



"""simple docstring"""

from ...processing_utils import ProcessorMixin




class        _UpperCAmelCase       (			lowerCAmelCase__):
 _lowerCAmelCase   :			List[str]							=		"""SpeechT5FeatureExtractor"""
 _lowerCAmelCase   :			Optional[Any]							=		"""SpeechT5Tokenizer"""

 def __init__(     self		:       List[Any]  ,     lowercase_		:       Any  ,     lowercase_		:       Tuple				):
     super().__init__(lowercase_  ,     lowercase_				)

 def __call__(     self		:       Optional[int]  ,     *lowercase_		:       List[Any]  ,     **lowercase_		:       List[str]				):
     snake_case_ :     Optional[Any]				=	kwargs.pop('''audio'''  ,     lowercase_				)
     snake_case_ :     Dict				=	kwargs.pop('''text'''  ,     lowercase_				)
     snake_case_ :     Dict				=	kwargs.pop('''text_target'''  ,     lowercase_				)
     snake_case_ :     Dict				=	kwargs.pop('''audio_target'''  ,     lowercase_				)
     snake_case_ :     Union[str, 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_ :     str				=	self.feature_extractor(lowercase_  ,     *lowercase_  ,     sampling_rate=lowercase_  ,     **lowercase_				)
     elif text is not None:
         snake_case_ :     Optional[Any]				=	self.tokenizer(lowercase_  ,     **lowercase_				)
     else:
         snake_case_ :     int				=	None

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

     if inputs is None:
         return targets

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

         snake_case_ :     Tuple				=	targets.get('''attention_mask'''				)
         if decoder_attention_mask is not None:
             snake_case_ :     List[str]				=	decoder_attention_mask

     return inputs

 def 						_snake_case					(     self		:       Tuple  ,     *lowercase_		:       str  ,     **lowercase_		:       List[Any]				):
     snake_case_ :     List[Any]				=	kwargs.pop('''input_values'''  ,     lowercase_				)
     snake_case_ :     Dict				=	kwargs.pop('''input_ids'''  ,     lowercase_				)
     snake_case_ :     List[str]				=	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_ :     Tuple				=	self.feature_extractor.pad(lowercase_  ,     *lowercase_  ,     **lowercase_				)
     elif input_ids is not None:
         snake_case_ :     Tuple				=	self.tokenizer.pad(lowercase_  ,     **lowercase_				)
     else:
         snake_case_ :     int				=	None

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

     if inputs is None:
         return targets

     if targets is not None:
         snake_case_ :     str				=	labels

         snake_case_ :     Optional[Any]				=	targets.get('''attention_mask'''				)
         if decoder_attention_mask is not None:
             snake_case_ :     Tuple				=	decoder_attention_mask

     return inputs

 def 						_snake_case					(     self		:       str  ,     *lowercase_		:       Optional[Any]  ,     **lowercase_		:       Union[str, Any]				):
     return self.tokenizer.batch_decode(*lowercase_  ,     **lowercase_				)






 def 						_snake_case					(     self		:       List[Any]  ,     *lowercase_		:       int  ,     **lowercase_		:       Dict				):
     return self.tokenizer.decode(*lowercase_  ,     **lowercase_				)










"""simple docstring"""

from functools import lru_cache




@lru_cache
def __lowercase			(							_a    ):
    if num < 0:
        raise ValueError('''Number should not be negative.'''    )

    return 1 if num in (0, 1) else num * factorial(num - 1    )


if __name__ == "__main__":
     import doctest

     doctest.testmod()





