






"""simple docstring"""






from ...processing_utils import ProcessorMixin





class       snake_case_       (    _lowerCamelCase       ):



	"""simple docstring"""

	SCREAMING_SNAKE_CASE_:							Union[str, Any]   			=					"""SpeechT5FeatureExtractor"""
	SCREAMING_SNAKE_CASE_:							List[Any]   			=					"""SpeechT5Tokenizer"""


	def __init__(      self  ,					__a  ,					__a						):


					"""simple docstring"""

					super().__init__(__a  ,					__a						)


	def __call__(      self  ,					*__a  ,					**__a						):


					"""simple docstring"""

					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       _UpperCAmelCase						(      self  ,					*__a  ,					**__a						):


					"""simple docstring"""

					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       _UpperCAmelCase						(      self  ,					*__a  ,					**__a						):


					"""simple docstring"""

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






	def       _UpperCAmelCase						(      self  ,					*__a  ,					**__a						):


					"""simple docstring"""

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










"""simple docstring"""






from math import ceil



def  __lowerCamelCase					(					lowerCAmelCase__       ,lowerCAmelCase__				):
				A__							 =       list(range(0       ,lowerCAmelCase__				)				)

				A__							 =       [item for sublist in list(device_map.values()				) for item in sublist]

				# Duplicate check
				A__							 =       []
				for i in device_map_blocks:
								if device_map_blocks.count(lowerCAmelCase__				) > 1 and i not in duplicate_blocks:
												duplicate_blocks.append(lowerCAmelCase__				)
    # Missing blocks
				A__							 =       [i for i in blocks if i not in device_map_blocks]
				A__							 =       [i for i in device_map_blocks if i not in blocks]

				if len(lowerCAmelCase__				) != 0:
								raise ValueError(
								    'Duplicate attention blocks specified in device_map. Attention blocks must be specified to one device.'
								    ' These attention blocks were specified more than once: ' + str(lowerCAmelCase__				)				)
				if len(lowerCAmelCase__				) != 0:
								raise ValueError(
								    'There are attention blocks for this model that are not specified in the device_map. Add these attention '
								    'blocks to a device on the device_map: ' + str(lowerCAmelCase__				)				)
				if len(lowerCAmelCase__				) != 0:
								raise ValueError(
								    'The device_map contains more attention blocks than this model has. Remove these from the device_map:'
								    + str(lowerCAmelCase__				)				)







def  __lowerCamelCase					(					lowerCAmelCase__       ,lowerCAmelCase__				):
				A__							 =       list(range(lowerCAmelCase__				)				)
				A__							 =       int(ceil(n_layers / len(lowerCAmelCase__				)				)				)
				A__							 =       [layers[i : i + n_blocks] for i in range(0       ,lowerCAmelCase__       ,lowerCAmelCase__				)]

				return dict(zip(lowerCAmelCase__       ,lowerCAmelCase__				)				)


