answer': 'Glass', 'gt_answer': 'mug'}, {'question_id': '202081474', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201185178', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201185172', 'answer': 'Ground', 'gt_answer': 'ground'}, {'question_id': '201185173', 'answer': 'Pavement', 'gt_answer': 'ground'}, {'question_id': '20891232', 'answer': 'Store', 'gt_answer': 'street'}, {'question_id': '20891231', 'answer': 'Store', 'gt_answer': 'street'}, {'question_id': '20891541', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '20652527', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '201455887', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20724222', 'answer': 'Man', 'gt_answer': 'snowboarder'}, {'question_id': '20810927', 'answer': 'Ornaments', 'gt_answer': 'ornament'}, {'question_id': '201346485', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20954197', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20954194', 'answer': 'Man', 'gt_answer': 'man'}, {'question_id': '20162099', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202228116', 'answer': 'Speaker', 'gt_answer': 'dvd player'}, {'question_id': '20982562', 'answer': 'Woman', 'gt_answer': 'woman'}, {'question_id': '20954191', 'answer': 'Woman', 'gt_answer': 'woman'}, {'question_id': '202012734', 'answer': 'Flowers', 'gt_answer': 'remote control'}, {'question_id': '20911295', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '201401768', 'answer': 'Gray', 'gt_answer': 'dark'}, {'question_id': '201882482', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202012733', 'answer': 'Flowers', 'gt_answer': 'remote control'}, {'question_id': '20724226', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201401762', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20342499', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20306355', 'answer': 'Man', 'gt_answer': 'woman'}, {'question_id': '20306354', 'answer': 'Man', 'gt_answer': 'woman'}, {'question_id': '20306357', 'answer': 'Pants', 'gt_answer': 'sweater'}, {'question_id': '201859542', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '201110489', 'answer': 'Bottom', 'gt_answer': 'bottom'}, {'question_id': '20149668', 'answer': 'Metal', 'gt_answer': 'plastic'}, {'question_id': '201832652', 'answer': 'Nightstand', 'gt_answer': 'nightstand'}, {'question_id': '20306358', 'answer': 'Jacket', 'gt_answer': 'sweater'}, {'question_id': '20120167', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '202257089', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201467424', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201467422', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202257086', 'answer': 'Beach', 'gt_answer': 'beach'}, {'question_id': '20317099', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20183235', 'answer': 'Young', 'gt_answer': 'old'}, {'question_id': '202053154', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202053153', 'answer': 'Short sleeved', 'gt_answer': 'long sleeved'}, {'question_id': '201920535', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '202241158', 'answer': 'Brown', 'gt_answer': 'black'}, {'question_id': '201548912', 'answer': 'Green', 'gt_answer': 'gray'}, {'question_id': '20709866', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20489464', 'answer': 'Sleeping', 'gt_answer': 'sleeping'}, {'question_id': '201758426', 'answer': 'Teddy bear', 'gt_answer': 'stuffed dog'}, {'question_id': '202012841', 'answer': 'Man', 'gt_answer': 'man'}, {'question_id': '201758429', 'answer': 'Stroller', 'gt_answer': 'stroller'}, {'question_id': '20403586', 'answer': 'Shelf', 'gt_answer': 'table'}, {'question_id': '202012848', 'answer': 'Television', 'gt_answer': 'television'}, {'question_id': '201498211', 'answer': 'Monitor', 'gt_answer': 'phone'}, {'question_id': '2053509', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201110662', 'answer': 'Tall', 'gt_answer': 'short'}, {'question_id': '20668033', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '2046539', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '2053501', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '2053505', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '2046530', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202174058', 'answer': 'Stove', 'gt_answer': 'oven'}, {'question_id': '201804455', 'answer': 'Computer mouse', 'gt_answer': 'computer monitor'}, {'question_id': '20157537', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20982385', 'answer': 'Sweater', 'gt_answer': 'shirt'}, {'question_id': '20320230', 'answer': 'Dirty', 'gt_answer': 'dirty'}, {'question_id': '202240953', 'answer': 'Brown', 'gt_answer': 'black'}, {'question_id': '2093835', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201156303', 'answer': 'Boy', 'gt_answer': 'boy'}, {'question_id': '20631894', 'answer': 'Waiting', 'gt_answer': 'waiting'}, {'question_id': '20978280', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '201156304', 'answer': 'Boy', 'gt_answer': 'boy'}, {'question_id': '201570788', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202021477', 'answer': 'Sign', 'gt_answer': 'artwork'}, {'question_id': '201491071', 'answer': 'Goat', 'gt_answer': 'goats'}, {'question_id': '201491070', 'answer': 'Cows', 'gt_answer': 'goats'}, {'question_id': '201445018', 'answer': 'Very', 'gt_answer': 'hard'}, {'question_id': '201623420', 'answer': 'Silver', 'gt_answer': 'silver'}, {'question_id': '20442165', 'answer': 'Cabinet', 'gt_answer': 'cabinet'}, {'question_id': '20442164', 'answer': 'Cabinet', 'gt_answer': 'cabinet'}, {'question_id': '202100414', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20245693', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20287908', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20492010', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201624139', 'answer': 'Spatula', 'gt_answer': 'spatula'}, {'question_id': '202125899', 'answer': 'People', 'gt_answer': 'audience'}, {'question_id': '20227104', 'answer': 'Man', 'gt_answer': 'menu'}, {'question_id': '20227105', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202012599', 'answer': 'Right', 'gt_answer': 'left'}, {'question_id': '201621484', 'answer': 'Table', 'gt_answer': 'desk'}, {'question_id': '201621489', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '201624134', 'answer': 'Pan', 'gt_answer': 'pan'}, {'question_id': '201536418', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20511621', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '202218593', 'answer': 'Bottom', 'gt_answer': 'bottom'}, {'question_id': '201975071', 'answer': 'Adidas', 'gt_answer': 'adidas'}, {'question_id': '201434265', 'answer': 'Glass', 'gt_answer': 'glass'}, {'question_id': '201654426', 'answer': 'Horses', 'gt_answer': 'horses'}, {'question_id': '201654424', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20211274', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20543092', 'answer': 'Elephant', 'gt_answer': 'elephant'}, {'question_id': '201412341', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201976777', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201641288', 'answer': 'Street sign', 'gt_answer': 'street sign'}, {'question_id': '20717109', 'answer': 'Bed', 'gt_answer': 'bed'}, {'question_id': '201975049', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201641287', 'answer': 'Traffic light', 'gt_answer': 'street sign'}, {'question_id': '201641286', 'answer': 'Traffic light', 'gt_answer': 'street sign'}, {'question_id': '201641282', 'answer': 'Pole', 'gt_answer': 'traffic light'}, {'question_id': '201794876', 'answer': 'Color', 'gt_answer': 'material'}, {'question_id': '20412052', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20412053', 'answer': 'Carrot', 'gt_answer': 'dessert'}, {'question_id': '20673114', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '2044674', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20673117', 'answer': 'Toilet paper', 'gt_answer': 'toilet paper'}, {'question_id': '20923068', 'answer': 'Truck', 'gt_answer': 'ambulance'}, {'question_id': '2017111', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20705812', 'answer': 'Desk', 'gt_answer': 'desk'}, {'question_id': '20550406', 'answer': 'Horse', 'gt_answer': 'horse'}, {'question_id': '20550407', 'answer': 'Horse', 'gt_answer': 'horse'}, {'question_id': '20673118', 'answer': 'Table', 'gt_answer': 'table'}, {'question_id': '20705816', 'answer': 'Desk', 'gt_answer': 'desk'}, {'question_id': '20468429', 'answer': 'Large', 'gt_answer': 'small'}, {'question_id': '201739230', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20248159', 'answer': 'Shirt', 'gt_answer': 'sweater'}, {'question_id': '202021472', 'answer': 'Red', 'gt_answer': 'red'}, {'question_id': '201068695', 'answer': 'Shirt', 'gt_answer': 'dress shirt'}, {'question_id': '20248154', 'answer': 'Shirt', 'gt_answer': 'sweater'}, {'question_id': '20144639', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201735564', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '201391831', 'answer': 'Wii controller', 'gt_answer': 'wii controller'}, {'question_id': '201391832', 'answer': 'Controller', 'gt_answer': 'wii controller'}, {'question_id': '201065519', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20204532', 'answer': 'Chair', 'gt_answer': 'chair'}, {'question_id': '202101231', 'answer': 'Right', 'gt_answer': 'left'}, {'question_id': '20258759', 'answer': 'Boy', 'gt_answer': 'child'}, {'question_id': '20262704', 'answer': 'Girl', 'gt_answer': 'girl'}, {'question_id': '202265747', 'answer': 'Woman', 'gt_answer': 'woman'}, {'question_id': '202024849', 'answer': 'Park', 'gt_answer': 'park'}, {'question_id': '20340632', 'answer': 'Resting', 'gt_answer': 'playing'}, {'question_id': '201498444', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201037030', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '2044903', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '201593873', 'answer': 'Tennis ball', 'gt_answer': 'tennis ball'}, {'question_id': '201593875', 'answer': 'Tennis ball', 'gt_answer': 'tennis ball'}, {'question_id': '201438282', 'answer': 'Home plate', 'gt_answer': 'net'}, {'question_id': '2075243', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20691469', 'answer': 'Basket', 'gt_answer': 'shelves'}, {'question_id': '201407334', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20691466', 'answer': 'Green', 'gt_answer': 'black'}, {'question_id': '201407331', 'answer': 'Fence', 'gt_answer': 'fence'}, {'question_id': '20177899', 'answer': 'Small', 'gt_answer': 'small'}, {'question_id': '201735690', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201987605', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20169603', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201983045', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201864553', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20706289', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202003684', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201481487', 'answer': 'Umbrella', 'gt_answer': 'umbrella'}, {'question_id': '201756642', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202285540', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202119928', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '20984434', 'answer': '16', 'gt_answer': 'young'}, {'question_id': '20340983', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20984387', 'answer': 'Boy', 'gt_answer': 'skater'}, {'question_id': '201347368', 'answer': 'Skating', 'gt_answer': 'skating'}, {'question_id': '20541514', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '20340988', 'answer': 'Wide', 'gt_answer': 'wide'}, {'question_id': '202006219', 'answer': 'Brown', 'gt_answer': 'tan'}, {'question_id': '20705745', 'answer': 'Computer', 'gt_answer': 'monitor'}, {'question_id': '201556920', 'answer': 'Laptop', 'gt_answer': 'laptop'}, {'question_id': '201956961', 'answer': 'Closed', 'gt_answer': 'closed'}, {'question_id': '202006213', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20756914', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '202243791', 'answer': 'Brown', 'gt_answer': 'red'}, {'question_id': '20330509', 'answer': 'Behind', 'gt_answer': 'behind'}, {'question_id': '20247860', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20596312', 'answer': 'Standing', 'gt_answer': 'standing'}, {'question_id': '201247081', 'answer': 'Plant', 'gt_answer': 'plant'}, {'question_id': '202245872', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20551315', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202162658', 'answer': 'Black', 'gt_answer': 'black'}, {'question_id': '20667821', 'answer': 'Tank top', 'gt_answer': 'shirt'}, {'question_id': '202158779', 'answer': 'Concrete', 'gt_answer': 'concrete'}, {'question_id': '201795384', 'answer': 'Bench', 'gt_answer': 'bench'}, {'question_id': '201795385', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201795382', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202158778', 'answer': 'Concrete', 'gt_answer': 'concrete'}, {'question_id': '201766528', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '202241056', 'answer': 'White', 'gt_answer': 'white'}, {'question_id': '202000663', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '2056075', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20968332', 'answer': 'Sign', 'gt_answer': 'pole'}, {'question_id': '20541546', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '201393608', 'answer': 'Gray', 'gt_answer': 'dark'}, {'question_id': '20954058', 'answer': 'Jacket', 'gt_answer': 'receipt'}, {'question_id': '20637305', 'answer': 'Stove', 'gt_answer': 'stove'}, {'question_id': '20516049', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '201393601', 'answer': 'Sock', 'gt_answer': 'sock'}, {'question_id': '201393603', 'answer': 'Socks', 'gt_answer': 'sock'}, {'question_id': '201795116', 'answer': 'Elephant', 'gt_answer': 'elephant'}, {'question_id': '20782987', 'answer': 'Chair', 'gt_answer': 'chair'}, {'question_id': '20151769', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202285048', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201882905', 'answer': 'Phone', 'gt_answer': 'television'}, {'question_id': '202262837', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201879568', 'answer': 'Truck', 'gt_answer': 'truck'}, {'question_id': '20596524', 'answer': 'Concrete', 'gt_answer': 'concrete'}, {'question_id': '20611554', 'answer': 'Large', 'gt_answer': 'large'}, {'question_id': '201669332', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201624192', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201804274', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201972699', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201770658', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201574214', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202156687', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201885214', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '201896270', 'answer': 'Large', 'gt_answer': 'large'}, {'question_id': '20982174', 'answer': 'Long sleeved', 'gt_answer': 'long sleeved'}, {'question_id': '20982179', 'answer': 'Black', 'gt_answer': 'black'}, {'question_id': '201896318', 'answer': 'Cake', 'gt_answer': 'cake'}, {'question_id': '20618861', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202147831', 'answer': 'Spectator', 'gt_answer': 'athlete'}, {'question_id': '201996785', 'answer': 'Black', 'gt_answer': 'gold'}, {'question_id': '20434808', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20618869', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201974935', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202258138', 'answer': 'Cloth', 'gt_answer': 'cloth'}, {'question_id': '202258139', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20385517', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '2062362', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201735165', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20699279', 'answer': 'Jacket', 'gt_answer': 'jacket'}, {'question_id': '201959852', 'answer': 'Large', 'gt_answer': 'large'}, {'question_id': '201439380', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201757688', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201429017', 'answer': 'Stove', 'gt_answer': 'stove'}, {'question_id': '202133566', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201902557', 'answer': 'Monitor', 'gt_answer': 'router'}, {'question_id': '202133564', 'answer': 'Man', 'gt_answer': 'skateboarder'}, {'question_id': '202133563', 'answer': 'Man', 'gt_answer': 'skateboarder'}, {'question_id': '201902552', 'answer': 'Speaker', 'gt_answer': 'router'}, {'question_id': '202244266', 'answer': 'Rice', 'gt_answer': 'rice'}, {'question_id': '201188320', 'answer': 'Man', 'gt_answer': 'man'}, {'question_id': '201951734', 'answer': 'Van', 'gt_answer': 'van'}, {'question_id': '20385778', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201188325', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20462152', 'answer': 'Woman', 'gt_answer': 'man'}, {'question_id': '20462153', 'answer': 'Woman', 'gt_answer': 'man'}, {'question_id': '20462158', 'answer': 'Woman', 'gt_answer': 'man'}, {'question_id': '201804491', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20953087', 'answer': 'Man', 'gt_answer': 'player'}, {'question_id': '20891689', 'answer': 'Child', 'gt_answer': 'child'}, {'question_id': '20953081', 'answer': 'Man', 'gt_answer': 'player'}, {'question_id': '201737879', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '20953088', 'answer': 'Man', 'gt_answer': 'player'}, {'question_id': '20827504', 'answer': 'Couch', 'gt_answer': 'chairs'}, {'question_id': '201322631', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '202174096', 'answer': 'Black', 'gt_answer': 'black'}, {'question_id': '201935943', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20978612', 'answer': 'Wood', 'gt_answer': 'wood'}, {'question_id': '20434779', 'answer': 'Plastic', 'gt_answer': 'wood'}, {'question_id': '201556748', 'answer': 'Keyboard', 'gt_answer': 'keyboard'}, {'question_id': '201030339', 'answer': 'Small', 'gt_answer': 'small'}, {'question_id': '20434770', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20827501', 'answer': 'Bar stool', 'gt_answer': 'chairs'}, {'question_id': '20636816', 'answer': 'Open', 'gt_answer': 'closed'}, {'question_id': '201935924', 'answer': 'Small', 'gt_answer': 'small'}, {'question_id': '20262487', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201185896', 'answer': 'Frisbee', 'gt_answer': 'frisbee'}, {'question_id': '201185893', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201935929', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20246006', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201735422', 'answer': 'Desk', 'gt_answer': 'desk'}, {'question_id': '201982950', 'answer': 'Brown', 'gt_answer': 'brown'}, {'question_id': '202023602', 'answer': 'Metal', 'gt_answer': 'metal'}, {'question_id': '202156925', 'answer': 'Elephant', 'gt_answer': 'elephants'}, {'question_id': '202156922', 'answer': 'Elephant', 'gt_answer': 'elephants'}, {'question_id': '201676219', 'answer': 'Laptop', 'gt_answer': 'computer'}, {'question_id': '202156920', 'answer': 'Elephant', 'gt_answer': 'elephants'}, {'question_id': '20785827', 'answer': 'Gray', 'gt_answer': 'white'}, {'question_id': '201804660', 'answer': 'Laptop', 'gt_answer': 'television'}, {'question_id': '201804661', 'answer': 'Speaker', 'gt_answer': 'television'}, {'question_id': '202244695', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202179615', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20827509', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '20734057', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '20783255', 'answer': 'Laptop', 'gt_answer': 'screen'}, {'question_id': '20929612', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20349965', 'answer': 'Sleeveless', 'gt_answer': 'sleeveless'}, {'question_id': '20783250', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202012477', 'answer': 'Television', 'gt_answer': 'cabinets'}, {'question_id': '202012476', 'answer': 'Man', 'gt_answer': 'woman'}, {'question_id': '20862758', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20661463', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20262625', 'answer': 'Tall', 'gt_answer': 'tall'}, {'question_id': '20661461', 'answer': 'Window', 'gt_answer': 'door'}, {'question_id': '201663548', 'answer': 'Clean', 'gt_answer': 'clean'}, {'question_id': '202223163', 'answer': 'Man', 'gt_answer': 'man'}, {'question_id': '201061187', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20226469', 'answer': 'Shirt', 'gt_answer': 'shirt'}, {'question_id': '20518474', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202023469', 'answer': 'Bed', 'gt_answer': 'bed'}, {'question_id': '202007027', 'answer': 'Cabinets', 'gt_answer': 'cabinet'}, {'question_id': '202007024', 'answer': 'Tiles', 'gt_answer': 'cabinet'}, {'question_id': '202023467', 'answer': 'Bed', 'gt_answer': 'bed'}, {'question_id': '20511438', 'answer': 'Heavy', 'gt_answer': 'heavy'}, {'question_id': '20511439', 'answer': 'Helicopter', 'gt_answer': 'helicopter'}, {'question_id': '201228219', 'answer': 'Black', 'gt_answer': 'black'}, {'question_id': '2075742', 'answer': 'Wide', 'gt_answer': 'wide'}, {'question_id': '201447004', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20120446', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202100662', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '20240980', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '201548696', 'answer': 'Smoothie', 'gt_answer': 'alcohol'}, {'question_id': '202081254', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201548698', 'answer': 'Bottle', 'gt_answer': 'blender'}, {'question_id': '20609189', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201064814', 'answer': 'Chair', 'gt_answer': 'sofa'}, {'question_id': '201983709', 'answer': 'Suitcase', 'gt_answer': 'papers'}, {'question_id': '20887489', 'answer': 'Large', 'gt_answer': 'large'}, {'question_id': '202003599', 'answer': 'Classroom', 'gt_answer': 'office'}, {'question_id': '202285228', 'answer': 'Beans', 'gt_answer': 'sausage'}, {'question_id': '201480233', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201056036', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201068788', 'answer': 'Shirt', 'gt_answer': 'blouse'}, {'question_id': '201068839', 'answer': 'Shirt', 'gt_answer': 'sweatshirt'}, {'question_id': '201068838', 'answer': 'Shirt', 'gt_answer': 'sweatshirt'}, {'question_id': '201752788', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20307097', 'answer': 'Camera', 'gt_answer': 'laptop'}, {'question_id': '201322493', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20902670', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20753536', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201430771', 'answer': 'Shirt', 'gt_answer': 'tie'}, {'question_id': '201393599', 'answer': 'Shirt', 'gt_answer': 'shirt'}, {'question_id': '20902678', 'answer': 'Bicycle', 'gt_answer': 'bike'}, {'question_id': '20672989', 'answer': 'Chair', 'gt_answer': 'chair'}, {'question_id': '201430779', 'answer': 'Shirt', 'gt_answer': 'dress shirt'}, {'question_id': '201430778', 'answer': 'Shirt', 'gt_answer': 'dress shirt'}, {'question_id': '201430953', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201446976', 'answer': 'Plastic', 'gt_answer': 'plastic'}, {'question_id': '201446977', 'answer': 'Plastic', 'gt_answer': 'plastic'}, {'question_id': '201438794', 'answer': 'Dirty', 'gt_answer': 'dirty'}, {'question_id': '20204824', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201983763', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '201896132', 'answer': 'Blue', 'gt_answer': 'blue'}, {'question_id': '201872912', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202100332', 'answer': 'Large', 'gt_answer': 'large'}, {'question_id': '201037203', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20349929', 'answer': 'Black', 'gt_answer': 'black'}, {'question_id': '20381511', 'answer': 'Shelf', 'gt_answer': 'shelf'}, {'question_id': '201412481', 'answer': 'Skiing', 'gt_answer': 'looking down'}, {'question_id': '20856731', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201412482', 'answer': 'Skier', 'gt_answer': 'skier'}, {'question_id': '201068165', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '202231269', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202049137', 'answer': 'Cake', 'gt_answer': 'plate'}, {'question_id': '202121992', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201760730', 'answer': 'Man', 'gt_answer': 'man'}, {'question_id': '201676175', 'answer': 'Plant', 'gt_answer': 'computer'}, {'question_id': '20741234', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20940298', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202265979', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202119416', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201434098', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202119414', 'answer': 'Elephant', 'gt_answer': 'elephant'}, {'question_id': '201434092', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20285445', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202003923', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20259043', 'answer': 'Open', 'gt_answer': 'open'}, {'question_id': '20177957', 'answer': 'Cutting board', 'gt_answer': 'cutting board'}, {'question_id': '20177951', 'answer': 'Cutting board', 'gt_answer': 'cutting board'}, {'question_id': '201264173', 'answer': 'Behind', 'gt_answer': 'front'}, {'question_id': '20479912', 'answer': 'Dirty', 'gt_answer': 'clean'}, {'question_id': '201391995', 'answer': 'Man', 'gt_answer': 'people'}, {'question_id': '20940127', 'answer': 'Small', 'gt_answer': 'small'}, {'question_id': '202116880', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202110114', 'answer': 'Sheep', 'gt_answer': 'sheep'}, {'question_id': '20550236', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '201997239', 'answer': 'Leather', 'gt_answer': 'metal'}, {'question_id': '201711229', 'answer': 'Very', 'gt_answer': 'hard'}, {'question_id': '201997232', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201428716', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '2055571', 'answer': 'White', 'gt_answer': 'white'}, {'question_id': '202102832', 'answer': 'Dishwasher', 'gt_answer': 'microwave'}, {'question_id': '202218628', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202162080', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201735221', 'answer': 'Shelf', 'gt_answer': 'shelves'}, {'question_id': '201735220', 'answer': 'Books', 'gt_answer': 'towel'}, {'question_id': '202243895', 'answer': 'Beans', 'gt_answer': 'beans'}, {'question_id': '201206923', 'answer': 'Red', 'gt_answer': 'red'}, {'question_id': '20857065', 'answer': 'Orange', 'gt_answer': 'white'}, {'question_id': '201662992', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201765606', 'answer': 'Beach', 'gt_answer': 'beach'}, {'question_id': '201873237', 'answer': 'Fire truck', 'gt_answer': 'fire truck'}, {'question_id': '20661261', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201952700', 'answer': 'Train', 'gt_answer': 'train'}, {'question_id': '201765979', 'answer': 'Sand', 'gt_answer': 'beach'}, {'question_id': '201765608', 'answer': 'Beach', 'gt_answer': 'beach'}, {'question_id': '201407360', 'answer': 'Tennis ball', 'gt_answer': 'tennis ball'}, {'question_id': '20308383', 'answer': 'Stove', 'gt_answer': 'oven'}, {'question_id': '202218778', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '2058536', 'answer': 'Large', 'gt_answer': 'small'}, {'question_id': '20340844', 'answer': 'Closed', 'gt_answer': 'closed'}, {'question_id': '202102901', 'answer': 'Square', 'gt_answer': 'square'}, {'question_id': '202262185', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201997153', 'answer': 'Chair', 'gt_answer': 'chair'}, {'question_id': '201859716', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201997150', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202243836', 'answer': 'Cupcake', 'gt_answer': 'cupcakes'}, {'question_id': '201826511', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20631451', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201640426', 'answer': 'Wine', 'gt_answer': 'wine'}, {'question_id': '201400164', 'answer': 'Bookshelf', 'gt_answer': 'couch'}, {'question_id': '201400167', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20385685', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201640422', 'answer': 'Glass', 'gt_answer': 'glass'}, {'question_id': '201640423', 'answer': 'Glass', 'gt_answer': 'glass'}, {'question_id': '201346713', 'answer': 'Soft', 'gt_answer': 'hard'}, {'question_id': '20797563', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20661199', 'answer': 'Bus', 'gt_answer': 'bus'}, {'question_id': '202262484', 'answer': 'Color', 'gt_answer': 'color'}, {'question_id': '201153564', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20968299', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '2012983', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201623712', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '2072778', 'answer': 'Dirty', 'gt_answer': 'dirty'}, {'question_id': '20441948', 'answer': 'Chair', 'gt_answer': 'table'}, {'question_id': '20441949', 'answer': 'Door', 'gt_answer': 'door'}, {'question_id': '201509727', 'answer': 'Helmet', 'gt_answer': 'seat'}, {'question_id': '20503773', 'answer': 'Wire', 'gt_answer': 'cables'}, {'question_id': '20503771', 'answer': 'Stop sign', 'gt_answer': 'traffic lights'}, {'question_id': '20503776', 'answer': 'Wire', 'gt_answer': 'traffic lights'}, {'question_id': '20503774', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20652501', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20782935', 'answer': 'Laptop', 'gt_answer': 'laptop'}, {'question_id': '20330319', 'answer': 'Statue', 'gt_answer': 'statue'}, {'question_id': '20503778', 'answer': 'Stop sign', 'gt_answer': 'stop sign'}, {'question_id': '20541288', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '201549013', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20542959', 'answer': 'Elephant', 'gt_answer': 'elephant'}, {'question_id': '2098049', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20982540', 'answer': 'Kitchen', 'gt_answer': 'table'}, {'question_id': '202228467', 'answer': 'Black', 'gt_answer': 'black'}, {'question_id': '20982544', 'answer': 'Table', 'gt_answer': 'table'}, {'question_id': '2098040', 'answer': 'Keyboard', 'gt_answer': 'keyboard'}, {'question_id': '201429046', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '201429043', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201959885', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20516188', 'answer': 'Black', 'gt_answer': 'black'}, {'question_id': '201758190', 'answer': 'Brown', 'gt_answer': 'brown'}, {'question_id': '202159019', 'answer': 'Dirty', 'gt_answer': 'clean'}, {'question_id': '20262814', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201758199', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '2053748', 'answer': 'Cow', 'gt_answer': 'cow'}, {'question_id': '20811131', 'answer': 'Dog', 'gt_answer': 'dog'}, {'question_id': '201859383', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '201902619', 'answer': 'Desk', 'gt_answer': 'desk'}, {'question_id': '20149600', 'answer': 'Drawer', 'gt_answer': 'sink'}, {'question_id': '201952854', 'answer': 'Train', 'gt_answer': 'train'}, {'question_id': '20120148', 'answer': 'Short', 'gt_answer': 'short'}, {'question_id': '202244398', 'answer': 'Square', 'gt_answer': 'round'}, {'question_id': '202023593', 'answer': 'Chair', 'gt_answer': 'bed'}, {'question_id': '202121739', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201347405', 'answer': 'Boy', 'gt_answer': 'skateboarder'}, {'question_id': '20157336', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20157335', 'answer': 'Bacon', 'gt_answer': 'bacon'}, {'question_id': '202121732', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '202258276', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202159012', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201739177', 'answer': 'Pants', 'gt_answer': 'pants'}, {'question_id': '201273203', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201548970', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201663197', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20411959', 'answer': 'Tray', 'gt_answer': 'food container'}, {'question_id': '201342313', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '2053524', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20403568', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20668016', 'answer': 'Cloth', 'gt_answer': 'cloth'}, {'question_id': '202144680', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '202144681', 'answer': 'Blender', 'gt_answer': 'blender'}, {'question_id': '20411955', 'answer': 'Fork', 'gt_answer': 'fork'}, {'question_id': '20866120', 'answer': 'Woman', 'gt_answer': 'woman'}, {'question_id': '20866123', 'answer': 'Container', 'gt_answer': 'sour cream'}, {'question_id': '20866124', 'answer': 'Spoon', 'gt_answer': 'sour cream'}, {'question_id': '20866126', 'answer': 'Salad', 'gt_answer': 'sour cream'}, {'question_id': '20866128', 'answer': 'Salad', 'gt_answer': 'sour cream'}, {'question_id': '2046513', 'answer': 'Chair', 'gt_answer': 'table'}, {'question_id': '20883226', 'answer': 'Boy', 'gt_answer': 'boy'}, {'question_id': '2046516', 'answer': 'Table', 'gt_answer': 'table'}, {'question_id': '202174598', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201804477', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201399994', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201621715', 'answer': 'Couch', 'gt_answer': 'couch'}, {'question_id': '201976914', 'answer': 'Right', 'gt_answer': 'left'}, {'question_id': '20340402', 'answer': 'Park', 'gt_answer': 'patio'}, {'question_id': '20342343', 'answer': 'Female', 'gt_answer': 'female'}, {'question_id': '201637357', 'answer': 'White', 'gt_answer': 'beige'}, {'question_id': '201175347', 'answer': 'Cat', 'gt_answer': 'cat'}, {'question_id': '201109202', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201935424', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '202144469', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20403308', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202258332', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '201886924', 'answer': 'Small', 'gt_answer': 'small'}, {'question_id': '201623404', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20492078', 'answer': 'Large', 'gt_answer': 'small'}, {'question_id': '20492071', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201068317', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20287923', 'answer': 'Short', 'gt_answer': 'short'}, {'question_id': '20724292', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20746561', 'answer': 'Birds', 'gt_answer': 'birds'}, {'question_id': '201641119', 'answer': 'Street', 'gt_answer': 'street'}, {'question_id': '201228021', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '20287929', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '202036874', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '202169389', 'answer': 'Woman', 'gt_answer': 'woman'}, {'question_id': '202053206', 'answer': 'Batter', 'gt_answer': 'umpire'}, {'question_id': '201982932', 'answer': 'Small', 'gt_answer': 'small'}, {'question_id': '202169380', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '20482414', 'answer': 'Blue', 'gt_answer': 'blue'}, {'question_id': '201704519', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201975091', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '201704511', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201056192', 'answer': 'Short', 'gt_answer': 'short'}, {'question_id': '20518735', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201704515', 'answer': 'Colorful', 'gt_answer': 'black and white'}, {'question_id': '20794134', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201434245', 'answer': 'Cell phone', 'gt_answer': 'phone'}, {'question_id': '20480122', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201412361', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20480129', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '20211216', 'answer': 'Top', 'gt_answer': 'top'}, {'question_id': '201322573', 'answer': 'Bus', 'gt_answer': 'bus'}, {'question_id': '202240204', 'answer': 'Girl', 'gt_answer': 'girl'}, {'question_id': '201873603', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201751683', 'answer': 'Stainless steel', 'gt_answer': 'stainless steel'}, {'question_id': '20756751', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20753327', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201528064', 'answer': 'Woman', 'gt_answer': 'girl'}, {'question_id': '20740889', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20416632', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201556973', 'answer': 'Small', 'gt_answer': 'large'}, {'question_id': '20936285', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20536173', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20468680', 'answer': 'Horse', 'gt_answer': 'horse'}, {'question_id': '202119498', 'answer': 'Hat', 'gt_answer': 'hat'}, {'question_id': '201235867', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20705833', 'answer': 'Large', 'gt_answer': 'large'}, {'question_id': '2044613', 'answer': 'White', 'gt_answer': 'khaki'}, {'question_id': '202100299', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202053203', 'answer': 'Player', 'gt_answer': 'umpire'}, {'question_id': '202100290', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201866571', 'answer': 'Sidewalk', 'gt_answer': 'walkway'}, {'question_id': '2075484', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '2075481', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20655425', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '2075483', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20655429', 'answer': 'Long', 'gt_answer': 'long'}, {'question_id': '202180239', 'answer': 'Color', 'gt_answer': 'shape'}, {'question_id': '20468933', 'answer': 'Sign', 'gt_answer': 'sign'}, {'question_id': '201997939', 'answer': 'Wood', 'gt_answer': 'wood'}, {'question_id': '202036848', 'answer': 'Pepper', 'gt_answer': 'pepper'}, {'question_id': '201065534', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '201972844', 'answer': 'Horse', 'gt_answer': 'woman'}, {'question_id': '20753183', 'answer': 'Bed', 'gt_answer': 'bed'}, {'question_id': '20753180', 'answer': 'Bed', 'gt_answer': 'bed'}, {'question_id': '20753181', 'answer': 'Bed', 'gt_answer': 'bed'}, {'question_id': '201972849', 'answer': 'Man', 'gt_answer': 'woman'}, {'question_id': '20862926', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20667687', 'answer': 'Table', 'gt_answer': 'coffee table'}, {'question_id': '20427658', 'answer': 'Picture', 'gt_answer': 'picture'}, {'question_id': '20741424', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '20427655', 'answer': 'Man', 'gt_answer': 'gentleman'}, {'question_id': '20427656', 'answer': 'Man', 'gt_answer': 'gentleman'}, {'question_id': '201752686', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '202246219', 'answer': 'Laptop', 'gt_answer': 'laptop'}, {'question_id': '202120103', 'answer': 'Glass', 'gt_answer': 'glasses'}, {'question_id': '201593816', 'answer': 'Woman', 'gt_answer': 'girl'}, {'question_id': '202120108', 'answer': 'Refrigerator', 'gt_answer': 'refrigerator'}, {'question_id': '201593815', 'answer': 'Woman', 'gt_answer': 'girl'}, {'question_id': '20320414', 'answer': 'Looking down', 'gt_answer': 'looking down'}, {'question_id': '20631682', 'answer': 'Catcher', 'gt_answer': 'batter'}, {'question_id': '201987624', 'answer': 'Color', 'gt_answer': 'shape'}, {'question_id': '201987626', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202006186', 'answer': 'Cabinets', 'gt_answer': 'shelves'}, {'question_id': '202006187', 'answer': 'Cabinet', 'gt_answer': 'shelves'}, {'question_id': '201111082', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202121856', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '20149850', 'answer': 'Dresser', 'gt_answer': 'cabinet'}, {'question_id': '202285561', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20953992', 'answer': 'Standing', 'gt_answer': 'standing'}, {'question_id': '20673137', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '201593721', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20984413', 'answer': 'Male', 'gt_answer': 'male'}, {'question_id': '202284962', 'answer': 'Banana', 'gt_answer': 'banana'}, {'question_id': '202231458', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201047376', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '20756977', 'answer': 'Small', 'gt_answer': 'large'}, {'question_id': '201956943', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20609451', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201738969', 'answer': 'Pitcher', 'gt_answer': 'pitcher'}, {'question_id': '201738968', 'answer': 'Pitcher', 'gt_answer': 'pitcher'}, {'question_id': '201738962', 'answer': 'Glove', 'gt_answer': 'pitcher'}, {'question_id': '20963659', 'answer': 'Flowers', 'gt_answer': 'flower pot'}, {'question_id': '20963658', 'answer': 'Gray', 'gt_answer': 'orange'}, {'question_id': '20345022', 'answer': 'Boy', 'gt_answer': 'athlete'}, {'question_id': '20345023', 'answer': 'Ball', 'gt_answer': 'tennis ball'}, {'question_id': '201738965', 'answer': 'Pitcher', 'gt_answer': 'pitcher'}, {'question_id': '201156526', 'answer': 'Backpack', 'gt_answer': 'backpack'}, {'question_id': '20752272', 'answer': 'Right', 'gt_answer': 'left'}, {'question_id': '20667844', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '20611794', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201235484', 'answer': 'Black', 'gt_answer': 'dark'}, {'question_id': '20541567', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '2056059', 'answer': 'Car', 'gt_answer': 'car'}, {'question_id': '2056058', 'answer': 'Car', 'gt_answer': 'car'}, {'question_id': '201481427', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20302888', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202041862', 'answer': 'Street', 'gt_answer': 'street'}, {'question_id': '202041863', 'answer': 'Street', 'gt_answer': 'street'}, {'question_id': '201766542', 'answer': 'Girl', 'gt_answer': 'girl'}, {'question_id': '2056050', 'answer': 'Store', 'gt_answer': 'apartment building'}, {'question_id': '201481421', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201576942', 'answer': 'Dog', 'gt_answer': 'goat'}, {'question_id': '20169881', 'answer': 'Brown', 'gt_answer': 'black'}, {'question_id': '201576499', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20954078', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20827017', 'answer': 'Couch', 'gt_answer': 'cupboard'}, {'question_id': '20827012', 'answer': 'Couch', 'gt_answer': 'cupboard'}, {'question_id': '201998309', 'answer': 'Table', 'gt_answer': 'table'}, {'question_id': '202262816', 'answer': 'Truck', 'gt_answer': 'car'}, {'question_id': '202162622', 'answer': 'Bed', 'gt_answer': 'bed'}, {'question_id': '201624170', 'answer': 'Pizza', 'gt_answer': 'pizza'}, {'question_id': '201577010', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201671802', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201794925', 'answer': 'Short sleeved', 'gt_answer': 'short sleeved'}, {'question_id': '20285127', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '20982158', 'answer': 'Sweater', 'gt_answer': 'blouse'}, {'question_id': '201434128', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20162468', 'answer': 'Green', 'gt_answer': 'brown'}, {'question_id': '202262005', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201346526', 'answer': 'Motorcycle', 'gt_answer': 'car'}, {'question_id': '201346524', 'answer': 'Van', 'gt_answer': 'van'}, {'question_id': '201479496', 'answer': 'Chicken', 'gt_answer': 'chicken'}, {'question_id': '20295378', 'answer': 'Speaker', 'gt_answer': 'television'}, {'question_id': '202258443', 'answer': 'Wood', 'gt_answer': 'wood'}, {'question_id': '20618843', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20618842', 'answer': 'Ground', 'gt_answer': 'courtyard'}, {'question_id': '202258110', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201974911', 'answer': 'Green', 'gt_answer': 'green'}, {'question_id': '201879319', 'answer': 'Truck', 'gt_answer': 'truck'}, {'question_id': '20385575', 'answer': 'Left', 'gt_answer': 'right'}, {'question_id': '20385576', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20541534', 'answer': 'Television', 'gt_answer': 'television'}, {'question_id': '201303245', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201556492', 'answer': 'Desk', 'gt_answer': 'desk'}, {'question_id': '2062343', 'answer': 'Top', 'gt_answer': 'top'}, {'question_id': '202162123', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20836453', 'answer': 'Table', 'gt_answer': 'table'}, {'question_id': '201623339', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20573530', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201360777', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20922878', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '202246021', 'answer': 'Computer', 'gt_answer': 'laptop'}, {'question_id': '202244204', 'answer': 'Vegetables', 'gt_answer': 'rice'}, {'question_id': '202244207', 'answer': 'Muffin', 'gt_answer': 'rice'}, {'question_id': '20211184', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202228707', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '201896258', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201908832', 'answer': 'Sandwich', 'gt_answer': 'sandwiches'}, {'question_id': '201908833', 'answer': 'Sandwich', 'gt_answer': 'sandwiches'}, {'question_id': '201908837', 'answer': 'Bread', 'gt_answer': 'sandwiches'}, {'question_id': '201873004', 'answer': 'Bus', 'gt_answer': 'truck'}, {'question_id': '201951712', 'answer': 'Flag', 'gt_answer': 'flag'}, {'question_id': '201951711', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201896255', 'answer': 'Bottom', 'gt_answer': 'bottom'}, {'question_id': '201030681', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '201175621', 'answer': 'Outlet', 'gt_answer': 'outlets'}, {'question_id': '201188344', 'answer': 'Cell phone', 'gt_answer': 'phone'}, {'question_id': '20385794', 'answer': 'Leather', 'gt_answer': 'cloth'}, {'question_id': '20710321', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201188341', 'answer': 'Cell phone', 'gt_answer': 'phone'}, {'question_id': '202004161', 'answer': 'Boy', 'gt_answer': 'man'}, {'question_id': '201984153', 'answer': 'Woman', 'gt_answer': 'woman'}, {'question_id': '20120559', 'answer': 'Behind', 'gt_answer': 'front'}, {'question_id': '20120557', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20120551', 'answer': 'Fence', 'gt_answer': 'fence'}, {'question_id': '20120553', 'answer': 'Bench', 'gt_answer': 'bench'}, {'question_id': '20120552', 'answer': 'Trees', 'gt_answer': 'bench'}, {'question_id': '20344960', 'answer': 'Yellow', 'gt_answer': 'green'}, {'question_id': '201738889', 'answer': 'Player', 'gt_answer': 'spectators'}, {'question_id': '20878996', 'answer': 'Man', 'gt_answer': 'skater'}, {'question_id': '20797799', 'answer': 'Top', 'gt_answer': 'top'}, {'question_id': '201030356', 'answer': 'Rocket', 'gt_answer': 'shuttle'}, {'question_id': '201983628', 'answer': 'Woman', 'gt_answer': 'woman'}, {'question_id': '201935909', 'answer': 'Shelf', 'gt_answer': 'shelf'}, {'question_id': '201030353', 'answer': 'Nothing', 'gt_answer': 'stars'}, {'question_id': '20811177', 'answer': 'Sniffing', 'gt_answer': 'standing'}, {'question_id': '201935905', 'answer': 'Shelf', 'gt_answer': 'shelf'}, {'question_id': '201935906', 'answer': 'Shelf', 'gt_answer': 'shelf'}, {'question_id': '201153042', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201030358', 'answer': 'Rocket', 'gt_answer': 'shuttle'}, {'question_id': '201889550', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '202042072', 'answer': 'Motorcycle', 'gt_answer': 'motorcycle'}, {'question_id': '202225979', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '202042070', 'answer': 'Motorcycle', 'gt_answer': 'motorcycle'}, {'question_id': '201796080', 'answer': 'Woman', 'gt_answer': 'woman'}, {'question_id': '20657100', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20456656', 'answer': 'Desk', 'gt_answer': 'table'}, {'question_id': '20456657', 'answer': 'Desk', 'gt_answer': 'table'}, {'question_id': '20246066', 'answer': 'Skateboarding', 'gt_answer': 'skateboarding'}, {'question_id': '20637333', 'answer': 'White', 'gt_answer': 'white'}, {'question_id': '20403173', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201738092', 'answer': 'Large', 'gt_answer': 'large'}, {'question_id': '202156672', 'answer': 'Elephants', 'gt_answer': 'elephants'}, {'question_id': '202156673', 'answer': 'Elephant', 'gt_answer': 'elephants'}, {'question_id': '202156907', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202156900', 'answer': 'Elephants', 'gt_answer': 'elephants'}, {'question_id': '201574098', 'answer': 'Wide', 'gt_answer': 'narrow'}, {'question_id': '201574090', 'answer': 'Tree', 'gt_answer': 'traffic light'}, {'question_id': '20136460', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201711314', 'answer': 'Suitcase', 'gt_answer': 'luggage'}, {'question_id': '201976752', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '202147674', 'answer': 'Short', 'gt_answer': 'short'}, {'question_id': '201935968', 'answer': 'Shelf', 'gt_answer': 'shelf'}, {'question_id': '20734077', 'answer': 'Man', 'gt_answer': 'people'}, {'question_id': '20734076', 'answer': 'Man', 'gt_answer': 'people'}, {'question_id': '201621526', 'answer': 'Sofa', 'gt_answer': 'tv stand'}, {'question_id': '201621527', 'answer': 'Couch', 'gt_answer': 'tv stand'}, {'question_id': '201982207', 'answer': 'Short sleeved', 'gt_answer': 'short sleeved'}, {'question_id': '202012416', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201227907', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202012412', 'answer': 'Cabinet', 'gt_answer': 'cabinets'}, {'question_id': '201947686', 'answer': 'Tank top', 'gt_answer': 'tank top'}, {'question_id': '201947687', 'answer': 'Tank top', 'gt_answer': 'tank top'}, {'question_id': '201498390', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201504984', 'answer': 'Surfboard', 'gt_answer': 'surfboard'}, {'question_id': '20320380', 'answer': 'Sitting', 'gt_answer': 'bending'}, {'question_id': '20647501', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '202245919', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20511454', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20511455', 'answer': 'Helicopter', 'gt_answer': 'helicopter'}, {'question_id': '20511456', 'answer': 'Helicopter', 'gt_answer': 'helicopter'}, {'question_id': '20177516', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20511450', 'answer': 'Helicopter', 'gt_answer': 'helicopter'}, {'question_id': '201859171', 'answer': 'Man', 'gt_answer': 'skateboarder'}, {'question_id': '20177513', 'answer': 'Metal', 'gt_answer': 'metal'}, {'question_id': '20511453', 'answer': 'Rope', 'gt_answer': 'cables'}, {'question_id': '201233859', 'answer': 'Ramp', 'gt_answer': 'pavement'}, {'question_id': '201704675', 'answer': 'Cow', 'gt_answer': 'cows'}, {'question_id': '202003723', 'answer': 'Projector', 'gt_answer': 'cables'}, {'question_id': '201704677', 'answer': 'Cows', 'gt_answer': 'cows'}, {'question_id': '201704671', 'answer': 'Cow', 'gt_answer': 'cows'}, {'question_id': '201704672', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201175443', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201492241', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201438261', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201998375', 'answer': 'Chairs', 'gt_answer': 'chairs'}, {'question_id': '201393820', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201497865', 'answer': 'Monitor', 'gt_answer': 'phone'}, {'question_id': '201735431', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201510328', 'answer': 'Apple', 'gt_answer': 'pear'}, {'question_id': '20716956', 'answer': 'Bed', 'gt_answer': 'bed'}, {'question_id': '20870435', 'answer': 'Player', 'gt_answer': 'player'}, {'question_id': '20870436', 'answer': 'Man', 'gt_answer': 'player'}, {'question_id': '20706437', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20716952', 'answer': 'Pillow', 'gt_answer': 'cord'}, {'question_id': '20716953', 'answer': 'Blanket', 'gt_answer': 'cord'}, {'question_id': '20870433', 'answer': 'White', 'gt_answer': 'caucasian'}, {'question_id': '202240256', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20468326', 'answer': 'Field', 'gt_answer': 'lawn'}, {'question_id': '20939885', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201481774', 'answer': 'Red', 'gt_answer': 'black'}, {'question_id': '20468328', 'answer': 'Outdoors', 'gt_answer': 'outdoors'}, {'question_id': '20939883', 'answer': 'Small', 'gt_answer': 'small'}, {'question_id': '201676183', 'answer': 'Table', 'gt_answer': 'table'}, {'question_id': '20452155', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201068819', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201480682', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201804642', 'answer': 'Metal', 'gt_answer': 'plastic'}, {'question_id': '201065400', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201760535', 'answer': 'Chair', 'gt_answer': 'chair'}, {'question_id': '20936078', 'answer': 'Elephant', 'gt_answer': 'elephant'}, {'question_id': '202117048', 'answer': 'Narrow', 'gt_answer': 'wide'}, {'question_id': '201247250', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201247254', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20244681', 'answer': 'Fence', 'gt_answer': 'road'}, {'question_id': '201975139', 'answer': 'Man', 'gt_answer': 'man'}, {'question_id': '20706380', 'answer': 'Monitor', 'gt_answer': 'keyboard'}, {'question_id': '202226052', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201110826', 'answer': 'Soft', 'gt_answer': 'hard'}, {'question_id': '202144325', 'answer': 'Color', 'gt_answer': 'material'}, {'question_id': '20118995', 'answer': 'Dress', 'gt_answer': 'dress'}, {'question_id': '201975134', 'answer': 'Woman', 'gt_answer': 'man'}, {'question_id': '20349903', 'answer': 'White', 'gt_answer': 'white'}, {'question_id': '201110827', 'answer': 'Cup', 'gt_answer': 'plates'}, {'question_id': '201623674', 'answer': 'Silver', 'gt_answer': 'silver'}, {'question_id': '202100609', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201055656', 'answer': 'Black', 'gt_answer': 'green'}, {'question_id': '202100352', 'answer': 'Sailboat', 'gt_answer': 'sailboat'}, {'question_id': '202100353', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202100350', 'answer': 'Water', 'gt_answer': 'sailboat'}, {'question_id': '20178224', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20818781', 'answer': 'Helmet', 'gt_answer': 'face mask'}, {'question_id': '201935962', 'answer': 'Shelf', 'gt_answer': 'shelf'}, {'question_id': '201410953', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20295689', 'answer': 'Bed', 'gt_answer': 'bed'}, {'question_id': '201639472', 'answer': 'Zebra', 'gt_answer': 'zebras'}, {'question_id': '202265950', 'answer': 'Pink', 'gt_answer': 'orange'}, {'question_id': '20790044', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201574246', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20427461', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201885221', 'answer': 'Yellow', 'gt_answer': 'blue'}, {'question_id': '20340509', 'answer': 'Metal', 'gt_answer': 'wood'}, {'question_id': '202060056', 'answer': 'Couch', 'gt_answer': 'couch'}, {'question_id': '202120071', 'answer': 'Floor', 'gt_answer': 'floor'}, {'question_id': '202246179', 'answer': 'Desk', 'gt_answer': 'computer desk'}, {'question_id': '202081453', 'answer': 'Color', 'gt_answer': 'shape'}, {'question_id': '20923189', 'answer': 'Heavy', 'gt_answer': 'heavy'}, {'question_id': '201988103', 'answer': 'Square', 'gt_answer': 'round'}, {'question_id': '20178082', 'answer': 'Fries', 'gt_answer': 'fries'}, {'question_id': '20178083', 'answer': 'Fries', 'gt_answer': 'fries'}, {'question_id': '20452116', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20753515', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201873580', 'answer': 'Street', 'gt_answer': 'street'}, {'question_id': '201873583', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20756564', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201879279', 'answer': 'No one', 'gt_answer': 'athlete'}, {'question_id': '201319790', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '202285609', 'answer': 'Tall', 'gt_answer': 'short'}, {'question_id': '202100849', 'answer': 'Stove', 'gt_answer': 'stove'}, {'question_id': '201795751', 'answer': 'Leather', 'gt_answer': 'wood'}, {'question_id': '202285604', 'answer': 'Top', 'gt_answer': 'top'}, {'question_id': '201143388', 'answer': 'Chair', 'gt_answer': 'table'}, {'question_id': '201153298', 'answer': 'Giraffe', 'gt_answer': 'giraffe'}, {'question_id': '20894178', 'answer': 'Boy', 'gt_answer': 'boy'}, {'question_id': '20550219', 'answer': 'Trailer', 'gt_answer': 'van'}, {'question_id': '20550218', 'answer': 'Trailer', 'gt_answer': 'van'}, {'question_id': '20754562', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20923231', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20836734', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201997214', 'answer': 'Blue', 'gt_answer': 'black'}, {'question_id': '202106378', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20836733', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201392050', 'answer': 'Long sleeved', 'gt_answer': 'short sleeved'}, {'question_id': '201878274', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201735241', 'answer': 'Yellow', 'gt_answer': 'beige'}, {'question_id': '201337071', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201711238', 'answer': 'Black', 'gt_answer': 'black'}, {'question_id': '201983819', 'answer': 'Red', 'gt_answer': 'red'}, {'question_id': '20308272', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201037166', 'answer': 'Stop sign', 'gt_answer': 'traffic sign'}, {'question_id': '201206907', 'answer': 'Table', 'gt_answer': 'mat'}, {'question_id': '20857040', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20247659', 'answer': 'Shirt', 'gt_answer': 'dress shirt'}, {'question_id': '20647946', 'answer': 'Sign', 'gt_answer': 'sign'}, {'question_id': '202262314', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20903215', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20899901', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201976645', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20247655', 'answer': 'Shirt', 'gt_answer': 'dress shirt'}, {'question_id': '20247304', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20381139', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20836590', 'answer': 'White', 'gt_answer': 'white'}, {'question_id': '202024751', 'answer': 'Long sleeved', 'gt_answer': 'sleeveless'}, {'question_id': '20836595', 'answer': 'Umbrella', 'gt_answer': 'shirts'}, {'question_id': '20836596', 'answer': 'Sign', 'gt_answer': 'shirts'}, {'question_id': '201510343', 'answer': 'Apple', 'gt_answer': 'pear'}, {'question_id': '2098205', 'answer': 'Large', 'gt_answer': 'little'}, {'question_id': '20302965', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20631475', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20691630', 'answer': 'Closed', 'gt_answer': 'closed'}, {'question_id': '201590123', 'answer': 'Frisbee', 'gt_answer': 'frisbee'}, {'question_id': '201590122', 'answer': 'Frisbee', 'gt_answer': 'frisbee'}, {'question_id': '201590120', 'answer': 'Man', 'gt_answer': 'player'}, {'question_id': '201590126', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201346735', 'answer': 'Motorcycle', 'gt_answer': 'motorcycle'}, {'question_id': '201576559', 'answer': 'Mountain', 'gt_answer': 'trees'}, {'question_id': '201590129', 'answer': 'Man', 'gt_answer': 'player'}, {'question_id': '201590128', 'answer': 'Man', 'gt_answer': 'player'}, {'question_id': '202060275', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20797547', 'answer': 'Shoe lace', 'gt_answer': 'shoe lace'}, {'question_id': '202081430', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20645836', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202081436', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201623848', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20226794', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202262558', 'answer': 'Man', 'gt_answer': 'man'}, {'question_id': '201623844', 'answer': 'Cabinets', 'gt_answer': 'cabinets'}, {'question_id': '201303187', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20226791', 'answer': 'Plant', 'gt_answer': 'chair'}, {'question_id': '20797548', 'answer': 'Shoe lace', 'gt_answer': 'shoe lace'}, {'question_id': '201982709', 'answer': 'Sitting', 'gt_answer': 'sitting'}, {'question_id': '20622048', 'answer': 'Horse', 'gt_answer': 'horses'}, {'question_id': '20149593', 'answer': 'Porcelain', 'gt_answer': 'porcelain'}, {'question_id': '20136611', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20622045', 'answer': 'Horses', 'gt_answer': 'horses'}, {'question_id': '20652566', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '2072790', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20622042', 'answer': 'Horses', 'gt_answer': 'horses'}, {'question_id': '20622043', 'answer': 'Horses', 'gt_answer': 'horses'}, {'question_id': '201752972', 'answer': 'Black', 'gt_answer': 'dark blue'}, {'question_id': '201902966', 'answer': 'Computer', 'gt_answer': 'keyboard'}, {'question_id': '20330339', 'answer': 'Tree', 'gt_answer': 'fence'}, {'question_id': '201765958', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202060059', 'answer': 'Couch', 'gt_answer': 'couch'}, {'question_id': '201959715', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201880402', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201765952', 'answer': 'Boats', 'gt_answer': 'boats'}, {'question_id': '201765955', 'answer': 'Boats', 'gt_answer': 'boats'}, {'question_id': '20716785', 'answer': 'Bed', 'gt_answer': 'bed'}, {'question_id': '201765957', 'answer': 'Sand', 'gt_answer': 'dirt'}, {'question_id': '201307404', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20149623', 'answer': 'Rectangle', 'gt_answer': 'square'}, {'question_id': '20414495', 'answer': 'Ramp', 'gt_answer': 'skateboard'}, {'question_id': '201055996', 'answer': 'Fence', 'gt_answer': 'car'}, {'question_id': '20120126', 'answer': 'Green', 'gt_answer': 'light brown'}, {'question_id': '201952874', 'answer': 'Tracks', 'gt_answer': 'train tracks'}, {'question_id': '201055993', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202100840', 'answer': 'Stove', 'gt_answer': 'stove'}, {'question_id': '201766020', 'answer': 'Dirty', 'gt_answer': 'dirty'}, {'question_id': '202249016', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '201055998', 'answer': 'Car', 'gt_answer': 'car'}, {'question_id': '201055999', 'answer': 'Car', 'gt_answer': 'car'}, {'question_id': '201639199', 'answer': 'Zebra', 'gt_answer': 'zebras'}, {'question_id': '202053116', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201885393', 'answer': 'Swimming pool', 'gt_answer': 'fence'}, {'question_id': '201548952', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '201757981', 'answer': 'Table', 'gt_answer': 'table'}, {'question_id': '20692580', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20644755', 'answer': 'Top', 'gt_answer': 'top'}, {'question_id': '202266123', 'answer': 'Pillow', 'gt_answer': 'pillows'}, {'question_id': '202023631', 'answer': 'Chair', 'gt_answer': 'chair'}, {'question_id': '202144666', 'answer': 'Blender', 'gt_answer': 'blender'}, {'question_id': '202179425', 'answer': 'Kite', 'gt_answer': 'bike'}, {'question_id': '202144664', 'answer': 'Blender', 'gt_answer': 'blender'}, {'question_id': '201080511', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202144668', 'answer': 'Blender', 'gt_answer': 'blender'}, {'question_id': '20891494', 'answer': '3', 'gt_answer': 'young'}, {'question_id': '20866146', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201571127', 'answer': 'Large', 'gt_answer': 'small'}, {'question_id': '20866141', 'answer': 'Kitchen', 'gt_answer': 'floor'}, {'question_id': '201407268', 'answer': 'Orange', 'gt_answer': 'orange'}, {'question_id': '201497890', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202023634', 'answer': 'Chair', 'gt_answer': 'chair'}, {'question_id': '202240990', 'answer': 'Full', 'gt_answer': 'full'}, {'question_id': '202258305', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201621736', 'answer': 'Wood', 'gt_answer': 'metal'}, {'question_id': '201156346', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202000979', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20797831', 'answer': 'People', 'gt_answer': 'man'}, {'question_id': '20733988', 'answer': 'Water', 'gt_answer': 'trees'}, {'question_id': '201109260', 'answer': 'Car', 'gt_answer': 'suv'}, {'question_id': '20518289', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20733986', 'answer': 'Beach', 'gt_answer': 'sand'}, {'question_id': '202023342', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202119181', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202265843', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20810780', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20480490', 'answer': 'Laptop', 'gt_answer': 'computer'}, {'question_id': '20480494', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20480324', 'answer': 'Bookshelf', 'gt_answer': 'bookcase'}, {'question_id': '201428862', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201902745', 'answer': 'Monitor', 'gt_answer': 'keyboard'}, {'question_id': '201428861', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '201902743', 'answer': 'Monitor', 'gt_answer': 'monitor'}, {'question_id': '201641133', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201481947', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20978796', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201301805', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201228041', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20754730', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201175109', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20754732', 'answer': 'Green', 'gt_answer': 'green'}, {'question_id': '20412546', 'answer': 'Chair', 'gt_answer': 'chair'}, {'question_id': '201770880', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '2017480', 'answer': 'Sheep', 'gt_answer': 'horse'}, {'question_id': '20518750', 'answer': 'Bowl', 'gt_answer': 'shower'}, {'question_id': '20518752', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '2017338', 'answer': 'Sheep', 'gt_answer': 'goat'}, {'question_id': '2017336', 'answer': 'Goat', 'gt_answer': 'goat'}, {'question_id': '2017333', 'answer': 'Horse', 'gt_answer': 'horse'}, {'question_id': '201704530', 'answer': 'Cows', 'gt_answer': 'cows'}, {'question_id': '20785981', 'answer': 'Tree', 'gt_answer': 'machine'}, {'question_id': '201497850', 'answer': 'Computer mouse', 'gt_answer': 'computer mouse'}, {'question_id': '20717149', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20785984', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202240228', 'answer': 'Floor', 'gt_answer': 'ground'}, {'question_id': '2055821', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201497858', 'answer': 'Computer', 'gt_answer': 'phone'}, {'question_id': '202240222', 'answer': 'Girl', 'gt_answer': 'girl'}, {'question_id': '201879074', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201528046', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201528048', 'answer': 'Woman', 'gt_answer': 'girl'}, {'question_id': '201882556', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201882554', 'answer': 'Bed', 'gt_answer': 'chair'}, {'question_id': '202005797', 'answer': 'Large', 'gt_answer': 'large'}, {'question_id': '20456469', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201481812', 'answer': 'White', 'gt_answer': 'white'}, {'question_id': '201770974', 'answer': 'Soap', 'gt_answer': 'toothpaste'}, {'question_id': '201770975', 'answer': 'Soap', 'gt_answer': 'toothpaste'}, {'question_id': '20899657', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20412091', 'answer': 'Caucasian', 'gt_answer': 'caucasian'}, {'question_id': '201175364', 'answer': 'Cat', 'gt_answer': 'cat'}, {'question_id': '201175365', 'answer': 'Cat', 'gt_answer': 'cat'}, {'question_id': '201175362', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '20412099', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '2044638', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20705853', 'answer': 'Wood', 'gt_answer': 'wood'}, {'question_id': '201711425', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201952894', 'answer': 'White', 'gt_answer': 'white'}, {'question_id': '201068593', 'answer': 'Taking picture', 'gt_answer': 'looking down'}, {'question_id': '20245743', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '2017157', 'answer': 'Dry', 'gt_answer': 'dry'}, {'question_id': '201068595', 'answer': 'Taking picture', 'gt_answer': 'looking down'}, {'question_id': '20655405', 'answer': 'Street sign', 'gt_answer': 'street sign'}, {'question_id': '20655403', 'answer': 'Street sign', 'gt_answer': 'street sign'}, {'question_id': '201735522', 'answer': 'Shelves', 'gt_answer': 'shelves'}, {'question_id': '20416735', 'answer': 'Brown', 'gt_answer': 'dark brown'}, {'question_id': '20789836', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '2075460', 'answer': 'Deer', 'gt_answer': 'deer'}, {'question_id': '201235642', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201935106', 'answer': 'Metallic', 'gt_answer': 'metallic'}, {'question_id': '201751709', 'answer': 'Net', 'gt_answer': 'clock'}, {'question_id': '20473114', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202036826', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201972828', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20588998', 'answer': 'Black', 'gt_answer': 'black'}, {'question_id': '20899323', 'answer': 'Plastic', 'gt_answer': 'plastic'}, {'question_id': '20427673', 'answer': 'Man', 'gt_answer': 'gentleman'}, {'question_id': '201403992', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '202231819', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20427674', 'answer': 'Man', 'gt_answer': 'gentleman'}, {'question_id': '20589022', 'answer': 'Ramp', 'gt_answer': 'graffiti'}, {'question_id': '20887361', 'answer': 'Desk', 'gt_answer': 'table'}, {'question_id': '20381301', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201593521', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20381305', 'answer': 'Leather', 'gt_answer': 'metal'}, {'question_id': '201687367', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20178147', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20178146', 'answer': 'Small', 'gt_answer': 'large'}, {'question_id': '201616253', 'answer': 'Toilet', 'gt_answer': 'table'}, {'question_id': '20672829', 'answer': 'Chair', 'gt_answer': 'chair'}, {'question_id': '201616254', 'answer': 'Cabinet', 'gt_answer': 'table'}, {'question_id': '201983080', 'answer': 'Large', 'gt_answer': 'small'}, {'question_id': '201616258', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201302074', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20827469', 'answer': 'Couch', 'gt_answer': 'sofa'}, {'question_id': '20827468', 'answer': 'Couch', 'gt_answer': 'sofa'}, {'question_id': '201467253', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202021395', 'answer': 'Color', 'gt_answer': 'color'}, {'question_id': '201795347', 'answer': 'People', 'gt_answer': 'woman'}, {'question_id': '20361323', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20210946', 'answer': 'Woman', 'gt_answer': 'woman'}, {'question_id': '201879046', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20361326', 'answer': 'Woman', 'gt_answer': 'snowboarder'}, {'question_id': '20361329', 'answer': 'Woman', 'gt_answer': 'snowboarder'}, {'question_id': '201879845', 'answer': 'Wet', 'gt_answer': 'dry'}, {'question_id': '201235840', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20149839', 'answer': 'Drawer', 'gt_answer': 'drawer'}, {'question_id': '20183254', 'answer': 'Man', 'gt_answer': 'woman'}, {'question_id': '20879028', 'answer': 'Gray', 'gt_answer': 'white'}, {'question_id': '20753709', 'answer': 'Female', 'gt_answer': 'female'}, {'question_id': '201878397', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201735383', 'answer': 'Chicken', 'gt_answer': 'chicken'}, {'question_id': '201735385', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20756952', 'answer': 'Stove', 'gt_answer': 'stove'}, {'question_id': '202073379', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20753702', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20705704', 'answer': 'Monitor', 'gt_answer': 'monitor'}, {'question_id': '20756956', 'answer': 'Stove', 'gt_answer': 'stove'}, {'question_id': '20609470', 'answer': 'Metal', 'gt_answer': 'metal'}, {'question_id': '201482336', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201639129', 'answer': 'Field', 'gt_answer': 'field'}, {'question_id': '201616070', 'answer': 'Toilet', 'gt_answer': 'toilet'}, {'question_id': '20183258', 'answer': 'Bench', 'gt_answer': 'steps'}, {'question_id': '20963671', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '201738981', 'answer': 'Pitcher', 'gt_answer': 'pitcher'}, {'question_id': '201738980', 'answer': 'Pitcher', 'gt_answer': 'pitcher'}, {'question_id': '201738984', 'answer': 'Pitcher', 'gt_answer': 'pitcher'}, {'question_id': '201738987', 'answer': 'Baseball', 'gt_answer': 'baseball'}, {'question_id': '20295746', 'answer': 'White', 'gt_answer': 'white'}, {'question_id': '20480529', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201156504', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '20667311', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20667642', 'answer': 'Table', 'gt_answer': 'coffee table'}, {'question_id': '20667644', 'answer': 'Coffee table', 'gt_answer': 'coffee table'}, {'question_id': '202125998', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20611779', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '202116785', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20667869', 'answer': 'Couch', 'gt_answer': 'couch'}, {'question_id': '20247538', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '202121522', 'answer': 'Window', 'gt_answer': 'window'}, {'question_id': '202121523', 'answer': 'Curtain', 'gt_answer': 'curtains'}, {'question_id': '201832266', 'answer': 'Bedroom', 'gt_answer': 'bedroom'}, {'question_id': '201143249', 'answer': 'Chairs', 'gt_answer': 'chairs'}, {'question_id': '20287254', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201976532', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20794202', 'answer': 'Mustard', 'gt_answer': 'mustard'}, {'question_id': '201143241', 'answer': 'Comfortable', 'gt_answer': 'uncomfortable'}, {'question_id': '201143246', 'answer': 'Bag', 'gt_answer': 'chairs'}, {'question_id': '201143245', 'answer': 'Bag', 'gt_answer': 'chairs'}, {'question_id': '201671758', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20722042', 'answer': 'Toilet', 'gt_answer': 'ladder'}, {'question_id': '20722045', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202021431', 'answer': 'Ugly', 'gt_answer': 'ugly'}, {'question_id': '201671757', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201428476', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20984478', 'answer': 'Skateboard', 'gt_answer': 'skateboard'}, {'question_id': '20984474', 'answer': 'Boy', 'gt_answer': 'skater'}, {'question_id': '20836693', 'answer': 'Hat', 'gt_answer': 'jewelry'}, {'question_id': '20611518', 'answer': 'Color', 'gt_answer': 'shape'}, {'question_id': '201711251', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20285100', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202133697', 'answer': 'Skateboard', 'gt_answer': 'skate park'}, {'question_id': '20162446', 'answer': 'Trees', 'gt_answer': 'trees'}, {'question_id': '201987450', 'answer': 'Man', 'gt_answer': 'driver'}, {'question_id': '201576611', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20982135', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201479126', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '201346500', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201576614', 'answer': 'Road', 'gt_answer': 'street'}, {'question_id': '201590244', 'answer': 'White', 'gt_answer': 'white'}, {'question_id': '202023458', 'answer': 'Closet', 'gt_answer': 'closet'}, {'question_id': '201795290', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '202258462', 'answer': 'Brown', 'gt_answer': 'light brown'}, {'question_id': '20317191', 'answer': 'Cutting board', 'gt_answer': 'coffee pot'}, {'question_id': '20317190', 'answer': 'Cutting board', 'gt_answer': 'coffee pot'}, {'question_id': '20618821', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20621988', 'answer': 'White', 'gt_answer': 'white'}, {'question_id': '201303519', 'answer': 'Tall', 'gt_answer': 'short'}, {'question_id': '20679414', 'answer': 'Tree', 'gt_answer': 'trees'}, {'question_id': '20482152', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201795563', 'answer': 'Bench', 'gt_answer': 'bench'}, {'question_id': '20385226', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '2062411', 'answer': 'People', 'gt_answer': 'people'}, {'question_id': '201303510', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20621983', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201795568', 'answer': 'Woman', 'gt_answer': 'child'}, {'question_id': '201064665', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '2012681', 'answer': 'Donut', 'gt_answer': 'donut'}, {'question_id': '2012682', 'answer': 'Donut', 'gt_answer': 'donut'}, {'question_id': '20836479', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '202179547', 'answer': 'People', 'gt_answer': 'man'}, {'question_id': '202228722', 'answer': 'Square', 'gt_answer': 'rectangular'}, {'question_id': '201879016', 'answer': 'Bottom', 'gt_answer': 'top'}, {'question_id': '202240764', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '202005709', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20596426', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20462196', 'answer': 'Gray', 'gt_answer': 'gray'}, {'question_id': '201908851', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202004255', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20655383', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202244087', 'answer': 'Cupcake', 'gt_answer': 'cupcakes'}, {'question_id': '201803838', 'answer': 'Couch', 'gt_answer': 'couch'}, {'question_id': '20257407', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20344942', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20257402', 'answer': 'Male', 'gt_answer': 'male'}, {'question_id': '201803833', 'answer': 'Pillow', 'gt_answer': 'pillow'}, {'question_id': '201480430', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20978300', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201030375', 'answer': 'Rocket', 'gt_answer': 'shuttle'}, {'question_id': '20866021', 'answer': 'Sign', 'gt_answer': 'poster'}, {'question_id': '201866684', 'answer': 'Small', 'gt_answer': 'small'}, {'question_id': '201974797', 'answer': 'Fence', 'gt_answer': 'fence'}, {'question_id': '202012653', 'answer': 'Shelf', 'gt_answer': 'shelves'}, {'question_id': '201608370', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201974790', 'answer': 'Woman', 'gt_answer': 'player'}, {'question_id': '201974791', 'answer': 'Woman', 'gt_answer': 'player'}, {'question_id': '202228019', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '202042055', 'answer': 'Policeman', 'gt_answer': 'policeman'}, {'question_id': '202042056', 'answer': 'Policeman', 'gt_answer': 'policeman'}, {'question_id': '20811159', 'answer': 'Large', 'gt_answer': 'large'}, {'question_id': '20818967', 'answer': 'Silver', 'gt_answer': 'black'}, {'question_id': '20456670', 'answer': 'Desk', 'gt_answer': 'cabinet'}, {'question_id': '201882797', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201557047', 'answer': 'Desk', 'gt_answer': 'desk'}, {'question_id': '201982916', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201079862', 'answer': 'Desk', 'gt_answer': 'desk'}, {'question_id': '202081905', 'answer': 'Cat', 'gt_answer': 'cat'}, {'question_id': '201438574', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '202133524', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20734093', 'answer': 'Sand', 'gt_answer': 'sand'}, {'question_id': '20734091', 'answer': 'Sand', 'gt_answer': 'sand'}, {'question_id': '20489761', 'answer': 'Drawers', 'gt_answer': 'shelves'}, {'question_id': '20489762', 'answer': 'Bed', 'gt_answer': 'shelves'}, {'question_id': '20783298', 'answer': 'Small', 'gt_answer': 'small'}, {'question_id': '201974536', 'answer': 'Color', 'gt_answer': 'material'}, {'question_id': '20811335', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201498370', 'answer': 'Keyboard', 'gt_answer': 'computer mouse'}, {'question_id': '202223129', 'answer': 'Short', 'gt_answer': 'short'}, {'question_id': '202169026', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20306188', 'answer': 'Blue', 'gt_answer': 'blue'}, {'question_id': '20262662', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20887111', 'answer': 'Computer', 'gt_answer': 'table lamp'}, {'question_id': '201879231', 'answer': 'Man', 'gt_answer': 'athlete'}, {'question_id': '20456586', 'answer': 'Dog', 'gt_answer': 'dog'}, {'question_id': '20887115', 'answer': 'Desk', 'gt_answer': 'table'}, {'question_id': '20456581', 'answer': 'Carpet', 'gt_answer': 'carpet'}, {'question_id': '20306182', 'answer': 'Jacket', 'gt_answer': 'sweater'}, {'question_id': '202223124', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201859190', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202245936', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '202119278', 'answer': 'Elephant', 'gt_answer': 'elephant'}, {'question_id': '202003707', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20157102', 'answer': 'Pancakes', 'gt_answer': 'eggs'}, {'question_id': '20183028', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20157107', 'answer': 'Egg', 'gt_answer': 'eggs'}, {'question_id': '201428985', 'answer': 'Refrigerator', 'gt_answer': 'refrigerator'}, {'question_id': '20157457', 'answer': 'Butter', 'gt_answer': 'butter'}, {'question_id': '202243255', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201393802', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201528184', 'answer': 'Cabinet', 'gt_answer': 'table'}, {'question_id': '201528182', 'answer': 'Dishwasher', 'gt_answer': 'stove'}, {'question_id': '201803709', 'answer': 'Color', 'gt_answer': 'shape'}, {'question_id': '20551634', 'answer': 'Train', 'gt_answer': 'train'}, {'question_id': '202174521', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20716935', 'answer': 'Gray', 'gt_answer': 'gray'}, {'question_id': '20306981', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '201996988', 'answer': 'Boy', 'gt_answer': 'pilot'}, {'question_id': '201996987', 'answer': 'Boy', 'gt_answer': 'pilot'}, {'question_id': '20636919', 'answer': 'Raw', 'gt_answer': 'raw'}, {'question_id': '201061277', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201996982', 'answer': 'Boy', 'gt_answer': 'pilot'}, {'question_id': '20827678', 'answer': 'Coffee table', 'gt_answer': 'coffee table'}, {'question_id': '20827679', 'answer': 'Table', 'gt_answer': 'coffee table'}, {'question_id': '20452178', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201763779', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20307051', 'answer': 'Table', 'gt_answer': 'chair'}, {'question_id': '20307050', 'answer': 'Snow', 'gt_answer': 'chair'}, {'question_id': '20452173', 'answer': 'Table', 'gt_answer': 'flowers'}, {'question_id': '20452172', 'answer': 'Table', 'gt_answer': 'flowers'}, {'question_id': '20452175', 'answer': 'Chair', 'gt_answer': 'chair'}, {'question_id': '20452177', 'answer': 'Chair', 'gt_answer': 'chair'}, {'question_id': '20721892', 'answer': 'Red', 'gt_answer': 'red'}, {'question_id': '201143422', 'answer': 'Table', 'gt_answer': 'table'}, {'question_id': '20785860', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '2044518', 'answer': 'Man', 'gt_answer': 'man'}, {'question_id': '20901983', 'answer': 'Rough', 'gt_answer': 'rough'}, {'question_id': '20345079', 'answer': 'Boy', 'gt_answer': 'athlete'}, {'question_id': '20740987', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201908668', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201505104', 'answer': 'Green', 'gt_answer': 'gray'}, {'question_id': '202049417', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20861269', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201908664', 'answer': 'Color', 'gt_answer': 'material'}, {'question_id': '20861264', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20861263', 'answer': 'Tie', 'gt_answer': 'sand'}, {'question_id': '20861262', 'answer': 'Carpet', 'gt_answer': 'sand'}, {'question_id': '20303097', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201623611', 'answer': 'Refrigerator', 'gt_answer': 'refrigerator'}, {'question_id': '201068865', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201654422', 'answer': 'Horse', 'gt_answer': 'horses'}, {'question_id': '202100379', 'answer': 'Sailboat', 'gt_answer': 'sailboats'}, {'question_id': '201064898', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201826648', 'answer': 'Large', 'gt_answer': 'large'}, {'question_id': '201641308', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20753286', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202006730', 'answer': 'Table', 'gt_answer': 'cabinet'}, {'question_id': '201490987', 'answer': 'Calf', 'gt_answer': 'goats'}, {'question_id': '201319553', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20468878', 'answer': 'Horse', 'gt_answer': 'helmet'}, {'question_id': '201639103', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201319550', 'answer': 'Window', 'gt_answer': 'window frame'}, {'question_id': '201410973', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201410972', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201639458', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20940258', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '2076254', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201599845', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201639454', 'answer': 'Zebra', 'gt_answer': 'zebras'}, {'question_id': '20468875', 'answer': 'Man', 'gt_answer': 'man'}, {'question_id': '201080374', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20786157', 'answer': 'Woman', 'gt_answer': 'woman'}, {'question_id': '201080373', 'answer': 'On', 'gt_answer': 'off'}, {'question_id': '202119839', 'answer': 'Refrigerator', 'gt_answer': 'refrigerator'}, {'question_id': '20259001', 'answer': 'Red', 'gt_answer': 'white'}, {'question_id': '20550581', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20472908', 'answer': 'Brown', 'gt_answer': 'white'}, {'question_id': '20865433', 'answer': 'Cow', 'gt_answer': 'cow'}, {'question_id': '20826986', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '2093735', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201412194', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201412193', 'answer': 'Green', 'gt_answer': 'brown'}, {'question_id': '202159091', 'answer': 'Left', 'gt_answer': 'right'}, {'question_id': '201654420', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201391955', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201430918', 'answer': 'Cabinet', 'gt_answer': 'cabinet'}, {'question_id': '20902639', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '20902982', 'answer': 'Blue', 'gt_answer': 'blue'}, {'question_id': '201391959', 'answer': 'Couch', 'gt_answer': 'sofa'}, {'question_id': '201360632', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201879700', 'answer': 'Yellow', 'gt_answer': 'light blue'}, {'question_id': '20247499', 'answer': 'Shirt', 'gt_answer': 'dress'}, {'question_id': '202285625', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201879255', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201480632', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '2044572', 'answer': 'Umbrella', 'gt_answer': 'umbrella'}, {'question_id': '2055536', 'answer': 'England', 'gt_answer': 'road'}, {'question_id': '20952949', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '2044576', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '2044579', 'answer': 'Steps', 'gt_answer': 'steps'}, {'question_id': '2044578', 'answer': 'People', 'gt_answer': 'man'}, {'question_id': '202101042', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201067544', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201711211', 'answer': 'Floor', 'gt_answer': 'rug'}, {'question_id': '201711212', 'answer': 'Carpet', 'gt_answer': 'rug'}, {'question_id': '20865410', 'answer': 'Trees', 'gt_answer': 'trees'}, {'question_id': '201997275', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '20865414', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201047296', 'answer': 'Man', 'gt_answer': 'man'}, {'question_id': '201444863', 'answer': 'Park', 'gt_answer': 'lawn'}, {'question_id': '2075698', 'answer': 'Rectangle', 'gt_answer': 'square'}, {'question_id': '201444866', 'answer': 'Cloudy', 'gt_answer': 'cloudy'}, {'question_id': '201047291', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202266002', 'answer': 'Couch', 'gt_answer': 'couch'}, {'question_id': '202081384', 'answer': 'Toaster', 'gt_answer': 'toaster'}, {'question_id': '20340640', 'answer': 'Table', 'gt_answer': 'table'}, {'question_id': '201952744', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20978749', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20644781', 'answer': 'Cat', 'gt_answer': 'cat'}, {'question_id': '20320209', 'answer': 'Street', 'gt_answer': 'sidewalk'}, {'question_id': '202231552', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20411736', 'answer': 'Plastic', 'gt_answer': 'plastic'}, {'question_id': '20411737', 'answer': 'Tray', 'gt_answer': 'food container'}, {'question_id': '20381119', 'answer': 'Speaker', 'gt_answer': 'computer monitor'}, {'question_id': '20411738', 'answer': 'Tray', 'gt_answer': 'food container'}, {'question_id': '20411739', 'answer': 'Table', 'gt_answer': 'table'}, {'question_id': '202081532', 'answer': 'Square', 'gt_answer': 'square'}, {'question_id': '201864490', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '20631495', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201882881', 'answer': 'Square', 'gt_answer': 'rectangular'}, {'question_id': '202100470', 'answer': 'City', 'gt_answer': 'trees'}, {'question_id': '201882885', 'answer': 'Laptop', 'gt_answer': 'television'}, {'question_id': '20151860', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201576574', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201490981', 'answer': 'Cow', 'gt_answer': 'goats'}, {'question_id': '202060213', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202060210', 'answer': 'Tree', 'gt_answer': 'tree'}, {'question_id': '201535799', 'answer': 'Woman', 'gt_answer': 'woman'}, {'question_id': '201570646', 'answer': 'Glass', 'gt_answer': 'glass'}, {'question_id': '201535791', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201972908', 'answer': 'Brown', 'gt_answer': 'black'}, {'question_id': '20652542', 'answer': 'Large', 'gt_answer': 'small'}, {'question_id': '20891254', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20622061', 'answer': 'Large', 'gt_answer': 'large'}, {'question_id': '20655264', 'answer': 'Black', 'gt_answer': 'black'}, {'question_id': '20541247', 'answer': 'Gray', 'gt_answer': 'gray'}, {'question_id': '20136673', 'answer': 'Silver', 'gt_answer': 'silver'}, {'question_id': '202100821', 'answer': 'Stove', 'gt_answer': 'stove'}, {'question_id': '20299608', 'answer': 'Mountain', 'gt_answer': 'mountain'}, {'question_id': '20299609', 'answer': 'Mountain', 'gt_answer': 'mountain'}, {'question_id': '20810940', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '2098088', 'answer': 'Laptop', 'gt_answer': 'bowl'}, {'question_id': '2098089', 'answer': 'Laptop', 'gt_answer': 'bowl'}, {'question_id': '201972792', 'answer': 'White', 'gt_answer': 'purple'}, {'question_id': '20516144', 'answer': 'Train', 'gt_answer': 'train'}, {'question_id': '20954138', 'answer': 'Woman', 'gt_answer': 'woman'}, {'question_id': '202257129', 'answer': 'Walking', 'gt_answer': 'playing'}, {'question_id': '20596522', 'answer': 'Concrete', 'gt_answer': 'concrete'}, {'question_id': '2098081', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20692211', 'answer': 'Large', 'gt_answer': 'small'}, {'question_id': '201765938', 'answer': 'Boats', 'gt_answer': 'boats'}, {'question_id': '201438588', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201765935', 'answer': 'Boats', 'gt_answer': 'boats'}, {'question_id': '201765930', 'answer': 'Boat', 'gt_answer': 'boats'}, {'question_id': '201765931', 'answer': 'Boat', 'gt_answer': 'boats'}, {'question_id': '202223354', 'answer': 'People', 'gt_answer': 'crowd'}, {'question_id': '202223355', 'answer': 'Car', 'gt_answer': 'trees'}, {'question_id': '202223356', 'answer': 'Car', 'gt_answer': 'trees'}, {'question_id': '202179621', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '202179627', 'answer': 'Kite', 'gt_answer': 'kite'}, {'question_id': '202223352', 'answer': 'People', 'gt_answer': 'crowd'}, {'question_id': '202179625', 'answer': 'Kite', 'gt_answer': 'kite'}, {'question_id': '202223358', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20836314', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '202227844', 'answer': 'Table', 'gt_answer': 'entertainment center'}, {'question_id': '20856868', 'answer': 'Candy', 'gt_answer': 'snack'}, {'question_id': '202227840', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201766006', 'answer': 'Brown', 'gt_answer': 'dark brown'}, {'question_id': '20856865', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201467699', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201273248', 'answer': 'Palm tree', 'gt_answer': 'palm tree'}, {'question_id': '201273249', 'answer': 'Palm tree', 'gt_answer': 'palm tree'}, {'question_id': '201959594', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202258092', 'answer': 'Large', 'gt_answer': 'large'}, {'question_id': '201959591', 'answer': 'Color', 'gt_answer': 'color'}, {'question_id': '202244312', 'answer': 'Small', 'gt_answer': 'small'}, {'question_id': '201998116', 'answer': 'Open', 'gt_answer': 'closed'}, {'question_id': '201873172', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202266148', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20903174', 'answer': 'Van', 'gt_answer': 'van'}, {'question_id': '201998118', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202262196', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201030589', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '201207184', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202023589', 'answer': 'Bed', 'gt_answer': 'chair'}, {'question_id': '20652814', 'answer': 'Green', 'gt_answer': 'green'}, {'question_id': '20442455', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '202023585', 'answer': 'Bookshelf', 'gt_answer': 'bookshelf'}, {'question_id': '201467691', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201346319', 'answer': 'Car', 'gt_answer': 'car'}, {'question_id': '20162168', 'answer': 'Tall', 'gt_answer': 'tall'}, {'question_id': '202053395', 'answer': 'Pitcher', 'gt_answer': 'baseball'}, {'question_id': '201346310', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '202053391', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '202053393', 'answer': 'Batter', 'gt_answer': 'batter'}, {'question_id': '201370416', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201303338', 'answer': 'Caucasian', 'gt_answer': 'caucasian'}, {'question_id': '201621759', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20896701', 'answer': 'Cabinets', 'gt_answer': 'cabinets'}, {'question_id': '2093853', 'answer': 'Giraffe', 'gt_answer': 'giraffe'}, {'question_id': '201621750', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '202158963', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '20342383', 'answer': 'Bus', 'gt_answer': 'bus'}, {'question_id': '202286908', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20342389', 'answer': 'White', 'gt_answer': 'blue'}, {'question_id': '20511394', 'answer': 'Cloudy', 'gt_answer': 'cloudy'}, {'question_id': '20508584', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201758057', 'answer': 'Table', 'gt_answer': 'table'}, {'question_id': '201593920', 'answer': 'Car', 'gt_answer': 'pole'}, {'question_id': '201759286', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '201322754', 'answer': 'Street sign', 'gt_answer': 'street sign'}, {'question_id': '201322752', 'answer': 'Street sign', 'gt_answer': 'street sign'}, {'question_id': '201322751', 'answer': 'Street sign', 'gt_answer': 'street sign'}, {'question_id': '202244413', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20710518', 'answer': 'Snow', 'gt_answer': 'ground'}, {'question_id': '201983988', 'answer': 'Man', 'gt_answer': 'woman'}, {'question_id': '201228066', 'answer': 'Bench', 'gt_answer': 'van'}, {'question_id': '202241133', 'answer': 'Table', 'gt_answer': 'shelf'}, {'question_id': '20308413', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20710512', 'answer': 'Child', 'gt_answer': 'child'}, {'question_id': '20710513', 'answer': 'Child', 'gt_answer': 'child'}, {'question_id': '20710514', 'answer': 'Jacket', 'gt_answer': 'pants'}, {'question_id': '201983984', 'answer': 'Suitcase', 'gt_answer': 'luggage cart'}, {'question_id': '20710517', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20754716', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20482456', 'answer': 'Woman', 'gt_answer': 'woman'}, {'question_id': '201185260', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201430645', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20403522', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201056156', 'answer': 'Boy', 'gt_answer': 'soccer player'}, {'question_id': '201056155', 'answer': 'Boy', 'gt_answer': 'soccer player'}, {'question_id': '202246360', 'answer': 'Desk', 'gt_answer': 'computer desk'}, {'question_id': '201704553', 'answer': 'Tall', 'gt_answer': 'tall'}, {'question_id': '201482068', 'answer': 'Umbrella', 'gt_answer': 'jacket'}, {'question_id': '201704559', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201804566', 'answer': 'Off', 'gt_answer': 'off'}, {'question_id': '202100785', 'answer': 'Stove', 'gt_answer': 'stove'}, {'question_id': '20381239', 'answer': 'Chair', 'gt_answer': 'desk'}, {'question_id': '201879336', 'answer': 'Tree', 'gt_answer': 'truck'}, {'question_id': '20734132', 'answer': 'Large', 'gt_answer': 'small'}, {'question_id': '2097685', 'answer': 'Speaker', 'gt_answer': 'poster'}, {'question_id': '20734137', 'answer': 'Buildings', 'gt_answer': 'buildings'}, {'question_id': '202036618', 'answer': 'Spinach', 'gt_answer': 'spinach'}, {'question_id': '202081011', 'answer': 'Small', 'gt_answer': 'tiny'}, {'question_id': '202286530', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202286533', 'answer': 'Bear', 'gt_answer': 'elephant'}, {'question_id': '202286532', 'answer': 'Bear', 'gt_answer': 'elephant'}, {'question_id': '20754574', 'answer': 'Male', 'gt_answer': 'male'}, {'question_id': '20306513', 'answer': 'Snowboard', 'gt_answer': 'cell phone'}, {'question_id': '201175380', 'answer': 'Cat', 'gt_answer': 'cat'}, {'question_id': '20536137', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20306517', 'answer': 'Camera', 'gt_answer': 'cell phone'}, {'question_id': '20456447', 'answer': 'Bow', 'gt_answer': 'tie'}, {'question_id': '20306514', 'answer': 'Snowboard', 'gt_answer': 'cell phone'}, {'question_id': '201859033', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201859035', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20984336', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201682449', 'answer': 'People', 'gt_answer': 'people'}, {'question_id': '201682446', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20245989', 'answer': 'Black', 'gt_answer': 'black'}, {'question_id': '201065572', 'answer': 'Black', 'gt_answer': 'black'}, {'question_id': '20416717', 'answer': 'Chair', 'gt_answer': 'chair'}, {'question_id': '202218477', 'answer': 'Color', 'gt_answer': 'material'}, {'question_id': '202218474', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201972804', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201593320', 'answer': 'Long', 'gt_answer': 'long'}, {'question_id': '2075442', 'answer': 'Deer', 'gt_answer': 'deer'}, {'question_id': '20258721', 'answer': 'Talking', 'gt_answer': 'talking'}, {'question_id': '202036806', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20244583', 'answer': 'Bus', 'gt_answer': 'bus'}, {'question_id': '20543134', 'answer': 'Gray', 'gt_answer': 'white'}, {'question_id': '202073126', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202000853', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20473229', 'answer': 'Shirt', 'gt_answer': 'jersey'}, {'question_id': '20427836', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20177729', 'answer': 'Burger', 'gt_answer': 'burger'}, {'question_id': '20953045', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20177724', 'answer': 'Burger', 'gt_answer': 'burger'}, {'question_id': '20177725', 'answer': 'Burger', 'gt_answer': 'burger'}, {'question_id': '20177722', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20473225', 'answer': 'Jersey', 'gt_answer': 'jersey'}, {'question_id': '20473226', 'answer': 'Jersey', 'gt_answer': 'jersey'}, {'question_id': '20178163', 'answer': 'Fries', 'gt_answer': 'mug'}, {'question_id': '20178162', 'answer': 'Fries', 'gt_answer': 'mug'}, {'question_id': '20177871', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201751533', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20753495', 'answer': 'Small', 'gt_answer': 'small'}, {'question_id': '201446899', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '202162463', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201935383', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20936136', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201479447', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201498631', 'answer': 'Plastic', 'gt_answer': 'plastic'}, {'question_id': '201498633', 'answer': 'Computer', 'gt_answer': 'keyboard'}, {'question_id': '20149819', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201479444', 'answer': 'Orange', 'gt_answer': 'orange'}, {'question_id': '20953955', 'answer': 'Airport', 'gt_answer': 'office'}, {'question_id': '20827448', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20942901', 'answer': 'Girl', 'gt_answer': 'soccer player'}, {'question_id': '20361307', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201879067', 'answer': 'Green', 'gt_answer': 'black'}, {'question_id': '2017176', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201798413', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20856690', 'answer': 'Cell phone', 'gt_answer': 'hair clip'}, {'question_id': '20757005', 'answer': 'Stove', 'gt_answer': 'stove'}, {'question_id': '201047331', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20879006', 'answer': 'Parking lot', 'gt_answer': 'parking lot'}, {'question_id': '201956902', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20879005', 'answer': 'Parking lot', 'gt_answer': 'parking lot'}, {'question_id': '201430584', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201956906', 'answer': 'Small', 'gt_answer': 'small'}, {'question_id': '20345066', 'answer': 'White', 'gt_answer': 'white'}, {'question_id': '20741395', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201207407', 'answer': 'Glass', 'gt_answer': 'glass'}, {'question_id': '201207408', 'answer': 'Glass', 'gt_answer': 'glass'}, {'question_id': '201795894', 'answer': 'Skinny', 'gt_answer': 'skinny'}, {'question_id': '20295729', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20381328', 'answer': 'Chair', 'gt_answer': 'chair'}, {'question_id': '201879056', 'answer': 'Short', 'gt_answer': 'short'}, {'question_id': '20611750', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20285347', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201412272', 'answer': 'Man', 'gt_answer': 'skier'}, {'question_id': '201412273', 'answer': 'Skier', 'gt_answer': 'skier'}, {'question_id': '202208445', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20381327', 'answer': 'Chair', 'gt_answer': 'chair'}, {'question_id': '201412275', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '202001064', 'answer': 'Short', 'gt_answer': 'short'}, {'question_id': '201576909', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202121546', 'answer': 'Stove', 'gt_answer': 'refrigerator'}, {'question_id': '202121544', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201360477', 'answer': 'Color', 'gt_answer': 'color'}, {'question_id': '2076755', 'answer': 'Car', 'gt_answer': 'cars'}, {'question_id': '2076751', 'answer': 'Car', 'gt_answer': 'cars'}, {'question_id': '2076752', 'answer': 'Cars', 'gt_answer': 'cars'}, {'question_id': '20794227', 'answer': 'Chicken', 'gt_answer': 'ham'}, {'question_id': '20827058', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20794225', 'answer': 'Peanut butter', 'gt_answer': 'ham'}, {'question_id': '2076758', 'answer': 'Cars', 'gt_answer': 'cars'}, {'question_id': '20611539', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201671739', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201624136', 'answer': 'Spatula', 'gt_answer': 'spatula'}, {'question_id': '20611533', 'answer': 'Dip', 'gt_answer': 'candy'}, {'question_id': '20611530', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20611536', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201879902', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20836672', 'answer': 'Table', 'gt_answer': 'table'}, {'question_id': '202000849', 'answer': 'Brown', 'gt_answer': 'brown'}, {'question_id': '201896362', 'answer': 'Woman', 'gt_answer': 'lady'}, {'question_id': '20836676', 'answer': 'Table', 'gt_answer': 'table'}, {'question_id': '20285163', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20984458', 'answer': 'Asian', 'gt_answer': 'caucasian'}, {'question_id': '20810887', 'answer': 'Chair', 'gt_answer': 'shelf'}, {'question_id': '201590261', 'answer': 'Background', 'gt_answer': 'field'}, {'question_id': '201590262', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20151608', 'answer': 'Woman', 'gt_answer': 'woman'}, {'question_id': '201479458', 'answer': 'Brown', 'gt_answer': 'light brown'}, {'question_id': '202147854', 'answer': 'Ball', 'gt_answer': 'racket'}, {'question_id': '202147855', 'answer': 'Opponent', 'gt_answer': 'athlete'}, {'question_id': '201156197', 'answer': 'Cloth', 'gt_answer': 'cloth'}, {'question_id': '202147850', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '20482139', 'answer': 'Gold', 'gt_answer': 'gold'}, {'question_id': '201360468', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201795231', 'answer': 'Bottom', 'gt_answer': 'bottom'}, {'question_id': '202158771', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201879934', 'answer': 'Female', 'gt_answer': 'female'}, {'question_id': '20550466', 'answer': 'Dry', 'gt_answer': 'dry'}, {'question_id': '202106393', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '202106395', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '2091013', 'answer': 'Yellow', 'gt_answer': 'black'}, {'question_id': '202244190', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20836414', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201428547', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20865628', 'answer': 'Brown', 'gt_answer': 'white'}, {'question_id': '201428542', 'answer': 'Remote control', 'gt_answer': 'remote control'}, {'question_id': '20865622', 'answer': 'Small', 'gt_answer': 'small'}, {'question_id': '201481465', 'answer': 'Sitting', 'gt_answer': 'sitting'}, {'question_id': '20865624', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202228746', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20257244', 'answer': 'Male', 'gt_answer': 'male'}, {'question_id': '202228745', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201511067', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201185858', 'answer': 'Thick', 'gt_answer': 'thin'}, {'question_id': '201760587', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201188385', 'answer': 'Jacket', 'gt_answer': 'jacket'}, {'question_id': '202122019', 'answer': 'Refrigerator', 'gt_answer': 'refrigerator'}, {'question_id': '20903027', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201188388', 'answer': 'Suit', 'gt_answer': 'jacket'}, {'question_id': '20903022', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201505196', 'answer': 'Large', 'gt_answer': 'large'}, {'question_id': '202179291', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20411508', 'answer': 'Pink', 'gt_answer': 'purple'}, {'question_id': '201185071', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201638717', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202284875', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20162319', 'answer': 'Boy', 'gt_answer': 'boy'}, {'question_id': '20162318', 'answer': 'Boy', 'gt_answer': 'boy'}, {'question_id': '20508001', 'answer': 'Color', 'gt_answer': 'color'}, {'question_id': '20162311', 'answer': 'Boy', 'gt_answer': 'boy'}, {'question_id': '201247119', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20162317', 'answer': 'Boy', 'gt_answer': 'boy'}, {'question_id': '20162316', 'answer': 'Boy', 'gt_answer': 'boy'}, {'question_id': '20308883', 'answer': 'Toaster', 'gt_answer': 'toaster'}, {'question_id': '201030390', 'answer': 'Stars', 'gt_answer': 'stars'}, {'question_id': '201972845', 'answer': 'Horse', 'gt_answer': 'woman'}, {'question_id': '201030394', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20978673', 'answer': 'Man', 'gt_answer': 'man'}, {'question_id': '20308884', 'answer': 'Toaster', 'gt_answer': 'toaster'}, {'question_id': '201882767', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20434710', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20899022', 'answer': 'Woman', 'gt_answer': 'woman'}, {'question_id': '20899023', 'answer': 'Woman', 'gt_answer': 'woman'}, {'question_id': '2062297', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20899024', 'answer': 'Jeans', 'gt_answer': 'shoe'}, {'question_id': '20536307', 'answer': 'Blue', 'gt_answer': 'light blue'}, {'question_id': '20306216', 'answer': 'White', 'gt_answer': 'light brown'}, {'question_id': '202042033', 'answer': 'Policeman', 'gt_answer': 'policeman'}, {'question_id': '20536301', 'answer': 'Field', 'gt_answer': 'plain'}, {'question_id': '202081503', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '201510337', 'answer': 'Apple', 'gt_answer': 'pear'}, {'question_id': '20541479', 'answer': 'Chairs', 'gt_answer': 'shelves'}, {'question_id': '20541478', 'answer': 'Black', 'gt_answer': 'black'}, {'question_id': '20461994', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20652734', 'answer': 'People', 'gt_answer': 'woman'}, {'question_id': '20652732', 'answer': 'Car', 'gt_answer': 'cars'}, {'question_id': '201735548', 'answer': 'Shelf', 'gt_answer': 'shelves'}, {'question_id': '20330169', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '202240360', 'answer': 'Black', 'gt_answer': 'gray'}, {'question_id': '20284983', 'answer': 'Chair', 'gt_answer': 'table'}, {'question_id': '20284982', 'answer': 'Color', 'gt_answer': 'material'}, {'question_id': '201273316', 'answer': 'Street sign', 'gt_answer': 'street sign'}, {'question_id': '20887179', 'answer': 'Closed', 'gt_answer': 'closed'}, {'question_id': '201110987', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202245957', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20721912', 'answer': 'Doll', 'gt_answer': 'doll'}, {'question_id': '202003761', 'answer': 'Desk', 'gt_answer': 'desk'}, {'question_id': '202003760', 'answer': 'Desk', 'gt_answer': 'desk'}, {'question_id': '20157127', 'answer': 'Egg', 'gt_answer': 'eggs'}, {'question_id': '201982676', 'answer': 'Shoe', 'gt_answer': 'skateboard'}, {'question_id': '201751761', 'answer': 'Woman', 'gt_answer': 'lady'}, {'question_id': '20896396', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202023617', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201037271', 'answer': 'Fire hydrant', 'gt_answer': 'fire hydrant'}, {'question_id': '2090994', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201983711', 'answer': 'People', 'gt_answer': 'woman'}, {'question_id': '20258953', 'answer': 'Crown', 'gt_answer': 'crown'}, {'question_id': '201438264', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20716910', 'answer': 'Mirror', 'gt_answer': 'mirror'}, {'question_id': '20258957', 'answer': 'Boy', 'gt_answer': 'child'}, {'question_id': '20258955', 'answer': 'Crown', 'gt_answer': 'crown'}, {'question_id': '20929669', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201997974', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201676186', 'answer': 'Desk', 'gt_answer': 'table'}, {'question_id': '20442249', 'answer': 'Refrigerator', 'gt_answer': 'microwave'}, {'question_id': '201061298', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '202208316', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20303068', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20452119', 'answer': 'Picture', 'gt_answer': 'picture'}, {'question_id': '20307038', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20303065', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20303066', 'answer': 'Man', 'gt_answer': 'man'}, {'question_id': '202174377', 'answer': 'Coffee maker', 'gt_answer': 'coffee maker'}, {'question_id': '201984182', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201902547', 'answer': 'Square', 'gt_answer': 'rectangular'}, {'question_id': '201804173', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20896577', 'answer': 'Microwave', 'gt_answer': 'microwave'}, {'question_id': '20295532', 'answer': 'Girl', 'gt_answer': 'woman'}, {'question_id': '20295531', 'answer': 'Laying down', 'gt_answer': 'sleeping'}, {'question_id': '201444894', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202082141', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201902595', 'answer': 'Desk', 'gt_answer': 'desk'}, {'question_id': '202100577', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20667431', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20667435', 'answer': 'Short sleeved', 'gt_answer': 'short sleeved'}, {'question_id': '201571044', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '20667439', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201623630', 'answer': 'Refrigerator', 'gt_answer': 'refrigerator'}, {'question_id': '20942345', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '20962320', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20962326', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20753261', 'answer': 'Bed', 'gt_answer': 'bed'}, {'question_id': '201751701', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20753265', 'answer': 'Bed', 'gt_answer': 'dresser'}, {'question_id': '202006869', 'answer': 'Empty', 'gt_answer': 'empty'}, {'question_id': '202053272', 'answer': 'Waiting', 'gt_answer': 'crouching'}, {'question_id': '20753268', 'answer': 'Bed', 'gt_answer': 'bed'}, {'question_id': '202106204', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20468818', 'answer': 'Trailer', 'gt_answer': 'trailer'}, {'question_id': '2076503', 'answer': 'House', 'gt_answer': 'houses'}, {'question_id': '2076502', 'answer': 'House', 'gt_answer': 'houses'}, {'question_id': '201639439', 'answer': 'Giraffe', 'gt_answer': 'zebras'}, {'question_id': '201639438', 'answer': 'Giraffe', 'gt_answer': 'zebras'}, {'question_id': '20472962', 'answer': 'Batting', 'gt_answer': 'playing'}, {'question_id': '20472963', 'answer': 'Boy', 'gt_answer': 'boy'}, {'question_id': '20923141', 'answer': 'Truck', 'gt_answer': 'fire truck'}, {'question_id': '20923140', 'answer': 'Truck', 'gt_answer': 'fire truck'}, {'question_id': '201504797', 'answer': 'Short sleeved', 'gt_answer': 'short sleeved'}, {'question_id': '201462495', 'answer': 'Catcher', 'gt_answer': 'catcher'}, {'question_id': '201462494', 'answer': 'Catcher', 'gt_answer': 'catcher'}, {'question_id': '2044774', 'answer': 'Black', 'gt_answer': 'blue'}, {'question_id': '201739331', 'answer': 'Pitcher', 'gt_answer': 'pitcher'}, {'question_id': '201832433', 'answer': 'Bed', 'gt_answer': 'bed'}, {'question_id': '201682381', 'answer': 'Red', 'gt_answer': 'red'}, {'question_id': '201832430', 'answer': 'Bed', 'gt_answer': 'bed'}, {'question_id': '20479972', 'answer': 'Bookshelf', 'gt_answer': 'desk'}, {'question_id': '20479973', 'answer': 'Bookcase', 'gt_answer': 'desk'}, {'question_id': '201407153', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20479971', 'answer': 'Bookshelf', 'gt_answer': 'desk'}, {'question_id': '20479977', 'answer': 'Shelf', 'gt_answer': 'bookcase'}, {'question_id': '20789976', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201951903', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20756524', 'answer': 'Shelf', 'gt_answer': 'shelf'}, {'question_id': '20756523', 'answer': 'Material', 'gt_answer': 'material'}, {'question_id': '201760574', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201760577', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '202053279', 'answer': 'Batter', 'gt_answer': 'batter'}, {'question_id': '202059916', 'answer': 'Indoors', 'gt_answer': 'indoors'}, {'question_id': '201879235', 'answer': 'No one', 'gt_answer': 'athlete'}, {'question_id': '201879234', 'answer': 'No one', 'gt_answer': 'athlete'}, {'question_id': '201879725', 'answer': 'Sunny', 'gt_answer': 'cloudy'}, {'question_id': '201879236', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201798334', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201795930', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201879721', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20468726', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201438604', 'answer': 'Swing', 'gt_answer': 'waiting'}, {'question_id': '201879239', 'answer': 'Woman', 'gt_answer': 'athlete'}, {'question_id': '201879238', 'answer': 'No one', 'gt_answer': 'athlete'}, {'question_id': '20600192', 'answer': 'Zebras', 'gt_answer': 'zebras'}, {'question_id': '20600193', 'answer': 'Zebra', 'gt_answer': 'zebras'}, {'question_id': '201392011', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20600191', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201832438', 'answer': 'Pillow', 'gt_answer': 'pillows'}, {'question_id': '201067528', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '20894130', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '2044513', 'answer': 'Reading', 'gt_answer': 'reading'}, {'question_id': '201997255', 'answer': 'Chair', 'gt_answer': 'chair'}, {'question_id': '20600198', 'answer': 'Zebras', 'gt_answer': 'zebras'}, {'question_id': '20600199', 'answer': 'Zebras', 'gt_answer': 'zebras'}, {'question_id': '20952968', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201067520', 'answer': 'Computer', 'gt_answer': 'calculator'}, {'question_id': '20827497', 'answer': 'Cabinet', 'gt_answer': 'chairs'}, {'question_id': '201206949', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20631706', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201467380', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202081508', 'answer': 'Black', 'gt_answer': 'gray'}, {'question_id': '202218647', 'answer': 'Indoors', 'gt_answer': 'outdoors'}, {'question_id': '20320453', 'answer': 'Large', 'gt_answer': 'large'}, {'question_id': '20240872', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201570944', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '20302783', 'answer': 'Beach', 'gt_answer': 'beach'}, {'question_id': '202161943', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '201998421', 'answer': 'Rug', 'gt_answer': 'rug'}, {'question_id': '20285576', 'answer': 'Yellow', 'gt_answer': 'yellow'}, {'question_id': '20340750', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20381179', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20647309', 'answer': '3 feet', 'gt_answer': 'tall'}, {'question_id': '20341193', 'answer': 'Tree', 'gt_answer': 'stairs'}, {'question_id': '202257495', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20341195', 'answer': 'Building', 'gt_answer': 'doors'}, {'question_id': '201393423', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '2098247', 'answer': 'Narrow', 'gt_answer': 'narrow'}, {'question_id': '201393427', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202218789', 'answer': 'Blue', 'gt_answer': 'blue'}, {'question_id': '201759499', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201153270', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201360614', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202225815', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202162572', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20984152', 'answer': 'City', 'gt_answer': 'street'}, {'question_id': '202080859', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202243929', 'answer': 'Cup', 'gt_answer': 'bowl'}, {'question_id': '201704692', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201246996', 'answer': 'Table', 'gt_answer': 'side table'}, {'question_id': '202000675', 'answer': 'Dirty', 'gt_answer': 'dirty'}, {'question_id': '202073222', 'answer': 'Zebra', 'gt_answer': 'horses'}, {'question_id': '201982741', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20136652', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20782996', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202040244', 'answer': 'Train', 'gt_answer': 'train'}, {'question_id': '202243924', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202243927', 'answer': 'Carrots', 'gt_answer': 'beans'}, {'question_id': '202243926', 'answer': 'Carrot', 'gt_answer': 'beans'}, {'question_id': '202228192', 'answer': 'Television', 'gt_answer': 'television'}, {'question_id': '202100513', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201393766', 'answer': 'Brown', 'gt_answer': 'brown'}, {'question_id': '201979353', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20596548', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20299624', 'answer': 'Bottom', 'gt_answer': 'bottom'}, {'question_id': '20596541', 'answer': 'Wet', 'gt_answer': 'wet'}, {'question_id': '20151606', 'answer': 'Coffee', 'gt_answer': 'coffee'}, {'question_id': '201765914', 'answer': 'Man', 'gt_answer': 'surfer'}, {'question_id': '201347437', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '202119663', 'answer': 'Red', 'gt_answer': 'red'}, {'question_id': '202012624', 'answer': 'Shelf', 'gt_answer': 'shelves'}, {'question_id': '2059604', 'answer': 'Player', 'gt_answer': 'batter'}, {'question_id': '20573724', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '201596045', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '2059600', 'answer': 'Umpire', 'gt_answer': 'batter'}, {'question_id': '20866369', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20836336', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201347316', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201638826', 'answer': 'Car', 'gt_answer': 'car'}, {'question_id': '202286708', 'answer': 'Looking down', 'gt_answer': 'looking down'}, {'question_id': '202121758', 'answer': 'Chairs', 'gt_answer': 'chairs'}, {'question_id': '20856845', 'answer': 'None', 'gt_answer': 'snack'}, {'question_id': '20856847', 'answer': 'Bed', 'gt_answer': 'bed'}, {'question_id': '201826689', 'answer': 'Elephant', 'gt_answer': 'elephant'}, {'question_id': '202121755', 'answer': 'Chairs', 'gt_answer': 'chairs'}, {'question_id': '20567702', 'answer': 'Large', 'gt_answer': 'small'}, {'question_id': '20151468', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20317016', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201998131', 'answer': 'Small', 'gt_answer': 'small'}, {'question_id': '20151463', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201832439', 'answer': 'Pillow', 'gt_answer': 'pillows'}, {'question_id': '202266164', 'answer': 'Square', 'gt_answer': 'square'}, {'question_id': '202286734', 'answer': 'Teddy bear', 'gt_answer': 'woman'}, {'question_id': '20797506', 'answer': 'Color', 'gt_answer': 'color'}, {'question_id': '20385928', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201153277', 'answer': 'Right', 'gt_answer': 'left'}, {'question_id': '201570567', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20385923', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201996668', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201883000', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201080554', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201590152', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '202119230', 'answer': 'Hat', 'gt_answer': 'shirt'}, {'question_id': '201080550', 'answer': 'Man', 'gt_answer': 'catcher'}, {'question_id': '20866185', 'answer': 'Refrigerator', 'gt_answer': 'refrigerator'}, {'question_id': '201574445', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201400130', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20162106', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202174587', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20982653', 'answer': 'Silver', 'gt_answer': 'silver'}, {'question_id': '201879278', 'answer': 'Chef', 'gt_answer': 'athlete'}, {'question_id': '201621771', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201621776', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20515124', 'answer': 'Trees', 'gt_answer': 'trees'}, {'question_id': '20515126', 'answer': 'Mountain', 'gt_answer': 'mountains'}, {'question_id': '20515123', 'answer': 'Trees', 'gt_answer': 'trees'}, {'question_id': '202179608', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '2053851', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201307444', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201737756', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '2053859', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202023304', 'answer': 'Closet', 'gt_answer': 'closet'}, {'question_id': '202023307', 'answer': 'Closet', 'gt_answer': 'closet'}, {'question_id': '202023309', 'answer': 'Bed', 'gt_answer': 'bed'}, {'question_id': '201428826', 'answer': 'Square', 'gt_answer': 'round'}, {'question_id': '20183217', 'answer': 'Resting', 'gt_answer': 'resting'}, {'question_id': '20308438', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '202244169', 'answer': 'Cupcake', 'gt_answer': 'cupcakes'}, {'question_id': '20865983', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '201481903', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20412508', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20412509', 'answer': 'Chair', 'gt_answer': 'boxes'}, {'question_id': '20412506', 'answer': 'Floor', 'gt_answer': 'floor'}, {'question_id': '201175144', 'answer': 'Cap', 'gt_answer': 'bottle cap'}, {'question_id': '201997538', 'answer': 'Brown', 'gt_answer': 'dark brown'}, {'question_id': '201175630', 'answer': 'Sweater', 'gt_answer': 'shirt'}, {'question_id': '201175140', 'answer': 'Plastic', 'gt_answer': 'plastic'}, {'question_id': '201175141', 'answer': 'Plastic', 'gt_answer': 'plastic'}, {'question_id': '2053586', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201175635', 'answer': 'Sweater', 'gt_answer': 'shirt'}, {'question_id': '20403509', 'answer': 'Metal', 'gt_answer': 'metal'}, {'question_id': '20442474', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202144620', 'answer': 'Beer', 'gt_answer': 'liquor'}, {'question_id': '202144621', 'answer': 'Beer', 'gt_answer': 'liquor'}, {'question_id': '20724189', 'answer': 'Snowboarding', 'gt_answer': 'riding'}, {'question_id': '201682186', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '202126132', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20896182', 'answer': 'Metal', 'gt_answer': 'metal'}, {'question_id': '20896184', 'answer': 'Counter', 'gt_answer': 'countertop'}, {'question_id': '20342508', 'answer': 'Bus', 'gt_answer': 'bus'}, {'question_id': '201411047', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201713532', 'answer': 'Bathroom', 'gt_answer': 'bathroom'}, {'question_id': '20978532', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202147712', 'answer': 'Colorful', 'gt_answer': 'colorful'}, {'question_id': '20754593', 'answer': 'Skateboard', 'gt_answer': 'skateboard'}, {'question_id': '201882512', 'answer': 'Brown', 'gt_answer': 'white'}, {'question_id': '201770939', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '20754597', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '202081037', 'answer': 'Coffee maker', 'gt_answer': 'toaster'}, {'question_id': '202036634', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20306535', 'answer': 'Man', 'gt_answer': 'man'}, {'question_id': '202081031', 'answer': 'Coffee maker', 'gt_answer': 'house'}, {'question_id': '201882519', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201109510', 'answer': 'Car', 'gt_answer': 'truck'}, {'question_id': '20887095', 'answer': 'Top', 'gt_answer': 'top'}, {'question_id': '201109515', 'answer': 'Car', 'gt_answer': 'suv'}, {'question_id': '201676064', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201676068', 'answer': 'Silver', 'gt_answer': 'pink'}, {'question_id': '201859010', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20655442', 'answer': 'Bottom', 'gt_answer': 'top'}, {'question_id': '20655446', 'answer': 'People', 'gt_answer': 'man'}, {'question_id': '201987466', 'answer': 'Motorcycle', 'gt_answer': 'motorcycle'}, {'question_id': '20511704', 'answer': 'Ocean', 'gt_answer': 'ocean'}, {'question_id': '201987464', 'answer': 'Man', 'gt_answer': 'driver'}, {'question_id': '201492336', 'answer': 'Open', 'gt_answer': 'closed'}, {'question_id': '20588950', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20588957', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202081155', 'answer': 'Brown', 'gt_answer': 'silver'}, {'question_id': '201737972', 'answer': 'Blue', 'gt_answer': 'blue'}, {'question_id': '20543116', 'answer': 'Dry', 'gt_answer': 'dry'}, {'question_id': '20427815', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20177744', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '202102739', 'answer': 'Metal', 'gt_answer': 'glass'}, {'question_id': '201536497', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201536494', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201056135', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201056134', 'answer': 'Ball', 'gt_answer': 'ball'}, {'question_id': '202005737', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201056130', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201056133', 'answer': 'Man', 'gt_answer': 'soccer player'}, {'question_id': '201056132', 'answer': 'Man', 'gt_answer': 'soccer player'}, {'question_id': '20178106', 'answer': 'Fries', 'gt_answer': 'fries'}, {'question_id': '20178105', 'answer': 'Fries', 'gt_answer': 'fries'}, {'question_id': '201987684', 'answer': 'Dirty', 'gt_answer': 'dirty'}, {'question_id': '201616213', 'answer': 'Shirt', 'gt_answer': 'shirt'}, {'question_id': '201616210', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20953971', 'answer': 'Woman', 'gt_answer': 'woman'}, {'question_id': '20636767', 'answer': 'Carrot', 'gt_answer': 'potato'}, {'question_id': '20210985', 'answer': 'Dessert', 'gt_answer': 'chocolate'}, {'question_id': '201235880', 'answer': 'Woman', 'gt_answer': 'woman'}, {'question_id': '20861180', 'answer': 'Top', 'gt_answer': 'top'}, {'question_id': '20861185', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20757021', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20757023', 'answer': 'Stove', 'gt_answer': 'stove'}, {'question_id': '202100589', 'answer': 'Water', 'gt_answer': 'sailboats'}, {'question_id': '20757029', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202100584', 'answer': 'Boat', 'gt_answer': 'sailboats'}, {'question_id': '20886945', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20452255', 'answer': 'Blue', 'gt_answer': 'blue'}, {'question_id': '2017190', 'answer': 'Pasture', 'gt_answer': 'pasture'}, {'question_id': '20756996', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201873150', 'answer': 'Fire truck', 'gt_answer': 'fire truck'}, {'question_id': '201110462', 'answer': 'Marshmallow', 'gt_answer': 'marshmallow'}, {'question_id': '201527499', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201482370', 'answer': 'Umbrella', 'gt_answer': 'sweater'}, {'question_id': '20752299', 'answer': 'Striped', 'gt_answer': 'striped'}, {'question_id': '202265878', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20752293', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202226281', 'answer': 'Mashed potatoes', 'gt_answer': 'mashed potatoes'}, {'question_id': '202226283', 'answer': 'Meat', 'gt_answer': 'mashed potatoes'}, {'question_id': '201207427', 'answer': 'Table', 'gt_answer': 'mat'}, {'question_id': '201207426', 'answer': 'Table', 'gt_answer': 'mat'}, {'question_id': '201738924', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202119828', 'answer': 'Refrigerator', 'gt_answer': 'refrigerator'}, {'question_id': '202119824', 'answer': 'Refrigerator', 'gt_answer': 'refrigerator'}, {'question_id': '20667606', 'answer': 'Coffee table', 'gt_answer': 'coffee table'}, {'question_id': '202119823', 'answer': 'Refrigerator', 'gt_answer': 'refrigerator'}, {'question_id': '20667603', 'answer': 'Table', 'gt_answer': 'coffee table'}, {'question_id': '20611885', 'answer': 'Pink', 'gt_answer': 'brown'}, {'question_id': '201637143', 'answer': 'Small', 'gt_answer': 'small'}, {'question_id': '201412252', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201638955', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20381347', 'answer': 'Brown', 'gt_answer': 'brown'}, {'question_id': '201412259', 'answer': 'Man', 'gt_answer': 'skier'}, {'question_id': '202120168', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201576923', 'answer': 'Dog', 'gt_answer': 'goat'}, {'question_id': '201264312', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '20169862', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201713476', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '202169403', 'answer': 'Man', 'gt_answer': 'woman'}, {'question_id': '202121568', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20542963', 'answer': 'Pen', 'gt_answer': 'zoo'}, {'question_id': '20940047', 'answer': 'Shade', 'gt_answer': 'curtain'}, {'question_id': '2076777', 'answer': 'Black', 'gt_answer': 'dark'}, {'question_id': '20722004', 'answer': 'Toilet', 'gt_answer': 'trees'}, {'question_id': '20794249', 'answer': 'Looking down', 'gt_answer': 'looking down'}, {'question_id': '201671716', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20482274', 'answer': 'Nike', 'gt_answer': 'nike'}, {'question_id': '201595920', 'answer': 'Motorcycle', 'gt_answer': 'motorcycle'}, {'question_id': '20711720', 'answer': 'Desk', 'gt_answer': 'table'}, {'question_id': '201711393', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '201623329', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20863724', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201878353', 'answer': 'Coat', 'gt_answer': 'coat'}, {'question_id': '20984305', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201878355', 'answer': 'Hat', 'gt_answer': 'hat'}, {'question_id': '202162362', 'answer': 'Map', 'gt_answer': 'map'}, {'question_id': '202162361', 'answer': 'Map', 'gt_answer': 'map'}, {'question_id': '201337117', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202060005', 'answer': 'Couch', 'gt_answer': 'couch'}, {'question_id': '20836659', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201479477', 'answer': 'Chicken', 'gt_answer': 'chicken'}, {'question_id': '20929452', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201479475', 'answer': 'Chicken', 'gt_answer': 'chicken'}, {'question_id': '202060004', 'answer': 'Couch', 'gt_answer': 'couch'}, {'question_id': '201590202', 'answer': 'Player', 'gt_answer': 'player'}, {'question_id': '201479471', 'answer': 'Chicken', 'gt_answer': 'chicken'}, {'question_id': '202144436', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '2066027', 'answer': 'Glove', 'gt_answer': 'baseball mitt'}, {'question_id': '2066026', 'answer': 'Girl', 'gt_answer': 'boy'}, {'question_id': '2066025', 'answer': 'Girl', 'gt_answer': 'boy'}, {'question_id': '2066024', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '201590208', 'answer': 'Grass', 'gt_answer': 'field'}, {'question_id': '20715772', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20667979', 'answer': 'Controller', 'gt_answer': 'wii controller'}, {'question_id': '201795214', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201896394', 'answer': 'Woman', 'gt_answer': 'lady'}, {'question_id': '20667973', 'answer': 'Girl', 'gt_answer': 'man'}, {'question_id': '201301840', 'answer': 'Smooth', 'gt_answer': 'smooth'}, {'question_id': '20247287', 'answer': 'Woman', 'gt_answer': 'woman'}, {'question_id': '20667976', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20667975', 'answer': 'Couch', 'gt_answer': 'pillow'}, {'question_id': '202102579', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202121628', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '202102574', 'answer': 'Sink', 'gt_answer': 'faucet'}, {'question_id': '202102577', 'answer': 'Sink', 'gt_answer': 'tiles'}, {'question_id': '201481480', 'answer': 'Shirt', 'gt_answer': 'pants'}, {'question_id': '2098187', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201481482', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20865605', 'answer': 'Tree', 'gt_answer': 'tree'}, {'question_id': '20865604', 'answer': 'Trees', 'gt_answer': 'tree'}, {'question_id': '20865607', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201654394', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202257525', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201064775', 'answer': 'Bench', 'gt_answer': 'sofa'}, {'question_id': '201030621', 'answer': 'Wii', 'gt_answer': 'wii controller'}, {'question_id': '202218922', 'answer': 'Pot', 'gt_answer': 'flower pot'}, {'question_id': '20692406', 'answer': 'Shower', 'gt_answer': 'cabinet'}, {'question_id': '20692405', 'answer': 'Rug', 'gt_answer': 'rug'}, {'question_id': '20395150', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202218928', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20692408', 'answer': 'Sink', 'gt_answer': 'cabinet'}, {'question_id': '20903045', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202053417', 'answer': 'Home plate', 'gt_answer': 'field'}, {'question_id': '201185017', 'answer': 'Color', 'gt_answer': 'material'}, {'question_id': '20721904', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201153399', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201804367', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '20414597', 'answer': 'Skateboarder', 'gt_answer': 'skateboarder'}, {'question_id': '20414596', 'answer': 'Skateboarder', 'gt_answer': 'skateboarder'}, {'question_id': '20414591', 'answer': 'People', 'gt_answer': 'spectators'}, {'question_id': '20647179', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202006408', 'answer': 'Tan', 'gt_answer': 'tan'}, {'question_id': '202102751', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20169718', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20491675', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20330492', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201482214', 'answer': 'Bottom', 'gt_answer': 'top'}, {'question_id': '20162377', 'answer': 'Wood', 'gt_answer': 'metal'}, {'question_id': '202125912', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20609567', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201758589', 'answer': 'Brown', 'gt_answer': 'brown'}, {'question_id': '201175142', 'answer': 'Cap', 'gt_answer': 'bottle cap'}, {'question_id': '202012614', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201983686', 'answer': 'Wide', 'gt_answer': 'narrow'}, {'question_id': '202286721', 'answer': 'Bear', 'gt_answer': 'elephant'}, {'question_id': '202012618', 'answer': 'Tall', 'gt_answer': 'short'}, {'question_id': '201758587', 'answer': 'Small', 'gt_answer': 'small'}, {'question_id': '201861459', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201570716', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20306236', 'answer': 'Open', 'gt_answer': 'open'}, {'question_id': '20344936', 'answer': 'Fence', 'gt_answer': 'lawn'}, {'question_id': '20456639', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20149767', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201859225', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201576980', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201576603', 'answer': 'Boy', 'gt_answer': 'child'}, {'question_id': '20541456', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '201235578', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20508281', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201175149', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201273169', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20257226', 'answer': 'White', 'gt_answer': 'green'}, {'question_id': '202240720', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201758368', 'answer': 'Stroller', 'gt_answer': 'stroller'}, {'question_id': '20709969', 'answer': 'Man', 'gt_answer': 'woman'}, {'question_id': '20709968', 'answer': 'Man', 'gt_answer': 'woman'}, {'question_id': '201175285', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202004165', 'answer': 'Computer', 'gt_answer': 'doors'}, {'question_id': '202231353', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20120319', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201207227', 'answer': 'Bread', 'gt_answer': 'sandwich'}, {'question_id': '201676410', 'answer': 'Rug', 'gt_answer': 'rug'}, {'question_id': '20953118', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201759185', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '201574381', 'answer': 'Glass', 'gt_answer': 'glass'}, {'question_id': '202003748', 'answer': 'Desk', 'gt_answer': 'desk'}, {'question_id': '20721933', 'answer': 'Doll', 'gt_answer': 'doll'}, {'question_id': '202257941', 'answer': 'Long', 'gt_answer': 'long'}, {'question_id': '20783128', 'answer': 'Plastic', 'gt_answer': 'plastic'}, {'question_id': '201434364', 'answer': 'Ground', 'gt_answer': 'pavement'}, {'question_id': '201998343', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201067854', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20637345', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20306942', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201947592', 'answer': 'Counter', 'gt_answer': 'countertop'}, {'question_id': '20258792', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '2090974', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20403274', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20403260', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '2090973', 'answer': 'Dark', 'gt_answer': 'dark'}, {'question_id': '20721859', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20435020', 'answer': 'Box', 'gt_answer': 'pizza box'}, {'question_id': '201438270', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20435022', 'answer': 'Pizza', 'gt_answer': 'pizza'}, {'question_id': '201886847', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201735403', 'answer': 'Desk', 'gt_answer': 'desk'}, {'question_id': '20939820', 'answer': 'Color', 'gt_answer': 'material'}, {'question_id': '201984162', 'answer': 'Suitcase', 'gt_answer': 'luggage cart'}, {'question_id': '20452134', 'answer': 'Lights', 'gt_answer': 'christmas light'}, {'question_id': '20452137', 'answer': 'Wall', 'gt_answer': 'picture frame'}, {'question_id': '20303041', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20452131', 'answer': 'Chair', 'gt_answer': 'chair'}, {'question_id': '201481762', 'answer': 'Purse', 'gt_answer': 'flowers'}, {'question_id': '201068256', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20922983', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20211037', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202244594', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20896517', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201621549', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20691661', 'answer': 'Long', 'gt_answer': 'long'}, {'question_id': '20896519', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20898758', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201920428', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201920420', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201676306', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '201886856', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20480640', 'answer': 'Laptop', 'gt_answer': 'radio'}, {'question_id': '201654526', 'answer': 'Car', 'gt_answer': 'car'}, {'question_id': '201885570', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20414531', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '20753240', 'answer': 'Bed', 'gt_answer': 'dresser'}, {'question_id': '20962302', 'answer': 'City', 'gt_answer': 'city'}, {'question_id': '20717045', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201528142', 'answer': 'Tall', 'gt_answer': 'tall'}, {'question_id': '201935064', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '201637227', 'answer': 'Stove', 'gt_answer': 'stove'}, {'question_id': '201504720', 'answer': 'Small', 'gt_answer': 'small'}, {'question_id': '201593628', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20472943', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20468525', 'answer': 'Trailer', 'gt_answer': 'trailer'}, {'question_id': '20468524', 'answer': 'Trailer', 'gt_answer': 'trailer'}, {'question_id': '20468529', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20705935', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20550547', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201061172', 'answer': 'Boy', 'gt_answer': 'boy'}, {'question_id': '20826942', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201067648', 'answer': 'Top', 'gt_answer': 'bottom'}, {'question_id': '201739317', 'answer': 'Player', 'gt_answer': 'player'}, {'question_id': '20182827', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201739319', 'answer': 'Pants', 'gt_answer': 'hat'}, {'question_id': '20303007', 'answer': 'Pink', 'gt_answer': 'pink'}, {'question_id': '201391912', 'answer': 'Leather', 'gt_answer': 'wood'}, {'question_id': '201391911', 'answer': 'Leather', 'gt_answer': 'wood'}, {'question_id': '20308745', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202262107', 'answer': 'Picture', 'gt_answer': 'napkin'}, {'question_id': '20308749', 'answer': 'Silver', 'gt_answer': 'dark'}, {'question_id': '201407175', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201391918', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20789953', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20119085', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20226398', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202286517', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201947738', 'answer': 'Wall', 'gt_answer': 'countertop'}, {'question_id': '20902945', 'answer': '20 pounds', 'gt_answer': 'heavy'}, {'question_id': '20299826', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '202265600', 'answer': 'White', 'gt_answer': 'white'}, {'question_id': '20899401', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '202265603', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201111100', 'answer': 'Long', 'gt_answer': 'long'}, {'question_id': '202059930', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201037170', 'answer': 'Stop sign', 'gt_answer': 'traffic sign'}, {'question_id': '201570596', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202106342', 'answer': 'Metal', 'gt_answer': 'metal'}, {'question_id': '20899611', 'answer': 'Wet', 'gt_answer': 'wet'}, {'question_id': '20894117', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201067504', 'answer': 'Screen', 'gt_answer': 'calculator'}, {'question_id': '201428795', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '201982319', 'answer': 'Chair', 'gt_answer': 'coffee table'}, {'question_id': '201763634', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20381480', 'answer': 'Thick', 'gt_answer': 'thick'}, {'question_id': '202174362', 'answer': 'Dishwasher', 'gt_answer': 'coffee maker'}, {'question_id': '202119981', 'answer': 'Table', 'gt_answer': 'table'}, {'question_id': '201982311', 'answer': 'Couch', 'gt_answer': 'couch'}, {'question_id': '201885238', 'answer': 'Swimming pool', 'gt_answer': 'fence'}, {'question_id': '201982315', 'answer': 'Couch', 'gt_answer': 'couch'}, {'question_id': '202101063', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20902498', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '202101066', 'answer': 'Skinny', 'gt_answer': 'skinny'}, {'question_id': '201589989', 'answer': 'Yellow', 'gt_answer': 'yellow'}, {'question_id': '20902494', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202101069', 'answer': 'Male', 'gt_answer': 'female'}, {'question_id': '20503819', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201639149', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '2076566', 'answer': 'Roof', 'gt_answer': 'houses'}, {'question_id': '202081831', 'answer': 'Orange', 'gt_answer': 'orange'}, {'question_id': '2076564', 'answer': 'Roof', 'gt_answer': 'chimney'}, {'question_id': '202073353', 'answer': 'Zebra', 'gt_answer': 'deer'}, {'question_id': '202073352', 'answer': 'Zebra', 'gt_answer': 'deer'}, {'question_id': '201639410', 'answer': 'Giraffes', 'gt_answer': 'zebras'}, {'question_id': '202073425', 'answer': 'Grass', 'gt_answer': 'grass'}, {'question_id': '20427484', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202073354', 'answer': 'Grass', 'gt_answer': 'grass'}, {'question_id': '201447155', 'answer': 'Mirror', 'gt_answer': 'sink'}, {'question_id': '201447154', 'answer': 'Mirror', 'gt_answer': 'sink'}, {'question_id': '202231519', 'answer': 'Trash can', 'gt_answer': 'trash can'}, {'question_id': '20285554', 'answer': 'Yellow', 'gt_answer': 'yellow'}, {'question_id': '20647364', 'answer': 'Pants', 'gt_answer': 'pants'}, {'question_id': '20381153', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202231516', 'answer': 'Man', 'gt_answer': 'man'}, {'question_id': '20381625', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20647362', 'answer': 'Glove', 'gt_answer': 'belt'}, {'question_id': '201411221', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '2062403', 'answer': 'Green', 'gt_answer': 'green'}, {'question_id': '20320243', 'answer': 'Gray', 'gt_answer': 'dark'}, {'question_id': '20320241', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201411227', 'answer': 'Yellow', 'gt_answer': 'black'}, {'question_id': '201616151', 'answer': 'Cabinet', 'gt_answer': 'table'}, {'question_id': '201430625', 'answer': 'Shirt', 'gt_answer': 'dress shirt'}, {'question_id': '20609355', 'answer': 'Spoon', 'gt_answer': 'whipped cream'}, {'question_id': '20963792', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20609358', 'answer': 'Fork', 'gt_answer': 'strawberry'}, {'question_id': '201482299', 'answer': 'Leather', 'gt_answer': 'leather'}, {'question_id': '201795913', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201795917', 'answer': 'Shirt', 'gt_answer': 'shirt'}, {'question_id': '20891558', 'answer': 'Shirt', 'gt_answer': 'shorts'}, {'question_id': '201235768', 'answer': 'Purse', 'gt_answer': 'handbag'}, {'question_id': '201903011', 'answer': 'Computer', 'gt_answer': 'computer mouse'}, {'question_id': '202162558', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202162559', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '202257164', 'answer': 'Bike', 'gt_answer': 'bicycle'}, {'question_id': '201482121', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201880434', 'answer': 'White', 'gt_answer': 'white'}, {'question_id': '202000899', 'answer': 'Man', 'gt_answer': 'skater'}, {'question_id': '201482126', 'answer': 'Blanket', 'gt_answer': 'blanket'}, {'question_id': '20797645', 'answer': 'Gray', 'gt_answer': 'gray'}, {'question_id': '20964070', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201795190', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20827399', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20596563', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201319481', 'answer': 'Light blue', 'gt_answer': 'light blue'}, {'question_id': '20827394', 'answer': 'Sofa', 'gt_answer': 'sofa'}, {'question_id': '20827393', 'answer': 'Couch', 'gt_answer': 'sofa'}, {'question_id': '20151660', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201621832', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '201987467', 'answer': 'Motorcycle', 'gt_answer': 'motorcycle'}, {'question_id': '201957334', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '20866345', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20866437', 'answer': 'Food', 'gt_answer': 'sour cream'}, {'question_id': '20866436', 'answer': 'Food', 'gt_answer': 'sour cream'}, {'question_id': '20866438', 'answer': 'Dip', 'gt_answer': 'sour cream'}, {'question_id': '20120418', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20183295', 'answer': 'Gray', 'gt_answer': 'gray'}, {'question_id': '20162295', 'answer': 'Boy', 'gt_answer': 'boy'}, {'question_id': '20162296', 'answer': 'Boy', 'gt_answer': 'boy'}, {'question_id': '20856821', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '202073233', 'answer': 'Zebra', 'gt_answer': 'horses'}, {'question_id': '201832412', 'answer': 'Pillows', 'gt_answer': 'pillows'}, {'question_id': '201346680', 'answer': 'Black', 'gt_answer': 'silver'}, {'question_id': '201861135', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20317034', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201998404', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201976541', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20508636', 'answer': 'Blond', 'gt_answer': 'blond'}, {'question_id': '202023544', 'answer': 'Small', 'gt_answer': 'small'}, {'question_id': '201455934', 'answer': 'Car', 'gt_answer': 'car'}, {'question_id': '201574461', 'answer': 'Metal', 'gt_answer': 'wood'}, {'question_id': '201455936', 'answer': 'Hill', 'gt_answer': 'hill'}, {'question_id': '201571143', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20503629', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20609280', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20982326', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20982323', 'answer': 'Man', 'gt_answer': 'man'}, {'question_id': '202244364', 'answer': 'Food', 'gt_answer': 'dip'}, {'question_id': '20982321', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201880341', 'answer': 'Small', 'gt_answer': 'small'}, {'question_id': '202244368', 'answer': 'Dip', 'gt_answer': 'dip'}, {'question_id': '20308981', 'answer': 'Stone', 'gt_answer': 'stone'}, {'question_id': '20982639', 'answer': 'Metal', 'gt_answer': 'metal'}, {'question_id': '20515106', 'answer': 'Fence', 'gt_answer': 'fence'}, {'question_id': '202012725', 'answer': 'Controller', 'gt_answer': 'remote control'}, {'question_id': '20710088', 'answer': 'Asphalt', 'gt_answer': 'asphalt'}, {'question_id': '201951498', 'answer': 'Small', 'gt_answer': 'large'}, {'question_id': '20515108', 'answer': 'Field', 'gt_answer': 'grass'}, {'question_id': '20515109', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201758116', 'answer': 'Stroller', 'gt_answer': 'table'}, {'question_id': '202036755', 'answer': 'White', 'gt_answer': 'white'}, {'question_id': '201737771', 'answer': 'Top', 'gt_answer': 'top'}, {'question_id': '2053834', 'answer': 'Pole', 'gt_answer': 'buildings'}, {'question_id': '2053835', 'answer': 'Pole', 'gt_answer': 'buildings'}, {'question_id': '201737959', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '20573746', 'answer': 'Toilet', 'gt_answer': 'toilet'}, {'question_id': '201757838', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201428809', 'answer': 'Black', 'gt_answer': 'orange'}, {'question_id': '2065946', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '201428803', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '202244106', 'answer': 'Cupcakes', 'gt_answer': 'cupcakes'}, {'question_id': '20935919', 'answer': 'Lush', 'gt_answer': 'lush'}, {'question_id': '201428491', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202244102', 'answer': 'Cupcake', 'gt_answer': 'cupcakes'}, {'question_id': '20978739', 'answer': 'Motorcycle', 'gt_answer': 'motorcycle'}, {'question_id': '201873677', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201030457', 'answer': 'Couch', 'gt_answer': 'couch'}, {'question_id': '201873675', 'answer': 'Blue', 'gt_answer': 'blue'}, {'question_id': '202244109', 'answer': 'Carrot', 'gt_answer': 'carrots'}, {'question_id': '201175618', 'answer': 'Hair dryer', 'gt_answer': 'cords'}, {'question_id': '20482492', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20754750', 'answer': 'Long sleeved', 'gt_answer': 'long sleeved'}, {'question_id': '202004089', 'answer': 'Classroom', 'gt_answer': 'office'}, {'question_id': '201882683', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201175610', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20412528', 'answer': 'Talking', 'gt_answer': 'talking'}, {'question_id': '201185222', 'answer': 'Striped', 'gt_answer': 'striped'}, {'question_id': '201185223', 'answer': 'Striped', 'gt_answer': 'striped'}, {'question_id': '20442418', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201997516', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201997511', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201509856', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201497831', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201037259', 'answer': 'Gold', 'gt_answer': 'silver'}, {'question_id': '20785925', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '201497834', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '201077075', 'answer': 'Metal', 'gt_answer': 'porcelain'}, {'question_id': '2097646', 'answer': 'Cell phone', 'gt_answer': 'keyboard'}, {'question_id': '2097644', 'answer': 'Keyboard', 'gt_answer': 'keyboard'}, {'question_id': '2076709', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '2097642', 'answer': 'Computer', 'gt_answer': 'monitor'}, {'question_id': '20783372', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20710438', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20456409', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20226566', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20204598', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20226564', 'answer': 'Table', 'gt_answer': 'chair'}, {'question_id': '20456407', 'answer': 'Teddy bear', 'gt_answer': 'teddy bear'}, {'question_id': '20226568', 'answer': 'Table', 'gt_answer': 'chair'}, {'question_id': '201504921', 'answer': 'Surfboard', 'gt_answer': 'sign'}, {'question_id': '201109572', 'answer': 'Left', 'gt_answer': 'right'}, {'question_id': '20473045', 'answer': 'Boy', 'gt_answer': 'boy'}, {'question_id': '20473046', 'answer': 'Boy', 'gt_answer': 'boy'}, {'question_id': '201080465', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '202023327', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '201982125', 'answer': 'White', 'gt_answer': 'caucasian'}, {'question_id': '201987441', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '20789893', 'answer': 'Brown', 'gt_answer': 'black'}, {'question_id': '201228177', 'answer': 'Square', 'gt_answer': 'rectangular'}, {'question_id': '20588977', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '20789890', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202012396', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '201336922', 'answer': 'Color', 'gt_answer': 'color'}, {'question_id': '202081131', 'answer': 'Soft', 'gt_answer': 'hard'}, {'question_id': '202073164', 'answer': 'Deer', 'gt_answer': 'zebra'}, {'question_id': '2046485', 'answer': 'Woman', 'gt_answer': 'boy'}, {'question_id': '201438385', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '2046483', 'answer': 'Man', 'gt_answer': 'man'}, {'question_id': '2046482', 'answer': 'Man', 'gt_answer': 'man'}, {'question_id': '201061319', 'answer': 'Sand', 'gt_answer': 'beach'}, {'question_id': '2046480', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '20511681', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201061315', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202073168', 'answer': 'Zebra', 'gt_answer': 'zebra'}, {'question_id': '20177767', 'answer': 'Burger', 'gt_answer': 'burger'}, {'question_id': '20177765', 'answer': 'Burger', 'gt_answer': 'burger'}, {'question_id': '20452098', 'answer': 'Lights', 'gt_answer': 'picture frame'}, {'question_id': '201704599', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201056119', 'answer': 'Male', 'gt_answer': 'male'}, {'question_id': '20452097', 'answer': 'Lights', 'gt_answer': 'picture frame'}, {'question_id': '201235672', 'answer': 'Bananas', 'gt_answer': 'bananas'}, {'question_id': '201616238', 'answer': 'Very', 'gt_answer': 'hard'}, {'question_id': '201599835', 'answer': 'Top', 'gt_answer': 'top'}, {'question_id': '20258584', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20706220', 'answer': 'Computer', 'gt_answer': 'monitor'}, {'question_id': '202000910', 'answer': 'Man', 'gt_answer': 'skater'}, {'question_id': '201639423', 'answer': 'Giraffes', 'gt_answer': 'zebras'}, {'question_id': '20861160', 'answer': 'Old', 'gt_answer': 'old'}, {'question_id': '201623778', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20178101', 'answer': 'Basket', 'gt_answer': 'serving dish'}, {'question_id': '20648037', 'answer': 'Motorcycle', 'gt_answer': 'motorcycle'}, {'question_id': '20648036', 'answer': 'Policeman', 'gt_answer': 'policeman'}, {'question_id': '20361349', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202119614', 'answer': 'Tall', 'gt_answer': 'tall'}, {'question_id': '201756554', 'answer': 'Cat', 'gt_answer': 'kitten'}, {'question_id': '20307200', 'answer': 'Camera', 'gt_answer': 'television'}, {'question_id': '202180458', 'answer': 'Top', 'gt_answer': 'top'}, {'question_id': '20757045', 'answer': 'Bottle', 'gt_answer': 'oil'}, {'question_id': '201548929', 'answer': 'Blender', 'gt_answer': 'picture'}, {'question_id': '20757043', 'answer': 'Thick', 'gt_answer': 'thick'}, {'question_id': '20886929', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20452279', 'answer': 'Chair', 'gt_answer': 'chair'}, {'question_id': '20886921', 'answer': 'Office', 'gt_answer': 'office'}, {'question_id': '20757048', 'answer': 'Pot', 'gt_answer': 'wine'}, {'question_id': '202228243', 'answer': 'Glass', 'gt_answer': 'wood'}, {'question_id': '201982992', 'answer': 'Man', 'gt_answer': 'man'}, {'question_id': '201711129', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201639085', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201400046', 'answer': 'Couch', 'gt_answer': 'couch'}, {'question_id': '20954242', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201207447', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '201822409', 'answer': 'Porcelain', 'gt_answer': 'porcelain'}, {'question_id': '202119770', 'answer': 'Wood', 'gt_answer': 'concrete'}, {'question_id': '202004239', 'answer': 'Chair', 'gt_answer': 'doors'}, {'question_id': '20596219', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '202119775', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202119779', 'answer': 'Gray', 'gt_answer': 'black'}, {'question_id': '202119808', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201264099', 'answer': 'Dirty', 'gt_answer': 'tinted'}, {'question_id': '20899058', 'answer': 'Woman', 'gt_answer': 'girl'}, {'question_id': '20381367', 'answer': 'Wall', 'gt_answer': 'rug'}, {'question_id': '20177831', 'answer': 'Small', 'gt_answer': 'small'}, {'question_id': '20381361', 'answer': 'Chair', 'gt_answer': 'chair'}, {'question_id': '20178128', 'answer': 'Top', 'gt_answer': 'top'}, {'question_id': '201264096', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20645600', 'answer': 'Closed', 'gt_answer': 'closed'}, {'question_id': '20741063', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201752809', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201623309', 'answer': 'Material', 'gt_answer': 'material'}, {'question_id': '201861423', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20722026', 'answer': 'Red', 'gt_answer': 'silver'}, {'question_id': '20940023', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201669333', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201795055', 'answer': 'Eating', 'gt_answer': 'staring'}, {'question_id': '202012583', 'answer': 'Shelves', 'gt_answer': 'cabinets'}, {'question_id': '20482257', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20940029', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201504737', 'answer': 'Flower', 'gt_answer': 'flower'}, {'question_id': '202023539', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202208386', 'answer': 'Street sign', 'gt_answer': 'traffic sign'}, {'question_id': '20316985', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202270900', 'answer': 'Skateboarder', 'gt_answer': 'skateboarder'}, {'question_id': '202270903', 'answer': 'Man', 'gt_answer': 'skateboarder'}, {'question_id': '20863702', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202270904', 'answer': 'Man', 'gt_answer': 'skateboarder'}, {'question_id': '202162303', 'answer': 'Small', 'gt_answer': 'small'}, {'question_id': '201735322', 'answer': 'Laptop', 'gt_answer': 'laptop'}, {'question_id': '201878373', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201337173', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201735499', 'answer': 'Brown', 'gt_answer': 'brown'}, {'question_id': '202218564', 'answer': 'Glass', 'gt_answer': 'metal'}, {'question_id': '2066009', 'answer': 'Girl', 'gt_answer': 'girl'}, {'question_id': '202060111', 'answer': 'Window', 'gt_answer': 'curtains'}, {'question_id': '202060113', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20752413', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202012859', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201795582', 'answer': 'Elephant', 'gt_answer': 'elephant'}, {'question_id': '201795584', 'answer': 'Elephant', 'gt_answer': 'elephant'}, {'question_id': '2066007', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '20340788', 'answer': 'Street', 'gt_answer': 'school'}, {'question_id': '20894309', 'answer': 'Trees', 'gt_answer': 'tree'}, {'question_id': '20706200', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201185787', 'answer': 'Orange', 'gt_answer': 'orange'}, {'question_id': '20340780', 'answer': 'Chair', 'gt_answer': 'chair'}, {'question_id': '20340781', 'answer': 'Chair', 'gt_answer': 'chair'}, {'question_id': '20894302', 'answer': 'Boy', 'gt_answer': 'boy'}, {'question_id': '2091058', 'answer': 'Small', 'gt_answer': 'small'}, {'question_id': '201713362', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201879470', 'answer': 'Truck', 'gt_answer': 'truck'}, {'question_id': '20631677', 'answer': 'Player', 'gt_answer': 'batter'}, {'question_id': '201510262', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201370362', 'answer': 'Scissors', 'gt_answer': 'paper'}, {'question_id': '201859673', 'answer': 'Table', 'gt_answer': 'table'}, {'question_id': '20865666', 'answer': 'Field', 'gt_answer': 'pasture'}, {'question_id': '201407286', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201407280', 'answer': 'Dirty', 'gt_answer': 'clean'}, {'question_id': '20169805', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201490922', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201030607', 'answer': 'Wii', 'gt_answer': 'wii controller'}, {'question_id': '201951792', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201030605', 'answer': 'Controller', 'gt_answer': 'wii controller'}, {'question_id': '201951790', 'answer': 'Tray', 'gt_answer': 'machine'}, {'question_id': '201030603', 'answer': 'Boy', 'gt_answer': 'boy'}, {'question_id': '202218900', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202262546', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '202262544', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201153620', 'answer': 'Man', 'gt_answer': 'man'}, {'question_id': '20903065', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20462051', 'answer': 'Woman', 'gt_answer': 'woman'}, {'question_id': '20247155', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201737899', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201737894', 'answer': 'Helmet', 'gt_answer': 'athletic shoe'}, {'question_id': '201638750', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '20866112', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '201737890', 'answer': 'Helmet', 'gt_answer': 'athletic shoe'}, {'question_id': '20247158', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20652443', 'answer': 'Cars', 'gt_answer': 'cars'}, {'question_id': '202284837', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201497592', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20652444', 'answer': 'Cars', 'gt_answer': 'cars'}, {'question_id': '201175306', 'answer': 'Bottom', 'gt_answer': 'bottom'}, {'question_id': '20655329', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20491651', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202107913', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '2012597', 'answer': 'Color', 'gt_answer': 'material'}, {'question_id': '20162357', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202133672', 'answer': 'Man', 'gt_answer': 'skateboarder'}, {'question_id': '20162353', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20609547', 'answer': 'Metal', 'gt_answer': 'metal'}, {'question_id': '20982405', 'answer': 'Green', 'gt_answer': 'green'}, {'question_id': '201590227', 'answer': 'Truck', 'gt_answer': 'truck'}, {'question_id': '20541130', 'answer': 'Striped', 'gt_answer': 'striped'}, {'question_id': '201576675', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201188246', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201570737', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201570731', 'answer': 'White', 'gt_answer': 'white'}, {'question_id': '201873360', 'answer': 'Fire truck', 'gt_answer': 'fire truck'}, {'question_id': '20149740', 'answer': 'Porcelain', 'gt_answer': 'porcelain'}, {'question_id': '20149742', 'answer': 'Porcelain', 'gt_answer': 'porcelain'}, {'question_id': '202223273', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20573686', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20120081', 'answer': 'Small', 'gt_answer': 'small'}, {'question_id': '201738019', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201557024', 'answer': 'Large', 'gt_answer': 'large'}, {'question_id': '201235514', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '201510995', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201510992', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '2017193', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '202121409', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '202133659', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202240700', 'answer': 'Body', 'gt_answer': 'undershirt'}, {'question_id': '202240701', 'answer': 'Body', 'gt_answer': 'undershirt'}, {'question_id': '202240702', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20151832', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20692420', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '20257209', 'answer': 'Male', 'gt_answer': 'male'}, {'question_id': '202240709', 'answer': 'Pink', 'gt_answer': 'pink'}, {'question_id': '20827186', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201511022', 'answer': 'Jacket', 'gt_answer': 'socks'}, {'question_id': '201175261', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201527491', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20963817', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201947425', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202223189', 'answer': 'Woman', 'gt_answer': 'crowd'}, {'question_id': '202223187', 'answer': 'Man', 'gt_answer': 'man'}, {'question_id': '20262688', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '20177594', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '2046678', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '201207201', 'answer': 'Apples', 'gt_answer': 'apples'}, {'question_id': '201571283', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20120338', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20883166', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202174622', 'answer': 'Table', 'gt_answer': 'table'}, {'question_id': '202174627', 'answer': 'Window', 'gt_answer': 'bench'}, {'question_id': '20317203', 'answer': 'Cutting board', 'gt_answer': 'coffee pot'}, {'question_id': '20451836', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20896683', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20451833', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20783148', 'answer': 'Laptop', 'gt_answer': 'laptop'}, {'question_id': '201404221', 'answer': 'Small', 'gt_answer': 'small'}, {'question_id': '20783141', 'answer': 'Glass', 'gt_answer': 'screen'}, {'question_id': '2093976', 'answer': 'Dirty', 'gt_answer': 'dirty'}, {'question_id': '202144725', 'answer': 'Blender', 'gt_answer': 'blender'}, {'question_id': '20306961', 'answer': 'Bed', 'gt_answer': 'chair'}, {'question_id': '20442201', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20657099', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202156896', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202156894', 'answer': 'Rock', 'gt_answer': 'rocks'}, {'question_id': '202156890', 'answer': 'Elephant', 'gt_answer': 'elephants'}, {'question_id': '202156891', 'answer': 'Elephants', 'gt_answer': 'elephants'}, {'question_id': '20508408', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20901962', 'answer': 'Brown', 'gt_answer': 'brown'}, {'question_id': '201984144', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20721832', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '202156898', 'answer': 'Elephant', 'gt_answer': 'elephants'}, {'question_id': '202156899', 'answer': 'Elephant', 'gt_answer': 'elephants'}, {'question_id': '201068277', 'answer': 'Shirt', 'gt_answer': 'blouse'}, {'question_id': '20211010', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '20211012', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201068273', 'answer': 'Girl', 'gt_answer': 'woman'}, {'question_id': '20898778', 'answer': 'Red', 'gt_answer': 'black'}, {'question_id': '20287791', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '202108087', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202126116', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '202126117', 'answer': 'Man', 'gt_answer': 'umpire'}, {'question_id': '201974558', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202012498', 'answer': 'Cabinet', 'gt_answer': 'cabinets'}, {'question_id': '20482531', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20709943', 'answer': 'Man', 'gt_answer': 'woman'}, {'question_id': '202012496', 'answer': 'Gold', 'gt_answer': 'gold'}, {'question_id': '20120496', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20482537', 'answer': 'Blue', 'gt_answer': 'dark blue'}, {'question_id': '20414479', 'answer': 'Man', 'gt_answer': 'skateboarder'}, {'question_id': '201676327', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '20746516', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20856795', 'answer': 'Cloth', 'gt_answer': 'cloth'}, {'question_id': '202156947', 'answer': 'Large', 'gt_answer': 'large'}, {'question_id': '201739058', 'answer': 'Heavy', 'gt_answer': 'heavy'}, {'question_id': '202100681', 'answer': 'Knife', 'gt_answer': 'utensils'}, {'question_id': '20942305', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '202102634', 'answer': 'Cabinets', 'gt_answer': 'cabinets'}, {'question_id': '202265875', 'answer': 'Top', 'gt_answer': 'top'}, {'question_id': '201068183', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20717065', 'answer': 'Black', 'gt_answer': 'dark'}, {'question_id': '202208481', 'answer': 'Narrow', 'gt_answer': 'wide'}, {'question_id': '20818708', 'answer': 'Home plate', 'gt_answer': 'home plate'}, {'question_id': '202006826', 'answer': 'Cabinet', 'gt_answer': 'cabinet'}, {'question_id': '201528165', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201751740', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '202006822', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '2090954', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '2044506', 'answer': 'Reading', 'gt_answer': 'reading'}, {'question_id': '201548676', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201770656', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201480380', 'answer': 'Boy', 'gt_answer': 'boy'}, {'question_id': '20705919', 'answer': 'Computer', 'gt_answer': 'computer mouse'}, {'question_id': '202144455', 'answer': 'Man', 'gt_answer': 'man'}, {'question_id': '201536289', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '20550523', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '2093757', 'answer': 'Large', 'gt_answer': 'small'}, {'question_id': '201682342', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '201920457', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '2093752', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201431008', 'answer': 'Tissue', 'gt_answer': 'tie'}, {'question_id': '201431009', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20648196', 'answer': 'People', 'gt_answer': 'policeman'}, {'question_id': '202208467', 'answer': 'Large', 'gt_answer': 'large'}, {'question_id': '202040124', 'answer': 'Platform', 'gt_answer': 'platform'}, {'question_id': '20902927', 'answer': 'Large', 'gt_answer': 'large'}, {'question_id': '202040122', 'answer': 'Platform', 'gt_answer': 'platform'}, {'question_id': '202049479', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201947714', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201947717', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '20899424', 'answer': 'Closed', 'gt_answer': 'closed'}, {'question_id': '20306616', 'answer': 'Camera', 'gt_answer': 'camera'}, {'question_id': '20340573', 'answer': 'Chair', 'gt_answer': 'chair'}, {'question_id': '20340572', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201504740', 'answer': 'Surfboard', 'gt_answer': 'surfboard'}, {'question_id': '20611737', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20340577', 'answer': 'Chair', 'gt_answer': 'chair'}, {'question_id': '20550290', 'answer': 'Trailer', 'gt_answer': 'van'}, {'question_id': '201593935', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201997291', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '201593931', 'answer': 'Blue', 'gt_answer': 'blue'}, {'question_id': '20865472', 'answer': 'Cow', 'gt_answer': 'calf'}, {'question_id': '20865473', 'answer': 'Cow', 'gt_answer': 'calf'}, {'question_id': '201411026', 'answer': 'Caucasian', 'gt_answer': 'caucasian'}, {'question_id': '2075362', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20691508', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20503835', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '202218682', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20691504', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '202266019', 'answer': 'Couch', 'gt_answer': 'couch'}, {'question_id': '202081859', 'answer': 'Small', 'gt_answer': 'small'}, {'question_id': '201935776', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20645457', 'answer': 'Gray', 'gt_answer': 'gray'}, {'question_id': '202100465', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20741297', 'answer': 'Skinny', 'gt_answer': 'thin'}, {'question_id': '201864453', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '201765829', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20940239', 'answer': 'Small', 'gt_answer': 'small'}, {'question_id': '20210825', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20647343', 'answer': 'Short', 'gt_answer': 'short'}, {'question_id': '20285532', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201444864', 'answer': 'Park', 'gt_answer': 'lawn'}, {'question_id': '201879696', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201430608', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201411240', 'answer': 'Plastic', 'gt_answer': 'plastic'}, {'question_id': '202243698', 'answer': 'Table', 'gt_answer': 'table'}, {'question_id': '201527825', 'answer': 'Cake', 'gt_answer': 'table'}, {'question_id': '20609373', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20963774', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '20827214', 'answer': 'Chair', 'gt_answer': 'side table'}, {'question_id': '201738866', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20752317', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20706070', 'answer': 'Right', 'gt_answer': 'left'}, {'question_id': '20752312', 'answer': 'Rack', 'gt_answer': 'wall'}, {'question_id': '20536034', 'answer': 'Plain', 'gt_answer': 'plain'}, {'question_id': '201798375', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201872909', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20468760', 'answer': 'Horse', 'gt_answer': 'horse'}, {'question_id': '20468762', 'answer': 'Trees', 'gt_answer': 'trees'}, {'question_id': '20541220', 'answer': 'Coffee table', 'gt_answer': 'table'}, {'question_id': '201878417', 'answer': 'Dog', 'gt_answer': 'dog'}, {'question_id': '20541225', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201047505', 'answer': 'Suit', 'gt_answer': 'dress shirt'}, {'question_id': '20954152', 'answer': 'Glasses', 'gt_answer': 'glasses'}, {'question_id': '20954153', 'answer': 'Glasses', 'gt_answer': 'glasses'}, {'question_id': '20954150', 'answer': 'Woman', 'gt_answer': 'girl'}, {'question_id': '202257181', 'answer': 'Brown', 'gt_answer': 'tan'}, {'question_id': '201832389', 'answer': 'Nightstand', 'gt_answer': 'nightstand'}, {'question_id': '20637027', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20637029', 'answer': 'Stainless steel', 'gt_answer': 'stainless steel'}, {'question_id': '20151646', 'answer': 'Sweater', 'gt_answer': 'sweatshirt'}, {'question_id': '20151641', 'answer': 'Jacket', 'gt_answer': 'sweatshirt'}, {'question_id': '20151642', 'answer': 'Jacket', 'gt_answer': 'sweatshirt'}, {'question_id': '20151649', 'answer': 'Sweater', 'gt_answer': 'sweatshirt'}, {'question_id': '20511652', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201957351', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201669479', 'answer': 'Frosting', 'gt_answer': 'candle'}, {'question_id': '20982581', 'answer': 'Table', 'gt_answer': 'table'}, {'question_id': '201447176', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202262979', 'answer': 'Parking lot', 'gt_answer': 'road'}, {'question_id': '2058595', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201766599', 'answer': 'Grass', 'gt_answer': 'field'}, {'question_id': '20786106', 'answer': 'Tree', 'gt_answer': 'cone'}, {'question_id': '20786109', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20853873', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201492393', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201885312', 'answer': 'Rectangle', 'gt_answer': 'rectangular'}, {'question_id': '201751873', 'answer': 'Large', 'gt_answer': 'small'}, {'question_id': '202147742', 'answer': 'Logo', 'gt_answer': 'socks'}, {'question_id': '202231566', 'answer': 'Shorts', 'gt_answer': 'shirt'}, {'question_id': '202082034', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20942893', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '202258271', 'answer': 'Horse', 'gt_answer': 'horse'}, {'question_id': '201663720', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20385650', 'answer': 'Calculator', 'gt_answer': 'keyboard'}, {'question_id': '20385653', 'answer': 'Calculator', 'gt_answer': 'keyboard'}, {'question_id': '201920432', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201998179', 'answer': 'Small', 'gt_answer': 'small'}, {'question_id': '201883045', 'answer': 'Desk', 'gt_answer': 'desk'}, {'question_id': '201861655', 'answer': 'Blue', 'gt_answer': 'blue'}, {'question_id': '20385382', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '202225857', 'answer': 'Chicken', 'gt_answer': 'turkey'}, {'question_id': '202225856', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202240653', 'answer': 'Controller', 'gt_answer': 'remote control'}, {'question_id': '201455952', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20942095', 'answer': 'Walking', 'gt_answer': 'walking'}, {'question_id': '20942097', 'answer': 'Woman', 'gt_answer': 'girl'}, {'question_id': '201479334', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201663241', 'answer': 'New', 'gt_answer': 'new'}, {'question_id': '202006798', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20308964', 'answer': '12 inches', 'gt_answer': 'narrow'}, {'question_id': '201908917', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201030297', 'answer': 'Round', 'gt_answer': 'round'}, {'question_id': '201908911', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202262284', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201188406', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201188403', 'answer': 'Cloth', 'gt_answer': 'cloth'}, {'question_id': '202107806', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202004311', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202102652', 'answer': 'Large', 'gt_answer': 'large'}, {'question_id': '20340918', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202053085', 'answer': 'Top', 'gt_answer': 'top'}, {'question_id': '20837028', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20837023', 'answer': 'Green', 'gt_answer': 'green'}, {'question_id': '202244123', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202228023', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20644681', 'answer': 'Shoe', 'gt_answer': 'sneakers'}, {'question_id': '201866569', 'answer': 'Stop sign', 'gt_answer': 'traffic sign'}, {'question_id': '201760532', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20935938', 'answer': 'Elephant', 'gt_answer': 'elephant'}, {'question_id': '202174260', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201866566', 'answer': 'Large', 'gt_answer': 'huge'}, {'question_id': '201896041', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '202262026', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20899163', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20899166', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201185759', 'answer': 'Frisbee', 'gt_answer': 'frisbee'}, {'question_id': '201175676', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201535625', 'answer': 'Donut', 'gt_answer': 'donuts'}, {'question_id': '201535624', 'answer': 'Donut', 'gt_answer': 'donuts'}, {'question_id': '201535627', 'answer': 'Donut', 'gt_answer': 'donuts'}, {'question_id': '20226601', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201055621', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201497782', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201997571', 'answer': 'Chairs', 'gt_answer': 'chairs'}, {'question_id': '201859583', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201079988', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '2046245', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20602784', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '202023526', 'answer': 'Black', 'gt_answer': 'black'}, {'question_id': '20136567', 'answer': 'Woman', 'gt_answer': 'woman'}, {'question_id': '201887214', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '202023522', 'answer': 'Shirt', 'gt_answer': 'jacket'}, {'question_id': '201713571', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201077055', 'answer': 'Urinal', 'gt_answer': 'urinal'}, {'question_id': '20734156', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20783357', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201621666', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20262749', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '202036678', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '20710412', 'answer': 'People', 'gt_answer': 'child'}, {'question_id': '201889329', 'answer': 'People', 'gt_answer': 'skier'}, {'question_id': '202036675', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201109556', 'answer': 'Car', 'gt_answer': 'truck'}, {'question_id': '202144482', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20473064', 'answer': 'Boy', 'gt_answer': 'boy'}, {'question_id': '201759222', 'answer': 'Coat', 'gt_answer': 'coats'}, {'question_id': '201759225', 'answer': 'Coats', 'gt_answer': 'coats'}, {'question_id': '20473063', 'answer': 'Boy', 'gt_answer': 'boy'}, {'question_id': '201759555', 'answer': 'Chair', 'gt_answer': 'chair'}, {'question_id': '20473061', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20480410', 'answer': 'Sleeping', 'gt_answer': 'lying'}, {'question_id': '201979388', 'answer': 'Square', 'gt_answer': 'square'}, {'question_id': '20827695', 'answer': 'Couch', 'gt_answer': 'coffee table'}, {'question_id': '20119188', 'answer': 'Suit', 'gt_answer': 'suit'}, {'question_id': '201979380', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '20588910', 'answer': 'Skateboarder', 'gt_answer': 'skater'}, {'question_id': '201548795', 'answer': 'Blender', 'gt_answer': 'blender'}, {'question_id': '202081115', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202036711', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20204712', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20543151', 'answer': 'Metal', 'gt_answer': 'metal'}, {'question_id': '202226354', 'answer': 'Table', 'gt_answer': 'table'}, {'question_id': '20861003', 'answer': 'Car', 'gt_answer': 'car'}, {'question_id': '20861005', 'answer': 'Car', 'gt_answer': 'car'}, {'question_id': '20473245', 'answer': 'Long sleeved', 'gt_answer': 'short sleeved'}, {'question_id': '201713582', 'answer': 'Small', 'gt_answer': 'small'}, {'question_id': '202053220', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '202174450', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '2017402', 'answer': 'Dark brown', 'gt_answer': 'dark brown'}, {'question_id': '202023470', 'answer': 'Bookshelf', 'gt_answer': 'bookshelf'}, {'question_id': '20785907', 'answer': 'Tree', 'gt_answer': 'cone'}, {'question_id': '20785906', 'answer': 'Tree', 'gt_answer': 'cone'}, {'question_id': '202241072', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201760690', 'answer': 'White', 'gt_answer': 'caucasian'}, {'question_id': '202012559', 'answer': 'Shelf', 'gt_answer': 'cabinets'}, {'question_id': '202226137', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20258739', 'answer': 'Little', 'gt_answer': 'young'}, {'question_id': '201756686', 'answer': 'Eating', 'gt_answer': 'playing'}, {'question_id': '20706242', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201480464', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201623758', 'answer': 'Right', 'gt_answer': 'left'}, {'question_id': '202245857', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202107956', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202037104', 'answer': 'Pepperoni', 'gt_answer': 'cheese'}, {'question_id': '20248194', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201068515', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '202081899', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202180474', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201987314', 'answer': 'Table', 'gt_answer': 'table'}, {'question_id': '20287585', 'answer': 'Player', 'gt_answer': 'batter'}, {'question_id': '201987317', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201621287', 'answer': 'Couch', 'gt_answer': 'desk'}, {'question_id': '201935095', 'answer': 'Metal', 'gt_answer': 'metal'}, {'question_id': '201247044', 'answer': 'Wood', 'gt_answer': 'wood'}, {'question_id': '202265837', 'answer': 'Short', 'gt_answer': 'short'}, {'question_id': '202162542', 'answer': 'Pillow', 'gt_answer': 'pillow'}, {'question_id': '201207463', 'answer': 'Colorful', 'gt_answer': 'black and white'}, {'question_id': '2076489', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201621284', 'answer': 'Material', 'gt_answer': 'material'}, {'question_id': '201207466', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202119751', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202119753', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '202119756', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20285323', 'answer': 'Rectangle', 'gt_answer': 'rectangular'}, {'question_id': '2072811', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '2072815', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202003808', 'answer': 'Black', 'gt_answer': 'black'}, {'question_id': '201412215', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '2075284', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20818934', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '20741080', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201639208', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20412437', 'answer': 'Bottom', 'gt_answer': 'bottom'}, {'question_id': '20645663', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201879546', 'answer': 'Truck', 'gt_answer': 'truck'}, {'question_id': '201795070', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20741089', 'answer': 'Flowers', 'gt_answer': 'flowers'}, {'question_id': '2076738', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202161984', 'answer': 'Air conditioner', 'gt_answer': 'heater'}, {'question_id': '202161985', 'answer': 'Air conditioner', 'gt_answer': 'heater'}, {'question_id': '202100898', 'answer': 'Stove', 'gt_answer': 'stove'}, {'question_id': '201861403', 'answer': 'Motorcycle', 'gt_answer': 'motorcycle'}, {'question_id': '201861402', 'answer': 'Motorcycle', 'gt_answer': 'motorcycle'}, {'question_id': '201623368', 'answer': 'Shelves', 'gt_answer': 'cupboards'}, {'question_id': '201886793', 'answer': 'Top', 'gt_answer': 'top'}, {'question_id': '201623367', 'answer': 'Round', 'gt_answer': 'rectangular'}, {'question_id': '20157006', 'answer': 'Pancakes', 'gt_answer': 'pancake'}, {'question_id': '20611590', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201861409', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20836960', 'answer': 'Woman', 'gt_answer': 'people'}, {'question_id': '202243388', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201623821', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20285183', 'answer': 'Pillows', 'gt_answer': 'pillows'}, {'question_id': '20600235', 'answer': 'Zebra', 'gt_answer': 'zebra'}, {'question_id': '202243936', 'answer': 'Carrot', 'gt_answer': 'beans'}, {'question_id': '20600238', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202270921', 'answer': 'Black', 'gt_answer': 'white'}, {'question_id': '201337154', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201481737', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20865393', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20416487', 'answer': 'Pepperoni', 'gt_answer': 'sausage'}, {'question_id': '20865390', 'answer': 'Green', 'gt_answer': 'green'}, {'question_id': '201206842', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20308158', 'answer': 'Square', 'gt_answer': 'rectangular'}, {'question_id': '201065442', 'answer': 'Chairs', 'gt_answer': 'chairs'}, {'question_id': '201481739', 'answer': 'Woman', 'gt_answer': 'man'}, {'question_id': '20752432', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201795258', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201952622', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201795255', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201438644', 'answer': 'Bat', 'gt_answer': 'bat'}, {'question_id': '201952625', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20667937', 'answer': 'Controller', 'gt_answer': 'wii controller'}, {'question_id': '20667934', 'answer': 'Controller', 'gt_answer': 'wii controller'}, {'question_id': '202106336', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20781863', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20863428', 'answer': 'Nice', 'gt_answer': 'street sign'}, {'question_id': '20621909', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20667939', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201859615', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20691718', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201982509', 'answer': 'Chair', 'gt_answer': 'side table'}, {'question_id': '201859611', 'answer': 'Small', 'gt_answer': 'large'}, {'question_id': '20302869', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '2075820', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20302866', 'answer': 'Ocean', 'gt_answer': 'bench'}, {'question_id': '20302864', 'answer': 'Ocean', 'gt_answer': 'bench'}, {'question_id': '2098148', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201576479', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201548821', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201576474', 'answer': 'Road', 'gt_answer': 'street'}, {'question_id': '201935230', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '201935234', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201153603', 'answer': 'Giraffe', 'gt_answer': 'giraffe'}, {'question_id': '201342112', 'answer': 'Runway', 'gt_answer': 'runway'}, {'question_id': '201153601', 'answer': 'Giraffe', 'gt_answer': 'giraffe'}, {'question_id': '201959835', 'answer': 'Airplane', 'gt_answer': 'airplane'}, {'question_id': '201153606', 'answer': 'Tree', 'gt_answer': 'walkway'}, {'question_id': '201153608', 'answer': 'Giraffe', 'gt_answer': 'giraffe'}, {'question_id': '20226837', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201623985', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20652469', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20863394', 'answer': 'Scooter', 'gt_answer': 'motorcycle'}, {'question_id': '202284818', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20441860', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20622100', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20652463', 'answer': 'Soccer ball', 'gt_answer': 'soccer ball'}, {'question_id': '20652462', 'answer': 'Man', 'gt_answer': 'woman'}, {'question_id': '20622106', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201982447', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20705693', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202246814', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '202133616', 'answer': 'Pants', 'gt_answer': 'jeans'}, {'question_id': '20810865', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '201987969', 'answer': 'Bottle', 'gt_answer': 'paper'}, {'question_id': '201987968', 'answer': 'Bottle', 'gt_answer': 'paper'}, {'question_id': '201479436', 'answer': 'Biscuit', 'gt_answer': 'bread'}, {'question_id': '20982422', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201481485', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '2066069', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '20151584', 'answer': 'Beans', 'gt_answer': 'plastic'}, {'question_id': '201758095', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201758546', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201064778', 'answer': 'Sofa', 'gt_answer': 'sofa'}, {'question_id': '202286763', 'answer': 'Teddy bear', 'gt_answer': 'woman'}, {'question_id': '201976972', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202223212', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201859264', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '201481489', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20866064', 'answer': 'Tall', 'gt_answer': 'tall'}, {'question_id': '202243386', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '202257952', 'answer': 'Rough', 'gt_answer': 'smooth'}, {'question_id': '20541143', 'answer': 'White', 'gt_answer': 'white'}, {'question_id': '201467567', 'answer': 'Chair', 'gt_answer': 'plant'}, {'question_id': '201235536', 'answer': 'Shelves', 'gt_answer': 'shelves'}, {'question_id': '20652794', 'answer': 'Behind', 'gt_answer': 'behind'}, {'question_id': '20652793', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201859269', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '2012711', 'answer': 'Female', 'gt_answer': 'male'}, {'question_id': '202041994', 'answer': 'White', 'gt_answer': 'gold'}, {'question_id': '201879145', 'answer': 'Clock', 'gt_answer': 'racket'}, {'question_id': '202240857', 'answer': 'Woman', 'gt_answer': 'woman'}, {'question_id': '201110713', 'answer': 'Marshmallows', 'gt_answer': 'chocolate'}, {'question_id': '201798470', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '20692448', 'answer': 'Soft', 'gt_answer': 'soft'}, {'question_id': '201996560', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20692443', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201391829', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20692441', 'answer': 'Brown', 'gt_answer': 'brown'}, {'question_id': '2046656', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201207269', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201061145', 'answer': 'Boy', 'gt_answer': 'boy'}, {'question_id': '201061143', 'answer': 'Beach', 'gt_answer': 'beach'}, {'question_id': '202004129', 'answer': 'Shirt', 'gt_answer': 'shirt'}, {'question_id': '201080477', 'answer': 'Man', 'gt_answer': 'umpire'}, {'question_id': '201080476', 'answer': 'Smiling', 'gt_answer': 'standing'}, {'question_id': '201573904', 'answer': 'Silver', 'gt_answer': 'gray'}, {'question_id': '2059436', 'answer': 'Field', 'gt_answer': 'field'}, {'question_id': '20721975', 'answer': 'Short', 'gt_answer': 'short'}, {'question_id': '2093912', 'answer': 'Giraffe', 'gt_answer': 'giraffe'}, {'question_id': '2093911', 'answer': 'Giraffe', 'gt_answer': 'giraffe'}, {'question_id': '202218927', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '2093918', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202240582', 'answer': 'Asian', 'gt_answer': 'asian'}, {'question_id': '201479292', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '201832621', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201998096', 'answer': 'Dirty', 'gt_answer': 'clean'}, {'question_id': '20783041', 'answer': 'White', 'gt_answer': 'beige'}, {'question_id': '20797735', 'answer': 'Cat', 'gt_answer': 'cat'}, {'question_id': '201998232', 'answer': 'Table', 'gt_answer': 'table'}, {'question_id': '20434954', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20403227', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '202244371', 'answer': 'Container', 'gt_answer': 'container'}, {'question_id': '201676125', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202100315', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20518691', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20721817', 'answer': 'Doll', 'gt_answer': 'doll'}, {'question_id': '201935806', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20241033', 'answer': 'Man', 'gt_answer': 'man'}, {'question_id': '20901904', 'answer': 'Brown', 'gt_answer': 'brown'}, {'question_id': '201902406', 'answer': 'Computer', 'gt_answer': 'computer'}, {'question_id': '201947834', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20287776', 'answer': 'Batter', 'gt_answer': 'catcher'}, {'question_id': '20287774', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201247164', 'answer': 'Chair', 'gt_answer': 'chair'}, {'question_id': '201064879', 'answer': 'Girl', 'gt_answer': 'girl'}, {'question_id': '20287779', 'answer': 'Home plate', 'gt_answer': 'sand'}, {'question_id': '20211079', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201663475', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20515865', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201974825', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201061097', 'answer': 'Beach', 'gt_answer': 'beach'}, {'question_id': '201399884', 'answer': 'Bookshelf', 'gt_answer': 'bookcase'}, {'question_id': '20709927', 'answer': 'Car', 'gt_answer': 'car'}, {'question_id': '20709926', 'answer': 'Car', 'gt_answer': 'car'}, {'question_id': '20482514', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20709924', 'answer': 'Table', 'gt_answer': 'car'}, {'question_id': '20709923', 'answer': 'Table', 'gt_answer': 'car'}, {'question_id': '20412483', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '20511525', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20518073', 'answer': 'Color', 'gt_answer': 'color'}, {'question_id': '202037035', 'answer': 'White', 'gt_answer': 'yellow'}, {'question_id': '20511521', 'answer': 'Orange', 'gt_answer': 'orange'}, {'question_id': '201623698', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201676341', 'answer': 'Wii controller', 'gt_answer': 'controller'}, {'question_id': '202246404', 'answer': 'Left', 'gt_answer': 'right'}, {'question_id': '201804036', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '2055924', 'answer': 'Bus', 'gt_answer': 'bus'}, {'question_id': '202240326', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20818726', 'answer': 'Catcher', 'gt_answer': 'batter'}, {'question_id': '20818727', 'answer': 'Catcher', 'gt_answer': 'batter'}, {'question_id': '20717005', 'answer': 'Glass', 'gt_answer': 'glass'}, {'question_id': '201641387', 'answer': 'Tree', 'gt_answer': 'traffic light'}, {'question_id': '201751760', 'answer': 'White', 'gt_answer': 'blond'}, {'question_id': '20753200', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '20753203', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20818721', 'answer': 'Bat', 'gt_answer': 'bat'}, {'question_id': '201713299', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201548657', 'answer': 'Color', 'gt_answer': 'color'}, {'question_id': '20258939', 'answer': 'Top', 'gt_answer': 'top'}, {'question_id': '201185941', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201498191', 'answer': 'Monitor', 'gt_answer': 'computer'}, {'question_id': '202106239', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20923126', 'answer': 'Truck', 'gt_answer': 'fire truck'}, {'question_id': '20705970', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '2055693', 'answer': 'Bus', 'gt_answer': 'bus'}, {'question_id': '2055690', 'answer': 'Bus', 'gt_answer': 'bus'}, {'question_id': '20245821', 'answer': 'Metal', 'gt_answer': 'metal'}, {'question_id': '20182864', 'answer': 'Umbrella', 'gt_answer': 'umbrella'}, {'question_id': '20182867', 'answer': 'Umbrella', 'gt_answer': 'umbrella'}, {'question_id': '2044716', 'answer': 'Umbrella', 'gt_answer': 'umbrella'}, {'question_id': '20182861', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '2044861', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '2044712', 'answer': 'Woman', 'gt_answer': 'woman'}, {'question_id': '201987760', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201987763', 'answer': 'Sandwich', 'gt_answer': 'cookie'}, {'question_id': '201682364', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201987765', 'answer': 'Sandwich', 'gt_answer': 'cookie'}, {'question_id': '201987764', 'answer': 'Tape dispenser', 'gt_answer': 'cookie'}, {'question_id': '20308704', 'answer': 'Stove', 'gt_answer': 'stove'}, {'question_id': '201987768', 'answer': 'Sandwich', 'gt_answer': 'tape'}, {'question_id': '20308700', 'answer': 'Tea kettle', 'gt_answer': 'tea kettle'}, {'question_id': '20308702', 'answer': 'Counter', 'gt_answer': 'stove'}, {'question_id': '201273292', 'answer': 'Sign', 'gt_answer': 'street sign'}, {'question_id': '201951964', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201873524', 'answer': 'Fire truck', 'gt_answer': 'fire truck'}, {'question_id': '201873521', 'answer': 'Fire truck', 'gt_answer': 'fire truck'}, {'question_id': '201822296', 'answer': 'White', 'gt_answer': 'white'}, {'question_id': '202059973', 'answer': 'Dog', 'gt_answer': 'dog'}, {'question_id': '20244663', 'answer': 'Bus', 'gt_answer': 'bus'}, {'question_id': '201998145', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '202265646', 'answer': 'Eating', 'gt_answer': 'eating'}, {'question_id': '201947772', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '201509720', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20899442', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20887264', 'answer': 'Dog', 'gt_answer': 'dog'}, {'question_id': '201467368', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202180318', 'answer': 'Girl', 'gt_answer': 'soccer player'}, {'question_id': '201593667', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '202120029', 'answer': 'Metal', 'gt_answer': 'metal'}, {'question_id': '201711298', 'answer': 'Large', 'gt_answer': 'huge'}, {'question_id': '202144538', 'answer': 'Wine', 'gt_answer': 'liquor'}, {'question_id': '201411006', 'answer': 'Fixing toilet', 'gt_answer': 'bending'}, {'question_id': '20416562', 'answer': 'Cutting board', 'gt_answer': 'pan'}, {'question_id': '20691524', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201439744', 'answer': 'Large', 'gt_answer': 'small'}, {'question_id': '2075615', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201527591', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202081301', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '201303158', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20645470', 'answer': 'Dirty', 'gt_answer': 'clean'}, {'question_id': '202081877', 'answer': 'Desk', 'gt_answer': 'mouse pad'}, {'question_id': '202081875', 'answer': 'Cat', 'gt_answer': 'cat'}, {'question_id': '202081306', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20171314', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201498735', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201752899', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202285331', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202285336', 'answer': 'Beans', 'gt_answer': 'beans'}, {'question_id': '202285337', 'answer': 'Beans', 'gt_answer': 'beans'}, {'question_id': '202144533', 'answer': 'Beer', 'gt_answer': 'liquor'}, {'question_id': '20929611', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202285449', 'answer': 'Egg', 'gt_answer': 'egg'}, {'question_id': '202120246', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20381665', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202119430', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20942911', 'answer': 'Green', 'gt_answer': 'green'}, {'question_id': '201527374', 'answer': 'Pink', 'gt_answer': 'blue'}, {'question_id': '201616113', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20609318', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20609316', 'answer': 'Strawberry', 'gt_answer': 'strawberry'}, {'question_id': '202257439', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '20345101', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201247181', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '20963754', 'answer': 'Square', 'gt_answer': 'square'}, {'question_id': '20656897', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20345108', 'answer': 'Metal', 'gt_answer': 'metal'}, {'question_id': '20706050', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201156445', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201798392', 'answer': 'Mirror', 'gt_answer': 'mirror'}, {'question_id': '202246131', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202040255', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202125916', 'answer': 'Gray', 'gt_answer': 'gray'}, {'question_id': '20667582', 'answer': 'Brown', 'gt_answer': 'light brown'}, {'question_id': '20340554', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201504767', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202119224', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '2075903', 'answer': 'Water', 'gt_answer': 'lake'}, {'question_id': '201047526', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201676456', 'answer': 'Shelf', 'gt_answer': 'shelf'}, {'question_id': '202119227', 'answer': 'Man', 'gt_answer': 'man'}, {'question_id': '20299685', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201393703', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201393708', 'answer': 'Closed', 'gt_answer': 'closed'}, {'question_id': '201393709', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20637008', 'answer': 'Small', 'gt_answer': 'small'}, {'question_id': '201936025', 'answer': 'Top', 'gt_answer': 'top'}, {'question_id': '201883208', 'answer': 'Chair', 'gt_answer': 'bed'}, {'question_id': '201957373', 'answer': 'White', 'gt_answer': 'white'}, {'question_id': '20482352', 'answer': 'Light blue', 'gt_answer': 'light blue'}, {'question_id': '201638830', 'answer': 'Car', 'gt_answer': 'car'}, {'question_id': '201883202', 'answer': 'Bed', 'gt_answer': 'bed'}, {'question_id': '201669416', 'answer': 'Bottom', 'gt_answer': 'bottom'}, {'question_id': '20644706', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20340887', 'answer': 'Brick', 'gt_answer': 'brick'}, {'question_id': '20711564', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20162250', 'answer': 'Gray', 'gt_answer': 'gray'}, {'question_id': '201739115', 'answer': 'Closed', 'gt_answer': 'closed'}, {'question_id': '202228689', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20786125', 'answer': 'Woman', 'gt_answer': 'woman'}, {'question_id': '202270878', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201400211', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20317072', 'answer': 'Green', 'gt_answer': 'gray'}, {'question_id': '201404056', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201896471', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201795620', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201976505', 'answer': 'Blue', 'gt_answer': 'white'}, {'question_id': '201303476', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201987306', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201570712', 'answer': 'Coat', 'gt_answer': 'coat'}, {'question_id': '202162516', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20600062', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201757780', 'answer': 'Laptop', 'gt_answer': 'laptop'}, {'question_id': '201757782', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201479357', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20596471', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201570711', 'answer': 'Coat', 'gt_answer': 'coat'}, {'question_id': '201935447', 'answer': 'Very', 'gt_answer': 'hard'}, {'question_id': '201479350', 'answer': 'Bottom', 'gt_answer': 'bottom'}, {'question_id': '201030272', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201188428', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '20692380', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201445044', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201663260', 'answer': 'Clean', 'gt_answer': 'clean'}, {'question_id': '2066213', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20692388', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201737911', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20621737', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20226993', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '20414435', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20414431', 'answer': 'Man', 'gt_answer': 'skateboarder'}, {'question_id': '20866476', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20491752', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201055938', 'answer': 'Dirty', 'gt_answer': 'tinted'}, {'question_id': '201887187', 'answer': 'Round', 'gt_answer': 'round'}, {'question_id': '20837044', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202244496', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20451973', 'answer': 'Glass', 'gt_answer': 'glass'}, {'question_id': '20978778', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '20837043', 'answer': 'Closed', 'gt_answer': 'closed'}, {'question_id': '20451979', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201663040', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201556644', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '201866542', 'answer': 'Street', 'gt_answer': 'walkway'}, {'question_id': '201030415', 'answer': 'Couch', 'gt_answer': 'couch'}, {'question_id': '201861267', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201030410', 'answer': 'Couch', 'gt_answer': 'couch'}, {'question_id': '20797694', 'answer': 'Cat', 'gt_answer': 'cat'}, {'question_id': '201207375', 'answer': 'Broccoli', 'gt_answer': 'broccoli'}, {'question_id': '20262580', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20899146', 'answer': 'Color', 'gt_answer': 'color'}, {'question_id': '201185777', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20226661', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20782927', 'answer': 'People', 'gt_answer': 'people'}, {'question_id': '201535605', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201887166', 'answer': 'Broccoli', 'gt_answer': 'broccoli'}, {'question_id': '201509815', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201509814', 'answer': 'Brick', 'gt_answer': 'stone'}, {'question_id': '201509811', 'answer': 'Plant', 'gt_answer': 'motorcycle'}, {'question_id': '20330248', 'answer': 'Black', 'gt_answer': 'black'}, {'question_id': '201455974', 'answer': 'Trees', 'gt_answer': 'hill'}, {'question_id': '201887160', 'answer': 'Cauliflower', 'gt_answer': 'cauliflower'}, {'question_id': '201455976', 'answer': 'Mountains', 'gt_answer': 'sky'}, {'question_id': '202000975', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20978593', 'answer': 'White', 'gt_answer': 'brown'}, {'question_id': '202126091', 'answer': 'Woman', 'gt_answer': 'umpire'}, {'question_id': '202126090', 'answer': 'Watching', 'gt_answer': 'looking up'}, {'question_id': '201621649', 'answer': 'Couch', 'gt_answer': 'couch'}, {'question_id': '20978599', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202126099', 'answer': 'Waiting', 'gt_answer': 'looking up'}, {'question_id': '201889346', 'answer': 'Skier', 'gt_answer': 'skier'}, {'question_id': '201889347', 'answer': 'Skier', 'gt_answer': 'skier'}, {'question_id': '202081092', 'answer': 'Toaster', 'gt_answer': 'toaster'}, {'question_id': '202081095', 'answer': 'Toaster', 'gt_answer': 'toaster'}, {'question_id': '201889343', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201889340', 'answer': 'Skier', 'gt_answer': 'skier'}, {'question_id': '202081099', 'answer': 'Toaster', 'gt_answer': 'toaster'}, {'question_id': '20953073', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20953077', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20647422', 'answer': 'Ball', 'gt_answer': 'baseball'}, {'question_id': '201676590', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20473008', 'answer': 'Boy', 'gt_answer': 'boy'}, {'question_id': '2053690', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20518265', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20473007', 'answer': 'Boy', 'gt_answer': 'boy'}, {'question_id': '20541605', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '202223028', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201759205', 'answer': 'Chair', 'gt_answer': 'mat'}, {'question_id': '201759204', 'answer': 'Chair', 'gt_answer': 'mat'}, {'question_id': '201322828', 'answer': 'No parking', 'gt_answer': 'street sign'}, {'question_id': '201207372', 'answer': 'Glass', 'gt_answer': 'broccoli'}, {'question_id': '201322827', 'answer': 'No parking', 'gt_answer': 'street sign'}, {'question_id': '202006206', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20480471', 'answer': 'Cat', 'gt_answer': 'cat'}, {'question_id': '20588934', 'answer': 'Girl', 'gt_answer': 'skater'}, {'question_id': '201972880', 'answer': 'Black', 'gt_answer': 'white'}, {'question_id': '20588933', 'answer': 'Boy', 'gt_answer': 'skater'}, {'question_id': '20754682', 'answer': 'Tall', 'gt_answer': 'tall'}, {'question_id': '20258636', 'answer': 'Wood', 'gt_answer': 'wood'}, {'question_id': '20258635', 'answer': 'Wood', 'gt_answer': 'wood'}, {'question_id': '201438346', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '20204738', 'answer': 'Laptop', 'gt_answer': 'laptop'}, {'question_id': '20411801', 'answer': 'Carrots', 'gt_answer': 'carrots'}, {'question_id': '20204734', 'answer': 'Computer mouse', 'gt_answer': 'charger'}, {'question_id': '202169258', 'answer': 'Brown', 'gt_answer': 'brown'}, {'question_id': '20204731', 'answer': 'Cord', 'gt_answer': 'charger'}, {'question_id': '2017461', 'answer': 'Sheep', 'gt_answer': 'goat'}, {'question_id': '201571092', 'answer': 'Purse', 'gt_answer': 'purse'}, {'question_id': '20861028', 'answer': 'Large', 'gt_answer': 'large'}, {'question_id': '201571094', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20898639', 'answer': 'Black', 'gt_answer': 'red'}, {'question_id': '20157223', 'answer': 'Pancakes', 'gt_answer': 'pancake'}, {'question_id': '2097607', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20157224', 'answer': 'Pancakes', 'gt_answer': 'pancake'}, {'question_id': '202208360', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201804077', 'answer': 'Laptop', 'gt_answer': 'keyboard'}, {'question_id': '20157228', 'answer': 'Pancakes', 'gt_answer': 'pancake'}, {'question_id': '20898634', 'answer': 'Long sleeved', 'gt_answer': 'long sleeved'}, {'question_id': '202053202', 'answer': 'Player', 'gt_answer': 'umpire'}, {'question_id': '202053200', 'answer': 'Umpire', 'gt_answer': 'umpire'}, {'question_id': '202162423', 'answer': 'Green', 'gt_answer': 'green'}, {'question_id': '202265587', 'answer': 'T-shirt', 'gt_answer': 'shirt'}, {'question_id': '20836777', 'answer': 'Cart', 'gt_answer': 'table'}, {'question_id': '20149892', 'answer': 'Drawer', 'gt_answer': 'cabinet'}, {'question_id': '20361477', 'answer': 'Pants', 'gt_answer': 'snow pants'}, {'question_id': '201536382', 'answer': 'Player', 'gt_answer': 'umpire'}, {'question_id': '201536381', 'answer': 'Standing', 'gt_answer': 'staring'}, {'question_id': '20861127', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20518484', 'answer': 'Small', 'gt_answer': 'large'}, {'question_id': '202125972', 'answer': 'Man', 'gt_answer': 'player'}, {'question_id': '20648072', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20856610', 'answer': 'Egg', 'gt_answer': 'snack'}, {'question_id': '20856611', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20611924', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202037127', 'answer': 'Pizza', 'gt_answer': 'pizza crust'}, {'question_id': '20480292', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202257407', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20452231', 'answer': 'Chair', 'gt_answer': 'chair'}, {'question_id': '20452237', 'answer': 'Chair', 'gt_answer': 'chair'}, {'question_id': '201889554', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201887106', 'answer': 'Broccoli', 'gt_answer': 'cabbage'}, {'question_id': '2097936', 'answer': 'Desk', 'gt_answer': 'desk'}, {'question_id': '20479894', 'answer': 'Black', 'gt_answer': 'silver'}, {'question_id': '201987375', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202180419', 'answer': 'Green', 'gt_answer': 'green'}, {'question_id': '2076132', 'answer': 'Large', 'gt_answer': 'large'}, {'question_id': '201247060', 'answer': 'Chair', 'gt_answer': 'chair'}, {'question_id': '20794117', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20427707', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '2017287', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20514905', 'answer': 'Clear', 'gt_answer': 'clear'}, {'question_id': '20611829', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20472845', 'answer': 'Red', 'gt_answer': 'red'}, {'question_id': '201498762', 'answer': 'Keyboard', 'gt_answer': 'phone'}, {'question_id': '202119734', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20308688', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20836809', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201067749', 'answer': 'Table', 'gt_answer': 'table'}, {'question_id': '201638935', 'answer': 'Man', 'gt_answer': 'men'}, {'question_id': '20836803', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20753413', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '201637116', 'answer': 'Orange', 'gt_answer': 'orange'}, {'question_id': '201480371', 'answer': 'Boy', 'gt_answer': 'boy'}, {'question_id': '20672884', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20482298', 'answer': 'Blue', 'gt_answer': 'blue'}, {'question_id': '20618692', 'answer': 'Tennis court', 'gt_answer': 'courtyard'}, {'question_id': '20618693', 'answer': 'Forest', 'gt_answer': 'courtyard'}, {'question_id': '20618697', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201638704', 'answer': 'Color', 'gt_answer': 'color'}, {'question_id': '201795014', 'answer': 'Elephant', 'gt_answer': 'elephant'}, {'question_id': '202262456', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20878933', 'answer': 'Gray', 'gt_answer': 'white'}, {'question_id': '20863749', 'answer': 'Man', 'gt_answer': 'skateboarder'}, {'question_id': '20550318', 'answer': 'Horse', 'gt_answer': 'horse'}, {'question_id': '20573542', 'answer': 'Toilet', 'gt_answer': 'toilet'}, {'question_id': '202284787', 'answer': 'Color', 'gt_answer': 'shape'}, {'question_id': '201439590', 'answer': 'None', 'gt_answer': 'horse'}, {'question_id': '202284789', 'answer': 'Egg', 'gt_answer': 'egg'}, {'question_id': '201206860', 'answer': 'Right', 'gt_answer': 'left'}, {'question_id': '201765994', 'answer': 'Sand', 'gt_answer': 'dirt'}, {'question_id': '201956981', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20320497', 'answer': 'Sign', 'gt_answer': 'newspaper'}, {'question_id': '20240972', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20240975', 'answer': 'People', 'gt_answer': 'customers'}, {'question_id': '20240976', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202006519', 'answer': 'Drawer', 'gt_answer': 'cabinet'}, {'question_id': '20781808', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20300456', 'answer': 'Large', 'gt_answer': 'large'}, {'question_id': '201037140', 'answer': 'Stop sign', 'gt_answer': 'traffic sign'}, {'question_id': '201434219', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20752459', 'answer': 'Rectangle', 'gt_answer': 'rectangular'}, {'question_id': '201980742', 'answer': 'Long', 'gt_answer': 'short'}, {'question_id': '201593499', 'answer': 'Rocky', 'gt_answer': 'rocky'}, {'question_id': '201959759', 'answer': 'Dense', 'gt_answer': 'dense'}, {'question_id': '20381273', 'answer': 'Pot', 'gt_answer': 'flower pot'}, {'question_id': '2091097', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '2091090', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20836492', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20381274', 'answer': 'Pot', 'gt_answer': 'flower pot'}, {'question_id': '201407247', 'answer': 'Blue', 'gt_answer': 'blue'}, {'question_id': '2098129', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202081643', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201935211', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20645648', 'answer': 'White', 'gt_answer': 'white'}, {'question_id': '201400022', 'answer': 'Bookshelf', 'gt_answer': 'bookcase'}, {'question_id': '201400023', 'answer': 'Couch', 'gt_answer': 'couch'}, {'question_id': '201624280', 'answer': 'Pizza', 'gt_answer': 'spatula'}, {'question_id': '201624281', 'answer': 'Pizza', 'gt_answer': 'spatula'}, {'question_id': '201624282', 'answer': 'Spatula', 'gt_answer': 'spatula'}, {'question_id': '201595894', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201153371', 'answer': 'Wooden', 'gt_answer': 'wooden'}, {'question_id': '20247194', 'answer': 'Long', 'gt_answer': 'long'}, {'question_id': '202080916', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20226855', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202080913', 'answer': 'Color', 'gt_answer': 'material'}, {'question_id': '20183067', 'answer': 'Light', 'gt_answer': 'light'}, {'question_id': '20891332', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201336976', 'answer': 'Skateboard', 'gt_answer': 'grass'}, {'question_id': '20183416', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201982425', 'answer': 'Window', 'gt_answer': 'mirror'}, {'question_id': '20891339', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '2012880', 'answer': 'Plastic', 'gt_answer': 'plastic'}, {'question_id': '2012881', 'answer': 'Orange juice', 'gt_answer': 'plastic'}, {'question_id': '202133633', 'answer': 'Male', 'gt_answer': 'male'}, {'question_id': '202125979', 'answer': 'Chairs', 'gt_answer': 'chairs'}, {'question_id': '202125978', 'answer': 'Chair', 'gt_answer': 'chairs'}, {'question_id': '202133636', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '202133635', 'answer': 'Asian', 'gt_answer': 'caucasian'}, {'question_id': '20810806', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '202125971', 'answer': 'Man', 'gt_answer': 'player'}, {'question_id': '20609506', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20810802', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201482064', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201752784', 'answer': 'Small', 'gt_answer': 'small'}, {'question_id': '20151562', 'answer': 'Blue', 'gt_answer': 'tan'}, {'question_id': '20668081', 'answer': 'Couch', 'gt_answer': 'couch'}, {'question_id': '2066045', 'answer': 'Jacket', 'gt_answer': 'baseball mitt'}, {'question_id': '20151569', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '2066047', 'answer': 'Baseball mitt', 'gt_answer': 'baseball mitt'}, {'question_id': '2066046', 'answer': 'Jacket', 'gt_answer': 'baseball mitt'}, {'question_id': '2066041', 'answer': 'Glove', 'gt_answer': 'baseball mitt'}, {'question_id': '20149708', 'answer': 'Small', 'gt_answer': 'small'}, {'question_id': '201599698', 'answer': 'Cat', 'gt_answer': 'cat'}, {'question_id': '201997173', 'answer': 'Chair', 'gt_answer': 'chair'}, {'question_id': '201482203', 'answer': 'Berries', 'gt_answer': 'berries'}, {'question_id': '20541165', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '201510486', 'answer': 'Clean', 'gt_answer': 'clean'}, {'question_id': '201510955', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '2012777', 'answer': 'Large', 'gt_answer': 'small'}, {'question_id': '20183336', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202144618', 'answer': 'Liquid', 'gt_answer': 'liquor'}, {'question_id': '202240839', 'answer': 'Pizza', 'gt_answer': 'shelf'}, {'question_id': '202240836', 'answer': 'Woman', 'gt_answer': 'woman'}, {'question_id': '20637278', 'answer': 'Spatula', 'gt_answer': 'pan'}, {'question_id': '201757669', 'answer': 'Paper', 'gt_answer': 'paper'}, {'question_id': '20668134', 'answer': 'Couch', 'gt_answer': 'couch'}, {'question_id': '20668132', 'answer': 'Cloth', 'gt_answer': 'cloth'}, {'question_id': '201061161', 'answer': 'Young', 'gt_answer': 'young'}, {'question_id': '20891599', 'answer': 'White', 'gt_answer': 'gray'}, {'question_id': '202024713', 'answer': 'Park', 'gt_answer': 'park'}, {'question_id': '20891595', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201061169', 'answer': 'Boy', 'gt_answer': 'boy'}, {'question_id': '201574364', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201571243', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201902391', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201640365', 'answer': 'Bottom', 'gt_answer': 'bottom'}, {'question_id': '201573865', 'answer': 'Traffic light', 'gt_answer': 'street light'}, {'question_id': '201152977', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201573867', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201573860', 'answer': 'Pole', 'gt_answer': 'stores'}, {'question_id': '2093936', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201434358', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201404266', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '201156262', 'answer': 'Boy', 'gt_answer': 'boy'}, {'question_id': '20434790', 'answer': 'Restaurant', 'gt_answer': 'restaurant'}, {'question_id': '201109160', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20797755', 'answer': 'Cat', 'gt_answer': 'cat'}, {'question_id': '20706086', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201153081', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201156268', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201920667', 'answer': 'Right', 'gt_answer': 'field'}, {'question_id': '20518672', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '202023268', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20508338', 'answer': 'Small', 'gt_answer': 'small'}, {'question_id': '20518674', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20508444', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202023260', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20508330', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '202023267', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201068231', 'answer': 'Child', 'gt_answer': 'girl'}, {'question_id': '201984101', 'answer': 'Phone', 'gt_answer': 'luggage cart'}, {'question_id': '202133891', 'answer': 'Wood', 'gt_answer': 'plastic'}, {'question_id': '201984103', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20211055', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201879280', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '202005853', 'answer': 'Cabinets', 'gt_answer': 'cabinet'}, {'question_id': '20211052', 'answer': 'Full', 'gt_answer': 'full'}, {'question_id': '201663452', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '20942863', 'answer': 'Ball', 'gt_answer': 'soccer ball'}, {'question_id': '201981992', 'answer': 'Color', 'gt_answer': 'material'}, {'question_id': '201556583', 'answer': 'Office chair', 'gt_answer': 'office chair'}, {'question_id': '201640444', 'answer': 'Black', 'gt_answer': 'pink'}, {'question_id': '201556581', 'answer': 'Desk', 'gt_answer': 'shelf'}, {'question_id': '201981998', 'answer': 'Television', 'gt_answer': 'phone'}, {'question_id': '201556586', 'answer': 'Chair', 'gt_answer': 'office chair'}, {'question_id': '201490846', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20709906', 'answer': 'Eating', 'gt_answer': 'skating'}, {'question_id': '201974842', 'answer': 'Woman', 'gt_answer': 'player'}, {'question_id': '20709903', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201974841', 'answer': 'Woman', 'gt_answer': 'player'}, {'question_id': '20746557', 'answer': 'Birds', 'gt_answer': 'birds'}, {'question_id': '20746556', 'answer': 'Birds', 'gt_answer': 'birds'}, {'question_id': '20480628', 'answer': 'Laptop', 'gt_answer': 'computer'}, {'question_id': '2017079', 'answer': 'Snow', 'gt_answer': 'snow'}, {'question_id': '20746551', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201654232', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201654231', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '202257876', 'answer': 'Color', 'gt_answer': 'color'}, {'question_id': '20480624', 'answer': 'Laptop', 'gt_answer': 'radio'}, {'question_id': '2017073', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202244681', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202240305', 'answer': 'Girl', 'gt_answer': 'girl'}, {'question_id': '201067918', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201528126', 'answer': 'Refrigerator', 'gt_answer': 'stove'}, {'question_id': '201528127', 'answer': 'Refrigerator', 'gt_answer': 'stove'}, {'question_id': '202036953', 'answer': 'Pepper', 'gt_answer': 'sausage'}, {'question_id': '201866734', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201889239', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201983737', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201947577', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '201770835', 'answer': 'Pink', 'gt_answer': 'orange'}, {'question_id': '20306389', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20306386', 'answer': 'Snow', 'gt_answer': 'gift'}, {'question_id': '20306387', 'answer': 'Man', 'gt_answer': 'gift'}, {'question_id': '20306384', 'answer': 'Man', 'gt_answer': 'woman'}, {'question_id': '201574126', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201996963', 'answer': 'Smiling', 'gt_answer': 'staring'}, {'question_id': '201996962', 'answer': 'Sitting', 'gt_answer': 'staring'}, {'question_id': '20394865', 'answer': 'Dress', 'gt_answer': 'dress'}, {'question_id': '202246683', 'answer': 'Desk', 'gt_answer': 'computer desk'}, {'question_id': '20394863', 'answer': 'Dress', 'gt_answer': 'dress'}, {'question_id': '201682306', 'answer': 'Man', 'gt_answer': 'man'}, {'question_id': '20245843', 'answer': 'Behind', 'gt_answer': 'behind'}, {'question_id': '202246689', 'answer': 'Desk', 'gt_answer': 'computer desk'}, {'question_id': '202246688', 'answer': 'Desk', 'gt_answer': 'computer desk'}, {'question_id': '20308723', 'answer': 'Coffee pot', 'gt_answer': 'tea kettle'}, {'question_id': '202262475', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201873549', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20588892', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201873546', 'answer': 'Bus', 'gt_answer': 'fire truck'}, {'question_id': '201873543', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20299841', 'answer': 'Sailboat', 'gt_answer': 'sailboat'}, {'question_id': '20244644', 'answer': 'Yellow', 'gt_answer': 'yellow'}, {'question_id': '201947754', 'answer': 'Tank top', 'gt_answer': 'tank top'}, {'question_id': '201227859', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20244648', 'answer': 'Bus', 'gt_answer': 'bus'}, {'question_id': '201068704', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20300558', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20306657', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201030624', 'answer': 'Controller', 'gt_answer': 'wii controller'}, {'question_id': '201920442', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201920441', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20511504', 'answer': 'Metal', 'gt_answer': 'metal'}, {'question_id': '202180330', 'answer': 'Girl', 'gt_answer': 'soccer player'}, {'question_id': '201593646', 'answer': 'Trees', 'gt_answer': 'pine trees'}, {'question_id': '201984169', 'answer': 'Woman', 'gt_answer': 'woman'}, {'question_id': '202180334', 'answer': 'Girl', 'gt_answer': 'soccer player'}, {'question_id': '202180335', 'answer': 'Field', 'gt_answer': 'field'}, {'question_id': '20381460', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20621963', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201467304', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202121686', 'answer': 'Clean', 'gt_answer': 'clean'}, {'question_id': '201593399', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20611686', 'answer': 'Grapes', 'gt_answer': 'grapes'}, {'question_id': '20381468', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '202006685', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202081979', 'answer': 'Brown', 'gt_answer': 'green'}, {'question_id': '20953870', 'answer': 'Girl', 'gt_answer': 'girl'}, {'question_id': '202081326', 'answer': 'Black', 'gt_answer': 'silver'}, {'question_id': '202285310', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '20953879', 'answer': 'Color', 'gt_answer': 'color'}, {'question_id': '201864418', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201498713', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201480340', 'answer': 'Boy', 'gt_answer': 'boy'}, {'question_id': '201676077', 'answer': 'Plant', 'gt_answer': 'lamp'}, {'question_id': '201480345', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201480348', 'answer': 'Boy', 'gt_answer': 'boy'}, {'question_id': '201751862', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '202006421', 'answer': 'Short', 'gt_answer': 'short'}, {'question_id': '201411289', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '202257414', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20902780', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20609330', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20551520', 'answer': 'Train', 'gt_answer': 'train'}, {'question_id': '20551522', 'answer': 'Train', 'gt_answer': 'train'}, {'question_id': '201908777', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '20345163', 'answer': 'Black', 'gt_answer': 'black'}, {'question_id': '201908771', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20827253', 'answer': 'Wood', 'gt_answer': 'wood'}, {'question_id': '201957175', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20622007', 'answer': 'Blanket', 'gt_answer': 'hair'}, {'question_id': '20827258', 'answer': 'Couch', 'gt_answer': 'chairs'}, {'question_id': '20827259', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201037333', 'answer': 'Concrete', 'gt_answer': 'concrete'}, {'question_id': '201480690', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20285284', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201064925', 'answer': 'Girl', 'gt_answer': 'boy'}, {'question_id': '201866695', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201434055', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '202120007', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201481567', 'answer': 'Umbrella', 'gt_answer': 'umbrella'}, {'question_id': '201444996', 'answer': 'Sign', 'gt_answer': 'rock'}, {'question_id': '201982132', 'answer': 'Couch', 'gt_answer': 'couch'}, {'question_id': '20637064', 'answer': 'Stove', 'gt_answer': 'stove'}, {'question_id': '201576841', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20637066', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20637062', 'answer': 'Stove', 'gt_answer': 'stove'}, {'question_id': '202258128', 'answer': 'Open', 'gt_answer': 'closed'}, {'question_id': '20245714', 'answer': 'Color', 'gt_answer': 'material'}, {'question_id': '201936009', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20794323', 'answer': 'Small', 'gt_answer': 'small'}, {'question_id': '201936007', 'answer': 'Square', 'gt_answer': 'square'}, {'question_id': '201247306', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20827330', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20644728', 'answer': 'Cat', 'gt_answer': 'cat'}, {'question_id': '201883221', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20118040', 'answer': 'Road', 'gt_answer': 'field'}, {'question_id': '201982135', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20118042', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20118048', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201669432', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202000909', 'answer': 'Boy', 'gt_answer': 'skater'}, {'question_id': '20285064', 'answer': 'Chair', 'gt_answer': 'couch'}, {'question_id': '201976693', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202159121', 'answer': 'Bus', 'gt_answer': 'cars'}, {'question_id': '201882931', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20942853', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202228667', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '202082077', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '2066188', 'answer': 'Glove', 'gt_answer': 'baseball mitt'}, {'question_id': '2066189', 'answer': 'Cap', 'gt_answer': 'baseball mitt'}, {'question_id': '201576760', 'answer': 'Dog', 'gt_answer': 'sheep'}, {'question_id': '201228068', 'answer': 'Car', 'gt_answer': 'car'}, {'question_id': '2066184', 'answer': 'Shirt', 'gt_answer': 'sweater'}, {'question_id': '201795606', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201498168', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '2066187', 'answer': 'Sweater', 'gt_answer': 'baseball mitt'}, {'question_id': '201623883', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201623881', 'answer': 'Off', 'gt_answer': 'off'}, {'question_id': '201883082', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20385619', 'answer': 'Smooth', 'gt_answer': 'rough'}, {'question_id': '2091139', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20954193', 'answer': 'Man', 'gt_answer': 'man'}, {'question_id': '201735648', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20144575', 'answer': 'Parking lot', 'gt_answer': 'pavement'}, {'question_id': '20744312', 'answer': 'Empty', 'gt_answer': 'empty'}, {'question_id': '201878458', 'answer': 'Window', 'gt_answer': 'windows'}, {'question_id': '201878459', 'answer': 'Window', 'gt_answer': 'windows'}, {'question_id': '20865560', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20865566', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201959640', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '202246016', 'answer': 'Nose', 'gt_answer': 'glasses'}, {'question_id': '20151797', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20151796', 'answer': 'Sweater', 'gt_answer': 'tank top'}, {'question_id': '20151795', 'answer': 'Shirt', 'gt_answer': 'tank top'}, {'question_id': '201030786', 'answer': 'Pants', 'gt_answer': 'pants'}, {'question_id': '201882487', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201873303', 'answer': 'American flag', 'gt_answer': 'bucket'}, {'question_id': '201873301', 'answer': 'American flag', 'gt_answer': 'bucket'}, {'question_id': '202262249', 'answer': 'Chair', 'gt_answer': 'chair'}, {'question_id': '20903122', 'answer': 'Small', 'gt_answer': 'large'}, {'question_id': '20903124', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202106107', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201347399', 'answer': 'Boy', 'gt_answer': 'skateboarder'}, {'question_id': '201347392', 'answer': 'Boy', 'gt_answer': 'skateboarder'}, {'question_id': '202284990', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '201982730', 'answer': 'Caucasian', 'gt_answer': 'caucasian'}, {'question_id': '202102615', 'answer': 'Cabinet', 'gt_answer': 'cabinets'}, {'question_id': '202102614', 'answer': 'Cabinets', 'gt_answer': 'cabinets'}, {'question_id': '202244281', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20866459', 'answer': 'Jeans', 'gt_answer': 'jeans'}, {'question_id': '202102618', 'answer': 'Cabinets', 'gt_answer': 'cabinets'}, {'question_id': '201974699', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20865903', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202073172', 'answer': 'Deer', 'gt_answer': 'horses'}, {'question_id': '201030434', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20692107', 'answer': 'Rectangle', 'gt_answer': 'square'}, {'question_id': '201803975', 'answer': 'Left', 'gt_answer': 'right'}, {'question_id': '201803972', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20226648', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202262061', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '202262790', 'answer': 'Red', 'gt_answer': 'black'}, {'question_id': '20899123', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20652619', 'answer': 'Small', 'gt_answer': 'small'}, {'question_id': '201535661', 'answer': 'Dark brown', 'gt_answer': 'dark brown'}, {'question_id': '202244253', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201492371', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201509830', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '201235474', 'answer': 'Ceiling', 'gt_answer': 'wall'}, {'question_id': '201055663', 'answer': 'Car', 'gt_answer': 'car'}, {'question_id': '20942182', 'answer': 'Woman', 'gt_answer': 'girl'}, {'question_id': '201887259', 'answer': 'Broccoli', 'gt_answer': 'broccoli'}, {'question_id': '20258753', 'answer': 'Boy', 'gt_answer': 'child'}, {'question_id': '201738114', 'answer': 'Wood', 'gt_answer': 'metal'}, {'question_id': '20783391', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '201882596', 'answer': 'Bed', 'gt_answer': 'bed'}, {'question_id': '201889365', 'answer': 'Skis', 'gt_answer': 'skis'}, {'question_id': '201882595', 'answer': 'Bed', 'gt_answer': 'bed'}, {'question_id': '201882593', 'answer': 'Rectangle', 'gt_answer': 'rectangular'}, {'question_id': '201889363', 'answer': 'Ski', 'gt_answer': 'skis'}, {'question_id': '20204530', 'answer': 'Chair', 'gt_answer': 'chair'}, {'question_id': '20204531', 'answer': 'Chair', 'gt_answer': 'chair'}, {'question_id': '20262705', 'answer': 'Girl', 'gt_answer': 'girl'}, {'question_id': '20204533', 'answer': 'Chair', 'gt_answer': 'chair'}, {'question_id': '202286593', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20536194', 'answer': 'Giraffe', 'gt_answer': 'giraffe'}, {'question_id': '20953056', 'answer': 'Man', 'gt_answer': 'player'}, {'question_id': '20953055', 'answer': 'Player', 'gt_answer': 'player'}, {'question_id': '20647406', 'answer': 'Player', 'gt_answer': 'man'}, {'question_id': '201976374', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20567615', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20567612', 'answer': 'Man', 'gt_answer': 'man'}, {'question_id': '20567611', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20567610', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201766601', 'answer': 'Girl', 'gt_answer': 'girl'}, {'question_id': '201759264', 'answer': 'Bottom', 'gt_answer': 'bottom'}, {'question_id': '202003666', 'answer': 'White', 'gt_answer': 'white'}, {'question_id': '2053761', 'answer': 'No one', 'gt_answer': 'crowd'}, {'question_id': '201548809', 'answer': 'Blender', 'gt_answer': 'blender'}, {'question_id': '202174273', 'answer': 'On', 'gt_answer': 'off'}, {'question_id': '201804273', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20480450', 'answer': 'Cat', 'gt_answer': 'cat'}, {'question_id': '201757502', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '202053048', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '2053763', 'answer': 'No one', 'gt_answer': 'crowd'}, {'question_id': '201548805', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201548807', 'answer': 'Bottle', 'gt_answer': 'blender'}, {'question_id': '201548806', 'answer': 'Bottles', 'gt_answer': 'blender'}, {'question_id': '201438368', 'answer': 'Batter', 'gt_answer': 'umpire'}, {'question_id': '20489530', 'answer': 'Bed', 'gt_answer': 'bed'}, {'question_id': '20489533', 'answer': 'Bed', 'gt_answer': 'bed'}, {'question_id': '20258659', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20258871', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202053171', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202023291', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201438366', 'answer': 'Crouching', 'gt_answer': 'looking up'}, {'question_id': '201438367', 'answer': 'Crouching', 'gt_answer': 'looking up'}, {'question_id': '20427892', 'answer': 'Square', 'gt_answer': 'square'}, {'question_id': '201207039', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20411825', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20636818', 'answer': 'Stove', 'gt_answer': 'oven'}, {'question_id': '201770960', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20757244', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '2017449', 'answer': 'Sheep', 'gt_answer': 'horse'}, {'question_id': '2017448', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20861041', 'answer': 'Old', 'gt_answer': 'old'}, {'question_id': '201593886', 'answer': 'Woman', 'gt_answer': 'girl'}, {'question_id': '201536489', 'answer': 'People', 'gt_answer': 'spectators'}, {'question_id': '20479939', 'answer': 'Speaker', 'gt_answer': 'radio'}, {'question_id': '201766474', 'answer': 'Field', 'gt_answer': 'field'}, {'question_id': '202158855', 'answer': 'Black', 'gt_answer': 'black'}, {'question_id': '2097625', 'answer': 'Laptop', 'gt_answer': 'monitor'}, {'question_id': '2097627', 'answer': 'Laptop', 'gt_answer': 'monitor'}, {'question_id': '20898610', 'answer': 'Wide', 'gt_answer': 'wide'}, {'question_id': '20412141', 'answer': 'Man', 'gt_answer': 'boy'}, {'question_id': '201759489', 'answer': 'Bottom', 'gt_answer': 'bottom'}, {'question_id': '201704688', 'answer': 'Cows', 'gt_answer': 'cows'}, {'question_id': '201616220', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20861104', 'answer': 'Car', 'gt_answer': 'car'}, {'question_id': '201079726', 'answer': 'Color', 'gt_answer': 'color'}, {'question_id': '201623710', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201704685', 'answer': 'Cow', 'gt_answer': 'cows'}, {'question_id': '201704684', 'answer': 'Cow', 'gt_answer': 'cows'}, {'question_id': '201704687', 'answer': 'Cows', 'gt_answer': 'cows'}, {'question_id': '20307261', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '20611942', 'answer': 'Round', 'gt_answer': 'rectangular'}, {'question_id': '20856631', 'answer': 'Phone', 'gt_answer': 'cell phone'}, {'question_id': '20492108', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20468832', 'answer': 'Trailer', 'gt_answer': 'trailer'}, {'question_id': '201739173', 'answer': 'Pants', 'gt_answer': 'pants'}, {'question_id': '201902614', 'answer': 'Desk', 'gt_answer': 'desk'}, {'question_id': '2097959', 'answer': 'Wall', 'gt_answer': 'curtain'}, {'question_id': '201228119', 'answer': 'Car', 'gt_answer': 'van'}, {'question_id': '2097953', 'answer': 'Plastic', 'gt_answer': 'plastic'}, {'question_id': '201228115', 'answer': 'Car', 'gt_answer': 'car'}, {'question_id': '201228117', 'answer': 'Van', 'gt_answer': 'van'}, {'question_id': '2097957', 'answer': 'Desk', 'gt_answer': 'desk'}, {'question_id': '2097956', 'answer': 'Desk', 'gt_answer': 'desk'}, {'question_id': '2097955', 'answer': 'Laptop', 'gt_answer': 'desk'}, {'question_id': '2076118', 'answer': 'Color', 'gt_answer': 'color'}, {'question_id': '201639021', 'answer': 'Cameras', 'gt_answer': 'phones'}, {'question_id': '20468939', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201639024', 'answer': 'On wall', 'gt_answer': 'sidewalk'}, {'question_id': '202286525', 'answer': 'Bear', 'gt_answer': 'elephant'}, {'question_id': '20468935', 'answer': 'Fence', 'gt_answer': 'fence'}, {'question_id': '20468936', 'answer': 'Fence', 'gt_answer': 'fence'}, {'question_id': '20468686', 'answer': 'Horse', 'gt_answer': 'horse'}, {'question_id': '2076115', 'answer': 'Stop sign', 'gt_answer': 'street sign'}, {'question_id': '20611802', 'answer': 'Yellow', 'gt_answer': 'orange'}, {'question_id': '20667684', 'answer': 'Table', 'gt_answer': 'coffee table'}, {'question_id': '20514965', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201536262', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20667683', 'answer': 'Remote control', 'gt_answer': 'remote control'}, {'question_id': '202246740', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202246745', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20836823', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20953099', 'answer': 'Man', 'gt_answer': 'player'}, {'question_id': '201067766', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20178188', 'answer': 'Top', 'gt_answer': 'top'}, {'question_id': '201760659', 'answer': 'Green', 'gt_answer': 'beige'}, {'question_id': '202226066', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201751594', 'answer': 'Racket', 'gt_answer': 'tennis ball'}, {'question_id': '201751593', 'answer': 'Woman', 'gt_answer': 'lady'}, {'question_id': '20753430', 'answer': 'Blue', 'gt_answer': 'green'}, {'question_id': '201760652', 'answer': 'Short sleeved', 'gt_answer': 'long sleeved'}, {'question_id': '201110807', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '201760654', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201429108', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202285105', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201360775', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201879193', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '202119704', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201639249', 'answer': 'In car', 'gt_answer': 'field'}, {'question_id': '202012845', 'answer': 'Wii', 'gt_answer': 'television'}, {'question_id': '201976750', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20878952', 'answer': 'Bushes', 'gt_answer': 'parking lot'}, {'question_id': '20550330', 'answer': 'Horse', 'gt_answer': 'horse'}, {'question_id': '20878950', 'answer': 'Parking lot', 'gt_answer': 'parking lot'}, {'question_id': '20144764', 'answer': 'Bus', 'gt_answer': 'bus'}, {'question_id': '20144765', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20600275', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20144767', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20600272', 'answer': 'Zebras', 'gt_answer': 'zebras'}, {'question_id': '20836922', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20600270', 'answer': 'Zebra', 'gt_answer': 'zebras'}, {'question_id': '201982090', 'answer': 'Looking down', 'gt_answer': 'looking down'}, {'question_id': '201959575', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '202218508', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20865357', 'answer': 'Pasture', 'gt_answer': 'pasture'}, {'question_id': '201047394', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202218502', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201047399', 'answer': 'Old', 'gt_answer': 'new'}, {'question_id': '20865358', 'answer': 'Field', 'gt_answer': 'pasture'}, {'question_id': '201065404', 'answer': 'Lights', 'gt_answer': 'light fixture'}, {'question_id': '202174430', 'answer': 'Brown', 'gt_answer': 'red'}, {'question_id': '201185793', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20240957', 'answer': 'Water', 'gt_answer': 'table'}, {'question_id': '20899909', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201037169', 'answer': 'Stop sign', 'gt_answer': 'traffic sign'}, {'question_id': '201763605', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20621945', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20621940', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '202106373', 'answer': 'Behind', 'gt_answer': 'behind'}, {'question_id': '20491900', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20647249', 'answer': 'Long', 'gt_answer': 'short'}, {'question_id': '201428933', 'answer': 'Refrigerator', 'gt_answer': 'refrigerator'}, {'question_id': '202243992', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20169578', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20631593', 'answer': 'Umpire', 'gt_answer': 'batter'}, {'question_id': '201490986', 'answer': 'Calves', 'gt_answer': 'goats'}, {'question_id': '20169575', 'answer': 'Street', 'gt_answer': 'road'}, {'question_id': '201490984', 'answer': 'Cow', 'gt_answer': 'goats'}, {'question_id': '2098106', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '201430786', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '2075866', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201490980', 'answer': 'Woman', 'gt_answer': 'woman'}, {'question_id': '20609295', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '201030309', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201623981', 'answer': 'Large', 'gt_answer': 'large'}, {'question_id': '2065971', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20171129', 'answer': 'Pan', 'gt_answer': 'baking pan'}, {'question_id': '20171126', 'answer': 'Pan', 'gt_answer': 'baking pan'}, {'question_id': '20171125', 'answer': 'Pan', 'gt_answer': 'baking pan'}, {'question_id': '201264135', 'answer': 'On sidewalk', 'gt_answer': 'grass'}, {'question_id': '20226871', 'answer': 'Candle', 'gt_answer': 'silverware'}, {'question_id': '20226870', 'answer': 'Napkin', 'gt_answer': 'wine glass'}, {'question_id': '20226872', 'answer': 'Candle', 'gt_answer': 'silverware'}, {'question_id': '201336956', 'answer': 'Skateboard', 'gt_answer': 'skateboard'}, {'question_id': '20652425', 'answer': 'Green', 'gt_answer': 'brunette'}, {'question_id': '201759554', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '201982407', 'answer': 'Couch', 'gt_answer': 'couch'}, {'question_id': '201235647', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201887084', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20756802', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20652429', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20756805', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201752760', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20169793', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '20954276', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '20169796', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20169798', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20434764', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201445019', 'answer': 'Parking sign', 'gt_answer': 'street sign'}, {'question_id': '201590281', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20151545', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20151540', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201360485', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '201360481', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202006453', 'answer': 'Chair', 'gt_answer': 'chair'}, {'question_id': '2012752', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20898809', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201055879', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20896220', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201510976', 'answer': 'Suitcase', 'gt_answer': 'suitcase'}, {'question_id': '20866023', 'answer': 'Ceiling', 'gt_answer': 'wall'}, {'question_id': '20786085', 'answer': 'Standing', 'gt_answer': 'playing'}, {'question_id': '20786087', 'answer': 'Woman', 'gt_answer': 'woman'}, {'question_id': '201983611', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '2056122', 'answer': 'Yellow', 'gt_answer': 'yellow'}, {'question_id': '20637216', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '202121435', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20637213', 'answer': 'Stove', 'gt_answer': 'burner'}, {'question_id': '20637212', 'answer': 'Stove', 'gt_answer': 'burner'}, {'question_id': '20287386', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201803701', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201998015', 'answer': 'Chair', 'gt_answer': 'table'}, {'question_id': '201883149', 'answer': 'Left', 'gt_answer': 'right'}, {'question_id': '201883141', 'answer': 'Square', 'gt_answer': 'rectangular'}, {'question_id': '201497601', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20503789', 'answer': 'Traffic light', 'gt_answer': 'light fixture'}, {'question_id': '20652282', 'answer': 'Backpack', 'gt_answer': 'backpack'}, {'question_id': '201574309', 'answer': 'Left', 'gt_answer': 'right'}, {'question_id': '20120396', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201080434', 'answer': 'Dirty', 'gt_answer': 'clean'}, {'question_id': '20503781', 'answer': 'Stop sign', 'gt_answer': 'stop sign'}, {'question_id': '201455835', 'answer': 'Road', 'gt_answer': 'road'}, {'question_id': '201455837', 'answer': 'Cloudless', 'gt_answer': 'cloudless'}, {'question_id': '201510605', 'answer': 'Square', 'gt_answer': 'round'}, {'question_id': '202228498', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201640349', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20657133', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201152950', 'answer': 'Forest', 'gt_answer': 'forest'}, {'question_id': '202228494', 'answer': 'Cat', 'gt_answer': 'cat'}, {'question_id': '202228495', 'answer': 'Cat', 'gt_answer': 'cat'}, {'question_id': '201156248', 'answer': 'Boy', 'gt_answer': 'boy'}, {'question_id': '201404246', 'answer': 'Cow', 'gt_answer': 'mother'}, {'question_id': '202169048', 'answer': 'Concrete', 'gt_answer': 'concrete'}, {'question_id': '201404241', 'answer': 'Calf', 'gt_answer': 'calf'}, {'question_id': '201404242', 'answer': 'Calf', 'gt_answer': 'calf'}, {'question_id': '201758503', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201758055', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20434911', 'answer': 'People', 'gt_answer': 'boy'}, {'question_id': '20434910', 'answer': 'People', 'gt_answer': 'boy'}, {'question_id': '201765936', 'answer': 'Boat', 'gt_answer': 'boats'}, {'question_id': '201624006', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '202240950', 'answer': 'Shirt', 'gt_answer': 'shirt'}, {'question_id': '20508310', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '201704477', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202231564', 'answer': 'Short sleeved', 'gt_answer': 'short sleeved'}, {'question_id': '201342218', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201623528', 'answer': 'Silver', 'gt_answer': 'gray'}, {'question_id': '20922900', 'answer': 'Wire', 'gt_answer': 'wire'}, {'question_id': '20922902', 'answer': 'Pole', 'gt_answer': 'telephone pole'}, {'question_id': '20922903', 'answer': 'Pole', 'gt_answer': 'telephone pole'}, {'question_id': '202246054', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20896594', 'answer': 'Beige', 'gt_answer': 'beige'}, {'question_id': '2097843', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201347398', 'answer': 'Boy', 'gt_answer': 'skateboarder'}, {'question_id': '202122169', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20412448', 'answer': 'Table', 'gt_answer': 'table'}, {'question_id': '20710259', 'answer': 'Child', 'gt_answer': 'child'}, {'question_id': '20710258', 'answer': 'Skiing', 'gt_answer': 'looking up'}, {'question_id': '201951669', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20679267', 'answer': 'Large', 'gt_answer': 'large'}, {'question_id': '20710253', 'answer': 'Child', 'gt_answer': 'child'}, {'question_id': '202122160', 'answer': 'Right', 'gt_answer': 'left'}, {'question_id': '20412445', 'answer': 'Vase', 'gt_answer': 'vase'}, {'question_id': '20412446', 'answer': 'Vase', 'gt_answer': 'vase'}, {'question_id': '201175202', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20899517', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201067500', 'answer': 'Laptop', 'gt_answer': 'laptop'}, {'question_id': '201175208', 'answer': 'Plastic', 'gt_answer': 'plastic'}, {'question_id': '20120485', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202248873', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '2055961', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '2055962', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20790006', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20414469', 'answer': 'Man', 'gt_answer': 'skateboarder'}, {'question_id': '202266073', 'answer': 'Couch', 'gt_answer': 'couch'}, {'question_id': '201804486', 'answer': 'Black', 'gt_answer': 'black'}, {'question_id': '201399965', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '202240367', 'answer': 'White', 'gt_answer': 'blue'}, {'question_id': '201391768', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20631868', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20783077', 'answer': 'Screen', 'gt_answer': 'screen'}, {'question_id': '201935871', 'answer': 'Potato', 'gt_answer': 'squash'}, {'question_id': '20811086', 'answer': 'Pants', 'gt_answer': 'pants'}, {'question_id': '20811087', 'answer': 'Pants', 'gt_answer': 'pants'}, {'question_id': '202162565', 'answer': 'Wall', 'gt_answer': 'bookcase'}, {'question_id': '201935876', 'answer': 'Bowl', 'gt_answer': 'bowls'}, {'question_id': '201935877', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202266070', 'answer': 'Rug', 'gt_answer': 'couch'}, {'question_id': '201983719', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201947514', 'answer': 'Toilet', 'gt_answer': 'shower curtain'}, {'question_id': '201947515', 'answer': 'Toilet', 'gt_answer': 'shower curtain'}, {'question_id': '201882969', 'answer': 'Desk', 'gt_answer': 'chair'}, {'question_id': '202156878', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201859379', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201763935', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201996945', 'answer': 'Young', 'gt_answer': 'young'}, {'question_id': '201920605', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202156871', 'answer': 'Dry', 'gt_answer': 'dry'}, {'question_id': '201682323', 'answer': 'Racket', 'gt_answer': 'racket'}, {'question_id': '201682322', 'answer': 'Racket', 'gt_answer': 'racket'}, {'question_id': '20245861', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201682325', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202119760', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20811228', 'answer': 'Sniffing', 'gt_answer': 'playing'}, {'question_id': '201080212', 'answer': 'Green', 'gt_answer': 'blue'}, {'question_id': '20588878', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202012520', 'answer': 'Shelf', 'gt_answer': 'shelves'}, {'question_id': '202012523', 'answer': 'Cabinets', 'gt_answer': 'cabinets'}, {'question_id': '20515976', 'answer': 'Blue', 'gt_answer': 'blue'}, {'question_id': '202012526', 'answer': 'Cabinets', 'gt_answer': 'cabinets'}, {'question_id': '20942865', 'answer': 'Girl', 'gt_answer': 'soccer player'}, {'question_id': '20177601', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '2017057', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '2017054', 'answer': 'Field', 'gt_answer': 'pasture'}, {'question_id': '2017055', 'answer': 'Field', 'gt_answer': 'pasture'}, {'question_id': '202231931', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202119768', 'answer': 'Concrete', 'gt_answer': 'concrete'}, {'question_id': '201669638', 'answer': 'Metal', 'gt_answer': 'metal'}, {'question_id': '2075302', 'answer': 'Brown', 'gt_answer': 'light brown'}, {'question_id': '201467325', 'answer': 'Small', 'gt_answer': 'small'}, {'question_id': '20611724', 'answer': 'Bread', 'gt_answer': 'sandwiches'}, {'question_id': '202180352', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20508151', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201751674', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201770769', 'answer': 'Toothbrush', 'gt_answer': 'toothbrush'}, {'question_id': '202121913', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '201896532', 'answer': 'Woman', 'gt_answer': 'lady'}, {'question_id': '202081340', 'answer': 'Coffee maker', 'gt_answer': 'toaster'}, {'question_id': '201498775', 'answer': 'Computer', 'gt_answer': 'computer'}, {'question_id': '201599882', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201498771', 'answer': 'Computer', 'gt_answer': 'computer'}, {'question_id': '201498770', 'answer': 'Keyboard', 'gt_answer': 'phone'}, {'question_id': '201498773', 'answer': 'Computer', 'gt_answer': 'computer'}, {'question_id': '202285379', 'answer': 'Beans', 'gt_answer': 'beans'}, {'question_id': '201412226', 'answer': 'Snowy', 'gt_answer': 'snowy'}, {'question_id': '201480363', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201480360', 'answer': 'Boy', 'gt_answer': 'boy'}, {'question_id': '201391885', 'answer': 'Couch', 'gt_answer': 'sofa'}, {'question_id': '201391886', 'answer': 'Sofa', 'gt_answer': 'sofa'}, {'question_id': '20879167', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201391882', 'answer': 'Couch', 'gt_answer': 'sofa'}, {'question_id': '20262530', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201951877', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201111090', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20898990', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '20963711', 'answer': 'Short', 'gt_answer': 'short'}, {'question_id': '20656857', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '201068336', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202265790', 'answer': 'Woman', 'gt_answer': 'woman'}, {'question_id': '201908710', 'answer': 'Ceramic', 'gt_answer': 'glass'}, {'question_id': '202073350', 'answer': 'Zebra', 'gt_answer': 'deer'}, {'question_id': '202219060', 'answer': 'Toaster', 'gt_answer': 'toaster'}, {'question_id': '201639140', 'answer': 'Green', 'gt_answer': 'dark'}, {'question_id': '20340515', 'answer': 'Chair', 'gt_answer': 'chair'}, {'question_id': '201504726', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20340516', 'answer': 'Chair', 'gt_answer': 'chair'}, {'question_id': '202100774', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20381409', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20894192', 'answer': 'Boy', 'gt_answer': 'boy'}, {'question_id': '201682317', 'answer': 'Man', 'gt_answer': 'man'}, {'question_id': '20381405', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '202120062', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201974820', 'answer': 'Woman', 'gt_answer': 'player'}, {'question_id': '202243980', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '20645561', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202081963', 'answer': 'Metal', 'gt_answer': 'glass'}, {'question_id': '201640322', 'answer': 'Woman', 'gt_answer': 'women'}, {'question_id': '201576820', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '2076651', 'answer': 'Car', 'gt_answer': 'entrance'}, {'question_id': '2076650', 'answer': 'Car', 'gt_answer': 'entrance'}, {'question_id': '20794305', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20901808', 'answer': 'Umbrella', 'gt_answer': 'umbrella'}, {'question_id': '20705819', 'answer': 'Desk', 'gt_answer': 'desk'}, {'question_id': '202021416', 'answer': 'Stop sign', 'gt_answer': 'stop sign'}, {'question_id': '201759450', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '2076303', 'answer': 'Bus', 'gt_answer': 'bus'}, {'question_id': '202021412', 'answer': 'Stop', 'gt_answer': 'stop sign'}, {'question_id': '2076301', 'answer': 'Bus', 'gt_answer': 'bus'}, {'question_id': '202021410', 'answer': 'Stop', 'gt_answer': 'stop sign'}, {'question_id': '201861586', 'answer': 'Black', 'gt_answer': 'gray'}, {'question_id': '201576932', 'answer': 'Dog', 'gt_answer': 'goat'}, {'question_id': '20482312', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201859575', 'answer': 'Table', 'gt_answer': 'table'}, {'question_id': '201462295', 'answer': 'Batter', 'gt_answer': 'man'}, {'question_id': '202023478', 'answer': 'Bed', 'gt_answer': 'bed'}, {'question_id': '201462299', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20511406', 'answer': 'Helicopter', 'gt_answer': 'helicopter'}, {'question_id': '202228644', 'answer': 'Square', 'gt_answer': 'rectangular'}, {'question_id': '202228649', 'answer': 'Cat', 'gt_answer': 'cat'}, {'question_id': '20645610', 'answer': 'Toilet', 'gt_answer': 'toilet'}, {'question_id': '201795377', 'answer': 'Elephant', 'gt_answer': 'man'}, {'question_id': '201896435', 'answer': 'Large', 'gt_answer': 'small'}, {'question_id': '201795374', 'answer': 'Woman', 'gt_answer': 'woman'}, {'question_id': '202023471', 'answer': 'Bookshelf', 'gt_answer': 'bookshelf'}, {'question_id': '201795370', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20182741', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201711128', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201303430', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201889437', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20621886', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20182748', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '2091118', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201079935', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20744331', 'answer': 'Black', 'gt_answer': 'brown'}, {'question_id': '20480810', 'answer': 'Dirty', 'gt_answer': 'clean'}, {'question_id': '201896084', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201067496', 'answer': 'Screen', 'gt_answer': 'calculator'}, {'question_id': '201067494', 'answer': 'Calculator', 'gt_answer': 'calculator'}, {'question_id': '201411223', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201428460', 'answer': 'Tall', 'gt_answer': 'tall'}, {'question_id': '20631672', 'answer': 'Batter', 'gt_answer': 'batter'}, {'question_id': '20631671', 'answer': 'Batter', 'gt_answer': 'batter'}, {'question_id': '20865545', 'answer': 'Brown', 'gt_answer': 'brown'}, {'question_id': '201909006', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20692344', 'answer': 'Large', 'gt_answer': 'large'}, {'question_id': '201030233', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20661330', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20710001', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201766585', 'answer': 'Girl', 'gt_answer': 'girl'}, {'question_id': '20414473', 'answer': 'Skateboard', 'gt_answer': 'skateboard'}, {'question_id': '20411622', 'answer': 'Apples', 'gt_answer': 'apples'}, {'question_id': '20411623', 'answer': 'Apple', 'gt_answer': 'apples'}, {'question_id': '20414478', 'answer': 'Man', 'gt_answer': 'skateboarder'}, {'question_id': '20655081', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20120493', 'answer': 'Shoe', 'gt_answer': 'sand'}, {'question_id': '20786160', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201826665', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '20935990', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201974674', 'answer': 'Black', 'gt_answer': 'silver'}, {'question_id': '202162379', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20262540', 'answer': 'Long', 'gt_answer': 'long'}, {'question_id': '20412210', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '201882602', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201663089', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202266013', 'answer': 'Couch', 'gt_answer': 'couch'}, {'question_id': '202081504', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20385893', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201988053', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20899103', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '201535640', 'answer': 'Donut', 'gt_answer': 'donuts'}, {'question_id': '201303292', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201988059', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201467661', 'answer': 'Couch', 'gt_answer': 'couch'}, {'question_id': '201055683', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201859565', 'answer': 'Plastic', 'gt_answer': 'plastic'}, {'question_id': '201859564', 'answer': 'Plastic', 'gt_answer': 'plastic'}, {'question_id': '20567583', 'answer': 'Shorts', 'gt_answer': 'trunks'}, {'question_id': '201079929', 'answer': 'Closed', 'gt_answer': 'open'}, {'question_id': '20330200', 'answer': 'Book', 'gt_answer': 'book'}, {'question_id': '20508816', 'answer': 'Controller', 'gt_answer': 'wii controller'}, {'question_id': '202144463', 'answer': 'Man', 'gt_answer': 'man'}, {'question_id': '20136502', 'answer': 'Round', 'gt_answer': 'square'}, {'question_id': '20516099', 'answer': 'Large', 'gt_answer': 'large'}, {'question_id': '202169189', 'answer': 'Man', 'gt_answer': 'man'}, {'question_id': '201889383', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202180438', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201889389', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20887032', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201770682', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '201638973', 'answer': 'Man', 'gt_answer': 'men'}, {'question_id': '20456489', 'answer': 'Dog', 'gt_answer': 'dog'}, {'question_id': '20567632', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201759241', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '202246309', 'answer': 'White', 'gt_answer': 'white'}, {'question_id': '201759247', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20157005', 'answer': 'Color', 'gt_answer': 'shape'}, {'question_id': '201766621', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20480106', 'answer': 'Tall', 'gt_answer': 'short'}, {'question_id': '20837080', 'answer': 'Table', 'gt_answer': 'table'}, {'question_id': '20183166', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '201264319', 'answer': 'Brown', 'gt_answer': 'brown'}, {'question_id': '201757525', 'answer': 'Color', 'gt_answer': 'color'}, {'question_id': '201638972', 'answer': 'People', 'gt_answer': 'men'}, {'question_id': '201273311', 'answer': 'Street sign', 'gt_answer': 'street sign'}, {'question_id': '20865960', 'answer': 'Food', 'gt_answer': 'sauce'}, {'question_id': '20865961', 'answer': 'Food', 'gt_answer': 'sauce'}, {'question_id': '201273314', 'answer': 'Pedestrian', 'gt_answer': 'street sign'}, {'question_id': '20865963', 'answer': 'Container', 'gt_answer': 'refrigerator'}, {'question_id': '201735337', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '20204770', 'answer': 'Blue', 'gt_answer': 'blue'}, {'question_id': '201185841', 'answer': 'Green', 'gt_answer': 'green'}, {'question_id': '20411845', 'answer': 'Carrots', 'gt_answer': 'hot dogs'}, {'question_id': '20411846', 'answer': 'Hot dog', 'gt_answer': 'hot dogs'}, {'question_id': '202285230', 'answer': 'Beans', 'gt_answer': 'sausage'}, {'question_id': '201235809', 'answer': 'Asian', 'gt_answer': 'caucasian'}, {'question_id': '201110717', 'answer': 'Marshmallows', 'gt_answer': 'chocolate'}, {'question_id': '201110718', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20411849', 'answer': 'Pizza', 'gt_answer': 'hot dogs'}, {'question_id': '201996888', 'answer': 'Dirty', 'gt_answer': 'clean'}, {'question_id': '20740868', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201498430', 'answer': 'Small', 'gt_answer': 'small'}, {'question_id': '20303108', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20303106', 'answer': 'Woman', 'gt_answer': 'man'}, {'question_id': '201399881', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20303102', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201908798', 'answer': 'Metal', 'gt_answer': 'metal'}, {'question_id': '201621359', 'answer': 'Wood', 'gt_answer': 'metal'}, {'question_id': '201621358', 'answer': 'Wood', 'gt_answer': 'metal'}, {'question_id': '202126055', 'answer': 'Man', 'gt_answer': 'player'}, {'question_id': '202126058', 'answer': 'Racket', 'gt_answer': 'racket'}, {'question_id': '20827485', 'answer': 'Couch', 'gt_answer': 'sofa'}, {'question_id': '20827487', 'answer': 'Coffee table', 'gt_answer': 'coffee table'}, {'question_id': '20827480', 'answer': 'Couch', 'gt_answer': 'sofa'}, {'question_id': '201109313', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201061222', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20442052', 'answer': 'Chair', 'gt_answer': 'chair'}, {'question_id': '20442051', 'answer': 'Chair', 'gt_answer': 'chair'}, {'question_id': '201654660', 'answer': 'Horse', 'gt_answer': 'horse'}, {'question_id': '202180433', 'answer': 'Blue', 'gt_answer': 'green'}, {'question_id': '202258186', 'answer': 'Horse', 'gt_answer': 'horse'}, {'question_id': '201322685', 'answer': 'Brick', 'gt_answer': 'concrete'}, {'question_id': '20136666', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '201068571', 'answer': 'Camera', 'gt_answer': 'cell phone'}, {'question_id': '20479852', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201739118', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202219030', 'answer': 'Toaster', 'gt_answer': 'toaster'}, {'question_id': '20679098', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '202004181', 'answer': 'Large', 'gt_answer': 'small'}, {'question_id': '201556790', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20679094', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201682453', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20511713', 'answer': 'Small', 'gt_answer': 'small'}, {'question_id': '20514946', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201983882', 'answer': 'Jacket', 'gt_answer': 'blouse'}, {'question_id': '20472801', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201056085', 'answer': 'Man', 'gt_answer': 'soccer player'}, {'question_id': '201056084', 'answer': 'Boy', 'gt_answer': 'soccer player'}, {'question_id': '20541654', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201056081', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202246765', 'answer': 'Sweater', 'gt_answer': 'shirt'}, {'question_id': '2055733', 'answer': 'Large', 'gt_answer': 'huge'}, {'question_id': '201556919', 'answer': 'Laptop', 'gt_answer': 'laptop'}, {'question_id': '201861325', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201951601', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201322423', 'answer': 'Street', 'gt_answer': 'street'}, {'question_id': '201322422', 'answer': 'Street', 'gt_answer': 'street'}, {'question_id': '201068842', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201951600', 'answer': 'Girl', 'gt_answer': 'girl'}, {'question_id': '202006534', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20818959', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20753459', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201153598', 'answer': 'Tree', 'gt_answer': 'forest'}, {'question_id': '201983888', 'answer': 'Jacket', 'gt_answer': 'blouse'}, {'question_id': '20862787', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201760639', 'answer': 'Grass', 'gt_answer': 'lawn'}, {'question_id': '201480553', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20673069', 'answer': 'Empty', 'gt_answer': 'empty'}, {'question_id': '201056227', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '201735418', 'answer': 'Black', 'gt_answer': 'black'}, {'question_id': '201735419', 'answer': 'Chicken', 'gt_answer': 'chicken'}, {'question_id': '202270987', 'answer': 'Man', 'gt_answer': 'skateboarder'}, {'question_id': '202270989', 'answer': 'Man', 'gt_answer': 'skateboarder'}, {'question_id': '202053394', 'answer': 'Pitcher', 'gt_answer': 'baseball'}, {'question_id': '20865374', 'answer': 'Trees', 'gt_answer': 'trees'}, {'question_id': '20248060', 'answer': 'Brown', 'gt_answer': 'dark brown'}, {'question_id': '20416421', 'answer': 'Bottom', 'gt_answer': 'top'}, {'question_id': '202248975', 'answer': 'Small', 'gt_answer': 'large'}, {'question_id': '20416424', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '202101167', 'answer': 'Boy', 'gt_answer': 'skateboarder'}, {'question_id': '201935073', 'answer': 'Tall', 'gt_answer': 'tall'}, {'question_id': '202101162', 'answer': 'Boy', 'gt_answer': 'skateboarder'}, {'question_id': '202101163', 'answer': 'Hat', 'gt_answer': 'jeans'}, {'question_id': '202101161', 'answer': 'Boy', 'gt_answer': 'skateboarder'}, {'question_id': '20240935', 'answer': 'Table', 'gt_answer': 'table'}, {'question_id': '20862875', 'answer': 'White', 'gt_answer': 'white'}, {'question_id': '202036951', 'answer': 'Sausage', 'gt_answer': 'sausage'}, {'question_id': '20794158', 'answer': 'Porcelain', 'gt_answer': 'porcelain'}, {'question_id': '20862872', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20862870', 'answer': 'Suit', 'gt_answer': 'suit'}, {'question_id': '20647268', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20621960', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20705810', 'answer': 'Desk', 'gt_answer': 'desk'}, {'question_id': '202119882', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20611688', 'answer': 'Fruit', 'gt_answer': 'grapes'}, {'question_id': '201462174', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20381235', 'answer': 'Desk', 'gt_answer': 'desk'}, {'question_id': '202159042', 'answer': 'Bus', 'gt_answer': 'buildings'}, {'question_id': '201407208', 'answer': 'People', 'gt_answer': 'crowd'}, {'question_id': '20381231', 'answer': 'Rectangle', 'gt_answer': 'rectangular'}, {'question_id': '201407207', 'answer': 'People', 'gt_answer': 'crowd'}, {'question_id': '20923063', 'answer': 'Truck', 'gt_answer': 'fire truck'}, {'question_id': '20491961', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20609271', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201979155', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201752607', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201979159', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201983173', 'answer': 'Square', 'gt_answer': 'square'}, {'question_id': '201879609', 'answer': 'Dog', 'gt_answer': 'dog'}, {'question_id': '202285129', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20706378', 'answer': 'Computer', 'gt_answer': 'speaker'}, {'question_id': '201360718', 'answer': 'Girl', 'gt_answer': 'boy'}, {'question_id': '201879600', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '202226085', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20706376', 'answer': 'Computer', 'gt_answer': 'speaker'}, {'question_id': '201535876', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20984506', 'answer': 'Red', 'gt_answer': 'yellow'}, {'question_id': '201336939', 'answer': 'Tree', 'gt_answer': 'bench'}, {'question_id': '201336930', 'answer': 'Color', 'gt_answer': 'material'}, {'question_id': '202162151', 'answer': 'Stained', 'gt_answer': 'clean'}, {'question_id': '201822321', 'answer': 'Porcelain', 'gt_answer': 'porcelain'}, {'question_id': '20756826', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '201882967', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201438689', 'answer': 'Batter', 'gt_answer': 'batter'}, {'question_id': '20248155', 'answer': 'Outfit', 'gt_answer': 'sweater'}, {'question_id': '202228250', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202060198', 'answer': 'Couch', 'gt_answer': 'couch'}, {'question_id': '201429105', 'answer': 'Rectangular', 'gt_answer': 'rectangular'}, {'question_id': '20151524', 'answer': 'Cream', 'gt_answer': 'straw'}, {'question_id': '20151525', 'answer': 'Cup', 'gt_answer': 'coffee'}, {'question_id': '20151526', 'answer': 'Cup', 'gt_answer': 'coffee'}, {'question_id': '201822226', 'answer': 'Shelf', 'gt_answer': 'shelf'}, {'question_id': '20752493', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '201879469', 'answer': 'Truck', 'gt_answer': 'truck'}, {'question_id': '201713271', 'answer': 'Indoors', 'gt_answer': 'indoors'}, {'question_id': '201763624', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201438339', 'answer': 'Glass', 'gt_answer': 'net'}, {'question_id': '20866006', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '20461952', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20541125', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201713275', 'answer': 'Color', 'gt_answer': 'color'}, {'question_id': '201739152', 'answer': 'Red', 'gt_answer': 'red'}, {'question_id': '20786308', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201713640', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '202174460', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201143404', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '20827106', 'answer': 'Gray', 'gt_answer': 'light brown'}, {'question_id': '20441884', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202119721', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201030632', 'answer': 'Boy', 'gt_answer': 'boy'}, {'question_id': '20786209', 'answer': 'Orange', 'gt_answer': 'orange'}, {'question_id': '202060197', 'answer': 'Couch', 'gt_answer': 'couch'}, {'question_id': '201574326', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201972765', 'answer': 'Brown', 'gt_answer': 'green'}, {'question_id': '201972767', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201391839', 'answer': 'Man', 'gt_answer': 'man'}, {'question_id': '20317289', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20929389', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201640321', 'answer': 'Woman', 'gt_answer': 'women'}, {'question_id': '201346477', 'answer': 'Top', 'gt_answer': 'top'}, {'question_id': '201832688', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20896604', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20317285', 'answer': 'Stove', 'gt_answer': 'toaster'}, {'question_id': '201391788', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20317287', 'answer': 'Stove', 'gt_answer': 'toaster'}, {'question_id': '20929384', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201751805', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202258265', 'answer': 'Horse', 'gt_answer': 'horse'}, {'question_id': '202257147', 'answer': 'Dog', 'gt_answer': 'dog'}, {'question_id': '201974551', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201624025', 'answer': 'Woman', 'gt_answer': 'woman'}, {'question_id': '201303359', 'answer': 'Chair', 'gt_answer': 'chair'}, {'question_id': '201713544', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '201758079', 'answer': 'Top', 'gt_answer': 'top'}, {'question_id': '202100630', 'answer': 'Sailboat', 'gt_answer': 'boats'}, {'question_id': '20480553', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201574180', 'answer': 'Left', 'gt_answer': 'right'}, {'question_id': '201302009', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20211095', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201757957', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202244532', 'answer': 'Beans', 'gt_answer': 'beans'}, {'question_id': '202240690', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201030570', 'answer': 'Boy', 'gt_answer': 'boy'}, {'question_id': '202108008', 'answer': 'Blue', 'gt_answer': 'blue'}, {'question_id': '20287719', 'answer': 'Batter', 'gt_answer': 'catcher'}, {'question_id': '202240699', 'answer': 'Shirt', 'gt_answer': 'shirt'}, {'question_id': '20247144', 'answer': 'Pavement', 'gt_answer': 'park'}, {'question_id': '20679244', 'answer': 'Giraffe', 'gt_answer': 'giraffe'}, {'question_id': '201951606', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20679242', 'answer': 'Tree', 'gt_answer': 'branches'}, {'question_id': '20489787', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201153599', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201556540', 'answer': 'Tan', 'gt_answer': 'tan'}, {'question_id': '201153597', 'answer': 'Tree', 'gt_answer': 'forest'}, {'question_id': '20856728', 'answer': 'Bed', 'gt_answer': 'bed'}, {'question_id': '201153595', 'answer': 'Giraffe', 'gt_answer': 'giraffe'}, {'question_id': '20412424', 'answer': 'Light', 'gt_answer': 'light'}, {'question_id': '201109677', 'answer': 'Green', 'gt_answer': 'black'}, {'question_id': '201399940', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201711361', 'answer': 'Thick', 'gt_answer': 'thick'}, {'question_id': '202053257', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '202012478', 'answer': 'Tv', 'gt_answer': 'cabinets'}, {'question_id': '20306470', 'answer': 'Fence', 'gt_answer': 'television'}, {'question_id': '20257312', 'answer': 'Large', 'gt_answer': 'small'}, {'question_id': '2055947', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20783053', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20734254', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20631803', 'answer': 'Long', 'gt_answer': 'short'}, {'question_id': '20911223', 'answer': 'Man', 'gt_answer': 'skater'}, {'question_id': '20911222', 'answer': 'Man', 'gt_answer': 'skater'}, {'question_id': '20911221', 'answer': 'Man', 'gt_answer': 'skater'}, {'question_id': '20715831', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202243313', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201947823', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201737886', 'answer': 'Player', 'gt_answer': 'player'}, {'question_id': '201109414', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20306474', 'answer': 'Camera', 'gt_answer': 'television'}, {'question_id': '201175487', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20412399', 'answer': 'Fireplace', 'gt_answer': 'chair'}, {'question_id': '20306471', 'answer': 'Fence', 'gt_answer': 'television'}, {'question_id': '20306472', 'answer': 'Camera', 'gt_answer': 'television'}, {'question_id': '20442283', 'answer': 'Gray', 'gt_answer': 'beige'}, {'question_id': '201859352', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201109411', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201795424', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201920629', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202081880', 'answer': 'Desk', 'gt_answer': 'mouse pad'}, {'question_id': '20518630', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20119029', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201407198', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '2097565', 'answer': 'Color', 'gt_answer': 'material'}, {'question_id': '201804759', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '201947798', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201822286', 'answer': 'Small', 'gt_answer': 'small'}, {'question_id': '201760705', 'answer': 'Ball', 'gt_answer': 'soccer ball'}, {'question_id': '202012500', 'answer': 'Cabinet', 'gt_answer': 'cabinets'}, {'question_id': '201207178', 'answer': 'Apples', 'gt_answer': 'apples'}, {'question_id': '201207171', 'answer': 'Vegetables', 'gt_answer': 'vegetables'}, {'question_id': '201207175', 'answer': 'Broccoli', 'gt_answer': 'vegetables'}, {'question_id': '201207174', 'answer': 'Broccoli', 'gt_answer': 'vegetables'}, {'question_id': '20887203', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20746593', 'answer': 'Birds', 'gt_answer': 'birds'}, {'question_id': '20746592', 'answer': 'Bird', 'gt_answer': 'birds'}, {'question_id': '201920480', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202156728', 'answer': 'Cloudy', 'gt_answer': 'cloudy'}, {'question_id': '20691468', 'answer': 'Towels', 'gt_answer': 'towels'}, {'question_id': '20746599', 'answer': 'Bridge', 'gt_answer': 'bridge'}, {'question_id': '201654586', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201982497', 'answer': 'Chair', 'gt_answer': 'side table'}, {'question_id': '201228327', 'answer': 'Concrete', 'gt_answer': 'concrete'}, {'question_id': '2075670', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201228320', 'answer': 'Silver', 'gt_answer': 'dark'}, {'question_id': '201570982', 'answer': 'Woman', 'gt_answer': 'woman'}, {'question_id': '201570981', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202081360', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20936256', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '201498752', 'answer': 'Computer mouse', 'gt_answer': 'phone'}, {'question_id': '201770744', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201982230', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '20609579', 'answer': 'Dirty', 'gt_answer': 'clean'}, {'question_id': '20705997', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '20169708', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202133646', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201407332', 'answer': 'Fence', 'gt_answer': 'fence'}, {'question_id': '202244019', 'answer': 'Cookie', 'gt_answer': 'cookies'}, {'question_id': '201920552', 'answer': 'Dirty', 'gt_answer': 'clean'}, {'question_id': '201482235', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '201446920', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201430866', 'answer': 'Counter', 'gt_answer': 'cabinet'}, {'question_id': '20753668', 'answer': 'Skiing', 'gt_answer': 'looking down'}, {'question_id': '20656876', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201207509', 'answer': 'Apple', 'gt_answer': 'apples'}, {'question_id': '201621810', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20752398', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20827295', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201207500', 'answer': 'Apple', 'gt_answer': 'apples'}, {'question_id': '20551568', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202125900', 'answer': 'Chairs', 'gt_answer': 'chairs'}, {'question_id': '201952896', 'answer': 'Train', 'gt_answer': 'car'}, {'question_id': '202059965', 'answer': 'Dog', 'gt_answer': 'dog'}, {'question_id': '201080174', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20381422', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '201687485', 'answer': 'Long sleeved', 'gt_answer': 'long sleeved'}, {'question_id': '201068636', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20381428', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201576772', 'answer': 'Sheep', 'gt_answer': 'sheep'}, {'question_id': '201576805', 'answer': 'Dog', 'gt_answer': 'goat'}, {'question_id': '201576775', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '202100579', 'answer': 'White', 'gt_answer': 'white'}, {'question_id': '201576808', 'answer': 'Dog', 'gt_answer': 'goat'}, {'question_id': '20645543', 'answer': 'Dirty', 'gt_answer': 'clean'}, {'question_id': '20794366', 'answer': 'Jeans', 'gt_answer': 'jeans'}, {'question_id': '20482338', 'answer': 'Hat', 'gt_answer': 'hat'}, {'question_id': '20794363', 'answer': 'Jeans', 'gt_answer': 'jeans'}, {'question_id': '20427536', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20482332', 'answer': 'Shirt', 'gt_answer': 'tank top'}, {'question_id': '20482330', 'answer': 'Shirt', 'gt_answer': 'tank top'}, {'question_id': '202021479', 'answer': 'Fence', 'gt_answer': 'sign post'}, {'question_id': '20482335', 'answer': 'Tank top', 'gt_answer': 'tank top'}, {'question_id': '202119527', 'answer': 'Leafy', 'gt_answer': 'leafy'}, {'question_id': '20644764', 'answer': 'Drinking', 'gt_answer': 'staring'}, {'question_id': '20285029', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201455937', 'answer': 'Hill', 'gt_answer': 'hill'}, {'question_id': '201462547', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '20863646', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '20285023', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20381687', 'answer': 'White', 'gt_answer': 'white'}, {'question_id': '202000940', 'answer': 'House', 'gt_answer': 'fence'}, {'question_id': '202286754', 'answer': 'Nothing', 'gt_answer': 'backpack'}, {'question_id': '20308094', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20177947', 'answer': 'Pan', 'gt_answer': 'cutting board'}, {'question_id': '20929555', 'answer': 'Building', 'gt_answer': 'vest'}, {'question_id': '20929553', 'answer': 'Motorcycle', 'gt_answer': 'wheelchair'}, {'question_id': '201174978', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201795316', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201879811', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201037163', 'answer': 'Woman', 'gt_answer': 'girl'}, {'question_id': '201902277', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20781983', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '202116756', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '2091178', 'answer': 'Car', 'gt_answer': 'car'}, {'question_id': '201392146', 'answer': 'Short sleeved', 'gt_answer': 'short sleeved'}, {'question_id': '202162027', 'answer': 'Bed', 'gt_answer': 'bed'}, {'question_id': '2012725', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201434114', 'answer': 'Cell phone', 'gt_answer': 'phone'}, {'question_id': '202073271', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20631651', 'answer': 'Batter', 'gt_answer': 'batter'}, {'question_id': '20631650', 'answer': 'Batter', 'gt_answer': 'batter'}, {'question_id': '20691493', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201047422', 'answer': 'Suit', 'gt_answer': 'dress shirt'}, {'question_id': '201445021', 'answer': 'Parking lot', 'gt_answer': 'lawn'}, {'question_id': '202218841', 'answer': 'Ceiling', 'gt_answer': 'hook'}, {'question_id': '201873347', 'answer': 'Fire truck', 'gt_answer': 'fire truck'}, {'question_id': '201908992', 'answer': 'Metal', 'gt_answer': 'metal'}, {'question_id': '201765732', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202244365', 'answer': 'Food', 'gt_answer': 'dip'}, {'question_id': '20903167', 'answer': 'Right', 'gt_answer': 'left'}, {'question_id': '20621758', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20226936', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20655060', 'answer': 'Large', 'gt_answer': 'small'}, {'question_id': '20411607', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201997065', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '201479319', 'answer': 'Closed', 'gt_answer': 'closed'}, {'question_id': '20411603', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201920486', 'answer': 'Running', 'gt_answer': 'playing'}, {'question_id': '20330592', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201987886', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20302691', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201859250', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20162233', 'answer': 'Leafy', 'gt_answer': 'leafy'}, {'question_id': '202228629', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201826642', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201640514', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201640512', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201882972', 'answer': 'Desk', 'gt_answer': 'chair'}, {'question_id': '201882626', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201983966', 'answer': '50 lbs', 'gt_answer': 'heavy'}, {'question_id': '201153459', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201153107', 'answer': 'Uncomfortable', 'gt_answer': 'uncomfortable'}, {'question_id': '201988072', 'answer': 'Sandwich', 'gt_answer': 'cookie'}, {'question_id': '201988073', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201185287', 'answer': 'Small', 'gt_answer': 'small'}, {'question_id': '201859509', 'answer': 'Donut', 'gt_answer': 'donut'}, {'question_id': '202225949', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '201438577', 'answer': 'Batter', 'gt_answer': 'catcher'}, {'question_id': '202158928', 'answer': 'Right', 'gt_answer': 'left'}, {'question_id': '201759036', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20226689', 'answer': 'Chair', 'gt_answer': 'chair'}, {'question_id': '201079906', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '20602871', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '201887291', 'answer': 'Basket', 'gt_answer': 'basket'}, {'question_id': '201887299', 'answer': 'Broccoli', 'gt_answer': 'broccoli'}, {'question_id': '201757720', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20596418', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20395054', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201959602', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20953018', 'answer': 'Jumping', 'gt_answer': 'jumping'}, {'question_id': '201758113', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202169108', 'answer': 'Narrow', 'gt_answer': 'wide'}, {'question_id': '20705744', 'answer': 'Computer', 'gt_answer': 'monitor'}, {'question_id': '20204571', 'answer': 'Chair', 'gt_answer': 'chair'}, {'question_id': '202227938', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '2059536', 'answer': 'Young', 'gt_answer': 'young'}, {'question_id': '20177477', 'answer': 'Pan', 'gt_answer': 'cutting board'}, {'question_id': '20177475', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20120216', 'answer': 'Girl', 'gt_answer': 'athlete'}, {'question_id': '20120217', 'answer': 'Girl', 'gt_answer': 'athlete'}, {'question_id': '201207348', 'answer': 'Top', 'gt_answer': 'top'}, {'question_id': '20120212', 'answer': 'Girl', 'gt_answer': 'athlete'}, {'question_id': '201956963', 'answer': 'Books', 'gt_answer': 'dvds'}, {'question_id': '202121601', 'answer': 'Chairs', 'gt_answer': 'chairs'}, {'question_id': '202053007', 'answer': 'Color', 'gt_answer': 'color'}, {'question_id': '201757894', 'answer': 'Orange', 'gt_answer': 'orange'}, {'question_id': '201879927', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201548842', 'answer': 'Short', 'gt_answer': 'tall'}, {'question_id': '20542864', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20258823', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201438326', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20411869', 'answer': 'Pink', 'gt_answer': 'pink'}, {'question_id': '2053901', 'answer': 'White', 'gt_answer': 'blue'}, {'question_id': '20204797', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '20204799', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20411861', 'answer': 'Hot dogs', 'gt_answer': 'carrots'}, {'question_id': '201574048', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '201596119', 'answer': 'Brown', 'gt_answer': 'brown'}, {'question_id': '20508746', 'answer': 'Couch', 'gt_answer': 'table'}, {'question_id': '2046420', 'answer': 'Man', 'gt_answer': 'woman'}, {'question_id': '201574040', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20508743', 'answer': 'Couch', 'gt_answer': 'table'}, {'question_id': '20508741', 'answer': 'Window', 'gt_answer': 'table'}, {'question_id': '20898658', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202240718', 'answer': 'Shirt', 'gt_answer': 'dress shirt'}, {'question_id': '20898653', 'answer': 'Large', 'gt_answer': 'large'}, {'question_id': '20896146', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20157288', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201511057', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20746438', 'answer': 'Blue', 'gt_answer': 'blue'}, {'question_id': '202144511', 'answer': 'Dirty', 'gt_answer': 'dirty'}, {'question_id': '20403450', 'answer': 'Large', 'gt_answer': 'small'}, {'question_id': '20442078', 'answer': 'White', 'gt_answer': 'brown'}, {'question_id': '201079761', 'answer': 'Lamp', 'gt_answer': 'books'}, {'question_id': '201536320', 'answer': 'Player', 'gt_answer': 'batter'}, {'question_id': '201676461', 'answer': 'Couch', 'gt_answer': 'shelf'}, {'question_id': '201536322', 'answer': 'Pitcher', 'gt_answer': 'dugout'}, {'question_id': '20856672', 'answer': 'Left', 'gt_answer': 'right'}, {'question_id': '201987597', 'answer': 'Left', 'gt_answer': 'right'}, {'question_id': '20182962', 'answer': 'Black', 'gt_answer': 'black'}, {'question_id': '20942240', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '201739131', 'answer': 'Black', 'gt_answer': 'red'}, {'question_id': '201322430', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20452292', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20709953', 'answer': 'Nothing', 'gt_answer': 'umbrella'}, {'question_id': '20287502', 'answer': 'Black', 'gt_answer': 'black'}, {'question_id': '201637318', 'answer': 'Stove', 'gt_answer': 'stove'}, {'question_id': '20479838', 'answer': 'Material', 'gt_answer': 'material'}, {'question_id': '201637316', 'answer': 'Stove', 'gt_answer': 'stove'}, {'question_id': '201663602', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20479830', 'answer': 'Living room', 'gt_answer': 'office'}, {'question_id': '20672797', 'answer': 'Rectangular', 'gt_answer': 'rectangular'}, {'question_id': '201548710', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20511731', 'answer': 'Clouds', 'gt_answer': 'sky'}, {'question_id': '20468644', 'answer': 'White', 'gt_answer': 'white'}, {'question_id': '20511732', 'answer': 'Clouds', 'gt_answer': 'sky'}, {'question_id': '20468648', 'answer': 'Man', 'gt_answer': 'man'}, {'question_id': '201549008', 'answer': 'Green', 'gt_answer': 'green'}, {'question_id': '201412297', 'answer': 'Brown', 'gt_answer': 'white'}, {'question_id': '20836869', 'answer': 'Sign', 'gt_answer': 'flag'}, {'question_id': '20416592', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201682285', 'answer': 'Spectators', 'gt_answer': 'people'}, {'question_id': '201682286', 'answer': 'Woman', 'gt_answer': 'people'}, {'question_id': '201067727', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201412299', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201067722', 'answer': 'Napkin', 'gt_answer': 'napkin'}, {'question_id': '20284979', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20753471', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '201439451', 'answer': 'Left', 'gt_answer': 'right'}, {'question_id': '201794965', 'answer': 'Closed', 'gt_answer': 'open'}, {'question_id': '202226209', 'answer': 'Dirty', 'gt_answer': 'clean'}, {'question_id': '20541561', 'answer': 'Books', 'gt_answer': 'television'}, {'question_id': '201207230', 'answer': 'Sandwich', 'gt_answer': 'sandwich'}, {'question_id': '20536048', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201030459', 'answer': 'Couch', 'gt_answer': 'couch'}, {'question_id': '201760610', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20412186', 'answer': 'Pink', 'gt_answer': 'pink'}, {'question_id': '20244744', 'answer': 'Driver', 'gt_answer': 'bus driver'}, {'question_id': '201504930', 'answer': 'Surfboard', 'gt_answer': 'surfboard'}, {'question_id': '201504933', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201504935', 'answer': 'Girl', 'gt_answer': 'woman'}, {'question_id': '201504934', 'answer': 'Girl', 'gt_answer': 'woman'}, {'question_id': '2091340', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20878992', 'answer': 'Man', 'gt_answer': 'skater'}, {'question_id': '202116920', 'answer': 'Concrete', 'gt_answer': 'concrete'}, {'question_id': '20878990', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20878997', 'answer': 'Man', 'gt_answer': 'skater'}, {'question_id': '20550377', 'answer': 'Brown', 'gt_answer': 'light brown'}, {'question_id': '202100817', 'answer': 'Stove', 'gt_answer': 'stove'}, {'question_id': '202100819', 'answer': 'Stove', 'gt_answer': 'stove'}, {'question_id': '20316966', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202000759', 'answer': 'Very', 'gt_answer': 'hard'}, {'question_id': '202120380', 'answer': 'Dirty', 'gt_answer': 'clean'}, {'question_id': '20316962', 'answer': 'Color', 'gt_answer': 'material'}, {'question_id': '202107840', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20248000', 'answer': 'Bench', 'gt_answer': 'bench'}, {'question_id': '201687545', 'answer': 'Shorts', 'gt_answer': 'shorts'}, {'question_id': '201735478', 'answer': 'Chicken', 'gt_answer': 'keyboard'}, {'question_id': '201065125', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20248008', 'answer': 'Woman', 'gt_answer': 'woman'}, {'question_id': '201439531', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201979212', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201207239', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '201979214', 'answer': 'Boy', 'gt_answer': 'skateboarder'}, {'question_id': '202241051', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20857105', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '20862856', 'answer': 'Dress', 'gt_answer': 'dress'}, {'question_id': '202101149', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202036976', 'answer': 'Box', 'gt_answer': 'pizza box'}, {'question_id': '20699197', 'answer': 'Man', 'gt_answer': 'man'}, {'question_id': '20117888', 'answer': 'Brown', 'gt_answer': 'brown'}, {'question_id': '201185738', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20412523', 'answer': 'Top', 'gt_answer': 'top'}, {'question_id': '201873202', 'answer': 'Man', 'gt_answer': 'pedestrian'}, {'question_id': '20381216', 'answer': 'Desk', 'gt_answer': 'desk'}, {'question_id': '202262345', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20691792', 'answer': 'Light', 'gt_answer': 'ceiling'}, {'question_id': '20691793', 'answer': 'Shower', 'gt_answer': 'floor'}, {'question_id': '20491941', 'answer': 'Bird', 'gt_answer': 'sky'}, {'question_id': '201407226', 'answer': 'Net', 'gt_answer': 'net'}, {'question_id': '201407225', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201407228', 'answer': 'Net', 'gt_answer': 'net'}, {'question_id': '202257975', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201393567', 'answer': 'Man', 'gt_answer': 'boy'}, {'question_id': '20171164', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '2065938', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '201756711', 'answer': 'Banana', 'gt_answer': 'banana'}, {'question_id': '20204893', 'answer': 'Table', 'gt_answer': 'chair'}, {'question_id': '202161904', 'answer': 'Chair', 'gt_answer': 'bookcase'}, {'question_id': '202174167', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '201756719', 'answer': 'Cat', 'gt_answer': 'kitten'}, {'question_id': '20673040', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '201879665', 'answer': 'Dog', 'gt_answer': 'dog'}, {'question_id': '20673046', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202004087', 'answer': 'Classroom', 'gt_answer': 'office'}, {'question_id': '201879661', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202284890', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201235609', 'answer': 'Banana', 'gt_answer': 'bananas'}, {'question_id': '201735292', 'answer': 'Shelf', 'gt_answer': 'shelves'}, {'question_id': '20541326', 'answer': 'Brown', 'gt_answer': 'brown'}, {'question_id': '2012820', 'answer': 'Large', 'gt_answer': 'small'}, {'question_id': '201393605', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20756843', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20954237', 'answer': 'Large', 'gt_answer': 'large'}, {'question_id': '202121385', 'answer': 'Cabinet', 'gt_answer': 'cupboard'}, {'question_id': '202121387', 'answer': 'Dishes', 'gt_answer': 'cups'}, {'question_id': '202121382', 'answer': 'Cabinet', 'gt_answer': 'cupboard'}, {'question_id': '20295655', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20878988', 'answer': 'Man', 'gt_answer': 'skater'}, {'question_id': '201037129', 'answer': 'Red', 'gt_answer': 'red'}, {'question_id': '2058479', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201879487', 'answer': 'Large', 'gt_answer': 'small'}, {'question_id': '201447068', 'answer': 'Large', 'gt_answer': 'large'}, {'question_id': '20667753', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201887290', 'answer': 'Basket', 'gt_answer': 'basket'}, {'question_id': '201669553', 'answer': 'Cupcake', 'gt_answer': 'cookies'}, {'question_id': '201510935', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20541497', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201510397', 'answer': 'Apple', 'gt_answer': 'pear'}, {'question_id': '201510398', 'answer': 'Apple', 'gt_answer': 'pear'}, {'question_id': '202082150', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '201624222', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201623905', 'answer': 'Dishwasher', 'gt_answer': 'stove'}, {'question_id': '20744273', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201455876', 'answer': 'Small', 'gt_answer': 'large'}, {'question_id': '201972749', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201640306', 'answer': 'Restaurant', 'gt_answer': 'restaurant'}, {'question_id': '202133780', 'answer': 'Open', 'gt_answer': 'open'}, {'question_id': '201573883', 'answer': 'Bottom', 'gt_answer': 'bottom'}, {'question_id': '202258249', 'answer': 'Standing', 'gt_answer': 'looking down'}, {'question_id': '202287013', 'answer': 'Orange', 'gt_answer': 'bananas'}, {'question_id': '201479210', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20306957', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201663386', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201404284', 'answer': 'Cow', 'gt_answer': 'mother'}, {'question_id': '201751826', 'answer': 'Nike', 'gt_answer': 'adidas'}, {'question_id': '201498491', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201951597', 'answer': 'Girl', 'gt_answer': 'girl'}, {'question_id': '201873285', 'answer': 'Wide', 'gt_answer': 'wide'}, {'question_id': '202240287', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201951593', 'answer': 'Girl', 'gt_answer': 'girl'}, {'question_id': '201556429', 'answer': 'Color', 'gt_answer': 'color'}, {'question_id': '201055838', 'answer': 'Large', 'gt_answer': 'small'}, {'question_id': '201303373', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '201481815', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202037009', 'answer': 'Pepperoni', 'gt_answer': 'spinach'}, {'question_id': '201055785', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201077076', 'answer': 'Urinal', 'gt_answer': 'urinal'}, {'question_id': '20922948', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202244555', 'answer': 'Beans', 'gt_answer': 'beans'}, {'question_id': '201757971', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '20837124', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20692551', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201896109', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '201663162', 'answer': 'Cabinet', 'gt_answer': 'cabinet'}, {'question_id': '201951621', 'answer': 'Van', 'gt_answer': 'van'}, {'question_id': '201861303', 'answer': 'Red', 'gt_answer': 'silver'}, {'question_id': '20679220', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201873133', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20412405', 'answer': 'Chair', 'gt_answer': 'chair'}, {'question_id': '20710216', 'answer': 'Child', 'gt_answer': 'parent'}, {'question_id': '20226749', 'answer': 'Long', 'gt_answer': 'long'}, {'question_id': '20412400', 'answer': 'Chair', 'gt_answer': 'chair'}, {'question_id': '202262430', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20710210', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201175714', 'answer': 'Cat', 'gt_answer': 'cat'}, {'question_id': '201185677', 'answer': 'Boy', 'gt_answer': 'boy'}, {'question_id': '201979208', 'answer': 'Boy', 'gt_answer': 'skateboarder'}, {'question_id': '201175710', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201175713', 'answer': 'Cat', 'gt_answer': 'cat'}, {'question_id': '20622098', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201109652', 'answer': 'Green', 'gt_answer': 'green'}, {'question_id': '20655292', 'answer': 'Man', 'gt_answer': 'man'}, {'question_id': '201497647', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201509796', 'answer': 'Motorcycle', 'gt_answer': 'motorcycle'}, {'question_id': '20655296', 'answer': 'Man', 'gt_answer': 'man'}, {'question_id': '201492454', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201509795', 'answer': 'Motorcycle', 'gt_answer': 'motorcycle'}, {'question_id': '201399926', 'answer': 'Brown', 'gt_answer': 'white'}, {'question_id': '20631828', 'answer': 'Leather', 'gt_answer': 'rubber'}, {'question_id': '201399921', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202244369', 'answer': 'Cookie', 'gt_answer': 'dip'}, {'question_id': '20717086', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201428945', 'answer': 'Refrigerator', 'gt_answer': 'refrigerator'}, {'question_id': '20734236', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20715810', 'answer': 'Rocks', 'gt_answer': 'sand'}, {'question_id': '20715811', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20715816', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20783031', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20978210', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20797842', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201401714', 'answer': 'Cloudy', 'gt_answer': 'cloudy'}, {'question_id': '20797845', 'answer': 'Cat', 'gt_answer': 'cat'}, {'question_id': '20797844', 'answer': 'Cat', 'gt_answer': 'cat'}, {'question_id': '202286636', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20647789', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20306456', 'answer': 'Snowboarding', 'gt_answer': 'sitting'}, {'question_id': '202223389', 'answer': 'Woman', 'gt_answer': 'people'}, {'question_id': '20456727', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '202223387', 'answer': 'Woman', 'gt_answer': 'people'}, {'question_id': '202081718', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201713523', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202156830', 'answer': 'Elephants', 'gt_answer': 'elephants'}, {'question_id': '202227898', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '20518349', 'answer': 'Heater', 'gt_answer': 'radiator'}, {'question_id': '20518619', 'answer': 'Can', 'gt_answer': 'tissue box'}, {'question_id': '20891759', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201641475', 'answer': 'Red', 'gt_answer': 'black'}, {'question_id': '20119041', 'answer': 'People', 'gt_answer': 'woman'}, {'question_id': '202174381', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20515932', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20515931', 'answer': 'Train', 'gt_answer': 'train'}, {'question_id': '20226355', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201079730', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202180376', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20226352', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202081050', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20941902', 'answer': 'Child', 'gt_answer': 'child'}, {'question_id': '20941900', 'answer': 'Girl', 'gt_answer': 'girl'}, {'question_id': '20480688', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20757140', 'answer': 'Wall', 'gt_answer': 'plates'}, {'question_id': '20757141', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20157544', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20287780', 'answer': 'Dirt', 'gt_answer': 'sand'}, {'question_id': '202006667', 'answer': 'Chair', 'gt_answer': 'table'}, {'question_id': '202006666', 'answer': 'Chair', 'gt_answer': 'table'}, {'question_id': '201228341', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201528095', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20753391', 'answer': 'Bed', 'gt_answer': 'bed'}, {'question_id': '201832305', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201528099', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201641219', 'answer': 'Man', 'gt_answer': 'man'}, {'question_id': '201770721', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201756948', 'answer': 'Counter', 'gt_answer': 'countertop'}, {'question_id': '20247294', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20936230', 'answer': 'Elephant', 'gt_answer': 'elephant'}, {'question_id': '20210881', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201980737', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201498001', 'answer': 'Desk', 'gt_answer': 'desk'}, {'question_id': '202101048', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202119151', 'answer': 'Man', 'gt_answer': 'man'}, {'question_id': '201080033', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201068435', 'answer': 'Girl', 'gt_answer': 'girl'}, {'question_id': '20492084', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20492087', 'answer': 'Gray', 'gt_answer': 'brown'}, {'question_id': '20307329', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '201804770', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201068382', 'answer': 'Asian', 'gt_answer': 'asian'}, {'question_id': '20518557', 'answer': 'Small', 'gt_answer': 'large'}, {'question_id': '201430804', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20381201', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20518226', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '202040090', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20902761', 'answer': 'Small', 'gt_answer': 'small'}, {'question_id': '201247103', 'answer': 'Cat', 'gt_answer': 'cat'}, {'question_id': '20244550', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202040099', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '202219022', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20295404', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202219027', 'answer': 'Square', 'gt_answer': 'rectangular'}, {'question_id': '20295400', 'answer': 'Purple', 'gt_answer': 'purple'}, {'question_id': '201175619', 'answer': 'Hair dryer', 'gt_answer': 'cords'}, {'question_id': '20349836', 'answer': 'Bottom', 'gt_answer': 'bottom'}, {'question_id': '201064981', 'answer': 'Chair', 'gt_answer': 'sofa'}, {'question_id': '201064980', 'answer': 'Chair', 'gt_answer': 'sofa'}, {'question_id': '20667504', 'answer': 'Couch', 'gt_answer': 'coffee table'}, {'question_id': '202100738', 'answer': 'Pot', 'gt_answer': 'utensil holder'}, {'question_id': '20667500', 'answer': 'Couch', 'gt_answer': 'couch'}, {'question_id': '202100737', 'answer': 'Pot', 'gt_answer': 'utensil holder'}, {'question_id': '2076639', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '20667508', 'answer': 'Table', 'gt_answer': 'couch'}, {'question_id': '202024802', 'answer': 'Playing', 'gt_answer': 'playing'}, {'question_id': '201987180', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '2076343', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '202081924', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '2076617', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202073201', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201983618', 'answer': 'Woman', 'gt_answer': 'woman'}, {'question_id': '20794360', 'answer': 'Jeans', 'gt_answer': 'jeans'}, {'question_id': '202021457', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202073207', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20741142', 'answer': 'Wood', 'gt_answer': 'wood'}, {'question_id': '20427555', 'answer': 'Cloth', 'gt_answer': 'cloth'}, {'question_id': '20427552', 'answer': 'Jacket', 'gt_answer': 'pants'}, {'question_id': '201763981', 'answer': 'Bed', 'gt_answer': 'beds'}, {'question_id': '201030469', 'answer': 'Couch', 'gt_answer': 'couch'}, {'question_id': '201739241', 'answer': 'Red', 'gt_answer': 'white'}, {'question_id': '201739240', 'answer': 'Cloth', 'gt_answer': 'cloth'}, {'question_id': '20942768', 'answer': 'Field', 'gt_answer': 'field'}, {'question_id': '202162226', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202270820', 'answer': 'Road', 'gt_answer': 'street'}, {'question_id': '202162223', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202270824', 'answer': 'Outdoors', 'gt_answer': 'outdoors'}, {'question_id': '201735595', 'answer': 'Wall', 'gt_answer': 'desk'}, {'question_id': '201391840', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201735597', 'answer': 'Shelf', 'gt_answer': 'desk'}, {'question_id': '202218489', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201735592', 'answer': 'Laptop', 'gt_answer': 'tray'}, {'question_id': '201936012', 'answer': 'Book', 'gt_answer': 'picture frame'}, {'question_id': '201065581', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201616191', 'answer': 'Pipe', 'gt_answer': 'pipes'}, {'question_id': '202218487', 'answer': 'Kettle', 'gt_answer': 'cutting board'}, {'question_id': '201616193', 'answer': 'Toilet', 'gt_answer': 'walls'}, {'question_id': '202218485', 'answer': 'Color', 'gt_answer': 'material'}, {'question_id': '201957088', 'answer': 'Wii controller', 'gt_answer': 'wii controller'}, {'question_id': '201640385', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201957081', 'answer': 'Wii controller', 'gt_answer': 'wii controller'}, {'question_id': '201795845', 'answer': 'People', 'gt_answer': 'man'}, {'question_id': '201957086', 'answer': 'Controller', 'gt_answer': 'wii controller'}, {'question_id': '20247586', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201976585', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201976584', 'answer': 'Nowhere', 'gt_answer': 'street'}, {'question_id': '20781968', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20894225', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202116770', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201411199', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201593880', 'answer': 'Woman', 'gt_answer': 'girl'}, {'question_id': '201411190', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201982245', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20655106', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201576758', 'answer': 'Dog', 'gt_answer': 'sheep'}, {'question_id': '202218731', 'answer': 'Rectangle', 'gt_answer': 'square'}, {'question_id': '201444936', 'answer': 'Leaves', 'gt_answer': 'sky'}, {'question_id': '202218735', 'answer': 'Black', 'gt_answer': 'black'}, {'question_id': '202243497', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201909041', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20169924', 'answer': 'Black', 'gt_answer': 'black'}, {'question_id': '201983037', 'answer': 'Remote control', 'gt_answer': 'phone'}, {'question_id': '202125893', 'answer': 'People', 'gt_answer': 'audience'}, {'question_id': '20710046', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20247720', 'answer': 'Caucasian', 'gt_answer': 'caucasian'}, {'question_id': '20699217', 'answer': 'Man', 'gt_answer': 'man'}, {'question_id': '20903147', 'answer': 'Van', 'gt_answer': 'van'}, {'question_id': '20984264', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '201535933', 'answer': 'Gray', 'gt_answer': 'white'}, {'question_id': '201737990', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201737996', 'answer': 'Rectangular', 'gt_answer': 'square'}, {'question_id': '201997004', 'answer': 'Chair', 'gt_answer': 'chair'}, {'question_id': '201997006', 'answer': 'Chair', 'gt_answer': 'chair'}, {'question_id': '201997001', 'answer': 'Chair', 'gt_answer': 'chair'}, {'question_id': '201997000', 'answer': 'Boy', 'gt_answer': 'pilot'}, {'question_id': '20655046', 'answer': 'Yellow', 'gt_answer': 'yellow'}, {'question_id': '202169211', 'answer': 'Right', 'gt_answer': 'left'}, {'question_id': '20655042', 'answer': 'Large', 'gt_answer': 'huge'}, {'question_id': '2098333', 'answer': 'Monitor', 'gt_answer': 'monitor'}, {'question_id': '2098330', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201887105', 'answer': 'Broccoli', 'gt_answer': 'cabbage'}, {'question_id': '20330578', 'answer': 'White', 'gt_answer': 'white'}, {'question_id': '201887109', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202228357', 'answer': 'Speaker', 'gt_answer': 'speaker'}, {'question_id': '201640162', 'answer': 'Short sleeved', 'gt_answer': 'short sleeved'}, {'question_id': '20385581', 'answer': 'Laptop', 'gt_answer': 'remote control'}, {'question_id': '20385585', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20797611', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201976851', 'answer': 'Blue', 'gt_answer': 'orange'}, {'question_id': '201988099', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20177768', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20797618', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201079969', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20567545', 'answer': 'Shorts', 'gt_answer': 'trunks'}, {'question_id': '201360707', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20968348', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '2012678', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201804627', 'answer': 'Black', 'gt_answer': 'black'}, {'question_id': '201947739', 'answer': 'Wall', 'gt_answer': 'countertop'}, {'question_id': '202121590', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '202121592', 'answer': 'Curtain', 'gt_answer': 'curtains'}, {'question_id': '20929280', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20609754', 'answer': 'Cake', 'gt_answer': 'cake'}, {'question_id': '20692309', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202081461', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201997560', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20692305', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201959629', 'answer': 'Large', 'gt_answer': 'large'}, {'question_id': '201207368', 'answer': 'Plate', 'gt_answer': 'fork'}, {'question_id': '201110579', 'answer': 'Small', 'gt_answer': 'small'}, {'question_id': '201758224', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20204557', 'answer': 'Chair', 'gt_answer': 'chair'}, {'question_id': '201429141', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201207362', 'answer': 'Table', 'gt_answer': 'plate'}, {'question_id': '20573836', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20541680', 'answer': 'Woman', 'gt_answer': 'woman'}, {'question_id': '20541681', 'answer': 'Woman', 'gt_answer': 'woman'}, {'question_id': '20883089', 'answer': 'Skate park', 'gt_answer': 'skate park'}, {'question_id': '20866210', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '20541689', 'answer': 'Remote', 'gt_answer': 'handbag'}, {'question_id': '202227952', 'answer': 'Shelf', 'gt_answer': 'entertainment center'}, {'question_id': '202227950', 'answer': 'Shelf', 'gt_answer': 'entertainment center'}, {'question_id': '202245898', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '202053020', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202174211', 'answer': 'Coffee maker', 'gt_answer': 'stove'}, {'question_id': '202053024', 'answer': 'Green', 'gt_answer': 'brown'}, {'question_id': '202121626', 'answer': 'Metal', 'gt_answer': 'metal'}, {'question_id': '201757566', 'answer': 'Very', 'gt_answer': 'hard'}, {'question_id': '20783627', 'answer': 'Beige', 'gt_answer': 'beige'}, {'question_id': '201974983', 'answer': 'Cap', 'gt_answer': 'hat'}, {'question_id': '201156145', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20317129', 'answer': 'Microwave', 'gt_answer': 'microwave'}, {'question_id': '201996714', 'answer': 'Helicopter', 'gt_answer': 'helicopter'}, {'question_id': '201175526', 'answer': 'Woman', 'gt_answer': 'woman'}, {'question_id': '201951735', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201735282', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '20411884', 'answer': 'Noodles', 'gt_answer': 'hot dogs'}, {'question_id': '201175520', 'answer': 'Herself', 'gt_answer': 'mirror'}, {'question_id': '201110750', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20462156', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201175529', 'answer': 'Camera', 'gt_answer': 'camera'}, {'question_id': '201571011', 'answer': 'Small', 'gt_answer': 'small'}, {'question_id': '20508721', 'answer': 'Wii controller', 'gt_answer': 'wii controller'}, {'question_id': '201571014', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20508723', 'answer': 'Wii controller', 'gt_answer': 'wii controller'}, {'question_id': '202249044', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20508728', 'answer': 'Wii controller', 'gt_answer': 'wii controller'}, {'question_id': '20508729', 'answer': 'Wii controller', 'gt_answer': 'wii controller'}, {'question_id': '201951730', 'answer': 'Van', 'gt_answer': 'van'}, {'question_id': '20211114', 'answer': 'Square', 'gt_answer': 'rectangular'}, {'question_id': '20211115', 'answer': 'Money', 'gt_answer': 'packet'}, {'question_id': '20896160', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20211117', 'answer': 'Money', 'gt_answer': 'packet'}, {'question_id': '201188326', 'answer': 'Man', 'gt_answer': 'man'}, {'question_id': '202126015', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201663597', 'answer': 'Oven', 'gt_answer': 'dishwasher'}, {'question_id': '201621315', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201621310', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20853887', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202169120', 'answer': 'Gray', 'gt_answer': 'beige'}, {'question_id': '202240190', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20435292', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20442091', 'answer': 'Door', 'gt_answer': 'door'}, {'question_id': '20442090', 'answer': 'Door', 'gt_answer': 'door'}, {'question_id': '20442097', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20518405', 'answer': 'Long', 'gt_answer': 'short'}, {'question_id': '202246540', 'answer': 'Shelf', 'gt_answer': 'computer desk'}, {'question_id': '202246541', 'answer': 'Shelf', 'gt_answer': 'computer desk'}, {'question_id': '202246542', 'answer': 'Desk', 'gt_answer': 'computer desk'}, {'question_id': '201759175', 'answer': 'Chair', 'gt_answer': 'table'}, {'question_id': '20746413', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201322641', 'answer': 'Red', 'gt_answer': 'black'}, {'question_id': '201654627', 'answer': 'Wood', 'gt_answer': 'metal'}, {'question_id': '20480210', 'answer': 'Bookshelf', 'gt_answer': 'desk'}, {'question_id': '20492167', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20480215', 'answer': 'Bookcase', 'gt_answer': 'bookcase'}, {'question_id': '201428953', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20480218', 'answer': 'Shelf', 'gt_answer': 'desk'}, {'question_id': '20942223', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201481874', 'answer': 'Woman', 'gt_answer': 'woman'}, {'question_id': '201637331', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '201752754', 'answer': 'Bike', 'gt_answer': 'bike'}, {'question_id': '20922779', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20922778', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20489593', 'answer': 'Bed', 'gt_answer': 'bed'}, {'question_id': '202246180', 'answer': 'Desk', 'gt_answer': 'computer desk'}, {'question_id': '20412213', 'answer': 'White', 'gt_answer': 'pink'}, {'question_id': '20489597', 'answer': 'Bed', 'gt_answer': 'bed'}, {'question_id': '201947474', 'answer': 'Mirror', 'gt_answer': 'mirror'}, {'question_id': '20515053', 'answer': 'Zebra', 'gt_answer': 'zebras'}, {'question_id': '20258810', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '201638808', 'answer': 'Car', 'gt_answer': 'car'}, {'question_id': '20657195', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202246183', 'answer': 'Desk', 'gt_answer': 'computer desk'}, {'question_id': '201056049', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '2017205', 'answer': 'Thick', 'gt_answer': 'thick'}, {'question_id': '2017200', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20182765', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201322469', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201713405', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201794943', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20317080', 'answer': 'Plastic', 'gt_answer': 'plastic'}, {'question_id': '20300419', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '201227990', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201227992', 'answer': 'Front', 'gt_answer': 'behind'}, {'question_id': '201030301', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201947617', 'answer': 'Sink', 'gt_answer': 'countertop'}, {'question_id': '202246675', 'answer': 'Desk', 'gt_answer': 'computer desk'}, {'question_id': '202265537', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201947612', 'answer': 'Silver', 'gt_answer': 'brown'}, {'question_id': '20456553', 'answer': 'Gray', 'gt_answer': 'gray'}, {'question_id': '20306152', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '202049336', 'answer': 'Black', 'gt_answer': 'black'}, {'question_id': '201504914', 'answer': 'Woman', 'gt_answer': 'woman'}, {'question_id': '201902991', 'answer': 'Computer', 'gt_answer': 'keyboard'}, {'question_id': '20316945', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201056264', 'answer': 'Player', 'gt_answer': 'soccer player'}, {'question_id': '202107827', 'answer': 'Boy', 'gt_answer': 'boy'}, {'question_id': '20316940', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20316943', 'answer': 'Cutting board', 'gt_answer': 'coffee pot'}, {'question_id': '202100830', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202180230', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20241047', 'answer': 'Man', 'gt_answer': 'man'}, {'question_id': '202180234', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20381583', 'answer': 'Computer mouse', 'gt_answer': 'computer monitor'}, {'question_id': '20381582', 'answer': 'Computer', 'gt_answer': 'computer monitor'}, {'question_id': '201439514', 'answer': 'Man', 'gt_answer': 'woman'}, {'question_id': '201982031', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201735450', 'answer': 'Bottom', 'gt_answer': 'bottom'}, {'question_id': '201822211', 'answer': 'Figurine', 'gt_answer': 'decoration'}, {'question_id': '201429138', 'answer': 'Ceiling', 'gt_answer': 'window'}, {'question_id': '201935036', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201972915', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201983204', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20258760', 'answer': 'Boy', 'gt_answer': 'child'}, {'question_id': '20258761', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201175136', 'answer': 'Closed', 'gt_answer': 'open'}, {'question_id': '202218718', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20258766', 'answer': 'Boy', 'gt_answer': 'child'}, {'question_id': '20427782', 'answer': 'Man', 'gt_answer': 'man'}, {'question_id': '201756553', 'answer': 'Cat', 'gt_answer': 'kitten'}, {'question_id': '2076429', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201639040', 'answer': 'Concrete', 'gt_answer': 'concrete'}, {'question_id': '20427784', 'answer': 'Man', 'gt_answer': 'gentleman'}, {'question_id': '20427785', 'answer': 'Man', 'gt_answer': 'gentleman'}, {'question_id': '20514980', 'answer': 'High', 'gt_answer': 'low'}, {'question_id': '201756558', 'answer': 'Cat', 'gt_answer': 'kitten'}, {'question_id': '202100895', 'answer': 'Window', 'gt_answer': 'counter'}, {'question_id': '20887430', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20647224', 'answer': 'Socks', 'gt_answer': 'socks'}, {'question_id': '20178039', 'answer': 'Bun', 'gt_answer': 'fries'}, {'question_id': '20647226', 'answer': 'Glove', 'gt_answer': 'socks'}, {'question_id': '20647221', 'answer': 'Pants', 'gt_answer': 'pants'}, {'question_id': '20647222', 'answer': 'Pants', 'gt_answer': 'pants'}, {'question_id': '20178030', 'answer': 'Fries', 'gt_answer': 'fries'}, {'question_id': '20647228', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20178035', 'answer': 'Onion', 'gt_answer': 'pickles'}, {'question_id': '201047484', 'answer': 'Suit', 'gt_answer': 'suit'}, {'question_id': '201527700', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '20672918', 'answer': 'Chair', 'gt_answer': 'ottoman'}, {'question_id': '201527706', 'answer': 'Cake', 'gt_answer': 'cake'}, {'question_id': '201527708', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201935292', 'answer': 'Bag', 'gt_answer': 'camera'}, {'question_id': '2075557', 'answer': 'Houses', 'gt_answer': 'lake'}, {'question_id': '201935295', 'answer': 'Camera', 'gt_answer': 'camera'}, {'question_id': '20861165', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201393504', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20171183', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201393500', 'answer': 'Sparse', 'gt_answer': 'dense'}, {'question_id': '201752641', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20827553', 'answer': 'Stool', 'gt_answer': 'chairs'}, {'question_id': '20827552', 'answer': 'Stools', 'gt_answer': 'chairs'}, {'question_id': '20827551', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201879023', 'answer': 'Chairs', 'gt_answer': 'chairs'}, {'question_id': '201438706', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20978611', 'answer': 'Wood', 'gt_answer': 'wood'}, {'question_id': '20827555', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202161927', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20706336', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20361210', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201957277', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201756771', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201756779', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20756863', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202248865', 'answer': 'City', 'gt_answer': 'street'}, {'question_id': '202248864', 'answer': 'Street', 'gt_answer': 'street'}, {'question_id': '20705671', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201445020', 'answer': 'Parking', 'gt_answer': 'street sign'}, {'question_id': '20954213', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20954212', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20299743', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202218847', 'answer': 'Kettle', 'gt_answer': 'pan'}, {'question_id': '201947589', 'answer': 'Silver', 'gt_answer': 'gray'}, {'question_id': '201319581', 'answer': 'Pizza', 'gt_answer': 'soup'}, {'question_id': '20295672', 'answer': 'Bed', 'gt_answer': 'bed'}, {'question_id': '201319585', 'answer': 'Table', 'gt_answer': 'table'}, {'question_id': '20295677', 'answer': 'Bed', 'gt_answer': 'bed'}, {'question_id': '20295679', 'answer': 'Bed', 'gt_answer': 'bed'}, {'question_id': '2076284', 'answer': 'Bus', 'gt_answer': 'bus'}, {'question_id': '201895947', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20611643', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20611645', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20340746', 'answer': 'Ground', 'gt_answer': 'patio'}, {'question_id': '201669538', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202240961', 'answer': 'Top', 'gt_answer': 'top'}, {'question_id': '2098293', 'answer': 'Desk', 'gt_answer': 'desk'}, {'question_id': '20786020', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201826565', 'answer': 'Shirt', 'gt_answer': 'polo shirt'}, {'question_id': '202107931', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201947708', 'answer': 'Toothbrush', 'gt_answer': 'toothbrush'}, {'question_id': '201882834', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201832537', 'answer': 'Drawer', 'gt_answer': 'nightstand'}, {'question_id': '201590195', 'answer': 'Man', 'gt_answer': 'man'}, {'question_id': '20827148', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201832532', 'answer': 'Bed', 'gt_answer': 'bed'}, {'question_id': '201832530', 'answer': 'Bed', 'gt_answer': 'bed'}, {'question_id': '201947705', 'answer': 'Woman', 'gt_answer': 'girl'}, {'question_id': '201590198', 'answer': 'Player', 'gt_answer': 'player'}, {'question_id': '201883124', 'answer': 'White', 'gt_answer': 'white'}, {'question_id': '201896554', 'answer': 'Woman', 'gt_answer': 'lady'}, {'question_id': '201896553', 'answer': 'Woman', 'gt_answer': 'lady'}, {'question_id': '201976440', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '202080991', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20984544', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20600162', 'answer': 'Zebras', 'gt_answer': 'zebras'}, {'question_id': '20744254', 'answer': 'Field', 'gt_answer': 'stadium'}, {'question_id': '202107938', 'answer': 'Right', 'gt_answer': 'left'}, {'question_id': '20600166', 'answer': 'Zebras', 'gt_answer': 'zebras'}, {'question_id': '20600165', 'answer': 'Zebra', 'gt_answer': 'zebras'}, {'question_id': '201902318', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201885188', 'answer': 'Swimming pool', 'gt_answer': 'swimming pool'}, {'question_id': '201983608', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201765677', 'answer': 'Sleeveless', 'gt_answer': 'short sleeved'}, {'question_id': '20205093', 'answer': 'Backpack', 'gt_answer': 'backpack'}, {'question_id': '202258518', 'answer': 'Blue', 'gt_answer': 'blue'}, {'question_id': '20205098', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20340565', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201758032', 'answer': 'Coffee cup', 'gt_answer': 'table'}, {'question_id': '201556444', 'answer': 'Material', 'gt_answer': 'material'}, {'question_id': '20679344', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20679343', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '20385467', 'answer': 'Thick', 'gt_answer': 'thick'}, {'question_id': '201671666', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202060018', 'answer': 'Hat', 'gt_answer': 'hat'}, {'question_id': '2059722', 'answer': 'People', 'gt_answer': 'boy'}, {'question_id': '202241126', 'answer': 'Plant', 'gt_answer': 'plant'}, {'question_id': '202241125', 'answer': 'Plant', 'gt_answer': 'plant'}, {'question_id': '201492296', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202241128', 'answer': 'Table', 'gt_answer': 'shelf'}, {'question_id': '20866512', 'answer': 'Long', 'gt_answer': 'long'}, {'question_id': '201510407', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201757914', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201593943', 'answer': 'Short sleeved', 'gt_answer': 'short sleeved'}, {'question_id': '20922967', 'answer': 'Brown', 'gt_answer': 'brown'}, {'question_id': '20257138', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201479098', 'answer': 'Orange', 'gt_answer': 'orange'}, {'question_id': '202060019', 'answer': 'Hat', 'gt_answer': 'hat'}, {'question_id': '20151923', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20715608', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20692532', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201861362', 'answer': 'Looking up', 'gt_answer': 'looking up'}, {'question_id': '202156924', 'answer': 'Elephant', 'gt_answer': 'elephants'}, {'question_id': '201109638', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202081480', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201639161', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20891515', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20891510', 'answer': 'Child', 'gt_answer': 'child'}, {'question_id': '201880477', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20782942', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201455854', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201621788', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20257351', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201621781', 'answer': 'Speaker', 'gt_answer': 'speaker'}, {'question_id': '20783546', 'answer': 'Chair', 'gt_answer': 'chair'}, {'question_id': '20783012', 'answer': 'Table', 'gt_answer': 'table'}, {'question_id': '20811060', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202081731', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20536217', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201498082', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20647817', 'answer': 'Tall', 'gt_answer': 'tall'}, {'question_id': '201109184', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201639020', 'answer': 'Cameras', 'gt_answer': 'phones'}, {'question_id': '20416534', 'answer': 'Pizza cutter', 'gt_answer': 'pan'}, {'question_id': '201883116', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '201982860', 'answer': 'Remote control', 'gt_answer': 'remote control'}, {'question_id': '201302044', 'answer': 'Black', 'gt_answer': 'black'}, {'question_id': '201759349', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20119067', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20119066', 'answer': 'Tie', 'gt_answer': 'suit'}, {'question_id': '201301897', 'answer': 'Tall', 'gt_answer': 'tall'}, {'question_id': '202012810', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '201976888', 'answer': '915 best', 'gt_answer': 'street sign'}, {'question_id': '202012816', 'answer': 'Playing wii', 'gt_answer': 'standing'}, {'question_id': '20226372', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20668069', 'answer': 'Table', 'gt_answer': 'coffee table'}, {'question_id': '20226371', 'answer': 'Color', 'gt_answer': 'color'}, {'question_id': '20668061', 'answer': 'Couch', 'gt_answer': 'couch'}, {'question_id': '20668062', 'answer': 'Couch', 'gt_answer': 'couch'}, {'question_id': '2053559', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '20668067', 'answer': 'Coffee table', 'gt_answer': 'coffee table'}, {'question_id': '2046588', 'answer': 'Left', 'gt_answer': 'right'}, {'question_id': '201596091', 'answer': 'Bench', 'gt_answer': 'mud'}, {'question_id': '201882970', 'answer': 'Window', 'gt_answer': 'chair'}, {'question_id': '20511588', 'answer': 'Beach', 'gt_answer': 'beach'}, {'question_id': '20757120', 'answer': 'Woman', 'gt_answer': 'lady'}, {'question_id': '202262106', 'answer': 'Picture', 'gt_answer': 'napkin'}, {'question_id': '20240822', 'answer': 'Indoors', 'gt_answer': 'indoors'}, {'question_id': '202053348', 'answer': 'Man', 'gt_answer': 'batter'}, {'question_id': '20783254', 'answer': 'Screen', 'gt_answer': 'screen'}, {'question_id': '202174007', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201399907', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '2097749', 'answer': 'Monitor', 'gt_answer': 'monitor'}, {'question_id': '201794881', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20936214', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201770701', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201623435', 'answer': 'Square', 'gt_answer': 'rectangular'}, {'question_id': '20551701', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20754835', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '201498066', 'answer': 'Desk', 'gt_answer': 'desk'}, {'question_id': '20963940', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201676091', 'answer': 'Bottom', 'gt_answer': 'bottom'}, {'question_id': '202169063', 'answer': 'Cars', 'gt_answer': 'cars'}, {'question_id': '201974652', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20381364', 'answer': 'Chair', 'gt_answer': 'sweatshirt'}, {'question_id': '201623498', 'answer': 'Oven', 'gt_answer': 'oven'}, {'question_id': '201882623', 'answer': 'Square', 'gt_answer': 'rectangular'}, {'question_id': '20661462', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201068414', 'answer': 'Girl', 'gt_answer': 'girl'}, {'question_id': '201068413', 'answer': 'Girl', 'gt_answer': 'girl'}, {'question_id': '201347474', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20724203', 'answer': 'Man', 'gt_answer': 'snowboarder'}, {'question_id': '201639408', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20896207', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201637296', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201984098', 'answer': 'Phone', 'gt_answer': 'luggage cart'}, {'question_id': '20724204', 'answer': 'Man', 'gt_answer': 'snowboarder'}, {'question_id': '201111032', 'answer': 'Woman', 'gt_answer': 'woman'}, {'question_id': '20656831', 'answer': 'City', 'gt_answer': 'street'}, {'question_id': '201247168', 'answer': 'Cat', 'gt_answer': 'cat'}, {'question_id': '201247166', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20618753', 'answer': 'Ground', 'gt_answer': 'courtyard'}, {'question_id': '20618754', 'answer': 'Racket', 'gt_answer': 'rackets'}, {'question_id': '20618756', 'answer': 'People', 'gt_answer': 'people'}, {'question_id': '201570615', 'answer': 'Cabinet', 'gt_answer': 'cabinet'}, {'question_id': '201669708', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201902771', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201570614', 'answer': 'Cabinet', 'gt_answer': 'cabinet'}, {'question_id': '201067889', 'answer': 'Chair', 'gt_answer': 'chair'}, {'question_id': '201068678', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '2055890', 'answer': 'Bus', 'gt_answer': 'bus'}, {'question_id': '20307165', 'answer': 'Left', 'gt_answer': 'right'}, {'question_id': '20307161', 'answer': 'Television', 'gt_answer': 'television'}, {'question_id': '202024862', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20818879', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201641237', 'answer': 'Short sleeved', 'gt_answer': 'long sleeved'}, {'question_id': '201319462', 'answer': 'Sweater', 'gt_answer': 'shirt'}, {'question_id': '20940323', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201795135', 'answer': 'People', 'gt_answer': 'child'}, {'question_id': '201639562', 'answer': 'Grass', 'gt_answer': 'field'}, {'question_id': '20245688', 'answer': 'Park', 'gt_answer': 'skate park'}, {'question_id': '201832698', 'answer': 'Nightstand', 'gt_answer': 'nightstand'}, {'question_id': '20711603', 'answer': 'Papers', 'gt_answer': 'counter'}, {'question_id': '20711602', 'answer': 'Teddy bear', 'gt_answer': 'stuffed bear'}, {'question_id': '201462581', 'answer': 'Black', 'gt_answer': 'black'}, {'question_id': '20144649', 'answer': 'Bus', 'gt_answer': 'bus'}, {'question_id': '201037015', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '202243652', 'answer': 'Cupcakes', 'gt_answer': 'cupcakes'}, {'question_id': '201711321', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201621491', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201663395', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '202006937', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20902705', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201935191', 'answer': 'Building', 'gt_answer': 'lamp'}, {'question_id': '201935190', 'answer': 'Building', 'gt_answer': 'lamp'}, {'question_id': '20902701', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20416859', 'answer': 'Table', 'gt_answer': 'pan'}, {'question_id': '201982613', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201879366', 'answer': 'Bushes', 'gt_answer': 'basket'}, {'question_id': '20667382', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20781942', 'answer': 'Dirty', 'gt_answer': 'clean'}, {'question_id': '201795863', 'answer': 'Standing', 'gt_answer': 'talking'}, {'question_id': '201467645', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201428488', 'answer': 'Wii controller', 'gt_answer': 'remote control'}, {'question_id': '20894200', 'answer': 'Boy', 'gt_answer': 'boy'}, {'question_id': '20894205', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20621824', 'answer': 'Old', 'gt_answer': 'old'}, {'question_id': '20381395', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '201439703', 'answer': 'Chicken', 'gt_answer': 'horse'}, {'question_id': '20655122', 'answer': 'Man', 'gt_answer': 'man'}, {'question_id': '201467295', 'answer': 'Bear', 'gt_answer': 'bear'}, {'question_id': '201467296', 'answer': 'Bear', 'gt_answer': 'bear'}, {'question_id': '201467297', 'answer': 'Chair', 'gt_answer': 'sidewalk'}, {'question_id': '20308367', 'answer': 'Refrigerator', 'gt_answer': 'oven'}, {'question_id': '201407382', 'answer': 'Man', 'gt_answer': 'man'}, {'question_id': '2094079', 'answer': 'Giraffe', 'gt_answer': 'giraffe'}, {'question_id': '202218754', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201360796', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '202218753', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201983017', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '201879684', 'answer': '10 feet', 'gt_answer': 'tall'}, {'question_id': '202081906', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201576730', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20654994', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20654992', 'answer': 'Hat', 'gt_answer': 'hat'}, {'question_id': '20226975', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20644671', 'answer': 'Blue', 'gt_answer': 'gray'}, {'question_id': '20247702', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20621796', 'answer': 'Narrow', 'gt_answer': 'wide'}, {'question_id': '202231423', 'answer': 'Street light', 'gt_answer': 'stop sign'}, {'question_id': '201997020', 'answer': 'Boy', 'gt_answer': 'pilot'}, {'question_id': '202102699', 'answer': 'Cabinets', 'gt_answer': 'cabinets'}, {'question_id': '201997026', 'answer': 'Window', 'gt_answer': 'control panel'}, {'question_id': '201997024', 'answer': 'Boy', 'gt_answer': 'pilot'}, {'question_id': '201887120', 'answer': 'Left', 'gt_answer': 'right'}, {'question_id': '202000893', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201987844', 'answer': 'Small', 'gt_answer': 'small'}, {'question_id': '201887127', 'answer': 'Purple', 'gt_answer': 'green'}, {'question_id': '20705794', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201527939', 'answer': 'Short sleeved', 'gt_answer': 'short sleeved'}, {'question_id': '201480459', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201527930', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202228377', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '20929516', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '2076716', 'answer': 'Closed', 'gt_answer': 'closed'}, {'question_id': '201303230', 'answer': 'Yellow', 'gt_answer': 'gold'}, {'question_id': '201303235', 'answer': 'Chair', 'gt_answer': 'chair'}, {'question_id': '20567561', 'answer': 'Waving', 'gt_answer': 'playing'}, {'question_id': '20567567', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201467601', 'answer': 'Large', 'gt_answer': 'large'}, {'question_id': '20461875', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20968362', 'answer': 'Sign', 'gt_answer': 'road'}, {'question_id': '201467606', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20968360', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20461871', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201079949', 'answer': 'Left', 'gt_answer': 'right'}, {'question_id': '202179534', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20954000', 'answer': 'Standing', 'gt_answer': 'talking'}, {'question_id': '202041896', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202041891', 'answer': 'Asphalt', 'gt_answer': 'concrete'}, {'question_id': '201757768', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202041892', 'answer': 'Concrete', 'gt_answer': 'concrete'}, {'question_id': '20609774', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '202228087', 'answer': 'Television', 'gt_answer': 'dvd player'}, {'question_id': '201873297', 'answer': 'Red', 'gt_answer': 'white'}, {'question_id': '202133489', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20692322', 'answer': 'Square', 'gt_answer': 'square'}, {'question_id': '20309019', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20395092', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201998397', 'answer': 'Large', 'gt_answer': 'large'}, {'question_id': '201030700', 'answer': 'Blue', 'gt_answer': 'blue'}, {'question_id': '20120528', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202223087', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202241243', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202147736', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201207302', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202241247', 'answer': 'Brown', 'gt_answer': 'light brown'}, {'question_id': '201110510', 'answer': 'Marshmallows', 'gt_answer': 'marshmallow'}, {'question_id': '202227974', 'answer': 'Table', 'gt_answer': 'entertainment center'}, {'question_id': '202227977', 'answer': 'Shelf', 'gt_answer': 'entertainment center'}, {'question_id': '201662997', 'answer': 'Tan', 'gt_answer': 'tan'}, {'question_id': '201763582', 'answer': 'Material', 'gt_answer': 'shape'}, {'question_id': '20856953', 'answer': 'Bed', 'gt_answer': 'bed'}, {'question_id': '2059576', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '201573960', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '201832701', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201757581', 'answer': 'Sweater', 'gt_answer': 'sweater'}, {'question_id': '202121643', 'answer': 'Chair', 'gt_answer': 'chairs'}, {'question_id': '20317103', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '201757589', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201996732', 'answer': 'Airplane', 'gt_answer': 'helicopter'}, {'question_id': '201996733', 'answer': 'Helicopter', 'gt_answer': 'helicopter'}, {'question_id': '201974617', 'answer': 'Short sleeved', 'gt_answer': 'long sleeved'}, {'question_id': '201996737', 'answer': 'Helicopter', 'gt_answer': 'helicopter'}, {'question_id': '20434856', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201498156', 'answer': 'Computer mouse', 'gt_answer': 'computer'}, {'question_id': '20480548', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20442307', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '201735324', 'answer': 'Laptop', 'gt_answer': 'laptop'}, {'question_id': '201935832', 'answer': 'Large', 'gt_answer': 'large'}, {'question_id': '202156976', 'answer': 'Soft', 'gt_answer': 'soft'}, {'question_id': '20508252', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201880317', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20211138', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '201574002', 'answer': 'People', 'gt_answer': 'people'}, {'question_id': '20942146', 'answer': 'Palm tree', 'gt_answer': 'trees'}, {'question_id': '201640115', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202126032', 'answer': 'Ball', 'gt_answer': 'tennis ball'}, {'question_id': '202126031', 'answer': 'Racket', 'gt_answer': 'racket'}, {'question_id': '202244293', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '202126034', 'answer': 'Ball', 'gt_answer': 'tennis ball'}, {'question_id': '202158811', 'answer': 'Bus', 'gt_answer': 'bus'}, {'question_id': '20489606', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20647440', 'answer': 'Player', 'gt_answer': 'man'}, {'question_id': '202258194', 'answer': 'Horse', 'gt_answer': 'horse'}, {'question_id': '201759403', 'answer': 'Brown', 'gt_answer': 'black'}, {'question_id': '202023434', 'answer': 'Bed', 'gt_answer': 'bed'}, {'question_id': '201759150', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20287545', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20480729', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '201878404', 'answer': 'Dog', 'gt_answer': 'dog'}, {'question_id': '20308565', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20462063', 'answer': 'Snow', 'gt_answer': 'snow'}, {'question_id': '20462064', 'answer': 'Snow', 'gt_answer': 'snow'}, {'question_id': '20462066', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201866613', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '201556554', 'answer': 'Laptop', 'gt_answer': 'desk'}, {'question_id': '201301954', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201068789', 'answer': 'Shirt', 'gt_answer': 'blouse'}, {'question_id': '20120213', 'answer': 'Girl', 'gt_answer': 'athlete'}, {'question_id': '201795580', 'answer': 'Elephant', 'gt_answer': 'elephant'}, {'question_id': '20754624', 'answer': 'Sitting', 'gt_answer': 'sitting'}, {'question_id': '20754620', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20306828', 'answer': 'Snowboard', 'gt_answer': 'lamp'}, {'question_id': '201175056', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201175505', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201996862', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '202049533', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201996866', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202053408', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201322443', 'answer': 'Street sign', 'gt_answer': 'street sign'}, {'question_id': '202060119', 'answer': 'Couch', 'gt_answer': 'couch'}, {'question_id': '201322441', 'answer': 'Color', 'gt_answer': 'material'}, {'question_id': '201434158', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201974895', 'answer': 'Metal', 'gt_answer': 'metal'}, {'question_id': '2017224', 'answer': 'Short', 'gt_answer': 'short'}, {'question_id': '201077102', 'answer': 'Faucet', 'gt_answer': 'sink'}, {'question_id': '20756669', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20412044', 'answer': 'Noodles', 'gt_answer': 'dessert'}, {'question_id': '20308625', 'answer': 'Brown', 'gt_answer': 'brown'}, {'question_id': '20783204', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '20721769', 'answer': 'Girl', 'gt_answer': 'girl'}, {'question_id': '201795276', 'answer': 'Metal', 'gt_answer': 'wood'}, {'question_id': '201947639', 'answer': 'Yellow', 'gt_answer': 'yellow'}, {'question_id': '201068836', 'answer': 'Jacket', 'gt_answer': 'sweatshirt'}, {'question_id': '20661435', 'answer': 'Bus', 'gt_answer': 'bus'}, {'question_id': '20385243', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20456571', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20781843', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202144558', 'answer': 'Man', 'gt_answer': 'man'}, {'question_id': '20306174', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20456576', 'answer': 'Dog', 'gt_answer': 'dog'}, {'question_id': '20403498', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202144557', 'answer': 'Man', 'gt_answer': 'man'}, {'question_id': '202121603', 'answer': 'Chair', 'gt_answer': 'chairs'}, {'question_id': '202144552', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202158981', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201370439', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201687509', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20381564', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '202121605', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201987532', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '201370431', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201758393', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201065482', 'answer': 'Black', 'gt_answer': 'dark'}, {'question_id': '20308196', 'answer': 'Cabinet', 'gt_answer': 'cabinets'}, {'question_id': '20308190', 'answer': 'Knives', 'gt_answer': 'microwave oven'}, {'question_id': '20308193', 'answer': 'Mixer', 'gt_answer': 'microwave oven'}, {'question_id': '201206886', 'answer': 'Table', 'gt_answer': 'papers'}, {'question_id': '201206885', 'answer': 'Glass', 'gt_answer': 'bowl'}, {'question_id': '20340782', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201979252', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20636988', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20609175', 'answer': 'Whipped cream', 'gt_answer': 'whipped cream'}, {'question_id': '2017102', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20171217', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '20468350', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20887456', 'answer': 'Computer', 'gt_answer': 'monitor'}, {'question_id': '20887451', 'answer': 'Computer', 'gt_answer': 'keyboard'}, {'question_id': '20468356', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201430956', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20427909', 'answer': 'Black and white', 'gt_answer': 'black and white'}, {'question_id': '20178011', 'answer': 'Fries', 'gt_answer': 'fries'}, {'question_id': '201428581', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20178012', 'answer': 'Fries', 'gt_answer': 'fries'}, {'question_id': '20178017', 'answer': 'Fries', 'gt_answer': 'fries'}, {'question_id': '201235879', 'answer': 'Woman', 'gt_answer': 'woman'}, {'question_id': '20609212', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201997647', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20672933', 'answer': 'White', 'gt_answer': 'white'}, {'question_id': '201822333', 'answer': 'Toilet', 'gt_answer': 'toilet'}, {'question_id': '20672935', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202006030', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201393526', 'answer': 'Front', 'gt_answer': 'front'}, {'question_id': '201752660', 'answer': 'Building', 'gt_answer': 'bandana'}, {'question_id': '201752661', 'answer': 'Building', 'gt_answer': 'bandana'}, {'question_id': '20964060', 'answer': 'Bathroom', 'gt_answer': 'floor'}, {'question_id': '201438721', 'answer': 'Large', 'gt_answer': 'large'}, {'question_id': '20204856', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '202226027', 'answer': 'Table', 'gt_answer': 'table'}, {'question_id': '20349971', 'answer': 'White', 'gt_answer': 'white'}, {'question_id': '20361234', 'answer': 'Snowboarder', 'gt_answer': 'snowboarder'}, {'question_id': '202285182', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '201798296', 'answer': 'Color', 'gt_answer': 'material'}, {'question_id': '202107808', 'answer': 'Boy', 'gt_answer': 'boy'}, {'question_id': '20673080', 'answer': 'Chair', 'gt_answer': 'chair'}, {'question_id': '201370360', 'answer': 'Scissors', 'gt_answer': 'paper'}, {'question_id': '201056243', 'answer': 'Female', 'gt_answer': 'male'}, {'question_id': '201447037', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202116964', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201056240', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20894051', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20894052', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20856767', 'answer': 'Bed', 'gt_answer': 'bed'}, {'question_id': '202121348', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20705651', 'answer': 'Color', 'gt_answer': 'color'}, {'question_id': '202226078', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202121340', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20637165', 'answer': 'Stainless steel', 'gt_answer': 'stainless steel'}, {'question_id': '202121345', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20295616', 'answer': 'Poster', 'gt_answer': 'picture frame'}, {'question_id': '20295615', 'answer': 'Poster', 'gt_answer': 'picture frame'}, {'question_id': '20295614', 'answer': 'Wood', 'gt_answer': 'plastic'}, {'question_id': '201064755', 'answer': 'Girl', 'gt_answer': 'girl'}, {'question_id': '20247931', 'answer': 'Man', 'gt_answer': 'woman'}, {'question_id': '201438323', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202119974', 'answer': 'Table', 'gt_answer': 'table'}, {'question_id': '20295618', 'answer': 'Wall', 'gt_answer': 'wall'}, {'question_id': '20340766', 'answer': 'White', 'gt_answer': 'white'}, {'question_id': '20411863', 'answer': 'Carrot', 'gt_answer': 'carrots'}, {'question_id': '201593432', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20340769', 'answer': 'Chair', 'gt_answer': 'chair'}, {'question_id': '20611668', 'answer': 'None', 'gt_answer': 'grapes'}, {'question_id': '201798458', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202082119', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201935412', 'answer': 'Skateboard', 'gt_answer': 'bricks'}, {'question_id': '202082114', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20637294', 'answer': 'Stove', 'gt_answer': 'burner'}, {'question_id': '201826548', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201766681', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20661257', 'answer': 'Bus', 'gt_answer': 'bus'}, {'question_id': '20978204', 'answer': 'Woman', 'gt_answer': 'woman'}, {'question_id': '201896533', 'answer': 'Table', 'gt_answer': 'table'}, {'question_id': '201623949', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201479495', 'answer': 'Chicken', 'gt_answer': 'chicken'}, {'question_id': '201896539', 'answer': 'Woman', 'gt_answer': 'lady'}, {'question_id': '201896538', 'answer': 'Woman', 'gt_answer': 'lady'}, {'question_id': '20836740', 'answer': 'Black', 'gt_answer': 'blue'}, {'question_id': '201976422', 'answer': 'Net', 'gt_answer': 'flags'}, {'question_id': '201976421', 'answer': 'Fence', 'gt_answer': 'flags'}, {'question_id': '20978201', 'answer': 'Girl', 'gt_answer': 'girl'}, {'question_id': '201902995', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202228141', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '202228410', 'answer': 'Cat', 'gt_answer': 'cat'}, {'question_id': '202228416', 'answer': 'Cat', 'gt_answer': 'cat'}, {'question_id': '201640613', 'answer': 'Lady', 'gt_answer': 'women'}, {'question_id': '201959784', 'answer': 'Mountain', 'gt_answer': 'mountain'}, {'question_id': '20542980', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20542981', 'answer': 'Front', 'gt_answer': 'front'}, {'question_id': '20982248', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202060026', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '2046421', 'answer': 'Man', 'gt_answer': 'woman'}, {'question_id': '20618933', 'answer': 'Girl', 'gt_answer': 'woman'}, {'question_id': '20434998', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20434993', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201556461', 'answer': 'Wires', 'gt_answer': 'cords'}, {'question_id': '201765659', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20434990', 'answer': 'Pizza', 'gt_answer': 'container'}, {'question_id': '202003777', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201624084', 'answer': 'Woman', 'gt_answer': 'woman'}, {'question_id': '20836564', 'answer': 'Blue', 'gt_answer': 'blue'}, {'question_id': '20836297', 'answer': 'Chair', 'gt_answer': 'cabinet'}, {'question_id': '20836566', 'answer': 'Table', 'gt_answer': 'table'}, {'question_id': '201492277', 'answer': 'White', 'gt_answer': 'white'}, {'question_id': '20340778', 'answer': 'Trash', 'gt_answer': 'chair'}, {'question_id': '20691640', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201510467', 'answer': 'Silver', 'gt_answer': 'silver'}, {'question_id': '201342137', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202108060', 'answer': 'Black', 'gt_answer': 'black'}, {'question_id': '202100620', 'answer': 'Ship', 'gt_answer': 'boats'}, {'question_id': '20692043', 'answer': 'Cream colored', 'gt_answer': 'cream colored'}, {'question_id': '20692514', 'answer': 'Cabinet', 'gt_answer': 'cabinet'}, {'question_id': '202286627', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20692513', 'answer': 'Cabinet', 'gt_answer': 'cabinet'}, {'question_id': '20978430', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201889247', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20715620', 'answer': 'Thick', 'gt_answer': 'thin'}, {'question_id': '202262166', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201153284', 'answer': 'Tree', 'gt_answer': 'post'}, {'question_id': '201153283', 'answer': 'Fence', 'gt_answer': 'post'}, {'question_id': '201153282', 'answer': 'Man', 'gt_answer': 'woman'}, {'question_id': '201153281', 'answer': 'Man', 'gt_answer': 'woman'}, {'question_id': '201175498', 'answer': 'Cat', 'gt_answer': 'cat'}, {'question_id': '20891575', 'answer': 'Green', 'gt_answer': 'blue'}, {'question_id': '201497684', 'answer': 'Round', 'gt_answer': 'round'}, {'question_id': '201185031', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201880417', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20883188', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20896410', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20786262', 'answer': 'Long', 'gt_answer': 'long'}, {'question_id': '20342449', 'answer': 'Bus', 'gt_answer': 'bus'}, {'question_id': '20342331', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '201889425', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202012702', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20899770', 'answer': 'Laptop', 'gt_answer': 'laptop'}, {'question_id': '20797804', 'answer': 'White', 'gt_answer': 'caucasian'}, {'question_id': '20306369', 'answer': 'Snowboard', 'gt_answer': 'camera'}, {'question_id': '20536279', 'answer': 'Buffalo', 'gt_answer': 'bison'}, {'question_id': '20536275', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20306367', 'answer': 'Snowboard', 'gt_answer': 'camera'}, {'question_id': '20204693', 'answer': 'Skinny', 'gt_answer': 'skinny'}, {'question_id': '201759369', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20541729', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '20567737', 'answer': 'Wide', 'gt_answer': 'narrow'}, {'question_id': '201759360', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20866397', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20866392', 'answer': 'T-shirt', 'gt_answer': 'shirt'}, {'question_id': '201759365', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20746641', 'answer': 'Thin', 'gt_answer': 'thick'}, {'question_id': '201737893', 'answer': 'Player', 'gt_answer': 'player'}, {'question_id': '202156820', 'answer': 'Elephants', 'gt_answer': 'elephants'}, {'question_id': '202053188', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201763559', 'answer': 'Indoors', 'gt_answer': 'indoors'}, {'question_id': '201109403', 'answer': 'Small', 'gt_answer': 'large'}, {'question_id': '201758419', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '2053572', 'answer': 'Plastic', 'gt_answer': 'plastic'}, {'question_id': '201498261', 'answer': 'Bottle', 'gt_answer': 'tape'}, {'question_id': '20411923', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '20668045', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201207117', 'answer': 'Broccoli', 'gt_answer': 'broccoli'}, {'question_id': '201207116', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202156783', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '2017506', 'answer': 'Hills', 'gt_answer': 'pasture'}, {'question_id': '20757105', 'answer': '40', 'gt_answer': 'old'}, {'question_id': '20177885', 'answer': 'Bottom', 'gt_answer': 'bottom'}, {'question_id': '20157505', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20940297', 'answer': 'Glass', 'gt_answer': 'glass'}, {'question_id': '202053365', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20257376', 'answer': 'Wet', 'gt_answer': 'wet'}, {'question_id': '201861643', 'answer': 'Dirty', 'gt_answer': 'clean'}, {'question_id': '20963968', 'answer': 'Shelf', 'gt_answer': 'shelf'}, {'question_id': '20412009', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20963966', 'answer': 'Sink', 'gt_answer': 'shelf'}, {'question_id': '201175314', 'answer': 'Sitting', 'gt_answer': 'standing'}, {'question_id': '20939972', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20939977', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '202100447', 'answer': 'Sailboat', 'gt_answer': 'boats'}, {'question_id': '202073385', 'answer': 'Sky', 'gt_answer': 'sky'}, {'question_id': '20790068', 'answer': 'Horse', 'gt_answer': 'horse'}, {'question_id': '201407189', 'answer': 'Man', 'gt_answer': 'crowd'}, {'question_id': '20287953', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20287952', 'answer': 'Fence', 'gt_answer': 'dugout'}, {'question_id': '20724223', 'answer': 'Man', 'gt_answer': 'snowboarder'}, {'question_id': '20287950', 'answer': 'Player', 'gt_answer': 'coach'}, {'question_id': '20896225', 'answer': 'Nothing', 'gt_answer': 'towel'}, {'question_id': '20896226', 'answer': 'Oven', 'gt_answer': 'cabinets'}, {'question_id': '20258945', 'answer': 'Gold', 'gt_answer': 'yellow'}, {'question_id': '20896228', 'answer': 'Oven', 'gt_answer': 'cabinets'}, {'question_id': '201360710', 'answer': 'Toothbrush', 'gt_answer': 'toothbrush'}, {'question_id': '201752874', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201639361', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '202125939', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20618739', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201080114', 'answer': 'Small', 'gt_answer': 'small'}, {'question_id': '201669720', 'answer': 'Bottom', 'gt_answer': 'bottom'}, {'question_id': '201576672', 'answer': 'Dog', 'gt_answer': 'goat'}, {'question_id': '201080118', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20307182', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20211244', 'answer': 'Woman', 'gt_answer': 'woman'}, {'question_id': '201068697', 'answer': 'Shirt', 'gt_answer': 'dress shirt'}, {'question_id': '201885420', 'answer': 'Fence', 'gt_answer': 'plant'}, {'question_id': '201903004', 'answer': 'Remote', 'gt_answer': 'keyboard'}, {'question_id': '20307189', 'answer': 'Camera', 'gt_answer': 'television'}, {'question_id': '201593338', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201360929', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201883078', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '2075383', 'answer': 'Top', 'gt_answer': 'top'}, {'question_id': '202006629', 'answer': 'Rectangular', 'gt_answer': 'square'}, {'question_id': '201346598', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202285516', 'answer': 'Waffle', 'gt_answer': 'waffles'}, {'question_id': '202285517', 'answer': 'Waffle', 'gt_answer': 'waffles'}, {'question_id': '202073242', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20673143', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201480490', 'answer': 'Tree', 'gt_answer': 'grass'}, {'question_id': '201235833', 'answer': 'Looking', 'gt_answer': 'crouching'}, {'question_id': '201235835', 'answer': 'Looking', 'gt_answer': 'crouching'}, {'question_id': '201861503', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20144668', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20863625', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20863626', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20245937', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201735552', 'answer': 'Shelf', 'gt_answer': 'shelves'}, {'question_id': '201735554', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20416834', 'answer': 'Pepper', 'gt_answer': 'sausage'}, {'question_id': '20416780', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20789844', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20416784', 'answer': 'Table', 'gt_answer': 'table'}, {'question_id': '20898918', 'answer': 'Long', 'gt_answer': 'long'}, {'question_id': '20753175', 'answer': 'Baby', 'gt_answer': 'boy'}, {'question_id': '201935172', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202006950', 'answer': 'Metal', 'gt_answer': 'wood'}, {'question_id': '20300607', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201111014', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202286961', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '201972832', 'answer': 'Nothing', 'gt_answer': 'kite'}, {'question_id': '201037065', 'answer': 'Octagon', 'gt_answer': 'octagonal'}, {'question_id': '201593842', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '20894261', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201067417', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20320461', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201593594', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201593591', 'answer': 'Red', 'gt_answer': 'white'}, {'question_id': '2091196', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201439727', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201909080', 'answer': 'Metal', 'gt_answer': 'metal'}, {'question_id': '201909081', 'answer': 'Metal', 'gt_answer': 'metal'}, {'question_id': '201909086', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201444978', 'answer': 'Parking sign', 'gt_answer': 'street sign'}, {'question_id': '201444977', 'answer': 'Bench', 'gt_answer': 'bench'}, {'question_id': '2097998', 'answer': 'White', 'gt_answer': 'white'}, {'question_id': '20169656', 'answer': 'Man', 'gt_answer': 'man'}, {'question_id': '201983074', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20573682', 'answer': 'Plastic', 'gt_answer': 'plastic'}, {'question_id': '20741181', 'answer': 'White', 'gt_answer': 'brown'}, {'question_id': '20247766', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20226957', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '20644659', 'answer': 'Floor', 'gt_answer': 'paper'}, {'question_id': '20644658', 'answer': 'Money', 'gt_answer': 'cable'}, {'question_id': '20340401', 'answer': 'Patio', 'gt_answer': 'patio'}, {'question_id': '202223270', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201347334', 'answer': 'Long', 'gt_answer': 'long'}, {'question_id': '202231441', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '20340405', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201671858', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201882478', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '20381084', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '202284939', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201593738', 'answer': 'Metal', 'gt_answer': 'metal'}, {'question_id': '201738918', 'answer': 'Baseball', 'gt_answer': 'pitcher'}, {'question_id': '201738919', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20963682', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '2066122', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '2066120', 'answer': 'Girl', 'gt_answer': 'girl'}, {'question_id': '20752223', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201738917', 'answer': 'Game', 'gt_answer': 'pitcher'}, {'question_id': '201738914', 'answer': 'Player', 'gt_answer': 'spectators'}, {'question_id': '202226255', 'answer': 'Meat', 'gt_answer': 'mashed potatoes'}, {'question_id': '201599790', 'answer': 'Chair', 'gt_answer': 'chair'}, {'question_id': '20573565', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '201976891', 'answer': '915 best', 'gt_answer': 'street sign'}, {'question_id': '20573568', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202162620', 'answer': 'Bookcase', 'gt_answer': 'bed'}, {'question_id': '201976899', 'answer': '915 best', 'gt_answer': 'street sign'}, {'question_id': '201319776', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201982675', 'answer': 'Shoe', 'gt_answer': 'skateboard'}, {'question_id': '20330280', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20541535', 'answer': 'Television', 'gt_answer': 'television'}, {'question_id': '20330284', 'answer': 'Black', 'gt_answer': 'dark'}, {'question_id': '20136582', 'answer': 'Toilet', 'gt_answer': 'toilet'}, {'question_id': '2056027', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20120095', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202180341', 'answer': 'Girl', 'gt_answer': 'soccer player'}, {'question_id': '201757741', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201757743', 'answer': 'Laptop', 'gt_answer': 'laptop'}, {'question_id': '201975144', 'answer': 'Woman', 'gt_answer': 'player'}, {'question_id': '20954029', 'answer': 'Girl', 'gt_answer': 'man'}, {'question_id': '201467233', 'answer': 'Sidewalk', 'gt_answer': 'sidewalk'}, {'question_id': '201998374', 'answer': 'Chairs', 'gt_answer': 'chairs'}, {'question_id': '202133584', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202218829', 'answer': 'Window', 'gt_answer': 'window'}, {'question_id': '20903186', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201952941', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20508099', 'answer': 'Small', 'gt_answer': 'small'}, {'question_id': '201987693', 'answer': 'White', 'gt_answer': 'yellow'}, {'question_id': '201983986', 'answer': 'Suitcase', 'gt_answer': 'luggage cart'}, {'question_id': '201110828', 'answer': 'Glass', 'gt_answer': 'plates'}, {'question_id': '20120500', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20162430', 'answer': 'Rectangular', 'gt_answer': 'rectangular'}, {'question_id': '20856970', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20982189', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201956973', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202121663', 'answer': 'Table', 'gt_answer': 'chairs'}, {'question_id': '202121662', 'answer': 'Cabinets', 'gt_answer': 'chairs'}, {'question_id': '201527400', 'answer': 'Candle', 'gt_answer': 'candles'}, {'question_id': '20482320', 'answer': 'Nike', 'gt_answer': 'nike'}, {'question_id': '201751620', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201156109', 'answer': 'Cloth', 'gt_answer': 'cloth'}, {'question_id': '201803999', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '20797652', 'answer': 'Cat', 'gt_answer': 'cat'}, {'question_id': '20434837', 'answer': 'Eating', 'gt_answer': 'sitting'}, {'question_id': '20434839', 'answer': 'Boy', 'gt_answer': 'boy'}, {'question_id': '20385811', 'answer': 'Ground', 'gt_answer': 'ground'}, {'question_id': '202240877', 'answer': 'Woman', 'gt_answer': 'woman'}, {'question_id': '201880330', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202023632', 'answer': 'Chair', 'gt_answer': 'chair'}, {'question_id': '201574028', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201880335', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201571058', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20508279', 'answer': 'Chair', 'gt_answer': 'chair'}, {'question_id': '201571057', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201574023', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201511025', 'answer': 'Jacket', 'gt_answer': 'socks'}, {'question_id': '201505086', 'answer': 'Sand', 'gt_answer': 'sand'}, {'question_id': '201738961', 'answer': 'Pitcher', 'gt_answer': 'pitcher'}, {'question_id': '201951768', 'answer': 'Truck', 'gt_answer': 'van'}, {'question_id': '201951769', 'answer': 'Truck', 'gt_answer': 'van'}, {'question_id': '201621687', 'answer': 'Sofa', 'gt_answer': 'couch'}, {'question_id': '201621686', 'answer': 'Couch', 'gt_answer': 'couch'}, {'question_id': '201951762', 'answer': 'Van', 'gt_answer': 'van'}, {'question_id': '201951763', 'answer': 'Van', 'gt_answer': 'van'}, {'question_id': '20462101', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201621688', 'answer': 'Sofa', 'gt_answer': 'couch'}, {'question_id': '201873053', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201188314', 'answer': 'Caucasian', 'gt_answer': 'caucasian'}, {'question_id': '201511020', 'answer': 'Pants', 'gt_answer': 'socks'}, {'question_id': '202158875', 'answer': 'Car', 'gt_answer': 'buildings'}, {'question_id': '20929653', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '202158870', 'answer': 'Bus', 'gt_answer': 'bus'}, {'question_id': '20345012', 'answer': 'Young', 'gt_answer': 'young'}, {'question_id': '20385729', 'answer': 'Silver', 'gt_answer': 'silver'}, {'question_id': '202246702', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201737843', 'answer': 'Top', 'gt_answer': 'top'}, {'question_id': '202023454', 'answer': 'Bed', 'gt_answer': 'bed'}, {'question_id': '20157198', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201704623', 'answer': 'Cows', 'gt_answer': 'cows'}, {'question_id': '201704622', 'answer': 'Cow', 'gt_answer': 'cows'}, {'question_id': '202004141', 'answer': 'Laptops', 'gt_answer': 'laptops'}, {'question_id': '201434110', 'answer': 'Cell phone', 'gt_answer': 'phone'}, {'question_id': '202023459', 'answer': 'Bookshelf', 'gt_answer': 'closet'}, {'question_id': '202004142', 'answer': 'Laptop', 'gt_answer': 'laptops'}, {'question_id': '201654371', 'answer': 'Horse', 'gt_answer': 'horse'}, {'question_id': '202243958', 'answer': 'Beans', 'gt_answer': 'beans'}, {'question_id': '20287562', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202244011', 'answer': 'Cookie', 'gt_answer': 'cookies'}, {'question_id': '202244013', 'answer': 'Container', 'gt_answer': 'bowl'}, {'question_id': '20978371', 'answer': 'Man', 'gt_answer': 'boy'}, {'question_id': '20308813', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20818661', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20308817', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20515091', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201065075', 'answer': 'Pink', 'gt_answer': 'pink'}, {'question_id': '20412258', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202218651', 'answer': 'Counter', 'gt_answer': 'chalkboard'}, {'question_id': '201065071', 'answer': 'Skirt', 'gt_answer': 't-shirt'}, {'question_id': '20412256', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201935953', 'answer': 'Wood', 'gt_answer': 'wood'}, {'question_id': '20706146', 'answer': 'Keyboard', 'gt_answer': 'keyboard'}, {'question_id': '201175074', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '202049511', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20306802', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '20442321', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20706140', 'answer': 'Keyboard', 'gt_answer': 'headphones'}, {'question_id': '202144715', 'answer': 'Blender', 'gt_answer': 'blender'}, {'question_id': '201175079', 'answer': 'Top', 'gt_answer': 'top'}, {'question_id': '201975143', 'answer': 'Woman', 'gt_answer': 'player'}, {'question_id': '20452185', 'answer': 'Vase', 'gt_answer': 'glass'}, {'question_id': '20452186', 'answer': 'Vase', 'gt_answer': 'glass'}, {'question_id': '20452180', 'answer': 'Picture', 'gt_answer': 'christmas light'}, {'question_id': '20303094', 'answer': 'Ocean', 'gt_answer': 'ocean'}, {'question_id': '20452182', 'answer': 'Flowers', 'gt_answer': 'flowers'}, {'question_id': '20452183', 'answer': 'Flowers', 'gt_answer': 'flowers'}, {'question_id': '201497965', 'answer': 'Keyboard', 'gt_answer': 'monitor'}, {'question_id': '201497966', 'answer': 'Keyboard', 'gt_answer': 'monitor'}, {'question_id': '20452188', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201322709', 'answer': 'Wide', 'gt_answer': 'wide'}, {'question_id': '20756642', 'answer': 'Shelf', 'gt_answer': 'shelf'}, {'question_id': '20756623', 'answer': 'Red', 'gt_answer': 'red'}, {'question_id': '20756646', 'answer': 'Shelf', 'gt_answer': 'shelf'}, {'question_id': '201077120', 'answer': 'Gray', 'gt_answer': 'black'}, {'question_id': '20244725', 'answer': 'Cobblestone', 'gt_answer': 'stone'}, {'question_id': '20899549', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201504952', 'answer': 'Surfboard', 'gt_answer': 'surfboard'}, {'question_id': '201504950', 'answer': 'Woman', 'gt_answer': 'woman'}, {'question_id': '201504956', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201080002', 'answer': 'Bed', 'gt_answer': 'bed'}, {'question_id': '201738964', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201109396', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '20177546', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20746451', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20518440', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201859121', 'answer': 'Skateboarder', 'gt_answer': 'skateboarder'}, {'question_id': '20177541', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201751799', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202180274', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201370411', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202180276', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '2075776', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201641371', 'answer': 'Traffic light', 'gt_answer': 'traffic light'}, {'question_id': '202180278', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20857164', 'answer': 'Phone', 'gt_answer': 'cell phone'}, {'question_id': '20857167', 'answer': 'Bed', 'gt_answer': 'bed'}, {'question_id': '20857161', 'answer': 'Plastic', 'gt_answer': 'plastic'}, {'question_id': '202262339', 'answer': 'Empty', 'gt_answer': 'empty'}, {'question_id': '202081203', 'answer': 'Toaster', 'gt_answer': 'toaster'}, {'question_id': '20857168', 'answer': 'Bed', 'gt_answer': 'bed'}, {'question_id': '201438290', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201760614', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201498453', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '20117798', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20258722', 'answer': 'Boy', 'gt_answer': 'child'}, {'question_id': '20887472', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '20543027', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20543021', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '2017243', 'answer': 'Hat', 'gt_answer': 'hair'}, {'question_id': '2017241', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '2017244', 'answer': 'Mountain', 'gt_answer': 'shirt'}, {'question_id': '201056001', 'answer': 'Fence', 'gt_answer': 'trees'}, {'question_id': '201430728', 'answer': 'Open', 'gt_answer': 'closed'}, {'question_id': '201047191', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201047193', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201047440', 'answer': 'Suit', 'gt_answer': 'dress shirt'}, {'question_id': '201951991', 'answer': 'Tree', 'gt_answer': 'tree leaves'}, {'question_id': '202262330', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20551449', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201760583', 'answer': 'Chair', 'gt_answer': 'chair'}, {'question_id': '20672953', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201760586', 'answer': 'Lawn', 'gt_answer': 'lawn'}, {'question_id': '202110154', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20936026', 'answer': 'Elephant', 'gt_answer': 'elephant'}, {'question_id': '201247282', 'answer': 'White', 'gt_answer': 'beige'}, {'question_id': '201752688', 'answer': 'Boy', 'gt_answer': 'boy'}, {'question_id': '201752689', 'answer': 'Bike', 'gt_answer': 'bike'}, {'question_id': '201804761', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20204876', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201957236', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20361254', 'answer': 'Standing', 'gt_answer': 'standing'}, {'question_id': '202161968', 'answer': 'White', 'gt_answer': 'tan'}, {'question_id': '20954060', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201037276', 'answer': 'Woman', 'gt_answer': 'girl'}, {'question_id': '201037274', 'answer': 'Woman', 'gt_answer': 'girl'}, {'question_id': '201037273', 'answer': 'Fire hydrant', 'gt_answer': 'fire hydrant'}, {'question_id': '201064806', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201447018', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201185211', 'answer': 'White', 'gt_answer': 'dark'}, {'question_id': '20667489', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20878993', 'answer': 'Man', 'gt_answer': 'skater'}, {'question_id': '201444893', 'answer': 'Tree', 'gt_answer': 'gravel'}, {'question_id': '20637106', 'answer': 'Silver', 'gt_answer': 'brown'}, {'question_id': '201143151', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201143156', 'answer': 'Wood', 'gt_answer': 'wood'}, {'question_id': '201393617', 'answer': 'Long', 'gt_answer': 'long'}, {'question_id': '20295637', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '201639194', 'answer': 'Field', 'gt_answer': 'field'}, {'question_id': '20790034', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201763807', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '202119912', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20631990', 'answer': 'Dirty', 'gt_answer': 'clean'}, {'question_id': '201037183', 'answer': 'Woman', 'gt_answer': 'girl'}, {'question_id': '201037182', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201935811', 'answer': 'Shelf', 'gt_answer': 'shelf'}, {'question_id': '20317202', 'answer': 'Cutting board', 'gt_answer': 'coffee pot'}, {'question_id': '201638825', 'answer': 'Car', 'gt_answer': 'car'}, {'question_id': '201462465', 'answer': 'Aluminum', 'gt_answer': 'aluminum'}, {'question_id': '202107846', 'answer': 'Blue', 'gt_answer': 'white'}, {'question_id': '201497778', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201175456', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20341127', 'answer': 'Building', 'gt_answer': 'tree'}, {'question_id': '20341125', 'answer': 'Fire hydrant', 'gt_answer': 'sculpture'}, {'question_id': '201640454', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201346744', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201056202', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201996583', 'answer': 'Wall', 'gt_answer': 'helicopter'}, {'question_id': '20940170', 'answer': 'Porcelain', 'gt_answer': 'porcelain'}, {'question_id': '20940174', 'answer': 'Porcelain', 'gt_answer': 'porcelain'}, {'question_id': '201996584', 'answer': 'Airplane', 'gt_answer': 'helicopter'}, {'question_id': '201896515', 'answer': 'Woman', 'gt_answer': 'lady'}, {'question_id': '202156796', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201624240', 'answer': 'Saucer', 'gt_answer': 'saucer'}, {'question_id': '201624241', 'answer': 'Saucer', 'gt_answer': 'saucer'}, {'question_id': '201879147', 'answer': 'Window', 'gt_answer': 'wristband'}, {'question_id': '202162437', 'answer': 'Curtain', 'gt_answer': 'drapes'}, {'question_id': '202162436', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '2012848', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201247114', 'answer': 'Cat', 'gt_answer': 'cat'}, {'question_id': '202162432', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201687544', 'answer': 'Shorts', 'gt_answer': 'shorts'}, {'question_id': '201481607', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202162439', 'answer': 'Ceiling', 'gt_answer': 'window'}, {'question_id': '201873269', 'answer': 'Large', 'gt_answer': 'large'}, {'question_id': '202228124', 'answer': 'Speaker', 'gt_answer': 'dvd player'}, {'question_id': '202228125', 'answer': 'Television', 'gt_answer': 'television'}, {'question_id': '201687546', 'answer': 'Man', 'gt_answer': 'man'}, {'question_id': '20542969', 'answer': 'Elephant', 'gt_answer': 'elephant'}, {'question_id': '201765981', 'answer': 'Trees', 'gt_answer': 'dirt'}, {'question_id': '201735479', 'answer': 'Chicken', 'gt_answer': 'keyboard'}, {'question_id': '202060003', 'answer': 'Tree', 'gt_answer': 'couch'}, {'question_id': '20692280', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202060001', 'answer': 'Dog', 'gt_answer': 'dog'}, {'question_id': '20710140', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201556485', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20550543', 'answer': 'Large', 'gt_answer': 'small'}, {'question_id': '201795499', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201735472', 'answer': 'Laptop', 'gt_answer': 'keyboard'}, {'question_id': '201492252', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20711558', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '201510332', 'answer': 'Apple', 'gt_answer': 'pear'}, {'question_id': '20302916', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201510335', 'answer': 'Apple', 'gt_answer': 'pear'}, {'question_id': '20691667', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201510338', 'answer': 'Apple', 'gt_answer': 'pear'}, {'question_id': '20691665', 'answer': 'Towel', 'gt_answer': 'towels'}, {'question_id': '20631423', 'answer': 'Catcher', 'gt_answer': 'umpire'}, {'question_id': '20600081', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20786060', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20692061', 'answer': 'Clean', 'gt_answer': 'clean'}, {'question_id': '20978416', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '201889229', 'answer': 'Cloudy', 'gt_answer': 'cloudy'}, {'question_id': '20349783', 'answer': 'Phone', 'gt_answer': 'cell phone'}, {'question_id': '201439392', 'answer': 'Left', 'gt_answer': 'right'}, {'question_id': '20978418', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20349785', 'answer': 'Phone', 'gt_answer': 'cell phone'}, {'question_id': '20645843', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201400165', 'answer': 'Bookcase', 'gt_answer': 'bookcase'}, {'question_id': '202262104', 'answer': 'Glass', 'gt_answer': 'flowers'}, {'question_id': '201185169', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202081447', 'answer': 'Computer', 'gt_answer': 'keyboard'}, {'question_id': '201428649', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201185165', 'answer': 'Umbrella', 'gt_answer': 'umbrella'}, {'question_id': '201185166', 'answer': 'Umbrella', 'gt_answer': 'umbrella'}, {'question_id': '201577009', 'answer': 'Low', 'gt_answer': 'high'}, {'question_id': '2072748', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20891556', 'answer': 'Boy', 'gt_answer': 'child'}, {'question_id': '20891557', 'answer': 'Boy', 'gt_answer': 'child'}, {'question_id': '2072741', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201859469', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '2072747', 'answer': 'Color', 'gt_answer': 'material'}, {'question_id': '201400160', 'answer': 'Bookshelf', 'gt_answer': 'bookcase'}, {'question_id': '20609638', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '202133725', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '2098039', 'answer': 'Computer', 'gt_answer': 'keyboard'}, {'question_id': '202100721', 'answer': 'Stove', 'gt_answer': 'stove'}, {'question_id': '201401779', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201882497', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201935898', 'answer': 'Shelf', 'gt_answer': 'shelf'}, {'question_id': '201935899', 'answer': 'Shelf', 'gt_answer': 'shelf'}, {'question_id': '201935896', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20306343', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201984141', 'answer': 'Cell phone', 'gt_answer': 'hair clip'}, {'question_id': '202262289', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201110492', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20573791', 'answer': 'White', 'gt_answer': 'white'}, {'question_id': '20306349', 'answer': 'Snowboard', 'gt_answer': 'floor'}, {'question_id': '20536259', 'answer': 'Giraffe', 'gt_answer': 'bison'}, {'question_id': '202227832', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201952882', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '20541704', 'answer': 'Woman', 'gt_answer': 'woman'}, {'question_id': '20518323', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '202249061', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201593617', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '201889331', 'answer': 'Skier', 'gt_answer': 'skier'}, {'question_id': '20257174', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202005896', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '201548924', 'answer': 'Small', 'gt_answer': 'small'}, {'question_id': '202126118', 'answer': 'Man', 'gt_answer': 'umpire'}, {'question_id': '201498209', 'answer': 'Computer', 'gt_answer': 'computer'}, {'question_id': '202012582', 'answer': 'Shelves', 'gt_answer': 'cabinets'}, {'question_id': '201068276', 'answer': 'Shirt', 'gt_answer': 'blouse'}, {'question_id': '201498206', 'answer': 'Left', 'gt_answer': 'right'}, {'question_id': '20489478', 'answer': 'Bed', 'gt_answer': 'bed'}, {'question_id': '201758431', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202012588', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20941964', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20411904', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '2053511', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201110674', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201068272', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '202036640', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20157521', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201185157', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20631888', 'answer': 'Batter', 'gt_answer': 'catcher'}, {'question_id': '2093826', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20631887', 'answer': 'Crouching', 'gt_answer': 'waiting'}, {'question_id': '2097872', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20754874', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201498639', 'answer': 'Monitor', 'gt_answer': 'keyboard'}, {'question_id': '201491007', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202049257', 'answer': 'White', 'gt_answer': 'white'}, {'question_id': '20403352', 'answer': 'Mirror', 'gt_answer': 'mirror'}, {'question_id': '20963988', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '202049253', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201109237', 'answer': 'Black', 'gt_answer': 'green'}, {'question_id': '202144456', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201080091', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '20648197', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20508519', 'answer': 'Wii controller', 'gt_answer': 'wii controller'}, {'question_id': '20518585', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20442171', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20939917', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20508512', 'answer': 'Wii controller', 'gt_answer': 'wii controller'}, {'question_id': '20508516', 'answer': 'Wii controller', 'gt_answer': 'wii controller'}, {'question_id': '20508515', 'answer': 'Wii controller', 'gt_answer': 'wii controller'}, {'question_id': '202100467', 'answer': 'Boats', 'gt_answer': 'ocean'}, {'question_id': '202073303', 'answer': 'Zebra', 'gt_answer': 'deer'}, {'question_id': '201984055', 'answer': 'Looking down', 'gt_answer': 'looking down'}, {'question_id': '20287970', 'answer': 'Player', 'gt_answer': 'spectators'}, {'question_id': '201481493', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201641416', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201641141', 'answer': 'Gray', 'gt_answer': 'brown'}, {'question_id': '201637253', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201621476', 'answer': 'Couch', 'gt_answer': 'desk'}, {'question_id': '201621470', 'answer': 'Table', 'gt_answer': 'tv stand'}, {'question_id': '201621472', 'answer': 'Couch', 'gt_answer': 'tv stand'}, {'question_id': '201766662', 'answer': 'Pink', 'gt_answer': 'pink'}, {'question_id': '201975068', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20427607', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '202231849', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20511632', 'answer': 'Beach', 'gt_answer': 'beach'}, {'question_id': '20511633', 'answer': 'Beach', 'gt_answer': 'beach'}, {'question_id': '201975064', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20794354', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202100796', 'answer': 'Pot', 'gt_answer': 'artwork'}, {'question_id': '20782939', 'answer': 'Laptop', 'gt_answer': 'laptop'}, {'question_id': '202100795', 'answer': 'Plant', 'gt_answer': 'artwork'}, {'question_id': '20480115', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20753378', 'answer': 'Short', 'gt_answer': 'short'}, {'question_id': '202049296', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '20211262', 'answer': 'Pie', 'gt_answer': 'pie'}, {'question_id': '20211261', 'answer': 'Dessert', 'gt_answer': 'pie'}, {'question_id': '20740872', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20330317', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20740876', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20717135', 'answer': 'Queen', 'gt_answer': 'large'}, {'question_id': '20709944', 'answer': 'Man', 'gt_answer': 'woman'}, {'question_id': '201959685', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20489705', 'answer': 'Blue', 'gt_answer': 'blue'}, {'question_id': '201556903', 'answer': 'Laptop', 'gt_answer': 'laptop'}, {'question_id': '20550478', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201235818', 'answer': 'Looking', 'gt_answer': 'looking down'}, {'question_id': '201235816', 'answer': 'Looking', 'gt_answer': 'looking down'}, {'question_id': '20923017', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201739220', 'answer': 'Clean', 'gt_answer': 'clean'}, {'question_id': '202270848', 'answer': 'Long', 'gt_answer': 'long'}, {'question_id': '201509729', 'answer': 'Motorcycle', 'gt_answer': 'motorcycle'}, {'question_id': '20245914', 'answer': 'Gray', 'gt_answer': 'gray'}, {'question_id': '202110076', 'answer': 'Overcast', 'gt_answer': 'overcast'}, {'question_id': '20416812', 'answer': 'Pizza', 'gt_answer': 'pizza'}, {'question_id': '202006979', 'answer': 'Utensils', 'gt_answer': 'utensils'}, {'question_id': '201391827', 'answer': 'Sofa', 'gt_answer': 'sofa'}, {'question_id': '201391824', 'answer': 'Couch', 'gt_answer': 'sofa'}, {'question_id': '20879187', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20416818', 'answer': 'Pepper', 'gt_answer': 'sausage'}, {'question_id': '20789865', 'answer': 'Sandy', 'gt_answer': 'sandy'}, {'question_id': '201982199', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202006976', 'answer': 'Clean', 'gt_answer': 'clean'}, {'question_id': '20119174', 'answer': 'Plastic', 'gt_answer': 'plastic'}, {'question_id': '20862915', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '2076626', 'answer': 'Building', 'gt_answer': 'windows'}, {'question_id': '201111074', 'answer': 'Gray', 'gt_answer': 'gray'}, {'question_id': '201669482', 'answer': 'Cake', 'gt_answer': 'cupcake'}, {'question_id': '202106233', 'answer': 'Striped', 'gt_answer': 'striped'}, {'question_id': '2044447', 'answer': 'Gray', 'gt_answer': 'gray'}, {'question_id': '20894248', 'answer': 'Short sleeved', 'gt_answer': 'short sleeved'}, {'question_id': '2044442', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20621865', 'answer': 'Top', 'gt_answer': 'top'}, {'question_id': '20781908', 'answer': 'Green', 'gt_answer': 'green'}, {'question_id': '20511543', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201429047', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20320445', 'answer': 'Top', 'gt_answer': 'top'}, {'question_id': '201959882', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201735684', 'answer': 'Yellow', 'gt_answer': 'white'}, {'question_id': '201654500', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201935338', 'answer': 'People', 'gt_answer': 'woman'}, {'question_id': '201444950', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '2094034', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20752309', 'answer': 'Mirror', 'gt_answer': 'towel'}, {'question_id': '202121861', 'answer': 'Green', 'gt_answer': 'white'}, {'question_id': '201983056', 'answer': 'Remote control', 'gt_answer': 'phone'}, {'question_id': '201983054', 'answer': 'Lamp', 'gt_answer': 'phone'}, {'question_id': '20940369', 'answer': 'Toilet', 'gt_answer': 'mat'}, {'question_id': '201462555', 'answer': 'Crouching', 'gt_answer': 'staring'}, {'question_id': '20940364', 'answer': 'Trash can', 'gt_answer': 'mat'}, {'question_id': '20940365', 'answer': 'Trash can', 'gt_answer': 'mat'}, {'question_id': '20940366', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20285601', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20285608', 'answer': 'Square', 'gt_answer': 'rectangular'}, {'question_id': '202284773', 'answer': 'Color', 'gt_answer': 'material'}, {'question_id': '201347351', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20705752', 'answer': 'Computer', 'gt_answer': 'monitor'}, {'question_id': '20330516', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201887161', 'answer': 'Cauliflower', 'gt_answer': 'cauliflower'}, {'question_id': '201887163', 'answer': 'Cauliflower', 'gt_answer': 'broccoli'}, {'question_id': '201616020', 'answer': 'Indoors', 'gt_answer': 'indoors'}, {'question_id': '20756921', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201616026', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20551309', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20596327', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201880490', 'answer': 'Empty', 'gt_answer': 'empty'}, {'question_id': '202226270', 'answer': 'Mashed potatoes', 'gt_answer': 'mashed potatoes'}, {'question_id': '201207498', 'answer': 'Broccoli', 'gt_answer': 'apples'}, {'question_id': '201739052', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20706128', 'answer': 'Computer', 'gt_answer': 'headphones'}, {'question_id': '201738937', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201738939', 'answer': 'Red', 'gt_answer': 'white'}, {'question_id': '201879320', 'answer': 'Truck', 'gt_answer': 'truck'}, {'question_id': '201207495', 'answer': 'Salad', 'gt_answer': 'vegetables'}, {'question_id': '2056002', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '202162609', 'answer': 'Bed', 'gt_answer': 'bookcase'}, {'question_id': '2056008', 'answer': 'Green', 'gt_answer': 'black'}, {'question_id': '20637318', 'answer': 'Oven', 'gt_answer': 'oven'}, {'question_id': '20637319', 'answer': 'Stove', 'gt_answer': 'oven'}, {'question_id': '201757694', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20516076', 'answer': 'Train', 'gt_answer': 'train'}, {'question_id': '20637310', 'answer': 'Stove', 'gt_answer': 'oven'}, {'question_id': '201393675', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201576997', 'answer': 'Mountain', 'gt_answer': 'hillside'}, {'question_id': '20899489', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '201061306', 'answer': 'Green', 'gt_answer': 'white'}, {'question_id': '20596493', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20151756', 'answer': 'Woman', 'gt_answer': 'woman'}, {'question_id': '201576998', 'answer': 'Mountain', 'gt_answer': 'hillside'}, {'question_id': '201671839', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201671835', 'answer': 'Light', 'gt_answer': 'light bulb'}, {'question_id': '201624182', 'answer': 'Pan', 'gt_answer': 'pan'}, {'question_id': '20654958', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20120294', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20508073', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201574228', 'answer': 'Waiting', 'gt_answer': 'waiting'}, {'question_id': '201574224', 'answer': 'Left', 'gt_answer': 'right'}, {'question_id': '20306179', 'answer': 'Jacket', 'gt_answer': 'sweater'}, {'question_id': '20982160', 'answer': 'Shirt', 'gt_answer': 'blouse'}, {'question_id': '20982161', 'answer': 'Shirt', 'gt_answer': 'blouse'}, {'question_id': '20982164', 'answer': 'Shirt', 'gt_answer': 'blouse'}, {'question_id': '20982167', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '2012828', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20295322', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '20434818', 'answer': 'Boy', 'gt_answer': 'boy'}, {'question_id': '202147825', 'answer': 'Taking a picture', 'gt_answer': 'sleeping'}, {'question_id': '20618877', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201996776', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '202231783', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20434817', 'answer': 'Eating', 'gt_answer': 'resting'}, {'question_id': '20434815', 'answer': 'Eating', 'gt_answer': 'resting'}, {'question_id': '20385290', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20385293', 'answer': 'Calculator', 'gt_answer': 'calculator'}, {'question_id': '2062370', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201873015', 'answer': 'Bus', 'gt_answer': 'truck'}, {'question_id': '20385832', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202240295', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20385298', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '2062379', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201303272', 'answer': 'Gold', 'gt_answer': 'gold'}, {'question_id': '201902528', 'answer': 'Silver', 'gt_answer': 'white'}, {'question_id': '20922822', 'answer': 'Truck', 'gt_answer': 'barn'}, {'question_id': '20922821', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20896496', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20942183', 'answer': 'Woman', 'gt_answer': 'girl'}, {'question_id': '20942180', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20922824', 'answer': 'Trees', 'gt_answer': 'trees'}, {'question_id': '202244258', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201908802', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20462129', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20489646', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202006225', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20953092', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20757218', 'answer': 'Metal', 'gt_answer': 'wood'}, {'question_id': '20953094', 'answer': 'Man', 'gt_answer': 'player'}, {'question_id': '201640235', 'answer': 'Short sleeved', 'gt_answer': 'short sleeved'}, {'question_id': '201110801', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20414500', 'answer': 'Man', 'gt_answer': 'skateboarder'}, {'question_id': '20414501', 'answer': 'Man', 'gt_answer': 'skateboarder'}, {'question_id': '201737862', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202004208', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201654314', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201654313', 'answer': 'Horse', 'gt_answer': 'horse'}, {'question_id': '20922793', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202240557', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20922796', 'answer': 'Green', 'gt_answer': 'blue'}, {'question_id': '20631941', 'answer': 'Batter', 'gt_answer': 'catcher'}, {'question_id': '20631940', 'answer': 'Batter', 'gt_answer': 'catcher'}, {'question_id': '20818646', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20818641', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201663689', 'answer': 'Drawers', 'gt_answer': 'drawers'}, {'question_id': '201556733', 'answer': 'Paper', 'gt_answer': 'keyboard'}, {'question_id': '20308832', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '201065051', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202244079', 'answer': 'Cupcake', 'gt_answer': 'cupcakes'}, {'question_id': '201983614', 'answer': 'Man', 'gt_answer': 'man'}, {'question_id': '202042088', 'answer': 'Silver', 'gt_answer': 'silver'}, {'question_id': '20657138', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20412279', 'answer': 'Pink', 'gt_answer': 'pink'}, {'question_id': '20456355', 'answer': 'Clock', 'gt_answer': 'shelves'}, {'question_id': '20456354', 'answer': 'Clock', 'gt_answer': 'shelves'}, {'question_id': '20456357', 'answer': 'Shelf', 'gt_answer': 'shelves'}, {'question_id': '20456356', 'answer': 'Drawers', 'gt_answer': 'shelves'}, {'question_id': '201185316', 'answer': 'Gray', 'gt_answer': 'gray'}, {'question_id': '201996826', 'answer': 'Shirt', 'gt_answer': 'sweater'}, {'question_id': '202144730', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20442346', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201996828', 'answer': 'Shirt', 'gt_answer': 'sweater'}, {'question_id': '201185319', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201497941', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '20785815', 'answer': 'Concrete', 'gt_answer': 'concrete'}, {'question_id': '201682203', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201682208', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20734044', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20783245', 'answer': 'Laptop', 'gt_answer': 'laptop'}, {'question_id': '20783242', 'answer': 'Chair', 'gt_answer': 'laptop'}, {'question_id': '20862765', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20120540', 'answer': 'Gray', 'gt_answer': 'gray'}, {'question_id': '202012444', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '201738860', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202144595', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '20511400', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20518467', 'answer': 'Wood', 'gt_answer': 'wood'}, {'question_id': '20511405', 'answer': 'Helicopter', 'gt_answer': 'helicopter'}, {'question_id': '201822336', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20511409', 'answer': 'Helicopter', 'gt_answer': 'helicopter'}, {'question_id': '20511408', 'answer': 'Helicopter', 'gt_answer': 'helicopter'}, {'question_id': '201873243', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20785779', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20240992', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202180324', 'answer': 'Girl', 'gt_answer': 'soccer player'}, {'question_id': '2053521', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202081266', 'answer': 'Small', 'gt_answer': 'small'}, {'question_id': '201570847', 'answer': 'Tall', 'gt_answer': 'tall'}, {'question_id': '20258700', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '201880509', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201770687', 'answer': 'Green', 'gt_answer': 'blue'}, {'question_id': '20870462', 'answer': 'Man', 'gt_answer': 'player'}, {'question_id': '20827685', 'answer': 'Couch', 'gt_answer': 'sofa'}, {'question_id': '20117806', 'answer': 'Small', 'gt_answer': 'small'}, {'question_id': '202285237', 'answer': 'Egg', 'gt_answer': 'sausage'}, {'question_id': '202285236', 'answer': 'Egg', 'gt_answer': 'sausage'}, {'question_id': '20827681', 'answer': 'Coffee table', 'gt_answer': 'coffee table'}, {'question_id': '20543049', 'answer': 'Metal', 'gt_answer': 'metal'}, {'question_id': '201056020', 'answer': 'Car', 'gt_answer': 'car'}, {'question_id': '201056026', 'answer': 'Car', 'gt_answer': 'car'}, {'question_id': '2017267', 'answer': 'Red', 'gt_answer': 'red'}, {'question_id': '2017261', 'answer': 'Colorful', 'gt_answer': 'colorful'}, {'question_id': '202246783', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202243469', 'answer': 'Truck', 'gt_answer': 'truck'}, {'question_id': '202037119', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201430749', 'answer': 'Tissue', 'gt_answer': 'tie'}, {'question_id': '20902685', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202243579', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '20551461', 'answer': 'Old fashioned', 'gt_answer': 'old fashioned'}, {'question_id': '201669557', 'answer': 'Table', 'gt_answer': 'table'}, {'question_id': '202117035', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20827539', 'answer': 'Chairs', 'gt_answer': 'chairs'}, {'question_id': '20827538', 'answer': 'Black', 'gt_answer': 'black'}, {'question_id': '201247267', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202218726', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '201438766', 'answer': 'Large', 'gt_answer': 'large'}, {'question_id': '202179405', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201879156', 'answer': 'Left', 'gt_answer': 'right'}, {'question_id': '20349930', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201037218', 'answer': 'Black', 'gt_answer': 'black'}, {'question_id': '201640398', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20856725', 'answer': 'Bed', 'gt_answer': 'bed'}, {'question_id': '20856726', 'answer': 'Bed', 'gt_answer': 'bed'}, {'question_id': '202120307', 'answer': 'Clock', 'gt_answer': 'sign'}, {'question_id': '202120306', 'answer': 'Counter', 'gt_answer': 'sign'}, {'question_id': '202023456', 'answer': 'Bed', 'gt_answer': 'bed'}, {'question_id': '20241021', 'answer': 'Eating', 'gt_answer': 'eating'}, {'question_id': '20241020', 'answer': 'Eating', 'gt_answer': 'eating'}, {'question_id': '202100329', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201951945', 'answer': 'Girl', 'gt_answer': 'girl'}, {'question_id': '20637120', 'answer': 'Pan', 'gt_answer': 'cutting board'}, {'question_id': '201143171', 'answer': 'Chairs', 'gt_answer': 'chairs'}, {'question_id': '201760700', 'answer': 'Lawn', 'gt_answer': 'lawn'}, {'question_id': '201760701', 'answer': 'Grass', 'gt_answer': 'lawn'}, {'question_id': '201760707', 'answer': 'Ball', 'gt_answer': 'soccer ball'}, {'question_id': '201143176', 'answer': 'Chair', 'gt_answer': 'chairs'}, {'question_id': '201143177', 'answer': 'Chairs', 'gt_answer': 'chairs'}, {'question_id': '201410987', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201319526', 'answer': 'Woman', 'gt_answer': 'women'}, {'question_id': '20790019', 'answer': 'People', 'gt_answer': 'people'}, {'question_id': '201319528', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '2076227', 'answer': 'Large', 'gt_answer': 'large'}, {'question_id': '20285458', 'answer': 'Fan', 'gt_answer': 'fan'}, {'question_id': '20285459', 'answer': 'Fan', 'gt_answer': 'fan'}, {'question_id': '202246122', 'answer': 'Small', 'gt_answer': 'small'}, {'question_id': '202003910', 'answer': 'Large', 'gt_answer': 'small'}, {'question_id': '20285451', 'answer': 'Metal', 'gt_answer': 'wood'}, {'question_id': '201462407', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201047429', 'answer': 'Jacket', 'gt_answer': 'dress shirt'}, {'question_id': '20178058', 'answer': 'Brown', 'gt_answer': 'brown'}, {'question_id': '201638800', 'answer': 'Car', 'gt_answer': 'car'}, {'question_id': '201047424', 'answer': 'Dress shirt', 'gt_answer': 'suit'}, {'question_id': '202100348', 'answer': 'Boat', 'gt_answer': 'sailboat'}, {'question_id': '2072906', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20645735', 'answer': 'Clean', 'gt_answer': 'clean'}, {'question_id': '201346716', 'answer': 'Motorcycle', 'gt_answer': 'tree'}, {'question_id': '201873359', 'answer': 'Bus', 'gt_answer': 'truck'}, {'question_id': '202081636', 'answer': 'Computer mouse', 'gt_answer': 'computer mouse'}, {'question_id': '201675989', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20550225', 'answer': 'Trailer', 'gt_answer': 'van'}, {'question_id': '20550226', 'answer': 'Trailer', 'gt_answer': 'van'}, {'question_id': '2055581', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20550222', 'answer': 'Bus', 'gt_answer': 'van'}, {'question_id': '2091259', 'answer': 'Dirty', 'gt_answer': 'dirty'}, {'question_id': '201711244', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202049475', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201337043', 'answer': 'Wood', 'gt_answer': 'plastic'}, {'question_id': '20308223', 'answer': 'Cabinets', 'gt_answer': 'cabinets'}, {'question_id': '20308222', 'answer': 'Cabinet', 'gt_answer': 'cabinets'}, {'question_id': '20308226', 'answer': 'Cabinets', 'gt_answer': 'cabinets'}, {'question_id': '20542945', 'answer': 'Gray', 'gt_answer': 'brown'}, {'question_id': '20205073', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '202060062', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202262341', 'answer': 'Flowers', 'gt_answer': 'glass'}, {'question_id': '202262343', 'answer': 'Picture', 'gt_answer': 'menu'}, {'question_id': '20710165', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201663309', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '201763750', 'answer': 'Bed', 'gt_answer': 'mirror'}, {'question_id': '20340856', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20647391', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20414337', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20414336', 'answer': 'Park', 'gt_answer': 'skate park'}, {'question_id': '20411747', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201492231', 'answer': 'Cap', 'gt_answer': 'hat'}, {'question_id': '20631446', 'answer': 'Player', 'gt_answer': 'umpire'}, {'question_id': '201400112', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201590173', 'answer': 'Playing', 'gt_answer': 'staring'}, {'question_id': '20691608', 'answer': 'Clean', 'gt_answer': 'clean'}, {'question_id': '201590177', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '202266110', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202262128', 'answer': 'Stainless steel', 'gt_answer': 'stainless steel'}, {'question_id': '202266114', 'answer': 'Round', 'gt_answer': 'square'}, {'question_id': '201885501', 'answer': 'Shorts', 'gt_answer': 'swimsuit'}, {'question_id': '202081460', 'answer': 'Computer mouse', 'gt_answer': 'computer mouse'}, {'question_id': '20797594', 'answer': 'Gray', 'gt_answer': 'gray'}, {'question_id': '202262127', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201983860', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '20441933', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '2072766', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '2072761', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201185146', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201467596', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202225880', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '201535727', 'answer': 'Yellow', 'gt_answer': 'yellow'}, {'question_id': '201549001', 'answer': 'Rubber', 'gt_answer': 'rubber'}, {'question_id': '201079843', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20136623', 'answer': 'Toilet', 'gt_answer': 'towels'}, {'question_id': '201079847', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20655214', 'answer': 'Brown', 'gt_answer': 'black'}, {'question_id': '20503742', 'answer': 'Rectangle', 'gt_answer': 'rectangular'}, {'question_id': '201902951', 'answer': 'Black', 'gt_answer': 'black'}, {'question_id': '20162086', 'answer': 'Field', 'gt_answer': 'field'}, {'question_id': '20609618', 'answer': 'Cake', 'gt_answer': 'cake'}, {'question_id': '20810939', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '20262801', 'answer': 'Grass', 'gt_answer': 'dress'}, {'question_id': '20262802', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20204655', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201110473', 'answer': 'Candy', 'gt_answer': 'marshmallow'}, {'question_id': '20149672', 'answer': 'Pipe', 'gt_answer': 'pipe'}, {'question_id': '201110477', 'answer': 'Color', 'gt_answer': 'color'}, {'question_id': '201510344', 'answer': 'Pear', 'gt_answer': 'pear'}, {'question_id': '20330582', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20120177', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201467436', 'answer': 'Dark', 'gt_answer': 'dark'}, {'question_id': '202223308', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '202023284', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20183208', 'answer': 'Walking', 'gt_answer': 'resting'}, {'question_id': '20183209', 'answer': 'Man', 'gt_answer': 'woman'}, {'question_id': '202023280', 'answer': 'Chair', 'gt_answer': 'chair'}, {'question_id': '201766706', 'answer': 'Brown', 'gt_answer': 'yellow'}, {'question_id': '202053140', 'answer': 'Field', 'gt_answer': 'field'}, {'question_id': '20157325', 'answer': 'Bacon', 'gt_answer': 'bacon'}, {'question_id': '202257093', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202240636', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '201548904', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201676008', 'answer': 'Brown', 'gt_answer': 'silver'}, {'question_id': '201548909', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20257159', 'answer': 'Yellow', 'gt_answer': 'yellow'}, {'question_id': '201273213', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20317086', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202004070', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201758454', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201498227', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '2053533', 'answer': 'People', 'gt_answer': 'crowd'}, {'question_id': '2053532', 'answer': 'Sign', 'gt_answer': 'street light'}, {'question_id': '202144693', 'answer': 'Bottle', 'gt_answer': 'blender'}, {'question_id': '201175686', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202179417', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202144694', 'answer': 'Blender', 'gt_answer': 'blender'}, {'question_id': '201080523', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '201080527', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20866139', 'answer': 'Kitchen', 'gt_answer': 'floor'}, {'question_id': '20883231', 'answer': 'Boy', 'gt_answer': 'boy'}, {'question_id': '20866135', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20866133', 'answer': 'Woman', 'gt_answer': 'woman'}, {'question_id': '20866131', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202174062', 'answer': 'Stove', 'gt_answer': 'microwave'}, {'question_id': '202053329', 'answer': '13', 'gt_answer': 'young'}, {'question_id': '202240947', 'answer': 'Shirt', 'gt_answer': 'shirt'}, {'question_id': '202240940', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202003816', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20342481', 'answer': 'Bus', 'gt_answer': 'bus'}, {'question_id': '202100437', 'answer': 'Sailboat', 'gt_answer': 'sailboat'}, {'question_id': '201156310', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20783521', 'answer': 'Man', 'gt_answer': 'people'}, {'question_id': '202003814', 'answer': 'Desk', 'gt_answer': 'desk'}, {'question_id': '201491062', 'answer': 'Cows', 'gt_answer': 'goats'}, {'question_id': '201491063', 'answer': 'Cows', 'gt_answer': 'goats'}, {'question_id': '201109219', 'answer': 'Small', 'gt_answer': 'large'}, {'question_id': '20611709', 'answer': 'Dessert', 'gt_answer': 'sandwiches'}, {'question_id': '201676561', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201153461', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201491068', 'answer': 'Cows', 'gt_answer': 'goats'}, {'question_id': '20442155', 'answer': 'Wood', 'gt_answer': 'glass'}, {'question_id': '20508533', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20262511', 'answer': 'Green', 'gt_answer': 'green'}, {'question_id': '20508534', 'answer': 'Woman', 'gt_answer': 'man'}, {'question_id': '201886910', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20899580', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20508539', 'answer': 'Window', 'gt_answer': 'table'}, {'question_id': '20262516', 'answer': 'Grass', 'gt_answer': 'grass'}, {'question_id': '20901813', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20963880', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20492007', 'answer': 'Man', 'gt_answer': 'man'}, {'question_id': '20385461', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201637239', 'answer': 'Large', 'gt_answer': 'huge'}, {'question_id': '201153465', 'answer': 'Plastic', 'gt_answer': 'glass'}, {'question_id': '20724265', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20227110', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202012879', 'answer': 'Television', 'gt_answer': 'television'}, {'question_id': '201301832', 'answer': 'Soft', 'gt_answer': 'hard'}, {'question_id': '201301834', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201861297', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201403947', 'answer': 'Sunny', 'gt_answer': 'cloudless'}, {'question_id': '201885465', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201654437', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201077086', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20717119', 'answer': 'Bed', 'gt_answer': 'bed'}, {'question_id': '201504990', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201641293', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20416605', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201641291', 'answer': 'Pedestrian', 'gt_answer': 'man'}, {'question_id': '201556965', 'answer': 'Left', 'gt_answer': 'right'}, {'question_id': '201770948', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '20754858', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201360960', 'answer': 'Shirt', 'gt_answer': 'jacket'}, {'question_id': '201360961', 'answer': 'Shirt', 'gt_answer': 'jacket'}, {'question_id': '20754855', 'answer': 'Wet', 'gt_answer': 'dry'}, {'question_id': '20899661', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20899663', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201770946', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20673101', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20923077', 'answer': 'Fire truck', 'gt_answer': 'fire truck'}, {'question_id': '20923076', 'answer': 'Car', 'gt_answer': 'ambulance'}, {'question_id': '20923073', 'answer': 'Fire truck', 'gt_answer': 'fire truck'}, {'question_id': '20923072', 'answer': 'Truck', 'gt_answer': 'fire truck'}, {'question_id': '20248140', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202225959', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201682418', 'answer': 'Watching', 'gt_answer': 'waiting'}, {'question_id': '202231536', 'answer': 'Orange', 'gt_answer': 'orange'}, {'question_id': '20416746', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201407060', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201935131', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201756491', 'answer': 'Color', 'gt_answer': 'color'}, {'question_id': '201573888', 'answer': 'Car', 'gt_answer': 'cars'}, {'question_id': '201756496', 'answer': 'Cat', 'gt_answer': 'kitten'}, {'question_id': '201037024', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20621841', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201185346', 'answer': 'Fence', 'gt_answer': 'fence post'}, {'question_id': '202006199', 'answer': 'Drawers', 'gt_answer': 'shelves'}, {'question_id': '20655182', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20655181', 'answer': 'Huge', 'gt_answer': 'huge'}, {'question_id': '201735551', 'answer': 'Shelf', 'gt_answer': 'shelves'}, {'question_id': '2075250', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20691479', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '20655184', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201407324', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201411119', 'answer': 'Shirt', 'gt_answer': 'shirt'}, {'question_id': '20691471', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20691470', 'answer': 'Cabinets', 'gt_answer': 'shelves'}, {'question_id': '20902895', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '20953980', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '20940347', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '201758014', 'answer': 'Small', 'gt_answer': 'small'}, {'question_id': '20149847', 'answer': 'Drawer', 'gt_answer': 'drawer'}, {'question_id': '20149844', 'answer': 'Drawer', 'gt_answer': 'drawer'}, {'question_id': '20149842', 'answer': 'Dresser', 'gt_answer': 'cabinet'}, {'question_id': '20149840', 'answer': 'Drawer', 'gt_answer': 'cabinet'}, {'question_id': '201347374', 'answer': 'Skating', 'gt_answer': 'looking up'}, {'question_id': '201347375', 'answer': 'Skateboarding', 'gt_answer': 'looking up'}, {'question_id': '202284971', 'answer': 'Egg', 'gt_answer': 'sausage'}, {'question_id': '202284977', 'answer': 'Egg', 'gt_answer': 'sausage'}, {'question_id': '202284976', 'answer': 'Banana', 'gt_answer': 'sausage'}, {'question_id': '202284974', 'answer': 'Banana', 'gt_answer': 'banana'}, {'question_id': '20984399', 'answer': 'Boy', 'gt_answer': 'skater'}, {'question_id': '202284978', 'answer': 'Egg', 'gt_answer': 'sausage'}, {'question_id': '201593731', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201687525', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20756904', 'answer': 'Shirt', 'gt_answer': 'robe'}, {'question_id': '20756908', 'answer': 'Sweater', 'gt_answer': 'robe'}, {'question_id': '201956970', 'answer': 'Bookshelf', 'gt_answer': 'tv stand'}, {'question_id': '201527401', 'answer': 'Candle', 'gt_answer': 'candles'}, {'question_id': '20963645', 'answer': 'Color', 'gt_answer': 'color'}, {'question_id': '20963646', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20551327', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20706144', 'answer': 'Headphones', 'gt_answer': 'headphones'}, {'question_id': '201957003', 'answer': 'Tv', 'gt_answer': 'tv stand'}, {'question_id': '201957005', 'answer': 'Shelf', 'gt_answer': 'tv stand'}, {'question_id': '201957004', 'answer': 'Tv', 'gt_answer': 'tv stand'}, {'question_id': '201957007', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202119893', 'answer': 'Square', 'gt_answer': 'rectangular'}, {'question_id': '20667855', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '20247500', 'answer': 'Shirt', 'gt_answer': 'dress'}, {'question_id': '20247502', 'answer': 'Shirt', 'gt_answer': 'dress'}, {'question_id': '2056061', 'answer': 'Car', 'gt_answer': 'car'}, {'question_id': '2056063', 'answer': 'Bus', 'gt_answer': 'bus'}, {'question_id': '202102581', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20183434', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202041875', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20954066', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202041872', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202041871', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202244095', 'answer': 'Cupcake', 'gt_answer': 'cupcakes'}, {'question_id': '201393461', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '20827023', 'answer': 'Chair', 'gt_answer': 'cupboard'}, {'question_id': '20151777', 'answer': 'Black', 'gt_answer': 'black'}, {'question_id': '20827024', 'answer': 'Couch', 'gt_answer': 'cupboard'}, {'question_id': '201998337', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20827028', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '202119913', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201595951', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201669329', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201671819', 'answer': 'Rectangular', 'gt_answer': 'rectangular'}, {'question_id': '201623394', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '2059599', 'answer': 'Umpire', 'gt_answer': 'batter'}, {'question_id': '201574205', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201972689', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201574200', 'answer': 'Black', 'gt_answer': 'black'}, {'question_id': '20302763', 'answer': 'Smooth', 'gt_answer': 'rough'}, {'question_id': '201972682', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20856934', 'answer': 'Green', 'gt_answer': 'green'}, {'question_id': '20162477', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201346534', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201479170', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201346531', 'answer': 'Car', 'gt_answer': 'car'}, {'question_id': '202162310', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20295302', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201346539', 'answer': 'Car', 'gt_answer': 'van'}, {'question_id': '202169132', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '202147805', 'answer': 'Chair', 'gt_answer': 'chair'}, {'question_id': '201337167', 'answer': 'Dirty', 'gt_answer': 'clean'}, {'question_id': '202240205', 'answer': 'Racket', 'gt_answer': 'racket'}, {'question_id': '20482181', 'answer': 'Modern', 'gt_answer': 'modern'}, {'question_id': '201303520', 'answer': 'Yellow', 'gt_answer': 'gold'}, {'question_id': '201303522', 'answer': 'Chair', 'gt_answer': 'chair'}, {'question_id': '201303257', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201434248', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '2062421', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '2062420', 'answer': 'Surfer', 'gt_answer': 'surfer'}, {'question_id': '202228718', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202133574', 'answer': 'Man', 'gt_answer': 'skateboarder'}, {'question_id': '202133575', 'answer': 'Man', 'gt_answer': 'skateboarder'}, {'question_id': '20783237', 'answer': 'Laptop', 'gt_answer': 'screen'}, {'question_id': '201902549', 'answer': 'Speaker', 'gt_answer': 'router'}, {'question_id': '202240793', 'answer': 'Chair', 'gt_answer': 'shelf'}, {'question_id': '201885423', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202240790', 'answer': 'Chair', 'gt_answer': 'chair'}, {'question_id': '201640171', 'answer': 'Left', 'gt_answer': 'right'}, {'question_id': '202228717', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201061246', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201663516', 'answer': 'Old', 'gt_answer': 'new'}, {'question_id': '201621395', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '202244552', 'answer': 'Carrot', 'gt_answer': 'carrots'}, {'question_id': '201908821', 'answer': 'Tray', 'gt_answer': 'napkin'}, {'question_id': '201951726', 'answer': 'Van', 'gt_answer': 'van'}, {'question_id': '201951727', 'answer': 'Van', 'gt_answer': 'van'}, {'question_id': '201951724', 'answer': 'Van', 'gt_answer': 'van'}, {'question_id': '201188351', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202004268', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201951720', 'answer': 'Van', 'gt_answer': 'van'}, {'question_id': '201951721', 'answer': 'Van', 'gt_answer': 'van'}, {'question_id': '20710339', 'answer': 'Pink', 'gt_answer': 'blue'}, {'question_id': '202004263', 'answer': 'Projector', 'gt_answer': 'screen'}, {'question_id': '20462148', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201987211', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20891699', 'answer': 'Fire hydrant', 'gt_answer': 'fire hydrant'}, {'question_id': '201110869', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '20891697', 'answer': 'Fire hydrant', 'gt_answer': 'fire hydrant'}, {'question_id': '20891690', 'answer': 'Child', 'gt_answer': 'child'}, {'question_id': '20411550', 'answer': 'Table', 'gt_answer': 'table'}, {'question_id': '20177460', 'answer': 'Restaurant', 'gt_answer': 'restaurant'}, {'question_id': '202106184', 'answer': 'Man', 'gt_answer': 'man'}, {'question_id': '201713334', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201804389', 'answer': 'Rectangle', 'gt_answer': 'rectangular'}, {'question_id': '20320217', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201713332', 'answer': 'Large', 'gt_answer': 'small'}, {'question_id': '20797788', 'answer': 'Fence', 'gt_answer': 'sky'}, {'question_id': '202012680', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201889281', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201556750', 'answer': 'Keyboard', 'gt_answer': 'keyboard'}, {'question_id': '20258892', 'answer': 'Phone', 'gt_answer': 'phone'}, {'question_id': '201796091', 'answer': 'Purse', 'gt_answer': 'basket'}, {'question_id': '201796090', 'answer': 'Woman', 'gt_answer': 'woman'}, {'question_id': '20785838', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '202042069', 'answer': 'Policeman', 'gt_answer': 'policeman'}, {'question_id': '20456334', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '202053469', 'answer': 'Field', 'gt_answer': 'field'}, {'question_id': '201987213', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201997723', 'answer': 'Open', 'gt_answer': 'closed'}, {'question_id': '202156912', 'answer': 'Elephants', 'gt_answer': 'elephants'}, {'question_id': '202156911', 'answer': 'Elephants', 'gt_answer': 'elephants'}, {'question_id': '202156910', 'answer': 'Elephant', 'gt_answer': 'elephants'}, {'question_id': '202156914', 'answer': 'Zoo', 'gt_answer': 'dirt'}, {'question_id': '20652767', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201880372', 'answer': 'Small', 'gt_answer': 'small'}, {'question_id': '202147669', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202262437', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '201766654', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202169078', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201227912', 'answer': 'Black', 'gt_answer': 'black'}, {'question_id': '20811369', 'answer': 'Right', 'gt_answer': 'left'}, {'question_id': '20226470', 'answer': 'Shirt', 'gt_answer': 'shirt'}, {'question_id': '20262631', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '202169071', 'answer': 'Car', 'gt_answer': 'cars'}, {'question_id': '20963887', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201491050', 'answer': 'Short sleeved', 'gt_answer': 'short sleeved'}, {'question_id': '201676489', 'answer': 'Chair', 'gt_answer': 'shelf'}, {'question_id': '201676488', 'answer': 'Couch', 'gt_answer': 'table'}, {'question_id': '202223177', 'answer': 'Woman', 'gt_answer': 'man'}, {'question_id': '20177501', 'answer': 'Small', 'gt_answer': 'small'}, {'question_id': '20511425', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202243967', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201822212', 'answer': 'Figurine', 'gt_answer': 'decoration'}, {'question_id': '202023491', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201704660', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20162488', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202053464', 'answer': 'Batter', 'gt_answer': 'batter'}, {'question_id': '202257286', 'answer': 'Right', 'gt_answer': 'left'}, {'question_id': '20609199', 'answer': 'Material', 'gt_answer': 'material'}, {'question_id': '201393859', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202174208', 'answer': 'Burner', 'gt_answer': 'stove'}, {'question_id': '201393857', 'answer': 'Off', 'gt_answer': 'off'}, {'question_id': '20899956', 'answer': 'Tomato', 'gt_answer': 'tomatoes'}, {'question_id': '202081246', 'answer': 'White', 'gt_answer': 'orange'}, {'question_id': '20550421', 'answer': 'White', 'gt_answer': 'light blue'}, {'question_id': '202218552', 'answer': 'Silver', 'gt_answer': 'white'}, {'question_id': '201832385', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20543066', 'answer': 'Brown', 'gt_answer': 'brown'}, {'question_id': '20468332', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20939898', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201711163', 'answer': 'Bookshelf', 'gt_answer': 'shelf'}, {'question_id': '20648255', 'answer': 'Man', 'gt_answer': 'woman'}, {'question_id': '20468335', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201997727', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20939890', 'answer': 'White', 'gt_answer': 'white'}, {'question_id': '201404178', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20939895', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20307061', 'answer': 'Camera', 'gt_answer': 'laptop'}, {'question_id': '20183241', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '202053463', 'answer': 'Player', 'gt_answer': 'player'}, {'question_id': '201804652', 'answer': 'Picture frame', 'gt_answer': 'television'}, {'question_id': '20785832', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201492361', 'answer': 'Fence', 'gt_answer': 'fence'}, {'question_id': '201804654', 'answer': 'Speaker', 'gt_answer': 'television'}, {'question_id': '201430767', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '20902667', 'answer': 'Dog', 'gt_answer': 'dog'}, {'question_id': '20902666', 'answer': 'Dog', 'gt_answer': 'dog'}, {'question_id': '201446904', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201393581', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201247247', 'answer': 'Chair', 'gt_answer': 'side table'}, {'question_id': '20692155', 'answer': 'Curtain', 'gt_answer': 'blinds'}, {'question_id': '20295540', 'answer': 'Sleeping', 'gt_answer': 'sleeping'}, {'question_id': '201247243', 'answer': 'Chair', 'gt_answer': 'side table'}, {'question_id': '20295546', 'answer': 'Bed', 'gt_answer': 'bed'}, {'question_id': '20508170', 'answer': 'Large', 'gt_answer': 'large'}, {'question_id': '20295549', 'answer': 'Bed', 'gt_answer': 'bed'}, {'question_id': '20349917', 'answer': 'Straight', 'gt_answer': 'straight'}, {'question_id': '20667443', 'answer': 'Gray', 'gt_answer': 'gray'}, {'question_id': '201480595', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20856708', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201480591', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20861211', 'answer': 'Tan', 'gt_answer': 'tan'}, {'question_id': '202100613', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201623664', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20856704', 'answer': 'Leather', 'gt_answer': 'leather'}, {'question_id': '20856705', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20456802', 'answer': 'Shelf', 'gt_answer': 'cabinet'}, {'question_id': '201412492', 'answer': 'Skier', 'gt_answer': 'skier'}, {'question_id': '201030483', 'answer': 'Shirt', 'gt_answer': 'shirt'}, {'question_id': '20381508', 'answer': 'Books', 'gt_answer': 'book'}, {'question_id': '20551402', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '2076204', 'answer': 'Building', 'gt_answer': 'houses'}, {'question_id': '2076205', 'answer': 'Fence', 'gt_answer': 'houses'}, {'question_id': '202121982', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '202240412', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201935852', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20940287', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201536211', 'answer': 'Batter', 'gt_answer': 'catcher'}, {'question_id': '201080344', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201438642', 'answer': 'Bat', 'gt_answer': 'bat'}, {'question_id': '202226049', 'answer': 'Large', 'gt_answer': 'large'}, {'question_id': '201175533', 'answer': 'Camera', 'gt_answer': 'camera'}, {'question_id': '20259057', 'answer': 'White', 'gt_answer': 'white'}, {'question_id': '202159045', 'answer': 'Trees', 'gt_answer': 'sky'}, {'question_id': '202218557', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20245895', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '2055629', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20245897', 'answer': 'Man', 'gt_answer': 'skateboarder'}, {'question_id': '201047402', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201391986', 'answer': 'Man', 'gt_answer': 'man'}, {'question_id': '201711305', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20645755', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202161879', 'answer': 'Bed', 'gt_answer': 'bookcase'}, {'question_id': '201872991', 'answer': 'Fire truck', 'gt_answer': 'truck'}, {'question_id': '20940134', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '202161870', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20861214', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '202060269', 'answer': 'Tree', 'gt_answer': 'tree'}, {'question_id': '202060268', 'answer': 'Christmas tree', 'gt_answer': 'tree'}, {'question_id': '202006981', 'answer': 'Cabinet', 'gt_answer': 'utensil holder'}, {'question_id': '20258829', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '202100965', 'answer': 'Silver', 'gt_answer': 'silver'}, {'question_id': '20550207', 'answer': 'White', 'gt_answer': 'yellow'}, {'question_id': '201407073', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201447170', 'answer': 'Camera', 'gt_answer': 'camera'}, {'question_id': '20789811', 'answer': 'Beach', 'gt_answer': 'beach'}, {'question_id': '2091230', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '2055565', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201997205', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201972847', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201337067', 'answer': 'Black', 'gt_answer': 'dark'}, {'question_id': '20734235', 'answer': 'Water', 'gt_answer': 'trees'}, {'question_id': '20308201', 'answer': 'Cabinets', 'gt_answer': 'cabinets'}, {'question_id': '201497817', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '20857079', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201185813', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202218617', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '20911205', 'answer': 'Man', 'gt_answer': 'skater'}, {'question_id': '201206930', 'answer': 'Table', 'gt_answer': 'table'}, {'question_id': '20899883', 'answer': 'Purple', 'gt_answer': 'gray'}, {'question_id': '20857077', 'answer': 'Bed', 'gt_answer': 'bed'}, {'question_id': '202218611', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201765946', 'answer': 'Boat', 'gt_answer': 'boats'}, {'question_id': '20903208', 'answer': 'Van', 'gt_answer': 'van'}, {'question_id': '2058506', 'answer': 'Green', 'gt_answer': 'white'}, {'question_id': '202012747', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201765941', 'answer': 'Boats', 'gt_answer': 'boats'}, {'question_id': '20710104', 'answer': 'Cloudy', 'gt_answer': 'cloudy'}, {'question_id': '20903207', 'answer': 'Van', 'gt_answer': 'van'}, {'question_id': '201765949', 'answer': 'Boats', 'gt_answer': 'boats'}, {'question_id': '201765948', 'answer': 'Boat', 'gt_answer': 'boats'}, {'question_id': '201446948', 'answer': 'Porcelain', 'gt_answer': 'porcelain'}, {'question_id': '201763568', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201763732', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20247330', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20863517', 'answer': 'Short', 'gt_answer': 'tall'}, {'question_id': '20340877', 'answer': 'Large', 'gt_answer': 'large'}, {'question_id': '20711515', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20411764', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20247338', 'answer': 'Woman', 'gt_answer': 'woman'}, {'question_id': '202102916', 'answer': 'Silver', 'gt_answer': 'silver'}, {'question_id': '201510378', 'answer': 'Green', 'gt_answer': 'green'}, {'question_id': '20631461', 'answer': 'Black', 'gt_answer': 'black'}, {'question_id': '201510205', 'answer': 'Restaurant', 'gt_answer': 'restaurant'}, {'question_id': '20734239', 'answer': 'Buildings', 'gt_answer': 'buildings'}, {'question_id': '201590118', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201590119', 'answer': 'Man', 'gt_answer': 'player'}, {'question_id': '201756488', 'answer': 'Apple', 'gt_answer': 'banana'}, {'question_id': '20715686', 'answer': 'Light', 'gt_answer': 'light'}, {'question_id': '20715689', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20645801', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '2012995', 'answer': 'Open', 'gt_answer': 'closed'}, {'question_id': '2072789', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20411498', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20411497', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201079861', 'answer': 'Couch', 'gt_answer': 'desk'}, {'question_id': '20411495', 'answer': 'Hot dogs', 'gt_answer': 'hot dogs'}, {'question_id': '20411493', 'answer': 'Hot dogs', 'gt_answer': 'hot dogs'}, {'question_id': '20441959', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20622036', 'answer': 'Horses', 'gt_answer': 'horses'}, {'question_id': '2098070', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '20330305', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '20922899', 'answer': 'Pole', 'gt_answer': 'telephone pole'}, {'question_id': '201861318', 'answer': 'Helmet', 'gt_answer': 'mirror'}, {'question_id': '20609674', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20724405', 'answer': 'Wet', 'gt_answer': 'wet'}, {'question_id': '201429053', 'answer': 'Refrigerator', 'gt_answer': 'stove'}, {'question_id': '201959899', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20716790', 'answer': 'Color', 'gt_answer': 'material'}, {'question_id': '20692243', 'answer': 'Silver', 'gt_answer': 'silver'}, {'question_id': '202060047', 'answer': 'Chair', 'gt_answer': 'couch'}, {'question_id': '202126062', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202060045', 'answer': 'Couch', 'gt_answer': 'couch'}, {'question_id': '201535804', 'answer': 'Box', 'gt_answer': 'box'}, {'question_id': '20922895', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202223369', 'answer': 'Playing', 'gt_answer': 'talking'}, {'question_id': '20204638', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '20320418', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201758619', 'answer': 'Brown', 'gt_answer': 'brown'}, {'question_id': '201055981', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20896403', 'answer': 'Chair', 'gt_answer': 'chair'}, {'question_id': '20149610', 'answer': 'Top', 'gt_answer': 'top'}, {'question_id': '201510593', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201467455', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201055988', 'answer': 'Car', 'gt_answer': 'car'}, {'question_id': '20149618', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20157302', 'answer': 'Bacon', 'gt_answer': 'bacon'}, {'question_id': '202249022', 'answer': 'Black', 'gt_answer': 'beige'}, {'question_id': '201832461', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202156836', 'answer': 'Elephants', 'gt_answer': 'elephants'}, {'question_id': '201247161', 'answer': 'Chair', 'gt_answer': 'chair'}, {'question_id': '201548964', 'answer': 'Large', 'gt_answer': 'large'}, {'question_id': '201156083', 'answer': 'Bat', 'gt_answer': 'bat'}, {'question_id': '20692027', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201663182', 'answer': 'Black', 'gt_answer': 'black'}, {'question_id': '20411934', 'answer': 'Metal', 'gt_answer': 'metal'}, {'question_id': '202144672', 'answer': 'Blender', 'gt_answer': 'blender'}, {'question_id': '202144675', 'answer': 'Blender', 'gt_answer': 'blender'}, {'question_id': '201175110', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202156833', 'answer': 'Elephant', 'gt_answer': 'elephants'}, {'question_id': '202100814', 'answer': 'Left', 'gt_answer': 'right'}, {'question_id': '201175119', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201571131', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201080505', 'answer': 'Man', 'gt_answer': 'umpire'}, {'question_id': '202265945', 'answer': 'Right', 'gt_answer': 'left'}, {'question_id': '201080500', 'answer': 'Man', 'gt_answer': 'umpire'}, {'question_id': '20883216', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20883214', 'answer': 'Boy', 'gt_answer': 'boy'}, {'question_id': '20883215', 'answer': 'Boy', 'gt_answer': 'boy'}, {'question_id': '20883210', 'answer': 'Skate park', 'gt_answer': 'skateboard'}, {'question_id': '20883211', 'answer': 'Skate park', 'gt_answer': 'skateboard'}, {'question_id': '2093863', 'answer': 'Giraffe', 'gt_answer': 'giraffe'}, {'question_id': '201804466', 'answer': 'Laptop', 'gt_answer': 'computer monitor'}, {'question_id': '201235692', 'answer': 'Apples', 'gt_answer': 'bananas'}, {'question_id': '201399988', 'answer': 'Couch', 'gt_answer': 'couch'}, {'question_id': '201752840', 'answer': 'Black', 'gt_answer': 'black'}, {'question_id': '20518618', 'answer': 'Trash can', 'gt_answer': 'tissue box'}, {'question_id': '201621708', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '202208280', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201623416', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20480330', 'answer': 'Bookshelf', 'gt_answer': 'bookcase'}, {'question_id': '201902759', 'answer': 'Computer', 'gt_answer': 'monitor'}, {'question_id': '202100427', 'answer': 'Clean', 'gt_answer': 'clean'}, {'question_id': '201571350', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20492069', 'answer': 'Mountain', 'gt_answer': 'mountain'}, {'question_id': '20492068', 'answer': 'Mountain', 'gt_answer': 'mountain'}, {'question_id': '20480487', 'answer': 'Laptop', 'gt_answer': 'computer'}, {'question_id': '201902750', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201902757', 'answer': 'Computer', 'gt_answer': 'monitor'}, {'question_id': '20896282', 'answer': 'Plastic', 'gt_answer': 'plastic'}, {'question_id': '20724281', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '202262178', 'answer': 'Glass', 'gt_answer': 'flowers'}, {'question_id': '202119404', 'answer': 'Elephant', 'gt_answer': 'elephant'}, {'question_id': '20896289', 'answer': 'Counter', 'gt_answer': 'countertop'}, {'question_id': '20287428', 'answer': 'Aluminum', 'gt_answer': 'aluminum'}, {'question_id': '20287939', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201866593', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201866596', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201920576', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202040353', 'answer': 'Large', 'gt_answer': 'large'}, {'question_id': '202225858', 'answer': 'Chicken', 'gt_answer': 'turkey'}, {'question_id': '20511675', 'answer': 'Tall', 'gt_answer': 'tall'}, {'question_id': '201704509', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202246359', 'answer': 'Desk', 'gt_answer': 'computer desk'}, {'question_id': '202228555', 'answer': 'Cat', 'gt_answer': 'cat'}, {'question_id': '20211227', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202240212', 'answer': 'Girl', 'gt_answer': 'girl'}, {'question_id': '202240213', 'answer': 'Girl', 'gt_answer': 'girl'}, {'question_id': '202240216', 'answer': 'Girl', 'gt_answer': 'girl'}, {'question_id': '20785991', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201751695', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '20929407', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201528079', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201360946', 'answer': 'Yellow', 'gt_answer': 'yellow'}, {'question_id': '201462197', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201556945', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '20936294', 'answer': 'People', 'gt_answer': 'child'}, {'question_id': '20480203', 'answer': 'Bookshelf', 'gt_answer': 'bookcase'}, {'question_id': '20716888', 'answer': 'Camera', 'gt_answer': 'phone'}, {'question_id': '201576662', 'answer': 'Dog', 'gt_answer': 'sheep'}, {'question_id': '20899648', 'answer': 'Sandwich', 'gt_answer': 'sandwich'}, {'question_id': '20716884', 'answer': 'Phone', 'gt_answer': 'phone'}, {'question_id': '201175370', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20716883', 'answer': 'Phone', 'gt_answer': 'phone'}, {'question_id': '20923055', 'answer': 'Truck', 'gt_answer': 'fire truck'}, {'question_id': '20705846', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201654656', 'answer': 'Cart', 'gt_answer': 'fence'}, {'question_id': '20863688', 'answer': 'Small', 'gt_answer': 'large'}, {'question_id': '202270883', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201068584', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '20878899', 'answer': 'Parking lot', 'gt_answer': 'parking lot'}, {'question_id': '201735537', 'answer': 'Bookshelf', 'gt_answer': 'shelves'}, {'question_id': '20248122', 'answer': 'Left', 'gt_answer': 'right'}, {'question_id': '201982159', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20797722', 'answer': 'Sitting', 'gt_answer': 'staring'}, {'question_id': '201735538', 'answer': 'Desk', 'gt_answer': 'desk'}, {'question_id': '201065524', 'answer': 'Brown', 'gt_answer': 'blond'}, {'question_id': '20452285', 'answer': 'Chair', 'gt_answer': 'chair'}, {'question_id': '20119135', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20588987', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201972831', 'answer': 'No one', 'gt_answer': 'woman'}, {'question_id': '201972833', 'answer': 'Stick', 'gt_answer': 'kite'}, {'question_id': '201951893', 'answer': 'Brown', 'gt_answer': 'beige'}, {'question_id': '202218988', 'answer': 'Bottom', 'gt_answer': 'bottom'}, {'question_id': '20452283', 'answer': 'Table', 'gt_answer': 'flowers'}, {'question_id': '202036836', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20940357', 'answer': 'Pink', 'gt_answer': 'white'}, {'question_id': '202081185', 'answer': 'Toaster', 'gt_answer': 'toaster'}, {'question_id': '20452282', 'answer': 'Flowers', 'gt_answer': 'flowers'}, {'question_id': '20887358', 'answer': 'Dog', 'gt_answer': 'dog'}, {'question_id': '201403968', 'answer': 'Cow', 'gt_answer': 'calf'}, {'question_id': '20982613', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '202231802', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20427860', 'answer': 'Chair', 'gt_answer': 'chair'}, {'question_id': '202265778', 'answer': 'Couch', 'gt_answer': 'couch'}, {'question_id': '201047306', 'answer': 'Cell phone', 'gt_answer': 'phone'}, {'question_id': '201403962', 'answer': 'Color', 'gt_answer': 'color'}, {'question_id': '201593535', 'answer': 'Cow', 'gt_answer': 'trees'}, {'question_id': '20320408', 'answer': 'Sitting', 'gt_answer': 'looking down'}, {'question_id': '201593537', 'answer': 'Mountain', 'gt_answer': 'mountains'}, {'question_id': '202120115', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201411130', 'answer': 'Yellow', 'gt_answer': 'white'}, {'question_id': '202244344', 'answer': 'Thick', 'gt_answer': 'thick'}, {'question_id': '201411134', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20320400', 'answer': 'Young', 'gt_answer': 'young'}, {'question_id': '201976966', 'answer': 'Court', 'gt_answer': 'motorcycle'}, {'question_id': '20631691', 'answer': 'Batter', 'gt_answer': 'batter'}, {'question_id': '201637327', 'answer': 'Stove', 'gt_answer': 'gas stove'}, {'question_id': '20672833', 'answer': 'Chair', 'gt_answer': 'table'}, {'question_id': '20827479', 'answer': 'Couch', 'gt_answer': 'sofa'}, {'question_id': '201864508', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20827471', 'answer': 'Window', 'gt_answer': 'window'}, {'question_id': '20567431', 'answer': 'Cloudy', 'gt_answer': 'sunny'}, {'question_id': '201879533', 'answer': 'Truck', 'gt_answer': 'truck'}, {'question_id': '20361330', 'answer': 'Woman', 'gt_answer': 'snowboarder'}, {'question_id': '20210930', 'answer': 'Triangle', 'gt_answer': 'triangular'}, {'question_id': '20210933', 'answer': 'Marshmallow', 'gt_answer': 'pie'}, {'question_id': '201859270', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20361335', 'answer': 'Woman', 'gt_answer': 'snowboarder'}, {'question_id': '20210936', 'answer': 'Pastry', 'gt_answer': 'pie'}, {'question_id': '20210939', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201235852', 'answer': 'Banana', 'gt_answer': 'handbag'}, {'question_id': '201235851', 'answer': 'Banana', 'gt_answer': 'handbag'}, {'question_id': '20361485', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20673120', 'answer': 'Table', 'gt_answer': 'table'}, {'question_id': '20361480', 'answer': 'Pants', 'gt_answer': 'snow pants'}, {'question_id': '20673122', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '2097987', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201735396', 'answer': 'Desk', 'gt_answer': 'desk'}, {'question_id': '20567434', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202161878', 'answer': 'Color', 'gt_answer': 'color'}, {'question_id': '201481784', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201616067', 'answer': 'Pipe', 'gt_answer': 'rug'}, {'question_id': '201616066', 'answer': 'Pipe', 'gt_answer': 'rug'}, {'question_id': '201616060', 'answer': 'Red', 'gt_answer': 'teal'}, {'question_id': '201616068', 'answer': 'Toilet', 'gt_answer': 'toilet'}, {'question_id': '20345031', 'answer': 'Boy', 'gt_answer': 'athlete'}, {'question_id': '20345030', 'answer': 'Boy', 'gt_answer': 'athlete'}, {'question_id': '20963664', 'answer': 'Shelf', 'gt_answer': 'shelf'}, {'question_id': '20963662', 'answer': 'Shelf', 'gt_answer': 'shelf'}, {'question_id': '20345034', 'answer': 'Racket', 'gt_answer': 'racket'}, {'question_id': '20963660', 'answer': 'Shelf', 'gt_answer': 'shelf'}, {'question_id': '201738976', 'answer': 'Pitcher', 'gt_answer': 'pitcher'}, {'question_id': '201738977', 'answer': 'Pitcher', 'gt_answer': 'pitcher'}, {'question_id': '201738972', 'answer': 'Pitcher', 'gt_answer': 'pitcher'}, {'question_id': '201738973', 'answer': 'Pitcher', 'gt_answer': 'pitcher'}, {'question_id': '201957027', 'answer': 'Playing wii', 'gt_answer': 'standing'}, {'question_id': '20667306', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20667875', 'answer': 'Couch', 'gt_answer': 'couch'}, {'question_id': '202119878', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20667878', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201676493', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201957028', 'answer': 'Man', 'gt_answer': 'man'}, {'question_id': '2056048', 'answer': 'Bus', 'gt_answer': 'car'}, {'question_id': '2056049', 'answer': 'Store', 'gt_answer': 'apartment building'}, {'question_id': '201766579', 'answer': 'Girl', 'gt_answer': 'girl'}, {'question_id': '2056044', 'answer': 'Bus', 'gt_answer': 'car'}, {'question_id': '20827009', 'answer': 'Couch', 'gt_answer': 'cupboard'}, {'question_id': '201393635', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20169893', 'answer': 'Dirty', 'gt_answer': 'clean'}, {'question_id': '201393631', 'answer': 'Short', 'gt_answer': 'short'}, {'question_id': '202121530', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20827000', 'answer': 'Stool', 'gt_answer': 'cupboard'}, {'question_id': '20827003', 'answer': 'Couch', 'gt_answer': 'cupboard'}, {'question_id': '201462359', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201143255', 'answer': 'Chairs', 'gt_answer': 'chairs'}, {'question_id': '20827004', 'answer': 'Couch', 'gt_answer': 'cupboard'}, {'question_id': '20169899', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20309093', 'answer': 'Cutting board', 'gt_answer': 'knife block'}, {'question_id': '201235480', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20309094', 'answer': 'Cutting board', 'gt_answer': 'knife block'}, {'question_id': '201654250', 'answer': 'Blue', 'gt_answer': 'light blue'}, {'question_id': '201595976', 'answer': 'Large', 'gt_answer': 'large'}, {'question_id': '20340468', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20162320', 'answer': 'Boy', 'gt_answer': 'boy'}, {'question_id': '20162321', 'answer': 'Boy', 'gt_answer': 'boy'}, {'question_id': '201346519', 'answer': 'Motorcycle', 'gt_answer': 'van'}, {'question_id': '20162323', 'answer': 'Boy', 'gt_answer': 'boy'}, {'question_id': '20162325', 'answer': 'Boy', 'gt_answer': 'boy'}, {'question_id': '20162326', 'answer': 'Boy', 'gt_answer': 'boy'}, {'question_id': '20162327', 'answer': 'Boy', 'gt_answer': 'boy'}, {'question_id': '201590250', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20982122', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20317183', 'answer': 'Cutting board', 'gt_answer': 'coffee pot'}, {'question_id': '201065611', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201479481', 'answer': 'Chicken', 'gt_answer': 'chicken'}, {'question_id': '202147865', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20295365', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201795574', 'answer': 'Child', 'gt_answer': 'child'}, {'question_id': '201030619', 'answer': 'Wii', 'gt_answer': 'wii controller'}, {'question_id': '202116896', 'answer': 'Train', 'gt_answer': 'train'}, {'question_id': '20679408', 'answer': 'Giraffe', 'gt_answer': 'giraffe'}, {'question_id': '201795578', 'answer': 'People', 'gt_answer': 'child'}, {'question_id': '201428519', 'answer': 'Tall', 'gt_answer': 'short'}, {'question_id': '202241028', 'answer': 'Pizza', 'gt_answer': 'pizza'}, {'question_id': '20573505', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '202162645', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20573508', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202241021', 'answer': 'Pizza', 'gt_answer': 'pizza'}, {'question_id': '202133511', 'answer': 'Blue', 'gt_answer': 'blue'}, {'question_id': '201640199', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201902568', 'answer': 'Speaker', 'gt_answer': 'router'}, {'question_id': '20395105', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20922860', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202158898', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201188378', 'answer': 'Dirty', 'gt_answer': 'dirty'}, {'question_id': '201972701', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201908842', 'answer': 'Sandwich', 'gt_answer': 'sandwiches'}, {'question_id': '201896225', 'answer': 'Table', 'gt_answer': 'table'}, {'question_id': '201951705', 'answer': 'Van', 'gt_answer': 'van'}, {'question_id': '20462167', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201908848', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202158897', 'answer': 'Street', 'gt_answer': 'sidewalk'}, {'question_id': '202004240', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201687547', 'answer': 'Man', 'gt_answer': 'man'}, {'question_id': '202042101', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201735230', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201887079', 'answer': 'Purple', 'gt_answer': 'purple'}, {'question_id': '201482076', 'answer': 'Sitting', 'gt_answer': 'posing'}, {'question_id': '20891675', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '20508035', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20411539', 'answer': 'Table', 'gt_answer': 'table'}, {'question_id': '202102701', 'answer': 'Cabinet', 'gt_answer': 'cabinets'}, {'question_id': '201713313', 'answer': 'Bottom', 'gt_answer': 'bottom'}, {'question_id': '201984161', 'answer': 'Suitcase', 'gt_answer': 'luggage cart'}, {'question_id': '20978648', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20936231', 'answer': 'Elephant', 'gt_answer': 'elephant'}, {'question_id': '201174984', 'answer': 'Material', 'gt_answer': 'material'}, {'question_id': '20308872', 'answer': 'Black', 'gt_answer': 'silver'}, {'question_id': '201738967', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201498008', 'answer': 'Desk', 'gt_answer': 'desk'}, {'question_id': '201803824', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20978315', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201974785', 'answer': 'Female', 'gt_answer': 'female'}, {'question_id': '201756945', 'answer': 'Bananas', 'gt_answer': 'banana bunch'}, {'question_id': '20679077', 'answer': 'Field', 'gt_answer': 'park'}, {'question_id': '202004136', 'answer': 'Boy', 'gt_answer': 'man'}, {'question_id': '201030343', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20434722', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202262686', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20345021', 'answer': 'Boy', 'gt_answer': 'athlete'}, {'question_id': '201549035', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202042046', 'answer': 'Gun', 'gt_answer': 'gun'}, {'question_id': '201175586', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202042043', 'answer': 'Policeman', 'gt_answer': 'policeman'}, {'question_id': '201738085', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202042041', 'answer': 'Man', 'gt_answer': 'policeman'}, {'question_id': '202042040', 'answer': 'Riding motorcycle', 'gt_answer': 'looking up'}, {'question_id': '20306719', 'answer': 'Camera', 'gt_answer': 'cell phone'}, {'question_id': '20968496', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202156647', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20136473', 'answer': 'Color', 'gt_answer': 'shape'}, {'question_id': '20136474', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201574089', 'answer': 'Tree', 'gt_answer': 'traffic light'}, {'question_id': '20652709', 'answer': 'Large', 'gt_answer': 'large'}, {'question_id': '20706152', 'answer': 'Computer', 'gt_answer': 'keyboard'}, {'question_id': '20734081', 'answer': 'Wetsuit', 'gt_answer': 'shirt'}, {'question_id': '201621519', 'answer': 'Long', 'gt_answer': 'short'}, {'question_id': '20734088', 'answer': 'People', 'gt_answer': 'people'}, {'question_id': '202053088', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20811307', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '20963862', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202012405', 'answer': 'Porcelain', 'gt_answer': 'porcelain'}, {'question_id': '20536088', 'answer': 'Giraffe', 'gt_answer': 'giraffe'}, {'question_id': '20262617', 'answer': 'Girl', 'gt_answer': 'girl'}, {'question_id': '202012401', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20262618', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201498387', 'answer': 'Computer', 'gt_answer': 'computer mouse'}, {'question_id': '20536081', 'answer': 'Zebra', 'gt_answer': 'bison'}, {'question_id': '202012408', 'answer': 'Doll', 'gt_answer': 'doll'}, {'question_id': '20536083', 'answer': 'Buffalo', 'gt_answer': 'giraffe'}, {'question_id': '201080294', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20511449', 'answer': 'Helicopter', 'gt_answer': 'helicopter'}, {'question_id': '20511447', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201206936', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20511445', 'answer': 'Boat', 'gt_answer': 'ship'}, {'question_id': '20511444', 'answer': 'Boat', 'gt_answer': 'ship'}, {'question_id': '20511442', 'answer': 'Helicopter', 'gt_answer': 'helicopter'}, {'question_id': '202100684', 'answer': 'Pot', 'gt_answer': 'utensil holder'}, {'question_id': '20978715', 'answer': 'Man', 'gt_answer': 'man'}, {'question_id': '20247511', 'answer': 'Green', 'gt_answer': 'gray'}, {'question_id': '20963877', 'answer': 'Wood', 'gt_answer': 'wood'}, {'question_id': '201228265', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201393834', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '201765945', 'answer': 'Surfboard', 'gt_answer': 'boats'}, {'question_id': '20543080', 'answer': 'Fence', 'gt_answer': 'fence'}, {'question_id': '20543081', 'answer': 'Fence', 'gt_answer': 'fence'}, {'question_id': '20543082', 'answer': 'Tree', 'gt_answer': 'bush'}, {'question_id': '20543083', 'answer': 'Tree', 'gt_answer': 'bush'}, {'question_id': '201498167', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20543088', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20716948', 'answer': 'Blue', 'gt_answer': 'blue'}, {'question_id': '20706425', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20706422', 'answer': 'Computer', 'gt_answer': 'cup'}, {'question_id': '20870420', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201947661', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '202244483', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201068772', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20307047', 'answer': 'Camera', 'gt_answer': 'laptop'}, {'question_id': '201068770', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20452165', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20307042', 'answer': 'Black', 'gt_answer': 'black'}, {'question_id': '201068801', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20227092', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201804102', 'answer': 'Laptop', 'gt_answer': 'keyboard'}, {'question_id': '20307048', 'answer': 'Snow', 'gt_answer': 'chair'}, {'question_id': '201068434', 'answer': 'Girl', 'gt_answer': 'girl'}, {'question_id': '201760522', 'answer': 'Field', 'gt_answer': 'lawn'}, {'question_id': '2056056', 'answer': 'Car', 'gt_answer': 'car'}, {'question_id': '201505138', 'answer': 'Wide', 'gt_answer': 'wide'}, {'question_id': '20936089', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20295564', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201430928', 'answer': 'Cabinet', 'gt_answer': 'cabinet'}, {'question_id': '202144330', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202174631', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201952714', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20118987', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202144337', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20667460', 'answer': 'Short', 'gt_answer': 'short'}, {'question_id': '201975120', 'answer': 'Fence', 'gt_answer': 'fence'}, {'question_id': '202107880', 'answer': 'Boy', 'gt_answer': 'boy'}, {'question_id': '202107887', 'answer': 'Boy', 'gt_answer': 'boy'}, {'question_id': '202107885', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201623609', 'answer': 'Tiles', 'gt_answer': 'refrigerator'}, {'question_id': '201739024', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201185756', 'answer': 'Boy', 'gt_answer': 'boy'}, {'question_id': '20818791', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20818790', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202180298', 'answer': 'Red', 'gt_answer': 'red'}, {'question_id': '202100802', 'answer': 'Stove', 'gt_answer': 'stove'}, {'question_id': '201639468', 'answer': 'Zebra', 'gt_answer': 'zebras'}, {'question_id': '2076266', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20295698', 'answer': 'Purple', 'gt_answer': 'purple'}, {'question_id': '201599775', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201599870', 'answer': 'Brown', 'gt_answer': 'brown'}, {'question_id': '20790056', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201639466', 'answer': 'Zebra', 'gt_answer': 'zebras'}, {'question_id': '202073327', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20468867', 'answer': 'Helmet', 'gt_answer': 'pants'}, {'question_id': '201599777', 'answer': 'Chair', 'gt_answer': 'chair'}, {'question_id': '20550592', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201669598', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20468869', 'answer': 'Helmet', 'gt_answer': 'pants'}, {'question_id': '201987171', 'answer': 'Street', 'gt_answer': 'street'}, {'question_id': '2055602', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '202003952', 'answer': 'Rectangular', 'gt_answer': 'rectangular'}, {'question_id': '20826993', 'answer': 'Chair', 'gt_answer': 'cupboard'}, {'question_id': '202052998', 'answer': 'Field', 'gt_answer': 'field'}, {'question_id': '20887113', 'answer': 'Desk', 'gt_answer': 'table'}, {'question_id': '201951831', 'answer': 'Checkered', 'gt_answer': 'dotted'}, {'question_id': '20826999', 'answer': 'Stool', 'gt_answer': 'cupboard'}, {'question_id': '20178097', 'answer': 'Fries', 'gt_answer': 'fries'}, {'question_id': '20178096', 'answer': 'Fries', 'gt_answer': 'fries'}, {'question_id': '201056218', 'answer': 'Ground', 'gt_answer': 'grass'}, {'question_id': '201264127', 'answer': 'Bus', 'gt_answer': 'bus'}, {'question_id': '20783171', 'answer': 'Laptop', 'gt_answer': 'laptop'}, {'question_id': '201264121', 'answer': 'Nothing', 'gt_answer': 'window'}, {'question_id': '201391967', 'answer': 'Wii controller', 'gt_answer': 'wii controller'}, {'question_id': '201264123', 'answer': 'Bus', 'gt_answer': 'bus'}, {'question_id': '201391965', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201434366', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201987172', 'answer': 'Street', 'gt_answer': 'street'}, {'question_id': '201319787', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201497786', 'answer': 'Computer mouse', 'gt_answer': 'speaker'}, {'question_id': '201711209', 'answer': 'Suitcase', 'gt_answer': 'suitcase'}, {'question_id': '201711208', 'answer': 'Suitcase', 'gt_answer': 'suitcase'}, {'question_id': '20551544', 'answer': 'Silver', 'gt_answer': 'gray'}, {'question_id': '2055541', 'answer': 'Color', 'gt_answer': 'color'}, {'question_id': '201492210', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201826551', 'answer': 'Young', 'gt_answer': 'old'}, {'question_id': '20320356', 'answer': 'Blue', 'gt_answer': 'black'}, {'question_id': '201711205', 'answer': 'Empty', 'gt_answer': 'empty'}, {'question_id': '201997263', 'answer': 'Chair', 'gt_answer': 'chair'}, {'question_id': '201047284', 'answer': 'Man', 'gt_answer': 'man'}, {'question_id': '201735255', 'answer': 'Empty', 'gt_answer': 'empty'}, {'question_id': '201998207', 'answer': 'Blue', 'gt_answer': 'gray'}, {'question_id': '20308267', 'answer': 'Cabinet', 'gt_answer': 'cabinets'}, {'question_id': '20308265', 'answer': 'Knives', 'gt_answer': 'cabinets'}, {'question_id': '20308264', 'answer': 'Knives', 'gt_answer': 'cabinets'}, {'question_id': '20857054', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202101018', 'answer': 'Tree', 'gt_answer': 'sign'}, {'question_id': '20308268', 'answer': 'Cabinet', 'gt_answer': 'cabinets'}, {'question_id': '20644798', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20710120', 'answer': 'Color', 'gt_answer': 'color'}, {'question_id': '20247647', 'answer': 'Yellow', 'gt_answer': 'green'}, {'question_id': '20711534', 'answer': 'Teddy bear', 'gt_answer': 'stuffed bear'}, {'question_id': '20711536', 'answer': 'Stuffed bear', 'gt_answer': 'stuffed bear'}, {'question_id': '20899812', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20411709', 'answer': 'Apples', 'gt_answer': 'apples'}, {'question_id': '20340813', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202024745', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20320212', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20381120', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20899818', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201510355', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20302977', 'answer': 'Cloth', 'gt_answer': 'cloth'}, {'question_id': '201510350', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20491816', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201527853', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201490893', 'answer': 'Petting', 'gt_answer': 'standing'}, {'question_id': '201590134', 'answer': 'Man', 'gt_answer': 'player'}, {'question_id': '201400155', 'answer': 'Bookshelf', 'gt_answer': 'couch'}, {'question_id': '202060209', 'answer': 'Ornaments', 'gt_answer': 'ornaments'}, {'question_id': '20609381', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '202060207', 'answer': 'Ornament', 'gt_answer': 'ornaments'}, {'question_id': '201143394', 'answer': 'Table', 'gt_answer': 'table'}, {'question_id': '202060203', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20385957', 'answer': 'Laptop', 'gt_answer': 'charger'}, {'question_id': '202081427', 'answer': 'Toaster', 'gt_answer': 'toaster'}, {'question_id': '201303198', 'answer': 'Glass', 'gt_answer': 'porcelain'}, {'question_id': '201153202', 'answer': 'Bottom', 'gt_answer': 'bottom'}, {'question_id': '202081422', 'answer': 'Light', 'gt_answer': 'wine bottle'}, {'question_id': '202081423', 'answer': 'Light', 'gt_answer': 'toaster'}, {'question_id': '20226787', 'answer': 'Table', 'gt_answer': 'table'}, {'question_id': '20226785', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201400179', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20149585', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201859401', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20891244', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20655258', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20330321', 'answer': 'Ground', 'gt_answer': 'stage'}, {'question_id': '20330320', 'answer': 'Statue', 'gt_answer': 'statue'}, {'question_id': '20330323', 'answer': 'Statue', 'gt_answer': 'statue'}, {'question_id': '20330322', 'answer': 'Ground', 'gt_answer': 'stage'}, {'question_id': '20652573', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20622052', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20330326', 'answer': 'Broom', 'gt_answer': 'broom'}, {'question_id': '201902915', 'answer': 'Keyboard', 'gt_answer': 'computer mouse'}, {'question_id': '202257136', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20810975', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '201902916', 'answer': 'Mouse pad', 'gt_answer': 'mouse pad'}, {'question_id': '201188253', 'answer': 'Yellow', 'gt_answer': 'blue'}, {'question_id': '20609654', 'answer': 'Strawberry', 'gt_answer': 'strawberry'}, {'question_id': '20151631', 'answer': 'Plastic', 'gt_answer': 'plastic'}, {'question_id': '20151632', 'answer': 'Plastic', 'gt_answer': 'plastic'}, {'question_id': '20205031', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '20205037', 'answer': 'Chair', 'gt_answer': 'chair'}, {'question_id': '20542904', 'answer': 'Elephant', 'gt_answer': 'elephant'}, {'question_id': '20542905', 'answer': 'Elephant', 'gt_answer': 'elephant'}, {'question_id': '201765925', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20692224', 'answer': 'Glass', 'gt_answer': 'metal'}, {'question_id': '20692223', 'answer': 'Glass', 'gt_answer': 'metal'}, {'question_id': '201765920', 'answer': 'Man', 'gt_answer': 'surfer'}, {'question_id': '20204615', 'answer': 'Chair', 'gt_answer': 'chair'}, {'question_id': '20204614', 'answer': 'Chair', 'gt_answer': 'chair'}, {'question_id': '202223341', 'answer': 'Van', 'gt_answer': 'car'}, {'question_id': '20414488', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20149639', 'answer': 'Drawer', 'gt_answer': 'drawer'}, {'question_id': '20836300', 'answer': 'Color', 'gt_answer': 'color'}, {'question_id': '20204619', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20414485', 'answer': 'Man', 'gt_answer': 'skateboarder'}, {'question_id': '20414484', 'answer': 'Man', 'gt_answer': 'skateboarder'}, {'question_id': '20856879', 'answer': 'Bed', 'gt_answer': 'bed'}, {'question_id': '20120134', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20786196', 'answer': 'Top', 'gt_answer': 'top'}, {'question_id': '20856877', 'answer': 'Bed', 'gt_answer': 'bed'}, {'question_id': '201548944', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201273252', 'answer': 'Building', 'gt_answer': 'building'}, {'question_id': '201832442', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20151455', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201030591', 'answer': 'Boy', 'gt_answer': 'boy'}, {'question_id': '201998106', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201996617', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20797579', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202144657', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '2046563', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20442446', 'answer': 'Lamp', 'gt_answer': 'plant'}, {'question_id': '20442447', 'answer': 'Lamp', 'gt_answer': 'plant'}, {'question_id': '202119260', 'answer': 'Elephant', 'gt_answer': 'elephant'}, {'question_id': '20442449', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201207199', 'answer': 'Green', 'gt_answer': 'brown'}, {'question_id': '201061098', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202119267', 'answer': 'Elephant', 'gt_answer': 'elephant'}, {'question_id': '20883270', 'answer': 'New', 'gt_answer': 'new'}, {'question_id': '202240983', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '202258318', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202244460', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201879246', 'answer': 'Man', 'gt_answer': 'athlete'}, {'question_id': '202258314', 'answer': 'Horse', 'gt_answer': 'horse'}, {'question_id': '202053354', 'answer': 'Waiting', 'gt_answer': 'playing'}, {'question_id': '20518522', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201859428', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '202023352', 'answer': 'Closet', 'gt_answer': 'bookshelf'}, {'question_id': '20733996', 'answer': 'Buildings', 'gt_answer': 'buildings'}, {'question_id': '20441950', 'answer': 'Door', 'gt_answer': 'door'}, {'question_id': '20733993', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20733992', 'answer': 'Beach', 'gt_answer': 'sand'}, {'question_id': '20442198', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '20733990', 'answer': 'Beach', 'gt_answer': 'sand'}, {'question_id': '201322762', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201068342', 'answer': 'Little', 'gt_answer': 'young'}, {'question_id': '20810791', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20480312', 'answer': 'Bookshelf', 'gt_answer': 'bookcase'}, {'question_id': '20492040', 'answer': 'Birds', 'gt_answer': 'birds'}, {'question_id': '20492042', 'answer': 'Rocks', 'gt_answer': 'ground'}, {'question_id': '202244150', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202005923', 'answer': 'Square', 'gt_answer': 'square'}, {'question_id': '20295376', 'answer': 'Poster', 'gt_answer': 'television'}, {'question_id': '202258441', 'answer': 'Wood', 'gt_answer': 'wood'}, {'question_id': '201641120', 'answer': 'City', 'gt_answer': 'street'}, {'question_id': '201641123', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20968281', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20489494', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201175179', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '20622037', 'answer': 'Horses', 'gt_answer': 'horses'}, {'question_id': '20489490', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '202004035', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201759224', 'answer': 'Coats', 'gt_answer': 'coats'}, {'question_id': '20518747', 'answer': 'Towel', 'gt_answer': 'soap'}, {'question_id': '201704521', 'answer': 'Cow', 'gt_answer': 'cow'}, {'question_id': '201704522', 'answer': 'Cow', 'gt_answer': 'cow'}, {'question_id': '201319446', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201056166', 'answer': 'Man', 'gt_answer': 'soccer player'}, {'question_id': '201056167', 'answer': 'Ball', 'gt_answer': 'ball'}, {'question_id': '202246806', 'answer': 'Dull', 'gt_answer': 'dull'}, {'question_id': '201434237', 'answer': 'Cell phone', 'gt_answer': 'phone'}, {'question_id': '2017494', 'answer': 'Mountain', 'gt_answer': 'mountains'}, {'question_id': '2017495', 'answer': 'Mountain', 'gt_answer': 'mountains'}, {'question_id': '2017493', 'answer': 'Snow', 'gt_answer': 'sky'}, {'question_id': '20518748', 'answer': 'Shampoo', 'gt_answer': 'soap'}, {'question_id': '201497846', 'answer': 'Monitor', 'gt_answer': 'monitor'}, {'question_id': '201497845', 'answer': 'Monitor', 'gt_answer': 'monitor'}, {'question_id': '201528054', 'answer': 'Knife', 'gt_answer': 'knife'}, {'question_id': '20462212', 'answer': 'Wood', 'gt_answer': 'wood'}, {'question_id': '20503763', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201342403', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20412085', 'answer': 'Smiling', 'gt_answer': 'talking'}, {'question_id': '201342407', 'answer': 'Blue', 'gt_answer': 'white'}, {'question_id': '20361493', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20306501', 'answer': 'Man', 'gt_answer': 'man'}, {'question_id': '20536143', 'answer': 'Brown', 'gt_answer': 'dark brown'}, {'question_id': '201109258', 'answer': 'Car', 'gt_answer': 'suv'}, {'question_id': '201174997', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20403335', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20403337', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201974830', 'answer': 'Woman', 'gt_answer': 'player'}, {'question_id': '201109525', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201109256', 'answer': 'Car', 'gt_answer': 'suv'}, {'question_id': '201109255', 'answer': 'Car', 'gt_answer': 'suv'}, {'question_id': '20886973', 'answer': 'Blue', 'gt_answer': 'blue'}, {'question_id': '202006048', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '20248101', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20245756', 'answer': 'Staring', 'gt_answer': 'staring'}, {'question_id': '20655417', 'answer': 'Gray', 'gt_answer': 'gray'}, {'question_id': '201987416', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201972819', 'answer': 'Small', 'gt_answer': 'large'}, {'question_id': '201982173', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201065548', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201987410', 'answer': 'Wood', 'gt_answer': 'metal'}, {'question_id': '2075474', 'answer': 'Deer', 'gt_answer': 'deer'}, {'question_id': '2075475', 'answer': 'Deer', 'gt_answer': 'deer'}, {'question_id': '2075476', 'answer': 'Lake', 'gt_answer': 'lake'}, {'question_id': '2075477', 'answer': 'City', 'gt_answer': 'lake'}, {'question_id': '201972815', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201752966', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '201498590', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201795036', 'answer': 'Large', 'gt_answer': 'large'}, {'question_id': '202036818', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20258686', 'answer': 'Chair', 'gt_answer': 'chair'}, {'question_id': '20177719', 'answer': 'Restaurant', 'gt_answer': 'restaurant'}, {'question_id': '20177718', 'answer': 'Restaurant', 'gt_answer': 'restaurant'}, {'question_id': '201763983', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202073131', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20887376', 'answer': 'Dog', 'gt_answer': 'dog'}, {'question_id': '20589037', 'answer': 'Blue', 'gt_answer': 'blue'}, {'question_id': '20177717', 'answer': 'Table', 'gt_answer': 'table'}, {'question_id': '201301990', 'answer': 'Dirty', 'gt_answer': 'clean'}, {'question_id': '20177714', 'answer': 'Table', 'gt_answer': 'table'}, {'question_id': '201987679', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20177846', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201467277', 'answer': 'Small', 'gt_answer': 'large'}, {'question_id': '201411154', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202270828', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20929347', 'answer': 'Large', 'gt_answer': 'small'}, {'question_id': '20672817', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20672815', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '20204972', 'answer': 'Woman', 'gt_answer': 'woman'}, {'question_id': '20204974', 'answer': 'Laptop', 'gt_answer': 'laptop'}, {'question_id': '20258611', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '20953949', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20827455', 'answer': 'Chair', 'gt_answer': 'sofa'}, {'question_id': '20361316', 'answer': 'Woman', 'gt_answer': 'snowboarder'}, {'question_id': '20210955', 'answer': 'Pie', 'gt_answer': 'pie'}, {'question_id': '20210954', 'answer': 'Dessert', 'gt_answer': 'pie'}, {'question_id': '20361313', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20361310', 'answer': 'Woman', 'gt_answer': 'snowboarder'}, {'question_id': '20757013', 'answer': 'Burner', 'gt_answer': 'stove'}, {'question_id': '20757015', 'answer': 'Stove', 'gt_answer': 'stove'}, {'question_id': '201047342', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201047345', 'answer': 'Collar', 'gt_answer': 'dress shirt'}, {'question_id': '201047346', 'answer': 'Collar', 'gt_answer': 'dress shirt'}, {'question_id': '201047347', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20756945', 'answer': 'Stove', 'gt_answer': 'stove'}, {'question_id': '202081293', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201616041', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '20756946', 'answer': 'Stove', 'gt_answer': 'stove'}, {'question_id': '201616046', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201878388', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '202081996', 'answer': 'Round', 'gt_answer': 'round'}, {'question_id': '201527444', 'answer': 'Silver', 'gt_answer': 'brown'}, {'question_id': '201342284', 'answer': 'Metal', 'gt_answer': 'metal'}, {'question_id': '202244209', 'answer': 'Rice', 'gt_answer': 'rice'}, {'question_id': '20551367', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201430599', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20706180', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '20706183', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201879652', 'answer': 'Dog', 'gt_answer': 'dog'}, {'question_id': '201879385', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '201738998', 'answer': 'Mound', 'gt_answer': 'field'}, {'question_id': '201412269', 'answer': 'Skier', 'gt_answer': 'skier'}, {'question_id': '20667327', 'answer': 'Green', 'gt_answer': 'green'}, {'question_id': '201264203', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20247813', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202161933', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201412263', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201412265', 'answer': 'Man', 'gt_answer': 'skier'}, {'question_id': '201412266', 'answer': 'Pole', 'gt_answer': 'skis'}, {'question_id': '201462385', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201481454', 'answer': 'Waiting', 'gt_answer': 'staring'}, {'question_id': '201481451', 'answer': 'Woman', 'gt_answer': 'woman'}, {'question_id': '201490977', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202121554', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202121557', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '202121551', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201832276', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '2076744', 'answer': 'Car', 'gt_answer': 'cars'}, {'question_id': '2076743', 'answer': 'Car', 'gt_answer': 'cars'}, {'question_id': '20794212', 'answer': 'Bread', 'gt_answer': 'ham'}, {'question_id': '20794211', 'answer': 'Bread', 'gt_answer': 'ham'}, {'question_id': '20794210', 'answer': 'Mustard', 'gt_answer': 'mustard'}, {'question_id': '20794215', 'answer': 'Chicken', 'gt_answer': 'ham'}, {'question_id': '20794214', 'answer': 'Peanut butter', 'gt_answer': 'ham'}, {'question_id': '201795080', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20722070', 'answer': 'Rectangle', 'gt_answer': 'rectangular'}, {'question_id': '20879120', 'answer': 'Brown', 'gt_answer': 'brown'}, {'question_id': '20611506', 'answer': 'Grapes', 'gt_answer': 'grapes'}, {'question_id': '20285113', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20836689', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20285116', 'answer': 'Chair', 'gt_answer': 'chair'}, {'question_id': '201704624', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20836684', 'answer': 'Dolls', 'gt_answer': 'dolls'}, {'question_id': '202000835', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201428685', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '202133684', 'answer': 'Man', 'gt_answer': 'skateboarder'}, {'question_id': '20982106', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201737916', 'answer': 'Player', 'gt_answer': 'player'}, {'question_id': '20942926', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201573989', 'answer': 'People', 'gt_answer': 'people'}, {'question_id': '202228538', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20120156', 'answer': 'Fence', 'gt_answer': 'fence'}, {'question_id': '201576606', 'answer': 'Jacket', 'gt_answer': 'jacket'}, {'question_id': '201576607', 'answer': 'Jacket', 'gt_answer': 'jacket'}, {'question_id': '201576604', 'answer': 'Boy', 'gt_answer': 'child'}, {'question_id': '201156181', 'answer': 'Red', 'gt_answer': 'black'}, {'question_id': '20715789', 'answer': 'Beige', 'gt_answer': 'beige'}, {'question_id': '20618816', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '201896251', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20482140', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20618818', 'answer': 'Girl', 'gt_answer': 'man'}, {'question_id': '20757206', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201879365', 'answer': 'Tree', 'gt_answer': 'basket'}, {'question_id': '20679424', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20863498', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201428534', 'answer': 'Asian', 'gt_answer': 'asian'}, {'question_id': '201428538', 'answer': 'Wii controller', 'gt_answer': 'remote control'}, {'question_id': '202102893', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20151849', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20151843', 'answer': 'Coffee', 'gt_answer': 'coffee'}, {'question_id': '20395127', 'answer': 'Table', 'gt_answer': 'table'}, {'question_id': '201663557', 'answer': 'Oven', 'gt_answer': 'dishwasher'}, {'question_id': '201908860', 'answer': 'Sandwich', 'gt_answer': 'sandwiches'}, {'question_id': '202158885', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20151844', 'answer': 'Coffee', 'gt_answer': 'coffee'}, {'question_id': '201188393', 'answer': 'Tan', 'gt_answer': 'brown'}, {'question_id': '202262515', 'answer': 'Green', 'gt_answer': 'green'}, {'question_id': '201974646', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201188342', 'answer': 'Cell phone', 'gt_answer': 'phone'}, {'question_id': '20903010', 'answer': 'Short sleeved', 'gt_answer': 'short sleeved'}, {'question_id': '20411518', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201638720', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20411516', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '2044425', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202053224', 'answer': 'Black', 'gt_answer': 'black'}, {'question_id': '20508012', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201887054', 'answer': 'Basket', 'gt_answer': 'table'}, {'question_id': '201347376', 'answer': 'Boy', 'gt_answer': 'skateboarder'}, {'question_id': '201887056', 'answer': 'Table', 'gt_answer': 'table'}, {'question_id': '20652493', 'answer': 'Man', 'gt_answer': 'woman'}, {'question_id': '201826736', 'answer': 'Standing', 'gt_answer': 'looking up'}, {'question_id': '201826737', 'answer': 'Standing', 'gt_answer': 'looking up'}, {'question_id': '20308891', 'answer': 'Toaster', 'gt_answer': 'toaster'}, {'question_id': '20978660', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20257411', 'answer': 'Beach', 'gt_answer': 'sand'}, {'question_id': '20783118', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20308899', 'answer': 'Toaster', 'gt_answer': 'toaster'}, {'question_id': '201984171', 'answer': 'Cell phone', 'gt_answer': 'papers'}, {'question_id': '201030367', 'answer': 'Blue', 'gt_answer': 'blue'}, {'question_id': '20811146', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20456669', 'answer': 'Window', 'gt_answer': 'cabinet'}, {'question_id': '201303388', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202265782', 'answer': 'Couch', 'gt_answer': 'couch'}, {'question_id': '202286714', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201153076', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '2062280', 'answer': 'Concrete', 'gt_answer': 'wood'}, {'question_id': '202286719', 'answer': 'Bear', 'gt_answer': 'elephant'}, {'question_id': '20456660', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201303383', 'answer': 'Blue', 'gt_answer': 'gray'}, {'question_id': '20456665', 'answer': 'Drawer', 'gt_answer': 'table'}, {'question_id': '20652729', 'answer': 'Ball', 'gt_answer': 'cars'}, {'question_id': '202223283', 'answer': 'Khaki', 'gt_answer': 'gray'}, {'question_id': '20246053', 'answer': 'Man', 'gt_answer': 'skateboarder'}, {'question_id': '202156626', 'answer': 'Brown', 'gt_answer': 'brown'}, {'question_id': '20246059', 'answer': 'Male', 'gt_answer': 'female'}, {'question_id': '20652727', 'answer': 'Ball', 'gt_answer': 'cars'}, {'question_id': '20330177', 'answer': 'Short', 'gt_answer': 'tall'}, {'question_id': '202246655', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201621574', 'answer': 'Brown', 'gt_answer': 'brown'}, {'question_id': '20284990', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20515819', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20811321', 'answer': 'Brown', 'gt_answer': 'dark'}, {'question_id': '20963846', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201498360', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20262674', 'answer': 'Little', 'gt_answer': 'young'}, {'question_id': '20226430', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201663187', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20306197', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20456593', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20226438', 'answer': 'Asian', 'gt_answer': 'asian'}, {'question_id': '20647537', 'answer': 'Blue', 'gt_answer': 'blue'}, {'question_id': '20262678', 'answer': 'Girl', 'gt_answer': 'girl'}, {'question_id': '202003718', 'answer': 'Laptop', 'gt_answer': 'laptops'}, {'question_id': '20511462', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202003710', 'answer': 'Laptop', 'gt_answer': 'laptops'}, {'question_id': '202003711', 'answer': 'Laptops', 'gt_answer': 'laptops'}, {'question_id': '202003712', 'answer': 'Laptop', 'gt_answer': 'laptops'}, {'question_id': '201319598', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20480784', 'answer': 'Left', 'gt_answer': 'right'}, {'question_id': '202240539', 'answer': 'Controller', 'gt_answer': 'remote control'}, {'question_id': '20157445', 'answer': 'Rectangular', 'gt_answer': 'rectangular'}, {'question_id': '202257932', 'answer': 'Blue', 'gt_answer': 'light blue'}, {'question_id': '201428998', 'answer': 'Refrigerator', 'gt_answer': 'stove'}, {'question_id': '202174125', 'answer': 'Closed', 'gt_answer': 'closed'}, {'question_id': '20344951', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '20724383', 'answer': 'Tall', 'gt_answer': 'tall'}, {'question_id': '202244092', 'answer': 'Beans', 'gt_answer': 'cookies'}, {'question_id': '201428993', 'answer': 'Microwave', 'gt_answer': 'stove'}, {'question_id': '201428990', 'answer': 'Refrigerator', 'gt_answer': 'refrigerator'}, {'question_id': '201682357', 'answer': 'White', 'gt_answer': 'white'}, {'question_id': '20827669', 'answer': 'Brown', 'gt_answer': 'light brown'}, {'question_id': '20936313', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20699174', 'answer': 'Brown', 'gt_answer': 'brown'}, {'question_id': '20551622', 'answer': 'Train', 'gt_answer': 'train'}, {'question_id': '20551620', 'answer': 'Train', 'gt_answer': 'train'}, {'question_id': '201886879', 'answer': 'Table', 'gt_answer': 'table'}, {'question_id': '201996991', 'answer': 'Boy', 'gt_answer': 'pilot'}, {'question_id': '201996992', 'answer': 'Boy', 'gt_answer': 'pilot'}, {'question_id': '201756847', 'answer': 'Banana', 'gt_answer': 'banana'}, {'question_id': '20636922', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201996995', 'answer': 'Boy', 'gt_answer': 'pilot'}, {'question_id': '201996996', 'answer': 'Boy', 'gt_answer': 'pilot'}, {'question_id': '201498101', 'answer': 'Left', 'gt_answer': 'right'}, {'question_id': '201996999', 'answer': 'Boy', 'gt_answer': 'pilot'}, {'question_id': '20636929', 'answer': 'Round', 'gt_answer': 'round'}, {'question_id': '201504786', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202133821', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20452100', 'answer': 'Painting', 'gt_answer': 'picture'}, {'question_id': '20452101', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20939859', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201804695', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '202262344', 'answer': 'Picture', 'gt_answer': 'menu'}, {'question_id': '20896561', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20827598', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202049400', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201444935', 'answer': 'Mountain', 'gt_answer': 'trees'}, {'question_id': '201623628', 'answer': 'Refrigerator', 'gt_answer': 'refrigerator'}, {'question_id': '202144350', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201430706', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20861258', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20667402', 'answer': 'Remote control', 'gt_answer': 'wii controller'}, {'question_id': '20667401', 'answer': 'Remote control', 'gt_answer': 'wii controller'}, {'question_id': '20169696', 'answer': 'Man', 'gt_answer': 'man'}, {'question_id': '202023383', 'answer': 'Metal', 'gt_answer': 'plastic'}, {'question_id': '202156839', 'answer': 'Elephant', 'gt_answer': 'elephants'}, {'question_id': '201037131', 'answer': 'Stop sign', 'gt_answer': 'traffic sign'}, {'question_id': '20753272', 'answer': 'Bed', 'gt_answer': 'dresser'}, {'question_id': '202006898', 'answer': 'Clean', 'gt_answer': 'clean'}, {'question_id': '202244544', 'answer': 'Carrots', 'gt_answer': 'beans'}, {'question_id': '201639441', 'answer': 'Giraffes', 'gt_answer': 'zebras'}, {'question_id': '201639442', 'answer': 'Giraffe', 'gt_answer': 'zebras'}, {'question_id': '202285432', 'answer': 'Round', 'gt_answer': 'round'}, {'question_id': '2076538', 'answer': 'Roof', 'gt_answer': 'roof'}, {'question_id': '201319541', 'answer': 'Woman', 'gt_answer': 'women'}, {'question_id': '202073306', 'answer': 'Zebra', 'gt_answer': 'deer'}, {'question_id': '202073309', 'answer': 'Zebra', 'gt_answer': 'zebra'}, {'question_id': '20472912', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '202285382', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20468802', 'answer': 'Shirt', 'gt_answer': 'helmet'}, {'question_id': '20923153', 'answer': 'Truck', 'gt_answer': 'fire truck'}, {'question_id': '20923154', 'answer': 'Truck', 'gt_answer': 'fire truck'}, {'question_id': '20923155', 'answer': 'Truck', 'gt_answer': 'fire truck'}, {'question_id': '20923158', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20923159', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201763889', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201067613', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '201067616', 'answer': 'Laptop', 'gt_answer': 'laptop'}, {'question_id': '2055667', 'answer': 'Bus', 'gt_answer': 'bus'}, {'question_id': '202243514', 'answer': 'Truck', 'gt_answer': 'truck'}, {'question_id': '202006097', 'answer': 'Clean', 'gt_answer': 'clean'}, {'question_id': '202243510', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '201879712', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20306250', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201879242', 'answer': 'Lady', 'gt_answer': 'athlete'}, {'question_id': '201795943', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '201879244', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201795761', 'answer': 'Paper', 'gt_answer': 'wood'}, {'question_id': '201795946', 'answer': 'Tree', 'gt_answer': 'tree'}, {'question_id': '201879247', 'answer': 'No one', 'gt_answer': 'athlete'}, {'question_id': '20341174', 'answer': 'Glass', 'gt_answer': 'metal'}, {'question_id': '20853902', 'answer': 'Right', 'gt_answer': 'left'}, {'question_id': '20340702', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20657206', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201976486', 'answer': 'Gray', 'gt_answer': 'gray'}, {'question_id': '20744297', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201392027', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20952979', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201392020', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201467391', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20899060', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201982347', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202101030', 'answer': 'Black', 'gt_answer': 'black'}, {'question_id': '20865409', 'answer': 'Trees', 'gt_answer': 'trees'}, {'question_id': '202218650', 'answer': 'Counter', 'gt_answer': 'chalkboard'}, {'question_id': '20857035', 'answer': 'Plastic', 'gt_answer': 'metal'}, {'question_id': '20857034', 'answer': 'Plastic', 'gt_answer': 'metal'}, {'question_id': '202121945', 'answer': 'Dark', 'gt_answer': 'dark'}, {'question_id': '20240864', 'answer': 'Table', 'gt_answer': 'table'}, {'question_id': '20240862', 'answer': 'Table', 'gt_answer': 'table'}, {'question_id': '20247661', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20247660', 'answer': 'Shirt', 'gt_answer': 'dress shirt'}, {'question_id': '202262984', 'answer': 'Red', 'gt_answer': 'red'}, {'question_id': '201235904', 'answer': 'Black', 'gt_answer': 'dark'}, {'question_id': '201447103', 'answer': 'Mirror', 'gt_answer': 'mirror'}, {'question_id': '201447100', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20899836', 'answer': 'Tomatoes', 'gt_answer': 'tomatoes'}, {'question_id': '20340834', 'answer': 'Wide', 'gt_answer': 'wide'}, {'question_id': '201952759', 'answer': 'Train', 'gt_answer': 'train'}, {'question_id': '20381106', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201886876', 'answer': 'Red', 'gt_answer': 'red'}, {'question_id': '20411728', 'answer': 'Pink', 'gt_answer': 'purple'}, {'question_id': '201411217', 'answer': 'Left', 'gt_answer': 'right'}, {'question_id': '201079983', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '20645793', 'answer': 'Top', 'gt_answer': 'top'}, {'question_id': '201822425', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201687440', 'answer': 'Paddle', 'gt_answer': 'paddle'}, {'question_id': '201738830', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20645799', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202060228', 'answer': 'Blue', 'gt_answer': 'white'}, {'question_id': '201669483', 'answer': 'Cake', 'gt_answer': 'cupcake'}, {'question_id': '202156901', 'answer': 'Elephant', 'gt_answer': 'elephants'}, {'question_id': '202003813', 'answer': 'Desk', 'gt_answer': 'desk'}, {'question_id': '201303175', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '202162562', 'answer': 'Map', 'gt_answer': 'pillow'}, {'question_id': '201109171', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202162567', 'answer': 'Bed', 'gt_answer': 'bookcase'}, {'question_id': '201982757', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201593692', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20136644', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20330346', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20541258', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20782981', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20782982', 'answer': 'Left', 'gt_answer': 'right'}, {'question_id': '20330340', 'answer': 'Tree', 'gt_answer': 'fence'}, {'question_id': '20637091', 'answer': 'Cutting board', 'gt_answer': 'cutting board'}, {'question_id': '201393775', 'answer': 'Chair', 'gt_answer': 'chair'}, {'question_id': '20516170', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20637096', 'answer': 'Cutting board', 'gt_answer': 'cutting board'}, {'question_id': '201482178', 'answer': 'Umbrella', 'gt_answer': 'berries'}, {'question_id': '201482179', 'answer': 'Berries', 'gt_answer': 'berries'}, {'question_id': '201247246', 'answer': 'Chair', 'gt_answer': 'side table'}, {'question_id': '20964022', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20964025', 'answer': 'Porcelain', 'gt_answer': 'porcelain'}, {'question_id': '201527589', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '202156909', 'answer': 'Elephant', 'gt_answer': 'elephants'}, {'question_id': '201765900', 'answer': 'Man', 'gt_answer': 'surfer'}, {'question_id': '201765906', 'answer': 'Man', 'gt_answer': 'surfer'}, {'question_id': '201765905', 'answer': 'Man', 'gt_answer': 'surfer'}, {'question_id': '20151611', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202060089', 'answer': 'Green', 'gt_answer': 'green'}, {'question_id': '201882735', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20120449', 'answer': 'Shallow', 'gt_answer': 'shallow'}, {'question_id': '20573716', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '201952808', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '2053841', 'answer': 'Man', 'gt_answer': 'crowd'}, {'question_id': '2053843', 'answer': 'Cow', 'gt_answer': 'crowd'}, {'question_id': '20866377', 'answer': 'Metal', 'gt_answer': 'granite'}, {'question_id': '20836322', 'answer': 'Boat', 'gt_answer': 'boats'}, {'question_id': '20836323', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201983605', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201766010', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202227935', 'answer': 'Rectangle', 'gt_answer': 'square'}, {'question_id': '201826699', 'answer': 'Elephant', 'gt_answer': 'elephant'}, {'question_id': '201879329', 'answer': 'Yellow', 'gt_answer': 'red'}, {'question_id': '201826694', 'answer': 'Elephant', 'gt_answer': 'elephant'}, {'question_id': '20856853', 'answer': 'Bed', 'gt_answer': 'bed'}, {'question_id': '201826690', 'answer': 'Elephant', 'gt_answer': 'elephant'}, {'question_id': '201803639', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201896486', 'answer': 'Glass', 'gt_answer': 'wood'}, {'question_id': '20151475', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202266177', 'answer': 'Pillow', 'gt_answer': 'pillows'}, {'question_id': '20385939', 'answer': 'Plastic', 'gt_answer': 'plastic'}, {'question_id': '20797537', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '202266179', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201982422', 'answer': 'Couch', 'gt_answer': 'couch'}, {'question_id': '201996679', 'answer': 'On', 'gt_answer': 'off'}, {'question_id': '20797530', 'answer': 'White', 'gt_answer': 'white'}, {'question_id': '202024894', 'answer': 'Large', 'gt_answer': 'large'}, {'question_id': '201596058', 'answer': 'Blue', 'gt_answer': 'blue'}, {'question_id': '201080545', 'answer': 'Man', 'gt_answer': 'catcher'}, {'question_id': '202119209', 'answer': 'Man', 'gt_answer': 'man'}, {'question_id': '20883250', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202023597', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20883257', 'answer': 'Blue', 'gt_answer': 'blue'}, {'question_id': '202119200', 'answer': 'Man', 'gt_answer': 'man'}, {'question_id': '201574452', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20982668', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20942029', 'answer': 'Large', 'gt_answer': 'large'}, {'question_id': '20982661', 'answer': 'Pot', 'gt_answer': 'bucket'}, {'question_id': '202053385', 'answer': 'Helmet', 'gt_answer': 'uniform'}, {'question_id': '202053383', 'answer': 'Player', 'gt_answer': 'batter'}, {'question_id': '201346304', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201247242', 'answer': 'Chair', 'gt_answer': 'side table'}, {'question_id': '201621746', 'answer': 'Empty', 'gt_answer': 'full'}, {'question_id': '20342394', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20744266', 'answer': 'Brown', 'gt_answer': 'brown'}, {'question_id': '201479101', 'answer': 'Color', 'gt_answer': 'shape'}, {'question_id': '201595808', 'answer': 'Cloudy', 'gt_answer': 'clear'}, {'question_id': '202012410', 'answer': 'Cabinet', 'gt_answer': 'cabinets'}, {'question_id': '201759290', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202023374', 'answer': 'Silver', 'gt_answer': 'white'}, {'question_id': '201623452', 'answer': 'Stove', 'gt_answer': 'microwave'}, {'question_id': '201322742', 'answer': 'No parking', 'gt_answer': 'street sign'}, {'question_id': '201322743', 'answer': 'Street sign', 'gt_answer': 'street sign'}, {'question_id': '20480371', 'answer': 'Cat', 'gt_answer': 'cat'}, {'question_id': '201428832', 'answer': 'Plastic', 'gt_answer': 'plastic'}, {'question_id': '20645440', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201428838', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '201873620', 'answer': 'Fire truck', 'gt_answer': 'fire truck'}, {'question_id': '20754702', 'answer': 'Skateboarding', 'gt_answer': 'looking down'}, {'question_id': '2053593', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201185703', 'answer': 'Tree', 'gt_answer': 'trees'}, {'question_id': '201185702', 'answer': 'Tree', 'gt_answer': 'trees'}, {'question_id': '201175625', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201185707', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20752167', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '202144637', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '2053598', 'answer': 'Dirty', 'gt_answer': 'dirty'}, {'question_id': '201497868', 'answer': 'Computer mouse', 'gt_answer': 'phone'}, {'question_id': '201885198', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201654496', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '201322524', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201497863', 'answer': 'Monitor', 'gt_answer': 'monitor'}, {'question_id': '201322520', 'answer': 'Car', 'gt_answer': 'car'}, {'question_id': '20287937', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '201704549', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201360988', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '2097699', 'answer': 'Computer monitor', 'gt_answer': 'monitor'}, {'question_id': '20978509', 'answer': 'Large', 'gt_answer': 'large'}, {'question_id': '202240253', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20756708', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20978503', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '201360984', 'answer': 'Black', 'gt_answer': 'gray'}, {'question_id': '201491089', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20899609', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202081002', 'answer': 'Wood', 'gt_answer': 'wood'}, {'question_id': '201491082', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20226596', 'answer': 'Bottom', 'gt_answer': 'bottom'}, {'question_id': '202081004', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201556988', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '202110256', 'answer': 'Looking up', 'gt_answer': 'standing'}, {'question_id': '201109509', 'answer': 'Car', 'gt_answer': 'truck'}, {'question_id': '20177480', 'answer': 'Color', 'gt_answer': 'shape'}, {'question_id': '20177487', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201637219', 'answer': 'Stove', 'gt_answer': 'stove'}, {'question_id': '20177485', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201987479', 'answer': 'Man', 'gt_answer': 'driver'}, {'question_id': '201987478', 'answer': 'Man', 'gt_answer': 'driver'}, {'question_id': '20886951', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201982111', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201654673', 'answer': 'Horse', 'gt_answer': 'horse'}, {'question_id': '201987472', 'answer': 'Man', 'gt_answer': 'driver'}, {'question_id': '201467625', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201407085', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201065567', 'answer': 'Suit', 'gt_answer': 'suit'}, {'question_id': '20588944', 'answer': 'Girl', 'gt_answer': 'skater'}, {'question_id': '20588945', 'answer': 'Skateboarder', 'gt_answer': 'skater'}, {'question_id': '20588947', 'answer': 'Helmet', 'gt_answer': 'headband'}, {'question_id': '20588948', 'answer': 'Helmet', 'gt_answer': 'headband'}, {'question_id': '201676491', 'answer': 'Sofa', 'gt_answer': 'shelf'}, {'question_id': '201065568', 'answer': 'Suit', 'gt_answer': 'suit'}, {'question_id': '201462263', 'answer': 'Umpire', 'gt_answer': 'man'}, {'question_id': '201704674', 'answer': 'Cow', 'gt_answer': 'cows'}, {'question_id': '202003720', 'answer': 'Laptops', 'gt_answer': 'laptops'}, {'question_id': '202073157', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20589015', 'answer': 'Skateboarder', 'gt_answer': 'skater'}, {'question_id': '20177733', 'answer': 'Burger', 'gt_answer': 'burger'}, {'question_id': '20427824', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20589011', 'answer': 'Crowd', 'gt_answer': 'audience'}, {'question_id': '20589013', 'answer': 'Skateboarder', 'gt_answer': 'skater'}, {'question_id': '20473234', 'answer': 'Green', 'gt_answer': 'green'}, {'question_id': '20753489', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20178113', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201616205', 'answer': 'Shirt', 'gt_answer': 'shirt'}, {'question_id': '201987473', 'answer': 'Man', 'gt_answer': 'driver'}, {'question_id': '2075239', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201616202', 'answer': 'Sweater', 'gt_answer': 'shirt'}, {'question_id': '202012878', 'answer': 'Tv', 'gt_answer': 'television'}, {'question_id': '20171080', 'answer': 'Blue', 'gt_answer': 'blue'}, {'question_id': '20171085', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20936124', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '202226142', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20210979', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20953963', 'answer': 'Blue', 'gt_answer': 'blue'}, {'question_id': '20706217', 'answer': 'Computer', 'gt_answer': 'monitor'}, {'question_id': '20210977', 'answer': 'Dessert', 'gt_answer': 'chocolate'}, {'question_id': '20210976', 'answer': 'Dessert', 'gt_answer': 'chocolate'}, {'question_id': '20827439', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20953968', 'answer': 'Sign', 'gt_answer': 'poster'}, {'question_id': '20953969', 'answer': 'Sign', 'gt_answer': 'poster'}, {'question_id': '201235896', 'answer': 'Shirt', 'gt_answer': 'sweater'}, {'question_id': '20856689', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201480434', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '2017167', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20705887', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20757036', 'answer': 'Yellow', 'gt_answer': 'dark'}, {'question_id': '20856681', 'answer': 'Plastic', 'gt_answer': 'plastic'}, {'question_id': '20757032', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20856684', 'answer': 'Bed', 'gt_answer': 'bed'}, {'question_id': '201047326', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202006282', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202101195', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '201527460', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201956911', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201482362', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20656922', 'answer': 'Bush', 'gt_answer': 'minivan'}, {'question_id': '20656924', 'answer': 'Bush', 'gt_answer': 'minivan'}, {'question_id': '20345078', 'answer': 'Boy', 'gt_answer': 'athlete'}, {'question_id': '20656927', 'answer': 'Car', 'gt_answer': 'minivan'}, {'question_id': '201759017', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20656929', 'answer': 'Car', 'gt_answer': 'minivan'}, {'question_id': '201908661', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20295714', 'answer': 'Wide', 'gt_answer': 'wide'}, {'question_id': '202246358', 'answer': 'Desk', 'gt_answer': 'computer desk'}, {'question_id': '201872894', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201957062', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '20752282', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20611740', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20667347', 'answer': 'Cloth', 'gt_answer': 'cloth'}, {'question_id': '202208490', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202119830', 'answer': 'Refrigerator', 'gt_answer': 'refrigerator'}, {'question_id': '202119832', 'answer': 'Refrigerator', 'gt_answer': 'refrigerator'}, {'question_id': '20285351', 'answer': 'Rectangle', 'gt_answer': 'rectangular'}, {'question_id': '20381359', 'answer': 'Chair', 'gt_answer': 'chair'}, {'question_id': '20340642', 'answer': 'Table', 'gt_answer': 'table'}, {'question_id': '201055798', 'answer': 'Car', 'gt_answer': 'car'}, {'question_id': '20381357', 'answer': 'Jacket', 'gt_answer': 'sweatshirt'}, {'question_id': '20177867', 'answer': 'Cucumber', 'gt_answer': 'onion'}, {'question_id': '20177866', 'answer': 'Cucumber', 'gt_answer': 'onion'}, {'question_id': '20645638', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '201428611', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201576918', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201590079', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201143215', 'answer': 'White', 'gt_answer': 'light brown'}, {'question_id': '202258190', 'answer': 'Horse', 'gt_answer': 'horse'}, {'question_id': '20722019', 'answer': 'Metal', 'gt_answer': 'metal'}, {'question_id': '202218861', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20482264', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201671702', 'answer': 'Color', 'gt_answer': 'shape'}, {'question_id': '201624101', 'answer': 'Woman', 'gt_answer': 'woman'}, {'question_id': '201624100', 'answer': 'Woman', 'gt_answer': 'woman'}, {'question_id': '20984344', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20550383', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20611520', 'answer': 'Cupcake', 'gt_answer': 'brownie'}, {'question_id': '20611522', 'answer': 'Pink', 'gt_answer': 'green'}, {'question_id': '20285175', 'answer': 'Square', 'gt_answer': 'square'}, {'question_id': '20836666', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201247196', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20984448', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20942902', 'answer': 'Girl', 'gt_answer': 'soccer player'}, {'question_id': '202228512', 'answer': 'Television', 'gt_answer': 'tissue box'}, {'question_id': '20982470', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202228510', 'answer': 'Cat', 'gt_answer': 'cat'}, {'question_id': '20827207', 'answer': 'Cabinet', 'gt_answer': 'side table'}, {'question_id': '201492366', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201479118', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202060144', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20870434', 'answer': 'Player', 'gt_answer': 'player'}, {'question_id': '20716957', 'answer': 'Bed', 'gt_answer': 'bed'}, {'question_id': '20247290', 'answer': 'Grass', 'gt_answer': 'bushes'}, {'question_id': '201879906', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201795205', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '2058488', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '202158763', 'answer': 'Clear', 'gt_answer': 'clear'}, {'question_id': '20667966', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201896363', 'answer': 'Woman', 'gt_answer': 'lady'}, {'question_id': '20781896', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20716955', 'answer': 'Bed', 'gt_answer': 'bed'}, {'question_id': '202244549', 'answer': 'Carrot', 'gt_answer': 'beans'}, {'question_id': '20781899', 'answer': 'Thick', 'gt_answer': 'thick'}, {'question_id': '201498172', 'answer': 'Computer mouse', 'gt_answer': 'computer'}, {'question_id': '201481865', 'answer': 'Woman', 'gt_answer': 'woman'}, {'question_id': '20302832', 'answer': 'Gray', 'gt_answer': 'black'}, {'question_id': '201481478', 'answer': 'Woman', 'gt_answer': 'woman'}, {'question_id': '201481479', 'answer': 'Man', 'gt_answer': 'woman'}, {'question_id': '202102561', 'answer': 'Sink', 'gt_answer': 'sink'}, {'question_id': '202037130', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202240215', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20786320', 'answer': 'Brown', 'gt_answer': 'dark'}, {'question_id': '20691766', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202162178', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '20692410', 'answer': 'Cabinet', 'gt_answer': 'cabinet'}, {'question_id': '20692411', 'answer': 'Cabinet', 'gt_answer': 'cabinet'}, {'question_id': '20245804', 'answer': 'Top', 'gt_answer': 'top'}, {'question_id': '20692413', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20395140', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201908887', 'answer': 'Sandwich', 'gt_answer': 'sandwiches'}, {'question_id': '201908889', 'answer': 'Sandwich', 'gt_answer': 'sandwiches'}, {'question_id': '201908888', 'answer': 'Sandwich', 'gt_answer': 'sandwiches'}, {'question_id': '20395149', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201481864', 'answer': 'Talking', 'gt_answer': 'posing'}, {'question_id': '201342160', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201185066', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201997712', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20414586', 'answer': 'Man', 'gt_answer': 'spectators'}, {'question_id': '20414584', 'answer': 'Standing', 'gt_answer': 'waiting'}, {'question_id': '20891634', 'answer': 'Standing', 'gt_answer': 'looking down'}, {'question_id': '20891632', 'answer': 'Standing', 'gt_answer': 'looking down'}, {'question_id': '202284841', 'answer': 'Thin', 'gt_answer': 'thin'}, {'question_id': '201987997', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20782843', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20120277', 'answer': 'Rubber', 'gt_answer': 'cloth'}, {'question_id': '20330480', 'answer': 'Car', 'gt_answer': 'car'}, {'question_id': '20330481', 'answer': 'Car', 'gt_answer': 'car'}, {'question_id': '201637211', 'answer': 'Plate', 'gt_answer': 'plate'}, {'question_id': '202265669', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20491668', 'answer': 'Painting', 'gt_answer': 'picture frame'}, {'question_id': '20491669', 'answer': 'Bear', 'gt_answer': 'bear'}, {'question_id': '20262478', 'answer': 'Green', 'gt_answer': 'purple'}, {'question_id': '201889508', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '201803862', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20811126', 'answer': 'Dog', 'gt_answer': 'dog'}, {'question_id': '20811125', 'answer': 'Dog', 'gt_answer': 'dog'}, {'question_id': '20811124', 'answer': 'Dog', 'gt_answer': 'dog'}, {'question_id': '20721744', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202042000', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202208351', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '202286731', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20899032', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202286739', 'answer': 'Oranges', 'gt_answer': 'bananas'}, {'question_id': '201185391', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201467530', 'answer': 'Metal', 'gt_answer': 'metal'}, {'question_id': '201467537', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201467536', 'answer': 'Window', 'gt_answer': 'vase'}, {'question_id': '201637108', 'answer': 'Orange', 'gt_answer': 'orange'}, {'question_id': '201235565', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20541442', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201951631', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202240734', 'answer': 'Shirt', 'gt_answer': 'dress shirt'}, {'question_id': '20257256', 'answer': 'Blue', 'gt_answer': 'blue'}, {'question_id': '201227973', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '202169018', 'answer': 'Sidewalk', 'gt_answer': 'sidewalk'}, {'question_id': '20303026', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201227975', 'answer': 'Black', 'gt_answer': 'silver'}, {'question_id': '201983066', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '202004171', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201974569', 'answer': 'Skinny', 'gt_answer': 'thin'}, {'question_id': '20262653', 'answer': 'Kite', 'gt_answer': 'kite'}, {'question_id': '20262652', 'answer': 'Girl', 'gt_answer': 'girl'}, {'question_id': '2046684', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202245962', 'answer': '20', 'gt_answer': 'young'}, {'question_id': '201759194', 'answer': 'Brown', 'gt_answer': 'tan'}, {'question_id': '20157130', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201654397', 'answer': 'Jockey', 'gt_answer': 'jockey'}, {'question_id': '201654396', 'answer': 'People', 'gt_answer': 'jockey'}, {'question_id': '201654391', 'answer': 'Female', 'gt_answer': 'male'}, {'question_id': '202003773', 'answer': 'Large', 'gt_answer': 'small'}, {'question_id': '20157136', 'answer': 'Egg', 'gt_answer': 'eggs'}, {'question_id': '20724360', 'answer': 'Wet', 'gt_answer': 'wet'}, {'question_id': '20724362', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20183052', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20896366', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '20344938', 'answer': 'Fence', 'gt_answer': 'plants'}, {'question_id': '20896368', 'answer': 'Chair', 'gt_answer': 'chair'}, {'question_id': '20724369', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20344935', 'answer': 'Fence', 'gt_answer': 'lawn'}, {'question_id': '201491043', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202147792', 'answer': 'Top', 'gt_answer': 'top'}, {'question_id': '20257430', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '20257432', 'answer': 'Sand', 'gt_answer': 'water'}, {'question_id': '202100280', 'answer': 'Overcast', 'gt_answer': 'cloudless'}, {'question_id': '201185938', 'answer': 'Fence', 'gt_answer': 'ground'}, {'question_id': '20536092', 'answer': 'Giraffe', 'gt_answer': 'bison'}, {'question_id': '201947583', 'answer': 'Chrome', 'gt_answer': 'chrome'}, {'question_id': '201185933', 'answer': 'Trees', 'gt_answer': 'trees'}, {'question_id': '20636946', 'answer': 'Cutting board', 'gt_answer': 'cutting board'}, {'question_id': '20636940', 'answer': 'Carrot', 'gt_answer': 'potato'}, {'question_id': '20636942', 'answer': 'Carrot', 'gt_answer': 'potato'}, {'question_id': '20258961', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20442251', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20300592', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20340749', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20939835', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201430930', 'answer': 'Cabinet', 'gt_answer': 'cabinet'}, {'question_id': '20452122', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202133801', 'answer': 'Metal', 'gt_answer': 'metal'}, {'question_id': '201984173', 'answer': 'Phone', 'gt_answer': 'papers'}, {'question_id': '20452121', 'answer': 'Flowers', 'gt_answer': 'flowers'}, {'question_id': '20901959', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201902470', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20303072', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20303071', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201804144', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '202126120', 'answer': 'Fence', 'gt_answer': 'shoes'}, {'question_id': '20287855', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202266105', 'answer': 'Brown', 'gt_answer': 'brown'}, {'question_id': '20896502', 'answer': 'Microwave', 'gt_answer': 'refrigerator'}, {'question_id': '20740974', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '201621552', 'answer': 'Gray', 'gt_answer': 'gray'}, {'question_id': '20898749', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '20295524', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202265615', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202121635', 'answer': 'Chairs', 'gt_answer': 'chairs'}, {'question_id': '202144374', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20302785', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '2075825', 'answer': 'Small', 'gt_answer': 'small'}, {'question_id': '202100346', 'answer': 'Boat', 'gt_answer': 'sailboat'}, {'question_id': '201885509', 'answer': 'Swimming pool', 'gt_answer': 'fence'}, {'question_id': '202073362', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20667423', 'answer': 'White', 'gt_answer': 'pink'}, {'question_id': '201412471', 'answer': 'Dry', 'gt_answer': 'wet'}, {'question_id': '201885503', 'answer': 'Shorts', 'gt_answer': 'swimsuit'}, {'question_id': '201996710', 'answer': 'Wall', 'gt_answer': 'helicopter'}, {'question_id': '201654537', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '202102532', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201510209', 'answer': 'Restaurant', 'gt_answer': 'restaurant'}, {'question_id': '202244614', 'answer': 'Carrots', 'gt_answer': 'carrots'}, {'question_id': '20717057', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20753257', 'answer': 'Bed', 'gt_answer': 'dresser'}, {'question_id': '201030592', 'answer': 'Boy', 'gt_answer': 'boy'}, {'question_id': '202147881', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20753250', 'answer': 'Bed', 'gt_answer': 'dresser'}, {'question_id': '201370346', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201866743', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20652581', 'answer': 'Round', 'gt_answer': 'round'}, {'question_id': '201751739', 'answer': 'Yellow', 'gt_answer': 'blond'}, {'question_id': '201997923', 'answer': 'Brown', 'gt_answer': 'dark'}, {'question_id': '20468822', 'answer': 'Fence', 'gt_answer': 'trees'}, {'question_id': '20468823', 'answer': 'Fence', 'gt_answer': 'trees'}, {'question_id': '20117995', 'answer': 'Large', 'gt_answer': 'large'}, {'question_id': '20468821', 'answer': 'Trailer', 'gt_answer': 'trailer'}, {'question_id': '20468534', 'answer': 'Horse', 'gt_answer': 'horse'}, {'question_id': '20427493', 'answer': 'Sleeveless', 'gt_answer': 'long sleeved'}, {'question_id': '20468536', 'answer': 'Horse', 'gt_answer': 'horse'}, {'question_id': '201676260', 'answer': 'Female', 'gt_answer': 'female'}, {'question_id': '201536272', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '202073369', 'answer': 'Wide', 'gt_answer': 'wide'}, {'question_id': '202246610', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201739327', 'answer': 'Player', 'gt_answer': 'player'}, {'question_id': '202003998', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201462483', 'answer': 'Man', 'gt_answer': 'catcher'}, {'question_id': '201739320', 'answer': 'Uniform', 'gt_answer': 'hat'}, {'question_id': '20756518', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201407040', 'answer': 'Color', 'gt_answer': 'color'}, {'question_id': '201067672', 'answer': 'Table', 'gt_answer': 'table'}, {'question_id': '201207098', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201067670', 'answer': 'Laptop', 'gt_answer': 'napkin'}, {'question_id': '201682391', 'answer': 'White', 'gt_answer': 'white'}, {'question_id': '20789966', 'answer': 'Horse', 'gt_answer': 'horse'}, {'question_id': '20753194', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20300520', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20300522', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202059909', 'answer': 'Living room', 'gt_answer': 'living room'}, {'question_id': '20898818', 'answer': 'Large', 'gt_answer': 'large'}, {'question_id': '201947723', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202144320', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201920509', 'answer': 'Grass', 'gt_answer': 'field'}, {'question_id': '201998276', 'answer': 'Table', 'gt_answer': 'table'}, {'question_id': '201947726', 'answer': 'Silver', 'gt_answer': 'gray'}, {'question_id': '20667280', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20667283', 'answer': 'White', 'gt_answer': 'white'}, {'question_id': '201798341', 'answer': 'Small', 'gt_answer': 'small'}, {'question_id': '201763976', 'answer': 'Beds', 'gt_answer': 'beds'}, {'question_id': '20667289', 'answer': 'Short sleeved', 'gt_answer': 'short sleeved'}, {'question_id': '202100902', 'answer': 'Stove', 'gt_answer': 'stove'}, {'question_id': '202100901', 'answer': 'Stove', 'gt_answer': 'stove'}, {'question_id': '20894106', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201972837', 'answer': 'Woman', 'gt_answer': 'woman'}, {'question_id': '202100904', 'answer': 'Window', 'gt_answer': 'wall'}, {'question_id': '201593962', 'answer': 'Gray', 'gt_answer': 'khaki'}, {'question_id': '201067511', 'answer': 'Screen', 'gt_answer': 'calculator'}, {'question_id': '201972836', 'answer': 'No one', 'gt_answer': 'woman'}, {'question_id': '201976395', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20648340', 'answer': 'White', 'gt_answer': 'caucasian'}, {'question_id': '201392008', 'answer': 'Couch', 'gt_answer': 'sofa'}, {'question_id': '201392009', 'answer': 'Couch', 'gt_answer': 'sofa'}, {'question_id': '20865427', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202106408', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201206959', 'answer': 'White', 'gt_answer': 'dark blue'}, {'question_id': '201935114', 'answer': 'Tree', 'gt_answer': 'lamp'}, {'question_id': '20857010', 'answer': 'Orange', 'gt_answer': 'gray'}, {'question_id': '201982329', 'answer': 'Table', 'gt_answer': 'side table'}, {'question_id': '201864488', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202100357', 'answer': 'Sailboat', 'gt_answer': 'sailboat'}, {'question_id': '201639134', 'answer': 'Tree', 'gt_answer': 'plant'}, {'question_id': '201342113', 'answer': 'Airport', 'gt_answer': 'runway'}, {'question_id': '201447126', 'answer': 'Shelf', 'gt_answer': 'shelf'}, {'question_id': '20631544', 'answer': 'People', 'gt_answer': 'crowd'}, {'question_id': '202024782', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '201462226', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20903082', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20247609', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201972838', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20320253', 'answer': 'Wide', 'gt_answer': 'wide'}, {'question_id': '20491856', 'answer': 'Bear', 'gt_answer': 'bear'}, {'question_id': '20691682', 'answer': 'Porcelain', 'gt_answer': 'porcelain'}, {'question_id': '202262561', 'answer': 'Woman', 'gt_answer': 'woman'}, {'question_id': '20341185', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20341181', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201804528', 'answer': 'Flowers', 'gt_answer': 'flowers'}, {'question_id': '20609349', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20299585', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20752343', 'answer': 'Cabinet', 'gt_answer': 'cabinet'}, {'question_id': '20752340', 'answer': 'Cabinets', 'gt_answer': 'cabinet'}, {'question_id': '20706026', 'answer': 'Computer', 'gt_answer': 'headphones'}, {'question_id': '20706024', 'answer': 'Keyboard', 'gt_answer': 'keyboard'}, {'question_id': '20340743', 'answer': 'Chair', 'gt_answer': 'table'}, {'question_id': '201303485', 'answer': 'Long', 'gt_answer': 'long'}, {'question_id': '202162548', 'answer': 'Bed', 'gt_answer': 'bed'}, {'question_id': '20984167', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20922883', 'answer': 'Beautiful', 'gt_answer': 'ugly'}, {'question_id': '20330361', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202080937', 'answer': 'Beautiful', 'gt_answer': 'beautiful'}, {'question_id': '202243932', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202257177', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20516111', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202243935', 'answer': 'Carrot', 'gt_answer': 'beans'}, {'question_id': '201482117', 'answer': 'Umbrella', 'gt_answer': 'jacket'}, {'question_id': '201393753', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202228183', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201438806', 'answer': 'Front', 'gt_answer': 'behind'}, {'question_id': '20609698', 'answer': 'Strawberry', 'gt_answer': 'strawberry'}, {'question_id': '202265779', 'answer': 'Couch', 'gt_answer': 'couch'}, {'question_id': '20863391', 'answer': 'Man', 'gt_answer': 'cyclist'}, {'question_id': '20836344', 'answer': 'White', 'gt_answer': 'white'}, {'question_id': '2059610', 'answer': 'Player', 'gt_answer': 'batter'}, {'question_id': '20302648', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20120466', 'answer': 'Gray', 'gt_answer': 'beige'}, {'question_id': '20863392', 'answer': 'Man', 'gt_answer': 'cyclist'}, {'question_id': '202286718', 'answer': 'Bear', 'gt_answer': 'elephant'}, {'question_id': '202053044', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201410954', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201273293', 'answer': 'Street sign', 'gt_answer': 'street sign'}, {'question_id': '20151495', 'answer': 'Bag', 'gt_answer': 'bag'}, {'question_id': '201273290', 'answer': 'Sticker', 'gt_answer': 'sticker'}, {'question_id': '20151498', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20692085', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20349725', 'answer': 'Woman', 'gt_answer': 'woman'}, {'question_id': '20473154', 'answer': 'Young', 'gt_answer': 'old'}, {'question_id': '201996654', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '20385660', 'answer': 'Calculator', 'gt_answer': 'keyboard'}, {'question_id': '20385662', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201623819', 'answer': 'Cabinets', 'gt_answer': 'cabinets'}, {'question_id': '201570574', 'answer': 'Color', 'gt_answer': 'color'}, {'question_id': '201336993', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '202106128', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20503650', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202073332', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201623386', 'answer': 'Cabinets', 'gt_answer': 'cupboards'}, {'question_id': '201880279', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202119229', 'answer': 'Hat', 'gt_answer': 'shirt'}, {'question_id': '201346320', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20982640', 'answer': 'Metal', 'gt_answer': 'metal'}, {'question_id': '20162115', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201864381', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20982317', 'answer': 'Wine glass', 'gt_answer': 'glass'}, {'question_id': '20741213', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20982314', 'answer': 'Wine glass', 'gt_answer': 'glass'}, {'question_id': '201346328', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20929272', 'answer': 'Color', 'gt_answer': 'color'}, {'question_id': '20162119', 'answer': 'Short', 'gt_answer': 'short'}, {'question_id': '201866728', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201156391', 'answer': 'White', 'gt_answer': 'white'}, {'question_id': '201951488', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201951486', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201951485', 'answer': 'Van', 'gt_answer': 'van'}, {'question_id': '202258359', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '201758105', 'answer': 'Man', 'gt_answer': 'woman'}, {'question_id': '202241127', 'answer': 'Table', 'gt_answer': 'shelf'}, {'question_id': '201624331', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201737745', 'answer': 'Color', 'gt_answer': 'color'}, {'question_id': '201883056', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201758108', 'answer': 'Chair', 'gt_answer': 'chair'}, {'question_id': '201757828', 'answer': 'Table', 'gt_answer': 'table'}, {'question_id': '202023314', 'answer': 'Closet', 'gt_answer': 'closet'}, {'question_id': '20953019', 'answer': 'Man', 'gt_answer': 'player'}, {'question_id': '201757823', 'answer': 'Table', 'gt_answer': 'table'}, {'question_id': '20480359', 'answer': 'Cat', 'gt_answer': 'cat'}, {'question_id': '20157092', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20157090', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202244115', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20865990', 'answer': 'Heavy', 'gt_answer': 'heavy'}, {'question_id': '201065138', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201663745', 'answer': 'Cabinets', 'gt_answer': 'cabinets'}, {'question_id': '202133612', 'answer': 'Pants', 'gt_answer': 'jeans'}, {'question_id': '20935904', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201882695', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201556616', 'answer': 'Brown', 'gt_answer': 'tan'}, {'question_id': '202257967', 'answer': 'Looking down', 'gt_answer': 'looking down'}, {'question_id': '202162319', 'answer': 'Brown', 'gt_answer': 'gray'}, {'question_id': '20482488', 'answer': 'Woman', 'gt_answer': 'woman'}, {'question_id': '20482489', 'answer': 'Woman', 'gt_answer': 'woman'}, {'question_id': '20412511', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201185727', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202144612', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20403516', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201110699', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202144615', 'answer': 'Bottle', 'gt_answer': 'cup'}, {'question_id': '201055612', 'answer': 'Color', 'gt_answer': 'color'}, {'question_id': '20652825', 'answer': 'Tree', 'gt_answer': 'trees'}, {'question_id': '20652824', 'answer': 'Tree', 'gt_answer': 'trees'}, {'question_id': '201713529', 'answer': 'Brush', 'gt_answer': 'brush'}, {'question_id': '201067861', 'answer': 'Silver', 'gt_answer': 'beige'}, {'question_id': '20785934', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20709931', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20785937', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201399878', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201621423', 'answer': 'Speaker', 'gt_answer': 'mirror'}, {'question_id': '20724191', 'answer': 'Man', 'gt_answer': 'snowboarder'}, {'question_id': '202125952', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201713525', 'answer': 'Woman', 'gt_answer': 'woman'}, {'question_id': '201713526', 'answer': 'Brush', 'gt_answer': 'brush'}, {'question_id': '20783301', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202147702', 'answer': 'Outfit', 'gt_answer': 'shirt'}, {'question_id': '202262369', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201428430', 'answer': 'Long sleeved', 'gt_answer': 'long sleeved'}, {'question_id': '20710420', 'answer': 'Snow', 'gt_answer': 'ground'}, {'question_id': '201889314', 'answer': 'Skier', 'gt_answer': 'skier'}, {'question_id': '20710422', 'answer': 'Child', 'gt_answer': 'child'}, {'question_id': '201412504', 'answer': 'Skier', 'gt_answer': 'skier'}, {'question_id': '202081023', 'answer': 'House', 'gt_answer': 'house'}, {'question_id': '20709786', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202036627', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '202036629', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20456416', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201109297', 'answer': 'Car', 'gt_answer': 'suv'}, {'question_id': '20887085', 'answer': 'Office', 'gt_answer': 'office'}, {'question_id': '201428959', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '20637192', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20518562', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201412501', 'answer': 'Short', 'gt_answer': 'tall'}, {'question_id': '2075430', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20655454', 'answer': 'Man', 'gt_answer': 'gentleman'}, {'question_id': '20655457', 'answer': 'Man', 'gt_answer': 'gentleman'}, {'question_id': '20655456', 'answer': 'Man', 'gt_answer': 'gentleman'}, {'question_id': '2075434', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201412502', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20655453', 'answer': 'Man', 'gt_answer': 'gentleman'}, {'question_id': '202081685', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201228160', 'answer': 'Car', 'gt_answer': 'car'}, {'question_id': '20789886', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201982138', 'answer': 'Boy', 'gt_answer': 'man'}, {'question_id': '202012386', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202036724', 'answer': 'Black', 'gt_answer': 'black'}, {'question_id': '202285574', 'answer': 'Green', 'gt_answer': 'green'}, {'question_id': '202073174', 'answer': 'Deer', 'gt_answer': 'horses'}, {'question_id': '201654629', 'answer': 'Wood', 'gt_answer': 'metal'}, {'question_id': '201879053', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20473213', 'answer': 'Woman', 'gt_answer': 'woman'}, {'question_id': '20473214', 'answer': 'Chair', 'gt_answer': 'folding chair'}, {'question_id': '20473216', 'answer': 'Chair', 'gt_answer': 'folding chair'}, {'question_id': '201056127', 'answer': 'Boy', 'gt_answer': 'soccer player'}, {'question_id': '201056123', 'answer': 'Man', 'gt_answer': 'soccer player'}, {'question_id': '201751501', 'answer': 'Color', 'gt_answer': 'material'}, {'question_id': '20672859', 'answer': 'Chair', 'gt_answer': 'chair'}, {'question_id': '201616225', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20672851', 'answer': 'Chair', 'gt_answer': 'chair'}, {'question_id': '20756563', 'answer': 'Bottom', 'gt_answer': 'bottom'}, {'question_id': '20936102', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '20936108', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202243527', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '202226166', 'answer': 'Left', 'gt_answer': 'right'}, {'question_id': '202241185', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20636773', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20706233', 'answer': 'Desk', 'gt_answer': 'desk'}, {'question_id': '20636771', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20636770', 'answer': 'Color', 'gt_answer': 'shape'}, {'question_id': '201879039', 'answer': 'Window', 'gt_answer': 'sign'}, {'question_id': '20861191', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202285579', 'answer': 'Plastic', 'gt_answer': 'plastic'}, {'question_id': '202100590', 'answer': 'Sailboat', 'gt_answer': 'sailboats'}, {'question_id': '202156773', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20492198', 'answer': 'Mountains', 'gt_answer': 'mountain'}, {'question_id': '202107906', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '2017189', 'answer': 'Horse', 'gt_answer': 'horse'}, {'question_id': '20757052', 'answer': 'Beer', 'gt_answer': 'wine'}, {'question_id': '20757053', 'answer': 'Wine', 'gt_answer': 'oil'}, {'question_id': '20886934', 'answer': 'Color', 'gt_answer': 'color'}, {'question_id': '201235538', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20361481', 'answer': 'Jacket', 'gt_answer': 'snow pants'}, {'question_id': '20492197', 'answer': 'Mountain', 'gt_answer': 'mountain'}, {'question_id': '2017182', 'answer': 'Mountain', 'gt_answer': 'pasture'}, {'question_id': '20492194', 'answer': 'Snow', 'gt_answer': 'snow'}, {'question_id': '20879072', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20879070', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20656904', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201872989', 'answer': 'Bus', 'gt_answer': 'truck'}, {'question_id': '20656903', 'answer': 'Plastic', 'gt_answer': 'metal'}, {'question_id': '201319667', 'answer': 'Wood', 'gt_answer': 'wood'}, {'question_id': '20345098', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202226292', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20285379', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20515054', 'answer': 'Zebras', 'gt_answer': 'zebras'}, {'question_id': '201235537', 'answer': 'Shelf', 'gt_answer': 'shelves'}, {'question_id': '202208475', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201235530', 'answer': 'Long', 'gt_answer': 'long'}, {'question_id': '20611897', 'answer': 'Ice cream', 'gt_answer': 'brownie'}, {'question_id': '20667369', 'answer': 'Controller', 'gt_answer': 'wii controller'}, {'question_id': '202003835', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201982990', 'answer': 'Couch', 'gt_answer': 'coffee table'}, {'question_id': '20754603', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202120172', 'answer': 'Square', 'gt_answer': 'rectangular'}, {'question_id': '20541412', 'answer': 'Sofa', 'gt_answer': 'couch'}, {'question_id': '201590051', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20645616', 'answer': 'Toilet', 'gt_answer': 'toilet'}, {'question_id': '20645617', 'answer': 'Toilet', 'gt_answer': 'toilet'}, {'question_id': '201548734', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '202286611', 'answer': 'Flower', 'gt_answer': 'ground'}, {'question_id': '201590059', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20940036', 'answer': 'Green', 'gt_answer': 'dark'}, {'question_id': '201879277', 'answer': 'Spectator', 'gt_answer': 'crowd'}, {'question_id': '20722035', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201757649', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20482240', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201879276', 'answer': 'No one', 'gt_answer': 'crowd'}, {'question_id': '201175032', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201623317', 'answer': 'Refrigerator', 'gt_answer': 'oven'}, {'question_id': '201976720', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201623314', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201976726', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202162373', 'answer': 'Small', 'gt_answer': 'small'}, {'question_id': '20644699', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20644698', 'answer': 'Dirty', 'gt_answer': 'dirty'}, {'question_id': '201233937', 'answer': 'Train', 'gt_answer': 'bushes'}, {'question_id': '201956953', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '201047308', 'answer': 'Cell phone', 'gt_answer': 'phone'}, {'question_id': '202000871', 'answer': 'Boy', 'gt_answer': 'skater'}, {'question_id': '201337106', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201735487', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20308162', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202107911', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201735482', 'answer': 'Desk', 'gt_answer': 'tray'}, {'question_id': '201735481', 'answer': 'Keyboard', 'gt_answer': 'keyboard'}, {'question_id': '201047303', 'answer': 'Man', 'gt_answer': 'man'}, {'question_id': '202060165', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '202258184', 'answer': 'Horse', 'gt_answer': 'horse'}, {'question_id': '202258188', 'answer': 'Horse', 'gt_answer': 'horse'}, {'question_id': '2066032', 'answer': 'Jacket', 'gt_answer': 'sweater'}, {'question_id': '2066033', 'answer': 'Jacket', 'gt_answer': 'sweater'}, {'question_id': '20715746', 'answer': 'Heavy', 'gt_answer': 'heavy'}, {'question_id': '2066094', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20385279', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '202004301', 'answer': 'Projector', 'gt_answer': 'screen'}, {'question_id': '201400158', 'answer': 'Bookshelf', 'gt_answer': 'bookcase'}, {'question_id': '201980790', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20863451', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201859640', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20836421', 'answer': 'Green', 'gt_answer': 'green'}, {'question_id': '2075931', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201795132', 'answer': 'Elephant', 'gt_answer': 'elephant'}, {'question_id': '201064742', 'answer': 'Brown', 'gt_answer': 'brown'}, {'question_id': '201490938', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20631504', 'answer': 'Short sleeved', 'gt_answer': 'short sleeved'}, {'question_id': '202257885', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20169871', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '20857202', 'answer': 'Bed', 'gt_answer': 'bed'}, {'question_id': '20416479', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20151807', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201030630', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20411697', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201392056', 'answer': 'Shirt', 'gt_answer': 'vest'}, {'question_id': '202262557', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201030639', 'answer': 'Couch', 'gt_answer': 'couch'}, {'question_id': '20442279', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20226800', 'answer': 'Front', 'gt_answer': 'front'}, {'question_id': '20226801', 'answer': 'Table', 'gt_answer': 'table'}, {'question_id': '201737889', 'answer': 'Player', 'gt_answer': 'player'}, {'question_id': '202218964', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201735249', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20394944', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20963663', 'answer': 'Shelf', 'gt_answer': 'shelf'}, {'question_id': '20891617', 'answer': 'Long sleeved', 'gt_answer': 'long sleeved'}, {'question_id': '20652455', 'answer': 'Man', 'gt_answer': 'woman'}, {'question_id': '20652457', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20491642', 'answer': 'Cloudy', 'gt_answer': 'partly cloudy'}, {'question_id': '202262665', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '20491641', 'answer': 'Cloudy', 'gt_answer': 'partly cloudy'}, {'question_id': '201498012', 'answer': 'Desk', 'gt_answer': 'desk'}, {'question_id': '201638769', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20810852', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201590238', 'answer': 'Truck', 'gt_answer': 'truck'}, {'question_id': '202228579', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201590231', 'answer': 'Van', 'gt_answer': 'truck'}, {'question_id': '201752887', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '201479464', 'answer': 'Soft', 'gt_answer': 'soft'}, {'question_id': '202125904', 'answer': 'Chairs', 'gt_answer': 'chairs'}, {'question_id': '201640241', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201935992', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201882757', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201882752', 'answer': 'Bed', 'gt_answer': 'table'}, {'question_id': '20797707', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201342325', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201153036', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201153030', 'answer': 'Giraffe', 'gt_answer': 'giraffe'}, {'question_id': '202121613', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201307396', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201976944', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '20306227', 'answer': 'Jacket', 'gt_answer': 'shirt'}, {'question_id': '201976943', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202180489', 'answer': 'Girl', 'gt_answer': 'spectator'}, {'question_id': '20149752', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20306228', 'answer': 'Jacket', 'gt_answer': 'shirt'}, {'question_id': '201467517', 'answer': 'Glass', 'gt_answer': 'metal'}, {'question_id': '2059728', 'answer': 'Lying', 'gt_answer': 'sitting'}, {'question_id': '20541429', 'answer': 'Brown', 'gt_answer': 'beige'}, {'question_id': '201757613', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202240865', 'answer': 'Chair', 'gt_answer': 'chair'}, {'question_id': '202240716', 'answer': 'Shirt', 'gt_answer': 'dress shirt'}, {'question_id': '202240867', 'answer': 'Chair', 'gt_answer': 'chair'}, {'question_id': '202240719', 'answer': 'Shirt', 'gt_answer': 'dress shirt'}, {'question_id': '20929641', 'answer': 'Motorcycle', 'gt_answer': 'mirror'}, {'question_id': '20929645', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20709955', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201758374', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20489732', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20963805', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202004154', 'answer': 'Boy', 'gt_answer': 'man'}, {'question_id': '201061137', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201061136', 'answer': 'Boy', 'gt_answer': 'boy'}, {'question_id': '20177580', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '202102603', 'answer': 'Dish', 'gt_answer': 'countertop'}, {'question_id': '202245988', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20120324', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20177589', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202003757', 'answer': 'Desk', 'gt_answer': 'desk'}, {'question_id': '201233828', 'answer': 'No one', 'gt_answer': 'skateboarder'}, {'question_id': '201207083', 'answer': 'Broccoli', 'gt_answer': 'broccoli'}, {'question_id': '20183074', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20492200', 'answer': 'Snow', 'gt_answer': 'snow'}, {'question_id': '201804303', 'answer': 'Small', 'gt_answer': 'small'}, {'question_id': '202174161', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20344915', 'answer': 'Color', 'gt_answer': 'color'}, {'question_id': '20724344', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201803848', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201360888', 'answer': 'Toothbrush', 'gt_answer': 'toothbrush'}, {'question_id': '201803842', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202270961', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20344919', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201663073', 'answer': 'Cabinets', 'gt_answer': 'cabinets'}, {'question_id': '20306483', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20657080', 'answer': 'Skinny', 'gt_answer': 'skinny'}, {'question_id': '20306956', 'answer': 'Bed', 'gt_answer': 'chair'}, {'question_id': '201438252', 'answer': 'Outdoors', 'gt_answer': 'outdoors'}, {'question_id': '2058513', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20935943', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201766571', 'answer': 'Girl', 'gt_answer': 'girl'}, {'question_id': '202119162', 'answer': 'Man', 'gt_answer': 'man'}, {'question_id': '20939817', 'answer': 'Color', 'gt_answer': 'material'}, {'question_id': '20435031', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20442230', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20721825', 'answer': 'Girl', 'gt_answer': 'girl'}, {'question_id': '201479203', 'answer': 'Chicken', 'gt_answer': 'orange'}, {'question_id': '201574137', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201068716', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '201902453', 'answer': 'Desk', 'gt_answer': 'desk'}, {'question_id': '20287838', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201984151', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20896522', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20287836', 'answer': 'Helmet', 'gt_answer': 'face mask'}, {'question_id': '20287837', 'answer': 'Mask', 'gt_answer': 'face mask'}, {'question_id': '201984158', 'answer': 'Woman', 'gt_answer': 'woman'}, {'question_id': '20247302', 'answer': 'Woman', 'gt_answer': 'woman'}, {'question_id': '201068421', 'answer': 'Cell phone', 'gt_answer': 'cell phone'}, {'question_id': '201663079', 'answer': 'Cabinets', 'gt_answer': 'cabinets'}, {'question_id': '20515853', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '202144390', 'answer': 'Hat', 'gt_answer': 't-shirt'}, {'question_id': '202144391', 'answer': 'T-shirt', 'gt_answer': 't-shirt'}, {'question_id': '202144396', 'answer': 'Hat', 'gt_answer': 't-shirt'}, {'question_id': '201068424', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201676339', 'answer': 'Wii controller', 'gt_answer': 'controller'}, {'question_id': '20511573', 'answer': 'Water', 'gt_answer': 'ocean'}, {'question_id': '20746502', 'answer': 'Water', 'gt_answer': 'river'}, {'question_id': '201676334', 'answer': 'Rug', 'gt_answer': 'pillow'}, {'question_id': '201676333', 'answer': 'Rug', 'gt_answer': 'pillow'}, {'question_id': '20511576', 'answer': 'Mountains', 'gt_answer': 'mountains'}, {'question_id': '20511574', 'answer': 'Water', 'gt_answer': 'ocean'}, {'question_id': '20899155', 'answer': 'Camera', 'gt_answer': 'ipod'}, {'question_id': '201885566', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201504915', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201804331', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20753236', 'answer': 'Bed', 'gt_answer': 'bed'}, {'question_id': '201751752', 'answer': 'Sad', 'gt_answer': 'happy'}, {'question_id': '201528173', 'answer': 'Dishwasher', 'gt_answer': 'stove'}, {'question_id': '202121533', 'answer': 'Blue', 'gt_answer': 'blue'}, {'question_id': '201548663', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201056265', 'answer': 'Man', 'gt_answer': 'soccer player'}, {'question_id': '201983886', 'answer': 'Jacket', 'gt_answer': 'blouse'}, {'question_id': '202107824', 'answer': 'Boy', 'gt_answer': 'boy'}, {'question_id': '201536299', 'answer': 'Umpire', 'gt_answer': 'batter'}, {'question_id': '20550535', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20183008', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '202246186', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202246676', 'answer': 'Desk', 'gt_answer': 'computer desk'}, {'question_id': '20826975', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201739300', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20245815', 'answer': 'Black', 'gt_answer': 'black'}, {'question_id': '20245816', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201143250', 'answer': 'Chair', 'gt_answer': 'chairs'}, {'question_id': '201047496', 'answer': 'Suit', 'gt_answer': 'dress shirt'}, {'question_id': '201067650', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '202246679', 'answer': 'Desk', 'gt_answer': 'computer desk'}, {'question_id': '20479982', 'answer': 'Desk', 'gt_answer': 'desk'}, {'question_id': '201407163', 'answer': 'Black', 'gt_answer': 'black'}, {'question_id': '20479987', 'answer': 'Bookshelf', 'gt_answer': 'bookcase'}, {'question_id': '201056263', 'answer': 'People', 'gt_answer': 'spectator'}, {'question_id': '201983681', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201497793', 'answer': 'Keyboard', 'gt_answer': 'speaker'}, {'question_id': '20827005', 'answer': 'Window', 'gt_answer': 'staircase'}, {'question_id': '202059921', 'answer': 'Couch', 'gt_answer': 'couch'}, {'question_id': '20299831', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20226388', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202059924', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201760543', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201947707', 'answer': 'Toothbrush', 'gt_answer': 'toothbrush'}, {'question_id': '20226381', 'answer': 'Table', 'gt_answer': 'chair'}, {'question_id': '20226380', 'answer': 'Color', 'gt_answer': 'material'}, {'question_id': '201676409', 'answer': 'Rug', 'gt_answer': 'rug'}, {'question_id': '201947700', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20340560', 'answer': 'Chair', 'gt_answer': 'chair'}, {'question_id': '20550283', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20340562', 'answer': 'Ground', 'gt_answer': 'patio'}, {'question_id': '20550286', 'answer': 'Hay', 'gt_answer': 'van'}, {'question_id': '2044527', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20550285', 'answer': 'Hay', 'gt_answer': 'van'}, {'question_id': '20340568', 'answer': 'Trash', 'gt_answer': 'chair'}, {'question_id': '20340569', 'answer': 'Chair', 'gt_answer': 'chair'}, {'question_id': '20550289', 'answer': 'Trailer', 'gt_answer': 'van'}, {'question_id': '201687470', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20308288', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20381495', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201411032', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20381490', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20691514', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20308283', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20416535', 'answer': 'Pizza cutter', 'gt_answer': 'pan'}, {'question_id': '20865442', 'answer': 'Large', 'gt_answer': 'large'}, {'question_id': '202101076', 'answer': 'Hat', 'gt_answer': 'jeans'}, {'question_id': '202218694', 'answer': 'Closed', 'gt_answer': 'closed'}, {'question_id': '201589998', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20902486', 'answer': 'Green', 'gt_answer': 'green'}, {'question_id': '20491807', 'answer': 'White', 'gt_answer': 'white'}, {'question_id': '202101079', 'answer': 'Boy', 'gt_answer': 'skateboarder'}, {'question_id': '20361321', 'answer': 'Woman', 'gt_answer': 'snowboarder'}, {'question_id': '20794082', 'answer': 'Color', 'gt_answer': 'color'}, {'question_id': '2076573', 'answer': 'Sign', 'gt_answer': 'entrance'}, {'question_id': '2076574', 'answer': 'Man', 'gt_answer': 'man'}, {'question_id': '2076576', 'answer': 'Man', 'gt_answer': 'entrance'}, {'question_id': '202285475', 'answer': 'Small', 'gt_answer': 'small'}, {'question_id': '20210831', 'answer': 'Material', 'gt_answer': 'material'}, {'question_id': '20797736', 'answer': 'Cat', 'gt_answer': 'cat'}, {'question_id': '201639403', 'answer': 'Giraffes', 'gt_answer': 'zebras'}, {'question_id': '202231508', 'answer': 'Man', 'gt_answer': 'man'}, {'question_id': '20381632', 'answer': 'Pot', 'gt_answer': 'flower pot'}, {'question_id': '20381630', 'answer': 'Full', 'gt_answer': 'full'}, {'question_id': '202120233', 'answer': 'Glass', 'gt_answer': 'wood'}, {'question_id': '202231503', 'answer': 'Stop', 'gt_answer': 'stop sign'}, {'question_id': '202231501', 'answer': 'Stop sign', 'gt_answer': 'stop sign'}, {'question_id': '201713567', 'answer': 'Blue', 'gt_answer': 'blue'}, {'question_id': '20491873', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201527833', 'answer': 'Dishwasher', 'gt_answer': 'stove'}, {'question_id': '201527832', 'answer': 'Dishwasher', 'gt_answer': 'stove'}, {'question_id': '201527830', 'answer': 'Window', 'gt_answer': 'stove'}, {'question_id': '201430635', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20609361', 'answer': 'Strawberry', 'gt_answer': 'strawberry'}, {'question_id': '201393419', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20345133', 'answer': 'Racket', 'gt_answer': 'racket'}, {'question_id': '20345134', 'answer': 'Ground', 'gt_answer': 'tennis ball'}, {'question_id': '20247449', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20706000', 'answer': 'Keyboard', 'gt_answer': 'keyboard'}, {'question_id': '20752362', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20706005', 'answer': 'Headphones', 'gt_answer': 'keyboard'}, {'question_id': '20752364', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20706007', 'answer': 'Computer', 'gt_answer': 'speaker'}, {'question_id': '20706006', 'answer': 'Computer', 'gt_answer': 'speaker'}, {'question_id': '201798367', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20468774', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201346736', 'answer': 'Motorcycle', 'gt_answer': 'motorcycle'}, {'question_id': '202082217', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201878405', 'answer': 'Dog', 'gt_answer': 'dog'}, {'question_id': '202125982', 'answer': 'Chairs', 'gt_answer': 'chairs'}, {'question_id': '202162523', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201879511', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201047512', 'answer': 'Suit', 'gt_answer': 'dress shirt'}, {'question_id': '201047514', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20541210', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201832399', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201393738', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '20516134', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201832393', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20954166', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '201832391', 'answer': 'Nightstand', 'gt_answer': 'nightstand'}, {'question_id': '201832395', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201064997', 'answer': 'Hot dog', 'gt_answer': 'hamburger'}, {'question_id': '201822331', 'answer': 'Toilet paper', 'gt_answer': 'sink'}, {'question_id': '20299655', 'answer': 'Large', 'gt_answer': 'large'}, {'question_id': '201400148', 'answer': 'Couch', 'gt_answer': 'bookcase'}, {'question_id': '201822334', 'answer': 'Toilet', 'gt_answer': 'toilet'}, {'question_id': '20827365', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20299651', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '20964062', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202173928', 'answer': 'Brown', 'gt_answer': 'green'}, {'question_id': '201936053', 'answer': 'Wood', 'gt_answer': 'wood'}, {'question_id': '2058588', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201669468', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '2058583', 'answer': 'Closed', 'gt_answer': 'closed'}, {'question_id': '201669460', 'answer': 'Top', 'gt_answer': 'top'}, {'question_id': '20866406', 'answer': 'Sleeveless', 'gt_answer': 'sleeveless'}, {'question_id': '201885243', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20302665', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20120401', 'answer': 'Gray', 'gt_answer': 'gray'}, {'question_id': '202082020', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20162282', 'answer': 'Boy', 'gt_answer': 'boy'}, {'question_id': '20317027', 'answer': 'Drawer', 'gt_answer': 'drawer'}, {'question_id': '20162288', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201156008', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201803678', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20349740', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20149569', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20385644', 'answer': 'Laptop', 'gt_answer': 'laptop'}, {'question_id': '201756551', 'answer': 'Cat', 'gt_answer': 'kitten'}, {'question_id': '201624317', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20385641', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '202225820', 'answer': 'Carrots', 'gt_answer': 'carrots'}, {'question_id': '201976553', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202036663', 'answer': 'Pepper', 'gt_answer': 'spinach'}, {'question_id': '201303132', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20385648', 'answer': 'Keyboard', 'gt_answer': 'keyboard'}, {'question_id': '201903007', 'answer': 'Remote control', 'gt_answer': 'keyboard'}, {'question_id': '20883295', 'answer': 'White', 'gt_answer': 'gray'}, {'question_id': '20162138', 'answer': 'Dirty', 'gt_answer': 'dirty'}, {'question_id': '201455942', 'answer': 'Road', 'gt_answer': 'road'}, {'question_id': '20982337', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '201623842', 'answer': 'Cabinets', 'gt_answer': 'cabinets'}, {'question_id': '202244355', 'answer': 'Carrots', 'gt_answer': 'dip'}, {'question_id': '20441964', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201479321', 'answer': 'Orange', 'gt_answer': 'orange'}, {'question_id': '201479320', 'answer': 'Orange', 'gt_answer': 'orange'}, {'question_id': '201908920', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20308996', 'answer': 'Brown', 'gt_answer': 'light brown'}, {'question_id': '201479328', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201760521', 'answer': 'Lawn', 'gt_answer': 'lawn'}, {'question_id': '202006676', 'answer': 'Cabinet', 'gt_answer': 'table'}, {'question_id': '20837018', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20480402', 'answer': 'Orange', 'gt_answer': 'orange'}, {'question_id': '20837017', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '201047489', 'answer': 'Shirt', 'gt_answer': 'dress shirt'}, {'question_id': '20978723', 'answer': 'Motorcycle', 'gt_answer': 'motorcycle'}, {'question_id': '201663012', 'answer': 'Clean', 'gt_answer': 'clean'}, {'question_id': '201047488', 'answer': 'Suit', 'gt_answer': 'dress shirt'}, {'question_id': '201920402', 'answer': 'Field', 'gt_answer': 'field'}, {'question_id': '20978726', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20978725', 'answer': 'Motorcycle', 'gt_answer': 'motorcycle'}, {'question_id': '202125985', 'answer': 'Chair', 'gt_answer': 'chairs'}, {'question_id': '201663018', 'answer': 'Oven', 'gt_answer': 'dishwasher'}, {'question_id': '20679175', 'answer': 'Gray', 'gt_answer': 'gray'}, {'question_id': '202266063', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201873667', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20434687', 'answer': 'Man', 'gt_answer': 'man'}, {'question_id': '20618836', 'answer': 'Girl', 'gt_answer': 'people'}, {'question_id': '201030460', 'answer': 'Couch', 'gt_answer': 'couch'}, {'question_id': '20754744', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20412536', 'answer': 'Fireplace', 'gt_answer': 'table'}, {'question_id': '20412531', 'answer': 'Talking', 'gt_answer': 'pointing'}, {'question_id': '20226631', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201175666', 'answer': 'Mirror', 'gt_answer': 'mirror'}, {'question_id': '201185217', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201319456', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201509849', 'answer': 'Dirty', 'gt_answer': 'dirty'}, {'question_id': '2046274', 'answer': 'Table', 'gt_answer': 'table'}, {'question_id': '2046276', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201887200', 'answer': 'Table', 'gt_answer': 'table'}, {'question_id': '20136573', 'answer': 'Woman', 'gt_answer': 'woman'}, {'question_id': '20136574', 'answer': 'Woman', 'gt_answer': 'woman'}, {'question_id': '201505088', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201887207', 'answer': 'Cauliflower', 'gt_answer': 'cauliflower'}, {'question_id': '201509845', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201439399', 'answer': 'Man', 'gt_answer': 'girl'}, {'question_id': '201077064', 'answer': 'Faucet', 'gt_answer': 'sink'}, {'question_id': '2097650', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '201077061', 'answer': 'Gray', 'gt_answer': 'white'}, {'question_id': '20978547', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20783366', 'answer': 'Sticker', 'gt_answer': 'keyboard'}, {'question_id': '20911333', 'answer': 'Top', 'gt_answer': 'bottom'}, {'question_id': '202147765', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201492444', 'answer': 'Black', 'gt_answer': 'black'}, {'question_id': '201889339', 'answer': 'Skis', 'gt_answer': 'mountain side'}, {'question_id': '201974829', 'answer': 'Woman', 'gt_answer': 'player'}, {'question_id': '202169193', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20262756', 'answer': 'Large', 'gt_answer': 'small'}, {'question_id': '201889330', 'answer': 'Skier', 'gt_answer': 'skier'}, {'question_id': '202081047', 'answer': 'House', 'gt_answer': 'house'}, {'question_id': '202081043', 'answer': 'Knife', 'gt_answer': 'house'}, {'question_id': '202081040', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20482512', 'answer': 'Racket', 'gt_answer': 'racket'}, {'question_id': '20403393', 'answer': 'Right', 'gt_answer': 'left'}, {'question_id': '20473052', 'answer': 'Boy', 'gt_answer': 'boy'}, {'question_id': '20473057', 'answer': 'Boy', 'gt_answer': 'boy'}, {'question_id': '20473058', 'answer': 'Boy', 'gt_answer': 'boy'}, {'question_id': '201879656', 'answer': 'Dog', 'gt_answer': 'dog'}, {'question_id': '20588909', 'answer': 'Skateboarding', 'gt_answer': 'looking down'}, {'question_id': '201972892', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201795579', 'answer': 'People', 'gt_answer': 'child'}, {'question_id': '20854010', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201676344', 'answer': 'Wii controller', 'gt_answer': 'controller'}, {'question_id': '202081102', 'answer': 'Toaster', 'gt_answer': 'toaster'}, {'question_id': '202081101', 'answer': 'Toaster', 'gt_answer': 'toaster'}, {'question_id': '202036705', 'answer': 'Bottom', 'gt_answer': 'top'}, {'question_id': '201983920', 'answer': 'Jacket', 'gt_answer': 'jacket'}, {'question_id': '202036708', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201401839', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201061302', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '202231886', 'answer': 'Wooden', 'gt_answer': 'wooden'}, {'question_id': '201207069', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201467540', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201711248', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201056104', 'answer': 'Boy', 'gt_answer': 'soccer player'}, {'question_id': '20157219', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '2017348', 'answer': 'Horse', 'gt_answer': 'goat'}, {'question_id': '201822197', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '2017435', 'answer': 'Horse', 'gt_answer': 'horse'}, {'question_id': '2017430', 'answer': 'Field', 'gt_answer': 'pasture'}, {'question_id': '2017344', 'answer': 'Sheep', 'gt_answer': 'horse'}, {'question_id': '2017433', 'answer': 'Field', 'gt_answer': 'pasture'}, {'question_id': '202006137', 'answer': 'Left', 'gt_answer': 'right'}, {'question_id': '201751564', 'answer': 'White', 'gt_answer': 'blond'}, {'question_id': '201739250', 'answer': 'Top', 'gt_answer': 'top'}, {'question_id': '201446986', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202147798', 'answer': 'White', 'gt_answer': 'white'}, {'question_id': '201407447', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20953924', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20706251', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20611918', 'answer': 'Car', 'gt_answer': 'container'}, {'question_id': '20361400', 'answer': 'Wet', 'gt_answer': 'wet'}, {'question_id': '202037111', 'answer': 'Pizza', 'gt_answer': 'pizza'}, {'question_id': '201438750', 'answer': 'White', 'gt_answer': 'white'}, {'question_id': '20611913', 'answer': 'Nothing', 'gt_answer': 'brownie'}, {'question_id': '20611914', 'answer': 'Cake', 'gt_answer': 'brownie'}, {'question_id': '20757077', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20757074', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '202060051', 'answer': 'Chair', 'gt_answer': 'couch'}, {'question_id': '20307212', 'answer': 'Camera', 'gt_answer': 'television'}, {'question_id': '20245739', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20452267', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20245735', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20245737', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202162135', 'answer': 'Wood', 'gt_answer': 'wood'}, {'question_id': '201739258', 'answer': 'Player', 'gt_answer': 'player'}, {'question_id': '201548783', 'answer': 'Blender', 'gt_answer': 'blender'}, {'question_id': '201935083', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20656965', 'answer': 'Car', 'gt_answer': 'minivan'}, {'question_id': '201446988', 'answer': 'Counter', 'gt_answer': 'shelf'}, {'question_id': '201669619', 'answer': 'Tray', 'gt_answer': 'cake stand'}, {'question_id': '20514931', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201080203', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '201669614', 'answer': 'Cupcake', 'gt_answer': 'cupcake'}, {'question_id': '201669616', 'answer': 'Cupcake', 'gt_answer': 'cupcake'}, {'question_id': '201207451', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202037070', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '202037077', 'answer': 'Meat', 'gt_answer': 'cheese'}, {'question_id': '202059998', 'answer': 'Dog', 'gt_answer': 'dog'}, {'question_id': '201638960', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '20285316', 'answer': 'Table', 'gt_answer': 'table'}, {'question_id': '20285314', 'answer': 'Chair', 'gt_answer': 'chair'}, {'question_id': '20285313', 'answer': 'Chair', 'gt_answer': 'chair'}, {'question_id': '202003812', 'answer': 'Rug', 'gt_answer': 'desk'}, {'question_id': '201951808', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20818920', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201976559', 'answer': 'Horse', 'gt_answer': 'cow'}, {'question_id': '2076725', 'answer': 'Car', 'gt_answer': 'cars'}, {'question_id': '201751764', 'answer': 'Woman', 'gt_answer': 'lady'}, {'question_id': '20645677', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '2076720', 'answer': 'Building', 'gt_answer': 'sign'}, {'question_id': '20645672', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '201641385', 'answer': 'Red', 'gt_answer': 'black'}, {'question_id': '20794274', 'answer': 'Spatula', 'gt_answer': 'mustard bottle'}, {'question_id': '20940010', 'answer': 'Soap', 'gt_answer': 'soap'}, {'question_id': '20940011', 'answer': 'Soap', 'gt_answer': 'soap'}, {'question_id': '20794271', 'answer': 'Bottle', 'gt_answer': 'mustard bottle'}, {'question_id': '2076728', 'answer': 'Car', 'gt_answer': 'cars'}, {'question_id': '202244211', 'answer': 'Rice', 'gt_answer': 'rice'}, {'question_id': '20878904', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '202121350', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201623372', 'answer': 'Cabinets', 'gt_answer': 'cupboards'}, {'question_id': '20863711', 'answer': 'Silver', 'gt_answer': 'white'}, {'question_id': '201878369', 'answer': 'Jacket', 'gt_answer': 'coat'}, {'question_id': '20711713', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201751762', 'answer': 'Woman', 'gt_answer': 'lady'}, {'question_id': '20836628', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20984377', 'answer': 'Black', 'gt_answer': 'black'}, {'question_id': '20600229', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20836624', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20836622', 'answer': 'Brown', 'gt_answer': 'black'}, {'question_id': '202144650', 'answer': 'Plastic', 'gt_answer': 'plastic'}, {'question_id': '202243663', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20857199', 'answer': 'Bed', 'gt_answer': 'bed'}, {'question_id': '202218578', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201735313', 'answer': 'Laptop', 'gt_answer': 'laptop'}, {'question_id': '201735588', 'answer': 'Plastic', 'gt_answer': 'wood'}, {'question_id': '20308148', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20308149', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201065472', 'answer': 'Stage', 'gt_answer': 'stage'}, {'question_id': '202218570', 'answer': 'Counter', 'gt_answer': 'canisters'}, {'question_id': '20857193', 'answer': 'Wallet', 'gt_answer': 'powder'}, {'question_id': '20857196', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201735318', 'answer': 'Keyboard', 'gt_answer': 'keyboard'}, {'question_id': '202060107', 'answer': 'Tree', 'gt_answer': 'couch'}, {'question_id': '20752425', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201795247', 'answer': 'Blue', 'gt_answer': 'orange'}, {'question_id': '20715722', 'answer': 'White', 'gt_answer': 'gray'}, {'question_id': '20667927', 'answer': 'Man', 'gt_answer': 'man'}, {'question_id': '20667921', 'answer': 'Couch', 'gt_answer': 'couch'}, {'question_id': '20340793', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20899954', 'answer': 'Fruit', 'gt_answer': 'tomatoes'}, {'question_id': '20899955', 'answer': 'Tomatoes', 'gt_answer': 'tomatoes'}, {'question_id': '20899952', 'answer': 'Bowl', 'gt_answer': 'bowl'}, {'question_id': '201188371', 'answer': 'Concrete', 'gt_answer': 'concrete'}, {'question_id': '201982518', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201735178', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201428596', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '202102836', 'answer': 'Dishwasher', 'gt_answer': 'microwave'}, {'question_id': '201370372', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '201510216', 'answer': 'Color', 'gt_answer': 'shape'}, {'question_id': '201407296', 'answer': 'Man', 'gt_answer': 'man'}, {'question_id': '201400019', 'answer': 'Couch', 'gt_answer': 'couch'}, {'question_id': '201590038', 'answer': 'White', 'gt_answer': 'white'}, {'question_id': '201400015', 'answer': 'Bookshelf', 'gt_answer': 'bookcase'}, {'question_id': '201935220', 'answer': 'Skateboard', 'gt_answer': 'skate park'}, {'question_id': '2098177', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201590031', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201935227', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201407299', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20247169', 'answer': 'Silver', 'gt_answer': 'dark'}, {'question_id': '202262576', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201342129', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201951789', 'answer': 'Tray', 'gt_answer': 'machine'}, {'question_id': '202122041', 'answer': 'Refrigerator', 'gt_answer': 'refrigerator'}, {'question_id': '201535802', 'answer': 'Box', 'gt_answer': 'box'}, {'question_id': '20441870', 'answer': 'Bookshelf', 'gt_answer': 'bookcase'}, {'question_id': '20863387', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '201997757', 'answer': 'Leafy', 'gt_answer': 'leafy'}, {'question_id': '201638740', 'answer': 'Car', 'gt_answer': 'car'}, {'question_id': '20441879', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201987955', 'answer': 'Closed', 'gt_answer': 'closed'}, {'question_id': '202102788', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '20810874', 'answer': 'Light brown', 'gt_answer': 'light brown'}, {'question_id': '2066010', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20169726', 'answer': 'Cap', 'gt_answer': 'hat'}, {'question_id': '202228228', 'answer': 'Gold', 'gt_answer': 'brown'}, {'question_id': '2097679', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '201640269', 'answer': 'Posing', 'gt_answer': 'posing'}, {'question_id': '20929402', 'answer': 'Red', 'gt_answer': 'silver'}, {'question_id': '201738008', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202286772', 'answer': 'Floor', 'gt_answer': 'ground'}, {'question_id': '20262431', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201976963', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202006918', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201976967', 'answer': 'Net', 'gt_answer': 'motorcycle'}, {'question_id': '20262438', 'answer': 'Heavy', 'gt_answer': 'heavy'}, {'question_id': '201738007', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '20461922', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '2012702', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20621839', 'answer': 'Closed', 'gt_answer': 'open'}, {'question_id': '20541155', 'answer': 'Short sleeved', 'gt_answer': 'short sleeved'}, {'question_id': '20182868', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202041989', 'answer': 'Top', 'gt_answer': 'top'}, {'question_id': '20637245', 'answer': 'Top', 'gt_answer': 'top'}, {'question_id': '201757637', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201273151', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202240842', 'answer': 'Table', 'gt_answer': 'shelf'}, {'question_id': '202240841', 'answer': 'Table', 'gt_answer': 'shelf'}, {'question_id': '202240840', 'answer': 'Painting', 'gt_answer': 'shelf'}, {'question_id': '201030610', 'answer': 'Controller', 'gt_answer': 'wii controller'}, {'question_id': '20827192', 'answer': 'Couch', 'gt_answer': 'side table'}, {'question_id': '201511034', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20151826', 'answer': 'Tall', 'gt_answer': 'short'}, {'question_id': '20827197', 'answer': 'Couch', 'gt_answer': 'side table'}, {'question_id': '201175272', 'answer': 'Curtain', 'gt_answer': 'curtain'}, {'question_id': '201175273', 'answer': 'Curtain', 'gt_answer': 'curtain'}, {'question_id': '20204460', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201952930', 'answer': 'Large', 'gt_answer': 'huge'}, {'question_id': '201061159', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201175275', 'answer': 'Window', 'gt_answer': 'window'}, {'question_id': '201434052', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202004139', 'answer': 'Laptop', 'gt_answer': 'laptops'}, {'question_id': '202004138', 'answer': 'Laptop', 'gt_answer': 'laptops'}, {'question_id': '201982423', 'answer': 'Couch', 'gt_answer': 'couch'}, {'question_id': '201319583', 'answer': 'Plate', 'gt_answer': 'table'}, {'question_id': '201887073', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '201593892', 'answer': 'Woman', 'gt_answer': 'girl'}, {'question_id': '2046643', 'answer': 'Glass', 'gt_answer': 'glass'}, {'question_id': '20182862', 'answer': 'Man', 'gt_answer': 'man'}, {'question_id': '2046644', 'answer': 'Glass', 'gt_answer': 'glass'}, {'question_id': '201593893', 'answer': 'Woman', 'gt_answer': 'girl'}, {'question_id': '201804320', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201319586', 'answer': 'Table', 'gt_answer': 'table'}, {'question_id': '20982299', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201573832', 'answer': 'Black', 'gt_answer': 'black'}, {'question_id': '20982293', 'answer': 'Door', 'gt_answer': 'door'}, {'question_id': '201887077', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20783172', 'answer': 'Laptop', 'gt_answer': 'laptop'}, {'question_id': '20783170', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '20317277', 'answer': 'Microwave', 'gt_answer': 'toaster'}, {'question_id': '201998202', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201682363', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201156214', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20118121', 'answer': 'Brown', 'gt_answer': 'brown'}, {'question_id': '201974703', 'answer': 'Front', 'gt_answer': 'behind'}, {'question_id': '201175417', 'answer': 'Woman', 'gt_answer': 'woman'}, {'question_id': '20435019', 'answer': 'Box', 'gt_answer': 'pizza box'}, {'question_id': '201185971', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201109158', 'answer': 'Black', 'gt_answer': 'gray'}, {'question_id': '20442212', 'answer': 'Small', 'gt_answer': 'large'}, {'question_id': '201984138', 'answer': 'Phone', 'gt_answer': 'hair clip'}, {'question_id': '201984137', 'answer': 'Woman', 'gt_answer': 'woman'}, {'question_id': '201982255', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '202257519', 'answer': 'Beach', 'gt_answer': 'beach'}, {'question_id': '20480092', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20740933', 'answer': 'Left', 'gt_answer': 'right'}, {'question_id': '201621261', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201621591', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20482522', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20709937', 'answer': 'No one', 'gt_answer': 'woman'}, {'question_id': '201861393', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20262694', 'answer': 'Girl', 'gt_answer': 'girl'}, {'question_id': '202012480', 'answer': 'Shelves', 'gt_answer': 'cabinets'}, {'question_id': '20482524', 'answer': 'Woman', 'gt_answer': 'woman'}, {'question_id': '201676355', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '2017081', 'answer': 'Mountain', 'gt_answer': 'mountains'}, {'question_id': '2017082', 'answer': 'Mountain', 'gt_answer': 'mountains'}, {'question_id': '20746527', 'answer': 'Tall', 'gt_answer': 'tall'}, {'question_id': '202246472', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201676359', 'answer': 'Couch', 'gt_answer': 'table'}, {'question_id': '202240339', 'answer': 'Girl', 'gt_answer': 'girl'}, {'question_id': '202240338', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '202059978', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '201885541', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20818712', 'answer': 'Batter', 'gt_answer': 'batter'}, {'question_id': '20631365', 'answer': 'Stadium', 'gt_answer': 'field'}, {'question_id': '202244653', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201713283', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20717019', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202012488', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '201548648', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201434049', 'answer': 'Runway', 'gt_answer': 'pavement'}, {'question_id': '201480393', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '20182873', 'answer': 'Street', 'gt_answer': 'street'}, {'question_id': '20394830', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '20394837', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201826563', 'answer': 'White', 'gt_answer': 'white'}, {'question_id': '20394835', 'answer': 'Suit', 'gt_answer': 'suit'}, {'question_id': '20394834', 'answer': 'Suit', 'gt_answer': 'suit'}, {'question_id': '20245833', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '2055680', 'answer': 'Car', 'gt_answer': 'car'}, {'question_id': '2093749', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201227868', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20789923', 'answer': 'Green', 'gt_answer': 'green'}, {'question_id': '201873515', 'answer': 'Clean', 'gt_answer': 'clean'}, {'question_id': '20299811', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20300566', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202262897', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201227865', 'answer': 'Car', 'gt_answer': 'van'}, {'question_id': '202162335', 'answer': 'Table', 'gt_answer': 'floor'}, {'question_id': '201227866', 'answer': 'Car', 'gt_answer': 'van'}, {'question_id': '201671884', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20306603', 'answer': 'White', 'gt_answer': 'gray'}, {'question_id': '20941993', 'answer': 'Boat', 'gt_answer': 'boat'}, {'question_id': '202059948', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202180309', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202120030', 'answer': 'Metal', 'gt_answer': 'metal'}, {'question_id': '201711281', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201711286', 'answer': 'Rectangle', 'gt_answer': 'rectangular'}, {'question_id': '201593924', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '202180302', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201593922', 'answer': 'Car', 'gt_answer': 'fence'}, {'question_id': '2091294', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '201504778', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20865464', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201439646', 'answer': 'Small', 'gt_answer': 'large'}, {'question_id': '201411014', 'answer': 'Female', 'gt_answer': 'male'}, {'question_id': '2075355', 'answer': 'Deer', 'gt_answer': 'giraffe'}, {'question_id': '2075356', 'answer': 'Giraffe', 'gt_answer': 'giraffe'}, {'question_id': '202073343', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202081846', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20456644', 'answer': 'Wood', 'gt_answer': 'metal'}, {'question_id': '20171305', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20752163', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201639176', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '202285450', 'answer': 'Egg', 'gt_answer': 'egg'}, {'question_id': '202285325', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20468579', 'answer': 'Long', 'gt_answer': 'long'}, {'question_id': '202285327', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '20984294', 'answer': 'Car', 'gt_answer': 'van'}, {'question_id': '20647350', 'answer': 'Blue', 'gt_answer': 'yellow'}, {'question_id': '202000937', 'answer': 'Bush', 'gt_answer': 'bush'}, {'question_id': '202000936', 'answer': 'Fence', 'gt_answer': 'sidewalk'}, {'question_id': '202000939', 'answer': 'House', 'gt_answer': 'fence'}, {'question_id': '20456319', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202257420', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201430619', 'answer': 'Shirt', 'gt_answer': 'dress shirt'}, {'question_id': '201527369', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '201616169', 'answer': 'Cabinet', 'gt_answer': 'table'}, {'question_id': '201430616', 'answer': 'Shirt', 'gt_answer': 'dress shirt'}, {'question_id': '201430617', 'answer': 'Shirt', 'gt_answer': 'dress shirt'}, {'question_id': '201997014', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20963763', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202226313', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20827202', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201156455', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201393479', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201872934', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20706063', 'answer': 'Computer', 'gt_answer': 'keyboard'}, {'question_id': '202082179', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '20953166', 'answer': 'Black', 'gt_answer': 'white'}, {'question_id': '201872930', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20706069', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '202265827', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202000682', 'answer': 'Concrete', 'gt_answer': 'concrete'}, {'question_id': '201878420', 'answer': 'Seat', 'gt_answer': 'seat'}, {'question_id': '201982967', 'answer': 'Wood', 'gt_answer': 'wood'}, {'question_id': '201878429', 'answer': 'Wall', 'gt_answer': 'windows'}, {'question_id': '202180317', 'answer': 'Girl', 'gt_answer': 'soccer player'}, {'question_id': '20299677', 'answer': 'Boat', 'gt_answer': 'bridge'}, {'question_id': '20954149', 'answer': 'Woman', 'gt_answer': 'girl'}, {'question_id': '201936035', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201438519', 'answer': 'Catcher', 'gt_answer': 'catcher'}, {'question_id': '201822359', 'answer': 'White', 'gt_answer': 'white'}, {'question_id': '201763797', 'answer': 'Small', 'gt_answer': 'small'}, {'question_id': '201976398', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20644716', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '201763978', 'answer': 'Bed', 'gt_answer': 'beds'}, {'question_id': '201447165', 'answer': 'Camera', 'gt_answer': 'camera'}, {'question_id': '201447167', 'answer': 'Camera', 'gt_answer': 'camera'}, {'question_id': '20247399', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201527923', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20340893', 'answer': 'Red', 'gt_answer': 'red'}, {'question_id': '20786116', 'answer': 'Left', 'gt_answer': 'right'}, {'question_id': '202121782', 'answer': 'Table', 'gt_answer': 'table'}, {'question_id': '202121783', 'answer': 'Table', 'gt_answer': 'table'}, {'question_id': '201885302', 'answer': 'Metal', 'gt_answer': 'metal'}, {'question_id': '201885301', 'answer': 'Metal', 'gt_answer': 'metal'}, {'question_id': '20942887', 'answer': 'Girl', 'gt_answer': 'soccer player'}, {'question_id': '20942886', 'answer': 'Girl', 'gt_answer': 'soccer player'}, {'question_id': '20942884', 'answer': 'Girl', 'gt_answer': 'soccer player'}, {'question_id': '20942883', 'answer': 'Girl', 'gt_answer': 'soccer player'}, {'question_id': '20942882', 'answer': 'Girl', 'gt_answer': 'soccer player'}, {'question_id': '201795054', 'answer': 'Eating', 'gt_answer': 'staring'}, {'question_id': '20518534', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201404023', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20514914', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20317040', 'answer': 'Cabinet', 'gt_answer': 'drawer'}, {'question_id': '20349765', 'answer': 'Texting', 'gt_answer': 'looking down'}, {'question_id': '20349766', 'answer': 'Woman', 'gt_answer': 'woman'}, {'question_id': '201207478', 'answer': 'Empty', 'gt_answer': 'empty'}, {'question_id': '201571136', 'answer': 'Brown', 'gt_answer': 'brown'}, {'question_id': '201109261', 'answer': 'Car', 'gt_answer': 'suv'}, {'question_id': '20349768', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '2012936', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20600059', 'answer': 'Zebra', 'gt_answer': 'zebras'}, {'question_id': '20600058', 'answer': 'Zebras', 'gt_answer': 'zebras'}, {'question_id': '202228610', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201428433', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202081873', 'answer': 'Cat', 'gt_answer': 'cat'}, {'question_id': '201346368', 'answer': 'Glass', 'gt_answer': 'glass'}, {'question_id': '202208292', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '202246227', 'answer': 'Computer', 'gt_answer': 'laptop'}, {'question_id': '201902880', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201447132', 'answer': 'Cabinet', 'gt_answer': 'shelf'}, {'question_id': '202258394', 'answer': 'Horse', 'gt_answer': 'horse'}, {'question_id': '202258395', 'answer': 'Horse', 'gt_answer': 'horse'}, {'question_id': '2066201', 'answer': 'Glove', 'gt_answer': 'baseball mitt'}, {'question_id': '202161864', 'answer': 'Bedroom', 'gt_answer': 'bedroom'}, {'question_id': '2066204', 'answer': 'Jacket', 'gt_answer': 'jacket'}, {'question_id': '201758140', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202240198', 'answer': 'Girl', 'gt_answer': 'girl'}, {'question_id': '202286894', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '202004300', 'answer': 'Projector', 'gt_answer': 'screen'}, {'question_id': '20744253', 'answer': 'Field', 'gt_answer': 'stadium'}, {'question_id': '201887198', 'answer': 'Broccoli', 'gt_answer': 'cauliflower'}, {'question_id': '202169053', 'answer': 'Cars', 'gt_answer': 'cars'}, {'question_id': '20573771', 'answer': 'Cabinet', 'gt_answer': 'cabinet'}, {'question_id': '20984542', 'answer': 'Boy', 'gt_answer': 'skater'}, {'question_id': '20573773', 'answer': 'Cabinet', 'gt_answer': 'cabinet'}, {'question_id': '202169054', 'answer': 'Car', 'gt_answer': 'cars'}, {'question_id': '20866466', 'answer': 'Jeans', 'gt_answer': 'jeans'}, {'question_id': '20573778', 'answer': 'Cabinet', 'gt_answer': 'cabinet'}, {'question_id': '201887197', 'answer': 'Broccoli', 'gt_answer': 'cauliflower'}, {'question_id': '201902749', 'answer': 'Computer', 'gt_answer': 'keyboard'}, {'question_id': '202053096', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20451989', 'answer': 'Flowers', 'gt_answer': 'glass'}, {'question_id': '20226419', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201307270', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202169057', 'answer': 'Cars', 'gt_answer': 'cars'}, {'question_id': '201873683', 'answer': 'Large', 'gt_answer': 'large'}, {'question_id': '20515079', 'answer': 'Brown', 'gt_answer': 'brown'}, {'question_id': '20536085', 'answer': 'Giraffe', 'gt_answer': 'giraffe'}, {'question_id': '202262744', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '201153170', 'answer': 'Man', 'gt_answer': 'man'}, {'question_id': '202262746', 'answer': 'Park', 'gt_answer': 'grass'}, {'question_id': '201030409', 'answer': 'Couch', 'gt_answer': 'couch'}, {'question_id': '201739104', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201988006', 'answer': 'Brown', 'gt_answer': 'tan'}, {'question_id': '201185763', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202241153', 'answer': 'Right', 'gt_answer': 'left'}, {'question_id': '201902747', 'answer': 'Computer', 'gt_answer': 'keyboard'}, {'question_id': '201185766', 'answer': 'Dirt', 'gt_answer': 'ground'}, {'question_id': '201175644', 'answer': 'Gray', 'gt_answer': 'gray'}, {'question_id': '201535630', 'answer': 'Donut', 'gt_answer': 'donuts'}, {'question_id': '201185768', 'answer': 'Dirt', 'gt_answer': 'ground'}, {'question_id': '201535636', 'answer': 'Donut', 'gt_answer': 'donuts'}, {'question_id': '202021389', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201497792', 'answer': 'Keyboard', 'gt_answer': 'speaker'}, {'question_id': '20652312', 'answer': 'Large', 'gt_answer': 'small'}, {'question_id': '20330278', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '20503619', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201055653', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201509867', 'answer': 'Pink', 'gt_answer': 'pink'}, {'question_id': '201492328', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '202023537', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '201455961', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20953147', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20508864', 'answer': 'Red', 'gt_answer': 'white'}, {'question_id': '201873619', 'answer': 'Fire truck', 'gt_answer': 'fire truck'}, {'question_id': '20783439', 'answer': 'Wine', 'gt_answer': 'drink'}, {'question_id': '20783438', 'answer': 'Wine', 'gt_answer': 'drink'}, {'question_id': '20734148', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201621671', 'answer': 'Couch', 'gt_answer': 'couch'}, {'question_id': '201621677', 'answer': 'Speaker', 'gt_answer': 'speaker'}, {'question_id': '20783343', 'answer': 'Black', 'gt_answer': 'black'}, {'question_id': '201889354', 'answer': 'Pole', 'gt_answer': 'pole'}, {'question_id': '202081069', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201889353', 'answer': 'Skier', 'gt_answer': 'skier'}, {'question_id': '201637106', 'answer': 'Orange', 'gt_answer': 'orange'}, {'question_id': '202262116', 'answer': 'Right', 'gt_answer': 'left'}, {'question_id': '202081060', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20226533', 'answer': 'Brown', 'gt_answer': 'brown'}, {'question_id': '20899187', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20887044', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20567667', 'answer': 'Black', 'gt_answer': 'brown'}, {'question_id': '20567664', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202262425', 'answer': 'Glass', 'gt_answer': 'mug'}, {'question_id': '202003650', 'answer': 'Laptops', 'gt_answer': 'laptops'}, {'question_id': '202003653', 'answer': 'Laptops', 'gt_answer': 'laptops'}, {'question_id': '202003652', 'answer': 'Laptops', 'gt_answer': 'laptops'}, {'question_id': '201434363', 'answer': 'Ground', 'gt_answer': 'pavement'}, {'question_id': '20480465', 'answer': 'Cat', 'gt_answer': 'cat'}, {'question_id': '20119194', 'answer': 'Coat', 'gt_answer': 'dress'}, {'question_id': '202100583', 'answer': 'Boat', 'gt_answer': 'sailboats'}, {'question_id': '20588925', 'answer': 'Girl', 'gt_answer': 'skater'}, {'question_id': '201467639', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '201548786', 'answer': 'Blender', 'gt_answer': 'blender'}, {'question_id': '202169246', 'answer': 'Black', 'gt_answer': 'dark'}, {'question_id': '201498514', 'answer': 'Keyboard', 'gt_answer': 'keyboard'}, {'question_id': '201498519', 'answer': 'Computer', 'gt_answer': 'phone'}, {'question_id': '20543141', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201548788', 'answer': 'Liquid', 'gt_answer': 'spices'}, {'question_id': '2017416', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202003830', 'answer': 'Laptops', 'gt_answer': 'laptops'}, {'question_id': '201571083', 'answer': 'Purse', 'gt_answer': 'purse'}, {'question_id': '20861033', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20452061', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201976475', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20785978', 'answer': 'Fence', 'gt_answer': 'cone'}, {'question_id': '20785979', 'answer': 'Tree', 'gt_answer': 'machine'}, {'question_id': '2097670', 'answer': 'On', 'gt_answer': 'on'}, {'question_id': '20794299', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202053234', 'answer': 'Thick', 'gt_answer': 'thin'}, {'question_id': '20157231', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201795454', 'answer': 'Sitting', 'gt_answer': 'staring'}, {'question_id': '20672898', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '201861415', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20482065', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20442001', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20661291', 'answer': 'Window', 'gt_answer': 'door'}, {'question_id': '201623740', 'answer': 'Refrigerator', 'gt_answer': 'oven'}, {'question_id': '201623741', 'answer': 'Refrigerator', 'gt_answer': 'oven'}, {'question_id': '202125967', 'answer': 'Shorts', 'gt_answer': 'shirt'}, {'question_id': '202107941', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202180460', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202100889', 'answer': 'Stove', 'gt_answer': 'stove'}, {'question_id': '202180468', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202006371', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20479881', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202110098', 'answer': 'Sheep', 'gt_answer': 'sheep'}, {'question_id': '2097909', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '201068528', 'answer': 'Small', 'gt_answer': 'small'}, {'question_id': '201247059', 'answer': 'Chair', 'gt_answer': 'chair'}, {'question_id': '201822188', 'answer': 'Toilet', 'gt_answer': 'decoration'}, {'question_id': '202265821', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20480568', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201757773', 'answer': 'Laptop', 'gt_answer': 'laptop'}, {'question_id': '20300392', 'answer': 'Red', 'gt_answer': 'black'}, {'question_id': '20611853', 'answer': 'Beef', 'gt_answer': 'ham'}, {'question_id': '202208435', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20611855', 'answer': 'Sandwich', 'gt_answer': 'sandwiches'}, {'question_id': '201669673', 'answer': 'Table', 'gt_answer': 'table'}, {'question_id': '202003875', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '2072802', 'answer': 'Female', 'gt_answer': 'male'}, {'question_id': '201434184', 'answer': 'Short sleeved', 'gt_answer': 'short sleeved'}, {'question_id': '20511661', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201638906', 'answer': 'Sitting', 'gt_answer': 'staring'}, {'question_id': '201434189', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201067754', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201067756', 'answer': 'Round', 'gt_answer': 'square'}, {'question_id': '2075297', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201055765', 'answer': 'Cars', 'gt_answer': 'car'}, {'question_id': '20878921', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '202006150', 'answer': 'Wood', 'gt_answer': 'wood'}, {'question_id': '201637101', 'answer': 'Orange', 'gt_answer': 'orange'}, {'question_id': '201861418', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201883218', 'answer': 'Desk', 'gt_answer': 'bed'}, {'question_id': '20794295', 'answer': 'Knife', 'gt_answer': 'mustard bottle'}, {'question_id': '20836972', 'answer': 'Cart', 'gt_answer': 'luggage'}, {'question_id': '20361282', 'answer': 'Female', 'gt_answer': 'female'}, {'question_id': '202285156', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '201639232', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '202285154', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201795002', 'answer': 'Elephant', 'gt_answer': 'elephant'}, {'question_id': '202100888', 'answer': 'Stove', 'gt_answer': 'stove'}, {'question_id': '20752339', 'answer': 'Cabinet', 'gt_answer': 'cabinet'}, {'question_id': '201735379', 'answer': 'Keyboard', 'gt_answer': 'laptop'}, {'question_id': '20894081', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20285195', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201976763', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20894084', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20600207', 'answer': 'Trees', 'gt_answer': 'tree branch'}, {'question_id': '20836971', 'answer': 'Woman', 'gt_answer': 'people'}, {'question_id': '20600205', 'answer': 'Zebras', 'gt_answer': 'zebras'}, {'question_id': '20600202', 'answer': 'Zebra', 'gt_answer': 'zebras'}, {'question_id': '202162332', 'answer': 'Bed', 'gt_answer': 'chair'}, {'question_id': '202162333', 'answer': 'Bed', 'gt_answer': 'chair'}, {'question_id': '201987585', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20827641', 'answer': 'Square', 'gt_answer': 'square'}, {'question_id': '20600208', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201065452', 'answer': 'Ceiling', 'gt_answer': 'stage'}, {'question_id': '201065453', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20308121', 'answer': 'Bread', 'gt_answer': 'bread'}, {'question_id': '20308123', 'answer': 'Counter', 'gt_answer': 'counter'}, {'question_id': '20211230', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20752443', 'answer': 'White', 'gt_answer': 'white'}, {'question_id': '20781871', 'answer': 'Gray', 'gt_answer': 'white'}, {'question_id': '201879431', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20870427', 'answer': 'Grassy', 'gt_answer': 'grassy'}, {'question_id': '201061262', 'answer': 'Top', 'gt_answer': 'top'}, {'question_id': '201593481', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20381262', 'answer': 'Desk', 'gt_answer': 'desk'}, {'question_id': '2091082', 'answer': 'Small', 'gt_answer': 'small'}, {'question_id': '202162196', 'answer': 'Small', 'gt_answer': 'small'}, {'question_id': '20302852', 'answer': 'Metal', 'gt_answer': 'metal'}, {'question_id': '201370353', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201822267', 'answer': 'Figurine', 'gt_answer': 'decoration'}, {'question_id': '201982538', 'answer': 'Large', 'gt_answer': 'huge'}, {'question_id': '20631545', 'answer': 'Umpire', 'gt_answer': 'crowd'}, {'question_id': '20491939', 'answer': 'Bear', 'gt_answer': 'bear'}, {'question_id': '20631547', 'answer': 'Batter', 'gt_answer': 'batter'}, {'question_id': '20631541', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '2075839', 'answer': 'Wood', 'gt_answer': 'metal'}, {'question_id': '20169836', 'answer': 'Blue', 'gt_answer': 'blue'}, {'question_id': '20341202', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202240220', 'answer': 'Girl', 'gt_answer': 'girl'}, {'question_id': '20645657', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202122064', 'answer': 'Stove', 'gt_answer': 'refrigerator'}, {'question_id': '201153615', 'answer': 'Giraffe', 'gt_answer': 'giraffe'}, {'question_id': '201763583', 'answer': 'Bed', 'gt_answer': 'nightstand'}, {'question_id': '20247183', 'answer': 'Green', 'gt_answer': 'dark'}, {'question_id': '202122060', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '202122062', 'answer': 'Sink', 'gt_answer': 'refrigerator'}, {'question_id': '20903094', 'answer': 'Shorts', 'gt_answer': 'shirt'}, {'question_id': '201763589', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '202080923', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202080920', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202080921', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202080927', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201336960', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201535825', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201527959', 'answer': 'Cutting cake', 'gt_answer': 'sitting'}, {'question_id': '201535821', 'answer': 'Girl', 'gt_answer': 'woman'}, {'question_id': '201535820', 'answer': 'Girl', 'gt_answer': 'woman'}, {'question_id': '20891306', 'answer': 'Sign', 'gt_answer': 'sign'}, {'question_id': '20411599', 'answer': 'Pink', 'gt_answer': 'purple'}, {'question_id': '20652412', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201752849', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202169031', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201574270', 'answer': 'Black', 'gt_answer': 'black'}, {'question_id': '20810814', 'answer': 'Chair', 'gt_answer': 'tree'}, {'question_id': '20810815', 'answer': 'Dog', 'gt_answer': 'tree'}, {'question_id': '202133604', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20810817', 'answer': 'Carpet', 'gt_answer': 'rug'}, {'question_id': '20491689', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '202125947', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201479426', 'answer': 'Bread', 'gt_answer': 'bread'}, {'question_id': '201479420', 'answer': 'Donut', 'gt_answer': 'bread'}, {'question_id': '2066076', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '202173885', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201206870', 'answer': 'Black', 'gt_answer': 'black'}, {'question_id': '202060120', 'answer': 'Couch', 'gt_answer': 'couch'}, {'question_id': '20169749', 'answer': 'Brown', 'gt_answer': 'green'}, {'question_id': '202173883', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201307350', 'answer': 'Sky', 'gt_answer': 'sky'}, {'question_id': '201303302', 'answer': 'Standing', 'gt_answer': 'standing'}, {'question_id': '202223209', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201467553', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201467552', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '20461905', 'answer': 'Snow', 'gt_answer': 'snow'}, {'question_id': '201982989', 'answer': 'Book', 'gt_answer': 'coffee table'}, {'question_id': '201510496', 'answer': 'Plate', 'gt_answer': 'tray'}, {'question_id': '201510495', 'answer': 'Plate', 'gt_answer': 'tray'}, {'question_id': '20541170', 'answer': 'Striped', 'gt_answer': 'dotted'}, {'question_id': '20461909', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20853982', 'answer': 'Man', 'gt_answer': 'soccer player'}, {'question_id': '20637269', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201757653', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20637261', 'answer': 'Silver', 'gt_answer': 'silver'}, {'question_id': '20692473', 'answer': 'Brown', 'gt_answer': 'brown'}, {'question_id': '20307045', 'answer': 'Snow', 'gt_answer': 'laptop'}, {'question_id': '20692477', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201030677', 'answer': 'Pillow', 'gt_answer': 'pillow'}, {'question_id': '201511019', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201996575', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '201497638', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201061171', 'answer': 'Boy', 'gt_answer': 'boy'}, {'question_id': '201061170', 'answer': 'Boy', 'gt_answer': 'boy'}, {'question_id': '2046625', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201080460', 'answer': 'Car', 'gt_answer': 'pitcher'}, {'question_id': '201080461', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201080463', 'answer': 'Donut', 'gt_answer': 'pitcher'}, {'question_id': '201080464', 'answer': 'Hand', 'gt_answer': 'pitcher'}, {'question_id': '20414613', 'answer': 'Trees', 'gt_answer': 'trees'}, {'question_id': '20414610', 'answer': 'Skateboard', 'gt_answer': 'skate park'}, {'question_id': '20414611', 'answer': 'Skate park', 'gt_answer': 'skate park'}, {'question_id': '201770978', 'answer': 'Cup', 'gt_answer': 'mug'}, {'question_id': '20508186', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202257249', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20508181', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201571275', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '2059442', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '2059441', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201548879', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202082273', 'answer': 'Mouse', 'gt_answer': 'keyboard'}, {'question_id': '2093905', 'answer': 'Giraffe', 'gt_answer': 'giraffe'}, {'question_id': '20896656', 'answer': 'Microwave', 'gt_answer': 'oven'}, {'question_id': '2093907', 'answer': 'Giraffe', 'gt_answer': 'giraffe'}, {'question_id': '20287603', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20567751', 'answer': 'Brown', 'gt_answer': 'light brown'}, {'question_id': '20317250', 'answer': 'Refrigerator', 'gt_answer': 'toaster'}, {'question_id': '20896309', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201152969', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20317255', 'answer': 'Refrigerator', 'gt_answer': 'toaster'}, {'question_id': '20911182', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201156274', 'answer': 'Boy', 'gt_answer': 'boy'}, {'question_id': '201998222', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20911181', 'answer': 'City', 'gt_answer': 'city'}, {'question_id': '201156273', 'answer': 'Boy', 'gt_answer': 'boy'}, {'question_id': '202121639', 'answer': 'Table', 'gt_answer': 'table'}, {'question_id': '20434789', 'answer': 'Restaurant', 'gt_answer': 'restaurant'}, {'question_id': '201156279', 'answer': 'Boy', 'gt_answer': 'boy'}, {'question_id': '201111151', 'answer': 'Table', 'gt_answer': 'table'}, {'question_id': '201109135', 'answer': 'Street', 'gt_answer': 'street'}, {'question_id': '202156847', 'answer': 'Walking', 'gt_answer': 'standing'}, {'question_id': '20518667', 'answer': 'Short', 'gt_answer': 'short'}, {'question_id': '202023275', 'answer': 'Closet', 'gt_answer': 'closet'}, {'question_id': '202023277', 'answer': 'Chair', 'gt_answer': 'chair'}, {'question_id': '20508455', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20901933', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '201430925', 'answer': 'Cabinet', 'gt_answer': 'cabinet'}, {'question_id': '201068200', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201873149', 'answer': 'Fire truck', 'gt_answer': 'fire truck'}, {'question_id': '201981981', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20740912', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202121885', 'answer': 'Granite', 'gt_answer': 'granite'}, {'question_id': '20412471', 'answer': 'White', 'gt_answer': 'white'}, {'question_id': '201438506', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201997912', 'answer': 'Large', 'gt_answer': 'small'}, {'question_id': '20482509', 'answer': 'Racket', 'gt_answer': 'racket'}, {'question_id': '20709919', 'answer': 'Man', 'gt_answer': 'woman'}, {'question_id': '201758331', 'answer': 'Laptop', 'gt_answer': 'chair'}, {'question_id': '202102817', 'answer': 'Silver', 'gt_answer': 'silver'}, {'question_id': '201920474', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20746540', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202147896', 'answer': 'Man', 'gt_answer': 'athlete'}, {'question_id': '201759069', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201759067', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201765896', 'answer': 'Man', 'gt_answer': 'surfer'}, {'question_id': '201887317', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20480614', 'answer': 'Laptop', 'gt_answer': 'computer'}, {'question_id': '20818733', 'answer': 'Umpire', 'gt_answer': 'batter'}, {'question_id': '20818734', 'answer': 'Umpire', 'gt_answer': 'batter'}, {'question_id': '201739089', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20480618', 'answer': 'Speaker', 'gt_answer': 'radio'}, {'question_id': '201528132', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201528130', 'answer': 'Refrigerator', 'gt_answer': 'stove'}, {'question_id': '201556828', 'answer': 'Large', 'gt_answer': 'large'}, {'question_id': '201920448', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201866724', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201883205', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201947563', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201947562', 'answer': 'White', 'gt_answer': 'white'}, {'question_id': '20536288', 'answer': 'Giraffe', 'gt_answer': 'bison'}, {'question_id': '20536289', 'answer': 'Giraffe', 'gt_answer': 'bison'}, {'question_id': '201185956', 'answer': 'Brown', 'gt_answer': 'brown'}, {'question_id': '201185951', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201434061', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20245858', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201264276', 'answer': 'Car', 'gt_answer': 'bus'}, {'question_id': '2044703', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20245856', 'answer': 'Tree', 'gt_answer': 'fence'}, {'question_id': '20245857', 'answer': 'Tree', 'gt_answer': 'fence'}, {'question_id': '201804704', 'answer': 'Tall', 'gt_answer': 'tall'}, {'question_id': '201987772', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201987773', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202246181', 'answer': 'Desk', 'gt_answer': 'computer desk'}, {'question_id': '201873532', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201998149', 'answer': 'Blue', 'gt_answer': 'blue'}, {'question_id': '201873536', 'answer': 'Fire truck', 'gt_answer': 'fire truck'}, {'question_id': '201873537', 'answer': 'Fire truck', 'gt_answer': 'fire truck'}, {'question_id': '201763905', 'answer': 'Large', 'gt_answer': 'large'}, {'question_id': '201430986', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201065556', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201947742', 'answer': 'Mirror', 'gt_answer': 'faucet'}, {'question_id': '201947744', 'answer': 'Sink', 'gt_answer': 'sink'}, {'question_id': '202231299', 'answer': 'Concrete', 'gt_answer': 'concrete'}, {'question_id': '20118967', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20887257', 'answer': 'Dog', 'gt_answer': 'dog'}, {'question_id': '20118963', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201593905', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201676279', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20241115', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201687439', 'answer': 'Paddle', 'gt_answer': 'paddle'}, {'question_id': '201593902', 'answer': 'Racket', 'gt_answer': 'racket'}, {'question_id': '201593903', 'answer': 'Racket', 'gt_answer': 'racket'}, {'question_id': '202180327', 'answer': 'Girl', 'gt_answer': 'soccer player'}, {'question_id': '201687437', 'answer': 'Man', 'gt_answer': 'man'}, {'question_id': '202180323', 'answer': 'Goalie', 'gt_answer': 'soccer player'}, {'question_id': '202180320', 'answer': 'Ball', 'gt_answer': 'soccer ball'}, {'question_id': '2075629', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201527562', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20416573', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201462252', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201447023', 'answer': 'Roll', 'gt_answer': 'shelf'}, {'question_id': '20645465', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202073384', 'answer': 'Sky', 'gt_answer': 'sky'}, {'question_id': '20117974', 'answer': 'Elephant', 'gt_answer': 'elephant'}, {'question_id': '202073386', 'answer': 'Tree', 'gt_answer': 'trees'}, {'question_id': '202073387', 'answer': 'Tree', 'gt_answer': 'trees'}, {'question_id': '20468554', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201759424', 'answer': 'Closed', 'gt_answer': 'closed'}, {'question_id': '20786126', 'answer': 'Woman', 'gt_answer': 'woman'}, {'question_id': '201671940', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201770973', 'answer': 'Blue', 'gt_answer': 'blue'}, {'question_id': '20753632', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202122066', 'answer': 'Window', 'gt_answer': 'countertop'}, {'question_id': '20609322', 'answer': 'Strawberry', 'gt_answer': 'strawberry'}, {'question_id': '20609323', 'answer': 'Strawberry', 'gt_answer': 'strawberry'}, {'question_id': '20609320', 'answer': 'Fork', 'gt_answer': 'strawberry'}, {'question_id': '20609321', 'answer': 'Fork', 'gt_answer': 'strawberry'}, {'question_id': '202036792', 'answer': 'Pepperoni', 'gt_answer': 'pepperoni'}, {'question_id': '20609324', 'answer': 'Plate', 'gt_answer': 'bowl'}, {'question_id': '20706049', 'answer': 'Headphones', 'gt_answer': 'keyboard'}, {'question_id': '20345171', 'answer': 'Boy', 'gt_answer': 'athlete'}, {'question_id': '20345172', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201957101', 'answer': 'Television', 'gt_answer': 'television'}, {'question_id': '20706040', 'answer': 'Computer', 'gt_answer': 'keyboard'}, {'question_id': '201574018', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20667597', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201037326', 'answer': 'Dirty', 'gt_answer': 'clean'}, {'question_id': '20340524', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '202116854', 'answer': 'Yellow', 'gt_answer': 'red'}, {'question_id': '202100745', 'answer': 'Counter', 'gt_answer': 'stove'}, {'question_id': '20285297', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202100746', 'answer': 'Stove', 'gt_answer': 'stove'}, {'question_id': '201624294', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201481571', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201064647', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201444986', 'answer': 'Bench', 'gt_answer': 'rock'}, {'question_id': '201444981', 'answer': 'Rock', 'gt_answer': 'lawn'}, {'question_id': '201109451', 'answer': 'Bottom', 'gt_answer': 'bottom'}, {'question_id': '20794339', 'answer': 'Yellow', 'gt_answer': 'yellow'}, {'question_id': '201438579', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201438578', 'answer': 'Catcher', 'gt_answer': 'catcher'}, {'question_id': '201247311', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20942173', 'answer': 'Woman', 'gt_answer': 'girl'}, {'question_id': '20794332', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201936017', 'answer': 'Shelf', 'gt_answer': 'shelf'}, {'question_id': '20741135', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201832359', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '201621801', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201983665', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202262908', 'answer': 'Red', 'gt_answer': 'red'}, {'question_id': '20118038', 'answer': 'Elephant', 'gt_answer': 'elephant'}, {'question_id': '20567759', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201621809', 'answer': 'Speaker', 'gt_answer': 'speaker'}, {'question_id': '201621808', 'answer': 'Speaker', 'gt_answer': 'speaker'}, {'question_id': '201883210', 'answer': 'Chair', 'gt_answer': 'bed'}, {'question_id': '20482344', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20644736', 'answer': 'Cat', 'gt_answer': 'cat'}, {'question_id': '201763917', 'answer': 'Mirror', 'gt_answer': 'doors'}, {'question_id': '20644734', 'answer': 'Cat', 'gt_answer': 'cat'}, {'question_id': '201763915', 'answer': 'Frame', 'gt_answer': 'glass'}, {'question_id': '20644732', 'answer': 'Floor', 'gt_answer': 'bedroom'}, {'question_id': '20644733', 'answer': 'Floor', 'gt_answer': 'bedroom'}, {'question_id': '201763910', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '202120272', 'answer': 'Black', 'gt_answer': 'yellow'}, {'question_id': '20285075', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201462240', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201763918', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20381186', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201885326', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '202228698', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201826611', 'answer': 'Man', 'gt_answer': 'man'}, {'question_id': '201826610', 'answer': 'Standing', 'gt_answer': 'looking up'}, {'question_id': '202082061', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '20177852', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202246148', 'answer': 'Dirty', 'gt_answer': 'clean'}, {'question_id': '2066199', 'answer': 'Baseball mitt', 'gt_answer': 'baseball mitt'}, {'question_id': '201404049', 'answer': 'Cows', 'gt_answer': 'cows'}, {'question_id': '201404047', 'answer': 'Cow', 'gt_answer': 'cows'}, {'question_id': '201404045', 'answer': 'Cows', 'gt_answer': 'cows'}, {'question_id': '2066197', 'answer': 'Jacket', 'gt_answer': 'jacket'}, {'question_id': '201404043', 'answer': 'Grazing', 'gt_answer': 'eating'}, {'question_id': '2066195', 'answer': 'Jacket', 'gt_answer': 'jacket'}, {'question_id': '201156041', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201861606', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201638993', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201795639', 'answer': 'Pink', 'gt_answer': 'green'}, {'question_id': '20600071', 'answer': 'Rock', 'gt_answer': 'brush'}, {'question_id': '20600070', 'answer': 'Sky', 'gt_answer': 'brush'}, {'question_id': '20744306', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20600072', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20600077', 'answer': 'Zebra', 'gt_answer': 'zebras'}, {'question_id': '201976511', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202162077', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201623919', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '202228071', 'answer': 'Tv', 'gt_answer': 'dvd player'}, {'question_id': '201319565', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202228074', 'answer': 'Television', 'gt_answer': 'dvd player'}, {'question_id': '202228076', 'answer': 'Cabinet', 'gt_answer': 'entertainment center'}, {'question_id': '202228077', 'answer': 'Cabinet', 'gt_answer': 'entertainment center'}, {'question_id': '202228078', 'answer': 'Cabinet', 'gt_answer': 'entertainment center'}, {'question_id': '20308950', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20308952', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201765767', 'answer': 'Long', 'gt_answer': 'long'}, {'question_id': '202218894', 'answer': 'Plant', 'gt_answer': 'paper towels'}, {'question_id': '202218897', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201030796', 'answer': 'Pants', 'gt_answer': 'pants'}, {'question_id': '201030793', 'answer': 'Shirt', 'gt_answer': 'pants'}, {'question_id': '202218892', 'answer': 'Plant', 'gt_answer': 'jar'}, {'question_id': '20903133', 'answer': 'Silver', 'gt_answer': 'silver'}, {'question_id': '201030798', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201908969', 'answer': 'Top', 'gt_answer': 'top'}, {'question_id': '201347388', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20226985', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20899214', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20899217', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '202106178', 'answer': 'Man', 'gt_answer': 'man'}, {'question_id': '20414423', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '201347387', 'answer': 'Boy', 'gt_answer': 'skateboarder'}, {'question_id': '201347386', 'answer': 'Boy', 'gt_answer': 'skateboarder'}, {'question_id': '20866443', 'answer': 'Refrigerator', 'gt_answer': 'refrigerator'}, {'question_id': '20866441', 'answer': 'Dairy', 'gt_answer': 'sour cream'}, {'question_id': '202169153', 'answer': 'Talking', 'gt_answer': 'talking'}, {'question_id': '201482070', 'answer': 'Woman', 'gt_answer': 'man'}, {'question_id': '20786132', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201997098', 'answer': 'Monitor', 'gt_answer': 'phone'}, {'question_id': '201920515', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20837055', 'answer': 'Table', 'gt_answer': 'table'}, {'question_id': '201030428', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20837053', 'answer': 'Table', 'gt_answer': 'table'}, {'question_id': '201663059', 'answer': 'Brown', 'gt_answer': 'light brown'}, {'question_id': '20837051', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '20692132', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20935960', 'answer': 'Elephant', 'gt_answer': 'elephant'}, {'question_id': '20692130', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201765815', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '20837058', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201882676', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201896076', 'answer': 'Gray', 'gt_answer': 'gray'}, {'question_id': '20797685', 'answer': 'Cat', 'gt_answer': 'cat'}, {'question_id': '20797684', 'answer': 'Cat', 'gt_answer': 'cat'}, {'question_id': '201153409', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '20797688', 'answer': 'Cat', 'gt_answer': 'cat'}, {'question_id': '201988024', 'answer': 'Round', 'gt_answer': 'square'}, {'question_id': '201079794', 'answer': 'Small', 'gt_answer': 'small'}, {'question_id': '201479425', 'answer': 'Biscuit', 'gt_answer': 'bread'}, {'question_id': '20508847', 'answer': 'Small', 'gt_answer': 'small'}, {'question_id': '201509809', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20783238', 'answer': 'Glass', 'gt_answer': 'screen'}, {'question_id': '201576684', 'answer': 'White', 'gt_answer': 'brown'}, {'question_id': '20978589', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20489694', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '202003954', 'answer': 'Wall', 'gt_answer': 'office'}, {'question_id': '201882583', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20262714', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20899681', 'answer': 'Glass', 'gt_answer': 'glass'}, {'question_id': '202081084', 'answer': 'Toaster', 'gt_answer': 'toaster'}, {'question_id': '202081089', 'answer': 'Toaster', 'gt_answer': 'toaster'}, {'question_id': '20262718', 'answer': 'Blond', 'gt_answer': 'blond'}, {'question_id': '20567605', 'answer': 'People', 'gt_answer': 'man'}, {'question_id': '20541619', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201759210', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20518275', 'answer': 'Long', 'gt_answer': 'long'}, {'question_id': '20473011', 'answer': 'Woman', 'gt_answer': 'woman'}, {'question_id': '20473010', 'answer': 'Woman', 'gt_answer': 'woman'}, {'question_id': '20157030', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201638862', 'answer': 'Bottom', 'gt_answer': 'bottom'}, {'question_id': '20480447', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '202174266', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20183117', 'answer': 'Gray', 'gt_answer': 'gray'}, {'question_id': '202258492', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20183113', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202081033', 'answer': 'Coffee maker', 'gt_answer': 'toaster'}, {'question_id': '20395038', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '20953156', 'answer': 'Short', 'gt_answer': 'short'}, {'question_id': '20489528', 'answer': 'Bed', 'gt_answer': 'bed'}, {'question_id': '20489526', 'answer': 'Bed', 'gt_answer': 'bed'}, {'question_id': '20480733', 'answer': 'Laptop', 'gt_answer': 'computer'}, {'question_id': '20754697', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20412285', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201067637', 'answer': 'Laptop', 'gt_answer': 'laptop'}, {'question_id': '201307351', 'answer': 'Sky', 'gt_answer': 'sky'}, {'question_id': '201067634', 'answer': 'Donut', 'gt_answer': 'laptop'}, {'question_id': '202005783', 'answer': 'Table', 'gt_answer': 'cabinet'}, {'question_id': '20204729', 'answer': 'Nothing', 'gt_answer': 'charger'}, {'question_id': '20887396', 'answer': 'Computer', 'gt_answer': 'monitor'}, {'question_id': '20435192', 'answer': 'Table', 'gt_answer': 'table'}, {'question_id': '201067635', 'answer': 'Laptop', 'gt_answer': 'laptop'}, {'question_id': '201738087', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20435196', 'answer': 'Chair', 'gt_answer': 'chair'}, {'question_id': '201879042', 'answer': 'Window', 'gt_answer': 'logo'}, {'question_id': '2017472', 'answer': 'Horse', 'gt_answer': 'horse'}, {'question_id': '20861050', 'answer': 'Car', 'gt_answer': 'car'}, {'question_id': '2017479', 'answer': 'Sheep', 'gt_answer': 'horse'}, {'question_id': '20149835', 'answer': 'Drawer', 'gt_answer': 'cabinet'}, {'question_id': '202053210', 'answer': 'Player', 'gt_answer': 'umpire'}, {'question_id': '202053211', 'answer': 'Player', 'gt_answer': 'umpire'}, {'question_id': '20157256', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201391961', 'answer': 'Couch', 'gt_answer': 'sofa'}, {'question_id': '20157252', 'answer': 'Egg', 'gt_answer': 'pancake'}, {'question_id': '20785959', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20210941', 'answer': 'Cake', 'gt_answer': 'pie'}, {'question_id': '202244325', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20412139', 'answer': 'Man', 'gt_answer': 'boy'}, {'question_id': '20636792', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '20648080', 'answer': 'Motorcycle', 'gt_answer': 'motorcycle'}, {'question_id': '20518498', 'answer': 'Clean', 'gt_answer': 'clean'}, {'question_id': '20361447', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20861138', 'answer': 'Blue', 'gt_answer': 'blue'}, {'question_id': '201798487', 'answer': 'Plastic', 'gt_answer': 'plastic'}, {'question_id': '202107965', 'answer': 'Brown', 'gt_answer': 'brown'}, {'question_id': '20611956', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20611953', 'answer': 'Pizza', 'gt_answer': 'cookie'}, {'question_id': '20307257', 'answer': 'Bed', 'gt_answer': 'couch'}, {'question_id': '201068544', 'answer': 'Camera', 'gt_answer': 'cell phone'}, {'question_id': '20856609', 'answer': 'Color', 'gt_answer': 'color'}, {'question_id': '201902605', 'answer': 'Desk', 'gt_answer': 'desk'}, {'question_id': '20307251', 'answer': 'Table', 'gt_answer': 'couch'}, {'question_id': '20655080', 'answer': 'Jacket', 'gt_answer': 'hat'}, {'question_id': '201896154', 'answer': 'Table', 'gt_answer': 'table'}, {'question_id': '2097926', 'answer': 'Desk', 'gt_answer': 'desk'}, {'question_id': '2097927', 'answer': 'Desk', 'gt_answer': 'desk'}, {'question_id': '202000703', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20818695', 'answer': 'Batting', 'gt_answer': 'playing'}, {'question_id': '201596093', 'answer': 'Bench', 'gt_answer': 'mud'}, {'question_id': '20818690', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202000826', 'answer': 'Wood', 'gt_answer': 'wood'}, {'question_id': '202156760', 'answer': 'Short', 'gt_answer': 'short'}, {'question_id': '20468943', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202246758', 'answer': 'Shirt', 'gt_answer': 'shirt'}, {'question_id': '20667690', 'answer': 'Coffee table', 'gt_answer': 'coffee table'}, {'question_id': '20667691', 'answer': 'Coffee table', 'gt_answer': 'coffee table'}, {'question_id': '202208410', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '201669651', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20611838', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '202246751', 'answer': 'Desk', 'gt_answer': 'computer desk'}, {'question_id': '202246752', 'answer': 'Shelf', 'gt_answer': 'computer desk'}, {'question_id': '201080248', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202246754', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202232021', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202248991', 'answer': 'Black', 'gt_answer': 'black'}, {'question_id': '20756692', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20756695', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201982980', 'answer': 'Couch', 'gt_answer': 'coffee table'}, {'question_id': '202006175', 'answer': 'Empty', 'gt_answer': 'full'}, {'question_id': '20705705', 'answer': 'Computer', 'gt_answer': 'monitor'}, {'question_id': '2013048', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201795022', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201795021', 'answer': 'Elephant', 'gt_answer': 'elephant'}, {'question_id': '201795020', 'answer': 'Elephant', 'gt_answer': 'elephant'}, {'question_id': '201360747', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201879188', 'answer': 'Left', 'gt_answer': 'right'}, {'question_id': '201861476', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201480560', 'answer': 'Short', 'gt_answer': 'short'}, {'question_id': '20673036', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20330477', 'answer': 'Tree', 'gt_answer': 'fence'}, {'question_id': '20600269', 'answer': 'Rock', 'gt_answer': 'rock'}, {'question_id': '2091313', 'answer': 'Wood', 'gt_answer': 'wood'}, {'question_id': '2091312', 'answer': 'Wood', 'gt_answer': 'wood'}, {'question_id': '201878324', 'answer': 'Woman', 'gt_answer': 'man'}, {'question_id': '201735420', 'answer': 'Chicken', 'gt_answer': 'chicken'}, {'question_id': '20308109', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201878321', 'answer': 'Coat', 'gt_answer': 'jacket'}, {'question_id': '201982088', 'answer': 'Boy', 'gt_answer': 'man'}, {'question_id': '201481747', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201481742', 'answer': 'Shirt', 'gt_answer': 'glasses'}, {'question_id': '202270958', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201175040', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201065437', 'answer': 'White', 'gt_answer': 'white'}, {'question_id': '202218534', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '20862808', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202006252', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20157561', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20621939', 'answer': 'Open', 'gt_answer': 'shut'}, {'question_id': '201763610', 'answer': 'Nothing', 'gt_answer': 'pillowcase'}, {'question_id': '201795284', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201795283', 'answer': 'Empty', 'gt_answer': 'full'}, {'question_id': '202231518', 'answer': 'Trash can', 'gt_answer': 'trash can'}, {'question_id': '202106365', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20899914', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20621736', 'answer': 'Street', 'gt_answer': 'pavement'}, {'question_id': '20647259', 'answer': 'Black', 'gt_answer': 'black'}, {'question_id': '201859627', 'answer': 'Table', 'gt_answer': 'table'}, {'question_id': '201859621', 'answer': 'White', 'gt_answer': 'white'}, {'question_id': '20491916', 'answer': 'Mountain', 'gt_answer': 'ground'}, {'question_id': '202147768', 'answer': 'Chair', 'gt_answer': 'chair'}, {'question_id': '201982550', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201430790', 'answer': 'Tissue', 'gt_answer': 'eye glasses'}, {'question_id': '201430792', 'answer': 'Tissue', 'gt_answer': 'eye glasses'}, {'question_id': '20171118', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201770700', 'answer': 'Sink', 'gt_answer': 'faucet'}, {'question_id': '20226867', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '201638780', 'answer': 'Tan', 'gt_answer': 'brown'}, {'question_id': '201638786', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '202000762', 'answer': 'Concrete', 'gt_answer': 'concrete'}, {'question_id': '201336943', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20136709', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201336940', 'answer': 'Tree', 'gt_answer': 'bench'}, {'question_id': '20655358', 'answer': 'Small', 'gt_answer': 'small'}, {'question_id': '20169769', 'answer': 'Pole', 'gt_answer': 'shirt'}, {'question_id': '201411099', 'answer': 'Gold', 'gt_answer': 'silver'}, {'question_id': '201590294', 'answer': 'Blue', 'gt_answer': 'blue'}, {'question_id': '202125966', 'answer': 'Short sleeved', 'gt_answer': 'short sleeved'}, {'question_id': '20169766', 'answer': 'Jacket', 'gt_answer': 'tie'}, {'question_id': '201752822', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '202173860', 'answer': 'Material', 'gt_answer': 'color'}, {'question_id': '2066050', 'answer': 'Pants', 'gt_answer': 'sweater'}, {'question_id': '2012745', 'answer': 'Donut', 'gt_answer': 'donut'}, {'question_id': '20340789', 'answer': 'Street', 'gt_answer': 'school'}, {'question_id': '2012746', 'answer': 'Donut', 'gt_answer': 'donut'}, {'question_id': '201055889', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '2012742', 'answer': 'Tattoo', 'gt_answer': 'shirt'}, {'question_id': '201608366', 'answer': 'Green', 'gt_answer': 'green'}, {'question_id': '201636988', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20968453', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20183328', 'answer': 'Wood', 'gt_answer': 'wood'}, {'question_id': '20541112', 'answer': 'Long', 'gt_answer': 'long'}, {'question_id': '201676178', 'answer': 'Laptop', 'gt_answer': 'computer'}, {'question_id': '2056116', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '2056110', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202240802', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201273119', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20865927', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201996550', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201803715', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201439462', 'answer': 'No one', 'gt_answer': 'woman'}, {'question_id': '201996557', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '202102551', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '202218959', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '2059466', 'answer': 'Wood', 'gt_answer': 'metal'}, {'question_id': '201207251', 'answer': 'Yellow', 'gt_answer': 'tan'}, {'question_id': '20652299', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '2046605', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '201207272', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20503792', 'answer': 'Wire', 'gt_answer': 'cables'}, {'question_id': '201030757', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20573433', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202257263', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202257261', 'answer': 'People', 'gt_answer': 'cyclist'}, {'question_id': '20162504', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20162506', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201794950', 'answer': 'Green', 'gt_answer': 'brown'}, {'question_id': '202257268', 'answer': 'People', 'gt_answer': 'cyclist'}, {'question_id': '201640374', 'answer': 'Clear', 'gt_answer': 'black'}, {'question_id': '201233881', 'answer': 'Yellow', 'gt_answer': 'gray'}, {'question_id': '201573876', 'answer': 'Pole', 'gt_answer': 'stores'}, {'question_id': '201030416', 'answer': 'Couch', 'gt_answer': 'couch'}, {'question_id': '20896672', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '202174619', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201404253', 'answer': 'Calf', 'gt_answer': 'calf'}, {'question_id': '201404251', 'answer': 'Calf', 'gt_answer': 'calf'}, {'question_id': '201404250', 'answer': 'Calf', 'gt_answer': 'calf'}, {'question_id': '20797763', 'answer': 'Cat', 'gt_answer': 'cat'}, {'question_id': '20797760', 'answer': 'Cat', 'gt_answer': 'cat'}, {'question_id': '201896060', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20434901', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202169059', 'answer': 'Cars', 'gt_answer': 'cars'}, {'question_id': '20434909', 'answer': 'Pizza', 'gt_answer': 'restaurant'}, {'question_id': '201758531', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202208385', 'answer': 'Street sign', 'gt_answer': 'traffic sign'}, {'question_id': '20518644', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201861265', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '202133881', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20480057', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20518649', 'answer': 'Shelf', 'gt_answer': 'countertop'}, {'question_id': '20227059', 'answer': 'People', 'gt_answer': 'woman'}, {'question_id': '201570673', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201663423', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201873168', 'answer': 'Bus', 'gt_answer': 'fire truck'}, {'question_id': '201873167', 'answer': 'Window', 'gt_answer': 'fire truck'}, {'question_id': '201974872', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '201873165', 'answer': 'Bridge', 'gt_answer': 'sticker'}, {'question_id': '201504902', 'answer': 'Woman', 'gt_answer': 'woman'}, {'question_id': '201556593', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202169094', 'answer': 'Cars', 'gt_answer': 'cars'}, {'question_id': '202169091', 'answer': 'Cars', 'gt_answer': 'cars'}, {'question_id': '202169093', 'answer': 'Cars', 'gt_answer': 'cars'}, {'question_id': '201175233', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '20412459', 'answer': 'Flowers', 'gt_answer': 'flowers'}, {'question_id': '201804498', 'answer': 'Couch', 'gt_answer': 'fireplace'}, {'question_id': '20480638', 'answer': 'Radio', 'gt_answer': 'radio'}, {'question_id': '201399971', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20480631', 'answer': 'Laptop', 'gt_answer': 'radio'}, {'question_id': '20480633', 'answer': 'Laptop', 'gt_answer': 'radio'}, {'question_id': '202162654', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20631852', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20978247', 'answer': 'Blond', 'gt_answer': 'blond'}, {'question_id': '201935869', 'answer': 'Nothing', 'gt_answer': 'squash'}, {'question_id': '201983707', 'answer': 'Suitcase', 'gt_answer': 'papers'}, {'question_id': '201935866', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '202082277', 'answer': 'Computer mouse', 'gt_answer': 'keyboard'}, {'question_id': '201935861', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201987210', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20306391', 'answer': 'Man', 'gt_answer': 'woman'}, {'question_id': '201859368', 'answer': 'Small', 'gt_answer': 'small'}, {'question_id': '201832264', 'answer': 'Bedroom', 'gt_answer': 'bedroom'}, {'question_id': '20306394', 'answer': 'Snow', 'gt_answer': 'couch'}, {'question_id': '20716983', 'answer': 'Glass', 'gt_answer': 'glass'}, {'question_id': '202082279', 'answer': 'Laptop', 'gt_answer': 'laptop'}, {'question_id': '20306399', 'answer': 'Desk', 'gt_answer': 'couch'}, {'question_id': '201996977', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201068418', 'answer': 'Phone', 'gt_answer': 'cell phone'}, {'question_id': '201676171', 'answer': 'Silver', 'gt_answer': 'silver'}, {'question_id': '201770829', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '2097555', 'answer': 'Keyboard', 'gt_answer': 'keyboard'}, {'question_id': '201068483', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20245877', 'answer': 'Leafy', 'gt_answer': 'leafy'}, {'question_id': '202110116', 'answer': 'Sheep', 'gt_answer': 'sheep'}, {'question_id': '201068489', 'answer': 'Bottom', 'gt_answer': 'bottom'}, {'question_id': '201804724', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20394879', 'answer': 'Suit', 'gt_answer': 'suit'}, {'question_id': '201804720', 'answer': 'Silver', 'gt_answer': 'silver'}, {'question_id': '20119011', 'answer': 'Walking', 'gt_answer': 'walking'}, {'question_id': '20308737', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20308731', 'answer': 'Cutting board', 'gt_answer': 'tea kettle'}, {'question_id': '201873559', 'answer': 'Bus', 'gt_answer': 'fire truck'}, {'question_id': '20119018', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202107833', 'answer': 'Boy', 'gt_answer': 'boy'}, {'question_id': '20299855', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201156275', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202102933', 'answer': 'Dishwasher', 'gt_answer': 'dishwasher'}, {'question_id': '20244652', 'answer': 'Bus', 'gt_answer': 'bus'}, {'question_id': '201068777', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201404276', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20887230', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201920454', 'answer': 'Short sleeved', 'gt_answer': 'long sleeved'}, {'question_id': '20746563', 'answer': 'Birds', 'gt_answer': 'birds'}, {'question_id': '20511516', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20118949', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20511510', 'answer': 'Helicopter', 'gt_answer': 'ship'}, {'question_id': '20511512', 'answer': 'Helicopter', 'gt_answer': 'ship'}, {'question_id': '202180344', 'answer': 'Girl', 'gt_answer': 'soccer player'}, {'question_id': '201411057', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20241172', 'answer': 'Ceramic', 'gt_answer': 'porcelain'}, {'question_id': '2075644', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20320333', 'answer': 'Bushy', 'gt_answer': 'bushy'}, {'question_id': '201641244', 'answer': 'Gray', 'gt_answer': 'green'}, {'question_id': '20241178', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202081338', 'answer': 'Mirror', 'gt_answer': 'toaster'}, {'question_id': '202006695', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201879915', 'answer': 'Short sleeved', 'gt_answer': 'short sleeved'}, {'question_id': '20936263', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20953862', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20953867', 'answer': 'Girl', 'gt_answer': 'girl'}, {'question_id': '20117918', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202285366', 'answer': 'Beans', 'gt_answer': 'beans'}, {'question_id': '201480354', 'answer': 'Boy', 'gt_answer': 'boy'}, {'question_id': '201391935', 'answer': 'Wide', 'gt_answer': 'wide'}, {'question_id': '201480359', 'answer': 'Boy', 'gt_answer': 'boy'}, {'question_id': '202246695', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201616124', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202006347', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201616120', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20345159', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20963724', 'answer': 'Window', 'gt_answer': 'pipe'}, {'question_id': '201908765', 'answer': 'Blue', 'gt_answer': 'blue'}, {'question_id': '202265787', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20902798', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201872973', 'answer': 'Black', 'gt_answer': 'brown'}, {'question_id': '202161890', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201957164', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '201080180', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20349863', 'answer': 'Brown', 'gt_answer': 'beige'}, {'question_id': '201593658', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '2044581', 'answer': 'Steps', 'gt_answer': 'steps'}, {'question_id': '202116876', 'answer': 'Concrete', 'gt_answer': 'concrete'}, {'question_id': '20340493', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20894186', 'answer': 'Ground', 'gt_answer': 'skateboard'}, {'question_id': '20894187', 'answer': 'Ground', 'gt_answer': 'skateboard'}, {'question_id': '20894184', 'answer': 'Boy', 'gt_answer': 'boy'}, {'question_id': '202116873', 'answer': 'Square', 'gt_answer': 'rectangular'}, {'question_id': '201593651', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201593652', 'answer': 'Trees', 'gt_answer': 'pine trees'}, {'question_id': '201504738', 'answer': 'Flower', 'gt_answer': 'flower'}, {'question_id': '201593654', 'answer': 'Fence', 'gt_answer': 'fence'}, {'question_id': '20307116', 'answer': 'Left', 'gt_answer': 'right'}, {'question_id': '201400151', 'answer': 'Couch', 'gt_answer': 'bookcase'}, {'question_id': '20169970', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201866742', 'answer': 'Tricycle', 'gt_answer': 'bicycle'}, {'question_id': '202243427', 'answer': 'Car', 'gt_answer': 'truck'}, {'question_id': '202243428', 'answer': 'Motorcycle', 'gt_answer': 'truck'}, {'question_id': '20637075', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201482195', 'answer': 'None', 'gt_answer': 'berries'}, {'question_id': '20427509', 'answer': 'Plastic', 'gt_answer': 'glass'}, {'question_id': '2076666', 'answer': 'Large', 'gt_answer': 'small'}, {'question_id': '2076661', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201505028', 'answer': 'Pink', 'gt_answer': 'orange'}, {'question_id': '201412315', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20716779', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201861595', 'answer': 'Dirty', 'gt_answer': 'dirty'}, {'question_id': '20968505', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20644759', 'answer': 'Petting cat', 'gt_answer': 'staring'}, {'question_id': '20285102', 'answer': 'White', 'gt_answer': 'dark'}, {'question_id': '201957389', 'answer': 'Glass', 'gt_answer': 'metal'}, {'question_id': '201763930', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201462514', 'answer': 'Catcher', 'gt_answer': 'catcher'}, {'question_id': '201462517', 'answer': 'Helmet', 'gt_answer': 'glove'}, {'question_id': '201462518', 'answer': 'Helmet', 'gt_answer': 'glove'}, {'question_id': '201337263', 'answer': 'Bench', 'gt_answer': 'bench'}, {'question_id': '201337265', 'answer': 'Tree', 'gt_answer': 'trees'}, {'question_id': '201337264', 'answer': 'Bench', 'gt_answer': 'bench'}, {'question_id': '201337266', 'answer': 'Tree', 'gt_answer': 'trees'}, {'question_id': '201346632', 'answer': 'Motorcycle', 'gt_answer': 'motorcycle'}, {'question_id': '202228670', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20929589', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201623851', 'answer': 'Cabinets', 'gt_answer': 'cabinets'}, {'question_id': '201640213', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201795348', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201623853', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201156060', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20258775', 'answer': 'Boy', 'gt_answer': 'child'}, {'question_id': '202258523', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201879840', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201505172', 'answer': 'Empty', 'gt_answer': 'empty'}, {'question_id': '201795611', 'answer': 'Long', 'gt_answer': 'short'}, {'question_id': '20645820', 'answer': 'Shelf', 'gt_answer': 'shelf'}, {'question_id': '201711139', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '2091125', 'answer': 'Car', 'gt_answer': 'car'}, {'question_id': '201303409', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20621894', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20480804', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '201640219', 'answer': 'Small', 'gt_answer': 'large'}, {'question_id': '202162017', 'answer': 'Chair', 'gt_answer': 'bookcase'}, {'question_id': '201735659', 'answer': 'Wood', 'gt_answer': 'wood'}, {'question_id': '202162013', 'answer': 'Bookcase', 'gt_answer': 'bookcase'}, {'question_id': '20865572', 'answer': 'Wood', 'gt_answer': 'wood'}, {'question_id': '20631662', 'answer': 'Batter', 'gt_answer': 'batter'}, {'question_id': '20962415', 'answer': 'Black', 'gt_answer': 'black'}, {'question_id': '20631665', 'answer': 'Bat', 'gt_answer': 'baseball bat'}, {'question_id': '20631666', 'answer': 'Bat', 'gt_answer': 'baseball bat'}, {'question_id': '201064667', 'answer': 'Pizza', 'gt_answer': 'hamburger'}, {'question_id': '20395003', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '201908948', 'answer': 'Tray', 'gt_answer': 'napkin'}, {'question_id': '201765708', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '20596422', 'answer': 'Right', 'gt_answer': 'left'}, {'question_id': '20308937', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201663217', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '20308930', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '20710033', 'answer': 'Car', 'gt_answer': 'car'}, {'question_id': '20710032', 'answer': 'Car', 'gt_answer': 'car'}, {'question_id': '20661328', 'answer': 'Stick', 'gt_answer': 'branch'}, {'question_id': '202106112', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201886889', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201492102', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201981984', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201492104', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201859406', 'answer': 'Closed', 'gt_answer': 'open'}, {'question_id': '202102606', 'answer': 'Dishwasher', 'gt_answer': 'washing machine'}, {'question_id': '202102605', 'answer': 'Cabinet', 'gt_answer': 'washing machine'}, {'question_id': '20491764', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20162261', 'answer': 'Short sleeved', 'gt_answer': 'short sleeved'}, {'question_id': '201407445', 'answer': 'Green', 'gt_answer': 'green'}, {'question_id': '20786154', 'answer': 'Water', 'gt_answer': 'water'}, {'question_id': '201983938', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20542857', 'answer': 'Pen', 'gt_answer': 'zoo'}, {'question_id': '20978741', 'answer': 'Motorcycle', 'gt_answer': 'motorcycle'}, {'question_id': '202000609', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201803966', 'answer': 'Chair', 'gt_answer': 'couch'}, {'question_id': '201882614', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '201983933', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20542858', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20177931', 'answer': 'Pickles', 'gt_answer': 'pickles'}, {'question_id': '201235873', 'answer': 'Man', 'gt_answer': 'woman'}, {'question_id': '201988045', 'answer': 'Sandwich', 'gt_answer': 'cookie'}, {'question_id': '202262077', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201570642', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201570645', 'answer': 'Wood', 'gt_answer': 'glass'}, {'question_id': '201988043', 'answer': 'Sandwich', 'gt_answer': 'cookie'}, {'question_id': '201153420', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20385888', 'answer': 'Bottom', 'gt_answer': 'bottom'}, {'question_id': '20891248', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20652608', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '201055699', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20330325', 'answer': 'Broom', 'gt_answer': 'broom'}, {'question_id': '20136510', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201492367', 'answer': 'Behind', 'gt_answer': 'front'}, {'question_id': '201235467', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201509822', 'answer': 'Ivy', 'gt_answer': 'vines'}, {'question_id': '201079803', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20609708', 'answer': 'Fruit', 'gt_answer': 'strawberry'}, {'question_id': '202122174', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20162199', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20516083', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202174299', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '201889391', 'answer': 'Trees', 'gt_answer': 'mountain side'}, {'question_id': '2062416', 'answer': 'Wall', 'gt_answer': 'bleachers'}, {'question_id': '201889393', 'answer': 'Snow', 'gt_answer': 'sky'}, {'question_id': '201889392', 'answer': 'Snow', 'gt_answer': 'sky'}, {'question_id': '201889394', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201758270', 'answer': 'Brown', 'gt_answer': 'brown'}, {'question_id': '201795130', 'answer': 'Elephant', 'gt_answer': 'elephant'}, {'question_id': '20567628', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20204527', 'answer': 'Laptop', 'gt_answer': 'rug'}, {'question_id': '20456497', 'answer': 'Dog', 'gt_answer': 'dog'}, {'question_id': '201896351', 'answer': 'Woman', 'gt_answer': 'lady'}, {'question_id': '20204528', 'answer': 'Laptop', 'gt_answer': 'rug'}, {'question_id': '20887003', 'answer': 'Desk', 'gt_answer': 'table'}, {'question_id': '2053640', 'answer': 'Cow', 'gt_answer': 'cow'}, {'question_id': '202227908', 'answer': 'Brown', 'gt_answer': 'brown'}, {'question_id': '202003616', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201766617', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202023390', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202003612', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20451949', 'answer': 'Round', 'gt_answer': 'round'}, {'question_id': '20837074', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201548811', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201752907', 'answer': 'Large', 'gt_answer': 'small'}, {'question_id': '202169281', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201175578', 'answer': 'Wood', 'gt_answer': 'wood'}, {'question_id': '20489546', 'answer': 'Wide', 'gt_answer': 'wide'}, {'question_id': '201951529', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201185852', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201796066', 'answer': 'Talking', 'gt_answer': 'talking'}, {'question_id': '201175577', 'answer': 'Wood', 'gt_answer': 'wood'}, {'question_id': '20427885', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20204746', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '2098091', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20648318', 'answer': 'Sign', 'gt_answer': 'sign'}, {'question_id': '201959873', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201752677', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20157276', 'answer': 'Butter', 'gt_answer': 'pancake'}, {'question_id': '20303111', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '2017455', 'answer': 'Sheep', 'gt_answer': 'goat'}, {'question_id': '20898605', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '2097631', 'answer': 'Speaker', 'gt_answer': 'keyboard'}, {'question_id': '20734184', 'answer': 'Long', 'gt_answer': 'long'}, {'question_id': '20783387', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '2097635', 'answer': 'Laptop', 'gt_answer': 'monitor'}, {'question_id': '20412157', 'answer': 'Suit', 'gt_answer': 'tie'}, {'question_id': '20412156', 'answer': 'Man', 'gt_answer': 'boy'}, {'question_id': '20827496', 'answer': 'Cabinet', 'gt_answer': 'chairs'}, {'question_id': '20596530', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201438469', 'answer': 'Wood', 'gt_answer': 'wood'}, {'question_id': '20412150', 'answer': 'Man', 'gt_answer': 'boy'}, {'question_id': '201109300', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20403401', 'answer': 'On', 'gt_answer': 'on'}, {'question_id': '202144525', 'answer': 'Beer', 'gt_answer': 'liquor'}, {'question_id': '202144524', 'answer': 'Yellow', 'gt_answer': 'yellow'}, {'question_id': '20412159', 'answer': 'Suit', 'gt_answer': 'tie'}, {'question_id': '20442044', 'answer': 'Wood', 'gt_answer': 'glass'}, {'question_id': '201446995', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20861113', 'answer': 'Old', 'gt_answer': 'old'}, {'question_id': '201079738', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '202246590', 'answer': 'Large', 'gt_answer': 'small'}, {'question_id': '20856626', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20942273', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20307270', 'answer': 'Brown', 'gt_answer': 'gray'}, {'question_id': '201765921', 'answer': 'Man', 'gt_answer': 'surfer'}, {'question_id': '20699250', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201412237', 'answer': 'Wet', 'gt_answer': 'wet'}, {'question_id': '201637363', 'answer': 'Cake', 'gt_answer': 'countertop'}, {'question_id': '2097942', 'answer': 'Desk', 'gt_answer': 'desk'}, {'question_id': '20479849', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '201889290', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20514958', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201756586', 'answer': 'Small', 'gt_answer': 'small'}, {'question_id': '201639012', 'answer': 'Short', 'gt_answer': 'tall'}, {'question_id': '201952869', 'answer': 'Train', 'gt_answer': 'train'}, {'question_id': '2076475', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '20468690', 'answer': 'Horse', 'gt_answer': 'horse'}, {'question_id': '202037014', 'answer': 'Pepperoni', 'gt_answer': 'pepperoni'}, {'question_id': '202037015', 'answer': 'Pepper', 'gt_answer': 'pepperoni'}, {'question_id': '202037016', 'answer': 'Pepper', 'gt_answer': 'pepperoni'}, {'question_id': '202286815', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '202246775', 'answer': 'Shirt', 'gt_answer': 'shirt'}, {'question_id': '202246770', 'answer': 'Shirt', 'gt_answer': 'shirt'}, {'question_id': '2055708', 'answer': 'Bus', 'gt_answer': 'bus'}, {'question_id': '201067716', 'answer': 'Donut', 'gt_answer': 'donut'}, {'question_id': '20753421', 'answer': '3 feet', 'gt_answer': 'tall'}, {'question_id': '202270905', 'answer': 'Man', 'gt_answer': 'skateboarder'}, {'question_id': '20818946', 'answer': 'Red', 'gt_answer': 'red'}, {'question_id': '20308697', 'answer': 'Pot', 'gt_answer': 'tea kettle'}, {'question_id': '20308694', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20308695', 'answer': 'Pot', 'gt_answer': 'tea kettle'}, {'question_id': '202144420', 'answer': 'Shorts', 'gt_answer': 'shorts'}, {'question_id': '202244672', 'answer': 'Carrots', 'gt_answer': 'carrots'}, {'question_id': '20936184', 'answer': 'People', 'gt_answer': 'child'}, {'question_id': '201676490', 'answer': 'Chair', 'gt_answer': 'shelf'}, {'question_id': '201447083', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20673018', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20550324', 'answer': 'Horse', 'gt_answer': 'horse'}, {'question_id': '202285118', 'answer': 'Paper', 'gt_answer': 'paper'}, {'question_id': '20878965', 'answer': 'Man', 'gt_answer': 'skater'}, {'question_id': '20600241', 'answer': 'Small', 'gt_answer': 'small'}, {'question_id': '20144778', 'answer': 'Bus', 'gt_answer': 'bus'}, {'question_id': '20836935', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20177467', 'answer': 'Fries', 'gt_answer': 'fries'}, {'question_id': '202243659', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20836930', 'answer': 'Tent', 'gt_answer': 'palm tree'}, {'question_id': '201481766', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202243658', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20865362', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201481761', 'answer': 'White', 'gt_answer': 'white'}, {'question_id': '20865360', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '2075783', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201065412', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '202218513', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20645343', 'answer': 'Indoors', 'gt_answer': 'indoors'}, {'question_id': '20862861', 'answer': 'Suit', 'gt_answer': 'suit'}, {'question_id': '20240947', 'answer': 'Napkin', 'gt_answer': 'table'}, {'question_id': '20794123', 'answer': 'Sandwich', 'gt_answer': 'sandwich'}, {'question_id': '20862868', 'answer': 'Suit', 'gt_answer': 'suit'}, {'question_id': '20781835', 'answer': 'Thick', 'gt_answer': 'thick'}, {'question_id': '201638802', 'answer': 'Car', 'gt_answer': 'car'}, {'question_id': '202106341', 'answer': 'Metal', 'gt_answer': 'metal'}, {'question_id': '20781836', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201037174', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201037177', 'answer': 'Woman', 'gt_answer': 'girl'}, {'question_id': '201037178', 'answer': 'Woman', 'gt_answer': 'girl'}, {'question_id': '20381224', 'answer': 'Desk', 'gt_answer': 'desk'}, {'question_id': '20647275', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20381221', 'answer': 'Shelf', 'gt_answer': 'shelf'}, {'question_id': '201407274', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202060188', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '20647278', 'answer': 'Short', 'gt_answer': 'short'}, {'question_id': '201896499', 'answer': 'Wood', 'gt_answer': 'wood'}, {'question_id': '2098117', 'answer': 'Small', 'gt_answer': 'large'}, {'question_id': '201983165', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201756729', 'answer': 'Cat', 'gt_answer': 'kitten'}, {'question_id': '201756728', 'answer': 'Cat', 'gt_answer': 'kitten'}, {'question_id': '20171133', 'answer': 'Pan', 'gt_answer': 'baking pan'}, {'question_id': '20171136', 'answer': 'Pan', 'gt_answer': 'baking pan'}, {'question_id': '20171139', 'answer': 'Pan', 'gt_answer': 'baking pan'}, {'question_id': '201756720', 'answer': 'Cat', 'gt_answer': 'kitten'}, {'question_id': '2059651', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201756724', 'answer': 'Counter', 'gt_answer': 'countertop'}, {'question_id': '20452053', 'answer': 'Table', 'gt_answer': 'leaves'}, {'question_id': '20781743', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20441893', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202286941', 'answer': 'Plastic', 'gt_answer': 'concrete'}, {'question_id': '20647183', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202053207', 'answer': 'Batter', 'gt_answer': 'umpire'}, {'question_id': '20136727', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201235674', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20330427', 'answer': 'Tall', 'gt_answer': 'short'}, {'question_id': '201235670', 'answer': 'Banana', 'gt_answer': 'bananas'}, {'question_id': '201982419', 'answer': 'Couch', 'gt_answer': 'couch'}, {'question_id': '201235679', 'answer': 'Bananas', 'gt_answer': 'bananas'}, {'question_id': '20756814', 'answer': 'Long', 'gt_answer': 'long'}, {'question_id': '20637194', 'answer': 'Deep', 'gt_answer': 'deep'}, {'question_id': '20637190', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20169786', 'answer': 'Flag', 'gt_answer': 'flag'}, {'question_id': '20169785', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201428489', 'answer': 'Controller', 'gt_answer': 'remote control'}, {'question_id': '201621414', 'answer': 'Wood', 'gt_answer': 'glass'}, {'question_id': '201822230', 'answer': 'Bathroom', 'gt_answer': 'bathroom'}, {'question_id': '202125988', 'answer': 'Chairs', 'gt_answer': 'chairs'}, {'question_id': '20151555', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201482038', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202173846', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20752482', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201434201', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '2058421', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201360495', 'answer': 'Poster', 'gt_answer': 'picture'}, {'question_id': '201952657', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '2058428', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201360491', 'answer': 'Blue', 'gt_answer': 'blue'}, {'question_id': '20860939', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201055866', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20573613', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '20302982', 'answer': 'Long sleeved', 'gt_answer': 'long sleeved'}, {'question_id': '20894206', 'answer': 'Boy', 'gt_answer': 'boy'}, {'question_id': '201235580', 'answer': 'Bananas', 'gt_answer': 'basket'}, {'question_id': '20302987', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20853943', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20637228', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '20853947', 'answer': '10 feet', 'gt_answer': 'short'}, {'question_id': '202106459', 'answer': 'Top', 'gt_answer': 'top'}, {'question_id': '202121403', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20183346', 'answer': 'Long', 'gt_answer': 'long'}, {'question_id': '20287338', 'answer': 'Black', 'gt_answer': 'black'}, {'question_id': '20183340', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201803779', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20151888', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '201143416', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201998003', 'answer': 'Skinny', 'gt_answer': 'thin'}, {'question_id': '201803770', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20744392', 'answer': 'Player', 'gt_answer': 'baseball players'}, {'question_id': '201883177', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201434069', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201996539', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201935883', 'answer': 'Bottom', 'gt_answer': 'bottom'}, {'question_id': '201509873', 'answer': 'Tall', 'gt_answer': 'tall'}, {'question_id': '20508146', 'answer': 'Chair', 'gt_answer': 'table'}, {'question_id': '201497672', 'answer': 'White', 'gt_answer': 'white'}, {'question_id': '201574315', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '202266155', 'answer': 'Red', 'gt_answer': 'brown'}, {'question_id': '20896617', 'answer': 'Metal', 'gt_answer': 'glass'}, {'question_id': '20896613', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20317296', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201873538', 'answer': 'Fire truck', 'gt_answer': 'fire truck'}, {'question_id': '20287644', 'answer': 'Boy', 'gt_answer': 'batter'}, {'question_id': '201998268', 'answer': 'Table', 'gt_answer': 'table'}, {'question_id': '20342299', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '202248985', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201751879', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202258279', 'answer': 'Horse', 'gt_answer': 'horse'}, {'question_id': '202258278', 'answer': 'Horse', 'gt_answer': 'horse'}, {'question_id': '20342293', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20385411', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '20385417', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201758047', 'answer': 'Clear', 'gt_answer': 'brown'}, {'question_id': '201624038', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201902907', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201535832', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202162292', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '20508362', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '20211086', 'answer': 'Glass', 'gt_answer': 'glass'}, {'question_id': '20211084', 'answer': 'Ice cream', 'gt_answer': 'ice cream'}, {'question_id': '202240686', 'answer': 'Shirt', 'gt_answer': 'shirt'}, {'question_id': '20211083', 'answer': 'Ice cream', 'gt_answer': 'ice cream'}, {'question_id': '202108016', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201663403', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20227079', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20692564', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201663406', 'answer': 'Cabinet', 'gt_answer': 'oven'}, {'question_id': '20692567', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20679273', 'answer': 'Green', 'gt_answer': 'green'}, {'question_id': '201861330', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201621285', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201536380', 'answer': 'Waiting', 'gt_answer': 'staring'}, {'question_id': '201951674', 'answer': 'Triangular', 'gt_answer': 'square'}, {'question_id': '202244081', 'answer': 'Cupcakes', 'gt_answer': 'cupcakes'}, {'question_id': '201974896', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202122173', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201556579', 'answer': 'Desk', 'gt_answer': 'shelf'}, {'question_id': '20710263', 'answer': 'Child', 'gt_answer': 'child'}, {'question_id': '20661155', 'answer': 'Bus', 'gt_answer': 'bus'}, {'question_id': '201185193', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201497710', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202179332', 'answer': 'Bottom', 'gt_answer': 'bottom'}, {'question_id': '202121824', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202240428', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '2097790', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201399959', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '202179459', 'answer': 'Red', 'gt_answer': 'green'}, {'question_id': '202240357', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201947749', 'answer': 'Tank top', 'gt_answer': 'tank top'}, {'question_id': '201399951', 'answer': 'Pillow', 'gt_answer': 'blanket'}, {'question_id': '20911238', 'answer': 'Man', 'gt_answer': 'skater'}, {'question_id': '20911239', 'answer': 'Skateboard', 'gt_answer': 'skateboard'}, {'question_id': '201391792', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202081902', 'answer': 'Cat', 'gt_answer': 'cat'}, {'question_id': '20631879', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '20342434', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20818778', 'answer': 'Helmet', 'gt_answer': 'face mask'}, {'question_id': '20911237', 'answer': 'Man', 'gt_answer': 'skater'}, {'question_id': '201947523', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201947522', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201947832', 'answer': 'Wall', 'gt_answer': 'countertop'}, {'question_id': '201770809', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20811091', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201935802', 'answer': 'Shelf', 'gt_answer': 'shelf'}, {'question_id': '202081763', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '202159034', 'answer': 'Cars', 'gt_answer': 'cars'}, {'question_id': '20518629', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202037129', 'answer': 'Pizza', 'gt_answer': 'pizza box'}, {'question_id': '20518627', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '20518620', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20891704', 'answer': 'Man', 'gt_answer': 'man'}, {'question_id': '20899521', 'answer': 'Leather', 'gt_answer': 'cloth'}, {'question_id': '202110134', 'answer': 'Bottom', 'gt_answer': 'bottom'}, {'question_id': '202110139', 'answer': 'Wood', 'gt_answer': 'metal'}, {'question_id': '201873574', 'answer': 'Fire truck', 'gt_answer': 'fire truck'}, {'question_id': '201873576', 'answer': 'Fire truck', 'gt_answer': 'fire truck'}, {'question_id': '202012532', 'answer': 'Shelf', 'gt_answer': 'shelves'}, {'question_id': '202012533', 'answer': 'Cabinets', 'gt_answer': 'cabinets'}, {'question_id': '201873578', 'answer': 'Bus', 'gt_answer': 'fire truck'}, {'question_id': '201504883', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20887216', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20247707', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201639261', 'answer': 'Giraffe', 'gt_answer': 'zebras'}, {'question_id': '201640486', 'answer': 'Round', 'gt_answer': 'square'}, {'question_id': '201826579', 'answer': 'Large', 'gt_answer': 'small'}, {'question_id': '202081675', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '201589910', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20644677', 'answer': 'Dirty', 'gt_answer': 'dirty'}, {'question_id': '201676373', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20963911', 'answer': 'Skinny', 'gt_answer': 'thin'}, {'question_id': '20963914', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202285343', 'answer': 'Beans', 'gt_answer': 'beans'}, {'question_id': '201480379', 'answer': 'Boy', 'gt_answer': 'boy'}, {'question_id': '201498764', 'answer': 'Keyboard', 'gt_answer': 'phone'}, {'question_id': '201756911', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '202285347', 'answer': 'Egg', 'gt_answer': 'waffles'}, {'question_id': '20117933', 'answer': 'Wide', 'gt_answer': 'narrow'}, {'question_id': '202285344', 'answer': 'Beans', 'gt_answer': 'beans'}, {'question_id': '20887252', 'answer': 'Dog', 'gt_answer': 'dog'}, {'question_id': '201480372', 'answer': 'Boy', 'gt_answer': 'boy'}, {'question_id': '201480374', 'answer': 'Dirt', 'gt_answer': 'mound'}, {'question_id': '201480376', 'answer': 'Dirt', 'gt_answer': 'mound'}, {'question_id': '201480377', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20411641', 'answer': 'Apple', 'gt_answer': 'apples'}, {'question_id': '2044837', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '202231425', 'answer': 'Stop sign', 'gt_answer': 'stop sign'}, {'question_id': '202240226', 'answer': 'Girl', 'gt_answer': 'girl'}, {'question_id': '202000602', 'answer': 'Color', 'gt_answer': 'material'}, {'question_id': '202231424', 'answer': 'Stop sign', 'gt_answer': 'stop sign'}, {'question_id': '202257442', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20753673', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20411645', 'answer': 'Carrot', 'gt_answer': 'carrots'}, {'question_id': '201393494', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20753679', 'answer': 'Snow', 'gt_answer': 'mountain side'}, {'question_id': '20551511', 'answer': 'Train', 'gt_answer': 'train'}, {'question_id': '201247133', 'answer': 'Happy', 'gt_answer': 'unhappy'}, {'question_id': '20656840', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20827269', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20567480', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20656845', 'answer': 'Street sign', 'gt_answer': 'traffic sign'}, {'question_id': '201908709', 'answer': 'Porcelain', 'gt_answer': 'glass'}, {'question_id': '20901792', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20827261', 'answer': 'Couch', 'gt_answer': 'cupboard'}, {'question_id': '20618787', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20427697', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201957146', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201143304', 'answer': 'Table', 'gt_answer': 'table'}, {'question_id': '20861304', 'answer': 'Wide', 'gt_answer': 'wide'}, {'question_id': '20349802', 'answer': 'Shirt', 'gt_answer': 'shirt'}, {'question_id': '201957143', 'answer': 'Wall', 'gt_answer': 'wall'}, {'question_id': '202180328', 'answer': 'Girl', 'gt_answer': 'soccer player'}, {'question_id': '201207518', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202100704', 'answer': 'Stove', 'gt_answer': 'burner'}, {'question_id': '20307138', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '20381414', 'answer': 'Full', 'gt_answer': 'full'}, {'question_id': '201593631', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20169956', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '2098350', 'answer': 'Desk', 'gt_answer': 'desk'}, {'question_id': '202121892', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201832310', 'answer': 'Orange', 'gt_answer': 'orange'}, {'question_id': '20169952', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202243440', 'answer': 'Car', 'gt_answer': 'truck'}, {'question_id': '202121899', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20636924', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201505046', 'answer': 'Tall', 'gt_answer': 'tall'}, {'question_id': '20741177', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202021409', 'answer': 'Stop', 'gt_answer': 'stop sign'}, {'question_id': '20427525', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20118072', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20644770', 'answer': 'Jeans', 'gt_answer': 'jeans'}, {'question_id': '20644777', 'answer': 'Man', 'gt_answer': 'man'}, {'question_id': '20644779', 'answer': 'Cat', 'gt_answer': 'cat'}, {'question_id': '20285036', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20923083', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20863656', 'answer': 'Silver', 'gt_answer': 'silver'}, {'question_id': '201676136', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '202159150', 'answer': 'Large', 'gt_answer': 'large'}, {'question_id': '20984271', 'answer': 'Black', 'gt_answer': 'black'}, {'question_id': '20929561', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '202258041', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201037296', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201795364', 'answer': 'Woman', 'gt_answer': 'woman'}, {'question_id': '201795366', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202119737', 'answer': 'Material', 'gt_answer': 'material'}, {'question_id': '20182751', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20182757', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201711116', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201303428', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '2091100', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201065609', 'answer': 'People', 'gt_answer': 'woman'}, {'question_id': '201735673', 'answer': 'Chicken', 'gt_answer': 'desk'}, {'question_id': '20865555', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '201735675', 'answer': 'Desk', 'gt_answer': 'desk'}, {'question_id': '201735676', 'answer': 'Desk', 'gt_answer': 'desk'}, {'question_id': '201623477', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202243990', 'answer': 'White', 'gt_answer': 'black'}, {'question_id': '20631647', 'answer': 'Batter', 'gt_answer': 'batter'}, {'question_id': '201909035', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202243994', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202147698', 'answer': 'Shorts', 'gt_answer': 'shirt'}, {'question_id': '202119195', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20308913', 'answer': 'Brown', 'gt_answer': 'light brown'}, {'question_id': '202218856', 'answer': 'Pot', 'gt_answer': 'flower pot'}, {'question_id': '202023357', 'answer': 'Bed', 'gt_answer': 'bed'}, {'question_id': '20340928', 'answer': 'Gray', 'gt_answer': 'gray'}, {'question_id': '20903175', 'answer': 'Van', 'gt_answer': 'van'}, {'question_id': '202246235', 'answer': 'Computer', 'gt_answer': 'computer mouse'}, {'question_id': '20903171', 'answer': 'Tree', 'gt_answer': 'van'}, {'question_id': '20903172', 'answer': 'Tree', 'gt_answer': 'van'}, {'question_id': '202023355', 'answer': 'Bed', 'gt_answer': 'bed'}, {'question_id': '20411630', 'answer': 'Apple', 'gt_answer': 'apples'}, {'question_id': '202102628', 'answer': 'Cabinets', 'gt_answer': 'cabinets'}, {'question_id': '202102629', 'answer': 'Cabinets', 'gt_answer': 'cabinets'}, {'question_id': '20411634', 'answer': 'Apple', 'gt_answer': 'apples'}, {'question_id': '202231498', 'answer': 'Man', 'gt_answer': 'man'}, {'question_id': '202102625', 'answer': 'Cabinets', 'gt_answer': 'cabinets'}, {'question_id': '20411639', 'answer': 'Carrot', 'gt_answer': 'hot dogs'}, {'question_id': '201987897', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202102621', 'answer': 'Cabinets', 'gt_answer': 'cabinets'}, {'question_id': '20609498', 'answer': 'Cream', 'gt_answer': 'fork'}, {'question_id': '20786177', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20491704', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202228650', 'answer': 'Cat', 'gt_answer': 'cat'}, {'question_id': '20492041', 'answer': 'Bird', 'gt_answer': 'birds'}, {'question_id': '20341037', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201882960', 'answer': 'Desk', 'gt_answer': 'desk'}, {'question_id': '20786179', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '2098302', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201346616', 'answer': 'Brown', 'gt_answer': 'brown'}, {'question_id': '20609497', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20567568', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201153112', 'answer': 'Man', 'gt_answer': 'man'}, {'question_id': '202262056', 'answer': 'Right', 'gt_answer': 'left'}, {'question_id': '201153446', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201988067', 'answer': 'Sign', 'gt_answer': 'tape'}, {'question_id': '20262533', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201997588', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201570624', 'answer': 'Cabinet', 'gt_answer': 'cabinet'}, {'question_id': '201570625', 'answer': 'Cabinet', 'gt_answer': 'cabinet'}, {'question_id': '201570623', 'answer': 'Cabinet', 'gt_answer': 'cabinet'}, {'question_id': '201570620', 'answer': 'Cabinet', 'gt_answer': 'cabinet'}, {'question_id': '2062389', 'answer': 'Rough', 'gt_answer': 'choppy'}, {'question_id': '201064885', 'answer': 'Brown', 'gt_answer': 'brown'}, {'question_id': '20226428', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '201535653', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20461886', 'answer': 'Purple', 'gt_answer': 'gray'}, {'question_id': '20652376', 'answer': 'Tall', 'gt_answer': 'tall'}, {'question_id': '201887285', 'answer': 'Broccoli', 'gt_answer': 'broccoli'}, {'question_id': '201608283', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201887287', 'answer': 'Broccoli', 'gt_answer': 'broccoli'}, {'question_id': '2056096', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201766580', 'answer': 'Girl', 'gt_answer': 'girl'}, {'question_id': '20330216', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20508800', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20330212', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201757736', 'answer': 'Chair', 'gt_answer': 'chair'}, {'question_id': '202228036', 'answer': 'Gold', 'gt_answer': 'black'}, {'question_id': '202228030', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202169115', 'answer': 'Stone', 'gt_answer': 'stone'}, {'question_id': '202169117', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201758256', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202006149', 'answer': 'Wood', 'gt_answer': 'wood'}, {'question_id': '20204506', 'answer': 'Black', 'gt_answer': 'black'}, {'question_id': '201207355', 'answer': 'Metal', 'gt_answer': 'metal'}, {'question_id': '201663704', 'answer': 'Cabinets', 'gt_answer': 'drawers'}, {'question_id': '202004164', 'answer': 'Screen', 'gt_answer': 'doors'}, {'question_id': '202227924', 'answer': 'Wood', 'gt_answer': 'wood'}, {'question_id': '201759251', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20541650', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20157071', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201804246', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201548834', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20451928', 'answer': 'Wood', 'gt_answer': 'metal'}, {'question_id': '20451927', 'answer': 'Wood', 'gt_answer': 'metal'}, {'question_id': '20652696', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '201273322', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '2053915', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '2053913', 'answer': 'Short sleeved', 'gt_answer': 'short sleeved'}, {'question_id': '201438336', 'answer': 'Bleachers', 'gt_answer': 'door'}, {'question_id': '20636869', 'answer': 'Cutting board', 'gt_answer': 'cutting board'}, {'question_id': '20636867', 'answer': 'Carrot', 'gt_answer': 'carrot'}, {'question_id': '2013003', 'answer': 'Plastic', 'gt_answer': 'plastic'}, {'question_id': '20636862', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20411855', 'answer': 'Carrot', 'gt_answer': 'apples'}, {'question_id': '2046418', 'answer': 'Man', 'gt_answer': 'boy'}, {'question_id': '2046416', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '20411859', 'answer': 'Hot dog', 'gt_answer': 'hot dogs'}, {'question_id': '20411858', 'answer': 'Carrot', 'gt_answer': 'hot dogs'}, {'question_id': '20157292', 'answer': 'Bacon', 'gt_answer': 'bacon'}, {'question_id': '20157293', 'answer': 'Bacon', 'gt_answer': 'bacon'}, {'question_id': '20157297', 'answer': 'Bacon', 'gt_answer': 'bacon'}, {'question_id': '201399895', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20724136', 'answer': 'Forest', 'gt_answer': 'forest'}, {'question_id': '20896426', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201621346', 'answer': 'Gray', 'gt_answer': 'gray'}, {'question_id': '201621611', 'answer': 'Table', 'gt_answer': 'desk'}, {'question_id': '201621612', 'answer': 'Table', 'gt_answer': 'desk'}, {'question_id': '201676459', 'answer': 'Couch', 'gt_answer': 'shelf'}, {'question_id': '20435261', 'answer': 'Rectangular', 'gt_answer': 'rectangular'}, {'question_id': '20511494', 'answer': 'Heavy', 'gt_answer': 'heavy'}, {'question_id': '20435264', 'answer': 'Napkin', 'gt_answer': 'table'}, {'question_id': '202119682', 'answer': 'Robe', 'gt_answer': 'umbrella'}, {'question_id': '20183450', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201079753', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202246576', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201654672', 'answer': 'Horse', 'gt_answer': 'horse'}, {'question_id': '20856646', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201976898', 'answer': 'Street sign', 'gt_answer': 'street sign'}, {'question_id': '201654671', 'answer': 'Horse', 'gt_answer': 'horse'}, {'question_id': '202100572', 'answer': 'Large', 'gt_answer': 'small'}, {'question_id': '201079758', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '2097963', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201322694', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201322695', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202244718', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201174975', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '202265645', 'answer': 'Eating', 'gt_answer': 'eating'}, {'question_id': '201866660', 'answer': 'Cars', 'gt_answer': 'cars'}, {'question_id': '20672782', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201548765', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201866664', 'answer': 'Street', 'gt_answer': 'roadway'}, {'question_id': '201866665', 'answer': 'Street', 'gt_answer': 'roadway'}, {'question_id': '20472834', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '201412283', 'answer': 'Man', 'gt_answer': 'skier'}, {'question_id': '201412282', 'answer': 'Man', 'gt_answer': 'skier'}, {'question_id': '201056092', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201056091', 'answer': 'Field', 'gt_answer': 'grass'}, {'question_id': '20785880', 'answer': 'Orange', 'gt_answer': 'orange'}, {'question_id': '201067732', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20836870', 'answer': 'Sign', 'gt_answer': 'flag'}, {'question_id': '20300407', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20395096', 'answer': 'Red', 'gt_answer': 'red'}, {'question_id': '201492260', 'answer': 'Fence', 'gt_answer': 'fence'}, {'question_id': '20244752', 'answer': 'Bus', 'gt_answer': 'bus'}, {'question_id': '20480170', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '202248882', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201055777', 'answer': 'Pink', 'gt_answer': 'pink'}, {'question_id': '20878981', 'answer': 'Man', 'gt_answer': 'skater'}, {'question_id': '20878982', 'answer': 'Man', 'gt_answer': 'skater'}, {'question_id': '20878983', 'answer': 'Man', 'gt_answer': 'skater'}, {'question_id': '201056232', 'answer': 'Boy', 'gt_answer': 'spectator'}, {'question_id': '20878989', 'answer': 'Man', 'gt_answer': 'skater'}, {'question_id': '201056230', 'answer': 'Watching', 'gt_answer': 'standing'}, {'question_id': '20316977', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202107876', 'answer': 'Boy', 'gt_answer': 'boy'}, {'question_id': '20316971', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20673079', 'answer': 'Chair', 'gt_answer': 'chair'}, {'question_id': '201987599', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202110304', 'answer': 'Grass', 'gt_answer': 'grass'}, {'question_id': '201735468', 'answer': 'White', 'gt_answer': 'white'}, {'question_id': '20345170', 'answer': 'Boy', 'gt_answer': 'athlete'}, {'question_id': '202243292', 'answer': 'Woman', 'gt_answer': 'woman'}, {'question_id': '202243297', 'answer': 'Woman', 'gt_answer': 'woman'}, {'question_id': '20119207', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '20306985', 'answer': 'Bed', 'gt_answer': 'chair'}, {'question_id': '20862842', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20964001', 'answer': 'Round', 'gt_answer': 'round'}, {'question_id': '20240920', 'answer': 'Table', 'gt_answer': 'table'}, {'question_id': '20414369', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '201980733', 'answer': 'Wide', 'gt_answer': 'narrow'}, {'question_id': '20621974', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20340711', 'answer': 'White', 'gt_answer': 'white'}, {'question_id': '20611694', 'answer': 'Pizza', 'gt_answer': 'sandwiches'}, {'question_id': '201428852', 'answer': 'Plastic', 'gt_answer': 'plastic'}, {'question_id': '20647219', 'answer': 'Glove', 'gt_answer': 'socks'}, {'question_id': '20611690', 'answer': 'Fruit', 'gt_answer': 'grapes'}, {'question_id': '20647214', 'answer': 'Pants', 'gt_answer': 'pants'}, {'question_id': '20543094', 'answer': 'Elephant', 'gt_answer': 'elephant'}, {'question_id': '20611699', 'answer': 'Vegetable', 'gt_answer': 'grapes'}, {'question_id': '201439591', 'answer': 'None', 'gt_answer': 'horse'}, {'question_id': '201407219', 'answer': 'Black', 'gt_answer': 'black'}, {'question_id': '2075859', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20879086', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20954322', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20699213', 'answer': 'Posing', 'gt_answer': 'posing'}, {'question_id': '201752633', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202243776', 'answer': 'Square', 'gt_answer': 'square'}, {'question_id': '201393570', 'answer': 'Fan', 'gt_answer': 'lamp'}, {'question_id': '202161910', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20204887', 'answer': 'Laptop', 'gt_answer': 'laptop'}, {'question_id': '20204882', 'answer': 'Square', 'gt_answer': 'rectangular'}, {'question_id': '20171150', 'answer': 'Deep', 'gt_answer': 'shallow'}, {'question_id': '2065922', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '201756703', 'answer': 'Cat', 'gt_answer': 'kitten'}, {'question_id': '20204889', 'answer': 'Chair', 'gt_answer': 'chair'}, {'question_id': '201360700', 'answer': 'Shelf', 'gt_answer': 'table'}, {'question_id': '2012834', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201735287', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202208331', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201752753', 'answer': 'Bike', 'gt_answer': 'bike'}, {'question_id': '20954220', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201752756', 'answer': 'People', 'gt_answer': 'bag'}, {'question_id': '201429136', 'answer': 'Ceiling', 'gt_answer': 'pipe'}, {'question_id': '201429134', 'answer': 'Plastic', 'gt_answer': 'plastic'}, {'question_id': '20299770', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201429131', 'answer': 'Plastic', 'gt_answer': 'plastic'}, {'question_id': '201438696', 'answer': 'Batter', 'gt_answer': 'batter'}, {'question_id': '201438697', 'answer': 'Player', 'gt_answer': 'batter'}, {'question_id': '201438691', 'answer': 'Fence', 'gt_answer': 'home plate'}, {'question_id': '20295646', 'answer': 'Colorful', 'gt_answer': 'black and white'}, {'question_id': '20151530', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20247254', 'answer': 'Young', 'gt_answer': 'young'}, {'question_id': '20541480', 'answer': 'Chairs', 'gt_answer': 'shelves'}, {'question_id': '201510432', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201983837', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '201510924', 'answer': 'Color', 'gt_answer': 'color'}, {'question_id': '20853961', 'answer': 'Green', 'gt_answer': 'green'}, {'question_id': '201510928', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20853964', 'answer': 'Man', 'gt_answer': 'soccer player'}, {'question_id': '2012787', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201497836', 'answer': 'Monitor', 'gt_answer': 'speaker'}, {'question_id': '202262176', 'answer': 'Picture', 'gt_answer': 'menu'}, {'question_id': '20667919', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20412086', 'answer': 'Woman', 'gt_answer': 'people'}, {'question_id': '20827111', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20785863', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201068297', 'answer': 'Texting', 'gt_answer': 'looking down'}, {'question_id': '202257370', 'answer': 'Dark', 'gt_answer': 'white'}, {'question_id': '201804113', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201883113', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201595829', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202080940', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20781763', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20781762', 'answer': 'Large', 'gt_answer': 'small'}, {'question_id': '201902348', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202121645', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201574330', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201574332', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201902341', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201639549', 'answer': 'Giraffe', 'gt_answer': 'giraffes'}, {'question_id': '202258529', 'answer': 'Large', 'gt_answer': 'small'}, {'question_id': '201548882', 'answer': 'Purple', 'gt_answer': 'gray'}, {'question_id': '201832697', 'answer': 'Nightstand', 'gt_answer': 'nightstand'}, {'question_id': '201663391', 'answer': 'Oven', 'gt_answer': 'oven'}, {'question_id': '201152989', 'answer': 'Green', 'gt_answer': 'brown'}, {'question_id': '202179497', 'answer': 'High', 'gt_answer': 'high'}, {'question_id': '201758068', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201751814', 'answer': 'Kia', 'gt_answer': 'adidas'}, {'question_id': '201556436', 'answer': 'Chair', 'gt_answer': 'office chair'}, {'question_id': '201758063', 'answer': 'Table', 'gt_answer': 'table'}, {'question_id': '20679332', 'answer': 'Giraffe', 'gt_answer': 'giraffe'}, {'question_id': '201737738', 'answer': 'Color', 'gt_answer': 'color'}, {'question_id': '201156298', 'answer': 'Boy', 'gt_answer': 'boy'}, {'question_id': '201307334', 'answer': 'Snowboarding', 'gt_answer': 'jumping'}, {'question_id': '201303362', 'answer': 'Chair', 'gt_answer': 'chair'}, {'question_id': '201303361', 'answer': 'Chair', 'gt_answer': 'chair'}, {'question_id': '201307332', 'answer': 'Snowboarding', 'gt_answer': 'jumping'}, {'question_id': '201303364', 'answer': 'Chair', 'gt_answer': 'chair'}, {'question_id': '201055842', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20480542', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20480541', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '20480016', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20480010', 'answer': 'Large', 'gt_answer': 'large'}, {'question_id': '20922932', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20837136', 'answer': 'Large', 'gt_answer': 'small'}, {'question_id': '202169197', 'answer': 'Sidewalk', 'gt_answer': 'sidewalk'}, {'question_id': '202108035', 'answer': 'Large', 'gt_answer': 'large'}, {'question_id': '20978464', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '201509813', 'answer': 'Brick', 'gt_answer': 'stone'}, {'question_id': '201247310', 'answer': 'Rug', 'gt_answer': 'carpet'}, {'question_id': '201873123', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20679255', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201556555', 'answer': 'Laptop', 'gt_answer': 'desk'}, {'question_id': '201556556', 'answer': 'Desk', 'gt_answer': 'desk'}, {'question_id': '201556557', 'answer': 'Desk', 'gt_answer': 'desk'}, {'question_id': '201951616', 'answer': 'Large', 'gt_answer': 'large'}, {'question_id': '20710202', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201859637', 'answer': 'Coffee', 'gt_answer': 'straw'}, {'question_id': '20489798', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201951619', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202244105', 'answer': 'Cupcake', 'gt_answer': 'cupcakes'}, {'question_id': '2046311', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201156126', 'answer': 'Ground', 'gt_answer': 'field'}, {'question_id': '2046314', 'answer': 'Skinny', 'gt_answer': 'thin'}, {'question_id': '201509781', 'answer': 'Black', 'gt_answer': 'green'}, {'question_id': '20622082', 'answer': 'Brown', 'gt_answer': 'brown'}, {'question_id': '201109669', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201497657', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201997642', 'answer': 'Wood', 'gt_answer': 'wood'}, {'question_id': '201682451', 'answer': 'Man', 'gt_answer': 'man'}, {'question_id': '20434859', 'answer': 'Boy', 'gt_answer': 'boy'}, {'question_id': '201682450', 'answer': 'People', 'gt_answer': 'people'}, {'question_id': '201155986', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '202106467', 'answer': 'Green', 'gt_answer': 'green'}, {'question_id': '20783027', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20783020', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20734221', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20911211', 'answer': 'Skating', 'gt_answer': 'riding'}, {'question_id': '20536222', 'answer': 'Giraffe', 'gt_answer': 'giraffe'}, {'question_id': '202081706', 'answer': 'Silver', 'gt_answer': 'black'}, {'question_id': '201621803', 'answer': 'Black', 'gt_answer': 'silver'}, {'question_id': '20306469', 'answer': 'People', 'gt_answer': 'man'}, {'question_id': '202286622', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20306467', 'answer': 'People', 'gt_answer': 'man'}, {'question_id': '201175497', 'answer': 'Cat', 'gt_answer': 'cat'}, {'question_id': '20647844', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201947818', 'answer': 'White', 'gt_answer': 'white'}, {'question_id': '202156821', 'answer': 'Elephants', 'gt_answer': 'elephants'}, {'question_id': '20746613', 'answer': 'Metal', 'gt_answer': 'concrete'}, {'question_id': '202156827', 'answer': 'Elephants', 'gt_answer': 'elephants'}, {'question_id': '20442290', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202156825', 'answer': 'Elephants', 'gt_answer': 'elephants'}, {'question_id': '201883217', 'answer': 'Desk', 'gt_answer': 'bed'}, {'question_id': '20518608', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201527783', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201228088', 'answer': 'Car', 'gt_answer': 'van'}, {'question_id': '201228089', 'answer': 'Car', 'gt_answer': 'van'}, {'question_id': '201975119', 'answer': 'Fence', 'gt_answer': 'fence'}, {'question_id': '201641466', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201228082', 'answer': 'Car', 'gt_answer': 'van'}, {'question_id': '201228086', 'answer': 'Tree', 'gt_answer': 'van'}, {'question_id': '201228087', 'answer': 'Tree', 'gt_answer': 'van'}, {'question_id': '201711375', 'answer': 'Suitcase', 'gt_answer': 'books'}, {'question_id': '20119115', 'answer': 'Black', 'gt_answer': 'black'}, {'question_id': '202012516', 'answer': 'Cabinets', 'gt_answer': 'cabinets'}, {'question_id': '20119114', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202179631', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202169321', 'answer': 'Very', 'gt_answer': 'hard'}, {'question_id': '202169324', 'answer': 'Bench', 'gt_answer': 'bench'}, {'question_id': '201763916', 'answer': 'Mirror', 'gt_answer': 'doors'}, {'question_id': '20941918', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201207163', 'answer': 'Apple', 'gt_answer': 'apples'}, {'question_id': '201207164', 'answer': 'Apple', 'gt_answer': 'apples'}, {'question_id': '201399984', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '201935215', 'answer': '20', 'gt_answer': 'young'}, {'question_id': '201804497', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201527506', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201527508', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201528081', 'answer': 'Woman', 'gt_answer': 'girl'}, {'question_id': '201528080', 'answer': 'Woman', 'gt_answer': 'girl'}, {'question_id': '201570995', 'answer': 'Carpet', 'gt_answer': 'carpet'}, {'question_id': '201570993', 'answer': 'Carpet', 'gt_answer': 'carpet'}, {'question_id': '201756930', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20716852', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20936228', 'answer': 'Elephant', 'gt_answer': 'elephant'}, {'question_id': '202081379', 'answer': 'Window', 'gt_answer': 'toaster'}, {'question_id': '202161925', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202100371', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20705988', 'answer': 'Headphones', 'gt_answer': 'keyboard'}, {'question_id': '20381182', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20705981', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202162297', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201480314', 'answer': 'Boy', 'gt_answer': 'boy'}, {'question_id': '20492097', 'answer': 'Sand', 'gt_answer': 'ground'}, {'question_id': '20492094', 'answer': 'Rock', 'gt_answer': 'rocks'}, {'question_id': '20644739', 'answer': 'Cat', 'gt_answer': 'cat'}, {'question_id': '201804765', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '201430870', 'answer': 'Shelf', 'gt_answer': 'cabinet'}, {'question_id': '201430876', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202040082', 'answer': 'Station', 'gt_answer': 'train station'}, {'question_id': '201482228', 'answer': 'Left', 'gt_answer': 'right'}, {'question_id': '20827283', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20306311', 'answer': 'Man', 'gt_answer': 'woman'}, {'question_id': '201908727', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201737790', 'answer': 'White', 'gt_answer': 'white'}, {'question_id': '20618768', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '20752381', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201064978', 'answer': 'Bench', 'gt_answer': 'sofa'}, {'question_id': '201064974', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '202100722', 'answer': 'Stove', 'gt_answer': 'stove'}, {'question_id': '201064976', 'answer': 'Bench', 'gt_answer': 'sofa'}, {'question_id': '202080918', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20427843', 'answer': 'Green', 'gt_answer': 'blue'}, {'question_id': '201068623', 'answer': 'Asian', 'gt_answer': 'asian'}, {'question_id': '201624289', 'answer': 'Pan', 'gt_answer': 'pan'}, {'question_id': '20307159', 'answer': 'Camera', 'gt_answer': 'television'}, {'question_id': '20741159', 'answer': 'Chair', 'gt_answer': 'chair'}, {'question_id': '202081958', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '20741157', 'answer': 'Chair', 'gt_answer': 'chair'}, {'question_id': '20902579', 'answer': 'Large', 'gt_answer': 'small'}, {'question_id': '2076628', 'answer': 'Cars', 'gt_answer': 'cars'}, {'question_id': '20177713', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202021465', 'answer': 'Ugly', 'gt_answer': 'ugly'}, {'question_id': '201669481', 'answer': 'Cake', 'gt_answer': 'cupcake'}, {'question_id': '20489800', 'answer': 'Lamp', 'gt_answer': 'table lamp'}, {'question_id': '20489803', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '2076627', 'answer': 'Sign', 'gt_answer': 'cars'}, {'question_id': '20550484', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201336972', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20550483', 'answer': 'Tall', 'gt_answer': 'short'}, {'question_id': '202162235', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '20245961', 'answer': 'Black', 'gt_answer': 'white'}, {'question_id': '202162236', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20381695', 'answer': 'Plastic', 'gt_answer': 'plastic'}, {'question_id': '201337224', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201337226', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201337222', 'answer': 'Bicycle', 'gt_answer': 'bike'}, {'question_id': '202218495', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '2066178', 'answer': 'Pink', 'gt_answer': 'pink'}, {'question_id': '2066171', 'answer': 'Shirt', 'gt_answer': 'sweater'}, {'question_id': '2066174', 'answer': 'Shirt', 'gt_answer': 'sweater'}, {'question_id': '20182778', 'answer': 'Brick', 'gt_answer': 'concrete'}, {'question_id': '20894233', 'answer': 'Yellow', 'gt_answer': 'yellow'}, {'question_id': '202116744', 'answer': 'River', 'gt_answer': 'railroad'}, {'question_id': '202116745', 'answer': 'City', 'gt_answer': 'railroad'}, {'question_id': '2091168', 'answer': 'Blue', 'gt_answer': 'blue'}, {'question_id': '201067447', 'answer': 'Chair', 'gt_answer': 'chair'}, {'question_id': '201067449', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201759375', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201639457', 'answer': 'Zebra', 'gt_answer': 'zebras'}, {'question_id': '20631621', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202218727', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20169683', 'answer': 'Flag', 'gt_answer': 'flag'}, {'question_id': '20330476', 'answer': 'Tree', 'gt_answer': 'fence'}, {'question_id': '202073315', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '201065621', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20169685', 'answer': 'Flag', 'gt_answer': 'flag'}, {'question_id': '201873356', 'answer': 'Fire truck', 'gt_answer': 'fire truck'}, {'question_id': '201873352', 'answer': 'Fire truck', 'gt_answer': 'fire truck'}, {'question_id': '20710073', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201056105', 'answer': 'Boy', 'gt_answer': 'soccer player'}, {'question_id': '20226921', 'answer': 'People', 'gt_answer': 'woman'}, {'question_id': '201482069', 'answer': 'Man', 'gt_answer': 'man'}, {'question_id': '201997077', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201887113', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20655070', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201407402', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '201056106', 'answer': 'Shorts', 'gt_answer': 'jersey'}, {'question_id': '20602905', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20602904', 'answer': 'Running', 'gt_answer': 'running'}, {'question_id': '20162224', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202228211', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '201882989', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20929540', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '20596394', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20385842', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20797620', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201988085', 'answer': 'Plastic', 'gt_answer': 'metal'}, {'question_id': '201153466', 'answer': 'Plastic', 'gt_answer': 'glass'}, {'question_id': '20385598', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20262515', 'answer': 'Kite', 'gt_answer': 'boots'}, {'question_id': '201307293', 'answer': 'Very', 'gt_answer': 'hard'}, {'question_id': '201763894', 'answer': 'Closed', 'gt_answer': 'closed'}, {'question_id': '20385593', 'answer': 'Plastic', 'gt_answer': 'plastic'}, {'question_id': '201307295', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201079918', 'answer': 'White', 'gt_answer': 'white'}, {'question_id': '201859518', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20567557', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201467651', 'answer': 'Red', 'gt_answer': 'red'}, {'question_id': '201663198', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201859512', 'answer': 'Box', 'gt_answer': 'basket'}, {'question_id': '201467655', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201859511', 'answer': 'Donut', 'gt_answer': 'donut'}, {'question_id': '201235856', 'answer': 'Purse', 'gt_answer': 'handbag'}, {'question_id': '202100276', 'answer': 'Ocean', 'gt_answer': 'ocean'}, {'question_id': '201959618', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201908982', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202100580', 'answer': 'Sailboat', 'gt_answer': 'sailboats'}, {'question_id': '201759219', 'answer': 'Coat', 'gt_answer': 'coats'}, {'question_id': '202004293', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202169133', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201235855', 'answer': 'Purse', 'gt_answer': 'handbag'}, {'question_id': '20204568', 'answer': 'Chair', 'gt_answer': 'chair'}, {'question_id': '20204564', 'answer': 'Chair', 'gt_answer': 'chair'}, {'question_id': '20120201', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201952933', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20120203', 'answer': 'Girl', 'gt_answer': 'athlete'}, {'question_id': '202227949', 'answer': 'Cable box', 'gt_answer': 'entertainment center'}, {'question_id': '202227948', 'answer': 'Book', 'gt_answer': 'entertainment center'}, {'question_id': '20120209', 'answer': 'Girl', 'gt_answer': 'athlete'}, {'question_id': '20120208', 'answer': 'Girl', 'gt_answer': 'athlete'}, {'question_id': '20883092', 'answer': 'Park', 'gt_answer': 'skate park'}, {'question_id': '202227946', 'answer': 'Full', 'gt_answer': 'full'}, {'question_id': '20177468', 'answer': 'Pickles', 'gt_answer': 'pickles'}, {'question_id': '20883097', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201889521', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '202012524', 'answer': 'Cabinets', 'gt_answer': 'cabinets'}, {'question_id': '20157055', 'answer': 'Large', 'gt_answer': 'small'}, {'question_id': '201757572', 'answer': 'Stainless steel', 'gt_answer': 'stainless steel'}, {'question_id': '201573953', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201573952', 'answer': 'White', 'gt_answer': 'tan'}, {'question_id': '202053012', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20542890', 'answer': 'Elephant', 'gt_answer': 'elephant'}, {'question_id': '201548857', 'answer': 'Brown', 'gt_answer': 'red'}, {'question_id': '20317153', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201273343', 'answer': 'Yellow', 'gt_answer': 'yellow'}, {'question_id': '20783618', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20542898', 'answer': 'Elephant', 'gt_answer': 'elephant'}, {'question_id': '20692154', 'answer': 'Curtain', 'gt_answer': 'blinds'}, {'question_id': '20295390', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '202049545', 'answer': 'Cake', 'gt_answer': 'plate'}, {'question_id': '2046432', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201175530', 'answer': 'Camera', 'gt_answer': 'camera'}, {'question_id': '201175021', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20982374', 'answer': 'Tie', 'gt_answer': 'collar'}, {'question_id': '201110743', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '2046438', 'answer': '70', 'gt_answer': 'old'}, {'question_id': '20411872', 'answer': 'Hot dog', 'gt_answer': 'hot dogs'}, {'question_id': '201571001', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20508758', 'answer': 'Front', 'gt_answer': 'front'}, {'question_id': '201574075', 'answer': 'People', 'gt_answer': 'woman'}, {'question_id': '201804591', 'answer': 'Rectangle', 'gt_answer': 'rectangular'}, {'question_id': '20922898', 'answer': 'Pole', 'gt_answer': 'telephone pole'}, {'question_id': '20724158', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20896405', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20896156', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20724407', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20896154', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20922890', 'answer': 'Green', 'gt_answer': 'brown'}, {'question_id': '20896402', 'answer': 'Chair', 'gt_answer': 'chair'}, {'question_id': '201621361', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20361315', 'answer': 'Woman', 'gt_answer': 'snowboarder'}, {'question_id': '202228079', 'answer': 'Cabinet', 'gt_answer': 'entertainment center'}, {'question_id': '20442082', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20746428', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20403441', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20442087', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20442089', 'answer': 'Chair', 'gt_answer': 'chair'}, {'question_id': '201412511', 'answer': 'Snow', 'gt_answer': 'ground'}, {'question_id': '20856666', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20452289', 'answer': 'Wall', 'gt_answer': 'christmas light'}, {'question_id': '20452288', 'answer': 'Picture', 'gt_answer': 'christmas light'}, {'question_id': '201654658', 'answer': 'Horse', 'gt_answer': 'horse'}, {'question_id': '201654659', 'answer': 'Horse', 'gt_answer': 'horse'}, {'question_id': '20480754', 'answer': 'Laptop', 'gt_answer': 'computer'}, {'question_id': '20480755', 'answer': 'Laptop', 'gt_answer': 'computer'}, {'question_id': '20480752', 'answer': 'Laptop', 'gt_answer': 'computer'}, {'question_id': '20480753', 'answer': 'Laptop', 'gt_answer': 'computer'}, {'question_id': '20452280', 'answer': 'Chair', 'gt_answer': 'chair'}, {'question_id': '20922767', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20287517', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20922762', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201637324', 'answer': 'Stove', 'gt_answer': 'gas stove'}, {'question_id': '201663610', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20287518', 'answer': 'Closed', 'gt_answer': 'open'}, {'question_id': '201663614', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20705867', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201866640', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '202262600', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20515028', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201947487', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '201548702', 'answer': 'Blender', 'gt_answer': 'blender'}, {'question_id': '201548700', 'answer': 'Bottle', 'gt_answer': 'blender'}, {'question_id': '20468387', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201434109', 'answer': 'Man', 'gt_answer': 'man'}, {'question_id': '20514992', 'answer': 'Blue', 'gt_answer': 'blue'}, {'question_id': '202246737', 'answer': 'Shelf', 'gt_answer': 'computer desk'}, {'question_id': '202006269', 'answer': 'Blue', 'gt_answer': 'light blue'}, {'question_id': '201301953', 'answer': 'Racket', 'gt_answer': 'racket'}, {'question_id': '201682270', 'answer': 'Playing tennis', 'gt_answer': 'playing'}, {'question_id': '201682273', 'answer': 'Man', 'gt_answer': 'man'}, {'question_id': '201798319', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20836894', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20836899', 'answer': 'Wide', 'gt_answer': 'wide'}, {'question_id': '20117865', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201959672', 'answer': 'Short', 'gt_answer': 'short'}, {'question_id': '202262080', 'answer': 'Red', 'gt_answer': 'gold'}, {'question_id': '201873339', 'answer': 'Fire truck', 'gt_answer': 'fire truck'}, {'question_id': '202174150', 'answer': 'Square', 'gt_answer': 'rectangular'}, {'question_id': '201794977', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20756639', 'answer': 'Shelf', 'gt_answer': 'shelf'}, {'question_id': '20756638', 'answer': 'Shelf', 'gt_answer': 'shelf'}, {'question_id': '201794970', 'answer': 'Long', 'gt_answer': 'long'}, {'question_id': '201047348', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201504928', 'answer': 'Girl', 'gt_answer': 'woman'}, {'question_id': '20412197', 'answer': 'Tables', 'gt_answer': 'tables'}, {'question_id': '202006262', 'answer': 'Granite', 'gt_answer': 'granite'}, {'question_id': '201504922', 'answer': 'Surfboard', 'gt_answer': 'sign'}, {'question_id': '20306141', 'answer': 'Camera', 'gt_answer': 'laptop'}, {'question_id': '20412198', 'answer': 'Table', 'gt_answer': 'tables'}, {'question_id': '201504927', 'answer': 'Woman', 'gt_answer': 'woman'}, {'question_id': '20306146', 'answer': 'Camera', 'gt_answer': 'cell phone'}, {'question_id': '201447047', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '202100803', 'answer': 'Stove', 'gt_answer': 'stove'}, {'question_id': '20306829', 'answer': 'Snowboard', 'gt_answer': 'lamp'}, {'question_id': '20381592', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20320350', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20381590', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20241054', 'answer': 'Man', 'gt_answer': 'man'}, {'question_id': '201037289', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201987501', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20248012', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20836457', 'answer': 'Chair', 'gt_answer': 'cabinet'}, {'question_id': '201439508', 'answer': 'No one', 'gt_answer': 'woman'}, {'question_id': '20416473', 'answer': 'Pizza', 'gt_answer': 'sausage'}, {'question_id': '20857136', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202243276', 'answer': 'Left', 'gt_answer': 'right'}, {'question_id': '201935029', 'answer': 'City', 'gt_answer': 'skate park'}, {'question_id': '201935028', 'answer': 'Park', 'gt_answer': 'skate park'}, {'question_id': '201482305', 'answer': 'Brown', 'gt_answer': 'brown'}, {'question_id': '202036902', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201756543', 'answer': 'Cat', 'gt_answer': 'kitten'}, {'question_id': '201756544', 'answer': 'Cat', 'gt_answer': 'kitten'}, {'question_id': '202116878', 'answer': 'Concrete', 'gt_answer': 'concrete'}, {'question_id': '20887403', 'answer': 'Computer', 'gt_answer': 'keyboard'}, {'question_id': '20887401', 'answer': 'Keyboard', 'gt_answer': 'keyboard'}, {'question_id': '2076436', 'answer': 'Red', 'gt_answer': 'red'}, {'question_id': '20887407', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '20647237', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201638899', 'answer': 'Bottom', 'gt_answer': 'bottom'}, {'question_id': '201347448', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20894156', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20178026', 'answer': 'Vegetable', 'gt_answer': 'pickles'}, {'question_id': '20178027', 'answer': 'Fries', 'gt_answer': 'fries'}, {'question_id': '20691785', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201392035', 'answer': 'Short', 'gt_answer': 'short'}, {'question_id': '201407231', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201711215', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20609240', 'answer': 'Brown', 'gt_answer': 'white'}, {'question_id': '20827549', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20699233', 'answer': 'Brown', 'gt_answer': 'brown'}, {'question_id': '2065903', 'answer': 'Pink shirt', 'gt_answer': 'sweater'}, {'question_id': '201935280', 'answer': 'White', 'gt_answer': 'caucasian'}, {'question_id': '20827540', 'answer': 'Couch', 'gt_answer': 'chairs'}, {'question_id': '20827542', 'answer': 'Counter', 'gt_answer': 'staircase'}, {'question_id': '201752659', 'answer': 'Boy', 'gt_answer': 'boy'}, {'question_id': '20706325', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201957263', 'answer': 'Tv', 'gt_answer': 'television'}, {'question_id': '201957265', 'answer': 'Television', 'gt_answer': 'television'}, {'question_id': '201957264', 'answer': 'Tv', 'gt_answer': 'television'}, {'question_id': '20673057', 'answer': 'Top', 'gt_answer': 'top'}, {'question_id': '201879658', 'answer': 'Truck', 'gt_answer': 'truck'}, {'question_id': '20836796', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201481678', 'answer': 'Smiling', 'gt_answer': 'looking up'}, {'question_id': '201481679', 'answer': 'Woman', 'gt_answer': 'woman'}, {'question_id': '201235635', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201047253', 'answer': 'Skinny', 'gt_answer': 'fat'}, {'question_id': '202121371', 'answer': 'Brown', 'gt_answer': 'light brown'}, {'question_id': '201738991', 'answer': 'Glove', 'gt_answer': 'pitcher'}, {'question_id': '20954207', 'answer': 'Behind', 'gt_answer': 'front'}, {'question_id': '202121377', 'answer': 'Cabinet', 'gt_answer': 'cupboard'}, {'question_id': '202121379', 'answer': 'Cabinet', 'gt_answer': 'cupboard'}, {'question_id': '20151514', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '2076293', 'answer': 'Bus', 'gt_answer': 'bus'}, {'question_id': '20491740', 'answer': 'Wet', 'gt_answer': 'dry'}, {'question_id': '201822273', 'answer': 'Glass', 'gt_answer': 'glass'}, {'question_id': '201143182', 'answer': 'Chair', 'gt_answer': 'chairs'}, {'question_id': '201319593', 'answer': 'Pizza', 'gt_answer': 'soup'}, {'question_id': '201143188', 'answer': 'Chair', 'gt_answer': 'chairs'}, {'question_id': '201952693', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201037139', 'answer': 'Stop', 'gt_answer': 'traffic sign'}, {'question_id': '20247907', 'answer': 'Caucasian', 'gt_answer': 'caucasian'}, {'question_id': '201037134', 'answer': 'Stop sign', 'gt_answer': 'traffic sign'}, {'question_id': '201037135', 'answer': 'Stop sign', 'gt_answer': 'traffic sign'}, {'question_id': '201957408', 'answer': 'Window', 'gt_answer': 'wall'}, {'question_id': '201037132', 'answer': 'Stop sign', 'gt_answer': 'traffic sign'}, {'question_id': '201952698', 'answer': 'Wires', 'gt_answer': 'wires'}, {'question_id': '20860977', 'answer': 'Shorts', 'gt_answer': 'pants'}, {'question_id': '20786034', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202041968', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20853909', 'answer': 'Large', 'gt_answer': 'small'}, {'question_id': '20786039', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201549041', 'answer': 'Blue', 'gt_answer': 'green'}, {'question_id': '20860978', 'answer': 'Shorts', 'gt_answer': 'pants'}, {'question_id': '20692139', 'answer': 'Brown', 'gt_answer': 'brown'}, {'question_id': '201510418', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201143328', 'answer': 'Table', 'gt_answer': 'floor'}, {'question_id': '201803949', 'answer': 'Black', 'gt_answer': 'black'}, {'question_id': '201143322', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201143325', 'answer': 'Flowers', 'gt_answer': 'flowers'}, {'question_id': '201143324', 'answer': 'Red', 'gt_answer': 'green'}, {'question_id': '201143326', 'answer': 'Vase', 'gt_answer': 'flowers'}, {'question_id': '202120138', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '201110906', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201883135', 'answer': 'Clean', 'gt_answer': 'clean'}, {'question_id': '20600178', 'answer': 'Small', 'gt_answer': 'large'}, {'question_id': '20952988', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20781786', 'answer': 'Small', 'gt_answer': 'small'}, {'question_id': '201976456', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20600175', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '202119852', 'answer': 'Metal', 'gt_answer': 'glass'}, {'question_id': '201885194', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201640317', 'answer': 'Man', 'gt_answer': 'man'}, {'question_id': '201479204', 'answer': 'Chicken', 'gt_answer': 'orange'}, {'question_id': '201479205', 'answer': 'Orange', 'gt_answer': 'orange'}, {'question_id': '201479206', 'answer': 'Orange', 'gt_answer': 'orange'}, {'question_id': '20287680', 'answer': 'Helmet', 'gt_answer': 'helmet'}, {'question_id': '201751835', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20710194', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201556454', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201624078', 'answer': 'Woman', 'gt_answer': 'woman'}, {'question_id': '20679350', 'answer': 'Small', 'gt_answer': 'small'}, {'question_id': '20385452', 'answer': 'Green', 'gt_answer': 'white'}, {'question_id': '20573652', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '20414341', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201492288', 'answer': 'Dirty', 'gt_answer': 'clean'}, {'question_id': '20863580', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20258978', 'answer': 'Shirt', 'gt_answer': 'shirt'}, {'question_id': '201055796', 'answer': 'Goal', 'gt_answer': 'car'}, {'question_id': '201055825', 'answer': 'Bottom', 'gt_answer': 'bottom'}, {'question_id': '202241111', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202240641', 'answer': 'Girl', 'gt_answer': 'woman'}, {'question_id': '201902497', 'answer': 'Chair', 'gt_answer': 'chair'}, {'question_id': '201902490', 'answer': 'Chair', 'gt_answer': 'chair'}, {'question_id': '201902491', 'answer': 'Chair', 'gt_answer': 'chair'}, {'question_id': '20837118', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202240646', 'answer': 'Wii controller', 'gt_answer': 'remote control'}, {'question_id': '202005826', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201663112', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20679233', 'answer': 'Brown', 'gt_answer': 'yellow'}, {'question_id': '20978199', 'answer': 'Boy', 'gt_answer': 'boy'}, {'question_id': '201974981', 'answer': 'Hat', 'gt_answer': 'hat'}, {'question_id': '201570661', 'answer': 'Shelf', 'gt_answer': 'cabinet'}, {'question_id': '202122132', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '201185664', 'answer': 'Long sleeved', 'gt_answer': 'long sleeved'}, {'question_id': '20226770', 'answer': 'Rectangular', 'gt_answer': 'rectangular'}, {'question_id': '20891291', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20891293', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20891295', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20899395', 'answer': 'Closed', 'gt_answer': 'closed'}, {'question_id': '2046334', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201887320', 'answer': 'Brown', 'gt_answer': 'brown'}, {'question_id': '201110978', 'answer': 'White', 'gt_answer': 'blond'}, {'question_id': '20652583', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '2046332', 'answer': 'Table', 'gt_answer': 'table'}, {'question_id': '202240467', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20631389', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201803899', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '20978222', 'answer': 'People', 'gt_answer': 'boy'}, {'question_id': '2093894', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '2097752', 'answer': 'Keyboard', 'gt_answer': 'monitor'}, {'question_id': '20631383', 'answer': 'Plastic', 'gt_answer': 'metal'}, {'question_id': '201832647', 'answer': 'Nightstand', 'gt_answer': 'nightstand'}, {'question_id': '20978224', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '202240398', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202012752', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20887186', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '20342476', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201889474', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201623767', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '201889478', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20899720', 'answer': 'Laptop', 'gt_answer': 'laptop'}, {'question_id': '202286600', 'answer': 'Bear', 'gt_answer': 'elephant'}, {'question_id': '20342479', 'answer': 'Bus', 'gt_answer': 'bus'}, {'question_id': '202082200', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20536205', 'answer': 'Buffalo', 'gt_answer': 'bison'}, {'question_id': '20306337', 'answer': 'Right', 'gt_answer': 'left'}, {'question_id': '20536203', 'answer': 'Buffalo', 'gt_answer': 'bison'}, {'question_id': '20536201', 'answer': 'Giraffe', 'gt_answer': 'giraffe'}, {'question_id': '201752869', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201859305', 'answer': 'Color', 'gt_answer': 'material'}, {'question_id': '201982810', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20891746', 'answer': 'Large', 'gt_answer': 'large'}, {'question_id': '202227882', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20794218', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20518372', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '202227886', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20518371', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '20340803', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20480563', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201302035', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '202102948', 'answer': 'Dishwasher', 'gt_answer': 'dishwasher'}, {'question_id': '201641197', 'answer': 'Skateboarding', 'gt_answer': 'looking down'}, {'question_id': '20588828', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20789989', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201621422', 'answer': 'Speaker', 'gt_answer': 'mirror'}, {'question_id': '202169309', 'answer': 'Brown', 'gt_answer': 'brown'}, {'question_id': '20709888', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '202231967', 'answer': 'Metal', 'gt_answer': 'metal'}, {'question_id': '2046595', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '2046594', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201207148', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '20757154', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20300628', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20394722', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201233938', 'answer': 'Train', 'gt_answer': 'bushes'}, {'question_id': '20940076', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20157556', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201228357', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201438594', 'answer': '200', 'gt_answer': 'heavy'}, {'question_id': '201492340', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201228350', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201794899', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20963956', 'answer': 'Shelf', 'gt_answer': 'shelf'}, {'question_id': '20716875', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20716879', 'answer': 'Woman', 'gt_answer': 'woman'}, {'question_id': '201756957', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201756954', 'answer': 'Cat', 'gt_answer': 'kitten'}, {'question_id': '201676082', 'answer': 'Table', 'gt_answer': 'shelf'}, {'question_id': '202081566', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201756953', 'answer': 'Cat', 'gt_answer': 'kitten'}, {'question_id': '201676086', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202208275', 'answer': 'Street', 'gt_answer': 'city'}, {'question_id': '201738992', 'answer': 'Baseball', 'gt_answer': 'baseball'}, {'question_id': '20394891', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20733964', 'answer': 'Sunny', 'gt_answer': 'sunny'}, {'question_id': '20733965', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201480339', 'answer': 'Boy', 'gt_answer': 'boy'}, {'question_id': '202119161', 'answer': 'Man', 'gt_answer': 'man'}, {'question_id': '201068393', 'answer': 'Girl', 'gt_answer': 'girl'}, {'question_id': '20307312', 'answer': 'Bed', 'gt_answer': 'couch'}, {'question_id': '201068396', 'answer': 'Phone', 'gt_answer': 'cell phone'}, {'question_id': '201984082', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20724210', 'answer': 'Man', 'gt_answer': 'snowboarder'}, {'question_id': '201068399', 'answer': 'Cell phone', 'gt_answer': 'cell phone'}, {'question_id': '201068428', 'answer': 'Girl', 'gt_answer': 'girl'}, {'question_id': '201068429', 'answer': 'Girl', 'gt_answer': 'girl'}, {'question_id': '20724216', 'answer': 'Man', 'gt_answer': 'snowboarder'}, {'question_id': '20724217', 'answer': 'Man', 'gt_answer': 'snowboarder'}, {'question_id': '20898924', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201206888', 'answer': 'Table', 'gt_answer': 'papers'}, {'question_id': '201430811', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20898929', 'answer': 'Gray', 'gt_answer': 'blond'}, {'question_id': '201621652', 'answer': 'Wall', 'gt_answer': 'speaker'}, {'question_id': '202219034', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201536478', 'answer': 'Woman', 'gt_answer': 'spectators'}, {'question_id': '201574148', 'answer': 'Metal', 'gt_answer': 'metal'}, {'question_id': '20618742', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20667511', 'answer': 'Coffee table', 'gt_answer': 'coffee table'}, {'question_id': '201536472', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20667515', 'answer': 'Couch', 'gt_answer': 'coffee table'}, {'question_id': '20618748', 'answer': 'Metal', 'gt_answer': 'metal'}, {'question_id': '201064992', 'answer': 'Salad', 'gt_answer': 'hamburger'}, {'question_id': '201064990', 'answer': 'Girl', 'gt_answer': 'boy'}, {'question_id': '201064996', 'answer': 'Pizza', 'gt_answer': 'hamburger'}, {'question_id': '2055882', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201064994', 'answer': 'Salad', 'gt_answer': 'hamburger'}, {'question_id': '20307172', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202024814', 'answer': 'Park', 'gt_answer': 'lawn'}, {'question_id': '20611505', 'answer': 'Brownie', 'gt_answer': 'brownie'}, {'question_id': '202024817', 'answer': 'Park', 'gt_answer': 'park'}, {'question_id': '201641228', 'answer': 'Cobblestone', 'gt_answer': 'cobblestone'}, {'question_id': '202006670', 'answer': 'Floor', 'gt_answer': 'floor'}, {'question_id': '20285115', 'answer': 'Chair', 'gt_answer': 'chair'}, {'question_id': '202006675', 'answer': 'Cabinet', 'gt_answer': 'table'}, {'question_id': '20818845', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201505083', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201639558', 'answer': 'Giraffes', 'gt_answer': 'giraffes'}, {'question_id': '201639559', 'answer': 'Giraffes', 'gt_answer': 'giraffes'}, {'question_id': '201623932', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202021449', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '201639552', 'answer': 'Giraffes', 'gt_answer': 'giraffes'}, {'question_id': '20427560', 'answer': 'Black', 'gt_answer': 'black'}, {'question_id': '201639551', 'answer': 'Giraffes', 'gt_answer': 'giraffes'}, {'question_id': '202285074', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202159192', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202081082', 'answer': 'Toaster', 'gt_answer': 'toaster'}, {'question_id': '20711616', 'answer': 'Desk', 'gt_answer': 'table'}, {'question_id': '202159196', 'answer': 'Rectangle', 'gt_answer': 'rectangular'}, {'question_id': '20711611', 'answer': 'Desk', 'gt_answer': 'table'}, {'question_id': '20863613', 'answer': 'Black', 'gt_answer': 'brown'}, {'question_id': '201337201', 'answer': 'Black', 'gt_answer': 'black'}, {'question_id': '202081225', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201984106', 'answer': 'Woman', 'gt_answer': 'woman'}, {'question_id': '201264275', 'answer': 'Car', 'gt_answer': 'bus'}, {'question_id': '201735587', 'answer': 'Wood', 'gt_answer': 'wood'}, {'question_id': '202081081', 'answer': 'Toaster', 'gt_answer': 'toaster'}, {'question_id': '202006459', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20753108', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201068665', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202006454', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20416868', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '202006451', 'answer': 'Chair', 'gt_answer': 'chair'}, {'question_id': '201956853', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201616181', 'answer': 'Silver', 'gt_answer': 'gray'}, {'question_id': '201711356', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201879352', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201795326', 'answer': 'Talking', 'gt_answer': 'staring'}, {'question_id': '201795327', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201957096', 'answer': 'Tv', 'gt_answer': 'television'}, {'question_id': '201957094', 'answer': 'Tv', 'gt_answer': 'television'}, {'question_id': '20287751', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20247595', 'answer': 'Porch', 'gt_answer': 'lawn'}, {'question_id': '202116764', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201504689', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20621837', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20894216', 'answer': 'Boy', 'gt_answer': 'boy'}, {'question_id': '20894217', 'answer': 'Boy', 'gt_answer': 'boy'}, {'question_id': '202120187', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20894211', 'answer': 'Skateboard', 'gt_answer': 'skateboard'}, {'question_id': '201593894', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20865519', 'answer': 'Cow', 'gt_answer': 'cow'}, {'question_id': '20865518', 'answer': 'Cow', 'gt_answer': 'cow'}, {'question_id': '20655138', 'answer': 'Wide', 'gt_answer': 'wide'}, {'question_id': '202040328', 'answer': 'Long', 'gt_answer': 'long'}, {'question_id': '20865515', 'answer': 'Calf', 'gt_answer': 'calf'}, {'question_id': '20468600', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '201593899', 'answer': 'Woman', 'gt_answer': 'girl'}, {'question_id': '201065645', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20169910', 'answer': 'Empty', 'gt_answer': 'empty'}, {'question_id': '2094048', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20982459', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20169668', 'answer': 'Man', 'gt_answer': 'man'}, {'question_id': '202040204', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202218749', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20902550', 'answer': 'Blue', 'gt_answer': 'blue'}, {'question_id': '20654986', 'answer': 'Coat', 'gt_answer': 'coat'}, {'question_id': '20654987', 'answer': 'Coat', 'gt_answer': 'coat'}, {'question_id': '20710050', 'answer': 'Car', 'gt_answer': 'car'}, {'question_id': '20710052', 'answer': 'Car', 'gt_answer': 'car'}, {'question_id': '20644660', 'answer': 'Wall', 'gt_answer': 'paper'}, {'question_id': '20644661', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201997016', 'answer': 'Boy', 'gt_answer': 'pilot'}, {'question_id': '202228291', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201997015', 'answer': 'Boy', 'gt_answer': 'pilot'}, {'question_id': '201110811', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201079833', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '2098341', 'answer': 'Black', 'gt_answer': 'dark'}, {'question_id': '201887134', 'answer': 'Broccoli', 'gt_answer': 'cabbage'}, {'question_id': '201887130', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20602928', 'answer': 'Large', 'gt_answer': 'huge'}, {'question_id': '2098349', 'answer': 'Desk', 'gt_answer': 'desk'}, {'question_id': '201873152', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201795558', 'answer': 'Child', 'gt_answer': 'child'}, {'question_id': '202228346', 'answer': 'Wood', 'gt_answer': 'plastic'}, {'question_id': '2066119', 'answer': 'Girl', 'gt_answer': 'girl'}, {'question_id': '201346659', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '20385866', 'answer': 'Silver', 'gt_answer': 'silver'}, {'question_id': '201370383', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201307275', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20385861', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202081558', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201976841', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20968350', 'answer': 'Man', 'gt_answer': 'man'}, {'question_id': '201497915', 'answer': 'Monitor', 'gt_answer': 'monitor'}, {'question_id': '201759216', 'answer': 'Coat', 'gt_answer': 'coats'}, {'question_id': '201079978', 'answer': 'Bed', 'gt_answer': 'table'}, {'question_id': '201079979', 'answer': 'Bed', 'gt_answer': 'table'}, {'question_id': '201079976', 'answer': 'Table', 'gt_answer': 'table'}, {'question_id': '201079974', 'answer': 'Bed', 'gt_answer': 'bed'}, {'question_id': '2012660', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20480442', 'answer': 'Cat', 'gt_answer': 'cat'}, {'question_id': '201757778', 'answer': 'Laptop', 'gt_answer': 'laptop'}, {'question_id': '20516027', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '201757771', 'answer': 'Laptop', 'gt_answer': 'laptop'}, {'question_id': '201175381', 'answer': 'Cat', 'gt_answer': 'cat'}, {'question_id': '20609764', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20309009', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201030718', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20151706', 'answer': 'Purse', 'gt_answer': 'purse'}, {'question_id': '201998380', 'answer': 'Chairs', 'gt_answer': 'chairs'}, {'question_id': '20929298', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20827090', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202003672', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201110509', 'answer': 'Marshmallows', 'gt_answer': 'marshmallow'}, {'question_id': '20414571', 'answer': 'Man', 'gt_answer': 'spectators'}, {'question_id': '201110506', 'answer': 'Soft', 'gt_answer': 'soft'}, {'question_id': '20567683', 'answer': 'Short', 'gt_answer': 'long'}, {'question_id': '20856948', 'answer': 'Bed', 'gt_answer': 'bed'}, {'question_id': '20541692', 'answer': 'Purse', 'gt_answer': 'handbag'}, {'question_id': '20541691', 'answer': 'Remote', 'gt_answer': 'handbag'}, {'question_id': '201574274', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201662987', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201467327', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20954134', 'answer': 'Woman', 'gt_answer': 'girl'}, {'question_id': '201766672', 'answer': 'Small', 'gt_answer': 'small'}, {'question_id': '20954135', 'answer': 'Girl', 'gt_answer': 'girl'}, {'question_id': '20183196', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '202121632', 'answer': 'Chairs', 'gt_answer': 'chairs'}, {'question_id': '201757594', 'answer': 'Blue', 'gt_answer': 'blue'}, {'question_id': '201548875', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20954137', 'answer': 'Woman', 'gt_answer': 'woman'}, {'question_id': '201573971', 'answer': 'People', 'gt_answer': 'people'}, {'question_id': '202262098', 'answer': 'Yellow', 'gt_answer': 'white'}, {'question_id': '201404151', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202147892', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201765895', 'answer': 'Man', 'gt_answer': 'surfer'}, {'question_id': '201974999', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201996729', 'answer': 'Airplane', 'gt_answer': 'helicopter'}, {'question_id': '201996728', 'answer': 'Helicopter', 'gt_answer': 'helicopter'}, {'question_id': '201765899', 'answer': 'Man', 'gt_answer': 'surfer'}, {'question_id': '201996726', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20797603', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20434860', 'answer': 'Boy', 'gt_answer': 'boy'}, {'question_id': '20411895', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202119310', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20411898', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201996878', 'answer': 'Blue', 'gt_answer': 'brown'}, {'question_id': '201574015', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201574011', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '201574013', 'answer': 'Store', 'gt_answer': 'stores'}, {'question_id': '20711630', 'answer': 'White', 'gt_answer': 'white'}, {'question_id': '20942177', 'answer': 'Surfboard', 'gt_answer': 'surfboard'}, {'question_id': '20942174', 'answer': 'Surfboard', 'gt_answer': 'surfboard'}, {'question_id': '20724171', 'answer': 'Man', 'gt_answer': 'snowboarder'}, {'question_id': '20942172', 'answer': 'Woman', 'gt_answer': 'girl'}, {'question_id': '201663583', 'answer': 'Dishwasher', 'gt_answer': 'dishwasher'}, {'question_id': '201663584', 'answer': 'Dishwasher', 'gt_answer': 'dishwasher'}, {'question_id': '20489611', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20262798', 'answer': 'Kite', 'gt_answer': 'kite'}, {'question_id': '201621303', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20262795', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201758211', 'answer': 'Chair', 'gt_answer': 'chair'}, {'question_id': '201758216', 'answer': 'Chair', 'gt_answer': 'chair'}, {'question_id': '201621308', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20182959', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20518415', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '202023403', 'answer': 'Window', 'gt_answer': 'bed'}, {'question_id': '202023406', 'answer': 'Bookshelf', 'gt_answer': 'bed'}, {'question_id': '201676418', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20480226', 'answer': 'Shelf', 'gt_answer': 'desk'}, {'question_id': '20480227', 'answer': 'Bookcase', 'gt_answer': 'bookcase'}, {'question_id': '20480225', 'answer': 'Desk', 'gt_answer': 'desk'}, {'question_id': '20258627', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201902687', 'answer': 'Brown', 'gt_answer': 'brown'}, {'question_id': '20480221', 'answer': 'Bookshelf', 'gt_answer': 'bookcase'}, {'question_id': '202244022', 'answer': 'Cookie', 'gt_answer': 'cookies'}, {'question_id': '201654639', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202173959', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201301948', 'answer': 'Woman', 'gt_answer': 'woman'}, {'question_id': '201065005', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201065004', 'answer': 'Shirt', 'gt_answer': 'sandal'}, {'question_id': '201065002', 'answer': 'Shirt', 'gt_answer': 'sandal'}, {'question_id': '201065001', 'answer': 'Girl', 'gt_answer': 'boy'}, {'question_id': '20412203', 'answer': 'Table', 'gt_answer': 'tables'}, {'question_id': '201185348', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20412201', 'answer': 'Tables', 'gt_answer': 'tables'}, {'question_id': '20306789', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20657189', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20657188', 'answer': 'On pole', 'gt_answer': 'street'}, {'question_id': '20412205', 'answer': 'Chair', 'gt_answer': 'tables'}, {'question_id': '20657185', 'answer': 'Speed limit', 'gt_answer': 'traffic sign'}, {'question_id': '201796005', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20657186', 'answer': 'Stop', 'gt_answer': 'traffic sign'}, {'question_id': '20306831', 'answer': 'Woman', 'gt_answer': 'woman'}, {'question_id': '201185347', 'answer': 'Fence', 'gt_answer': 'fence post'}, {'question_id': '20657182', 'answer': 'Metal', 'gt_answer': 'metal'}, {'question_id': '201056053', 'answer': 'Man', 'gt_answer': 'soccer player'}, {'question_id': '201056055', 'answer': 'People', 'gt_answer': 'soccer player'}, {'question_id': '20394977', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201056059', 'answer': 'Boy', 'gt_answer': 'soccer player'}, {'question_id': '201908869', 'answer': 'Dip', 'gt_answer': 'sandwiches'}, {'question_id': '201434160', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20349975', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20411813', 'answer': 'Carrots', 'gt_answer': 'carrots'}, {'question_id': '201713415', 'answer': 'Mirror', 'gt_answer': 'mirror'}, {'question_id': '201713417', 'answer': 'Coffee cup', 'gt_answer': 'mirror'}, {'question_id': '201439461', 'answer': 'Eating', 'gt_answer': 'standing'}, {'question_id': '201322478', 'answer': 'White', 'gt_answer': 'cream colored'}, {'question_id': '201682258', 'answer': 'Playing tennis', 'gt_answer': 'standing'}, {'question_id': '201360893', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201360890', 'answer': 'Toothbrush', 'gt_answer': 'toothbrush'}, {'question_id': '201360896', 'answer': 'Girl', 'gt_answer': 'girl'}, {'question_id': '20244719', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201227982', 'answer': 'Car', 'gt_answer': 'car'}, {'question_id': '201360898', 'answer': 'Shirt', 'gt_answer': 'jacket'}, {'question_id': '201492498', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20300402', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20899241', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201504901', 'answer': 'Woman', 'gt_answer': 'woman'}, {'question_id': '20536073', 'answer': 'Zebra', 'gt_answer': 'bison'}, {'question_id': '201798294', 'answer': 'Trash bag', 'gt_answer': 'trash bag'}, {'question_id': '20887391', 'answer': 'Computer', 'gt_answer': 'monitor'}, {'question_id': '20899572', 'answer': 'Off', 'gt_answer': 'off'}, {'question_id': '201504907', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201109362', 'answer': 'Car', 'gt_answer': 'car'}, {'question_id': '20403466', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20403460', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201109365', 'answer': 'Car', 'gt_answer': 'car'}, {'question_id': '201430839', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20381570', 'answer': 'Computer', 'gt_answer': 'computer monitor'}, {'question_id': '20381571', 'answer': 'Computer', 'gt_answer': 'computer monitor'}, {'question_id': '20381575', 'answer': 'Light switch', 'gt_answer': 'computer monitor'}, {'question_id': '202107830', 'answer': 'Boy', 'gt_answer': 'boy'}, {'question_id': '20381577', 'answer': 'Computer', 'gt_answer': 'computer monitor'}, {'question_id': '20320335', 'answer': 'Building', 'gt_answer': 'road'}, {'question_id': '201370443', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20241072', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201687514', 'answer': 'Choppy', 'gt_answer': 'wavy'}, {'question_id': '201064822', 'answer': 'Leather', 'gt_answer': 'steel'}, {'question_id': '201430835', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '201972923', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '2053705', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20862888', 'answer': 'Long', 'gt_answer': 'long'}, {'question_id': '20258774', 'answer': 'Boy', 'gt_answer': 'child'}, {'question_id': '20887420', 'answer': 'Computer', 'gt_answer': 'monitor'}, {'question_id': '202285240', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201756564', 'answer': 'Cat', 'gt_answer': 'kitten'}, {'question_id': '201795862', 'answer': 'Sitting', 'gt_answer': 'talking'}, {'question_id': '201756560', 'answer': 'Cat', 'gt_answer': 'kitten'}, {'question_id': '202100852', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20177932', 'answer': 'Cucumber', 'gt_answer': 'pickles'}, {'question_id': '20381716', 'answer': 'Keyboard', 'gt_answer': 'keyboard'}, {'question_id': '201983731', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '20381710', 'answer': 'Desk', 'gt_answer': 'keyboard'}, {'question_id': '2075891', 'answer': 'Brown', 'gt_answer': 'brown'}, {'question_id': '201047494', 'answer': 'Dress shirt', 'gt_answer': 'dress shirt'}, {'question_id': '20178004', 'answer': 'Cutting board', 'gt_answer': 'cutting board'}, {'question_id': '201947437', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20178000', 'answer': 'Red', 'gt_answer': 'red'}, {'question_id': '202158892', 'answer': 'Bus', 'gt_answer': 'bus'}, {'question_id': '20609229', 'answer': 'Table', 'gt_answer': 'table'}, {'question_id': '20609228', 'answer': 'Table', 'gt_answer': 'table'}, {'question_id': '201393533', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201393537', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20171190', 'answer': 'Pan', 'gt_answer': 'baking pan'}, {'question_id': '20171196', 'answer': 'Pan', 'gt_answer': 'baking pan'}, {'question_id': '201446990', 'answer': 'Shelf', 'gt_answer': 'shelf'}, {'question_id': '201446991', 'answer': 'Shelf', 'gt_answer': 'shelf'}, {'question_id': '20204848', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '201438730', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201064838', 'answer': 'Blue', 'gt_answer': 'blue'}, {'question_id': '201957240', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '201447024', 'answer': 'Roll', 'gt_answer': 'shelf'}, {'question_id': '201957249', 'answer': 'Man', 'gt_answer': 'woman'}, {'question_id': '202243838', 'answer': 'Cupcake', 'gt_answer': 'cupcakes'}, {'question_id': '201481657', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201047278', 'answer': 'Healthy', 'gt_answer': 'unhealthy'}, {'question_id': '202101080', 'answer': 'Boy', 'gt_answer': 'skateboarder'}, {'question_id': '202243831', 'answer': 'Sprinkles', 'gt_answer': 'sprinkles'}, {'question_id': '202248871', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '202243345', 'answer': 'Motorcycle', 'gt_answer': 'motorcycle'}, {'question_id': '20756870', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202248875', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20609594', 'answer': 'Cake', 'gt_answer': 'cake'}, {'question_id': '20637175', 'answer': 'Silver', 'gt_answer': 'silver'}, {'question_id': '201996964', 'answer': 'Boy', 'gt_answer': 'pilot'}, {'question_id': '201822253', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201109453', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20118110', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201669526', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201669520', 'answer': 'Cookie', 'gt_answer': 'cookies'}, {'question_id': '20896464', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201462454', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '202159036', 'answer': 'Cars', 'gt_answer': 'cars'}, {'question_id': '201935408', 'answer': 'Skinny', 'gt_answer': 'fat'}, {'question_id': '20853927', 'answer': 'Black', 'gt_answer': 'black'}, {'question_id': '20786015', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201826574', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201935401', 'answer': 'Small', 'gt_answer': 'large'}, {'question_id': '202041949', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '2098285', 'answer': 'Laptop', 'gt_answer': 'speaker'}, {'question_id': '2098282', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '2098281', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201143309', 'answer': 'Table', 'gt_answer': 'table'}, {'question_id': '20827154', 'answer': 'Left', 'gt_answer': 'right'}, {'question_id': '201143302', 'answer': 'Old fashioned', 'gt_answer': 'old fashioned'}, {'question_id': '20722106', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201896544', 'answer': 'Woman', 'gt_answer': 'lady'}, {'question_id': '201896545', 'answer': 'Woman', 'gt_answer': 'lady'}, {'question_id': '201896546', 'answer': 'Cake', 'gt_answer': 'cake'}, {'question_id': '201623957', 'answer': 'Spatula', 'gt_answer': 'pan'}, {'question_id': '201068498', 'answer': 'Black', 'gt_answer': 'pink'}, {'question_id': '201624270', 'answer': 'Wood', 'gt_answer': 'metal'}, {'question_id': '201623953', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201889466', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201428779', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20984557', 'answer': 'Car', 'gt_answer': 'car'}, {'question_id': '202102761', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20984553', 'answer': 'Black', 'gt_answer': 'black'}, {'question_id': '201972737', 'answer': 'Short', 'gt_answer': 'tall'}, {'question_id': '201623359', 'answer': 'Closed', 'gt_answer': 'closed'}, {'question_id': '202228404', 'answer': 'Cat', 'gt_answer': 'cat'}, {'question_id': '201479269', 'answer': 'Lid', 'gt_answer': 'lid'}, {'question_id': '202228156', 'answer': 'On', 'gt_answer': 'on'}, {'question_id': '201959794', 'answer': 'Blue', 'gt_answer': 'dark'}, {'question_id': '20205086', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202262399', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20434988', 'answer': 'Pizza', 'gt_answer': 'pizza box'}, {'question_id': '202258292', 'answer': 'Horse', 'gt_answer': 'horse'}, {'question_id': '202173947', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202122065', 'answer': 'Mirror', 'gt_answer': 'countertop'}, {'question_id': '20679377', 'answer': 'Fence', 'gt_answer': 'park'}, {'question_id': '20385477', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201758022', 'answer': 'Clear', 'gt_answer': 'white'}, {'question_id': '20385472', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20899370', 'answer': 'Blue', 'gt_answer': 'blue'}, {'question_id': '201055804', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201055773', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202241136', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '2059757', 'answer': 'Player', 'gt_answer': 'umpire'}, {'question_id': '2059756', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '20120075', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '20860953', 'answer': 'Walking', 'gt_answer': 'talking'}, {'question_id': '2059758', 'answer': 'Player', 'gt_answer': 'umpire'}, {'question_id': '20866501', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20860955', 'answer': 'Walking', 'gt_answer': 'talking'}, {'question_id': '20922974', 'answer': 'Car', 'gt_answer': 'car'}, {'question_id': '201663139', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20922970', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '202244562', 'answer': 'Carrot', 'gt_answer': 'cookies'}, {'question_id': '201663131', 'answer': 'Light brown', 'gt_answer': 'light brown'}, {'question_id': '20922978', 'answer': 'Truck', 'gt_answer': 'car'}, {'question_id': '20692504', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20679215', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '201030527', 'answer': 'Rocket', 'gt_answer': 'shuttle'}, {'question_id': '201556510', 'answer': 'Large', 'gt_answer': 'small'}, {'question_id': '201896155', 'answer': 'Table', 'gt_answer': 'table'}, {'question_id': '20715639', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201360709', 'answer': 'Toothbrush', 'gt_answer': 'toothbrush'}, {'question_id': '202262175', 'answer': 'Picture', 'gt_answer': 'menu'}, {'question_id': '202262174', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201663273', 'answer': 'Microwave', 'gt_answer': 'microwave'}, {'question_id': '20891509', 'answer': 'Standing', 'gt_answer': 'playing'}, {'question_id': '20226715', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201175720', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201207182', 'answer': 'Apple', 'gt_answer': 'vegetables'}, {'question_id': '201109623', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201492405', 'answer': 'Tall', 'gt_answer': 'tall'}, {'question_id': '201832644', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '201880467', 'answer': 'Wood', 'gt_answer': 'glass'}, {'question_id': '201887308', 'answer': 'Broccoli', 'gt_answer': 'broccoli'}, {'question_id': '201497698', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202119647', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201879230', 'answer': 'No one', 'gt_answer': 'athlete'}, {'question_id': '20285417', 'answer': 'Beige', 'gt_answer': 'gray'}, {'question_id': '20811075', 'answer': 'Chair', 'gt_answer': 'chair'}, {'question_id': '201739148', 'answer': 'Colorful', 'gt_answer': 'black and white'}, {'question_id': '201889415', 'answer': 'Small', 'gt_answer': 'small'}, {'question_id': '202179448', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '20536264', 'answer': 'Buffalo', 'gt_answer': 'bison'}, {'question_id': '20536265', 'answer': 'Giraffe', 'gt_answer': 'giraffe'}, {'question_id': '202023584', 'answer': 'Bed', 'gt_answer': 'bookshelf'}, {'question_id': '20536260', 'answer': 'Giraffe', 'gt_answer': 'bison'}, {'question_id': '201889418', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20204685', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20541739', 'answer': 'Beige', 'gt_answer': 'beige'}, {'question_id': '20473137', 'answer': 'Right', 'gt_answer': 'right'}, {'question_id': '20891762', 'answer': 'Dirty', 'gt_answer': 'clean'}, {'question_id': '201982874', 'answer': 'Remote control', 'gt_answer': 'remote control'}, {'question_id': '201759373', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20508383', 'answer': 'Small', 'gt_answer': 'large'}, {'question_id': '201759370', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20480503', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20899460', 'answer': 'Earbuds', 'gt_answer': 'notebook'}, {'question_id': '201342366', 'answer': 'Right', 'gt_answer': 'left'}, {'question_id': '201826546', 'answer': 'Tall', 'gt_answer': 'short'}, {'question_id': '202012554', 'answer': 'Cabinet', 'gt_answer': 'cabinets'}, {'question_id': '201570768', 'answer': 'Top', 'gt_answer': 'top'}, {'question_id': '201947751', 'answer': 'Tank top', 'gt_answer': 'tank top'}, {'question_id': '2053547', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '202102692', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201902607', 'answer': 'Desk', 'gt_answer': 'desk'}, {'question_id': '201976900', 'answer': 'Grass', 'gt_answer': 'street'}, {'question_id': '20668072', 'answer': 'Couch', 'gt_answer': 'couch'}, {'question_id': '201498258', 'answer': 'Keyboard', 'gt_answer': 'coffee cup'}, {'question_id': '201498259', 'answer': 'Keyboard', 'gt_answer': 'coffee cup'}, {'question_id': '20668077', 'answer': 'Table', 'gt_answer': 'coffee table'}, {'question_id': '20941952', 'answer': 'Green', 'gt_answer': 'green'}, {'question_id': '202156791', 'answer': 'Rock', 'gt_answer': 'rocks'}, {'question_id': '202156792', 'answer': 'Trees', 'gt_answer': 'rocks'}, {'question_id': '20757139', 'answer': 'Plates', 'gt_answer': 'plates'}, {'question_id': '20757138', 'answer': 'Woman', 'gt_answer': 'lady'}, {'question_id': '201574483', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201574486', 'answer': 'Green', 'gt_answer': 'green'}, {'question_id': '20394707', 'answer': 'Color', 'gt_answer': 'color'}, {'question_id': '20898569', 'answer': 'Color', 'gt_answer': 'color'}, {'question_id': '201637342', 'answer': 'Black', 'gt_answer': 'black'}, {'question_id': '202174017', 'answer': 'Square', 'gt_answer': 'rectangular'}, {'question_id': '20898561', 'answer': 'Color', 'gt_answer': 'color'}, {'question_id': '20340412', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '202240916', 'answer': 'Wood', 'gt_answer': 'wood'}, {'question_id': '20827718', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20320496', 'answer': 'Sign', 'gt_answer': 'newspaper'}, {'question_id': '201535904', 'answer': 'Scarf', 'gt_answer': 'rope'}, {'question_id': '20827715', 'answer': 'Window', 'gt_answer': 'window'}, {'question_id': '20818696', 'answer': 'Swing', 'gt_answer': 'playing'}, {'question_id': '2046712', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '20818697', 'answer': 'Man', 'gt_answer': 'batter'}, {'question_id': '20648146', 'answer': 'White', 'gt_answer': 'white'}, {'question_id': '20648141', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202100453', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202100456', 'answer': 'Sailboat', 'gt_answer': 'sailboats'}, {'question_id': '201595885', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201984030', 'answer': 'Woman', 'gt_answer': 'woman'}, {'question_id': '201641425', 'answer': 'Sign', 'gt_answer': 'street sign'}, {'question_id': '201641427', 'answer': 'Pole', 'gt_answer': 'traffic light'}, {'question_id': '201641426', 'answer': 'Street sign', 'gt_answer': 'street sign'}, {'question_id': '20896232', 'answer': 'Cabinet', 'gt_answer': 'cabinets'}, {'question_id': '201641420', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20724230', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20896231', 'answer': 'Cabinets', 'gt_answer': 'cabinets'}, {'question_id': '201247151', 'answer': 'Black and white', 'gt_answer': 'black and white'}, {'question_id': '201247156', 'answer': 'Cat', 'gt_answer': 'cat'}, {'question_id': '202180331', 'answer': 'Girl', 'gt_answer': 'soccer player'}, {'question_id': '202265708', 'answer': 'Sitting', 'gt_answer': 'sitting'}, {'question_id': '201247158', 'answer': 'Cat', 'gt_answer': 'cat'}, {'question_id': '20295472', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20541465', 'answer': 'Wood', 'gt_answer': 'wood'}, {'question_id': '201080103', 'answer': 'Blue', 'gt_answer': 'blue'}, {'question_id': '201536452', 'answer': 'Playing', 'gt_answer': 'staring'}, {'question_id': '20307199', 'answer': 'Camera', 'gt_answer': 'television'}, {'question_id': '201885418', 'answer': 'Fence', 'gt_answer': 'plant'}, {'question_id': '201067897', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202024871', 'answer': 'Black', 'gt_answer': 'black'}, {'question_id': '201360938', 'answer': 'Small', 'gt_answer': 'large'}, {'question_id': '201637062', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20818866', 'answer': 'Wood', 'gt_answer': 'wood'}, {'question_id': '20753349', 'answer': 'Wide', 'gt_answer': 'narrow'}, {'question_id': '201319475', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '202285502', 'answer': 'Brown', 'gt_answer': 'brown'}, {'question_id': '202073235', 'answer': 'Deer', 'gt_answer': 'horses'}, {'question_id': '202285056', 'answer': 'Yellow', 'gt_answer': 'yellow'}, {'question_id': '201879596', 'answer': 'White', 'gt_answer': 'light brown'}, {'question_id': '202119574', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201976623', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20711632', 'answer': 'Desk', 'gt_answer': 'table'}, {'question_id': '20863630', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202006198', 'answer': 'Stove', 'gt_answer': 'shelves'}, {'question_id': '201391879', 'answer': 'Couch', 'gt_answer': 'sofa'}, {'question_id': '20984486', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201391870', 'answer': 'Couch', 'gt_answer': 'sofa'}, {'question_id': '201391871', 'answer': 'Couch', 'gt_answer': 'sofa'}, {'question_id': '201264250', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '202003859', 'answer': 'Narrow', 'gt_answer': 'wide'}, {'question_id': '201391877', 'answer': 'Chair', 'gt_answer': 'sofa'}, {'question_id': '201974832', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201935188', 'answer': 'Tree', 'gt_answer': 'tree'}, {'question_id': '20753693', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '202244084', 'answer': 'Cupcake', 'gt_answer': 'cupcakes'}, {'question_id': '20862942', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201935187', 'answer': 'Tree', 'gt_answer': 'tree'}, {'question_id': '201795695', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20781688', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '201080243', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20320478', 'answer': 'Car', 'gt_answer': 'suv'}, {'question_id': '201067409', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '20894271', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20518512', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20381380', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20600090', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201909091', 'answer': 'Black', 'gt_answer': 'black'}, {'question_id': '20984562', 'answer': 'No', 'gt_answer': 'yes'}, {'question_id': '202003850', 'answer': 'Dark', 'gt_answer': 'dark'}, {'question_id': '20655151', 'answer': 'Black', 'gt_answer': 'black'}, {'question_id': '201982279', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '201439719', 'answer': 'Chicken', 'gt_answer': 'horse'}, {'question_id': '20655158', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '202208419', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201576703', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201759539', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '201935363', 'answer': 'Skateboard', 'gt_answer': 'bricks'}, {'question_id': '201935365', 'answer': 'Man', 'gt_answer': 'man'}, {'question_id': '202040263', 'answer': 'Station', 'gt_answer': 'train station'}, {'question_id': '202040262', 'answer': 'Train', 'gt_answer': 'train'}, {'question_id': '201738978', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20903195', 'answer': 'Van', 'gt_answer': 'van'}, {'question_id': '20903190', 'answer': 'No', 'gt_answer': 'no'}, {'question_id': '20644644', 'answer': 'Living room', 'gt_answer': 'bedroom'}, {'question_id': '20226963', 'answer': 'Left', 'gt_answer': 'left'}, {'question_id': '201535906', 'answer': 'Ceiling', 'gt_answer': 'coat'}, {'question_id': '201997034', 'answer': 'Black', 'gt_answer': 'brown'}, {'question_id': '201878974', 'answer': 'Yes', 'gt_answer': 'no'}, {'question_id': '20330545', 'answer': 'Thin', 'gt_answer': 'fat'}, {'question_id': '20330547', 'answer': 'Car', 'gt_answer': 'car'}, {'question_id': '20602944', 'answer': 'Horse', 'gt_answer': 'horse'}, {'question_id': '20602943', 'answer': 'Horse', 'gt_answer': 'horse'}, {'question_id': '20341099', 'answer': 'Yes', 'gt_answer': 'yes'}, {'question_id': '20341098', 'answer': 'Sidewalk', 'gt_answer': 'grass'}, {'question_id': '20341097', 'answer': 'Street', 'gt_answer': 'grass'}, {'question_id': '20602949', 'answer': 'Field', 'gt_answer': 'ground'}, {'question_id': '201738903', 'answer': 'Man', 'gt_answer': 'spectators'}], 'vision_bits': 8, 'language_bits': 4}\n" + ] + } + ], + "source": [ + "# gqa ans\n", + "ans_path = '/fs/cfar-projects/low-bit-vision/llava/full_precision/gqa_test_do_pad/results_v8_l4.json'\n", + "\n", + "with open(ans_path, 'r') as f:\n", + " results = json.load(f)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "659a13d7", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "dict_keys(['answers', 'vision_bits', 'language_bits'])" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "results.keys()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3ac5f5c5", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'agg_metrics': 61.46, 'acc': 61.46}\n" + ] + } + ], + "source": [ + "for res in results:\n", + " res['answer'] = res['answer'].split('ASSISTANT: ')[-1]\n", + "\n", + "def compute_gqa_results(results, scorer, save_path=None):\n", + " gqa_results = scorer.compute_scores(results, \"gqa\")\n", + " print(gqa_results)\n", + "# if save_path:\n", + "# with open(save_path, \"w\") as f:\n", + "# json.dump(gqa_results, f)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "8a72a455", + "metadata": {}, + "outputs": [], + "source": [ + "def compute_gqa_results(results, scorer, save_path=None):\n", + " gqa_results = scorer.compute_scores(results, \"gqa\")\n", + " print(gqa_results)\n", + " # if save_path:\n", + " # with open(save_path, \"w\") as f:\n", + " # json.dump(gqa_results, f)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "65e5d656", + "metadata": {}, + "outputs": [], + "source": [ + "# vqav2 ans\n", + "ans_path = '/fs/cfar-projects/low-bit-vision/llava/full_precision/vqav2_test_do_pad/results_v8_l4.json'\n", + "\n", + "with open(ans_path, 'r') as f:\n", + " results = json.load(f)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "05eaa30d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "dict_keys(['answers', 'vision_bits', 'language_bits'])" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "results.keys()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "6753cbba", + "metadata": {}, + "outputs": [], + "source": [ + "answers = results['answers']\n", + "for ans in answers:\n", + " ans['answer'] = ans['answer'].split('ASSISTANT: ')[-1]\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "e5786bee", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'question_id': 262148000, 'answer': 'Down'}" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "results['answers'][0]" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "1308c4b1", + "metadata": {}, + "outputs": [], + "source": [ + "ann_root = '/fs/cfar-projects/low-bit-vision/datasets/vqav2/annotations'\n", + "q_root = '/fs/cfar-projects/low-bit-vision/datasets/vqav2/questions'\n", + "\n", + "# results[\"answers\"] = answers\n", + "results[\"annotations\"] = os.path.join(ann_root, \"v2_mscoco_val2014_annotations.json\")\n", + "results[\"questions\"] = os.path.join(q_root, \"v2_OpenEnded_mscoco_val2014_questions.json\")" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "52955eae", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "dict_keys(['answers', 'vision_bits', 'language_bits', 'annotations', 'questions'])" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "results.keys()" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "c39f27ec", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Adding current path to python system paths\n", + "loading VQA annotations and questions into memory...\n", + "0:00:09.746300\n", + "creating index...\n", + "index created!\n", + "Loading and preparing results... \n", + "DONE (t=0.33s)\n", + "creating index...\n", + "index created!\n", + "computing accuracy\n", + "Finshed Percent: [####################] 99% Done computing accuracy\n", + "{'agg_metrics': 75.69, 'other': 68.86, 'yes/no': 91.04, 'number': 57.4}\n" + ] + } + ], + "source": [ + "\n", + "def compute_vqa_results(results, scorer, save_path=None):\n", + " vqa_results = scorer.compute_scores(results, \"vqav2\")\n", + " print(vqa_results)\n", + " if save_path:\n", + " with open(save_path, \"w\") as f:\n", + " json.dump(vqa_results, f)\n", + "\n", + "\n", + "\n", + "scorer = ScoringPipeline()\n", + "compute_vqa_results(results, scorer)" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "id": "bd270633", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "214354" + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(results)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/llava_runs/llava_scoring.py b/llava_runs/llava_scoring.py new file mode 100644 index 0000000..6dba5bd --- /dev/null +++ b/llava_runs/llava_scoring.py @@ -0,0 +1,70 @@ +import sys +sys.path.append('..') +from scoring_pipeline import ScoringPipeline +import json +import os +import torch +import pandas as pd +from tqdm import tqdm + + +def compute_scores(results_dir, task): + + scorer = ScoringPipeline() + + gather = [] + for results_file in tqdm(os.listdir(results_dir)): + + results_path = os.path.join(results_dir, results_file) + + with open(results_path, 'r') as f: + results = json.load(f) + + # post-processing llava output + answers = results['answers'] + for ans in answers: + ans['answer'] = ans['answer'].split('ASSISTANT: ')[-1] + + + if task == 'vqav2': + ann_root = '/fs/cfar-projects/low-bit-vision/datasets/vqav2/annotations' + q_root = '/fs/cfar-projects/low-bit-vision/datasets/vqav2/questions' + + # results["answers"] = answers + results["annotations"] = os.path.join(ann_root, "v2_mscoco_val2014_annotations.json") + results["questions"] = os.path.join(q_root, "v2_OpenEnded_mscoco_val2014_questions.json") + + score = scorer.compute_scores(results, task) + # print(score) + + record = dict( + vision_bits = results['vision_bits'], + language_bits = results['language_bits'], + ) + + record.update(score) + + print(record) + + elif task == 'gqa': + score = scorer.compute_scores(answers, task)['acc'] + + record = dict( + vision_bits = results['vision_bits'], + language_bits = results['language_bits'], + acc = score + ) + + gather.append(record) + + return pd.DataFrame(gather) + + +results_dir = '/fs/cfar-projects/low-bit-vision/llava/gptq/vqav2' +df_vqav2_gptq = compute_scores(results_dir, 'vqav2') + + +output_path = '/fs/cfar-projects/low-bit-vision/final_results/llava/gptq_vqav2.csv' +df_vqav2_gptq.to_csv(output_path, index=None) + +print(f'output file written to: {output_path}') diff --git a/llava_runs/multi_sbatch_awq_gqa.py b/llava_runs/multi_sbatch_awq_gqa.py new file mode 100644 index 0000000..9dda596 --- /dev/null +++ b/llava_runs/multi_sbatch_awq_gqa.py @@ -0,0 +1,384 @@ +import os +from datetime import datetime +import argparse +import shutil +import math +import time +import socket +import itertools +import subprocess + + +def run(cmd): + return subprocess.check_output(cmd, shell=True).decode('UTF-8').splitlines() + +def present_in_list(string, gpu_list): + return any([x in string for x in gpu_list]) + +def split(a, n): + k, m = divmod(len(a), n) + return (a[i*k+min(i, m):(i+1)*k+min(i+1, m)] for i in range(n)) + +def get_exclude_string(gpu_list, default_exclude=None): + if gpu_list[0] == 'any': + if default_exclude is None: + return '' + else: + return '#SBATCH --exclude='+','.join(default_exclude) + memdata = run('sinfo -O nodehost,gres -h') + superset = set([x.split()[0] for x in memdata]) + blacklist = [] + for x in memdata: + nodehost, gres = x.strip().split() + if present_in_list(gres, gpu_list): + blacklist.append(nodehost) + + exclude_list = superset - set(blacklist) + if default_exclude: + exclude_list = exclude_list.union(set(default_exclude)) + exclude_string = ','.join(sorted(exclude_list)) + if exclude_string: + exclude_string = '#SBATCH --exclude='+exclude_string+'\n' + return exclude_string + else: + return '' + +def get_include_string(gpu_list, default_include=None): + if gpu_list[0] == 'any': + raise Exception("That's too much, man! (It's a Bojack reference. Watch it if you haven't already, you degenerate)") + memdata = run('sinfo -O nodehost,gres -h') + include_list = [] + for x in memdata: + nodehost, gres = x.strip().split() + if present_in_list(gres, gpu_list): + include_list.append(nodehost) + include_string = ','.join(sorted(include_list)) + if include_string: + include_string = '#SBATCH --nodelist='+include_string+'\n' + return include_string + else: + return '' + +# Function to chec for validity of QOS +#TODO: Add time check for QOS + +qos_dict = { + "scav" : {"nhrs" : 72, "cores": 32, "mem":256}, + "high" : {"gpu":4, "cores": 16, "mem":128, "nhrs": 36}, + "medium" : {"gpu":2, "cores": 8, "mem":64, "nhrs": 72}, + "default" : {"gpu":1, "cores": 4, "mem":32, "nhrs": 168}} + + +def check_qos(args): + + for qos in args.qos: + for key, max_value in qos_dict[qos].items(): + val_from_args = getattr(args, key) + if val_from_args != None: + if val_from_args > max_value: + raise ValueError("Invalid parameter for {} for {}".format(key, qos)) + else: + setattr(args, key, max_value) + return args + + +#TODO: Add day funtionality too +parser = argparse.ArgumentParser() +parser.add_argument('--nhrs', type=int, default=None) +parser.add_argument('--base-dir', default=f'{os.getcwd()}') +parser.add_argument('--output-dirname', default='outputs') +parser.add_argument('--partition', default='vulcan', choices=['vulcan','cml','nexus']) +parser.add_argument('--dryrun', action='store_true') +parser.add_argument('--qos', default=None, type=str, nargs='*', help='Qos to run') +parser.add_argument('--env', type=str, help = "Set the name of the dir you want to dump") +parser.add_argument('--gpu', default=None, type=int, help='Number of gpus') +parser.add_argument('--gpu-type', type=str, help='Type of gpu to use (can be multiple)', default=['any'], + choices=['any','p6000','gtx','rtx2080','a4000','a5000','a6000'], nargs='*') +parser.add_argument('--cores', default=None, type=int, help='Number of cpu cores') +parser.add_argument('--mem', default=None, type=int, help='RAM in G') +parser.add_argument('--single', action='store_true') +parser.add_argument('--filename', default=None, type=str, help='Slurm file name') +parser.add_argument('--max_jobs', default=80, type=int, help='Maximum number of jobs running in parallel') +parser.add_argument('--offset', default=0, type=int, help='Offset') +parser.add_argument('--batchsize', default=500, type=int, help='Offset') + +args = parser.parse_args() + +if args.filename is None: + args.filename = args.env + +output_dir = os.path.join(args.base_dir, args.output_dirname, args.env) +if os.path.exists(output_dir): + shutil.rmtree(output_dir) +if not os.path.exists(output_dir): + os.makedirs(output_dir) +print("Output Directory: %s" % output_dir) + +if "nexus" in socket.gethostname(): + root = 'root' ## TODO +else: + raise Exception("Not on nexus") + + +# Define parameters for grid search +bit_options = [2,3,4,5,6,8,16] +params = { + 'task': ['--task', 't', ['gqa']], + 'seed': ['--seed', 's', ['0']], + 'output_dir': ['--output_dir', '', ['/fs/cfar-projects/low-bit-vision/llava/awq/gqa']], + 'vision_bits': ['--vision-bits', 'vb', bit_options], + 'language_bits': ['--language-bits', 'lb', bit_options] +} +####################################################################### + +class Argument(object): + + def __init__(self, name, cmd_line, string_id, val): + self.name = name + self.val = val + if isinstance(val,list): + if len(val) == 0: + + if isinstance(cmd_line, list): + self.cmd_string = '' + for cur_line in cmd_line: + self.cmd_string += ' '+cur_line+' []' + else: + self.cmd_string = ' '+cmd_line+' []' + else: + if isinstance(cmd_line, list): + self.cmd_string = '' + for cur_line in cmd_line: + self.cmd_string += ' '+cur_line+' '+','.join([str(e) for e in val]) + else: + self.cmd_string = ' '+cmd_line+' '+','.join([str(e) for e in val]) + else: + + if isinstance(cmd_line, list): + self.cmd_string = '' + for cur_line in cmd_line: + self.cmd_string += ' '+cur_line+' '+str(val) + else: + self.cmd_string = ' '+cmd_line+' '+str(val) + if isinstance(val,bool): + if not val: + self.job_string = '' + self.cmd_string = '' + self.name = '' + else: + self.job_string = '_'+string_id if string_id else '' + if isinstance(cmd_line, list): + self.cmd_string = '' + for cur_line in cmd_line: + self.cmd_string += ' '+cur_line+' ' + self.cmd_string = ' '+cmd_line+' ' + elif isinstance(val,list): + self.job_string = '_'+string_id+'_'.join([str(v) for v in val]) + else: + self.job_string = '_'+string_id+str(val) + if string_id == 'none': + self.job_string = '' + + def copy(self): + new_arg = Argument(self.name, cmd_line='', string_id='', val=self.val) + new_arg.cmd_string = self.cmd_string + new_arg.job_string = self.job_string + return new_arg + + +os.makedirs(f'{args.base_dir}/{args.output_dirname}/{args.env}',exist_ok=True) +n_jobs = 0 +# Making text files which will store the python command to run, stdout, and error if any +with open(f'{args.base_dir}/{args.output_dirname}/{args.env}/now.txt', "w") as nowfile,\ + open(f'{args.base_dir}/{args.output_dirname}/{args.env}/log.txt', "w") as output_namefile,\ + open(f'{args.base_dir}/{args.output_dirname}/{args.env}/err.txt', "w") as error_namefile,\ + open(f'{args.base_dir}/{args.output_dirname}/{args.env}/name.txt', "w") as namefile: + + arg_list = [] + for key, param in params.items(): + cur_arg_list = [] + if not isinstance(param[2],list): + param[2] = [param[2]] + + if len(param[2])>1 and key!="dataset": + assert param[1]!='none', f"{param[0]} set to none with multiple values!" + + for value in param[2]: + cur_arg_list.append(Argument(key, param[0],param[1], value)) + + arg_list.append(cur_arg_list) + + arg_list = list(itertools.product(*arg_list)) + n_jobs = 0 + for idx,job_args in enumerate(arg_list): + + # Allows modification of current set of args + job_args = {arg.name:arg.copy() for arg in job_args} + + job_string = '' + python_cmd = 'python awq_llava.py' + for arg_name, arg in job_args.items(): + python_cmd += arg.cmd_string + job_string += arg.job_string + + job_string = f'{n_jobs}_'+job_string + cmd_line_str = python_cmd + + # cmd_line_str = python_cmd + + n_jobs += 1 + + nowfile.write(f'{cmd_line_str}\n') + namefile.write(f'{(os.path.join(output_dir, job_string))}.log\n') + output_namefile.write(f'{(os.path.join(output_dir, job_string))}_log.txt\n') + error_namefile.write(f'{(os.path.join(output_dir, job_string))}_error.txt\n') + if args.single: + break + +print(f"\nGenerated {n_jobs} jobs for all bit combinations") +########################################################################### +if len(args.qos)>1: + splits = split(range(0,n_jobs), len(args.qos)) + for qos in args.qos: + cur_dir = os.path.join(args.base_dir, args.output_dirname, args.env, qos) + if os.path.exists(cur_dir): + shutil.rmtree(cur_dir) + if not os.path.exists(cur_dir): + os.makedirs(cur_dir) + + with open(f'{args.base_dir}/{args.output_dirname}/{args.env}/log.txt', "r") as output_namefile,\ + open(f'{args.base_dir}/{args.output_dirname}/{args.env}/err.txt', "r") as error_namefile: + logs = output_namefile.read().splitlines() + errs = error_namefile.read().splitlines() + + with open(f'{args.base_dir}/{args.output_dirname}/{args.env}/log.txt', "w") as output_namefile,\ + open(f'{args.base_dir}/{args.output_dirname}/{args.env}/err.txt', "w") as error_namefile: + for i,log in enumerate(logs): + qos_idx = math.floor(i/math.ceil(n_jobs/len(args.qos))) + folder, basename = os.path.split(log) + new_log_name = os.path.join(folder, args.qos[qos_idx], basename) + folder, basename = os.path.split(errs[i]) + new_err_name = os.path.join(folder, args.qos[qos_idx], basename) + output_namefile.write(f'{new_log_name}\n') + error_namefile.write(f'{new_err_name}\n') + + + +########################################################################### +#slurm_script_path = os.path.join(output_dir, '%s.slurm' % name) +id = args.env.split('run')[-1] +filenames = [] +if len(args.qos)==1: + filenames = [f'{args.qos[0][:2]}_r{id}.slurm' if not args.filename else args.filename] +else: + for qos in args.qos: + filenames.append(f'{qos[:2]}_r{id}.slurm' if not args.filename else qos[0]+args.filename) + +print("Filenames:") +print(filenames) +slurm_script_paths = [os.path.join(output_dir, filename) for filename in filenames] +slurm_commands = ["sbatch %s" % slurm_script_path for slurm_script_path in slurm_script_paths] +shutil.copyfile(os.path.abspath(__file__), + os.path.join(output_dir, + os.path.basename(os.path.abspath(__file__)))) + + +idx = 0 +start_idx, end_idx = [], [] +for i in range(len(args.qos)): + start_idx += [idx+1] + idx += math.ceil(n_jobs/len(args.qos)) + end_idx += [min(idx, n_jobs)] + +for i,slurm_script_path in enumerate(slurm_script_paths): + print(f"writing to {slurm_script_path}") + with open(slurm_script_path, 'w') as slurmfile: + slurmfile.write("#!/bin/bash\n") + if args.max_jobs>0: + slurmfile.write(f"#SBATCH --array={start_idx[i]}-{end_idx[i]}%{args.max_jobs}\n") + else: + slurmfile.write(f"#SBATCH --array={start_idx[i]}-{end_idx[i]}\n") + slurmfile.write("#SBATCH --output=/dev/null\n") + slurmfile.write("#SBATCH --error=/dev/null\n") + slurmfile.write("#SBATCH --requeue\n") + args = check_qos(args) + + default_include_list = [] + default_exclude_list = [] + if args.qos[i] == "scav": + if "vulcan" in args.partition: + slurmfile.write("#SBATCH --account=vulcan\n") + slurmfile.write("#SBATCH --partition=vulcan-scavenger\n") + slurmfile.write("#SBATCH --qos=vulcan-scavenger\n") + default_exclude_list = ["janus[02-04]"] + elif "nexus" in args.partition: + slurmfile.write("#SBATCH --account=scavenger\n") + slurmfile.write("#SBATCH --partition=scavenger\n") + slurmfile.write("#SBATCH --qos=scavenger\n") + elif "cml" in args.partition: + slurmfile.write("#SBATCH --account=cml-abhinav\n") + slurmfile.write("#SBATCH --partition=cml-scavenger\n") + slurmfile.write("#SBATCH --qos=cml-scavenger\n") + elif args.qos[i] == "high" or args.qos[i] == "medium" or args.qos[i] == "default": + if "vulcan" in args.partition: + slurmfile.write("#SBATCH --account=vulcan-abhinav\n") + slurmfile.write("#SBATCH --partition=vulcan-ampere\n") + slurmfile.write(f"#SBATCH --qos=vulcan-{args.qos[i]}\n") + default_exclude_list = ["janus[02-04]"] + elif "nexus" in args.partition: + slurmfile.write("#SBATCH --account=nexus\n") + slurmfile.write(f"#SBATCH --qos={args.qos[i]}\n") + elif "cml" in args.partition: + slurmfile.write("#SBATCH --account=cml-abhinav\n") + slurmfile.write("#SBATCH --partition=cml-dpart\n") + slurmfile.write(f"#SBATCH --qos=cml-{args.qos[i]}\n") + + slurmfile.write("#SBATCH --time=%d:00:00\n" % args.nhrs) + slurmfile.write("#SBATCH --cpus-per-task=%d\n" % args.cores) + slurmfile.write("#SBATCH --mem=%dG\n" % args.mem) + + + if not args.gpu is None: + if len(args.gpu_type)==1: + if 'any' in args.gpu_type: + slurmfile.write("#SBATCH --gres=gpu:%d\n" % args.gpu) + elif "rtx2080" in args.gpu_type: + slurmfile.write("#SBATCH --gres=gpu:rtx2080ti:%d\n" % args.gpu) + elif "gtx" in args.gpu_type: + slurmfile.write("#SBATCH --gres=gpu:gtx1080ti:%d\n" % args.gpu) + elif "p6000" in args.gpu_type: + slurmfile.write("#SBATCH --gres=gpu:p6000:%d\n" % args.gpu) + elif "a4000" in args.gpu_type: + slurmfile.write("#SBATCH --gres=gpu:rtxa4000:%d\n" % args.gpu) + elif "a5000" in args.gpu_type: + slurmfile.write("#SBATCH --gres=gpu:rtxa5000:%d\n" % args.gpu) + elif "a6000" in args.gpu_type: + slurmfile.write("#SBATCH --gres=gpu:rtxa6000:%d\n" % args.gpu) + else: + assert len(args.gpu_type)>1 + slurmfile.write("#SBATCH --gres=gpu:%d\n" % args.gpu) + # slurmfile.write(get_include_string(args.gpu_type,default_include_list)) + slurmfile.write(get_exclude_string(args.gpu_type,default_exclude_list)) + else: + raise ValueError("Specify the number of gpus") + + slurmfile.write("\n") + if "vulcan" in socket.gethostname() or "nexus" in socket.gethostname(): + slurmfile.write(f"cd {root}") #TODO + # slurmfile.write('conda activate {env}\n') #TODO + slurmfile.write('source ~/.bashrc') + slurmfile.write("module load cuda\n") + slurmfile.write('micromamba activate MMQ_LLAVA\n') + + num_exps = 1 + for n in reversed(range(num_exps)): + slurmfile.write(f"srun --output=$(head -n $SLURM_ARRAY_TASK_ID {args.base_dir}/{args.output_dirname}/{args.env}/log.txt | tail -n 1) $(head -n $(expr {num_exps} \* $SLURM_ARRAY_TASK_ID - {n}) {args.base_dir}/{args.output_dirname}/{args.env}/now.txt | tail -n 1)\n") + slurmfile.write("\n") + +for i,slurm_command in enumerate(slurm_commands): + print(slurm_command) + print("Running on {}, with {} gpus, {} cores, {} mem for {} hour".format(args.qos[i], args.gpu, args.cores, args.mem , args.nhrs)) + +if not args.dryrun: + for slurm_command in slurm_commands: + os.system("%s &" % slurm_command) diff --git a/llava_runs/multi_sbatch_awq_vqav2.py b/llava_runs/multi_sbatch_awq_vqav2.py new file mode 100644 index 0000000..e5f8d70 --- /dev/null +++ b/llava_runs/multi_sbatch_awq_vqav2.py @@ -0,0 +1,384 @@ +import os +from datetime import datetime +import argparse +import shutil +import math +import time +import socket +import itertools +import subprocess + + +def run(cmd): + return subprocess.check_output(cmd, shell=True).decode('UTF-8').splitlines() + +def present_in_list(string, gpu_list): + return any([x in string for x in gpu_list]) + +def split(a, n): + k, m = divmod(len(a), n) + return (a[i*k+min(i, m):(i+1)*k+min(i+1, m)] for i in range(n)) + +def get_exclude_string(gpu_list, default_exclude=None): + if gpu_list[0] == 'any': + if default_exclude is None: + return '' + else: + return '#SBATCH --exclude='+','.join(default_exclude) + memdata = run('sinfo -O nodehost,gres -h') + superset = set([x.split()[0] for x in memdata]) + blacklist = [] + for x in memdata: + nodehost, gres = x.strip().split() + if present_in_list(gres, gpu_list): + blacklist.append(nodehost) + + exclude_list = superset - set(blacklist) + if default_exclude: + exclude_list = exclude_list.union(set(default_exclude)) + exclude_string = ','.join(sorted(exclude_list)) + if exclude_string: + exclude_string = '#SBATCH --exclude='+exclude_string+'\n' + return exclude_string + else: + return '' + +def get_include_string(gpu_list, default_include=None): + if gpu_list[0] == 'any': + raise Exception("That's too much, man! (It's a Bojack reference. Watch it if you haven't already, you degenerate)") + memdata = run('sinfo -O nodehost,gres -h') + include_list = [] + for x in memdata: + nodehost, gres = x.strip().split() + if present_in_list(gres, gpu_list): + include_list.append(nodehost) + include_string = ','.join(sorted(include_list)) + if include_string: + include_string = '#SBATCH --nodelist='+include_string+'\n' + return include_string + else: + return '' + +# Function to chec for validity of QOS +#TODO: Add time check for QOS + +qos_dict = { + "scav" : {"nhrs" : 72, "cores": 32, "mem":256}, + "high" : {"gpu":4, "cores": 16, "mem":128, "nhrs": 36}, + "medium" : {"gpu":2, "cores": 8, "mem":64, "nhrs": 72}, + "default" : {"gpu":1, "cores": 4, "mem":32, "nhrs": 168}} + + +def check_qos(args): + + for qos in args.qos: + for key, max_value in qos_dict[qos].items(): + val_from_args = getattr(args, key) + if val_from_args != None: + if val_from_args > max_value: + raise ValueError("Invalid parameter for {} for {}".format(key, qos)) + else: + setattr(args, key, max_value) + return args + + +#TODO: Add day funtionality too +parser = argparse.ArgumentParser() +parser.add_argument('--nhrs', type=int, default=None) +parser.add_argument('--base-dir', default=f'{os.getcwd()}') +parser.add_argument('--output-dirname', default='outputs') +parser.add_argument('--partition', default='vulcan', choices=['vulcan','cml','nexus']) +parser.add_argument('--dryrun', action='store_true') +parser.add_argument('--qos', default=None, type=str, nargs='*', help='Qos to run') +parser.add_argument('--env', type=str, help = "Set the name of the dir you want to dump") +parser.add_argument('--gpu', default=None, type=int, help='Number of gpus') +parser.add_argument('--gpu-type', type=str, help='Type of gpu to use (can be multiple)', default=['any'], + choices=['any','p6000','gtx','rtx2080','a4000','a5000','a6000'], nargs='*') +parser.add_argument('--cores', default=None, type=int, help='Number of cpu cores') +parser.add_argument('--mem', default=None, type=int, help='RAM in G') +parser.add_argument('--single', action='store_true') +parser.add_argument('--filename', default=None, type=str, help='Slurm file name') +parser.add_argument('--max_jobs', default=80, type=int, help='Maximum number of jobs running in parallel') +parser.add_argument('--offset', default=0, type=int, help='Offset') +parser.add_argument('--batchsize', default=500, type=int, help='Offset') + +args = parser.parse_args() + +if args.filename is None: + args.filename = args.env + +output_dir = os.path.join(args.base_dir, args.output_dirname, args.env) +if os.path.exists(output_dir): + shutil.rmtree(output_dir) +if not os.path.exists(output_dir): + os.makedirs(output_dir) +print("Output Directory: %s" % output_dir) + +if "nexus" in socket.gethostname(): + root = 'root' ## TODO +else: + raise Exception("Not on nexus") + + +# Define parameters for grid search +bit_options = [2,3,4,5,6,8,16] +params = { + 'task': ['--task', 't', ['vqav2']], + 'seed': ['--seed', 's', ['0']], + 'output_dir': ['--output_dir', '', ['/fs/cfar-projects/low-bit-vision/llava/awq/vqav2']], + 'vision_bits': ['--vision-bits', 'vb', bit_options], + 'language_bits': ['--language-bits', 'lb', bit_options] +} +####################################################################### + +class Argument(object): + + def __init__(self, name, cmd_line, string_id, val): + self.name = name + self.val = val + if isinstance(val,list): + if len(val) == 0: + + if isinstance(cmd_line, list): + self.cmd_string = '' + for cur_line in cmd_line: + self.cmd_string += ' '+cur_line+' []' + else: + self.cmd_string = ' '+cmd_line+' []' + else: + if isinstance(cmd_line, list): + self.cmd_string = '' + for cur_line in cmd_line: + self.cmd_string += ' '+cur_line+' '+','.join([str(e) for e in val]) + else: + self.cmd_string = ' '+cmd_line+' '+','.join([str(e) for e in val]) + else: + + if isinstance(cmd_line, list): + self.cmd_string = '' + for cur_line in cmd_line: + self.cmd_string += ' '+cur_line+' '+str(val) + else: + self.cmd_string = ' '+cmd_line+' '+str(val) + if isinstance(val,bool): + if not val: + self.job_string = '' + self.cmd_string = '' + self.name = '' + else: + self.job_string = '_'+string_id if string_id else '' + if isinstance(cmd_line, list): + self.cmd_string = '' + for cur_line in cmd_line: + self.cmd_string += ' '+cur_line+' ' + self.cmd_string = ' '+cmd_line+' ' + elif isinstance(val,list): + self.job_string = '_'+string_id+'_'.join([str(v) for v in val]) + else: + self.job_string = '_'+string_id+str(val) + if string_id == 'none': + self.job_string = '' + + def copy(self): + new_arg = Argument(self.name, cmd_line='', string_id='', val=self.val) + new_arg.cmd_string = self.cmd_string + new_arg.job_string = self.job_string + return new_arg + + +os.makedirs(f'{args.base_dir}/{args.output_dirname}/{args.env}',exist_ok=True) +n_jobs = 0 +# Making text files which will store the python command to run, stdout, and error if any +with open(f'{args.base_dir}/{args.output_dirname}/{args.env}/now.txt', "w") as nowfile,\ + open(f'{args.base_dir}/{args.output_dirname}/{args.env}/log.txt', "w") as output_namefile,\ + open(f'{args.base_dir}/{args.output_dirname}/{args.env}/err.txt', "w") as error_namefile,\ + open(f'{args.base_dir}/{args.output_dirname}/{args.env}/name.txt', "w") as namefile: + + arg_list = [] + for key, param in params.items(): + cur_arg_list = [] + if not isinstance(param[2],list): + param[2] = [param[2]] + + if len(param[2])>1 and key!="dataset": + assert param[1]!='none', f"{param[0]} set to none with multiple values!" + + for value in param[2]: + cur_arg_list.append(Argument(key, param[0],param[1], value)) + + arg_list.append(cur_arg_list) + + arg_list = list(itertools.product(*arg_list)) + n_jobs = 0 + for idx,job_args in enumerate(arg_list): + + # Allows modification of current set of args + job_args = {arg.name:arg.copy() for arg in job_args} + + job_string = '' + python_cmd = 'python awq_llava.py' + for arg_name, arg in job_args.items(): + python_cmd += arg.cmd_string + job_string += arg.job_string + + job_string = f'{n_jobs}_'+job_string + cmd_line_str = python_cmd + + # cmd_line_str = python_cmd + + n_jobs += 1 + + nowfile.write(f'{cmd_line_str}\n') + namefile.write(f'{(os.path.join(output_dir, job_string))}.log\n') + output_namefile.write(f'{(os.path.join(output_dir, job_string))}_log.txt\n') + error_namefile.write(f'{(os.path.join(output_dir, job_string))}_error.txt\n') + if args.single: + break + +print(f"\nGenerated {n_jobs} jobs for all bit combinations") +########################################################################### +if len(args.qos)>1: + splits = split(range(0,n_jobs), len(args.qos)) + for qos in args.qos: + cur_dir = os.path.join(args.base_dir, args.output_dirname, args.env, qos) + if os.path.exists(cur_dir): + shutil.rmtree(cur_dir) + if not os.path.exists(cur_dir): + os.makedirs(cur_dir) + + with open(f'{args.base_dir}/{args.output_dirname}/{args.env}/log.txt', "r") as output_namefile,\ + open(f'{args.base_dir}/{args.output_dirname}/{args.env}/err.txt', "r") as error_namefile: + logs = output_namefile.read().splitlines() + errs = error_namefile.read().splitlines() + + with open(f'{args.base_dir}/{args.output_dirname}/{args.env}/log.txt', "w") as output_namefile,\ + open(f'{args.base_dir}/{args.output_dirname}/{args.env}/err.txt', "w") as error_namefile: + for i,log in enumerate(logs): + qos_idx = math.floor(i/math.ceil(n_jobs/len(args.qos))) + folder, basename = os.path.split(log) + new_log_name = os.path.join(folder, args.qos[qos_idx], basename) + folder, basename = os.path.split(errs[i]) + new_err_name = os.path.join(folder, args.qos[qos_idx], basename) + output_namefile.write(f'{new_log_name}\n') + error_namefile.write(f'{new_err_name}\n') + + + +########################################################################### +#slurm_script_path = os.path.join(output_dir, '%s.slurm' % name) +id = args.env.split('run')[-1] +filenames = [] +if len(args.qos)==1: + filenames = [f'{args.qos[0][:2]}_r{id}.slurm' if not args.filename else args.filename] +else: + for qos in args.qos: + filenames.append(f'{qos[:2]}_r{id}.slurm' if not args.filename else qos[0]+args.filename) + +print("Filenames:") +print(filenames) +slurm_script_paths = [os.path.join(output_dir, filename) for filename in filenames] +slurm_commands = ["sbatch %s" % slurm_script_path for slurm_script_path in slurm_script_paths] +shutil.copyfile(os.path.abspath(__file__), + os.path.join(output_dir, + os.path.basename(os.path.abspath(__file__)))) + + +idx = 0 +start_idx, end_idx = [], [] +for i in range(len(args.qos)): + start_idx += [idx+1] + idx += math.ceil(n_jobs/len(args.qos)) + end_idx += [min(idx, n_jobs)] + +for i,slurm_script_path in enumerate(slurm_script_paths): + print(f"writing to {slurm_script_path}") + with open(slurm_script_path, 'w') as slurmfile: + slurmfile.write("#!/bin/bash\n") + if args.max_jobs>0: + slurmfile.write(f"#SBATCH --array={start_idx[i]}-{end_idx[i]}%{args.max_jobs}\n") + else: + slurmfile.write(f"#SBATCH --array={start_idx[i]}-{end_idx[i]}\n") + slurmfile.write("#SBATCH --output=/dev/null\n") + slurmfile.write("#SBATCH --error=/dev/null\n") + slurmfile.write("#SBATCH --requeue\n") + args = check_qos(args) + + default_include_list = [] + default_exclude_list = [] + if args.qos[i] == "scav": + if "vulcan" in args.partition: + slurmfile.write("#SBATCH --account=vulcan\n") + slurmfile.write("#SBATCH --partition=vulcan-scavenger\n") + slurmfile.write("#SBATCH --qos=vulcan-scavenger\n") + default_exclude_list = ["janus[02-04]"] + elif "nexus" in args.partition: + slurmfile.write("#SBATCH --account=scavenger\n") + slurmfile.write("#SBATCH --partition=scavenger\n") + slurmfile.write("#SBATCH --qos=scavenger\n") + elif "cml" in args.partition: + slurmfile.write("#SBATCH --account=cml-abhinav\n") + slurmfile.write("#SBATCH --partition=cml-scavenger\n") + slurmfile.write("#SBATCH --qos=cml-scavenger\n") + elif args.qos[i] == "high" or args.qos[i] == "medium" or args.qos[i] == "default": + if "vulcan" in args.partition: + slurmfile.write("#SBATCH --account=vulcan-abhinav\n") + slurmfile.write("#SBATCH --partition=vulcan-ampere\n") + slurmfile.write(f"#SBATCH --qos=vulcan-{args.qos[i]}\n") + default_exclude_list = ["janus[02-04]"] + elif "nexus" in args.partition: + slurmfile.write("#SBATCH --account=nexus\n") + slurmfile.write(f"#SBATCH --qos={args.qos[i]}\n") + elif "cml" in args.partition: + slurmfile.write("#SBATCH --account=cml-abhinav\n") + slurmfile.write("#SBATCH --partition=cml-dpart\n") + slurmfile.write(f"#SBATCH --qos=cml-{args.qos[i]}\n") + + slurmfile.write("#SBATCH --time=%d:00:00\n" % args.nhrs) + slurmfile.write("#SBATCH --cpus-per-task=%d\n" % args.cores) + slurmfile.write("#SBATCH --mem=%dG\n" % args.mem) + + + if not args.gpu is None: + if len(args.gpu_type)==1: + if 'any' in args.gpu_type: + slurmfile.write("#SBATCH --gres=gpu:%d\n" % args.gpu) + elif "rtx2080" in args.gpu_type: + slurmfile.write("#SBATCH --gres=gpu:rtx2080ti:%d\n" % args.gpu) + elif "gtx" in args.gpu_type: + slurmfile.write("#SBATCH --gres=gpu:gtx1080ti:%d\n" % args.gpu) + elif "p6000" in args.gpu_type: + slurmfile.write("#SBATCH --gres=gpu:p6000:%d\n" % args.gpu) + elif "a4000" in args.gpu_type: + slurmfile.write("#SBATCH --gres=gpu:rtxa4000:%d\n" % args.gpu) + elif "a5000" in args.gpu_type: + slurmfile.write("#SBATCH --gres=gpu:rtxa5000:%d\n" % args.gpu) + elif "a6000" in args.gpu_type: + slurmfile.write("#SBATCH --gres=gpu:rtxa6000:%d\n" % args.gpu) + else: + assert len(args.gpu_type)>1 + slurmfile.write("#SBATCH --gres=gpu:%d\n" % args.gpu) + # slurmfile.write(get_include_string(args.gpu_type,default_include_list)) + slurmfile.write(get_exclude_string(args.gpu_type,default_exclude_list)) + else: + raise ValueError("Specify the number of gpus") + + slurmfile.write("\n") + if "vulcan" in socket.gethostname() or "nexus" in socket.gethostname(): + slurmfile.write(f"cd {root}") #TODO + # slurmfile.write('conda activate {env}\n') #TODO + slurmfile.write('source ~/.bashrc') + slurmfile.write("module load cuda\n") + slurmfile.write('micromamba activate MMQ_LLAVA\n') + + num_exps = 1 + for n in reversed(range(num_exps)): + slurmfile.write(f"srun --output=$(head -n $SLURM_ARRAY_TASK_ID {args.base_dir}/{args.output_dirname}/{args.env}/log.txt | tail -n 1) $(head -n $(expr {num_exps} \* $SLURM_ARRAY_TASK_ID - {n}) {args.base_dir}/{args.output_dirname}/{args.env}/now.txt | tail -n 1)\n") + slurmfile.write("\n") + +for i,slurm_command in enumerate(slurm_commands): + print(slurm_command) + print("Running on {}, with {} gpus, {} cores, {} mem for {} hour".format(args.qos[i], args.gpu, args.cores, args.mem , args.nhrs)) + +if not args.dryrun: + for slurm_command in slurm_commands: + os.system("%s &" % slurm_command) diff --git a/llava_runs/multi_sbatch_gptq_gqa.py b/llava_runs/multi_sbatch_gptq_gqa.py new file mode 100644 index 0000000..38b4383 --- /dev/null +++ b/llava_runs/multi_sbatch_gptq_gqa.py @@ -0,0 +1,384 @@ +import os +from datetime import datetime +import argparse +import shutil +import math +import time +import socket +import itertools +import subprocess + + +def run(cmd): + return subprocess.check_output(cmd, shell=True).decode('UTF-8').splitlines() + +def present_in_list(string, gpu_list): + return any([x in string for x in gpu_list]) + +def split(a, n): + k, m = divmod(len(a), n) + return (a[i*k+min(i, m):(i+1)*k+min(i+1, m)] for i in range(n)) + +def get_exclude_string(gpu_list, default_exclude=None): + if gpu_list[0] == 'any': + if default_exclude is None: + return '' + else: + return '#SBATCH --exclude='+','.join(default_exclude) + memdata = run('sinfo -O nodehost,gres -h') + superset = set([x.split()[0] for x in memdata]) + blacklist = [] + for x in memdata: + nodehost, gres = x.strip().split() + if present_in_list(gres, gpu_list): + blacklist.append(nodehost) + + exclude_list = superset - set(blacklist) + if default_exclude: + exclude_list = exclude_list.union(set(default_exclude)) + exclude_string = ','.join(sorted(exclude_list)) + if exclude_string: + exclude_string = '#SBATCH --exclude='+exclude_string+'\n' + return exclude_string + else: + return '' + +def get_include_string(gpu_list, default_include=None): + if gpu_list[0] == 'any': + raise Exception("That's too much, man! (It's a Bojack reference. Watch it if you haven't already, you degenerate)") + memdata = run('sinfo -O nodehost,gres -h') + include_list = [] + for x in memdata: + nodehost, gres = x.strip().split() + if present_in_list(gres, gpu_list): + include_list.append(nodehost) + include_string = ','.join(sorted(include_list)) + if include_string: + include_string = '#SBATCH --nodelist='+include_string+'\n' + return include_string + else: + return '' + +# Function to chec for validity of QOS +#TODO: Add time check for QOS + +qos_dict = { + "scav" : {"nhrs" : 72, "cores": 32, "mem":256}, + "high" : {"gpu":4, "cores": 16, "mem":128, "nhrs": 36}, + "medium" : {"gpu":2, "cores": 8, "mem":64, "nhrs": 72}, + "default" : {"gpu":1, "cores": 4, "mem":32, "nhrs": 168}} + + +def check_qos(args): + + for qos in args.qos: + for key, max_value in qos_dict[qos].items(): + val_from_args = getattr(args, key) + if val_from_args != None: + if val_from_args > max_value: + raise ValueError("Invalid parameter for {} for {}".format(key, qos)) + else: + setattr(args, key, max_value) + return args + + +#TODO: Add day funtionality too +parser = argparse.ArgumentParser() +parser.add_argument('--nhrs', type=int, default=None) +parser.add_argument('--base-dir', default=f'{os.getcwd()}') +parser.add_argument('--output-dirname', default='outputs') +parser.add_argument('--partition', default='vulcan', choices=['vulcan','cml','nexus']) +parser.add_argument('--dryrun', action='store_true') +parser.add_argument('--qos', default=None, type=str, nargs='*', help='Qos to run') +parser.add_argument('--env', type=str, help = "Set the name of the dir you want to dump") +parser.add_argument('--gpu', default=None, type=int, help='Number of gpus') +parser.add_argument('--gpu-type', type=str, help='Type of gpu to use (can be multiple)', default=['any'], + choices=['any','p6000','gtx','rtx2080','a4000','a5000','a6000'], nargs='*') +parser.add_argument('--cores', default=None, type=int, help='Number of cpu cores') +parser.add_argument('--mem', default=None, type=int, help='RAM in G') +parser.add_argument('--single', action='store_true') +parser.add_argument('--filename', default=None, type=str, help='Slurm file name') +parser.add_argument('--max_jobs', default=80, type=int, help='Maximum number of jobs running in parallel') +parser.add_argument('--offset', default=0, type=int, help='Offset') +parser.add_argument('--batchsize', default=500, type=int, help='Offset') + +args = parser.parse_args() + +if args.filename is None: + args.filename = args.env + +output_dir = os.path.join(args.base_dir, args.output_dirname, args.env) +if os.path.exists(output_dir): + shutil.rmtree(output_dir) +if not os.path.exists(output_dir): + os.makedirs(output_dir) +print("Output Directory: %s" % output_dir) + +if "nexus" in socket.gethostname(): + root = 'root' ## TODO +else: + raise Exception("Not on nexus") + + +# Define parameters for grid search +bit_options = [2,3,4,5,6,8,16] +params = { + 'task': ['--task', 't', ['gqa']], + 'seed': ['--seed', 's', ['0']], + 'output_dir': ['--output_dir', '', ['/fs/cfar-projects/low-bit-vision/llava/gptq/gqa']], + 'vision_bits': ['--vision-bits', 'vb', bit_options], + 'language_bits': ['--language-bits', 'lb', bit_options] +} +####################################################################### + +class Argument(object): + + def __init__(self, name, cmd_line, string_id, val): + self.name = name + self.val = val + if isinstance(val,list): + if len(val) == 0: + + if isinstance(cmd_line, list): + self.cmd_string = '' + for cur_line in cmd_line: + self.cmd_string += ' '+cur_line+' []' + else: + self.cmd_string = ' '+cmd_line+' []' + else: + if isinstance(cmd_line, list): + self.cmd_string = '' + for cur_line in cmd_line: + self.cmd_string += ' '+cur_line+' '+','.join([str(e) for e in val]) + else: + self.cmd_string = ' '+cmd_line+' '+','.join([str(e) for e in val]) + else: + + if isinstance(cmd_line, list): + self.cmd_string = '' + for cur_line in cmd_line: + self.cmd_string += ' '+cur_line+' '+str(val) + else: + self.cmd_string = ' '+cmd_line+' '+str(val) + if isinstance(val,bool): + if not val: + self.job_string = '' + self.cmd_string = '' + self.name = '' + else: + self.job_string = '_'+string_id if string_id else '' + if isinstance(cmd_line, list): + self.cmd_string = '' + for cur_line in cmd_line: + self.cmd_string += ' '+cur_line+' ' + self.cmd_string = ' '+cmd_line+' ' + elif isinstance(val,list): + self.job_string = '_'+string_id+'_'.join([str(v) for v in val]) + else: + self.job_string = '_'+string_id+str(val) + if string_id == 'none': + self.job_string = '' + + def copy(self): + new_arg = Argument(self.name, cmd_line='', string_id='', val=self.val) + new_arg.cmd_string = self.cmd_string + new_arg.job_string = self.job_string + return new_arg + + +os.makedirs(f'{args.base_dir}/{args.output_dirname}/{args.env}',exist_ok=True) +n_jobs = 0 +# Making text files which will store the python command to run, stdout, and error if any +with open(f'{args.base_dir}/{args.output_dirname}/{args.env}/now.txt', "w") as nowfile,\ + open(f'{args.base_dir}/{args.output_dirname}/{args.env}/log.txt', "w") as output_namefile,\ + open(f'{args.base_dir}/{args.output_dirname}/{args.env}/err.txt', "w") as error_namefile,\ + open(f'{args.base_dir}/{args.output_dirname}/{args.env}/name.txt', "w") as namefile: + + arg_list = [] + for key, param in params.items(): + cur_arg_list = [] + if not isinstance(param[2],list): + param[2] = [param[2]] + + if len(param[2])>1 and key!="dataset": + assert param[1]!='none', f"{param[0]} set to none with multiple values!" + + for value in param[2]: + cur_arg_list.append(Argument(key, param[0],param[1], value)) + + arg_list.append(cur_arg_list) + + arg_list = list(itertools.product(*arg_list)) + n_jobs = 0 + for idx,job_args in enumerate(arg_list): + + # Allows modification of current set of args + job_args = {arg.name:arg.copy() for arg in job_args} + + job_string = '' + python_cmd = 'python gptq_llava.py' + for arg_name, arg in job_args.items(): + python_cmd += arg.cmd_string + job_string += arg.job_string + + job_string = f'{n_jobs}_'+job_string + cmd_line_str = python_cmd + + # cmd_line_str = python_cmd + + n_jobs += 1 + + nowfile.write(f'{cmd_line_str}\n') + namefile.write(f'{(os.path.join(output_dir, job_string))}.log\n') + output_namefile.write(f'{(os.path.join(output_dir, job_string))}_log.txt\n') + error_namefile.write(f'{(os.path.join(output_dir, job_string))}_error.txt\n') + if args.single: + break + +print(f"\nGenerated {n_jobs} jobs for all bit combinations") +########################################################################### +if len(args.qos)>1: + splits = split(range(0,n_jobs), len(args.qos)) + for qos in args.qos: + cur_dir = os.path.join(args.base_dir, args.output_dirname, args.env, qos) + if os.path.exists(cur_dir): + shutil.rmtree(cur_dir) + if not os.path.exists(cur_dir): + os.makedirs(cur_dir) + + with open(f'{args.base_dir}/{args.output_dirname}/{args.env}/log.txt', "r") as output_namefile,\ + open(f'{args.base_dir}/{args.output_dirname}/{args.env}/err.txt', "r") as error_namefile: + logs = output_namefile.read().splitlines() + errs = error_namefile.read().splitlines() + + with open(f'{args.base_dir}/{args.output_dirname}/{args.env}/log.txt', "w") as output_namefile,\ + open(f'{args.base_dir}/{args.output_dirname}/{args.env}/err.txt', "w") as error_namefile: + for i,log in enumerate(logs): + qos_idx = math.floor(i/math.ceil(n_jobs/len(args.qos))) + folder, basename = os.path.split(log) + new_log_name = os.path.join(folder, args.qos[qos_idx], basename) + folder, basename = os.path.split(errs[i]) + new_err_name = os.path.join(folder, args.qos[qos_idx], basename) + output_namefile.write(f'{new_log_name}\n') + error_namefile.write(f'{new_err_name}\n') + + + +########################################################################### +#slurm_script_path = os.path.join(output_dir, '%s.slurm' % name) +id = args.env.split('run')[-1] +filenames = [] +if len(args.qos)==1: + filenames = [f'{args.qos[0][:2]}_r{id}.slurm' if not args.filename else args.filename] +else: + for qos in args.qos: + filenames.append(f'{qos[:2]}_r{id}.slurm' if not args.filename else qos[0]+args.filename) + +print("Filenames:") +print(filenames) +slurm_script_paths = [os.path.join(output_dir, filename) for filename in filenames] +slurm_commands = ["sbatch %s" % slurm_script_path for slurm_script_path in slurm_script_paths] +shutil.copyfile(os.path.abspath(__file__), + os.path.join(output_dir, + os.path.basename(os.path.abspath(__file__)))) + + +idx = 0 +start_idx, end_idx = [], [] +for i in range(len(args.qos)): + start_idx += [idx+1] + idx += math.ceil(n_jobs/len(args.qos)) + end_idx += [min(idx, n_jobs)] + +for i,slurm_script_path in enumerate(slurm_script_paths): + print(f"writing to {slurm_script_path}") + with open(slurm_script_path, 'w') as slurmfile: + slurmfile.write("#!/bin/bash\n") + if args.max_jobs>0: + slurmfile.write(f"#SBATCH --array={start_idx[i]}-{end_idx[i]}%{args.max_jobs}\n") + else: + slurmfile.write(f"#SBATCH --array={start_idx[i]}-{end_idx[i]}\n") + slurmfile.write("#SBATCH --output=/dev/null\n") + slurmfile.write("#SBATCH --error=/dev/null\n") + slurmfile.write("#SBATCH --requeue\n") + args = check_qos(args) + + default_include_list = [] + default_exclude_list = [] + if args.qos[i] == "scav": + if "vulcan" in args.partition: + slurmfile.write("#SBATCH --account=vulcan\n") + slurmfile.write("#SBATCH --partition=vulcan-scavenger\n") + slurmfile.write("#SBATCH --qos=vulcan-scavenger\n") + default_exclude_list = ["janus[02-04]"] + elif "nexus" in args.partition: + slurmfile.write("#SBATCH --account=scavenger\n") + slurmfile.write("#SBATCH --partition=scavenger\n") + slurmfile.write("#SBATCH --qos=scavenger\n") + elif "cml" in args.partition: + slurmfile.write("#SBATCH --account=cml-abhinav\n") + slurmfile.write("#SBATCH --partition=cml-scavenger\n") + slurmfile.write("#SBATCH --qos=cml-scavenger\n") + elif args.qos[i] == "high" or args.qos[i] == "medium" or args.qos[i] == "default": + if "vulcan" in args.partition: + slurmfile.write("#SBATCH --account=vulcan-abhinav\n") + slurmfile.write("#SBATCH --partition=vulcan-ampere\n") + slurmfile.write(f"#SBATCH --qos=vulcan-{args.qos[i]}\n") + default_exclude_list = ["janus[02-04]"] + elif "nexus" in args.partition: + slurmfile.write("#SBATCH --account=nexus\n") + slurmfile.write(f"#SBATCH --qos={args.qos[i]}\n") + elif "cml" in args.partition: + slurmfile.write("#SBATCH --account=cml-abhinav\n") + slurmfile.write("#SBATCH --partition=cml-dpart\n") + slurmfile.write(f"#SBATCH --qos=cml-{args.qos[i]}\n") + + slurmfile.write("#SBATCH --time=%d:00:00\n" % args.nhrs) + slurmfile.write("#SBATCH --cpus-per-task=%d\n" % args.cores) + slurmfile.write("#SBATCH --mem=%dG\n" % args.mem) + + + if not args.gpu is None: + if len(args.gpu_type)==1: + if 'any' in args.gpu_type: + slurmfile.write("#SBATCH --gres=gpu:%d\n" % args.gpu) + elif "rtx2080" in args.gpu_type: + slurmfile.write("#SBATCH --gres=gpu:rtx2080ti:%d\n" % args.gpu) + elif "gtx" in args.gpu_type: + slurmfile.write("#SBATCH --gres=gpu:gtx1080ti:%d\n" % args.gpu) + elif "p6000" in args.gpu_type: + slurmfile.write("#SBATCH --gres=gpu:p6000:%d\n" % args.gpu) + elif "a4000" in args.gpu_type: + slurmfile.write("#SBATCH --gres=gpu:rtxa4000:%d\n" % args.gpu) + elif "a5000" in args.gpu_type: + slurmfile.write("#SBATCH --gres=gpu:rtxa5000:%d\n" % args.gpu) + elif "a6000" in args.gpu_type: + slurmfile.write("#SBATCH --gres=gpu:rtxa6000:%d\n" % args.gpu) + else: + assert len(args.gpu_type)>1 + slurmfile.write("#SBATCH --gres=gpu:%d\n" % args.gpu) + # slurmfile.write(get_include_string(args.gpu_type,default_include_list)) + slurmfile.write(get_exclude_string(args.gpu_type,default_exclude_list)) + else: + raise ValueError("Specify the number of gpus") + + slurmfile.write("\n") + if "vulcan" in socket.gethostname() or "nexus" in socket.gethostname(): + slurmfile.write(f"cd {root}") #TODO + # slurmfile.write('conda activate {env}\n') #TODO + slurmfile.write('source ~/.bashrc') + slurmfile.write("module load cuda\n") + slurmfile.write('micromamba activate MMQ_LLAVA\n') + + num_exps = 1 + for n in reversed(range(num_exps)): + slurmfile.write(f"srun --output=$(head -n $SLURM_ARRAY_TASK_ID {args.base_dir}/{args.output_dirname}/{args.env}/log.txt | tail -n 1) $(head -n $(expr {num_exps} \* $SLURM_ARRAY_TASK_ID - {n}) {args.base_dir}/{args.output_dirname}/{args.env}/now.txt | tail -n 1)\n") + slurmfile.write("\n") + +for i,slurm_command in enumerate(slurm_commands): + print(slurm_command) + print("Running on {}, with {} gpus, {} cores, {} mem for {} hour".format(args.qos[i], args.gpu, args.cores, args.mem , args.nhrs)) + +if not args.dryrun: + for slurm_command in slurm_commands: + os.system("%s &" % slurm_command) diff --git a/llava_runs/multi_sbatch_gptq_vqav2.py b/llava_runs/multi_sbatch_gptq_vqav2.py new file mode 100644 index 0000000..07f781e --- /dev/null +++ b/llava_runs/multi_sbatch_gptq_vqav2.py @@ -0,0 +1,384 @@ +import os +from datetime import datetime +import argparse +import shutil +import math +import time +import socket +import itertools +import subprocess + + +def run(cmd): + return subprocess.check_output(cmd, shell=True).decode('UTF-8').splitlines() + +def present_in_list(string, gpu_list): + return any([x in string for x in gpu_list]) + +def split(a, n): + k, m = divmod(len(a), n) + return (a[i*k+min(i, m):(i+1)*k+min(i+1, m)] for i in range(n)) + +def get_exclude_string(gpu_list, default_exclude=None): + if gpu_list[0] == 'any': + if default_exclude is None: + return '' + else: + return '#SBATCH --exclude='+','.join(default_exclude) + memdata = run('sinfo -O nodehost,gres -h') + superset = set([x.split()[0] for x in memdata]) + blacklist = [] + for x in memdata: + nodehost, gres = x.strip().split() + if present_in_list(gres, gpu_list): + blacklist.append(nodehost) + + exclude_list = superset - set(blacklist) + if default_exclude: + exclude_list = exclude_list.union(set(default_exclude)) + exclude_string = ','.join(sorted(exclude_list)) + if exclude_string: + exclude_string = '#SBATCH --exclude='+exclude_string+'\n' + return exclude_string + else: + return '' + +def get_include_string(gpu_list, default_include=None): + if gpu_list[0] == 'any': + raise Exception("That's too much, man! (It's a Bojack reference. Watch it if you haven't already, you degenerate)") + memdata = run('sinfo -O nodehost,gres -h') + include_list = [] + for x in memdata: + nodehost, gres = x.strip().split() + if present_in_list(gres, gpu_list): + include_list.append(nodehost) + include_string = ','.join(sorted(include_list)) + if include_string: + include_string = '#SBATCH --nodelist='+include_string+'\n' + return include_string + else: + return '' + +# Function to chec for validity of QOS +#TODO: Add time check for QOS + +qos_dict = { + "scav" : {"nhrs" : 72, "cores": 32, "mem":256}, + "high" : {"gpu":4, "cores": 16, "mem":128, "nhrs": 36}, + "medium" : {"gpu":2, "cores": 8, "mem":64, "nhrs": 72}, + "default" : {"gpu":1, "cores": 4, "mem":32, "nhrs": 168}} + + +def check_qos(args): + + for qos in args.qos: + for key, max_value in qos_dict[qos].items(): + val_from_args = getattr(args, key) + if val_from_args != None: + if val_from_args > max_value: + raise ValueError("Invalid parameter for {} for {}".format(key, qos)) + else: + setattr(args, key, max_value) + return args + + +#TODO: Add day funtionality too +parser = argparse.ArgumentParser() +parser.add_argument('--nhrs', type=int, default=None) +parser.add_argument('--base-dir', default=f'{os.getcwd()}') +parser.add_argument('--output-dirname', default='outputs') +parser.add_argument('--partition', default='vulcan', choices=['vulcan','cml','nexus']) +parser.add_argument('--dryrun', action='store_true') +parser.add_argument('--qos', default=None, type=str, nargs='*', help='Qos to run') +parser.add_argument('--env', type=str, help = "Set the name of the dir you want to dump") +parser.add_argument('--gpu', default=None, type=int, help='Number of gpus') +parser.add_argument('--gpu-type', type=str, help='Type of gpu to use (can be multiple)', default=['any'], + choices=['any','p6000','gtx','rtx2080','a4000','a5000','a6000'], nargs='*') +parser.add_argument('--cores', default=None, type=int, help='Number of cpu cores') +parser.add_argument('--mem', default=None, type=int, help='RAM in G') +parser.add_argument('--single', action='store_true') +parser.add_argument('--filename', default=None, type=str, help='Slurm file name') +parser.add_argument('--max_jobs', default=80, type=int, help='Maximum number of jobs running in parallel') +parser.add_argument('--offset', default=0, type=int, help='Offset') +parser.add_argument('--batchsize', default=500, type=int, help='Offset') + +args = parser.parse_args() + +if args.filename is None: + args.filename = args.env + +output_dir = os.path.join(args.base_dir, args.output_dirname, args.env) +if os.path.exists(output_dir): + shutil.rmtree(output_dir) +if not os.path.exists(output_dir): + os.makedirs(output_dir) +print("Output Directory: %s" % output_dir) + +if "nexus" in socket.gethostname(): + root = 'root' ## TODO +else: + raise Exception("Not on nexus") + + +# Define parameters for grid search +bit_options = [2,3,4,5,6,8,16] +params = { + 'task': ['--task', 't_', ['vqav2']], + 'seed': ['--seed', 's', ['0']], + 'output_dir': ['--output_dir', '', ['/fs/cfar-projects/low-bit-vision/llava/gptq/vqav2_subset']], + 'vision_bits': ['--vision-bits', 'vb', bit_options], + 'language_bits': ['--language-bits', 'lb', bit_options] +} +####################################################################### + +class Argument(object): + + def __init__(self, name, cmd_line, string_id, val): + self.name = name + self.val = val + if isinstance(val,list): + if len(val) == 0: + + if isinstance(cmd_line, list): + self.cmd_string = '' + for cur_line in cmd_line: + self.cmd_string += ' '+cur_line+' []' + else: + self.cmd_string = ' '+cmd_line+' []' + else: + if isinstance(cmd_line, list): + self.cmd_string = '' + for cur_line in cmd_line: + self.cmd_string += ' '+cur_line+' '+','.join([str(e) for e in val]) + else: + self.cmd_string = ' '+cmd_line+' '+','.join([str(e) for e in val]) + else: + + if isinstance(cmd_line, list): + self.cmd_string = '' + for cur_line in cmd_line: + self.cmd_string += ' '+cur_line+' '+str(val) + else: + self.cmd_string = ' '+cmd_line+' '+str(val) + if isinstance(val,bool): + if not val: + self.job_string = '' + self.cmd_string = '' + self.name = '' + else: + self.job_string = '_'+string_id if string_id else '' + if isinstance(cmd_line, list): + self.cmd_string = '' + for cur_line in cmd_line: + self.cmd_string += ' '+cur_line+' ' + self.cmd_string = ' '+cmd_line+' ' + elif isinstance(val,list): + self.job_string = '_'+string_id+'_'.join([str(v) for v in val]) + else: + self.job_string = '_'+string_id+str(val) + if string_id == 'none': + self.job_string = '' + + def copy(self): + new_arg = Argument(self.name, cmd_line='', string_id='', val=self.val) + new_arg.cmd_string = self.cmd_string + new_arg.job_string = self.job_string + return new_arg + + +os.makedirs(f'{args.base_dir}/{args.output_dirname}/{args.env}',exist_ok=True) +n_jobs = 0 +# Making text files which will store the python command to run, stdout, and error if any +with open(f'{args.base_dir}/{args.output_dirname}/{args.env}/now.txt', "w") as nowfile,\ + open(f'{args.base_dir}/{args.output_dirname}/{args.env}/log.txt', "w") as output_namefile,\ + open(f'{args.base_dir}/{args.output_dirname}/{args.env}/err.txt', "w") as error_namefile,\ + open(f'{args.base_dir}/{args.output_dirname}/{args.env}/name.txt', "w") as namefile: + + arg_list = [] + for key, param in params.items(): + cur_arg_list = [] + if not isinstance(param[2],list): + param[2] = [param[2]] + + if len(param[2])>1 and key!="dataset": + assert param[1]!='none', f"{param[0]} set to none with multiple values!" + + for value in param[2]: + cur_arg_list.append(Argument(key, param[0],param[1], value)) + + arg_list.append(cur_arg_list) + + arg_list = list(itertools.product(*arg_list)) + n_jobs = 0 + for idx,job_args in enumerate(arg_list): + + # Allows modification of current set of args + job_args = {arg.name:arg.copy() for arg in job_args} + + job_string = '' + python_cmd = 'python gptq_llava.py' + for arg_name, arg in job_args.items(): + python_cmd += arg.cmd_string + job_string += arg.job_string + + job_string = f'{n_jobs}_'+job_string + cmd_line_str = python_cmd + + # cmd_line_str = python_cmd + + n_jobs += 1 + + nowfile.write(f'{cmd_line_str}\n') + namefile.write(f'{(os.path.join(output_dir, job_string))}.log\n') + output_namefile.write(f'{(os.path.join(output_dir, job_string))}_log.txt\n') + error_namefile.write(f'{(os.path.join(output_dir, job_string))}_error.txt\n') + if args.single: + break + +print(f"\nGenerated {n_jobs} jobs for all bit combinations") +########################################################################### +if len(args.qos)>1: + splits = split(range(0,n_jobs), len(args.qos)) + for qos in args.qos: + cur_dir = os.path.join(args.base_dir, args.output_dirname, args.env, qos) + if os.path.exists(cur_dir): + shutil.rmtree(cur_dir) + if not os.path.exists(cur_dir): + os.makedirs(cur_dir) + + with open(f'{args.base_dir}/{args.output_dirname}/{args.env}/log.txt', "r") as output_namefile,\ + open(f'{args.base_dir}/{args.output_dirname}/{args.env}/err.txt', "r") as error_namefile: + logs = output_namefile.read().splitlines() + errs = error_namefile.read().splitlines() + + with open(f'{args.base_dir}/{args.output_dirname}/{args.env}/log.txt', "w") as output_namefile,\ + open(f'{args.base_dir}/{args.output_dirname}/{args.env}/err.txt', "w") as error_namefile: + for i,log in enumerate(logs): + qos_idx = math.floor(i/math.ceil(n_jobs/len(args.qos))) + folder, basename = os.path.split(log) + new_log_name = os.path.join(folder, args.qos[qos_idx], basename) + folder, basename = os.path.split(errs[i]) + new_err_name = os.path.join(folder, args.qos[qos_idx], basename) + output_namefile.write(f'{new_log_name}\n') + error_namefile.write(f'{new_err_name}\n') + + + +########################################################################### +#slurm_script_path = os.path.join(output_dir, '%s.slurm' % name) +id = args.env.split('run')[-1] +filenames = [] +if len(args.qos)==1: + filenames = [f'{args.qos[0][:2]}_r{id}.slurm' if not args.filename else args.filename] +else: + for qos in args.qos: + filenames.append(f'{qos[:2]}_r{id}.slurm' if not args.filename else qos[0]+args.filename) + +print("Filenames:") +print(filenames) +slurm_script_paths = [os.path.join(output_dir, filename) for filename in filenames] +slurm_commands = ["sbatch %s" % slurm_script_path for slurm_script_path in slurm_script_paths] +shutil.copyfile(os.path.abspath(__file__), + os.path.join(output_dir, + os.path.basename(os.path.abspath(__file__)))) + + +idx = 0 +start_idx, end_idx = [], [] +for i in range(len(args.qos)): + start_idx += [idx+1] + idx += math.ceil(n_jobs/len(args.qos)) + end_idx += [min(idx, n_jobs)] + +for i,slurm_script_path in enumerate(slurm_script_paths): + print(f"writing to {slurm_script_path}") + with open(slurm_script_path, 'w') as slurmfile: + slurmfile.write("#!/bin/bash\n") + if args.max_jobs>0: + slurmfile.write(f"#SBATCH --array={start_idx[i]}-{end_idx[i]}%{args.max_jobs}\n") + else: + slurmfile.write(f"#SBATCH --array={start_idx[i]}-{end_idx[i]}\n") + slurmfile.write("#SBATCH --output=/dev/null\n") + slurmfile.write("#SBATCH --error=/dev/null\n") + slurmfile.write("#SBATCH --requeue\n") + args = check_qos(args) + + default_include_list = [] + default_exclude_list = [] + if args.qos[i] == "scav": + if "vulcan" in args.partition: + slurmfile.write("#SBATCH --account=vulcan\n") + slurmfile.write("#SBATCH --partition=vulcan-scavenger\n") + slurmfile.write("#SBATCH --qos=vulcan-scavenger\n") + default_exclude_list = ["janus[02-04]"] + elif "nexus" in args.partition: + slurmfile.write("#SBATCH --account=scavenger\n") + slurmfile.write("#SBATCH --partition=scavenger\n") + slurmfile.write("#SBATCH --qos=scavenger\n") + elif "cml" in args.partition: + slurmfile.write("#SBATCH --account=cml-abhinav\n") + slurmfile.write("#SBATCH --partition=cml-scavenger\n") + slurmfile.write("#SBATCH --qos=cml-scavenger\n") + elif args.qos[i] == "high" or args.qos[i] == "medium" or args.qos[i] == "default": + if "vulcan" in args.partition: + slurmfile.write("#SBATCH --account=vulcan-abhinav\n") + slurmfile.write("#SBATCH --partition=vulcan-ampere\n") + slurmfile.write(f"#SBATCH --qos=vulcan-{args.qos[i]}\n") + default_exclude_list = ["janus[02-04]"] + elif "nexus" in args.partition: + slurmfile.write("#SBATCH --account=nexus\n") + slurmfile.write(f"#SBATCH --qos={args.qos[i]}\n") + elif "cml" in args.partition: + slurmfile.write("#SBATCH --account=cml-abhinav\n") + slurmfile.write("#SBATCH --partition=cml-dpart\n") + slurmfile.write(f"#SBATCH --qos=cml-{args.qos[i]}\n") + + slurmfile.write("#SBATCH --time=%d:00:00\n" % args.nhrs) + slurmfile.write("#SBATCH --cpus-per-task=%d\n" % args.cores) + slurmfile.write("#SBATCH --mem=%dG\n" % args.mem) + + + if not args.gpu is None: + if len(args.gpu_type)==1: + if 'any' in args.gpu_type: + slurmfile.write("#SBATCH --gres=gpu:%d\n" % args.gpu) + elif "rtx2080" in args.gpu_type: + slurmfile.write("#SBATCH --gres=gpu:rtx2080ti:%d\n" % args.gpu) + elif "gtx" in args.gpu_type: + slurmfile.write("#SBATCH --gres=gpu:gtx1080ti:%d\n" % args.gpu) + elif "p6000" in args.gpu_type: + slurmfile.write("#SBATCH --gres=gpu:p6000:%d\n" % args.gpu) + elif "a4000" in args.gpu_type: + slurmfile.write("#SBATCH --gres=gpu:rtxa4000:%d\n" % args.gpu) + elif "a5000" in args.gpu_type: + slurmfile.write("#SBATCH --gres=gpu:rtxa5000:%d\n" % args.gpu) + elif "a6000" in args.gpu_type: + slurmfile.write("#SBATCH --gres=gpu:rtxa6000:%d\n" % args.gpu) + else: + assert len(args.gpu_type)>1 + slurmfile.write("#SBATCH --gres=gpu:%d\n" % args.gpu) + # slurmfile.write(get_include_string(args.gpu_type,default_include_list)) + slurmfile.write(get_exclude_string(args.gpu_type,default_exclude_list)) + else: + raise ValueError("Specify the number of gpus") + + slurmfile.write("\n") + if "vulcan" in socket.gethostname() or "nexus" in socket.gethostname(): + slurmfile.write(f"cd {root}") #TODO + # slurmfile.write('conda activate {env}\n') #TODO + slurmfile.write('source ~/.bashrc') + slurmfile.write("module load cuda\n") + slurmfile.write('micromamba activate MMQ_LLAVA\n') + + num_exps = 1 + for n in reversed(range(num_exps)): + slurmfile.write(f"srun --output=$(head -n $SLURM_ARRAY_TASK_ID {args.base_dir}/{args.output_dirname}/{args.env}/log.txt | tail -n 1) $(head -n $(expr {num_exps} \* $SLURM_ARRAY_TASK_ID - {n}) {args.base_dir}/{args.output_dirname}/{args.env}/now.txt | tail -n 1)\n") + slurmfile.write("\n") + +for i,slurm_command in enumerate(slurm_commands): + print(slurm_command) + print("Running on {}, with {} gpus, {} cores, {} mem for {} hour".format(args.qos[i], args.gpu, args.cores, args.mem , args.nhrs)) + +if not args.dryrun: + for slurm_command in slurm_commands: + os.system("%s &" % slurm_command) diff --git a/llava_runs/submit_gqa_awq.sh b/llava_runs/submit_gqa_awq.sh new file mode 100644 index 0000000..98c66da --- /dev/null +++ b/llava_runs/submit_gqa_awq.sh @@ -0,0 +1,27 @@ +#!/bin/bash + +#SBATCH --job-name=llava_gqa_awq # sets the job name +#SBATCH --output=llava_gqa_awq.%j # indicates a file to redirect STDOUT to; %j is the jobid. If set, must be set to a file instead of a directory or else submission will fail. +#SBATCH --error=llava_gqa_awq.%j # indicates a file to redirect STDERR to; %j is the jobid. If set, must be set to a file instead of a directory or else submission will fail. +#SBATCH --time=03:00:00 # how long you would like your job to run; format=hh:mm:ss + +#SBATCH --partition=vulcan-scavenger +#SBATCH --qos=vulcan-scavenger # set QOS, this will determine what resources can be requested +#SBATCH --account=vulcan-abhinav +#SBATCH --gres=gpu:rtxa5000:1 + +#SBATCH --nodes=1 # number of nodes to allocate for your job +#SBATCH --ntasks=1 +#SBATCH --ntasks-per-node=1 +#SBATCH --mem=32gb # (cpu) memory required by job; if unit is not specified MB will be assumed + +module load cuda +source ~/.bashrc +micromamba activate MMQ_LLAVA + +python awq_llava.py --task gqa \ + --seed 0 \ + --output_dir ./test \ + --vision-bits 16 \ + --language-bits 4 + diff --git a/llava_runs/submit_gqa_gptq.sh b/llava_runs/submit_gqa_gptq.sh new file mode 100644 index 0000000..1dab796 --- /dev/null +++ b/llava_runs/submit_gqa_gptq.sh @@ -0,0 +1,28 @@ +#!/bin/bash + +#SBATCH --job-name=llava_gqa_fp # sets the job name +#SBATCH --output=llava_gqa_fp.%j # indicates a file to redirect STDOUT to; %j is the jobid. If set, must be set to a file instead of a directory or else submission will fail. +#SBATCH --error=llava_gqa_fp.%j # indicates a file to redirect STDERR to; %j is the jobid. If set, must be set to a file instead of a directory or else submission will fail. +#SBATCH --time=02:00:00 # how long you would like your job to run; format=hh:mm:ss + +#SBATCH --partition=vulcan-scavenger +#SBATCH --qos=vulcan-scavenger # set QOS, this will determine what resources can be requested +#SBATCH --account=vulcan-abhinav +#SBATCH --gres=gpu:rtxa6000:1 + +#SBATCH --nodes=1 # number of nodes to allocate for your job +#SBATCH --ntasks=1 +#SBATCH --ntasks-per-node=1 +#SBATCH --mem=32gb # (cpu) memory required by job; if unit is not specified MB will be assumed + +module load cuda +source ~/.bashrc +micromamba activate MMQ_LLAVA + +python gptq_llava.py --task gqa \ + --seed 0 \ + --output_dir /fs/cfar-projects/low-bit-vision/full_precision/gqa_test_do_pad \ + --no_quant + # --vision-bits 4 \ + # --language-bits 4 + diff --git a/llava_runs/submit_llava_scoring.sh b/llava_runs/submit_llava_scoring.sh new file mode 100644 index 0000000..8590a89 --- /dev/null +++ b/llava_runs/submit_llava_scoring.sh @@ -0,0 +1,21 @@ +#!/bin/bash + +#SBATCH --job-name=llava_scoring # sets the job name +#SBATCH --output=llava_scoring.%j # indicates a file to redirect STDOUT to; %j is the jobid. If set, must be set to a file instead of a directory or else submission will fail. +#SBATCH --error=llava_scoring.%j # indicates a file to redirect STDERR to; %j is the jobid. If set, must be set to a file instead of a directory or else submission will fail. +#SBATCH --time=03:00:00 # how long you would like your job to run; format=hh:mm:ss + +#SBATCH --partition=tron +#SBATCH --qos=high # set QOS, this will determine what resources can be requested +#SBATCH --account=nexus + + +#SBATCH --nodes=1 # number of nodes to allocate for your job +#SBATCH --ntasks=1 +#SBATCH --ntasks-per-node=1 +#SBATCH --mem=32gb # (cpu) memory required by job; if unit is not specified MB will be assumed + +source ~/.bashrc +micromamba activate MMQ_LLAVA + +srun python llava_scoring.py diff --git a/llava_runs/submit_vqav2_awq.sh b/llava_runs/submit_vqav2_awq.sh new file mode 100644 index 0000000..c334cf5 --- /dev/null +++ b/llava_runs/submit_vqav2_awq.sh @@ -0,0 +1,27 @@ +#!/bin/bash + +#SBATCH --job-name=llava_gqa_awq # sets the job name +#SBATCH --output=llava_gqa_awq.%j # indicates a file to redirect STDOUT to; %j is the jobid. If set, must be set to a file instead of a directory or else submission will fail. +#SBATCH --error=llava_gqa_awq.%j # indicates a file to redirect STDERR to; %j is the jobid. If set, must be set to a file instead of a directory or else submission will fail. +#SBATCH --time=03:00:00 # how long you would like your job to run; format=hh:mm:ss + +#SBATCH --partition=vulcan-scavenger +#SBATCH --qos=vulcan-scavenger # set QOS, this will determine what resources can be requested +#SBATCH --account=vulcan-abhinav +#SBATCH --gres=gpu:rtxa5000:1 + +#SBATCH --nodes=1 # number of nodes to allocate for your job +#SBATCH --ntasks=1 +#SBATCH --ntasks-per-node=1 +#SBATCH --mem=32gb # (cpu) memory required by job; if unit is not specified MB will be assumed + +module load cuda +source ~/.bashrc +micromamba activate MMQ_LLAVA + +python awq_llava.py --task vqav2 \ + --seed 0 \ + --output_dir /fs/cfar-projects/low-bit-vision/llava/awq \ + --vision-bits 5 \ + --language-bits 3 + diff --git a/llava_runs/submit_vqav2_gptq.sh b/llava_runs/submit_vqav2_gptq.sh new file mode 100644 index 0000000..a542572 --- /dev/null +++ b/llava_runs/submit_vqav2_gptq.sh @@ -0,0 +1,26 @@ +#!/bin/bash + +#SBATCH --job-name=llava_vqav2_v4_l2 # sets the job name +#SBATCH --output=llava_vqav2_v4_l2.%j # indicates a file to redirect STDOUT to; %j is the jobid. If set, must be set to a file instead of a directory or else submission will fail. +#SBATCH --error=llava_vqav2_v4_l2.%j # indicates a file to redirect STDERR to; %j is the jobid. If set, must be set to a file instead of a directory or else submission will fail. +#SBATCH --time=3:00:00 # how long you would like your job to run; format=hh:mm:ss + +#SBATCH --partition=tron +#SBATCH --qos=high # set QOS, this will determine what resources can be requested +#SBATCH --account=nexus +#SBATCH --gres=gpu:rtxa5000:1 + +#SBATCH --nodes=1 # number of nodes to allocate for your job +#SBATCH --ntasks=1 +#SBATCH --ntasks-per-node=1 +#SBATCH --mem=32gb # (cpu) memory required by job; if unit is not specified MB will be assumed + +module load cuda +source ~/.bashrc +micromamba activate MMQ_LLAVA + +python gptq_llava.py --task vqav2 \ + --seed 42 \ + --output_dir /fs/cfar-projects/low-bit-vision/llava/gptq/vqav2_subset \ + --vision-bits 4 \ + --language-bits 2 \ No newline at end of file diff --git a/llava_test.ipynb b/llava_test.ipynb new file mode 100644 index 0000000..d6c13a2 --- /dev/null +++ b/llava_test.ipynb @@ -0,0 +1,583 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "c788b940", + "metadata": {}, + "outputs": [], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "6438068d", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/fs/nexus-scratch/vla/micromamba/envs/MMQ_LLAVA/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + " from .autonotebook import tqdm as notebook_tqdm\n" + ] + } + ], + "source": [ + "import torch\n", + "from transformers import AutoProcessor, LlavaForConditionalGeneration\n", + "\n", + "from dataset import VQAv2Eval\n", + "# from inference_pipeline import InferencePipeline\n", + "# import time\n", + "# from scoring_pipeline import ScoringPipeline\n", + "\n", + "from dataset import VQAv2Eval\n", + "# import os\n", + "from awq.llava_quantizer import LlavaAWQQuantizer\n", + "from transformers.models.llava.image_processing_llava import LlavaImageProcessor" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "cb450a6c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "device(type='cuda')" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "if torch.backends.mps.is_available():\n", + " device = torch.device(\"mps\")\n", + "else:\n", + " device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", + " \n", + "device" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "b32f52a8", + "metadata": {}, + "outputs": [], + "source": [ + "# VQAv2 dataset paths\n", + "ann_root = '/fs/cfar-projects/low-bit-vision/datasets/vqav2/annotations'\n", + "q_root = '/fs/cfar-projects/low-bit-vision/datasets/vqav2/questions'\n", + "image_root = '/fs/cfar-projects/low-bit-vision/datasets/vqav2/val2014'\n", + "\n", + "llava_prompt = 'USER: \\n{}\\nAnswer the question using a single word or phrase. ASSISTANT:'\n", + "\n", + "dataset = VQAv2Eval(image_root=image_root,\n", + " ann_root=ann_root,\n", + " q_root=q_root,\n", + " prompt=llava_prompt)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "2a65652c", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Loading checkpoint shards: 100%|██████████| 3/3 [00:00<00:00, 13.14it/s]\n" + ] + }, + { + "data": { + "text/plain": [ + "LlavaForConditionalGeneration(\n", + " (vision_tower): CLIPVisionModel(\n", + " (vision_model): CLIPVisionTransformer(\n", + " (embeddings): CLIPVisionEmbeddings(\n", + " (patch_embedding): Conv2d(3, 1024, kernel_size=(14, 14), stride=(14, 14), bias=False)\n", + " (position_embedding): Embedding(577, 1024)\n", + " )\n", + " (pre_layrnorm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n", + " (encoder): CLIPEncoder(\n", + " (layers): ModuleList(\n", + " (0-23): 24 x CLIPEncoderLayer(\n", + " (self_attn): CLIPSdpaAttention(\n", + " (k_proj): Linear(in_features=1024, out_features=1024, bias=True)\n", + " (v_proj): Linear(in_features=1024, out_features=1024, bias=True)\n", + " (q_proj): Linear(in_features=1024, out_features=1024, bias=True)\n", + " (out_proj): Linear(in_features=1024, out_features=1024, bias=True)\n", + " )\n", + " (layer_norm1): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n", + " (mlp): CLIPMLP(\n", + " (activation_fn): QuickGELUActivation()\n", + " (fc1): Linear(in_features=1024, out_features=4096, bias=True)\n", + " (fc2): Linear(in_features=4096, out_features=1024, bias=True)\n", + " )\n", + " (layer_norm2): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n", + " )\n", + " )\n", + " )\n", + " (post_layernorm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n", + " )\n", + " )\n", + " (multi_modal_projector): LlavaMultiModalProjector(\n", + " (linear_1): Linear(in_features=1024, out_features=4096, bias=True)\n", + " (act): GELUActivation()\n", + " (linear_2): Linear(in_features=4096, out_features=4096, bias=True)\n", + " )\n", + " (language_model): LlamaForCausalLM(\n", + " (model): LlamaModel(\n", + " (embed_tokens): Embedding(32064, 4096)\n", + " (layers): ModuleList(\n", + " (0-31): 32 x LlamaDecoderLayer(\n", + " (self_attn): LlamaAttention(\n", + " (q_proj): Linear(in_features=4096, out_features=4096, bias=False)\n", + " (k_proj): Linear(in_features=4096, out_features=4096, bias=False)\n", + " (v_proj): Linear(in_features=4096, out_features=4096, bias=False)\n", + " (o_proj): Linear(in_features=4096, out_features=4096, bias=False)\n", + " )\n", + " (mlp): LlamaMLP(\n", + " (gate_proj): Linear(in_features=4096, out_features=11008, bias=False)\n", + " (up_proj): Linear(in_features=4096, out_features=11008, bias=False)\n", + " (down_proj): Linear(in_features=11008, out_features=4096, bias=False)\n", + " (act_fn): SiLU()\n", + " )\n", + " (input_layernorm): LlamaRMSNorm((4096,), eps=1e-05)\n", + " (post_attention_layernorm): LlamaRMSNorm((4096,), eps=1e-05)\n", + " )\n", + " )\n", + " (norm): LlamaRMSNorm((4096,), eps=1e-05)\n", + " (rotary_emb): LlamaRotaryEmbedding()\n", + " )\n", + " (lm_head): Linear(in_features=4096, out_features=32064, bias=False)\n", + " )\n", + ")" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Load the model\n", + "model = LlavaForConditionalGeneration.from_pretrained(\"llava-hf/llava-1.5-7b-hf\", torch_dtype=torch.float16)\n", + "processor = AutoProcessor.from_pretrained(\"llava-hf/llava-1.5-7b-hf\", pad_token = '', use_fast = False)\n", + "# need to use this image processor w/ do_pad=True according to \"Note regarding reproducing original implementation\"\n", + "# https://huggingface.co/docs/transformers/en/model_doc/llava\n", + "image_processor = LlavaImageProcessor.from_pretrained(\"llava-hf/llava-1.5-7b-hf\",\n", + " do_pad=True)\n", + "\n", + "processor.image_processor = image_processor\n", + "\n", + "model.to(device)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "30fc8f4b", + "metadata": {}, + "outputs": [], + "source": [ + "# FP output\n", + "conversation = [\n", + " {\n", + " \"role\": \"user\",\n", + " \"content\": [\n", + " {\"type\": \"image\", \"url\": \"https://www.ilankelman.org/stopsigns/australia.jpg\"},\n", + " {\"type\": \"text\", \"text\": \"What is shown in this image?\"},\n", + " ],\n", + " },\n", + "]\n", + "\n", + "inputs = processor.apply_chat_template(\n", + " conversation,\n", + " add_generation_prompt=True,\n", + " tokenize=True,\n", + " return_dict=True,\n", + " return_tensors=\"pt\"\n", + ").to(model.device, torch.float16)\n", + "\n", + "with torch.no_grad():\n", + " # Generate\n", + " generate_ids = model.generate(**inputs, max_new_tokens=30)\n", + " \n", + " print(processor.batch_decode(generate_ids, skip_special_tokens=True))" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "642653d1", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'llm_layers': {'self_attn': 4, 'mlp': 4}}" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "config = {}\n", + "\n", + "# config['vision_layers'] = {\n", + "# 'self_attn':16,\n", + "# 'mlp': 16\n", + "# }\n", + "\n", + "config['llm_layers'] = {\n", + " 'self_attn': 4,\n", + " 'mlp': 4\n", + "}\n", + "\n", + "config" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "bae2be69", + "metadata": {}, + "outputs": [], + "source": [ + "quantizer = LlavaAWQQuantizer(model, device, processor, dataset, config)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "725c5a92", + "metadata": {}, + "outputs": [], + "source": [ + "quantizer.n_samples = 128" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "727ed47a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "128\n" + ] + } + ], + "source": [ + "print(quantizer.n_samples)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "9ff10b5e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "layer_groups: {'llm_layers': ModuleList(\n", + " (0-31): 32 x LlamaDecoderLayer(\n", + " (self_attn): LlamaAttention(\n", + " (q_proj): Linear(in_features=4096, out_features=4096, bias=False)\n", + " (k_proj): Linear(in_features=4096, out_features=4096, bias=False)\n", + " (v_proj): Linear(in_features=4096, out_features=4096, bias=False)\n", + " (o_proj): Linear(in_features=4096, out_features=4096, bias=False)\n", + " )\n", + " (mlp): LlamaMLP(\n", + " (gate_proj): Linear(in_features=4096, out_features=11008, bias=False)\n", + " (up_proj): Linear(in_features=4096, out_features=11008, bias=False)\n", + " (down_proj): Linear(in_features=11008, out_features=4096, bias=False)\n", + " (act_fn): SiLU()\n", + " )\n", + " (input_layernorm): LlamaRMSNorm((4096,), eps=1e-05)\n", + " (post_attention_layernorm): LlamaRMSNorm((4096,), eps=1e-05)\n", + " )\n", + ")}\n", + "Calibration set size: 128\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Quantizing llm_layers: 100%|██████████| 32/32 [39:38<00:00, 74.34s/it]\n" + ] + } + ], + "source": [ + "model.to(device)\n", + "quantizer.quantize()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "a434ad6e", + "metadata": {}, + "outputs": [ + { + "data": { + "image/jpeg": "/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAgGBgcGBQgHBwcJCQgKDBQNDAsLDBkSEw8UHRofHh0aHBwgJC4nICIsIxwcKDcpLDAxNDQ0Hyc5PTgyPC4zNDL/2wBDAQkJCQwLDBgNDRgyIRwhMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjL/wAARCAGsAoADASIAAhEBAxEB/8QAHwAAAQUBAQEBAQEAAAAAAAAAAAECAwQFBgcICQoL/8QAtRAAAgEDAwIEAwUFBAQAAAF9AQIDAAQRBRIhMUEGE1FhByJxFDKBkaEII0KxwRVS0fAkM2JyggkKFhcYGRolJicoKSo0NTY3ODk6Q0RFRkdISUpTVFVWV1hZWmNkZWZnaGlqc3R1dnd4eXqDhIWGh4iJipKTlJWWl5iZmqKjpKWmp6ipqrKztLW2t7i5usLDxMXGx8jJytLT1NXW19jZ2uHi4+Tl5ufo6erx8vP09fb3+Pn6/8QAHwEAAwEBAQEBAQEBAQAAAAAAAAECAwQFBgcICQoL/8QAtREAAgECBAQDBAcFBAQAAQJ3AAECAxEEBSExBhJBUQdhcRMiMoEIFEKRobHBCSMzUvAVYnLRChYkNOEl8RcYGRomJygpKjU2Nzg5OkNERUZHSElKU1RVVldYWVpjZGVmZ2hpanN0dXZ3eHl6goOEhYaHiImKkpOUlZaXmJmaoqOkpaanqKmqsrO0tba3uLm6wsPExcbHyMnK0tPU1dbX2Nna4uPk5ebn6Onq8vP09fb3+Pn6/9oADAMBAAIRAxEAPwD3+iiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooATFFLRQAlJTqMUCG0UuKMUAJRRRTFYTFFLTJZFijaRzhVGSfQUXFYXFLSA5GR3pcUCCimb08wx7hvAyVzzj1xTqYBRRmmeYnmeXvXzMbtueceuKBWMrXLlljjslEi/ag4aZP8AlmFAJ6ckn0HbJ7VV0BrazivLSKVXjtpyq+XGyoA5yAD0Y5POOBnFVPEd7LJqI0+CGDzRGHEsrFWQcklSO3GO2C3euQOoTWFv5un3apEbiOUJcsGXaQNw68YO5uOpXPTGeGpiVCd3t/Wy6lcraOjuLaC3k1KaaS4W8uP3iwhh5oUj5lLnggkAYHOMVxequssMhuLh4rh4QRcpGFhjXdsVZmB6syEccDaB2NXbXXWuZ5Wl1BZ52KRnz4h8jcZY8g7TjGV6cg9qi8TT6Xf2cV9ZIkEoPlLcgqVeUZGAecLhjjAUcryeK5KsoVby27f1+n/BC1ita+GNRuo5V0u5hu3unZ54PtuYRgncskez5ct3z3PYYqRr57SBtJ1SKWWQo3kyRuYl/eZzvJyDtdjj+7jr1qlpLzWcEUlpbW+nyJLgSRIY0wygBpXZ8gqSwGTkgjIGOLWs6Zqkckeo6heWU0N9s+0XFjMqsCXxlf73BCkDAwoOc06qShzRWul9rf8AB/ryJW4j3skmoiPbPAjARsLbCbEXgDLZ8xAN4JABAPJxioLWOzn1KCS9nkQ2sylY35z1ym5RhS2AM8/nVOLU5jI0V3BJHqMii0gnYYQRjO1e6hSVcEY54B4zS27DRZndZI0HlhBJIWX5iuCoXgZzu6jnjGOK4JSSqKd/+D6b/wDBKsaWqaZex+HobuLTtDt9HeLzNrSNJJIwUKrDcATJgkfdHcHHfzu5+0JayXBswsPmrhQ21QwBBIyxPOCOMKAMDnFdRFeo+nxNb6crfZ90cDruH3+GUkk46k8DKnPIrLv44tLma3utPtWvY1WGTflY0baS5wOGK7xgk9RnqK741udbbEpC+HtHEul3d/dRXeyMpm4gcFArN1THGeCCpyV644p+p6NMokbTbZNjAI0cuxZBkYHL8gkg+mc8Zzms/SNT1XZNcWt09wgQ4FzOUVXbnYoJXzG4B6clV5x1t2eri+tbW1nW6+3qEESGN2UKnJl293LFjls5ZiTgYFZ1OdTcou/ddv1/q5WlhLXxe9lt01o3HlfJMVYF5piRvWR8EYGWPy89MdM10Gk2s0l9pUjpJCt0RLBZ+a7ST4Cquc8qBn75BPAxjFUPtmk3NvDb3kxj8kpNFIqsUYMQF5QZQjHuWLeoGN7Sxp90s84uLrzLiRjPPMGWREKtHHGH7Rbt/QY7NnBJzlJc3NBWb331v+Hz3+TGkjNkjIkt9M0tpSDcOjvayfKkbHB2ufT5hu46njmrNlLDBe2yAARoxMDMpcrtXblc8Y3DBPbnoDVSC9vHluo9LVt7SBISnzbQyndhyOCBgk+gPrgLcJB+7gXzJrmRWZ3jUZU9yo543MDgDnA5xjHNJNy/r8S0RSPNJqj752hNu5DXMZ8tnHYkEcAAAg+5yTWLqWoWklpNNJqNxd35O2OK6QsoQHnB+6flPHU8bsjIFaj2Ti2WKdY0ln4NvLIS+xW2lmUEfNwxGSAQeNvBqyWTT4JI7eRpzJhQzZkyrcEH5eAVVvlUDjqWrenNQ97oJn0VRRRX0ABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFNJxTfM5xinYV0SUUgOaWkMKKKKACiiigAooooAKKKKACiiigAoNFFACVkat4gsdJRvPuEV1wzJyxC9ScDnp2rXNeR+MZNPl1p57cvKkcwWVt42q5ByQSTnv1GAfWsa9V046bhY9Ut7hZ7aKba0YlUEK4wwz0BHr7Vhy6vFq1/FZ2ly0cIjaaeQYBZeQqj6gM2R0wOmRXM2PinToNbinuJbjyIovIRniLnBAOScnnPJJGfmHbOKPhqBdW1vU5rF0hnuEURALhY0ztlYY5zgBAeOue3C9pzNRM3ex6FpOrw6jLcwRRSRm18sFZM7sMgYZ/l+FWp9RtLeZoZp0SVYjMyE8hB1bHpXHWl2vhO2uHa9fUj+7iMaFflbnkHrnGflzjCjpmuIvtdvrm+dWMxaeUMcg7nbO0qoP3TgkYHUZFKpiFBpbscU2rnokOtQ3Hi0LDFIjtFH5m58F1YHZhTwBySe+cD1rrQR071wngu9umlmV8yrI3mSzOm0r7888gDjoPyp0moXkWpz3EVve3du0hmeC027WwqrgkjJ+bHOcYHpVwqLlT3uB3JPvXMDzLfxV51xfQQNLwIG4aRAGwB64xu/A+lJ4s1JU0y3jW7ezmlZZQe6gYJB/PpznBxnFc7p2s2k8M0epWFuZXWQ+fI5Idgm09eVzgjA7BvelUrwg7Sdga7FzXGiWf7Obc2378yNNGS5kzzu3N7FeOo7cVzhETXFu5ncC2lCZLMhkUlmduOOvBzjsMZOKlniu75Lm0ie4ubidBIxS3z5OCu0ow4Cks+B8xx6Yq/p/hPU7swzXzi32Ef60BpSAScHaTjOc9c885NeVJyq3ly6vy9bXZpGL6Gc0EF/qMSXOmwQzOuyN4X3yyx78jEfBHHJP8AtY+Y5NYeox20d3dSFktgrpcG6nkJJXghSGLAFVOcAYPy+mK6Dx1ax6B4bhSCY/a7q9xPcgbJHXa7BMjkIPlAXOMCvOY7+6QKsdzJ8mNo3Z24yeM9ByePc1X1eTlzN69rux0RwkpRumb9+trp8cV9druvJgJI4SVVWV0wu0bWA+UjPBOByCcEULKUW888ts1mYXj+dlJSFlVvnZCcg7kGN52/xHjiqkWqXMcjvJtmd2DFpRk5GOeOM/KozjPAqNLqKIr5NnGm1vMBd3kIfcfmOT83Bxg9SATTcaq1W/8AXoQ8HVXQW8v0gs49Ft0DG1QjzUl4WUSMSV5+Uc98ZPU9KwdPvgt8slwtzNbrh3jz97DZBfOdy7uME9O4NdDPdw3O9WadFVgYkG1FbLZdn2gnc3HzDPToRWNcaTZSeZdJdNE6s2y3MLPsH8OG7jIHbPOT3FOkrK8lYylRqLdMsanrEd1cyahY2xtQ8iN5MTBIkK42hUGOAQfcZHrUdze3hill/tGWOcsbmQowROTtXCA5z82eR0b35xhPeo0pSObEqNCy8kHf94YPqBVbc0iny94jlK7yQecHOPzB9entWvsV1MndbnWeF7u/05lmtbH7XE0w87aMNcEAlDu2N91gP4h8350r6+05i06wsIb/AFAMyMwcPbud7OoCD5Nihj/EACByQvNDSNS07T4Lu4OniaWRyoRnkbZERznBxj5sZI57HJrcuLi2m1SO40+CxmEkaLCCskm2IBlkAA/5ZjGCQB16muarpVbcHotH00767a+Q0ypLNqRUa1KkdsJQkLoYiCGIGeQQqk5JBzj5cHoc9T4bN81tf6pHIL+G1jSEu21EWRPm+6cBsKSCQATnjjk4Vtd3ep3KW1iks5urjc0UCq0aKI8eVjAxIdo5BGMZ/hrrodFFjpdhoysSodZL+OGUOLeQ8lQT96THy7v5cmsJ6dLfd/w5Ue5lWrSarcyCL7FbyKXaFywjYIOpzu6jH5455FL5/wDY8VtE9xFDehw4mR8ZA+U7wSehHHbvzgCpmW2me5tLWKGdrVFi3lhJvywXAI5PJyf6AVFqq2thoZi85be5m2sYQCZFUY3cg4LFh0/hGAAck1MLSlsMxLyfzL22QXcUAz58ihlCwuSemMk4ATg5PJJ5renntrTSLSO8gVLkRpI6zht+/wCXe2c7s8nnkgA9ulPRPDt20tzLeAfZ7ZftTxs6qJpMcD5uFAwASOn44qxJfx2Vz9jRzcSy5bzSisV2gZwpOMEt1OBxkciqqPmaUdvIZ9A0UUV9EIKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACkJpaawyKBMjdqjXl+al8vPWgRDNVcizHA0+kCgUtSWgooooGFFFFABRRRQAUUUUAFFFJmgBaYXUMoLAFugJ602eVYYXlbO1FLHHoBXDah4wIkM0cRS5iBEcDjd1znIxnd1+6cdM9ayqVYwV2B3MuxwYy3JGQAeeO9eQarDavosn21s3sUu2J4lKAIM/K5HBIIzkY5b0rT1fV5dR+zWtvdA3BtjBJKrCN5PlBdWLHg5BIwccHJ7Vj36Q3trcPkNdeWm5UmBBcdZFOMdMj5c+vAGa5atXm+EaRxd2TpMvlTvIk5HmqVkXY4PTbt/E54+9+J77wv4n0jSpWht7kwvJFHJObjL4AGdqAhQvUkkk5JHU4FcxbLFNem4ubVJLe1kG1mXdEQoO0ED5mIKl8fQ9OnP3d7dX97e3KoBLNN508xiUHeQyttx0XLDAz1wTz0cJW97qK19Ds/EN1i4uvst2uXlYxMVZBtb5iAOq8nGeprJVnt5SzSB5EhfAcE7MqQFx/eHUkDjp70/T9JvNRgs1gs7m8l2L9yIlAAAFBJ4A49QMY64rqNL+H2qzmSXUZ4bVJFUeUT58i8HcQc4BJPqelcrhKd7eZpysybSddO1FTFNLtuJQo2Y2RhGwQN3UEKf656VoWF3NNeRXGlSST3V3EdpgDP9lViSVbA+TGcDJHOTggcddpvgrRtLYyskt1MVIaS5fcOc5+XgDOT61tLNBFGIbdR5aDCxwr8o9uOBW8IOLdxqjc4aXwbrerXkdzeXkNqgHzKcyvnnqM46Hj5u3fNdND4W00Jtuke+OQT9oOVB7kL0HPNaBmuXJ2xxxDOMud5/IcUCDcMSyPMT2bgfgo4ojSgul/U2VNLces1vBGIo9oROFiiXIH4DgUwzzsP3cKp6GU8/kP8agu9RsNMAS6uoLcnpGzDcfog+Y/lVY6pczp/oOk3MgPSW7Ito//AB7Ln8Fq79DRR8jjPibIwsbISMrytctlggXgIeg/4EO/YV5gyK7MWVT9RXofxMlkP9kRTbBLtlkkCElQ3yA4zzjOcZrzo+buYqyBTnAK5pHZSVoIeI1A4yPoxoOR/Ef50z99/fj/AO+P/r0n73+/H/3yf8aVzVEmW9QfqKaWYN90H6NTP3396P8A75P+NIfN9Y/yNFwsSlwRhlbHuM0wtFkE7QRnBIxjIwfzFMJm/wCmX60h87H/ACz/ADNFyZRT3FWC2EbIlvBhu4QZH0PbpUYs4gZSNy+aCr7TtypxleOcHAyPSnEP3SI/nTSJR0VR9HNG5jLDUn9k0NKmhsfEFvqcyuwjJOIcKyfKQNg+7gZ6GteTVorrUIHeZfJSU7EnBCRFmyX6HcACc55OPSuYzP1G0/U5/pSebOD/AKpT9GxWcqMZNNmTwdPpdHe2zQyXn2pNWttzSNIZILjEqlTw6oWChiOMHj5umBRqs8rXllb6RbSWenwujyySoocljjPfexx0GOxUYFcKJWf78JH1IP8AWlWTy2Vk3Iw6FQRj8RWfsGm7Mh4LtI7S8uWsBeG4PmR3cYmUiIooQSMcHJzjqScHr161zcVssEii5twzTBfPe4Jj2qOpC5IZeVOevoKqi/uQ2RdykkYyz7uMFcc57Mw/E1GsrrEI/lZORgoDkFix5xn7xJ604UnFWMpYKp0aPraiiivXOQKKKKACiiigAooooAKKKKACiiigAooooAKTNIc0wk07Et2JM0tRAmng0WGncdRRRSGFFFFABRRRQAUUUUAFFGapapNNb6dNLblBMgyu9dwJz0xuX+YoegF2is7SdXtdYsxcWsqOB8rhWB2NjkHBI/WpbK9N4bgNbTQGGZov3gHzgYwy4PIOaVxXRcopM1FPcRW0LzTyJHEgyzscAU7jJqKx7LxFY6hOIoPNyzYQtGQGGPvD2zkZPcEVsDkUk01dCTuFFFFMYUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAVWvUElnMhZkDRsNynBXjqM1ZqtfWzXdnLbrM0JkUrvQZI+lKWwHmuuahc22lOk+oRpptxk2QUOWmwQpU55zyTk8E81hyWcUdubhbiMPC5k8gLsZkbA3ovcglxx2zXrK6VY2yxsI4/PjjCI7DpjoQOgPuK5U+Dpbm3kgvtTZVZmO6zXY7AtuILHOOcHgdq4atJXu1d/15lxTex5lq9hqNvKXuiMKREw3gZLcEcdBxjOe4rV0/SdR1TUTJpdlemzIJQOAsKNno+7hgMnoPSvTItH0PSzE721v50a7UkmUPJ1J4GOuSegq5Jfz7d1tZSTHt5riJQPfOTj8Kl0ltfQ1UHuzkNK8BXEDCS9ngt1ZArRW4Ls6gYIZjhfyHQDnjNasXg3w3otvDI9lbkGZWea/ffgAElju+UcDHStOEatNIZL65tokz8sVoh6e7vz+QFQ3lrpcIF1qEdqGHSa8IP5Fz/KrtFbFRplm31rTrhBHph+0xIdubZMRL7buF/Kp2kuXHDRxD2G8/mcCsyPU43jVdOsbq7RfuGKLyoh9HfaMfTNTeVq9x9+a0sU9IlNxJ/3021R/3yaLtmigkWxAGO5y8pA6ucgfh0FU5Nb09JDDHObqYf8ALG0QzsPrtyB+JFL/AGFZykNetPfsOf8AS5S6j6IMJ/47WiiJFEIo1WOMdEQBVH4Digd0Zf2nVrj/AI99PitF/v30u5v+/cef1YU4aTPOP9P1S6nB6xQYto/yT5j+LVdubiCzi826mit4v78zhB+ZNUf7dhuB/wAS+1vL/P8AFBDtj/7+PtX8iaNOo9XsXLTT7LTgfsVpBbk9WjQBj9W6n8TUwUseASfbms3GuXI6WOnof7xNzJ+Q2oPzNRvoUVwP+JheXt+OpSaXZH/37j2r+eaBW7v+v68zzf4nTbtes4x0S1J/76kb/wCJrhQy4HzL+ddX8RZjL4slGSfLt4V/Qt/7NXJ+VGP+WaflUHoQ0ih2R6j86PxH50zyov8Anmn/AHyKPJi/55J/3yKCh9Jg0wwRf88k/wC+aQwRE/6tPyoAfg0EH0P5VH5EX/PNfypDBF/zzWgCTafQ0hB9D+VRmGP/AJ5ik8mLsg/OgRJtPofypNp9DUfkx/3f1NHlR+n/AI8aAH7T6Gkx7GmeUnof++j/AI0nlJ6N/wB9GgCT6j86aVHpTfKT/a/77NJ5a+r/APfZoEfX1FFFeieAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFIRmlooAZspcU6ii4rIBRRRQMKKKKACiiigArE1zxFaaEkb3H3Wfax5AUbSQc4xjIAPpnJrTury3so1e5mSJGYIGdsDJ6CsjxLFfy2YSz8kI/wAkruvMSH7zg59Bjp3z2qZXtoJvTQqz+IZo5tMkn2W0LqHuh58ZALAfL3JxuBPAyCDmtDXZPNsZbKJoXndNzQOCS8ecHABGMnAyeB34rww3aWn2iWKaQiSWXMzzZO5kI+UA7ioAA3HnAGD1A6ma9ul0yHS3kghE7DEoYu03GX3jJPTb3HJ+uMHVVnchNmnaeI7zSrnV7eWZBKkKFZZGjOxgudzHanY57gYPrirfhDVdSnWVLi4JgxOXMbAeQ+4ktJuHGeCAOBu6Y6ef63dxi0aF1SSViwkaQn52AI27Og42npg5656b3w+8RJaNJb3e+VpQFZfszyPIdvOG5HZcgsMjoves6VSUnzN2DY7HT/Ea6jpF+1zL51jDCqGV42iMoPDNv9cEZAHXoeeMq/8AHFmEMWnypFcsgUxS5dYlBzllHA+XJOOemcVw+vXk9vcXc1pK4tmfZPC8vEchYncynAA4OCCME4+uPa6l/aJvoIf3STK0ksIyqqVzhh2wuThfc+2D2spK4Hp/gydpdR324RXLqjHyy4CcswHQqfmXqBjIHzdvSx0rxnwl4nSynu4LWaM77fass0btsKLsQt1O0nGecDB9c169Y3Au7C3uAHAljV8OhRhkZ5U9D7V0UVaCHF9CzRRRWpYUUUUAFFFJmgBaKh85d5QcsBkihLiN1JDDjqPTp/jQK5NRTVOe1OoGFFNZwvUgfWoXuQDhQSaTkluNJvYsZpjSKn3iBWfNfqH2GQbv7iDJ/IVAZpnPyxhB6yHJ/If41k6q6GsaTe5ovcjB2Ak1UlvgrFWl+b+4nJ/Ic1WeDzUPnSPIvcZ2oPwH9c1XGpWQJjtS1y4PKWcfmY+pHyj8TWbqNmsaaRa8yV2ysO0esjY/Qf8A1qTyZJG+eZ2/2EG0fpz+tQB9TnH7u3gtF/vTv5r/APfKEKP++jSNpSzD/Tbu5ugeqF/Lj/74TGfxJqNy9ENe+06wlMRmiWYj/VRgvKf+AqC1Ma7vpgRbac0akf6y8cR/+OLuY/jtrRt7eC0jMNpBHCn9yFAoP4Cq11qdjaP5U11Esv8AzyU73/74XLfpQCd3oiH+z7yf/j61SVV/552aCEf99Hc/6in2uk6dZyedFaR+fnmaTMkh/wCBtlv1pG1C6mOLXSrgj+/dMLdfyOX/APHaYLXVJ8effw2y/wBy0h3N/wB9yZ/RRS6hr1f9fI0WYkF2yQOrHt+NZ763p/mGOKf7VKP+WdojTt+OwED8SKP7CsHYNdRveuP4ryVpv0Pyj8BWiqrGgjQBUHRFGB+Qp6hoZpu9Un/49tMWBT/HezhT/wB8R7j+ZFH9nXs//H3q820/8s7KMW6/99fM/wD48KuXV7a2Cbry5ht1/wCm0gTP5mqY1mOYf6FaXt7/ALUUBRP++5Nq/kTSGr7pDrbRdMtJvOisojNn/XSgyyf99uSf1rQZ8nkkt78msvdrdwflhsbJfWWRrh/yXav/AI8aDo7zL/p2q31wD1jjcW8f5RgE/ixo9A66ssXmpWdgub27gtgegmkCk/QHk/lVP+2Y5+La1vZY2BzcG3McS8ddz7Sf+Ag1ZtNM07TjusrG3gfu6Rjefqx+Y/nRqD/6LJySzYHueaGVFJux4d40m83xbqbddrqgH0RRWD5knXyT/wB9itDXpvtHiPUnznddyY+gYj+lUc1B3jN7/wDPE/8AfYo8x/8Anif++xTs0UDGmRv+eLf99Ck8xv8Ani3/AH0KdmkNIQ3zG/55P+Y/xpPMb/nk/wCY/wAadRQAwyH/AJ5P+n+NJ5h/55P+n+NPpDQA3zD/AM8pPyH+NJ5n/TOT8h/jTv8AOaQ0wGmT/pnJ+X/16TzP+mcn/fP/ANenUdaAuM8wj/lnJ/3z/wDXo3n/AJ5yf980+koA+vKKKK9E+fCiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAoopDQBn600cekXMssPnJHGXZPl5AHP3vl6Z61xNl4zF1OsMMQkuhZnyyqKu88YTbuPTDkk4Axj3roNU1f+y7W6j1B2uWJaSFY0jVyN64VULEsRuHOOcV5v4gsobVl1yGGQ3MLpHMBbsyyK6O+5g4ICjjgZ4G3qaxqSa2M5blDxK0K5lghAF1uYmRTuWMsdu0D5cEYIwPlOPlPLVyVveCEx2yOyJnyX4U7eQSPlIDDrya2ZXgvtPu0WSB7ZlV1VB8kA+8GzkDI3/KCT3Hrjj5GlsZDG6q52ZDKSccnOcZ5zjrnt65rnivaBsdbc6jcNoU1pGsh3LuKkHG0kN3HqCTjqfzrnNM1e5toZ1WRwlwwDgqdr7WDAcHnGPTqAOlWL+7c2dlMkiF5IcuFUg5HHLE8nn3x1rCk2w5YbGcEqME4z3A/P2q6ULRaYM6a71O5vZpEjWOd5UkDyNk4HGWILDDAD8OvXmsS9mZL+XzJkZWfLMCGV+/bjPt2z61SMwPyfd4OQO9aulBoy91HPbwySFowolIcLjLADGMcAZ5q1HkDc9g+ENtZy2s08z2rXduWiTy2G50cDl+7HgKCeMcDnNejafrVtql1PHZ5mhh4a5RlaMt3UYOc/hivm6O+uvs9oLWJEvrqRo0jiQIdpC4UsG3EkopAbHzfMOmK9v+Hv2eDShC5mGpTIJ51mh8osCByqgDA5xyAxOc81tF3QRdtDtc4pa4XWfGltLqqaRZ3rQ/6SkM91Cy7omLDAw4wynDAkZxx613IzjmmnctO4tNZsKSeB606o5GVEJdgqjqScCmMSOUOgOeTVee6ELhScM3GeoU9uPfBrmZ7jUotUmtdPjkkRm3FlICRLnBAJBA45xnqDwKsS6ZdXRcS3IhBO7MJLSc4x8x4GMccfyrKVW2yFFSlsiOXXFWT7TGQ3mRZaST5QnYDIH1469PWrOkXE19YkupUM4G7zD91QO/rz+NV47XQrCQosa3FxksUwZ3yTnoOB+OKvi4nkXKRJCOg8w5I/AcD86xVSV7s1jh29zWE6qNqAnAx0xVWTUUViplG4fwR/M35DmqFw0MMYN9dAKe0jhFP0Hf9aalyW4srGV17Oy+RH+bcn8FNN1JM2VKKLfnzSElItn+1Kf6D/Go5VVIzLd3GIh1LsI0/nz+dMFvezf667SBf7lqnP8A32+T+QFOi020jlDrCJJv+espMr/m2SPwxUasvRFaLUIGDrY209wN3WGPbH2/jbav5E1IIdSn+/Jb2aeiDzn/ADOFH5GpJNSsrZzHJcp5xYkRgl5Dz/dGW/Smi7vJ/wDj20+QDs904iH/AHyNzfoKB69A/sm1bDXXmXjDvdPvA+i/dH5VakkitrcNK8cMCjguQiD8+Kq/ZL6b/j41Axj+5aRhP/H23N+WKWPS7GGUSi3SSbP+tmJlf/vpiT+VAvVjP7Xhm/484bi994Ivk/77bC/qaTOr3A4W0slPdiZ3/IbVH5mrdzdQ2qb7qeOFf70zhR+tUhq0c3/Hnb3V3/tRQkJ/32+1f1NHqNLql/X5DjpMco/0y7u7v1R5dkf/AHwm0fnmrFvawWiiK1gjgUkfLCgXP5daq51abolpaKf77NO/5Dav6mmtpxlIF3fXdwD1Tf5Sf98x4z+JNA7Pq/6/It3V9Z2A/wBKuYICegkkCk/h1NVRqwmGbSyvbkdnWHy0/wC+pNo/LNTW9nZ2PNrawQE9WjQKT9T1P51Oz4UyMcL1LsePzpBZIpBtYnPEdjZqe7u07j8BtX9TS/2Y8oxeanezg9UjcQJ+UYB/Nqz7/wAZeHdNyLnWLYuOscLea35LmsGb4nWkr+XpOj6jfueASoiUn/x4/pRoGp2ltpun2L77Wygik7uqAuf+BHn9as7y54yx/OvMr7xL48uYi8GjQ6dEemYjLL+TH+lYcv8AaGojPiLxHrcSHrF9hmSMf98DbS5l0E+7Z6vqGvaXpYJ1DUrS2I/hllUMf+A9f0rmb34oaBESlmt7qEg7W8G1T/wJ8fyrhItH8P27Pc22s6JLGh5F6rKc++2QNn8PwpZfEtmiC3Tw/YX9v3ezicJ+cqD+dK7FzI6Wbxx4q1FSdJ8L+RGRxLc7n/8AiV/nXOavP46v0eO+mvDCww0ViibSD2+Qg/zqC9uoWt45NK0Brd3j3s0V3MGT5iMbY2Az9T+BxUOmL4mlj3TazdwRyfdV3O4D+Y/OpcurY7voYs9qLQM1xDcQE5Be5ikGT35IAzUXkEBcCRl2K4Yc5U8A/Qkda6W+8NxXRja8v7i5KOP9ad+RnkHOf/rVbSK3tHKNNDEqbABkDhe3r1qHNdBpy6nHHYpwwI/3lp6iIjJ249Sp/wAa6+bU9NjJxl+/yJwfzxWdNqFrKSqaVAx9XX/4nH86m7Ku+5gqISxG6I+wf/69PWFT/B+uauSWguGJFtFGD2AOB+ZNImjJJjA257qKA5n3KptV67Bn6mj7Jk/Kv5GrQ0O4wfKuWGPUn/69I2kasp+QpJx/eB/nii/mHMyu1kRj5CPxNIbJsZ2H/vo1K8WrQjc9nIR6hCf5VH/aEqkB4WB+v+Ipr1Dml3GCxYDGw/maGsXA+6f++qnXU0B+dWA91z/Wp01G3PVhk+oIxT1Fzy7mY1lODwp/OgWUpwcHH1rdS7s3zl8fQircbWjAYcDjvTSYueXc5gWM2Dx+tH2GU9FJ/GuvjtYTnG0g+hqQWkYAwtOzD2ku59AUVA91DHL5TSoJNpYR7huI9QOtVBr2kmbyV1K1aTaW2rKpOB16H9K9K5wGlRXn9/8AEhQhFhYurE/LJdAhcZPYc56HHv61NpPxGspN66uy23zDZIsb7focjj69KLk8yO6orPs9a0zUIy9pqFtOqqWYpKDgDqT6VRt/GXh65YLFqkJJBPzZXj8RRcq5vUVzp8a6CJ2i+3DIUNu2Haecda1rHUrLUoWlsrqKdFOCY2zg+9CaYXLlFJmjNMBaKKKACiiigAooooAKKKKACiiigAopM0ZoAWmSb9jeXt3443dM07NRzM6xM0SB3HRS2M/jQBzHiWCK7aOQzxsNrRrbsAzswzkx88N1zjkhccZrx/U7kWs7q7L9nSZ1dINyhgrE/KuAFB9fQjkHFen6xDLaSG+sp5U8otII0jy6xj/WJlfRuduRk8E15J4t1K41a5upri3immR0E+yMBkwp+YEY25UDPJ6YwOa5KnvSsQ11MXUGj8u4lgu/NDToIvmCBWIB3EZ67iV6fwnk4rKu59/7l4QXRidwPJyc9vfNRC5PlSopcx7t+CfmUDHIbGc8Dn396dNJHLcMwidH2R7mCqvO0A8Z65/+v1q4U+XQRejeGTSDCyRllJMJ8kglRkna+OMnI69u9YZLMyICWA4XnA5P+Na/mkaXG6rbMRLtUpGhIwFPzcfMemc9PfNYzsC7ONqtknAwuD06VcOoDWc7jnIYHGD1FTRXcoVUU5UHIz09vxpJEDRCVSD/AHs/Lg46fkP1pFVnyCwHUAnnn0q9wN7SL+CzkhvI7gjUVkIiV4l8pQcgvISMldpYBBznOSOBWpqPjzVNQt0tAYYrJZxNDZJHmNMLtC9fujg7TwWJJ6VzOm2N5qM/2fT7OW6uOm2CNncH8Pu9QK9A8O/B7Xr+WN9W8vS7Qj51JWSc8HoBkDr3P4dKLpDUGzovhOy3+rNPfWUt1ckGZbl4cCNizHeSepJMgBX2GOte0ZAGegrmtG0bT9As/slirlRIXAHJAydq8dlBwM/XqTWj58zs37vbzwXO49uw/wAah1V0NYUXbU0GnUZxyfaqF1fQOphcLLn70apv/Pt+dUri5t1kMU9w0kn/ADxBLN/3wo/mKYXu5VxDaCJP71w238kXJ/MispVGzojRS3C4vNSnDJbQwWynID3B8xvqEU4/NqikaCAqt/eGSRudkhxk+0a/4GpfsLyD/SbyVweqRfuV/wDHfmP/AH1Ug+w6XHj9xaBvorP/AFb9azd3uaKy2K8c08u9LWwkVC5O+ciFT0528sf++RUy2Ny//Hxeso/uWy+WP++jlvyxSx3jybhaWM8nJO+RfJT825P4KakFvfS/626jgH923Tcf++3/AKLRYGx0NnaWQM0cUcX96Zvvfi7c/rUf9qQSMfsyy3Zz1t03L/32cL+tPXTLTzBJJEbiRed87GUj/vrIH4AUkmp2iP5f2hZJBx5cQMjD/gK5xT2J38xudSnHC21ov+2TM/5Dao/M0h0xJRi7uLi6HdZH2p/3wmB+eaQ3V7NxBYMg/v3UgjH/AHyNzfoKRre8cZmv9gxnZaxhP/Hm3N+WKCtSxDFb2FrmNIbeLqzKqxqc+uMCoP7XtXJFt5t2R2tYzIP++vu/rTItMso2V2gE0oAxJcMZWH0L5x+FS3eoW9nCJLu6igjxwZpAg/Wi4WV7jDPqUv8Aq7WC3X+9cS72/wC+U4/8epv2OaUf6VqNy47pBiBf/Hfm/wDHq568+I3hy3by4ruS9l/552cRk/XgfrWMfiDqeoymHR9HtkIOC19exqR/wAMP5mpuPRHeQWNjav5kNrCsh/5aFdzn/gTZb9alub2G2jMt1PHEg6vM4Ufm1eZ3E3ja7kKXl3fW0B/6BVojg/Qh91c/eWWhW9+p1HVS85GRHrMNwjt75B5H0FHN2E5Lqej33xC8N2RYf2kty4/gtUMn6/d/WsG8+Js2PNsdFZYeQJr6fy1P4L/jXNtqWnRXMccWh7kGNtzp11PBknpjeFz1680641jxAIX+x3F9CSQIvNukulwD82/cikcdME81Ll3FzPojWGveKtZBMGt6RbZ6RWU8Jf6ZJYis250mQobnxFHrV0o5ZzdQyoB68yZx+FUp01LWgyajOoUKEMa28ahjjlsqu49ezD0qW10OCKwW0lmkNujl8MVXLeucZz75qXND95jLSbw8sUtxp965SMbvLn0dZ8cgYH3cnJH8VQza+95dxwyeH9NvYz/y0nt/szY+gZ8D9farBj0S2GBJGrZG4oSxOD0JFSNr1igxBbSOBwCqhaXP2QuXuyrNp+qzopsYodKDc7rOafI9vmkx/wCO1etrW/8ANgjvL+6naNSWLzNsckjGVGAcDscge5NV2129kyILVIwB/Hk/4VA02p3P37h1z1CDb/LFS5SY1FFyDQNOsLl5iAm/J5IQD8amlv8ATION8bMO6Lu/X/69ZaaUzkNIzMx6kmrKaVGOqjr1NT6sod/blvHI7W9s7s2ASTtzj6Z7VXl1TUbjPlxpEPZefzOa0I7BQMEduacyW9vkyyInPVmApXQamPJbXt1n7RcO4PYk4/Lp+lLFo4D9Wxjoa0G1SwQFY2aZgMny1JH501dSmkJMFiwGBzK20c9+nNO7FoRLpiKcBRUosVB6VI73JY75Io8DPyKT+pNVJLy2Qj7Re5yRkeYBx+FTdsZc8qKNcuyqPfio/tForEK4cgchAWx+VZqappcDs20u3GDsJ/U1AfFNvEhSOHtgAv0/AVSixXRuLKSTstpME8F8Ln9c05TcyMcGKMcjjLf4Vyc/iqd8bAq+m1P8aoS6/eTNjzpPoHx+gqlSkLmR2KxyFRJNeONv8K4QCq9xdaaqOss8bt7yF2P0rimubl8j1OTxn+dPt9O1O/8A+Pa1uZx/0yjZh+gx+tX7PuyeY6O41TRgo/0RGPGflCcfXrWLcatpZJ8uONP92UucfTFR3/g3XIbI3dzZm3t1K7mlIyCTgfKDn/8AXWT/AGQIwS11GSBnAH/160jCHclyZbfWLVc4WZse2P506LWbYjdiZPoAcfkayvsoLYMyH64/xrtfBfgq7k26ldWlncWUqMqRXRZCcHhxjtnP1Gfam4wSDmZlx6yi8i88v2kBH860YdemA+S+gf2EgP8AWvRLXR3tRiBNLtB/0ws9x/NiP5VK+liRczXUr9OEjjjHB/2Vz+tZehV+5lXBkHnTyF5Jp4xukdt2TwfmPJYZx0549DWFLehJIt62rkAPtjttg74O7g9P6Vo6peQWtp5GXSd8SDbGY4mKrgjBHUbsHGPfpWM2pR31yPtNvGQx28HaVGcjae38q6sMpNc7W5507bGms1z5efs82HkP+pQfNkZ5JyD19ulVpr94iUcZIO1nmbLsO+McDj/9dWpNW2MyxL5cSrhUTgjHYf8A1qyJxHdL/o0YjcHmM53nPH49v/rVvCOuqJfkaJ1OH7PueQuScJtQIeOmT1+v1FOm1PZBxAxV+S5beWPfkYAP9PzrBmt57UkSgAZx8rAjP4e1IszIpUDKE5ZT0P1q/ZReqJ5mXjfeeF3Pjb0JBIPfG0e9WdO1+80u6W5tZ3imQYDKTyOhyDwc59KxpJN75wBnsBjFNDjG4Hjt3rXkQrnoGl/EPWobcRzXfmrHlg8mCxGMYPduua6TSfiXMzLFfWDu7kFTGQmAfY9se/8ASvIYZdjj7hB4ww4/z71aM029fN3MxHytnOQPfPSs3DXQtTZ9D6N4k0zXjKtjOWeLG9HUqwB6HB7e9a+a+drHVrvSLi11O1dRJCdgDLxgjGMdMdef8K9E8O/EYXeoGDUmj8uZlEUkUZHlsf4WGenofzqbGimnuei0UgOaWgsKKM0UAFFGaQnHWgAJwM1l3mvWVkkjPICEi83IPBHYZ9ax9U8SzafqZ5R7RWw6YGSMc4J98Z9K5bXNcjMsz7g8jkqQ8WSwBBUAE9MYBOO1c06/8oHTXXjOBWBhmixH8s69drfLn8ufr9eKk0rxbHcJcy3y+QkYXaxPysSOnsa8yA+y2MxYMgZBJJFwCHPckEscY+7z147U0XIhguZLYsWABkMZOQMY3fMe3481zfWpc2gtT1PVdf0+ZRb/AGl1YhJopY1faGySuSvUZUgg+3HNMsvG+kzxtvucbSq7iODlN+Sew7c45BFeYT67cDR4J7aZrqBRHuiJVt2PRGGcZyCRg8jtVPUr1tYt3mu4/IeSbzJY4ZsCMqCDkEkdgMgn7w57Vq8TZXFqdZPJc+Iba6ks7iZtQiWX/Ri4cncVkQsTwoTO0gjGMgcnnyXVHljkmiWOPfMQzpDu+VQCSoBY5U84OegH94V0cZvxfsjNP5cCjKSZDQNjOfMAyRuP3cH5sH3rnrvTLzzZJIrdtgLL+6O/GCBtHU5yQenp2qKdRKVmDMcBLNbqGWCOS4IAUs4Z4gRkBccBsEEntjjmrekadcSRJcxWkd/JKGAiX7ydQHJYbQMg5Of1xTXuxLawxzwQIka4ijABG8vgnOd/Z/lJIBIIHNdf4S8G+IL+7gmi0D7Np7n9411mIEHHzfN8znA7DHJwRXTKTtoCVzjZpSqyQO4Yj+MMwDg4OACOnA98gVTFtLeXSRRQO7ueIY/ncj0UAE5r3Sw+EmjRTh9YvZLx8k/ZrYGKPOcknBLZPGeVFdrpmm6fosBi0jTLSwi25ZlQA/8AAiOT9S1LnSRpGi3ueF6N8IPEuqusk1uNMg6hrtvnAzz+7X5vzxXoejfCTwvpe037T6tcjgqfljz/ALi/+zMa7X7XFOSsZmvcdREuUB/3uE/U0/beyhVBhtUzyFHmMPzwo/I1LqN7GypJbj7G3hs7UQWNnBZ26/8ALOJAoHbouBTftlsxKo8l24P3YV34/L5R+JpJLWygQPfS+Zzw13JkfgvC/kKkF6HQC2tZ5UH3SE8tPzbH6A1Lb6l2XQA15KMJBFbr6ytvP/fK8f8Aj1M/s8yuftNzPOMcoreWnX+6uM/iTTyL2X70kFuvoimRvzbA/Q1GbC3YNJdvJcKve4k+Qf8AAeF/SkMRLuwtc29t5eR1itU3nPuEH86DLdy8RWgiX+9cPj/x1cn8yKVL+1CCK1zMo42WsZZR+Q2j86PNvZB+7tY4h6zy5P8A3ymf/QqLjsN+xzSD/SL6Ugj7kAEK/mMt/wCPU9Y7DS18zbBbbusjEBm/4EfmP50n2WaTm4vpSP7sAEK/mMt/49SOmnaUhuJBb2oHWaVgpP8AwJjk/nRYGxYr8SJm1tbibOTu2eWnU/xPj9AaUi/l+9LBbr6RqZW/NsL+hrAu/H3h+2GyG8a9lA+5aRtL+bcD9a52/wDibOcrZadDCezXU3mN/wB8R9PxaldBY75rG3f/AI+WluvaeQlf++Rhf0p095Z6XbEzzW9nAP77LEv5cCvHLvxdr+oHD6nPGrcbLULAv5gFv1rOgsxc3QdlaabOclfMc/i240Rd9gk7bnqdx8QdBjYx2s1xqEo42WMDSf8Aj3C/rWLf+OtYdmhh0eHTlK483VJWXAPfAAA/76rHvtK12608pa2SrdbCkEjpHEsJPG44wcgEkYHWszSPhnrsYDXvi/Ubf/Ys5JGx+JYfyqlC+7IdS2xsSX3iTUTm71y4FqxwDo7W4VuM4B8xW/U1kpYaLb3yfaZUurmU5QanbXKs3bhkZlPPeuib4faRcwxx6vret6kkZ3Kl5e4VWxgkDHBx70Dwd4E09dptrVQpz+8vZOD6/eFP2XmR7VmRa30ltJ/oGlalbRtkrNBeGNGIH92aPcB77aZcXOszSyNdNZyQCPKia1jnkz/vbE9u1arQfD20bI/s9GByCl1LkH14enNq/gp8r/aYUHut3Lj9c1MqM+g1Uh1MBdAg88zu00gZQHjQiKJvqkYUY9jTBpcNtOZLeGNFKKqLFHjHJJweuOa07m08F34OzxTNG3+3dLIPydf60suiXUtvGmi+ItCugiBFW4QxsQOnzI7D9KydKp1NFUh0M+2tUlsbdXA3wsRuz1KkrnHpVXUr+9tpHigggZVx8zEk9M9OlXo9D8S2dyJLjTy1tuYlrQ+cgyc/w84z7VVvwzXTKIpWdoxwsbE8ZB7cVi007M0umjHW51W76zGNT2Tj+VSxaTJKQZpHYnjnmr9srqFItzkAZyQKuRtICAfKQH8/6UOXYLFODSI0OGTPueavJp6In3RVZr+CN187UUHBDBCo/lk1Sude0qJjmRpAO5zz/wB9Gi0mK6NgrbRuNzp+eaUyxADZHI+fRMD9cVy03jO1hXbbxKM9MknP5Cs258bzudsa47cIB+pJqlSm+gc8Ud4s0pGFiRef4mz+gpktwyKGlukjBOeAB+przZvEt/c7lMj49N5P8sVT87U5zuxKM90iJ/XBqlQfVk+0R6RJqenJnzbtpsdgzNn8uKzG1vS4GJW2DnPV9o7++TXK2Gm31/KyAuWC7iJdy8Zx1ateLwtc4zJcQp3IXLf0FDhCOjYXb6F2bxcdpENvGAeM/M3+ArPn8T303CyFR6IoX+QJrQi8L2qgGWeVznsoX/GriaJp0WP3Bf8A33Jo5oLoFpM5ObUbuclnfJ7ljn+Zp1pZ6lqLlbSOa4PUiBS2P++RXUtpmn7j/oNtn3iU0qWlnDPJi3t4wUUE+Wq92/wp+0XRBysyIvB2tTMBJBHBkZ/0ieOPH4Fs/pVyDwdblitz4i0pGXhkgkMzA+mBirbyadDGWaS3A6ZAX+lXNC1a2hllWKRYt3zsCNhwByecHGBnPSlzyYmkhLXwVoxY5l1e8IAIMFqIlOc/xPWrB4V01PuaEh/2r69L/wDjqZFTS+JdOtrCW/kuHe3jB3yRxM2MNtPb+8cVz9z8UdMjQGCxvJd33d7xx5HrjLHFOPNPYhzijoD4XsMEHTtEiHUCOw3n82P9KsWGlQWkEsMM80UXnuTHAVgQnPXCAdsVxU3xJ1CW1ae00aNVBK5kkZiWP3cD5c8cn/69V9L8eXkksp1C5jt0d2IESLGq5ByQ3zMSCFwO+apwla4vaI9HXTLAkk2qTEd5SZf/AEImkmttLiUm4tdOjUckyxRKAP8AgQrzKTxVCWiFzqd9egcuFlZQw5O0YAI6hSec4JGPlrJGuRGW5lnSSaSUtJGUiRRDKTkugYce2MdTTVNk+0fY9efVdI02ASrJbQxdmgt8jpngovoRzWNP8SNDQkI15cPnBKwbBn6uQf0ry/8AtmNJGEdmDHj5RcTu204ALDBAzwOMEcDrVA3YeQEhZJDgbmzIx7e2fyqo0n1FzyPTrz4mfZ5ZI49FkVkbYzXNxgKfoi/1rFuPiXrlxayyxQ6ZbRKOP3TOzHI4XcSCRnJ4AA/CuKM8+4zhJNwAbzBHjjoDk547Ui+fMsgDgCPlg8uASOi+5PYVoqaW5PM+567LbLqkExaMLHwbZWZ8AqOSGYhQG6Hd90bcgGufvrOWHZugEEgGGiHJHJIJ9+g9xg55rYu9TMF1bwrZi3aH78kbsm7cVJO7PyggDP49qZcxrp809xPG8kmZUjQsT8xYgOQ2OADxjnjkCuejiJU7JLTov6fU1+qp2cuphvIyqoI+fHfpUKuVYEHaR0x2p8+0xo/IJ+XkfzqAH/PSvWpPmjc4sRT9nUcUTGaRtxZicjnPOaaCduR2FRhsg9hT5I5IgodCu9cqDxkGtNEYhkYBApzvvcsSzM3JJ7moiccHg+hFSwW09yD5ELygddozR5gIp/On7iHDD7woNrOikvDIpGMqUOefb8KellczTmJLeVm/3DjpnrSugsyXzCYCPn6fwk449RU8U5a1G5lHl/Lhl6g9gfY81ShSQIZFyMMFwM7s9elOkWSNiJY3SQ4Yh1IOD9aVugz07wl8QriykSz1uV5rUr+7nK5dPTJ7jr7ivRP+EisMZzNg9xESP0r5ztpRuGW2n1K5rpNI1a6McllDO+XXCqz8DA5bOew7fSspprVFxm9j0648cxR3rJHZh7YZHmNMqsT2wp9efetG38WWEpAlWWHjOWXI7cZFeMzRXxjZJI3L53ZDZ3gdD7j0/GlilvHaFYZmEoxGFEoz6jHNJWtqw9pK57WPE+mOWCzsSvHKEc5/+vXHePPF5sIopLO7ktShB8wH/WgnG0dRng9s/L2HXi9R1KXTZ7Zb2Ry0kbKskTrsDhuo4GOcDnjNYviPxM19ZeTI5huomG3IJLjH8RYcdR3Oa5HVc3ZbGt31L0Xii0u9Wm1C61AKwV9kJOFYkFeMY28Y4O4EjoOK0LCa7lma6ksbhYFVdkjQuwljI4ZWOP4SfXO4815tY3FzLexKZZDtcOQIjNwOTlBywwOfau8t7m102C5jt5CsyZmeOHcrAkbsdRjB4xwB09qxrwjDRdQiS3OpRT3CSRvugJAUxLwpOTjdxg5zzj09ecxNQK3sltsKyuuxvPZgHTd8xXcQO3O70Hes7xPqPk6s6QEITHHJJGOAzPGj5x2OW7elUtOTUPEOq2cdnZXd1LEcSJbjhFJycnHA/wB44P4mnHD6XY7m9p5S0WfakG8l8Q20bEo+0OwYk8Z7qOgB46YoWN9A+o20FzF5cQlUxSZwQw4ALDqCF2j36DFegxfDO4umT+0b2GxgUlvJRVnkbIxjbjYmMnH3zySSeMdXp/hPw9ox+1JpsPmrjF1fkMwwB0zwvQdAKShu2XGnJnl2m+HfEOppEulWVy8TEEzXBVIRjK4OcA9f4QenSu+tvAql4JdU1ELLGySeRZruwVzld7gsQc8nAJwK6o3bXAzEk9yD3UeXH/302M/hmjybkxEtNFbRjtAgOP8AgTDH/jtVyRata5sqSW5TsdD0fRpXubHSbS2l/iupQC5+rHn9RVpb1blT5fnXhzyYVxGfqxIX9TUSNpwk3RI99MP4wDOQf94/Kv5irW++lA2xQ247NM/mN/3yvH/j1V5GlkugwR3rhYx9ntU54RfNb+ij8jUUsOmwSqLqX7ROOizt5r/hGBgfgop80MUfzahfOUx915BCh5/urgn8zT4Zook2WFi+090jEKfm2M/kaQx/n3UvENoUXs1y2zH0UZb88UC1nlYGa7kIHJW3HlD8+W/UUFbyT700MAPaNTI35tgf+O1WvP7NsgJtTuowoGd17OAv5HC/kKYiWN9NtZD5CxtN38lTLJ+JGT+ZqUz3Mh/d2u0f3p5Av/jq7j/KueufHug20RFtLNdqvazgJT/vo4X9ay5fG+rXQH9n6NDDGRlZby43/wDjsQOPxIpXHp1OyMVy/wDrbzYP7sEYX/x5tx/lVd4bC2MlxciP5GwZbp92OB3c4H4V5ze+JdSdT/aPiH7OmPmhskSEjPTn53I/Ko7LSn1GaOSDTLm6faWF1cxMyH0y0xIz6YWle+yDmsdvdeOtBgIjS9N2+cBLSNpefTP3f1rNuvG99yLXRfs6/wAMupTeWv5ID/6EKq2vhrVZFVZHgt4wTviSSSQH6bSifoatWvgWFfmuLmeVwcnyNtvj2zGA5H1anZsjmOd1LxTrc1rJPda6lnCiNI6WUSqAqjnEimRicfSuXGpaBc3Ekl3NfKRGZDcagN/CjJKsWUkk4AG05JH1r0XVfCWp308i6V5VjJIojE/lKbgZ+80krlmCDsqAFvUVq+HvhZoWiuLq5Q6jfnlrq8+c59QDkD9TWkYJrUhza2Z4zb2fiHxOQNC0S7+z7iBPcybYyO3GFA/8ersNI+FGtOFfV9Yjj5B8q0hDEe24/wCH417P9njj2gDjt6AVS1fVbPQdNa6lUuc7Y4wfmkc9Bz/PsKpwSV3oiVNt2W5x0nhHwt4bsftmrSARrxvupCdx9Ao+8fYA1jXPxJ0mwQxaPoV1Ko6MyrboffH3v0riPE3iqfVtXluZHWeVSV3s22KAf3F9vXHJ7mucfWpd2PtFoP8AdhZv61inUetOOnmW+RfE/uO3vviP4kuSRbWNrar7HcR+JrBuvEHii8z5tywz6P8A/XrEbWJAOLyLPtan/Gq8ut3ZyEuAQR1EIFUniO0fxF+58y7L/asxJlnkb6y1Ta1nz8ygn3Oak0rVLi4vJFnmyiRMxBAHORjoKoX+rGebv9nU5KhtuQD3+tVCriHU5Glp6ilCly31LJgmXqjfgKQQSkj5TViXT206FTfu0FxIgkWwtsBo1blTNI24gkEELy2OTtqkHn3EieQL2+cmt+efS39fMz5YLv8A18ibyJuu000QSkZERPuBULXVwvBkb8+tKksrorebICRng9KOep5By0/MtQz31o4eCS5hYdDG7Lj8q27bx14mtY2hbU7ieFlKtHcDzAynqDnnH41zh809J5B+NTW1zc2soYSebHnmOQdfx7Gpm52vZMqKhfdo2DrllOS0ti9pKRjzLK4ZB/3w+4fgMVWkjtrrPka2Yyf4byIj/wAfXcP0FNu/EVtBN5fkSSKUDbgwHXsQajXX9Jk4lt5F+sQb+RrjdWb1dNm6UNuf8CtNo2r/ADSoj3cAHL2comUfXYcj8QKrW2nvcMSqqMHB3PjH5CtVb/QHcOpEbjo2xkYfQgVdjuLS8ceVfrO/YPJvb8z81S8RZaxa+RSpp7STKMGhQOm6Rd8gJXbvIXj3xmmzeHDLLmKOCJMAY3k/0rXH2W12m+1A25kkYKBbZUfV2YAflVePXtFz8zXpGwn55BndzgbY19cZO7gHuRTjOUlzRd0Q7RdmP0bQobCY3MmySYEeWU3YT1Puea3TfRRgK1wqn03/ANK5mPxNp6runsQT12xQhgP+Bzu3bOfkFV28YXn2UQW+6MkqWkiYRsSGJwPLAABGARz04NDpzk9Q9olsjrVkmacMsNy6GLr5L44b1Ix39aSW6EcMk7vbxRR8SPNcxrtJOBkAk9eOlcBLqV5cXMd2EZXjHlK6mRssRzksx+Yg+3TpSy6hqihw8kyeamwgqAWUYIXnJx904H1p+wJdVnWzeIrGG3E32sSR7ygaC3kkDHqQCwQHHseOKqnxPbs2PJv2weSBGgx9QH/nXIzT3P2NC1y+x2cLEJSCDgZbbwADnGe+D6VRM/y9Bj164rVUIEOq+51d14omLFYLS3Az965nduP90OPbtWe2vX7sdsscBxgvBEo3EZxj5eMZ9e1Zlm0NzfbHmeK0QFpJNoL7QMnABxuPQZ4okEUj5toyERSz+ZJ8zBeS3YdMDA79K0VOK6EufmWpNRu5XDS6lfSFQQu6XGM8HHJ9T2ottTkso7oQcPcqElmdmeQoDu2g8cEgE+uKimeyTzJWRJZZEDpDbuUih3D7rAjcSuOmcHPXrmpBcmCTzWghkIz8ssO4NkY/lkj6CqcE1qTzIsyXriIRuwWNjvCsOGIJ5wxOTknn3NWobPUrqQrHG2dvmFjIqqq4HzEjoOapWl+bCSYrZwzw3MBR4poeAG6FDncuCOCCDxg8E16TaXOlaT4TgaL98JTgTSRyAygAKGVD1OVKjkELtHrWGIqulFcsbthzHBNpF6ZzHIIlfJUmR+Mg8DJ45PSr9joFlLHM9zrHkxx4CyRWu5CcdCc/Lk5wenB6V0t2893FPNdRm0tIzuHmRgvGAdrIDghScHGWxx+NcM1wkX2iKQsmJXBU4dgM8A//AK8cGnCUpITky/HbaJDCPt1xfNKyFgy4VOWwpAwSRgZJ3DrgVvXXhnSrOxtr1LVpFcr8k0hZjlNxDorHaMYIO4cnnjFcogubUW+oKkTwiQ+V5jhlJHX5T1GSM8YPQ960rfV7bTbd76K4L306uCsgyRhgFDrs28puHBIH4mh02+rEpM6GRvC/2K2CaRapeMhVSsbFCgz8zZUkykj04GOgOaz77VEuI1uLdI7IlPLRreFFXY24MGC/dbBPPTA6ZxXNahcQwW+n20cyZ8lZJTEoKozdAcDJYLt3cnk4GMEVSivnSykX7QrM42eU8ZOAe4PQGn7EGzVnUWlxKt2shYZUwsctH/vKcYBJyDwR1xVeaP8A0WNtoAYfux7A9VHofXnp17VRkub26jSSS5LtnCs0hLKMY289BjHH0FLHPHbkwzHzdjFcgHBGOV7HGf5VfJYVj2CGOWXWNsq28LRoqq8cADsGXC4BIUcZ56kkdahvbSZ7RVSOdbdM+WmVCq/AJbJHPr09OxqOO6jiVzlJRjdl8lQemcA4XngnvkEVNAwLOECxxOyfL5vmBSMDBJ49jk8Z9q8mT5bS7HXGtZp9jKt7ITxul1mMGNpEI4yVPYE+5q9Fo1m1vNLIFB2I3l4IdePvAE9M9ePx9dCRo2mnuljuIXANu8XmlVKED5Xc/wAJHPHUA4z1pPsrtcrHNcsqRQHKbEVpudnysScZUK3f29a7aWJl7O7dv6uY4iSqT5rGLqcEEVvAkIkZUUgNsAz6kj8q1Ujjk1OS3hBZFBiyy5ypUKeB1Pymo7y+hEIs7WOYW/nbW80Dfx17cDDDg/X1p9rbRm6tpbmJLmPj93cMybjkdGUjnv1zXVCo2k3puc/LqZN/ZxWjxbS5WQblBbIxx369+9ERjIDfNlRwVbBX34/lW1Jb2V5PcRw2lsc7gNhkA3DnOc/Mcmqn9mqmEVofbbub6LgH6fjW8ayaXcnlfQkF7crbCcTt5qI6hsDoevGPYc0g1q7ulCPN+6AbKqoX+XNPOmXU00FmrvE8mFBELZJ5Pfntisa+t76yVortAD1J7gZI+YDpyDwetNWZTckXDKROtxa5E0TbgQM8jo35/wBKq3kDKudh2uvzOW3kuep56GnaQYn1KBZ7qa2hIPmywJufaBkgAdenpXS3194aubV4otNv42yfLeOUAsAeMs5PXH93NNtpiUeZHFqCpwwZHxkAjFaNkqvEd0R3gghw3GPTH171es3tyGhuGkAYnaUUNgkY9C3p06+1dUvhS5ii09H0+3/dHMztc+WZs5BBwuQRkHuc8U5VLbgqVzk0imuLlPKyZC3c9+uc1fWLYZLpXnxOwWNl0xnjY/whTnB7gkdx0NO1ApbWOovbLDAYN0cjeZuK4JyDzk8LjI4rM0y0WGzgjunjme5kBDDe2FKq+No5AGcE9OO/bir1W7roWoKL1HCwg1zUvtk09obSR1dRKzh8Lwe3HIUYDc+o5zzXiaZjqb29xbrHLGmwuWYEgZwWznnp+g6V2y6YUshBpltcXUgQ+VAh3kHceSB3B7n69qlf4V3uq6w15qV9HYxMkWYox507MI1B46LyO5NY0GnK/RF8jlscBoVlcyarFdWNrLKkTASeVIPkOMHhsn9K7H/hHNf8QIP7LsxGpwrzSJ5KY6Elj97nsAfbvn0fTfDuh6HBb28Vqsktum2KS9PnyD6IOB+AFbcvny5dl8qNRy9yfuj/AHAcD8SPpVSfNPmZrGhbc5CL4a6Tdzw3OtTS6hLHDFGYoSY4RsjVTlurDK57da6e1jgtLRbTToo4rZThbfTYwiDn+KTgfXkH61KqwzYYJNf88M+BGPpnCfkDUs7SqiG5uo7VM4VY8E/QM39FpuTZuoJCMsscRlnuIrOI9dhBJ+rtxn6D8aSP7OJfMtrOW4lxxNKP/Z5Of++RRGEWQS29nJJJ2nuDt/8AHmy35CnytMimW6vEgTHJjAUf99vn+QpDJGW8kUvLcRQL3Ma7z/32/H/jtUx/ZzvuCy6hICOcGfH/ALIP0rNu/EXh6zYtLN9rkXvhpyPxb5R+YrFvPiWpwtnYqFHR7qQnb/wFAcfnUuS6jSO4868kACwxQKOnnPuI/wCArx/49TJk8uIyXt/IsfchlgT8+v8A49XnkXifWNYbZFcXxB6rp9gyg+2/DH8circHhG+1BxLNZOXPO+/lLOcnrg7iMfhTUm9tROyN9vFHh2wdzaOLmb/pyhMznufn/wAWrJvfHmoyA/YNGCA8h7qYMx9PkT+rVoW/gaZwRcaiyjJylunT05bgH8DWrB4I0tTmWCS4Iz/x8ysy88EbRgEe2KpQmzN1Inl2o+L/ABNdyGEvfDLbTHZoI+T0Hybn7etQ2vhfxRqMnmrpMkO7rLcgIfqWkJb9K9xttNt7GFYreNIIxwscK7B+QqwtrEvzMAT6mr9i3uR7bseS23wxv7lg1/qkOB/cjaT8i2BW/p3wx0K2KvNavet1Jm2hM/7qgfqTXeh0z8vQcHC9acGY/wAJH1qlQiiXWZj2egWOngLbWdrb56CKFVP5gZq8lihIaY5fHQEkf/XqcLLlizjGeAFxgfiaZJLDEP3kyj/ebFacsVuRzSew4wxKOFH4ikZY3GNgYewqjNrul2pAa7jyegQZzVJ/FMEsxgsLK+1CZVDOsCABAc7dzMQoJx0zmnZB7xtxIII9qKiDOcDAApSxHVh7YFYH9o+IZziHRLW3H966vgSPwjB/nVC+8VxaBlPEV3bxTscxJaIzfLjJPPPcDOB1prshW7nUTTfZ4i212JyfX/Irw74geKZbuCaZJmGG8iAdNmT8zD3wD+ldLP40tNYvru3tLS9u7RoQQ0tq4RsMuSSeBwX69cCvK/FBCaVaKAMCbgD/AHTXJipP2kKT6vU2pJKMpHPRsXXac4A+UdhV4wWTNKWuAo3jZsHRffI61kFyDwePY4pvmv2c59jXRKLlszA2TaWHzE3AOWAGWGR09ueM89Kz5o40kdV2soOFYc5HrVXz22j5mz7mjzXJHP4mlGDT3uK4QymM3uOvlbB+LAVHakC5R2VXCsH2sMhsEYB9qdb/ADXFxn0/+vTLU5mTPtVR+KRq9ka00095cTXc7F5ZXaSRyerE5J/WofNXoBkeoPWq94MqSB823AP171N/YV2rHE0uQf8Anl/9lUzqQh8TsKMJS2ElIK5wc+9SwgCJO/FQnSNQU7RK/PYxtz+tRDTL0NtWYjjI/dvgjpUqvS7leyn2L+5cZyCKQPwB3JrLuYb+1jLvLkD/AHh/MVBb3Vy86q0rY3AHB681rGUZK6JcWtzUv4xJbs46oMj6d6yCTW5MuIJcE/cPGfasQKcjj8KokN1Lu9abk5YY6DAz3J6U8r6D9KLAbmmah9qiOm3xMsMoxGxPzK3YZP6en0rsJ/CeiT61Z2tjFJHbFBPLJLcNIZEONsfl8MrNhseueOK81RinzDgjkfhXYQa/eabrsctp5cTRhGwIwRKWwD5nI3Z4HXHHTrXn16TjL927XT27mjk3DXob13p9hpOjSWiaJBHqKWsc8t1LGjiJpH+VN3Tcp4BHBAPpiuVOu22qebLd6eUvdm43NlOLddgXA/dYwSTj7pG7Aq1eazcJFnzII45SC1rbxDykdeVb5wfnTIGRnIGM1i3AuNSV7oR2qxmRsCPy4gCepCDBAz6Age1ZUKNk3P8AN/1+hkzUuNUYadpSedawTWxJZYmZGIMm7eWHJb5cEqcj6njCurqO/uROLZLeDKxM4YuTtXkljySfvHippltRaxLFnzWOZGlxtz2568dznByOPlNT2U1jetBbahA6HiJZo28vyVx9GDDkscqGPPzc11RhGOqEUXk860SFLVWYoFM7sSxdST8rcADBA2nPb2xFO8d3bzXEzyLMCixncCpXGCD0PAAwR9D610F2+itYJY6aLq1uV4mW9OYm+YNvyQP4QPl28+54rm7mV5XAmTbJjGwRiIJ3Ix6f59K0hK/SwEov7rfGFnEZtS3krgfuyep6denv0pHZfs/k2yuU++xlVS44xkHqM5PA7Y6nmleSORV3fO0IKrKWK7lzxhfUZJ/E5zULM7Mqx28kZZAFC55yMDr6j8OeKsCZLy5mht9NREZRvVI1jALM5HORgs3A6noAOlJIUubqT7NbXRiVVUwli7LGu0AFu2W64GBnA9Kkj1J4dOu7eMQn7Yix3DuillCsCFU9vurk4z2qrCBeXLtPduJyAUIGS7dMZJGOO/tT23An0rSp9cna2tfKSbb+7DOTuyRxnOAAMnPt6kV6lD4Xh028ktHY3UEUIPzxbpS7nCAR4yYwuwnnO7t0FYHgDwrDqGpG+adXs0ikWaPaHlbIwSgHTqBz1OQAcZXqXs08LXMlpqlxdSLLII3ltdyK7qhzuYg/vArAE5xjBIBGRzVpX0KS0uc3qNhrI1a5tINtvNJOZIhFqK7SFzhBIzA8ZJ5GSQSOOa4+SCCacpvD3hb946uZUZix3MXzgDJHJ+tdLqVqNItZ57mGWOG7RmH2naWkLqcf3u4xycgA55wa5TVLuCPyUt9oUxjdsGAB1wfUjPJ6HFVSlzbEsvJNpdta3gvdt5cCJ4YnVcgtjapDZ4VeoOMn6VSttG1HWGC2UX2janmNmQKsKFtoLMxCqCfesuRs/dkHJxg9fauk8L6Lca1HPaW842LEZ5lEeAgDclycZwozjOB9a1fuq4GVcaLf2UFvNd2hhjnjEyF+NybiAcdgSOCevalh00vby3CYMIXlnCqBjBIGTnPIA7nNaS6hNc2aqwinSBfmkLFmIwSEO45IUtxjpnp0qoI4I13GN5I1b7iMeVHJ+b19PrUObvZAIumXL2k9/b2SPaWw2zHzP9WSeOCc8g+/esu7if5nKxASSZBT1xyB9PT6VsXNxbuLmCF4vsvmKx2rtaQDPI4HPJByMZrNO+JvMgkdFLn905yApA5yeD/9YU6blrcD0xJYp5MRvGEQb4nchh83GDx1HTk561pW14rSyrHGUyNoWYAbyxyexIXI55x1HQ1UiQR3CNhkt0Eyb3bEYTaxCjhSxwOpPJP41I7o8nnJGPKjbChweH7K5HQAcgZ/h715Ul06FvQl8xoUlullMRK7187f+8XIwqjPYg4OMYqbTVujK0FtHEwWOMSYx8+4uy9QDuHzn8OT0zCXAheFxciV2AjZhkxhFPzFs4+bzCTuGBkdMVcs7d9Omku4nmMcqFclAWPToe+0g8jjBx/DXTBQjDUjW5mQ20890gJZEBExIY4Vsn5ce/09aNdjKwCUED5wgCsx3rjIJz0PQdf51ajvI0O8pEu6fD+U2e/Pylsk8cisy7jvtTu41SKSTzcYjRCABwBgnGQO2e3XnNb0qjlLXSxL2K1nfmKdTKHMe0rISTu54+UjkHOB9auJqq20kLCzWOCYKw2MUdsd+vGDmtB9IeAQtbrbmMK6SM6jIIBJ5HuelY8+hX9/dTyRxxhUyrNLKI9+BngHHTp9a1VSlJ3bsiWpI0ft9jdQBi8kUyghWZnbaw9Dk9fanPEBtjBS4kYHAUFiQRx9ec1j3Ph/VbK38yazZo8Z3x/OFGM846DFT+GLm8jvGNgw8xVzlUB2AEEkN0GeAehx3rSLja6ldApO9mWIvDurW86brePIJG3z0OeOe/1/KrQt45IY3MaRtjlRMuTnOOWPXofzrqtS1K6vURoNyT27GbylZHYOUwvG09yRjHPFY10NXuJFN44t4SUKxTQQyNIy4JI2qCQCBkZ74p+1jPVSRXLbYzB9gMihZ5oz1WeM7sHt6Y5xz2p8Oo31tG8ztOqkrwssyBsDAw6MOO/XBPPJrofsMcE0p1e00+3ivGeQy7N7QfKNwVVBJjzgqeNp4PrVC3GhJJCbmGzlVE8i4MF46724IuAOCwPOV4I7A00r6FJuJQhuWgsJrqQi6lKq6iWTJAXPIyp5B/n3PTT8Lpa6jPpck6CKCaweWSCN/JQOHKgjByP4u/fA4Apmo2FtdxxxWWlI1kD8iSSlnK5zkc7gT1xnoevatfwfpiafcW8pEcU6WssQKlpNoMpbao6Hr19u9cLkm5J9zSCvNHbW0QgtvLtrdIIByePKT6n+JvqcU5dki4V5Z1P8MA2R/i2Rn/vo0hXP7wxbtpz5t03A99o4H6VlX3i3RLHi61lZGHHl2wJ/Vc/+hUHbsbRVrePGbeyjbr3Y/wAgf1oEauystvLcOD8slycD8AR/Ja4S7+J1hA+NK0wvKTgSSnBP0Ayx+madaWnjvxS485X06zfk7yYAR/uL+8b8SBTSvsS2ludnfalbWCb9T1SO3/6Zo2wn+bn8MVjw+J7W4vkt9E0yW6upCdryEQg8ZJLPl8D6Vc0z4d6dYANcyy3Mp64/dKx+g+b8zXR2mi2NioWGCKDjBEYwT9W+8fzq1Tk3qZyqpbHnmu3/AI1XUksLexu5SybnbSY1EcRPRWml6nHJxjqKgb4f63qQSWe8eGclt5up/PZRj5SNvJ56/MPxr1dUiHyqp474/lTg23hVAA961VFdTJ1Wee2XwnsM79Q1C8uiDnIxGMemfmP610tl4K0CwKmHSrbcv8cq+Y31+YmtuSVVwXkVF754z+dVZdUsEUh7kN7Kc/ypqnBPYOactrlpbeONQoG1AMY6Y/wpylDlUX5RweO9Y8niK2DYggklYeg/yazbzxfJAwQ/ZLdyu/bPKA23OM4JHGeP/wBVaeiBU5M6wlsDag69zjFLggdQBXn0niq6mzt1AvjjFrAz/qF/rVOW/u5su0F5LgcfaJVjB/Alj+lUotlqh3Z6DLeWMM26W7Xe2FC+Zn8gO9BvY85WByfVgF/nXm6zXSTIyx2UOx1YbjJKeGB9VFd2YLjdta8lzk5EMax5/Qn9amaa6kzgoPYlvdTuLaxnuI7dW8pC+3JJbHbpXHXPjq8xIr3NhZPj5UaVWY+vAOR+Vb+q6fbyaTeiffN/o8nE8zOM7Tj5c4/SvN/Lay2qFWBCpb92hXb26YU5/nnrV0YKV7mbb6GnceJL26yPt97OO4trZ8fmQo/Woop5zNvNnM7ZbH2q6ROceihzVWe/SaRnLTHJyBwoH5kmiK8RlfHmIRlj+83ZyP8AdGDW3s0loiW5Pc1pre/2lmOnQhcr8iSTsO/BZkX/AMdrb8FRuseqSyTGdnukBcoEHyxIMBRwAM/zrj/7auAoDGMLgqw8sdD7nJzz1rodI1uDw94TN7cRTS+fqDxRQxAb2bhe/HAjYkn0qa0XGOoovU7nPFcJ4p14aBqF9fRqhu3kitIWMe8r8m5jjq2Oy5AywzU1v47mub22ibSGt7eVwpkmnBYA8ZAHXnGea4P4n3DS6rNHnhZLqQfgAn/stc0Gty7GppviyQW9zJqWo3JVGILqqs24gHafLctnCnG7AxmvPPFbE6fadeZC3/jv/wBerVrYG08PGZRtSWNQFAwDjOPx6/nVPxZxYWY9Gb9EFcuISlXpT6u/4I3hpCS9Ct/ZtrL5KNM4kMIbAVf8KwyFdchVye2M/wAq6YDlDsHCD59xGOB7+1cyJHO3CE8dhW8J81znasIVG3PyHjAHQ0RMDIgMSlc889KcT5mSy9B6U9JvLO9FxgHqOnFUIq2xxJdHphP8abaY+0Lnpn+lOC+XLfD0UDjjt/8AXptp/wAfK/j/ACNUvikW9kWrwDt2I/mK7YRjceO9cZdY8kd2JXB9PmFdwPvH6mvJzR/D8/0OrC9SPyhvHA6UjRD0qfHz/hQwryOZnUc/4gQDSrg9Puj/AMeFcjAMXKYOfnHPrXY+I/8AkFTfVf8A0IVyFuP9Ii/3hXv5b/Cb8/8AI4sR8RszY8iQHJG08/hWXLFsQH5tx6A961LnizmI4Gw8fhWa+HChVOcfy4P+frXezmK6xlpAcknrn61ZWJSpyxHrgDApi8tkfLnnntUkkZYcHBY/l6UNiuRzp5cQZZCwJIzjArobjMEhlaGHadixtKS29woBGOgADDJIPTjqaxdRt/s1rbKSCGUOAOwYZAPvXYf2dpa2h1K51uO1vROixRqN7R8ADcgyxycsMdAM+grkrayj8/0NPsv5fqcwt9Ol3bm3jgjlimRwiDCIy4Ayv4c+uWz141tU+13aS6pe3sQe/UyPEArSb8EKy8Z8tvm2gEcYB4GTTuILTzpRLdW8jTLJjztxdFwAhJUEiQkk4yeFOetPgmtFsLy41GGG61F2ijhZ5sOqbcEqFIAxsCnOMBuPcndK6X9XMzJ3u0sbPAJFztwGwXGemR3x3pVufNtZrdg4dpAwPBEajg8DBLH5Rx2zxzUMkZmdyEgUkYGGz+J5NSeWbePzDJBKFhYBZIy2w89O2R2PTmtlF9gsOm+80ErPmIlPn3IVVOMY5A+mOP1pYrSCS2llF2jyquFzINxcAnhTj5ML1PI44qCWFpXSdp4C8zEOBJ/qj0JO0YC4Pv3HarFvGbHU4BOrW0bOjGVQyAxlsrIu4fdIwRweBnmjYRQ+a3LCW3k8zA5lBUr6ZHpV2CG3uJUkuppZHkRBHHE+8n5woXA5Xav8JIJ+XGAafqF5/aEt1cSyvLdSSb2Jw4JzglXPJXGMDr+ArOG14vNk3ZGFTbtXDDGM9z8ue3XHNO10GxPqV2kOq3YsBcQ25DQrHMFDomSCpxwcDIz1qGzt7a5Mq3Nz5ThP3RMZcEjsQOcY70wrDvciV8q37tlH3gD1PcGrMLSNbthF+zBhuYxEkk+hx7HGT/exTSskg6no+heILHw9okdvFhJ8r5dvp84klacR8zMVyW6hcDCgbgMncaxhrF5caqi7JIJ7NVhhs5YX3tIu0yKqjq2UHD5BzisqTxBbLcWjaXZQWYtk2sfJG2Z2PzFwSfkCjAGSeOTyadfeJr3VJo7mJh9oRHUKSVLRgAtuG7BL4ycdhjpxWThcq5e+IDtN4ivHdC+QjGQjJPA4bnAIJxjnH44HEXJWR2JJ3E5J9T36dB7VoXGqyTaaLWWGMbJ2kGxNhBPUH9PoB7msxiUVJA6OrEnHcY7EdqulDlViepPbW5WTabOSRpl2wjLKdzEYYAfe46D3qc3Ulo00NtPNFBNGIpRESBMB13DuMjpQblIrSGYSxOc7REfvIRyWA6Edsnk88cCotQvIbq8e4itvJjc5WNpC5Hbk8ZOcnt9MVerAZbyTNMsURf5jtCR8Fs8bfxq9JNbQgITndHzsGNrE5APPJGMe2c9abpfkWkSXgnIunkZQQzIYE4Bbd0JOSMc8ZpmqNcPduskajYi/cIbjaMHjgDGKl2vYBiyLJKgiBWQv8pDdPTJ7fWtCKOe0tPtsMwRSqugZMjPBIGRggHHt+dYSy7ZF3IGGPmHr/hWybwyxPIwBlwVKRr8sa9gvpjIwMEd85NTOL6AdnBNL5ryzNE20na8U3MYwVDN/dT72eMscDitqyuEF4qrJsmlVPKckL5bZOFZAucEtgjPTHP3QcmK/gu1UMYtqDbwpBRmX73oEDDpyRz1zmrMlxu3l7eKTzd/8XLfuyACw93J6dwe2a4GrOzWg3bob0lyIb47vIi3FswPApEcRydwKnLjORzyvQg4zUsrRMjEouwyndtAJwCA+fQgDPvkjIxmuYstXngnBR5I1JyAWPyqeMKSwwMcE4+916Vukm8hWOMRKV/dB8E5A+Y9R8uCQPlOevqaJQ2bC9xzoszeX+7f5xsRMgcfdbcc/Xp269aVJjJGYImWWbeFVtzHbzwVzxxkntgfhVWWR7eLyvs0krM2whD93cRjdtOemeemTzgDBW3u4LERJ50jhnLOoXhe2eCd3HeoUbMVzSz5VrLcLlY40LFMcDnt+HNV3a2v0UXKhlU/uz5mzeO+fVTnp3x1pbotdqskMrRFXLOUbawznp6dB+Brmb66tbePYil1hXCHO5tx45wex/wD1DNVGHNtuDdjoGVYLoXFvIYxGjeaiyfKcg8Ed+R19KdFezXRdWjWG2BBCxkIsrEYKtj04OfqK56016O5jkinDEMxJlY5eNOpA7E5A5PP4Vq6fbl7jypXje2Clo93RgQDlgecf4VbpW+JAnc5zU9T1KDVpZJwIHZwyqFGNoyFI9uOD3qsZ7jULKT943nRuZt3mYwm07/w79fwrR8Tx2szpNbhPNU7CFV8soyT3xhcHoO9YEM/lz7yMoeGweoPXH4V20knBNLUylozpNH8XtHJbwamkVxBDEse6QE/Kn3QQCMgeldmPEVgIPNfS9NOVyuIWOOmM/PnoR2zXmV1o8kISe1eC4gmJ8sKSSuOSGGABj/Cuw8P2UktojXJSOVjvPmfMrZKqO3GADzzgj3qpThHVFxv1OjneOeEMNkS+UD5iR7EjBGSe+D0wPrTZNT1Gw857ECKUhgZZIgVwHJxyfl4BOcHt61UvZRbTokkSTMiEgREHaOqhh2BPUeg9eKNSkuLTT4ZbJVW8zIYgp3lTkHgrzjPXpjNebe2v9bo1Ta1Rxl14uvtS1NItSdpEJA3zSNtjz324wBz6ZrUubt7G5tLYQiee4RCkcJwdzOUCjP8AtDFc34j0W90yQSTRjyWBPmbgcNnkE5564yOvWulhvDeXnhK9ggjSURq6nZxGV8wH5gDwCgIHb1ya6lCFTlktmUq1RaNntvhzwxb6JapIVjkvduJZyOh7qp7KP1xzWlNq+nWfE+oW6+xkBY/lXCaPayapqnlani5jitjIsM0hkG4sqgsCcZxngjvUVzpN697dy2sE0dosrhTGvlxqqnHVV6DHrXXGCT5VoEnfV6nZy+KNLDlozPOQMfu4m2j8TxWZP45to2IjgiT3luEX9BzXLPpMe1JLl0O5N6pLKjuRtzgKXLE9e3vSwjSFcxw6rZysASI7WTd0/wBpU2/rV2it2K3ka0/ji8l/1JQD/plbSSfqwAom1vUZsZ+1YP8AfljiH6E/yrLW40CaPMDXc7tGXSV7V0jJK8fM7A4z6Cn2viLTy0Hk6VKY2IHmzXKJkA43bUDHt0OO1S5U7GlN23LJe9lIPlwAdyWklP6BaTyb1yAZkPX5RF5Y7eu4msx/FFxbXN1aW9lp7LbSGMzXKySM5xknYCFHJPGaiv8AxVqttcWosrlLQz2wmlNvbpySQoChw21eGOPeh1IrRGjkbh02edgk5Lp/ChLgEk9hxu/Kq1v4N1CPxbJfmCKK0WySKL5QMNu6YJ68Hn3HvWG+u6tc+HtRMup3kkhwqStIA6EtGDtKgbevaqtvdRx6gjPawTEsoVrjMjA54IZiTmolVtZoOZpaHZH7DCpa/wBXtLViflS4mCswHGQvXFMlk0yeyMljeC4Cv87iCREVcEkgtw33egrlJ9Vnt9ZvpIbgo7C3XIGW4iB64z/Gaux3uoXem3ay/aJy0Eiwho2LMfKbheMschRx60/ay5+Ud5XMSX4r6ZFj7H4fvpgyhlNxepCCD0OI1J/Wutl+IV9c/DOfxPDZW8V6tx9mSIlpIw5lCBjnBPB6etZHgn4YWbWkd94pRYhhdto82w4AwAwB6YHTrz+fQ+N9HiuvCzaboC2VtD9rgkVY1xGNvzMcIrZOcdqHLuc7bb1PJtV+J3jWa5ltv7beBFbpawJHj8QCcfjW7puo3up6Fp91qN3NdXJhIMszbmI82XAJ+gFa9l4R8PaVbR/2hKlxI3zSTTW5Bkbv/rSqgVBftYPclNMI+xIqJGFKEDC8/cJX7xPStsO7zE9NSvuz0z6U+NsGTp9z+ZFN8vaBkcmgKxEgHTCj9Tn+VdjJuOk/1Z9yB+ZrT1Ej/hHPDaHkPJe3XP8AvbR/6HWWwbdEMZJdeOueRWjqrFNO8LQYyV0t5P8AvuRP8K58U/dQRSuaeoN9o1DRLcAr5NtbxYPclh+VcB49uTN4kfnIa0upPzkl/wAK6/SF8zV7IAAA3UfTjowP9K8+8USF9aG45I0onk/3t7f+zVyUtmzSVr6F1+PD8ed3+rUcn29KyvFv/Hvaj/bfp/uitqf/AJFyAdMhRnPsRWH4uP7q2H+3J/IVzVv4tH1l+RcPgn8jPnjXcmAh4B5qFk7/ADHPJ7VeljUOCFGcDOB7VERknjIroSOdspNynygDCjPPXmq85Ow5fk5H1ODWlMg8osOGx/hWTcKPLYkZwD7dsj/GmtQWpFvMhvHIwWC9PypLU/6QMe/8qReI7r6qKW2/13X1qo7v+uhb2RfuASgBH8cY/wDHhXbj7x+prh3yVQZ6zR/+hCu4X7x+prx813j8/wBDqw3UePv/AIUrdKQffP0pT0ryDrOf8Sf8gmX/AHl/nXJW4/0mL/eFdb4m/wCQTJ7uuPzrk7b/AI+4h/tV9Dlv8H5/5HDiPiNW84sZ88HYazoWJYEtwRyB6dcVdvHH2WePHRfvVUA9Gx1H3c8dK72zmZJuwOSCvG4H26fhTcooOfnP5554oUO5y8gwScADmprWzaWMsxw3mBGBHqpbP1yKm6W4rEV9zbRAd3P8qs6tO1rrUk6JtKlf3jLuXG3BGO4PcdKr38bRwW4Ygkktx2zg4/pXRXWtX5u4LMRo0FlcFkLwqVBxuywP32G47c8LnHOawm7ST8n+hovgfy/UzEOmGx0gfIXDSfacAsQO3Hr7f0q7bQ6QWVItOvZA/wArFopCAM9j26detaGm+LJLSxVU066klSFxzNhNsfBfnt8pzj6ZqzaeLNb1HVE063XTo5nRiolEvLhSdnXgnHUjHrim6vInfZeZUpJu5XtIjJEiP4dvZAIkDbrQgOVXpj3IAz+dYV5oeqx2l1PJp00UKI7sW2jaB7ZzxWlc+NtciJXzLAspwSlvxkjIwW69fT86y7rxTq1zBNbST2yx3CESCO0jTeGHPOM/jVxlLoQ3EzLCQi5g22azSfJtjKtgnOegIznGP/r1p6z4hOsywNeabAiQMd6237pmAXaiA4O1VA+7jnn1456MhURgQBgFg3IP+fSrUc3+iSRiY7WkTMTBtpxn5/7vGSOQT8xxUunFzU2tUQU3kkbKhW54A646VpWmnEWd1cSO6tEjBUMWcAYBZgRwMN25z6YzSpNc2QM2nzXEMZKt+7ly3yn5SSAOMngkdc1FcN5k0kk11NJIQzGRlJdnOTzk55PUn16GtbgQWtrc32bW1Ekjj51hRclyByfwFSqgGmW7fbIpfNY77ZN4dFB6sdu3B7YJPPvVUZk+VQW5yFx3+n0rRS+GnacRZXIZr6Dy7hCv+pHmAgA57lcn2x68Jt9BGeILiW3eWBZGggXMjhcLHk8ZPTnFTNc3lxbJFsLW1mrMAicJuPdhyeQOp7VWSfaHTc6iRQhI9BjjH4Cqw54J49KqzAv31wt9MrwxtFEkaxgE5yR7KMDJzx0qGSB4CEuI3jKnDAjDL68HvUlpNbR7muLcSgI+1VcjLHhdxHOB1461Xuk8qZlIkDD7wkXDA45z+NAAwXaSWwR2Hep9NtJbzUre3jieZpCMRqfmYYyQD64BqmJCAQQCGGOas2F1NZSC4gJSeNg0Tgcqw7/pTewzqfEVrDp7WtoAIpZUy8cbBWRejK68hTuUYBO4Ac8njBt7nckloXnMTMXYxZIBC4yRnBH1/Oqb/ab+5nuZd8rySF5ZCOrMSck+pOav29tPYRNMZnijni2EIxHmKe3uPl57duorNqysBHHYyRyTKyoDChY/OvOSBgHPPXtUD3MhjjXzQPL5UDqMnkk/gP0q4LpVh+zSBPKPyFnAPqepyV69vSsu5iWGcqsiyA4O5Tkc8/1HH4VUbvcD0rdbzCB4Le3Eax/vkVsIX3EqMnofcHHpyKJVu9OtZI7yR4EnwMN828KcgnGRtXjoeODyRVAPNbqqxgEyZOdx+QEnOOfcZ9CKt34WSEx3loUlL8onKxgDnrnByBz1PPauFw112C10RzTNGJJrcs8QJGzzAxCYwcgcg9zjj5ucVp22oQKu0S+XI52xxLbAjHAPAbjHHAPbqTWPJHJOtqyMZ33+ULfyWAAwirll+UKeQBwR3zVe3uxbqqqi5Jy24EMQOByMH/PpW3LzRI2Z0M91Lc2SlIra3tHILBlbkMeeFb5uexFYMN7Itx+9VQqn5VH8IB4A9qqPdeYS8gG3AA2kqR79P5jvVeOZdzHA54FaQpKKE2dPbXttJdu7OwiIAYLg5yAMAdPbv1qrq6LBLbi2t0SNowVjBzIBnq3NYyysZlZCFduy4UDHf2rX1CQnRoFa3kSVGDI5wBsOSeDyck9u/WlyKMkFytcSrcw/alKJKPlZQ53e3Xt149/SrdncmOBcq+xfl3AfdGOg/nWECz7ieO5Aq3DJEV2v93HY461q4K1ib6nT2OoxRZQiIDhS7EkkDseent0NQah4egnMf9loIysRkdHYkvnO3Hck4x2AFc15rxgR4AI7Y5x6D8q6jQJporWR5ot4L+WgkYfMTxtwD9evrWE4OHvxZW+jK+mJbw3aWl+ZoEx8wXncT0HsPft1rtEuojALa9SJY5slN+FbaAQy7se2MjOeeucVjzW5v7zTtRkRtouEV08nIMZxz7dOpIxnPFbptY47h5ZSWLRqBGw49DjHGPvHt0rlr1UrNmkVYoI11ELmO4VVjI3phzIVIOQTxg8MO+c/pLc3++CxkuVWRPNlDSZyVb5QQMg5zuP61HOubl/MVo5nG0ZXk9MkY9Bkbhk8jtTLjz/7GgEspM8k8ithMsc7V4Pt7YzXJBOacpdV+qHcs3scOsR2ttNE/kiXLMWyzH5lIx+LHtU81nFbQWz4jDW8ZCoqbBtyc/KOAMf5yaw7TWL0aejwsH82Z7fYoJbcFD5/JvzzWpPNNNpIufs48o2s5LHO5ME44z04I6HtW1KM6c1G+mn5oadyDwpqd69ukrztDIdwzA3lmTDNsDEcnLEE464HarcgvbjWrnUfKu5IIryRxMUkMcaq2Mg/dA4rN8LRE2+mKCBuuEzz/wBNKhN1fz2bTSahdPFiQrC93IUAy2FCZxjAHFenJ3lr3RotjattF1Wws47qXSZbdQPmd4VjJd2wAehyS36099Jv9MtYftU9tGURIhE17GWyQq9AeAD1PQDnpXKSRD+z5GZVkfyDmQvvbO3nqSc0jQwRRzCKGJNscmCsSg/dI9KUbaeo3c6UW9rp9rGz63osssaIogt7oSu5AAOMDHqfoKLaPQo7eEL4lhuJkUbY7azmcO3Yb8YwT3NYbxJFC5jkX5UbCr7Kff2qxYwB5bJVlLAPHwTnoRUrlsvX/Iavc1Ffw8ZLmSe51uaSSV3lW2s1VVbJyoZzzjpnvimy6jocdwpuNL1S5kESiNVuo4VWPJZQ3Od3Jzjjpisq1hZ7UuFOGJOT7kmm3if8TF1yPljiXGf+mY/xo5tyraGtLrWni1Z4fD8aW4ljRrae+ZxKxO4MzAZGNg4HWiTxTMF8m20Hw/b78J5ixO8ig8ZDHGCM1lzREacgPRrtT+CxvRBGr3sS558xcD8qd9YoLbGynirWbGa4trK8t7WKKZo1MdmjSMAcZZ2PJ/DsKiv9b1a4s7m6uNVup5oYlMLusa+UzOoJUKo/XNZEgLTXEm75muZTj0/eN/hVu+iMekzqGJD/AGcE4/6aZ/pQpPnfzGkrtkB1G+k+d9T1DJHOLpk/RNtaGsrnwDoMLO7+dfXMjb5GYsAD1JOSORWUsKs+0SADA5I9hWlqE6vomh2rfMsEl709D5ZB/JzUJtpkNLQwYrC0RMraQBs9RGM/yrTtgRHEuQFyeB+FJHHbMqkfLyOc49K1NRtBa/2aUwFlsYZPxwQf/QRXTgpfvH6E1VoQtjd14xRC3zSdfvKMfgTRtLKWPpg81CCVDEcZcf8AoNegYklw6qYzjgNk++ATWzrCr9s0aPGTFotsOvTLMf8A2WucuiTE57hHPP8AuNXR6+DH4pWIZAj060UfgshrlxeyHEj07cl/b4GCJC+f91Wb+leceJm/4qCcf3dNVf8AyCP8a9EspStyHIJxDcN9MQSf415v4nYjxFqJbJ2weWD1/wCWSKP51z0tmWzo5UQ+E7RgPmJIJ+haub8WZxbY7s+f0roxlvC1rzx5z4H0ZqxvEdq1xYiZAS0EjMwA/hIwT+BANceIko1qV+8jWmr05W8ivPHwMDnOKgC84IAHrnrVmLVLObSrcSThLgO3mIyHHsQeQQajmutPVNwvoiQeeCP6V2I5WmU7vCRFccHHTvz0rPlh/wBEkcnLMrHOMZyDz/ntinyzx3N5sMgEKnJJ4z6/4fTNWbhrYW4b7TFISSCgOSBz7U2hq5hqcw3P++uP1p1p/rSfY0yPiKb/AH1/rT7Xlz9Kcepb6F8/PJD6tcR/+hV2qdT9a4xV2T2YHJFzF9PvZrs1OST3JzXj5pvE6sNsyQfeP0FKelNH3j9KU9K8g6jnvE//ACCmHrIv9a5a2/4/I/r/AI11Hig40z/tqv8AWuXtBm+jAHf0r6LLv4PzOHEfEXros0MyqflOOM98jFV2G59qA/LwcnFXhEZrnYMEk8Lt9qedHufvxQSA7huGM5/OuxvUwTK1vbgvuZMpwAD1YnOfpV6GMx6cBHuUGcn585P7s85qaCzu7ferWrl2GBn17n3pPss8QZZIpUUtvGYz124+lZyuxGbqfKW3GOTx+VdNdJa/Z2KxILyaQwxpgKCwYEyucfMBkKASBn6YrmrpRc3lrbwqd5cKVx0JI/wzU8l0+n+JJr61Z1u0nEkbhsbScgHj0xu/Dmspq8kl2f6GlvcIYZJ7O3a4E2yRJH8lpDgHD8bAeo3HJAzjqaNK1S8g1BhaXpAulMMtxNAXUbgCwwc7uRjnr6c1Qa7mluGlkZpmjmbYshDJlmJI2njk88DFPaeRrNrUzFrfc8wQjI3YC7vqcAVq6cXdSV0zOxp6nLOXkW/mkkvjOZLgNOjK52gKQF6nGckHgbQOaxiEWcBSQHXcpZcZ/AZ9xU+FCGAKmS4jPyjA4DNj0Ix1+tWlu3a0xHs+zvGvmrKEJ38qrqoA54wDglc9eRRZroFjJ3DMHnY8vYobyiGO3vyT97H5U5TKsCTEJ5cbfLmMEFjztPHPHPPSorcNJsAJ3Daq+x7n8BU7wQtiYlmXDSnPpnC/iTWjXcLMrq8iuAGOSMcHjFPCRbcySuGYblbqDnrk5yD7U+WDBPy4YqsY2jALd/wA6+9WIJSsP2ZC3kSybGXeQJEU7jn2zz9eeooCzKE0wZywRUYtkBPlA/AcCmRTGKVHQ/MnTBIq1DBE77/uoSZO/wDqwen40q28SENIgAAM0i56L0Vfzp3FysfK1xdiWSK3tgLdMyPbQgAZYnORx/Fj0wMdqoPhpWmdAFZj8sY2jPcCtFG+zRnAMe2EGbYxG8k5VD68gHHTipJZ5ZIngKrsUKGiUYVpcEDj1AJyfWpu7jsZKpudVjVzIxAAAzn/ADxSPJIxBlcsQCNrE8f/AF81otHGkriMcRgQqx43O3XP0BqNkjh3fIGEEezkfeds/wAs/pVJhYpxhY497ID5gIUk8KR+NMVkKtndk9COg+taQRICCQp+zDc/+1I3QY/L8qjjREZN4GIQZZP949BRcfKVY7qaKJoI5CiSld6jjJHTP0Jrp9SGlWUdlaR6ks8Jt4riZ4IvnjfGCmOAWGSecZ4+6c5xI1+aFXOMf6RLgdAOgpQDJGFGBLdNuY46KDSaQWIZpFk3GMFiwJC+i+/5mqYUkg7flZuAO59q0S2Q8qAZf9zDgdu5oG2NyQcxWq4/3nP/ANemtAsdnp4SSaBE2ZXZlFcEsMHvjHPXHYcGrFzcwTXzwRR+dlXVT5vKqRhsYPzkkkknjk8EZqXTlh8xIYQPI3SZhcFsLsJGAC3vkDkf7WKr3drc2srZlkhuQMTZYBgNuMA8EKQcbR1HX0rn66FuNoDrgrc2jTPc2sULHBtjA5QAruB+U5PKnkD+Eda5udJ5nci1ddpUMsUDKEYjgY9Tgn8+1b1vqjQlmEzsrKQxBCnBGONvt74HpzWdd65q9vOYVvrxNrB13/KxyAefw2j3CjNFJSWiMHYzpopIWVZI5I5MD5JFKnBGRwfbFCYbC9+nSlnupbu4865JZzgEkdTjv6n3powMjjNdSvbXchksJxJu45bODXRahnU9EM+1EeEAt/ug4P45Ye5x3rmhnacEAjsa2bKGSfTlQJG6Gb96GcjtxzjjnIzWdRJNSBGSQABz27DBpYSuCN349K6mLQIbuyjWGNHlkWNmZA2xflXIHpzjk+p45pkmlRWUwhnDsrpnO3aqllOffI/nml9YjsPke5zSuWdWcMQvT6VpWl8j2kkcpV2HzxjbglunDAce5PYVJrOjpYzxSWm9rWZfkZ3DNkZBz/P/ABwTVfSNJa7vhEd+zaSWTgj0/XFOUoOPN0BJ3sdtHfu2tLAbqRy+BI0J3nHpwMqDyfY4HNbCTmWdyyBSARKHYDBGCc7egxyKw1WbS7SEWikzGWCBmVRvIZ9o6nngA+nr0xUXiLVNRshFHstpH8tv3iIVCHeV28+igkexZjgYrgcOf4TdRa1Jb+4iefOxZA37tm3YC5PG4Y7Y/So7+9D6JZswfb9pkVlibaOEjODg9T61j3uuXkUyyXkEYhYcoky7cPv25JBOMHK99pLH7wqhcapdiBbGdYWxM0jLuVfmIGeSuRkIv/ATzy1Ojh3FEM0dMupTdeW08wjlYyGOMZG4gqxByFzgfeP9BXTXkkH9jTbze20y2cxjS6VpDOWByu/oCAqNkjowA71y9lustKtXKxRyX8zyFWBGVRSvz7VyMuT8oH8OK2tPvIbkafY3MbySxCRrlAQvytwUDAAbsKc4GFJxyRVUkqlW76afd/wUUosu+E13f2UewlRuR/tZrJgi3aevr5DE+v3T/jXomi6fbPpt3cCNECyyyRpEqqqAxKQvA4x7Ec1z/wBouDsbbbqylXDpaRK5I5GWAyeQPrXWouUro0TsYM8Za2lQ4KiM4OOn41bv3S4SZvKUNtIbZwpxx+H51rfa7jzY322qNGdy+XaQx5OD1wPmHJ4PFKLm4EkcgFsrRklfLsokGcY+YKPmGCeDxTVGWmo3IzL8brG6QImWRzkdfumr8D2s97ATahJvNUh06ZBz/T0qcXVyWRytspjYMpis44zkdM4HI9u9aMbtNbxlre0XOGJhso4249GAyOaSotWuVD3paHNWBC6TED3Ayp/3cf1qfz/s17eIyKf3qHLHGQI0Hp04rp3lmlDhoLTcxJLf2fHuye+cdfemLK4VFENq4QbVM1kkjAZzjcwz1NSqLs1c05XY5kM8UNnKis4Fw+dpx/yyK9fxq9HeQzzrEQQ5YY3IM8Hr+lbCzTQySsiwhpMB1Nqvl4GMAJjA6ZpsrzTIVkjtlTIY+VZpGx743AZ61XstU+w+R3Oeha0FqFnEKziSTIcAMMyMf5GoL3MmkMyYKiaIkjnAByT9BXXfbr1n3KYCwAAY2aMxA4GTjJ6VEJrtLl5xIfPddpPkDbt6Y24xj/GhUWm3cFBnG/aINrAmHIA+bcMjH4+4/Kp9Sjkje0YjbCsMnzHgBzsGM/Rensa69bzUFb/WRr9LJf8ACn6NdTWepW8Syt5U0nlzCRNwZmB2sfcN/wChGkqLSepMotRuzgvOTYf3iNwT1HNdt4r04x6Po9wFwYYxbv8AioYfqGruN823AlVfpDjH61nazp/9paPPZRMFYqDHuXd8y8r37kY/Gij+7lc55PmR5lnauSMknoRmoAq4Y4wd/wDSnFH/AL6genlkf1p4QrEqhvm6klepJJPGa9LmRnYq3YBicYHKP091x/Wut8TWk8fiyS5MUjQPbworqhIyqkEcfX9a5d4yw+aQMThBhcYBIz3PoPyr17UdOtrqd5ZmuUZMqDBdSQ8ZzzsYZ/GuXFO9kVFHm8UTs1ySjqEsLpsspH8AXv8A74/OuZ1Hw5c6x4qkvIbO4n02UqkzW43lG8tdynHKklRhsY57816xrGl2ljoGrXMX2h5msnj33FzJMQuc7RvJ2gnB464HpXl1wgkuppCmRvI3Yz09+lRQikmN6k+opHbWVvpsNjJaxW4L/vi3mOWPU59s9ABzVEZy/BJyTxUpRVQhQB04Axnmq4lliJeJtr5IB4OOnYg56V5WaRhzwjLRanXh78rsUpdI0q4cyG3CE8t5UhQH8AcVn6j4aiaJDYnDBssJJSa6H7bfsebiM/WCP/4mkN3eEcywH/t2j/8Aia46ddQatUf3f8E0cL7xX9fI5+HwtELUCRybjGSQ5C5z9Klfw1ZKm7dcKSOQJQf/AGWtg3d1j/l1P/bsn+FV5bq5ZCPKsTn1tR/jWnt3J/xX9wuRL7Jx+safBYInkO7eY3O8g9P/ANdZ9p96tTxDM0soWQjzFc7wq7QMgYxWXa9/p/hXsUL+z1dzkqfEaQGbuyz1+0pXYR9K4+NT/aNhyPmnRuO3WuwQ/KK8nNPij6HRhtmPH3z9KjmkKdwo4GSM9akH3jSOMjpn2rzI2T1OlnK+IrsTWqxh42/eg/Kwzxntn3rCtCBeISQB6n8a6PxPEiWCsEXd5o5A9jXO2gBu1B6YP8q+kwbi6ScFZHn1k1LU0orUX8xgWQKzEncwJGB7VcXw1OBxeQj/AIA3+NQ6bKsN4JHBMKhyT5gTt6kV0aX1p5qxGGfzGAIUXCZOen8NZYmdWM7Qkkv68i6UYtaoxh4fvgOL6L2/1n+NKdA1AnH2+Mj03yVsjU9N8tpP9I2KQpIuIyAT0HT6/lUhu7LAJF4AV3jBiOR69envXN7XEr7S/A05KfYo6bocWnOZ3fzZ8EBgMBc9cD19zXLapk6lcgHDeYfwLDav5KCfxruoLu0njkWDz5CRw7BCq+vKseefSuKuLaS71e6jjJA3tufHTP8Ago/WrwzmqspVZXdv1Jq25FZGSGIupAh4V2KD3PA/x/CrCkIoOSyrhgB3VOAPxY/pTJInjvpYypR95C8dd3AP5ZxU0ZDuixjKlhtBPVV+VB+LGvTurXOYjKPtEZbBOYy3+0fmc/gKlQgsHzgErJgddvRF/rWjPbQ+QtjuAbHzSjnDE/MR9eazMlWMpUdQ+z0boi/gOazp1FUV0FynaJu+Qvt3ZQEdh/EfyFWd6ld5GIyfN2/7C8IPxNVIATuUHBfMa+wzkn8quhhLIpUAhiHwTxjoi/nWrAicuuBuLSJwOesj9fyzTiowUQ8H9xGfYffaiQ7ZmKKN0QCjD7g0p4zn8zSOpZhGjAEYt0IGP99qSAVQJUBPypLwT/diT/Ej9KcSrZaXgN+/kHog+6tACSPjIEcnC+0Sf4mjIkbdLwr/AL6Qeka/dX8aAJSqYJaUF1ZZnVlwGdugJ7BeKjIWDcGbe1sd5IHEkjdP6dqYG53zDpm4lHufurQXMQXzDkxDzpAe7t90UWAla2aC2iuHKlEdgFzlnlwM59gWA/P0qMKsQ+bLCAeY/wDtSN0H8qfGqQu33nEY8xiVwWY9Af1/OnQxStcxQIpklLb3C9XkbhVH5/rRcCuy+WgEhzsPmy/7TnoP8+tGwcRynr++nPt2FSAPBd7JCA8ZMk5OGGQSMdweaRlVnSJlcPIN9xuIG0D+nQfgaAECGVAjNtkuTvf2jHb/AD6UjyF1aRQQ858qMf3Y/wDP86WVZg7+ZG0c85CKrjBVOvT6Ukr4VpkHLfu4R+mf5mgAyAzSoRsh/dRD/a7mrkWnqukzXM5YQwOoBGP3kx+bBBOcBATkA87QetUgEjcKcGG1XLZ/ic1JNI1wtvbsoVQm+XBP1Ofcn+Q9KAPRLa1t5NfuRbKgUyOiQhvn7bS25doGQOeg5GTV3UtJt9Rvy1sI7xokLPF9oWIF8uW5JBwDjp949xzV+Rbp76e5hiVI4THGTbyEJucl1BHU8c4H90dqy5oZJZ47srvaAB45ZLf5VIfKHeV5Gc4455ODXNGzsvxOiUbrlMzU/D9tYK9x/aFjHE8SmIGd53dgBvUeWu0fNnknHvmse923dy8UM5Nkkgdj5HlrHn5cbdx7Y7+tdRcTq1oWn8yS8disbTRkRHHzSheo74yRznI5Fc5PILyTzo41gspsNcXJ+Tc+OQCx5552r13dOKqDTdlrb+v68zCrCKdkY6wlCyhkDDggHI49KeqHZuLKR161tx6TavC32S2vJ+c+fOwgTHsCCx/Kp00hbUGW4uEtI0YIzCLdhsZ2guWLN7AZrbnRn7JmJFbTTtiCJpCDkle2auQvfWeRGUi3EbkLfl8rY9OoreW0kkUtHGxVVIMupyMSw9REp47DBPfoKsR2dwqLDbXMoz8gEAW2Q+2EGccnncc1Eqie5apFLTrjV9KjYgKIZkCZwyqFzxg4xjj16Y9qszFrptroJHBztSQOc4/2Qx9T+NOjhzO9vZW8M8sDtG97K5I3DOQo58wgnGTgZHfrWjFbznMUqm48wqvlySbY1J44UDAyc9c9+uKzdr3tqaKOhjgLIosPOiALgNE9wWKnIxngYGevI6c10+maK+neckn2X958gWPcwI98k/zqnBJcsSsAS0tU3KkUSqhlAJUZ9FJGQo5I781XGrav4dlW6uLaz1XT2jErwzKTsBbB2t1GCCRnOBwelRP3lybXDlSLniFDFoMckqliLu3ALx+aTiQdFU/MOAcZ5PHvWFf6vPrSRwRxSyXFxG+JLh0iCxg52hmwrZILP0DMAo+VcHudN+IHg+7jMWo6TbWDN+7Pm2wkRlPbcB/Ouig0LwL4gQXEWm6PdrgLuRFGABgDg9gPwrOlTdPSXcfJfVM8MljbWIRJbWtw1zJMiGOOIOzNgsTvP8TEM3qx5OFWq0lsHvoY0hu1jdEl+4N3lkliwDdRjccnGSC5wMV9FxeDdDgtnhsbCziifeHi8vKkOAG/MAA+wqrc+DdOnkld9LtJZJY2ikdc7nRsZBOQew56jHGK2U5LRLQOQ8P1fVNPkniit7GS2htG8uKOOfejrzuYk85PGMdj2NX9IuEuNcilSFYVktiREGyqYLcD0Udh2GOvWvRZ/hbokzs0tjdbj1P2qQZ/Kqd74NXRruDVrZkWKzjjhjthvJXDk7t2cnOSMY7561UOVNWQLq+5uaAyjw7fEHgGXPH/AEyFcv2HGPw9q6jR/n0HUHK4aR5mOfXYK5cK5I+YeXt+6D37H8s1tTerJaJntStvHNuB3nG0DoMZprR7CofaN6h15HII4ra0+dYHs5WlMSqCS4ycZGB0/H8qvXeqLPp0ifa1kLhwQepGOBgj1PXNXKbTsgscwFBQ8DPsfatGzG63XjpkYz71TSMBvTOe1WrHAV0465puRdPSRYkaJCA8kal+EVnALH2HfqKeq4jXjtVW5XOp2WCBtjkyMdf3sGPp3ra/sljFCyzoN4XqDxkZ9ajnXU3ukZu0byfpTWAOQPr0rUfSJPLDRzw4C4LMxA3DOe1VJ9Pli82UYESELy4LZwM8Y55NNTQ1JFcjr9abgZzTW69TQSAACxxnnHXHt71VyiTv1NRyIzIVRiH/AITn7p6g/nitmWOzTSmWKAGULuSZx8+0Nj5sH73XmshyQQOOlTGdyYtSR2lldi+sobpePNXcR/dbow/A5FTjOfvH8D0rl9Avjb3LWcmBHcNviJPSTuv/AALg/Ue9dOCfQdfWspaM5JRs7HC+KdMNlqDXMa/uLolhg/dfqy/j1H4+lc3K7ZQBmHy5+8a9WureG+tmt7iLzIn6jPIPYg9iPWuKvvCN8s+bQpcxAYUlgjY7ZGcH8DXRSqq1mS0c6jyPLGrO5XcvBY4+8K9ilG55BxyT1+tePxtq0OuNpV/CYrW1lhMOe7PtLcgkd69eZiHb3JqK8uZpoErGX4lOPDOogf8APuR+orgYtRsrTwzcQTxXTG5uXXeJV8sFmwpI68bFziu88SBn8OX6gFi0W0ADJOWWvK9VDRaZDay5S4a5V/KZCHI8xuenSsJ2cLPuv1LhuQygrHyOdyg/mKqqM49C1aVzbyCyeYq6qtxFHhgRyxJ/kprPgAJ59TXn5w/fidGGWjMS414R3csUNtJN5eQSp9OvbpS22sXd7v8As2lXM2zBfyzu256ZOK3Phhe6TY+Kdbl1SSOJfsr+W0kgTnzBkDPfGPyrovAk2ijwXDOsCO1vOxvF8gzMpLn7yjpuUrh8Y4xT+rUlH4L7dWHM2/isecS+IzGWWSylRlYqQzYKkdQeOvtUCeJE8zY9vKqE43ghgKv+PL2xufFCz2SYWOIRyFkC5YE4yM9QpA9elcpMfMkBHcgHA966I4Oi4/Db5slylq+YuayhlvZv4fnyCfpVOGExggsDkdq0fEoO+LJzy3WsqxAQ7senGOvNb4aXNSTMaqtNmhaEHU7FR2nB/wDHTXYxngVykQxr1qu3A8/I+mw4rq06CvMzP44+h0Yf4WSL1NDUi9TQ1eX1Og53xSf9AjHrMP5GubtDtuxgZODxXReKf+PKL/rt/Q1ztpn7WdpAO08k4xX0WX/wEcNf4zQtTCrStPIio0TpkrnBIwDjv1q+NTsReJMLgqF27isT7jtXbt67cH6ZrBueYR1++O9O/dmFVCDfjli3JNa1aEZu8mwpuWyN5NTt1Ejm/g3uFXmKRVwAR16oeei8Dt1pwutN2AC7hybT7OzmJlLnjBOB0wMU7w5Ai6c16IEZ2n8hp3RWEIxnGCMAnk5PpW14U8Lab4g8SXtrcNCMWM00SH5Vd1ZQGyvQYJ/wrnnhYRTld/h/kONRt2K+m/LayMssbxyMSnluXVRgDG48nkE8+tYV04t0mxktNK7nr64H14q94d+SynAOV89sEdCMDms26R55GWNogYw7ENIBnknv356Vy0qb9tKO5VW7gjKuXaQoyg71zEp+vT8smrGllRK83zbYU3rj0Hyr/wCzGqJZigRSRvPBPbP/ANbNakUQt9NEpjwlwM789l+6APxr0Zq1PlOaMW1oVnnd5CScAn8v/r1DK58xmH3lO/Hq7cKPwHNXf7OMdks8rRxvKA0aSkruQjO8eoyCPrVUrbGEnz2LoScqMgscAZHoBnpTppJjUGle5mw9wM5b5QfQZ5P5VcSbywjKdvymQ4Pb7qio9KEC38T3UXm2yEu6ZYb0HJXKjK5OFz2zRlXJcgBD87AdAo+6ordiJ3ZUI8tT+6QfeIJMrfTtnJHXimhQPkQ/d/cJ9T99qYWZB5jcun7xh6yN90fgKdtZT5acso8lD6u33jSsA9Ard8K/yL7Rr1/M03IcfOcCb94+e0a9BQQkhKK21G/dIfRF+8fxpBtlOW+VZPmb/ZiXoPxpgOyMhps4/wCPiUe38K03AbLTOMqfPlHPzZ6D+X50rncMyYBkPnyjPRRwq0kEbTkAozFg1zMFGSEUZ/LjNAEiMI8eb9//AF0g9WP3V/8ArU5XKzBjIyOv792UkEHsMiun0yxj0nS7i4vty3l6kiPuK+Wi9kzg9flO4djj1NYg0yOSVVkmMcEpJeTAbJHRBzzyR9BzWSqJuxooNopx2Ut1ZXFxGipCmx7hiwG1ScKoB6k+g7AnoKWS2jyUM+154gxIQnZ8uQgx1z8oz059qlv9NFuxV2k8xmKSSeX8idwF7kEY5OOvtWfv+dyilS2IogOw7mtFqQ1Z6kjMG3SKTlv3MRPOB3NOLpGry4BEPyxj/a/zj8qhLqhZ05WAeXGPVj1NPUKm1WPyW43uf7z07CBcw7IsjKAyzEjPJ6U352iCdJLltxH91B/9akVWkAV/+Wh8yU+3pTzufMyH95LlUGPuqO/5UAe0ypcWVylu5tpGUgFowFDrtySWJ+6G4AA6H0IqZL9Ee3YXmyOeXyt8aCdEkyANy8KuFGMfw4qpK9q1zFHERdzP5iqJECsRnIDDPzdM8Z54qGe8VtRh+1O6AEllMfkEHOWQjO7ovPGcDg15iqS3jp/X6f1c29q4u8SW6h0+KGW3hlure5SMQwW4nIM0ikk8x7dqqWIDdCGP0rNXToft9zc4a7eOQ/v5ARhc/cTBIQD0HYZqebVr/TrKRrKWJbb7OBMsIQNGGGBiJhnaD/tE8gnpXM6jq80n2afftSdizIgRQkmFZ8Ko4GSAO+B+A6170E479QcoqN0tTpbOBb0xbsqryou7qRucDAB68mqKTi5uHvJI1UlnWBc8QRqxAVfQd2bqWrmBq9+2YVlMflyCRSihDuByDuxn0q7Ff3N3ZvcWkioy/PdwFQdrZG6ReM7Gxz/dPsQapQdiOc6W5uVlG85VeSMEHvjP4Eela9usV3Y+ZHCscycqBlRuXuQB/u4PvXNWs0VxCHwXHCld+3ae4x9CMdj+FXIb6S1ljkgdXEZH7p324BI4IHYe3cDPSsJxfTc08zVgWO1BSLetszMFOMlCWJKn+6Vyfr1FTXMBvTmKVRLtJCdhzj8sn/OKrefFduZbNI4ruTBePduR1Xlgw+6ecdu3tUK36ruS4At2RseXNzGDjI2v1XHHBJHT5qhtvVaMGtLotyTO0hSUfZ5y3zLIwCy+6nP3jkZU4GfunnbWfOyxRzmVdvmr5cqS7tsYBBJI49uCcf1m1G6juLHyLq3ZLnaG/gAweFI5IYYxkjNQW0M0kNrbmaK42qXEEoc+SM4+Vx93sMAnr901UdV72gr9yjqVtZXVzAPsqxNbrLv2gR4+UYJIwN2cnGMcH1qpN4O1aK/kW2iV5EGQ9vMUc8gYAOGJ9q1LmzaAiV2e2TDBTdQAxuSeQsyAKOCfvBadFdapZpHueWOKRPldVDxNjJ4Zchic+pBx1rW8ktA0Zg22ueI7B54V1m+ieIgGOdtxzkA8MOMVtWfxU8TWn/HwLe57ENuQ9Ovf+Vbtp4kud7CRTIp2Yik2sx27RnthuAfu46UySbSNTllW4t7UtFE7kyuq5LdAzZIB2jpgnB4AOKXMuqKs+5NafGmTP+m6TKqesbh/bgZXvV3XPiZoGs+HprS2NzFeyGMpG8DKxw4JAOCOme9c9/Y2jieRI0QxuCqu0DEhd2WPUYHbgHA9+TJJ4U0qW/nWO3kQJKdn2ebIAGPvEdBkYJAzyPQmlzRBqRp+H/Efm2txaw3LBWXeQ8Q7jacfkp/Gqb6k8Fz9nmktg4OCrEA8cHvXN+Ibx/D1wi2Wm6ZYI67rS9SB45Sn91xnbnBUk4BOR71hWkGq63dG6OoWpnubkROPtMQckkDd5bEEjHOQD0q4X1dyHoeo22objH5tss0IzlUfjJ+nvU0uqae0RH2K4hYD5SsmQD715ZcXCW97ItvIvkh2WN9uG4PGcEdRg1attT1MyCOOaY5GQEcnPrwciiM21e5TVnY9F/tC2bA85lJPG5ev6VPb3sHngi6jIPGCR3rzwa7fpL5cqlymDgxA/T7pz60//hIlziSKMMOfvFD+taKRK0dz01o2mZCs3yhgSqEYbByAeDxkdiKtx3d1Gq7JWAGMfMMcdOorzSLXLSVfmt5VOfvIQ38sVdh1q3VVVLyaIgY53D+WaLo3UkzvhqNzgZw2M/wKe/tTpL5ri3nSYLGSAy7UIy24E5/AVxkGsuygJqCOfR2Un9RV5NSviQFSOQHuF/wNHuj0Nj5M/eH50MoLEA5Ge1UP7UuEAEsAyemGP9RSnU4zzLbEEeymq5l3KOkOoQtov2MrL5u3GSi7fv7uuc9KytoPpVOPU7Jz/En/AAAj+RqVb21Y8TAKehMhH86SsthRioqyJnhEkewg4PcdqvQ+Ib+1jWO4thdYOPNyVY/Xgg/kKqK0Z+7KT9Crf0pDGXIO/GM4JQj+Rp77ilBSNH/hLQA2dOlBAOMSZ+n8NNs/F1iYV329zGMYCqFIUDj+97VQG8YO5OP9oj+lRCLc20xqV9FZTxSUYmbo+ZQ1G9iv/E7Sww3Co88LJJLCUVwBGPlPfBzn0r0n5g7hmB+diOOgzwK8/u4lN1bTLaqjGdFZwgztBGMkfSvQSMO2f7xpTVrIylGzszJ12Uw2lu5k8uIXcZlJOBtDA8+wIz+FZWoeJLBbiFYJrW7meQhP3XnYbjhW3Lg59M9ulafiQ50G8BYhSqjHr+8XJzXnslnEr5DSqDIH+WTGOfYU6UFK9xqm5LQ6zUdUe68J3RuZh5kzSMoc7SQOAFBOcc9ia81jOPzqzI8sus6lFLcTzJClu6+a5fBKtnBPTt+VVlHyivGzSPLNLyOqhFxTuZl1oFndXDynehc5IVhjNZ7aXZQvK0ZvzHG3lyzxEbVPccfMwBPOBgV03Ix+dUEhvbbzYbfyPLeVpEmdzmMMcsCuPmOSccjrzXPRxVRK3N+JUoR7GIdBtDcLGGu0R5GiSUquxnAJIHfsecYOKsw+HLeGYSPLJJt5CtgDI+laT29xJfRTOIR5UjETBjuaI5xGVxjuMnPbirLD+VVPF1bJKXQSpx7HI+JuHg/4FWVaDKH8K1PE5zLD9GqhYqWhbA7qP517GD/gI5K3xsv225tcs2Y55PGOmI66pOlcvaKBrdqo6qH/APQK6da87Mv4i9P1Zvh/hJAeT9aCcCkXqfrSsOK806Dm/FBH2SHP/Pb/ANlNc7bj9+f930rofFH/AB7QZ/56n/0E1z1v/r3P+z/hX0OA/gI4a/xl60sRqDSRFtjKN4YDPIIq3/wjc5/5eI/++DS6Bzdy8f8ALP8AqK6VTXNi8TUp1OWLNKUIyjdmPp1lrGkmb7DqCRrNH5cqGIOrr6FWBH0PUVENM1iO4a4jvyszqULoxQ7T1HHb2rV1KWWK3gMOSTK2VEhTcNo64IzWQmo3MqPJE77HXCkXbEIRnJGWz3H5Cuij7erTU+ffyIm4QlaxqabY/YLPyS25iSzEDGSf/wBVchNeyRXM4VUOXccr1y2efXoK7+f/AFsnpk15zco32iU7W5dv4T61z4FuVSbkaVVaKSJ9PufOu1jmRXRmJYseBng4H096k1WVTIIo0RUUYGByPr71l+bJGwAzjPI209ZTPvJXbjHGSf516XKr3Oa/Qkkud8ZWSNXbaoDMTlcenPTGBVYztvbgDcMHbwKeceo/Oq8mCD9KpJCaQ+LhCqkjzMgkHt1qzHtaMbh8g+dvdR0H6VXiTJ27gCeB7etW0CuvzfcbLt/uL0/M02xJCqSu53XLr+8PPV2+6PyoXcBw2XUeUhJ/jbqfwFSnIVWeNcovmvjPLn7oPvQYzHwpy8WFH+1K/U/hU3HYYQHfap4JEKf7o+8f6UpCyHkgJKcn/rkv+JpRDk7Y+efs8RH/AI8386UxiUYXhZTsU+kS9T+dFwsQoY7iRvPZ1EoMhCjsPuj2HfNXdPu2sNRiuUEEzAGQiVSUChcAEe2T+I71BGiy43KVWcnG04KxgdT+n600L53zEkfaOTk9I1/xobT0FY0rm7a7SO4QSQ5HABySox0A6YPJ7c/hVKe9uIoVj8xEUgIQhBbZnPTHrn65otlWVmeQAmVSyAj7qrgD+dUbuNVkXgfd6fjUxgkNuXVkl7eJcTM4eVlk+Yg/wt3+vFQySShjJIrBtoWMnPPbjP0/SoBtDfMoIwe3fFOBAZnKghenPStbCbu7lofIodo/ljGVxwGfp0709lUbYmIKqN8pPc9f8TUMYA2rIxxGPNfvz6VKytMFjOMyfO59v8/ypMESPbvJDiHa8si+ZIysCFXIH6ZA496mtreaS7ECxrH5oVIWmG1SDzuyegOOo7CqzyDynkCHzGby4gfTpx+tSSTO8++Vy8dum35m3YwMBR7AcCpdwPXI/tcl0kauU8xJGtwv3wRjLKQByVxwSOO+erdOKPbeZbEXBBTMM8bblVTznPzbeo2t8qkHoKtW1zLYJA94fMvo4cpE6vtXdJj5yeBjoCck5wax4pLXT9QmltJozPJE6/Z7xM5BDZKtv5clRwABkjgEYrxYTm7pf13/AA2GmaF3PptvJ/xMZbJpRKjOs0GI0YMSCqA8nONzds4A6muV8V65barIoENwZVO1/MCx5bC9VXvxg42+tN1vUVvLU2rIsSrGkkCQbduWyW3DceuT79+OlUbQjUreKC+Ma3RTZa3DyL+9UYAjkPbrhJG4/hJxyPUo86haT/ryLVSXK49DJG0SI0S5Kj58HOR3/rW/YWttpuswfbrj7NNLEJImhmDiAuvySOy+xBwM/LnIOcVVi0a7WO6ne3lWKzOLtj8nlHcFAIJyCSQMY9cUtzKt3bJFDb2sXlgbnUEngcAE8qMHkew96qcm/d28yNh9heJdFpEEdldfdkTd5dvOfTP/ACzP1+X0K9K1EuXecwXEU6TIuHgkb5wcAA4Oc9eMA5z2ArLt9LvJFQCF1Dtt8yQYXJwB17knAHf8Dizq0h0TUl02WA3EEaLiCY4kt2K5KpIM49cDKHIwKlzi5cvUE2ttjYiumTDIHG1z+7kQFgOd3Tk8jr0/OrB2Nai1kdtx/eCBX+7k5AycEN14zz15FUrCYX3mfYWNzKi/OnlD7REp4O5Bw4wfvKSPUDmrsWnXAd3lAUwP5oRm2qvG7nOecZHtnvkVlO0XZm0ZJosW1zbwtIs8Mq27lcRsN6k464we2OQAeetMltbUyJLa+ZDFJzDPbDzY2A6EITuAzzgFu3GajW3bTPL3+Z5ZKyFehUEKVGAeucjk/wAsVKlwUmiXy5FlQI/y7c9wMDpgbuuMD8zULR3THypkSafdWszMXd4EGZHsyTjb0Zxjd3Ocr296ngnWWBrlZ1hnYlUFihgaU5J2nHyOdpP3hj3qzczz3MMBngV5mkzbSREoxCkA7SOQfv8A3SCT24qGWSYvJM6RzLKAii4jPmhG6DzY/mzwOu/+dUqkmtdCeQoG4VbgxT2dtKVZozs3W0iZ6k7QY88dNvUUJdJ9+O+8nH/LK+i2HBOdvnJlePfaeT0qVobNAT+8spRFtUzIrRqTyW8xFxnHGWVTxnNRXUNzBC08cDpbFdktyJVkikHB++uR1465PcDmtbphqhJIL2KAXL24MKFjFNCNybsgqEaMsCCRjkjqfXNQ2t7eWlwJIb6VpQzKFXbNy/J5yued2QMn3prj7JqLPFAYJCpIltZjGxyTyGiPzEjjP3Rj0zmymo3PlNNNcWlwDF8ovLf96+cblEke1wck9S3APrRpbQLsZruoWTaTFDqE0v2u7YfamhUAQxIyKu6Irkna7kFcEkZJIxXMz63bW+gPo1jpNsIZiWmnuId8pGT5e0nlcJtPHcn1q1r0n2iFjDZ+QiM5YK7S4Y+UOGKjjjgHnOR7nd0TwnFqWlTabqCQSXltLFGsyctArhGKbuOm5uuQCeOlOLjGN2Ek2zhrPw5e3GkTawkkFrYrN9nM05ZVLkZwMKaZFHqOnC4gktv+PY5lbBGwnONx4xkjAFauv3LyWiWdnHMmn6dM8TRoCY0dpXxk+4Bxn3qC8vxD4XjsY43DXbRzzSk/60hc49cqTznruzWqfNqQ1ylRdWQpvKNG6uF2rJuLKQSSM/QfnViPVrdyCJ9rfdImQqOfUiucMQznFamnWLXptreIosk06xqXOAMt3PpVOERXZt6jE1l/Z8k8ERF5F58DRuG3IeOcdD0pNxwFTzo3YfIDk5/nT/FVzFNp3hqC3lhneLThC4XaxVt+R2ypPHXBxxWvHeQeErXwZqcdqrT+VPPPIjFHlOCqZYhufm446Y6dsei76ldTIW5VwNlyGbuGUEqfQ4xzTluLjcQNgKn+FivHY1zdrqLRz8oHMpCsWUdBx271eOo+SImms5U3LuBik659Ae3Q1TiwUjoI9Zv7bbtmuVycfLLuHt1NWx4nvwB5su4dP3sAP6gf1rm11eydcGeaIk9JY84/EA1bhulmG4XFu5b+623NJplKZ0UXixmJWS3tJPUKxUj8Mmrkfia2RWWSzl+Y9nBH6gVyEv7vdM8JKEAPgBh7Gh2giywzEBjlgUwfw461JXOzuovEWkScM0sf+/CT+ozUiazbLIxgvo2RjkAOVIwOnOK89ikDMY/tDE7sBmYEfiSOKmTzCeHBGe6/4GjQaqHocWsSs6nzZSPZg4/lVlNTuo3AYoVYZ3FK84u1utOsLO8nhx9qXeio3O3eVVvxKmoYPEjxj5Li7i9skj9D7elOLvqmU523PS7nVZESKYwxOsUqv8rHnB5FdvH4lSVtzWxKt8wKP2PIPNeFp4iuAHC3avkAkSR568g8iuz8O62b3TEIYEwnyzg546r+nH4UTbsRKzdztte1i1m0S5+SZThScpnADqT0PoDXGzanCXDRSM24YIEZJKnr+nftW/Fi9haJmh2yAqwZ9vBGDz071w02yxjWGaVUuIQyuCR1Vivb3U//AKiKqhPXUum7aEFtdJc63q0iFgrJEo3LtJKKQePrmlaSOCAyyuqIg3MzdAKs2n2K4kkkhMPnNG5bbwx+Un8RxWVrD7dAvWPA8hv515maR5sRCPdL8zeOkWxf7d0o8f2la49C/wD9aj+2tLJ/5CNp/wB/K4EMGGcMR7CgjIwA1af2TD+ZnN9afY7/APtjSz/zEbTP/XUVG2t6WgP/ABMLUnHGJBXANCAP8eKClv5bY2k4780LKoL7TD6y+xqa/eQXU0bQTRyAKc7GBxTNLI8skk4DoOP+BVkRKPMI2ADb1xjNaWnSeXAZOBiRf4t3QGvQhTVOmoIxcnKV2asOf+EkUEYAWQjIx/CK6JDXK210Tr3nzMBuWQ5Jx1Ax1Nb6XsBGfNjx7Sp/8VXk4+DdReh0UWki9GRkk+tSsQRWcb2JQcOh/wC2if40JqEMmdrrx/trz+tcLoyetjbmRk+J/wDURDr+8P8A6Ca56DPnOR2HXOK2vEdwkkMIQ5Ic5/75rDt/vSHoQBXvYFNUVc46z983fDyn7VOe3ljH510a1xCX8+nMz2zIrMcNvXd0qUeJdSVdxa2we5i/+vXNisJUq1HKJdOrGMbM6+8theQLH58kJBY7kXOcjHr7Vljw2gt/J+3uVznBh4/LdWJ/wleoj+G0P/bM/wCNIfF+ojrBaHt91v8AGnTp4ynFQjayCUqTd2dtM28SNj72Tioku12qpcHAA61n6NqM2p6a886xqwkKARggYAB9fetERIVHB6dhWGGg4SlGW5pN3SaF86JuoQ/lSN9kkGHhiYehQGl8hPQ/lR9njzyf0/8Ar116EakRs9Nfg2sH/foUw6XpZ/5dYBn0TFWfssR6P/47/wDXpPsqHGJk/WnfzCxUOjaa3/LGOopNB05hjykAPFXzaHjawP40n2WQHIUn6Gnd9xWXYz28P2mMBEAJB4dhyOneoz4fiDBlbBDlx+8P3jwTWmYWXqrD6g0bCO36Ucz7isjGPh0qqCOWRQisikMG4bqarv4duQhVZ+sXlDMXRfwNdDkjuPzoyfb86fPIOVHMSaJf7ZQpiJdBGOGXAHXsetQT6XqP71RFF86quA54UduRXXb2HHIpfM45x+dUqkhcqOSW2u47iSWW2ZFKhVIIO1fTj3rOv0IkTPBK5/M8V324dcLn6ij5G6op/KmqjQnA80ZNwwGBPtUiBVIG5Sq8nH8Rr0UxW5BBiTn/AGRTDaWhyfLA+nFV7byF7M4FdhCqzr87bpDnsO3+fSneaHVyrYeZ/LAz91f/ANVd0bC0bqg/GozpFk5yYkOPVQf6UvaoORnHk7XaUKSsS7YhjO5j39+f5UCAfu7Y55HmzN69xXVHw/YnpDH/AN81G3h23IO1FAIwcMw/rT9og5WdtZ6vN9sW7skS2aYsoaRuCe7BedzBcAnHygn1zU8Em6GOzgnZ5I3RZIJYlbDnljvzxgbhgDII45yagKWV7drcSxi2sxEZbeOKPJGSY1bjJ3NtOSO4xz3j1m8uotQuLZ7ICy84SjyZ87FLFfvHJznOe4wegrx171m/u0/r7jPY1Bp0DNIs8NibaOR41b7NGxlB/jbnIyOeTn5vWvNtUjuJ7uXS7a1aVku2ATyyz5+7sHfYfl+XPYfWvR7/AFNdOtc3cyXMu9XhiD+U90xKgAblJbGT0HPauR1TxGmnavdSWGmywXl0wxcyLu82MgxsqA8lH/vdSfSu3Du+q1NG1axqaZZX+qaDeQa3bQWIxuSR4gJFCABW2bs4QFhk54YgU+08L6VbX32iaHUo4Io/nbbgSuqgmRMhg4H3uMryKzdIsjEskklvHI21lj06G4OI3GN77CSGz0ZN3JB5FB8SR2mrGG4u5ZbQKkUGpSMYWA4JwmCAByCo4+UfjjUi25+zk297L9d/l+BF7GM9wLuWa5uGaKFiEt5pQ5ZFJO3GM5O3qB3zgimtKkskmpXu2/LTATvLJtG85PK/eIIU+gqfV4J9Ou5oblYWUhmtJgRIkq7uisBjABJwMbSOlUYfPvUFtFbRySli3mRLhgg67scHrwTyPpXbGNldf0hJl+8166nuILiOztokhRSI44kCl1yFdeODg479BViPxhJeXol1OCGQbY491suyQbVAwVYlXHHIOO4BArCkHzEKQcMBlTlW7df6ikMbFTIqSNglQSAOT6Dvx3ojRpqzS20+QeaPTLK9tNXtHuiwutsSwuLQHfCh6l48FhjpkZHAwTWbJaoYLoogRHJfaZN2/kYGc4LAFQerHBx6V54VvLG7S6heWKaP5o5omKOv0I/pXR2PjqSTbHrEHm4Py3tmipMvB+8owrjnnBVvej6ulrA0jO25vqtxHJHaiIvP5YUmNFG/dk4A2jAPXBOeOxalW7eNwZBMqwku0ZZNwBwCUOR0GSScnBPqamSYanYNe2d5HfR5Xznj3ZjGGALI3+r9ORtx3NVYwgDySKsiSuOi7MsSBk5++OvBPYcgcVnZr4jaLTWhZtD/AKEhjjz5YxujiYocjggEEfeKnIznjoKWG3jhaSRHmsLxdytJHI0Rd+DsZuhwWxyCOnrVR7hPKjDskwCMqxkqMYIKnIO7djngYzgYbmmQzKYWjjjPm7S2DvyR/EvXvnrjf8uMYxRYdzb0+wvZpJHmS0ARWaS4YfZpOFBYlogY36qvzJk5x61kTJo96yPDMLNWG3ffFl38HH75d0fXAwBGKtxh3lS0zKYd4WaMSmJio53YLA8DPXAxjrxTpkjiv9wtl2gorK+8kgZwmVPPJAPJHHscNNpiaW5S1Czu7eCZpreSKym8sqkJZoH2tlRlSwYgg/xd+vQHntX0zVpdemurFnkmmdP3tvcKvmSFcMUII+XKnGa6KO2ksQbnTZp7ZWZQ0sDeVuzyzugXYUHQZXDZHNNvL7Umsp1ntbW4cL5u+Mm3kG04BkCZjJxnH3Tz+FVGbE4nLXgmGkXq6hI7X731u8hlZS5ASYOQVJyuR1rn7nzoHFm6/L8smPQleCPquK29f1WK90WKKzT/AF8qyTo0aqVaNWUAMAAQQ+cDp9av3Wm6be6cI7QSz3UViv783hZVKxjPDcBchhweuAOuK2TstTJq7OSMP7gyZA4PB9iB/WuusPDccng601JLhlu5ZOAjhkQ+YQpyO/TvXLWMQudTgtXEhiYDeExu2kbjjPGcetdlHcwQ6ZFp9pq4jS2XzTHcKG3EEPgH5Tyx27RnvxRNtWSGrMTUPBn2HRNWvpJIHlsRBOjbCCV3OrL+ZU/8BrH+2ya/oi28rrHJo9qIrVI1JEys3JbJ4xhT8tbuoeIrq9tbiwMVkbS72RXEkV2u75ZNwMYcrxk4IIP14rgpXewu2KyxuegeMkqRnsccilBSd77hLlWwWpjhuw8sKzKm792xIBwCBnHvg+9W9Ult3js/KkhJEGHEbsdrbjwQw44A9eDWfNLmRZCoj83DYzwM9/ap513xQ/udqpHtd0IYOdzEtkexH5VrYi5V3BXDLhh0NdroHw31/wAU6Uuq2doDasWRSZFDNjglQSMjI4PqD6Vw7gIeoBzwfX0NfT3grX7BfBmmQWs0f7q0jUoCMghBnI+uf1rKtPkSZpTjzux4dqvgnW9B3SXUF5DCv3jNGyp6ct0qbwfrEOneIoLzUrhks4N28uvnAEqQPlxk8/XrXb/F7X5rlNN0uOVhbFXuZFB4dgdq5+nzH8a8gmiZ2wilmPYUoy9pHUcocrLMV1tnmnOyVCxYq2cJn0//AFUXGpnh7ZWjAXkKARmoNN0/UL/U4NNt0dZLyRYgHU4yT1PsOv0r1I/DLRbbTJLh7i7vIEYRtPbXCrltuTuRo/l4OcZOMgHmqnaOrIWrsc7deJfDmutbwzDVLJLeOGGF90LBVRNoLA4J+Yljg9+lYl6NFigzZaw8rAFPJmsnjZflCjJBZegPTueldVY6ZpXgu+m1YFrhwgW1+0qrGJ+dzADgnGMHtk98VzHifxAPEU6zzWsSzKTmYLh3B7Njr7Z5pU4x+zsEm+pU0rUYbW5PnxJdx7SFQSun6qQfXj3NdNa+ItEt3WR9Hu4D3a21WaJm/wC+gR+Brz8oFbcvBHccU+S8uHjCOd4B6mrlTuJSPUJ/iDo9vbxtYSa20jDLR3Qt5An0baCR+Ncjq+unU9WkvoVdRJgyLgKQcAHA3H+6D165rm3YLaROynBJ6HHc1adQEiZSwJQYI78CkoKI+a5uJdzbUkSRS45BOcVJqWqX09pLbMLZo3QqxWNgR3/vfrWPbSsAEDjfnhWXH6Ve3t5IfAJAPT2qJU4SknNXsPmklZMw/LVGLBOT170vlxs4YpnHTI4q+945P3U/Fc1H9oc84TI/2RW9zPUgLEJx+gqpKCeSpPrxWgZ3AONuP9wf4VE07/7P/fIppiZnDaB1OMd1rUsZrZoBFJcpB8+4s6scDHsD3qMsW5YL+VUpUd3JXAXPT1pbjRsebpkspW4v2IXIV1gZgfwJB/SoGl03zFUSP5Y6uUOevp9PesYbulaWl20VxPILjJRYywAbbk5HGaLASzTacJmMLExfwmRMN+IHFAnsP4nX6bD/AIVI1tYFH2xuCpGMy53f5FCWmnMikgZ7jzOBTJGGew5x5begK4pontQSUdFJ64NU76COC62REYKr0bPJpyrGvVAcUDJLiVHxtYOOc7TyKjSOa5/1e7CYzzg1IsqDpEP0/wAKd9t2tiJACeucfh2poWoxre72kkuMd9+ahzccbC7H86sm/kDbSo98UgnHaJR9BRoGvU63wNbHULmPRjMkE882UdlLLyvOSOFwFr0h/hfrijMM9pKP99l/oa830nTriyiaW5spzK4xgR7gi+nAIz61rQat9lb91LLbsOyStGf0IrgnGPO3HqdML8up0dx4F8SW5/48UlA7xXCt+hxVG48Pa9a8y6VeAeqpv/8AQc0618Z63AP3Os3mPR5RIPycGtSH4k6/FjdNZzAcYkgAz9SpFKzKOZkW6h/10Eyf78bL/SohcrkDcmfTdXeQ/FG4ZQt1pNpKO/lysv6EGraeO/Dl18t5oUqjvtEcg/XH8qNewHnnmnH3CR7UnnJ3Rv8AvmvQ5Lz4e3h3PbiE472hX/0Ef1qM6H4Fu8mHWVhB6AzOn/of+FHyD5nAecAPlDCjzm/2vpmu4fwHps/Nj4it3yeB50bf4VXk+GmrbSbe7tpgPr/7LuouhHH+c5/+vS7mI5VTn1XNb03gXxDBn/Q1kA/55yj+uKzZtB1a2/1unXSj1AB/kaace4WZR4HWNfyowh6xkfQ0SpNbnE0U8WP78TL/AEqH7Sn/AD0zVJE3JCiZ+6wP0BoMa/8A61pnngj+LA9BSGdAdpZgR2p2YXHFfTbQVIx8h/Cmm4TrvH4mk86MjiVQfXNPlZNx24A4IYe2ab5igck+nWlDA87iR9aa23H3c/hmjlDmFMqeo/Ok+0J3I/OoisRP3cH1ziojAhB6jjqGp8gcx2MGpx3F1BLcRLag28cTS2zBYpQnRlK87d7E4ABBDfMelEmof2Fqf7i7YzPIpRWX5JDv2n74z8wGMkAdB0qfTY32m5LPbeRJsS5Dguu0KFjxnIU4Y5K8Dbzg1XaOKG2fVkmW9m3I6pOhlkbeoYyNJuG1j5gwORxjHIrzJW5/N6+X/D3/ACMtkV9U166TXZibedZ7mJ1ae7aPOD8pcFVYoAT2J4AxjaKx7TQtKuL2GfUFItQHVYLbam/YAQQ7HGWJJJ4B29iTjob6C3lslvpmt57l2Eyxy7IELZBcFd2GwSF46DJwcnE1xYzXNva3MsFybc7tyW8ZXoegZjsUFi/yIOFC5yTWsJKFPn69X3Hp3M1pdRW1tLu1Ky21rMfs/wBtYDY20oI42RBkDAJxx8oxUckNxqFlH/ZcdvDdkyFUgR5BMWGAvzKW3gEjjJ55HIrobyCzt7C5szfC2V7nyYppJdrFSu5tyD5GTqucAkg84Fc1IYMvHJPJLG8ckrwwuqtuHfB2qvyALu5zt6EUUkm1K1m9U+uvfRd38yklbUTw/DHo+nX2iX7PfReft/sksvDrw7JIOVmUDOFODggntTEs9YsAtx4de3kgk3qDJsSWF93YMFYOAqk53EEZ6YqYwarcxC+S9tCY5N5dY3S4Qq3G4nBBBAbIPIA4qFNWmtdUjvhctBfAs0d3KfM81iDuSVcZkJyV3ADC9e9dUnzXjLX5Fct9jmZtF1a0AYWU10suSJYEd146jIHUE85A/KtPSNHnvIn82C5tZEkXY0itHuc84+bHbuCO3U110EdtrvntpUSW2qFd82mlgyucYEsBPDj/AGev8qzbW7Nruk1CbyYYV5wApDhiFXp5hG0n5cjPPPFKc58nn5E+zMrU9HnWA7I555FYpEkA38gLljgcggccDHfms2Pwxe3kAMdhdR3BwFMilFcE8EBgM8Z7810U2rQM0UEvmCS5cNLtJDMcEriM8bm4Gejd8da2ltra2gKTsqP57G6SA7lfcD8pBBPcrnplcYqPbShBX3Cy6nDQWGo6LqcV9DcXFjcQHC3cIyFXOOSMgkgn5c455rrYfEFrOwm8Q2G7Ydh1bSflK/xFZYenf5iv51O6Twh5J2jzPKQsrKUdzg9wU2k8nlsAVktozRTJJp0zqwTaskKfIqt94oF3BSckbi/rn0rT2imjSKS2NW60otFDqGlpbazpyApFLBF5qRKVACsN4ZcEA/MB0PNZ8UUCSsZpiSLdhFtnAQfLuIHJ6sAAfu7jjJxSaLp9zJqXnWlxLpOpFxGs9g4dGOOVkK/IxwCzZIxkA+taD+I1uM/8JNarG0eANV0wqdytlfni7cZ6bajmi5OKepSdzR8LxyPfTwXMqltihgsTKqBWUlU9huAOAPXpTdYMF3frP8ih5AfKaME7F+UOxyCgwOmTnsT0qbTbeawF14gsp4tXskiDJPaTZMa78soBG9Dg5+ueuBXPiJdRmUW9qpmuD5UYDFkZSRkrj7uMgd+vFZKnas5Psae0bjy9C4txDZ28CGcMG2tD5TCNNysSTv8Au4b7owOwyfmplxbzvDDbSWkyxpEuRHCkQORjgYYYIwuNuTn+EAtWXJBHa6gMalDJJHiNo51LeXKucxAHhQCOFGeoJ9rcMyB900F1HKqOC7QBwxwN5DA4yMYzztG3ityCprPh1LuWWKK5k8ktv/cukkZkVcElsZYAHaOm45rmdR8L3umPbiG5iunmjMm2FWDLgnPBHIwAQe4NdlI0LXiyxRzmeaIDESqqICwRY3IJG1iWKqp+X3OcWVijfSzaWRhXJwkluqhACyqwG09OD3LYLY96UmhOKZ5SbYtcK80cyySfPnBDEddwBGD/ACrTh0aW5iK2muWkcwkO20vv3MhxyrB2GzcQQfvA16NcQK4nguFRLiYeTHBY4K5Py7iVJG35V44xgDBJqje2aqkbTx26K0WxV81PLlULtjYqRlifmKkkdMYxirdV9CVBHl2q6JrGjt/xMbK5t1fo8iko/wBHGVb8DWYQ2B19q9VtYWs12WOofY2bcZ4LaQtGBjOGgcMH9DjuD6VVudLtLqIzahoUDAsV+06a/wBilyOuYnzGx57Yq1X7ol0ux5ozMVVS3C9B6UgypyuQfUHFdFP4fL6qbSwn3BhlBe4tm6dDuO3Pbg4PaquqeHtV0fb9v0+5gU8rIyZRvo4yp/A1spxexm4tbmYsxY/vcuM5yeSD/WtTT9VnsQViQNH1GxiCv9ayypXkr+OKZyDnP40SipKzCLa1R08+qf2lChlmkzECFEzdAeSAappMkEqy7lZR15HIrDO4gdW/WlWV1YEY46AgEflWapJbFuo+p6H4d12zsNQgvmjM7xvzFkjbEyMry9DnaD0989qfpGpTX3jaOeWZJWKPGpRSq7VjPAB6D29cmuL0nVo9Pv47h7dCNrxyBRjKOpVuOnRv0ru/CdpDDpf9umfcJtRisIsEKowoldmJ7bRtHvUShyjjK5uan4XudbtWhWKIgNuRluE3IfcH2PSuL1bwD4k00Fzpks8PaWEhvzGcg16suBayMy8N0Yjj8DXNa94VGq/Yrm0jjmlXbHPG+DhSx55/ujqPQj0rCnNxdr6FySavY8mtbO4vtShsYUInmkESh+AGJxz6Ad69W1HQvDei+GpJUg0x7yGEbF1CydWlbOAB843McE4/hBG7b0rVl8NaPaOtxbabawvuKlo0IO0ggjrjp7V5Tqt/JfmNpbneY4yiEJtOM8Djg+ue+a3VRVCOSxO92kcEcqeXYxSRu3kQxs0ZcOV4BYkcDJ5PSsfU5DIkMzKpLqCcDjJANdLpWmaRqOjWLajryafdL5gjSa33o4Dk5Lbh3J/KsvxhGkGreWk0MyIqKJYRhJP3acqPQ1SfvWFbS5BYqGSDAI4HAPtS2cUj6qYUjUPNNtEmOmTyTz0x2pmmXcUjxQKGDgAcjuBWvoEXna7IxH+qSRufUnaP5morzdOEpdkVTjzSSKN7ot9bszJF50fYw8nHuvX+dZPnICQSQR1GDXprxKTzxVC+0u1vh/pECSHs54Yf8CHNedRzLpUR0zwq3izgfM342DIzgn0qPzQAN/ynGcV01z4Z2BjaTjpwk4/9mH9RWBfWF5bRubm12Y+66rlT/wAC5H616dKvTqfCzknTlHdEPnLt4POO9IpAFVCp3AL830FTAyKm6SOTbnG7BHNbEESKxOQu76Vahyd2YizAZAxn8aRkUKQpHNPRX2HaduRgEUXBjTdoygEAd+BUawk4byweM5/lU1tpks7FUAc7S2CwXgdaULPEcYUL6N1FMRWZSk484bO/TNSloAP9cSRxgKaSYNJMAw8xiOcDJAFNhs7iQjZbTMM87Yyf6Um0tx7ib89F9OfrRlCSd5APYCppUmtiUeExnGVWRSDipYtPnmVWMawgj+PjI9h1NJyildsFF7Ipq6AN1PpmnxxTXIPlxnYOrk4UfU1rwaZEnOwyN6vwP++R/Uml1NDBp7yMctwik9s+npxmsfrEW1GJp7J2uzIWX7JIBBdsD3aIsoz9e9advr+pKNq6pMw9Hk3j/wAezWAacqnd29a3cU9zNM6yLWr84LRWk3PV7ZD+qgVaTXMH97pduf8ArlJJH/U1yCjO3qvPUVMDOgGyeTj3PFQ4oq7Oyj1jT3GZLa8h90lWX+YH86nTUdLk6ahNGfSW0P8ANWNcUtzco/JVwfUA1KL6Xbh7dOOe4pckQuzt1mtH/wBXqlieOju0f/oS/wBasR29xKQLcwT56CGdXz+ANefrqSBstA6jpwwOP0qyt9bMVCl09SV7/hS9mg5mdo1tcFS0llIADyXgPH5iiK6ltm3QyvCw7xuU/liuai1aaLBg1KSP6Tsv8zV9PEOtbQF1FpuP+Wm2QcfUUuRD52dXb+LdftgBFrN5gdA0m8f+PZq+vj7xACBJdwTgdPOtUP8AICuNTxBfuGE9vZS57tapkfiAP61MNctyT5ujwj/rjPImPw5qXTTDnO2T4hagwAuLHT5xnP3GQ/of6VKvjTT5igvPDds6dwkoP5Blx+tcXHf6XPn93fQ/ikg/kKmAsnGYtRVe2JYGX+RP8qn2KK9ozqm1Twdeupu9Euoic7/KEbY9MdP6VEul+A7lVzd3dqSPmLxyYH0xkVl6V4dvNZkZLK/0xivXfdbD+RGf0rfh+GWtyLuN5ZeuISZT+gFS4qPVjvcpL4R8J3JAt/E8cbEfdkkC4/76Xmnn4aecP9D1qCbjgAo38iPf8q2E+GMy4Fxqkw9ksz/Vqni+HOloQZby8ZvZY0/9lqXNrqUqd+hyc/wx1uPJRrZwOmWZSf0NUJfAniCAZ+xqw6/u5gf54r1Sx8P22nkGG91MgfwtqDlfyGBWz5jAdT+ppe3kh+xR4LL4e1uDPmabee5C7v5E1Tltru34mt54/wDrpCy/0r6FLBm+bafqKPLiIOYgQRg8CqWIfYToHjenyCcy3d4H06Gyhjhl+2xiOR8Nvwqk4fLKo2HjAOTirVxc7dNWeNIY3uYRKlzc3CPJ52SAxUfcbacAn0zk7cVnWvxPhuCsOs6OjIOPMgk3FT67WB/LNO1DUtK1ITvoE4hWK3Mk7SoIlLO5Vsgj5m+bgD1OOlY1MMow5eXT1OZpLVGjaaiYhPLKYJI0QiZ0jWUqigYi24yobGcdR14zWZcax5lvJHb3UMafaP3jNiRpFZRhyAx3AAYwOvJPU0sHk/uoXnkeeNIspKjwqhxzlgDlRwvTLZ9OaufZL7Sw6RxLblVEcgERMKRuWC5faW3ghuSSCOc9Kx9yMm29dv60KWpzEOl3VxKbp/NYNCRNJHHs8thxjGOBkgdOgPap5IZfPeO4mVIkIVfMy+DsGcEMCSAOvYc4xUrGG2+0fa4HaKNFjOyRB23bRnl+C3H8OBjrWLJrRG9I42QNCEzHLs2qPZcZ6d+ecnJrujFvVGmiNK2nja7bz7SN1kwuHJzICrEJ1APYnAzkYq0ttHPK5ggtoLBQm1YGlh3tnG1SAykvx1wOmPSufbWZZLr7S0UMUhDL5ixl3AYYYZJxzk9f73p00Uvra8w0wjbfsjlEpcnywRjKhstxjPQenJpuLQ1JMtO408W0V3cDykXzIZbYjzYDgkCFVY/LuPJPX05wOok1GK6uorLxFbrBq8hEcOpKmIrnI4E3dTjuOfqK42BotPjZoIJHMmWaC1cgAjIX5DyMYbDcnH5i691rOtWUWn3VisFjbogQzxHCqdpGXyWYnIwMk4PbqJd1r0HuX9Vt7m1vIYGuII9wSZ4ZLkFGQfMu0/xruAPBxjp7Kn9rT6s5S4tpQVLDyHQqm7HzBS/Cgg8E46YNWbC8n0ayuNP1KykvvDEc8kNtKn+utlIB3IwzxhjkfXII5qxf6U8GmjUtOv7S602QZWdjthVU5RXUL8jg4xztbHqQKh2a5o6g1/NuMuLlJy5lms0gdFBa6u0wyHscOFzg5xu4HGT0qqk6NEGuNV0i6ZEBPl3UDuzk/cDOGVVVeAAoJxRY282p2JuJLqNZLcNmDYoi6ZAO7OAct83fHTIqSTT44LSYsFlA3ySBwioJB3HX5sAKABkfMScHFY05wjePYTi4JX6ln+2PItrmE3cBijtXicCRWTLsCQdxjXG3PICjHFc83z5Gn3kkiK+9oYyZDIOG25jT5QRx1xg+lb0cUV7LeCF4muv9fI20RxsQuE2ls5HGTzyfqcdbpM7XUj+S4iVoVYxRRBSrEnIIBGDmuPE42OETk1e/9f5kp2+Z5xp11dafds1qZdD1e8cHMMZRI41+bLr7kHoOhAx1rSPiSzvGE3ibTDbTnfHHrOmAtEWBwWaLO0kEDnqMVq6zokkv2hNlxskjDTFLeJDLzx++kOMcAdvxwa5a5VdMn3W13BJa242x2BnE7NkEZYqpVgCzfePX6CvQo1VVgprZ6miOmbQi2lLe6bex6jbLEI2ubU/IoJwNygh0UZywwd2OTjIrHt7S1mYx/bZJ447coEAZ1UAZO9QxGMsORjJXbWbao2nanb/2dfSaTrkjEuLZv3Cqcn5gTkAADJ5Hf1xtf8JZbTlIfFunGFgdianpj5TcDuBZAdpOTnjB59605ew7kUkVsLYoqW7RO4i2tJl1GzlSilgwJU7VX5tvcd5bTUXlne3jaOD9+NhMbSyRmPhXcYIVtgwBjCgcDIrVn0y6m0yG+0maHU7dJvtH2iPMpzkEMytl1xtwSAc5OfUZsQkklmjsRZWdwR5olkbyMuOqZyCwPLZ/TrSBWew3yEE8qBZvNJRmkE5UhCzKD84UkgFupCgEsOeaktrN5NzSXSizKrJENx35UYXG3sRk5GBtCgdOKlpLeQadIltZ+XG6u9y8beZLOpbO/IxkqBxzheOCc06Ge5WWeeOBoQSdgdGmBYDIaRmwq44LDPbHSm/IEWbS0SWSGaW4mVtu4CEeX5e/JLBmxhmAPQEkDgYGahu5LWeFreaCcyy7br9wwX51wFJwMdQuc8lgOgpiTrdW8U0lsb8BvOZJUMKMBtRV2KMbfl7sxAGO+KtLJPJc3FsTDJcuRLN5sihXwdxTkMNq5I5xwADyKXUCtq2iRTCK+1S4MzlwxUuFiXdyw27MdQMAkbsj7o65VzpCR2y6ba3l5DJcPsktYlcQA7vkJV+NuOc+x45Fb8d8wT5Ena5RSBIhb5ZRyxy5DsM5BI2tjag4NU1UOwiF2IZGmECrC0fnOAV4SLko27HzfMRg9hmhXAxbnR4Yx9k1jwzbrPGoDvpdwIZ14yCUJKPxzwO/asaTwrp91Ky6XrcCzZ4tdSQ2sw9s8oT+Ir0AReVblbPULmS+tXUh7u3X5VHVVky24FWDYHXK56CqUlhD5b2l1HbXZYB/mmZiueVSNSpJ44ORkMev8NUqkkJwizzTVPDWraMM3+nzwxt0lK7o2+jrlT+dZLIQexHqK9Piim0m0lntNVk00wIpngWUSw5bJCqrE7+mOPcnArK1/Tbme2iuZ9KskVgZPtlhH5XnJ03FCMEZ5yB3rWNb+YzlS7HBhecYz9a7DT9RgPgJtKN9ah1vzeNCxZX2lBHgcbT13degrBaw7Ruh/wBlxtP+FNm0rUIbVbuSzuFtiTicxN5Z5x97p6961laStchXRr6bd39vbSNp2qLb+QpJhglcvIoOSdoBU4B68cLWzafEDV7ZClyLW8ToSf3b/TK8Z/CuDQyRtmN2VsEBkbHbnkVdiUFyMjD4YfjUypp7jUj0G7+IVnfaLcwNbXltevGREOJVZjx97gjjPOK5DT9Ki1KDUbua6+z2mnwq8hERdnZm2qoAIx35zwKybl2RhwvAJB/QVr6TcNFpmp2kiN593GmzHzB9rEnHphTn8KlQUVdFNtkc0Zjs7FGOAvmruJ44kbv+VV/EAyLHHObaHp/1yWuo8N+Jtb0rSrqKynheC3cTNbTRbgwfgtnI6ELwex9q5vXdTOu6i92LaO2JK/u4j8gwADgYGBx0oi3zA9jGhgkLFdjfMDgY645rsvB0EkUtz5qsGEYHJ5Hzf/WrmY1JuF3tkKr55P8AdrsPB8ywQ3DJjJCBs89C9Z4z3qLj/W46Gk0zpCAe4z+VMaP1AFSrcRP96NT9OKXMBPy71+hzXz7w8lsz0udFV4VziqrwbckHBPUjjNaoiUj5ZVJP94YqN4JOT5Yb3Qg0lCpHoDlFmAltDayySQxJG74DFBtzjnkDis7X1MmjXIJJxtfk+jD+hravF8twWG0H+9xWbfYuLKeFNrGSMqPmHXH19a7KUpc8ZPyMJrRpHn5UgkfpVxbnbGA4+gHNWv7BuzgN5OR/01FH/CP3rtndAPbzP/rV7jrU+6OH2cuxFb6oLWQsq7iQRg9OfxpbjU1uSuYwhUYO0dec5NSN4cvFxmSHGf754/Tn8KRvD9yB/rY/yb/Cp9rS3uHJPsX/AA04fUJpVyCkBA/FhXSWyPKwA3En3rn9Et/7LeZp2DeYABsVuMHnqK6C31W1QBUL85z8ledirzqNrY6KStHUr6lahb9WCrvEYG/HOPY9u9QLbc9OT196vySC4n8wAgEAfN7UoUDP8q53UexqkiqsAFZPiRdunJx1mGfyNdAFxxgjjkVkeIYfN0iQgf6tlfH04/rV4eX72Nyai91nHAevTuRT/JO8qeQB16VHjmpGZzjcT7Zr3DhHoFXlSQfVamWWQkDKY6cEjNV1xkksM9qkRjndkcc80hlhZ1yu9M47KM1IXjcjyTs4yQeOfxqBQjMWIYHqMcilRUkk+ZgF75pWC5IIzjJjzk9aleKP7uAg9Md6i+zwqcJclT7Zp0cM+VCXDEddu7P8+KVgHvaCSJFCAEdSO9RPZKvIOw5xjHFPM86Oy7kZQcAlef0pReuPleEEZ42tiizHcbEk8UxjiuQdw6bjg1aS+v4t+9yVUfMCobI/EVGLyFeodTjjK5watJeRTW0lubrlxlcucZHYj3oBsig1+6t3+e2gk2tn7pB/nVhPEIZiTZuo6nY+R+RFUobXzgQiA4HJBqe3svJJMq8+ikHjvmk7AjY0nxXa2GpwXB81YxlZEaLOVOM9/wAa7yLxR4fm5t9ctYy3IWQtEef94D+deVXFiHO+MBge49qgNqyMnyE4G0/qM/qKylSjPVmkajjoj3Ww12Zx/oOuRPt5xFeqcYyem6tH+39fSNXE88iYAyyh1avnqbTcRGTgsApC7c7h3IP41Jbre2jILS6lhLYx5chTH5EYqHRa2kUqifQ+gv8AhKtTWWESi1YZbcr26gtgHjj8/wAKujxUNimTTrZs4wVd0z/P0rwJNV8TWqhzqt4yxtuBM5fB6Z+bPbirr+OPECE4uxMpOdlxaoe394YqPZy6MrnXY95XxNp+MS2N1HyMiOYMB+YqZNc0eY8z3UPGf3luG4+orwWL4i6min7RptlJ2OwyIf8A0I1pw/EuA4NxpcyMOrR3II59Ayj+dJ059hqce5xW0byNvzDAADYJJ71q+H7meGe4WBInE8QR943bV3qcgeuQOPrWdtBQF9uSOrZyPetvwlZQah4gW1uZooo5IpcyzBcJhDzyCARjIPbGa6qi5oNM5GdjHfXFnd6d5tm159kl8tkl2rGRuziMKu7cT0+9wTuxjNULzUJrG5mhuAC8hmtrqQ7jFJGeCqgHapGcYXj1x3ZJq9td27k20Ul7HCsUlyxYop24DhG4OQGGOuG45BFZV/YXmopDHY3kBs4YisMXmsoiYruaJVbJxyfr715tOlC15q34L+t2K/Y1rnUtLtrixu4AmpBIFW5glkSUQ7sZjjO0E4OCew+ZRwa4ieOFb2ULKjRB2WN8EblBIDY7dvpU66VJE0pdZCV3fMQFGAOTk8fjUltpV7eI86KRbxgFpG+VEBICn5sbs57ZJrvhThBuUWXZvRlEzQ7FyCDknO75SMdAcZz1phk3AqwV1xztOD9R9OK15vDuoRzIUtXUMvRo9rvtOC2zO5R9cVUXT2WFHdCuSCMHJAPHXoOT9fzFWpJ7Dsx1tqRtT5LKoQA+a2wEyg9N5P3ucYyeldXp0eoWlvNLa6fN5TRB3eazYbVA42HjP1CnGeveovAnhz7brX277dHEmnAXLKsTK5ySFIJAA9cjOO1eg6nrFtaaRNLDK0ioroElkLmSQgYDFz/tDnPQGvLxmOhGp7GEeaWl+xcL3PMTqD29+uoACSYqWVZyDjKEHcoIHQ8HggEnrW34bLzNDeaLfyQGVW+12YiV4JGCjfuXdgqS3TvkdCM1habp8E99axmBEmR0DsbwBZMkdnU46nHPp3r1vUNJtorOSWIG22EmN4kVH3bSoZmAznB70swxscK4xWjezLhJX95aHI2sWna/ZXdnoEhsbyb5LvSixJbact5PzDjj7v3u+SKz5ruaxs5IlYQWz5RYRvhPy5GCH43HpjJ4GBt5zyl1d/aF+1EJa3cTolvJChQFenzHOAcg8fXGMV6VZeIIrzSba81RLeO+lXypZCitLOyHBJCjPU8E8/U81piqioU+blvd7fqRKS6mHFrUtqkVjczrIEn2IyTl9+58gEsFVcHbnH0wK6jw9cRW1nNdyks/lnfnJDBc9TXKoIi73DfIsTSlRm4VABwAXQHIyckZGM9eann1lFkXS7O2juYLgGU+XNIQOrbdw2NxjO3qxIFcmLwaxUFyq13r6E6uzQefZ6xq/wDacc9ncXD5aKF7OYh2KnGOcqCMLuAI4PU1Mby1t4pBFdXUDmFd0Uc0dpGh28KCdwVsbiOMkD5fU0F1GONd0p0aCdIzGryyyu4UEkryW2YPUDkEGpLuMm2tFiureCAxx+arW0jeY20AlwxDFCBkYCjlcivSUVHTZGlyK5tIWgc3NvZafOp8sNc3UstwMjcDuVt24g/3c45NVDZ6pBpslrornULaedt0lvbSSEHjcTuB3cEnJx7ZIFa6TmO3jijVIJtzo8sUTQyopOQAwjDDJ4OCueB0BJb5Op6nO8F5p97eF0DRi5vSqkDk4UqSOwzjpux60XGJfpHZ3BTRr2bS3tj8ssUZCDG0LnnOCT0xnnvkVGPF8Fx5K+MNKWZGOY9V09sb8ZXJA+Vxy361Je6XaiGGMDTbK3I3OrXMgd3LYwAGzj5uuM9BwKx703+nx2+mWhkmtmZgv7twzDOSMMgDcbRu+uOTmppaK17+pMUdFbeFre7C6joeoQarbxJwxMjTLjld67i/3gPu/pVRzYANmb7MRuM6bmdyAwIyrrvQBcY4yS+ecEjE+zWUmuSf8I5eyWF3HvcvHLiMbc8HuucN7ADk84rZj8bzMEtvGOlx38eNq6jasBIoI67l68EH6Vra5WxHqN9cQiG3m+1wCcIsDy3HnHcBtLKu4FuMnnIOVGQOC5roXgKFbq+8kInlIVUxktkLhdqDLFegJzuzgcV0VjYRXtv9r8MamdRzIJGAkWK4XH8R/vsBx7gLnpWdOjW9rJazXFzbXMERlFr56oz71y7DbtxllAwScEZzjip2HuMtw+mwDfPbhJXSD7FCweeU5JJDBvkI2HrjJJ4HFF/cx2umM+nu7XSsfs8ayCQbjg7pXUnHcYDfNxuPGAtu1iNN8hIJxE6KVheHzZHLddzEgntwp6E4BHAjkh06xvYIpoLi4C7kijZQjM5XGSoAZyS3QYwEOMjkoZLLJcac0VydQtszjyxD5is5gPzEgLjKsTkkBV47ACoLq7ntrqW6+0EQy+Y8moTEKGOw7gr4AycsABljuUcAGmTXX2jTZYZpgQ0eChZFjYbss2WVvl4XJ+Yt24AFX9M166klihlt4bq2syktvawFkkHOSSrjopODnq3UkjFMRFMbmGSJNPVXYwhpGlugssYKkkOQMfKvO1cAtnOSMUl1Ha6nYwSobucTQM0EIeMLuJLByzIWB9vu4HUkU1X066guHgWa5ntZGgFvAcESbt3mSyLgON3TIALDABJpnlX2m6YI2htMhVWXztpiDlQFVUyd7BQTk7zuA6A0AU7jwpFq888/l6fp0cLKk6RSAYPy5IXA2jDGRs54GPlrL0y1utPnkXSdcvIEMhjjkMYSFsKXbeGbGAACSOPm+tdNLsZxHaQpPI0ThLcWzTqhAXcGCOVzyxZicnbUNxO6LK0izSTTK+66tpBh23jbG+35VGBjA3ZIxk4JovpYOpiX9jJIzDXfC9tcsG2tdab/AKNNu75X7rH2xXKXQ06zuJhYR3LosJSEXqqHRm9cYHy5bHFd1FaRwKbspLbSxbxumIzJIX6Kh+Ygljnkck5IHFYus2GsatLCRa242r5kmDFEsQJwASWyOq5z0PbiqpyadugpKLPPnSdBiRGAzwx5AH1pElkiKlHZOcgocEH1rpNT0O80gK0gRkMaSM9vIHVd4yA2DwfY+9ZbwJIm4orA914/lxXUpJoxcSzoesw2LXYu7YXQnh8pUkfCfeBO4DkggdiCDg1Tnkgkw0UJgV+Qquzf+hHNQvaA/dkx6Bxj9RRBBKGaN125O5D1XI7flRZXuhXfUeYlSNgrDn1OKmtNVudJjbycZkPzK3I46H9T+dVr0+Wix/xHk/0qvdyB3UJkALg8UcqkrMd7ao2P+Ex1MHhLf8UP+NSR+M9R8xd62+zI3YjOcd+9c2WPGKTPNR9Xpfyh7Wfc9c+0MenIxkYHbt/SnC6+bZn5iM15QLy6AAFzMMdMORitHT/EN5ZbxIzXCnGBK5+X6GuKWBml7rubrELqj0Z7hyQSxP15polhb70KE+uwCuKHjJ/4rQfhJ/8AWqRPGKfxWjj6SD/CsPquIXQ09vDudmBbMOYyPo3+NBtrY/dYr9RXJL4xth1tpx9GU1IPGNh/FFc8/wCyv+NT9WqdYD9tHudBdWaKEKsHyT07VV+z/wCzWWfGNgVx5d1/3wv/AMVUEniy3IOy3nY/7TqP8aX1WtfSIvaw7my1tk5PpSJbop5xmuWn8TXDtiO3jj/32Yn+lQDXr4nBMa+wT/69bLBVWtWS60TtgqDtmmXJmFtKYFIlCEoduRuxxXGjW74kbXTnPSOqUss87ljJJg9gxwPYDNVDASvdsUq6toi43iTU2PMkY+kQqOTWdQuEaKSf5HBVgEUcflVFYn2H1JxQI3AJZDx716CpU1tFHPzy7girk59Kkb5cAA47HNMjwTgkqfU9KnIKBgw6jOQcj/OasQwgcEKdvTpShYwGGRuGQKcEYNyucnIIalJ3McgZ7FhTEAVRwHI9cik8p0UNtPzfrSmN1Y4yAD9cU/aNwZHBwOQRg0ALMpFwUznpg+vFJwofIOcHtU0ayPxhSoG4gsDjj0pzTLIh3Qx7z0bkEflQBVLkk4UnHf69KedpxuBGOuOac/liUtGHQcNtY56c9aRU4jIZWDE5HTHbnPFAiN2LNkEHjPWntF85+UhlcZO7HFKbaWOXZhWKkHKsCDjv9KsqkaOWniIVnYbjnjn39qdhXI1SFJB+8kjQsSCVViD65Hb/AAqSEzE+XHfSb2fapbkH045OSagBQLhcsBnBPPGeM0+1KmWMHkZ6fTmlZDHwy34DMJ1KKeUIBB65I9uP1pyalK8fzRI2ehBxz9Krm9Mw3S28BYjlkTYf0pYlhcoA7Rh2CnI3Y59se9HKguaNvqKAxB4po2Tk4UHqOffBpVENw+5G8sLgbX+Un35rPji3XDqsyggbQWzz26/TmpUSWSQSeXlOh74HSpcQTNkC4EPzMr8f3etZ0lrtQkPIoGe/QelU3RY3YrI0ZUkfK+KeLq6RceeWU8DeA1Z8li7ltraVbcAlXbOFx19TQlpJJBJIyhcYyCeSOmcUJqDImBbFgMDAbGD7cVaR5LiNQthMxO4Bo9rfnz707DKuGB4zkc5Iq1p+qNo1+LowQXIKvDJFMm5XRxgjH071CPugE9KfZWkE9/HFOIniYkHznMaZ2nGWXkc4/wD1U27JtkFu0vdLkZ/M1GS1MmD5bIWRSOGJOPT7uOnfitZHmFksYiikdY/LTz4jjYNx3FsgNkHGD7DmskyWsenwH7Ctt5oRl3je/wApwSpBHXkHIzn860YQiz27QgGMuZfLGGyCSFKr74IAyzZzgdqwe9hwtfREkFr58lzC9qhfBJNshKZBznLEZG4bcZ5245HS1JYWsw8/zIIL5iJFDYCs3ViAxwjcd1IPHXiqyCebUFjubCaMb2jU3OQEyp6jJw23sc43cnrVi3vRHDLfSRrCjuGMhJEYwcZ+VRngkbTnv6mjU1QssNrIrOsHnyKkcSyyu8wHy5IJycjJwWJUZ4AIyKhiFkFnlt3tYnGIcSFgIGxhhu3KFPDDOOeoFSCOG/1B2lFx5krJnN2gdGHzGQqQBtAA+6BtHYk4E2prcaVMILq/2xwxkCYfOkhbgYx90nkkbCw29qXkUmavhW7trITqojRb1NokEuQ7Kc7s+mSRk/xZGegpytEyai09m14GLSBXfygxXd9w7zlcjG7jJHfAq/pGnaY+hWuoJf3Sv5jsjo6KjlWJDlCNuCRkA8jPXJqhBo140EJulkiwSVijnjbzM5whGRjagJLZ+UbvqfMXsJ15yT12fqu33DSSdzGubV1uYN6xS7m8qVY7kSBWBDAJkAY+bock857V3K+IWv8AwsLmeJklVCZGUYRmAydvUZx25x06VzGkyH/hHQ88lo1vDfoIo1XaVzESwYHgcKmMZyTnPOKgudHv7SMXNpLb+YisGEiFAqNyWx1Y/NtyF3dfbDxVKnXmqdXRq1n+n9eRne7aM7SdHg1q5SYNHbTLDy07AoMnaNoHLHGeQevPAreitYfC+qQi9jbUo7iQ+TPDlpIRgfKFPBAAyctnnjdzWj4S0iKCSG6OxzJEcjytrDAHUAnHPYc/yGj4m8Parqbwx2SWUcDxGNp5t7NGc5OQBjHAHc89ua5K2P58S6E3+7s7/wDDgk22iC2imgh1PR7qFJ7VpRKkMyAiNjxuBzkHI6gj+tZV/wCEza2S3Om2oltBuV/LuXcwuVK4aLb8xwePmAOecV0UUMPh7THu9bv4FChbdZI4im/aMDCZODz9OOwrmbzWNW0zW31fS52likAjksZpFJUADMZH8LDnKsMHsehqstxFapUkpO8ErJ9L6ChFmJPMbOG3Q6aXlDgIy26yAAD5Y/LBxnnJ6Yzjkk1sXN3Pa6bHFc6fqVxLNuhnWRFDMCwJ542ochRkfl1rUt4NM8YwfaNCni0+/Egae0khDfOuT8g4Kvu5xn3HrXNy6T9k1AQXV4iTiKXCQ27wFpGU7yWmXDPk9N3K+9ezJJ7l2udBp8cslk0kR1GISuXMU0hA3YA+UrhcEEk+nH1qBtOhlvg81lO58xHR7m/+RWOQdqPxhemPWq2mX4iia0V7eBvNEkcKoq+YuQpwN20/Mpxg8nOMjJrRzFFIpWViqSOB5sxjAbOOhOCcDPPTJPSuSU5RqO5DbT1IWuTDdxWipwu4IzcRqzbtrHnIAI64yfXNNupmdmE2vXkKIQr29hEw25+VVBVEyN2ccngY6hqbFYQIhvZ7kO5jE/lLMSUJPDdcgEAccD29Va/tbX7MvmMsrYkxbmVi25jn5SP9pTjA5z6YpRfK7bgpcplf2fFcWs0Gk6Xqc17sAN1JJvdsgk8xxsgPb73OeTVOWHUfDmmzi6tmuJJpfOmRhHKScEnfh88nCliAflOK101S1v47kXWrX05ijx9niiLSR/NjOC21G+XO3k4J47VWMKXF872nhK8vw58xbu5ViGbYASyqEHXjjrjPPWuzXqaehkfYLKDSrXV7HU3sbxkBkaNH2FyxA6DAyQcA88V0S+N9R06RLHxlpEd7HEw23IQb0IPByR6jPINRX3hOQzrf3/8AY9qyAJEqzNBGuMng7izbc9NvOe1Zd5rVzJfWtolwl/ZQFQxtju+0JGgLDZtG0cEttyee/ShO+w2u512n6XZayz3uha5LfySMG8q6unSVMD7uFIVwe+QSRx7ViapJLZyTQ6pdTQXcYdgiCSFp3J7ZzvGDkY/TFZWoaVpo1qGPRLya3u52IjZNoQ4BPPzcDIwNwGSela0PjnWNJC6f4psV1C0Q4WYrl0+h6j/PFNWYWaHXKXVxCNRjF5KnmedETBJcEeX8u4sSF3qAPughSDnpT5LyWSVo5TFDZyhS++986KPIbBkXcWOST8oIJYk461qJBp3iZJrrw/q0Ul1cECWC/ALMoyfLBIxjJHbgDG2sfWbOSy1O2s9TinglLJhoiVjIUcYZh87gjAVjwBjuBRr1AfDDMYv7NsoZ9QktZ2Dwyyoqx/u8IQVGMruGFzwSTtJNM04zRTTX93aG8uuJbW3a3yYo3yQWbaAkZLNkY3cZCrVWG6U2tzhdZt1tbeRlhdkSUICQAQgXYuQOepzgcNgyxu9rFBCs5huYv3colRURFLAvK0gJGUACAKO+0Dnks9mBYSw0+0vxBNqdnE6SOk9pEAknGchApJAY8fxHBUDJzgvdThE80CztawKxW2EkJWRSoYfcyAAoJCh+5Y8say7qO0v5PtFrDey+VmRp9oZIgBk5kcEAZA7sSOAM5zqa1cXM08cYnLGVI4Xu7eI7Xj6uSMFyMfw8bsZ6Gl1DoNtYBp1gbox3kskZwZJraNg3ZcHO9iAvKqoxkcgDJNPsijMs8Vu0soW6la+mEUfJY4U5KOUPOMHk9SRT9ghspJVaeMl5fszq4eRJGAG6RwOGCq27A4yVU9SK8EVha21y2o3MrX5ki3qkxk8xSoYbnJPzEnBUfN8wAAHVgQXWizb4xLpjLcjfK9zPbKJD82NwUBi+MgbieenGM1R1TQoNQumhS9tEkjhOyQqd+2M7WBUbe5+8egXFai3d6tvK97dRxRvIY/skRErTKPnIDYYHlzk4YnpnPAnNy/kXU10ZZp1mVUtLffGcFj99pQMHarfID7kE8Bq61EcbrPhlNFjaJr1nuFKoqNDhXzjBVgx3Dr/D2Pfrz5gdZXTaVZXKHb6j/PSvT7JVcGYR2yssGZfLVI/LjTBjRnIz5rbARtAIyP71QXLgSy2lwNVtrtVj8mMSNH8pySZSoyOcknIJ4+XirU2tyXFHmMlvG/3o1bnqDg/nUD2KN92QqfSQcfmK7u88K/bXllilcMiCR2Nk4cuV3dB1UgjnkgHJ45rMm0KOOKNFuk+07GldWIUIg4AxnJYnnAB6gYzmrVRdCXA5i30PUb1nWzsp7rYu8iBC5A6ZIHNUXheJ2jkVkdeCrDBH4Gux07StZh1EJaQXkMyv5ckkMbFUPG7JxjAzznp3xSa/JqN3JHJqV7HqCsMIwl3YA4zjPHQ/iDVKr71hcmlzjcY/rSVrNYwOTtd4z6PyPzqBtNlXJUCQdihz+nWtFJEuLKG3NG2rLW7xu0bKUfurLg/kaQwuOMDPpmncRW2n3pKshDyMAUFFx1UnOPSi4rFYe1KDVn7NnpgnpxzSfZmI+XtyaLgRB2B604NnOR6dKUwMp2lfm7gc0CPnH6GkUOwCe/496cUbsDyAQQKYAQRg4IqVXI6qD7g4NACqrDdkHmphGighlcnryMUgmG0rllB6gjIqeJ14KseuflIpAMCKUAK4bsOufrUgtlKhhjBGSwHQ09GTOWRlAGeelSLhUx5DEnnfjG4d8fzoEVFs/Mk+TduPJwOCPX+X50ptyihcKxAycHrVpcxlsSuffgn6VIkY8ogfMzfxFegpgZ5SNpFIUruHIVvTjv8AnSsjAABg4zwMYI71ekMewFVJ3A/KwwQQcZI9P5002yPjqM8Y2jPT6f54oAzg3OcDGeParP7xlXA8xSAdv3v0q0sITYEmbHIwV+9j2PFQSRMs524jKYyBxj8hQBDtVE27MHsdx5pyhGChwV29Sp696eZSLhy22bPygyL16ds9aa8LBVB+TKkjceo9v1oEJMq5Ro5Cx6YfjGOmD/T2p8Uk6OsvmIwVsspkBP4qevSo/KZfvDJxngZ6Uh425Cjb/s4xQgY6FjPKEIX5iCxHB4HPP0q0YYBGWh81JVJKg4YH2zx+dV0cg7gFyFwDjp/+scU6Mgxs3mhWBGAQTkf4/Wh36AMNpEjGN5ijLjnbuXpmo1TyWflG9wevNTS5wsnBQj5Tn8Kh/wBY6hQOh9vehgi5Hbo20Bvv9R7etJeYjmREVQuFwR3xx1plushlCRjnkkAgcDnvU7yzRhPNicJIuVLp97I6iktAZWdjFJtDHB6Cmyo0bOhy2w4LDpUolWIHyxkOACCc4xz17Ukm2VnkwIwxOVGWGe/P40kMbu5HPHervlTNDHLExO5dx+bkYOKqbQwycH0zxTthKqI2O5WwDgjr/wDXoYHWweBfEU43jTyuflxJIoOe/eibwXr9izS/YRIVO7904f8A/XXdy/ETw9bPtW6unw+0lIdoX3HrU0fj3w7qMSB7sxO3CiYbD+Pasf3nYu0TyeXUJ7aE2D21wrhnYxyIOWYAbmBXPIHbHTvWudHukgs5pvK+wXIk+zvEqtJvABbIQFhjrxz16V6hIml6hGVXyrtGHPAbisq98L2dxD5cYnhA4CJOdnTGdh4zgDn2rGUpJpJW7j5WtjgQNLS4kS2t1leSNdkAG2RmwSHwGOM5x95jj0NTxzPPIiXFnNuRhELea5JCykHDhSfl654B47knNbWueBneKO4trdXuXIZnaTYrdsBegz69ue3FczdeDtUGJJYIgyxNmI3iBig7KBnj27+laRaa10DUusPNmZoLdoxu/wCPi3CiQFe4bqB0yeMZ5NWbSyuZpJ1hmRB5UhmMLeWDyQUP8W0HaOFIP3fWubsZ7myDL9peBNjsqvG7RyOoBAXggseOSAPX2sJe3ROJRFK8p2vOQjsDnKlXJLKM91XOenIApuD6ApHb2V/dXOjX+mzWaW1yYme1DIIwHCrvUoAOMYOee4znArZ1HTZ9VEFrHJGrq53M4JjDYJx047/41x/hvTLu81aVWuIJ7m32yMxunyqZwxJHznkgAepz0Ga7fULpdOubcRbgpfczORxnI+UdeQc+tfO46PssQvY6yd36af18xSetzI1TTxoGl2cMl95EGZTGqI++WUooGxiT5OMA7uMZOME1CVOoavbQ3u4qwMcq/ccgKWMqq65KkjhiB0zik1LVLW/1NXupbgi2VY42imw0e7ksMEYzgDPPQ8ZFZ+g30GnMswuJWs7mV/nm3ArgAh1H8TZUZPzHqORzXVyVamG5p/xEr/f/AMAduZHUWNt5N8Ld9RkNnbRCOG1eFEzlVI8xl5I4ORn5iPwrW07WYrl9RimkWOG1bcx2eWAp685x97P51zsUN3FBc3VyRCu5TDLLKqiWMbjlSTxyRgelVnmN3pd1qDrBsukH3WGGKZK7iTgcjv3Arx505VHab00Wlt3b8XqJTcWQeIfElxqb289sun2lvHG5ijvZQ5kbdtAYEjGVPIPQEVzumNLptwmq37QyIkbIs7SN5eHONufLK4O5ieSxHIwRVi9W6u4LZ5YjFFAjSKHjU7AqqQqsz5G0DuApyM5NXr2+tba8gs59Z1CfIEwiu1MccY5bO0Exscg8KoHXkmvqKNKFGmqcFoa9SjbwNrAuNW0hZbS5t5Cg27nLqMckYG5N2Rj7y4rqdM8Ww65Euj+IGlsNS24iu4m5Yevo6+uRkfrXJ3Rm1WBU021vWaOQzzXcaKiGR2UFlWL5WYLwDwQoPfFT6jHpa2Frp1ys5uo2S3UiPBeQYAJ+csrgEEuPl7cnir20Q9zV1fRb7w3bRLLrrLaFVLXE80oEoAziORAdpJJwDk84Hesi6e5OkBtQnjm8lYwmGfzXYnHdOe3PJ4yODWnpvifUPCzPpmvRm80stsdpV5T/AHx2P+0ODWvq/hqPWLb7XoEWn3dowQvHIAkkJ77jyHBXs3HFJxUtdyWu5xt7rhlg+y2moh2CxlEFoqtCAMtnjJYHHOAByeeBW/peo3GJJI9SiRC0cSNLGElBOWY+YI3JY4xg5IHJAxWZaXVzfWEt8Ps8kEMqriOclvLU7XaUKNyHGMbcIMthe9Xl1Oa3uVtYL+1kjkd8kXWwJzgLhl2pkbQBlmbBxSlFJWjHUVrK6KVzf6l50lhqd4JI5ZhAyIqGFkVzuwNqqfl4O3B5BOKuW11YS2ssMfilLrbIqxsEmjW1Ct8qhAhUrxyDxx1qoYdQ8q9tLC0DXk94nlz3KYmXnLqA7/OQdpDADgMSc4q+LHxCZhJPYWzxW0axwRz2tmzykE/OxZxt9cg9T0zzVqzSuNXC2FhGDJDDqV/ZGPzftxuFgVU3EnCZyFH3s/eOcfSxDL5lxJHp+iIhX5JLmS0kuiWIyOWIJG085HU+lOkuLiPRTDfJaQXyzkBrd4o442A3KB5TdTlTz61BBJPqls1sLww2wlRZDJerAoOCWCnljjGcD88nFc7l7zVhKVnYx/8AhFry3nN5d3Vjp8rSM/nlo7YrgcDaM5y2Dg+nvUN/4gu7jUgZpRcWELqZZophIkqKBwVUYTcQxO3nn+LFbUMPhq1up7u61WK+uI23+dYxM2yMcAZVNgPbcMEdsHmklTToEnkGnXVwYn37r2/ijXe3ICqhZt2O5ywxya3v3Lt2MfWtG0i2H9padqLWmVVt6xyBfm6LnA5P93g+orUt/GniDw7nTvEVkNQs0O0+YuSv+H6fSs+60qS51BpxZTMXAWJYIp5wgxjcVdl55AAOQSxJ6GpdW1fUdN06Kz1DT5FnhjKiSWxbY/IClmLEgAZJ2nk+ooV9Enf1B92joEtfDfim2U6HeQ2syjP2Schct1Hz/wB7gAFhkAfKB1qrdaLrVozwTKloYREvmzSJHEE6HGWBPUAEkZ5B4JNYb+FYLrSLfVbK5S2mVQskqSqqiQcHBDYALZwMg+1T2HjPxB4ej+yatBHqenIxRg4Eirjgj2OeONv40009P6/r+rBZos3OnXQiNla3lxqCyo6rN57RQuAQWCIBhEQjG0YHTIOQA+Cz0mwvZL+8LCMM5RpYlLGQKcq2xmO3cSAOMt15U1o2cXhLxNaSjRbpdIv3KuqSklImBJ+XkbQSTwQAeOpFZ93pWseG4vK1DSYrq2S0MZlUkwuFYspYKcsBk/LwGJ6nJy7MVyOHUY0uLgrp63UCDcVaNmdlOBsVUwGBAJ+6AOhUDq5ft0DSraaeLi/kHmTiDmO2AQEKwLY3L8w59Cx4xUf2aXWdQH9p/wCjyunmvJpkqFoNyj95Mi8DOOmcjHpgVJbaWI7B7OG5c2zQnYhljVHkkcMS8ij52EaDKdwqg/L1WnUfoRWuqTadcQ2thcNY3+3e4glB8xdu9QZF4xyWAXjkZDGnRXM72+26jZXtLR5nWZDGZWbe7sXxuCZAz0Zt3LdjNZWl9YM10ksr3csW6OZLdmVm+43lncvUjlsBQBgZ60ttdQanboLBROgl8mQS26QLPNwdzCIneoUdFBOSOT0ouLzLEV7qe6Ff7Ov3tLdWQrbNv34wWk3SE7cnC4Y7sBuM5w/Sru3Z5n1OyknE5jleIQsignopdUw7t8ueFAVe4BzknUZF0240yS4lnihLTXqSFUiiAbEShVO8fMc7T8xZlB6GrGrS6jpttcbdQWSZHd3zeeUUU4HliJnOc/L1Hclsk4DaC5MtrZX0621tfWbyGeTfcRIuy4/jEaM53uFALGT7vABxgGq0OmTxSXky2yXtuxIgRX8oFhk+YefMZMgkKCN2AfTMdnqVukrzC+uhYFGWd7a3WLzANrERqBwgIcbgB05wWzSxxmC5e6U3NwSftFwirIjwKSQdwxzzwVDqTzk8ZpW1H0Jbi1X7BcJcyI807KAn2mRw7YPBMagM3zD+IbSB15FZuqeFb28unuzIpleIM+9S3mKCAShwMhWyC2ATye4B1JbbULC4g1O9k1BvNlFrFC7gtcIRs4QsMYbnAGPmX5uhpLmytYInjgmitbYCVp7lXM80mcL5aMHwOnUsFyWxmmm+gjnJvDESW7NFPJJcoV3xFQhIPRU65Y8nOccdj1wbnTrixlXzk8qUAN8rg4PXr6/Wu3i02C9ijBlkngMKpJO8hMQJcAESJjoDnGzqcnI5OlMum6JZ2t1pdnd2LMi4kguMRTKWCrJKx6k4Jw21evc03JoLJnNQavr0tjE1/ZQavaSDKpcxhmxkjIyD6HpVR4/CWoFo5be70WcnOFy8ef8AdbPH/AhW1esdX1G5WVZ0dYj504CmafZgNESuFAwuTjK+m0EmtmfS9MvoD9u/0CXjMdzCTGGbkKjZYdCON2eazcnH/gFqKkcJP4EuZE83Sru01FD2ikEbt/wFjj8iawbrTL3TpjBd209tIRyk0RXP4Ec/hXWR6Ek+pJbaX9rt72RHlSOFlfMa9WO1j3yMZJyDx62v7Q8T6WhtpxFqFsvBhmTOR/ukY/Q1oq0lpczdJPY8/MLg4ZMswyARzxxUghkMbMcDpj5ec9h9a7Frvwxfhor7TrjSpm+81ucL/wB8nI/9Bpsng/7VFu0rVrO8hJyEc+U38ypP/Aq1VaPXQzdN9NTkRDGe6DP8I9elSGzBGBGPl9ePrWpqGk6lp7lL6znh8xxteVc8f754P4VTZZEHmeWuRjMpY8+hrVNPYhprcpfY1Py5xzwU7/UGke0kXOAGHYnCmrwuQYgsifUcgZ7D1yaerW8UiBwRIDtOw7uPqeKYjJEEmGYo2BySq5UD600RhxuUq3uDWxJbxP8AuzuOM7WDk8deOaYLOJ9xXy1/iABJ3/XFIdzKVnTG1yOM4PNSpcyL2B/3TirLWShX8snfkHJIK4zg9sn8Kje3kjUl0U9gUzz+BosO4LdREr5iAgZADjjn3FWYXhKFRwgyUEbdM+uetZ5DKQGUpx1cFc/nSmI/e2/iP8aNUGhfcbyuThUXAG3gj39P1pSYwMLEZSowSqnp7YqirzRnh2yOzVKl7JGfmTI/2Tj9KVwsXY1jDOd7O5GF5zs+tNJa2iaUAHkNnaVH4nv3NQR3cAcMSVbP8Y6+oq2Jw75UIUPUZ+99enGKLisxyTNCzkOxkUYGMLnnBwevtUD+YqnFsVEeTkkAYzz17Z5pZVVt2EdM8gsy/KB6ce/SnBPsm9dwdJB8xIywB+vb/HtVCIjC4VWjdYzIM7FbOR6GmR2cpD7VjYMucxnP+eak3qs7NHMpA4MqsQAO5Ax+p9acsbrvRYyoBO98kjBxgA/SgCoEWKMLKknPXHPvmiJoVjJ3nzM/LjoPXNWVEjoriPEQJ2k47nHXrj2qxLbRwIJNgB+6JHIwSe/5UXAoFtwYls7QAMd6SIAhhzzwcYqy1su1/wB5lwRiMELnJ7CmfYpUdtuNqkgtnAz6c80hkUVs+x8fKAe7YzTTGUQlC2B0PWrSjZIC6MEIwAHxnPT8aQvFyCgHXJUkZHoR6+9LULoiaUzfMw3HaMlhyTzn+lQlXzjJHYg84p3lk5w2AOP/AK9IwdTjaWx3FMRKrOBkLkeuOnvTVbCcMc5H4c02NtpJ3MinuCRToppIHEiFS2f4hn/Peiw7jJIzjIOMHrSopbhuvvUhQMw559adsUgNnJPbNK4DPNlgZXgmeNx3Rip/St6x8d61pwVEnS6QDkXKbiT9etYpUZ+gquy7skDHNG+4bbHo9j8UMAJdWZgVsfvIn3AH6GutsNe07WdOMkZtZ4UYkgj5lbscdfpXhJDbRwcelSWs8sFwGikeJsYJQ4JqXTXQfM+p1OsM+oavdSW8bpGHMY2Bhz2Yhe+Cemf1qlHNNHLbwiONVViBK6gMTj7wVSVHHHc1o+GdBh8RtNG949q6nmXZvyCM+oHWpZ9EFk15a2s6SeXN5e6YIMYJDZJOBjIOM5+Za51Xp87op6oauzV8I6jb6Xf3KXzMxS1xvWAjpmRV9sg9D7VFNeXvia1uboxRmaFVSUIjBfLbOMFckEHrnjHpVTTb+O31S2lKxXLvJ++eMqzOGGxizHHXJx0HGOprZsphoNnNHZFHzeP5siscNGceWOOOzD6qa83EQVPEe1jG8ml6db+nQmd00zO0i2n0u8nnYx7Gt2ihWTkbmwcjeSeAGIBxyB2rR0aL+1bORnlnd7G3RdkRd5ZcuNsTOxPf5SBxgemal/4RebWbOaazvLKHCi5Fu8e6Qx9d3oNp3fkOlWfCkf8AZTyXBFvLt2hXtgR5oOTnLE8twADgDGAOTTr4tOk3B2kaJ6X7nS6za2t5bKt06vEsq+exHCZBB555GePwrmrlbqPww+l22mysk+4I8qLLsgzuQsvVT6lhhecirmuX/nasIYryYIkCzGKUbFjIfsD8pYg5GM9OKwNfuZrfTbaNroQ5bc7yuRuQ8jYRyT8pJB7Ff7wrysBh2pw5tbu/oJfFqZk8YjkF00dpdzQOsV25RRHKpAOwM7lQ4GcYUDglW4rXt7zVY9KNrbabbRiTHzzWTu8XzAFlJ3bjwBjbzt+UCon1aHUvtLR2N3IH8mJy7zs7I7bhhs7BuwNoG0D+8QDmobGVrYtZw/Y/tMTm4up9xiEauARET8rYP8RwDjPcY+ku+psag0jV9bP+l6TPGYpFli+zwSWq78fM3llSoGQOW5J7hRRbWOpWd69rqN3i4mSONIo9PlRlA4BaTYoA2j36ijQxpP2WO4t9Ru7WSPhLi4lMRXOCFB3nbyuSoPIJ7YNaWxVhuJNO1XW7+Vgss0tvFuLgn+FJJBtO0cnZ298VzzqU6kJRbstu35kXUtGZ0ttqN7q1lpqfY7mJwy/M6mRQFIVQw6jOM5B6VnabFrXhvXxLpsvkWalUlyxMUTnd8ob+EErjaeP0punSBoLuG00++tmeNod04WJUXBbbgAHdx1Pzc1qWGrwx6RdAyQTm5fzZ3WIs+xQMALwc7uOeATz61z+0rU5qnHXZf8Eaa+Fs7ddNtvE0VybO3to9UTBlUY2NIR9/AzgjGASCRXNaXoGo2usNZ6jLfW4tixhj+17lSJWwpyijDEkj8TgYFcjOd2pPdaFI9lfWqh5CkW10ycbZSCfMzjrk8EV1fhDxVcal/wASu6tvKvbWFmTax/eRlhkA87hkgjuOmTW2IlUp4WUov30u342/pCV4vXVFrUdF862nTm5m3YUFJJNoXOFZxJuIAA6cjGcZrEnudOGlWq3SXk86ZiJiJ2sEDYC7wWZ3bGcggDGcE11fiBbmysrSeBrc23lEyebN5cTg8EM4DYyScn17iuKn0m1knt0m0/SlvyjSuUuC7XEQIwIx5mc4G3JUlsE9OKxy6dWpByr9+1mjST7EN2bK6insvK161WMhi0TQSHJG4xkDp2OCe2eMVYgstAuIpG/svUUOBvM95EoBxySFDbfUjORxxxWhod3LdiJbPRdOSVpZD5P9i+WUQNn5nkPXBwMAk9+hpYLmWa6muodMKSpH9nkuooRFbkEkuAW6IjN9/G4nBGK7puyM3bcZLqGmG9geEyNcXAYo8V5DNtx8oJ3RsQenTgDtmkhn1ezu5GC2sEjyL5hltvOYM20BUB+Y/wAWMnnfk9KuQ6tqtz5lsZHs9u+Mq52FyVZgUGPvDAAOQ3fnNVbT7dZF/sGqxWrruaJ5Zm27BuJwGbbgOxJDdCPwrHn2X/BFzk1zZ+Kbz7L5lzdzQ7z58ds4tiQBweMKAT/D2Hc1ntocVkGudRvtFsZA0TXEygzyLtJOMYwu7AJwO3p1umz1m/y8+s6d5vlxDyryZyHJGZGGePmywxhv0pJdF0FdNkSbXrFFjd5pIrGDz/KLELgHBKnOFzgkk1rCV9mWmnqcY95F9kEunw3MFxLLuYQ2HmJIBuVWDFs5YMfoeOlamgX8WlWL6Tq1suwJJNEyXETB2XO/BzkZIx1IyDwa2bifR7GA29v4ZvLwGUgvcwrbI7gbCCeCQMY+71zxnNYUl5qF9aTWml6Fo0Nt5Rfy4IhcMRuC7c/U55GAMmreqs1oC0d0VjpNjquqSwafi2uYk3+ZHMCjNuxtQ4XccEE4wevHFXbDxl4o8JSC2uCLq1wSYpVyCO59uvXj61Xt/CPivT5YZ9Jjkt7vdmRPKjiiU8gdTh+DyQCBnrUesajqEd69tNHdwOzLHJNcAeXtGGO0kcbmB+YHoenYUt/ddwe2qOztdU8H+NPLUSHStQBDKpbABDA/K38OcYzjp/FRqHha80a0QLaWjwea07mJZf3pPQhVbawUHHUluc1yF34YtdTthqGnYSNxvBV1O32JBwCPrTtL8W+KvB58h2a6ssAmGcblK9jz/P8AWi6lotGFmjSWXT7mFtYGnGe7EqwRvfXCxkgY+bG5VJCnCqFwBkYzUEU8+nXNtDbW7RWUc2yEZiBaQDhh5Xzk/MGKhgOoJ4ro4PEPhHxykcV276XqIGIy7ZA/3WOcDk8ciquq+Ar3SoI59JeApsEbO6mQyr3PmIp65OUAHHr0p8tg5jK1K3cSW89vdzSrPP8AfUnzQd3yhVbHOAP3pBA7YABpt7e2cVzbQ6dbW0M4lWJi+Llp1GPvxkBVwSz8ncSRkZNVkt1aC7/tKe9t5ETIXaUSRNu4FzklVJ6rjJ4HU4qtcaZGL25ml0+4ht43aMOmXQAoMozszEtjjgEqeOTxSTXUC6ianDqMQi+03GpJOFhijj3rboceWSnCoWbnb0GD0xilt9Ia4uUWRNSBt5pCLa6IUF1O5nbeQN3IJJwoGMluQW2MEyStDbTnT0tw/wAm/ZMWKFtolKhmfHPzdMkKOlPtl1C8jtrWKznu7SNd0NkUE6oGPLY+UMpAZlLE7iOaNhmjaDVhq0GrTzR3txI22G5AY+aDgO0KMn3RkqXwijIPXioroatBqEZnvmmaNgZEtoWjjtmGDuZDGq7gOhOcflVlJWF/aXs9t9ijVGliWeIiWebzD5eVxgbQC3IEaluhNU9FjS4nknh1O3sooZjdRA3P3pc7t7gg5G/gZJzg7VJOaW+ouhf0zUpEgX7Tp8EZMccLGeQbTGGL8qV7EYC4OzAzyKhsJLi4jcRQTXdtLPteWKzdoFiTDswH3pDuYhcAAH6gVBCNU8Ord6yixrObhglwYPkhLMxcI0rKGdiR90HADDIyajv5pdHt9u2BrplJmkbECH7hGXLElMngEKdzE84FO2unULmjpz6jf6gyTaWunQl1lkkhl8pi+A0cbpk88bm4yM5NRyJI8pmsPtDyRpLGk6zSN5jNuzMzfMwwARtwBgjkZNWJEtr6ysLGwMFjpd5IrTizClmX+KVyQCAT8q8kcBj1AqEqJLG60+a2iHkFLeOSQzXbKSdxRUJKlgAxy5ULn7oyaXUOhk2Gl3G6XY0kNnHNHtTy1gc4AB3Rjfl34PcYUk44rpDqkt7BdQ3xs7jCN9mMsP2eRzyB947GOQTgY4XnBODnz/2pPLqFs+mtfMXkeBWCbI1OApXOGOAhOcKCc445ot4bOXVL+JlsLd44DMJLoEJbKOjiJSAMHcxDDdllqZRUtylJx0RiW8K315FBerPDZyRsY2u7XdI7kqqqCAqlc5wV659qs6j4Ans5fMtS0chyQ1tIWzjrxnOOR371dhkuPscl1fO8EbuZ0E5cvJIeFLYBwQSCOF25Jx6VP7WmkuhNJb2kbXds4t45CscaQhWJDMBukJ4bZ3JGc8Cjllf3WPmVtUZttf8AiXTIswzRX9qTt25BD+2Oh/JqYNZ0G8Pl6npD2E3eS3Gzn1xgj/x0VtWFvJLrLXkGmK2wNbLFPGFErn75WNAvzhVXcSRtIIJ9NC5s/DWtEwWl7HHc5wtrcrgsf9kk8jg4Kk0m+V7fcNR5luc0fDttqKebpWq2l0NpxHcqIyD/ALwJXp6laxtQ0XUdPIa70+VY2OFdl3R+5DKSv4Zq5f6JZWty7JcSW0iH5ZY921zu24Q9W+Y44Pv0qxDeeJ9FldIrgXKjhkb7xx2PQn8c1qqkl1+8ydOLOcntolcrFGzQk/IShORzzTYYgAqvCQBtIbbxj6d66YeIdFvHCaxoot5d2TJAChz77Rz+KfjVpdE0XVDNPpupb2IzHbytsRGxjlgGIHU/w81p7a3xKxHsn0OQJG0OeFUcgNgHPQAdRgf5xUtvPFuwrLGzLkFwc5HUV0Fz4UvVGPLjtrdQCGkO+JmJA4kBbtz07e9VotBv7mP7VDPaNG8myN3JiFwwH8IcDlQOSSPz4q1Ui+pDhJGMyvc3LNKzSR+vKj2xz6Uk1jAsZJMcLlsl4wVP0wOAK2ItC1CXzUhRrp0RWVbYiTBJOFOCcMeeO3esxrO5tLkKyqjj5WMgARfcHHTIbmqUk9mJxaM17OR/3gkBRjywjPOD7f4VF5b+YU8iUnk5RdwIHfjtW4kgSNWLhB0+XgOOzKMcj+dROyw5YQl0X7zO2SD6UxXMdkwCVO7BIIxgjHrmmbAGOzIIOMjit0RILlJyisYiHYKOeOnH1qG6V7uWaTynExIUEDIUY4PuKLDuZiTzx9JSR6NU8V+6E7o8c5zGcc9KGtrkDPkpKvqp2n8uagkwHIRWJJwq7CMelTYdy9HfQngOEY92XBOPf/8AXVi3guLq5WOzRpZ5m4EZyzenGOT+FZXlMcgDp1B6j8KIo5VlQQrIJM/KIwSSfYDvRd9B2R0N1pOoWEO270+5imJ5MiOoGe/IrOYStbpEFZwgyTIPu5PTnvWvp3jjxHYoUW4a4hXhkkQkD644/MVor4w0XU0Caz4fiP8A01tztI98r/8AE1n7Sa3X3Fezi9mcu3lRMriCRZDltwbop4G49RnntThGyBkcugHL5J47YP8AnvXVw6X4S1KUvputTWVwzbwlwokw3r/e/TFSS+BtSmnZreW0v1Odn2aYbgT1Yoee+elNVoPTYTpSWpy8MaQxpC1u0lyWyskTbiPrn88U0pHFuaaTzJQdpBHyKCRnJ7H+X61qah4dubGQxyBsjgfu3yx6HC4z784yD+FRaZpdxeXflOxEQwx3TooK9BhAcscjHH41XtI2uTyO9ina6f8A2kUht4XkuTG0jBT91FGS+McD3J/nWpN4Ys49PiZr+JL0wrcNCrl96uQEQbVOG5zW/YSaRYCbTILi4N2qv9snVd2AuMRoMkDlV3NksMELjBIv3Vh5dv5jXUlpOYjci8ltwRGmdmxEJLBj8xGMlt248CspVHfQ0UFbU5ceAb+5e2gsmFxcMrlwv3A6kBghAzgZGWbHJA61iXHhy6inuIIlmnktFP2tVj4hIbaec5K543EDPpiu3k02PRNNv7nVJr9jP5arC7qbiNGZn3Ha6kZ4HOMOfunrSTf2Hq+hLDo8V55bTLbgXEkKJLIq5LSAsGc5bJyTnAyQFwWpyDlR5tlRg7ue4FL5gGSUznoaixuAGQMdjUoAII4B659K2MhFOScqRmkIzzjBpVDAc8e1Kq8sc0ARAbRnvn1pm7jnqKl47io3GM8UIR13hHV7XRLK+vrmYb42jaKDg+cTkYx7dSewHvWXJqE1zcxq9wC5YNLliqs+4kNg5xyc5Hr0qHRLWO+kYPNFEY8ODKc7gOoC5BY9OBzW4ugR35BsrW8j2bUZZ5Q0i5BOW+6qnGSAT0wc1yKnTpVZVHu7X/JFRWlxYlW8YDzl8kLI00jQJMzqV4wSAFGc4B56e1TQ+Rp80UNzI0AeIGRTKZi+3+HPPl5AGcE54HtVK98LXulW0c8mlkQvjbLLIpw56AhTgHH+e1aOjRRCC3AWSJ7oiKGS2RVVMNgsGH3hweWPIHTkU5Sg4txdxt6am1EdUhvl1DTbF7eztoxH9pkmbypA0ZdgpI7DI5HPArWgW1tfEF7cXmr2Btr2NYGdJg+6T7wPBONrLt5HJrIuJJ7Wxv7VHb7DIxnuLRMRtJhiDlgTk7cE4HJ9uK5izuy3iGwS4t0e3trpVEaxAeYgYE52jBOD/TpXlToQrpxWnmt7f53HeLtY6XXrhJddtfKa9mto08lZLRvlY9XwdvC84yCfwzV/UNP0/W0mlurfaq7ds0rl/LQDOAOijjp0NdJrfiqx0nU1vrR4bppIWjgSOUBeBGckjooHb2xXIrriyaVIInjlM6q73Djau8Yz8uMbCeB0JzXHRliakIKlBxS8/v7f8Ecr7Iu3V7pp0OC+j02OSZnQwtPF84DKcsWweQE9e3DYzWbCtwbG/livbK8skWMSyyqzyRouOAGz84Hy4AxuY9xWp/aX26wkCW8M97LCYTC7AAEDGwsAQBtzjPUHtmsq70jVbbRTqF3DbxQqBOyJe7WOAFX95jIIwcKhPOOlejhXZWb1bej1e+xUHe7ZXhiln8TM12v2KPT38w+aFMYbjAYbPnzgAjHHUtmqnhzUJrTWhNnBllZZBt35DHnjvyfWof7P1Cy0hbKwgWa1mcSyySRhlJPPzBhndjgk4UeuTT5bWVLGzmaOGMLL+8kitTCZe4K5/g7A4APNbYinCcbS22JqRvZmtpiXCQi1mEk5E3nNLvMiINwG0Ek5Py5657dqyb3R3a/1S3MDJBLFvXdOVPOGXGPvfNjjkAZJ6V0FhdhCurXrLBF/qFJjZd7HkYGORt5yoxgCnaslrdTbYIPtEEq7mEkGbfeR80gYHduVO2QG9DXFSm3jZaad/MlK8zntLhu/D6S6ndWBtbcKRtmmkWR2J+XLbQm1ichW7AsoNXtI1hJtVv109ILa/No8cLRT5VmZhvaLIBGF9epye1XtW8OCyv7bT4r1VV1R54riHLtuOApDANhhgY3AjoBWEI4dBke+jIS9cyu0M6xl3cjEbCAEsuCfvZz16gkV6bUK0fU3aa0exb0LxfqfhK4NtqGbrTyxV2lU454/eD+E/wC0OvcGum1HwpoPiaym1PRIp3lOyWS1t7pYnGOm3IKkehzgnuDxXn7+IjqV+IPKn1FdhAZIFErE45baMFVPGMd/XAq2I9V8I6j52lu4RWLCANjju0ZPT3U/jWm1lIm19jd1SfT9MiaG5vtYlSOUKbZLl5VV16A4YZIxgDJweSKp/wBoWVtp6JFAsV07Bpg9wMqN2AMjce65L9cCuq0zW9A8dRRvewWkWrJhUuJIh94fdDDqpyemcHse1Z+qaVdaU8lu+i7JIR5kU8AJWZsjKgAhj3G0lSB0PNROOlmDXMc9Y3+nXm0/2kUlJRPLYucO5wMEAbT278gZpUvi2v4njSWKKZpWMkiv93dwc4XqOTjk+/ShbWGn38v2ZobKOGGQSTs2JXm+Y5G8Hco4J+8T07VYNxo11qczy6feSWkjloTFcSBmcHIYpyAoBwByfbnFKVFX0ZnyGxYtZ3nns8lzKFBKjzEdnwCxA3c8jHQjoQc9Khti0utGW3tV1OFEVmia1RSu1iVwZJAQ+QxwAQc5A6YZpkWh3Gq2z2+l6owZpJBK10TGxXPLIOcEjGMY7deKvzfZksZpntrTVCXdXJkIeQ5AKlWzjagXPJx6g5qU+SXKNaC2usaJZax9kbQ2tLnYzNLfTrmMZyRjk5yBgHHbpUl/4s1yWe5isprKCOJN6uu0tIevyqxJJycfdrLTXLW0u4pLfw/YR3EoMkNxLdH95jnKnBOOwY4yentdt9TnZLh7bSdDEsCjYsStK0jFyCiBclmzuOMDoScd75ba2NbmY5v9atFafV9a1AmbDCOOSKFk7GMopGeDnd9PWpoPA7S6bfwm1CSyFXSRyAQQegO1mVe56EkDtUo8T6le3jpe6kNLzGxhj+zrCdq/xM0udvUfKDn6VTGm33iVku4JDf2yPsEc11hZcYOSSCDzuJyOhAGMU3dbuwrrsQarbWGi6e0Vnc6a82I0lSXAMm0g4KoCvUdxzn16Zmkajb2V1LDq0TRpdHczeS+1NoIQp0KjHy9CAFrr28C21qEnuprbTgJDMAsnyqxPGGYLwowBkHJ5PpVWf/hCdHLpFfOZZBszE0kkmSPmIPQFhjJGOOmAaFJNW1Y7O99jC1jQtLMjmK6RMIZTIFPlABQ2C+Nu7BBx15A68UumeJvFHhCTbFO89r1MUvzKR75/rmqOo3gupmisJ9QubdGTEEtsnl/IoCqTuORheAffvmt3QdVj1KOGw1GyuFkJZY5R++zjOFIHzAgDA6547mq96K01Fo3qdPpfjXwt4m2RahAum3rFWyUDxuwORx16+hrW1Pw5p0UcmpQeHbfUdyf6xNQmaFsDIOzPX0GR1PNeWXul2F7cW8UMM9vdXZJhjOxt6BS2/KkjHGMdc554p2na14m8HXO61uJWiHVSSQR/PH5ijR+oao6o/aYBJEmjWOkC4HmEiYOUjH30TByd2Oc4UkEk4FV9RvLee4l8t4Jwka73ubj9xEcKqLGiZD7VJHJOOfcnV0zx74b8SI1rrVmlhcTJ5byIgMcoJBKsvQjIB4IPHSpv+EHaw/07Qpra7ty3MjASrGhzuXG0lc57jsOcDBOW24cxhXlw91DZ6cIbGxG7yYmnQmVlCkeZvZQAMAZ+Ube2T0r28Osano7XEUXnR28zO8iXBiSJVHMrSPgjgkAHruY4HSrz20qy3F9qViZbiO1aNJbyQRo0mdvmjLcEDOGIOefwyo1t5C9ra6nqTzTb1W0VxccEkryT8pHAztJ+YnjjCi0xtE3nQQNE9xO97f242CPMkaoSnyLGuAoAORyOncZ4S0vNSNlaXRuTZWvmDYWVmVMyYAG4FZGdiSXY445PFXb3QdHtLG2ju1uNPaLyv7QgMYkkUEfMU+ZjwAc8D344qSNLXTRHALq7isJ3Jgtxbqs8sITIcNJ93c7jlVUn5u+MHNcGireXpe1d59UW7E85nWa5LSSSBSFWKNyq5AIPzLgZc46GnWG/UTqenxS39wscbbYpYg0YQOFKLAmcndg59sY5qrdR3fiTULnVZYwLezMdvb+Q6RRgKePKJJUYwcAe+T2JFdR2epPfPpFxGXRUWC2mj89gBgq+SSmeC21Bn2AINC1NzS7e2gtxdmxgCBMQtOgWSZsHfuBwY8suMsTxj1OKdwzXNpb3F1c2dgj26XKwKkcSsNw27AVdnOcdMckcnHGPprafJse50y9vBPGzvASbmZl3YUgnakY+YDdz7AVsNcWsZQS6j/ZeqSNG8iyRorDYrCGLfCpwACpLbePlHUUmtQuWLLTNSmEpv/tTSzJJJbRTWrTSEFeXYF1UNgEAH/vn5iClrqa6VOZrWCO2YKixnUJizMyL96Ty+XwQCI145xikuLeTTdYguZpHnuLKXyJh58rI2Ru2ztyxZiRw20f7PaqMFzFJeg4ihvi3l2bpZsZJN5yVT2GSA2c/N/BigT7mlY3lu9lOuix3zFkWe8l3rGXYE72SE4O1idgwWbnpuzm001xda6kkUgieaR3uoDGreSqxbTG+WK8KVAGNoJbjORVCHw0zzpdW01zevD+8VrRRD8xBO4SsWMhCjG1ehYYYdarXdvd2emm2v9NmhlihRW86VLPEHUIgBZVyRltxyzEGlo9itepTudLmvbi5dFW8eO4jJS1mllmU7snY7gAtjGSBnjoO25bz3dxZRNqmiTOpJDXD3cbSKcAszhiMKM5ySMdOcVVvkkttEtT5zLBbzJKxjiPyL5Y+bzs4QZIAzuZtu7PKiqt/r2najp8WnaUkiTSkmZNqsZeP9YzgDe45ZmOOO1KUeYcZND9a0+AK4trdZ/KUyTQ3D7GVRswQMHGd/BYhTjgnNYA8NQ6hE95YRzxxpIUSRWBLAY+fbncqknjnpXU6OtwdSgg0+5a7Z7STz0ESNF5KrswSVXzRgeoIY7R0Y1FbQ3OnwP50wOroT5TTQLiFFJMgZAepXuCoxtwTmlZx0iF7/Ec5FN4l0eTda3ZnHpuO78ejfqavW3jkKWi1KxlhkcFZHgYoxBxuzs2nnAzkHoK6yBHutMtp9UtIxLO8axwRRv5pD/dOGGB68Oe2K5zVYbUXErhIZrCI7ZFOWlUbckjPy5ByCnVfl5JOKSu/iRTt0Zo6TqmmSzpLpl/iRU2eVM7kYxgD5CCAP90ccU5bSa10iOCW0tri2gdp3l8gXALHjChcgfKBweSc9K5p/BzXdjBfLayQb0Dsscgk8k9drDqCB16VVifxDpUu+1vGuAnGGYlh7ckMPwahWvo/vE07ao25442vZvOFlEBAZ5XSRVc4b5ckq2ATtXrnBHGKtWmiaNfSIbuQWlxK8z29msiwySRqTgM5yNwKnI/wNZEXjdfMC6xpq+YDnzNvzjv1G1uoB/i6V0Ca7puvzNPFqai4YLuWRQWBB4bKgOrAZw23I/CqcpLdE8qexz0uno8RV0uIQu0Ga33XEK7lLqXdnB+7/dHGD9KlOhafI9vFa375kBAdogyswGTgZUAAEZJY9CBmtq20u4toI4YA88SviS4S6892Dhg5YbcovzZ246jPJ5qK4ubqK6EkGlwvYpHiSVVLw7m5VFV26gE8NyDn5V4q/at7Mn2a6oy73w2sc0EdnczzuFUXDsqeTA/UhirZz9eM8biazn0C8eaRRdQfJE0jFCHbZjHCqC3frjHfPIJ6KwuYvsl+97fMJLeHYsFuNkTRggBS2chgfmxjc2eMVZ0s3EVnJd292THLJHObf7EIpihb5jKxfG0qvA3EEYxjcM1zyRPIjl7XwRql6if8S2bYwykjMsW3IzncxyfwrQtPBGpaS8ty9xBJE1vLCUllIzvQrnfhcYz2P6E1r6df20Ovps1YXYkkePyftOI4OTgKS+xRgZJOcDAxzioNVU3tpO7Rm4guvKFr9oR90cRDehAOHOMhf4vpUuc31sNRiYlrYrYwW1y8dslqokaRBdlohIqhdzSDO9jyAqAhd31AvSaVomqeX5c8UU8sfmJHKrLvXJG5GZRuTIIDD0rdslisNSvZLDS7J7hT5CJJAglTHzRhV3KqKXyofJJ25+bNOttC1f7HEqXF7EJnV71LG4YGSM/8s/MDYC5LAdiWZj0AqJ2bu3YuN0tjkr3wHcqm+MExkZBB3A/TP+NZB07W9LkCQyyq2NyoHxkdMhW4Iz6V3o8EvaWwm0nUbrRpGcsUgdpIlBbhTufDYHfArJ1GK4bW7WO4u9NldmiU36yK08TLkCQIpBPAUbRxhemTmlGTel7/ACG7b2sZkHjvxLpf7i9/exr8pjuExjHBGCCv6Vs2HxF07/l40+SzYlSZLX5RkdDwGHB/2RUFkmnxaOZ5Yg2j2p8h7+NX3b92BvXqQSc7se2KiPhvSNWBfSr60uSRnbHIA4/AYP6Um4p6q3mNJvrc2bD/AIRW9gMdlewmTaFi+0b1aEZJwrRn/ab7y/xGrY8OiW4hzYQ3cZiMMs8NxI7opAyV2MSAecjbx6HmuB1TwlPp8Lz3DCGKNgGkmxgE9Bng54PHtVdBrunJFJBPN5b5MREgcNjGSA3OORyKaV17rFtujrLyNYLlL/UXUtZJNbQxGNnKgIdmPM+ZkG7gn5ic9K1YbKzY6ZFaW9myaZg3T3bqqQNGv8W5BvcklsAbc4Jz0HK23xE162Xyb0R3SD+Gdcn8nDfpir6+KvC2q28ltqujfZllxvaH5ckdDnD9/wDdpvm6oWnQ4HaM88e1AwXI3AAd6va7ZHTdevrU9IpmAx6ZyP0NUFDb8kd+DXWndXOZokX7vBHNLnggdKMHk5pSCRyRkCgCPAxn9Kg5KEEnPappHKLkA8U0EBGyoyR19KaBml4ent4bk/aZliV1MRZlzjcpGcj0OPzrsvC5gma90y+up1vMJOjSuuEIGPlGfvHcMY6gYrzZZni+ZD+JFbOh6rqNpe/abWwluLgqFR0DAYPHQDB/pXLi8P7WnJJ6tfitgSuejancy2WmfYg/k3YYPF5cYbKr824A4BXt1GDxXLaVqEk96xlE4u1DOktxIXyQp+8CCV+Ug56ADn1rQu5NX8QW/wBqu9OgEqNiSKWQAbQwAVBnjvk55/Ws+PSLm01NrSGeNjPIsSxyfNDIueu4rt2jhj7Ac1wYGkoUnTk1e/TuUl0NGF7ybarq1xPhGKKW8z52IAYhcAkd+B6Zqx4h0Q21gWs7gT21u7Rgq4K+Xu4xjjjpXS6/p+naJp9rBokkV5cPH58sglAWaQYClAflX+IgngZ/i6VypvLOSJLJbaeWcSR3Esfn7kiP3WVNuFfkDnGOePWs8VGSalDaL1X9bCkouN0aVj4TtZJ7UZeafyC0rrKq+XtcAnkdMH1z6EGs+bT5NPedba3ZriGTyG3bXBVvmDOd2RnJOQuR6cbq3ZJmGuywMTGksQWb7OCmN8g2gEdDlSCc8cDr0w9cmt/7XkKB7RJsQ+aAQBHyrOO5BBxnHUHr3ywuIqtJT1TX5/0ynKySsXvDetS+G4LzSb+xmntpg8kMiAAwr1yw7jcQd3sfSr9mG1yxDXfytAPKjaNGjdSB8zKW4H8IyBng4POay9TDWSS30GJppJ0lTCFtp2lFk8sn944Az83y5I+UZzTZr/VDLd3ivqKtEVV0u7EQqEYkg5LEuQcD2/k62Hi4vEUnaT6+e347FPRXRu/Z4ZYkgmjt4FQgI/mMnsCXzyT05z7VELbR501C8ugkFgsXyLFbybml+6oi+XkHkc+xxzmsXXdf0y9sBHK7wEr+9Tcd4UH5VQDKnuxZgemMcisOXQP7GunMWqpAVj84maXbMFwcsixknof4iv4VvgKdWVNyr3u2EW2rD5/Kj8wwPHbSxTKZxPL5+C38Pyj6Aj/DFWPP+yXFzJFC93NMBHPJE6qwOB8qoW7N1A7+gGK6LwWird3Ud9DKby42uZgDmVVHVvTjnjOSetQfENdHiXT7iHToFv4kLARRldi5BDOAANgG7jrzQ8XBYpYfl36/IuSSRnasb77AL5J28q1QRzBH8/czNtJ3Yxu6dyVAx0rPdWuba3L6msb3OVMEQyUBPCtEPmbJUe2MnpwYTqk11KgumnZmkUC+EixCMdASuxgQB29PrWzH4X1PVNOutWjthKjxmM3UjwkM2TuOC+IxjCnJLZzwMiu9R5VZiVrWKLPa6JrEH2jWUv8AVLlYW82DzD5AbH7tcMig7T0YEY7LVWbW57m4hh+3eess0b3GSjgEuMxxAL8jbcjAJ6LgnNXk02HRYJP+Jhp0EsZV45vtESSRtz1yGkbnOQMD5fet+5+HupXlw0M19p7tMU8mSa7MkyKMsSgWMfMcsT1AH5htx6js+hzes6E8Fx/aOnySRMRlZjGVJU9pUP8AUV0Hhn4gspTSPEEJOMBQTk47GNj94f7JOfQ9qx/EkuqaZqz2Bi1aX7OBHbH59s8mQWLYGXGHUHdnIHUE4qudGOq6Ii3K+bfoWedFj2qNzHaY2A29McD09RUp2im3oNq7O28Q2GqRWtnqnh+VdStI7kSvgAuFxgggAE49sMOvJFcu2q6ssSxXFvbWtrJKBOX86NN5PEiRswLnle3O3nb2zdE8V6r4Tu9s0rTWZIUysu7gdpF7/X7w9+lelRXcXiq1SbQ9T+xX+3cYH/exSr3IHG4e4OR3FPlX2UK/c48XWl6LoxsNHg+2FojcTTYwpG7aV34B42/dLHqRxWfD4iNvJeSWtvaz229pGE25o1AzgNhfvkcdcn8c1vTWmtSbo9V8Nx3TLKoEsUSSxsvQkqXypxnB5x6Gq+ralJpcflyWdvcTbT5OzSG8leflVpHIOB6qCf5VkoRbd935kuC3MZvHV8irPFDoe1ohxHGxfbjdgJuBwOnp1rQutY1kzCSOWR7mby4DbRJGYY8gbUOws43ZHJGeewzRY+IdXj1G2SaCxCXUO6O3jgIMSruJfCgttJGAM5xg96qWF/qdjealMJHtJpXEz2tnpk0rTydMFpfuZz3zjJ44wdnbshq/coWNzd2t3dPa6IbWMfu5nt1N2GKknaJJNyBieN3I9q24JNXuJ1gD6ubafy2QxWRiIA++GaPHcEZAx0xkckl8T63Hpkt7dXlvZMZFS3ilthHwTyS0jc4AJAXg+o6Vs31zqltfxBTFONoT91EJZZCR8r9vRjt5wB361nUnZq/UluxyWs6FO+pi3hgWW9b5o4kI3hRg7mTcG2jP3mOSenHTf07wjqaS+dGE0pUXamy5MrnnJ3Nt44AyQck55C/LUGoya/c3JjsJ4pLsWy5KhP3LBvmZHKqehCkjIyeMcVQsfC3iV5oJL6e2uHRS3kX9z5sb/KAqty2cD2xnvTi3KCuyk/I07iDRLOJLfV/EpnEMrzGGEq5LNxufAbOM4yQOprOv7/wzIzLZaRd3VyBsWUMsTAANnbuJC4VSeAOOa0W8M6pNuVoNIsIZF/fNbAszt/CAAAo29Mn1PWtSbR9OtMebdSxW8jbmWR0XJbAJZ2yzHIBzwRxjjiobSfmU7nl8VndC0nv7A7IEAjInvFkL9CAuAoyuQRzx26V0uneIoNVs511DS5IpbeFn3IwML7RnAkJ+U/meDjJwK3r/AFHwxpFtKJ7SC4+zBYY4DGsjqwzlV3nGc8njjHPPXD1XxbZ6podwTpcDKjIggnuDlgxIJ2xgY24Xv/EPpV8zqK7joJNLqYM2n2uoXkdpHAY72VnJEMgeMKFyCTjqTkbTyMHmlsdS8R+EbkPaXEyqnbJxj+YH5isi3lnDSPYaehVUCyqFaRSM5GdzHHTGRg4ziuw0zxBba1bywXemSwXMFu04jQZV1XA+UnG3ryTwACc8VpLmW2qErPc39M+JOh66iweIrCOGZj/x8Ii4J9SCNrfoa25/Dkt7Ak+i61cXNtE4lCxyF2iI6FVPKEdm+bGTivL7rTLS9vo7JbeW3v5Q7bA6uoRVyG3DrnkYIB4OarW02veF7oSWc8qBDkBScfl1H4GjR+TA9J0zwnrISCWC502QRSiVrUzvsLglgHGzc5zzz1IzSap4R1xoybWHSUnG4mNlLlwxJJZ5EyDknHRfpWfpXxR0/VUFv4nsVZmGw3KAAke56MPZh+NbqeFtMvrfzvDclhMCwlMRt1MjY5wQfmC/7hqOVplcxxFzb3VtYRwa2l5FfxThoVeNZXEI/iCjj7y4GS3U4AHNTxSW0MNvp2kJcRWzu1xLcXcbBmJGMrs5J/h6FQTgcsc6F/4a1e608DbGb2ArHsjjeR8HJdl2KHRQT0w3c5HOasHh7xFPCUOn3smnSYkKQxLErsnCLmZ9wA54YcZPGTup3utQ9BsM90t+63a273Fy/wBnFrMisIVA2gRov3n4CZJ4yw6ZISwkurPWNTjsbX7NKXRRPDbLLK7DJdAzMqocgklV4wMYxy+ZY9PsbV9btfsLwyKBZ28G+6l+YkIXAwoYFslm6M2Fxiqcusme5e+tEvI4XPnypcqIRbyHcWWPAAl5ORuHU9Cc09bXFuySNR9laeUyW80oaKysWHloiMWGQiOXLkjvkNkFmqvZsLO8eZXUSWYEwS4ugz4CYdTGF4OepbAwF565UH+17lr8adZQQkmK3t2LJnyx8zynnc+MgbgQWH53La60LStLF49vBO8g2QQjDPJ0Y8EDyoVPQMCWZucACne/qBYlmt7+Rbi71VkitmRrW0WBZU3cHpuO4fKv8K9j04ot9RubO2je5ikuJGlFwgWKSSBmdQAkjJjzHKhThiFXccgnioNP1C+W3SSHUo1sfPaOJbBdkqo212jgjUHD8sNxGcDJIGKbHfu2r5k0+4sbPT7rbEs90Uhs5JSxYyM4PzAnPIJbAzwMEV9gZeW8vEn1CzNtbQPBGbW1eRkZoWGC5LyAAoOMk8s5A+YDAp28UGpSi1kkjWwKnfbRXgke4KsVJlfjbubgBF5yMABcindTxy6rDp+myRX8N2Q1t5wbaq4JYgOOudwZyATtx6mtBdWgj0mGaKG3tUQoIrm8iwyKFyCiKDgjnbt4+bjJyQtg3Lr3X2extRNPZaXeyNCrKVErQMWwu5duEVVAUDJA9AWIK2oKanZRI1wwuynkxMgiZyTsDy4YZfkkDgbVz7U28u9M8JWocQR3l9Eu/S7drsu0Jk5DCFVKqSCG+dixJHc5pdYmF5qCWkN21xqEUT3N/ZzTbbSFyqgpnk4TcQVzySct2ot1DyKatdXWtvLAtot8Vlj86OaQxlD8jStI2cqCDh2I5HHAGXSXOptrkdzBK62dqvNrJMt2u1kwMCThWYjcN/3Rz7GSY6Lp0weS4VQ0UixCKRlJuAQqhULkhVB3ZkAIwCOabojLp0VhJFKZrKK4aS5uFMjOsSMSXVVbYqbtqgMSXbPpih3eoaLQ09Fsda0+Ke7DQI0hledhbRuIoolVnwhxl2ZlQZwg2lsfNmtW6Md+JV1HTgjLZfbRcOyHaNrOATGinaVRuSOGA27gazrDULea0ii0+9u57pHEh8iFPNtywKoEXbtkUpGVfefmO05Gc1n6trN5YwzvPplzBcXCeS7zxC0Z+QQI4Y2YueANxY8DaMZII4qWjBSaLXhSx1WDV9TXyx9r/si4a3U25ieCYlQoIYkLn7ykkNjqBmpPEvhy5u9Bht/Ehzei+Vre7EayzxW2MSMxGMrnGATnIrO8MR3ekXV5dypeLb3WmXEarteQpOdoBk3YCsSpPPZhzitKSW70XwWun6fHf3Eq3AubMXsH7uVWHzwLg7lPIbLYyckDFO9laL1Fa71RzkPhfW9I8QapYyasy2ulQyzyTFN5KLgptUnIL7lxhu59K2PDV63iSaGxdru41UxknzdNYQxyBSTGZw/mKeCN3Q11F411caLp9hqumCz1bUIgl0TOhjEcDER5fI+8WBHXO2qx1nT9L8QaXex6le/2VYwRKbK3jdY0yCm/JZVZSxJK7TISM4xzQuVu0kDvbRmLbQ6FrHhq01S6vZLeyN29sLPy/NLuF3H+6x++S2Sc4GSeKbqPhe3svtMkcU+orcRxXNkto65kiIO0sjnKkYxnDdOAD0wWgsNW8Fw6RaiQS2+pzXZi2guIygTey9RkjOPcc0zxBd3skOgjS5Gc2GlxWsyyIOZFLE4DdRyOQaT5VomO0nq0eiQ6rqraFfXN1eWlzdpdxMEthHGYomVz5fzKQGO3OM5JPUVhaBEviDUo55Ly7urwyeWZLfTsQ20pzhWm5YMSB86LtHHFc7beOgnhu707XLU3Fy91AyQzQmRTGobcfnPByRjn6V1GleMPCk8mhSxalf2P9nBFbSoIykZYHLP1G7PfO447Z5qk39olr+Uq3Vtc23h8a3LdKZJdRk3rcs0iIwY7U2g4lGBwx5BDYyOK22H9maV4nt9V1Vr2O3hgLwD91EJDKC2dpHRiqEjGQpAAxijwyWF7fSyzNdaPDFHcbbcYkWaOXfGNjAHLglSOd3sMVmw+c2i67ayTxNqmpxR3EkaFw28ymR0fndgKAuQDjOM9KOaK1uFnsLqFjoy6jBDqniK6tLy4Ku8dpCYUHmj5I5Dvb5QrDhB8q8nrmpNO0WG5lubnUbfT7O80W8WC4aW1SRJARgBOQCxAHUcbh6U28h0zX9Ug1K9uLxJyI4r+xhtDc7vLKqWUjhQcLyR8vPWkm1sapb660WmXJuru/hlWCVQm4qzYWTH3BtK8ZLHBPHWh8oXZnf2ZJcW72jXV1euknnSieTKPk/LtI+SN9oyVIxgDrmluPBMOpPNLcLK00zI++RhI+VABZmAXceWAAwBx6Yqpe6thYElll1OaxUsLUyshhbdwX2EqsagAf6w+hx20JPEgZ5rSd4JZSnnPH5LvA6hiSxZSCyA5xgZJC9RWbUujKuupVvtIms4JYrDVoHZJN6RXsxxCB1CtzjK5QnPQ46ms6GLSNL1SGG7WRbg2Za62wKsKPvyYkTGUUMx+bqT7VfNzq11qLWSwtPbpELme207y4J423ZVo965fDEHA/E56s07VIbrXbmW4me0mjj2nyoGMjSMChCIF6BQSWyCc4zjihRaVnqO+uhjC58P3sEBuZG0+WePesd0hRTzgkE5XGQR2ps3g1J4zLZFJY/78DZH5gkV1ulwXCSFrm2u764vo42eWXyo4tu4OUdMgqBwT97OSAOCTFqngw3s8T2LW+kmMkGe1R1lK5JGArAdcn5ufm6jAFRts7fiVe+6OV+Itg1p4l8/gLdRA59xwa49nOwIRwDkV6n8UoxLpFhKxJZJSB9CK8pz09666b905pbljaFCnKkEZxnpQcFeASfakZt7ZIAwAOKUnjI4z6VYivLg0h+4QBz9KJSTyacqjDUxBZJbPKTcIXjVSdqkjJxx05612+jeDE1bW7bS/OntVWOTzpPlJQqBlU+Y9c9z0B6VxmmyyRX8Zicoy52svUcVrafrmoaN9nvrObbLbzNsBGVORzkd85IPtWGIU3B+zdnbQqDXU9X07TdOg3aLaTo81mTHtaRTIyABgzAdOCRzjpWP4pgFnaaerDbHcXGUCE5AXo3Py53NwOee3Jqjb3bw/ETVJEVMeWZTGRlWO1W5zzjLHvTNFt18TeLLJ9XZ7jIuHK7iq/u1DKoAxhcscgYr56jhZQq/WJSulHmfe7Q9G3Ykmki8ywtbOS4uz9n8v94FTbGmcswH3WGTkdOM0aT4UvLvV2kt/IsbG3snLksxAjzgKAMsjE5PTPccmuwl060kkiHkorTF43ZRglByF9xnH5Vk287nSftQIWUI4YqPv+Xuxn1+6P16VzwzOc4uKWr01JSscz4b1dbK+n8mRWLKiybhhjGW9+gwfu9Pl9eam1OYmS4vZo0e3H7jeZWaSFSzBdvb1ODyfYEUeFEj1G8SC7iWWJZCdjdDhQAD6jHH0p3hW9mu9euJbwi6YwTOPOG4KVwVI9MYHSuqpVUZTlbZK/wDX3k3bVu4Xl7fJLp1oyLHeSOImBiMTwRkY35JX5iN33SMAcnmqplvUllmuLmGJ3AZkslkjjAIyHIZdh4/uL83HIzk6FulxPPfypfXEFyk+03MO0SvlSfmcqSepH0+grBtrye41wXN04uHjuF2iVQV5AJyMc56c9hivSpuNOjotEjTnjGNyfSrJZb+C5ubSC5yCVhuJkQKf4coWx1G4tkqAe+MVvwC4jht102xuLa6uZX+zgQxyRwYGMoUBGcf3sHnr6VLiX7Bd3sdnHHbr9pMX7pdhKgjuOecn/wCtWh4NdmsTLnDLOdo7DcnPWuPEY7ko88IjjUSWhqeHZb+HVLu1uG8qJYwEQy784K4yepbqSeeKy/FUDxeJIEkKxqRuklWPBCnJBb6DgD2JrR1O7mjs7vUFYC4hglVGAAAwwUcDvhjz615/a3rWTR3TRpdXCSMiy3TNIwRSAE6/d+Yn8a58HCeJqPESaWlvmNXnuW723gvdMVG0zUtNnJM370nByM+YcqCRgdSeOvtWbDYnS9JUC+ujPeKJZIoLTcTEG4ZZCeRg7s9OBnkVt6fqdzPZ29xKVk+26gUnSUeYHVTtVctltoznGevXNT6iy6NGrW0aGa6voo5ZXHJX7uMDC9Ce3PfNe4p2biaOPU5mwXT7bzrq21KaLMebcGOIsAR0YsdqE44IB/qbOnaLJKJLqHUUjniAkll86TMAbIGAgy7sflwrE9e3SU30+p+I4NPnKbZIgzzBAZGLJuPXIAyBwAAOwFRJepp+koBp9jOrO5cXEAfcQSFOeqkZ/hIrW7ZNrGnJc6jrl4i2kvm3VnG0UNtCZXk8sAb0VcsOp5YsWZgeRjFRWE8+k6Puivbk2ktyLe3hmtjmJjyxjXlenTJ255x2pnh6GK61CLT4/PtLSfJmitrqZBLtUYDfOeKZFfpDozW8Gm6dHFcXAEg+zBjgIMAM2SPvnkHPv1zNruyDZXCO7XUtTe3u7yy8hUeNbuQIjTyDBAZVJUDBK9skevFVXs9Q8O3nm6eGAB3taliAT/eQ9Qfpz9elZFpdz6HeWtzZvzIrh0dQyso/hI9PlHPX0NdZLrU19rGp6ZPb27QWsJljIDbtwGeu6pknF3WxUddzqtC+IWm61Z+Tq0skMoG1rlTteM9P3gH/AKGAR6iotVm1/wAL2rSmc6zYyZkj1GW62+WrEYDhQdw91z2GB0rzvWrSIW/21Mx3KFf3iHBOfX1rf+GWs3n9oQaW7h7K7kYPC2cKcE7k/un6VpGKmrkNuLJdL1UXWtS6lb6RYx3EcW6W9bzYi7EYI2BgvJ7MecAnnFM1G81bxXNY2kqzw6e7qohhkEZkk2sT/rDt4wepOO2TXW67HFBq406aGO7gcg/6Su8joRzxnGepyarSaBpMkZLafBuVSQwXDDr/ABDn9eO1ZOai9UXy3WhztrdappenrbSWtqul25Zo471orqZhnOxMHAYnJyQAo9eBV/Xr+6gaeT7GYHmC7RJKoeEg9SSSvXuDjpkjpVyLwrpdj4ks5bWOWIrC90FWUkbskEc9v19+K5PxW8FjfNZwWVqtuVVGQpnOcMTknIOfQiolBVZL+v1IlHQ07vWbi0SWCy1dItscbrGkMJX7uW2sw2soPBYkAYHLVWn8WXcWmXMh1hpZrkrHAltOjyKytyUGMLnuT17dcjqtP8BaDFnyYJY9zljiUt07c59K5LxFrF7onimew06Vbe0gaHMKRKFl3KpO/jnrj6VrTjH4Yis4rUbqtzrlnaRXF7f6jCjO0QgTUg08nUlmCnC4+7jaMehNW5bNNVltry5t/JklgR0M5kMkca4AxjggA53HBbJyAeK2NT8WXunGQwQW+QT13+vswrnrzxjqOoQxXEsdurxlmQpvGCCBnO7Ofm654wMYrKpzOzQprqa1/on9szRTW6bCC32VlAGwZDbiEAbIYYOfY+tWP+ER3wXMV8dOMlyNyyrC7OXBHzEF9qjnp+fSs3T9Wu4tFM8RjjlQqissa8AxeYe3J3AHmtbxXbsmmWYlurqc3MqhjLKcKNwGFUYXGB6HqazTcfdbEpImtNI8P+H7Q2d5eK0pBcGSJEOR1KpjnoT1JwOOuK5jW9d8KXME0Vtp14zzA77mFwjSZO45ZwWOTzyKsapYIupkiWbe2Zy5bLblwMc9iGOR04GMYrE1HRbO21G0ijVtlxLArjd0Em0tj0+8cenFbU0nrIvnb0MD+0BbO3kmQmRQp8+Xccg8EY24bqM9eT0rstG8T6brk0On3kBtrp2WOMEl0cngfMeQfr+dc5PJLbu1tayvb28xHmRx9G6DknJPHvXb6d4O0uw0iLVozM12kuI2Yr+7KkEMuAORjrWlRwt7yHC97I5rVLLR5lmnSSWBFUNHNJCVE4JwCh/iz15xxzVFYda8OXPmWksibDnCdPrt/qtZzahd3uqRWd1MZohcHhwDksxBYjpk5JzjP5DHo9hH9t0xzdMZmWaVQz4JwrkDnHPA6nn3pTvTRUfeY3RvislyqW/iO0W4APFxnDr9H6j8R+NdW+m2PiZ4r3RtbupZQQ4t3uWDNjsUztcfSvNNZ0azDllVlbbu3A81y1tfXWmu0tpO8ZXnA6E+46U42mtCX7p6nqOi6k6rY353CNsiUJlk6gnY3HQsMgNjrmsyS2igf7LplhdMFmB810lAwwAAZgAVHJzt3EAdua6TwL4q1DxAbGx1UQ3cU6/ekUl0/wB1s5/Mmun183GlSSw2l7cgBGZS7ByMf7wPp3qORpXRXNrZnmt6kojtbG5ubmzijZkEsJ5nPfZv2hI0QBehJx71V0CwgtNE1aP7NbXMs0bok8rRpBEhJxNIWPBPG1OSuzOPmqxF4v1rVAgubpf72Y4lQ43AFcgZ2kcEd6qWl5cv4ktVa5nH22aR52jmaMtsIwMqRgfMeBxVqLtZibW5JY6zc6Lp0VlpVo/mzWvlea86hwZCc/wgR9FPXkBegq5Fe6Wuk2txP9g1DUTuK3F88zwK/PzKFj2sQMD5iT91RgVU1G8Q3s9rDaQw+U42zB5JJMsr5OZHbnPIqrJFJBbat5N3cxiyjjhiVZMAq0mG3DueS31JotfQLlnzdV0y5bXZ1tptSfENta+UJY1dhtAXccFxkHCggbsH0EL6NFcXKTa1qFxqF+R5lz9jj+0KgO4DfLuwWzgBU7/SoDYfb/DVtq9zd3Ut1Ley2vzyZVIwRgLn0znkmus0UJfa/HEYkgW2v5beD7OvlmKOKPaoUjkE7mJb7xJ69KeqVhaNnPWWkWNhcWk9vb3F9NFMN0rb44ocDcu9Yy53EDPJAGB65FuHw7/ZgtPI8/VDdLuS3ikitklKuDgOSTwSrMy59MDGRoeJzcW/g+01mG/vUkuZmha3+0u0KhZDhgCSd3B+Yknk81Bp3iG81GCy8N3UdtJYlLWSRmiDPKWO9gxOQQSBngfmSaSbY7EUVo1xY6j5EukI0dxBbxoivLDBI2cxoxQ73z2yeSWLet1LXSbTRo3bzp7dyzSJEY7W1l25+aXzT84Uk4wcNnjjk4NnYINOsXkllkgFrPeJas2IUlaAyZCrjowHXPAA55qfxVe3DXhMkhkeGQRxux5Ay46dDxEnBGBzjFNrXQV9DelfS/C+jtHBLLBc3yGVVijk2kBfkBPQffB69ePYpFbNptnGdqjXkDNbQhy85ySqlmPzjPU52keoArO0q1TxHfapd3byxTx+Qm+2laMvld+WwfmIZQRnp+Axoz3F3JNbafJeSvHfW0omdlXeTlSTnHU5+lZ2LM9LS10gWGqXcJnuwrMDfxqnQEIoVOZXGR0wAxBJzxRFLa6dpUaaZqSWuoGRGMS3ASVhxsBLbu/zEbeuSx7VY8FTpqGs3ZuLS1Y2dlcPb4iA8tkIAbjqTgZJ5zzwSTXLaXdtHos7xw2wlgEcayvbpIxBAP8AGDg5JORjr7DFK71E7bHoE08Wmy6hrUuuyTTFQk17PGkzphmAWMgDZyW28dCMYFc9Z6lrOh3Cf2akOpT3aM6yhPNbYOSyyOcpz1yDjj2q54jifRUjW2uJHYXCpvmCu2WRSz9PvHJGfTjpWdGRGzxxoifbNLF1cuqjdKxZQAT/AHR2UcZ5OeKL3FaxHosLWk0AttOuZtZM+Y4hbuYohgCZnYvufAGOCF+Y+tdJbN/bbFLjRJY3QlTLHIksJwM8OpB9eo4xWHbanPBoluISYx5jQALI/wAqFCdo+boDyB0HYVT8KaxfT39jHLKsju32QTSRK8iIx28MwJyO2cj2xxUThzJsuM3HRG7qvhSRbCaa0t5L5Y9u6GII+MjPLMQAMD68j1ripNEsL0W4g8xJ7jftgCHcmwZfep+7jj654rory/vtG8UTaPZ3kqWwuzFKRhWuFZin7wqBnCrgdOpqO+uRp/h9buyhjtr1lvYPtUTOJFjib5VB3YHQZwMmnGDWzCUk3qYTv4hstPi063v5JLGBzIltxtDHqSrDk/jx2xU8HjbUrbbBqUBlRT91hux7hX6f8BIrvrPT4dQ0WGW9zcT4VDMwAdhjPO0DJ9+/fNc9rem29vIYQC8ePuvhu30rP2t3aSL9nZXiyPTte8PXV0ZvKihunXb8yBsdf4JDjPPZue+a19NtlguswvDdqYGiVBK0cw3ZBIWQkMegHULk4B4FcJqOj2a2rTRoyH+6DlT+BzWLDf3dlEwt7iRUAz5ZO5D/AMBORWihzfCzNu256qMRsiLbGxDrsMUs0cTvKZOC0YALqAOF3c57kkU66sbme4vrm3e7itI7ggRQxktNKcDK+ZtJ5xjLdegxXLeHfEmo3mmSGR02Jx5RXdGw91bI/LFdhYINQsl1bLQXsGI45I2J2ryMDduwOTwOPas72dirXVzHaEaWlxfPaX10twZMpdJFEhCkbnZVJf5VJ5XqeccVcurSNr5oL6SYXRhBiaa5iiVNoBOFRCEwCAGJZieOxNWNNQ6v4gSS8YvM9kHeXA3sC3KE4+7jjFQaJaxaxo813dIgKIFEMSKkZJYgsVA+Zj5a8tn8qtslIy7vVZLq6tb++uRJJblXWOe68kx/eG7KgSE7iDjAyOpwRUmoapf3EEL6lrkVjDMFSFLW65uCWOZCqgtGu3Gdx69PSuik0SxtvE+nzCFHlljYszovAdQpAwBgAE49zk5rKvrOKKa72DC2E8LxLgYJaXbzx6DIxjkk9aaktNCWmf/Z", + "image/png": "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", + "text/plain": [ + "" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dataset[100]['image']" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "d18399d6", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "USER: \n", + "What is the background metal structure?\n", + "Answer the question using a single word or phrase. ASSISTANT:\n" + ] + } + ], + "source": [ + "img = dataset[100]['image']\n", + "prompt = 'USER: \\n' + dataset.qa_pairs[100]['question'] + '\\nAnswer the question using a single word or phrase. ASSISTANT:'\n", + "\n", + "print(prompt)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "13756414", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "dict_keys(['input_ids', 'attention_mask', 'pixel_values'])" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model = model.to('cuda')\n", + "samples = processor(images = [img],\n", + " text=[prompt],\n", + " return_tensors='pt',\n", + " padding=True).to(model.device)\n", + "\n", + "samples.keys()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "7bb0a0f0", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['USER: \\nWhat is the background metal structure?\\nAnswer the question using a single word or phrase. ASSISTANT:49444440404040000000']" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Generate\n", + "# generate_ids = model.generate(**inputs, max_new_tokens=30)\n", + "generate_ids = model.generate(**samples)\n", + "processor.batch_decode(generate_ids, skip_special_tokens=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "d554f63b", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1/1 [00:02<00:00, 2.59s/it]\n" + ] + } + ], + "source": [ + "from torch.utils.data import DataLoader\n", + "from inference_pipeline import InferencePipeline\n", + "\n", + "dataset.set_max_samples(10)\n", + "\n", + "dataloader = DataLoader(dataset,\n", + " batch_size=16,\n", + " num_workers=1,\n", + " pin_memory=False,\n", + " shuffle=False,\n", + " collate_fn = dataset.collater)\n", + "\n", + "inferencer = InferencePipeline(model, device, processor)\n", + "\n", + "# set this according to huggingface usage tips: https://huggingface.co/docs/transformers/en/model_doc/llava\n", + "processor.tokenizer.padding_side = \"left\"\n", + "processor_kwargs = dict(padding=True)\n", + "\n", + "# greedy decoding\n", + "generate_kwargs = {\n", + " 'num_beams': 1,\n", + " 'do_sample': False\n", + "}\n", + "\n", + "results = inferencer.run_inference(\n", + " dataloader,\n", + " task = 'vqav2',\n", + " processor_kwargs = processor_kwargs,\n", + " generate_kwargs = generate_kwargs\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "d82ecd07", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[{'question_id': 262148000,\n", + " 'answer': 'USER: \\nWhere is he looking?\\nAnswer the question using a single word or phrase. ASSISTANT:44444444444444444444'},\n", + " {'question_id': 262148001,\n", + " 'answer': 'USER: \\nWhat are the people in the background doing?\\nAnswer the question using a single word or phrase. ASSISTANT:44444444444444444444'},\n", + " {'question_id': 262148002,\n", + " 'answer': 'USER: \\nWhat is he on top of?\\nAnswer the question using a single word or phrase. ASSISTANT:44444444444444444444'},\n", + " {'question_id': 393225000,\n", + " 'answer': 'USER: \\nWhat website copyrighted the picture?\\nAnswer the question using a single word or phrase. ASSISTANT:44444444444444444444'},\n", + " {'question_id': 393225001,\n", + " 'answer': 'USER: \\nIs this a creamy soup?\\nAnswer the question using a single word or phrase. ASSISTANT:44444444444444444444'},\n", + " {'question_id': 393225002,\n", + " 'answer': 'USER: \\nIs this rice noodle soup?\\nAnswer the question using a single word or phrase. ASSISTANT:44444444444444444444'},\n", + " {'question_id': 393225003,\n", + " 'answer': 'USER: \\nWhat is to the right of the soup?\\nAnswer the question using a single word or phrase. ASSISTANT:44444444444444444444'},\n", + " {'question_id': 393226000,\n", + " 'answer': 'USER: \\nWhat is the man doing in the street?\\nAnswer the question using a single word or phrase. ASSISTANT:00000000000000000000'},\n", + " {'question_id': 393226001,\n", + " 'answer': \"USER: \\nHow many photo's can you see?\\nAnswer the question using a single word or phrase. ASSISTANT:00000000000000000000\"},\n", + " {'question_id': 393226002,\n", + " 'answer': 'USER: \\nWhat does the truck on the left sell?\\nAnswer the question using a single word or phrase. ASSISTANT:00000000000000000000'}]" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "results" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "ee7bd08e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[{'question_id': 262148000,\n", + " 'answer': 'USER: \\nWhere is he looking?\\nAnswer the question using a single word or phrase. ASSISTANT: Down'},\n", + " {'question_id': 262148001,\n", + " 'answer': 'USER: \\nWhat are the people in the background doing?\\nAnswer the question using a single word or phrase. ASSISTANT: Watching'},\n", + " {'question_id': 262148002,\n", + " 'answer': 'USER: \\nWhat is he on top of?\\nAnswer the question using a single word or phrase. ASSISTANT: Table'},\n", + " {'question_id': 393225000,\n", + " 'answer': 'USER: \\nWhat website copyrighted the picture?\\nAnswer the question using a single word or phrase. ASSISTANT: Foodiebakercom'},\n", + " {'question_id': 393225001,\n", + " 'answer': 'USER: \\nIs this a creamy soup?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes'},\n", + " {'question_id': 393225002,\n", + " 'answer': 'USER: \\nIs this rice noodle soup?\\nAnswer the question using a single word or phrase. ASSISTANT: Yes'},\n", + " {'question_id': 393225003,\n", + " 'answer': 'USER: \\nWhat is to the right of the soup?\\nAnswer the question using a single word or phrase. ASSISTANT: Chopsticks'},\n", + " {'question_id': 393226000,\n", + " 'answer': 'USER: \\nWhat is the man doing in the street?\\nAnswer the question using a single word or phrase. ASSISTANT: Walking'},\n", + " {'question_id': 393226001,\n", + " 'answer': \"USER: \\nHow many photo's can you see?\\nAnswer the question using a single word or phrase. ASSISTANT: 1\"},\n", + " {'question_id': 393226002,\n", + " 'answer': 'USER: \\nWhat does the truck on the left sell?\\nAnswer the question using a single word or phrase. ASSISTANT: Ice cream'}]" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "results" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "MMQ_LLAVA", + "language": "python", + "name": "mmq_llava" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/llava_test.py b/llava_test.py new file mode 100644 index 0000000..13b1606 --- /dev/null +++ b/llava_test.py @@ -0,0 +1,80 @@ +import torch +from transformers import AutoProcessor, LlavaForConditionalGeneration + +from dataset import VQAv2Eval +# from inference_pipeline import InferencePipeline +import time +# from scoring_pipeline import ScoringPipeline + +from dataset import VQAv2Eval +# import os +from awq.llava_quantizer import LlavaAWQQuantizer +import gc + + +if torch.backends.mps.is_available(): + device = torch.device("mps") +else: + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + + +# VQAv2 dataset paths +ann_root = '/fs/cfar-projects/low-bit-vision/datasets/vqav2/annotations' +q_root = '/fs/cfar-projects/low-bit-vision/datasets/vqav2/questions' +image_root = '/fs/cfar-projects/low-bit-vision/datasets/vqav2/val2014' + +# short answer prompting according to: https://github.com/haotian-liu/LLaVA/blob/main/docs/Evaluation.md +llava_prompt = 'USER: \n{}\nAnswer the question using a single word or phrase. ASSISTANT:' + +dataset = VQAv2Eval(image_root=image_root, + ann_root=ann_root, + q_root=q_root, + prompt = llava_prompt) + +# Load the model +model = LlavaForConditionalGeneration.from_pretrained("llava-hf/llava-1.5-7b-hf", torch_dtype=torch.float16, device_map="auto") +processor = AutoProcessor.from_pretrained("llava-hf/llava-1.5-7b-hf", pad_token = '') + +config = {} + +config['vision_layers'] = { + 'self_attn':4, + 'mlp': 4 +} + +config['llm_layers'] = { + 'self_attn': 4, + 'mlp': 4 +} + +quantizer = LlavaAWQQuantizer(model, device, processor, dataset, config) +quantizer.n_samples = 128 + +start_time = time.time() +quantizer.quantize() +elapsed_time = time.time() - start_time + +print(f'Elapsed time: {elapsed_time} seconds') + +print(model) +model = model.to('cpu') + +gc.collect() +torch.cuda.empty_cache() + +pass + + +img = dataset[42]['image'] +prompt = dataset[42]['text_input'] +# prompt = 'USER: \n' + dataset.qa_pairs[42]['question'] + '\nAnswer the question using a single word or phrase. ASSISTANT:' + +model = model.to('cuda') +pass +samples = processor(images = [img], + text=[prompt], + return_tensors='pt', + padding=True).to(model.device) + +generate_ids = model.generate(**samples) +print(processor.batch_decode(generate_ids, skip_special_tokens=True)) diff --git a/run_awq.py b/run_awq.py new file mode 100644 index 0000000..1e9a315 --- /dev/null +++ b/run_awq.py @@ -0,0 +1,95 @@ +from awq.quantizer import Blip2ForConditionalGenerationAWQQuantizer, Blip2ForImageTextRetrievalAWQQuantizer +from dataset import COCODataset, Flickr30kEvalDataset +from inference_pipeline import InferencePipeline +from scoring_pipeline import ScoringPipeline + +import torch +import torchvision.transforms as transforms +from transformers import Blip2Processor, Blip2ForConditionalGeneration, AutoProcessor, Blip2ForImageTextRetrieval + +import numpy as np +import json +import os +import argparse + + +def main(config_path, task): + + config = json.load(open(config_path)) + + # Set up device + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + + # define model,dataset,quantizer based on task + if task == 'image_captioning': + model_name = "Salesforce/blip2-opt-2.7b" + model = Blip2ForConditionalGeneration.from_pretrained(model_name) + processor = Blip2Processor.from_pretrained(model_name) + dataset = COCODataset(ann_file='/fs/cfar-projects/low-bit-vision/datasets/cocow/annotations/captions_val2017.json', + img_dir='/fs/cfar-projects/low-bit-vision/datasets/cocow/images/val2017') + + quantizer = Blip2ForConditionalGenerationAWQQuantizer(model, device, processor, dataset, config) + + + elif task == 'image_text_retrieval': + model_name = "Salesforce/blip2-itm-vit-g-coco" + model = Blip2ForImageTextRetrieval.from_pretrained(model_name) + processor = Blip2Processor.from_pretrained(model_name) + + # NOTE: img_transform set to None so that AWQ can use the Blip2Processor for calibration set + dataset = Flickr30kEvalDataset(ann_file='/fs/cfar-projects/low-bit-vision/datasets/flickr30k/annotations/test.json', + img_dir = '/fs/cfar-projects/low-bit-vision/datasets/flickr30k/images/flickr30k-images', + img_transform=None) + + quantizer = Blip2ForImageTextRetrievalAWQQuantizer(model, device, processor, dataset, config) + + img_transform = transforms.Compose( + [ + transforms.Resize( + (364, 364), interpolation=transforms.InterpolationMode.BICUBIC + ), + transforms.ToTensor(), + transforms.Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711)) + ] + ) + + # APPLY AWQ + print('Applying AWQ Quantization...') + quantizer.quantize() + + # need to set this for the inference pipeline + if task == 'image_text_retrieval': + dataset.img_transform = img_transform + + + model = model.to(device) + # RUN INFERENCE + print(f'Running inference on {task} task...') + pipeline = InferencePipeline(model, device, processor) + results = pipeline.run_inference(dataset, task = task) + + os.makedirs(f'{task}_results', exist_ok=True) + result_path = os.path.join(f"{task}_results", os.path.basename(config_path)) + print(f"Inference Finished, Saving Results to {result_path}...") + + results['model_size'] = quantizer.model_size + + if task == 'image_text_retrieval': + for key in results: + # cast these to lists so we can jsonify properly + if type(results[key]) == np.ndarray: + results[key] = results[key].tolist() + + pipeline.save_results(results, result_path) + + +if __name__ == '__main__': + parser = argparse.ArgumentParser( + description="Run inference with a given quantization config" + ) + parser.add_argument("--config_path", help="Path to the quantization config JSON file") + parser.add_argument("--task", choices = ['image_captioning', 'image_text_retrieval']) + + args = parser.parse_args() + + main(args.config_path, args.task) \ No newline at end of file diff --git a/scoring_pipeline.py b/scoring_pipeline.py index d31981b..6e5e606 100644 --- a/scoring_pipeline.py +++ b/scoring_pipeline.py @@ -6,6 +6,8 @@ from pycocoevalcap.rouge.rouge import Rouge from pycocoevalcap.cider.cider import Cider from pycocoevalcap.spice.spice import Spice +from vqa_tools.vqa import VQA +from vqa_tools.vqa_eval import VQAEval import os import sys @@ -36,6 +38,10 @@ def load_results(self, filename): def compute_scores(self, results, task, **kwargs): if task == 'image_captioning': return self._compute_image_captioning_scores(results) + elif task == "vqav2": + return self._compute_vqa_scores(results) + elif task == "gqa": + return self._compute_gqa_scores(results) elif task == "image_text_retrieval": return self._compute_retrieval_scores(results, **kwargs) else: @@ -64,6 +70,47 @@ def _compute_image_captioning_scores(self, results): return scores + def _compute_vqa_scores(self, results): + answers = results["answers"] + annotations = results["annotations"] + questions = results["questions"] + vqa = VQA(annotations, questions) + + quesIds = [ans["question_id"] for ans in answers] + vqa_result = vqa.loadRes(answers, quesFile=questions) + vqa_scorer = VQAEval(vqa, vqa_result, n=2) + vqa_scorer.evaluate(quesIds=quesIds) + + metrics = {"agg_metrics": vqa_scorer.accuracy["overall"]} + + for ans_type in vqa_scorer.accuracy["perAnswerType"]: + metrics[ans_type] = vqa_scorer.accuracy["perAnswerType"][ans_type] + + return metrics + + def _compute_gqa_scores(self, results): + acc = [] + vqa_tool = VQAEval() + + for res in results: + gt_ans = res["gt_answer"] + pred_ans = res["answer"] + + pred_ans = vqa_tool.processPunctuation(pred_ans) + pred_ans = vqa_tool.processDigitArticle(pred_ans) + + gt_ans = vqa_tool.processPunctuation(gt_ans) + gt_ans = vqa_tool.processDigitArticle(gt_ans) + + vqa_acc = 1 if pred_ans == gt_ans else 0 + + acc.append(vqa_acc) + + accuracy = round((sum(acc) / len(acc) * 100), 2) + metrics = {"agg_metrics": accuracy, "acc": accuracy} + return metrics + + def _compute_retrieval_scores(self, results): scores_i2t = results["scores_i2t"] scores_t2i = results["scores_t2i"] diff --git a/slurm_files/vqa/vqa_submit.sh b/slurm_files/vqa/vqa_submit.sh new file mode 100644 index 0000000..0aed6b7 --- /dev/null +++ b/slurm_files/vqa/vqa_submit.sh @@ -0,0 +1,26 @@ +#!/bin/bash + +#SBATCH --job-name=blip2-vqav2 +#SBATCH --output=vqa_baseline.%j +#SBATCH --error=vqa_baseline.%j +#SBATCH --time=20:00:00 + +#SBATCH --partition=vulcan-scavenger +#SBATCH --qos=vulcan-scavenger +#SBATCH --acount=vulcan-abhinav +#SBATCH --gres=gpu:p6000:8 + +#SBATCH --nodes=1 +#SBATCH --ntasks=1 +#SBATCH --ntasks-per-node=1 +#SBATCH --mem=128gb + +module load cuda + +source ~/.bashrc + +micromamba activate blip + +python -m torch.distributed.run --nproc_per_node=8 vqav2.py --batch-size=32 + +wait diff --git a/vqa_tools/__pycache__/vqa.cpython-38.pyc b/vqa_tools/__pycache__/vqa.cpython-38.pyc new file mode 100644 index 0000000..0d3adad Binary files /dev/null and b/vqa_tools/__pycache__/vqa.cpython-38.pyc differ diff --git a/vqa_tools/__pycache__/vqa_eval.cpython-38.pyc b/vqa_tools/__pycache__/vqa_eval.cpython-38.pyc new file mode 100644 index 0000000..1a4a730 Binary files /dev/null and b/vqa_tools/__pycache__/vqa_eval.cpython-38.pyc differ diff --git a/vqa_tools/vqa.py b/vqa_tools/vqa.py new file mode 100644 index 0000000..3631f34 --- /dev/null +++ b/vqa_tools/vqa.py @@ -0,0 +1,170 @@ +__author__ = 'aagrawal' +__version__ = '0.9' + +# Interface for accessing the VQA dataset. + +# This code is based on the code written by Tsung-Yi Lin for MSCOCO Python API available at the following link: +# (https://github.com/pdollar/coco/blob/master/PythonAPI/pycocotools/coco.py). + +# The following functions are defined: +# VQA - VQA class that loads VQA annotation file and prepares data structures. +# getQuesIds - Get question ids that satisfy given filter conditions. +# getImgIds - Get image ids that satisfy given filter conditions. +# loadQA - Load questions and answers with the specified question ids. +# showQA - Display the specified questions and answers. +# loadRes - Load result file and create result object. + +# Help on each function can be accessed by: "help(COCO.function)" + +import json +import datetime +import copy + +class VQA: + def __init__(self, annotation_file=None, question_file=None): + """ + Constructor of VQA helper class for reading and visualizing questions and answers. + :param annotation_file (str): location of VQA annotation file + :return: + """ + # load dataset + self.dataset = {} + self.questions = {} + self.qa = {} + self.qqa = {} + self.imgToQA = {} + if not annotation_file == None and not question_file == None: + print('loading VQA annotations and questions into memory...') + time_t = datetime.datetime.utcnow() + dataset = json.load(open(annotation_file, 'r')) + questions = json.load(open(question_file, 'r')) + print(datetime.datetime.utcnow() - time_t) + self.dataset = dataset + self.questions = questions + self.createIndex() + + def createIndex(self): + # create index + print('creating index...') + imgToQA = {ann['image_id']:[] for ann in self.dataset['annotations']} + qa = {ann['question_id']:[] for ann in self.dataset['annotations']} + qqa = {ann['question_id']:[] for ann in self.dataset['annotations']} + for ann in self.dataset['annotations']: + imgToQA[ann['image_id']] += [ann] + qa[ann['question_id']] = ann + for ques in self.questions['questions']: + qqa[ques['question_id']] = ques + + print('index created!') + # create class members + self.qa = qa + self.qqa = qqa + self.imgToQA = imgToQA + + def getQuesIds(self, imgIds=[], quesTypes=[], ansTypes=[]): + """ + Get question ids that satisfy given filter conditions. default skips that filter + :param imgIds (int array) : get question ids for given imgs + quesTypes (str array) : get question ids for given question types + ansTypes (str array) : get question ids for given answer types + :return: ids (int array) : integer array of question ids + """ + imgIds = imgIds if type(imgIds) == list else [imgIds] + quesTypes = quesTypes if type(quesTypes) == list else [quesTypes] + ansTypes = ansTypes if type(ansTypes) == list else [ansTypes] + + if len(imgIds) == len(quesTypes) == len(ansTypes) == 0: + anns = self.dataset['annotations'] + else: + if not len(imgIds) == 0: + anns = sum([self.imgToQA[imgId] for imgId in imgIds if imgId in self.imgToQA],[]) + else: + anns = self.dataset['annotations'] + anns = anns if len(quesTypes) == 0 else [ann for ann in anns if ann['question_type'] in quesTypes] + anns = anns if len(ansTypes) == 0 else [ann for ann in anns if ann['answer_type'] in ansTypes] + ids = [ann['question_id'] for ann in anns] + return ids + + def getImgIds(self, quesIds=[], quesTypes=[], ansTypes=[]): + """ + Get image ids that satisfy given filter conditions. default skips that filter + :param quesIds (int array) : get image ids for given question ids + quesTypes (str array) : get image ids for given question types + ansTypes (str array) : get image ids for given answer types + :return: ids (int array) : integer array of image ids + """ + quesIds = quesIds if type(quesIds) == list else [quesIds] + quesTypes = quesTypes if type(quesTypes) == list else [quesTypes] + ansTypes = ansTypes if type(ansTypes) == list else [ansTypes] + + if len(quesIds) == len(quesTypes) == len(ansTypes) == 0: + anns = self.dataset['annotations'] + else: + if not len(quesIds) == 0: + anns = sum([self.qa[quesId] for quesId in quesIds if quesId in self.qa],[]) + else: + anns = self.dataset['annotations'] + anns = anns if len(quesTypes) == 0 else [ann for ann in anns if ann['question_type'] in quesTypes] + anns = anns if len(ansTypes) == 0 else [ann for ann in anns if ann['answer_type'] in ansTypes] + ids = [ann['image_id'] for ann in anns] + return ids + + def loadQA(self, ids=[]): + """ + Load questions and answers with the specified question ids. + :param ids (int array) : integer ids specifying question ids + :return: qa (object array) : loaded qa objects + """ + if type(ids) == list: + return [self.qa[id] for id in ids] + elif type(ids) == int: + return [self.qa[ids]] + + def showQA(self, anns): + """ + Display the specified annotations. + :param anns (array of object): annotations to display + :return: None + """ + if len(anns) == 0: + return 0 + for ann in anns: + quesId = ann['question_id'] + print("Question: %s" %(self.qqa[quesId]['question'])) + for ans in ann['answers']: + print("Answer %d: %s" %(ans['answer_id'], ans['answer'])) + + def loadRes(self, results, quesFile): + """ + Load result file and return a result object. + :param resFile (str) : file name of result file + :return: res (obj) : result api object + """ + res = VQA() + res.questions = json.load(open(quesFile)) + res.dataset['info'] = copy.deepcopy(self.questions['info']) + res.dataset['task_type'] = copy.deepcopy(self.questions['task_type']) + res.dataset['data_type'] = copy.deepcopy(self.questions['data_type']) + res.dataset['data_subtype'] = copy.deepcopy(self.questions['data_subtype']) + res.dataset['license'] = copy.deepcopy(self.questions['license']) + + print('Loading and preparing results... ') + time_t = datetime.datetime.utcnow() + anns = results + assert type(anns) == list, 'results is not an array of objects' + annsQuesIds = [ann['question_id'] for ann in anns] + #assert set(annsQuesIds) == set(self.getQuesIds()), \ + #'Results do not correspond to current VQA set. Either the results do not have predictions for all question ids in annotation file or there is atleast one question id that does not belong to the question ids in the annotation file.' + for ann in anns: + quesId = ann['question_id'] + if res.dataset['task_type'] == 'Multiple Choice': + assert ann['answer'] in self.qqa[quesId]['multiple_choices'], 'predicted answer is not one of the multiple choices' + qaAnn = self.qa[quesId] + ann['image_id'] = qaAnn['image_id'] + ann['question_type'] = qaAnn['question_type'] + ann['answer_type'] = qaAnn['answer_type'] + print('DONE (t=%0.2fs)'%((datetime.datetime.utcnow() - time_t).total_seconds())) + + res.dataset['annotations'] = anns + res.createIndex() + return res \ No newline at end of file diff --git a/vqa_tools/vqa_eval.py b/vqa_tools/vqa_eval.py new file mode 100644 index 0000000..ee808b3 --- /dev/null +++ b/vqa_tools/vqa_eval.py @@ -0,0 +1,324 @@ +""" + Copyright (c) 2022, salesforce.com, inc. + All rights reserved. + SPDX-License-Identifier: BSD-3-Clause + For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause +""" + +# coding=utf-8 + +__author__ = "aagrawal" + +# This code is based on the code written by Tsung-Yi Lin for MSCOCO Python API available at the following link: +# (https://github.com/tylin/coco-caption/blob/master/pycocoevalcap/eval.py). +import sys +import re + + +class VQAEval: + def __init__(self, vqa=None, vqaRes=None, n=2): + self.n = n + self.accuracy = {} + self.evalQA = {} + self.evalQuesType = {} + self.evalAnsType = {} + self.vqa = vqa + self.vqaRes = vqaRes + if vqa is not None: + self.params = {"question_id": vqa.getQuesIds()} + self.contractions = { + "aint": "ain't", + "arent": "aren't", + "cant": "can't", + "couldve": "could've", + "couldnt": "couldn't", + "couldn'tve": "couldn't've", + "couldnt've": "couldn't've", + "didnt": "didn't", + "doesnt": "doesn't", + "dont": "don't", + "hadnt": "hadn't", + "hadnt've": "hadn't've", + "hadn'tve": "hadn't've", + "hasnt": "hasn't", + "havent": "haven't", + "hed": "he'd", + "hed've": "he'd've", + "he'dve": "he'd've", + "hes": "he's", + "howd": "how'd", + "howll": "how'll", + "hows": "how's", + "Id've": "I'd've", + "I'dve": "I'd've", + "Im": "I'm", + "Ive": "I've", + "isnt": "isn't", + "itd": "it'd", + "itd've": "it'd've", + "it'dve": "it'd've", + "itll": "it'll", + "let's": "let's", + "maam": "ma'am", + "mightnt": "mightn't", + "mightnt've": "mightn't've", + "mightn'tve": "mightn't've", + "mightve": "might've", + "mustnt": "mustn't", + "mustve": "must've", + "neednt": "needn't", + "notve": "not've", + "oclock": "o'clock", + "oughtnt": "oughtn't", + "ow's'at": "'ow's'at", + "'ows'at": "'ow's'at", + "'ow'sat": "'ow's'at", + "shant": "shan't", + "shed've": "she'd've", + "she'dve": "she'd've", + "she's": "she's", + "shouldve": "should've", + "shouldnt": "shouldn't", + "shouldnt've": "shouldn't've", + "shouldn'tve": "shouldn't've", + "somebody'd": "somebodyd", + "somebodyd've": "somebody'd've", + "somebody'dve": "somebody'd've", + "somebodyll": "somebody'll", + "somebodys": "somebody's", + "someoned": "someone'd", + "someoned've": "someone'd've", + "someone'dve": "someone'd've", + "someonell": "someone'll", + "someones": "someone's", + "somethingd": "something'd", + "somethingd've": "something'd've", + "something'dve": "something'd've", + "somethingll": "something'll", + "thats": "that's", + "thered": "there'd", + "thered've": "there'd've", + "there'dve": "there'd've", + "therere": "there're", + "theres": "there's", + "theyd": "they'd", + "theyd've": "they'd've", + "they'dve": "they'd've", + "theyll": "they'll", + "theyre": "they're", + "theyve": "they've", + "twas": "'twas", + "wasnt": "wasn't", + "wed've": "we'd've", + "we'dve": "we'd've", + "weve": "we've", + "werent": "weren't", + "whatll": "what'll", + "whatre": "what're", + "whats": "what's", + "whatve": "what've", + "whens": "when's", + "whered": "where'd", + "wheres": "where's", + "whereve": "where've", + "whod": "who'd", + "whod've": "who'd've", + "who'dve": "who'd've", + "wholl": "who'll", + "whos": "who's", + "whove": "who've", + "whyll": "why'll", + "whyre": "why're", + "whys": "why's", + "wont": "won't", + "wouldve": "would've", + "wouldnt": "wouldn't", + "wouldnt've": "wouldn't've", + "wouldn'tve": "wouldn't've", + "yall": "y'all", + "yall'll": "y'all'll", + "y'allll": "y'all'll", + "yall'd've": "y'all'd've", + "y'alld've": "y'all'd've", + "y'all'dve": "y'all'd've", + "youd": "you'd", + "youd've": "you'd've", + "you'dve": "you'd've", + "youll": "you'll", + "youre": "you're", + "youve": "you've", + } + self.manualMap = { + "none": "0", + "zero": "0", + "one": "1", + "two": "2", + "three": "3", + "four": "4", + "five": "5", + "six": "6", + "seven": "7", + "eight": "8", + "nine": "9", + "ten": "10", + } + self.articles = ["a", "an", "the"] + + self.periodStrip = re.compile("(?!<=\d)(\.)(?!\d)") + self.commaStrip = re.compile("(\d)(,)(\d)") + self.punct = [ + ";", + r"/", + "[", + "]", + '"', + "{", + "}", + "(", + ")", + "=", + "+", + "\\", + "_", + "-", + ">", + "<", + "@", + "`", + ",", + "?", + "!", + ] + + def evaluate(self, quesIds=None): + if quesIds == None: + quesIds = [quesId for quesId in self.params["question_id"]] + gts = {} + res = {} + for quesId in quesIds: + gts[quesId] = self.vqa.qa[quesId] + res[quesId] = self.vqaRes.qa[quesId] + + # ================================================= + # Compute accuracy + # ================================================= + accQA = [] + accQuesType = {} + accAnsType = {} + print("computing accuracy") + step = 0 + for quesId in quesIds: + resAns = res[quesId]["answer"] + resAns = resAns.replace("\n", " ") + resAns = resAns.replace("\t", " ") + resAns = resAns.strip() + resAns = self.processPunctuation(resAns) + resAns = self.processDigitArticle(resAns) + gtAcc = [] + gtAnswers = [ans["answer"] for ans in gts[quesId]["answers"]] + if len(set(gtAnswers)) > 1: + for ansDic in gts[quesId]["answers"]: + ansDic["answer"] = self.processPunctuation(ansDic["answer"]) + for gtAnsDatum in gts[quesId]["answers"]: + otherGTAns = [ + item for item in gts[quesId]["answers"] if item != gtAnsDatum + ] + matchingAns = [item for item in otherGTAns if item["answer"] == resAns] + acc = min(1, float(len(matchingAns)) / 3) + gtAcc.append(acc) + quesType = gts[quesId]["question_type"] + ansType = gts[quesId]["answer_type"] + avgGTAcc = float(sum(gtAcc)) / len(gtAcc) + accQA.append(avgGTAcc) + if quesType not in accQuesType: + accQuesType[quesType] = [] + accQuesType[quesType].append(avgGTAcc) + if ansType not in accAnsType: + accAnsType[ansType] = [] + accAnsType[ansType].append(avgGTAcc) + self.setEvalQA(quesId, avgGTAcc) + self.setEvalQuesType(quesId, quesType, avgGTAcc) + self.setEvalAnsType(quesId, ansType, avgGTAcc) + if step % 100 == 0: + self.updateProgress(step / float(len(quesIds))) + step = step + 1 + + self.setAccuracy(accQA, accQuesType, accAnsType) + print("Done computing accuracy") + + def processPunctuation(self, inText): + outText = inText + for p in self.punct: + if (p + " " in inText or " " + p in inText) or ( + re.search(self.commaStrip, inText) != None + ): + outText = outText.replace(p, "") + else: + outText = outText.replace(p, " ") + outText = self.periodStrip.sub("", outText, re.UNICODE) + return outText + + def processDigitArticle(self, inText): + outText = [] + tempText = inText.lower().split() + for word in tempText: + word = self.manualMap.setdefault(word, word) + if word not in self.articles: + outText.append(word) + else: + pass + for wordId, word in enumerate(outText): + if word in self.contractions: + outText[wordId] = self.contractions[word] + outText = " ".join(outText) + return outText + + def setAccuracy(self, accQA, accQuesType, accAnsType): + self.accuracy["overall"] = round(100 * float(sum(accQA)) / len(accQA), self.n) + self.accuracy["perQuestionType"] = { + quesType: round( + 100 * float(sum(accQuesType[quesType])) / len(accQuesType[quesType]), + self.n, + ) + for quesType in accQuesType + } + self.accuracy["perAnswerType"] = { + ansType: round( + 100 * float(sum(accAnsType[ansType])) / len(accAnsType[ansType]), self.n + ) + for ansType in accAnsType + } + + def setEvalQA(self, quesId, acc): + self.evalQA[quesId] = round(100 * acc, self.n) + + def setEvalQuesType(self, quesId, quesType, acc): + if quesType not in self.evalQuesType: + self.evalQuesType[quesType] = {} + self.evalQuesType[quesType][quesId] = round(100 * acc, self.n) + + def setEvalAnsType(self, quesId, ansType, acc): + if ansType not in self.evalAnsType: + self.evalAnsType[ansType] = {} + self.evalAnsType[ansType][quesId] = round(100 * acc, self.n) + + def updateProgress(self, progress): + barLength = 20 + status = "" + if isinstance(progress, int): + progress = float(progress) + if not isinstance(progress, float): + progress = 0 + status = "error: progress var must be float\r\n" + if progress < 0: + progress = 0 + status = "Halt...\r\n" + if progress >= 1: + progress = 1 + status = "Done...\r\n" + block = int(round(barLength * progress)) + text = "\rFinshed Percent: [{0}] {1}% {2}".format( + "#" * block + "-" * (barLength - block), int(progress * 100), status + ) + sys.stdout.write(text) + sys.stdout.flush() diff --git a/vqav2.py b/vqav2.py new file mode 100644 index 0000000..567a172 --- /dev/null +++ b/vqav2.py @@ -0,0 +1,163 @@ +import argparse +import os +import json +import builtins as __builtin__ + +import torch +import torch.distributed as dist +from torch.utils.data import DistributedSampler, DataLoader +from transformers import Blip2ForConditionalGeneration, Blip2Processor + +from datasets import VQAv2Eval +from inference_pipeline import InferencePipeline +from scoring_pipeline import ScoringPipeline + +def init_distributed(): + rank = int(os.environ["RANK"]) + world_size = int(os.environ["WORLD_SIZE"]) + gpu = int(os.environ["LOCAL_RANK"]) + dist.init_process_group(backend="nccl", init_method="env://", rank=rank, world_size=world_size) + torch.cuda.set_device(gpu) + + builtin_print = __builtin__.print + def print(*args, **kwargs): + if rank == 0: + builtin_print(*args, **kwargs) + __builtin__.print = print + + return rank, world_size, gpu + +def compute_vqa_results(results, scorer, save_path=None): + vqa_results = scorer.compute_scores(results, "vqav2") + print(vqa_results) + if save_path: + with open(save_path, "w") as f: + json.dump(vqa_results, f) + +if __name__ == "__main__": + parser = argparse.ArgumentParser( + prog='VQAv2 Eval', + description='Performs VQA evaluation using BLIP2 on VQAv2', + ) + + parser.add_argument("--distributed", action="store_true") + parser.add_argument("--batch_size", default=64, type=int) + parser.add_argument("--num_workers", default=1, type=int) + parser.add_argument("--output_dir", default="./output", type=str) + parser.add_argument("--dataset_dir", default="./data/vqav2", type=str) + + args = parser.parse_args() + os.makedirs(args.output_dir, exist_ok=True) + + processor = Blip2Processor.from_pretrained("salesforce/blip2-opt-2.7b", padding_side="left") + vqav2 = VQAv2Eval( + os.path.join(args.dataset_dir, "val2014"), + os.path.join(args.dataset_dir, "annotations"), + os.path.join(args.dataset_dir, "questions"), + ) + + if args.distributed: + rank, world_size, gpu = init_distributed() + dist.barrier() + + try: + sampler = DistributedSampler( + vqav2, + shuffle=False, + num_replicas=world_size, + rank=rank + ) + + dataloader = DataLoader( + vqav2, + batch_size=args.batch_size, + num_workers=args.num_workers, + pin_memory=False, + shuffle=False, + sampler=sampler, + collate_fn=vqav2.collater, + ) + model = Blip2ForConditionalGeneration.from_pretrained("Salesforce/blip2-opt-2.7b", device_map=gpu) + + inferencer = InferencePipeline(model, gpu, processor) + scorer = ScoringPipeline() + + # T5 kwargs + # processor_kwargs={"padding": "longest", "max_length": 32, "truncation": True} + # generate_kwargs={"num_beams": 5, "max_new_tokens": 10, "min_length": 1, "length_penalty": -1, "do_sample": False} + # OPT kwargs + processor_kwargs={"padding": "longest", "max_length": 32, "truncation": True} + generate_kwargs={"num_beams": 5, "max_new_tokens": 10, "min_length": 1, "length_penalty": 0, "do_sample": False} + + results = inferencer.run_inference( + dataloader, + task="vqav2", + processor_kwargs=processor_kwargs, + generate_kwargs=generate_kwargs + ) + + with open(os.path.join(args.output_dir, f"{rank}_results.json"), 'w') as f: + json.dump(results, f) + dist.barrier() + + if rank == 0: + results = { + "answers": [], + "annotations": os.path.join(args.dataset_dir, "annotations/v2_mscoco_val2014_annotations.json"), + "questions": os.path.join(args.dataset_dir, "questions/v2_OpenEnded_mscoco_val2014_questions.json") + } + + question_ids = set() + for rank_id in range(world_size): + with open(os.path.join(args.output_dir, f"{rank_id}_results.json"), 'r') as f: + rank_results = json.load(f) + for answer in rank_results: + question_id = answer["question_id"] + if question_id not in question_ids: + results["answers"].append(answer) + question_ids.add(question_id) + + compute_vqa_results(results, scorer, os.path.join(args.output_dir, "results.json")) + finally: + dist.destroy_process_group() + else: + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + + model = Blip2ForConditionalGeneration.from_pretrained("Salesforce/blip2-opt-2.7b", device_map=device) + + inferencer = InferencePipeline(model, device, processor) + scorer = ScoringPipeline() + + # T5 kwargs + # processor_kwargs={"padding": "longest", "max_length": 32, "truncation": True} + # generate_kwargs={"num_beams": 5, "max_new_tokens": 10, "min_length": 1, "length_penalty": -1, "do_sample": False} + # OPT kwargs + processor_kwargs={"padding": "longest", "max_length": 32, "truncation": True} + generate_kwargs={"num_beams": 5, "max_new_tokens": 10, "min_length": 1, "length_penalty": 0, "do_sample": False} + + dataloader = DataLoader( + vqav2, + batch_size=args.batch_size, + num_workers=args.num_workers, + pin_memory=False, + shuffle=False, + collate_fn=vqav2.collater, + ) + + results = inferencer.run_inference( + dataloader, + task="vqav2", + proecssor_kwargs=processor_kwargs, + generate_kwargs=generate_kwargs + ) + + with open(os.path.join(args.output_dir, "answers.json"), 'w') as f: + json.dump(results, f) + + #results["annotations"] = "./data/vqav2/annotations/v2_mscoco_val2014_annotations.json" + results["annotations"] = os.path.join(args.dataset_dir, "annotations/v2_mscoco_val2014_annotations.json") + results["questions"] = os.path.join(args.dataset_dir, "questions/v2_OpenEnded_mscoco_val2014_questions.json") + + compute_vqa_results(results, scorer, os.path.join(args.output_dir, "results.json")) + +