




import argparse
import json
import re
import string
import sys
from collections import Counter
def 						lowerCAmelCase_       (	__a )       ->			Union[str, Any]:





							"""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			: List[str]	     =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 )       ->			Any:





							"""simple docstring"""





							SCREAMING_SNAKE_CASE			: str	     =normalize_answer(__a ).split()
							SCREAMING_SNAKE_CASE			: str	     =normalize_answer(__a ).split()
							SCREAMING_SNAKE_CASE			: List[Any]	     =Counter(__a ) & Counter(__a )
							SCREAMING_SNAKE_CASE			: Union[str, Any]	     =sum(common.values() )
							if num_same == 0:
														return 0
							SCREAMING_SNAKE_CASE			: Optional[Any]	     =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 )       ->			Any:





							"""simple docstring"""





							return normalize_answer(__a ) == normalize_answer(__a )
def 						lowerCAmelCase_       (	__a	,	__a	,	__a )       ->			Union[str, Any]:





							"""simple docstring"""





							SCREAMING_SNAKE_CASE			: Optional[int]	     =[]
							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 )       ->			List[str]:





							"""simple docstring"""





							SCREAMING_SNAKE_CASE			: Dict	     =0
							for article in dataset:
														for paragraph in article["paragraphs"]:
																					for qa in paragraph["qas"]:
																												total += 1
																												if qa["id"] not in predictions:
																																			SCREAMING_SNAKE_CASE			: Any	     ='''Unanswered question ''' + qa['''id'''] + ''' will receive score 0.'''
																																			print(__a	,	file=sys.stderr )
																																			continue
																												SCREAMING_SNAKE_CASE			: Union[str, Any]	     =[x['''text'''] for x in qa['''answers''']]
																												SCREAMING_SNAKE_CASE			: Any	     =predictions[qa['''id''']]
																												exact_match += metric_max_over_ground_truths(__a	,	__a	,	__a )
																												fa += metric_max_over_ground_truths(__a	,	__a	,	__a )

							SCREAMING_SNAKE_CASE			: str	     =100.0 * exact_match / total
							SCREAMING_SNAKE_CASE			: int	     =100.0 * fa / total

							return {"exact_match": exact_match, "f1": fa}


if __name__ == "__main__":
							_A							    =	"""1.1"""
							_A							    =	argparse.ArgumentParser(description="""Evaluation for SQuAD """ + expected_version)
							parser.add_argument("""dataset_file""", help="""Dataset file""")
							parser.add_argument("""prediction_file""", help="""Prediction File""")
							_A							    =	parser.parse_args()
							with open(args.dataset_file) as dataset_file:
														_A							    =	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,
																					)
														_A							    =	dataset_json["""data"""]
							with open(args.prediction_file) as prediction_file:
														_A							    =	json.load(prediction_file)
							print(json.dumps(evaluate(dataset, predictions)))












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()






