


'''simple docstring'''
import argparse
import json
import re
import string
import sys
from collections import Counter




def      __snake_case       (       lowerCamelCase_ :				Dict						):
				'''simple docstring'''




				def remove_articles(lowerCamelCase_ :				str						):
								return re.sub(R"\b(a|an|the)\b"	,  " "	,  lowerCamelCase_						)

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

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

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

				return white_space_fix(remove_articles(remove_punc(lower(lowerCamelCase_						)						)						)						)




def      __snake_case       (       lowerCamelCase_ :				int	,  lowerCamelCase_ :				List[str]						):
				'''simple docstring'''




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




def      __snake_case       (       lowerCamelCase_ :				Any	,  lowerCamelCase_ :				int						):
				'''simple docstring'''




				return normalize_answer(lowerCamelCase_						) == normalize_answer(lowerCamelCase_						)




def      __snake_case       (       lowerCamelCase_ :				Optional[Any]	,  lowerCamelCase_ :				Union[str, Any]	,  lowerCamelCase_ :				str						):
				'''simple docstring'''




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




def      __snake_case       (       lowerCamelCase_ :				Optional[Any]	,  lowerCamelCase_ :				Dict						):
				'''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(lowerCamelCase_	,  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(lowerCamelCase_	,  lowerCamelCase_	,  lowerCamelCase_						)
												if int(_exact_match						) == 1:
																correct_ids.append(qa["id"]						)
												exact_match += _exact_match

												fa += metric_max_over_ground_truths(lowerCamelCase_	,  lowerCamelCase_	,  lowerCamelCase_						)

				__magic_name__	  = exact_match / total
				__magic_name__	  = fa / total

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


if __name__ == "__main__":
						__magic_name__   :   Optional[int]  							='1.0'
						__magic_name__   :   Dict  							=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.')
						__magic_name__   :   str  							=parser.parse_args()

						with open(args.data_file) as data_file:
												__magic_name__   :   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,
																		)
												__magic_name__   :   Tuple  							=dataset_json['data']

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

						__magic_name__							,    __magic_name__   :   Optional[int]  							=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'''
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__magic_name__   :   List[Any]  							={'configuration_yoso': ['YOSO_PRETRAINED_CONFIG_ARCHIVE_MAP', 'YosoConfig']}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__magic_name__   :   Union[str, Any]  							=[
						    'YOSO_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'YosoForMaskedLM',
						    'YosoForMultipleChoice',
						    'YosoForQuestionAnswering',
						    'YosoForSequenceClassification',
						    'YosoForTokenClassification',
						    'YosoLayer',
						    'YosoModel',
						    'YosoPreTrainedModel',
						]


if TYPE_CHECKING:
						from .configuration_yoso import YOSO_PRETRAINED_CONFIG_ARCHIVE_MAP, YosoConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_yoso import (
												    YOSO_PRETRAINED_MODEL_ARCHIVE_LIST,
												    YosoForMaskedLM,
												    YosoForMultipleChoice,
												    YosoForQuestionAnswering,
												    YosoForSequenceClassification,
												    YosoForTokenClassification,
												    YosoLayer,
												    YosoModel,
												    YosoPreTrainedModel,
												)


else:
						import sys

						__magic_name__   :   Optional[int]  							=_LazyModule(__name__, globals()['__file__'], _import_structure)



