





import argparse
import json

from tqdm import tqdm






def 	_SCREAMING_SNAKE_CASE		(      )      ->   Tuple:
 __lowerCAmelCase					:	Dict      =       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"""   ,   )
 __lowerCAmelCase					:	Union[str, Any]      =       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					:	Optional[Any]      =       json.load(SCREAMING_SNAKE_CASE   )
  for dpr_record in tqdm(SCREAMING_SNAKE_CASE   ):
   __lowerCAmelCase					:	Union[str, Any]      =       dpr_record["""question"""]
   __lowerCAmelCase					:	Dict      =       [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 ...configuration_utils import PretrainedConfig
from ...utils import logging


_UpperCAmelCase             =							logging.get_logger(__name__)

_UpperCAmelCase             =							{
    'microsoft/biogpt': 'https://huggingface.co/microsoft/biogpt/resolve/main/config.json',
    # See all BioGPT models at https://huggingface.co/models?filter=biogpt
}






class        snake_case_    (		__lowercase						):
  A_      							=   'biogpt'






  def __init__(							self			:					int       ,  _snake_case			:					Any=42384       ,  _snake_case			:					Any=1024       ,  _snake_case			:					List[Any]=24       ,  _snake_case			:					Any=16       ,  _snake_case			:					List[str]=4096       ,  _snake_case			:					Dict="gelu"       ,  _snake_case			:					Tuple=0.1       ,  _snake_case			:					str=0.1       ,  _snake_case			:					Tuple=1024       ,  _snake_case			:					Tuple=0.02       ,  _snake_case			:					Tuple=1E-12       ,  _snake_case			:					Optional[int]=True       ,  _snake_case			:					Optional[int]=True       ,  _snake_case			:					Any=0.0       ,  _snake_case			:					Tuple=0.0       ,  _snake_case			:					str=1       ,  _snake_case			:					Dict=0       ,  _snake_case			:					str=2       ,  **_snake_case			:					Union[str, Any]       ,  )->Dict:






   '''simple docstring'''

   __lowerCAmelCase					:	List[Any]      =       vocab_size
   __lowerCAmelCase					:	Dict      =       max_position_embeddings
   __lowerCAmelCase					:	str      =       hidden_size
   __lowerCAmelCase					:	Dict      =       num_hidden_layers
   __lowerCAmelCase					:	List[Any]      =       num_attention_heads
   __lowerCAmelCase					:	Optional[Any]      =       intermediate_size
   __lowerCAmelCase					:	int      =       hidden_act
   __lowerCAmelCase					:	Any      =       hidden_dropout_prob
   __lowerCAmelCase					:	Any      =       attention_probs_dropout_prob
   __lowerCAmelCase					:	Any      =       initializer_range
   __lowerCAmelCase					:	int      =       layer_norm_eps
   __lowerCAmelCase					:	Optional[int]      =       scale_embedding
   __lowerCAmelCase					:	List[Any]      =       use_cache
   __lowerCAmelCase					:	str      =       layerdrop
   __lowerCAmelCase					:	Dict      =       activation_dropout
   super().__init__(pad_token_id=_snake_case       ,  bos_token_id=_snake_case       ,  eos_token_id=_snake_case       ,  **_snake_case      )