





"""simple docstring"""





from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding





class        _lowerCamelCase      (       _UpperCamelCase ):
						UpperCAmelCase_ =			"ClapFeatureExtractor"
						UpperCAmelCase_ =			("RobertaTokenizer", "RobertaTokenizerFast")


						def __init__(self   ,							__a   ,							__a		) ->   Optional[Any]:
								super().__init__(_UpperCAmelCase   ,							_UpperCAmelCase		)


						def __call__(self   ,							__a=None   ,							__a=None   ,							__a=None   ,							**__a		) ->   Dict:
								UpperCamelCase   =	kwargs.pop("sampling_rate"   ,							_UpperCAmelCase		)

								if text is None and audios is None:
										raise ValueError("You have to specify either text or audios. Both cannot be none."		)

								if text is not None:
										UpperCamelCase   =	self.tokenizer(_UpperCAmelCase   ,							return_tensors=_UpperCAmelCase   ,							**_UpperCAmelCase		)

								if audios is not None:
										UpperCamelCase   =	self.feature_extractor(
										    _UpperCAmelCase   ,							sampling_rate=_UpperCAmelCase   ,							return_tensors=_UpperCAmelCase   ,							**_UpperCAmelCase		)

								if text is not None and audios is not None:
										UpperCamelCase   =	audio_features.input_features
										return encoding
								elif text is not None:
										return encoding
								else:
										return BatchEncoding(data=dict(**_UpperCAmelCase		)   ,							tensor_type=_UpperCAmelCase		)


						def     snake_case_   (self   ,							*__a   ,							**__a		) ->   Optional[Any]:
								return self.tokenizer.batch_decode(*_UpperCAmelCase   ,							**_UpperCAmelCase		)


						def     snake_case_   (self   ,							*__a   ,							**__a		) ->   Optional[Any]:
								return self.tokenizer.decode(*_UpperCAmelCase   ,							**_UpperCAmelCase		)




						@property
						def     snake_case_   (self		) ->   List[Any]:
								UpperCamelCase   =	self.tokenizer.model_input_names
								UpperCamelCase   =	self.feature_extractor.model_input_names
								return list(dict.fromkeys(tokenizer_input_names + feature_extractor_input_names		)		)













"""simple docstring"""





from diffusers.utils.testing_utils import require_onnxruntime





@require_onnxruntime
class        _lowerCamelCase      :
						pass






