code: from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
  
  
  UpperCAmelCase     :   Any					=				{
      '''configuration_deformable_detr''': ['''DEFORMABLE_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DeformableDetrConfig'''],
  }
  
  try:
   if not is_vision_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   UpperCAmelCase     :   int					=				['''DeformableDetrFeatureExtractor''']
   UpperCAmelCase     :   str					=				['''DeformableDetrImageProcessor''']
  
  try:
   if not is_torch_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   UpperCAmelCase     :   Tuple					=				[
       '''DEFORMABLE_DETR_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''DeformableDetrForObjectDetection''',
       '''DeformableDetrModel''',
       '''DeformableDetrPreTrainedModel''',
   ]
  
  
  if TYPE_CHECKING:
   from .configuration_deformable_detr import DEFORMABLE_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP, DeformableDetrConfig
  
   try:
    if not is_vision_available():
     raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
    pass
   else:
    from .feature_extraction_deformable_detr import DeformableDetrFeatureExtractor
    from .image_processing_deformable_detr import DeformableDetrImageProcessor
  
   try:
    if not is_torch_available():
     raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
    pass
   else:
    from .modeling_deformable_detr import (
        DEFORMABLE_DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
        DeformableDetrForObjectDetection,
        DeformableDetrModel,
        DeformableDetrPreTrainedModel,
    )
  
  else:
   import sys
  
   UpperCAmelCase     :   Tuple					=				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
  
  
  
  
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)))
  
  
  
  