


'''simple docstring'''





from ...configuration_utils import PretrainedConfig
from ...utils import logging


__lowerCamelCase												=						logging.get_logger(__name__)

__lowerCamelCase												=						{
    '''facebook/s2t-wav2vec2-large-en-de''': (
        '''https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/config.json'''
    ),
    # See all Speech2Text models at https://huggingface.co/models?filter=speech2text2
}




class 				UpperCAmelCase			(			_snake_case						):
 UpperCAmelCase				=					"speech_to_text_2"
 UpperCAmelCase				=					["past_key_values"]
 UpperCAmelCase				=					{"num_attention_heads": "decoder_attention_heads", "hidden_size": "d_model"}


 def __init__(      self							:	List[Any]			,       __lowerCamelCase							:	List[str]=1_0_0_0_0			,       __lowerCamelCase							:	Dict=6			,       __lowerCamelCase							:	Union[str, Any]=2_0_4_8			,       __lowerCamelCase							:	List[str]=4			,       __lowerCamelCase							:	List[Any]=0.0			,       __lowerCamelCase							:	List[str]=True			,       __lowerCamelCase							:	Union[str, Any]="relu"			,       __lowerCamelCase							:	Optional[int]=2_5_6			,       __lowerCamelCase							:	Dict=0.1			,       __lowerCamelCase							:	Tuple=0.0			,       __lowerCamelCase							:	str=0.0			,       __lowerCamelCase							:	Optional[int]=0.02			,       __lowerCamelCase							:	Union[str, Any]=2			,       __lowerCamelCase							:	int=True			,       __lowerCamelCase							:	Optional[int]=1			,       __lowerCamelCase							:	Dict=0			,       __lowerCamelCase							:	Union[str, Any]=2			,       __lowerCamelCase							:	List[str]=1_0_2_4			,       **__lowerCamelCase							:	str			,       ):
    UpperCAmelCase__	:Tuple      =      vocab_size
    UpperCAmelCase__	:Tuple      =      d_model
    UpperCAmelCase__	:Dict      =      decoder_ffn_dim
    UpperCAmelCase__	:Dict      =      decoder_layers
    UpperCAmelCase__	:Any      =      decoder_attention_heads
    UpperCAmelCase__	:Optional[int]      =      dropout
    UpperCAmelCase__	:List[str]      =      attention_dropout
    UpperCAmelCase__	:Optional[int]      =      activation_dropout
    UpperCAmelCase__	:Union[str, Any]      =      activation_function
    UpperCAmelCase__	:Optional[int]      =      init_std
    UpperCAmelCase__	:Any      =      decoder_layerdrop
    UpperCAmelCase__	:Any      =      use_cache
    UpperCAmelCase__	:Tuple      =      decoder_layers
    UpperCAmelCase__	:str      =      scale_embedding  # scale factor will be sqrt(d_model) if True
    UpperCAmelCase__	:str      =      max_target_positions

    super().__init__(
        pad_token_id=UpperCamelCase__			,       bos_token_id=UpperCamelCase__			,       eos_token_id=UpperCamelCase__			,       decoder_start_token_id=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()
