


import argparse
import json

from tqdm import tqdm

def 						_UpperCAmelCase				(			):
     __UpperCamelCase    							=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    							=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    							=json.load(SCREAMING_SNAKE_CASE__				)
          for dpr_record in tqdm(SCREAMING_SNAKE_CASE__				):
               __UpperCamelCase    							=dpr_record['question']
               __UpperCamelCase    							=[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()









from typing import TYPE_CHECKING

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


_A		       =						{'configuration_vit_mae': ['VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ViTMAEConfig']}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A		       =						[
       'VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST',
       'ViTMAEForPreTraining',
       'ViTMAELayer',
       'ViTMAEModel',
       'ViTMAEPreTrainedModel',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A		       =						[
       'TFViTMAEForPreTraining',
       'TFViTMAEModel',
       'TFViTMAEPreTrainedModel',
   ]

if TYPE_CHECKING:
   from .configuration_vit_mae import VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMAEConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_vit_mae import (
          VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST,
          ViTMAEForPreTraining,
          ViTMAELayer,
          ViTMAEModel,
          ViTMAEPreTrainedModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_vit_mae import TFViTMAEForPreTraining, TFViTMAEModel, TFViTMAEPreTrainedModel


else:
   import sys

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





