
'''simple docstring'''







import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin







class UpperCAmelCase__	( lowercase__	):





  """simple docstring"""

  __UpperCAmelCase				:				Optional[Any]   =					'''Speech2TextFeatureExtractor'''
  __UpperCAmelCase				:				Any   =					'''Speech2TextTokenizer'''


  def __init__(		self      :     Tuple							,_a      :     List[str]							,_a      :     Any       ):


     '''simple docstring'''






     super().__init__(_a							,_a       )
     _a				:	str			=   self.feature_extractor
     _a				:	List[str]			=   False


  def __call__(		self      :     List[str]							,*_a      :     str							,**_a      :     List[Any]       ):


     '''simple docstring'''






     if self._in_target_context_manager:
        return self.current_processor(*_a							,**_a       )

     if "raw_speech" in kwargs:
        warnings.warn('Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.'       )
        _a				:	Optional[int]			=   kwargs.pop('raw_speech'       )
     else:
        _a				:	Optional[Any]			=   kwargs.pop('audio'							,_a       )
     _a				:	Any			=   kwargs.pop('sampling_rate'							,_a       )
     _a				:	str			=   kwargs.pop('text'							,_a       )
     if len(_a       ) > 0:
        _a				:	str			=   args[0]
        _a				:	Dict			=   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:
        _a				:	Optional[Any]			=   self.feature_extractor(_a							,*_a							,sampling_rate=_a							,**_a       )
     if text is not None:
        _a				:	Any			=   self.tokenizer(_a							,**_a       )

     if text is None:
        return inputs
     elif audio is None:
        return encodings
     else:
        _a				:	int			=   encodings['input_ids']
        return inputs


  def   __lowercase		(		self      :     List[str]							,*_a      :     Optional[Any]							,**_a      :     Union[str, Any]       ):


     '''simple docstring'''






     return self.tokenizer.batch_decode(*_a							,**_a       )


  def   __lowercase		(		self      :     List[Any]							,*_a      :     Optional[Any]							,**_a      :     Optional[Any]       ):


     '''simple docstring'''






     return self.tokenizer.decode(*_a							,**_a       )


  @contextmanager
  def   __lowercase		(		self      :     List[str]       ):


     '''simple docstring'''






     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.'       )
     _a				:	int			=   True
     _a				:	Dict			=   self.tokenizer
     yield
     _a				:	Any			=   self.feature_extractor
     _a				:	Any			=   False





'''simple docstring'''







from __future__ import annotations

from collections.abc import Iterator
from typing import Generic, TypeVar

__lowerCAmelCase         =    TypeVar("""T""")







class UpperCAmelCase__	( Generic[T]	):





  """simple docstring"""

  def __init__(		self      :     Tuple							,_a      :     T       ):


     '''simple docstring'''






     _a				:	List[str]			=   data
     _a				:	Node[T] | None			=   None


  def __str__(		self      :     Dict       ):


     '''simple docstring'''






     return F"""{self.data}"""







class UpperCAmelCase__	( Generic[T]	):





  """simple docstring"""

  def __init__(		self      :     Optional[int]       ):


     '''simple docstring'''






     _a				:	Node[T] | None			=   None


  def __iter__(		self      :     str       ):


     '''simple docstring'''






     _a				:	Tuple			=   self.top
     while node:
        yield node.data
        _a				:	int			=   node.next


  def __str__(		self      :     str       ):


     '''simple docstring'''






     return "->".join([str(_a       ) for item in self]       )


  def __len__(		self      :     Optional[Any]       ):


     '''simple docstring'''






     return len(tuple(iter(self       )       )       )


  def   __lowercase		(		self      :     str       ):


     '''simple docstring'''






     return self.top is None


  def   __lowercase		(		self      :     List[Any]							,_a      :     T       ):


     '''simple docstring'''






     _a				:	int			=   Node(_a       )
     if not self.is_empty():
        _a				:	Optional[Any]			=   self.top
     _a				:	List[str]			=   node


  def   __lowercase		(		self      :     Tuple       ):


     '''simple docstring'''






     if self.is_empty():
        raise IndexError('pop from empty stack'       )
     assert isinstance(self.top							,_a       )
     _a				:	List[Any]			=   self.top
     _a				:	int			=   self.top.next
     return pop_node.data


  def   __lowercase		(		self      :     List[str]       ):


     '''simple docstring'''






     if self.is_empty():
        raise IndexError('peek from empty stack'       )

     assert self.top is not None
     return self.top.data


  def   __lowercase		(		self      :     List[str]       ):


     '''simple docstring'''






     _a				:	Optional[int]			=   None


if __name__ == "__main__":
  from doctest import testmod

  testmod()



