



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


def 	lowercase_						( lowerCAmelCase__						:	str			):

			"""simple docstring"""





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

			def white_space_fix(lowerCAmelCase__						:	List[str]			):
						return " ".join(text.split()			)

			def remove_punc(lowerCAmelCase__						:	Any			):
						__UpperCAmelCase :   Optional[int]    					=  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(_UpperCamelCase			)			)			)			)


def 	lowercase_						( lowerCAmelCase__						:	Any      ,				lowerCAmelCase__						:	int			):

			"""simple docstring"""





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


def 	lowercase_						( lowerCAmelCase__						:	Union[str, Any]      ,				lowerCAmelCase__						:	str			):

			"""simple docstring"""





			return normalize_answer(_UpperCamelCase			) == normalize_answer(_UpperCamelCase			)


def 	lowercase_						( lowerCAmelCase__						:	Dict      ,				lowerCAmelCase__						:	Tuple      ,				lowerCAmelCase__						:	Dict			):

			"""simple docstring"""





			__UpperCAmelCase :   List[str]    					=  []
			for ground_truth in ground_truths:
						__UpperCAmelCase :   Union[str, Any]    					=  metric_fn(_UpperCamelCase      ,				_UpperCamelCase			)
						scores_for_ground_truths.append(_UpperCamelCase			)
			return max(_UpperCamelCase			)


def 	lowercase_						( lowerCAmelCase__						:	Union[str, Any]      ,				lowerCAmelCase__						:	int			):

			"""simple docstring"""





			__UpperCAmelCase :   Union[str, Any]    					=  0
			for article in dataset:
						for paragraph in article["paragraphs"]:
									for qa in paragraph["qas"]:
												total += 1
												if qa["id"] not in predictions:
															__UpperCAmelCase :   List[str]    					=  '''Unanswered question ''' + qa['''id'''] + ''' will receive score 0.'''
															print(_UpperCamelCase      ,				file=sys.stderr			)
															continue
												__UpperCAmelCase :   Optional[int]    					=  [x['''text'''] for x in qa['''answers''']]
												__UpperCAmelCase :   Optional[Any]    					=  predictions[qa['''id''']]
												exact_match += metric_max_over_ground_truths(_UpperCamelCase      ,				_UpperCamelCase      ,				_UpperCamelCase			)
												fa += metric_max_over_ground_truths(_UpperCamelCase      ,				_UpperCamelCase      ,				_UpperCamelCase			)

			__UpperCAmelCase :   Dict    					=  100.0 * exact_match / total
			__UpperCAmelCase :   Dict    					=  100.0 * fa / total

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


if __name__ == "__main__":
					_UpperCamelCase									=      '1.1'
					_UpperCamelCase									=      argparse.ArgumentParser(description='''Evaluation for SQuAD ''' + expected_version)
					parser.add_argument('''dataset_file''', help='''Dataset file''')
					parser.add_argument('''prediction_file''', help='''Prediction File''')
					_UpperCamelCase									=      parser.parse_args()
					with open(args.dataset_file) as dataset_file:
										_UpperCamelCase									=      json.load(dataset_file)
										if dataset_json["version"] != expected_version:
															print(
															    '''Evaluation expects v-''' + expected_version + ''', but got dataset with v-''' + dataset_json['''version'''],
															    file=sys.stderr,
															)
										_UpperCamelCase									=      dataset_json['data']
					with open(args.prediction_file) as prediction_file:
										_UpperCamelCase									=      json.load(prediction_file)
					print(json.dumps(evaluate(dataset, predictions)))









from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_torch_available


UpperCAmelCase__			:   Optional[int]      			=    {
    'configuration_ernie_m': ['ERNIE_M_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ErnieMConfig'],
}

try:
					if not is_sentencepiece_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCAmelCase__			:   List[Any]      			=    ['ErnieMTokenizer']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCAmelCase__			:   Dict      			=    [
					    'ERNIE_M_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'ErnieMForMultipleChoice',
					    'ErnieMForQuestionAnswering',
					    'ErnieMForSequenceClassification',
					    'ErnieMForTokenClassification',
					    'ErnieMModel',
					    'ErnieMPreTrainedModel',
					    'ErnieMForInformationExtraction',
					]


if TYPE_CHECKING:
					from .configuration_ernie_m import ERNIE_M_PRETRAINED_CONFIG_ARCHIVE_MAP, ErnieMConfig

					try:
										if not is_sentencepiece_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_ernie_m import ErnieMTokenizer

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_ernie_m import (
										    ERNIE_M_PRETRAINED_MODEL_ARCHIVE_LIST,
										    ErnieMForInformationExtraction,
										    ErnieMForMultipleChoice,
										    ErnieMForQuestionAnswering,
										    ErnieMForSequenceClassification,
										    ErnieMForTokenClassification,
										    ErnieMModel,
										    ErnieMPreTrainedModel,
										)


else:
					import sys

					UpperCAmelCase__			:   Union[str, Any]      			=    _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
