code: 
  
  
  
  
  
  
  import argparse
  import json
  from pathlib import Path
  
  import requests
  import torch
  from huggingface_hub import cached_download, hf_hub_url
  from PIL import Image
  
  from transformers import DeformableDetrConfig, DeformableDetrForObjectDetection, DeformableDetrImageProcessor
  from transformers.utils import logging
  
  
  logging.set_verbosity_info()
  lowerCamelCase			:							Tuple       =logging.get_logger(__name__)
  
  
  
  
  
  
  
  def 	SCREAMING_SNAKE_CASE							(      __lowerCAmelCase    )				->			List[Any]:
       if "backbone.0.body" in orig_key:
            UpperCamelCase__    :     Union[str, Any]        =						orig_key.replace("backbone.0.body"      ,					"backbone.conv_encoder.model"    )
       if "transformer" in orig_key:
            UpperCamelCase__    :     Dict        =						orig_key.replace("transformer."      ,					""    )
       if "norm1" in orig_key:
            if "encoder" in orig_key:
                 UpperCamelCase__    :     Any        =						orig_key.replace("norm1"      ,					"self_attn_layer_norm"    )
            else:
                 UpperCamelCase__    :     int        =						orig_key.replace("norm1"      ,					"encoder_attn_layer_norm"    )
       if "norm2" in orig_key:
            if "encoder" in orig_key:
                 UpperCamelCase__    :     List[Any]        =						orig_key.replace("norm2"      ,					"final_layer_norm"    )
            else:
                 UpperCamelCase__    :     Union[str, Any]        =						orig_key.replace("norm2"      ,					"self_attn_layer_norm"    )
       if "norm3" in orig_key:
            UpperCamelCase__    :     Union[str, Any]        =						orig_key.replace("norm3"      ,					"final_layer_norm"    )
       if "linear1" in orig_key:
            UpperCamelCase__    :     Union[str, Any]        =						orig_key.replace("linear1"      ,					"fc1"    )
       if "linear2" in orig_key:
            UpperCamelCase__    :     int        =						orig_key.replace("linear2"      ,					"fc2"    )
       if "query_embed" in orig_key:
            UpperCamelCase__    :     Union[str, Any]        =						orig_key.replace("query_embed"      ,					"query_position_embeddings"    )
       if "cross_attn" in orig_key:
            UpperCamelCase__    :     List[str]        =						orig_key.replace("cross_attn"      ,					"encoder_attn"    )
  
       return orig_key
  
  
  
  
  
  
  
  def 	SCREAMING_SNAKE_CASE							(      __lowerCAmelCase    )				->			Tuple:
       # transformer decoder self-attention layers
       for i in range(6    ):
            # read in weights + bias of input projection layer of self-attention
            UpperCamelCase__    :     int        =						state_dict.pop(f'decoder.layers.{i}.self_attn.in_proj_weight'    )
            UpperCamelCase__    :     Optional[int]        =						state_dict.pop(f'decoder.layers.{i}.self_attn.in_proj_bias'    )
            # next, add query, keys and values (in that order) to the state dict
            UpperCamelCase__    :     List[str]        =						in_proj_weight[:256, :]
            UpperCamelCase__    :     Union[str, Any]        =						in_proj_bias[:256]
            UpperCamelCase__    :     int        =						in_proj_weight[256:512, :]
            UpperCamelCase__    :     Tuple        =						in_proj_bias[256:512]
            UpperCamelCase__    :     Optional[int]        =						in_proj_weight[-256:, :]
            UpperCamelCase__    :     str        =						in_proj_bias[-256:]
  
  
  
  
  
  
  
  def 	SCREAMING_SNAKE_CASE							(      )				->			Union[str, Any]:
       UpperCamelCase__    :     List[str]        =						"http://images.cocodataset.org/val2017/000000039769.jpg"
       UpperCamelCase__    :     List[Any]        =						Image.open(requests.get(__lowerCAmelCase      ,					stream=__lowerCAmelCase    ).raw    )
  
       return im
  
  
  
  
  
  
  
  @torch.no_grad()
  def 	SCREAMING_SNAKE_CASE							(      __lowerCAmelCase      ,					__lowerCAmelCase      ,					__lowerCAmelCase      ,					__lowerCAmelCase      ,					__lowerCAmelCase      ,					__lowerCAmelCase      ,					__lowerCAmelCase      ,					)				->			Optional[Any]:
       UpperCamelCase__    :     List[Any]        =						DeformableDetrConfig()
       # set config attributes
       if single_scale:
            UpperCamelCase__    :     Tuple        =						1
       UpperCamelCase__    :     List[Any]        =						dilation
       UpperCamelCase__    :     List[Any]        =						with_box_refine
       UpperCamelCase__    :     str        =						two_stage
       # set labels
       UpperCamelCase__    :     List[Any]        =						91
       UpperCamelCase__    :     Dict        =						"huggingface/label-files"
       UpperCamelCase__    :     Any        =						"coco-detection-id2label.json"
       UpperCamelCase__    :     Optional[int]        =						json.load(open(cached_download(hf_hub_url(__lowerCAmelCase      ,					__lowerCAmelCase      ,					repo_type="dataset"    )    )      ,					"r"    )    )
       UpperCamelCase__    :     List[Any]        =						{int(__lowerCAmelCase    ): v for k, v in idalabel.items()}
       UpperCamelCase__    :     Any        =						idalabel
       UpperCamelCase__    :     int        =						{v: k for k, v in idalabel.items()}
  
       # load image processor
       UpperCamelCase__    :     Any        =						DeformableDetrImageProcessor(format="coco_detection"    )
  
       # prepare image
       UpperCamelCase__    :     Tuple        =						prepare_img()
       UpperCamelCase__    :     Optional[Any]        =						image_processor(images=__lowerCAmelCase      ,					return_tensors="pt"    )
       UpperCamelCase__    :     str        =						encoding["pixel_values"]
  
       logger.info("Converting model..."    )
  
       # load original state dict
       UpperCamelCase__    :     Any        =						torch.load(__lowerCAmelCase      ,					map_location="cpu"    )["model"]
       # rename keys
       for key in state_dict.copy().keys():
            UpperCamelCase__    :     Any        =						state_dict.pop(__lowerCAmelCase    )
            UpperCamelCase__    :     List[Any]        =						val
       # query, key and value matrices need special treatment
       read_in_q_k_v(__lowerCAmelCase    )
       # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
       UpperCamelCase__    :     Tuple        =						"model."
       for key in state_dict.copy().keys():
            if not key.startswith("class_embed"    ) and not key.startswith("bbox_embed"    ):
                 UpperCamelCase__    :     Optional[int]        =						state_dict.pop(__lowerCAmelCase    )
                 UpperCamelCase__    :     Optional[int]        =						val
      # finally, create HuggingFace model and load state dict
       UpperCamelCase__    :     Optional[int]        =						DeformableDetrForObjectDetection(__lowerCAmelCase    )
       model.load_state_dict(__lowerCAmelCase    )
       model.eval()
  
       UpperCamelCase__    :     str        =						"cuda" if torch.cuda.is_available() else "cpu"
       model.to(__lowerCAmelCase    )
       # verify our conversion
       UpperCamelCase__    :     Tuple        =						model(pixel_values.to(__lowerCAmelCase    )    )
  
       UpperCamelCase__    :     List[str]        =						torch.tensor(
           [[-9.6_6_4_5, -4.3_4_4_9, -5.8_7_0_5], [-9.7_0_3_5, -3.8_5_0_4, -5.0_7_2_4], [-1_0.5_6_3_4, -5.3_3_7_9, -7.5_1_1_6]]    )
       UpperCamelCase__    :     int        =						torch.tensor([[0.8_6_9_3, 0.2_2_8_9, 0.2_4_9_2], [0.3_1_5_0, 0.5_4_8_9, 0.5_8_4_5], [0.5_5_6_3, 0.7_5_8_0, 0.8_5_1_8]]    )
  
       if single_scale:
            UpperCamelCase__    :     Union[str, Any]        =						torch.tensor(
                [[-9.9_0_5_1, -4.2_5_4_1, -6.4_8_5_2], [-9.6_9_4_7, -4.0_8_5_4, -6.8_0_3_3], [-1_0.0_6_6_5, -5.8_4_7_0, -7.7_0_0_3]]    )
            UpperCamelCase__    :     Dict        =						torch.tensor([[0.7_2_9_2, 0.4_9_9_1, 0.5_5_3_2], [0.7_9_5_9, 0.2_4_2_6, 0.4_2_3_6], [0.7_5_8_2, 0.3_5_1_8, 0.4_4_5_1]]    )
  
       if single_scale and dilation:
            UpperCamelCase__    :     Any        =						torch.tensor(
                [[-8.9_6_5_2, -4.1_0_7_4, -5.6_6_3_5], [-9.0_5_9_6, -4.9_4_4_7, -6.6_0_7_5], [-1_0.1_1_7_8, -4.5_2_7_5, -6.2_6_7_1]]    )
            UpperCamelCase__    :     Optional[int]        =						torch.tensor([[0.7_6_6_5, 0.4_1_3_0, 0.4_7_6_9], [0.8_3_6_4, 0.1_8_4_1, 0.3_3_9_1], [0.6_2_6_1, 0.3_8_9_5, 0.7_9_7_8]]    )
  
       if with_box_refine:
            UpperCamelCase__    :     Any        =						torch.tensor(
                [[-8.8_8_9_5, -5.4_1_8_7, -6.8_1_5_3], [-8.4_7_0_6, -6.1_6_6_8, -7.6_1_8_4], [-9.0_0_4_2, -5.5_3_5_9, -6.9_1_4_1]]    )
            UpperCamelCase__    :     Tuple        =						torch.tensor([[0.7_8_2_8, 0.2_2_0_8, 0.4_3_2_3], [0.0_8_9_2, 0.5_9_9_6, 0.1_3_1_9], [0.5_5_2_4, 0.6_3_8_9, 0.8_9_1_4]]    )
  
       if with_box_refine and two_stage:
            UpperCamelCase__    :     str        =						torch.tensor(
                [[-6.7_1_0_8, -4.3_2_1_3, -6.3_7_7_7], [-8.9_0_1_4, -6.1_7_9_9, -6.7_2_4_0], [-6.9_3_1_5, -4.4_7_3_5, -6.2_2_9_8]]    )
            UpperCamelCase__    :     Optional[int]        =						torch.tensor([[0.2_5_8_3, 0.5_4_9_9, 0.4_6_8_3], [0.7_6_5_2, 0.9_0_6_8, 0.4_8_8_2], [0.5_4_9_0, 0.2_7_6_3, 0.0_5_6_4]]    )
  
       print("Logits:"      ,					outputs.logits[0, :3, :3]    )
  
       assert torch.allclose(outputs.logits[0, :3, :3]      ,					expected_logits.to(__lowerCAmelCase    )      ,					atol=1E-4    )
       assert torch.allclose(outputs.pred_boxes[0, :3, :3]      ,					expected_boxes.to(__lowerCAmelCase    )      ,					atol=1E-4    )
  
       print("Everything ok!"    )
  
       # Save model and image processor
       logger.info(f'Saving PyTorch model and image processor to {pytorch_dump_folder_path}...'    )
       Path(__lowerCAmelCase    ).mkdir(exist_ok=__lowerCAmelCase    )
       model.save_pretrained(__lowerCAmelCase    )
       image_processor.save_pretrained(__lowerCAmelCase    )
  
       # Push to hub
       if push_to_hub:
            UpperCamelCase__    :     List[str]        =						"deformable-detr"
            model_name += "-single-scale" if single_scale else ""
            model_name += "-dc5" if dilation else ""
            model_name += "-with-box-refine" if with_box_refine else ""
            model_name += "-two-stage" if two_stage else ""
            print("Pushing model to hub..."    )
            model.push_to_hub(repo_path_or_name=__lowerCAmelCase      ,					organization="nielsr"      ,					commit_message="Add model"    )
  
  
  if __name__ == "__main__":
   lowerCamelCase			:							Optional[int]       =argparse.ArgumentParser()
  
   parser.add_argument(
       '''--checkpoint_path''',
       type=str,
       default='''/home/niels/checkpoints/deformable_detr/r50_deformable_detr-checkpoint.pth''',
       help='''Path to Pytorch checkpoint (.pth file) you\'d like to convert.''',
   )
   parser.add_argument('''--single_scale''', action='''store_true''', help='''Whether to set config.num_features_levels = 1.''')
   parser.add_argument('''--dilation''', action='''store_true''', help='''Whether to set config.dilation=True.''')
   parser.add_argument('''--with_box_refine''', action='''store_true''', help='''Whether to set config.with_box_refine=True.''')
   parser.add_argument('''--two_stage''', action='''store_true''', help='''Whether to set config.two_stage=True.''')
   parser.add_argument(
       '''--pytorch_dump_folder_path''',
       default=None,
       type=str,
       required=True,
       help='''Path to the folder to output PyTorch model.''',
   )
   parser.add_argument(
       '''--push_to_hub''', action='''store_true''', help='''Whether or not to push the converted model to the 🤗 hub.'''
   )
   lowerCamelCase			:							Dict       =parser.parse_args()
   convert_deformable_detr_checkpoint(
       args.checkpoint_path,
       args.single_scale,
       args.dilation,
       args.with_box_refine,
       args.two_stage,
       args.pytorch_dump_folder_path,
       args.push_to_hub,
   )
style_context: 
  
  
  
  
  
  
  import unittest
  
  from transformers import (
      MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
      TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
      PreTrainedTokenizer,
      is_vision_available,
  )
  from transformers.pipelines import ImageClassificationPipeline, pipeline
  from transformers.testing_utils import (
      is_pipeline_test,
      nested_simplify,
      require_tf,
      require_torch,
      require_torch_or_tf,
      require_vision,
      slow,
  )
  
  from .test_pipelines_common import ANY
  
  
  if is_vision_available():
   from PIL import Image
  else:
  
  
  
  
  
   class     __a						:
  
  
  
  
  
  
  
    @staticmethod
    def       __lowercase      (	*SCREAMING_SNAKE_CASE      :     Optional[Any]							,     **SCREAMING_SNAKE_CASE      :     Union[str, Any]		):
  
  
         '''simple docstring'''
  
  
  
  
  
  
         pass
  
  
  
  
  
  @is_pipeline_test
  @require_torch_or_tf
  @require_vision
  class     __a						(					unittest.TestCase ):
   _lowerCAmelCase : Tuple 				=				MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
   _lowerCAmelCase : str 				=				TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
  
  
  
  
  
  
  
   def       __lowercase      (	self      :     Union[str, Any]							,     SCREAMING_SNAKE_CASE      :     str							,     SCREAMING_SNAKE_CASE      :     Union[str, Any]							,     SCREAMING_SNAKE_CASE      :     Optional[Any]		):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        UpperCamelCase__    :     Dict        =						ImageClassificationPipeline(model=SCREAMING_SNAKE_CASE							,     image_processor=SCREAMING_SNAKE_CASE							,     top_k=2		)
        UpperCamelCase__    :     Union[str, Any]        =						[
            Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"		),
            "http://images.cocodataset.org/val2017/000000039769.jpg",
        ]
        return image_classifier, examples
  
  
  
  
  
  
  
   def       __lowercase      (	self      :     Tuple							,     SCREAMING_SNAKE_CASE      :     Tuple							,     SCREAMING_SNAKE_CASE      :     Tuple		):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        UpperCamelCase__    :     Optional[Any]        =						image_classifier("./tests/fixtures/tests_samples/COCO/000000039769.png"		)
  
        self.assertEqual(
            SCREAMING_SNAKE_CASE							,     [
                {"score": ANY(SCREAMING_SNAKE_CASE		), "label": ANY(SCREAMING_SNAKE_CASE		)},
                {"score": ANY(SCREAMING_SNAKE_CASE		), "label": ANY(SCREAMING_SNAKE_CASE		)},
            ]							,     )
  
        import datasets
  
        UpperCamelCase__    :     int        =						datasets.load_dataset("hf-internal-testing/fixtures_image_utils"							,     "image"							,     split="test"		)
  
        # Accepts URL + PIL.Image + lists
        UpperCamelCase__    :     Tuple        =						image_classifier(
            [
                Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"		),
                "http://images.cocodataset.org/val2017/000000039769.jpg",
                # RGBA
                dataset[0]["file"],
                # LA
                dataset[1]["file"],
                # L
                dataset[2]["file"],
            ]		)
        self.assertEqual(
            SCREAMING_SNAKE_CASE							,     [
                [
                    {"score": ANY(SCREAMING_SNAKE_CASE		), "label": ANY(SCREAMING_SNAKE_CASE		)},
                    {"score": ANY(SCREAMING_SNAKE_CASE		), "label": ANY(SCREAMING_SNAKE_CASE		)},
                ],
                [
                    {"score": ANY(SCREAMING_SNAKE_CASE		), "label": ANY(SCREAMING_SNAKE_CASE		)},
                    {"score": ANY(SCREAMING_SNAKE_CASE		), "label": ANY(SCREAMING_SNAKE_CASE		)},
                ],
                [
                    {"score": ANY(SCREAMING_SNAKE_CASE		), "label": ANY(SCREAMING_SNAKE_CASE		)},
                    {"score": ANY(SCREAMING_SNAKE_CASE		), "label": ANY(SCREAMING_SNAKE_CASE		)},
                ],
                [
                    {"score": ANY(SCREAMING_SNAKE_CASE		), "label": ANY(SCREAMING_SNAKE_CASE		)},
                    {"score": ANY(SCREAMING_SNAKE_CASE		), "label": ANY(SCREAMING_SNAKE_CASE		)},
                ],
                [
                    {"score": ANY(SCREAMING_SNAKE_CASE		), "label": ANY(SCREAMING_SNAKE_CASE		)},
                    {"score": ANY(SCREAMING_SNAKE_CASE		), "label": ANY(SCREAMING_SNAKE_CASE		)},
                ],
            ]							,     )
  
  
  
  
  
  
  
   @require_torch
   def       __lowercase      (	self      :     List[str]		):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        UpperCamelCase__    :     List[Any]        =						"hf-internal-testing/tiny-random-vit"
        UpperCamelCase__    :     List[str]        =						pipeline("image-classification"							,     model=SCREAMING_SNAKE_CASE		)
  
        UpperCamelCase__    :     List[str]        =						image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg"		)
        self.assertEqual(
            nested_simplify(SCREAMING_SNAKE_CASE							,     decimals=4		)							,     [{"label": "LABEL_1", "score": 0.5_7_4}, {"label": "LABEL_0", "score": 0.4_2_6}]							,     )
  
        UpperCamelCase__    :     Optional[Any]        =						image_classifier(
            [
                "http://images.cocodataset.org/val2017/000000039769.jpg",
                "http://images.cocodataset.org/val2017/000000039769.jpg",
            ]							,     top_k=2							,     )
        self.assertEqual(
            nested_simplify(SCREAMING_SNAKE_CASE							,     decimals=4		)							,     [
                [{"label": "LABEL_1", "score": 0.5_7_4}, {"label": "LABEL_0", "score": 0.4_2_6}],
                [{"label": "LABEL_1", "score": 0.5_7_4}, {"label": "LABEL_0", "score": 0.4_2_6}],
            ]							,     )
  
  
  
  
  
  
  
   @require_tf
   def       __lowercase      (	self      :     int		):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        UpperCamelCase__    :     str        =						"hf-internal-testing/tiny-random-vit"
        UpperCamelCase__    :     int        =						pipeline("image-classification"							,     model=SCREAMING_SNAKE_CASE							,     framework="tf"		)
  
        UpperCamelCase__    :     List[Any]        =						image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg"		)
        self.assertEqual(
            nested_simplify(SCREAMING_SNAKE_CASE							,     decimals=4		)							,     [{"label": "LABEL_1", "score": 0.5_7_4}, {"label": "LABEL_0", "score": 0.4_2_6}]							,     )
  
        UpperCamelCase__    :     int        =						image_classifier(
            [
                "http://images.cocodataset.org/val2017/000000039769.jpg",
                "http://images.cocodataset.org/val2017/000000039769.jpg",
            ]							,     top_k=2							,     )
        self.assertEqual(
            nested_simplify(SCREAMING_SNAKE_CASE							,     decimals=4		)							,     [
                [{"label": "LABEL_1", "score": 0.5_7_4}, {"label": "LABEL_0", "score": 0.4_2_6}],
                [{"label": "LABEL_1", "score": 0.5_7_4}, {"label": "LABEL_0", "score": 0.4_2_6}],
            ]							,     )
  
  
  
  
  
  
  
   def       __lowercase      (	self      :     Optional[int]		):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        UpperCamelCase__    :     Any        =						PreTrainedTokenizer()
  
        # Assert that the pipeline can be initialized with a feature extractor that is not in any mapping
        UpperCamelCase__    :     str        =						pipeline(
            "image-classification"							,     model="hf-internal-testing/tiny-random-vit"							,     tokenizer=SCREAMING_SNAKE_CASE		)
  
        self.assertIs(image_classifier.tokenizer							,     SCREAMING_SNAKE_CASE		)
  
  
  
  
  
  
  
   @slow
   @require_torch
   def       __lowercase      (	self      :     int		):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        UpperCamelCase__    :     Optional[Any]        =						pipeline("image-classification"							,     model="deepmind/vision-perceiver-conv"		)
        UpperCamelCase__    :     Any        =						image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg"		)
        self.assertEqual(
            nested_simplify(SCREAMING_SNAKE_CASE							,     decimals=4		)							,     [
                {"score": 0.4_3_8_5, "label": "tabby, tabby cat"},
                {"score": 0.3_2_1, "label": "tiger cat"},
                {"score": 0.0_5_0_2, "label": "Egyptian cat"},
                {"score": 0.0_1_3_7, "label": "crib, cot"},
                {"score": 0.0_0_7, "label": "radiator"},
            ]							,     )
  
        UpperCamelCase__    :     Optional[Any]        =						pipeline("image-classification"							,     model="deepmind/vision-perceiver-fourier"		)
        UpperCamelCase__    :     List[str]        =						image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg"		)
        self.assertEqual(
            nested_simplify(SCREAMING_SNAKE_CASE							,     decimals=4		)							,     [
                {"score": 0.5_6_5_8, "label": "tabby, tabby cat"},
                {"score": 0.1_3_0_9, "label": "tiger cat"},
                {"score": 0.0_7_2_2, "label": "Egyptian cat"},
                {"score": 0.0_7_0_7, "label": "remote control, remote"},
                {"score": 0.0_0_8_2, "label": "computer keyboard, keypad"},
            ]							,     )
  
        UpperCamelCase__    :     List[str]        =						pipeline("image-classification"							,     model="deepmind/vision-perceiver-learned"		)
        UpperCamelCase__    :     Optional[int]        =						image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg"		)
        self.assertEqual(
            nested_simplify(SCREAMING_SNAKE_CASE							,     decimals=4		)							,     [
                {"score": 0.3_0_2_2, "label": "tabby, tabby cat"},
                {"score": 0.2_3_6_2, "label": "Egyptian cat"},
                {"score": 0.1_8_5_6, "label": "tiger cat"},
                {"score": 0.0_3_2_4, "label": "remote control, remote"},
                {"score": 0.0_0_9_6, "label": "quilt, comforter, comfort, puff"},
            ]							,     )