


'''simple docstring'''





from typing import Dict

from .base import GenericTensor, Pipeline




class 				UpperCAmelCase			(			_snake_case						):


 def        __SCREAMING_SNAKE_CASE					(      self							:	List[Any]			,       __lowerCamelCase							:	Optional[int]=None			,       __lowerCamelCase							:	List[str]=None			,       __lowerCamelCase							:	List[Any]=None			,       **__lowerCamelCase							:	Union[str, Any] ):
    if tokenize_kwargs is None:
       UpperCAmelCase__	:Optional[Any]      =      {}

    if truncation is not None:
       if "truncation" in tokenize_kwargs:
          raise ValueError(
              '''truncation parameter defined twice (given as keyword argument as well as in tokenize_kwargs)''' )
       UpperCAmelCase__	:List[str]      =      truncation

    UpperCAmelCase__	:Optional[int]      =      tokenize_kwargs

    UpperCAmelCase__	:List[Any]      =      {}
    if return_tensors is not None:
       UpperCAmelCase__	:Tuple      =      return_tensors

    return preprocess_params, {}, postprocess_params


 def        __SCREAMING_SNAKE_CASE					(      self							:	List[Any]			,       __lowerCamelCase							:	int			,       **__lowerCamelCase							:	Union[str, Any] ):
    UpperCAmelCase__	:str      =      self.framework
    UpperCAmelCase__	:Any      =      self.tokenizer(UpperCamelCase__			,       return_tensors=UpperCamelCase__			,       **UpperCamelCase__ )
    return model_inputs


 def        __SCREAMING_SNAKE_CASE					(      self							:	Optional[int]			,       __lowerCamelCase							:	List[str] ):
    UpperCAmelCase__	:Any      =      self.model(**UpperCamelCase__ )
    return model_outputs


 def        __SCREAMING_SNAKE_CASE					(      self							:	int			,       __lowerCamelCase							:	List[str]			,       __lowerCamelCase							:	Optional[Any]=False ):
    # [0] is the first available tensor, logits or last_hidden_state.
    if return_tensors:
       return model_outputs[0]
    if self.framework == "pt":
       return model_outputs[0].tolist()
    elif self.framework == "tf":
       return model_outputs[0].numpy().tolist()





 def __call__(      self							:	Any			,       *__lowerCamelCase							:	Dict			,       **__lowerCamelCase							:	Dict ):
    return super().__call__(*UpperCamelCase__			,       **UpperCamelCase__ )






'''simple docstring'''







from bisect import bisect
from itertools import accumulate
def    UpperCAmelCase__     (   UpperCAmelCase__,							UpperCAmelCase__,							UpperCAmelCase__,							UpperCAmelCase__					)      ->      Dict:
       A_						       =   sorted(zip(UpperCAmelCase__,							UpperCAmelCase__					),							key=lambda UpperCAmelCase__					: x[0] / x[1],							reverse=UpperCAmelCase__					)
       A_       ,      A_						       =   [i[0] for i in r], [i[1] for i in r]
       A_						       =   list(accumulate(UpperCAmelCase__					)					)
       A_						       =   bisect(UpperCAmelCase__,							UpperCAmelCase__					)
       return (
           0
           if k == 0
           else sum(vl[:k]					) + (w - acc[k - 1]) * (vl[k]) / (wt[k])
           if k != n
           else sum(vl[:k]					)
       )


if __name__ == "__main__":
      import doctest

      doctest.testmod()
