


import argparse
import json

from tqdm import tqdm




def       __lowercase						(    )     ->Optional[Any]:







      """simple docstring"""

      lowercase      : Any        =		argparse.ArgumentParser()

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


if __name__ == "__main__":
  main()





from typing import TYPE_CHECKING

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


__snake_case   							=						{'''configuration_unispeech''': ['''UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''UniSpeechConfig''']}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __snake_case   							=						[
        '''UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''UniSpeechForCTC''',
        '''UniSpeechForPreTraining''',
        '''UniSpeechForSequenceClassification''',
        '''UniSpeechModel''',
        '''UniSpeechPreTrainedModel''',
    ]

if TYPE_CHECKING:
    from .configuration_unispeech import UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP, UniSpeechConfig

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_unispeech import (
            UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST,
            UniSpeechForCTC,
            UniSpeechForPreTraining,
            UniSpeechForSequenceClassification,
            UniSpeechModel,
            UniSpeechPreTrainedModel,
        )

else:
    import sys

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


