






from ...processing_utils import ProcessorMixin



class   lowerCamelCase      (lowerCAmelCase_			):







		'''simple docstring'''



		_snake_case    :    List[str]       						=  '''SpeechT5FeatureExtractor'''
		_snake_case    :    List[Any]       						=  '''SpeechT5Tokenizer'''



		def __init__(							self				,		_UpperCamelCase				,		_UpperCamelCase   )	->							int:
							super().__init__(__snake_case				,		__snake_case   )



		def __call__(							self				,		*_UpperCamelCase				,		**_UpperCamelCase   )	->							List[Any]:
							UpperCAmelCase_    :      Dict            =						kwargs.pop('audio'				,		__snake_case   )
							UpperCAmelCase_    :      Any            =						kwargs.pop('text'				,		__snake_case   )
							UpperCAmelCase_    :      Optional[int]            =						kwargs.pop('text_target'				,		__snake_case   )
							UpperCAmelCase_    :      Optional[Any]            =						kwargs.pop('audio_target'				,		__snake_case   )
							UpperCAmelCase_    :      Union[str, Any]            =						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:
												UpperCAmelCase_    :      Tuple            =						self.feature_extractor(__snake_case				,		*__snake_case				,		sampling_rate=__snake_case				,		**__snake_case   )
							elif text is not None:
												UpperCAmelCase_    :      Optional[int]            =						self.tokenizer(__snake_case				,		**__snake_case   )
							else:
												UpperCAmelCase_    :      int            =						None

							if audio_target is not None:
												UpperCAmelCase_    :      Union[str, Any]            =						self.feature_extractor(audio_target=__snake_case				,		*__snake_case				,		sampling_rate=__snake_case				,		**__snake_case   )
												UpperCAmelCase_    :      Optional[Any]            =						targets['input_values']
							elif text_target is not None:
												UpperCAmelCase_    :      Any            =						self.tokenizer(__snake_case				,		**__snake_case   )
												UpperCAmelCase_    :      Union[str, Any]            =						targets['input_ids']
							else:
												UpperCAmelCase_    :      Tuple            =						None

							if inputs is None:
												return targets

							if targets is not None:
												UpperCAmelCase_    :      Union[str, Any]            =						labels

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

							return inputs



		def 					__UpperCAmelCase			(							self				,		*_UpperCamelCase				,		**_UpperCamelCase   )	->							Dict:
							UpperCAmelCase_    :      Optional[int]            =						kwargs.pop('input_values'				,		__snake_case   )
							UpperCAmelCase_    :      Dict            =						kwargs.pop('input_ids'				,		__snake_case   )
							UpperCAmelCase_    :      Dict            =						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:
												UpperCAmelCase_    :      List[str]            =						self.feature_extractor.pad(__snake_case				,		*__snake_case				,		**__snake_case   )
							elif input_ids is not None:
												UpperCAmelCase_    :      Dict            =						self.tokenizer.pad(__snake_case				,		**__snake_case   )
							else:
												UpperCAmelCase_    :      Any            =						None

							if labels is not None:
												if "input_ids" in labels or (isinstance(__snake_case				,		__snake_case   ) and "input_ids" in labels[0]):
																	UpperCAmelCase_    :      int            =						self.tokenizer.pad(__snake_case				,		**__snake_case   )
																	UpperCAmelCase_    :      Optional[int]            =						targets['input_ids']
												else:
																	UpperCAmelCase_    :      str            =						self.feature_extractor.feature_size
																	UpperCAmelCase_    :      Optional[int]            =						self.feature_extractor.num_mel_bins
																	UpperCAmelCase_    :      Any            =						self.feature_extractor.pad(__snake_case				,		*__snake_case				,		**__snake_case   )
																	UpperCAmelCase_    :      Optional[Any]            =						feature_size_hack
																	UpperCAmelCase_    :      Tuple            =						targets['input_values']
							else:
												UpperCAmelCase_    :      Union[str, Any]            =						None

							if inputs is None:
												return targets

							if targets is not None:
												UpperCAmelCase_    :      Any            =						labels

												UpperCAmelCase_    :      List[Any]            =						targets.get('attention_mask'   )
												if decoder_attention_mask is not None:
																	UpperCAmelCase_    :      Dict            =						decoder_attention_mask

							return inputs



		def 					__UpperCAmelCase			(							self				,		*_UpperCamelCase				,		**_UpperCamelCase   )	->							Optional[int]:
							return self.tokenizer.batch_decode(*__snake_case				,		**__snake_case   )



		def 					__UpperCAmelCase			(							self				,		*_UpperCamelCase				,		**_UpperCamelCase   )	->							List[str]:
							return self.tokenizer.decode(*__snake_case				,		**__snake_case   )









from ...configuration_utils import PretrainedConfig
from ...utils import logging


_lowerCAmelCase					:			Tuple  				=			logging.get_logger(__name__)

_lowerCAmelCase					:			Union[str, Any]  				=			{"ctrl": "https://huggingface.co/ctrl/resolve/main/config.json"}






class 	__magic_name__    (			lowerCAmelCase_      ):
			SCREAMING_SNAKE_CASE							   =						'ctrl'
			SCREAMING_SNAKE_CASE							   =						['past_key_values']
			SCREAMING_SNAKE_CASE							   =						{
			    'max_position_embeddings': 'n_positions',
			    'hidden_size': 'n_embd',
			    'num_attention_heads': 'n_head',
			    'num_hidden_layers': 'n_layer',
			}





			def __init__(		self					,  __snake_case=24_6534					,  __snake_case=256					,  __snake_case=1280					,  __snake_case=8192					,  __snake_case=48					,  __snake_case=16					,  __snake_case=0.1					,  __snake_case=0.1					,  __snake_case=1e-6					,  __snake_case=0.02					,  __snake_case=True					,  **__snake_case					,  )   -> Union[str, Any]:




						'''simple docstring'''
						__a        =vocab_size
						__a        =n_positions
						__a        =n_embd
						__a        =n_layer
						__a        =n_head
						__a        =dff
						__a        =resid_pdrop
						__a        =embd_pdrop
						__a        =layer_norm_epsilon
						__a        =initializer_range

						__a        =use_cache

						super().__init__(**__snake_case					)






