code: 
  
  import argparse
  import json
  from collections import OrderedDict
  from pathlib import Path
  
  import requests
  import torch
  from huggingface_hub import hf_hub_download
  from PIL import Image
  
  from transformers import DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor
  from transformers.utils import logging
  
  
  logging.set_verbosity_info()
  UpperCAmelCase_       :				int		   =      logging.get_logger(__name__)
  
  # here we list all keys to be renamed (original name on the left, our name on the right)
  UpperCAmelCase_       :				Union[str, Any]		   =      []
  for i in range(6):
  			# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
  			rename_keys.append(
  			    (f"""transformer.encoder.layers.{i}.self_attn.out_proj.weight""", f"""encoder.layers.{i}.self_attn.out_proj.weight""")
  			)
  			rename_keys.append(
  			    (f"""transformer.encoder.layers.{i}.self_attn.out_proj.bias""", f"""encoder.layers.{i}.self_attn.out_proj.bias""")
  			)
  			rename_keys.append((f"""transformer.encoder.layers.{i}.linear1.weight""", f"""encoder.layers.{i}.fc1.weight"""))
  			rename_keys.append((f"""transformer.encoder.layers.{i}.linear1.bias""", f"""encoder.layers.{i}.fc1.bias"""))
  			rename_keys.append((f"""transformer.encoder.layers.{i}.linear2.weight""", f"""encoder.layers.{i}.fc2.weight"""))
  			rename_keys.append((f"""transformer.encoder.layers.{i}.linear2.bias""", f"""encoder.layers.{i}.fc2.bias"""))
  			rename_keys.append(
  			    (f"""transformer.encoder.layers.{i}.norm1.weight""", f"""encoder.layers.{i}.self_attn_layer_norm.weight""")
  			)
  			rename_keys.append((f"""transformer.encoder.layers.{i}.norm1.bias""", f"""encoder.layers.{i}.self_attn_layer_norm.bias"""))
  			rename_keys.append((f"""transformer.encoder.layers.{i}.norm2.weight""", f"""encoder.layers.{i}.final_layer_norm.weight"""))
  			rename_keys.append((f"""transformer.encoder.layers.{i}.norm2.bias""", f"""encoder.layers.{i}.final_layer_norm.bias"""))
  			# decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
  			rename_keys.append(
  			    (f"""transformer.decoder.layers.{i}.self_attn.out_proj.weight""", f"""decoder.layers.{i}.self_attn.out_proj.weight""")
  			)
  			rename_keys.append(
  			    (f"""transformer.decoder.layers.{i}.self_attn.out_proj.bias""", f"""decoder.layers.{i}.self_attn.out_proj.bias""")
  			)
  			rename_keys.append(
  			    (
  			        f"""transformer.decoder.layers.{i}.multihead_attn.out_proj.weight""",
  			        f"""decoder.layers.{i}.encoder_attn.out_proj.weight""",
  			    )
  			)
  			rename_keys.append(
  			    (
  			        f"""transformer.decoder.layers.{i}.multihead_attn.out_proj.bias""",
  			        f"""decoder.layers.{i}.encoder_attn.out_proj.bias""",
  			    )
  			)
  			rename_keys.append((f"""transformer.decoder.layers.{i}.linear1.weight""", f"""decoder.layers.{i}.fc1.weight"""))
  			rename_keys.append((f"""transformer.decoder.layers.{i}.linear1.bias""", f"""decoder.layers.{i}.fc1.bias"""))
  			rename_keys.append((f"""transformer.decoder.layers.{i}.linear2.weight""", f"""decoder.layers.{i}.fc2.weight"""))
  			rename_keys.append((f"""transformer.decoder.layers.{i}.linear2.bias""", f"""decoder.layers.{i}.fc2.bias"""))
  			rename_keys.append(
  			    (f"""transformer.decoder.layers.{i}.norm1.weight""", f"""decoder.layers.{i}.self_attn_layer_norm.weight""")
  			)
  			rename_keys.append((f"""transformer.decoder.layers.{i}.norm1.bias""", f"""decoder.layers.{i}.self_attn_layer_norm.bias"""))
  			rename_keys.append(
  			    (f"""transformer.decoder.layers.{i}.norm2.weight""", f"""decoder.layers.{i}.encoder_attn_layer_norm.weight""")
  			)
  			rename_keys.append(
  			    (f"""transformer.decoder.layers.{i}.norm2.bias""", f"""decoder.layers.{i}.encoder_attn_layer_norm.bias""")
  			)
  			rename_keys.append((f"""transformer.decoder.layers.{i}.norm3.weight""", f"""decoder.layers.{i}.final_layer_norm.weight"""))
  			rename_keys.append((f"""transformer.decoder.layers.{i}.norm3.bias""", f"""decoder.layers.{i}.final_layer_norm.bias"""))
  
  # convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
  rename_keys.extend(
      [
          ('''input_proj.weight''', '''input_projection.weight'''),
          ('''input_proj.bias''', '''input_projection.bias'''),
          ('''query_embed.weight''', '''query_position_embeddings.weight'''),
          ('''transformer.decoder.norm.weight''', '''decoder.layernorm.weight'''),
          ('''transformer.decoder.norm.bias''', '''decoder.layernorm.bias'''),
          ('''class_embed.weight''', '''class_labels_classifier.weight'''),
          ('''class_embed.bias''', '''class_labels_classifier.bias'''),
          ('''bbox_embed.layers.0.weight''', '''bbox_predictor.layers.0.weight'''),
          ('''bbox_embed.layers.0.bias''', '''bbox_predictor.layers.0.bias'''),
          ('''bbox_embed.layers.1.weight''', '''bbox_predictor.layers.1.weight'''),
          ('''bbox_embed.layers.1.bias''', '''bbox_predictor.layers.1.bias'''),
          ('''bbox_embed.layers.2.weight''', '''bbox_predictor.layers.2.weight'''),
          ('''bbox_embed.layers.2.bias''', '''bbox_predictor.layers.2.bias'''),
      ]
  )
  def        __SCREAMING_SNAKE_CASE    (      a__						:      Union[str, Any] ,a__						:      List[str] ,a__						:      Union[str, Any]					)				->							List[Any]:
  	__A   : int								= state_dict.pop(a__					)
  	__A   : List[str]								= val
  def        __SCREAMING_SNAKE_CASE    (      a__						:      Optional[Any]					)				->							Dict:
  	__A   : Any								= OrderedDict()
  	for key, value in state_dict.items():
  		if "backbone.0.body" in key:
  			__A   : int								= key.replace("""backbone.0.body""" ,"""backbone.conv_encoder.model"""					)
  			__A   : Tuple								= value
  		else:
  			__A   : List[Any]								= value
  
  	return new_state_dict
  def        __SCREAMING_SNAKE_CASE    (      a__						:      Any ,a__						:      List[str]=False					)				->							Any:
  	__A   : Tuple								= """"""
  	if is_panoptic:
  		__A   : Any								= """detr."""
  
  	# first: transformer encoder
  	for i in range(6					):
  		# read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
  		__A   : Union[str, Any]								= state_dict.pop(f"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"""					)
  		__A   : Union[str, Any]								= state_dict.pop(f"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias"""					)
  		# next, add query, keys and values (in that order) to the state dict
  		__A   : List[str]								= in_proj_weight[:256, :]
  		__A   : int								= in_proj_bias[:256]
  		__A   : Union[str, Any]								= in_proj_weight[256:512, :]
  		__A   : Optional[Any]								= in_proj_bias[256:512]
  		__A   : Union[str, Any]								= in_proj_weight[-256:, :]
  		__A   : List[Any]								= in_proj_bias[-256:]
  	# next: transformer decoder (which is a bit more complex because it also includes cross-attention)
  	for i in range(6					):
  		# read in weights + bias of input projection layer of self-attention
  		__A   : int								= state_dict.pop(f"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight"""					)
  		__A   : int								= state_dict.pop(f"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias"""					)
  		# next, add query, keys and values (in that order) to the state dict
  		__A   : int								= in_proj_weight[:256, :]
  		__A   : Any								= in_proj_bias[:256]
  		__A   : Optional[int]								= in_proj_weight[256:512, :]
  		__A   : Dict								= in_proj_bias[256:512]
  		__A   : List[Any]								= in_proj_weight[-256:, :]
  		__A   : List[Any]								= in_proj_bias[-256:]
  		# read in weights + bias of input projection layer of cross-attention
  		__A   : Dict								= state_dict.pop(
  		    f"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"""					)
  		__A   : Dict								= state_dict.pop(f"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias"""					)
  		# next, add query, keys and values (in that order) of cross-attention to the state dict
  		__A   : Optional[int]								= in_proj_weight_cross_attn[:256, :]
  		__A   : Optional[int]								= in_proj_bias_cross_attn[:256]
  		__A   : Union[str, Any]								= in_proj_weight_cross_attn[256:512, :]
  		__A   : Tuple								= in_proj_bias_cross_attn[256:512]
  		__A   : Any								= in_proj_weight_cross_attn[-256:, :]
  		__A   : Union[str, Any]								= in_proj_bias_cross_attn[-256:]
  def        __SCREAMING_SNAKE_CASE    (      )				->							List[str]:
  	__A   : str								= """http://images.cocodataset.org/val2017/000000039769.jpg"""
  	__A   : Any								= Image.open(requests.get(a__ ,stream=a__					).raw					)
  
  	return im
  @torch.no_grad()
  def        __SCREAMING_SNAKE_CASE    (      a__						:      str ,a__						:      int					)				->							int:
  	__A   : Optional[Any]								= DetrConfig()
  	# set backbone and dilation attributes
  	if "resnet101" in model_name:
  		__A   : Optional[int]								= """resnet101"""
  	if "dc5" in model_name:
  		__A   : List[Any]								= True
  	__A   : Optional[int]								= """panoptic""" in model_name
  	if is_panoptic:
  		__A   : List[Any]								= 250
  	else:
  		__A   : Optional[int]								= 91
  		__A   : Dict								= """huggingface/label-files"""
  		__A   : str								= """coco-detection-id2label.json"""
  		__A   : List[Any]								= json.load(open(hf_hub_download(a__ ,a__ ,repo_type="""dataset"""					) ,"""r"""					)					)
  		__A   : List[str]								= {int(a__					): v for k, v in idalabel.items()}
  		__A   : List[str]								= idalabel
  		__A   : Dict								= {v: k for k, v in idalabel.items()}
  
  	# load image processor
  	__A   : List[str]								= """coco_panoptic""" if is_panoptic else """coco_detection"""
  	__A   : Optional[int]								= DetrImageProcessor(format=a__					)
  
  	# prepare image
  	__A   : List[str]								= prepare_img()
  	__A   : Tuple								= image_processor(images=a__ ,return_tensors="""pt"""					)
  	__A   : Any								= encoding["""pixel_values"""]
  
  	logger.info(f"""Converting model {model_name}..."""					)
  
  	# load original model from torch hub
  	__A   : int								= torch.hub.load("""facebookresearch/detr""" ,a__ ,pretrained=a__					).eval()
  	__A   : Dict								= detr.state_dict()
  	# rename keys
  	for src, dest in rename_keys:
  		if is_panoptic:
  			__A   : int								= """detr.""" + src
  		rename_key(a__ ,a__ ,a__					)
  	__A   : int								= rename_backbone_keys(a__					)
  	# query, key and value matrices need special treatment
  	read_in_q_k_v(a__ ,is_panoptic=a__					)
  	# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
  	__A   : Optional[int]								= """detr.model.""" if is_panoptic else """model."""
  	for key in state_dict.copy().keys():
  		if is_panoptic:
  			if (
  			    key.startswith("""detr"""					)
  			    and not key.startswith("""class_labels_classifier"""					)
  			    and not key.startswith("""bbox_predictor"""					)
  			):
  				__A   : str								= state_dict.pop(a__					)
  				__A   : List[str]								= val
  			elif "class_labels_classifier" in key or "bbox_predictor" in key:
  				__A   : int								= state_dict.pop(a__					)
  				__A   : Optional[int]								= val
  			elif key.startswith("""bbox_attention"""					) or key.startswith("""mask_head"""					):
  				continue
  			else:
  				__A   : List[str]								= state_dict.pop(a__					)
  				__A   : int								= val
  		else:
  			if not key.startswith("""class_labels_classifier"""					) and not key.startswith("""bbox_predictor"""					):
  				__A   : Any								= state_dict.pop(a__					)
  				__A   : Union[str, Any]								= val
      # finally, create HuggingFace model and load state dict
  	__A   : Optional[Any]								= DetrForSegmentation(a__					) if is_panoptic else DetrForObjectDetection(a__					)
  	model.load_state_dict(a__					)
  	model.eval()
  	# verify our conversion
  	__A   : Optional[Any]								= detr(a__					)
  	__A   : Tuple								= model(a__					)
  	assert torch.allclose(outputs.logits ,original_outputs["""pred_logits"""] ,atol=1E-4					)
  	assert torch.allclose(outputs.pred_boxes ,original_outputs["""pred_boxes"""] ,atol=1E-4					)
  	if is_panoptic:
  		assert torch.allclose(outputs.pred_masks ,original_outputs["""pred_masks"""] ,atol=1E-4					)
  
  	# Save model and image processor
  	logger.info(f"""Saving PyTorch model and image processor to {pytorch_dump_folder_path}..."""					)
  	Path(a__					).mkdir(exist_ok=a__					)
  	model.save_pretrained(a__					)
  	image_processor.save_pretrained(a__					)
  
  
  if __name__ == "__main__":
  			UpperCAmelCase_       :				List[str]		   =      argparse.ArgumentParser()
  
  			parser.add_argument(
  			    '''--model_name''', default='''detr_resnet50''', type=str, help='''Name of the DETR model you\'d like to convert.'''
  			)
  			parser.add_argument(
  			    '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the folder to output PyTorch model.'''
  			)
  			UpperCAmelCase_       :				int		   =      parser.parse_args()
  			convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)
  
  
  
  
  
  
style_context: 
  
  import math
  
  
  
  
  
  
  class     lowerCamelCase_			:
  
  
  
  
  	def __init__(    self	:    Dict				,			__A	:    int				,			__A	:    int				,			__A	:    int				,			__A	:    Any       ):
  		__A   : Union[str, Any]								= x
  		__A   : Union[str, Any]								= y
  		__A   : Optional[int]								= step_size
  		__A   : Dict								= function_to_optimize
  
  
  
  
  	def 					lowerCAmelCase_					(    self	:    Optional[int]       ):
  		return self.function(self.x				,			self.y       )
  
  
  
  
  	def 					lowerCAmelCase_					(    self	:    int       ):
  		__A   : Dict								= self.step_size
  		return [
  		    SearchProblem(__A				,			__A				,			__A				,			self.function       )
  		    for x, y in (
  		        (self.x - step_size, self.y - step_size),
  		        (self.x - step_size, self.y),
  		        (self.x - step_size, self.y + step_size),
  		        (self.x, self.y - step_size),
  		        (self.x, self.y + step_size),
  		        (self.x + step_size, self.y - step_size),
  		        (self.x + step_size, self.y),
  		        (self.x + step_size, self.y + step_size),
  		    )
  		]
  
  
  
  
  	def __hash__(    self	:    str       ):
  		return hash(str(self       )       )
  
  
  
  
  	def __eq__(    self	:    Union[str, Any]				,			__A	:    Optional[int]       ):
  		if isinstance(__A				,			__A       ):
  			return hash(str(self       )       ) == hash(str(__A       )       )
  		return False
  
  
  
  
  	def __str__(    self	:    List[Any]       ):
  		return F"""x: {self.x} y: {self.y}"""
  
  
  
  
  
  
  def        __SCREAMING_SNAKE_CASE    (      a__						:      Optional[Any] ,a__						:      bool = True ,a__						:      float = math.inf ,a__						:      float = -math.inf ,a__						:      float = math.inf ,a__						:      float = -math.inf ,a__						:      bool = False ,a__						:      int = 10000 ,)				->							SearchProblem:
  	__A   : int								= search_prob
  	__A   : List[str]								= []  # list to store the current score at each iteration
  	__A   : Optional[Any]								= 0
  	__A   : Any								= False
  	__A   : Dict								= set()
  	while not solution_found and iterations < max_iter:
  		visited.add(a__					)
  		iterations += 1
  		__A   : Union[str, Any]								= current_state.score()
  		scores.append(a__					)
  		__A   : List[Any]								= current_state.get_neighbors()
  		__A   : Tuple								= -math.inf
  		__A   : str								= math.inf
  		__A   : Optional[int]								= None  # to hold the next best neighbor
  		for neighbor in neighbors:
  			if neighbor in visited:
  				continue  # do not want to visit the same state again
  			if (
  			    neighbor.x > max_x
  			    or neighbor.x < min_x
  			    or neighbor.y > max_y
  			    or neighbor.y < min_y
  			):
  				continue  # neighbor outside our bounds
  			__A   : int								= neighbor.score() - current_score
  			if find_max:  # finding max
  				# going to direction with greatest ascent
  				if change > max_change and change > 0:
  					__A   : str								= change
  					__A   : Optional[int]								= neighbor
  			else:  # finding min
  				# to direction with greatest descent
  				if change < min_change and change < 0:
  					__A   : List[Any]								= change
  					__A   : List[str]								= neighbor
  		if next_state is not None:
  			# we found at least one neighbor which improved the current state
  			__A   : Tuple								= next_state
  		else:
  			# since we have no neighbor that improves the solution we stop the search
  			__A   : str								= True
  
  	if visualization:
  		from matplotlib import pyplot as plt
  
  		plt.plot(range(a__					) ,a__					)
  		plt.xlabel("""Iterations"""					)
  		plt.ylabel("""Function values"""					)
  		plt.show()
  
  	return current_state
  
  
  if __name__ == "__main__":
  			import doctest
  
  			doctest.testmod()
  			def        __SCREAMING_SNAKE_CASE    (      a__						:      str ,a__						:      Any					)				->							Dict:
  				return (x**2) + (y**2)
  
  			# starting the problem with initial coordinates (3, 4)
  			UpperCAmelCase_       :				Tuple		   =      SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
  			UpperCAmelCase_       :				Any		   =      hill_climbing(prob, find_max=False)
  			print(
  			    '''The minimum score for f(x, y) = x^2 + y^2 found via hill climbing: '''
  			    f"""{local_min.score()}"""
  			)
  
  			# starting the problem with initial coordinates (12, 47)
  			UpperCAmelCase_       :				List[Any]		   =      SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
  			UpperCAmelCase_       :				Any		   =      hill_climbing(
  			    prob, find_max=False, max_x=100, min_x=5, max_y=50, min_y=-5, visualization=True
  			)
  			print(
  			    '''The minimum score for f(x, y) = x^2 + y^2 with the domain 100 > x > 5 '''
  			    f"""and 50 > y > - 5 found via hill climbing: {local_min.score()}"""
  			)
  			def        __SCREAMING_SNAKE_CASE    (      a__						:      int ,a__						:      List[str]					)				->							Any:
  				return (3 * x**2) - (6 * y)
  
  			UpperCAmelCase_       :				Tuple		   =      SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
  			UpperCAmelCase_       :				Dict		   =      hill_climbing(prob, find_max=True)
  
  
  
  
  
  
  
  			print(
  			    '''The maximum score for f(x, y) = x^2 + y^2 found via hill climbing: '''
  			    f"""{local_min.score()}"""
  			)
  
  
  
  
  
  