


'''simple docstring'''






import argparse
import json

from tqdm import tqdm


def    _snake_case  (	)		->       Optional[Any]:
							lowerCAmelCase__			  =      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__			  =      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__			  =      json.load(A			)
														for dpr_record in tqdm(A			):
																					lowerCAmelCase__			  =      dpr_record['''question''']
																					lowerCAmelCase__			  =      [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()



'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__UpperCAmelCase       							=				{
    '''configuration_clipseg''': [
        '''CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''CLIPSegConfig''',
        '''CLIPSegTextConfig''',
        '''CLIPSegVisionConfig''',
    ],
    '''processing_clipseg''': ['''CLIPSegProcessor'''],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__UpperCAmelCase       							=				[
		    '''CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''CLIPSegModel''',
		    '''CLIPSegPreTrainedModel''',
		    '''CLIPSegTextModel''',
		    '''CLIPSegVisionModel''',
		    '''CLIPSegForImageSegmentation''',
		]

if TYPE_CHECKING:
		from .configuration_clipseg import (
		    CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    CLIPSegConfig,
		    CLIPSegTextConfig,
		    CLIPSegVisionConfig,
		)
		from .processing_clipseg import CLIPSegProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_clipseg import (
				    CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST,
				    CLIPSegForImageSegmentation,
				    CLIPSegModel,
				    CLIPSegPreTrainedModel,
				    CLIPSegTextModel,
				    CLIPSegVisionModel,
				)

else:
		import sys

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