





from __future__ import annotations
def      A__       (    SCREAMING_SNAKE_CASE_   :      list[int]				,	SCREAMING_SNAKE_CASE_   :      list[int]				,	SCREAMING_SNAKE_CASE_   :      int						)  ->  tuple[float, list[float]]:






   """simple docstring"""







   _UpperCAmelCase           =   list(range(len(SCREAMING_SNAKE_CASE_						)						)						)
   _UpperCAmelCase           =   [v / w for v, w in zip(SCREAMING_SNAKE_CASE_				,	SCREAMING_SNAKE_CASE_						)]
   index.sort(key=lambda SCREAMING_SNAKE_CASE_						: ratio[i]				,	reverse=SCREAMING_SNAKE_CASE_						)

   _UpperCAmelCase           =   0
   _UpperCAmelCase           =   [0] * len(SCREAMING_SNAKE_CASE_						)
   for i in index:
      if weight[i] <= capacity:
         _UpperCAmelCase           =   1
         max_value += value[i]
         capacity -= weight[i]
      else:
         _UpperCAmelCase           =   capacity / weight[i]
         max_value += value[i] * capacity / weight[i]
         break

   return max_value, fractions


if __name__ == "__main__":
   import doctest

   doctest.testmod()






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 			__UpperCamelCase    (							A__    ):
     __A	:  Any							  =   """biogpt"""





     def __init__(     self				,							_UpperCamelCase=42384				,							_UpperCamelCase=1024				,							_UpperCamelCase=24				,							_UpperCamelCase=16				,							_UpperCamelCase=4096				,							_UpperCamelCase="gelu"				,							_UpperCamelCase=0.1				,							_UpperCamelCase=0.1				,							_UpperCamelCase=1024				,							_UpperCamelCase=0.02				,							_UpperCamelCase=1e-12				,							_UpperCamelCase=True				,							_UpperCamelCase=True				,							_UpperCamelCase=0.0				,							_UpperCamelCase=0.0				,							_UpperCamelCase=1				,							_UpperCamelCase=0				,							_UpperCamelCase=2				,							**_UpperCamelCase				,							):
        _UpperCAmelCase           =   vocab_size
        _UpperCAmelCase           =   max_position_embeddings
        _UpperCAmelCase           =   hidden_size
        _UpperCAmelCase           =   num_hidden_layers
        _UpperCAmelCase           =   num_attention_heads
        _UpperCAmelCase           =   intermediate_size
        _UpperCAmelCase           =   hidden_act
        _UpperCAmelCase           =   hidden_dropout_prob
        _UpperCAmelCase           =   attention_probs_dropout_prob
        _UpperCAmelCase           =   initializer_range
        _UpperCAmelCase           =   layer_norm_eps
        _UpperCAmelCase           =   scale_embedding
        _UpperCAmelCase           =   use_cache
        _UpperCAmelCase           =   layerdrop
        _UpperCAmelCase           =   activation_dropout
        super().__init__(pad_token_id=_UpperCamelCase				,							bos_token_id=_UpperCamelCase				,							eos_token_id=_UpperCamelCase				,							**_UpperCamelCase					)