code: from __future__ import annotations
  
  
  def SCREAMING_SNAKE_CASE				(     lowercase_:	list		,						lowercase_:	list				):
         _UpperCamelCase     :       List[Any]  						=  [None] * (len(lowercase_				) + len(lowercase_				))
  
         _UpperCamelCase     :       Dict  						=  0  # pointer to current index for left Half
         _UpperCamelCase     :       str  						=  0  # pointer to current index for the right Half
         _UpperCamelCase     :       Any  						=  0  # pointer to current index for the sorted array Half
  
         while pointera < len(lowercase_				) and pointera < len(lowercase_				):
                if left_half[pointera] < right_half[pointera]:
                       _UpperCamelCase     :       Dict  						=  left_half[pointera]
                       pointera += 1
                       index += 1
                else:
                       _UpperCamelCase     :       Dict  						=  right_half[pointera]
                       pointera += 1
                       index += 1
         while pointera < len(lowercase_				):
                _UpperCamelCase     :       Dict  						=  left_half[pointera]
                pointera += 1
                index += 1
  
         while pointera < len(lowercase_				):
                _UpperCamelCase     :       List[Any]  						=  right_half[pointera]
                pointera += 1
                index += 1
  
         return sorted_array
  
  
  def SCREAMING_SNAKE_CASE				(     lowercase_:	list				):
         if len(lowercase_				) <= 1:
                return array
         # the actual formula to calculate the middle element = left + (right - left) // 2
         # this avoids integer overflow in case of large N
         _UpperCamelCase     :       Dict  						=  0 + (len(lowercase_				) - 0) // 2
  
         # Split the array into halves till the array length becomes equal to One
         # merge the arrays of single length returned by mergeSort function and
         # pass them into the merge arrays function which merges the array
         _UpperCamelCase     :       Optional[Any]  						=  array[:middle]
         _UpperCamelCase     :       List[str]  						=  array[middle:]
  
         return merge(merge_sort(lowercase_				)		,						merge_sort(lowercase_				)				)
  
  
  if __name__ == "__main__":
   import doctest
  
   doctest.testmod()
  
  
  
  
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)))
  
  
  
  