





'''simple docstring'''




import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin







class   A_	(  lowerCAmelCase_			):
 _lowerCamelCase  : Optional[int]           =							"""Speech2TextFeatureExtractor"""
 _lowerCamelCase  : int           =							"""Speech2TextTokenizer"""




 def __init__(					self       :    Tuple	,			snake_case_       :    List[Any]	,			snake_case_       :    Dict    ):
      super().__init__(__lowerCamelCase	,			__lowerCamelCase    )
      _UpperCAmelCase     							=   self.feature_extractor
      _UpperCAmelCase     							=   False




 def __call__(					self       :    Union[str, Any]	,			*snake_case_       :    List[Any]	,			**snake_case_       :    List[Any]    ):
      # For backward compatibility
      if self._in_target_context_manager:
           return self.current_processor(*__lowerCamelCase	,			**__lowerCamelCase    )

      if "raw_speech" in kwargs:
           warnings.warn("Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead."    )
           _UpperCAmelCase     							=   kwargs.pop("raw_speech"    )
      else:
           _UpperCAmelCase     							=   kwargs.pop("audio"	,			__lowerCamelCase    )
      _UpperCAmelCase     							=   kwargs.pop("sampling_rate"	,			__lowerCamelCase    )
      _UpperCAmelCase     							=   kwargs.pop("text"	,			__lowerCamelCase    )
      if len(__lowerCamelCase    ) > 0:
           _UpperCAmelCase     							=   args[0]
           _UpperCAmelCase     							=   args[1:]

      if audio is None and text is None:
           raise ValueError("You need to specify either an `audio` or `text` input to process."    )

      if audio is not None:
           _UpperCAmelCase     							=   self.feature_extractor(__lowerCamelCase	,			*__lowerCamelCase	,			sampling_rate=__lowerCamelCase	,			**__lowerCamelCase    )
      if text is not None:
           _UpperCAmelCase     							=   self.tokenizer(__lowerCamelCase	,			**__lowerCamelCase    )

      if text is None:
           return inputs
      elif audio is None:
           return encodings
      else:
           _UpperCAmelCase     							=   encodings["input_ids"]
           return inputs




 def      lowercase	(					self       :    Optional[int]	,			*snake_case_       :    Any	,			**snake_case_       :    Any    ):
      return self.tokenizer.batch_decode(*__lowerCamelCase	,			**__lowerCamelCase    )




 def      lowercase	(					self       :    Optional[Any]	,			*snake_case_       :    List[Any]	,			**snake_case_       :    List[Any]    ):
      return self.tokenizer.decode(*__lowerCamelCase	,			**__lowerCamelCase    )




 @contextmanager
 def      lowercase	(					self       :    Dict    ):
      warnings.warn(
          "`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your "
          "labels by using the argument `text` of the regular `__call__` method (either in the same call as "
          "your audio inputs, or in a separate call."    )
      _UpperCAmelCase     							=   True
      _UpperCAmelCase     							=   self.tokenizer
      yield
      _UpperCAmelCase     							=   self.feature_extractor
      _UpperCAmelCase     							=   False








from __future__ import annotations
def 							_UpperCAmelCase				(UpperCamelCase__     :  list[int]				,							UpperCamelCase__     :  list[int]				,							UpperCamelCase__     :  int						):
       _A      :       Dict				    = list(range(len(UpperCamelCase__						)						)						)
       _A      :       Any				    = [v / w for v, w in zip(UpperCamelCase__				,							UpperCamelCase__						)]
       index.sort(key=lambda UpperCamelCase__						: ratio[i]				,							reverse=UpperCamelCase__						)

       _A      :       float				    = 0
       _A      :       list[float]				    = [0] * len(UpperCamelCase__						)
       for i in index:
              if weight[i] <= capacity:
                     _A      :       Union[str, Any]				    = 1
                     max_value += value[i]
                     capacity -= weight[i]
              else:
                     _A      :       Optional[Any]				    = capacity / weight[i]
                     max_value += value[i] * capacity / weight[i]
                     break

       return max_value, fractions


if __name__ == "__main__":
    import doctest

    doctest.testmod()
