code: import argparse
  import json
  import re
  import string
  import sys
  from collections import Counter
  
  
  def SCREAMING_SNAKE_CASE				(     lowercase_:	Union[str, Any]				):
  
         def remove_articles(lowercase_:	Optional[int]				):
                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_:	Tuple				):
                _UpperCamelCase     :       List[Any]  						=  set(string.punctuation				)
                return "".join(ch for ch in text if ch not in exclude				)
  
         def lower(lowercase_:	List[Any]				):
                return text.lower()
  
         return white_space_fix(remove_articles(remove_punc(lower(lowercase_				)				)				)				)
  
  
  def SCREAMING_SNAKE_CASE				(     lowercase_:	Optional[Any]		,						lowercase_:	Optional[Any]				):
         _UpperCamelCase     :       Optional[int]  						=  normalize_answer(lowercase_				).split()
         _UpperCamelCase     :       Union[str, Any]  						=  normalize_answer(lowercase_				).split()
         _UpperCamelCase     :       Union[str, Any]  						=  Counter(lowercase_				) & Counter(lowercase_				)
         _UpperCamelCase     :       Union[str, Any]  						=  sum(common.values()				)
         if num_same == 0:
                return 0
         _UpperCamelCase     :       List[Any]  						=  1.0 * num_same / len(lowercase_				)
         _UpperCamelCase     :       List[str]  						=  1.0 * num_same / len(lowercase_				)
         _UpperCamelCase     :       Any  						=  (2 * precision * recall) / (precision + recall)
         return fa
  
  
  def SCREAMING_SNAKE_CASE				(     lowercase_:	List[str]		,						lowercase_:	Optional[int]				):
         return normalize_answer(lowercase_				) == normalize_answer(lowercase_				)
  
  
  def SCREAMING_SNAKE_CASE				(     lowercase_:	Optional[int]		,						lowercase_:	Union[str, Any]		,						lowercase_:	int				):
         _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_:	int		,						lowercase_:	Optional[int]				):
         _UpperCamelCase     :       List[Any]  						=  0
         _UpperCamelCase     :       Tuple  						=  []
         for passage in dataset:
                for qa in passage["qas"]:
                       total += 1
                       if qa["id"] not in predictions:
                              _UpperCamelCase     :       Optional[Any]  						=  F"Unanswered question {qa['id']} will receive score 0."
                              print(lowercase_		,						file=sys.stderr				)
                              continue
  
                       _UpperCamelCase     :       List[Any]  						=  [x['text'] for x in qa['answers']]
                       _UpperCamelCase     :       Any  						=  predictions[qa['id']]
  
                       _UpperCamelCase     :       Any  						=  metric_max_over_ground_truths(lowercase_		,						lowercase_		,						lowercase_				)
                       if int(_exact_match				) == 1:
                              correct_ids.append(qa['id']				)
                       exact_match += _exact_match
  
                       fa += metric_max_over_ground_truths(lowercase_		,						lowercase_		,						lowercase_				)
  
         _UpperCamelCase     :       Tuple  						=  exact_match / total
         _UpperCamelCase     :       str  						=  fa / total
  
         return {"exact_match": exact_match, "f1": fa}, correct_ids
  
  
  if __name__ == "__main__":
   UpperCAmelCase					=				'''1.0'''
   UpperCAmelCase					=				argparse.ArgumentParser('''Official evaluation script for ReCoRD v1.0.''')
   parser.add_argument('''data_file''', help='''The dataset file in JSON format.''')
   parser.add_argument('''pred_file''', help='''The model prediction file in JSON format.''')
   parser.add_argument('''--output_correct_ids''', action='''store_true''', help='''Output the correctly answered query IDs.''')
   UpperCAmelCase					=				parser.parse_args()
  
   with open(args.data_file) as data_file:
    UpperCAmelCase					=				json.load(data_file)
    if dataset_json["version"] != expected_version:
     print(
         f'''Evaluation expects v-{expected_version}, but got dataset with v-{dataset_json["version"]}''',
         file=sys.stderr,
     )
    UpperCAmelCase					=				dataset_json['''data''']
  
   with open(args.pred_file) as pred_file:
    UpperCAmelCase					=				json.load(pred_file)
  
   UpperCAmelCase		,					UpperCAmelCase					=				evaluate(dataset, predictions)
  
   if args.output_correct_ids:
    print(f'''Output {len(correct_ids)} correctly answered question IDs.''')
    with open('''correct_ids.json''', '''w''') as f:
     json.dump(correct_ids, f)
  
  
  
  
style_context: from .chunk_utils import chunk_layer
  from .data_transforms import make_atomaa_masks
  from .feats import atomaa_to_atomaa, frames_and_literature_positions_to_atomaa_pos, torsion_angles_to_frames
  from .loss import compute_predicted_aligned_error, compute_tm
  from .protein import Protein as OFProtein
  from .protein import to_pdb
  from .rigid_utils import Rigid, Rotation
  from .tensor_utils import dict_multimap, flatten_final_dims, permute_final_dims
  
  
  
  