


"""simple docstring"""


from typing import TYPE_CHECKING

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


_snake_case          =							{
    "configuration_gpt_neo": ["GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTNeoConfig", "GPTNeoOnnxConfig"],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _snake_case          =							[
       "GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST",
       "GPTNeoForCausalLM",
       "GPTNeoForQuestionAnswering",
       "GPTNeoForSequenceClassification",
       "GPTNeoForTokenClassification",
       "GPTNeoModel",
       "GPTNeoPreTrainedModel",
       "load_tf_weights_in_gpt_neo",
   ]

try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _snake_case          =							[
       "FlaxGPTNeoForCausalLM",
       "FlaxGPTNeoModel",
       "FlaxGPTNeoPreTrainedModel",
   ]


if TYPE_CHECKING:
   from .configuration_gpt_neo import GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoConfig, GPTNeoOnnxConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_gpt_neo import (
          GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST,
          GPTNeoForCausalLM,
          GPTNeoForQuestionAnswering,
          GPTNeoForSequenceClassification,
          GPTNeoForTokenClassification,
          GPTNeoModel,
          GPTNeoPreTrainedModel,
          load_tf_weights_in_gpt_neo,
      )

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel, FlaxGPTNeoPreTrainedModel


else:
   import sys

   _snake_case          =							_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)







"""simple docstring"""


import argparse
import json

from tqdm import tqdm



def   snake_case						(				)->		List[Any]:
      '''simple docstring'''


      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()



