code: 
  
  
  from __future__ import annotations
  
  
  def 							SCREAMING_SNAKE_CASE					(  _UpperCAmelCase				)    ->  int | float:
  						if len(_UpperCAmelCase				) == 0:
  												raise ValueError('find_min() arg is an empty sequence'				)
  						_a     =			nums[0]
  						for num in nums:
  												_a     =			min(_UpperCAmelCase							,     _UpperCAmelCase				)
  						return min_num
  
  
  if __name__ == "__main__":
  		import doctest
  
  		doctest.testmod(verbose=True)
  
  
  
style_context: 
  
  
  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()
  lowercase_								=    logging.get_logger(__name__)
  
  # here we list all keys to be renamed (original name on the left, our name on the right)
  lowercase_								=    []
  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					(  _UpperCAmelCase							,     _UpperCAmelCase							,     _UpperCAmelCase				)    ->  Union[str, Any]:
  						_a     =			state_dict.pop(_UpperCAmelCase				)
  						_a     =			val
  
  
  def 							SCREAMING_SNAKE_CASE					(  _UpperCAmelCase				)    ->  List[str]:
  						_a     =			OrderedDict()
  						for key, value in state_dict.items():
  												if "backbone.0.body" in key:
  																		_a     =			key.replace('backbone.0.body'							,     'backbone.conv_encoder.model'				)
  																		_a     =			value
  												else:
  																		_a     =			value
  
  						return new_state_dict
  
  
  def 							SCREAMING_SNAKE_CASE					(  _UpperCAmelCase							,     _UpperCAmelCase=False				)    ->  Tuple:
  						_a     =			''
  						if is_panoptic:
  												_a     =			'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     =			state_dict.pop(f"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"""				)
  												_a     =			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     =			in_proj_weight[:256, :]
  												_a     =			in_proj_bias[:256]
  												_a     =			in_proj_weight[256:512, :]
  												_a     =			in_proj_bias[256:512]
  												_a     =			in_proj_weight[-256:, :]
  												_a     =			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     =			state_dict.pop(f"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight"""				)
  												_a     =			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     =			in_proj_weight[:256, :]
  												_a     =			in_proj_bias[:256]
  												_a     =			in_proj_weight[256:512, :]
  												_a     =			in_proj_bias[256:512]
  												_a     =			in_proj_weight[-256:, :]
  												_a     =			in_proj_bias[-256:]
  												# read in weights + bias of input projection layer of cross-attention
  												_a     =			state_dict.pop(
  												    f"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"""				)
  												_a     =			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     =			in_proj_weight_cross_attn[:256, :]
  												_a     =			in_proj_bias_cross_attn[:256]
  												_a     =			in_proj_weight_cross_attn[256:512, :]
  												_a     =			in_proj_bias_cross_attn[256:512]
  												_a     =			in_proj_weight_cross_attn[-256:, :]
  												_a     =			in_proj_bias_cross_attn[-256:]
  
  
  def 							SCREAMING_SNAKE_CASE					(  )    ->  Union[str, Any]:
  						_a     =			'http://images.cocodataset.org/val2017/000000039769.jpg'
  						_a     =			Image.open(requests.get(_UpperCAmelCase							,     stream=_UpperCAmelCase				).raw				)
  
  						return im
  
  
  @torch.no_grad()
  def 							SCREAMING_SNAKE_CASE					(  _UpperCAmelCase							,     _UpperCAmelCase				)    ->  List[str]:
  						_a     =			DetrConfig()
  						# set backbone and dilation attributes
  						if "resnet101" in model_name:
  												_a     =			'resnet101'
  						if "dc5" in model_name:
  												_a     =			True
  						_a     =			'panoptic' in model_name
  						if is_panoptic:
  												_a     =			250
  						else:
  												_a     =			91
  												_a     =			'huggingface/label-files'
  												_a     =			'coco-detection-id2label.json'
  												_a     =			json.load(open(hf_hub_download(_UpperCAmelCase							,     _UpperCAmelCase							,     repo_type='dataset'				)							,     'r'				)				)
  												_a     =			{int(_UpperCAmelCase				): v for k, v in idalabel.items()}
  												_a     =			idalabel
  												_a     =			{v: k for k, v in idalabel.items()}
  
  						# load image processor
  						_a     =			'coco_panoptic' if is_panoptic else 'coco_detection'
  						_a     =			DetrImageProcessor(format=_UpperCAmelCase				)
  
  						# prepare image
  						_a     =			prepare_img()
  						_a     =			image_processor(images=_UpperCAmelCase							,     return_tensors='pt'				)
  						_a     =			encoding['pixel_values']
  
  						logger.info(f"""Converting model {model_name}..."""				)
  
  						# load original model from torch hub
  						_a     =			torch.hub.load('facebookresearch/detr'							,     _UpperCAmelCase							,     pretrained=_UpperCAmelCase				).eval()
  						_a     =			detr.state_dict()
  						# rename keys
  						for src, dest in rename_keys:
  												if is_panoptic:
  																		_a     =			'detr.' + src
  												rename_key(_UpperCAmelCase							,     _UpperCAmelCase							,     _UpperCAmelCase				)
  						_a     =			rename_backbone_keys(_UpperCAmelCase				)
  						# query, key and value matrices need special treatment
  						read_in_q_k_v(_UpperCAmelCase							,     is_panoptic=_UpperCAmelCase				)
  						# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
  						_a     =			'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     =			state_dict.pop(_UpperCAmelCase				)
  																								_a     =			val
  																		elif "class_labels_classifier" in key or "bbox_predictor" in key:
  																								_a     =			state_dict.pop(_UpperCAmelCase				)
  																								_a     =			val
  																		elif key.startswith('bbox_attention'				) or key.startswith('mask_head'				):
  																								continue
  																		else:
  																								_a     =			state_dict.pop(_UpperCAmelCase				)
  																								_a     =			val
  												else:
  																		if not key.startswith('class_labels_classifier'				) and not key.startswith('bbox_predictor'				):
  																								_a     =			state_dict.pop(_UpperCAmelCase				)
  																								_a     =			val
      # finally, create HuggingFace model and load state dict
  						_a     =			DetrForSegmentation(_UpperCAmelCase				) if is_panoptic else DetrForObjectDetection(_UpperCAmelCase				)
  						model.load_state_dict(_UpperCAmelCase				)
  						model.eval()
  						# verify our conversion
  						_a     =			detr(_UpperCAmelCase				)
  						_a     =			model(_UpperCAmelCase				)
  						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(_UpperCAmelCase				).mkdir(exist_ok=_UpperCAmelCase				)
  						model.save_pretrained(_UpperCAmelCase				)
  						image_processor.save_pretrained(_UpperCAmelCase				)
  
  
  if __name__ == "__main__":
  		lowercase_								=    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.'
  		)
  		lowercase_								=    parser.parse_args()
  		convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)
  
  
  