



'''simple docstring'''






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





def 				_SCREAMING_SNAKE_CASE(					snake_case_    :     Optional[Any]  )		->str:






							'''simple docstring'''

							def remove_articles(snake_case_    :     Union[str, Any]  ):
														return re.sub(R'''\b(a|an|the)\b''' ,       ''' ''' ,       snake_case_  )

							def white_space_fix(snake_case_    :     Optional[int]  ):
														return " ".join(text.split()  )

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

							def lower(snake_case_    :     Optional[Any]  ):
														return text.lower()

							return white_space_fix(remove_articles(remove_punc(lower(snake_case_  )  )  )  )





def 				_SCREAMING_SNAKE_CASE(					snake_case_    :     List[Any] ,       snake_case_    :     Union[str, Any]  )		->Union[str, Any]:






							'''simple docstring'''
							_lowercase							:		Any				=   normalize_answer(snake_case_  ).split()
							_lowercase							:		str				=   normalize_answer(snake_case_  ).split()
							_lowercase							:		Union[str, Any]				=   Counter(snake_case_  ) & Counter(snake_case_  )
							_lowercase							:		Any				=   sum(common.values()  )
							if num_same == 0:
														return 0
							_lowercase							:		Dict				=   1.0 * num_same / len(snake_case_  )
							_lowercase							:		str				=   1.0 * num_same / len(snake_case_  )
							_lowercase							:		Dict				=   (2 * precision * recall) / (precision + recall)
							return fa





def 				_SCREAMING_SNAKE_CASE(					snake_case_    :     Union[str, Any] ,       snake_case_    :     Any  )		->List[str]:






							'''simple docstring'''
							return normalize_answer(snake_case_  ) == normalize_answer(snake_case_  )





def 				_SCREAMING_SNAKE_CASE(					snake_case_    :     List[Any] ,       snake_case_    :     Optional[Any] ,       snake_case_    :     Optional[Any]  )		->List[str]:






							'''simple docstring'''
							_lowercase							:		List[Any]				=   []
							for ground_truth in ground_truths:
														_lowercase							:		Dict				=   metric_fn(snake_case_ ,       snake_case_  )
														scores_for_ground_truths.append(snake_case_  )
							return max(snake_case_  )





def 				_SCREAMING_SNAKE_CASE(					snake_case_    :     int ,       snake_case_    :     Union[str, Any]  )		->List[Any]:






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

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

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

																					fa += metric_max_over_ground_truths(snake_case_ ,       snake_case_ ,       snake_case_  )

							_lowercase							:		Optional[Any]				=   exact_match / total
							_lowercase							:		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)








'''simple docstring'''






import abc
import copy
import dataclasses
from dataclasses import dataclass
from typing import ClassVar, Dict, Type, TypeVar

from ..features import Features


lowerCamelCase__	  =		TypeVar('T', bound='TaskTemplate')

@dataclass(frozen=__A       )
class 	_lowerCAmelCase     (    abc.ABC       ):






		'''simple docstring'''
		# `task` is not a ClassVar since we want it to be part of the `asdict` output for JSON serialization
		snake_case_							= 42
		snake_case_							= 42
		snake_case_							= 42
		def 						__lowercase      (						self							:       T      ,		UpperCamelCase_							:       Features		)      ->			T:







									'''simple docstring'''





									return copy.deepcopy(self		)
		@property
		def 						__lowercase      (						self							:       int		)      ->			Features:







									'''simple docstring'''





									return Features(**self.input_schema      ,		**self.label_schema		)
		@property
		@abc.abstractmethod
		def 						__lowercase      (						self							:       List[str]		)      ->			Dict[str, str]:







									'''simple docstring'''





									raise NotImplementedError





		@classmethod
		def 						__lowercase      (						cls							:       Type[T]      ,		UpperCamelCase_							:       dict		)      ->			T:







									'''simple docstring'''





									_lowercase							:		List[str]				=   {f.name for f in dataclasses.fields(cls		)}
									return cls(**{k: v for k, v in template_dict.items() if k in field_names}		)



