Standardise processing requires (#50042)

* remove redundant requires

* cleanup more requires

* more cleanup

* even more

* and more

Author: guarin

src/transformers/models/chmv2/image_processing_chmv2.py

@@ -33,7 +33,7 @@
 from ...image_utils import ChannelDimension, ImageInput, PILImageResampling, SizeDict
 from ...modeling_outputs import DepthEstimatorOutput
 from ...processing_utils import ImagesKwargs, Unpack
-from ...utils import TensorType, auto_docstring, is_torch_available, requires_backends
+from ...utils import TensorType, auto_docstring, is_torch_available
 
 
 class CHMv2ImageProcessorKwargs(ImagesKwargs, total=False):
@@ -402,7 +402,6 @@ def post_process_depth_estimation(
             `List[Dict[str, TensorType]]`: A list of dictionaries of tensors representing the processed depth
             predictions.
         """
-        requires_backends(self, "torch")
 
         predicted_depth = outputs.predicted_depth
 

src/transformers/models/chmv2/modular_chmv2.py

@@ -25,7 +25,7 @@
 from ...modeling_outputs import DepthEstimatorOutput
 from ...modeling_utils import PreTrainedModel
 from ...processing_utils import ImagesKwargs, Unpack
-from ...utils import TensorType, TransformersKwargs, auto_docstring, can_return_tuple, requires_backends
+from ...utils import TensorType, TransformersKwargs, auto_docstring, can_return_tuple
 from ..auto import AutoConfig
 from ..depth_anything.modeling_depth_anything import (
     DepthAnythingPreActResidualLayer,
@@ -166,7 +166,6 @@ def post_process_depth_estimation(
             `List[Dict[str, TensorType]]`: A list of dictionaries of tensors representing the processed depth
             predictions.
         """
-        requires_backends(self, "torch")
 
         predicted_depth = outputs.predicted_depth
 

src/transformers/models/conditional_detr/image_processing_pil_conditional_detr.py

@@ -50,7 +50,7 @@
     validate_annotations,
 )
 from ...processing_utils import ImagesKwargs, Unpack
-from ...utils import TensorType, auto_docstring, is_torch_available, is_vision_available, logging, requires_backends
+from ...utils import TensorType, auto_docstring, is_torch_available, is_vision_available, logging
 from ...utils.import_utils import requires
 
 
@@ -862,7 +862,6 @@ def post_process_object_detection(
             `list[Dict]`: A list of dictionaries, each dictionary containing the scores, labels and boxes for an image
             in the batch as predicted by the model.
         """
-        requires_backends(self, ["torch"])
         out_logits, out_bbox = outputs.logits, outputs.pred_boxes
 
         if target_sizes is not None:
@@ -929,7 +928,6 @@ def post_process_semantic_segmentation(
             `(height, width)`) and `segmentation_scores` (shape `(num_classes, height, width)`). In both cases,
             `(height, width)` corresponds to the target size (if `target_sizes` is specified).
         """
-        requires_backends(self, ["torch"])
         class_queries_logits = outputs.logits  # [batch_size, num_queries, num_classes]
         masks_queries_logits = outputs.pred_masks  # [batch_size, num_queries, height, width]
 
@@ -1012,11 +1010,6 @@ def post_process_instance_segmentation(
                 - **label_id** -- An integer representing the label / semantic class id corresponding to `segment_id`.
                 - **score** -- Prediction score of segment with `segment_id`.
         """
-        if not is_torch_available():
-            raise ImportError("PyTorch is required for post-processing.")
-        import torch
-        from torch import nn
-
         class_queries_logits = outputs.logits  # [batch_size, num_queries, num_classes+1]
         masks_queries_logits = outputs.pred_masks  # [batch_size, num_queries, height, width]
 
@@ -1110,11 +1103,6 @@ def post_process_panoptic_segmentation(
             logger.warning_once("`label_ids_to_fuse` unset. No instance will be fused.")
             label_ids_to_fuse = set()
 
-        if not is_torch_available():
-            raise ImportError("PyTorch is required for post-processing.")
-        import torch
-        from torch import nn
-
         class_queries_logits = outputs.logits  # [batch_size, num_queries, num_classes+1]
         masks_queries_logits = outputs.pred_masks  # [batch_size, num_queries, height, width]
 

src/transformers/models/conditional_detr/modular_conditional_detr.py

@@ -32,7 +32,6 @@
     TransformersKwargs,
     auto_docstring,
     logging,
-    requires_backends,
 )
 from ...utils.generic import can_return_tuple, merge_with_config_defaults
 from ...utils.import_utils import requires
@@ -258,7 +257,6 @@ def post_process_object_detection(
             `list[Dict]`: A list of dictionaries, each dictionary containing the scores, labels and boxes for an image
             in the batch as predicted by the model.
         """
-        requires_backends(self, ["torch"])
         out_logits, out_bbox = outputs.logits, outputs.pred_boxes
 
         if target_sizes is not None:
@@ -325,7 +323,6 @@ def post_process_semantic_segmentation(
             `(height, width)`) and `segmentation_scores` (shape `(num_classes, height, width)`). In both cases,
             `(height, width)` corresponds to the target size (if `target_sizes` is specified).
         """
-        requires_backends(self, ["torch"])
         class_queries_logits = outputs.logits  # [batch_size, num_queries, num_classes]
         masks_queries_logits = outputs.pred_masks  # [batch_size, num_queries, height, width]
 

src/transformers/models/deepseek_ocr2/image_processing_pil_deepseek_ocr2.py

@@ -37,7 +37,6 @@
 )
 from ...processing_utils import ImagesKwargs, Unpack
 from ...utils import TensorType, auto_docstring
-from ...utils.import_utils import requires
 
 
 class DeepseekOcr2ImageProcessorKwargs(ImagesKwargs, total=False):
@@ -139,7 +138,6 @@ def get_optimal_tiled_canvas(
     return best_grid
 
 
-@requires(backends=("vision",))
 @auto_docstring
 class DeepseekOcr2ImageProcessorPil(PilBackend):
     valid_kwargs = DeepseekOcr2ImageProcessorKwargs

src/transformers/models/deepseek_ocr2/modular_deepseek_ocr2.py

@@ -41,7 +41,6 @@
 from ...processing_utils import Unpack
 from ...utils import TensorType, TransformersKwargs, auto_docstring, can_return_tuple, logging
 from ...utils.generic import merge_with_config_defaults
-from ...utils.import_utils import requires
 from ...utils.output_capturing import capture_outputs
 from ..deepseek_v2.configuration_deepseek_v2 import DeepseekV2Config
 from ..deepseek_v2.modeling_deepseek_v2 import (
@@ -317,7 +316,6 @@ def get_number_of_image_patches(self, height: int, width: int, images_kwargs: di
         return num_patches
 
 
-@requires(backends=("vision",))
 @auto_docstring
 class DeepseekOcr2ImageProcessorPil(GotOcr2ImageProcessorPil):
     image_mean = IMAGENET_STANDARD_MEAN

src/transformers/models/deformable_detr/image_processing_pil_deformable_detr.py

@@ -49,7 +49,7 @@
 )
 from ...processing_utils import ImagesKwargs, Unpack
 from ...utils import TensorType, auto_docstring, is_torch_available, is_vision_available
-from ...utils.import_utils import requires, requires_backends
+from ...utils.import_utils import requires
 
 
 if is_vision_available():
@@ -696,7 +696,6 @@ def post_process_object_detection(
             `list[Dict]`: A list of dictionaries, each dictionary containing the scores, labels and boxes for an image
             in the batch as predicted by the model.
         """
-        requires_backends(self, ["torch"])
         out_logits, out_bbox = outputs.logits, outputs.pred_boxes
 
         if target_sizes is not None:

src/transformers/models/deformable_detr/modular_deformable_detr.py

@@ -37,7 +37,7 @@
     torch_compilable_check,
 )
 from ...utils.generic import can_return_tuple, merge_with_config_defaults
-from ...utils.import_utils import requires, requires_backends
+from ...utils.import_utils import requires
 from ...utils.output_capturing import OutputRecorder, capture_outputs
 from ..detr.image_processing_detr import DetrImageProcessor
 from ..detr.image_processing_pil_detr import DetrImageProcessorPil
@@ -155,7 +155,6 @@ def post_process_object_detection(
             `list[Dict]`: A list of dictionaries, each dictionary containing the scores, labels and boxes for an image
             in the batch as predicted by the model.
         """
-        requires_backends(self, ["torch"])
         out_logits, out_bbox = outputs.logits, outputs.pred_boxes
 
         if target_sizes is not None:

src/transformers/models/depth_pro/image_processing_depth_pro.py

@@ -32,7 +32,6 @@
     TensorType,
     auto_docstring,
     logging,
-    requires_backends,
 )
 
 
@@ -88,7 +87,6 @@ def post_process_depth_estimation(
         target_sizes: TensorType | list[tuple[int, int]] | None = None,
     ) -> list[dict[str, TensorType]]:
         """Post-processes the raw depth predictions from the model."""
-        requires_backends(self, "torch")
         predicted_depth = outputs.predicted_depth
         fov = outputs.field_of_view
         batch_size = len(predicted_depth)

src/transformers/models/detr/image_processing_pil_detr.py

@@ -57,6 +57,7 @@
 
 if is_torch_available():
     import torch
+    from torch import nn
 
 if is_vision_available():
     import PIL.Image
@@ -861,11 +862,6 @@ def post_process_object_detection(
             `list[Dict]`: A list of dictionaries, each dictionary containing the scores, labels and boxes for an image
             in the batch as predicted by the model.
         """
-        if not is_torch_available():
-            raise ImportError("PyTorch is required for post-processing.")
-        import torch
-        from torch import nn
-
         out_logits, out_bbox = outputs.logits, outputs.pred_boxes
 
         if target_sizes is not None:
@@ -929,11 +925,6 @@ def post_process_semantic_segmentation(
             `(height, width)`) and `segmentation_scores` (shape `(num_classes, height, width)`). In both cases,
             `(height, width)` corresponds to the target size (if `target_sizes` is specified).
         """
-        if not is_torch_available():
-            raise ImportError("PyTorch is required for post-processing.")
-        import torch
-        from torch import nn
-
         class_queries_logits = outputs.logits  # [batch_size, num_queries, num_classes+1]
         masks_queries_logits = outputs.pred_masks  # [batch_size, num_queries, height, width]
 
@@ -1016,11 +1007,6 @@ def post_process_instance_segmentation(
                 - **label_id** -- An integer representing the label / semantic class id corresponding to `segment_id`.
                 - **score** -- Prediction score of segment with `segment_id`.
         """
-        if not is_torch_available():
-            raise ImportError("PyTorch is required for post-processing.")
-        import torch
-        from torch import nn
-
         class_queries_logits = outputs.logits  # [batch_size, num_queries, num_classes+1]
         masks_queries_logits = outputs.pred_masks  # [batch_size, num_queries, height, width]
 
@@ -1114,11 +1100,6 @@ def post_process_panoptic_segmentation(
             logger.warning_once("`label_ids_to_fuse` unset. No instance will be fused.")
             label_ids_to_fuse = set()
 
-        if not is_torch_available():
-            raise ImportError("PyTorch is required for post-processing.")
-        import torch
-        from torch import nn
-
         class_queries_logits = outputs.logits  # [batch_size, num_queries, num_classes+1]
         masks_queries_logits = outputs.pred_masks  # [batch_size, num_queries, height, width]