


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



class 							_UpperCamelCase			(a_			):
      snake_case_  =							'ClapFeatureExtractor'
      snake_case_  =							('RobertaTokenizer', 'RobertaTokenizerFast')


      def __init__(	self					,						__UpperCamelCase					,						__UpperCamelCase	)->	Any:
           super().__init__(__UpperCamelCase					,						__UpperCamelCase	)


      def __call__(	self					,						__UpperCamelCase=None					,						__UpperCamelCase=None					,						__UpperCamelCase=None					,						**__UpperCamelCase	)->	Union[str, Any]:
           __lowerCAmelCase								=					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:
                __lowerCAmelCase								=					self.tokenizer(__UpperCamelCase					,						return_tensors=__UpperCamelCase					,						**__UpperCamelCase	)

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

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


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


      def  __UpperCAmelCase		(	self					,						*__UpperCamelCase					,						**__UpperCamelCase	)->	Dict:
           return self.tokenizer.decode(*__UpperCamelCase					,						**__UpperCamelCase	)


      @property
      def  __UpperCAmelCase		(	self	)->	Optional[int]:
           __lowerCAmelCase								=					self.tokenizer.model_input_names
           __lowerCAmelCase								=					self.feature_extractor.model_input_names
           return list(dict.fromkeys(tokenizer_input_names + feature_extractor_input_names	)	)














lowerCamelCase			:					str         =	'''
# Transformers 설치 방법
! pip install transformers datasets
# 마지막 릴리스 대신 소스에서 설치하려면, 위 명령을 주석으로 바꾸고 아래 명령을 해제하세요.
# ! pip install git+https://github.com/huggingface/transformers.git
'''

lowerCamelCase			:					Tuple         =	[{'''type''': '''code''', '''content''': INSTALL_CONTENT}]
lowerCamelCase			:					Dict         =	{
    '''{processor_class}''': '''FakeProcessorClass''',
    '''{model_class}''': '''FakeModelClass''',
    '''{object_class}''': '''FakeObjectClass''',
}






