




import warnings
from contextlib import contextmanager

from ....processing_utils import ProcessorMixin


class    SCREAMING_SNAKE_CASE_				(       _UpperCamelCase   ):



	"""simple docstring"""


	__magic_name__   :       Union[str, Any]      	=		'MCTCTFeatureExtractor'
	__magic_name__   :       Union[str, Any]      	=		'AutoTokenizer'






	def __init__(					self		:						List[str]					,     lowerCAmelCase		:						Optional[Any]					,     lowerCAmelCase		:						Any	)	->       Any:







						"""simple docstring"""







						super().__init__(lowerCAmelCase					,     lowerCAmelCase	)
						__UpperCamelCase					:	Optional[Any]             =      self.feature_extractor
						__UpperCamelCase					:	List[Any]             =      False






	def __call__(					self		:						List[str]					,     *lowerCAmelCase		:						Dict					,     **lowerCAmelCase		:						int	)	->       List[str]:







						"""simple docstring"""







						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					:	Optional[int]             =      kwargs.pop("""raw_speech"""	)
						else:
											__UpperCamelCase					:	List[str]             =      kwargs.pop("""audio"""					,     lowerCAmelCase	)
						__UpperCamelCase					:	Union[str, Any]             =      kwargs.pop("""sampling_rate"""					,     lowerCAmelCase	)
						__UpperCamelCase					:	str             =      kwargs.pop("""text"""					,     lowerCAmelCase	)
						if len(lowerCAmelCase	) > 0:
											__UpperCamelCase					:	Dict             =      args[0]
											__UpperCamelCase					:	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					:	Union[str, Any]             =      self.feature_extractor(lowerCAmelCase					,     *lowerCAmelCase					,     sampling_rate=lowerCAmelCase					,     **lowerCAmelCase	)
						if text is not None:
											__UpperCamelCase					:	List[Any]             =      self.tokenizer(lowerCAmelCase					,     **lowerCAmelCase	)

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






	def 					lowerCamelCase__     (					self		:						str					,     *lowerCAmelCase		:						int					,     **lowerCAmelCase		:						Union[str, Any]	)	->       int:







						"""simple docstring"""







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






	def 					lowerCamelCase__     (					self		:						Union[str, Any]					,     *lowerCAmelCase		:						str					,     **lowerCAmelCase		:						str	)	->       Tuple:







						"""simple docstring"""







						if self._in_target_context_manager:
											return self.current_processor.pad(*lowerCAmelCase					,     **lowerCAmelCase	)

						__UpperCamelCase					:	Any             =      kwargs.pop("""input_features"""					,     lowerCAmelCase	)
						__UpperCamelCase					:	Any             =      kwargs.pop("""labels"""					,     lowerCAmelCase	)
						if len(lowerCAmelCase	) > 0:
											__UpperCamelCase					:	Union[str, Any]             =      args[0]
											__UpperCamelCase					:	int             =      args[1:]

						if input_features is not None:
											__UpperCamelCase					:	Optional[int]             =      self.feature_extractor.pad(lowerCAmelCase					,     *lowerCAmelCase					,     **lowerCAmelCase	)
						if labels is not None:
											__UpperCamelCase					:	List[Any]             =      self.tokenizer.pad(lowerCAmelCase					,     **lowerCAmelCase	)

						if labels is None:
											return input_features
						elif input_features is None:
											return labels
						else:
											__UpperCamelCase					:	List[Any]             =      labels["""input_ids"""]
											return input_features






	def 					lowerCamelCase__     (					self		:						Any					,     *lowerCAmelCase		:						Optional[Any]					,     **lowerCAmelCase		:						Optional[Any]	)	->       Tuple:







						"""simple docstring"""







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






	@contextmanager
	def 					lowerCamelCase__     (					self		:						Tuple	)	->       List[Any]:







						"""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."""	)
						__UpperCamelCase					:	str             =      True
						__UpperCamelCase					:	Tuple             =      self.tokenizer
						yield
						__UpperCamelCase					:	Union[str, Any]             =      self.feature_extractor
						__UpperCamelCase					:	int             =      False












def 				A__ (snake_case   :		float		,	snake_case   :		int				)     ->						float:
					if digit_amount > 0:
										return round(number - int(snake_case				)		,	snake_case				)
					return number - int(snake_case				)


if __name__ == "__main__":
	print(decimal_isolate(1.53, 0))
	print(decimal_isolate(35.345, 1))
	print(decimal_isolate(35.345, 2))
	print(decimal_isolate(35.345, 3))
	print(decimal_isolate(-14.789, 3))
	print(decimal_isolate(0, 2))
	print(decimal_isolate(-14.123, 1))
	print(decimal_isolate(-14.123, 2))
	print(decimal_isolate(-14.123, 3))






