


'''simple docstring'''







from ...processing_utils import ProcessorMixin


class  lowerCAmelCase_						(       snake_case__      ):





  """simple docstring"""



  a_       :List[Any]												="""SpeechT5FeatureExtractor"""
  a_       :Dict												="""SpeechT5Tokenizer"""

  def __init__(					self     :							int				,							SCREAMING_SNAKE_CASE__     :							List[str]				,							SCREAMING_SNAKE_CASE__     :							Union[str, Any]						):





       '''simple docstring'''

       super().__init__(SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__						)

  def __call__(					self     :							Union[str, Any]				,							*SCREAMING_SNAKE_CASE__     :							Optional[Any]				,							**SCREAMING_SNAKE_CASE__     :							Tuple						):





       '''simple docstring'''

       __a       =   kwargs.pop("""audio"""				,							SCREAMING_SNAKE_CASE__						)
       __a       =   kwargs.pop("""text"""				,							SCREAMING_SNAKE_CASE__						)
       __a       =   kwargs.pop("""text_target"""				,							SCREAMING_SNAKE_CASE__						)
       __a       =   kwargs.pop("""audio_target"""				,							SCREAMING_SNAKE_CASE__						)
       __a       =   kwargs.pop("""sampling_rate"""				,							SCREAMING_SNAKE_CASE__						)

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

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

       if inputs is None:
            return targets

       if targets is not None:
            __a       =   labels

            __a       =   targets.get("""attention_mask"""						)
            if decoder_attention_mask is not None:
                 __a       =   decoder_attention_mask

       return inputs

  def __a						(					self     :							int				,							*SCREAMING_SNAKE_CASE__     :							List[str]				,							**SCREAMING_SNAKE_CASE__     :							List[Any]						):





       '''simple docstring'''

       __a       =   kwargs.pop("""input_values"""				,							SCREAMING_SNAKE_CASE__						)
       __a       =   kwargs.pop("""input_ids"""				,							SCREAMING_SNAKE_CASE__						)
       __a       =   kwargs.pop("""labels"""				,							SCREAMING_SNAKE_CASE__						)

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

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

       if inputs is None:
            return targets

       if targets is not None:
            __a       =   labels

            __a       =   targets.get("""attention_mask"""						)
            if decoder_attention_mask is not None:
                 __a       =   decoder_attention_mask

       return inputs

  def __a						(					self     :							Union[str, Any]				,							*SCREAMING_SNAKE_CASE__     :							Any				,							**SCREAMING_SNAKE_CASE__     :							Optional[Any]						):





       '''simple docstring'''

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


  def __a						(					self     :							int				,							*SCREAMING_SNAKE_CASE__     :							str				,							**SCREAMING_SNAKE_CASE__     :							Tuple						):





       '''simple docstring'''

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










'''simple docstring'''







def        __lowercase						(      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE		)				->					str:
     """simple docstring"""




     __a       =   """"""
     for word_or_phrase in separated:
          if not isinstance(__SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE		):
               raise Exception("""join() accepts only strings to be joined"""		)
          joined += word_or_phrase + separator
     return joined.strip(__SCREAMING_SNAKE_CASE		)


if __name__ == "__main__":
 from doctest import testmod

 testmod()






