AdversarialAttacks.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "provenance": [],
      "collapsed_sections": [
        "U4Q5q6v4Y9Nc",
        "47bXEwGclGSB",
        "Tg5QEvvVY_4R",
        "FkYV8W9xLnl0",
        "tQiNcd7OZEXy",
        "H8Y85D2TZHJG"
      ]
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "U4Q5q6v4Y9Nc"
      },
      "source": [
        "## Libs"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4YS8GJzdHkLU",
        "outputId": "da214597-df6d-4502-a15a-30cae66e73ab"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "import numpy as np\n",
        "\n",
        "from torch.utils.data import DataLoader, Dataset\n",
        "import torchvision.transforms as T\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import copy\n",
        "import os\n",
        "from tqdm import tqdm\n",
        "\n",
        "from torchvision.datasets import ImageFolder\n",
        "from torch.nn import Module, Dropout2d, Flatten\n",
        "from torch.nn import Conv2d\n",
        "from torch.nn import Linear\n",
        "from torch.nn import MaxPool2d\n",
        "from torch.nn import ReLU\n",
        "from torch.nn import LogSoftmax, BatchNorm2d\n",
        "from torch import flatten\n",
        "import torch.optim as optim\n",
        "from torch.optim.lr_scheduler import StepLR\n",
        "from torch.nn.functional import max_pool2d, dropout\n",
        "from torch.utils.data import TensorDataset\n",
        "\n",
        "\n",
        "\n",
        "from torchvision import models\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "from sklearn.metrics import classification_report\n",
        "\n",
        "import sys\n",
        "import warnings\n",
        "import cv2\n",
        "\n",
        "if not sys.warnoptions:\n",
        "    warnings.simplefilter(\"ignore\")\n",
        "\n",
        "from google.colab import drive\n",
        "drive._mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "47bXEwGclGSB"
      },
      "source": [
        "## Stage 1. Model training."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Tg5QEvvVY_4R"
      },
      "source": [
        "#### Dataset reading and preparing"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Xb1SOPRkIPDn",
        "outputId": "bcce2cc8-d7ab-4835-caf2-5f94f26491cb"
      },
      "source": [
        "dataset_path = \"drive/MyDrive/Dataset\"\n",
        "os.listdir(dataset_path)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "text/plain": [
              "['training', 'validation']"
            ]
          },
          "execution_count": 2,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Qvzml0UkJb2W"
      },
      "source": [
        "train_dataset = ImageFolder(dataset_path + '/training', transform=T.Compose([T.ToTensor(), T.Resize(256), T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])]))\n",
        "test_dataset = ImageFolder(dataset_path + '/validation', transform=T.Compose([T.ToTensor(), T.Resize(256), T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])]))\n",
        "batch_size = 32\n",
        "train_loader = DataLoader(train_dataset, batch_size, shuffle=True, num_workers=2)\n",
        "test_loader = DataLoader(test_dataset, batch_size, num_workers=2)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FkYV8W9xLnl0"
      },
      "source": [
        "#### Functions for visualisation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 301
        },
        "id": "ZYY87ho9S1qQ",
        "outputId": "bb19cb74-81e8-4295-a606-6b1974a33ebe"
      },
      "source": [
        "def show_example(img, label):\n",
        "    print('Label: ', train_dataset.classes[label], \"(\"+str(label)+\")\")\n",
        "    plt.imshow(img.permute(1, 2, 0))\n",
        "    \n",
        "show_example(*train_dataset[1000])\n",
        "\n",
        "def showInRow(list_of_images, titles = None):\n",
        "  count = len(list_of_images)\n",
        "  for idx in range(count):\n",
        "    subplot = plt.subplot(1, count, idx+1)\n",
        "    if titles is not None:\n",
        "      subplot.set_title(titles[idx])\n",
        "      \n",
        "    img = list_of_images[idx]\n",
        "    subplot.imshow(img.permute(1, 2, 0))\n",
        "  plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Label:  Tiger (1)\n"
          ]
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tQiNcd7OZEXy"
      },
      "source": [
        "#### Model "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CWeNCKseOK_4",
        "outputId": "9754b886-65d3-4b35-cd4d-3cc335a99a9d"
      },
      "source": [
        "torch.manual_seed(43)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<torch._C.Generator at 0x7fe7008d4670>"
            ]
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RXz3VSKwQIZv"
      },
      "source": [
        "class CustomCNN(nn.Module):\n",
        "  def __init__(self, numChannels, classes):\n",
        "\n",
        "    super(CustomCNN, self).__init__()\n",
        "\n",
        "    self.dropout = nn.Dropout(0.2)\n",
        "  \n",
        "    self.conv1 = Conv2d(in_channels=numChannels, out_channels=32, kernel_size=(5, 5))\n",
        "    self.relu1 = ReLU()\n",
        "    self.maxpool1 = MaxPool2d(kernel_size=(2, 2), stride=(2, 2))\n",
        "    self.conv1_bn = nn.BatchNorm2d(32)\n",
        "\n",
        "    self.conv2 = Conv2d(in_channels=32, out_channels=64, kernel_size=(5, 5))\n",
        "    self.relu2 = ReLU()\n",
        "    self.maxpool2 = MaxPool2d(kernel_size=(2, 2), stride=(2, 2))\n",
        "    self.conv2_bn = nn.BatchNorm2d(64)\n",
        "\n",
        "\n",
        "    self.conv3 = Conv2d(in_channels=64, out_channels=128, kernel_size=(3, 3))\n",
        "    self.relu3 = ReLU()\n",
        "    self.maxpool3 = MaxPool2d(kernel_size=(2, 2), stride=(2, 2))\n",
        "    self.conv3_bn = nn.BatchNorm2d(128)\n",
        "\n",
        "\n",
        "    self.fc1 = Linear(in_features=107648, out_features=1000)\n",
        "    self.relu3 = ReLU()\n",
        "    self.fc2 = Linear(in_features=1000, out_features=classes)\n",
        "\n",
        "  def forward(self, x):\n",
        "\n",
        "    x = self.conv1(x)\n",
        "    x = self.conv1_bn(x)\n",
        "    x = self.relu1(x)\n",
        "    x = self.maxpool1(x)\n",
        "\n",
        "    x = self.conv2(x)\n",
        "    x = self.conv2_bn(x)\n",
        "    x = self.relu2(x)\n",
        "    x = self.maxpool2(x)\n",
        "\n",
        "    x = self.conv3(x)\n",
        "    x = self.conv3_bn(x)\n",
        "    x = self.relu3(x)\n",
        "    x = self.maxpool3(x)\n",
        "    x = self.dropout(x)\n",
        "\n",
        "    x = flatten(x, 1)\n",
        "\n",
        "    x = self.fc1(x)\n",
        "    x = self.relu3(x)\n",
        "\n",
        "    x = self.fc2(x)\n",
        "\n",
        "\n",
        "    return x"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H8Y85D2TZHJG"
      },
      "source": [
        "#### Training Loop"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yAANHSFJPxi7"
      },
      "source": [
        "def train(model, optimizer, loss_fn, train_loader, val_loader, scheduler=None, epochs=20, device=\"cuda\"):\n",
        "    best_loss = 1000\n",
        "    best_model = copy.deepcopy(model)\n",
        "    \n",
        "    for epoch in range(1, epochs+1):\n",
        "        training_loss = 0.0\n",
        "        valid_loss = 0.0\n",
        "\n",
        "        model.train()\n",
        "        for batch in tqdm(train_loader):\n",
        "            optimizer.zero_grad()\n",
        "            inputs, targets = batch\n",
        "            inputs = inputs.to(device)\n",
        "            targets = targets.to(device)\n",
        "            output = model(inputs)\n",
        "            loss = loss_fn(output, targets)\n",
        "            loss.backward()\n",
        "            optimizer.step()\n",
        "            training_loss += loss.data.item() * inputs.size(0)\n",
        "        training_loss /= len(train_loader.dataset)\n",
        "        \n",
        "        model.eval()\n",
        "        num_correct = 0 \n",
        "        num_examples = 0\n",
        "        for batch in tqdm(val_loader):\n",
        "          with torch.no_grad():\n",
        "            inputs, targets = batch\n",
        "            inputs = inputs.to(device)\n",
        "            output = model(inputs)\n",
        "            targets = targets.to(device)\n",
        "            loss = loss_fn(output,targets) \n",
        "            valid_loss += loss.data.item() * inputs.size(0)\n",
        "            correct = torch.eq(torch.max(output, dim=1)[1], targets).view(-1)\n",
        "            num_correct += torch.sum(correct).item()\n",
        "            num_examples += correct.shape[0]\n",
        "        \n",
        "        if scheduler != None:\n",
        "          scheduler.step()\n",
        "        valid_loss /= len(val_loader.dataset)\n",
        "\n",
        "        print('Epoch: {}, Training Loss: {:.2f}, Validation Loss: {:.2f}, accuracy = {:.2f}'.format(epoch, training_loss,\n",
        "        valid_loss, num_correct / num_examples))\n",
        "\n",
        "        if valid_loss < best_loss:\n",
        "          best_model.load_state_dict(model.state_dict())\n",
        "          best_loss = valid_loss\n",
        "        \n",
        "    return best_model"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NCkR5gYVSJqV",
        "outputId": "94bf7211-84a9-4b21-b3f1-5273b9e031c9"
      },
      "source": [
        "model_2 = CustomCNN(3, 2).to(\"cuda\")\n",
        "lr = 1e-4\n",
        "optimizer = optim.Adam(model_2.parameters(), lr=lr)\n",
        "scheduler = StepLR(optimizer, step_size=5, gamma=0.1)\n",
        "loss =  nn.CrossEntropyLoss()\n",
        "best_model = train(model_2, optimizer, loss, train_loader, test_loader, epochs=10, device=\"cuda\")\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 57/57 [06:59<00:00,  7.36s/it]\n",
            "100%|██████████| 7/7 [00:44<00:00,  6.33s/it]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch: 1, Training Loss: 1.57, Validation Loss: 0.77, accuracy = 0.71\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 57/57 [00:23<00:00,  2.40it/s]\n",
            "100%|██████████| 7/7 [00:02<00:00,  3.07it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch: 2, Training Loss: 0.60, Validation Loss: 0.63, accuracy = 0.76\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 57/57 [00:23<00:00,  2.39it/s]\n",
            "100%|██████████| 7/7 [00:02<00:00,  3.16it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch: 3, Training Loss: 0.24, Validation Loss: 0.31, accuracy = 0.85\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 57/57 [00:23<00:00,  2.38it/s]\n",
            "100%|██████████| 7/7 [00:02<00:00,  3.06it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch: 4, Training Loss: 0.13, Validation Loss: 0.37, accuracy = 0.85\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 57/57 [00:24<00:00,  2.36it/s]\n",
            "100%|██████████| 7/7 [00:02<00:00,  3.09it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch: 5, Training Loss: 0.08, Validation Loss: 0.43, accuracy = 0.81\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 57/57 [00:23<00:00,  2.40it/s]\n",
            "100%|██████████| 7/7 [00:02<00:00,  3.13it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch: 6, Training Loss: 0.07, Validation Loss: 0.46, accuracy = 0.83\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 57/57 [00:24<00:00,  2.37it/s]\n",
            "100%|██████████| 7/7 [00:02<00:00,  3.08it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch: 7, Training Loss: 0.09, Validation Loss: 0.47, accuracy = 0.84\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 57/57 [00:24<00:00,  2.35it/s]\n",
            "100%|██████████| 7/7 [00:02<00:00,  3.09it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch: 8, Training Loss: 0.06, Validation Loss: 0.41, accuracy = 0.85\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 57/57 [00:23<00:00,  2.38it/s]\n",
            "100%|██████████| 7/7 [00:02<00:00,  3.04it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch: 9, Training Loss: 0.12, Validation Loss: 0.53, accuracy = 0.84\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 57/57 [00:23<00:00,  2.38it/s]\n",
            "100%|██████████| 7/7 [00:02<00:00,  3.01it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch: 10, Training Loss: 0.03, Validation Loss: 0.38, accuracy = 0.88\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_D4SOswEYOKb"
      },
      "source": [
        "torch.save(best_model.state_dict(), 'best_model.pt')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iuhLdw0NVqKv",
        "outputId": "6fdf97d4-9a96-4d52-c05f-2673de8e7b9f"
      },
      "source": [
        "best_model = CustomCNN(3, 2).to(\"cuda\")\n",
        "best_model.load_state_dict(torch.load(\"/content/best_model.pt\"))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<All keys matched successfully>"
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ix1fP-e9ZKcG"
      },
      "source": [
        "#### Model asessment before attack"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HzxPShIjDRmC",
        "outputId": "63da575c-1e80-4a50-8b56-f50b0fb4f6ad"
      },
      "source": [
        "def model_report(model, test_loader):\n",
        "  preds = []\n",
        "  targets = []\n",
        "  valid_loss = 0.0\n",
        "  loss_fn = nn.CrossEntropyLoss()\n",
        "\n",
        "  for batch in tqdm(test_loader):\n",
        "    with torch.no_grad():\n",
        "      inputs, target = batch\n",
        "      inputs, target = batch = inputs.to(\"cuda\"), target.to(\"cuda\")\n",
        "      output = model(inputs)\n",
        "      pred = torch.max(output, dim=1)[1]\n",
        "      preds += pred.to(\"cpu\")\n",
        "      targets += target.to(\"cpu\")\n",
        "      loss = loss_fn(output, target) \n",
        "      valid_loss += loss.data.item() * inputs.size(0)\n",
        "\n",
        "  valid_loss /= len(test_loader.dataset)\n",
        "\n",
        "  print()\n",
        "  print()\n",
        "  print(\"Validation loss is: {}\".format(valid_loss))\n",
        "  print(classification_report(preds, targets))\n",
        "\n",
        "model_report(model_2, test_loader)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 7/7 [00:02<00:00,  2.91it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "Validation loss is: 0.3775999677181244\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.93      0.85      0.89       110\n",
            "           1       0.83      0.92      0.87        90\n",
            "\n",
            "    accuracy                           0.88       200\n",
            "   macro avg       0.88      0.88      0.88       200\n",
            "weighted avg       0.89      0.88      0.88       200\n",
            "\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PBWY1B7IZpcb"
      },
      "source": [
        "## Stage 2. Attack & Defence"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P6Jf4R-wIWgK"
      },
      "source": [
        "attack_set = [10, 11, 111, 112]\n",
        "attack_dataset = torch.utils.data.Subset(test_dataset, attack_set)\n",
        "attack_loader = DataLoader(attack_dataset, 1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tdRjYH-iQpoA"
      },
      "source": [
        "def attack_output(model, attack_f, args):\n",
        "  images = []\n",
        "  preds = []\n",
        "  for X, y in attack_loader:\n",
        "    X, y = X.to(\"cuda\"), y.to(\"cuda\")\n",
        "    delta = attack_f(model, loss, X, y, *args)\n",
        "    altered_img = X.to(\"cuda\")+delta\n",
        "    pred = train_dataset.classes[torch.argmax(best_model(altered_img)).item()]\n",
        "\n",
        "    images.append(altered_img.squeeze().to(\"cpu\"))\n",
        "    preds.append(pred)\n",
        "    \n",
        "\n",
        "  showInRow(images, preds)\n",
        "\n",
        "\n",
        "def attacked_model_report(model, test_loader, attack_f, args):\n",
        "  preds = []\n",
        "  targets = []\n",
        "  loss_fn = nn.CrossEntropyLoss()\n",
        "  valid_loss = 0.0\n",
        "\n",
        "  for (X, y) in tqdm(test_loader):\n",
        "      X, y = X.to(\"cuda\"), y.to(\"cuda\")\n",
        "      delta = attack_f(model, loss_fn, X, y, *args)\n",
        "      output = model(X+delta)\n",
        "      pred = torch.max(output, dim=1)[1]\n",
        "      preds += list(pred.to(\"cpu\"))\n",
        "      targets += list(y.to(\"cpu\"))\n",
        "      loss = loss_fn(output, y)\n",
        "      valid_loss += loss.data.item() * X.size(0)\n",
        "  \n",
        "  valid_loss /= len(test_loader.dataset)\n",
        "\n",
        "  print()\n",
        "  print()\n",
        "  print(\"Validation loss is: {}\".format(valid_loss))\n",
        "  print(classification_report(preds, targets))\n",
        "  "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0kuBTSDGH2gZ"
      },
      "source": [
        "### Building attacks"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nSr2-sIib5Pc"
      },
      "source": [
        "FGSM"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Dv0s3_IlZrmF"
      },
      "source": [
        "def fgsm(model, loss_fn, X, y, e):\n",
        "    delta = torch.zeros_like(X, requires_grad=True).to(\"cuda\")\n",
        "    loss = loss_fn(model(X + delta), y)\n",
        "    loss.backward()\n",
        "    g = delta.grad.detach()\n",
        "    return e * g.sign()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        },
        "id": "35r62ju4HEf_",
        "outputId": "82116a14-9c8c-4a9d-bc35-deabdc15a9e9"
      },
      "source": [
        "args = [0.1]\n",
        "attack_output(model_2, fgsm, args)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
          ]
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 4 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7pzkK5BvOUQT",
        "outputId": "d3aaf2a4-e5b1-44f8-ee52-8af3a8561042"
      },
      "source": [
        "attacked_model_report(model_2, test_loader, fgsm, args)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 7/7 [00:03<00:00,  2.03it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "Validation loss is: 6.279686584472656\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.23      0.26      0.24        88\n",
            "           1       0.35      0.31      0.33       112\n",
            "\n",
            "    accuracy                           0.29       200\n",
            "   macro avg       0.29      0.29      0.29       200\n",
            "weighted avg       0.30      0.29      0.29       200\n",
            "\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E-iOrZbcW10d"
      },
      "source": [
        "PGD"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o_VEt9QnR1TL"
      },
      "source": [
        "def pgd(model, loss_fn, X, y, e, alpha, num_iter, verbose=False):\n",
        "    delta = torch.zeros_like(X, requires_grad=True).to(\"cuda\")\n",
        "    \n",
        "    if verbose:\n",
        "      iter_obj = tqdm(range(num_iter))\n",
        "    else:\n",
        "      iter_obj = range(num_iter)\n",
        "\n",
        "    for t in range(num_iter):\n",
        "        loss = loss_fn(model(X + delta), y)\n",
        "        loss.backward()\n",
        "        delta.data = (delta + X.shape[0] * alpha * delta.grad.data).clamp(-e, e)\n",
        "        delta.grad.zero_()\n",
        "    return delta.detach()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        },
        "id": "whYF1dBAXRDi",
        "outputId": "976bcd1d-988d-4c58-f2ae-3e9687bdc440"
      },
      "source": [
        "args = [0.1, 1e4, 100]\n",
        "attack_output(model_2, pgd, args)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
          ]
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 4 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fVBKB05kXs-a",
        "outputId": "4426b325-000e-4936-9a3e-02d650fc7238"
      },
      "source": [
        "attacked_model_report(model_2, test_loader, pgd, args)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 7/7 [02:44<00:00, 23.44s/it]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "Validation loss is: 4.582411241531372\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.12      0.13      0.12        95\n",
            "           1       0.17      0.16      0.17       105\n",
            "\n",
            "    accuracy                           0.14       200\n",
            "   macro avg       0.15      0.14      0.14       200\n",
            "weighted avg       0.15      0.14      0.15       200\n",
            "\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "teaieK-TXHvG"
      },
      "source": [
        "C&W attack with $l_2$ norm"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LkFFZflsuVZc"
      },
      "source": [
        "# original paper: https://arxiv.org/pdf/1608.04644.pdf\n",
        "\n",
        "def cw_l2(model, loss_fn, X, y, c, num_iter, lr):\n",
        "\n",
        "  # f_1 functional from original paper\n",
        "  def f(delta, X, y, model, loss_fn):\n",
        "    return -loss_fn(model(X+delta), y)\n",
        "\n",
        "  # Change of variables from original paper\n",
        "  w = torch.zeros_like(X, requires_grad=True).to(\"cuda\")\n",
        "  \n",
        "  optimizer = optim.Adam([w], lr=lr)\n",
        "  loss_d = nn.MSELoss(reduction='sum')\n",
        "  loss_f = torch.sum\n",
        "\n",
        "  for i in range(num_iter):\n",
        "    # Change of variables from original paper\n",
        "    delta = 1/2 * (nn.Tanh()(w) + 1) - X\n",
        "    \n",
        "    # minimizing both negative loss of target and distance of attack\n",
        "    overall_loss = loss_d(delta+X, X) + loss_f(c*f(delta, X, y, model, loss_fn))\n",
        "    \n",
        "    overall_loss.backward()\n",
        "    optimizer.step()\n",
        "    optimizer.zero_grad()\n",
        "  \n",
        "  return delta.detach()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YgJOXwAI3iqu"
      },
      "source": [
        "Note: constant c was found by brute forcing instead of binary search like it was in original paper due to the lack of resources (colab contantly was taking gpu from 1.5 day of working)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        },
        "id": "fpf59aWlXJ7T",
        "outputId": "c89f06eb-9eb2-4133-dc0b-a89e8789b9e1"
      },
      "source": [
        "args = [10, 100, 1e-1]\n",
        "attack_output(model_2, cw_l2, args)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 4 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XVdV3-FEXz4u",
        "outputId": "216ac278-8bac-4647-b482-f8b63775c25d"
      },
      "source": [
        "attacked_model_report(model_2, test_loader, cw_l2, args)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 7/7 [02:50<00:00, 24.29s/it]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "Validation loss is: 0.7157291603088379\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.59      0.70      0.64        84\n",
            "           1       0.75      0.65      0.69       116\n",
            "\n",
            "    accuracy                           0.67       200\n",
            "   macro avg       0.67      0.67      0.67       200\n",
            "weighted avg       0.68      0.67      0.67       200\n",
            "\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mebYdrufiuFq"
      },
      "source": [
        "C&W attack with $l_{1}$"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iSISiiWIefBv"
      },
      "source": [
        "# original paper: https://arxiv.org/pdf/1608.04644.pdf\n",
        "\n",
        "def cw_l_1(model, loss_fn, X, y, c, num_iter, lr):\n",
        "\n",
        "  # f_1 functional from original paper\n",
        "  def f(delta, X, y, model, loss_fn):\n",
        "    return -loss_fn(model(X+delta), y)\n",
        "\n",
        "  # Change of variables from original paper\n",
        "  w = torch.zeros_like(X, requires_grad=True).to(\"cuda\")\n",
        "  \n",
        "  optimizer = optim.Adam([w], lr=lr)\n",
        "  loss_d = nn.MSELoss(reduction='sum')\n",
        "  loss_f = torch.sum\n",
        "\n",
        "  for i in range(num_iter):\n",
        "    # Change of variables from original paper\n",
        "    delta = 1/2 * (nn.Tanh()(w) + 1) - X\n",
        "    \n",
        "    # minimizing both negative loss of target and distance of attack\n",
        "    overall_loss =  torch.norm(delta, 1) + loss_f(c*f(delta, X, y, model, loss_fn))\n",
        "    overall_loss.backward()\n",
        "    optimizer.step()\n",
        "    optimizer.zero_grad()\n",
        "  \n",
        "  return delta.detach()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        },
        "id": "iTLonOS2pUv3",
        "outputId": "e7e26b65-a092-4f82-c613-c02a45243968"
      },
      "source": [
        "args = [100, 500, 1e-1]\n",
        "attack_output(model_2, cw_l_1, args)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "image/png": 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xeT0c6emgu68TJZPGtrMzoZrYWbtuTw7dvPZr9yVugNeU/XQ5XoqDrK063YhN90TyVU+A4hKQ71AZz1lZARhGhqOdHSfyqEB5fiGj8Si9Rw/gIPttjNPR1ukA3Q0d7Rqp2Bl+58Kd+z+d+98LSY/JWMBEnw2KR+C5xE1mvYXRlSb9J+j5dBpTP4b/cqh2+ije2UxPRy/beg/ydF+S4YhFClBdIBRIpwye3bmfw1s30XqxzUO2JNgOIhrld52PEf5aHa0bY9gmOFE49uwEP9j9e4ocDo4VR0hu2s3FTVfy+1YnqboA3d4gD3RLLiyB0nlgOwpQGlRm64K+Ywnclxrwp7+hcQeQUj4CPPJq83tdKgvKVGSBQUm1xBq3QYkSkuD0Q56jkP7oBHrI5KaqGu7b103SY+DLg+mzVeqli1X1K/ninkeorwjwrtVv5f9u+CPFKCQqIKlAQJEoPpuRKIQs+Ms+yfJUmhnTHaxeNpsPXv0l/JkYNZkHaF4cYefzg6idJgOxTsrKHIT2dFBxjZ+KtYuwgwYdLb1Mbwyg7DnI8tlzeeeb62jbt58DLWFaDo5QWOxld0cIPQlSAZcbho/C9FKFqGFjZCCShM1PmbhroUBkPaG1zW7aDiTBDdV10H8UWnedgb7CRpXJ42HsE3F2hMSWKmTAUiSKVJDCQiGNaaoIVcEplWzc3BJYQsG2BKahZneEYEZ5GQUeUBUNSBIZ7Ke7r5fCypn8Zd1fWf/08wzu7UTMUIglhiEC1Sp8rEGlvDLGzvnw43u6WV7ezS86oEmBtfVQHIAiL9y9PkwyEWbt3A6sIGwfNNm7O8z/+biPlTMV1kVK+dHeEOOmja47sEwTIVQKnTXcdsU7+ehbb8ahOwgmJvj5pvu597Hf0t/fiy3s7MPOtP+H+/tar90X03/s7MfbT0WGrKU8XV7Lo0hTwaFmHyM6UOlzoVgG4+HwiTwGELUypAyD/UPDjCWeOlnWa9RWd0F/N2RGdDjTjxhZZG/E3GPQMRfKvgmh74MxEzKaTXowiRbUCKT8xPtixJ0SouBZLujWBxiNOmjb0UWr0U0mX6G+KZ9gZ4KwapKPTrPDxYHBFN09NuocHWWXRaMGqlPw8K0m6b1tWDnBl8xtYjDez4GuKL5AAm8JJMYmKPsHlX3bRunalEL9sORPhyB/FszyK+wfaaM64MDo9/DkwQR3rCrhf07keZLXxZS/irCYWScJ+iCsgOG06WjvJJPJYKTBLwxmzlFozVhs39/BoJH1hPNMF517MoQKx3F09TB8UCdmxvjFH9ezZzBFeZGCK6xjeA1iFjAmGYlAQAO7Ara2SgrrMrx95uUktmwn7tAxfEuoXFCG+tyvYCxB7c2XUmn3oxgOVFccUWGS9Lt5+j+OsXjNQvxXNHHoqR6aVzVRfOlyIn3PsU+R7H86QfEyhXnlkq0Dkh4PBMeguMTGdkG9V9A/CDWNEssNB4/B3GmCPM2DoSaxFagpBV2H2mJ4pOX0tJVIpEijoCFtM+ulK6BJia1oYApQTaQUKEKgCwUhHRi2QkpRcOaaRRULEGDlGkFVoVLoldjje9FKlhPs7uLwnp20DY3xo498kT0H94ACsxfOo2HmMtb/7hfkGQZ+S7Az0c2bVsGycugfgmQQ3lWjUDqngCXLi3DVTyMxEMHTP8bzj3dTWSHZccSiPQ4XzdTo3ZsgNtzBJXOD3LU5zkRK4F1RglA1Ll18DQy7WH/XOq6sns+CVfMp8xTwL1fcytrpi3n/D/6JjoFuVGkiTQvLOnuetpSSQ10dr5zxLHNuAkH/E7fLicujQjyBDUyrLGRM0bJvcpPoikSwJZRaFrZ1+q3AiRSEHwNrNPmatlOdAiv94qc2VM+vJJgaRXZnuP0WL8dWxYh/D3beoZCabmM/J3CpCldcfjnBoXYe27AfmQeZMYEVzTBYv5vwzBixb2ksksXcWJ1HoqKAX+9tY3V+MY7DKqommH0h7DCO0XgNdD4M7T8zSY9BeYkHr6uIroEBwvEYa9ZU43F2MRRIk45LrrmmjFFjD9HyCYLbTZR2jbmFVfiK+9gjbJxBeE4avGteNRf6+licP5uXaLY4wevDuDtgQNoYMUH7YdAVKHCmMUcFiWLJA3+OkvJDpgfkEFx7XRExI4RtpKgucjM+muHhgTb8qkXasHj08BDzavI42pKkRFVJFBu4TXA7wAjAuAFVAhaugPkVBdiDrUTSbp5e18KhrhgL5pVw3R2rqFg4E8WnwKCBnSxGtLUh69+KXlpFQ8GjqBNdaLqJgyTx9Q+Sd+18iip83PzOC6lt7uGRzUdx5VkUOkGpy4afUg4o0XQWlHpRMmHSbsFgryQ+CO4Sne3tKeI6OHWFTS02pdUw2PeKEp4SW0hkLgpr5XoH2SqACQI0O9ufRUhAt0BYKDZIIUgLgSZAtRUURUUKialKrIxKcGiYgfYWSuxK7rv7Lr778wdQhaR3LIjf4+ADH3wrH7/9OvY9vh1nieQtc5z8YEOalojgopQHdyrBP1yk0XPYYMYcF/mLaylfspj2w4MEFqxmySUuKpv3wUAX/RuPkBA2zS4FfCqxpM5P1k1w8+w8gnkBMnk6dfOv5Y5b/xf/def3+PbmR3jXe9r51te/zNKlc6iob2RFbTNfes8/8cnvfp5ILIltW2Cexc9a2jZmJnP29neWEIAuBBkpOem7vnZ0p4u8Ig+MJLCAVRes5Y+PPonxIjtqy6xnLy2b6Bnoa6TBCpP7zAg57/ul82o6WGY2uWSJwnCLhYzkEp0qlFrUfHkEbYlF358lQ5UJKgVo00BelceuTJRUmYl32MWGjVuYGBtDZkCkwVxvU/LRARZ1ltJ5l8oaZzE1Pg17eIhvP3uMy+sFb55XRMtwGjMSYd9OjRUxHwUVBt49Fu2joGsqhuVgPBpDUxWSiVHKXAnedlseuwrHeawjzXOHh6moHCNepOGoU0mNWLgLahkpasKq2E+5N8JRt8Gh+DDNMyXbHt17Sv1eF8Y9loYnW8GhSNJ9UFQKHRkbOyBJJQVRQ+KOwlgfqCYcOhLFWQYra2D/SJrQmE1PPEG5E6qEQkhCWQEcGbEZnmlQUQGOcRhNwVgE8svA2Qe+tlJufu/neP436yhZCMWeDFosQVFRCZWXvR8hTBh8ADImxqik42iAn/16HZWNiwgGVW5IVKIvdjN31WKCP/4pnet28OHvJ6mom4VbN8l4TJgL08YUUCTlzeCsFnQdshlpS9CIB9ObQK8U1FVAkVclEklQ4IeuLpvOIzASBZk4Q4GVk90flVz/RMs+2aNQUUBa2WiLaYGq2ijK8W6IuVC9yHUhBFQJQtEw0zGOPPskCbuE//zZ7+gPxlAVQaVfcOvlVdx21Wy2/e4vPPPrh/nACo1HDhqkNcFXv7mWyvoqfnXXE9w0w8NoZxeZZJp7/tJB6VNBbvrQGmQ4TLizj6bVi8kc8fCtz+o8u7uf9MEQdz0imbMgwH6HyUJd5av/eiUZAdLZhNvn4ua3rqW7q5P7fvkX3nn7bdx06Wp+ct89KK4irqyZT/OseTy37elsaMY6u98sPq7R6wmXqlDictATT522YQeIJFK4nGXAIIoQDI0G6Rweecm8TqBQEfRHoqdd3guc/leoJkvJal9T56HvUAJZApww7hZ8GvZ6M5gamB+Cp6XNAsAQ4HiPpDE1n4OtexkLRtAdGRTVCZjoNvzvlkLmDa4g75DEmqPiLhjnoV1R/uvpEBNpiw0DCguGhlClSaluMl7l55L3zeNGxxCffrgNMBFCkshk3+4SaYNUXKUCg7lr59AW3ou3J403Y2G5JQObklhbBZ7VgljxKNq097Fm/kpSwWdY5ujHRyU9mV7S/tgpNXldGHczA8EE6BloWAT1+U727skQqAZdkcy9CMwxsD1ACsIigycDuycgE7DxC6ithIk90NZqU1QiCPWmSXXZqFUwmIb8BMQGoNAJl86fzrJlc1CPxamoWsyb//lCjJEWtEN3M70uxfyLVyO0JpDboaIZyq7AHHuUiisXcUl9gJ/c/ShtA16WRC7mxvplIENodQHso0Eap5fzmW98gW0b/8IP7z9KgaGwY5ON4oflNwjqi7w0X1HC+vt6KCVBSY1kuiIoKhX8dVMSIaDQAa5CqG8UFFWAisJzW87s5Vvk+s7J3A0j7ezNIMjZN5EdZzDZM7JtTjwBhABx/IGgQnFRgOuuuRr1iT8x+twvCTizx5fvVnjfRYUUm0F++C/fY8tgmndOl4QTLu5tTeF0CPJqqxnqCHPBggae2dJBqMumplrjk59ezc/u3kNf6xD3P97KPc8O8fWbGrhironL1FlRpmLrTo7uSfGr56N85wM15Lt1hGVhdhzlnj88yrUf9NN8xbv57D/ZhBMxnnr8ebbs3kP33qeoab6UkrJpXLPsYjbvfgLLTJ95LHcSiXic0f6Bs7a/s0XSsumJv3zD26slZZi4ctdHWb6Pffv3YVj2iU4ok7HJviF6kZzaBJ2CxEvseDLHhz1oIIrBNwYxO5mdCQayjREW2eV1kH5HrrrLIFgPm9JAGuZ6q/DlFcBaBfvPJunUyTCQV4c11/iYick3t0Yw2/axf1TQM5ZmrmITc8Mcv833N4RZNUMlNWFyYUWaf7ntWZoCGrOmqThaTQzTJhM9+dGmooCXqppiRjNBymtV/CrotoWnWDBvFbQ8LZm1DFqfbuexoZ/C7CLcMT8ldcs5EttNMBHBaIhwKl4Xs0K63FDWCO44OIZVhgfBdEpUJxwdhJEkBE1w+0H3QH0F1FVA2gmhCIScMKMaLrlSUF6u4ioXJAMGcqGNvwj8lqCi0cGi8kqqj5RQ/pDGRQ1r6B83+eqHPoKGi/ymG7jyfV9g+XUrqV16K3bSj9EdQYqL2fPXPWjT15C/6MPc8NYF/OXR7/PEkw/w4J8fIzo6BHaavLVLKVwyi/n5GnOmmZSW5lFUA7u32FTXgNcDzoSCqoBZNIx/KZiNbvbHIS4k+1ts5jSArYNbhdI6WLESrlygcsNa75kJrE7yvNXcX+7GON6lTky+EnL5FB0U7eQ6cbxPvArYNoWF06hdvpD00AFub8ywzAHz8iWrFxWSiRkErASuZIYLr64jlNApcMLVjQqxbdvZv2k37W1dHDgwzIYBeKpNok+M8aYL8tn5wDZW1UgqPZAZ7EIJj2FJhY1PhNBIsbhacDAsefDxIJoTNv9mG+G+EZ7qivK/v/F9zPgA85sv4z++8llm1JXQMRTk/R/6Ao/95h6QJlXSwmWBMIwXd4U8I+KpFH1jY6+c8TxxfOzO6eJVBaFgtgGvzuciOJE1Vi+1zyQwaNucvt9O1ls/vvMXF6ICpbnfHtAbYF6lTlXAieIANa3jLHcCoHnAOyAIbFKQt5P9ku0vIfMsmEcVJmIxOnv3oyg2zH9hWRc2KLCzn2+8bwPqk9uwDYN3vdfHvZ9xU1cNW2Lgd8KvP+Tjzh9N5+qbixkZBTkh+fFBg/070jTliWxX4klcMdOgK9pL1742phXFqG1UqbsAHFIy4oHCOaA4JLFRg+72MX7/2B7u2bWRZ6O7yDiTdAXihF9h2N3rwnOXFlSNQtFKyEOwuT9NQRk0lSsEHDaJNLgA1QnlBVA7x0PfUBKfJYkmgUQ2Lj13usq8OU729CWI5NvMWKsws1ohEFeYFi6kZuZyym9Yxf1/fYS2fY/y9nevQi+cie4pJDkyhK9sJstv/SmIfJKD63BY48RHR2m+9utoziJAgG6gMYFLhJAaEN0EahzLWYunvI9lF3j45me+TWcmhV4CVg/4vAojus3z2y0O9UdZuEYQH1URYRMLUJpV4jEbMy5ZOEeh0q/QHrVoGZcMmSbh/lM/oU+FAPTjXrrIeeyAOilUY5HtCZHt/33SCMicwVcUkGrWuKtWNj2eiNLStZOlDQ3s/pXFkRaLt1dA9SydRDDCyMEkfROSW2qgXgkx4k7zw2s81DbXIL1u3nLrLHY/eZBfjkourYe8PBsjFqNxcRXOVBRHJMo7PTDTlgx0pHFV9VNdJMkv1qkVcInboLM7TrBrkKd22XzsjsW8Y6WJ4S9D1bNDaIeObSUTGUc4NJ4/MsiWjbu58u297H/iYTLxFOjyrLZGxkIhzGM3vKQAACAASURBVPTrL+Z+HF1k21Ey9uk1IrtcLiLJDJqicOnyhbQPhul+fvtLhnokEAfcL+XWv1oUsvGdDP+znizwNArcDghHJV4fNJXqHDqawOlXqZtdA36N1omjeAJeHPUQCyaybwP94NkryAxJbK9NT1cPqgsqyt1MvCtBegLMo1DoUbh9icbvn8jwwCjcc7mb2I2L2fV8N7/aNMbu9uwbys/aYf1dcRqf7uG6i2fw9s8t4kMffiIbFVLgxjkqX99y8g3R5YAKT5KW/TB9rkqD18WqylL2lPWTGDHoz0DT9dA+Ado8CUqUnjGJS3VwsHOY2VUF9A5blBWcWtjXhXFPRWBfO9SmoLTBhDxIJGGw2yYRhsoacITBWwxmQNB22MKQ2W597qhkWplKbMQiUFfBweEQcUtSVSqYVuglvd3gyvkziBgaqb4O/rrxEHbUJOXO40/7n+Ty69qxQ/fwrTsP84FP3MDsm74PWOgON0rRmxjb9gi1qy8HO54bMz8XM9XD9/71bXzicx/DW1XKfV//LA9u2cVQb4SwpTBuOwnMDxGohsIBqK2wSY5CrUNnxDQYa3PStj9FVQBmzHSRSVgYWIwYEGyDnX0WCbekqBA6DZCO079DhABVnOi9eKJbtyRn3JWs0dZF1sCLXF4l9yBAZreRMheWycXiJTaJVJDxUY17tsQZn4AF9ZCIGGjBJKXe7LQLsxugb3uQ4Va47oPTKFw2nfj2gzz61S70zASNJYLbPlrB4T0TjPcOs+vpXi5eodC3I4IvAXmKpLFOZTCeJtVv0eGWdEegLANlNkS6k4xHJff/+SC3fOJaXJWNCHscpJ/lKy7gl9/r487f7+YP63biUIbp2fdHDh45gGVY2YfXWeyWfqyzk0jsjHzVc0r6RKWfPhkpKfY6uXDNRcywSnh4zwGiyWwYY3Jjrcgtm2dSnCBr3NMnV7k9YNoCy5bMmCWYPtPJwa0SfVmaeTYEMjqDtkQthLHOKHggOZFG5AuMsKToTRB/RME8LJB+C8UNZpfEboZgPIk2D7RisI7Bhy6pYLhvgp8NZajyqvg+8n72dzpJpaPMTfQxqxTuHICbCuHqShtl1XRcK25j2soa7nrffm781TD3jsI/19ssrVHY0ZtVx1kDj8+CklovPRVJki1xtip9hIZsHLqkyg+6P2sXpRMiARs9pFI/24sZLaEv3sP8eo1Cr8VLP1o5UR/nHUWHQg/UuASDg1CfD4troXI6mCrUFoEvX1DV4KW9U7Jxa5oRU+J2qgQV2N1qQS3UV7tori7iklmCRWUFBEJeRjcrFAeq6T+apL+tlyc2tVOp9vHVn+/Do1o0LZ1Dy+Ex5q2aQcMVV4DcB5hI1xyEYx7lM97KseceBeEEoQNgGCqjIaitmYtwXEW87mPsHXSzqS2KVtDEv3z5M1y0pIAGn2D2SihphKaFCs5Sk5q5EEiluXipQqBZAZ+HTMZJfgGUuAWte20SAorLodoDviLBAqf71AK+SgQ545xrWFVU0JTs0HZNya5TtWzPA03NefMqaFrWC3DkwjIoIIXk8W0beXz/HsakpMwDoTHoDUkKHBnqKuEti2B6k4Nf7IKoBr7UBOOPbuPZeztJj45z3z7JzjCYZbWsensTXr9C7EiU6M4w+3ZL7g9DSzcEQ5AaVRgahuF2G69pM2MaFHthPAjTihR+8lyEYE8/xPoxt/0Que+LqNEnaPAOUhzs4L2NgrcsdlMSMLj67W/B5/Men+DmrJEOh7Ffh182UwW41DM/Ua8ApyKYW13JkZajPLNpExgnjctkMyPJOtzpF+/ktaCS7TjvPLnKlILCZgXpg2CFzcSKDKWNKt1pyZHFSZYsd1M3Q9LW3sVYehQmstNTxLoNZKlCukBhwT9Pp5BCrO1Qt1iloElB9kHqUQkJcCyExTN8VLqSfGVznIU6/OC2KmZfcDOXX/th4obK5UsFnUb2nB8Pw6AftvYNMzJi4ym9itmffh/LKwVFwMOtNl+5sZqLllZmI56lkF8J7aMW3cUazx+FlMtAFFo4l0JziZO5y4qxI+CNwpLpLtSQxVA6znBLH219SfYJg5beU4cUXxfG3VsIl1zoQJSCkobm2WDnQ6AaFq2FhID8GsnBliSJqEZBDYz3wc6tJj4HOOohrkFrTzeHWwcYbpUUZdKMrU/xz++bQUWVyYpleWzcFSURNIkOm4yNWwztOEB03ToYDZJoaaHrV1/j2R98iWD3UWzTonPrw+j5M6lbuDpn2LMTESXG2gkOjZIc3IsQMe74wEd46ME/svayC7j//j9RUeajrT2Ku9TNkAbrj0JfVNIekVQ4YF65oK7BjSZsmsurKYk6WRYSREMSz0wobobZ+Qqzq12snK0RdZ7Zq76q5rx3JedRKVkDrmpZw60o2Ti6pmUnUHNouW1yhl7Xsv3tlZxxVwSoOnQPD/BEWyvXX+BiRjk8Ow4L5uuYwymWNAvmrgqgFBfwbFwhFYWJvRGOPTVKQQCWLYUOHbymJHngAOldxxBHQyxqgEO74a8heFLCtjFQCqcxnsinsQkqZkHlNMF1a1WiFgwNS2K9Nj4pYWII94xFiKKZpAfaSRzaidfl58vfu4Obb5nOX+/fxu9/+jua6mtoXjQvK85ZfHdVzmL8/mzSUD+Nf/3gtbjUlz9Zn6684nNuZmMtVVVl+Mw4z+09wFMbn6K2qAqneOlhVJIzjHqZgAfc006uMpKS4RYL3BCrgv1Ri75witggbM2A01+Jr1TFtCV5Mx0oAdBmChwLwB62iYVtRgfCfPnO25k2p4LOVouJ9TaqB5T50FAAS/8RvvhuFy1HksxxQ5sJ8caZHOuy+fY3v8F1t1zDzzo97ByHAJA24FcdCh/74kdwSpNnNzzCw8+EKZ3uZ5YH8jX43h9G+PZNleTnKZifAO9VcO3VChHToukqQVSBYTeMR8EosBlXogTqwCqDyLCJUCHem0bzpQkNQf8+SIyd+uJ9XRh3BcCArjGJOwB9wxCMQ3sPjAYFxVUu2sYEhYVuVhfXEwrBSAxKCwSrGwQFThgbhB29GSLjJokeF52PSDY9GeX2j+5jw1e3IA8fY3mdwrYY3LvPYl6JwkXzVD79pRZu+dEwP98WwVeYweEOs/5nX2f02Ga8hZUIRcVMHx8FlhXTWTiPN93xKZxlc7FsD7Zl4PYW8YV/+zc2Hd7AA5vvxRHIIEUan5btOiiHJYYp2NcpiNvF7DuQZrQbypUiCooUDpkq0zwKRSlBWY9ObUWAmOVD6XFxeP+Z9ehQlUl/6snukMdDNceNvy6y8XnNzi5r2sltVPXkAyE78RhYArqtEKFimxiwaraGZpnkucA7rQHLm0cwqTKYge4ghPqzD+9ACrY9A5EYXFQBAVPSs9fknvssWrfA5k7YbWSdNlOH9oN9FBZNUJgPeCGDxM7YNBRnb6z+IFxdCi7NwhjuwiTK8IRGMqVz9LldOGSGGSsWsq3H4N4Hj7Jx63NEImOoCmc2vPNFvB67QQIIK8173n8Vq+cG0IBSHWodUOLKzqr5DyuL2PifN/DvH1iDS395QTJpE9WUxEIR9h45Sn94BNXpptZXhFucA1PiUAhc68FXr73wDSuZDVv4nDDeAX1uC3sMIv2STMDEcmg4SqBslU7z1cU05ZdQ0KDimefAMQ36ZYb7Hv81S986A2EIpCKwjkB+HbSFYXFDMailPNye5MoLHCS9GtsOp3ns6b1ceell9A4Wc+RQio/kQwWwRIG8MZu/rA/SPzDKzn2H+cm6oxSUNdFqC957sU7rWIpjW1q45BKBVg4XeF2UzpDIYotdKcnYOMQUGM9AOG3QEkkTioDhgM6USSogKCjWGQ+Z+IKQtwOaZ5760fm6iLlbJrQMZwhnIL9A0BeTzA6ohJIeUuMGVzcvQTi7uP/+ISr8Y3icgsFWyfBc6BqDPAWSEcj3qgQKoN7n5vmnE/SNWMxQ4eN/iFKqgyOTNbTdKfhEnaBnt8njQfh4PWQMmx/89zBzKmw2HjxKrSdM3uqP0d3+CzRnCYvKZgAeQODJy2fRsiVs3N2Gun+QkhIPgiBHg7u5b/29zPBHUDRJV6eNRxMUOxR6hySXr/GQCKRoG49R49JJRWz2de9DVljsfNrmioDC6uk6vSPQ1pakxKNTV+rn+hUpfvz4q50K6oUcj6Efjy+rSjaGLnINqsfThQBhAWqu94wAdZIjaudGqCo62XfRXNfISNRmu0iT74K5HosiF+SVeMlftpKRA0dIJoaRpo00YbQL2lVwpyETy76y7x2Ax34Zx/DBrAro6INd9slxd26vIB6WhIIGeqGKvyyP3pZxQm2S2lKBv0Zh+IjNnHpoebKTrsP/zWjIZMaqcqy8Ep5Z383e7XfhKRSU5ZscDcKGlq30RGPoelaDs+Fv24bBxgf/eBb2dPZpqM7HldfIZXNqGOkMoQn47E0NTKQNnt05wL9/YgmVejWlawp4sjXEY1sOveR+th5p4x+Xz6dg3uXc+fBTmLbJob4jzCuvg+QEzeXTONjXjn1ycuYzQnEJxAyDuGKCDxoqBaEeSdgAl54N9WXsbG+4wumCvFJJLBInaYF3gSCmJnEH0qxcPZ/8SzS27sxnW6wV4hM8/3iIOX4VtV/BNCwYAa0hO8FYxUSA/bsGufoCJ+/5chWtX7cJJgP88e67uOZtt7CioZI6U/LrMWgCBm2otmHf/m6aF8zjN7/9NUXeKi6/8X24ZRs3fqaIEUcv9++1+NaP5/F9VxvlZRX0WQmiRhKPE9YUwkYXhI9BoSqIpiRD94LjPeD1QygsGUkYZJJQUAkLSgQD42+ABlXDzHpgJMAYlcRHYfOETaErg68Q7nvoIIlDaWZVWoSUOOPPSeQE9BVIYkegoRJclsAKqRzrzLDl+Qk+u8BN7Aj4PIJjYUmeBTssuCwf3rECHt5rMRC3+PmF0JGA3YNwqC3OhRmbY51gTRziD9/8FDd/8EYGEip7dx3A4fCiahouj0Z5aS3fufunPPbQb1l0kYNLF6o4fCYj/SlEiYRCwdAIlBfqBFMZGmfqXL16Fr3hHgaiSTaMRMnEIZJKMnAM/MJmqNsmXmUypIBSKiivNYgFXBwdPT3DfoLjxjtn4JXj01of9+Bz1k3kHKTjxl6dNPWKkmtsteRJ46+I7AUUsSCvWKesUFJRplF83VtwzLqKgW3DHGk/yEIFGnVwV0BmFI6FYZuEAWCBhGI/TKShVIGDZGO1yeyhcnRCUkKakEPnokW1aNUFdO4+QGYgxdwywcXvnkZM76XQ4aA7qfDIX2PoZQFWvHseLZs388yRFB4NPE4Q+VC90kW7nUBiZ99GZLa80yUZmcDMpFE1jQlFP4M9nRscquDa5YU8+N0P4ZsY48dfrODupywKp6/hxg++jVse+gyeUBf9IRP3yptZNDfyssY9YUksaeHxFTGRykbTM7ZJz/gIpmWxctlqOkcHiaXjKIBX03FJycjpDhRzWsRNC0cduN4Mi8o1Wn9ukNahZFF2fvdYNUR/LxFRiW8FJEt8tO8OYc6TzCx3Mj7u5cKbGtkS7SXa3Y2igfAqWM02rT3dyFYJMeByGOmHaTEXV+Qv5IM7HuJHX/Uw+GSUdFzlfTfeSNxYT/+eDWw55maXZVMJrCQ7k/BzwIEnHsdvj+PNz2f+4pV0H9xJZ59ESaS544sV/OGGAQ4/n+Tz776ObebjOD1FmBJKbKguAiUGshi6hKTIBWI62E7w50NoP3hXgxnLTqiWboJK0032TnlpXhfGXdogI3DJPA+tfQkaLZ1u2yA8kqbA72T77hhDcZPqKojHM6RS4CyFPE1hUa2P8b4kew4Z+IsyVLZCaUTy1ycT7JKwQJEsKYIto1CmwcE4/OdmWFUn+Ow1kif3w0NtsLhZ8IePFjGRdjDxqSF++XCIWXMKKCkZ4tD2drZsfZp7fvsMsXgat9/JOz9yG+VNDi6/1cVgXwx/xMnuLSYL50FnCCYmJNEQDO/OoPmgpFDycPsxKioMxrwW6Qz48sHlzeBP2ehzYagPrAjoBYKuqGTOALTtC1HblAeEX1HHl0NRODkB+CSXSpHZN5l4btee4my6crwPfC7GbucmHYNsTxnFyuUBVEXgsVzMLvAyu9ZAzfPgnnkR8bibpFrB4LDFLAOGBQwHIRiEgDw5PsWZgfAEVPjhQC8ctGEb2W50CtCZBENVeOub5lG6cBmK10Z4+og4h+hP22TiJmsuKqFt4xgrV1XQOxTnkjfVMdIzwuDhOD4N5s2BcROSeYKglSYYkSd6Bqln6F5GJ0JsffSvLL/qeg4d+dtOGvZqKPUqjG86xMMHJ4gXOthZMIe3XVVN3xPrSFynk9d8Ce3/6zfICosdz/0nFfUzX9brLnDq2JkU3d29ZCYZ7LFktqtuRiRQ1azHYAEp0yB9Bi3WNqBkBIVOjQlTpV1kcJUCg1C8VGGZ9POr2jDheqAJhhOgTXex/KIy+sp7KLcSeC7yEio9ihYSdO/IYC2F4lo/nvkWaafBcF8ShoEqEC643l9J10O95GkG4a4UX/tJlB0hwWPv/QyhUIp8Twa3JpggG5I5QjZ8mAJi8TRbntnNzmCE5zZtw+N2YacS7Lgpyv13lbBolsa3ftTOAzPKqL6+iHvNdsZGYFYZpPKy56vHwTMORZWQvwj0SsgrBNULXi9Ek1DtdRPOJDk6ceo5d14Xxl1LgmdQUNEsGQqBI2BjprOeJC6DQCU0ztFJDFoURiUpJevd3/y2BgwZpykiEfUWA6M2M4HtKrgLoDEJM8pAi2SnRr2mEporoT0DH71R4bu/tdg5CB+4UuG2KxV29en84P4o73lPDTOnuTCCMQbWreNgt+CSy9bC29fyvbsfIjwa5b51P2HhxbBgVoxwEPoshfd8qp628SH+tD3G+FGwMqAWQEUteBVw+BI8u8vC5RI0/D/m3jtMkrO69/+8VdW5p7unJ+e4O7M7m7O0klZplUESkhBCMmCESQYMtsH4cg2XcLFNsLFNlC8CwQUhopCQQFkr7a4259kwYSfnnumcKr2/P6pXu7JhhVY896fzPP10VXVNTdXpt7/veU/4nvUqiWmFCd2FNHIkFJjTIGSDyIKZhMd/LXE1SYoDrzO9rmSJ27YTOFVsQHFoZBScZd/LrhmcyVYp+eOlcIjGFEopkCXAV0uf66akusbD1VUVBLMTGG4Pj337FzzwxCkWYgsMTelELegGnpmGo7YThPLj9EtwSTi0AB05xx2zHZg659b7DBhP2ORfGKN68QYqKRIsc/PcnKRYrtB3ZBrbFOiGyUTvNKvaJGX0U4y7sFSFTBZePAyGAspiyFc4mUFnqrfk68xzr2pqZtM112FYFvH8668C/VOKpgjCmuSh3gSNERfv/pt7yctO1m5t4p5//jGrv/Qoa/75f9HyrgRf/ddnqGldxpQa/oPulICQ5AybmdFxlNIK7lyX1qlTp1FKy0ENCAAhxcWIfYFrIwlaXqFmhQdPucLQEQ2lxgWdcfQym0iZG5+pkF5r420UCB9MFIc56cmyLATJvMW0nmdHfIhcIkPOY4AJiUyaaLMHv0cwE8Xp7qqB34YrihqP/p8jLA/Bz39ZZJkfdk/DkpXL2f3CDpI5i0vW1LHtwCRl0gnZhHE6RUVqgozlbaSEnhXLCXpVovsOcGoGtr2gc8OWEL/eNkcylaJZvYG54g/IzyRJ+Z2mKWrRqYg9nnIoR267SmX7pM3YtMTdDaoPfEshXW4yMAW5VymGfkOAu1WEYF5y/EQew6VxzZWr8B88TkbV8RdV0m6TYsZEzEiaKlyUNcJ8m42HGANzeZZVSeoKgtnDGuVek6Ll8FIkDbikBrbPgEuFJybh+WmYsaGuStK8TKO63MJnQrE1zLGfZFhTLbmx00X3TfXseHSAl2b9FDI6Mzu2s2+okatvupbnR39NsD1JqijoNyFpQqFJcHRhgqJfYVWLYHFIIa5aCEXgkiqzcyahQIAr64PsOzFDpMVFc4WLGRuyfoV8wqa4AFUVKpFaOHHSYmYc/CZEV70Or3DJcDrjMxeU/O6lpGRFgMfrnCNLgC2Us+digeZxrHWpAlrJF18CxVwBpsZzVF62iOLeHF/56gi/SY0wdw6b1BiOZWPaJc4RnKVsAcdCn8XZOch/X59YQFrC9qEYBz99H61VLvScxbgOW5pdCF1nakxSVg5zCzqeoMb4KZ3aepWMYtCbg8E0BL2wKKCSNCyHOO1M7v/rtNyF0NDcHvY9v43x0dHXd7E/sVy2bDGtmQlmpzJcdcednB7x8refejfB0DR33l5DfmCG4oMPYl7+Tl4cfox1rhT/97lH/+D1JnI6E6bCytWbeHpomLTxyslsIbVAvng2510HfB4f5C8M3DUXlF8k8JUFiY1O06l0kK/yM78qzrgORlWO6gXJQgMEWsAWCqsDbTyhnGA2Jgl2SYoxk1WhVp5cOI19mY4ioabRZmFeJzlnUt/UyfTOQSIVkvUNKvKBOUy/RS4OYwWodcHNURibH+fmt1zPif5R9p7o5SLgo2XQn4NGF7Tr8M3pJOlgmKu2bsFlC+xdL+HywbsaYGdfmLs25TAkxLIaF7u3cq1xiCdSLzJuQ7sGlV4YTkCdphLyS3adsoi5IG+CkYHqdpifgcE5A60GZ/Y8j7whsmUC5Sq1V3vQawTRDj+4BCNpnUDaZnbeYjpnMdonOTkBE/MK8wWTOVXy6N4UyWmD54d0evsttnY08uab6ilIGLQgYcIP90DIAneJI6roha/dpCIEbKyBm6728dVtNpNmM6cKPr79bJqhXYN84WP7+Nb98wzlw8RsjT7dzUI+jtDHuXmLxpuuD5JyS04Ow2gBJs0i6aRgarpIKgGzCza5AahKqbhGBfasQr2oozmg0FAJtmWxcq2PqzZ62drpoz2ooCcgK2wmJiVWDMp6QFZD8vdzM/1xIp2Jzj4D3CV+GVmi/i215nAM2VLg1Sr9jXUm2Gid/VulxC8jVOd8VYVKXFRgE2xr5VBOvgLYz/z5cWAYGMAJllql/+vFAYFTOJPA+dYoWUvSO63TX2qScPRwkeF+SXWNRrSpgmBEYcmmJuIJyY4XM+iGQHXDknqo7hHMaxaZvHSeSzrj4U8RTLX0Av/nO98gnc3+Ca72pxFVUTCnZmAsQ0+nn+veci2rulwk0vNYVoTRbBXtdzfxwiN7UM0Fll60iWD3EoqpJIHfgwoKTh/X7adHmY/HsWwbj3L2RAFMToxhlNw1FtAQraIsELrgZwgEoDJoElTjpKWAiinqbprCFYJ8ApZ31NFkuTEmIXZQMnPaYnghSX5EMh5XOD4EJ+M5nus/ymgxQ1W7k2XTvtmFHjIwEpKsmMW9AewcvL+2ktq8QmuVyqksCJeCDHhZ7IaZoUFe3LabVSs2Mpcz8QPLG2FzJVzZAyvKAUPiVjVCCvzumefJ54sMqhpLW8MMDhloagVCgRdejJE/foQbPC1cukgwnwSZgcp58Cog3RbBiE0+CmkdgtUQ2Qwpt0OQRg6MAmhN59ffGwLcPT6VvnGDoq3Q0uglPz9NNCgRhqS4IHDjRiQ0VBUuvSTKJevcLAtBQdosaY1wc0sVXkugpwT/+IN5PnFTkEc/VcPNHRrlAgYLUK5Cqw8210BPg82uUzbPHFeZTPtZ36zS4onxoRvLeOyBjYTXN3Hftgzp2Tyrq6Nceu0GtKoonsICp4cGMFQXYwM5ggVoKsBaFHIzFpqryHTcpO+ERBherKDK7kmLCbekolxhbjjBrv4FZAEKLsGUYvLkYzl+8mCWzhovPVcKvN0wadhYAaciV62HqiUXrluJUyUobRAmYDnbtu0ct3GYIHXLyTwoSmf/DMCfaVZ0BhCxnFWAZTsuDUUFpZAjfvgg9ugQXtspfvp9cqaC0cZxk0mcVOYwzhIyhLPMLVPOX1skhEOJvEOH0TT0DlgEgjZYCpo7wIrLOonrgkjQxfIaWNnloqWzDF07O2lZZun5Xjf9gET1Bulete71XuhPKj6PlzbL5MOd8LYNPsJlKjt29ZPNJtj5xEPc//OTHHbXcczv5sgj3+f61bXMJk7RHTZ5V+N/v54GYINumgxO9FGwdMrcZ6uLJNAUCVNR5nReUhHoxQIF/bVxsZ8rfg948zCXN0kJyQlyTLljVCmgxcBr2Jiq0yksFwQrC8+OjrGQNBkftTBd4LIlw3mbVF7i9wMhsNQiFfXg8bip8lRiWZKlbheXWStoW9NKoKYKtdxFtKuOTe+6nW8lfdgG+ObmePynP6ADeLsHZBK6FzsGUVsFbPbCXCzG8We2sVxARkLnlZfScP0deF1lpLTF2DYcPTbJ87/cTj0dLFbLUDIwWISmrEpzVFDb6qQO6xZ460DEIDkIXk0hagjye6DVDVbi/Pp7Q4B7qmDg0lTcwkVstsBEX5JEVjAiVfJ+yUJcZ3zIhIKgpbKLkGZx08oISzpV5txZ2nsaWNNSQVs4h78qyGjcR+9xi6G4JGXBad0Bs4QCR0fggz+U1Pnh8JhJ+5Ioq9fV8JVv5Pizfxhl4qROebScbb+8jH/8SIT+Y8fpPTpCuDzEYEplPp4nVSiSHQdXAnqaXNx4RTVdFR5G0hbTc5AsQrDOw0LGIm5CzpBMTZscGJ4nqRgUM9ARVTh8Ike+zCC4HPIqyLBkNCPIZgSLrwZ1JZS3QnPd69OvfQbMbQfA9RJwGzYUbCgKKEgoWk7WUt4CwygBYGnb0B3qc8N0wN82HbA3LQiWqXi8CocPzrG0u4pb37z5v91DxA3NGqzFAfc0UAEsF7DEBV1eWK3COi+sjZ4f3FesaOPDNy2m2w0zGUhkJMMn01Sv6aJi0+XUrVlNZ72CVVRIFgQVvhAXre5BUzTnmUrPZZp/CsZfgSwmmJ0Yeb0X+pNKJp+jmMmw6BJYuaqJjKsOv6+Gn37rGzz04H/w2U90EG5qxbtkDT/cdpoN04UEvwAAIABJREFUq2fYt/cYK5vg9qVQcc4EXakJKr0qSSBvWrh8CooQpM2zWVwuoDzof3nfQFIdLCeoXjjEpHUwLJgpSDwhMLwwlYXKNpAtEE8XWa+6WBrSuLI1iisIhs9m/TqFUA3MTkIhADnN6eNQVa+hmYIDgzA+De4mk0TLFEzC7e0BvLkq9k65OJlyEaivItSxhKbVW7htcyfBPKxD4isUWFmq0s4FwbNJIZlyxtJVIWgRsFQ6Tb0XVIW33nQ1dTfdTtuajeyebyRjgeEuo2+2iGZ387bKDjbUg+GCPtvC1CTtAUEkqVLIQzYOVUGor4Fc0kYJSLxhqKhSYP78+ntDgLu0JNG8hj5hs39vml/tSDEzpNI3anPyWZPijKCggKq5Cdu1VLkqGE+A22uRzOs8cmqB6kATP3t+FlW1ueVtG3mmz+TOdd6XszwmJXRVwI2bfVRGBH//7iBdIfCEyzB0iBd8LGkO8PDTp/nZb0ZpCut84aEC0UY/d7+thqBMcGlPGG/AZHxK4fCYTbFNIRcOotRWUBlxEe7ykPJAU7ebKSVJ2A8hl8LyqhoqNBidlEwlYccABDUPI8ctFuYlXi8MTtlM9UI4KLnljkrWbK7DrITsFIy/DtyQJSvVLFnmZ16m5byKFhRNB+h12/kxGYZjNeRt53OzBISGDroOpuGcUyw4lm9NZZTm9pX85oTkziu6uPcDf4M3EHzFfdRXuLinSuVNQbgtDOsVaAPWN8IVN6hcswkub4G3NAmaG7U/6AqvrKviUx/5c9ZXC7b4IWNAKAKtXbUsvWQT4bVb8ISDVLW6ObUny/S8RJFFCoUYubyFbTnPYRhOwFu+zixTgOL8OMcGh17/hf6EogAbVoC/BWbCTXzmM1/l5N4X2f7UDu5YnkJdSLEwDS0btqBX+vEXD/CxS33cvQ7W1sGfVZ6dYCMeL9lSxypbSnYcOoktJcY5jThMYDqZJp52CH7LVReJZJyBxKsg0HmkYIDLDfGMjW2ByDhu1YQBYS94MymaPRaYgvY6H20dMO0FWW1TTIM5DsYMzJ1wDJr4rI3XdqKg5gmwPDapqTxWFFStgfyoRqCsDX91M/NJP82NVyL8USaUCvoRTEm43AP1KhywIdoOSm2QmotUnpx3VsL/0OHQiusKLG6MsnHzVajeRSzqXsmp8VmkBL0syrpLL0NIhWWBy3HnXIwkYKEIbVUKb22MsKzDzdYV0FMB8TKYb4KJNDQEofESKJbZtLa8+hj4/12kgN7+PMvqVZILksgKDzUVLiKqhlEEtUJS2QGRoMbA2BAdzZ1MZLIk0tC7WzJ6OsFsfIR7ru/g7Td08/DD+7hmi5eLlrqIVir0VMI3bnBhFwWXdktuWaPw4R8U6e4O89jTae547+Vk8iZVja3Mz1lce896Tgy4Wb/SS2Wtj6d+fZoHnsuwbHUnEX8FK1dVESiH2bjN3GSaeebwBt1M7YyQScPElCQ+qLJQgIAtySdjeLNugobGUJ/N/AQMjQqmh21GTtkIF6R0hSo1ArqLQ7MJRsfmMQ9CcxwKU6+uwz+o2zP+85LVbZfcKnpp/2VL3HbOK9qOv7RYsux1eRb09RKoF4oO6Bekc52NGzcRc3WwL2bgGz3Buugs1775es61vys0my2dkrddDXfd4eKyCsdiX//mAIvfu5mlW6u58jqVFT2QTtq/F9zLyoJ88gO3o5x6nhNPDDBahKcMeGEMBk4uILERepZgUzudG9cQqPOSNGFmtMDw7CyJouNhlxaOo1/nfM3j/2hxl9diZV9vR5U/pai8pULwrmsFFOGpf/gtW1b2cPGK1Vw2MU5dsMgvdieoiTRSFnWjeNxYqSBXX6Sxxue4AT5+N3SUAnZD2TzJczhkEqb1snvtXPGfCeYAWcskbxlYyuuAGBUGRiDX7/R8EHWw3A/lChTGIT2Q4eRsjnSVzYA9zdQMVGtwqh8KSRCTwHNAEiwXDB63iW8DcxCoA08thCegLuji+vpleNsXobSspGnNVvomDdRwPUd3T7Fq43rC4QhJVbAoCL8xnRiSGnGCTkqLh7QLvj0PsYTT5a2jVbBnPs91t7yD79/3IFVVzYxNpFBUheauxYQrG8BdQYA0NzdGCGYcN9RC2ub4pMGuuQJPjMLxnTD2BKR2g3vCMfYWdMgegi2vF9yFEPcLIWaFEMfOORYVQjwlhOgvvZeXjgshxL8LIQaEEEeEEGv+mO/Q54dNV7tpXxnk6ltVulYarFxvctFVLiquhI4tAu8SiPkLDBZP8/Pne9m23SAdg/wkdDQF6R2Jc+B0Dhlo4IMf2swzLxT4+58k2VJhc/dS+OpLBq6i5FMPFZiYtVheZXHRFWHqC0kGjk2zZnkVo0Mz5PASFSaPbztNYT7PptU+nt85y5f+ZgmtbV4uamtkXflSOsLQXuYDy8POQ/M8PhJn775ZPMdcDA8YjO5SKe5VWJiwGYxZDLtNaqu8WDGHmjSteZFuUKfguU/Bni/miFa6OT6qM9Bn8MxnddKPwOBBqIi8rPcL0q8lS6X80rHIi8ZZH7uO814oAbmNkzaoKw7oG4YD5HkL8tKhHNBxtvOWE7hbvbib+aNHub5Msv3ZefZ+7p/4/DsvZfMVl6C53ICgHIulGzUqmhUqKhV8IVjph7KGAKK+hvCadTS01tLSojGbPwvtQggURaG+rZ2/vOcK8tue4ctfe47xokFri5OSedCEJ/fmePpbj5EfPIRIxanrWsEOXWNEwsMnTXqPJtELODffCxwCTvIyuF+obgFyBcHUxB/uZfn/Wmrdkn+8AiJljh5XqRb3VP2Oi448SG9EI3jJVla2NFOz6DomhxUWtSxDjXgJLo3g6oB0ASri8BeLS9OzOJ+TzBGvIoj63RglcNeRzBmFUq0qXIhubR28YSdLy+ODljqFnK5yIAmxo7D3pGTcLZk8bTFUtMjHnZqJiSy4E1DfqSGyApECsR/Y5rSXNJ8C2Q+pExDvg2uXllHtjSNdbbStuo6GlqUk4jnGM9N843vf5Gtf+zarF0cdCuMiBIXgrhYFf0cZtifIxJSF7hdM2U6nuGwevO4wt91yHclsjn/9l6/gqowyMjSIz++jsqWDcFMbQimngIuot8AtSyBYDvoY9CYDeIIS47AT08IP0g1GHmI2hF3QPgP2/vNzZ/wx0+r3gev+y7FPAs9IKRcBz5T2Aa4HFpVe7wW+9cd8iUUdNraVc1lbFZ6wTXmFzaxpYJk6q5YKynSJWoQVazTizNOXTRGL29RF4I4b6pgagkJCIRwOk52a45Mf/y0968Os6HJzPAv/cghq6xT2m/BnaxSWLCvjmZOSf/vhPN4V1fzi8V4uvnk94fpqTk9n+ein9/D2m8q567oydr0wT7Vm8YN/303AH6BgGvzohy8RaIBiMk/eyqJN2MRPuMkkVU7uMlgYg2ADTM9K9FmHH2XKBzUdKq09PjpafMwPpvFIWLFa5ZK7NTQVlgYyFGwYfxwUP1zyAQ0RhtiRl1X12vUrSz53WcqQKQVXMSkhvtOdRhqORS/PnFs65UywNSedfcUPmtu5Vi4F2qxGdH6Omf5TRANQ8MDcyDCBp77Bzx74Ej/68Q+5+643U9fkJdQUZXbMZuaojkxC2xI3nk09kExgj/fidhfRfB4KJWxvb6zhrz52L/d9/zvc/3e3Yv/2BQ4/08ecLokvwK/7nUkmIeFgEX76u1l+/OkHme8fYKB3P1cvDXJdFDQB5jzYaZyUHF9Jg35g7nXoFkBKcvl5ZjNvhEwZgYrKO7ts2mqkUzDQC00JycR3DpNY7+a2z32Cuu5P0drYTcS/lBPD86zcfDfam+6F5Zvgao3cnQo7e+H2jdASgEgpwO06j/9cE4JDc/GX9xXArbip10LgxMxfs27tHCRtEJXQWAPFCRsFi3weaIftftihg3kAhsYhr8HpaaiMQF0DGGmJV/UjT4AcA9xuRLeGaHX0IvtBtICJJK1cheapp7Z6CQVdQVEzfO8b/4FIpbnhYx9BBOuoUyXJrIISVFn6/i3M9lzHzswqJrtu4ua7lrHGC9c0wtYWwdycynv+6pN8+t+/DAEX81aM6dkZiqaO7qogWL0IRCNC+Dhg2RgLEHJBOgNHxpNYtnDiWkBNCMoDYEQhacGyRgjOCFpexV37qnnuUsoXhBCt/+XwzcDlpe0HgOeBvysd/4GUUgK7hBARIUSdlPK8joWIprGstZrfvHSacJ2byViR4ycsGmoEikshW5RUV9tMa0WkB4JaAF82z9CoTaA1wf5dRTZIjfZFS1B9M3zuK1fj0Wze8fMJmkrW6Zu7Va6sk7hViW+owCeu9/H9PTobru5h+SI/B3ceoqe9nAPHyjg6uMDWu3tZFLaYLwi66qG70SI5M8/6dbUYIzb7T59ABiS4FEIeF5otWNwQYaY/hloJKaOIrIV4Jay+VKUt6+b49iIbuqooGipzyRjGCR/pWwxkxMTtAtso0uxWODppE1kqCLok3m4Y/90r9P7a9CtxQPyc7ktnuGFeUbEqcDJpzhhplvPSS0DrNsFTBl4NAj7QDRWRsSBpMfj0Lh7ZO8fecfjgMkF1jYZqzFJrjnH7W26ns7OdX39hF1o0SNUiN0pNBVusWcJdLkTYj5yaIH1qEi3oZi6tMpuSBANePv35v+e2627C603y4vu/gDqWpCkAS2oEWVvy0rSzegA4DUzmYOqZGTzh7Sxki/QPGehupxgulqDElQA048xUIeBsavoFjV2HuMeD/YZghHTTGdS59+0CpR3spyRi0pn8Bqq9ZKpuY8NFN2CKTmLuDnyhMHPjY/S87y/ANQIVbRCJ0xg4xMjuWSpNk+ua4dsnJIoQ+DWNtKX//vRR4bTzK20SEQpJaZCwcwARLkS3FpQXwIyrWPU28QxkbBUrbYIKEy3gSwFLwSsg74boSri8LcJjC0mSaQuMAu6uMPpwEs1vILMSRYDLr6DFJRVzXi695TqqVQuXu46C4qJz5XrC9a3MJ4dpW3Yl9liCay+/lYM7X6K73OKpaoX9T85SpuZ5zmjg2mCeQ7EUw1JgRSQTwoWRyTJwcoQynxvblDz92EsUsjlUVaOAl6IdAGwUNN6sXMZ97meZmtLxVUrmeotc5NMI9RiMDJcygSRoI2D44DLhYdW6Gv7lxNh51XehDrGac76YaaCmtN2AYxudkfHSsfNK2ja5b/tRClaWS7o6mdyp0SrchEMKuZwHG0E9GtYszByFZKbA3KhNrKAwmxLUNLi47s3rWbF+GXnfEu775ghD/Rm+9p+3Eqz1cnkjfO8Zg/kJSX5Y8qmXDE4PFhgfLjDan8aq6eGBJ8c42hfjE5+8g4///duoLnfxV7cH+dwdbt6xVLDtkEV+aoKBCcFUTDI1IsmnBLYuiSs67p4CZjTOjbcLPvRuL2+6uJLmDohWCCK1bnIJDzWRFiKFeXqW6QTyeS6/NIRRkHhOOdZ1JiCJ6hpShzVXVLKAoFgJ9tmg3wXp9+VEds55L4E3Bq90ngrHJ21KJ4iqGeDXoCsCUQHhIrTg47bFzVwe9VIct3jw0QFOzpi4Ij6WrmukaVk1tddfC8EoQgj8Hh8NVX7Q4wRrvfhXdNF8cxvR7nJweyCVoayjhkBXK7YvQsaGqy5ezY23vhVfVR1M7UHNzrFyjeCWO8u55c8aaFmssSR0dgDrOAVRA0X4yq9jfPaxND86WsDjE3i8Aq1ccVI6TJyJrlSlew4n7YXpFpv58UmKb4gcd5WLu8to7a6EcJiTL8HcKVDDELrtTta89cN88CP/g2NHT9C9rIdsLo+tBqisbiLRHwZ7DchG3F644st1BC8KcO0iB6xtKUkVdRQc9tD/KoZlv9yYQ0UQLa9AAkXbBEfzr1m3qipI9EHDYh9r3Mtp0ivwpBWMBNS6oTgGxRmgAPkxUHxQrbjZPpYieULiVkDMWATqPShRQWODn79b2sK94UquC0YJDCh8bEMrb1/Rw0R6nCJN6JYs2Tkaxnyeof6THO3bj5Aq+6TKw5bG8s7L+fjvTvHwb/fy1id/wc8ffZxPvjjGDffcyp5EgFMJnSOBalLZJC88v53W1sU8+tCDIGWpC5qLuUQaMDGMInt29HNrcA0ipaCMQ6VH4iqYXL/Ex01LvbS3Qc2EYFlAsLHVy61trSwui+KtLjuv/l53QLU0G7/mOj8hxHuFEPuEEPuMHEjVjVt4CBfdREICtx/UvMFV9XVsDPhZ4hVs8bhY7hXETll44lDv9dNgC268soIr1zVi5xK86z1vxZ0rEK0OEi5MsbjBwh1UiBnwjQF4fMIB0oaeEEs3hDm18wieoYN84oPr+Z//fD0b1nVQnZogloV7v55m584Cn/mtTRbJaO8Mo8MTzFqT1HZAahCGJiSneyXjozAyZzGLZPtgEbspTXOHoGox1LYX6Z2bp7xgI+phZCxGR1OEUEMeV8Ciqd6Dxw1+d4A5xUACBStNVrNIJ157P4lzdYtJyZEOL2+fSXIoFTahOe9CLbXTsx1q35AHPAY0WtAdcHFJSGWZ1HhPVx1/ffX13N64iEIWfjNk0JuGJp9gPCEZGpxFyix4nVll5PRpuhdVUZjOgtuFyBVwbVyHsmIVjPUjChnU6kqE6gRGMzqsWrmcSMCPkjlG8fRekjmd6q4g1WtWkRFheocsDsSc2z+Tl2PheFmO5CBuOYOymJYYRUnBts/S+0qckX+Bo/9c/c7NxZgYPULuDdFeT6FKhJmd38rz+5up7gnh74ZQh8ZMUcVYWOA/vvpPfOPrXycxl2BsfJJAwIequXApFnbcwuybcpipvDbirfUs3uqlTD3bWckGvPKVqlOAoOtsznsQiGoailAo9/t5LXKubgMujYYuH2JVgWXLW1moTJJL64gpyGZAmYXMPgiudHzd0TQ0WzA/I/DmFLQouHSFFafcrNQaKV+QGJM2T+yNUXMyzu0VCldcXuC5fTuYT7ThVv34NIGiKSxbswFF86BqGh/5y79B1lShR/y8aNlcvvk6Nt78Vn4l4eMSHjEVbv3ze7n6LXfw7xM2v0p7uOvtb6Wto52lKzZQsCGbcrKGKppaGTnVT5nfi2SakKuWza2dtIoNLA9JvAm4yx9ia9rF1ILB8KyFJwF/2ebh7muDbGn1oYoCMf04+fHz05JcKLjPCCHqSl9GHaUKcpziw3Prpho5y976CpFS3ielXCelXOf2qzS3+fFHyrDcKdZscnN8Qae63E2LFwxN0jtlMKLbzOQV2jpVGtfC6hVRvKqPtQ0bGT4OfX19DB3fQ8/WJXi8Vdzy7l1M5N20dQapbhZ85xq4rB6WVEFjZxmb21X2HEgxkQ3ztW8f5rEH97IwO8eDT5zAG/BhA1uXCtY0wq2LXRw7nWfb/iEmiimKaWhvlSxyQ7UfUoegdhMsCDh1WHJ8f5GbbvKwdomGuiDJTsNUdpiDY0UGhg2GXWl+uytFug9a3G5UVaN/ziQZk2hep5ReMxSW10Go/GW1/VH6PVe3Z4D7FRVEJXqBV7xK7htNQFCFiAlVArwpuKYOVhbA2meT2G/hm8hS4cmwYUUH9QGFlC1ISzgxVeDwiRmSLg+xoSns6V4wJ0jNDFOem2Zgj4FEIAs6BELgCjgO/OYaiAQpzMwzM5/DklBbEUEYCYrje5ju78XXHWFeWhx7cR8Hnuvj9JSkM+A8So0PgiUH47kQKymlPZpOMAqbEhE9Z632szGpCxq7lZEIs3tewHojdGAK5ilaLv7tn3bwy/4leG/owrhJ8MhCgIsvuZxfPLqLqqZm7vnzd5FH8tyzO6hpbEHRNAJNi8EfZWJvBrwtYNVDYCvV9R0EAuBTNVziLCnYuRa8EILmiihqKfCaBaKhEC5F5YoVq8ExLV6zbqOVfjwF8OgaYW2GIibGfoXgOi/6kjKCtW5ctQIrCtEVGjev9jMwqqMNugiGXVy8EOXO6ko+s7qOe6/rZKzf4NlfjpE9Dh+6y4tZEDyxc4zt23ZSpbcgsJkvmDRWVbB23Tq8Pj+qbfGdr3yd+koP9//r2/j39y9nxaZmvnv/17n4ulvY5+pk9eZr+OoXv0R7XZh/+MAlfPnzb6JnRRXpZIZnf/YQg8cPI0vVcp3dS7j0+hvRDRMhAXuQnsZWKgOwrkKhy++lyR3Azoco9Jkc22UwtwOqqk0WlSnUZnM8d2SMYkc9kej5h8OFgvsjwDtL2+8Efn3O8XeUMg82AclX9asBurTo7c+iRIqMJGfZubdAU7XC1evaaV6qsrsvR41XZSptM+mxUCMQaRM0VMzjjccZ3JumZW0LoUgt82mVx357HCNX4Jtfv42//vSVpPPwb1cq7BmFL/RDAcHB7VM88Eya2rCL518c5k2X1tEZclFbvsBH/+7tbL1mLQlT8L9fkDw1Al/YrpP3eJCaSSIpmTsoSM4rWAlByAd+CQ1ZD21VsCoCmSRMxXUKEyZiRHJVo0rKdGOofpZ1VLDzmM7sFDT7NA6eymNaNiNJi7algoouGD8haW1TKAy5qOx62Xa/IP2+7JYp8cYo4pxjJUteVSFcBtVB8JngKUJZHMxR8KehRtg8c0JyYEiy8/kYhekJWpp0Pn9VGXdtbnXmCK+gPKrhq6ojawUpjJ4GCePHD1GjFehYoWFNZskcGyf7mx1OwrzXhRwewZqN4Wpfibc2hAKkYzNgjCJcFrhcnDiU5AeP5Xjw8TS/229wPA9TBbi6y8Xieoickzggznns4ZyT7WOYnDXzU6UPE5zLz3FhupU2Lz2361VP+38imXL8l9yEdsV1fPGLX8bX9RE8V/yA6374W6IdV/Lu97+bkd4TNDdWc/mlmxnc38sly1diFArophfFV0HVlo+CXQvTOZBLyczVgoCcZVKQNiFFUKGpKIqgzuNQHFtScmxuDvPMBCck+8aG8Ssq8UQcHE2/Zt3mixnKGixyE3D/k/2YIY3olVUYM2BLg542lWVbw5gSNtQoZBIFBk5DclsB66TBxLMZ9DaVufJBnvjPw9jHLXIRcBmgjhbYs0fnV2Mmc0YYT1WSnBHj1NQcPlWhsiJKOFpJRW09q7ZcypH+0/zFJ3/Bp3/Qz19/9hv834d+yfs++QV6NiznQ5/9Krv2HeR/feqbfPp7L/GRLzzLL58Y4re/+R1FMrQvaaexsxlPMMzc1DTe6nIKVgFLt5D2ahLzA+Sz22BGI5/T2H14mpf2LRDMwq01sHwt7Dho8uhIkjGXTrbd5oWRGN7E+bNlXjWgKoR4ECd4WimEGAc+A/wT8FMhxL3ACPDW0umPAzfgUIjkgD9/teuDY12dGjLwui2MDPSO27zpyjBDpxS6FwVZvbGKGt1gbXMV2tFRMofymC2CJ4/lWN3s4rKLWqmuDXL9rd34Qg3c87ZDlDVEWVKe4RMf382e/WnkJX6+eSrLp66A25cLBmIq//s9IXrWdnAyZrFrv8nVm9rZ88I02w7GuO22m7nv+8/z9KhBxC1Y3uHh4EiRtF9iLwBVkpGkZCoLjSHw2HBse5Gllwp8URUjbZGZt3nkUdi0VaHBNlncEmLpkg5e3D1GfiTGoh6Fo7stYsMSswCZ7+lYN6o0X2Fx5CF47O8NtDLBqvfC4IsXrt8z1rkinNQqu0TZKyXIBYeWoKIbohqMjUNtGYSCoExANgF7tsG1twdpL0+zc9ZmxpKY/Sfxdnez+tqLGdiZxLV9iGTKovd4Aa8cQ9Ez1C7qJF/Qic2PojUEcRWyzB4qsnf3LGs2FAl0hYAY9kQSQzFwr2+muaUcv5YgmUmAmUbV4xx9aZxnDxuU++BIFsZNiEmH/72xzs+RU0nCLpgrOg1APDimogUMnXFBTQBVQCUOkXwSZ/RX40DPheoWyWzhT2O1i9K9n/GevZYQrSpACoNE0uCT//AZgpXVGK4N5DJpbHcVzz78PFfcdgvVjbUYlkTPFTlybA/v+9A9nOztozIapK6tA3/rlRB/DmQ9WBejTn+Tc9uvpmxJxrYwgdHC2WCQfk5BkyEhViwSdXsYnZsGR9uneY26zdmwKFzO3sdnGEkWqLimnNiuefK2iVqAfTqEZIGqehBzOgf2KHysqZEfpqfZfGMDO+8fRZRP87cjktT+CmTRZmwAKsthwLKIlUNuCnrkAtmxx5jKN2LoHcRyRabHB7jjvX9JS0c7NfUNbH/8dyxbvYGphTnKKhcTCXVwcmaBogjQOzfN8vIoiy+6ilSxSEpCLptg40VXs3bNZUi/xoFjL5DI5CgUTMzMPNPTC3RVN+LSahk9cZpfHJvkuXiRvoUCt5U7Fb4vTIK+G9o/DFEbhjOQz4BfF4zO57moU+N+zD+ovz8mW+auP/DRVb/nXAn85R/zxZ0rUZ9KZ72C2ys5eshkkV/hZC7N8dF+7qlcT4e2iBd2HOCtb+9henaSbAXMpAQZ3U3/gs5//stPWYi5aVu5nC/8z7eTHI5x1z/9ht8+9T62XFzLne9tIVw0WN55DGUyze3329y1xOIXj8XoXpzkb++t5Mg+m+7OaX72217edNN6FnV5uPcdV/DN7z5FQUrcqsma5QEO6xkKabBjECoDdwO0dfhwhzTSaZvBhRybO92kJywqAkW0AAzHTKy05J4bqmlo3sC3nt2Pu9zpFXvnezaw78gUTx6Zwx8oMjRiUylcdN8N5UWbgm5RH6kA5i9Yv3CWDRLONulAOtTC7iIwB54A9OhQpUE9CmMSdGmzawZ+93yK1oLkKDATs0nldcLhKEq1j57mOJc0e5lM6yy4VU5NF+lqs/C3LmVwZIjKcIaj2zIs63ARaXZx8XQCt2YhJ6YR7jR2AaTbhtQCZYogoEL/sVNYuNAUi1VX9BCt8HG6b4Dh3QaJuLPkrCuDx3enyBcg6AKv20mrLVHovCyBKGR9OFa6iuMgOBNULf02LlS3EkHmAlkP/6u4gEoF2qTggJSviHe/2vThlqBSZPupF/nmv32RW+/4M9Z1rcVA4sLfAAAgAElEQVQVzPLT//wuz24/SlEJcPOdN3Ps5DEsXTIeS5C1TTqam/G5zwwKN7a4BhkJIWM2xcLYK2iRz510BNDq8ZCybeKGgaYo6KWsIU2AbVukhXPnUsrXrNt8yuLoY3NY0iGtK7cSgMSMg1YO+d1g1ks2rIT0hOD2ihDaMcmc7eJkpJ2G5hksrcjMDPi0BQolCo74GGwfgIVZheKoYLLCx49//hKtW5Yyn5xjKtxMriDYfP0NVLtCTI71Eilr4JHto9gVNmoDPL1/P9lihqGxIzz644exb76WwYlJtGgde558lPKgj5aOdi6+fitHTkyw54Xd2JbO4hU9JCf6sAMVFHJl2MZ2fnVkgm//qkjG4xhez3ZDedRxMaYLDpVBKAFyAtq8kEpKvEGb7uXn5854Q1D+ehWNyxYpPNRXIGmAr1bS2wvlEZuFZIyZKYvqlW5qajtZ1N5L//wprLTN1GyB6haF9/3tXzExrGEZk0zGbDa85aN8Yd0xdmwf4LuPnOLLXS18+h9PEcgWuKJesiIKl17p59Jbfdz1uRnKflHgkhs3oUUj/ONnrmZswuQzH/06W665mB/63CSzRfqmJXYoy6XvFBTnYE5X6Frr5flTWdR6jS2rPYydCDIYGqZgGzQQYnJe5+2bqxjKJVCFhdAq+fGzD6NU5VkmoLsuwuh0gUHFgMoiqldSmZGMHDZI2uB3Q0e7wMy/vjS7l10xZyww6dAsezSHY0VfgFQfNC2FO5uhp8aFmbYYqYa9fTAv4QcnJF021AjYXAaHnk2SzB/jCz8a5v2XR6iv0DidtFDKykjlUkSqyxGq5LmHf8Lxw0M8smeOH3XUEWoLotlFCDtIKw2Bq6sCmSgSHxpheDBL3oDR2Tn0bApXeTVNy9qguMDokKQ1BHuTTgXtTAYWDElAOGXZ1Qrsi/EKW0YIsAI4hNtneNxf/pDXnVJg63lmU689U0bFsdKLOOn2WZzmDw0KtEiJsBwWTQkslD73AS5UqrCYAryoZFTLKUxDgNeib6CM//G+NZw8doSFsRH+/vP/zMDIOCKQIaecJlzuJ1hTRW1FlE/89Qfo6mqiLBgq1SkZYE8h8nGU6ovJH/wBC0E3iT/weBJISEkQiaUorKtrJO1W2Ds0jCEhbprEZ2Zes27OSMAFE0kbmQfPaph6SOITHlQMXEUbPQGVy6AtAgvjGpUtNp/79gTRd3ThOrQb4bc5Pi/Ri+AXkoAJoTwsDsPB7VDM2QSkwui+OcyIQGnewZHpNEsr4iyO+nAXchSw6du7h727d5AuDpMftYjPZlhzvc7JZ3ZgLJzmwPQQIj+OMTbNifkDFHIGuVScb/zrvzKVS7HxsjtJ5gWp8WEWLWpmYmSQu95yIx7fLPMjk+x/yaJCBdEI0RTMH4acC+wFKHjh8GFQpiFtwySSzi54zyURju47fwOfNwS4W0Khwq5kpXuSyTmLXdMSRYe8LpmsynJoKI2mpBEbpzB1Bf+4IHgKtLREU1UqFgeoKQ/ztS/+DCVYzsa15bS3hTiw18+1Wzfx0oFJZsZ1WsIqn95pYgFrRvN88BMtfMtXQ1XI5OTADL5mD888PcKXvrObFe31NNaEuWhNM797sR+pKKQDNrkhCNdCosJiW3+W6T6wzDRiqkj7UjBTkJUGi4RNbEAQK+a4csNSxo0sh45Ms7K6ml88O4RbheqKKu57spflW6LET0iUlKQmqKJ2WPhcDttc35SkPxV/VR2eV2znWrblAL00QJ6G6i6IS0hboM5DVVYhVJRU64LxScn+I5LmkvtmznIM3wngc30QHdB5y+4TvJCAxak8GbdgLgdF26KgguoLoCdSJEemqKspI1aXpWLTSrRIDVrzGNboCMKQ0NwFAQnpk2RjCcoifjoqFU5NpDh9eD9L28MI1Y23rpmU9xhlwTQNKhQVBxTLPJARJaD5PckDvjDYVZw15c9Mcmes9tfrUbFMshdwkVocb1ArDnjX4nR7u0OB3Ta8zQ3/op+NPNYi2IDERZD9pKhBoVnx0x+1Kc5liSPRChp1HYOsX7uM5u7VjA0OEc/GyOYLCKPAzmd3szj0Ez75pa8Sqg3x7g9/AEM3ePSB+7nh7neiKQbWVC/IWsycyfaHHmDw8B/OyIhoGrZtkZOCgOYmWFPJ6roGZmdnGc/mXqZ1vlBxm+CpF2iHJOUF0BZBR12UvqEF3nUpHC1oSF+W4RlYrlv87HiGbJ0XlyfNrJZjzWUuju0DhsHwgJmFtyyCmVkYyTm54/GsTXIe+vslWf9xCsUZfHUnCdRuYN/PHuBtd1/Ds88/RbSzA9XrxZyfwyyOcuA3DyNNE6Qb29Q59OKzWKaOZRooqori8VA0DXY+s5MV62+ku+cipnOztLY1oScnSKeTNFSUMTuhIo9adK0v4/CJLAO7bSo2OGnIgRwsHIfRJYCE3CjMmLCkEiYW0hzaeX7tviHAPacX+VHfBJkJiTUHqzoq8Oo6u3dn6O/Ls9gqZ05LMT49yHO/nibmlyxfJOhugHCTzS+f/hnN0y1ITeOGm6/lvi/fR6pgc+ctK1i7RPDwI0WalwR51y3l3D2zwPCwzmVbIzzwxSHu32XwqXfV8eXvnmbJotN89vPv4quLe1i0uI6nf/ICetH54U7FDS7a4mF/rEhrpUbvUyZGBWgWpEdg3DSYycwzokOlH+qUBO11Cs8dz/JS/xiVVY2c2N+PVdeJHZNYYY2nn4sxus9i+RINpeDm1GiRjk7Jxq1+jvabpHM2edPE9r4O5cpSBeqZ5hQ2yElQ4tCc42WrzJSQOGKTM+G7e3R2JsDOQ7cKVwbge1nHYQqO+0BR4OrlMH8IhufhtEeQNWxmEgUWd/tQypsZPPn/Uffe0XGV1xr375TpfdR7sy3LtuSCuw0u2GB67wRIJ4TkpnBTbhLSQ5IbEtIg4YbcAAktdAgdDO69S5Zk9V5nNL2c9v1xJGyIQ/jsu77Ft9eaJWk0M+ecd/Z53v3u/bzP7ifPGeGcIohOX4g1txrcboxsHNEVgapSjEQI+sdI9cVxxFXqZpZycWScHz0/xOEjrdROW0jHjp1gsyM6HRwKxRjRzM5WPQoU2CCcMVcmBS4gcwJ11Ap6JWRdmDIDUwB/YtvB09x7pKoamRPUEafsg/qA2CfHUALCmKDuxwT5XGC+AFEJ1gC7gJU4qcLF64wyQQQ3sEHUGLDE0CfcpPFwtuBBY4Bwp0QiNMqwLFE5o4ZLLr6E++77C7IuE3D7+cI3voTH5UIzeUuoWYVdW97igo/dRGrgEKOH9lJ+zh2Ee7sZSUC3mE/a6DvpdaR1HR1TsyJPtrBw9kzCQyGWlFZQFhohq2t0ROOMKKcmvRkKQ63VwGODxQOQLINq+xDueV6Gj2YZaDbwLBRpPWJQGtKZvUBCrrHT/vogiVw4EFAIZiEVhYQTUGBLj9lgJuEEPQ32XDOVl4mBNhAiHU/TF8pi2z+EUlvFU5s66W3ZT38kzoRZHMZXUMbnvvtzmvYdIRxOgqpz/voVjPS08Yd7f0omFUeUdLIZld7WZh74zU8oK60gSga3S+L5p7dw5UXLyaGarFKGmJI5f1Y9Lz++DVGF8QEoiEFWAE2B3uSky6rgsMD4XmiXNDb9m94wHwlwlwQr9n6dlq1ZLFaRCpdAxGEno8Y4djRMxB8mZ5ZB65CAYtWxzQCrU8SJQU+fTpw+bq0+i5XB6Txw9+9YumwusxYv4Eu338U5Z9XhzbFQP83CUNzBL/8a51urJVr2hHFaVGaVylTOzeGll5aSTor84idPsHhuKY8+GMVisVNXW862g52UL7DhKHQx1jiK3qNiKYJMM8iF4AyAqBlkBmDmEmjZAZvcBhtcAsagwZ7eKAXT2vEFweLWyDkDxltEOjtCFM2SePmhQUrrDGo8IuvmuOlrMyCsIkYMbA4oFgX6TzXEnAIwZbKTUhYYBpsGs1LQmjbJIxngsAKpw5AwoM2Y3LRqwDIbBBMm+NiABgHmyBAfhmki/EWBHkXHDoyMZtEXFTMWV2ndvYlzr1yH/+jL2MrK0Tw1SC4BpbcX1RLEEtKI9w0hZocIJ3Ri/VCzKpdr6s7igc1/5vmntnLhmcX0Hm2ndyTCQAJGU5DQIToJyr1p8/KiWbBMerMsglWGjBvS1snrn6KEnkilsXDakbuWSqKcBLw+6GOLMNMsMiawF3N807BVhs864M445NvAmoX1hsqfGCMDnAmUSTDkhH0uEIfjrMJOwJJi3JDxJJP8+Sff53ul1Qx1ZblwzRIee/jPrK4sI64lePvZv3Hdl7+NPeEmlgqDVWfOwoUIapixnqMUr/4simLQvPU1erJ5LLjmXPTX7zrpFWm6joIJPJJVoqexhaGBIWpqZ7Ng8VnY7DJDva3c9crrpzS2eUEBZxoqVYHNUYNZEYFYQGJ9xkZbt4D3jCyXXFPMmR0BDj1/FCGhkl8dIx0U2RfRyG4CnxVkO6iCuVO5c8JUNm2Y7kdzxIn3qu8KySUcGsOdCdxOjf5mg8rYCII9QFhJ4EpEwNCx2Z3c9cvfcsklGzh2/ga8DgExm0XVRGYXX4Yiy/z+rm+TTZoTvpJNEhoYpL/tGEIqjFvMkopG2LNpI8uXVlNeO4uI1cHBphCGZAYt8RQMbgfJA+ISiHZCeb2p7W7zeYh1J3DXytSXZTn04L8ev4+EKmTQ66E+KyE7BPxnWHCXpgiUpggugpJ5IoEGHVsJaPktFCyLY3VAmSGhtkClIVJhC3H4yA7q66qYPfcMEukMocExfnznrVx1y6UMd2foDvsIWkTOmeNG0zQ+9ccMrx/UuGc9FHR18o3PvsBl1z/D7f9xIbJFRLRb+dSt15BVdHwBmeJ1GqFMDHkQjIzZLKBkFiCabJPiSoGJEoFQ3FSb83thWFXpHIZRVSWUThFxZ3lz7CgFM0Wc5Ror10BpjUgmZhAahPFencMtcY4MxbBnspQLOh4J5sw59W42wPGdqBlMhM6YG1GKxuCiAnBPAl4COKibwA7m7dyqwzthmCWYaZmL3XBVvrliuf8YbIuaW5TtAlTbwCMbOC0yutVGOi1TVZjHyLjIM8838cbTb9G+rYnmEQdvDAd5rSnDpt1jdPal+cdGhTe6JDKjzVQUxFkwzcXhtmE2P/goBw+MM65Zya/0UlVixyNDoQ3sknlpOU6wStBt9mlG0SGZBZ8bRJnjxGwJs0vIlNcLnMhzPyXTDYOY+uFnCBnzq4hzXN6naPKhYPaMdTshxws9WSg1oIMsIxjMQiRXEHhTE9kSs1IRkSnwutHcaWYIFkIWF/0YHHhrPzevWsnqi1fw8Vs+g5BN09vezqcvWMLyhfMZ6WglFu3kzb/fy3hvC5ff8hn0cBcl8y5CduRxdMsbPPC3B1l86Y3kBQsQ/8VUZZn8aRUFCh12bIJOjtvBxFA/0bEQufkl5BScejOCPJ8VKU8gd7GLspRAhWhn66hCS1uCZ3ckiLsUnvxziDotyzGnRpMAlWsNktU6+n7ItItYU34sFmDcTEdm05hLJ2uK0hIJLQxkwbDBkQ7IRnRCYoaYITJ9UZDu9k1IghubpoFuIIpBysvKcMsaRc4Q4c5XmBjbSWUu2GSRPJsNw8hw4pIwFRllxcpVOCwCSmQIPSPQdeAw6egIBcUhFi2z8deNzVgUU2ZbT4GtRsAeFFH2g2UPhN6AY78GS9yC9ZMWtGkK4r8p9XwkIvex9Di2eh9+TaWyRCfrStLdYmDPSngsBr1AsQZ7hseYN9tCQVJHO2owLQ/2DGg4AyIFgkpraxcFZT727x8hp6CBX911F6uXuIlN9PGVL2/gnrueZnmRxjynwM83iJw5w+CaP2WomCnzhat9DARr6Nh/BEP0UFEZwG0ZJzzYzUQqgyyBUSDgmAtiypQliTVCcAFoYzA0bOCRoL8DMhGQZ5gKdAMi5Mw3qK0Ae0RHypcYHtcpWCiyZIWdI4cyLLgA0hNQNl/iQLfBovMkjBGNTMhgSYmT8aHTnIOnON5T3G7dfGo8bv6cK8A2459jMxETcA4YcLUVlvjhjOmw5Sj8VTH/d04AlDA4ZYGMXaI/qhIeHScSj1JRW0W4f5ifPtzNk2OdFOz2UZXvJmNxM5LQiSXSFKYjnOnV2NUNFy20Y0RSJN95Fa+RpTtusL8rSeOoztyqIDleCwtq4wQ8IuGYztAEtA5NyjOIxyVywPxdRkSWdbJTZJZJ/Zx3d+X+v+UbnsSUrEJK+3DgbgECmPPrFFXTgkm9nwMcBY4JQBB8CWgZhetlaFVNFucEBoZVZH2ZnwPhCElN4CtXFeMf7+bLL4ywGJFlhsEjqoK1f5x+oMLi5g9fXcJvXuygYVkZlStXgiUHJRHh3Bs+ic0dQJSt6GohguwiG4vw2lMPMX/5uSxbspLm+24g6IDR9ykaOziulpzVDRKKQs/wCIl4Cl1TiegCg5EQOR4Lp2oJA9q3GKR3x3DEoGs0RSYH2i0SzFZpbjJIvKRwTp4Fmy7hq9XpzULn2waMQFbR6dg/cXyz2hTX1A7hQZEV5wZp3N1vOnoKtBRM6BBv1JmebyG3rIx4YyO5QQcxdQRBMEilxnnogV8RFDdwcPfLvPTCRixOFzVzFnDlpTdypHPvP11HOhVFFjK4fD5cYgyHXaR/LIVF60BLpLl6bSlPvhKi19DM84yC3GInp9ZFYnQMTTMlju3zIZAbItvs5lC5wsHGD/a7jwS4Z1M66VgC1VBQhi1Iq2B0N8StGkkkVi9x0ZVOUuoyaD6oUOyXEdwBxjtGKS0SsQtultTMZfbMGWzbc5CzzlpMRW01V994Ex7pMAoG433NRBSRMYeH72zT2NeSpr5apqZYQ7RaCMcF7nv8IOum+4g5Cjn//IV8/st/Q7D4WLOwnrH2RoacOk4fTByDTAWUV4NuB0WG3GILMUHFHzCIWqGoXKBnyECsMnn8xyIa5cWQCuhEjoEa0zh2VGP/IQO7CkPjoCYNZiyD3l6N4umQslrp2a2wadf/gVa4isk2jptgIgKvJM0bdI0MYR0GBMgzzDxwBFPK73wnnDkXZucIWMMGYdks6qQmP+NQ2ATSKr9MfbGdRw/GeKcpxoWXB5k5Zw5GbILdupUJLclEf5iW/vcWh3uAI0Pmefgb41x+hg+rM05ljkRKhbvfThIAsPURGRV483CWrAL1lQJ6BsSgQXcIxifT3vLkRq2sBrGogWARJvsDcnxX6lQO5P8gcs8q6ocSDfNiFkftIhzSzQXESkxd8G5M4vdfgVER9GJYlIR72mFaKYwnYMko7BSCXHWGwk41lxx/LrdftpwVqwq58Kp7ODco8hnByreH0pRiThQC4CDOrAY3X5x9By8dPMZtq7oRXfnYbAUcn86ziN4yMkmV+3/2I0JigP/63OfZ+8iPyCk8eNK2iSfK4OtAczxBbVUVkXg3hiwzloiQUVWaj556A+DB4SyZYdAjYC02OzHlARE1gW8u9HcIyHMMNh/uwFYpkVJUBnplhL7J6MXJux3XRQsUL7Ay0JtFL7dSXCmzstrLE2o/epL3TPJq2uDKc2cw1NxNJqNwRrWfzTuGkQRQ9RRPPvxXtrz8LJFokoShQRbEf7Ty4P3/YOwk1CJVNejpbqOgogxJ1REFjb7RFGLoMF072jn4Vpj/WG7h+y9pxDLme5RIFnswAGmz/7HuB30QNt0JDn+cC79uR3w1wwclAD8SaRlFhWS1haGt0HZUoWfCIGkIiC4Y6NCIpiU8pQKt3dAyARGbwDtHwzDNoMzpYL5zKWX+BD0Du0mODvPQo09y1/e+S45H55lntjPaNcFv/nsfD/xsFnMaXHzqYgs/uV7mu0+oyFaBz5/vIlDipbLIzgU3r6emIsA9v32Br96+nq985QqWzMwnOWohRwSjGdQMZGSwFUmMdZuNNuqnydgUEUsZeK1QWghzc6A83+RZF8yAs2sElvpF3FZwWARkIOsxkHWzK1A2qKMFdAKzwKEIlDqd7OhQ0U6noAomoKWAEXAp0IDp912YTIy3VVglwRdz4Nog3CDBTMwbeF4+nFkDXU0Gd++E72+BxsRxNYMRTBBZVeNAnkQBRdfp7W4lWFxO84jK0dAHd8SYure60jA0miBquKmrr0WWBCKKmV834gob92bJJqArDFubDQaHDRIpcyk75eKKYQI7gB4w0PQTnH+K+jhV7TTD+9OyrKKiG8Z70vnvN68Aa2xmKiaqm9G6hrmXygF0AvcAl3qhohgO5MHcc6G4GBy1AiV1AhUeWH+WgaMqnzPXrmTDuuvw5kznJ3c+R7E9w48v0sm603RhMm80wCnAdy7zMtLVy/pLz6e9N04o6TZJ4+8OiBnOZrNw93e+xbYd+/ncl+8gtfuvNKzcSFY1OBnT8/2CUk67hKIlycsrpGpOvSniNtLHeOrUu6FohoEQN5eUqgS+lSITbbCoxMpNa8qQwwJiEDqTaawFWYSjEBj1YhuVze81O3mSViAIQ0eyGC4JKQpFcRGfLYGsg/XEUxTBF7AwKw/mFoMWjbOixsfCigrck/6t6wYDIzESac1MdRpm/DASiv3LiX54sJf8vDy0VAJJFqj1ZJFbGtnxXBepoXHyLKrZ8nHSibJhjZ6mgeOBSAmQD0rMrNFM7Mgi/RvVqY8EuLv8MKwmKJwDBXUm33Pa5bBkvUBJBbRGoxxs1AkfAz0KB95RSQwpZP0y0ZDBxesvxleyjOS4xtwVa7ji8rXcfNP16DhZsLiBm774CZYtLeeZvw1y67e7eOWNBC+8qdISgf1HddZ8fQBd0vjKZwqQW3cx64w6li9dTG5VLV/7zh9JJ7PkKfmURWX0EASqQJ6A8UENTxGU1+lsOZRiMKShSyAMQpVHRJQgWAYZj7lZKNsro6ZFxsehs1OiN2PB4oXeKJTNgZxC6DwiMNwD27cKHB2Kk+g3SLef5gBPblJCMVkZVszUwBQTcBBolWDNdFhVBVVO0zFKgJ4RuONZuK0DjqjQZcACN1RP+pUCLAnA0oIYY31mmCRnNbo7MviL5hHIy8f5IaNjTYSQZCVq81A/by65fgcAtV6z6bbdAU6vSGG+xEASulKQFsDh+OfPEoAiK9gtmDf6FIhPaetMveg0TbbZ37MQONEsk8+tLYKs02x5mcXMDCwEFgOf9sEvaiFjA6cEt54NL4dFnJflc/FVlYzW51GwtJiwU+b8c9dgKauncsY01ly8nsee3siu7gl+9tNV5M610ySZk0UYE9zPywXdU0TtkgXIlglu/NgnaNp+AIz3pko0VeMfj/0vcmKQX/z+bsoqA+Qtt2NxZTn0vwk+jJrxWCLL/o4+KqZVsmjpInQBFMNg4jSa1AoI767EbEEYekXDlQc9eWnefDzFeqWY3BGRQr+IK2XQtRlefzCErmtmAckPjkqwzwIpagZQZDS0pizDQzHy83zoooDqNOU3JNHUuptX5KXQl6aw3I5DU+jPWKiuzJI1Tj2Hl4nFGB0eoqmxjVRGw20Vybb2s6NVwR3S0ZxOSnPM3KLLCnZJID3G8VTidtB3AQqkU6B36+TN+/8BuGeAER0KF0PFSlAHoGOnQZ5XoC8i0HoIunebudXIVpDaDS4/y4ZDMdj9toZgn0lhzQbk/Bns3Lqb3vYwRw4coW+kC2fedAoLDTbuC4E9wIt3FPPZ82ysmevgY0vhi8vgEytt6MNpLv94I7fd301JoI+gV6NQ6uC+n1/Poobp3HThlURiIrnzoWqaSL4P7DXgtZvUx5FeWFRmo2zYTNuMZnUURTB1TXqgpwuea1I4OqDSvQkcukFPt0rvWxAMihQWSQzuh/pyiUwbhNohO6SjW8D6bwSC/q1N6eFi5tcLOF4MAxPkOxR4ej/cvx8eiZnL+jbg2SQMxM3V7YgE1+bBwmKoc5jO4xPg2kLwyQZ5blMrO6WCy1uIzVPAjDkNLJj2IZRzgdklApJLJBDwUlC/hNLSXAC6YxBOQiYFsVEdKaqBDjHVPJYwdTFTvi6C4TdXeL6A+F4vf79Y2mmaPhkFv6vJJoDXZsENrLSZxerxBByJmiuUIcyvQwG6Bch3wOc2wA+vE3g8AW9sAVW1MKSu4Kpbb6ZbKKKoIJ9wrpdAQSFDoxozZi7k4ccew5tXwIUb1lK3ZD1jPdCnmzn9MCAhc+nlfmYv6SckLkRS3NTOP4O+oXG0zAncdcOgdf9O3n5jEx//z+9SMr0e5ADJmMGWe0ZZXQRVH6wsC5h598GMQiwZRVQ1HLKF0XQK/2m02RNUs42j6DNXPE4fRMJwUDd4++FRZFmibmYx+Yt15DGRaDsoVnC6LDicAkyAkoDSMrC7wFoqECiQkICMz0YsCQ5DoMgpEPSL5PjM70bLJhDCMdxSjIgg0tUdRhOCaMqpOYwsgC5IDA7388jWMYZDCtFoliNHdfZ0GIyFwJ3nZ8VcLwjg8AosWOBE0MBiw/RVBUiavi57QcxCX7f2gcf9SIB7OgYT/TARh9gYOMNmAUFtk8gOGzj6QA5BeRWUVoOvDPRKnYxf4oqLV1NUWoMtp4a33jxAXnUZ4YkeVGOUpsYjlLllHn1kPzMrnOjefL71xASX/jpNKqmwtwtufx1cdpmG9SX813cu4PYvbmB02Mf3f/MmDz59mJ/+4jFe27ybtoM7yZWrKakpwDacy43L13JV3XJW1XiYiAoUNUBOhcpYP8yrhsN7DKSEzEQC7Jr5pYxJoEclZi2UCFQKyBGN0jpwyTrduzVyA1YyukB12sKSUgnBLeAogZLc0xhcAxPYJ/0yY5hAnXnfyzoNeCxtsmN2M+nkmDsmByffHtIg5RVpWGrli/PhMisUGXCgHYb7IT25SWosC3GrCwQVZ83ZXHPLte+mbP6V2SVYNNuHzRtE8ORjtTpZ1DANARhR4dAIHAnB0QwkBTMiD3rA4oDwFFaZHf3MRwVECwxi2Ump3ylP1ycvbKr6euqBJQDGZFi7YfEsPn7eSt8pL6AAACAASURBVFxWC7eesxRZFmlSTBbR3giMaMcPuUKGc2fAxy+Ax73wbNbNBd9fyNf/ay1/jTrIevy8+qYDr6cch+Jk02GZotpqrIXTqWtYRjIrsmf/PkZCY1z7idt59qledg/YaIqIFGBSW1fkaFxwvUbtNfN5ac920rIHt8fNcCTNkUOHJ0/eYPBYM7s2vslnv/ZNcqrrQBSJjPbyxn1/Zvl1ScqvgKXVH24sdMEgqyaJRqPUVdQgA2n91GdQv9/G9bcsx5oHpCEzAGoR9O0zf9/4eh+XX17OylkOvKWw8gI/DENVQCVvuYW8qy1ItQKpTabS6arFLi6bXonFDtNrCgkbtbidAha3DVdAojIg4rTJXLx+OhXlVjLDOhedMROHKNPSNfoexVEByDnJivFkZgASBqGhMbKTDedLEzqP7jOwJ2F/F+TKE3zsAif5NgGnbtDSlsCWEvC5pOP1AC8YDgilTUnu5MAHH/cjAe6yAENN4MqHUAomorB4ncyQolBeJlJYJGCvNDUhKiuh0A0dQwrlvhK++fXfY3e4EASBG2+6BCE5hiw7sdvyKckNkqaQTErhyo/fQmdTG1+6bTnf/XQ9sxu8dIREZufDxl1xPvb5I1TL7Yx1dmLNyWfHjt9y7qolXHf5OcyfV8pELElZUuE7Cz7Hb77wW86s/Qzn5H+WOcJalpYUMqtaxlspMpgHO1VIpQRcATvT50MmYxaF6nOhsADmrBNobFNxVgvMWwZ5dRLeEshxSZSnfRzq1ugLKUiSxvQKsBaexuCKmKCH6ZA9wDFOThKxAysFM2c7FQSP6GYkCJAjwRlF4Bc0im1wfiGcIZmtzu7eDzv6TccVBDCEJEpGBtHGJdfexJf/80u4fP6TnqLFInHR2dNZc8U6XGUz0CwuhrsOM29GALvdggoMaiZ7AgECeTJFFQKeXCiotODzTOrlTNWXJvf2q0B0qhY9hTESx8F+CuhPw2ySldrSIooKffznJzcgWyy83dhGTNMZmeTjJydPTQYucUG+HaZXwtV3FfCHB6p4eKfEQPwLfOEbj/Gr+37Pg6/FaG0ZYGzLPxgcGuRgX4bbvn0Po0MG1TNqefyxx1m1ZjXTplVTXTmDzduOINfU4ckI9IjmhHLHVTZ88zfgcCxjbm2AgWPHAIHrb7wBbXyQvpaj6JqOpiRZsGoVM+vqEEWBTCrNk3/8Gcs/4cE9xwqlsLDsA4fgXUtkNUZHw4yNjmKzWyjy+xk7jbRMIp1BnX+EmhWQXwH2Elh5o8RCfz52B0TtGm8NNFNQoTFuGKQSCfy5UO4xEKeLRI4pZLYbEIOxMQgWerlpkRenCpkBhZauIc5cZ+WmT80hz2KlujJIRZWbNWd48NsU6kskLlpXQiYeobF79ISFnkRV0MLs/A9XsDEAiwixpHnX5dihNAhb+qHOCU0a5PdHWXWWj8/eUIIaB1sKslGDaPqEOzUJ1hSsXQd6DijxDz7uRwLcDaBwLhQETErgeAbkXIlAjpXgNAl7jgADMNwGrR2g6Q4aShws9dZx8NVXObLtVbrbW8gvP4PQaISzz91Afq4LX14+vkAesjqKpvZxpCuGfXyQoY5RfvlshB9dm8t/bnBw+7k2wkMJnn6mm5/88TA733iLl/7nlzzz3A5mVKsc3nuY8y9YhREsoL8ng89XyqMP/BFZcPHy84NcOO2b5IwuYnxUQBmQCdvMQukrz8Yw2iDgBJ8OC+c4UOyg+zQqF4JmE1HTIgGLjHuGg+BC0PUscY8pdOX2wewSqMw7vcGVo8fHuQ2zgPd+TBOBHCBXgv+ww6V+s+g6LsGyXBEHMKzBn3fovPCSxo6dZtHYX25OFkcz0DfJWFF1ONLYh5FNAQb5pTP5wfd+zG8ffAjnCQBvsVpZsGIF3/r+bfzoB1+gZPZi/BWVpCSRsdA4L7y4kVRaQbLI2Nx2BKC0wIqtKED1vFJy8wNEUwIZi/Be1ksQM/k8tYFrKlJ/P1fywyhy/RsbSyTwGim27W6iZc8uVFWhbyiENkktnVoh+SRY4oGvXCXwpa+JPNkj8ODfPBSf8XkKKwt5/ZGHkKR9TKuI4LaKLDTaePpP++nqSXDZVVczfdYZpFSFmhkz2LNvPxWVVazdsIrtuzaStUg0Dbu5coFBDKhywNIr88BeBcI1VMy6ni2b96AqSYIFBXT1j+HNLeTYoUNINid5eWUgihiGwQP3/5GZCxeRW30ReIsw3GaR+sOUJySgaTyGJMm09HZj9wWxyadesRYlGD2aonCJhwkdxEJo3qZRMi/GslsEXOdBKD+EZsmSpwoUVJVzw1eLyfpktL06vm5zR2fCAqoP3tg3TmdHp0lDVcIQi/DtT09joukoS2sFQjGYU+mjacyLUlqL4ClmuGMYWYkyHlfM9nwCgMDCYpnmkQ83cekGJDMqU4rI03NFMm4rfYpZ9M5IEBQEhNEMn759OTluK8NxMw2dTRgIolkPcHvBkoCDf4cz3SIfv+zkwdK743fKI/9/aHbZLCYV5YjEo5CfJ5GMZcApEneqdGZ1giVQXAVeBTSbijtbyDzHGhRbHrLbx6bXX0JEYPUl19HWE2U0apCj6vzqZ3fizF3Aff+zk+tvOofnmpMMqyq9spM9zUlufDCFhso3r7LzjR8s4HufLufci6ezrzHN1qYJ7vtzE7sOhHnjzSNcffEljA2E6ehs57qrlqMmevnE1cuYWXsGJXouqVGRibiOVYfkBATzBcIJs3HvwnoX2coM7TGNyJhBXbnA3HwbTQd0XLIVi2gjqqVxF6XJLzAorAWfJLPjGPScRuHPLsG0ExLsBu/Nt09ZETAfM4WUyYAjC9eIcM86O59cYMUP1AlwSY1AeRAKi0C2vTcTcmL7tQNHOjmw9SkSY/uIDTciC3GuPG8VN3zuJixWK6IosP6Cxfzu3v/k5puvRnJ7CKUyHGluYfP2ozz1xD/YcSgMgoggWvC5bcxZVMTMdUtI+wsRLTnUz1+ExWbB6jUbewMmwrh5L3hPcfw54bkpucXTLKpmVI2jgxMMjUV5/u1GNM1gPH086RWQ4JI8eOmzhdz38VKyihWh1+B/f1zA/p1jvPHnZ8iRFUb6DqFs+jF/+tpPqbKksRk+ntg9QM+oQsO8GSiZFA2zFzLcl8RvtTC3YQ5Ll63jj/c/wsJFK8jPh6biAhZa4EvnwfPPxskmFoKYT0FhHUMjaUTRisVmA4cL2W6lsm4WFpsDT44fURTp6OggGh1h6bobgQq0VBFHD0mMDYH3fRh9Qnnj3b8tAhiGTm84iqRJzK2dTVXBh6u3nMxku0SF30/vMwaSBpYSUFJWth1IYWBQUA2DfdDfJ1NQKPD6Xzp47bVB9gxpJIYUFMkMNBS/QKBWwClCyF/CwqVuAsFiNlxwJtt2RLh1rcK0UpFIKs2GCxoYifZjyytB70uT5/YR6k2RB5xdHUCURPxOneYJnVDK9P+TtR18vyUmczo2AWryLWzqUymVoUWFHCt4xw04OkBJjZOvX1NyXOwPcEkmaUGPg5QDoxZ48ZBO65EPpkh/JHjuCFBaBZ3DBkVekWLZwviogC0nTaZfQkTDGQQ9Ab6ZMDKk0DacpfK8ixnZ3orXX0h3199Rs+fxnW98mx///E7UgkLsikLDgWMsW76AgDyC6PYwEnNw8XILa9ZY8KcHGMvC+LDCr15IcVv/QX78fIxnt2f5n19dAtYKWtvHSSXA5s2jpuEsdhz9LW+9+Cg2RFadeymNzUO8uedXiNhpSkr4fCJ9AzrzpkNJJbx9DHw2gTXrbWxXkpS6ZVrfkvCUagwaKebWyiwozmX3gQEKAvDqtiypNCCC3yEgeSQsWZl/zpJ/OPOI4DsBzS3ABqBThMMnUAhHMQXNDxsmJ/vtJNSLcC0Z0A2+Ugp+K1QHDV7cD2+lIdoBcf14UKyeUKScSGTZumU75eVFyDl1GKkwgjXI8pWzePwBF9lEgqA8yOZn/xuL3Y0WSTDe1oFfjzAWSnGwTyKqAoaOJAlkDIMFK89CLCwn3dRIOjNEydwF2Hv7SHS3YGiTaxEfJrgrHPfuqQBr6mJP1Jj5P7C4DoJu8PCuDtTJHLNbgMsdUOqA2zfYKPzRt0ip83n6T7/nqV8/xedVWFlm45lf76QtrrNQMvjl3igPNafJ06HxmUYuz4XdCZE3/udezvvaTJIC9Pa3IPnc+PxOmg4fZvuOrXzhU1fiVAxefG4HXztXoOEbAm2Kl0d+/yc+9qV87M4lnLF0GaFQiNy8fFadcx6aZnD0wCEyo70sv/RyopEILz79JLfefgdiZoDE2w/z8ku7KArAV78u8NxtBofGjl/z1FBaOJ4NS+gGAY8HJImxZIwioKa0gj29Hac0rh7RhiddyrlzYjx3qJPu3RqEs4j94F4DviCQkMhosO1tDSwg2EQWrpjGcGMnraOgSAqKS8AYF5hdYmHFkgISBCnKd6AOd3K0ZZRiWUdy2HD7fUwvKCTTfRTN4iFck0tg01HmxhXOrnPhqrGztzNMaZ6VgVAWFTOVWRsQOBYzSP6zxNA/WcBhijw1ZySWF8DeMZ35drO1pdKUwTK+j3VXVlD+SCcdkxz9jAa7Q6D5IE8XuW61zNH2LFsOfLDU9EcC3DUD8vMlZs4MkszLUqF5OJYx6DGGGU/qxHVTrzseBsUuELEZ9BwTaTm4DZciko7ZETIp0tkY3/vhjympmM0dt9/E1751OzMWLKZp1z4am1MUlfTwzTvW8ccfPoTXIvDwxjFe/GstB1/pY26nwrmfqGPlzYXEJiZ46r6/U3vmOWx6pZXqPAv1a9dx+YVX4LX7ueWT11BZlKBvOIKuQH3DIqZXWGh9q4k4jZTHTRW/aMhAy0BtoYXtTQqNUQFv2E59mUBhVYrHmiHPbkOSYqSsWXo6BDIRg2QIJFmkfVAnPAQrFwQ5yL9vCnQyS8bg4Al/i0ChBc5wQigCw5g4OCk5wzgmD17AlCK4a7PBN6rhkjkwEIZDPfDXFIy+jz5uw4ySrJOfpRvwl6e3s2jteSyZNQuraCVrGBi+aaiGiCLa2NopkT+jAr8RQ0yPozidDI3GaB2AwwMKmcmbJZNMgi5jBEuQZCddjS0kI0NMJA0U0WJqw0Y1RJeM7lWPi4KpmB4+Fb1PRVhTaZqpTU2nYVMLAgPeBXYv8BUHXFcAylnlpBfNI9wShTIPV3/sAqS4hKIMsfPxtygotrJ/VOHthIEwoTLNDusbnOwY0rj9wmn0bR6lZ6yDv/3lAabXL2XXI09R5suj+eAR+keizJk9i/lLV/LGcwOcu7SIhvldCKLC9PwBjqgTvPPa46y9dCnV1TV0t3eQm5dPICfIcPcAQb+Dgvq1AOzdtYuLLr0Mvy2KuvkanM69XPFDEcHmQt0do8gJh05y/e9PTHQODhGwutE0gd6ebjKnkXNXsikaUwc4dNigrtJHz/YIxhDoAbNAXSDAeJNGU4PBRBTWnW9lfoOTXD1EdyEMPK9jnCVQEtIRXDIVM71Uzarj2gIrj764lZffaefaWpl0SmD2NAtjlhlEh/o476INuEvmkJsYZfCZCLeck0/xx+ex47/e4a4Gie5qDz99xuyJKkmAKHH2HAdvNSVIZP41XVIA/A6Z7YMGqaRKWY2DpwaSnFsjoJUYSG6gfxB3QRFnVdjpOGQS8JWp9pAJCCsGB1pFygQZd75KX/e/Hr+PBLinEjDepbF7IETrLpF5ORliYY0hj4oOBCVwGFA7G+Ixg4wVhDGDoZ44S5bN4sXn99CwfAn3//ZPLF4wnb/87+/56T2/5O2NB9h9+AAXnX8F4d37sIcs3H33G/QMqfzirjNxVzaz+bFufvRSmqBFYKKtkxe2tFESFHlyq0Bh+2YuPDuP2fOXsvtwL/f//m78hbWIlhQP3v8HHGI/133qJrBY+dn3v8fAsSiJApNXG7ZbaWrMkG6GnplZtoxkOWeJHz2UJBTKkiiE4YMwpCfocApk9xoMHTQgI5D1GFRbrMR08ElposLoKY9t4n055SzwqAJGxAxwb7aBxQMPjZmFvxzgLCeUSvCPmNls+kA7HOwxqWh217v12XfNgUnBG+G9hVrdXUh+9XxEwUyIZ3WYiFnQRBcWr4f8OSvx1C1n7ZkLGG7ehp5OEOppQd+1mWOpXobjx3eXqKrBQM8YKaUFl9dN/6E0oa79eCpcWHwCNr8fWfIRinYfT71MgTi8F/BPzL2f5g7Vk1mOJOCyCvjmGuR/8Tr6xUX8/S//w97GR1g8vRJP7TrCx17jmgU2Zl9ZywXRebx1aJSqsjzuvPvvTP/YhQztCyOfdRZVqf3ElTY6jhxk1oIzefSt1ymYXs3spXM4tL+NtevPwektoPXgbj65WkPIKvAMsExhxbwUd973AivO/jYVVWW4J8VIRFFE1bOkFA2n1082k8btclA9fRok/4zsaTJn7wEd7Cl6njQZbSez98+NAYeNaDLGeCyJZGikM6e+iUmSBcpzbOy0pShYEME9ISDIEB83SO6GUT+EU/D6bp0ZDqgvE9i5MY7mU5ldCp+qsdG8VGfsPgW5U0WulBjpn0BUPCTCOunMBGq/ROnSGUgqLJnpRkvrlCxZhmjJZ7g1w707Ynz+tmUYYhkzZyqcuaaCDns5Oxv3srs3jarpLJiZQ/W0AkLGGNsODvCv2ulKkkDW7qZvOMLiUi+vD2fIaOC2g2QFa8aAEYU9rY1kLQ5y3CrjcXNylAUBlwV0i4hoh75hnaUNMuw+jU5M/1+YTRLQRw1CeRpjnRpv9CmoKfDXgZIUcLoMLAKMjomUO8ApGtywaj318+Zz72//m9mzzibgC/DJj0/DG6xGRWGgbw8zaxvIETJ4gz5u+9bd7Hj1XqbXDFPWcBP2rp1csFDj2q9rqAasn2PhY18fYe0sL/2DTp546htsf+cITiFE07EDdPfnc9XMBg4eeIu2lqOkEykuuHQlP/nOneSXz+DCi1YT2NbNYKaNPaE9NGtZxvpM4f3wEBRdAIe3xdFUjYEDII2C2g2NbtgaipMKmzm1+JiBxQ7j4xm0jMHq1RIH2k9TAOUEMzCpcgLQ4IPbzgSPDl2vwnbNBO62DKz1gy7DDCtsTsJwCrxJWFcKOQL0n+DAdqAW80YfmnrO5SSeSfD6zh1UVTaQFaAlkmDvwSOgKWRCY7TtP8j4qkVEdTezl12DmuxlKODlWPsA3YNt7zlvS8BHPB1nuLOF5Og402eX0tM3QEbJIElW0pqGRUy9txG4ggneU+JhGsd72DH5v9MsqJ6MLu/wO3m9MMg0sZDiR1+k7qZKXtl8lFcO9vDwxqM4rRsx1DSLCmx8c4YF97QAt99xJYLLyrOvHWBCraWqzsKura08+8KbKKiUFc1gTn0DYjCH8UicZDRN4+GDXH7jNWhKlnnCIDmpXvOaXgCiIvlfnMk8Xx/dbS3MmL+avoExPEVxRFHE47GwZ9Pb1M2tJ5tKUlNTbXaHaHsZnk6alKlZQDFUf0HkcwMa+x4388tR4+SyPAIQjifIpBUMyUpvZJwcu/OUxzaS1Og5kOb8s7zMLM3wWiqDZS74ByBrhZ5hWFJu+uvCQpm2djubUxFmBCXOmRvEVTKboHqYfVcIVKl+8qvKUViBPb2fm686k0OHVf7+QBM/++Qy2ne0Mhpux1UwHVF2g5rAMdbIWyGVOrWAyzs7CFwyE0v1DGYkQjzw45Vs2T7EDx46DHlB5i+cgd0VoKc/Qu+oOYlapeO7pQH8LgvugB/3RJL6+dN56NWDyALMqy1FnhbDiOkoqsC9fwxRVhXgq+tmc+fdWygssnHmbB8+TWJr4xi+pI2WoQxzL7byQVzejwS4l/vd5Co6u0YTaN0QHoOac8Dqh1KvSHG+iJ5QyC0O4OhRSfVH+eFjz3DtDQ68niCV1RUIdg9XXncz9/7qa8QmMuza3UZ748tcf8UleIJudr7zv/zwV3+nwquwYF6KN7YNcc58jU+uclBXH6SiKMa2YwpfviOPF95J09G+i1B3D2OZPh56aQxvYS3xZIR9b73Jlj1d/Ore33HsyJO8tbWFWxtWcKylC5s7n89eOY/m+1vY3xqGHrPRhc2AMwpFDrWqDA8AGsjjUFwrcOwNg9q50D4K00qt9NoVIt0GWqXAwtUyh/er2DOnjkASJ888uAT45jpoWGQlqztZfWSCV3tMXbF+DY6Nw89nQCYND/Sa/Hi7Cm3dcGEOdIyZ2+nBXA3YJx9TJogSQ22d7NpzlCUrulFdfn7zu9/wwr33kRk3VyKhw/t5/Oe/IDLWw7rV6wlaYvR2N9I32IPd7iSrmSpfgqAwe+1qyqtLObTxNVIDcfTCLAtXzaG9rxtvXgntOzpIJEbMnqgZ3qsbMyUWNgXuJ6ZkThPcT/b2lvEEF1+4ms1Zkef/+AqLX/kmb7cnSWsAOkIqiQq81pdi7+/289XLXMxbW0bWUclQUmXONImDO7q494nniAxEGARC4iAHm5spKC5m/97dbNm4nbGRYVw2B12dXdhGmhAEBZ7gXakJ8j9GsOEVWtuGmTEf7E43jdvexuuBQCCP8y67AkEQkWSwkoZt/4Hx9efQO0D6KiZ1StcgrjNrGuSJEDegUIRpOTYG4gqqJOAUdNoTBggiMV0npWTQsxkMQyCpnHoLQrsFAoEcrs8vZO6sLF3zx9g1EidbrtL7to67HiL5UCEIzE5qbHQk+c519cwPWskqAfaIXeyVLGzeOsEt8+I89cwIB/aNseGsWjxWkbPXXog9plFUU0MqbiG2/Ulm5ZdAuh+UFNmtw/wgIFA2M48RwcfRxjDJ5l7OWuDFlx3n3BlZFvzlBjoGrficErPrJa7oL+T+lzq5aLabvmiGre1mrUwSoCjXg5CMsWZlLfsGYiSSCnkuC7UrFyPPyoBb4Ml/9NDa3cq3vqoxfcN8mo8NsX9PLy++PY7PIbKk1sloPE11hcGEcZoFVUEQyoCHMDc2GsD9hmH8WhCEIPA4ZjOZLuBqwzDCgiAIwK8xmw0ngVsMw9j3QcfoGI3Td8BAHzUbr1efDa5CAYsDxnp0qoMyTZ0C7pEM/YMqNy+axfU33onNZaFuVj0tBxpJxkJ8786fYGRHuPz68xkclmlufhBDyueSy24k3zHBM0/cxvDATg69NsDy5RUsnmHlKz/fz4ZjcVbOg+aQwRMPDfDbt9I89fIzlPjttI5beOAPd9Cw4gas7kK++P3fcGsqhkU2SPZozK6fSZ4vRV3dSvJrFrB3y4tYR0sRMmE8CyC9DVyVEEtYkPxZ9GaDWBT89WAbE9DGDdq3gqbDSKmC7jUwnND6rE7r33WsVhCdZjh6KmP7r1LKCQMO9MGGKz3YXQ7qSiIU9RoMGeZ7eoCwApvGIDKJYGlMYbEqARZKsEUzX5sHVIrmBqgpS8Vi+H1uouODvL7xKfa0dPCPex9EiR5fphu6zujRdv727Z+zZfHfOXPtcgpsMdqbu0mlUqDpCLKbornzqJkzi562vahJ82YJhRN0tPSj2gxGBgZQI2kTsGVMbeIMZuTeM/lTwKRI5nL8ArNMUYck4JTG92QmAPc/+hpJw2B+RTEvtPSTUI9/E6kTXqdlNRoyTQw8P8zWRAEDx1o4+uIT3Pd0J62DyXczTH39wzx4988YH+xD1zTe2LoJ3TDoOtaIqiepDIxCLxjtIEhg5IgI4jTy8mYgTtI5XC4HE70TZAyVpCcXvyuf9ubDlFf5cLT9BL75LEKHjmTDXMIlJqPCJJRXgNdmKifmi2DVMkzoAmnVwCaLLKsp4IKl89nffIznjnSRVTQwDJKqCe6nMrZWUeaaq+s51tFI29A4VasDPPdfWRIJk2GXmwsLPB5uWpXPREsX2oDGsL2Tu55PEHKI6LpIiRDkhzetYHVhlpIZRfzjmR2UXDrBj3/6Gl+5wou/sBZH3kwy+hGyMZWSWdUYSCAWYzVULlhqIbA2QHZsEGZXkIplkZ7axF9eSpC7ppirrryYaYKX7U/ewzNP7GNMEVgz08O2rgR94eNRtc0mkUEgIMqEYhn2H+hAFATWzi+lfMEc9AqZJ3//LA882MjDnxGZXZkEsYPv/+A2rrrtp/w/zJ13mBxXlfZ/VdXVOU3P9OSs0SjnZMmSZTngnMAJgwlml7As4F3iwpqwhiV+JuyCwSSzxjYYHMFZyJItS5aV02hmNDlPT0/nXOF+f9SMJYEDNvzBeZ5+uqe7p7rr9K1zz33vOe+bHYmQcxs8nkxy+VonN2xy8eLjZ1IL/7n9NZm7DnxSCHFAkiQfsF+SpGeB9wF/EkJ8Q5KkzwGfAz4LXALMnbmtA+6cuX9NM2SBKUNpGoxaGB8F16igNAJaLcyrNnBPCnYOZPAvkjistXLblkt5+J6fM3hyL+0NCzlxZCcbN1zAsR6Nz33uX7jlA//Jf3/r26STk/z4rrtIDx1FVb08s/1ljrwwSjjo4BgBLltTTa0vxyd/neBrt1Rz7a3LuaIrTSpqZ9HmmxA4OLT9QXb/7HkKVWtpaG7m45/6FpdfsIzqcIBrL9vE//zwJyzbMM0lm9P88am9eFQb5W4/E8MpKuZByoBdO4p4fBJiEjBgYh+UsibYQK2UMadNJgcEpgLkQQlYlMFaHsSpRoY37dvXMocEK1vsGCmTxB9GOatRcE8EHs3AiSxcUAZtArblrfg3WwgwCfxyChZ74AIgJVlKTS4JGiLQb542oQgDr9fN7++7j56uUbTMq1f8mEWD/p09jBwYwFdmJ58qoOkCZBVFNVl/xaU0zall1x/vQ2gGwrSERyYiU4RayskO5jB141T3VZZTZY4NWIFKxyrId2EtTzxAy8wJZZhtE3vT/p3dpz39EtOBWMny2IHeITyqjCKdIqc83Yq6YHTexRzceognt29HzcPD/3fcqgybOVYOQJg8//yLsywS5ArWJPnlL32JTy0r4+xVIDogPQQuJ6g5FaPk4+GnO/inT1xK3MaG8AAAIABJREFUPhNDcfoob5mLKudxBEKMDfQxPbmP1rLd8PO7EZ2mtaip4dTkaAAR65hVDovPp0Ky1IxWlEksagmSzcv8vnuKFaODDMUTbFlYz4evOZdfPrmf8USOncdPviXfllcEWDU3zLEnclziK2fbkEm9TcLWYGPBApnaFUXmuUts/1OEiWGBZoMf3p0hWANLb3by8vfzTMtxVvsc1Mb7ueaqdi674guoup36ul5C7Q6MRBdkXmbRsnmM79qBIfKUIj04wsuoXe/HFfQg+ZzYbQWwtWJ+616k55PcHHYTuXgtP/vGnbilFBdeciVXnJ1mMDrK7fdGiaTOhEsKRYORkRiuhjKOH+mlVNRZ3FzL5etriO7fxS/uOMG9W0f5yXUSi1bUQMUl4NxC08Jl/PzOMt526YeZjBcxR6HcYaLmTSrl14dr37AYTAgxPjvDCiHSWLQjdcBVwK9m3vYr4OqZx1cB/ycsewkISpL0+oz9OigzOp82tyUDl+gBf4WlpJLu0ck6QF8Afb0Cmz6frt2PkMsUUNz1qOH5/Pj+bUR69pA3FdasuIiAr5zf/up7XLR5Az5HD8s3tvPLb/8vbdXgKKvn7e84H/f4FDdf72Q0nqc9AI89O8UfHzxEpSPN7hcGGO7ezSc/9ln6YyqHJ3Jc895vcmDnUdrr59DS0MxXv/8M/ZEIjW1ruPqSi8kZWS64YDkDPSNM9qTRDkJ6GNInId0BxBUUUyZsQF0PGAMzHNL9JvkEmEWsK1lYAtYiN1O/fSoovHnfzpj0Z4/fVgcbFsjkxjUKdlD80NwC19fCjzfABy+CQyY8I87M/gNYae5Teaj3wYfbYFU7rFklc8tKiXbZgmd8QDZbYDoSBWeY5ZsuxeMvf+0vKEDL6sRGcuRTpoVnmRqheW1UNlQzOT7C1NA4WsZ8RVVJnxYITSfcWGsdY5Zu0cB6j2PmuVmysNlSnjQWg5o8c0LWfvBb8q+CtQk9W+//51YUEC+Zr0q+JbCy+Hsee4G+3kkWeSzGP58dalRrH8MPhCWolaBchnqnTEiWXsnK+oYm2f9MJ5lOEOPQa8JkAUiWSE30UMrqTA938fz2Zzl2/Ai+ympwl6NpBczsblbn70T68f/BLhNJAblx5kOlGd/FgYjFcVIbYIZSAKrKZdYsr6a5rZnymnLCfgdT6RhHR6ZpsBfpOHKQxSGTntFXeuTftG+FITH81A7eefEi/OEQFXUOPvupWt57XQBZ0bgo5KDNq9BYLnH2kjA3bGnk2s1hvnyui5vqm3n/jRXcdutyrj2/lrnnhEhGDzDV+zB2Z5RPf+Y2wuGLmbN0CaYhY7oMateuR9K8OBrng8tAXbcM9bxl4FSRlAza9n3kto9TavMjN6s0lrq4Iv4ia6cP4Bi4h/OuaKT/ZJxc4S9/bFOAqZsMjGXIZg3cdhuXbWyhRo3zgx9s4/89NMLNIcGK1krYfDk0bgJpH/AbFs33cOt7tqBkwK/DmhV2aiTbDMz32vamMHdJkpqxel32AFVCiNn6vAks2AaswD982r+NzDz3mrV8Zg5IWkykktvSM5TDkFUgJEPYA+lySE3ClouaeP+7349d1zm/vg3FGeLAnq3ceefPmLdoMaGOB/GqQe793U9YvmIT9Q0LMQwffZ0HkcIulq4OcPk7LuCee17i9u15Lh8a4HcnTTbXg6KYfOO7kwysnebZYS8yz3Gyq4jDNcwXb7+NqtYeLr/2Cq79YB6DAI3zN2ETJ5BMncqqJHf84Fd89faPs2RJG8cfmcLhslHo1UGxgvT4IR0JCBgwrPP69db5V3nuLfgWrGTVgRXTDOBsN/zncpgeK3Ksr0j7QhcyRZQm6DxhsugKF9mOHPm01aU6O1RtwDlY3C5HTdiThbMVyGowf14FdXN0ruqPsTMFYxoMGIJdT/4Je30r6y5YRUXjArKJF17vq55hnuom1l99Han0GMde2oqZ1l+hF5C8CjabQbGQJp0oWtF1thpG4VRQn2WEzGPhSrNZ/CwZk/nKqb0l/xqAJoFbWNVHeayFwyzSLGNtA1TJMCFgWpy5pwtwpKMfU4YlQXApYOjgkqGkQqUOfgViwoJbHJiUKeArk+jNQEYTRIrQOwqrVkBrBNwNQKtBLtLDyGSE+x96Hm9VLWs3rOPw4f3Ee57n6qWHcCaOIf0whnFghg15HdZKh5kvrQGdWNzEIeucdKDRA36XjCLpjEyN09MTY3i6xIawTE4zsRsFioU8esEgmX0FhnvTvhVCRZTK6U05Od43xp7tEW75Fx+2hiL/2majrTrArhMySnmRsE/nzp9NUHIJfGFBv4jxmyciBD0xOmuGqQwVufDKxSxc0crwka3sfMHghg/+G4XRuWQPb8O9IUSpWECxVYE8H2H0Ey+04PLlITcJySSZR44jylXcF69HVCnsvGsX2UMFNp8jYRsbRu6O8KWaPBdfaeP2vTovj1g16rOmCSgVijhVG+/csoj1TQItkWFf2sZSp0lNVlCw+3GLdhAGyBnQtyKn7uO6c+v4wZ12Vq+sYElVOYd+2UX30Ouz3/3VwV2SJC/wIHCrECJlQWizP4IQkiS99qe8+vE+CHxw9u/M84AbjGFAsqh90wqYjbBjCESjRdRzzQVvo65+Hlouyy03XMI5q4OsPOc65sxpYKyzg8998kd89fb3su/gOJde28wDf/gj8ch2Hrn3AN+58zgDhya5bO0gtRV2li2u4xO3r+fa4/vZ/sAAl212MFGykZc95E6WmDabePiRVfzsJ39i+47nuPGma9nx6Ff57o+28vO7v87Y8F6MfJpV7Q186+u/5hMfuJD85Ah+RUE2oDi7NJv5gUXO+inif+6M15mBZTuYb3JP6s992ybBLc3w9DR4fRKff5uCNqZz7a8FfXnYeDjHbZslKkxobhXEoibffxYeTEMUS2RiuQM8BixUob8INgE9aXj8OJSFYV25m6m+ODkFFGFt2CqAWSgxfbKbE/6dVLUsZPDIXx/c565Zx/JVy3jydz+i+4W9SLMD2QQzb2AzJaSMTGE8fyorn+WNmVXfAQti6MfaHJidSDXe8mbq6f6VgQqbREkTeLGQIUUChxscDhmfQ0ZG0BC0I3SD3mmD6ZKBKYPLrlCQBZJiY5lXxis0FBd4vRJ5HRw5E2/apFQAv99BvKQzmjRwSiDyAtO0GqiiTpiYqekPLMSK1COgvfQYu7r6SR3uwelw8MgjD9HgyvDLKzRc3Zq1Bh8FxQ0swOIgXoUV0D3ALiwlEQXESUgVrYXlcBECukk8kQOnmx0jGm0Owcn+tMV1PpFlarpEuNKHeJNOPt23DXV+lKpKHty2l5cOlPjPrwfpzxW5YWEjRmaMbCpDd6+ES5FYuqmcSzbL9I3p+NtcHNg+zZVrJW6+qIyvfC1FMGBQGc4jMj2EggHay/vJDe/H5qnFtbyVQPkEK86qxMzPqrkkMNQyKA9BcRTh8REbKxJYNA/buW2URkfZ05HhrJV2jh4r0b0nz4HRHG1FuNEDTy6BnWvgK7th96h1bvUBGU+ogivWN7J57UImThzhv+4dJVEQrApJPKEL2gZN5o0F8DjzMLkNjvUjJnQaa/Ocv9rP2rkOBo5HqG40iXY6OFM25Uz7q4K7JEkqVmC/Vwjx0MzTk5Ik1QghxmeWV7OSK6Ocmv/Big2jf35MIcRdwF0zxxeyAmaVxXbmaAI5I5HpEqRGIFcEqQCOOSpLK+YAgqHhY7S21iB52nBKbr7woXfynvfdzFXXXEPt0vO58+71uN1t7H34azz36CP889d/zIqNTdRXN3P3978NJwZ4+NEPIRNjXoObnz7g57rvJ7DZYMlihccf/zKhMuh8fg8/v/8krU153n79+8iLOgamdAo089RTd1PKRxGyyuIF1fT0dnO8I0HLonqsye81BvYsfHB6+7vTqufPaqBr4HCC3Q0FGYrT1kT+Vn0bCsL7bnFxXaSIT5HIo/C+3+scnAFwn45AxR7Bty+WWTHPzf0PZLkreWrx0CLDTVXQOQ0RAd2SdQoKMFWEF0eg9fFhchmTB2fIxNNYDJFCAgyTse4Omuctw+Z0oxfeWFmqrLaV+evWMDHcT8fWveQnC6d4EzQQefDUgNDNU2Rhs5k6WIF+tixyEAsncmEF+hlZtVcEHU7Vk71p/9okSSQ0gZNTRTlOoEqBMr/A5wWvz4XLqZJJFWmzSYTyAk2XCPkknKqgtaGRYLmdkp4hHstSKuqUDKgwIZ0skkoVCTf6GInl0LQcZhESJdBmSleH7AqOsAszlbFglQ7ADzVDvVyy1s+uXXHKnUVqlTS337aKVuUwvKhZPnABc4DzZ25lMsRMq0RiJs/OReDkAIzHLMRmVAMlIoilCxxM51hc7WVpuY3IZBxHHsamCniUAn3jaWynLoE37duVy+rEvA3r2JTMsbhmAslt0B6YT6hko+BIc+ylJCO9WYplEsunFa64cAueWB8JZz8b6mRuXO/B5pvDsqXH2by5Aq9Zi+wKEGhsJpCz8bUvfYub/+1ylm9cSXq8C0Wdi7taAXqRjGGqW+yQ6wNfGcWxBI51QYJXLoOqOaiFcjYveILjERXGSzyYEGSL1p59VwrW7YK3LYRFH3Hz5WeKPLHfpNwrU5RMFjZ4eWHH86QmcoznoaQL/jQp8AHy1hLf++cY/PJrsD0OfVBsBOeSSa5fVcG8865nePfPOTigMDTw+j0Ef021jAT8HDghhLjjtJceA94LfGPm/tHTnv9XSZJ+g7VhkjwNvnl1U0ANQWkK7OXg7ALTkCzAchL0amAaGpuDNFfOB7GNYuo4odpqbnj3e+nfvYOOkQL79mxj02U3kItN8uEPfIzP/ce/c9NHvsuVlywmOtHHl77wTW7/3EfYtiNGNmtw4d4hPvaFB3nXlXV8/VsX89A9L7K6Wac/6Ufow+z4/TaKzjU8+/SPmZjs5OMf/xCf/rebefLJX9C1bxv/+V9fIpXIccv7PsgN59bz6J96eOCZAcqPdp2pAAR/SVhl45SagwK1y1WcCY2GZhg/ai3x/Wslhg4Kaiqgv+Mt+hYQNkiPFCj0CirPsbN1p8HW05pSdKC+DOwuQSFfIFunkO85tZw4ZILRCMMluHfCwt+uUCGiWXDBNHDvcYOSsC782X4hjFMbscVUgqGunQTLgsQn8ngqmklFRwCQHV6C9W0EAk5M08TpL2PN+W8jme9h133PkI3EYAaWEDN+lH0gvAqFlGEFbgd/KXw9K3dkw3qPNnOydqxifzez9ZyJmf980/5VsQpwKiQIOcFuA59LpjzkwGY3MHQTm6zgsNspOEp4ZBnF4cTj8eGQFUKhMsoCfrRSmmxCI1wRxuv1k85kmZqK4XH4CYcV5ICCGrDjcgqiUzpOm43WrMFoUqcjazKQFJRaPMiVTux1GpRKODwSt66+nuT+nxIKwrot62nacBnYHIjWSaS7TqDpZShyFHkNVlp+wITjkB+HiUF4KQJ7MwoN1TZWtJg0ZkwyRZNSSdCZ1vHIYGYLaNV+2ubV0JyLUFBkWtwmkZTJHI/C4SntLflWokB5VYF33jCXyKiMEWqmOtCOpPqw9dzD6KhMKgP+Fpmy/AT/98uthPNpLloT4Pz16ygLdlNQBPIchTKnB3tgOWpZLRgm3SOPUawH5/AhiokipVwTnnI3OKdAm4TEJKqkQrITPBegZNLYnHmKpoyqOTCGjrFwcxn2XieFC5pZfP8AD57M0A2sx6owK+uBup05fnQJHHunylfuViiJACMDEfxumXHJxOdWiaasQoM0MO2vwp2agP9LwjEQPjBdQFTnvAu9qPOrGH7c4LmjGo0e6E68tv/+msz9bOBm4KgkSYdmnvs8VlB/QJKkD2DlRtfPvPYEVrlTz8xwef8bfoIExQio86E0BPkcVm3gbD3yCCheOLvJy+P/+x0WLLYxZ+FCAvokXQceo3bxAr7+jc/jdJrI5gmmItVsOOty3MF67rrrB5RXVVBeFWBuexinfxV3fK+e7PQBqlsXYhpP0Dck8d5FZQQqbTy7dZQ/HInwyNN3Mb8myBNH7ucPDy+ltyvKYEeEbEpDtUdJGx4++JGPctUlF/Pzn3yUf/vU9/jMJ1eyZEUr/+9Xe0GcOavKNiuTNYoW1BJ0glYJ6S5rn2HqiIYhWUpUxmpI7oGRZwV2F7hvxMrG3opvsWCdjhOCs5eAzWXSEZfPaH1QJFjeCHpRwlPt5sWBMzPrPOCvkpiblyhNmIwDbtVigRzAiqFdqVPo0qvu4QtBYnSale2rODb1BFUL5tHb5yYzPoCjvJ45m9/GurMXo+tFqpuqcEjw628+wNCLnWd2mdoAHcy0JdZhZoS1KTr72qwQsjTjoRRWMB/BOo6TUxsQWWYn3dkg86b9KwC7DD7JKgjwemQaaz00NrXisis4VRlVtSE5XEzFJ1Ftdvy+ENicBCpC1NQ1kUrGyESGKfMGEaqEYlfRihLrVs/F4QpgOp2UTIOh4R4Wz5VJxmOc6ByiMJKh2q/Sn9DxXfZ+MucvJ3LsONVzyvBI+0nENSrLNrJkwf0Ea/zULFxFNlGDUf4pIsYgrR/fSak3ifcXj8OkSfaIwolRhdiEyZFeE7xu5i7y86GWIEJxsTCtEstmiaWn6Zky2P7iJDV+O30licHuLJctcXHpqgoe2DtN15iOxyZx5TwPh6cSb8m3+UKeQk5nx7bnMCWVJesdKGo7A/1bKR0dJ5wQVJcL3rHCgd3hYUov41hvgWvPTxH2C0w09Kle5tlstK9bQjpewmFOgzbB0vlz2Ld/kKZ1dhTZids5TCmdxBlwQzoDeo5U0YfqrMOlhDEkjWS3TmBLBOIRBrsn0BQvTz86TEVrkoVNXnb3ZQgb1u58EnAXwdYBqg1WXKvxm8/q9HUkeWqfzrYjKXYMm2T/jI+mqbESecczFpdHExTjoKaAYXCn4+h9T1BM6CgJk9FX35d7xd4wuAshdvLa3Hnnv8r7BfDRNzruGaaDuxxcbrBtgOgBkIsgN1hZp0hDdYOd3JE4v9g/RMYr87uH13L5288mJ4d57JHHyU0f46JLNhJLH+e/vvMzfvfY8zhcbiYOf4nQ/Mt5Ytt9nLW5hTlLW/jX936f3u4DPPLkV/nAu9rxBGTyY/Dw76c4MQ6339JOQ0uQ8PwNfCwYoPvEAQ4f7eFXv/kpD9x7F398che/efhZujv6iY/1cvcPH6J/LMFP/3CEZx+dIBr7S5Dc7gbhmNk0roCgacOxUoerQVEl4iWrlKvMCdoULH+fjcP98N5zFrA30slxtLfmW+ClJOyfhs01KrpsY3y6cEZzU70DWgU4yu2IiirsmTN1/SRAUd2YovCKvsWEsCLi7CShizcuvSoaJZZs3gA9+5i36Syaz7qawb4+PH43G87bjOIuoWcnSU8PEcunyMfjZwR2ITiVoRfBLOrWEme2C1XmFF+7gZVWN3OqganIKazJxalZKGu54q341w7MdYCigiRJlPkUTGFjaipFQVJYMa8Zm6yg2FUWzl+KJAmc/nJsXj8ub4BgKIw7GECtryM5GmE6m8DpdhGPTpEvlChJBQJ+D2bJYOG8xQhJQSvlqKpu4kT3EJPxLPpAnMr2zYiqDYRFEE/jHGT7uRgn+/B4W2lsbCSnFLjrtw/jqF3C5vpGGhYvJju1AmlVHl7o4MRjfewa9NAwp4kFq8tpnT+JECaB6nqKeYPJaJxsYhyXvYzG2jra57q48vx1GIqD2Ngk6UyMTCGHyxXgPy4OMT0RJ5Eu4fRYoeOt+Lak6SSj+wjZBa1zgkz2DVOaeJrtv+tHL+rUSRZTad+eEnODgmsubqDm3IV4q0bI+UeRCjqJqJvFpSTpQyfwrgwj8hkkPYkzNY5juoBc1oZiOJCZxulygciCM23R1I53I4WWQH6QUv84oVUh7GYSPV8gs3+AaGqakqZRUczy4wNJ5phWEZYNq8IkC8wdA4dpwc3ONYKFnigLlsF1N1dwy5d0th9PYArwOO3UeBSu3LIGOXXUGp8zwJVqghkBqSfDtt0v89y+LAU7eGclvV7D/iE6VFWPRMtaQfUSmYxLgahOOiIIt4FUUgjINm5Y2kpwMM1TxQQ6Jo888RyOcJGp5FHkEZknXzjK5osu4tcP7+SWd27Gppp87YufZvzELr591T8z0mHy6JNHOG+Twa2fvJldW6fp79rHrhcm2XdwgM0bG7j352v405Pd+FrC+BwFrr35B3zl81cyEpF5efcAyWyea9/1L7S1tpLP9vHJL9zEA7+4k2//dJCAX+W3vxnCeA04WS+BUmspStVWwfDLOvYgtLZCySEQMQtb15bDghoo2HUaPHC4ewDvotNk3N+CKRIsXaJi0yUygyVuWWOn1lXkTyPgc8FFFTClQX1VJUZJcG6TxB9jgvxMYLWrULmols3qGJ7DWTIGTOnQ5oTDMwuUEFbGMksa4MOKo6evEDKRKM/cfxdNuRxFVaG1rY3qOfW4nAo+v0p5VTmdx/uJxYfY8cijjHedPPNEDIsHRzt9KTpTFonMaXjQjJ3OAjl7M//s8d/ILaOqMopLotKvoJUEmq7icnuYt6SdUH0NFRXlFFNpSpk8gWAQ4VBx+8pxeX04AkFiEzEqG1rJT8epbC2jocqP5PZgmnZMfw2KBNpEN+loFLuvDH8ghJ5PMiefob51iJwhk/n9s3zvR3fz+c/XMvTyHiorOxFldeQmuqgxj3Ljv/4zmuRCefxluo/uJ96zj0uuuY58No3bozIYbKFUCrDlbCfeQAWqJPB4VXSjhMNdhqoISrpCMqOB043pUMllUngMF6GKAMGmRtJ6Bcl4AglBPhnDXu7CVoDYcOwt+zaR0Xjk8QGaw4JFeHAbBmWhNgLqBKG2Gh58dBjNkNiSL1EXruDhw2M4liyh0aXj9LRg5PZT401Qf14TueOTJPu6sLuXE6grUVHfyk1vL6Fl7EiZToSrhL2sDooZ8AQQmSnSfePULWyHZBJvowNffRCpqhJTcTA2kaU0WWQqDka1Rn8e5gprOMWwSNbcQEGH2glomAIOAZMgLYOGYoJff7ed73yzyM7JJt5x/nKmB3dTG56CJ6YQR0A0gagD/CCXg5g2MPIysYxEEgWneP1ayH+I4F5TYae6vMiaBjcTEQ1XvYIRllFDGhNjBisUmS2tIb772CDjBWitFRx/6jhSbZwnTg5QHIO1K+Yxf00LyzsXct41K3lpx+3UVnt4YYeGUKf47Fc3808fv4xAucZzP3mc//heJ0sWTPCVf72QPzzfw3NPdHP5Zc3c/UCEwcQkzz54Dh/a4mFsYpxLL11CQ2gDmamjDBzr5f4Hd/G1O+7its+dw9bfP4tDaIyOaxiv42unBKUchOZYkoKmAo52iDxn4WqLLoahbmhWZcYSJloHZA0YMdPY3hoh5CtW7ZFYstjHeG+CHArL1jppDZf4gCZhd0kc6IQPbDO41Yzw/iubuP7GVh4d62PnJOSFSZNXpbXMQVfRoEqGjGGtMOoDcGSEVwTmAzOfJwONblhUKfPSoElWgmkTEIKO/kEm7A42DvcRiyfoHR9m7TnncPzYYepq6oiOj7B/13YmDg5g5k6bGkygaK187D4olbBGr5NTmflskJ8N3LOZ+SzWXuLUnoc+c3P8bb51uFxsvOgs/MKGVsqTysSpKPficfoxMzqR5DhVNeVkS2li03HwOIlEM5i5Ak1z5yF0Qbz7JM4KPxkjjz6Zx+H3Yw83IfQMhs2FK1SPy1+JcHow83mQ7fjr6mm0KTgdAVrmLWVf3xCP3XcXL+3eQyKTRnU6aa0N86VPvZfgko2UBob556vK0SQbRi6KY+QwSjqOcLqpO/966rQEIpVD11IU8xoujw9nfQ1IDgqZAiGthKOQIzMxDbJMJjmOGzuyIRNoqAabTHJsDKHl8bRUM9I/gk9MMDD2OqDwG5kOdUYAb16h6KmlNp8CUmy+diVGvpqDux5jKJ3DrUu4/YLLl6wgXNeMac+gyiEUhw/qowiljMBZPgqqE2eFF9Qc9koPc3zViPQU2dIoqq8GtDiU0pDuRxSiVLZKkOyDTAFT1ZHtHsToGFpyglpPlnQA6hSIRiQWCUHjzNDKYiXe4ZkhJwss4LoW6yIZBQYdVO3p4ptfmMOEejm79gmeemSKT190PzjzSH4oZkFOYVUu+SQMj8S2P2boTBucjFkr7tezf4jgbrdJXNzmZNf+LKok07YUiknBSAJc9TIt/nome8d5uiuPUg3BMkvFKOWDad0gkxO8sLeHlw48TU1TluM9h/jvbz7Ph953Pn/8/VXkx+4nFbZx68efxshJfOHjl/K/vgsIVgfoP3mSzctDrLrgLL5z19OsmAPnCZPbbtvLTdfPYWDazY77nue324b4lw/mWLtlPRduztN//AQf/cRj5NIaGdOqXJjFOWw2G0KYqApU1NiJTxlkShr2MNgbIDdkqazoMWssCQU8TaA2QiRpohswGZdwOwWN9TK5UXi9NuM3sotqBKWTKdJZhYpGGclm4goouBSJYknh9/1FhkswmbHhXbWeYsfLvGuuDUdJ46k41LsNxjomebyjiFeGNTa4eLFCjc3gt6OQFxbi8UrzEtCoQp0dFjjh+GmNqaZuMm0UOPTCDiaHx9HtMD60l2I6jSfoJjWcIhtNYeZe/Xy1HJRVSShVDqKpAmJWv27WZoU4TmeB1LAqxsRpN5m/y+gPhCrYeMmNqCZ43Q50oaMXM9jQ0dMp7HY7/qCPhvlryGZT5DIJipk805kxjr+0j0AoRH1DLaVxA7ksgOqV0dMFzMIIBgaoKprLhbemEcXhRhIO7C4XemYCKVfCVuGmauE8Llm2ggsuvgRdE+SzaRw2DVewCluoGmQ7jvn1OPJTVmZaDIJdQUxnEYoBsorssmZJu6Li8gaRdCxYy1mOPZNDj41idh3F7VEpZdM4chJjQ71MRjMER2pIDfWzv2uvlS42AAAbmUlEQVSavKSgelQchRLRkSjHsm993Ia9MnVekwmtiJn1QlrDseQ8wmEDDB8fuGmQSHcnIpXCuTqMUr0R1WWSHXoeHM+hKgny/Xkccj801lCM9uOqcIGsYmijSCMRTLkMLwIycQrjeZyVgGpDlhSkMoEo6SBSKNEUk4ej+NvDKJKTYG2QyVQUe6WEPqeO5ckx5qkg0iYO4OJKqNWgVAWOd6iW1NzeTujMwHVOzEuuRc41I/t3Utu2iEWeFj4d3UaootfCdDJg1oLLi4V/qoLICh8v9MTI56DCbaO+UuJo32vjMv8Qwb2g6yQNJ/tPlHDUmMRyArVosNQpcXn7St65rI3PfOlh8pIgWACbAtuGM4yke1Bd4AlBYrzINe/6BQ2LTbz6Ed51TYDDJw6zYL7CT795EmebjVTW4PjBHLH8IAsXh6mdfy7Xf/sJptMSutnAiWNF5re2845ravjQF17it1tHuf2O9XQcnEvtxCEOHRslevJ/8LdV87Evv4f9JybYtWeE3MzyX5IkFjQ18p5rLiKq55mOj2LL9jNmn+TAfp3UPEFcs/BpRwXkx6xks7keRFa2VM39JtK01RQTkKEubadDe2tCHbP2zCDctNZJQ5WJUysQ7TNJG2AUDQIhP9tGC1Ta4YqN1Wj9Xby89SQLWmx8pl4m9GyB8xfZ+MGLae45LrAB6/2wZa5Kf4dBrQp9JWgCGiToFdZe5UspGEqblKS/hAWFEAz3DmAYJuQhcmgAhERKiWHmTV6vNFpkwVa0s+DCObyw4zhidntjdldoNmOfnWw1TrXRgzXiZ7t+Z29/g9ntdhZs2oRNSEg+i5Ql2dNHZnIYGx5cPje+yiBmUSPgD+ATYYyijNP0odpHqG9vQSvp+BqacNrt5ItJbH4XzmAl2ViU7PAgsm6QHBzGU1ZGIFRGTkjIxTze2nqUmgbMQg5KGo5QAw6HC480s3svnUZib7eDvXZmZZMFJYBUCaKUApvd2hQCjFKe9PggpUg/U70vkZ9KkukbZM9LXQxESyRKBWqd4BVFlrbXU8ppDOzeQ1tjACmdIJPSkVSF6RKUSRC2y/A6HOevZ26vSgoF2QnaUJREX4rK1ZOUigkKwyWGnu0lsMxHoNyLZqZQo/dTmvaQ3dOD60IZUVFAiglwF6DagzNUBbZyMCuITxXJx3pRswrlQTeKlsCWl0D3QEWllXInkoisiVzQ0dUKst44hZyGfyJJMZVA0qG50ceUV2ALKay+agEjDx6hvgFq54CcA0cb0OCBGz/JnqkdLJ3+Cc7lZzFYuIrnu6LccPWtHN6fZv78JuZ+5m449A6oSmGeL5NdGsYxGUPp1qBaRs6m8GDJdg7ndZIlF68Huv9DBPdYSudHv8tgVIAZBzEMNWXw9ou8lKtZfnX342w9UsTVDq68xKERgVQPZtTacD3vHTa2PqRTOddG/Uo7qQHBySmJ++4+zMFuDx+58mx+9PCLbLxa5+bNfrzOIXQBTz3/I/Z2pfjGf7yf6jlbiBbuwFdeT/tlZ/Ozimru/N5e/vfbv2F5YwObN66ipb6M53a8yPReH4vWniBR1Ii/Etjh4kvW8p/vvpL6+Q188YvfZTQeZ1NtmpMpDec8hciIjpIFXbdEr1UvFHpBnoD4iEkaC5tL5qBSgiPdIOs6kejfFoGSAnZ36yy8uR1HdoJtuxL8eKdGWx38+3UuLqtWuOzcWtZuWc5Tv3uBTzyo4VU1PrwU3r1Sxtng5MsvZCjOZOgH8nCgQ9DXBwvdsMRmscMWNZBmxlpcQEFAQLK6Mv/cDOPUBS8KVpT9q85SQCEj43X6OaNt7vTHp8MyglNwDaf9ffp7/waz2e04KmutEiibDIqNoMNLoKkeSZYgFUPoWYyREaKDg/irK3H4w4RbWvDXhcmX8thUF57aJkoTYzh9FagOG/lsjlQyRd3SleCrxHQ6kWUDKT2FovqQNB3ZbwdHAFloVpYthLW5YwNRKqKlEuQTUTyhMIonCFoeYeSRFBnsYBh2YmMDHNi+HXs2SXNziBM7nuWFrftoDro4NJogky9RH/BiIuN2+sjb/SQzKbKmSsdAlMlEGlvRoG8oTUA1MR02PIqgxqkwlhccSf8V8kSvYaJkYGRS2ANuhD6Msy1EsidFYPlKUof3MmUUkbrjCHeAqloZTc6iajbKL/KBlCM1KnBVK5hGESXXYAkKyINQOkqgfC1BZZDMiMTJ/Qnmrqskk9Aoq7SBsxJKU0jeasREgfy0RvbkOI3LvFBdT2/3CUa1cvqUBIuaoLt3mmeyGq5InmCVjWODOjfVW/qoYhCk5qWMTc+lqzFH2f5f0GJbjn/hhTx3z5e5/MINRIsGhzsmGRnq5Z1LPoq0+rskPQqZ6SqSf4zQ3KRgu+lC+h6Z4tNXn+Rn+1Qi/THmOF+rzmVmbL5lz/8drZiFOrfCuivKeOpElFwWpqrh/r4M0eET2G2w8kaZ7jHBeJfA6QLJZeHWShm8FDP4xEfewdy5vTgrnfzs513sik1Qcykc6yxga1C54FIvvzo4yUUhmR98pwtHLsLd/7OG73ymmas/MI/BzvtY2+xm+cZ1HDw8wffueIoGj53Gehef/8EJvvhRJ8MH8wRsYW74t9V84cu/5D3XrAd1N4f6Jmms9XPrJ97PsUNH+O2zz3Hp5espq+rnxMsvc2W9wkMPFRiOw6IN0OezmpUkGYIuOKtOZlxTmTxUxLfM0jDNeAW6AuNdOvJbZ00FrGt+2aZmAhUqR7oEH3u6xEgJOopwdV+a//r8crxLl5Lt6+WOZ6P0z2S73z0MX1snWGAH1XZqIMVLsGe0yPY0hFX4jwUwmoTt/VCnwvjM9VzAalX4+7HRAxL4/DA53oM5O3pnuWROr8XUTr3/FW6Z2b6yM/l6/jazO2bWzqd9RY8Hya5aEIhdRtL9ZOJxfI01uCrrkWwuFF8Iu17CUSpBSaeUy+PwlqE2W+SrqjeEe56GojjA6UeWJKtkWLIhSkUc5dWIYhJjvIvEUC8GKiIZI9lxCE9zJd27DzE2PIZbyMgOhfqmVmqq3fQMx8hqKv4KB7riZcdjzzIZzzG3zEEs7CeSzOKWFDRTYXVjBeb0FOW1NgqqAyE7cfgqiA7ZGBwdI5XUUTUNYZc5Fjc4q0LFY0pIHhtmQacx5GClASN/jf7cq5hekljc4scmfITaA5TSfrSChqQG8fjKWblpLp5gFVIViMJWcgmwVwaR63OIqE66WwGPjk8xIX0MVdVAc4CioDryYKoUCyVcHheGEgQ1ZnWkZlIgqeBuJD95lPRglsTeaUqpJI6VNp7anqBlcR37TkSRG528/W1LeXl0H9/8Qw8+G4RtML8HFjlAz0qEP3UhYwU/2/d3IuxhWsJLKEoy3vJ6fvjQC1RVeJjfupRf/vIXLLvtn9AX/RNPPnGYq9rCzLkphu3mLZiUM2R0ceHFMos/eyv//e+fxjn4+kUW/xDBXXJCWjXo0dK4bJAPWVjVZBCuX+MmGMzxRI9MdJ9O7RrQ81BdDbGQpdsZqpfwL4+R0ew0Vy9nzbm9HBtW+NNBHWEY/PtPnkYqMxmKwmP5EnKjSXnGTj5V4rndg+yOfYEjRw0yo07Oefdxciis31RB0Ahy4fXzaVx0lK6Hj7Oz2yTQmsKlFLjxsnNYtCjBhyrbeWJgmgp3iHt+cRvb9yS45ZZGMq6j/PC2aTp1gy1XCqYkCDXDkvUwMWaVeoocVKwD1RPg6NYE6ji4lsGC+TKHd5qUe2DpOmgzXPy45w2KWl/HypxQrY1jZKr4yfYUIzOTxXgOkrINl0fFHB9g5EAvRyOnUtnePNx3RPBeM4NknIqGAhjJWFJnBQOmUvBQFE4o0OzmlZagvwPq8RdWVSOzeE0DfZkpxOkkLbPc7Kc3Bs9Ww8zWvs9m9LOc7n+XWedVzlCSrKBvt1t86Jj4V5WDKILiBCOPZApkSUUpWheorpgY08cpjfajY6A4y8l0HEaeimKf28yJfZ20rzuLgw/eQ1VjHY6qVpqXzEUIgYgcQ7L5kQwHXncZE3v2YCtlaJANEpkcGcPHaDzDVHcHmZJE0VCIRfyk8RJLmRQzGjFZRpRieJ02cukChWgSmwwL6oJ4ZZUav5N4SUYvZHDoKVoq3AgtTyljIpxuhCnRmzaokDQyRR27Afm0gU1560K1QgjKWquxB+dAYQBHewUOyQ3FcpzzNuHwVqAs3gC5ZxE9z+FPFsnaMjCRRYqDWq4iD8jITQUY7UVRJAgHLIY+dwDkCcI1VYjwEoyxYdxaCcajVilkZRXEp3A1KTgXX4gr9SIoeWyeCtbVFzi8e5LWOWUYOZmqGpXWWjcD6RwBr0ImovFfJ+GjHnDKsCiS52i8k+M9B3D4qlgdXM7DD99PJjHIsb197H7uST7woQ9jqA6++N2HWblxA84Fm3n2D3cz94476Ix0kugaZsTRTl734ZUrqJ+zime2/eF1/fcPEdwDHhUzKJgYLVKIQnktePJg5iS6piSOmBKZQRlXJeRl8ARAbYfLG1QO9GpULjD56jeeo2GljP70AVxJHW+Fgt4DRhKOderIAhougWzJYHIczr4wwHB+itbNQe7/Y4y2Nujpz3PH81uZTgpqdIlz1wR4dNsUO/eN8dzWAmvf6UI4x3juTzG6RrrZM5gh2ltgwG8juKVAV98UVavht8le0g9C+wKZsrygMwnuGhiKgGQHfxOYvVBtB+rBdLlYMaYRc2Y4cQjsDYIFq2SiKcFwTjBx+PXbjN/InAIKusrwkSjPdJ/C7xWgFM8hIlOYqsKJiETytFWCAHbE4aqiwhXzVLoP5l5JguvdEsGiIKvDpBNeTFt7CbbCmRTBf0+TZAhVeKidO5fIBEh9catj9RQB2Kkvbp52P5u1nz4B/J2WE9KrKn5IFl/E1EmM0UOkk0eJDPahazp1LfPJTmdIjgxSEazB8Pjp6x2gzKVQM6+e2LEBfAK6Ok+ieHwEigm6tu7CF6rCsV5jzVXvJF9KUhrqIvLEfoZHE9g8PkxNpbyhjejUFIZuMqfRzeHhKEXNTrDSR9CeI14sEKxrIuC2kxwbI+DMkKpwcjKdweOy0dRcRV4zqC+ME6ouJzIVx+exE42UONaTQqgSfgUq/D4UxUaBIodTJoV8idoyHy0eF8cHxokWddxColgyOPqaLTJvbKrLRrJ/mtDmdciTHUi5I7DqepAV5IoV4NZBPw6JFByQMHsMphqzSAVrHg2ZGiIPNFWBqAFzDEIOmMxCdhyyGWRvHRgdKKvXQCyKEFGkoBsxnGHymQjhpTUo7a24biinc0c/znQFC6+9jPZSnpefP8KiOTJDJ7ppdlfyvWsqyWbT/OqoQWcuxucmsgSExoqv3U1vw1G6jnbQn48x9rXbOLr7KLm0hk11oBU0fvHTnwESbfNWsfICN3v2PcGmeevodCzi3q37UaNxBpNJqsur6b7jR6RlD2+gj/2PEdzzhoYUkEifhMa5KgEUTg4UaLCbHChk8cyHTHOJpnbwpSEUhlxBortfUBWWiD8q0GMQOWjitJmMDoOjQVDrsjOeLyGaLU6MLYsVOo8YOKqhmIlw/1iO4WkNsxZe6oB5m8BWKTM1qNO+WjCajHPwQJxElcTGmx0cSZssbrDz6EMZAgtyyFmTyoUwHocdh1M4/DLjEyb+Y1Yg0iVwhiRsNYI5lRLadkFnv4LaI7N+kUbIDjvHobApzsBAgbU2CNaCqwKyKZPaGonpPRLDf1tsp9mn0D6vgp6OCeKnBTUDmBgr0N81QbDcweGDqb8Q7arwwIp54JjU8EqWxJpTgnMXOoi8XKDKD61uiywsZ0K0BEHpTAHtv5f5fBINc8KMJBJ0RwYRCqcoFmeXCacH89ksfTZ7P13N+nQ8/m8xabb2crZEB+s+0w/H7yF7eIDJ0TGSw/1gCFLLTfyVVTh9QcR4AsmdxDURweOWSScm8JaHUWtbWHT2eaiBGuwOiWDnMYItq7GZY+R6OylMJwg1tRAbHWHJmiWkdZ1Dz/eQmt5JY2UVqttJPp6lde0S4idjFE2ZyeEkQvKQ6uwlLiTKwk7yBTflfhnfWW1EO4bo7htkNKfjlaC6OkiDt5Jctkg46KRomExLNsrL7EiqRjyqUe7z4HeZCF0jE08iyRI1VSHE8BSdGlQ6VZqL2iu9D2/WhOyAcDWK6ic/KXBUychaFuwGTD4NZXUgiohsktyYYGoYHk2D4zBsXAKqohBWJXB4EZkcktcD2RH+f3vnFhtXccbx33d29+zV69v67ji+kKS5QRNSCCJQoAhoXgBVlSohwUNfeGvVSihS+9qH9qEPVFRVJR5ArVoqCmoo4hqIBCEEQkKujuPEduz4tmt7s977nj1n+rAb2UVAiB1nN6v5SaudM7vWfvM/336e2ZlvhqSJ45nFsFsgtIgzm6domxi+MAsjvTRvTeM0P4jvvjZk4wAEg5j9QqdvEY8vRLitFRF4+KEk2Iuo2DQ/LoboioTwhZt52BFiczEyqTSZ+Vle3b+fT17fT24hzmIhz3//9jaiFM6y0XAhUxqZT4yd5cA7r3Pk/YM8/fqbjF2aYXBsiuFPP8Jf34q3yeC1V95jyzovbRE3i7EqP2ZPOVA4qfBug5GjFvachb8Zhtzw2FMmZlyREYvkebj4OfzwezATUhhzNv7NwuhxRaAf7u31MjmeJ1qE3LhDLlNg0x4vM3MWzpxD2jDovgMC4hCVReJSpLHBJJCxyY1DQ6tJIm5xd5fBQtZh5jx4vMKVBuHyYB6PGw6dEIpuRdewm9aNioWARW7aYXgww46tJt0DMD9cINQD64MmZm+RoxNFXLaLlnCR8Ys2d+3w4vXaeL3QknaIZ7KMXoa0CbvbwWUqpmfBNBWWgs7dkLju84BKeIBnHwkSbvKRD7RgOUvrjv0e2NTjZfRinsaEw+7+BlpOzRMrLjldfwHe/chi+1aTeyLCezFFvwfMnMUDjbD+Nsg4Bn6XQ9KBrAM9lHa+9LF0FN9qEQMiDULXwACnUwnSp/NLgfzqSUvLD8a+GmeX9+C/7rf2Vf8Tupoe+5WvUt0G+OFzhO/NEDr9Glc+OYx/4w4kHieXylJczNLY2YhnYx/NP9hJsT6MBMOIy4917guyI8fADBGfGCc7P4P70ikC6/twBV10dm4hMzRENJXn0D8OcN/9m9jWH8Rw+8mlsng6unGHg1iuOgx/FCuZpDjl5fLwKO2RJmJj0xTrXZDM4ORyTJybYuu6CG0b2tm+MINpO7T39DI3n0I88xRdNkbcosXnxR/04Xi9tIc9nDk7zplYku56kw2RAOfmkwRDJlFbWLBs3IaBuYoJa0sJwc51XJm0yUV20LYuUjo81ReCQgJiUSjMYg1PszghLI5AwgPeKFw6KaSTgtOj6JlTSD4HgR6YGoexIgyo0h4WoTuRzp/ijtwHHpOWAQuMWVxyBw09y+6pGyL97f9nnyccJJ2eonNgGx730ryLiaI7ePV6G9/f8yDP/Xofh4+c5MMDb/LRFycYPHuadCZF0bZRaqnHlYzPc/CNt3C5TCZjSfBN8PE7/0IyRXyZFK+8fIJkMkesKct08dudtyqCey4NvhYwU5CJlxYeGIAdgdgYTEVtZsbBugwtLmgqwhULfHshnVEE26GQhe11AUZS+VKWaALMeyA6bGHlHTo3QvKcYsSw2XCviwavMB83mI5D0RA67ja4eNTiJw804L/D4cU3Uixi41EKBhUyAr45yKLo3QKbu0w+GE6Sj4IzoXCbELpicamgYDO0dXnISoFgD6ghOHyySLMX6kKwYGQY+xgSs2A1QmMU8qOQqBNidxrkzzrkJhWnLHC3lTJMV0pTEDatb2BqGs6cSeNVS5uEig0XRvNcGFU8+ZBD/+0DPHU8zgsjNhalrNMfNcFfLsHvbi/yq10uWo8X2dMv1NcpBuvht5/BbRFFr1nKclXAJhOswtds+bcK3G4wTaGuOUJDSxDxCyqqlnbWdFhKUrraI3cvKy/XcHmC05ohYNSDWY+x81matj+FFTuDOvYOanIKNTtPvmcz6YkJvP19uKcmKC7OY3Z0kptOENp2Oy6Xjbmhl/gHhzDCYebHhgm5Dfzb63CFAgTbu5Hzkxx/+zMaejrwNoVp9LkZOzyIlU3R1lTHQjJPoC5IMa3o7IrQFDAwXS1s3LWZsVNT9JkFujvCbHry5+Q+f5WYaiUXT+D3OXQ98gSJQwdRhQS9nX0k8jmOD0eBFGL7cYouNndFiM8nOT2VwjQVsynFbSEPjQETl22jXAascK27gR/kfuo9UwQskHQjBLeXNvFxwqVjAF2NGHMzmHaQgXtMnh53OHgyQa4ZIq4AdjSDTAMtnTCRwnlXYbSBnU9Cl4FhbUB670eop5SlkaO0mX0pNNq2heEqpUGXXMihaNm4PR4ULlzmegzl/8qd/8oX1jAId3Tw6BMdPPr4I2RzeUaHLjI5PcvRL08xODKCIy4sy03uyiQjQ8cYuTTLn5//E619ARZicdwCqWyKoioF9HRC0dFaz3D8mydVRak1GD9fJyKSBIYqbccaE6G0PfpKWa+UarnePxKRGKW8otV8drVTEW1B++53ZKW+q7W9Nt+obVX03IEhpdSuShuxlojI0Uq0USnVUqnPvllUuH3ad9cOre0qWPk6JY1Go9FULTq4azQaTQ1SLcH9r5U24CZQyTbWur5a27WlUm3U2q6CqphQ1Wg0Gs2NpVp67hqNRqO5gVQ8uIvIYyIyJCIXRGRfpe1ZCSKyTkQ+FJGzInJGRH5Rrm8SkfdEZLj83FiuFxF5vtzmkyKyc43suuW1herUV2urffdaVFxbpVTFHpTS+y4C/ZQO8zkBbKmkTStsRwews1yuA85T2gX3D8C+cv0+4Pfl8l7gLUqpNbuBI1rbW0dfra323VtB20r33O8CLiilRpRSBeCfwOMVtum6UUpNK6WOlctJYBDootSWl8pvewl4olx+HHhZlfgUaBCRjhtsVk1oC1Wpr9ZW++41qbS2lQ7uXcDEsuvL5bpbFhHpBXYAR4A2pdTVE1BngLZy+Wa0u+a0harRV2u7hPbd70AltK10cK8pRCQE/Bv4pVJqcflrqjTu0kuTVoHWd+3Q2q4dldK20sF9Eli37LqbG7vf1E1DRDyUbuDflVKvlatnrw6rys/Rcv3NaHfNaAtVp6/Wdgntu99CJbWtdHD/HNggIn0iYgI/A/ZX2KbrRkQEeBEYVEr9cdlL+4FnyuVngP8sq3+6PDu+G0gsG6bdKGpCW6hKfbW22nevScW1rYIZ5b2UZpEvAr+ptD0rbMMeSkOrk8CX5cdeoBk4AAwD7wNN5fcL8EK5zaeAXVrbW0tfra323WrXVmeoajQaTQ1S6Z9lNBqNRrMG6OCu0Wg0NYgO7hqNRlOD6OCu0Wg0NYgO7hqNRlOD6OCu0Wg0NYgO7hqNRlOD6OCu0Wg0Ncj/AJuobROCg+HIAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 4 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ezczT7C_paYE",
        "outputId": "a318221b-9b34-4226-a5dd-08ea680e930d"
      },
      "source": [
        "attacked_model_report(model_2, test_loader, cw_l_1, args)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 7/7 [13:58<00:00, 119.79s/it]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "Validation loss is: 0.7119308304786682\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.60      0.71      0.65        85\n",
            "           1       0.75      0.65      0.70       115\n",
            "\n",
            "    accuracy                           0.68       200\n",
            "   macro avg       0.68      0.68      0.67       200\n",
            "weighted avg       0.69      0.68      0.68       200\n",
            "\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pl5zw5Cd8lP7"
      },
      "source": [
        "As in the paper, my implemented cw with l_1 and l_inf norm attacks perform worse than l_2 "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nNB5cdPKuU_T"
      },
      "source": [
        "### Building defense"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ECoTtLkluatj"
      },
      "source": [
        "Adversarial training "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VLy20dXu7MXf"
      },
      "source": [
        "# I do not want cuda out of memory warning, so I decreased batch size\n",
        "train_loader = DataLoader(train_dataset, 8, shuffle=True, num_workers=2)\n",
        "test_loader = DataLoader(test_dataset, 8, num_workers=2)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HzRVlbYskNn0"
      },
      "source": [
        "Universal adversarial training function for all attacks"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Yl53paYvaupm"
      },
      "source": [
        "def adversarial_train(model, optimizer, loss_fn, train_loader, val_loader, attack_f, args, \n",
        "                      scheduler=None, epochs=20, device=\"cuda\"):\n",
        "    best_loss = 1000\n",
        "    best_model = copy.deepcopy(model)\n",
        "    \n",
        "    for epoch in range(1, epochs+1):\n",
        "        training_loss = 0.0\n",
        "        valid_loss = 0.0\n",
        "\n",
        "        model.train()\n",
        "        for batch in tqdm(train_loader):\n",
        "            optimizer.zero_grad()\n",
        "            inputs, targets = batch\n",
        "            inputs, targets = inputs.to(device), targets.to(device)\n",
        "\n",
        "            delta = attack_f(model, loss_fn, inputs, targets, *args)\n",
        "            output = model(inputs+delta)\n",
        "\n",
        "            loss = loss_fn(output, targets)\n",
        "            loss.backward()\n",
        "            optimizer.step()\n",
        "            training_loss += loss.data.item() * inputs.size(0)\n",
        "        training_loss /= len(train_loader.dataset)\n",
        "        \n",
        "        model.eval()\n",
        "        num_correct = 0 \n",
        "        num_examples = 0\n",
        "        for batch in tqdm(val_loader):\n",
        "          #with torch.no_grad():\n",
        "            inputs, targets = batch\n",
        "            inputs, targets = inputs.to(device), targets.to(device)\n",
        "            delta = attack_f(model, loss_fn, inputs, targets, *args)\n",
        "\n",
        "            output = model(inputs+delta)\n",
        "            loss = loss_fn(output, targets) \n",
        "            valid_loss += loss.data.item() * inputs.size(0)\n",
        "            correct = torch.eq(torch.max(output, dim=1)[1], targets).view(-1)\n",
        "            num_correct += torch.sum(correct).item()\n",
        "            num_examples += correct.shape[0]\n",
        "        \n",
        "        if scheduler != None:\n",
        "          scheduler.step()\n",
        "        valid_loss /= len(val_loader.dataset)\n",
        "\n",
        "        print('Epoch: {}, Training Loss: {:.2f}, Validation Loss: {:.2f}, accuracy = {:.2f}'.format(epoch, training_loss,\n",
        "        valid_loss, num_correct / num_examples))\n",
        "\n",
        "        if valid_loss < best_loss:\n",
        "          best_model.load_state_dict(model.state_dict())\n",
        "          best_loss = valid_loss\n",
        "        \n",
        "    return best_model"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "41GBrVUWkCvE"
      },
      "source": [
        "Adversarial training for fgsm attack"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/"
        },
        "id": "x6QtYB484uop",
        "outputId": "06fde9f7-85b9-46c1-dcda-4bdd8b5cbe10"
      },
      "source": [
        "model_2_adv = CustomCNN(3, 2).to(\"cuda\")\n",
        "args = [0.1]\n",
        "lr_2_adv = 1e-3\n",
        "optimizer_2_adv = optim.Adam(model_2_adv.parameters(), lr=lr_2_adv)\n",
        "scheduler_2_adv = StepLR(optimizer_2_adv, step_size=15, gamma=0.1)\n",
        "loss = nn.CrossEntropyLoss()\n",
        "best_adv_model = adversarial_train(model_2_adv, optimizer_2_adv, loss, train_loader, test_loader, fgsm, args, scheduler=scheduler_2_adv, epochs=30, device=\"cuda\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 225/225 [01:02<00:00,  3.60it/s]\n",
            "100%|██████████| 25/25 [00:03<00:00,  6.32it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch: 1, Training Loss: 2.69, Validation Loss: 0.70, accuracy = 0.43\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 225/225 [01:02<00:00,  3.61it/s]\n",
            "100%|██████████| 25/25 [00:03<00:00,  6.26it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch: 2, Training Loss: 0.71, Validation Loss: 0.71, accuracy = 0.53\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 225/225 [01:02<00:00,  3.60it/s]\n",
            "100%|██████████| 25/25 [00:03<00:00,  6.34it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch: 3, Training Loss: 0.71, Validation Loss: 0.73, accuracy = 0.52\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 225/225 [01:02<00:00,  3.58it/s]\n",
            "100%|██████████| 25/25 [00:03<00:00,  6.34it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch: 4, Training Loss: 0.72, Validation Loss: 0.71, accuracy = 0.54\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 225/225 [01:03<00:00,  3.56it/s]\n",
            "100%|██████████| 25/25 [00:03<00:00,  6.31it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch: 5, Training Loss: 0.72, Validation Loss: 0.71, accuracy = 0.48\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 225/225 [01:03<00:00,  3.56it/s]\n",
            "100%|██████████| 25/25 [00:03<00:00,  6.34it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch: 6, Training Loss: 0.72, Validation Loss: 0.86, accuracy = 0.54\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 225/225 [01:03<00:00,  3.55it/s]\n",
            "100%|██████████| 25/25 [00:03<00:00,  6.26it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch: 7, Training Loss: 0.71, Validation Loss: 0.81, accuracy = 0.55\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 225/225 [01:03<00:00,  3.54it/s]\n",
            "100%|██████████| 25/25 [00:03<00:00,  6.35it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch: 8, Training Loss: 0.70, Validation Loss: 0.73, accuracy = 0.53\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 225/225 [01:03<00:00,  3.55it/s]\n",
            "100%|██████████| 25/25 [00:03<00:00,  6.29it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch: 9, Training Loss: 0.68, Validation Loss: 0.76, accuracy = 0.58\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 225/225 [01:03<00:00,  3.57it/s]\n",
            "100%|██████████| 25/25 [00:03<00:00,  6.35it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch: 10, Training Loss: 0.66, Validation Loss: 0.83, accuracy = 0.58\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 225/225 [01:03<00:00,  3.56it/s]\n",
            "100%|██████████| 25/25 [00:03<00:00,  6.31it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch: 11, Training Loss: 0.65, Validation Loss: 0.99, accuracy = 0.57\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 225/225 [01:03<00:00,  3.56it/s]\n",
            "100%|██████████| 25/25 [00:03<00:00,  6.32it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch: 12, Training Loss: 0.63, Validation Loss: 0.75, accuracy = 0.66\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 225/225 [01:03<00:00,  3.57it/s]\n",
            "100%|██████████| 25/25 [00:03<00:00,  6.41it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch: 13, Training Loss: 0.60, Validation Loss: 0.71, accuracy = 0.68\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 225/225 [01:03<00:00,  3.57it/s]\n",
            "100%|██████████| 25/25 [00:03<00:00,  6.38it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch: 14, Training Loss: 0.58, Validation Loss: 0.68, accuracy = 0.65\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 225/225 [01:03<00:00,  3.57it/s]\n",
            "100%|██████████| 25/25 [00:03<00:00,  6.26it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch: 15, Training Loss: 0.56, Validation Loss: 0.73, accuracy = 0.63\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 225/225 [01:03<00:00,  3.55it/s]\n",
            "100%|██████████| 25/25 [00:03<00:00,  6.31it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch: 16, Training Loss: 0.46, Validation Loss: 0.76, accuracy = 0.67\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 225/225 [01:03<00:00,  3.54it/s]\n",
            "100%|██████████| 25/25 [00:03<00:00,  6.30it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch: 17, Training Loss: 0.45, Validation Loss: 0.67, accuracy = 0.70\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 225/225 [01:03<00:00,  3.56it/s]\n",
            "100%|██████████| 25/25 [00:03<00:00,  6.29it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch: 18, Training Loss: 0.44, Validation Loss: 0.67, accuracy = 0.69\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 225/225 [01:03<00:00,  3.54it/s]\n",
            "100%|██████████| 25/25 [00:04<00:00,  6.22it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch: 19, Training Loss: 0.42, Validation Loss: 0.71, accuracy = 0.69\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 225/225 [01:03<00:00,  3.55it/s]\n",
            "100%|██████████| 25/25 [00:03<00:00,  6.33it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch: 20, Training Loss: 0.42, Validation Loss: 0.68, accuracy = 0.71\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 225/225 [01:03<00:00,  3.54it/s]\n",
            "100%|██████████| 25/25 [00:03<00:00,  6.35it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch: 21, Training Loss: 0.41, Validation Loss: 0.69, accuracy = 0.69\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 225/225 [01:03<00:00,  3.55it/s]\n",
            "100%|██████████| 25/25 [00:03<00:00,  6.30it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch: 22, Training Loss: 0.39, Validation Loss: 0.68, accuracy = 0.71\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 225/225 [01:03<00:00,  3.54it/s]\n",
            "100%|██████████| 25/25 [00:03<00:00,  6.29it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch: 23, Training Loss: 0.39, Validation Loss: 0.65, accuracy = 0.72\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 225/225 [01:03<00:00,  3.54it/s]\n",
            "100%|██████████| 25/25 [00:03<00:00,  6.27it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch: 24, Training Loss: 0.39, Validation Loss: 0.69, accuracy = 0.73\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 225/225 [01:03<00:00,  3.54it/s]\n",
            "100%|██████████| 25/25 [00:03<00:00,  6.29it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch: 25, Training Loss: 0.38, Validation Loss: 0.68, accuracy = 0.69\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 225/225 [01:03<00:00,  3.56it/s]\n",
            "100%|██████████| 25/25 [00:03<00:00,  6.44it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch: 26, Training Loss: 0.37, Validation Loss: 0.68, accuracy = 0.72\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 225/225 [01:03<00:00,  3.56it/s]\n",
            "100%|██████████| 25/25 [00:03<00:00,  6.30it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch: 27, Training Loss: 0.34, Validation Loss: 0.70, accuracy = 0.72\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 225/225 [01:03<00:00,  3.55it/s]\n",
            "100%|██████████| 25/25 [00:03<00:00,  6.31it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch: 28, Training Loss: 0.38, Validation Loss: 0.72, accuracy = 0.72\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 225/225 [01:03<00:00,  3.55it/s]\n",
            "100%|██████████| 25/25 [00:03<00:00,  6.25it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch: 29, Training Loss: 0.35, Validation Loss: 0.72, accuracy = 0.71\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 225/225 [01:03<00:00,  3.55it/s]\n",
            "100%|██████████| 25/25 [00:03<00:00,  6.34it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch: 30, Training Loss: 0.31, Validation Loss: 0.73, accuracy = 0.72\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "GkhW52Lg47T9",
        "outputId": "12026865-ef4e-4eeb-be7c-56b63d66b212"
      },
      "source": [
        "torch.save(best_adv_model.state_dict(), 'best_adv_model.pt')\n",
        "best_adv_model = CustomCNN(3, 2).to(\"cuda\")\n",
        "best_adv_model.load_state_dict(torch.load(\"/content/best_adv_model.pt\"))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<All keys matched successfully>"
            ]
          },
          "execution_count": null,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5GKlq1ylIURy"
      },
      "source": [
        "### Model asessmemnt after attack"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "orjqt-p_IxNA"
      },
      "source": [
        "Metrics are not so good, but much better than instantly after attack applying"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "UyecYUaM4iSd",
        "outputId": "aeca0451-b862-4684-edd5-a611a20e622c"
      },
      "source": [
        "args = [0.1]\n",
        "attack_output(best_adv_model, fgsm, args)\n",
        "attacked_model_report(best_adv_model, test_loader, fgsm, args)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
          ]
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 4 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 25/25 [00:04<00:00,  6.03it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "Validation loss is: 1.1910822820663451\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.59      0.57      0.58       104\n",
            "           1       0.55      0.57      0.56        96\n",
            "\n",
            "    accuracy                           0.57       200\n",
            "   macro avg       0.57      0.57      0.57       200\n",
            "weighted avg       0.57      0.57      0.57       200\n",
            "\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wkB_yKIZkTwb"
      },
      "source": [
        "Adversarial training for pgd attack"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7R6xlDkMIbd_"
      },
      "source": [
        "# it run for 4 hours and failed due to the limit of colab gpus\n",
        "model_2 = CustomCNN(3, 2).to(\"cuda\")\n",
        "args = [0.1, 1e4, 100]\n",
        "lr = 1e-3\n",
        "optimizer = optim.Adam(model_2.parameters(), lr=lr)\n",
        "scheduler = StepLR(optimizer, step_size=7, gamma=0.1)\n",
        "loss =  nn.CrossEntropyLoss()\n",
        "best_adv_model = adversarial_train(model_2, optimizer, loss, train_loader, test_loader, pgd, args, epochs=10, device=\"cuda\")\n",
        "\n",
        "torch.save(best_adv_model.state_dict(), 'best_adv_model.pt')\n",
        "best_adv_model = CustomCNN(3, 2).to(\"cuda\")\n",
        "best_adv_model.load_state_dict(torch.load(\"/content/best_adv_model.pt\"))\n",
        "\n",
        "args = [0.1, 1e5, 10]\n",
        "attack_output(best_adv_model, fgsm, args)\n",
        "attacked_model_report(best_adv_model, test_loader, fgsm, args)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IK9ftZYSx-Qz"
      },
      "source": [
        "## Stage 3. Adaptive attack implementation"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2bI6MhmRrFZe"
      },
      "source": [
        "Adversarial Retraining implementation \n",
        "\n",
        "Both approaches from original paper - https://arxiv.org/pdf/1705.07263.pdf\n",
        "\n",
        "However, due to the lack of resources, I implemented attacks and defense using fgsm. Nevertheless, the same code could be uses with any (pgd/c&w attack).\n",
        "\n",
        " My experiments proved the fact that fgsm is not effective for attacking adversarial retrained model."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pBku748W374T"
      },
      "source": [
        "def generate_adversarial_samples(model, loader, attack_f, args):\n",
        "  images = []\n",
        "  for (X, y) in tqdm(loader):\n",
        "    X, y = X.to(\"cuda\"), y.to(\"cuda\")\n",
        "    delta = attack_f(model, loss, X, y, *args)\n",
        "    altered_img = X.to(\"cuda\")+delta\n",
        "\n",
        "    images.append(altered_img.to(\"cpu\"))    \n",
        "  return images "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B3TUrwGozvLU"
      },
      "source": [
        "### Zero-Knowledge Attack Evaluation"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fuvx7QlsrVAA"
      },
      "source": [
        "#### Approach woth $F_{base}$ and $F_{secure}$"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EtyNBAkUJgSn",
        "outputId": "a5a2604a-a07e-4315-c8b5-c31f96e3cc44"
      },
      "source": [
        "args = [0.1]\n",
        "loss = nn.CrossEntropyLoss()\n",
        "train_adv_tensors = generate_adversarial_samples(best_model, train_loader, fgsm, args)\n",
        "test_adv_tensors = generate_adversarial_samples(best_model, test_loader, fgsm, args)\n",
        "\n",
        "adv_data_train = TensorDataset(torch.cat(train_adv_tensors), \n",
        "                               torch.tensor([2 for i in range(len(torch.cat(train_adv_tensors)))]))\n",
        "adv_data_test = TensorDataset(torch.cat(test_adv_tensors),\n",
        "                              torch.tensor([2 for i in range(len(torch.cat(test_adv_tensors)))]))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 57/57 [00:25<00:00,  2.25it/s]\n",
            "100%|██████████| 7/7 [00:02<00:00,  2.48it/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IBnS4A_OMYyX"
      },
      "source": [
        "def collate(batch):\n",
        "    \n",
        "    # item: a tuple of (img, label)\n",
        "    data = [item[0] for item in batch]\n",
        "    target = [item[1] for item in batch]\n",
        "    data = torch.stack(data)\n",
        "    target = torch.LongTensor(target)\n",
        "    return [data, target]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zjLb7B3pDEmR"
      },
      "source": [
        "train_adv_dataset = torch.utils.data.ConcatDataset([adv_data_train, train_dataset])\n",
        "test_adv_dataset = torch.utils.data.ConcatDataset([adv_data_test, test_dataset])\n",
        "\n",
        "train_adv_loader = DataLoader(train_adv_dataset, 8, shuffle=True, collate_fn=collate, num_workers=2)\n",
        "test_adv_loader = DataLoader(test_adv_dataset, 8, collate_fn=collate, num_workers=2)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hl4zxWxZA-zf",
        "outputId": "6e4e322e-393e-4da0-ed2a-294c71e6a39e"
      },
      "source": [
        "model_3 = CustomCNN(3, 3).to(\"cuda\")\n",
        "lr_3 = 1e-4\n",
        "optimizer_3 = optim.Adam(model_3.parameters(), lr=lr_3)\n",
        "scheduler_3 = StepLR(optimizer_3, step_size=5, gamma=0.1)\n",
        "loss =  nn.CrossEntropyLoss()\n",
        "best_model_3 = train(model_3, optimizer_3, loss, train_adv_loader, test_adv_loader, epochs=10, device=\"cuda\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 450/450 [01:17<00:00,  5.80it/s]\n",
            "100%|██████████| 50/50 [00:03<00:00, 12.69it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch: 1, Training Loss: 1.80, Validation Loss: 1.05, accuracy = 0.58\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 450/450 [01:17<00:00,  5.80it/s]\n",
            "100%|██████████| 50/50 [00:03<00:00, 12.53it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch: 2, Training Loss: 0.76, Validation Loss: 0.88, accuracy = 0.66\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 450/450 [01:18<00:00,  5.72it/s]\n",
            "100%|██████████| 50/50 [00:03<00:00, 13.16it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch: 3, Training Loss: 0.42, Validation Loss: 0.34, accuracy = 0.85\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 450/450 [01:18<00:00,  5.74it/s]\n",
            "100%|██████████| 50/50 [00:03<00:00, 12.68it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch: 4, Training Loss: 0.32, Validation Loss: 0.29, accuracy = 0.88\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 450/450 [01:18<00:00,  5.74it/s]\n",
            "100%|██████████| 50/50 [00:04<00:00, 12.34it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch: 5, Training Loss: 0.24, Validation Loss: 0.24, accuracy = 0.90\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 450/450 [01:18<00:00,  5.72it/s]\n",
            "100%|██████████| 50/50 [00:04<00:00, 12.30it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch: 6, Training Loss: 0.23, Validation Loss: 0.24, accuracy = 0.91\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 450/450 [01:18<00:00,  5.72it/s]\n",
            "100%|██████████| 50/50 [00:04<00:00, 12.48it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch: 7, Training Loss: 0.16, Validation Loss: 0.25, accuracy = 0.90\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 450/450 [01:18<00:00,  5.71it/s]\n",
            "100%|██████████| 50/50 [00:04<00:00, 12.34it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch: 8, Training Loss: 0.16, Validation Loss: 0.13, accuracy = 0.96\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 450/450 [01:18<00:00,  5.72it/s]\n",
            "100%|██████████| 50/50 [00:04<00:00, 12.36it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch: 9, Training Loss: 0.13, Validation Loss: 0.32, accuracy = 0.91\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 450/450 [01:18<00:00,  5.72it/s]\n",
            "100%|██████████| 50/50 [00:04<00:00, 12.24it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch: 10, Training Loss: 0.14, Validation Loss: 0.25, accuracy = 0.92\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oSrJeZwtyH5R"
      },
      "source": [
        "torch.save(best_model_3.state_dict(), 'best_model_3.pt')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pO7roqHQyGcc",
        "outputId": "b21f0e7e-72e1-4eec-c076-fd0daa92ec70"
      },
      "source": [
        "best_model_3 = CustomCNN(3, 3).to(\"cuda\")\n",
        "best_model_3.load_state_dict(torch.load(\"/content/best_model_3.pt\"))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<All keys matched successfully>"
            ]
          },
          "execution_count": 20,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G6QQ8kuQXoqz",
        "outputId": "6ed92347-1996-4d43-e141-0a30bdf36b6d"
      },
      "source": [
        "model_report(model_3, test_adv_loader)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 50/50 [00:03<00:00, 13.45it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "Validation loss is: 0.18358927626919466\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.90      0.87      0.89       103\n",
            "           1       0.86      0.93      0.90        92\n",
            "           2       1.00      0.98      0.99       205\n",
            "\n",
            "    accuracy                           0.94       400\n",
            "   macro avg       0.92      0.93      0.92       400\n",
            "weighted avg       0.94      0.94      0.94       400\n",
            "\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "o7-wZkjhntIi"
      },
      "source": [
        "#### Detector approach\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gTNs5Cz9Yghd"
      },
      "source": [
        "def collate_for_detector(batch):\n",
        "    data = [item[0] for item in batch]\n",
        "    target = []\n",
        "\n",
        "    for item in batch:\n",
        "      if int(item[1]) < 2:\n",
        "        target.append(0)\n",
        "      else:\n",
        "        target.append(1)\n",
        "    data = torch.stack(data)\n",
        "    target = torch.LongTensor(target)\n",
        "    return [data, target]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SnXIaMVKXCSw"
      },
      "source": [
        "train_loader = DataLoader(train_dataset, batch_size, shuffle=True, num_workers=2)\n",
        "test_loader = DataLoader(test_dataset, batch_size, num_workers=2)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8HysNOX4oL3P"
      },
      "source": [
        "train_adv_loader_d = DataLoader(train_adv_dataset, 8, shuffle=True, collate_fn = collate_for_detector, num_workers=2)\n",
        "test_adv_loader_d = DataLoader(test_adv_dataset, 8, collate_fn = collate_for_detector, num_workers=2)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a7roG5bpoTwI",
        "outputId": "ccec7900-75a6-4dfe-824c-296db4955268"
      },
      "source": [
        "model_d = CustomCNN(3, 2).to(\"cuda\")\n",
        "lr_d = 1e-4\n",
        "optimizer_d = optim.Adam(model_d.parameters(), lr=lr_d)\n",
        "scheduler_d = StepLR(optimizer_d, step_size=5, gamma=0.1)\n",
        "loss = nn.CrossEntropyLoss()\n",
        "best_model_d = train(model_d, optimizer_d, loss, train_adv_loader_d, test_adv_loader_d, epochs=6, device=\"cuda\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 450/450 [01:17<00:00,  5.82it/s]\n",
            "100%|██████████| 50/50 [00:04<00:00, 11.50it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch: 1, Training Loss: 1.21, Validation Loss: 0.93, accuracy = 0.53\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 450/450 [01:17<00:00,  5.79it/s]\n",
            "100%|██████████| 50/50 [00:04<00:00, 11.67it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch: 2, Training Loss: 0.66, Validation Loss: 0.42, accuracy = 0.82\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 450/450 [01:18<00:00,  5.76it/s]\n",
            "100%|██████████| 50/50 [00:04<00:00, 11.19it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch: 3, Training Loss: 0.33, Validation Loss: 0.19, accuracy = 0.92\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 450/450 [01:18<00:00,  5.74it/s]\n",
            "100%|██████████| 50/50 [00:04<00:00, 11.39it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch: 4, Training Loss: 0.19, Validation Loss: 0.13, accuracy = 0.94\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 450/450 [01:18<00:00,  5.73it/s]\n",
            "100%|██████████| 50/50 [00:04<00:00, 11.23it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch: 5, Training Loss: 0.16, Validation Loss: 0.10, accuracy = 0.97\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 450/450 [01:18<00:00,  5.74it/s]\n",
            "100%|██████████| 50/50 [00:04<00:00, 11.41it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch: 6, Training Loss: 0.14, Validation Loss: 0.13, accuracy = 0.94\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HLesCRgTyWM8"
      },
      "source": [
        "torch.save(best_model_d.state_dict(), 'best_model_d.pt')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q3yhwFXpyUtx",
        "outputId": "dbf45a22-9722-4c26-e045-30e101aad5bf"
      },
      "source": [
        "best_model_d = CustomCNN(3, 2).to(\"cuda\")\n",
        "best_model_d.load_state_dict(torch.load(\"/content/best_model_d.pt\"))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<All keys matched successfully>"
            ]
          },
          "execution_count": null,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w_qYRDliGiH_",
        "outputId": "92e5224d-c0fa-4b59-b50c-bb27389e9e87"
      },
      "source": [
        "model_report(model_d, test_adv_loader_d)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 50/50 [00:04<00:00, 11.27it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "Validation loss is: 0.13098596154093683\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.99      0.90      0.95       219\n",
            "           1       0.90      0.99      0.94       181\n",
            "\n",
            "    accuracy                           0.94       400\n",
            "   macro avg       0.94      0.95      0.94       400\n",
            "weighted avg       0.95      0.94      0.94       400\n",
            "\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1qmFvJqm0Raq"
      },
      "source": [
        "### Perfect-Knowledge Attack Evaluation"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UFkgja600VNl"
      },
      "source": [
        "#### Approach woth $F_{base}$ and $F_{secure}$"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D8IJQvGFsYbg"
      },
      "source": [
        "def attacked_model_report(model, test_loader, attack_f, args):\n",
        "  preds = []\n",
        "  targets = []\n",
        "  loss_fn = nn.CrossEntropyLoss()\n",
        "  valid_loss = 0.0\n",
        "\n",
        "  for (X, y) in tqdm(test_loader):\n",
        "      X, y = X.to(\"cuda\"), torch.full(torch.Size([X.size()[0]]), 2).to(\"cuda\")\n",
        "      delta = attack_f(model, loss_fn, X, y, *args)\n",
        "      output = model(X+delta)\n",
        "      pred = torch.max(output, dim=1)[1]\n",
        "      preds += list(pred.to(\"cpu\").numpy())\n",
        "      targets += list([2 for i in range(len(y.to(\"cpu\").numpy()))])\n",
        "      loss = loss_fn(output, y)\n",
        "      valid_loss += loss.data * X.size(0)\n",
        "  \n",
        "  valid_loss /= len(test_loader.dataset)\n",
        "\n",
        "  print()\n",
        "  print()\n",
        "  print(\"Validation loss is: {}\".format(valid_loss))\n",
        "  print(classification_report(preds, targets, labels=[2]))\n",
        "  "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kuTOOM1mzdJJ",
        "outputId": "51686b36-84c8-4fdc-c2fe-768825232d2d"
      },
      "source": [
        "args = [0.1]\n",
        "attacked_model_report(model_3, test_loader, fgsm, args)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 7/7 [00:04<00:00,  1.61it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "Validation loss is: 0.49319323897361755\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           2       0.79      1.00      0.88       158\n",
            "\n",
            "   micro avg       0.79      1.00      0.88       158\n",
            "   macro avg       0.79      1.00      0.88       158\n",
            "weighted avg       0.79      1.00      0.88       158\n",
            "\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c858uZnz0tYQ"
      },
      "source": [
        "#### Detector approach"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mZRoVPEqXOiS"
      },
      "source": [
        "train_loader = DataLoader(train_dataset, 1, shuffle=True, num_workers=2)\n",
        "test_loader = DataLoader(test_dataset, 1, num_workers=2)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0f5b4od70dGV"
      },
      "source": [
        "def adaptive_attacked_model_report(model, model_d, test_loader, attack_f, args):\n",
        "  preds = []\n",
        "  targets = []\n",
        "  loss_fn = nn.CrossEntropyLoss()\n",
        "  valid_loss = 0.0\n",
        "\n",
        "  for (X, y) in tqdm(test_loader):\n",
        "      X, y = X.to(\"cuda\"), torch.full(torch.Size([X.size()[0]]), 1).to(\"cuda\")\n",
        "      delta = attack_f(model, model_d, X, y[0], *args)\n",
        "      output = model_d(X+delta)\n",
        "      pred = torch.max(output, dim=1)[1]\n",
        "      preds += list(pred.to(\"cpu\"))\n",
        "      targets += list(y.to(\"cpu\"))\n",
        "      loss = loss_fn(output, y)\n",
        "      valid_loss += loss.data.item() * X.size(0)\n",
        "  \n",
        "  valid_loss /= len(test_loader.dataset)\n",
        "\n",
        "  print()\n",
        "  print()\n",
        "  print(\"Validation loss is: {}\".format(valid_loss))\n",
        "  print(classification_report(preds, targets, labels=[1]))\n",
        "  "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G9rR-LqhQUVv"
      },
      "source": [
        "def adaptive_fgsm(model, model_d, X, y, e):\n",
        "    m = nn.Sigmoid()\n",
        "    loss_fn = nn.BCELoss()\n",
        "    delta = torch.zeros_like(X, requires_grad=True, device=\"cuda\")\n",
        "    X, y = X.to(\"cuda\"), torch.tensor(1).to(\"cuda\")\n",
        "\n",
        "    Z_d = model_d(X + delta)\n",
        "    Z_f = model(X + delta)\n",
        "\n",
        "    if torch.argmax(Z_d) == 0:\n",
        "      loss = loss_fn(m(Z_f[0][y.item()]), y.float())\n",
        "\n",
        "    else:\n",
        "\n",
        "      loss = loss_fn(m((Z_d[0][1]+1)*torch.max(Z_f[0])), y.float())\n",
        "\n",
        "    loss.backward()\n",
        "\n",
        "\n",
        "    g = delta.grad.detach()\n",
        "    return e * g.sign()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7knNT0iBb4vZ",
        "outputId": "373d9e32-46e8-4a40-85bd-5ddec20ab7bc"
      },
      "source": [
        "adaptive_attacked_model_report(model_2, model_d, test_loader, adaptive_fgsm, [0.1])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 200/200 [00:09<00:00, 20.29it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "Validation loss is: 0.2467489069249632\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           1       0.89      1.00      0.94       178\n",
            "\n",
            "   micro avg       0.89      1.00      0.94       178\n",
            "   macro avg       0.89      1.00      0.94       178\n",
            "weighted avg       0.89      1.00      0.94       178\n",
            "\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E4CDEUQGaAhI"
      },
      "source": [
        "### Limited-Knowledge Attack Evaluation"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AK_cShI70-co"
      },
      "source": [
        "#### Approach woth $F_{base}$ and $F_{secure}$"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nWNWwOTnqxzT"
      },
      "source": [
        "subset_a, subset_d = torch.utils.data.random_split(train_adv_dataset, [int(len(train_adv_dataset)/2), int(len(train_adv_dataset)/2)])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ukyqgFS08D9g"
      },
      "source": [
        "train_loader_a = DataLoader(subset_a, 32, shuffle=True, collate_fn=collate, num_workers=2)\n",
        "train_loader_d = DataLoader(subset_d, 32, shuffle=True, collate_fn=collate, num_workers=2)\n",
        "test_adv_loader = DataLoader(test_adv_dataset, 32, collate_fn=collate, num_workers=2)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DX5tTp_A8MV7",
        "outputId": "c17bb746-2d46-4145-dc67-736fe9c486ba"
      },
      "source": [
        "model_3_defender = CustomCNN(3, 3).to(\"cuda\")\n",
        "lr_3_defender = 1e-4\n",
        "optimizer_3_defender = optim.Adam(model_3_defender.parameters(), lr=lr_3_defender)\n",
        "scheduler_3_defender = StepLR(optimizer_3_defender, step_size=5, gamma=0.1)\n",
        "loss = nn.CrossEntropyLoss()\n",
        "best_model_3_defender = train(model_3_defender, optimizer_3_defender, loss, train_loader_a, test_adv_loader, epochs=10, device=\"cuda\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 57/57 [00:18<00:00,  3.01it/s]\n",
            "100%|██████████| 13/13 [00:03<00:00,  4.02it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch: 1, Training Loss: 2.35, Validation Loss: 1.22, accuracy = 0.38\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 57/57 [00:18<00:00,  3.02it/s]\n",
            "100%|██████████| 13/13 [00:03<00:00,  4.03it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch: 2, Training Loss: 1.01, Validation Loss: 1.11, accuracy = 0.43\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 57/57 [00:18<00:00,  3.01it/s]\n",
            "100%|██████████| 13/13 [00:03<00:00,  4.05it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch: 3, Training Loss: 0.85, Validation Loss: 1.01, accuracy = 0.61\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 57/57 [00:18<00:00,  3.01it/s]\n",
            "100%|██████████| 13/13 [00:03<00:00,  4.06it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch: 4, Training Loss: 0.62, Validation Loss: 0.72, accuracy = 0.72\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 57/57 [00:18<00:00,  3.03it/s]\n",
            "100%|██████████| 13/13 [00:03<00:00,  4.07it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch: 5, Training Loss: 0.42, Validation Loss: 0.70, accuracy = 0.69\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 57/57 [00:19<00:00,  2.99it/s]\n",
            "100%|██████████| 13/13 [00:03<00:00,  3.99it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch: 6, Training Loss: 0.28, Validation Loss: 0.86, accuracy = 0.75\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 57/57 [00:18<00:00,  3.02it/s]\n",
            "100%|██████████| 13/13 [00:03<00:00,  3.99it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch: 7, Training Loss: 0.29, Validation Loss: 0.78, accuracy = 0.79\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 57/57 [00:18<00:00,  3.03it/s]\n",
            "100%|██████████| 13/13 [00:03<00:00,  4.04it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch: 8, Training Loss: 0.20, Validation Loss: 0.95, accuracy = 0.75\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 57/57 [00:18<00:00,  3.06it/s]\n",
            "100%|██████████| 13/13 [00:03<00:00,  4.09it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch: 9, Training Loss: 0.15, Validation Loss: 0.40, accuracy = 0.86\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 57/57 [00:18<00:00,  3.02it/s]\n",
            "100%|██████████| 13/13 [00:03<00:00,  4.14it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch: 10, Training Loss: 0.10, Validation Loss: 0.40, accuracy = 0.86\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-rOqIMi2-zn1",
        "outputId": "8de6f9ef-104f-46d6-991d-c8531decb012"
      },
      "source": [
        "model_3_attacker = CustomCNN(3, 3).to(\"cuda\")\n",
        "lr_3_attacker = 1e-4\n",
        "optimizer_3_attacker = optim.Adam(model_3_attacker.parameters(), lr=lr_3_attacker)\n",
        "scheduler_3_attacker = StepLR(optimizer_3_attacker, step_size=5, gamma=0.1)\n",
        "loss = nn.CrossEntropyLoss()\n",
        "best_model_3_attacker = train(model_3_attacker, optimizer_3_attacker, loss, train_loader_d, test_adv_loader, epochs=10, device=\"cuda\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 57/57 [00:18<00:00,  3.01it/s]\n",
            "100%|██████████| 13/13 [00:03<00:00,  4.17it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch: 1, Training Loss: 2.73, Validation Loss: 1.08, accuracy = 0.50\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 57/57 [00:18<00:00,  3.03it/s]\n",
            "100%|██████████| 13/13 [00:03<00:00,  4.09it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch: 2, Training Loss: 1.00, Validation Loss: 1.11, accuracy = 0.50\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 57/57 [00:18<00:00,  3.04it/s]\n",
            "100%|██████████| 13/13 [00:03<00:00,  4.01it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch: 3, Training Loss: 0.95, Validation Loss: 1.03, accuracy = 0.51\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 57/57 [00:18<00:00,  3.02it/s]\n",
            "100%|██████████| 13/13 [00:03<00:00,  4.01it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch: 4, Training Loss: 0.89, Validation Loss: 0.97, accuracy = 0.49\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 57/57 [00:18<00:00,  3.02it/s]\n",
            "100%|██████████| 13/13 [00:03<00:00,  4.00it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch: 5, Training Loss: 0.84, Validation Loss: 0.90, accuracy = 0.55\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 57/57 [00:18<00:00,  3.01it/s]\n",
            "100%|██████████| 13/13 [00:03<00:00,  3.96it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch: 6, Training Loss: 0.72, Validation Loss: 0.73, accuracy = 0.70\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 57/57 [00:18<00:00,  3.03it/s]\n",
            "100%|██████████| 13/13 [00:03<00:00,  4.01it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch: 7, Training Loss: 0.48, Validation Loss: 0.62, accuracy = 0.72\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 57/57 [00:18<00:00,  3.00it/s]\n",
            "100%|██████████| 13/13 [00:03<00:00,  4.07it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch: 8, Training Loss: 0.30, Validation Loss: 0.55, accuracy = 0.79\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 57/57 [00:18<00:00,  3.01it/s]\n",
            "100%|██████████| 13/13 [00:03<00:00,  3.95it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch: 9, Training Loss: 0.26, Validation Loss: 0.32, accuracy = 0.87\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 57/57 [00:18<00:00,  3.04it/s]\n",
            "100%|██████████| 13/13 [00:03<00:00,  3.97it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch: 10, Training Loss: 0.17, Validation Loss: 0.41, accuracy = 0.83\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vJJp8M9e94c9"
      },
      "source": [
        "def limited_attacked_model_report(model_d, model_a, test_loader, attack_f, args):\n",
        "  preds = []\n",
        "  targets = []\n",
        "  loss_fn = nn.CrossEntropyLoss()\n",
        "  valid_loss = 0.0\n",
        "\n",
        "  for (X, y) in tqdm(test_loader):\n",
        "      X, y = X.to(\"cuda\"), torch.full(torch.Size([X.size()[0]]), 2).to(\"cuda\")\n",
        "      delta = attack_f(model_a, loss_fn, X, y, *args)\n",
        "      output = model_d(X+delta)\n",
        "      pred = torch.max(output, dim=1)[1]\n",
        "      preds += list(pred.to(\"cpu\"))\n",
        "      targets += list(y.to(\"cpu\"))\n",
        "      loss = loss_fn(output, y)\n",
        "      valid_loss += loss.data.item() * X.size(0)\n",
        "  \n",
        "  valid_loss /= len(test_loader.dataset)\n",
        "\n",
        "  print()\n",
        "  print()\n",
        "  print(\"Validation loss is: {}\".format(valid_loss))\n",
        "  print(classification_report(preds, targets, labels=[2]))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NUowtK1kBJvQ",
        "outputId": "64a4f417-f7d9-4c9e-f143-3637b0957fa5"
      },
      "source": [
        "limited_attacked_model_report(model_3_defender, model_3_attacker, test_adv_loader, fgsm, [0.1])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 13/13 [00:05<00:00,  2.59it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "Validation loss is: 0.03834956189384684\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           2       0.98      1.00      0.99       394\n",
            "\n",
            "   micro avg       0.98      1.00      0.99       394\n",
            "   macro avg       0.98      1.00      0.99       394\n",
            "weighted avg       0.98      1.00      0.99       394\n",
            "\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "APTDTs3E1PeW"
      },
      "source": [
        "#### Approach with detector"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WqdPpjGQRQDF"
      },
      "source": [
        "train_adv_loader_d = DataLoader(subset_d, 8, shuffle=True, collate_fn = collate_for_detector, num_workers=2)\n",
        "train_adv_loader_a = DataLoader(subset_a, 8, shuffle=True, collate_fn = collate_for_detector, num_workers=2)\n",
        "test_loader = DataLoader(test_dataset, 1, num_workers=2)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6RPqRIM2RhEf",
        "outputId": "da73d651-1e50-4950-dd58-5910b637434e"
      },
      "source": [
        "model_defender = CustomCNN(3, 2).to(\"cuda\")\n",
        "lr_defender = 1e-4\n",
        "optimizer_defender = optim.Adam(model_defender.parameters(), lr=lr_defender)\n",
        "scheduler_defender = StepLR(optimizer_defender, step_size=5, gamma=0.1)\n",
        "loss = nn.CrossEntropyLoss()\n",
        "best_model_defender = train(model_defender, optimizer_defender, loss, train_adv_loader_d, test_adv_loader_d, epochs=6, device=\"cuda\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 225/225 [00:39<00:00,  5.74it/s]\n",
            "100%|██████████| 50/50 [00:03<00:00, 12.75it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch: 1, Training Loss: 1.89, Validation Loss: 1.10, accuracy = 0.52\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 225/225 [00:39<00:00,  5.76it/s]\n",
            "100%|██████████| 50/50 [00:03<00:00, 13.00it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch: 2, Training Loss: 0.84, Validation Loss: 0.70, accuracy = 0.66\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 225/225 [00:39<00:00,  5.76it/s]\n",
            "100%|██████████| 50/50 [00:03<00:00, 14.54it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch: 3, Training Loss: 0.50, Validation Loss: 0.25, accuracy = 0.89\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 225/225 [00:39<00:00,  5.71it/s]\n",
            "100%|██████████| 50/50 [00:03<00:00, 15.98it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch: 4, Training Loss: 0.25, Validation Loss: 0.58, accuracy = 0.79\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 225/225 [00:39<00:00,  5.69it/s]\n",
            "100%|██████████| 50/50 [00:03<00:00, 16.38it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch: 5, Training Loss: 0.19, Validation Loss: 0.13, accuracy = 0.94\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 225/225 [00:39<00:00,  5.70it/s]\n",
            "100%|██████████| 50/50 [00:03<00:00, 16.15it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch: 6, Training Loss: 0.22, Validation Loss: 0.29, accuracy = 0.86\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NwfFZvvWR9tl",
        "outputId": "39255915-6d0c-47f7-a15c-c2f99745b68a"
      },
      "source": [
        "model_attacker = CustomCNN(3, 2).to(\"cuda\")\n",
        "lr_attacker = 1e-4\n",
        "optimizer_attacker = optim.Adam(model_attacker.parameters(), lr=lr_attacker)\n",
        "scheduler_attacker = StepLR(optimizer_attacker, step_size=5, gamma=0.1)\n",
        "loss = nn.CrossEntropyLoss()\n",
        "best_model_defender = train(model_attacker, optimizer_attacker, loss, train_adv_loader_a, test_adv_loader_d, epochs=6, device=\"cuda\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 225/225 [00:38<00:00,  5.78it/s]\n",
            "100%|██████████| 50/50 [00:03<00:00, 16.46it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch: 1, Training Loss: 1.80, Validation Loss: 0.80, accuracy = 0.56\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 225/225 [00:38<00:00,  5.79it/s]\n",
            "100%|██████████| 50/50 [00:03<00:00, 16.43it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch: 2, Training Loss: 0.84, Validation Loss: 0.71, accuracy = 0.59\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 225/225 [00:38<00:00,  5.78it/s]\n",
            "100%|██████████| 50/50 [00:02<00:00, 17.00it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch: 3, Training Loss: 0.63, Validation Loss: 0.48, accuracy = 0.77\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 225/225 [00:39<00:00,  5.74it/s]\n",
            "100%|██████████| 50/50 [00:02<00:00, 16.89it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch: 4, Training Loss: 0.43, Validation Loss: 0.36, accuracy = 0.84\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 225/225 [00:39<00:00,  5.70it/s]\n",
            "100%|██████████| 50/50 [00:03<00:00, 16.39it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch: 5, Training Loss: 0.33, Validation Loss: 0.25, accuracy = 0.89\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 225/225 [00:39<00:00,  5.70it/s]\n",
            "100%|██████████| 50/50 [00:03<00:00, 15.78it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch: 6, Training Loss: 0.26, Validation Loss: 0.19, accuracy = 0.92\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1ASFUXgfhHjZ"
      },
      "source": [
        "def limited_adaptive_attack_model_report(model, model_detector_defenser,\n",
        "                                         model_detector_attacker, test_loader, attack_f, args):\n",
        "  preds = []\n",
        "  targets = []\n",
        "  loss_fn = nn.CrossEntropyLoss()\n",
        "  valid_loss = 0.0\n",
        "\n",
        "  for (X, y) in tqdm(test_loader):\n",
        "      X, y = X.to(\"cuda\"), torch.full(torch.Size([X.size()[0]]), 1).to(\"cuda\")\n",
        "      delta = attack_f(model, model_detector_attacker, X, y, *args)\n",
        "      output = model_detector_defenser(X+delta)\n",
        "      pred = torch.max(output, dim=1)[1]\n",
        "      preds += [pred.to(\"cpu\").item()]\n",
        "      targets += [y.to(\"cpu\").item()]\n",
        "      loss = loss_fn(output, y)\n",
        "      valid_loss += loss.data.item() * X.size(0)\n",
        "  \n",
        "  valid_loss /= len(test_loader.dataset)\n",
        "\n",
        "  print()\n",
        "  print()\n",
        "  print(\"Validation loss is: {}\".format(valid_loss))\n",
        "  print(classification_report(preds, targets, labels=[1]))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rOkKabRGhsk7",
        "outputId": "3e9ff266-ad1b-414e-b744-58c44abbbd57"
      },
      "source": [
        "limited_adaptive_attack_model_report(model_2, model_defender, model_attacker, test_loader, adaptive_fgsm, [0.1])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 200/200 [00:10<00:00, 19.08it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "Validation loss is: 0.2860299956036351\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           1       0.87      1.00      0.93       174\n",
            "\n",
            "   micro avg       0.87      1.00      0.93       174\n",
            "   macro avg       0.87      1.00      0.93       174\n",
            "weighted avg       0.87      1.00      0.93       174\n",
            "\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        }
      ]
    }
  ]
}