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()
  a__				: Optional[int]									=    logging.get_logger(__name__)
  
  
  
  
  def 	__lowerCamelCase (  UpperCAmelCase_       )      ->Tuple:
  	if "backbone.0.body" in orig_key:
  		snake_case__  					=      orig_key.replace('backbone.0.body'				,							'backbone.conv_encoder.model'       )
  	if "transformer" in orig_key:
  		snake_case__  					=      orig_key.replace('transformer.'				,							''       )
  	if "norm1" in orig_key:
  		if "encoder" in orig_key:
  			snake_case__  					=      orig_key.replace('norm1'				,							'self_attn_layer_norm'       )
  		else:
  			snake_case__  					=      orig_key.replace('norm1'				,							'encoder_attn_layer_norm'       )
  	if "norm2" in orig_key:
  		if "encoder" in orig_key:
  			snake_case__  					=      orig_key.replace('norm2'				,							'final_layer_norm'       )
  		else:
  			snake_case__  					=      orig_key.replace('norm2'				,							'self_attn_layer_norm'       )
  	if "norm3" in orig_key:
  		snake_case__  					=      orig_key.replace('norm3'				,							'final_layer_norm'       )
  	if "linear1" in orig_key:
  		snake_case__  					=      orig_key.replace('linear1'				,							'fc1'       )
  	if "linear2" in orig_key:
  		snake_case__  					=      orig_key.replace('linear2'				,							'fc2'       )
  	if "query_embed" in orig_key:
  		snake_case__  					=      orig_key.replace('query_embed'				,							'query_position_embeddings'       )
  	if "cross_attn" in orig_key:
  		snake_case__  					=      orig_key.replace('cross_attn'				,							'encoder_attn'       )
  
  	return orig_key
  
  
  
  
  def 	__lowerCamelCase (  UpperCAmelCase_       )      ->List[str]:
  	# transformer decoder self-attention layers
  	for i in range(6       ):
  		# read in weights + bias of input projection layer of self-attention
  		snake_case__  					=      state_dict.pop(f'''decoder.layers.{i}.self_attn.in_proj_weight'''       )
  		snake_case__  					=      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
  		snake_case__  					=      in_proj_weight[:2_56, :]
  		snake_case__  					=      in_proj_bias[:2_56]
  		snake_case__  					=      in_proj_weight[2_56:5_12, :]
  		snake_case__  					=      in_proj_bias[2_56:5_12]
  		snake_case__  					=      in_proj_weight[-2_56:, :]
  		snake_case__  					=      in_proj_bias[-2_56:]
  
  
  
  
  def 	__lowerCamelCase (  )      ->Tuple:
  	snake_case__  					=      'http://images.cocodataset.org/val2017/000000039769.jpg'
  	snake_case__  					=      Image.open(requests.get(UpperCAmelCase_				,							stream=UpperCAmelCase_       ).raw       )
  
  	return im
  
  
  
  
  @torch.no_grad()
  def 	__lowerCamelCase (  UpperCAmelCase_				,							UpperCAmelCase_				,							UpperCAmelCase_				,							UpperCAmelCase_				,							UpperCAmelCase_				,							UpperCAmelCase_				,							UpperCAmelCase_				,							)      ->Optional[Any]:
  	snake_case__  					=      DeformableDetrConfig()
  	# set config attributes
  	if single_scale:
  		snake_case__  					=      1
  	snake_case__  					=      dilation
  	snake_case__  					=      with_box_refine
  	snake_case__  					=      two_stage
  	# set labels
  	snake_case__  					=      91
  	snake_case__  					=      'huggingface/label-files'
  	snake_case__  					=      'coco-detection-id2label.json'
  	snake_case__  					=      json.load(open(cached_download(hf_hub_url(UpperCAmelCase_				,							UpperCAmelCase_				,							repo_type='dataset'       )       )				,							'r'       )       )
  	snake_case__  					=      {int(UpperCAmelCase_       ): 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__  					=      DeformableDetrImageProcessor(format='coco_detection'       )
  
  	# prepare image
  	snake_case__  					=      prepare_img()
  	snake_case__  					=      image_processor(images=UpperCAmelCase_				,							return_tensors='pt'       )
  	snake_case__  					=      encoding['pixel_values']
  
  	logger.info('Converting model...'       )
  
  	# load original state dict
  	snake_case__  					=      torch.load(UpperCAmelCase_				,							map_location='cpu'       )['model']
  	# rename keys
  	for key in state_dict.copy().keys():
  		snake_case__  					=      state_dict.pop(UpperCAmelCase_       )
  		snake_case__  					=      val
  	# query, key and value matrices need special treatment
  	read_in_q_k_v(UpperCAmelCase_       )
  	# 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__  					=      'model.'
  	for key in state_dict.copy().keys():
  		if not key.startswith('class_embed'       ) and not key.startswith('bbox_embed'       ):
  			snake_case__  					=      state_dict.pop(UpperCAmelCase_       )
  			snake_case__  					=      val
      # finally, create HuggingFace model and load state dict
  	snake_case__  					=      DeformableDetrForObjectDetection(UpperCAmelCase_       )
  	model.load_state_dict(UpperCAmelCase_       )
  	model.eval()
  
  	snake_case__  					=      'cuda' if torch.cuda.is_available() else 'cpu'
  	model.to(UpperCAmelCase_       )
  	# verify our conversion
  	snake_case__  					=      model(pixel_values.to(UpperCAmelCase_       )       )
  
  	snake_case__  					=      torch.tensor(
  	    [[-9.6645, -4.3449, -5.8705], [-9.7035, -3.8504, -5.0724], [-10.5634, -5.3379, -7.5116]]       )
  	snake_case__  					=      torch.tensor([[0.8693, 0.2289, 0.2492], [0.3150, 0.5489, 0.5845], [0.5563, 0.7580, 0.8518]]       )
  
  	if single_scale:
  		snake_case__  					=      torch.tensor(
  		    [[-9.9051, -4.2541, -6.4852], [-9.6947, -4.0854, -6.8033], [-10.0665, -5.8470, -7.7003]]       )
  		snake_case__  					=      torch.tensor([[0.7292, 0.4991, 0.5532], [0.7959, 0.2426, 0.4236], [0.7582, 0.3518, 0.4451]]       )
  
  	if single_scale and dilation:
  		snake_case__  					=      torch.tensor(
  		    [[-8.9652, -4.1074, -5.6635], [-9.0596, -4.9447, -6.6075], [-10.1178, -4.5275, -6.2671]]       )
  		snake_case__  					=      torch.tensor([[0.7665, 0.4130, 0.4769], [0.8364, 0.1841, 0.3391], [0.6261, 0.3895, 0.7978]]       )
  
  	if with_box_refine:
  		snake_case__  					=      torch.tensor(
  		    [[-8.8895, -5.4187, -6.8153], [-8.4706, -6.1668, -7.6184], [-9.0042, -5.5359, -6.9141]]       )
  		snake_case__  					=      torch.tensor([[0.7828, 0.2208, 0.4323], [0.0892, 0.5996, 0.1319], [0.5524, 0.6389, 0.8914]]       )
  
  	if with_box_refine and two_stage:
  		snake_case__  					=      torch.tensor(
  		    [[-6.7108, -4.3213, -6.3777], [-8.9014, -6.1799, -6.7240], [-6.9315, -4.4735, -6.2298]]       )
  		snake_case__  					=      torch.tensor([[0.2583, 0.5499, 0.4683], [0.7652, 0.9068, 0.4882], [0.5490, 0.2763, 0.0564]]       )
  
  	print('Logits:'				,							outputs.logits[0, :3, :3]       )
  
  	assert torch.allclose(outputs.logits[0, :3, :3]				,							expected_logits.to(UpperCAmelCase_       )				,							atol=1E-4       )
  	assert torch.allclose(outputs.pred_boxes[0, :3, :3]				,							expected_boxes.to(UpperCAmelCase_       )				,							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(UpperCAmelCase_       ).mkdir(exist_ok=UpperCAmelCase_       )
  	model.save_pretrained(UpperCAmelCase_       )
  	image_processor.save_pretrained(UpperCAmelCase_       )
  
  	# Push to hub
  	if push_to_hub:
  		snake_case__  					=      '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=UpperCAmelCase_				,							organization='nielsr'				,							commit_message='Add model'       )
  
  
  if __name__ == "__main__":
  			a__				: List[Any]									=    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.'''
  			)
  			a__				: List[Any]									=    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'''
  
  
  
  
  a__				: Any									=    {
      0: '''underwear''',
      1: '''pants''',
      2: '''belt''',
      3: '''suit''',
      4: '''shoe''',
      5: '''socks''',
      6: '''shirt''',
      7: '''tie''',
      8: '''watch''',
  }
  
  a__				: Optional[Any]									=    [[1, 4], [2, 4], [3], [], [], [4], [2, 7], [3], []]
  
  a__				: List[str]									=    [0 for x in range(len(graph))]
  a__				: List[Any]									=    []
  
  
  
  
  def 	__lowerCamelCase (  UpperCAmelCase_				,							UpperCAmelCase_       )      ->Dict:
  	snake_case__  					=      1
  	while stack:
  		snake_case__  					=      stack.pop()
  		print(UpperCAmelCase_				,							clothes[current_clothing]       )
  		order += 1
  
  
  
  
  def 	__lowerCamelCase (  UpperCAmelCase_				,							UpperCAmelCase_				,							UpperCAmelCase_       )      ->Dict:
  	snake_case__  					=      1
  	for v in graph[u]:
  		if not visited[v]:
  			depth_first_search(UpperCAmelCase_				,							UpperCAmelCase_				,							UpperCAmelCase_       )
  
  	stack.append(UpperCAmelCase_       )
  
  
  
  
  def 	__lowerCamelCase (  UpperCAmelCase_				,							UpperCAmelCase_       )      ->Union[str, Any]:
  	for v in range(len(UpperCAmelCase_       )       ):
  		if not visited[v]:
  			depth_first_search(UpperCAmelCase_				,							UpperCAmelCase_				,							UpperCAmelCase_       )
  
  
  if __name__ == "__main__":
  			topological_sort(graph, visited)
  			print(stack)
  			print_stack(stack, clothes)
  
  
  
  