


import argparse
import json
import re
import string
import sys
from collections import Counter
def 				UpperCamelCase_	(					__snake_case      :    Tuple  )->   Optional[int]:




						'''simple docstring'''

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

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

						def remove_punc(__snake_case      :    Union[str, Any]  ):
												__magic_name__					=      set(string.punctuation  )
												return "".join(ch for ch in text if ch not in exclude  )

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

						return white_space_fix(remove_articles(remove_punc(lower(__snake_case  )  )  )  )
def 				UpperCamelCase_	(					__snake_case      :    Union[str, Any]						,      __snake_case      :    Optional[int]  )->   Dict:




						'''simple docstring'''
						__magic_name__					=      normalize_answer(__snake_case  ).split()
						__magic_name__					=      normalize_answer(__snake_case  ).split()
						__magic_name__					=      Counter(__snake_case  ) & Counter(__snake_case  )
						__magic_name__					=      sum(common.values()  )
						if num_same == 0:
												return 0
						__magic_name__					=      1.0 * num_same / len(__snake_case  )
						__magic_name__					=      1.0 * num_same / len(__snake_case  )
						__magic_name__					=      (2 * precision * recall) / (precision + recall)
						return fa
def 				UpperCamelCase_	(					__snake_case      :    Optional[Any]						,      __snake_case      :    Optional[Any]  )->   Dict:




						'''simple docstring'''
						return normalize_answer(__snake_case  ) == normalize_answer(__snake_case  )
def 				UpperCamelCase_	(					__snake_case      :    Dict						,      __snake_case      :    Tuple						,      __snake_case      :    Optional[Any]  )->   Dict:




						'''simple docstring'''
						__magic_name__					=      []
						for ground_truth in ground_truths:
												__magic_name__					=      metric_fn(__snake_case						,      __snake_case  )
												scores_for_ground_truths.append(__snake_case  )
						return max(__snake_case  )
def 				UpperCamelCase_	(					__snake_case      :    Optional[Any]						,      __snake_case      :    Union[str, Any]  )->   Optional[Any]:




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

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

																		__magic_name__					=      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  )

						__magic_name__					=      exact_match / total
						__magic_name__					=      fa / total

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


if __name__ == "__main__":
					UpperCamelCase			:					List[Any]         =							'''1.0'''
					UpperCamelCase			:					Union[str, 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.''')
					UpperCamelCase			:					Optional[int]         =							parser.parse_args()

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

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

					UpperCamelCase     ,UpperCamelCase			:					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)





from ..utils import DummyObject, requires_backends

class      __snake_case				(   metaclass=_UpperCAmelCase):
					SCREAMING_SNAKE_CASE_		:       str								=			["torch", "transformers", "k_diffusion"]
					def __init__(						self       :						Tuple	,      *_lowerCAmelCase       :						Union[str, Any]	,      **_lowerCAmelCase       :						Union[str, Any]  )							->					Dict:



											'''simple docstring'''





											requires_backends(self	,      ['torch', 'transformers', 'k_diffusion']  )
					@classmethod
					def 	a			(						cls       :						Optional[int]	,      *_lowerCAmelCase       :						Any	,      **_lowerCAmelCase       :						Optional[Any]  )							->					Optional[Any]:



											'''simple docstring'''





											requires_backends(cls	,      ['torch', 'transformers', 'k_diffusion']  )







					@classmethod
					def 	a			(						cls       :						Union[str, Any]	,      *_lowerCAmelCase       :						Tuple	,      **_lowerCAmelCase       :						str  )							->					Tuple:



											'''simple docstring'''





											requires_backends(cls	,      ['torch', 'transformers', 'k_diffusion']  )

