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






def    UpperCAmelCase__(						lowerCamelCase_  ):



			"""simple docstring"""





			def remove_articles(lowerCamelCase_  ):
						return re.sub(R"\b(a|an|the)\b"							,	" "							,	lowerCamelCase_  )

			def white_space_fix(lowerCamelCase_  ):
						return " ".join(text.split()  )

			def remove_punc(lowerCamelCase_  ):
						_snake_case              =      set(string.punctuation  )
						return "".join(ch for ch in text if ch not in exclude  )

			def lower(lowerCamelCase_  ):
						return text.lower()

			return white_space_fix(remove_articles(remove_punc(lower(lowerCamelCase_  )  )  )  )






def    UpperCAmelCase__(						lowerCamelCase_							,	lowerCamelCase_  ):



			"""simple docstring"""





			_snake_case              =      normalize_answer(lowerCamelCase_  ).split()
			_snake_case              =      normalize_answer(lowerCamelCase_  ).split()
			_snake_case              =      Counter(lowerCamelCase_  ) & Counter(lowerCamelCase_  )
			_snake_case              =      sum(common.values()  )
			if num_same == 0:
						return 0
			_snake_case              =      1.0 * num_same / len(lowerCamelCase_  )
			_snake_case              =      1.0 * num_same / len(lowerCamelCase_  )
			_snake_case              =      (2 * precision * recall) / (precision + recall)
			return fa






def    UpperCAmelCase__(						lowerCamelCase_							,	lowerCamelCase_  ):



			"""simple docstring"""





			return normalize_answer(lowerCamelCase_  ) == normalize_answer(lowerCamelCase_  )






def    UpperCAmelCase__(						lowerCamelCase_							,	lowerCamelCase_							,	lowerCamelCase_  ):



			"""simple docstring"""





			_snake_case              =      []
			for ground_truth in ground_truths:
						_snake_case              =      metric_fn(lowerCamelCase_							,	lowerCamelCase_  )
						scores_for_ground_truths.append(lowerCamelCase_  )
			return max(lowerCamelCase_  )






def    UpperCAmelCase__(						lowerCamelCase_							,	lowerCamelCase_  ):



			"""simple docstring"""





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

									_snake_case              =      [x["text"] for x in qa["answers"]]
									_snake_case              =      predictions[qa["id"]]

									_snake_case              =      metric_max_over_ground_truths(lowerCamelCase_							,	lowerCamelCase_							,	lowerCamelCase_  )
									if int(_exact_match  ) == 1:
												correct_ids.append(qa["id"]  )
									exact_match += _exact_match

									fa += metric_max_over_ground_truths(lowerCamelCase_							,	lowerCamelCase_							,	lowerCamelCase_  )

			_snake_case              =      exact_match / total
			_snake_case              =      fa / total

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


if __name__ == "__main__":
			_SCREAMING_SNAKE_CASE:						Tuple    	=       '''1.0'''
			_SCREAMING_SNAKE_CASE:						Optional[Any]    	=       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.''')
			_SCREAMING_SNAKE_CASE:						Dict    	=       parser.parse_args()

			with open(args.data_file) as data_file:
						_SCREAMING_SNAKE_CASE:						Union[str, Any]    	=       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,
									)
						_SCREAMING_SNAKE_CASE:						List[Any]    	=       dataset_json['''data''']

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

			_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE:						Tuple    	=       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)


from math import gcd






def    UpperCAmelCase__(						lowerCamelCase_ = 12000  ):



			"""simple docstring"""





			_snake_case              =      0
			for d in range(max_d + 1  ):
						for n in range(d // 3 + 1							,	(d + 1) // 2  ):
									if gcd(lowerCamelCase_							,	lowerCamelCase_  ) == 1:
												fractions_number += 1
			return fractions_number


if __name__ == "__main__":
			print(F"{solution() = }")

