

def   __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__	):


						if len(str(_A	)	) == 1 or len(str(_A	)	) == 1:
												return a * b

						snake_case_       =     max(len(str(_A	)	)  ,		len(str(_A	)	)	)
						snake_case_       =     ma // 2

						snake_case_,   snake_case_       =     divmod(_A  ,		10**ma	)
						snake_case_,   snake_case_       =     divmod(_A  ,		10**ma	)

						snake_case_       =     karatsuba(_A  ,		_A	)
						snake_case_       =     karatsuba((aa + aa)  ,		(ba + ba)	)
						snake_case_       =     karatsuba(_A  ,		_A	)

						return (z * 10 ** (2 * ma)) + ((y - z - x) * 10 ** (ma)) + (x)


def   __SCREAMING_SNAKE_CASE ():
						print(karatsuba(15463  ,		23489	)	)


if __name__ == "__main__":
				main()


import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin





class      snake_case_       (		__A ):







			'''simple docstring'''

			SCREAMING_SNAKE_CASE					:       List[str]						      =    ["image_processor", "tokenizer"]
			SCREAMING_SNAKE_CASE					:       List[Any]						      =    "AutoImageProcessor"
			SCREAMING_SNAKE_CASE					:       Optional[int]						      =    "AutoTokenizer"







			def __init__(					self			:    List[str]							,       _UpperCamelCase			:    List[str]=None							,       _UpperCamelCase			:    Any=None							,       **_UpperCamelCase			:    Tuple							)				->str:
									snake_case_       =     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							,       )
															snake_case_       =     kwargs.pop('''feature_extractor'''							)

									snake_case_       =     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							)
									snake_case_       =     self.image_processor
									snake_case_       =     False







			def __call__(					self			:    int							,       *_UpperCamelCase			:    Tuple							,       **_UpperCamelCase			:    Any							)				->Tuple:
									# For backward compatibility
									if self._in_target_context_manager:
															return self.current_processor(*_UpperCamelCase							,       **_UpperCamelCase							)

									snake_case_       =     kwargs.pop('''images'''							,       _UpperCamelCase							)
									snake_case_       =     kwargs.pop('''text'''							,       _UpperCamelCase							)
									if len(_UpperCamelCase							) > 0:
															snake_case_       =     args[0]
															snake_case_       =     args[1:]

									if images is None and text is None:
															raise ValueError('''You need to specify either an `images` or `text` input to process.'''							)

									if images is not None:
															snake_case_       =     self.image_processor(_UpperCamelCase							,       *_UpperCamelCase							,       **_UpperCamelCase							)
									if text is not None:
															snake_case_       =     self.tokenizer(_UpperCamelCase							,       **_UpperCamelCase							)

									if text is None:
															return inputs
									elif images is None:
															return encodings
									else:
															snake_case_       =     encodings['''input_ids''']
															return inputs







			def     snake_case__(					self			:    List[Any]							,       *_UpperCamelCase			:    Union[str, Any]							,       **_UpperCamelCase			:    Tuple							)				->Any:
									return self.tokenizer.batch_decode(*_UpperCamelCase							,       **_UpperCamelCase							)







			def     snake_case__(					self			:    Optional[Any]							,       *_UpperCamelCase			:    List[Any]							,       **_UpperCamelCase			:    Union[str, Any]							)				->List[str]:
									return self.tokenizer.decode(*_UpperCamelCase							,       **_UpperCamelCase							)







			@contextmanager
			def     snake_case__(					self			:    Any							)				->Any:
									warnings.warn(
									    '''`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '''
									    '''labels by using the argument `text` of the regular `__call__` method (either in the same call as '''
									    '''your images inputs, or in a separate call.'''							)
									snake_case_       =     True
									snake_case_       =     self.tokenizer
									yield
									snake_case_       =     self.image_processor
									snake_case_       =     False







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