




import argparse
import json
import re
import string
import sys
from collections import Counter






def    snake_case   (				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     ):
		lowerCamelCase_				:						List[Any]				  =				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    snake_case   (				A		,  A     )				->				Optional[Any]:




	'''simple docstring'''


	lowerCamelCase_				:						List[str]				  =				normalize_answer(A     ).split()
	lowerCamelCase_				:						int				  =				normalize_answer(A     ).split()
	lowerCamelCase_				:						Optional[int]				  =				Counter(A     ) & Counter(A     )
	lowerCamelCase_				:						Optional[Any]				  =				sum(common.values()     )
	if num_same == 0:
		return 0
	lowerCamelCase_				:						Dict				  =				1.0 * num_same / len(A     )
	lowerCamelCase_				:						List[Any]				  =				1.0 * num_same / len(A     )
	lowerCamelCase_				:						Optional[int]				  =				(2 * precision * recall) / (precision + recall)
	return fa






def    snake_case   (				A		,  A     )				->				Tuple:




	'''simple docstring'''


	return normalize_answer(A     ) == normalize_answer(A     )






def    snake_case   (				A		,  A		,  A     )				->				Any:




	'''simple docstring'''


	lowerCamelCase_				:						Any				  =				[]
	for ground_truth in ground_truths:
		lowerCamelCase_				:						Optional[int]				  =				metric_fn(A		,  A     )
		scores_for_ground_truths.append(A     )
	return max(A     )






def    snake_case   (				A		,  A     )				->				str:




	'''simple docstring'''


	lowerCamelCase_				:						Dict				  =				0
	lowerCamelCase_				:						List[Any]				  =				[]
	for passage in dataset:
		for qa in passage["qas"]:
			total += 1
			if qa["id"] not in predictions:
				lowerCamelCase_				:						str				  =				f"Unanswered question {qa['id']} will receive score 0."
				print(A		,  file=sys.stderr     )
				continue

			lowerCamelCase_				:						str				  =				[x['text'] for x in qa['answers']]
			lowerCamelCase_				:						int				  =				predictions[qa['id']]

			lowerCamelCase_				:						Dict				  =				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     )

	lowerCamelCase_				:						str				  =				exact_match / total
	lowerCamelCase_				:						Union[str, Any]				  =				fa / total

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


if __name__ == "__main__":
							UpperCamelCase__						=		'1.0'
							UpperCamelCase__						=		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.')
							UpperCamelCase__						=		parser.parse_args()

							with open(args.data_file) as data_file:
														UpperCamelCase__						=		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,
																					)
														UpperCamelCase__						=		dataset_json['data']

							with open(args.pred_file) as pred_file:
														UpperCamelCase__						=		json.load(pred_file)

							UpperCamelCase__			,					UpperCamelCase__						=		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)




'''simple docstring'''







import math
from timeit import timeit







def _UpperCamelCase			(	__A       )				->						int:


      '''simple docstring'''



      UpperCamelCase__      =							0
      UpperCamelCase__      =							abs(__A       )
      while True:
            UpperCamelCase__      =							n // 10
            digits += 1
            if n == 0:
                  break
      return digits







def _UpperCamelCase			(	__A       )				->						int:


      '''simple docstring'''



      return 1 if n == 0 else math.floor(math.log(abs(__A       ) ,     10       ) + 1       )







def _UpperCamelCase			(	__A       )				->						int:


      '''simple docstring'''



      return len(str(abs(__A       )       )       )







def _UpperCamelCase			(	)				->						None:


      '''simple docstring'''



      from collections.abc import Callable

      def benchmark_a_function(__A ,     __A       ) -> None:
            UpperCamelCase__      =							F'''{func.__name__}({value})'''
            UpperCamelCase__      =							timeit(F'''__main__.{call}''' ,     setup="import __main__"       )
            print(F'''{call}: {func(__A       )} -- {timing} seconds'''       )

      for value in (262144, 1125899906842624, 1267650600228229401496703205376):
            for func in (num_digits, num_digits_fast, num_digits_faster):
                  benchmark_a_function(__A ,     __A       )
            print()


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  benchmark()





