code: 
  
  
  
  
  '''simple docstring'''
  
  
  
  
  
  
  
  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[Any]										=						logging.get_logger(__name__)
  
  # here we list all keys to be renamed (original name on the left, our name on the right)
  _lowerCamelCase							:		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 	_lowercase	(			lowercase_		,							lowercase_		,							lowercase_						):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		_snake_case			 = state_dict.pop(lowercase_						)
  		_snake_case			 = val
  
  
  
  
  
  
  def 	_lowercase	(			lowercase_						):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		_snake_case			 = OrderedDict()
  		for key, value in state_dict.items():
  				if "backbone.0.body" in key:
  						_snake_case			 = key.replace("backbone.0.body"		,							"backbone.conv_encoder.model"						)
  						_snake_case			 = value
  				else:
  						_snake_case			 = value
  
  		return new_state_dict
  
  
  
  
  
  
  def 	_lowercase	(			lowercase_		,							lowercase_=False						):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		_snake_case			 = ""
  		if is_panoptic:
  				_snake_case			 = "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)
  				_snake_case			 = state_dict.pop(f'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'''						)
  				_snake_case			 = 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
  				_snake_case			 = in_proj_weight[:256, :]
  				_snake_case			 = in_proj_bias[:256]
  				_snake_case			 = in_proj_weight[256:512, :]
  				_snake_case			 = in_proj_bias[256:512]
  				_snake_case			 = in_proj_weight[-256:, :]
  				_snake_case			 = 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
  				_snake_case			 = state_dict.pop(f'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight'''						)
  				_snake_case			 = 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
  				_snake_case			 = in_proj_weight[:256, :]
  				_snake_case			 = in_proj_bias[:256]
  				_snake_case			 = in_proj_weight[256:512, :]
  				_snake_case			 = in_proj_bias[256:512]
  				_snake_case			 = in_proj_weight[-256:, :]
  				_snake_case			 = in_proj_bias[-256:]
  				# read in weights + bias of input projection layer of cross-attention
  				_snake_case			 = state_dict.pop(
  				    f'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight'''						)
  				_snake_case			 = 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
  				_snake_case			 = in_proj_weight_cross_attn[:256, :]
  				_snake_case			 = in_proj_bias_cross_attn[:256]
  				_snake_case			 = in_proj_weight_cross_attn[256:512, :]
  				_snake_case			 = in_proj_bias_cross_attn[256:512]
  				_snake_case			 = in_proj_weight_cross_attn[-256:, :]
  				_snake_case			 = in_proj_bias_cross_attn[-256:]
  
  
  
  
  
  
  def 	_lowercase	(			):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		_snake_case			 = "http://images.cocodataset.org/val2017/000000039769.jpg"
  		_snake_case			 = Image.open(requests.get(lowercase_		,							stream=lowercase_						).raw						)
  
  		return im
  
  
  
  
  
  
  @torch.no_grad()
  def 	_lowercase	(			lowercase_		,							lowercase_						):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		_snake_case			 = DetrConfig()
  		# set backbone and dilation attributes
  		if "resnet101" in model_name:
  				_snake_case			 = "resnet101"
  		if "dc5" in model_name:
  				_snake_case			 = True
  		_snake_case			 = "panoptic" in model_name
  		if is_panoptic:
  				_snake_case			 = 250
  		else:
  				_snake_case			 = 91
  				_snake_case			 = "huggingface/label-files"
  				_snake_case			 = "coco-detection-id2label.json"
  				_snake_case			 = json.load(open(hf_hub_download(lowercase_		,							lowercase_		,							repo_type="dataset"						)		,							"r"						)						)
  				_snake_case			 = {int(lowercase_						): v for k, v in idalabel.items()}
  				_snake_case			 = idalabel
  				_snake_case			 = {v: k for k, v in idalabel.items()}
  
  		# load image processor
  		_snake_case			 = "coco_panoptic" if is_panoptic else "coco_detection"
  		_snake_case			 = DetrImageProcessor(format=lowercase_						)
  
  		# prepare image
  		_snake_case			 = prepare_img()
  		_snake_case			 = image_processor(images=lowercase_		,							return_tensors="pt"						)
  		_snake_case			 = encoding["pixel_values"]
  
  		logger.info(f'''Converting model {model_name}...'''						)
  
  		# load original model from torch hub
  		_snake_case			 = torch.hub.load("facebookresearch/detr"		,							lowercase_		,							pretrained=lowercase_						).eval()
  		_snake_case			 = detr.state_dict()
  		# rename keys
  		for src, dest in rename_keys:
  				if is_panoptic:
  						_snake_case			 = "detr." + src
  				rename_key(lowercase_		,							lowercase_		,							lowercase_						)
  		_snake_case			 = rename_backbone_keys(lowercase_						)
  		# query, key and value matrices need special treatment
  		read_in_q_k_v(lowercase_		,							is_panoptic=lowercase_						)
  		# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
  		_snake_case			 = "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"						)
  						):
  								_snake_case			 = state_dict.pop(lowercase_						)
  								_snake_case			 = val
  						elif "class_labels_classifier" in key or "bbox_predictor" in key:
  								_snake_case			 = state_dict.pop(lowercase_						)
  								_snake_case			 = val
  						elif key.startswith("bbox_attention"						) or key.startswith("mask_head"						):
  								continue
  						else:
  								_snake_case			 = state_dict.pop(lowercase_						)
  								_snake_case			 = val
  				else:
  						if not key.startswith("class_labels_classifier"						) and not key.startswith("bbox_predictor"						):
  								_snake_case			 = state_dict.pop(lowercase_						)
  								_snake_case			 = val
      # finally, create HuggingFace model and load state dict
  		_snake_case			 = DetrForSegmentation(lowercase_						) if is_panoptic else DetrForObjectDetection(lowercase_						)
  		model.load_state_dict(lowercase_						)
  		model.eval()
  		# verify our conversion
  		_snake_case			 = detr(lowercase_						)
  		_snake_case			 = model(lowercase_						)
  		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(lowercase_						).mkdir(exist_ok=lowercase_						)
  		model.save_pretrained(lowercase_						)
  		image_processor.save_pretrained(lowercase_						)
  
  
  if __name__ == "__main__":
  						_lowerCamelCase							:		int										=						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							:		Union[str, Any]										=						parser.parse_args()
  						convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)
  
  
  
  
  
  
style_context: 
  
  
  
  
  '''simple docstring'''
  
  
  
  
  
  
  
  from __future__ import annotations
  
  from collections.abc import Iterable, Iterator
  from typing import Any, Generic, TypeVar
  
  _lowerCamelCase							:		List[str]										=						TypeVar('T', bound=bool)
  
  
  
  
  
  class   lowerCamelCase_							( Generic[T]							):
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  					def __init__(					self,						__UpperCamelCase				):
  							_snake_case			 = value
  							_snake_case			 = None
  							_snake_case			 = None
  
  
  
  
  
  
  					@property
  					def        __SCREAMING_SNAKE_CASE			(					self				):
  							return self._value
  
  
  
  
  
  
  					@staticmethod
  					def        __SCREAMING_SNAKE_CASE			(					__UpperCamelCase,						__UpperCamelCase				):
  							if not roota:
  									return roota
  
  							if not roota:
  									return roota
  
  							if roota.value > roota.value:
  									_snake_case ,					_snake_case			 = roota, roota
  
  							_snake_case			 = roota
  							_snake_case			 = roota.right
  							_snake_case			 = roota.left
  							_snake_case			 = SkewNode.merge(__UpperCamelCase,						__UpperCamelCase				)
  
  							return result
  
  
  
  
  
  class   lowerCamelCase_							( Generic[T]							):
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  					def __init__(					self,						__UpperCamelCase = ()				):
  							_snake_case			 = None
  							if data:
  									for item in data:
  											self.insert(__UpperCamelCase				)
  
  
  
  
  
  
  					def __bool__(					self				):
  							return self._root is not None
  
  
  
  
  
  
  					def __iter__(					self				):
  							_snake_case			 = []
  							while self:
  									result.append(self.pop()				)
  
  							# Pushing items back to the heap not to clear it.
  							for item in result:
  									self.insert(__UpperCamelCase				)
  
  							return iter(__UpperCamelCase				)
  
  
  
  
  
  
  					def        __SCREAMING_SNAKE_CASE			(					self,						__UpperCamelCase				):
  							_snake_case			 = SkewNode.merge(self._root,						SkewNode(__UpperCamelCase				)				)
  
  
  
  
  
  
  					def        __SCREAMING_SNAKE_CASE			(					self				):
  							_snake_case			 = self.top()
  							_snake_case			 = (
  							    SkewNode.merge(self._root.left,						self._root.right				) if self._root else None
  							)
  
  							return result
  
  
  
  
  
  
  					def        __SCREAMING_SNAKE_CASE			(					self				):
  							if not self._root:
  									raise IndexError("Can't get top element for the empty heap."				)
  							return self._root.value
  
  
  
  
  
  
  					def        __SCREAMING_SNAKE_CASE			(					self				):
  							_snake_case			 = None
  
  
  if __name__ == "__main__":
  						import doctest
  
  						doctest.testmod()
  
  
  
  
  
  