
lowercase_					     =	"""ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/"""




def     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	)       ->     List[str]:
  # Make sure the supplied data is a bytes-like object
  if not isinstance(_lowerCAmelCase	,		_lowerCAmelCase	):
    lowercase__			    =						F"""a bytes-like object is required, not '{data.__class__.__name__}'"""
    raise TypeError(_lowerCAmelCase	)

  lowercase__			    =						''.join(bin(_lowerCAmelCase	)[2:].zfill(8	) for byte in data	)

  lowercase__			    =						len(_lowerCAmelCase	) % 6 != 0

  if padding_needed:
    # The padding that will be added later
    lowercase__			    =						b'=' * ((6 - len(_lowerCAmelCase	) % 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(_lowerCAmelCase	) % 6)
  else:
    lowercase__			    =						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(_lowerCAmelCase	)	,		6	)	).encode()
      + padding
  )




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

  # 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(_lowerCAmelCase	,		_lowerCAmelCase	):
    try:
      lowercase__			    =						encoded_data.decode('utf-8'	)
    except UnicodeDecodeError:
      raise ValueError('base64 encoded data should only contain ASCII characters'	)

  lowercase__			    =						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(_lowerCAmelCase	) % 4 == 0 and padding < 3, "Incorrect padding"

  if padding:
    # Remove padding if there is one
    lowercase__			    =						encoded_data[:-padding]

    lowercase__			    =						''.join(
        bin(B64_CHARSET.index(_lowerCAmelCase	)	)[2:].zfill(6	) for char in encoded_data	)[: -padding * 2]
  else:
    lowercase__			    =						''.join(
        bin(B64_CHARSET.index(_lowerCAmelCase	)	)[2:].zfill(6	) for char in encoded_data	)

  lowercase__			    =						[
      int(binary_stream[index : index + 8]	,		2	)
      for index in range(0	,		len(_lowerCAmelCase	)	,		8	)
  ]

  return bytes(_lowerCAmelCase	)


if __name__ == "__main__":
      import doctest

      doctest.testmod()









from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class        SCREAMING_SNAKE_CASE     (UpperCAmelCase   ):
   _UpperCamelCase					:	Tuple				      = 'ClapFeatureExtractor'
   _UpperCamelCase					:	Union[str, Any]				      = ('RobertaTokenizer', 'RobertaTokenizerFast')


   def __init__(					self    :       List[Any]    ,  a    :       int    ,  a    :       str )->     Any:





     """simple docstring"""







     super().__init__(a    ,  a )


   def __call__(					self    :       Any    ,  a    :       Tuple=None    ,  a    :       Optional[int]=None    ,  a    :       int=None    ,  **a    :       Optional[int] )->     Union[str, Any]:





     """simple docstring"""







     lowercase__			    =						kwargs.pop('sampling_rate'    ,  a )

     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:
       lowercase__			    =						self.tokenizer(a    ,  return_tensors=a    ,  **a )

     if audios is not None:
       lowercase__			    =						self.feature_extractor(
           a    ,  sampling_rate=a    ,  return_tensors=a    ,  **a )

     if text is not None and audios is not None:
       lowercase__			    =						audio_features.input_features
       return encoding
     elif text is not None:
       return encoding
     else:
       return BatchEncoding(data=dict(**a )    ,  tensor_type=a )


   def  SCREAMING_SNAKE_CASE_							(					self    :       str    ,  *a    :       Dict    ,  **a    :       int )->     Optional[int]:





     """simple docstring"""







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


   def  SCREAMING_SNAKE_CASE_							(					self    :       List[Any]    ,  *a    :       int    ,  **a    :       Dict )->     Dict:





     """simple docstring"""







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


   @property
   def  SCREAMING_SNAKE_CASE_							(					self    :       Union[str, Any] )->     Optional[int]:





     """simple docstring"""







     lowercase__			    =						self.tokenizer.model_input_names
     lowercase__			    =						self.feature_extractor.model_input_names
     return list(dict.fromkeys(tokenizer_input_names + feature_extractor_input_names ) )


