code: def SCREAMING_SNAKE_CASE				(     lowercase_:	int		,						lowercase_:	int		,						lowercase_:	int		,						lowercase_:	int				):
         return len_board * len_board_column * row + column
  
  
  def SCREAMING_SNAKE_CASE				(     lowercase_:	list[list[str]]		,						lowercase_:	str		,						lowercase_:	int		,						lowercase_:	int		,						lowercase_:	int		,						lowercase_:	set[int]		,						):
  
         if board[row][column] != word[word_index]:
                return False
  
         if word_index == len(lowercase_				) - 1:
                return True
  
         _UpperCamelCase     :       List[Any]  						=  [(0, 1), (0, -1), (-1, 0), (1, 0)]
         _UpperCamelCase     :       List[Any]  						=  len(lowercase_				)
         _UpperCamelCase     :       Union[str, Any]  						=  len(board[0]				)
         for direction in traverts_directions:
                _UpperCamelCase     :       Optional[Any]  						=  row + direction[0]
                _UpperCamelCase     :       Union[str, Any]  						=  column + direction[1]
                if not (0 <= next_i < len_board and 0 <= next_j < len_board_column):
                       continue
  
                _UpperCamelCase     :       int  						=  get_point_key(lowercase_		,						lowercase_		,						lowercase_		,						lowercase_				)
                if key in visited_points_set:
                       continue
  
                visited_points_set.add(lowercase_				)
                if exits_word(lowercase_		,						lowercase_		,						lowercase_		,						lowercase_		,						word_index + 1		,						lowercase_				):
                       return True
  
                visited_points_set.remove(lowercase_				)
  
         return False
  
  
  def SCREAMING_SNAKE_CASE				(     lowercase_:	list[list[str]]		,						lowercase_:	str				):
         _UpperCamelCase     :       Dict  						=  (
             'The board should be a non empty matrix of single chars strings.'
         )
  
         _UpperCamelCase     :       int  						=  len(lowercase_				)
         if not isinstance(lowercase_		,						lowercase_				) or len(lowercase_				) == 0:
                raise ValueError(lowercase_				)
  
         for row in board:
                if not isinstance(lowercase_		,						lowercase_				) or len(lowercase_				) == 0:
                       raise ValueError(lowercase_				)
  
                for item in row:
                       if not isinstance(lowercase_		,						lowercase_				) or len(lowercase_				) != 1:
                              raise ValueError(lowercase_				)
  
      # Validate word
         if not isinstance(lowercase_		,						lowercase_				) or len(lowercase_				) == 0:
                raise ValueError(
                    'The word parameter should be a string of length greater than 0.'				)
  
         _UpperCamelCase     :       Any  						=  len(board[0]				)
         for i in range(lowercase_				):
                for j in range(lowercase_				):
                       if exits_word(
                           lowercase_		,						lowercase_		,						lowercase_		,						lowercase_		,						0		,						{get_point_key(lowercase_		,						lowercase_		,						lowercase_		,						lowercase_				)}				):
                              return True
  
         return False
  
  
  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)))
  
  
  
  