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()
  __lowercase:		Tuple							=     logging.get_logger(__name__)
  
  
  
  def        SCREAMING_SNAKE_CASE__(		_UpperCamelCase      :				List[str]   )		->					Any:
  
  
  
  
  
  			'''simple docstring'''
  
  
  
  
  
  
  
  			if "backbone.0.body" in orig_key:
  						UpperCamelCase__ 						=						orig_key.replace("backbone.0.body"    ,		"backbone.conv_encoder.model"   )
  			if "transformer" in orig_key:
  						UpperCamelCase__ 						=						orig_key.replace("transformer."    ,		""   )
  			if "norm1" in orig_key:
  						if "encoder" in orig_key:
  									UpperCamelCase__ 						=						orig_key.replace("norm1"    ,		"self_attn_layer_norm"   )
  						else:
  									UpperCamelCase__ 						=						orig_key.replace("norm1"    ,		"encoder_attn_layer_norm"   )
  			if "norm2" in orig_key:
  						if "encoder" in orig_key:
  									UpperCamelCase__ 						=						orig_key.replace("norm2"    ,		"final_layer_norm"   )
  						else:
  									UpperCamelCase__ 						=						orig_key.replace("norm2"    ,		"self_attn_layer_norm"   )
  			if "norm3" in orig_key:
  						UpperCamelCase__ 						=						orig_key.replace("norm3"    ,		"final_layer_norm"   )
  			if "linear1" in orig_key:
  						UpperCamelCase__ 						=						orig_key.replace("linear1"    ,		"fc1"   )
  			if "linear2" in orig_key:
  						UpperCamelCase__ 						=						orig_key.replace("linear2"    ,		"fc2"   )
  			if "query_embed" in orig_key:
  						UpperCamelCase__ 						=						orig_key.replace("query_embed"    ,		"query_position_embeddings"   )
  			if "cross_attn" in orig_key:
  						UpperCamelCase__ 						=						orig_key.replace("cross_attn"    ,		"encoder_attn"   )
  
  			return orig_key
  
  
  
  def        SCREAMING_SNAKE_CASE__(		_UpperCamelCase      :				Tuple   )		->					Dict:
  
  
  
  
  
  			'''simple docstring'''
  
  
  
  
  
  
  
  			for i in range(6   ):
  						# read in weights + bias of input projection layer of self-attention
  						UpperCamelCase__ 						=						state_dict.pop(F'decoder.layers.{i}.self_attn.in_proj_weight'   )
  						UpperCamelCase__ 						=						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__ 						=						in_proj_weight[:2_56, :]
  						UpperCamelCase__ 						=						in_proj_bias[:2_56]
  						UpperCamelCase__ 						=						in_proj_weight[2_56:5_12, :]
  						UpperCamelCase__ 						=						in_proj_bias[2_56:5_12]
  						UpperCamelCase__ 						=						in_proj_weight[-2_56:, :]
  						UpperCamelCase__ 						=						in_proj_bias[-2_56:]
  
  
  
  def        SCREAMING_SNAKE_CASE__(		)		->					Union[str, Any]:
  
  
  
  
  
  			'''simple docstring'''
  
  
  
  
  
  
  
  			UpperCamelCase__ 						=						"http://images.cocodataset.org/val2017/000000039769.jpg"
  			UpperCamelCase__ 						=						Image.open(requests.get(lowercase__    ,		stream=lowercase__   ).raw   )
  
  			return im
  
  
  
  @torch.no_grad()
  def        SCREAMING_SNAKE_CASE__(		_UpperCamelCase      :				Any    ,		_UpperCamelCase      :				Dict    ,		_UpperCamelCase      :				Optional[int]    ,		_UpperCamelCase      :				int    ,		_UpperCamelCase      :				List[str]    ,		_UpperCamelCase      :				str    ,		_UpperCamelCase      :				Optional[int]    ,		)		->					int:
  
  
  
  
  
  			'''simple docstring'''
  
  
  
  
  
  
  
  			UpperCamelCase__ 						=						DeformableDetrConfig()
  			# set config attributes
  			if single_scale:
  						UpperCamelCase__ 						=						1
  			UpperCamelCase__ 						=						dilation
  			UpperCamelCase__ 						=						with_box_refine
  			UpperCamelCase__ 						=						two_stage
  			# set labels
  			UpperCamelCase__ 						=						91
  			UpperCamelCase__ 						=						"huggingface/label-files"
  			UpperCamelCase__ 						=						"coco-detection-id2label.json"
  			UpperCamelCase__ 						=						json.load(open(cached_download(hf_hub_url(lowercase__    ,		lowercase__    ,		repo_type="dataset"   )   )    ,		"r"   )   )
  			UpperCamelCase__ 						=						{int(lowercase__   ): v for k, v in idalabel.items()}
  			UpperCamelCase__ 						=						idalabel
  			UpperCamelCase__ 						=						{v: k for k, v in idalabel.items()}
  
  			# load image processor
  			UpperCamelCase__ 						=						DeformableDetrImageProcessor(format="coco_detection"   )
  
  			# prepare image
  			UpperCamelCase__ 						=						prepare_img()
  			UpperCamelCase__ 						=						image_processor(images=lowercase__    ,		return_tensors="pt"   )
  			UpperCamelCase__ 						=						encoding["pixel_values"]
  
  			logger.info("Converting model..."   )
  
  			# load original state dict
  			UpperCamelCase__ 						=						torch.load(lowercase__    ,		map_location="cpu"   )["model"]
  			# rename keys
  			for key in state_dict.copy().keys():
  						UpperCamelCase__ 						=						state_dict.pop(lowercase__   )
  						UpperCamelCase__ 						=						val
  			# query, key and value matrices need special treatment
  			read_in_q_k_v(lowercase__   )
  			# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
  			UpperCamelCase__ 						=						"model."
  			for key in state_dict.copy().keys():
  						if not key.startswith("class_embed"   ) and not key.startswith("bbox_embed"   ):
  									UpperCamelCase__ 						=						state_dict.pop(lowercase__   )
  									UpperCamelCase__ 						=						val
      # finally, create HuggingFace model and load state dict
  			UpperCamelCase__ 						=						DeformableDetrForObjectDetection(lowercase__   )
  			model.load_state_dict(lowercase__   )
  			model.eval()
  
  			UpperCamelCase__ 						=						"cuda" if torch.cuda.is_available() else "cpu"
  			model.to(lowercase__   )
  			# verify our conversion
  			UpperCamelCase__ 						=						model(pixel_values.to(lowercase__   )   )
  
  			UpperCamelCase__ 						=						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], [-10.56_34, -5.3_3_7_9, -7.5_1_1_6]]   )
  			UpperCamelCase__ 						=						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__ 						=						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], [-10.06_65, -5.8_4_7_0, -7.7_0_0_3]]   )
  						UpperCamelCase__ 						=						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__ 						=						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], [-10.11_78, -4.5_2_7_5, -6.2_6_7_1]]   )
  						UpperCamelCase__ 						=						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__ 						=						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__ 						=						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__ 						=						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__ 						=						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(lowercase__   )    ,		atol=1e-4   )
  			assert torch.allclose(outputs.pred_boxes[0, :3, :3]    ,		expected_boxes.to(lowercase__   )    ,		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(lowercase__   ).mkdir(exist_ok=lowercase__   )
  			model.save_pretrained(lowercase__   )
  			image_processor.save_pretrained(lowercase__   )
  
  			# Push to hub
  			if push_to_hub:
  						UpperCamelCase__ 						=						"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=lowercase__    ,		organization="nielsr"    ,		commit_message="Add model"   )
  
  
  if __name__ == "__main__":
  							__lowercase:		List[str]							=     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."
  							)
  							__lowercase:		List[str]							=     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'''
  
  from dataclasses import dataclass
  
  __lowercase:		List[str]							=     "0.1"
  __lowercase:		Union[str, Any]							=     "Lucia Harcekova"
  
  
  
  
  
  
  
  @dataclass
  class  UpperCAmelCase :
   _lowerCamelCase :					int
   _lowerCamelCase :					int
   _lowerCamelCase :					str
  
   def __repr__( self :	List[str]					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      return f'({self.offset}, {self.length}, {self.indicator})'
  
  
  
  
  
  
  
  class  UpperCAmelCase :
  
   def __init__( self :	Optional[int],					a_ :	int = 13,					a_ :	int = 6					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      UpperCamelCase__ 						=						window_size
      UpperCamelCase__ 						=						lookahead_buffer_size
      UpperCamelCase__ 						=						self.window_size - self.lookahead_buffer_size
  
   def 		lowercase_						( self :	Optional[int],					a_ :	str					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      UpperCamelCase__ 						=						[]
      UpperCamelCase__ 						=						""
  
      # while there are still characters in text to compress
      while text:
         # find the next encoding phrase
         # - triplet with offset, length, indicator (the next encoding character)
         UpperCamelCase__ 						=						self._find_encoding_token(a_,					a_					)
  
         # update the search buffer:
         # - add new characters from text into it
         # - check if size exceed the max search buffer size, if so, drop the
         #   oldest elements
         search_buffer += text[: token.length + 1]
         if len(a_					) > self.search_buffer_size:
            UpperCamelCase__ 						=						search_buffer[-self.search_buffer_size :]
  
         # update the text
         UpperCamelCase__ 						=						text[token.length + 1 :]
  
         # append the token to output
         output.append(a_					)
  
      return output
  
   def 		lowercase_						( self :	List[Any],					a_ :	list[Token]					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      UpperCamelCase__ 						=						""
  
      for token in tokens:
         for _ in range(token.length					):
            output += output[-token.offset]
         output += token.indicator
  
      return output
  
   def 		lowercase_						( self :	Any,					a_ :	str,					a_ :	str					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      if not text:
         raise ValueError("We need some text to work with."					)
  
      # Initialise result parameters to default values
      UpperCamelCase__						,		UpperCamelCase__ 						=						0, 0
  
      if not search_buffer:
         return Token(a_,					a_,					text[length]					)
  
      for i, character in enumerate(a_					):
         UpperCamelCase__ 						=						len(a_					) - i
         if character == text[0]:
            UpperCamelCase__ 						=						self._match_length_from_index(a_,					a_,					0,					a_					)
            # if the found length is bigger than the current or if it's equal,
            # which means it's offset is smaller: update offset and length
            if found_length >= length:
               UpperCamelCase__						,		UpperCamelCase__ 						=						found_offset, found_length
  
      return Token(a_,					a_,					text[length]					)
  
   def 		lowercase_						( self :	Optional[int],					a_ :	str,					a_ :	str,					a_ :	int,					a_ :	int					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      if not text or text[text_index] != window[window_index]:
         return 0
      return 1 + self._match_length_from_index(
          a_,					window + text[text_index],					text_index + 1,					window_index + 1					)
  
  
  if __name__ == "__main__":
         from doctest import testmod
  
         testmod()
         # Initialize compressor class
         __lowercase:		Optional[Any]							=     LZaaCompressor(window_size=13, lookahead_buffer_size=6)
  
         # Example
         __lowercase:		Union[str, Any]							=     "cabracadabrarrarrad"
         __lowercase:		Optional[int]							=     lzaa_compressor.compress(TEXT)
         print(lzaa_compressor.compress("ababcbababaa"))
         __lowercase:		Optional[int]							=     lzaa_compressor.decompress(compressed_text)
         assert decompressed_text == TEXT, "The LZ77 algorithm returned the invalid result."