code: 
  
  
  """simple docstring"""
  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()
  UpperCAmelCase   =	logging.get_logger(__name__)
  
  def 							_snake_case			(      __snake_case       :			Any      ):
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     if "backbone.0.body" in orig_key:
        _lowerCamelCase  :				str      =		orig_key.replace("""backbone.0.body"""	,   """backbone.conv_encoder.model"""      )
     if "transformer" in orig_key:
        _lowerCamelCase  :				Tuple      =		orig_key.replace("""transformer."""	,   """"""      )
     if "norm1" in orig_key:
        if "encoder" in orig_key:
           _lowerCamelCase  :				Any      =		orig_key.replace("""norm1"""	,   """self_attn_layer_norm"""      )
        else:
           _lowerCamelCase  :				Optional[Any]      =		orig_key.replace("""norm1"""	,   """encoder_attn_layer_norm"""      )
     if "norm2" in orig_key:
        if "encoder" in orig_key:
           _lowerCamelCase  :				Any      =		orig_key.replace("""norm2"""	,   """final_layer_norm"""      )
        else:
           _lowerCamelCase  :				Union[str, Any]      =		orig_key.replace("""norm2"""	,   """self_attn_layer_norm"""      )
     if "norm3" in orig_key:
        _lowerCamelCase  :				Dict      =		orig_key.replace("""norm3"""	,   """final_layer_norm"""      )
     if "linear1" in orig_key:
        _lowerCamelCase  :				str      =		orig_key.replace("""linear1"""	,   """fc1"""      )
     if "linear2" in orig_key:
        _lowerCamelCase  :				str      =		orig_key.replace("""linear2"""	,   """fc2"""      )
     if "query_embed" in orig_key:
        _lowerCamelCase  :				Union[str, Any]      =		orig_key.replace("""query_embed"""	,   """query_position_embeddings"""      )
     if "cross_attn" in orig_key:
        _lowerCamelCase  :				Optional[Any]      =		orig_key.replace("""cross_attn"""	,   """encoder_attn"""      )
  
     return orig_key
  
  def 							_snake_case			(      __snake_case       :			Dict      ):
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     for i in range(6      ):
        # read in weights + bias of input projection layer of self-attention
        _lowerCamelCase  :				List[str]      =		state_dict.pop(F'decoder.layers.{i}.self_attn.in_proj_weight'      )
        _lowerCamelCase  :				Tuple      =		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
        _lowerCamelCase  :				Optional[Any]      =		in_proj_weight[:256, :]
        _lowerCamelCase  :				Union[str, Any]      =		in_proj_bias[:256]
        _lowerCamelCase  :				Union[str, Any]      =		in_proj_weight[256:512, :]
        _lowerCamelCase  :				str      =		in_proj_bias[256:512]
        _lowerCamelCase  :				Union[str, Any]      =		in_proj_weight[-256:, :]
        _lowerCamelCase  :				Optional[int]      =		in_proj_bias[-256:]
  
  def 							_snake_case			(      ):
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     _lowerCamelCase  :				Union[str, Any]      =		"http://images.cocodataset.org/val2017/000000039769.jpg"
     _lowerCamelCase  :				Any      =		Image.open(requests.get(__snake_case	,   stream=__snake_case      ).raw      )
  
     return im
  
  @torch.no_grad()
  def 							_snake_case			(      __snake_case       :			Union[str, Any]	,   __snake_case       :			Optional[int]	,   __snake_case       :			Optional[int]	,   __snake_case       :			Tuple	,   __snake_case       :			List[str]	,   __snake_case       :			str	,   __snake_case       :			Optional[Any]	,   ):
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     _lowerCamelCase  :				Tuple      =		DeformableDetrConfig()
     # set config attributes
     if single_scale:
        _lowerCamelCase  :				Tuple      =		1
     _lowerCamelCase  :				Tuple      =		dilation
     _lowerCamelCase  :				List[str]      =		with_box_refine
     _lowerCamelCase  :				Tuple      =		two_stage
     # set labels
     _lowerCamelCase  :				Union[str, Any]      =		91
     _lowerCamelCase  :				List[str]      =		"huggingface/label-files"
     _lowerCamelCase  :				Tuple      =		"coco-detection-id2label.json"
     _lowerCamelCase  :				int      =		json.load(open(cached_download(hf_hub_url(__snake_case	,   __snake_case	,   repo_type="""dataset"""      )      )	,   """r"""      )      )
     _lowerCamelCase  :				List[Any]      =		{int(__snake_case      ): v for k, v in idalabel.items()}
     _lowerCamelCase  :				Dict      =		idalabel
     _lowerCamelCase  :				Union[str, Any]      =		{v: k for k, v in idalabel.items()}
  
     # load image processor
     _lowerCamelCase  :				Optional[int]      =		DeformableDetrImageProcessor(format="""coco_detection"""      )
  
     # prepare image
     _lowerCamelCase  :				List[str]      =		prepare_img()
     _lowerCamelCase  :				Tuple      =		image_processor(images=__snake_case	,   return_tensors="""pt"""      )
     _lowerCamelCase  :				int      =		encoding["pixel_values"]
  
     logger.info("""Converting model..."""      )
  
     # load original state dict
     _lowerCamelCase  :				Optional[int]      =		torch.load(__snake_case	,   map_location="""cpu"""      )["model"]
     # rename keys
     for key in state_dict.copy().keys():
        _lowerCamelCase  :				Dict      =		state_dict.pop(__snake_case      )
        _lowerCamelCase  :				Union[str, Any]      =		val
     # query, key and value matrices need special treatment
     read_in_q_k_v(__snake_case      )
     # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
     _lowerCamelCase  :				List[str]      =		"model."
     for key in state_dict.copy().keys():
        if not key.startswith("""class_embed"""      ) and not key.startswith("""bbox_embed"""      ):
           _lowerCamelCase  :				Optional[int]      =		state_dict.pop(__snake_case      )
           _lowerCamelCase  :				Any      =		val
      # finally, create HuggingFace model and load state dict
     _lowerCamelCase  :				Optional[int]      =		DeformableDetrForObjectDetection(__snake_case      )
     model.load_state_dict(__snake_case      )
     model.eval()
  
     _lowerCamelCase  :				Dict      =		"cuda" if torch.cuda.is_available() else "cpu"
     model.to(__snake_case      )
     # verify our conversion
     _lowerCamelCase  :				int      =		model(pixel_values.to(__snake_case      )      )
  
     _lowerCamelCase  :				List[Any]      =		torch.tensor(
         [[-9.6645, -4.3449, -5.8705], [-9.7035, -3.8504, -5.0724], [-10.5634, -5.3379, -7.5116]]      )
     _lowerCamelCase  :				List[str]      =		torch.tensor([[0.8693, 0.2289, 0.2492], [0.3150, 0.5489, 0.5845], [0.5563, 0.7580, 0.8518]]      )
  
     if single_scale:
        _lowerCamelCase  :				Optional[Any]      =		torch.tensor(
            [[-9.9051, -4.2541, -6.4852], [-9.6947, -4.0854, -6.8033], [-10.0665, -5.8470, -7.7003]]      )
        _lowerCamelCase  :				Tuple      =		torch.tensor([[0.7292, 0.4991, 0.5532], [0.7959, 0.2426, 0.4236], [0.7582, 0.3518, 0.4451]]      )
  
     if single_scale and dilation:
        _lowerCamelCase  :				Optional[Any]      =		torch.tensor(
            [[-8.9652, -4.1074, -5.6635], [-9.0596, -4.9447, -6.6075], [-10.1178, -4.5275, -6.2671]]      )
        _lowerCamelCase  :				Dict      =		torch.tensor([[0.7665, 0.4130, 0.4769], [0.8364, 0.1841, 0.3391], [0.6261, 0.3895, 0.7978]]      )
  
     if with_box_refine:
        _lowerCamelCase  :				Any      =		torch.tensor(
            [[-8.8895, -5.4187, -6.8153], [-8.4706, -6.1668, -7.6184], [-9.0042, -5.5359, -6.9141]]      )
        _lowerCamelCase  :				Tuple      =		torch.tensor([[0.7828, 0.2208, 0.4323], [0.0892, 0.5996, 0.1319], [0.5524, 0.6389, 0.8914]]      )
  
     if with_box_refine and two_stage:
        _lowerCamelCase  :				str      =		torch.tensor(
            [[-6.7108, -4.3213, -6.3777], [-8.9014, -6.1799, -6.7240], [-6.9315, -4.4735, -6.2298]]      )
        _lowerCamelCase  :				int      =		torch.tensor([[0.2583, 0.5499, 0.4683], [0.7652, 0.9068, 0.4882], [0.5490, 0.2763, 0.0564]]      )
  
     print("""Logits:"""	,   outputs.logits[0, :3, :3]      )
  
     assert torch.allclose(outputs.logits[0, :3, :3]	,   expected_logits.to(__snake_case      )	,   atol=1E-4      )
     assert torch.allclose(outputs.pred_boxes[0, :3, :3]	,   expected_boxes.to(__snake_case      )	,   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(__snake_case      ).mkdir(exist_ok=__snake_case      )
     model.save_pretrained(__snake_case      )
     image_processor.save_pretrained(__snake_case      )
  
     # Push to hub
     if push_to_hub:
        _lowerCamelCase  :				Union[str, Any]      =		"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=__snake_case	,   organization="""nielsr"""	,   commit_message="""Add model"""      )
  
  
  if __name__ == "__main__":
   UpperCAmelCase   =	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."""
   )
   UpperCAmelCase   =	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: 
  
  
  """simple docstring"""
  import subprocess
  from argparse import ArgumentParser
  from typing import List, Union
  
  from huggingface_hub.hf_api import HfFolder, create_repo, whoami
  from requests.exceptions import HTTPError
  
  from . import BaseTransformersCLICommand
  
  
  
  
  class   lowercase__							(				A_							):
  
  
  
  
  
     @staticmethod
     def 		UpperCamelCase_     (       SCREAMING_SNAKE_CASE)     ->     Dict:
        _lowerCamelCase  :				Optional[Any]      =		parser.add_parser("""login"""			,      help="""Log in using the same credentials as on huggingface.co""")
        login_parser.set_defaults(func=lambda SCREAMING_SNAKE_CASE: LoginCommand(SCREAMING_SNAKE_CASE))
        _lowerCamelCase  :				Any      =		parser.add_parser("""whoami"""			,      help="""Find out which huggingface.co account you are logged in as.""")
        whoami_parser.set_defaults(func=lambda SCREAMING_SNAKE_CASE: WhoamiCommand(SCREAMING_SNAKE_CASE))
        _lowerCamelCase  :				Optional[int]      =		parser.add_parser("""logout"""			,      help="""Log out""")
        logout_parser.set_defaults(func=lambda SCREAMING_SNAKE_CASE: LogoutCommand(SCREAMING_SNAKE_CASE))
  
        # new system: git-based repo system
        _lowerCamelCase  :				Optional[int]      =		parser.add_parser(
            """repo"""			,      help="""Deprecated: use `huggingface-cli` instead. Commands to interact with your huggingface.co repos."""			,      )
        _lowerCamelCase  :				Optional[Any]      =		repo_parser.add_subparsers(
            help="""Deprecated: use `huggingface-cli` instead. huggingface.co repos related commands""")
        _lowerCamelCase  :				Dict      =		repo_subparsers.add_parser(
            """create"""			,      help="""Deprecated: use `huggingface-cli` instead. Create a new repo on huggingface.co""")
        repo_create_parser.add_argument(
            """name"""			,      type=SCREAMING_SNAKE_CASE			,      help="""Name for your model's repo. Will be namespaced under your username to build the model id."""			,      )
        repo_create_parser.add_argument("""--organization"""			,      type=SCREAMING_SNAKE_CASE			,      help="""Optional: organization namespace.""")
        repo_create_parser.add_argument("""-y"""			,      """--yes"""			,      action="""store_true"""			,      help="""Optional: answer Yes to the prompt""")
        repo_create_parser.set_defaults(func=lambda SCREAMING_SNAKE_CASE: RepoCreateCommand(SCREAMING_SNAKE_CASE))
  
  
  
  
  
  
  
  class   lowercase__							:
     __UpperCAmelCase       					=				'''\u001b[1m'''
     __UpperCAmelCase       					=				'''\u001b[31m'''
     __UpperCAmelCase       					=				'''\u001b[90m'''
     __UpperCAmelCase       					=				'''\u001b[0m'''
  
  
  
  
  
     @classmethod
     def 		UpperCamelCase_     (       cls			,      SCREAMING_SNAKE_CASE)     ->     Dict:
        return F'{cls._bold}{s}{cls._reset}'
  
  
  
  
  
     @classmethod
     def 		UpperCamelCase_     (       cls			,      SCREAMING_SNAKE_CASE)     ->     Union[str, Any]:
        return F'{cls._bold}{cls._red}{s}{cls._reset}'
  
  
  
  
  
     @classmethod
     def 		UpperCamelCase_     (       cls			,      SCREAMING_SNAKE_CASE)     ->     Tuple:
        return F'{cls._gray}{s}{cls._reset}'
  
  
  
  
  
  
  
  def 							_snake_case			(      __snake_case       :			List[List[Union[str, int]]]	,   __snake_case       :			List[str]      ):
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     _lowerCamelCase  :				str      =		[max(len(str(__snake_case      )      ) for x in col      ) for col in zip(*__snake_case	,   __snake_case      )]
     _lowerCamelCase  :				Any      =		("""{{:{}}} """ * len(__snake_case      )).format(*__snake_case      )
     _lowerCamelCase  :				Optional[Any]      =		[]
     lines.append(row_format.format(*__snake_case      )      )
     lines.append(row_format.format(*["""-""" * w for w in col_widths]      )      )
     for row in rows:
        lines.append(row_format.format(*__snake_case      )      )
     return "\n".join(__snake_case      )
  
  
  
  
  class   lowercase__							:
  
  
  
  
  
     def __init__(       self			,      SCREAMING_SNAKE_CASE)     ->     List[str]:
        _lowerCamelCase  :				Any      =		args
  
  
  
  
  
  
  
  class   lowercase__							(				A_							):
  
  
  
  
  
     def 		UpperCamelCase_     (       self)     ->     int:
        print(
            ANSI.red(
                """ERROR! `huggingface-cli login` uses an outdated login mechanism """
                """that is not compatible with the Hugging Face Hub backend anymore. """
                """Please use `huggingface-cli login instead."""))
  
  
  
  
  
  
  
  class   lowercase__							(				A_							):
  
  
  
  
  
     def 		UpperCamelCase_     (       self)     ->     Tuple:
        print(
            ANSI.red(
                """WARNING! `transformers-cli whoami` is deprecated and will be removed in v5. Please use """
                """`huggingface-cli whoami` instead."""))
        _lowerCamelCase  :				Tuple      =		HfFolder.get_token()
        if token is None:
           print("""Not logged in""")
           exit()
        try:
           _lowerCamelCase      ,						_lowerCamelCase  :				int      =		whoami(SCREAMING_SNAKE_CASE)
           print(SCREAMING_SNAKE_CASE)
           if orgs:
              print(ANSI.bold("""orgs: """)			,      """,""".join(SCREAMING_SNAKE_CASE))
        except HTTPError as e:
           print(SCREAMING_SNAKE_CASE)
           print(ANSI.red(e.response.text))
           exit(1)
  
  
  
  
  
  
  
  class   lowercase__							(				A_							):
  
  
  
  
  
     def 		UpperCamelCase_     (       self)     ->     Union[str, Any]:
        print(
            ANSI.red(
                """ERROR! `transformers-cli logout` uses an outdated logout mechanism """
                """that is not compatible with the Hugging Face Hub backend anymore. """
                """Please use `huggingface-cli logout instead."""))
  
  
  
  
  
  
  
  class   lowercase__							(				A_							):
  
  
  
  
  
     def 		UpperCamelCase_     (       self)     ->     List[Any]:
        print(
            ANSI.red(
                """WARNING! Managing repositories through transformers-cli is deprecated. """
                """Please use `huggingface-cli` instead."""))
        _lowerCamelCase  :				Union[str, Any]      =		HfFolder.get_token()
        if token is None:
           print("""Not logged in""")
           exit(1)
        try:
           _lowerCamelCase  :				Union[str, Any]      =		subprocess.check_output(["""git""", """--version"""]).decode("""utf-8""")
           print(ANSI.gray(stdout.strip()))
        except FileNotFoundError:
           print("""Looks like you do not have git installed, please install.""")
  
        try:
           _lowerCamelCase  :				List[str]      =		subprocess.check_output(["""git-lfs""", """--version"""]).decode("""utf-8""")
           print(ANSI.gray(stdout.strip()))
        except FileNotFoundError:
           print(
               ANSI.red(
                   """Looks like you do not have git-lfs installed, please install."""
                   """ You can install from https://git-lfs.github.com/."""
                   """ Then run `git lfs install` (you only have to do this once)."""))
        print("""""")
  
        _lowerCamelCase      ,						_lowerCamelCase  :				str      =		whoami(SCREAMING_SNAKE_CASE)
        _lowerCamelCase  :				str      =		self.args.organization if self.args.organization is not None else user
        _lowerCamelCase  :				Union[str, Any]      =		F'{namespace}/{self.args.name}'
        print(F'You are about to create {ANSI.bold(SCREAMING_SNAKE_CASE)}')
  
        if not self.args.yes:
           _lowerCamelCase  :				Tuple      =		input("""Proceed? [Y/n] """).lower()
           if not (choice == "" or choice == "y" or choice == "yes"):
              print("""Abort""")
              exit()
        try:
           _lowerCamelCase  :				int      =		create_repo(SCREAMING_SNAKE_CASE			,      name=self.args.name			,      organization=self.args.organization)
        except HTTPError as e:
           print(SCREAMING_SNAKE_CASE)
           print(ANSI.red(e.response.text))
           exit(1)
        print("""\nYour repo now lives at:""")
        print(F'  {ANSI.bold(SCREAMING_SNAKE_CASE)}')
        print("""\nYou can clone it locally with the command below, and commit/push as usual.""")
        print(F'\n  git clone {url}')
        print("""""")
  
  