
"""simple docstring"""


from ...processing_utils import ProcessorMixin







class  lowerCAmelCase_ (					_lowercase			):



						'''simple docstring'''


						_lowerCamelCase:			Any					=			'''SpeechT5FeatureExtractor'''
						_lowerCamelCase:			List[Any]					=			'''SpeechT5Tokenizer'''


						def __init__(       self   :					Optional[Any]     ,A_   :					str     ,A_   :					List[str]					)   ->      Union[str, Any]:
							super().__init__(A_     ,A_					)


						def __call__(       self   :					List[str]     ,*A_   :					Any     ,**A_   :					Union[str, Any]					)   ->      Dict:
							A        =      kwargs.pop('audio'     ,A_					)
							A        =      kwargs.pop('text'     ,A_					)
							A        =      kwargs.pop('text_target'     ,A_					)
							A        =      kwargs.pop('audio_target'     ,A_					)
							A        =      kwargs.pop('sampling_rate'     ,A_					)

							if audio is not None and text is not None:
								raise ValueError(
								    'Cannot process both `audio` and `text` inputs. Did you mean `audio_target` or `text_target`?'					)
							if audio_target is not None and text_target is not None:
								raise ValueError(
								    'Cannot process both `audio_target` and `text_target` inputs. Did you mean `audio` or `text`?'					)
							if audio is None and audio_target is None and text is None and text_target is None:
								raise ValueError(
								    'You need to specify either an `audio`, `audio_target`, `text`, or `text_target` input to process.'					)

							if audio is not None:
								A        =      self.feature_extractor(A_     ,*A_     ,sampling_rate=A_     ,**A_					)
							elif text is not None:
								A        =      self.tokenizer(A_     ,**A_					)
							else:
								A        =      None

							if audio_target is not None:
								A        =      self.feature_extractor(audio_target=A_     ,*A_     ,sampling_rate=A_     ,**A_					)
								A        =      targets['input_values']
							elif text_target is not None:
								A        =      self.tokenizer(A_     ,**A_					)
								A        =      targets['input_ids']
							else:
								A        =      None

							if inputs is None:
								return targets

							if targets is not None:
								A        =      labels

								A        =      targets.get('attention_mask'					)
								if decoder_attention_mask is not None:
									A        =      decoder_attention_mask

							return inputs


						def   _SCREAMING_SNAKE_CASE					(       self   :					List[Any]     ,*A_   :					Optional[int]     ,**A_   :					Tuple					)   ->      int:
							A        =      kwargs.pop('input_values'     ,A_					)
							A        =      kwargs.pop('input_ids'     ,A_					)
							A        =      kwargs.pop('labels'     ,A_					)

							if input_values is not None and input_ids is not None:
								raise ValueError('Cannot process both `input_values` and `input_ids` inputs.'					)
							if input_values is None and input_ids is None and labels is None:
								raise ValueError(
								    'You need to specify either an `input_values`, `input_ids`, or `labels` input to be padded.'					)

							if input_values is not None:
								A        =      self.feature_extractor.pad(A_     ,*A_     ,**A_					)
							elif input_ids is not None:
								A        =      self.tokenizer.pad(A_     ,**A_					)
							else:
								A        =      None

							if labels is not None:
								if "input_ids" in labels or (isinstance(A_     ,A_					) and "input_ids" in labels[0]):
									A        =      self.tokenizer.pad(A_     ,**A_					)
									A        =      targets['input_ids']
								else:
									A        =      self.feature_extractor.feature_size
									A        =      self.feature_extractor.num_mel_bins
									A        =      self.feature_extractor.pad(A_     ,*A_     ,**A_					)
									A        =      feature_size_hack
									A        =      targets['input_values']
							else:
								A        =      None

							if inputs is None:
								return targets

							if targets is not None:
								A        =      labels

								A        =      targets.get('attention_mask'					)
								if decoder_attention_mask is not None:
									A        =      decoder_attention_mask

							return inputs


						def   _SCREAMING_SNAKE_CASE					(       self   :					int     ,*A_   :					str     ,**A_   :					Any					)   ->      Dict:
							return self.tokenizer.batch_decode(*A_     ,**A_					)




						def   _SCREAMING_SNAKE_CASE					(       self   :					Dict     ,*A_   :					Any     ,**A_   :					Any					)   ->      Dict:
							return self.tokenizer.decode(*A_     ,**A_					)



'''simple docstring'''
def 			a      (			__a ,					__a   )  ->	int:



					'''simple docstring'''






					if len(__a   ) != len(__a   ):
										raise ValueError('''String lengths must match!'''   )

					UpperCamelCase__   :Union[str, Any]												= 0

					for chara, chara in zip(__a ,					__a   ):
										if chara != chara:
															count += 1

					return count


if __name__ == "__main__":
		import doctest

		doctest.testmod()