
import argparse
import json
import re
import string
import sys
from collections import Counter
def 					lowerCAmelCase__     (    SCREAMING_SNAKE_CASE_	)							->						List[str]:

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

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

			def remove_punc(SCREAMING_SNAKE_CASE_	):
						lowerCAmelCase__ : Optional[int]   =						set(string.punctuation	)
						return "".join(ch for ch in text if ch not in exclude	)

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

			return white_space_fix(remove_articles(remove_punc(lower(SCREAMING_SNAKE_CASE_	)	)	)	)
def 					lowerCAmelCase__     (    SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_	)							->						int:
			lowerCAmelCase__ : Any   =						normalize_answer(SCREAMING_SNAKE_CASE_	).split()
			lowerCAmelCase__ : Union[str, Any]   =						normalize_answer(SCREAMING_SNAKE_CASE_	).split()
			lowerCAmelCase__ : List[Any]   =						Counter(SCREAMING_SNAKE_CASE_	) & Counter(SCREAMING_SNAKE_CASE_	)
			lowerCAmelCase__ : Union[str, Any]   =						sum(common.values()	)
			if num_same == 0:
						return 0
			lowerCAmelCase__ : str   =						1.0 * num_same / len(SCREAMING_SNAKE_CASE_	)
			lowerCAmelCase__ : Dict   =						1.0 * num_same / len(SCREAMING_SNAKE_CASE_	)
			lowerCAmelCase__ : Tuple   =						(2 * precision * recall) / (precision + recall)
			return fa
def 					lowerCAmelCase__     (    SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_	)							->						Optional[int]:
			return normalize_answer(SCREAMING_SNAKE_CASE_	) == normalize_answer(SCREAMING_SNAKE_CASE_	)
def 					lowerCAmelCase__     (    SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_	)							->						Optional[Any]:
			lowerCAmelCase__ : Dict   =						[]
			for ground_truth in ground_truths:
						lowerCAmelCase__ : Optional[Any]   =						metric_fn(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_	)
						scores_for_ground_truths.append(SCREAMING_SNAKE_CASE_	)
			return max(SCREAMING_SNAKE_CASE_	)
def 					lowerCAmelCase__     (    SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_	)							->						List[Any]:
			lowerCAmelCase__ : Optional[Any]   =						0
			lowerCAmelCase__ : Optional[Any]   =						[]
			for passage in dataset:
						for qa in passage["qas"]:
									total += 1
									if qa["id"] not in predictions:
												lowerCAmelCase__ : List[Any]   =						F'''Unanswered question {qa["id"]} will receive score 0.'''
												print(SCREAMING_SNAKE_CASE_			,				file=sys.stderr	)
												continue

									lowerCAmelCase__ : List[str]   =						[x['text'] for x in qa['answers']]
									lowerCAmelCase__ : Union[str, Any]   =						predictions[qa['id']]

									lowerCAmelCase__ : str   =						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_	)

			lowerCAmelCase__ : List[str]   =						exact_match / total
			lowerCAmelCase__ : int   =						fa / total

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


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

					with open(args.data_file) as data_file:
										lowerCamelCase__        =						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,
															)
										lowerCamelCase__        =						dataset_json["""data"""]

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

					lowerCamelCase__ ,   lowerCamelCase__        =						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() = }""")