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




def 		lowerCAmelCase_     (     _SCREAMING_SNAKE_CASE  )   ->      Dict:

							"""simple docstring"""






							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  ):
														_UpperCAmelCase  :			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:

							"""simple docstring"""






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




def 		lowerCAmelCase_     (     _SCREAMING_SNAKE_CASE      ,	_SCREAMING_SNAKE_CASE  )   ->      Any:

							"""simple docstring"""






							return normalize_answer(_SCREAMING_SNAKE_CASE  ) == normalize_answer(_SCREAMING_SNAKE_CASE  )




def 		lowerCAmelCase_     (     _SCREAMING_SNAKE_CASE      ,	_SCREAMING_SNAKE_CASE      ,	_SCREAMING_SNAKE_CASE  )   ->      Tuple:

							"""simple docstring"""






							_UpperCAmelCase  :			Any	       =		[]
							for ground_truth in ground_truths:
														_UpperCAmelCase  :			int	       =		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  )   ->      Dict:

							"""simple docstring"""






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

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

																					_UpperCAmelCase  :			Dict	       =		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  :			str	       =		exact_match / total
							_UpperCAmelCase  :			Any	       =		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




def 		lowerCAmelCase_     (     _SCREAMING_SNAKE_CASE  )   ->      list[int]:

							"""simple docstring"""






							if not isinstance(_SCREAMING_SNAKE_CASE      ,	_SCREAMING_SNAKE_CASE  ):
														_UpperCAmelCase  :			Optional[Any]	       =		F"""Input value of [number={number}] must be an integer"""
														raise TypeError(_SCREAMING_SNAKE_CASE  )
							if number < 1:
														_UpperCAmelCase  :			Dict	       =		F"""Input value of [number={number}] must be a positive integer"""
														raise ValueError(_SCREAMING_SNAKE_CASE  )
							_UpperCAmelCase  :			List[Any]	       =		[number]
							while number != 1:
														if number % 2 == 0:
																					_UpperCAmelCase  :			List[str]	       =		math.floor(math.sqrt(_SCREAMING_SNAKE_CASE  )  )
														else:
																					_UpperCAmelCase  :			Dict	       =		math.floor(
																					    math.sqrt(_SCREAMING_SNAKE_CASE  ) * math.sqrt(_SCREAMING_SNAKE_CASE  ) * math.sqrt(_SCREAMING_SNAKE_CASE  )  )
														sequence.append(_SCREAMING_SNAKE_CASE  )
							return sequence


if __name__ == "__main__":
				import doctest

				doctest.testmod()




