from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__lowercase							 =					{
    '''configuration_clap''': [
        '''CLAP_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''ClapAudioConfig''',
        '''ClapConfig''',
        '''ClapTextConfig''',
    ],
    '''processing_clap''': ['''ClapProcessor'''],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowercase							 =					[
        '''CLAP_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''ClapModel''',
        '''ClapPreTrainedModel''',
        '''ClapTextModel''',
        '''ClapTextModelWithProjection''',
        '''ClapAudioModel''',
        '''ClapAudioModelWithProjection''',
    ]
    __lowercase							 =					['''ClapFeatureExtractor''']

if TYPE_CHECKING:
    from .configuration_clap import (
        CLAP_PRETRAINED_MODEL_ARCHIVE_LIST,
        ClapAudioConfig,
        ClapConfig,
        ClapTextConfig,
    )
    from .processing_clap import ClapProcessor

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .feature_extraction_clap import ClapFeatureExtractor
        from .modeling_clap import (
            CLAP_PRETRAINED_MODEL_ARCHIVE_LIST,
            ClapAudioModel,
            ClapAudioModelWithProjection,
            ClapModel,
            ClapPreTrainedModel,
            ClapTextModel,
            ClapTextModelWithProjection,
        )


else:
    import sys

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






import argparse
import json

from tqdm import tqdm



def lowerCamelCase  (   ):
    '''simple docstring'''







    __UpperCamelCase     :Tuple			  =		argparse.ArgumentParser()

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


if __name__ == "__main__":
    main()





