code: 
  
  
  
  
  import argparse
  import json
  import re
  import string
  import sys
  from collections import Counter
  def 						lowerCAmelCase_       (	__a )       ->			Optional[int]:
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  							def remove_articles(__a ):
  														return re.sub(r'''\b(a|an|the)\b'''	,	''' '''	,	__a )
  
  							def white_space_fix(__a ):
  														return " ".join(text.split() )
  
  							def remove_punc(__a ):
  														SCREAMING_SNAKE_CASE			: Dict	     =set(string.punctuation )
  														return "".join(ch for ch in text if ch not in exclude )
  
  							def lower(__a ):
  														return text.lower()
  
  							return white_space_fix(remove_articles(remove_punc(lower(__a ) ) ) )
  def 						lowerCAmelCase_       (	__a	,	__a )       ->			str:
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  							SCREAMING_SNAKE_CASE			: Optional[int]	     =normalize_answer(__a ).split()
  							SCREAMING_SNAKE_CASE			: List[Any]	     =normalize_answer(__a ).split()
  							SCREAMING_SNAKE_CASE			: Dict	     =Counter(__a ) & Counter(__a )
  							SCREAMING_SNAKE_CASE			: Any	     =sum(common.values() )
  							if num_same == 0:
  														return 0
  							SCREAMING_SNAKE_CASE			: Optional[int]	     =1.0 * num_same / len(__a )
  							SCREAMING_SNAKE_CASE			: Any	     =1.0 * num_same / len(__a )
  							SCREAMING_SNAKE_CASE			: int	     =(2 * precision * recall) / (precision + recall)
  							return fa
  def 						lowerCAmelCase_       (	__a	,	__a )       ->			Tuple:
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  							return normalize_answer(__a ) == normalize_answer(__a )
  def 						lowerCAmelCase_       (	__a	,	__a	,	__a )       ->			str:
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  							SCREAMING_SNAKE_CASE			: Optional[Any]	     =[]
  							for ground_truth in ground_truths:
  														SCREAMING_SNAKE_CASE			: Optional[Any]	     =metric_fn(__a	,	__a )
  														scores_for_ground_truths.append(__a )
  							return max(__a )
  def 						lowerCAmelCase_       (	__a	,	__a )       ->			Dict:
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  							SCREAMING_SNAKE_CASE			: Optional[int]	     =0
  							SCREAMING_SNAKE_CASE			: Tuple	     =[]
  							for passage in dataset:
  														for qa in passage["qas"]:
  																					total += 1
  																					if qa["id"] not in predictions:
  																												SCREAMING_SNAKE_CASE			: List[Any]	     =f'Unanswered question {qa["id"]} will receive score 0.'
  																												print(__a	,	file=sys.stderr )
  																												continue
  
  																					SCREAMING_SNAKE_CASE			: Dict	     =[x['''text'''] for x in qa['''answers''']]
  																					SCREAMING_SNAKE_CASE			: Optional[int]	     =predictions[qa['''id''']]
  
  																					SCREAMING_SNAKE_CASE			: Union[str, Any]	     =metric_max_over_ground_truths(__a	,	__a	,	__a )
  																					if int(_exact_match ) == 1:
  																												correct_ids.append(qa['''id'''] )
  																					exact_match += _exact_match
  
  																					fa += metric_max_over_ground_truths(__a	,	__a	,	__a )
  
  							SCREAMING_SNAKE_CASE			: Union[str, Any]	     =exact_match / total
  							SCREAMING_SNAKE_CASE			: Optional[Any]	     =fa / total
  
  							return {"exact_match": exact_match, "f1": fa}, correct_ids
  
  
  if __name__ == "__main__":
  							_A							    =	"""1.0"""
  							_A							    =	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.""")
  							_A							    =	parser.parse_args()
  
  							with open(args.data_file) as data_file:
  														_A							    =	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,
  																					)
  														_A							    =	dataset_json["""data"""]
  
  							with open(args.pred_file) as pred_file:
  														_A							    =	json.load(pred_file)
  
  							_A    , _A							    =	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 __future__ import annotations
  def 						lowerCAmelCase_       (	__a	,	__a	,	__a	,	__a )       ->			None:
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  							SCREAMING_SNAKE_CASE     ,							SCREAMING_SNAKE_CASE			: Any	     =m - 1, 0
  							while i >= 0 and j < n:
  														if key == mat[i][j]:
  																					print(f'Key {key} found at row- {i + 1} column- {j + 1}' )
  																					return
  														if key < mat[i][j]:
  																					i -= 1
  														else:
  																					j += 1
  							print(f'Key {key} not found' )
  def 						lowerCAmelCase_       (	)       ->			None:
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  							SCREAMING_SNAKE_CASE			: int	     =[[2, 5, 7], [4, 8, 13], [9, 11, 15], [12, 17, 20]]
  							SCREAMING_SNAKE_CASE			: List[str]	     =int(input('''Enter the element to be searched:''' ) )
  							print(__a )
  							search_in_a_sorted_matrix(__a	,	len(__a )	,	len(mat[0] )	,	__a )
  
  
  if __name__ == "__main__":
  							import doctest
  
  							doctest.testmod()
  							main()
  
  
  
  
  
  
  