


"""simple docstring"""







from ...processing_utils import ProcessorMixin







class  UpperCamelCase	(    a__						):
   lowercase							=						"SpeechT5FeatureExtractor"
   lowercase							=						"SpeechT5Tokenizer"
   def __init__(    self  ,__UpperCamelCase  ,__UpperCamelCase    )			->   Optional[Any]:


        '''simple docstring'''


        super().__init__(lowerCamelCase_  ,lowerCamelCase_    )
   def __call__(    self  ,*__UpperCamelCase  ,**__UpperCamelCase    )			->   Dict:


        '''simple docstring'''


        lowercase_			:       Tuple									=			kwargs.pop('audio'  ,lowerCamelCase_    )
        lowercase_			:       Dict									=			kwargs.pop('text'  ,lowerCamelCase_    )
        lowercase_			:       Union[str, Any]									=			kwargs.pop('text_target'  ,lowerCamelCase_    )
        lowercase_			:       Optional[int]									=			kwargs.pop('audio_target'  ,lowerCamelCase_    )
        lowercase_			:       Dict									=			kwargs.pop('sampling_rate'  ,lowerCamelCase_    )

        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_			:       Union[str, Any]									=			self.feature_extractor(lowerCamelCase_  ,*lowerCamelCase_  ,sampling_rate=lowerCamelCase_  ,**lowerCamelCase_    )
        elif text is not None:
             lowercase_			:       Union[str, Any]									=			self.tokenizer(lowerCamelCase_  ,**lowerCamelCase_    )
        else:
             lowercase_			:       Union[str, Any]									=			None

        if audio_target is not None:
             lowercase_			:       Dict									=			self.feature_extractor(audio_target=lowerCamelCase_  ,*lowerCamelCase_  ,sampling_rate=lowerCamelCase_  ,**lowerCamelCase_    )
             lowercase_			:       int									=			targets['input_values']
        elif text_target is not None:
             lowercase_			:       Optional[int]									=			self.tokenizer(lowerCamelCase_  ,**lowerCamelCase_    )
             lowercase_			:       Dict									=			targets['input_ids']
        else:
             lowercase_			:       Tuple									=			None

        if inputs is None:
             return targets

        if targets is not None:
             lowercase_			:       str									=			labels

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

        return inputs
   def     _UpperCAmelCase     (    self  ,*__UpperCamelCase  ,**__UpperCamelCase    )			->   Union[str, Any]:


        '''simple docstring'''


        lowercase_			:       Any									=			kwargs.pop('input_values'  ,lowerCamelCase_    )
        lowercase_			:       str									=			kwargs.pop('input_ids'  ,lowerCamelCase_    )
        lowercase_			:       Union[str, Any]									=			kwargs.pop('labels'  ,lowerCamelCase_    )

        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_			:       List[str]									=			self.feature_extractor.pad(lowerCamelCase_  ,*lowerCamelCase_  ,**lowerCamelCase_    )
        elif input_ids is not None:
             lowercase_			:       Tuple									=			self.tokenizer.pad(lowerCamelCase_  ,**lowerCamelCase_    )
        else:
             lowercase_			:       List[str]									=			None

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

        if inputs is None:
             return targets

        if targets is not None:
             lowercase_			:       str									=			labels

             lowercase_			:       Tuple									=			targets.get('attention_mask'    )
             if decoder_attention_mask is not None:
                  lowercase_			:       Any									=			decoder_attention_mask

        return inputs
   def     _UpperCAmelCase     (    self  ,*__UpperCamelCase  ,**__UpperCamelCase    )			->   Dict:


        '''simple docstring'''


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







   def     _UpperCAmelCase     (    self  ,*__UpperCamelCase  ,**__UpperCamelCase    )			->   Union[str, Any]:


        '''simple docstring'''


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


"""simple docstring"""







__SCREAMING_SNAKE_CASE								="ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/"



def     lowercase__( __SCREAMING_SNAKE_CASE							:  bytes   ):
     # Make sure the supplied data is a bytes-like object
     if not isinstance(__SCREAMING_SNAKE_CASE  ,    __SCREAMING_SNAKE_CASE   ):
          lowercase_			:       Dict									=			F'''a bytes-like object is required, not \'{data.__class__.__name__}\''''
          raise TypeError(__SCREAMING_SNAKE_CASE   )

     lowercase_			:       List[Any]									=			''.join(bin(__SCREAMING_SNAKE_CASE   )[2:].zfill(8   ) for byte in data   )

     lowercase_			:       List[Any]									=			len(__SCREAMING_SNAKE_CASE   ) % 6 != 0

     if padding_needed:
          # The padding that will be added later
          lowercase_			:       Optional[int]									=			b'=' * ((6 - len(__SCREAMING_SNAKE_CASE   ) % 6) // 2)

          # Append binary_stream with arbitrary binary digits (0's by default) to make its
          # length a multiple of 6.
          binary_stream += "0" * (6 - len(__SCREAMING_SNAKE_CASE   ) % 6)
     else:
          lowercase_			:       List[Any]									=			b''

     # Encode every 6 binary digits to their corresponding Base64 character
     return (
         "".join(
             B64_CHARSET[int(binary_stream[index : index + 6]  ,    2   )]
             for index in range(0  ,    len(__SCREAMING_SNAKE_CASE   )  ,    6   )   ).encode()
         + padding
     )



def     lowercase__( __SCREAMING_SNAKE_CASE							:  str   ):
     # Make sure encoded_data is either a string or a bytes-like object
     if not isinstance(__SCREAMING_SNAKE_CASE  ,    __SCREAMING_SNAKE_CASE   ) and not isinstance(__SCREAMING_SNAKE_CASE  ,    __SCREAMING_SNAKE_CASE   ):
          lowercase_			:       Tuple									=			(
              'argument should be a bytes-like object or ASCII string, '
              F'''not \'{encoded_data.__class__.__name__}\''''
          )
          raise TypeError(__SCREAMING_SNAKE_CASE   )

     # In case encoded_data is a bytes-like object, make sure it contains only
     # ASCII characters so we convert it to a string object
     if isinstance(__SCREAMING_SNAKE_CASE  ,    __SCREAMING_SNAKE_CASE   ):
          try:
               lowercase_			:       List[str]									=			encoded_data.decode('utf-8'   )
          except UnicodeDecodeError:
               raise ValueError('base64 encoded data should only contain ASCII characters'   )

     lowercase_			:       List[str]									=			encoded_data.count('='   )

     # Check if the encoded string contains non base64 characters
     if padding:
          assert all(
              char in B64_CHARSET for char in encoded_data[:-padding]   ), "Invalid base64 character(s) found."
     else:
          assert all(
              char in B64_CHARSET for char in encoded_data   ), "Invalid base64 character(s) found."

     # Check the padding
     assert len(__SCREAMING_SNAKE_CASE   ) % 4 == 0 and padding < 3, "Incorrect padding"

     if padding:
          # Remove padding if there is one
          lowercase_			:       Union[str, Any]									=			encoded_data[:-padding]

          lowercase_			:       List[Any]									=			''.join(
              bin(B64_CHARSET.index(__SCREAMING_SNAKE_CASE   )   )[2:].zfill(6   ) for char in encoded_data   )[: -padding * 2]
     else:
          lowercase_			:       int									=			''.join(
              bin(B64_CHARSET.index(__SCREAMING_SNAKE_CASE   )   )[2:].zfill(6   ) for char in encoded_data   )

     lowercase_			:       str									=			[
         int(binary_stream[index : index + 8]  ,    2   )
         for index in range(0  ,    len(__SCREAMING_SNAKE_CASE   )  ,    8   )
     ]

     return bytes(__SCREAMING_SNAKE_CASE   )


if __name__ == "__main__":
       import doctest

       doctest.testmod()

