



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






class SCREAMING_SNAKE_CASE__					(		SCREAMING_SNAKE_CASE__   ):




    '''simple docstring'''






    __lowerCamelCase      :			Dict =				"ClapFeatureExtractor"
    __lowerCamelCase      :			Any =				("RobertaTokenizer", "RobertaTokenizerFast")






    def __init__(	self,		lowerCamelCase__,		lowerCamelCase__		):
        super().__init__(lowerCamelCase__,		lowerCamelCase__		)






    def __call__(	self,		lowerCamelCase__=None,		lowerCamelCase__=None,		lowerCamelCase__=None,		**lowerCamelCase__		):
        A			:	Optional[int]										=       kwargs.pop("""sampling_rate""",		lowerCamelCase__		)

        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:
            A			:	Any										=       self.tokenizer(lowerCamelCase__,		return_tensors=lowerCamelCase__,		**lowerCamelCase__		)

        if audios is not None:
            A			:	Dict										=       self.feature_extractor(
                lowerCamelCase__,		sampling_rate=lowerCamelCase__,		return_tensors=lowerCamelCase__,		**lowerCamelCase__		)

        if text is not None and audios is not None:
            A			:	List[Any]										=       audio_features.input_features
            return encoding
        elif text is not None:
            return encoding
        else:
            return BatchEncoding(data=dict(**lowerCamelCase__		),		tensor_type=lowerCamelCase__		)






    def       _lowerCAmelCase (	self,		*lowerCamelCase__,		**lowerCamelCase__		):
        return self.tokenizer.batch_decode(*lowerCamelCase__,		**lowerCamelCase__		)






    def       _lowerCAmelCase (	self,		*lowerCamelCase__,		**lowerCamelCase__		):
        return self.tokenizer.decode(*lowerCamelCase__,		**lowerCamelCase__		)






    @property
    def       _lowerCAmelCase (	self		):
        A			:	List[Any]										=       self.tokenizer.model_input_names
        A			:	Dict										=       self.feature_extractor.model_input_names
        return list(dict.fromkeys(tokenizer_input_names + feature_extractor_input_names		)		)







from __future__ import annotations




def      __UpperCamelCase     (  _lowerCAmelCase		)	->  list[int]:



    """simple docstring"""






    A			:	Tuple										=       2
    A			:	List[Any]										=       []
    while i * i <= n:
        if n % i:
            i += 1
        else:
            n //= i
            factors.append(_lowerCAmelCase		)
    if n > 1:
        factors.append(_lowerCAmelCase		)
    return factors


if __name__ == "__main__":
     import doctest

     doctest.testmod()


