code: 
  
  import warnings
  from contextlib import contextmanager
  
  from ...processing_utils import ProcessorMixin
  
  class __UpperCamelCase    (							_a		):
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  					__magic_name__     			=      "AutoFeatureExtractor"
  					__magic_name__     			=      "Speech2Text2Tokenizer"
  
  
  
  
  
  					def __init__(							self							,			lowerCamelCase__							,			lowerCamelCase__	):
  						super().__init__(lowerCamelCase__							,			lowerCamelCase__	)
  						UpperCAmelCase__:  Optional[Any]		       =   self.feature_extractor
  						UpperCAmelCase__:  str		       =   False
  
  
  
  
  
  					def __call__(							self							,			*lowerCamelCase__							,			**lowerCamelCase__	):
  						# 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__:  List[str]		       =   kwargs.pop("raw_speech"	)
  						else:
  							UpperCAmelCase__:  Tuple		       =   kwargs.pop("audio"							,			lowerCamelCase__	)
  						UpperCAmelCase__:  Dict		       =   kwargs.pop("sampling_rate"							,			lowerCamelCase__	)
  						UpperCAmelCase__:  Dict		       =   kwargs.pop("text"							,			lowerCamelCase__	)
  						if len(lowerCamelCase__	) > 0:
  							UpperCAmelCase__:  Tuple		       =   args[0]
  							UpperCAmelCase__:  Optional[int]		       =   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__:  str		       =   self.feature_extractor(lowerCamelCase__							,			*lowerCamelCase__							,			sampling_rate=lowerCamelCase__							,			**lowerCamelCase__	)
  						if text is not None:
  							UpperCAmelCase__:  Optional[Any]		       =   self.tokenizer(lowerCamelCase__							,			**lowerCamelCase__	)
  
  						if text is None:
  							return inputs
  						elif audio is None:
  							return encodings
  						else:
  							UpperCAmelCase__:  List[Any]		       =   encodings["input_ids"]
  							return inputs
  
  
  
  
  
  					def       _UpperCAmelCase						(							self							,			*lowerCamelCase__							,			**lowerCamelCase__	):
  						return self.tokenizer.batch_decode(*lowerCamelCase__							,			**lowerCamelCase__	)
  
  
  
  
  
  					def       _UpperCAmelCase						(							self							,			*lowerCamelCase__							,			**lowerCamelCase__	):
  						return self.tokenizer.decode(*lowerCamelCase__							,			**lowerCamelCase__	)
  
  
  
  
  
  					@contextmanager
  					def       _UpperCAmelCase						(							self	):
  						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__:  List[str]		       =   True
  						UpperCAmelCase__:  str		       =   self.tokenizer
  						yield
  						UpperCAmelCase__:  Any		       =   self.feature_extractor
  						UpperCAmelCase__:  Optional[int]		       =   False
style_context: 
  
  from __future__ import annotations
  
  
  
  def  _A  (			SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE			):
  	assert n > 1 and a > 0 and greatest_common_divisor(SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE			) == 1
  	((UpperCAmelCase__)       ,	(UpperCAmelCase__)       ,	(UpperCAmelCase__)):  Optional[Any]		       =   extended_gcd(SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE			)  # Implemented below
  	UpperCAmelCase__:  List[str]		       =   (b * s) % n
  	return x
  
  
  
  def  _A  (			SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE			):
  	((UpperCAmelCase__)       ,	(UpperCAmelCase__)):  int		       =   extended_euclid(SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE			)  # Implemented below
  	if b < 0:
  		UpperCAmelCase__:  List[str]		       =   (b % n + n) % n
  	return b
  
  
  
  def  _A  (			SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE			):
  	UpperCAmelCase__:  Union[str, Any]		       =   invert_modulo(SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE			)
  	UpperCAmelCase__:  Any		       =   (b * s) % n
  	return x
  
  
  
  def  _A  (			SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE			):
  	assert a >= 0 and b >= 0
  
  	if b == 0:
  		UpperCAmelCase__       ,	UpperCAmelCase__       ,	UpperCAmelCase__:  List[Any]		       =   a, 1, 0
  	else:
  		((UpperCAmelCase__)       ,	(UpperCAmelCase__)       ,	(UpperCAmelCase__)):  Tuple		       =   extended_gcd(SCREAMING_SNAKE_CASE  ,a % b			)
  		UpperCAmelCase__:  Union[str, Any]		       =   q
  		UpperCAmelCase__:  Union[str, Any]		       =   p - q * (a // b)
  
  	assert a % d == 0 and b % d == 0
  	assert d == a * x + b * y
  
  	return (d, x, y)
  
  
  
  def  _A  (			SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE			):
  	if b == 0:
  		return (1, 0)
  	((UpperCAmelCase__)       ,	(UpperCAmelCase__)):  List[str]		       =   extended_euclid(SCREAMING_SNAKE_CASE  ,a % b			)
  	UpperCAmelCase__:  Optional[Any]		       =   a // b
  	return (y, x - k * y)
  
  
  
  def  _A  (			SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE			):
  	if a < b:
  		UpperCAmelCase__       ,	UpperCAmelCase__:  str		       =   b, a
  
  	while a % b != 0:
  		UpperCAmelCase__       ,	UpperCAmelCase__:  Union[str, Any]		       =   b, a % b
  
  	return b
  
  
  if __name__ == "__main__":
  					from doctest import testmod
  
  					testmod(name="""modular_division""", verbose=True)
  					testmod(name="""modular_division2""", verbose=True)
  					testmod(name="""invert_modulo""", verbose=True)
  					testmod(name="""extended_gcd""", verbose=True)
  					testmod(name="""extended_euclid""", verbose=True)
  					testmod(name="""greatest_common_divisor""", verbose=True)