code: 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()
  UpperCAmelCase					=				logging.get_logger(__name__)
  
  
  def SCREAMING_SNAKE_CASE				(     lowercase_:	List[Any]				):
         if "backbone.0.body" in orig_key:
                _UpperCamelCase     :       Dict  						=  orig_key.replace('backbone.0.body'		,						'backbone.conv_encoder.model'				)
         if "transformer" in orig_key:
                _UpperCamelCase     :       Optional[Any]  						=  orig_key.replace('transformer.'		,						''				)
         if "norm1" in orig_key:
                if "encoder" in orig_key:
                       _UpperCamelCase     :       Optional[int]  						=  orig_key.replace('norm1'		,						'self_attn_layer_norm'				)
                else:
                       _UpperCamelCase     :       Tuple  						=  orig_key.replace('norm1'		,						'encoder_attn_layer_norm'				)
         if "norm2" in orig_key:
                if "encoder" in orig_key:
                       _UpperCamelCase     :       Dict  						=  orig_key.replace('norm2'		,						'final_layer_norm'				)
                else:
                       _UpperCamelCase     :       Dict  						=  orig_key.replace('norm2'		,						'self_attn_layer_norm'				)
         if "norm3" in orig_key:
                _UpperCamelCase     :       str  						=  orig_key.replace('norm3'		,						'final_layer_norm'				)
         if "linear1" in orig_key:
                _UpperCamelCase     :       Any  						=  orig_key.replace('linear1'		,						'fc1'				)
         if "linear2" in orig_key:
                _UpperCamelCase     :       List[str]  						=  orig_key.replace('linear2'		,						'fc2'				)
         if "query_embed" in orig_key:
                _UpperCamelCase     :       Dict  						=  orig_key.replace('query_embed'		,						'query_position_embeddings'				)
         if "cross_attn" in orig_key:
                _UpperCamelCase     :       Dict  						=  orig_key.replace('cross_attn'		,						'encoder_attn'				)
  
         return orig_key
  
  
  def SCREAMING_SNAKE_CASE				(     lowercase_:	Tuple				):
         # transformer decoder self-attention layers
         for i in range(6				):
                # read in weights + bias of input projection layer of self-attention
                _UpperCamelCase     :       Any  						=  state_dict.pop(F"decoder.layers.{i}.self_attn.in_proj_weight"				)
                _UpperCamelCase     :       Any  						=  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
                _UpperCamelCase     :       List[Any]  						=  in_proj_weight[:256, :]
                _UpperCamelCase     :       List[str]  						=  in_proj_bias[:256]
                _UpperCamelCase     :       int  						=  in_proj_weight[256:512, :]
                _UpperCamelCase     :       List[str]  						=  in_proj_bias[256:512]
                _UpperCamelCase     :       int  						=  in_proj_weight[-256:, :]
                _UpperCamelCase     :       int  						=  in_proj_bias[-256:]
  
  
  def SCREAMING_SNAKE_CASE				(     ):
         _UpperCamelCase     :       int  						=  'http://images.cocodataset.org/val2017/000000039769.jpg'
         _UpperCamelCase     :       int  						=  Image.open(requests.get(lowercase_		,						stream=lowercase_				).raw				)
  
         return im
  
  
  @torch.no_grad()
  def SCREAMING_SNAKE_CASE				(     lowercase_:	Tuple		,						lowercase_:	List[Any]		,						lowercase_:	List[str]		,						lowercase_:	str		,						lowercase_:	Optional[int]		,						lowercase_:	List[str]		,						lowercase_:	Tuple		,						):
         _UpperCamelCase     :       List[Any]  						=  DeformableDetrConfig()
         # set config attributes
         if single_scale:
                _UpperCamelCase     :       str  						=  1
         _UpperCamelCase     :       Union[str, Any]  						=  dilation
         _UpperCamelCase     :       Any  						=  with_box_refine
         _UpperCamelCase     :       Any  						=  two_stage
         # set labels
         _UpperCamelCase     :       Union[str, Any]  						=  91
         _UpperCamelCase     :       Tuple  						=  'huggingface/label-files'
         _UpperCamelCase     :       List[str]  						=  'coco-detection-id2label.json'
         _UpperCamelCase     :       List[Any]  						=  json.load(open(cached_download(hf_hub_url(lowercase_		,						lowercase_		,						repo_type='dataset'				)				)		,						'r'				)				)
         _UpperCamelCase     :       Dict  						=  {int(lowercase_				): v for k, v in idalabel.items()}
         _UpperCamelCase     :       int  						=  idalabel
         _UpperCamelCase     :       List[str]  						=  {v: k for k, v in idalabel.items()}
  
         # load image processor
         _UpperCamelCase     :       Any  						=  DeformableDetrImageProcessor(format='coco_detection'				)
  
         # prepare image
         _UpperCamelCase     :       str  						=  prepare_img()
         _UpperCamelCase     :       Optional[Any]  						=  image_processor(images=lowercase_		,						return_tensors='pt'				)
         _UpperCamelCase     :       Any  						=  encoding['pixel_values']
  
         logger.info('Converting model...'				)
  
         # load original state dict
         _UpperCamelCase     :       Union[str, Any]  						=  torch.load(lowercase_		,						map_location='cpu'				)['model']
         # rename keys
         for key in state_dict.copy().keys():
                _UpperCamelCase     :       Tuple  						=  state_dict.pop(lowercase_				)
                _UpperCamelCase     :       Union[str, Any]  						=  val
         # query, key and value matrices need special treatment
         read_in_q_k_v(lowercase_				)
         # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
         _UpperCamelCase     :       int  						=  'model.'
         for key in state_dict.copy().keys():
                if not key.startswith('class_embed'				) and not key.startswith('bbox_embed'				):
                       _UpperCamelCase     :       Dict  						=  state_dict.pop(lowercase_				)
                       _UpperCamelCase     :       Dict  						=  val
      # finally, create HuggingFace model and load state dict
         _UpperCamelCase     :       int  						=  DeformableDetrForObjectDetection(lowercase_				)
         model.load_state_dict(lowercase_				)
         model.eval()
  
         _UpperCamelCase     :       Any  						=  'cuda' if torch.cuda.is_available() else 'cpu'
         model.to(lowercase_				)
         # verify our conversion
         _UpperCamelCase     :       Dict  						=  model(pixel_values.to(lowercase_				)				)
  
         _UpperCamelCase     :       Optional[Any]  						=  torch.tensor(
             [[-9.6645, -4.3449, -5.8705], [-9.7035, -3.8504, -5.0724], [-10.5634, -5.3379, -7.5116]]				)
         _UpperCamelCase     :       Dict  						=  torch.tensor([[0.8693, 0.2289, 0.2492], [0.3150, 0.5489, 0.5845], [0.5563, 0.7580, 0.8518]]				)
  
         if single_scale:
                _UpperCamelCase     :       Optional[int]  						=  torch.tensor(
                    [[-9.9051, -4.2541, -6.4852], [-9.6947, -4.0854, -6.8033], [-10.0665, -5.8470, -7.7003]]				)
                _UpperCamelCase     :       Any  						=  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:
                _UpperCamelCase     :       Optional[Any]  						=  torch.tensor(
                    [[-8.9652, -4.1074, -5.6635], [-9.0596, -4.9447, -6.6075], [-10.1178, -4.5275, -6.2671]]				)
                _UpperCamelCase     :       int  						=  torch.tensor([[0.7665, 0.4130, 0.4769], [0.8364, 0.1841, 0.3391], [0.6261, 0.3895, 0.7978]]				)
  
         if with_box_refine:
                _UpperCamelCase     :       Dict  						=  torch.tensor(
                    [[-8.8895, -5.4187, -6.8153], [-8.4706, -6.1668, -7.6184], [-9.0042, -5.5359, -6.9141]]				)
                _UpperCamelCase     :       List[str]  						=  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:
                _UpperCamelCase     :       int  						=  torch.tensor(
                    [[-6.7108, -4.3213, -6.3777], [-8.9014, -6.1799, -6.7240], [-6.9315, -4.4735, -6.2298]]				)
                _UpperCamelCase     :       Optional[Any]  						=  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(lowercase_				)		,						atol=1e-4				)
         assert torch.allclose(outputs.pred_boxes[0, :3, :3]		,						expected_boxes.to(lowercase_				)		,						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(lowercase_				).mkdir(exist_ok=lowercase_				)
         model.save_pretrained(lowercase_				)
         image_processor.save_pretrained(lowercase_				)
  
         # Push to hub
         if push_to_hub:
                _UpperCamelCase     :       List[str]  						=  '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=lowercase_		,						organization='nielsr'		,						commit_message='Add model'				)
  
  
  if __name__ == "__main__":
   UpperCAmelCase					=				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.'''
   )
   UpperCAmelCase					=				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: import argparse
  import json
  import re
  import string
  import sys
  from collections import Counter
  
  
  def SCREAMING_SNAKE_CASE				(     lowercase_:	List[Any]				):
  
         def remove_articles(lowercase_:	Dict				):
                return re.sub(R'\b(a|an|the)\b'		,						' '		,						lowercase_				)
  
         def white_space_fix(lowercase_:	Optional[Any]				):
                return " ".join(text.split()				)
  
         def remove_punc(lowercase_:	List[str]				):
                _UpperCamelCase     :       str  						=  set(string.punctuation				)
                return "".join(ch for ch in text if ch not in exclude				)
  
         def lower(lowercase_:	List[str]				):
                return text.lower()
  
         return white_space_fix(remove_articles(remove_punc(lower(lowercase_				)				)				)				)
  
  
  def SCREAMING_SNAKE_CASE				(     lowercase_:	List[Any]		,						lowercase_:	Tuple				):
         _UpperCamelCase     :       int  						=  normalize_answer(lowercase_				).split()
         _UpperCamelCase     :       int  						=  normalize_answer(lowercase_				).split()
         _UpperCamelCase     :       Union[str, Any]  						=  Counter(lowercase_				) & Counter(lowercase_				)
         _UpperCamelCase     :       Optional[int]  						=  sum(common.values()				)
         if num_same == 0:
                return 0
         _UpperCamelCase     :       Optional[Any]  						=  1.0 * num_same / len(lowercase_				)
         _UpperCamelCase     :       Optional[int]  						=  1.0 * num_same / len(lowercase_				)
         _UpperCamelCase     :       List[str]  						=  (2 * precision * recall) / (precision + recall)
         return fa
  
  
  def SCREAMING_SNAKE_CASE				(     lowercase_:	str		,						lowercase_:	Union[str, Any]				):
         return normalize_answer(lowercase_				) == normalize_answer(lowercase_				)
  
  
  def SCREAMING_SNAKE_CASE				(     lowercase_:	Optional[int]		,						lowercase_:	int		,						lowercase_:	Union[str, Any]				):
         _UpperCamelCase     :       Any  						=  []
         for ground_truth in ground_truths:
                _UpperCamelCase     :       Optional[Any]  						=  metric_fn(lowercase_		,						lowercase_				)
                scores_for_ground_truths.append(lowercase_				)
         return max(lowercase_				)
  
  
  def SCREAMING_SNAKE_CASE				(     lowercase_:	Union[str, Any]		,						lowercase_:	List[Any]				):
         _UpperCamelCase     :       List[Any]  						=  0
         for article in dataset:
                for paragraph in article["paragraphs"]:
                       for qa in paragraph["qas"]:
                              total += 1
                              if qa["id"] not in predictions:
                                     _UpperCamelCase     :       Union[str, Any]  						=  'Unanswered question ' + qa['id'] + ' will receive score 0.'
                                     print(lowercase_		,						file=sys.stderr				)
                                     continue
                              _UpperCamelCase     :       Dict  						=  [x['text'] for x in qa['answers']]
                              _UpperCamelCase     :       List[str]  						=  predictions[qa['id']]
                              exact_match += metric_max_over_ground_truths(lowercase_		,						lowercase_		,						lowercase_				)
                              fa += metric_max_over_ground_truths(lowercase_		,						lowercase_		,						lowercase_				)
  
         _UpperCamelCase     :       Dict  						=  100.0 * exact_match / total
         _UpperCamelCase     :       Optional[Any]  						=  100.0 * fa / total
  
         return {"exact_match": exact_match, "f1": fa}
  
  
  if __name__ == "__main__":
   UpperCAmelCase					=				'''1.1'''
   UpperCAmelCase					=				argparse.ArgumentParser(description='''Evaluation for SQuAD ''' + expected_version)
   parser.add_argument('''dataset_file''', help='''Dataset file''')
   parser.add_argument('''prediction_file''', help='''Prediction File''')
   UpperCAmelCase					=				parser.parse_args()
   with open(args.dataset_file) as dataset_file:
    UpperCAmelCase					=				json.load(dataset_file)
    if dataset_json["version"] != expected_version:
     print(
         '''Evaluation expects v-''' + expected_version + ''', but got dataset with v-''' + dataset_json['''version'''],
         file=sys.stderr,
     )
    UpperCAmelCase					=				dataset_json['''data''']
   with open(args.prediction_file) as prediction_file:
    UpperCAmelCase					=				json.load(prediction_file)
   print(json.dumps(evaluate(dataset, predictions)))
  
  
  
  