


'''simple docstring'''
import warnings

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






class        lowerCAmelCase_  (     UpperCAmelCase	):
       __UpperCAmelCase					=['image_processor', 'tokenizer']
       __UpperCAmelCase					='ViTImageProcessor'
       __UpperCAmelCase					=('CLIPTokenizer', 'CLIPTokenizerFast')






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

            _A				=							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__(_UpperCamelCase			,     _UpperCamelCase   )






       def __call__( self			,     _UpperCamelCase=None			,     _UpperCamelCase=None			,     _UpperCamelCase=None			,     _UpperCamelCase=None			,     **_UpperCamelCase   )->      int:
            if text is None and visual_prompt is None and images is None:
                 raise ValueError('You have to specify either text, visual prompt or images.'   )

            if text is not None and visual_prompt is not None:
                 raise ValueError('You have to specify exactly one type of prompt. Either text or visual prompt.'   )

            if text is not None:
                 _A				=							self.tokenizer(_UpperCamelCase			,     return_tensors=_UpperCamelCase			,     **_UpperCamelCase   )

            if visual_prompt is not None:
                 _A				=							self.image_processor(_UpperCamelCase			,     return_tensors=_UpperCamelCase			,     **_UpperCamelCase   )

            if images is not None:
                 _A				=							self.image_processor(_UpperCamelCase			,     return_tensors=_UpperCamelCase			,     **_UpperCamelCase   )

            if visual_prompt is not None and images is not None:
                 _A				=							{
                     'pixel_values': image_features.pixel_values,
                     'conditional_pixel_values': prompt_features.pixel_values,
                 }
                 return encoding
            elif text is not None and images is not None:
                 _A				=							image_features.pixel_values
                 return encoding
            elif text is not None:
                 return encoding
            elif visual_prompt is not None:
                 _A				=							{
                     'conditional_pixel_values': prompt_features.pixel_values,
                 }
                 return encoding
            else:
                 return BatchEncoding(data=dict(**_UpperCamelCase   )			,     tensor_type=_UpperCamelCase   )






       def   UpperCamelCase						( self			,     *_UpperCamelCase			,     **_UpperCamelCase   )->      Any:
            return self.tokenizer.batch_decode(*_UpperCamelCase			,     **_UpperCamelCase   )






       def   UpperCamelCase						( self			,     *_UpperCamelCase			,     **_UpperCamelCase   )->      Optional[int]:
            return self.tokenizer.decode(*_UpperCamelCase			,     **_UpperCamelCase   )






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






       @property
       def   UpperCamelCase						( self   )->      Tuple:
            warnings.warn(
                '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'			,     _UpperCamelCase			,     )
            return self.image_processor









'''simple docstring'''
def 					lowerCamelCase_ ( __UpperCamelCase					:       int							,    __UpperCamelCase					:       int		)					->	int:







     """simple docstring"""
     while second != 0:
          _A				=							first & second
          first ^= second
          _A				=							c << 1
     return first


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    lowerCAmelCase						=					int(input("""Enter the first number: """).strip())
    lowerCAmelCase						=					int(input("""Enter the second number: """).strip())
    print(F'{add(first, second) = }')





