



"""simple docstring"""







from ...processing_utils import ProcessorMixin

class       _a  ( SCREAMING_SNAKE_CASE__):
			__magic_name__     					=     """SpeechT5FeatureExtractor"""
			__magic_name__     					=     """SpeechT5Tokenizer"""



			def __init__(			self							:				Dict   ,	_lowercase							:				str   ,	_lowercase							:				Dict		)							->					List[str]:
									super().__init__(_lowercase   ,	_lowercase		)



			def __call__(			self							:				Optional[Any]   ,	*_lowercase							:				Optional[int]   ,	**_lowercase							:				Optional[Any]		)							->					int:
									snake_case   :			Dict						    = kwargs.pop("audio"   ,	_lowercase		)
									snake_case   :			Optional[Any]						    = kwargs.pop("text"   ,	_lowercase		)
									snake_case   :			str						    = kwargs.pop("text_target"   ,	_lowercase		)
									snake_case   :			int						    = kwargs.pop("audio_target"   ,	_lowercase		)
									snake_case   :			Optional[Any]						    = kwargs.pop("sampling_rate"   ,	_lowercase		)

									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:
															snake_case   :			Tuple						    = self.feature_extractor(_lowercase   ,	*_lowercase   ,	sampling_rate=_lowercase   ,	**_lowercase		)
									elif text is not None:
															snake_case   :			Optional[int]						    = self.tokenizer(_lowercase   ,	**_lowercase		)
									else:
															snake_case   :			List[Any]						    = None

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

									if inputs is None:
															return targets

									if targets is not None:
															snake_case   :			Any						    = labels

															snake_case   :			Optional[Any]						    = targets.get("attention_mask"		)
															if decoder_attention_mask is not None:
																					snake_case   :			Optional[int]						    = decoder_attention_mask

									return inputs



			def 				__lowercase					(			self							:				List[Any]   ,	*_lowercase							:				str   ,	**_lowercase							:				int		)							->					Optional[int]:
									snake_case   :			Union[str, Any]						    = kwargs.pop("input_values"   ,	_lowercase		)
									snake_case   :			Union[str, Any]						    = kwargs.pop("input_ids"   ,	_lowercase		)
									snake_case   :			int						    = kwargs.pop("labels"   ,	_lowercase		)

									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:
															snake_case   :			int						    = self.feature_extractor.pad(_lowercase   ,	*_lowercase   ,	**_lowercase		)
									elif input_ids is not None:
															snake_case   :			Optional[Any]						    = self.tokenizer.pad(_lowercase   ,	**_lowercase		)
									else:
															snake_case   :			Optional[Any]						    = None

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

									if inputs is None:
															return targets

									if targets is not None:
															snake_case   :			List[Any]						    = labels

															snake_case   :			Dict						    = targets.get("attention_mask"		)
															if decoder_attention_mask is not None:
																					snake_case   :			str						    = decoder_attention_mask

									return inputs



			def 				__lowercase					(			self							:				Optional[int]   ,	*_lowercase							:				int   ,	**_lowercase							:				Optional[int]		)							->					Union[str, Any]:
									return self.tokenizer.batch_decode(*_lowercase   ,	**_lowercase		)



			def 				__lowercase					(			self							:				Optional[int]   ,	*_lowercase							:				Tuple   ,	**_lowercase							:				Any		)							->					Dict:
									return self.tokenizer.decode(*_lowercase   ,	**_lowercase		)








"""simple docstring"""







A		=						8.31_4462  # Unit - J mol-1 K-1

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							float     ,		lowerCamelCase_:							float     ,		lowerCamelCase_:							float ):
						"""simple docstring"""






						if moles < 0 or kelvin < 0 or volume < 0:
												raise ValueError("Invalid inputs. Enter positive value." )
						return moles * kelvin * UNIVERSAL_GAS_CONSTANT / volume

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							float     ,		lowerCamelCase_:							float     ,		lowerCamelCase_:							float ):
						"""simple docstring"""






						if moles < 0 or kelvin < 0 or pressure < 0:
												raise ValueError("Invalid inputs. Enter positive value." )
						return moles * kelvin * UNIVERSAL_GAS_CONSTANT / pressure


if __name__ == "__main__":
			from doctest import testmod

			testmod()



