"""simple docstring"""





import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding


class 	UpperCAmelCase_      (       UpperCamelCase__):
      lowerCamelCase__						:       List[Any]					    =    ["image_processor", "tokenizer"]
      lowerCamelCase__						:       Dict					    =    "CLIPImageProcessor"
      lowerCamelCase__						:       List[Any]					    =    ("CLIPTokenizer", "CLIPTokenizerFast")


      def __init__(  self						,     a=None						,     a=None						,     **a				)			->   Optional[Any]:
             lowercase__     :						str         =      None
             if "feature_extractor" in kwargs:
                    warnings.warn(
                        'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
                        ' instead.'						,     lowercase_						,     )
                    lowercase__     :						str         =      kwargs.pop('feature_extractor'				)

             lowercase__     :						Optional[Any]         =      image_processor if image_processor is not None else feature_extractor
             if image_processor is None:
                    raise ValueError('You need to specify an `image_processor`.'				)
             if tokenizer is None:
                    raise ValueError('You need to specify a `tokenizer`.'				)

             super().__init__(lowercase_						,     lowercase_				)


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

             if text is None and images is None:
                    raise ValueError('You have to specify either text or images. Both cannot be none.'				)

             if text is not None:
                    lowercase__     :						Dict         =      self.tokenizer(lowercase_						,     return_tensors=lowercase_						,     **lowercase_				)

             if images is not None:
                    lowercase__     :						Tuple         =      self.image_processor(lowercase_						,     return_tensors=lowercase_						,     **lowercase_				)

             if text is not None and images is not None:
                    lowercase__     :						int         =      image_features.pixel_values
                    return encoding
             elif text is not None:
                    return encoding
             else:
                    return BatchEncoding(data=dict(**lowercase_				)						,     tensor_type=lowercase_				)


      def 						_UpperCAmelCase			(  self						,     *a						,     **a				)			->   Dict:
             return self.tokenizer.batch_decode(*lowercase_						,     **lowercase_				)


      def 						_UpperCAmelCase			(  self						,     *a						,     **a				)			->   List[str]:
             return self.tokenizer.decode(*lowercase_						,     **lowercase_				)


      @property
      def 						_UpperCAmelCase			(  self				)			->   Optional[int]:
             lowercase__     :						str         =      self.tokenizer.model_input_names
             lowercase__     :						List[Any]         =      self.image_processor.model_input_names
             return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names				)				)


      @property
      def 						_UpperCAmelCase			(  self				)			->   str:
             warnings.warn(
                 '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'						,     lowercase_						,     )
             return self.image_processor_class


      @property
      def 						_UpperCAmelCase			(  self				)			->   List[Any]:
             warnings.warn(
                 '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'						,     lowercase_						,     )
             return self.image_processor








'''simple docstring'''

from string import ascii_uppercase

__lowerCAmelCase				:      Union[str, Any]						      ={str(ord(c) - 55): c for c in ascii_uppercase}





def 		UpperCamelCase							(				_lowerCamelCase   :   int		,			_lowerCamelCase   :   int			):
    if isinstance(_lowerCamelCase		,			_lowerCamelCase			):
        raise TypeError("int() can't convert non-string with explicit base"			)
    if num < 0:
        raise ValueError("parameter must be positive int"			)
    if isinstance(_lowerCamelCase		,			_lowerCamelCase			):
        raise TypeError("'str' object cannot be interpreted as an integer"			)
    if isinstance(_lowerCamelCase		,			_lowerCamelCase			):
        raise TypeError("'float' object cannot be interpreted as an integer"			)
    if base in (0, 1):
        raise ValueError("base must be >= 2"			)
    if base > 36:
        raise ValueError("base must be <= 36"			)
    A__							=     ""
    A__							=     0
    A__							=     0
    while div != 1:
        A__,      A__							=     divmod(_lowerCamelCase		,			_lowerCamelCase			)
        if base >= 11 and 9 < mod < 36:
            A__							=     ALPHABET_VALUES[str(_lowerCamelCase			)]
        else:
            A__							=     str(_lowerCamelCase			)
        new_value += actual_value
        A__							=     num // base
        A__							=     div
        if div == 0:
            return str(new_value[::-1]			)
        elif div == 1:
            new_value += str(_lowerCamelCase			)
            return str(new_value[::-1]			)

    return new_value[::-1]


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  for base in range(2, 37):
    for num in range(1000):
      assert int(decimal_to_any(num, base), base) == num, (
          num,
          base,
          decimal_to_any(num, base),
          int(decimal_to_any(num, base), base),
      )



