


'''simple docstring'''

from ...processing_utils import ProcessorMixin
class     __lowerCAmelCase		(lowercase_	):





      '''simple docstring'''






      lowerCAmelCase__   :	int       =				"""SpeechT5FeatureExtractor"""
      lowerCAmelCase__   :	Dict       =				"""SpeechT5Tokenizer"""



      def __init__(self       :       Any  ,		UpperCamelCase       :       Any  ,		UpperCamelCase       :       str					):

             '''simple docstring'''







             super().__init__(UpperCamelCase  ,		UpperCamelCase					)



      def __call__(self       :       int  ,		*UpperCamelCase       :       Union[str, Any]  ,		**UpperCamelCase       :       Optional[int]					):

             '''simple docstring'''







             lowercase__      =					kwargs.pop('''audio'''  ,		UpperCamelCase					)
             lowercase__      =					kwargs.pop('''text'''  ,		UpperCamelCase					)
             lowercase__      =					kwargs.pop('''text_target'''  ,		UpperCamelCase					)
             lowercase__      =					kwargs.pop('''audio_target'''  ,		UpperCamelCase					)
             lowercase__      =					kwargs.pop('''sampling_rate'''  ,		UpperCamelCase					)

             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:
                    lowercase__      =					self.feature_extractor(UpperCamelCase  ,		*UpperCamelCase  ,		sampling_rate=UpperCamelCase  ,		**UpperCamelCase					)
             elif text is not None:
                    lowercase__      =					self.tokenizer(UpperCamelCase  ,		**UpperCamelCase					)
             else:
                    lowercase__      =					None

             if audio_target is not None:
                    lowercase__      =					self.feature_extractor(audio_target=UpperCamelCase  ,		*UpperCamelCase  ,		sampling_rate=UpperCamelCase  ,		**UpperCamelCase					)
                    lowercase__      =					targets['''input_values''']
             elif text_target is not None:
                    lowercase__      =					self.tokenizer(UpperCamelCase  ,		**UpperCamelCase					)
                    lowercase__      =					targets['''input_ids''']
             else:
                    lowercase__      =					None

             if inputs is None:
                    return targets

             if targets is not None:
                    lowercase__      =					labels

                    lowercase__      =					targets.get('''attention_mask'''					)
                    if decoder_attention_mask is not None:
                           lowercase__      =					decoder_attention_mask

             return inputs



      def  UpperCamelCase__						(self       :       Tuple  ,		*UpperCamelCase       :       str  ,		**UpperCamelCase       :       Dict					):

             '''simple docstring'''







             lowercase__      =					kwargs.pop('''input_values'''  ,		UpperCamelCase					)
             lowercase__      =					kwargs.pop('''input_ids'''  ,		UpperCamelCase					)
             lowercase__      =					kwargs.pop('''labels'''  ,		UpperCamelCase					)

             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:
                    lowercase__      =					self.feature_extractor.pad(UpperCamelCase  ,		*UpperCamelCase  ,		**UpperCamelCase					)
             elif input_ids is not None:
                    lowercase__      =					self.tokenizer.pad(UpperCamelCase  ,		**UpperCamelCase					)
             else:
                    lowercase__      =					None

             if labels is not None:
                    if "input_ids" in labels or (isinstance(UpperCamelCase  ,		UpperCamelCase					) and "input_ids" in labels[0]):
                           lowercase__      =					self.tokenizer.pad(UpperCamelCase  ,		**UpperCamelCase					)
                           lowercase__      =					targets['''input_ids''']
                    else:
                           lowercase__      =					self.feature_extractor.feature_size
                           lowercase__      =					self.feature_extractor.num_mel_bins
                           lowercase__      =					self.feature_extractor.pad(UpperCamelCase  ,		*UpperCamelCase  ,		**UpperCamelCase					)
                           lowercase__      =					feature_size_hack
                           lowercase__      =					targets['''input_values''']
             else:
                    lowercase__      =					None

             if inputs is None:
                    return targets

             if targets is not None:
                    lowercase__      =					labels

                    lowercase__      =					targets.get('''attention_mask'''					)
                    if decoder_attention_mask is not None:
                           lowercase__      =					decoder_attention_mask

             return inputs



      def  UpperCamelCase__						(self       :       List[str]  ,		*UpperCamelCase       :       Optional[int]  ,		**UpperCamelCase       :       Optional[int]					):

             '''simple docstring'''







             return self.tokenizer.batch_decode(*UpperCamelCase  ,		**UpperCamelCase					)






      def  UpperCamelCase__						(self       :       List[Any]  ,		*UpperCamelCase       :       int  ,		**UpperCamelCase       :       Optional[int]					):

             '''simple docstring'''







             return self.tokenizer.decode(*UpperCamelCase  ,		**UpperCamelCase					)








'''simple docstring'''

def _SCREAMING_SNAKE_CASE (A    ,					A    ,					A			)			->			list:






       """simple docstring"""







       lowercase__      =					len(A			)
       lowercase__      =					[[0] * n for i in range(A			)]
       for i in range(A			):
              lowercase__      =					y_points[i]

       for i in range(2    ,					A			):
              for j in range(A    ,					A			):
                     lowercase__      =					(
                         (xa - x_points[j - i + 1]) * q[j][i - 1]
                         - (xa - x_points[j]) * q[j - 1][i - 1]
                     ) / (x_points[j] - x_points[j - i + 1])

       return [q[n - 1][n - 1], q]


if __name__ == "__main__":
 import doctest

 doctest.testmod()




