code: 
  
  
  """simple docstring"""
  
  
  
  
  from __future__ import annotations
  
  from enum import Enum
  
  
  
  
  
  class __snake_case		(					_SCREAMING_SNAKE_CASE      ):
  
  
  
  
  
  
  
  			"""simple docstring"""
  
  
  
  
  			lowerCAmelCase_				:   List[str]     	=			24
  			lowerCAmelCase_				:   Union[str, Any]     	=			21
  			lowerCAmelCase_				:   int     	=			18
  			lowerCAmelCase_				:   Optional[Any]     	=			15
  			lowerCAmelCase_				:   Tuple     	=			12
  			lowerCAmelCase_				:   Tuple     	=			9
  			lowerCAmelCase_				:   str     	=			6
  			lowerCAmelCase_				:   Optional[int]     	=			3
  			lowerCAmelCase_				:   Union[str, Any]     	=			2
  			lowerCAmelCase_				:   Optional[Any]     	=			1
  			lowerCAmelCase_				:   Union[str, Any]     	=			-1
  			lowerCAmelCase_				:   int     	=			-2
  			lowerCAmelCase_				:   List[Any]     	=			-3
  			lowerCAmelCase_				:   str     	=			-6
  			lowerCAmelCase_				:   List[str]     	=			-9
  			lowerCAmelCase_				:   Dict     	=			-12
  			lowerCAmelCase_				:   Optional[int]     	=			-15
  			lowerCAmelCase_				:   Dict     	=			-18
  			lowerCAmelCase_				:   List[str]     	=			-21
  			lowerCAmelCase_				:   Union[str, Any]     	=			-24
  
  
  
  
  
  
  class __snake_case		(					_SCREAMING_SNAKE_CASE      ):
  
  
  
  
  
  
  
  			"""simple docstring"""
  
  
  
  
  			lowerCAmelCase_				:   Optional[Any]     	=			8
  			lowerCAmelCase_				:   List[Any]     	=			7
  			lowerCAmelCase_				:   List[Any]     	=			6
  			lowerCAmelCase_				:   Union[str, Any]     	=			5
  			lowerCAmelCase_				:   Union[str, Any]     	=			4
  			lowerCAmelCase_				:   Union[str, Any]     	=			3
  			lowerCAmelCase_				:   Union[str, Any]     	=			2
  			lowerCAmelCase_				:   Optional[int]     	=			1
  
  
  
  
  
  
  def 						__a      (  a,		a,		a,		):
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  
  
  							if isinstance(a,		a					):
  														_a          =				SIUnit[known_prefix.lower()]
  							if isinstance(a,		a					):
  														_a          =				SIUnit[unknown_prefix.lower()]
  							_a          =				known_amount * (
  							    1_0 ** (known_prefix.value - unknown_prefix.value)
  							)
  							return unknown_amount
  
  
  
  def 						__a      (  a,		a,		a,		):
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  
  
  							if isinstance(a,		a					):
  														_a          =				BinaryUnit[known_prefix.lower()]
  							if isinstance(a,		a					):
  														_a          =				BinaryUnit[unknown_prefix.lower()]
  							_a          =				known_amount * (
  							    2 ** ((known_prefix.value - unknown_prefix.value) * 1_0)
  							)
  							return unknown_amount
  
  
  if __name__ == "__main__":
  			import doctest
  
  			doctest.testmod()
  
style_context: 
  
  
  """simple docstring"""
  
  
  
  
  import warnings
  from contextlib import contextmanager
  
  from ...processing_utils import ProcessorMixin
  
  
  
  
  
  class __snake_case		(					_SCREAMING_SNAKE_CASE      ):
  
  
  
  
  
  
  
     """simple docstring"""
  
  
  
  
     lowerCAmelCase_				:   str     	=			'AutoFeatureExtractor'
     lowerCAmelCase_				:   List[Any]     	=			'Speech2Text2Tokenizer'
  
  
  
  
     def __init__(		self							:int				,  UpperCamelCase__							:List[str]				,  UpperCamelCase__							:Union[str, Any]       ):
            super().__init__(UpperCamelCase__				,  UpperCamelCase__       )
            _a          =				self.feature_extractor
            _a          =				False
  
  
  
  
     def __call__(		self							:List[str]				,  *UpperCamelCase__							:List[str]				,  **UpperCamelCase__							:Dict       ):
            # For backward compatibility
            if self._in_target_context_manager:
                   return self.current_processor(*UpperCamelCase__				,  **UpperCamelCase__       )
  
            if "raw_speech" in kwargs:
                   warnings.warn("Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead."       )
                   _a          =				kwargs.pop("raw_speech"       )
            else:
                   _a          =				kwargs.pop("audio"				,  UpperCamelCase__       )
            _a          =				kwargs.pop("sampling_rate"				,  UpperCamelCase__       )
            _a          =				kwargs.pop("text"				,  UpperCamelCase__       )
            if len(UpperCamelCase__       ) > 0:
                   _a          =				args[0]
                   _a          =				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          =				self.feature_extractor(UpperCamelCase__				,  *UpperCamelCase__				,  sampling_rate=UpperCamelCase__				,  **UpperCamelCase__       )
            if text is not None:
                   _a          =				self.tokenizer(UpperCamelCase__				,  **UpperCamelCase__       )
  
            if text is None:
                   return inputs
            elif audio is None:
                   return encodings
            else:
                   _a          =				encodings["input_ids"]
                   return inputs
  
  
  
  
     def SCREAMING_SNAKE_CASE_      (		self							:Union[str, Any]				,  *UpperCamelCase__							:int				,  **UpperCamelCase__							:Optional[Any]       ):
            return self.tokenizer.batch_decode(*UpperCamelCase__				,  **UpperCamelCase__       )
  
  
  
  
     def SCREAMING_SNAKE_CASE_      (		self							:Optional[Any]				,  *UpperCamelCase__							:List[Any]				,  **UpperCamelCase__							:List[Any]       ):
            return self.tokenizer.decode(*UpperCamelCase__				,  **UpperCamelCase__       )
  
  
  
  
     @contextmanager
     def SCREAMING_SNAKE_CASE_      (		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."       )
            _a          =				True
            _a          =				self.tokenizer
            yield
            _a          =				self.feature_extractor
            _a          =				False
  