






"""simple docstring"""



import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin







class        lowercase__      (							snake_case__       ):
			_UpperCAmelCase		:List[Any]             =			"Speech2TextFeatureExtractor"
			_UpperCAmelCase		:str             =			"Speech2TextTokenizer"


			def __init__(				self					:  Any       ,	snake_case__					:  Optional[Any]       ,	snake_case__					:  Any ):
										super().__init__(snake_case__       ,	snake_case__ )
										lowerCamelCase_	:							int									=self.feature_extractor
										lowerCamelCase_	:							Tuple									=False


			def __call__(				self					:  int       ,	*snake_case__					:  Optional[int]       ,	**snake_case__					:  Any ):
										# For backward compatibility
										if self._in_target_context_manager:
																	return self.current_processor(*snake_case__       ,	**snake_case__ )

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

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


			def      UpperCAmelCase__						(				self					:  Dict       ,	*snake_case__					:  Tuple       ,	**snake_case__					:  Union[str, Any] ):
										return self.tokenizer.batch_decode(*snake_case__       ,	**snake_case__ )


			def      UpperCAmelCase__						(				self					:  Union[str, Any]       ,	*snake_case__					:  Optional[int]       ,	**snake_case__					:  int ):
										return self.tokenizer.decode(*snake_case__       ,	**snake_case__ )


			@contextmanager
			def      UpperCAmelCase__						(				self					:  Optional[int] ):
										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." )
										lowerCamelCase_	:							List[str]									=True
										lowerCamelCase_	:							List[Any]									=self.tokenizer
										yield
										lowerCamelCase_	:							int									=self.feature_extractor
										lowerCamelCase_	:							List[Any]									=False











"""simple docstring"""



def 			_snake_case    (					lowerCamelCase__     :   int  )    ->     int:

							if not isinstance(lowerCamelCase__    ,						lowerCamelCase__  ):
														raise TypeError("only integers accepted as input"  )
							else:
														lowerCamelCase_	:							str									=str(abs(lowerCamelCase__  )  )
														lowerCamelCase_	:							Tuple									=[list(lowerCamelCase__  ) for char in range(len(lowerCamelCase__  )  )]
														for index in range(len(lowerCamelCase__  )  ):
																					num_transpositions[index].pop(lowerCamelCase__  )
														return max(
														    int("".join(list(lowerCamelCase__  )  )  ) for transposition in num_transpositions  )


if __name__ == "__main__":
						__import__('doctest').testmod()



