code: import timeit
  
  
  
  
  class    __lowerCAmelCase  :
  
  
  
   '''simple docstring'''
  
   def __init__(			self       :		Union[str, Any]       ,			lowerCamelCase       :		Union[str, Any]  ) ->							int:
          if isinstance(lowerCamelCase       ,			lowerCamelCase  ):  # Initializes a matrix identical to the one provided.
                 _UpperCamelCase     :       List[str]  						=  arg
                 _UpperCamelCase     :       List[str]  						=  len(lowerCamelCase  )
          else:  # Initializes a square matrix of the given size and set values to zero.
                 _UpperCamelCase     :       List[str]  						=  arg
                 _UpperCamelCase     :       Tuple  						=  [[0 for _ in range(self.n  )] for _ in range(self.n  )]
  
   def __mul__(			self       :		str       ,			lowerCamelCase       :		Union[str, Any]  ) ->							Any:
          _UpperCamelCase     :       str  						=  Matrix(self.n  )
          for i in range(self.n  ):
                 for j in range(self.n  ):
                        for k in range(self.n  ):
                               matrix.t[i][j] += self.t[i][k] * b.t[k][j]
          return matrix
  
  
  
  
  
  
  
  def SCREAMING_SNAKE_CASE				(     lowercase_:	Tuple		,						lowercase_:	Any				):
         _UpperCamelCase     :       Tuple  						=  Matrix([[1, 0], [0, 1]]				)
         while b > 0:
                if b & 1:
                       matrix *= a
                a *= a
                b >>= 1
         return matrix
  
  
  def SCREAMING_SNAKE_CASE				(     lowercase_:	int		,						lowercase_:	Optional[Any]		,						lowercase_:	List[Any]				):
         # Trivial Cases
         if n == 1:
                return fa
         elif n == 2:
                return fa
         _UpperCamelCase     :       int  						=  Matrix([[1, 1], [1, 0]]				)
         _UpperCamelCase     :       List[Any]  						=  modular_exponentiation(lowercase_		,						n - 2				)
         return fa * matrix.t[0][0] + fa * matrix.t[0][1]
  
  
  def SCREAMING_SNAKE_CASE				(     lowercase_:	Optional[int]		,						lowercase_:	Tuple		,						lowercase_:	str				):
         # Trivial Cases
         if n == 1:
                return fa
         elif n == 2:
                return fa
  
         _UpperCamelCase     :       List[Any]  						=  fa
         _UpperCamelCase     :       str  						=  fa
         n -= 2
  
         while n > 0:
                _UpperCamelCase						,      _UpperCamelCase     :       Tuple  						=  fn_a + fn_a, fn_a
                n -= 1
  
         return fn_a
  
  
  def SCREAMING_SNAKE_CASE				(     ):
         _UpperCamelCase     :       str  						=  '\nfrom random import randint\nfrom __main__ import fibonacci_with_matrix_exponentiation\n'
         _UpperCamelCase     :       Tuple  						=  'fibonacci_with_matrix_exponentiation(randint(1,70000), 1, 1)'
         _UpperCamelCase     :       Tuple  						=  timeit.timeit(setup=lowercase_		,						stmt=lowercase_		,						number=100				)
         print('With matrix exponentiation the average execution time is '		,						exec_time / 100				)
         return exec_time
  
  
  def SCREAMING_SNAKE_CASE				(     ):
         _UpperCamelCase     :       Tuple  						=  '\nfrom random import randint\nfrom __main__ import simple_fibonacci\n'
         _UpperCamelCase     :       Optional[Any]  						=  'simple_fibonacci(randint(1,70000), 1, 1)'
         _UpperCamelCase     :       str  						=  timeit.timeit(setup=lowercase_		,						stmt=lowercase_		,						number=100				)
         print(
             'Without matrix exponentiation the average execution time is '		,						exec_time / 100				)
         return exec_time
  
  
  def SCREAMING_SNAKE_CASE				(     ):
         matrix_exponentiation_time()
         simple_fibonacci_time()
  
  
  if __name__ == "__main__":
   main()
  
  
  
  
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)))
  
  
  
  