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()
  __lowerCamelCase       :       List[str]				   =			logging.get_logger(__name__)
  
  # here we list all keys to be renamed (original name on the left, our name on the right)
  __lowerCamelCase       :       str				   =			[]
  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__						(							snake_case_,	snake_case_,	snake_case_	)			->							List[Any]:
  				A__     :							Any   				=state_dict.pop(snake_case_	)
  				A__     :							Union[str, Any]   				=val
  
  
  
  def   SCREAMING_SNAKE_CASE__						(							snake_case_	)			->							int:
  				A__     :							Tuple   				=OrderedDict()
  				for key, value in state_dict.items():
  								if "backbone.0.body" in key:
  												A__     :							Dict   				=key.replace('''backbone.0.body''',	'''backbone.conv_encoder.model'''	)
  												A__     :							Optional[Any]   				=value
  								else:
  												A__     :							Optional[int]   				=value
  
  				return new_state_dict
  
  
  
  def   SCREAMING_SNAKE_CASE__						(							snake_case_,	snake_case_=False	)			->							Dict:
  				A__     :							List[Any]   				=''''''
  				if is_panoptic:
  								A__     :							Tuple   				='''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__     :							Any   				=state_dict.pop(f'{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'	)
  								A__     :							int   				=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[:2_5_6, :]
  								A__     :							Tuple   				=in_proj_bias[:2_5_6]
  								A__     :							Union[str, Any]   				=in_proj_weight[2_5_6:5_1_2, :]
  								A__     :							List[str]   				=in_proj_bias[2_5_6:5_1_2]
  								A__     :							Optional[int]   				=in_proj_weight[-2_5_6:, :]
  								A__     :							str   				=in_proj_bias[-2_5_6:]
  				# 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__     :							Tuple   				=state_dict.pop(f'{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight'	)
  								A__     :							Dict   				=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__     :							str   				=in_proj_weight[:2_5_6, :]
  								A__     :							str   				=in_proj_bias[:2_5_6]
  								A__     :							List[str]   				=in_proj_weight[2_5_6:5_1_2, :]
  								A__     :							int   				=in_proj_bias[2_5_6:5_1_2]
  								A__     :							Optional[Any]   				=in_proj_weight[-2_5_6:, :]
  								A__     :							Tuple   				=in_proj_bias[-2_5_6:]
  								# read in weights + bias of input projection layer of cross-attention
  								A__     :							List[Any]   				=state_dict.pop(
  								    f'{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight'	)
  								A__     :							List[Any]   				=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__     :							Any   				=in_proj_weight_cross_attn[:2_5_6, :]
  								A__     :							Tuple   				=in_proj_bias_cross_attn[:2_5_6]
  								A__     :							List[Any]   				=in_proj_weight_cross_attn[2_5_6:5_1_2, :]
  								A__     :							Optional[int]   				=in_proj_bias_cross_attn[2_5_6:5_1_2]
  								A__     :							Union[str, Any]   				=in_proj_weight_cross_attn[-2_5_6:, :]
  								A__     :							Union[str, Any]   				=in_proj_bias_cross_attn[-2_5_6:]
  
  
  
  def   SCREAMING_SNAKE_CASE__						(							)			->							List[str]:
  				A__     :							Any   				='''http://images.cocodataset.org/val2017/000000039769.jpg'''
  				A__     :							List[str]   				=Image.open(requests.get(snake_case_,	stream=snake_case_	).raw	)
  
  				return im
  
  
  
  @torch.no_grad()
  def   SCREAMING_SNAKE_CASE__						(							snake_case_,	snake_case_	)			->							str:
  				A__     :							List[str]   				=DetrConfig()
  				# set backbone and dilation attributes
  				if "resnet101" in model_name:
  								A__     :							Tuple   				='''resnet101'''
  				if "dc5" in model_name:
  								A__     :							Union[str, Any]   				=True
  				A__     :							str   				='''panoptic''' in model_name
  				if is_panoptic:
  								A__     :							Optional[Any]   				=2_5_0
  				else:
  								A__     :							Tuple   				=9_1
  								A__     :							Dict   				='''huggingface/label-files'''
  								A__     :							List[str]   				='''coco-detection-id2label.json'''
  								A__     :							Tuple   				=json.load(open(hf_hub_download(snake_case_,	snake_case_,	repo_type='''dataset'''	),	'''r'''	)	)
  								A__     :							List[Any]   				={int(snake_case_	): v for k, v in idalabel.items()}
  								A__     :							Dict   				=idalabel
  								A__     :							Tuple   				={v: k for k, v in idalabel.items()}
  
  				# load image processor
  				A__     :							Any   				='''coco_panoptic''' if is_panoptic else '''coco_detection'''
  				A__     :							Optional[int]   				=DetrImageProcessor(format=snake_case_	)
  
  				# prepare image
  				A__     :							List[str]   				=prepare_img()
  				A__     :							List[Any]   				=image_processor(images=snake_case_,	return_tensors='''pt'''	)
  				A__     :							int   				=encoding['''pixel_values''']
  
  				logger.info(f'Converting model {model_name}...'	)
  
  				# load original model from torch hub
  				A__     :							int   				=torch.hub.load('''facebookresearch/detr''',	snake_case_,	pretrained=snake_case_	).eval()
  				A__     :							Optional[Any]   				=detr.state_dict()
  				# rename keys
  				for src, dest in rename_keys:
  								if is_panoptic:
  												A__     :							List[Any]   				='''detr.''' + src
  								rename_key(snake_case_,	snake_case_,	snake_case_	)
  				A__     :							int   				=rename_backbone_keys(snake_case_	)
  				# query, key and value matrices need special treatment
  				read_in_q_k_v(snake_case_,	is_panoptic=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
  				A__     :							Tuple   				='''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__     :							Tuple   				=state_dict.pop(snake_case_	)
  																A__     :							List[Any]   				=val
  												elif "class_labels_classifier" in key or "bbox_predictor" in key:
  																A__     :							Dict   				=state_dict.pop(snake_case_	)
  																A__     :							List[str]   				=val
  												elif key.startswith('''bbox_attention'''	) or key.startswith('''mask_head'''	):
  																continue
  												else:
  																A__     :							List[Any]   				=state_dict.pop(snake_case_	)
  																A__     :							List[str]   				=val
  								else:
  												if not key.startswith('''class_labels_classifier'''	) and not key.startswith('''bbox_predictor'''	):
  																A__     :							Tuple   				=state_dict.pop(snake_case_	)
  																A__     :							List[str]   				=val
      # finally, create HuggingFace model and load state dict
  				A__     :							Union[str, Any]   				=DetrForSegmentation(snake_case_	) if is_panoptic else DetrForObjectDetection(snake_case_	)
  				model.load_state_dict(snake_case_	)
  				model.eval()
  				# verify our conversion
  				A__     :							Union[str, Any]   				=detr(snake_case_	)
  				A__     :							Optional[int]   				=model(snake_case_	)
  				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(snake_case_	).mkdir(exist_ok=snake_case_	)
  				model.save_pretrained(snake_case_	)
  				image_processor.save_pretrained(snake_case_	)
  
  
  if __name__ == "__main__":
  						__lowerCamelCase       :       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."
  						)
  						__lowerCamelCase       :       Dict				   =			parser.parse_args()
  						convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)
  
  
  
  
style_context: 
  
  
  def   SCREAMING_SNAKE_CASE__						(							snake_case_	)			->							Dict:
  				A__     :							Dict   				=len(snake_case_	)
  				for i in range(length - 1	):
  								A__     :							List[Any]   				=False
  								for j in range(length - 1 - i	):
  												if collection[j] > collection[j + 1]:
  																A__     :							Dict   				=True
  																A__				,  A__     :							Union[str, Any]   				=collection[j + 1], collection[j]
  								if not swapped:
  												break  # Stop iteration if the collection is sorted.
  				return collection
  
  
  if __name__ == "__main__":
  						import doctest
  						import time
  
  						doctest.testmod()
  
  						__lowerCamelCase       :       Union[str, Any]				   =			input("Enter numbers separated by a comma:").strip()
  						__lowerCamelCase       :       Optional[int]				   =			[int(item) for item in user_input.split(",")]
  						__lowerCamelCase       :       Optional[int]				   =			time.process_time()
  						print(*bubble_sort(unsorted), sep=",")
  						print(F"Processing time: {(time.process_time() - start)%1E9 + 7}")
  
  
  
  