"""simple docstring"""






import argparse
import json

from tqdm import tqdm



def   a_						(					):
				UpperCAmelCase__             =							argparse.ArgumentParser()

				# Required parameters
				parser.add_argument(
				    '--src_path'					,			type=lowerCamelCase					,			default='biencoder-nq-dev.json'					,			help='Path to raw DPR training data'					,			)
				parser.add_argument(
				    '--evaluation_set'					,			type=lowerCamelCase					,			help='where to store parsed evaluation_set file'					,			)
				parser.add_argument(
				    '--gold_data_path'					,			type=lowerCamelCase					,			help='where to store parsed gold_data_path file'					,			)
				UpperCAmelCase__             =							parser.parse_args()

				with open(args.src_path					,			'r'       ) as src_file, open(args.evaluation_set					,			'w'       ) as eval_file, open(
				    args.gold_data_path					,			'w'       ) as gold_file:
								UpperCAmelCase__             =							json.load(lowerCamelCase       )
								for dpr_record in tqdm(lowerCamelCase       ):
												UpperCAmelCase__             =							dpr_record['question']
												UpperCAmelCase__             =							[context['title'] for context in dpr_record['positive_ctxs']]
												eval_file.write(question + '\n'       )
												gold_file.write('\t'.join(lowerCamelCase       ) + '\n'       )


if __name__ == "__main__":
						main()










from typing import TYPE_CHECKING

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


UpperCAmelCase						:   Optional[int]			  =     {
    '''configuration_xlm''': ['''XLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''XLMConfig''', '''XLMOnnxConfig'''],
    '''tokenization_xlm''': ['''XLMTokenizer'''],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCAmelCase						:   Union[str, Any]			  =     [
				    '''XLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
				    '''XLMForMultipleChoice''',
				    '''XLMForQuestionAnswering''',
				    '''XLMForQuestionAnsweringSimple''',
				    '''XLMForSequenceClassification''',
				    '''XLMForTokenClassification''',
				    '''XLMModel''',
				    '''XLMPreTrainedModel''',
				    '''XLMWithLMHeadModel''',
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCAmelCase						:   Optional[Any]			  =     [
				    '''TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
				    '''TFXLMForMultipleChoice''',
				    '''TFXLMForQuestionAnsweringSimple''',
				    '''TFXLMForSequenceClassification''',
				    '''TFXLMForTokenClassification''',
				    '''TFXLMMainLayer''',
				    '''TFXLMModel''',
				    '''TFXLMPreTrainedModel''',
				    '''TFXLMWithLMHeadModel''',
				]


if TYPE_CHECKING:
				from .configuration_xlm import XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMConfig, XLMOnnxConfig
				from .tokenization_xlm import XLMTokenizer

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_xlm import (
								    XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
								    XLMForMultipleChoice,
								    XLMForQuestionAnswering,
								    XLMForQuestionAnsweringSimple,
								    XLMForSequenceClassification,
								    XLMForTokenClassification,
								    XLMModel,
								    XLMPreTrainedModel,
								    XLMWithLMHeadModel,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_xlm import (
								    TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFXLMForMultipleChoice,
								    TFXLMForQuestionAnsweringSimple,
								    TFXLMForSequenceClassification,
								    TFXLMForTokenClassification,
								    TFXLMMainLayer,
								    TFXLMModel,
								    TFXLMPreTrainedModel,
								    TFXLMWithLMHeadModel,
								)

else:
				import sys

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




