



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



def        __a     (    lowerCAmelCase_   :					Dict				)      -> Union[str, Any]:






						'''simple docstring'''



						def remove_articles(lowerCAmelCase_   :					Optional[Any]				):
												return re.sub(r"""\b(a|an|the)\b"""			,""" """			,lowerCAmelCase_				)

						def white_space_fix(lowerCAmelCase_   :					List[Any]				):
												return " ".join(text.split()				)

						def remove_punc(lowerCAmelCase_   :					List[str]				):
												UpperCAmelCase_=   set(string.punctuation				)
												return "".join(ch for ch in text if ch not in exclude				)

						def lower(lowerCAmelCase_   :					Any				):
												return text.lower()

						return white_space_fix(remove_articles(remove_punc(lower(lowerCAmelCase_				)				)				)				)



def        __a     (    lowerCAmelCase_   :					Optional[Any]			,lowerCAmelCase_   :					List[Any]				)      -> Dict:






						'''simple docstring'''



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



def        __a     (    lowerCAmelCase_   :					Union[str, Any]			,lowerCAmelCase_   :					Any				)      -> List[str]:






						'''simple docstring'''



						return normalize_answer(lowerCAmelCase_				) == normalize_answer(lowerCAmelCase_				)



def        __a     (    lowerCAmelCase_   :					List[str]			,lowerCAmelCase_   :					str			,lowerCAmelCase_   :					Optional[Any]				)      -> Optional[int]:






						'''simple docstring'''



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



def        __a     (    lowerCAmelCase_   :					Tuple			,lowerCAmelCase_   :					Optional[int]				)      -> int:






						'''simple docstring'''



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

																		UpperCAmelCase_=   [x["""text"""] for x in qa["""answers"""]]
																		UpperCAmelCase_=   predictions[qa["""id"""]]

																		UpperCAmelCase_=   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_				)

						UpperCAmelCase_=   exact_match / total
						UpperCAmelCase_=   fa / total

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


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

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

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

	__A    ,			__A				    =						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 typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__A				    =						{
    '''configuration_mpnet''': ['''MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MPNetConfig'''],
    '''tokenization_mpnet''': ['''MPNetTokenizer'''],
}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__A				    =						['''MPNetTokenizerFast''']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__A				    =						[
	    '''MPNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''MPNetForMaskedLM''',
	    '''MPNetForMultipleChoice''',
	    '''MPNetForQuestionAnswering''',
	    '''MPNetForSequenceClassification''',
	    '''MPNetForTokenClassification''',
	    '''MPNetLayer''',
	    '''MPNetModel''',
	    '''MPNetPreTrainedModel''',
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__A				    =						[
	    '''TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''TFMPNetEmbeddings''',
	    '''TFMPNetForMaskedLM''',
	    '''TFMPNetForMultipleChoice''',
	    '''TFMPNetForQuestionAnswering''',
	    '''TFMPNetForSequenceClassification''',
	    '''TFMPNetForTokenClassification''',
	    '''TFMPNetMainLayer''',
	    '''TFMPNetModel''',
	    '''TFMPNetPreTrainedModel''',
	]


if TYPE_CHECKING:
	from .configuration_mpnet import MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP, MPNetConfig
	from .tokenization_mpnet import MPNetTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_mpnet_fast import MPNetTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_mpnet import (
		    MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
		    MPNetForMaskedLM,
		    MPNetForMultipleChoice,
		    MPNetForQuestionAnswering,
		    MPNetForSequenceClassification,
		    MPNetForTokenClassification,
		    MPNetLayer,
		    MPNetModel,
		    MPNetPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_mpnet import (
		    TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFMPNetEmbeddings,
		    TFMPNetForMaskedLM,
		    TFMPNetForMultipleChoice,
		    TFMPNetForQuestionAnswering,
		    TFMPNetForSequenceClassification,
		    TFMPNetForTokenClassification,
		    TFMPNetMainLayer,
		    TFMPNetModel,
		    TFMPNetPreTrainedModel,
		)

else:
	import sys

	__A				    =						_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




