


'''simple docstring'''






from ...processing_utils import ProcessorMixin




class 	a__       (							UpperCAmelCase_						):





		'''simple docstring'''






		lowercase__    :     Tuple					=   "SpeechT5FeatureExtractor"
		lowercase__    :     int					=   "SpeechT5Tokenizer"







		def __init__(	self      ,					lowerCamelCase_      ,					lowerCamelCase_     ) ->							str:
									super().__init__(_snake_case      ,					_snake_case     )







		def __call__(	self      ,					*lowerCamelCase_      ,					**lowerCamelCase_     ) ->							Optional[int]:
									lowerCAmelCase__      =			kwargs.pop('''audio'''      ,					_snake_case     )
									lowerCAmelCase__      =			kwargs.pop('''text'''      ,					_snake_case     )
									lowerCAmelCase__      =			kwargs.pop('''text_target'''      ,					_snake_case     )
									lowerCAmelCase__      =			kwargs.pop('''audio_target'''      ,					_snake_case     )
									lowerCAmelCase__      =			kwargs.pop('''sampling_rate'''      ,					_snake_case     )

									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:
																lowerCAmelCase__      =			self.feature_extractor(_snake_case      ,					*_snake_case      ,					sampling_rate=_snake_case      ,					**_snake_case     )
									elif text is not None:
																lowerCAmelCase__      =			self.tokenizer(_snake_case      ,					**_snake_case     )
									else:
																lowerCAmelCase__      =			None

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

									if inputs is None:
																return targets

									if targets is not None:
																lowerCAmelCase__      =			labels

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

									return inputs







		def 						__SCREAMING_SNAKE_CASE							(	self      ,					*lowerCamelCase_      ,					**lowerCamelCase_     ) ->							Optional[int]:
									lowerCAmelCase__      =			kwargs.pop('''input_values'''      ,					_snake_case     )
									lowerCAmelCase__      =			kwargs.pop('''input_ids'''      ,					_snake_case     )
									lowerCAmelCase__      =			kwargs.pop('''labels'''      ,					_snake_case     )

									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:
																lowerCAmelCase__      =			self.feature_extractor.pad(_snake_case      ,					*_snake_case      ,					**_snake_case     )
									elif input_ids is not None:
																lowerCAmelCase__      =			self.tokenizer.pad(_snake_case      ,					**_snake_case     )
									else:
																lowerCAmelCase__      =			None

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

									if inputs is None:
																return targets

									if targets is not None:
																lowerCAmelCase__      =			labels

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

									return inputs







		def 						__SCREAMING_SNAKE_CASE							(	self      ,					*lowerCamelCase_      ,					**lowerCamelCase_     ) ->							Optional[Any]:
									return self.tokenizer.batch_decode(*_snake_case      ,					**_snake_case     )







		def 						__SCREAMING_SNAKE_CASE							(	self      ,					*lowerCamelCase_      ,					**lowerCamelCase_     ) ->							Optional[int]:
									return self.tokenizer.decode(*_snake_case      ,					**_snake_case     )



'''simple docstring'''






def       _snake_case     (  A )		->					bool:
							if not isinstance(A				,					A ):
														raise ValueError('''Input series is not valid, valid series - [2, 4, 6]''' )
							if len(A ) == 0:
														raise ValueError('''Input list must be a non empty list''' )
							if len(A ) == 1:
														return True
							lowerCAmelCase__      =			series[1] - series[0]
							for index in range(len(A ) - 1 ):
														if series[index + 1] - series[index] != common_diff:
																					return False
							return True


def       _snake_case     (  A )		->					float:
							if not isinstance(A				,					A ):
														raise ValueError('''Input series is not valid, valid series - [2, 4, 6]''' )
							if len(A ) == 0:
														raise ValueError('''Input list must be a non empty list''' )
							lowerCAmelCase__      =			0
							for val in series:
														answer += val
							return answer / len(A )


if __name__ == "__main__":
		import doctest

		doctest.testmod()