




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




def  a_		(							__snake_case					)	->			List[str]:

			'''simple docstring'''


			def remove_articles(__snake_case					):
						return re.sub(r'\b(a|an|the)\b'     ,		' '     ,		SCREAMING_SNAKE_CASE_					)

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

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

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

			return white_space_fix(remove_articles(remove_punc(lower(SCREAMING_SNAKE_CASE_					)					)					)					)




def  a_		(							__snake_case     ,		__snake_case					)	->			int:

			'''simple docstring'''


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




def  a_		(							__snake_case     ,		__snake_case					)	->			Optional[int]:

			'''simple docstring'''


			return normalize_answer(SCREAMING_SNAKE_CASE_					) == normalize_answer(SCREAMING_SNAKE_CASE_					)




def  a_		(							__snake_case     ,		__snake_case     ,		__snake_case					)	->			Optional[Any]:

			'''simple docstring'''


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




def  a_		(							__snake_case     ,		__snake_case					)	->			List[Any]:

			'''simple docstring'''


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

									UpperCamelCase_     				= [x['text'] for x in qa['answers']]
									UpperCamelCase_     				= predictions[qa['id']]

									UpperCamelCase_     				= metric_max_over_ground_truths(SCREAMING_SNAKE_CASE_     ,		SCREAMING_SNAKE_CASE_     ,		SCREAMING_SNAKE_CASE_					)
									if int(_exact_match					) == 1:
												correct_ids.append(qa['id']					)
									exact_match += _exact_match

									fa += metric_max_over_ground_truths(SCREAMING_SNAKE_CASE_     ,		SCREAMING_SNAKE_CASE_     ,		SCREAMING_SNAKE_CASE_					)

			UpperCamelCase_     				= exact_match / total
			UpperCamelCase_     				= fa / total

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


if __name__ == "__main__":
				__a			:	Tuple							     =		"""1.0"""
				__a			:	int							     =		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			:	int							     =		parser.parse_args()

				with open(args.data_file) as data_file:
								__a			:	int							     =		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			:	Dict							     =		dataset_json["""data"""]

				with open(args.pred_file) as pred_file:
								__a			:	Union[str, Any]							     =		json.load(pred_file)

				__a     ,   __a			:	List[Any]							     =		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)


import math
from decimal import Decimal, getcontext
def 					lowerCAmelCase__     (    SCREAMING_SNAKE_CASE_ = 4_000_000	)							->						int:

			try:
						lowerCAmelCase__ : str   =						int(SCREAMING_SNAKE_CASE_	)
			except (TypeError, ValueError):
						raise TypeError('Parameter n must be int or castable to int.'	)
			if n <= 0:
						raise ValueError('Parameter n must be greater than or equal to one.'	)
			lowerCAmelCase__ : Union[str, Any]   =						100
			lowerCAmelCase__ : List[Any]   =						(Decimal(5	) ** Decimal(0.5	) + 1) / Decimal(2	)

			lowerCAmelCase__ : Tuple   =						(math.floor(math.log(n * (phi + 2)			,				SCREAMING_SNAKE_CASE_	) - 1	) // 3) * 3 + 2
			lowerCAmelCase__ : Optional[int]   =						Decimal(round(phi ** Decimal(index + 1	)	)	) / (phi + 2)
			lowerCAmelCase__ : Tuple   =						num // 2
			return int(SCREAMING_SNAKE_CASE_	)


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