


import argparse
import json

from tqdm import tqdm






def 					lowerCamelCase_   (	):




		"""simple docstring"""






		lowerCAmelCase__       :      Any   					= argparse.ArgumentParser()

		# Required parameters
		parser.add_argument(
		    '''--src_path'''     ,       type=_a     ,       default='''biencoder-nq-dev.json'''     ,       help='''Path to raw DPR training data'''     ,       )
		parser.add_argument(
		    '''--evaluation_set'''     ,       type=_a     ,       help='''where to store parsed evaluation_set file'''     ,       )
		parser.add_argument(
		    '''--gold_data_path'''     ,       type=_a     ,       help='''where to store parsed gold_data_path file'''     ,       )
		lowerCAmelCase__       :      Optional[int]   					= 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:
				lowerCAmelCase__       :      Any   					= json.load(_a	)
				for dpr_record in tqdm(_a	):
						lowerCAmelCase__       :      Union[str, Any]   					= dpr_record['''question''']
						lowerCAmelCase__       :      Any   					= [context['''title'''] for context in dpr_record['''positive_ctxs''']]
						eval_file.write(question + '''\n'''	)
						gold_file.write('''\t'''.join(_a	) + '''\n'''	)


if __name__ == "__main__":
		main()








from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_torch_available,
    is_vision_available,
)


lowerCamelCase						=		{'''configuration_beit''': ['''BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BeitConfig''', '''BeitOnnxConfig''']}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCamelCase						=		['''BeitFeatureExtractor''']
		lowerCamelCase						=		['''BeitImageProcessor''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCamelCase						=		[
		    '''BEIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''BeitForImageClassification''',
		    '''BeitForMaskedImageModeling''',
		    '''BeitForSemanticSegmentation''',
		    '''BeitModel''',
		    '''BeitPreTrainedModel''',
		]


try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCamelCase						=		[
		    '''FlaxBeitForImageClassification''',
		    '''FlaxBeitForMaskedImageModeling''',
		    '''FlaxBeitModel''',
		    '''FlaxBeitPreTrainedModel''',
		]

if TYPE_CHECKING:
		from .configuration_beit import BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, BeitConfig, BeitOnnxConfig

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_beit import BeitFeatureExtractor
				from .image_processing_beit import BeitImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_beit import (
				    BEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    BeitForImageClassification,
				    BeitForMaskedImageModeling,
				    BeitForSemanticSegmentation,
				    BeitModel,
				    BeitPreTrainedModel,
				)

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_beit import (
				    FlaxBeitForImageClassification,
				    FlaxBeitForMaskedImageModeling,
				    FlaxBeitModel,
				    FlaxBeitPreTrainedModel,
				)


else:
		import sys

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




