
import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType

class        _UpperCamelCase				(					_A  ):






			'''simple docstring'''

			__UpperCamelCase      :						Optional[int]					      = ["""image_processor""", """tokenizer"""]
			__UpperCamelCase      :						Tuple					      = """ViltImageProcessor"""
			__UpperCamelCase      :						Any					      = ("""BertTokenizer""", """BertTokenizerFast""")

			def __init__(       self :       Optional[int]						,	snake_case_ :       str=None						,	snake_case_ :       Optional[int]=None						,	**snake_case_ :       str      ):
							UpperCamelCase_:							List[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."""						,	snake_case_						,	)
											UpperCamelCase_:							List[str]	=						kwargs.pop("""feature_extractor"""      )

							UpperCamelCase_:							Tuple	=						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__(snake_case_						,	snake_case_      )
							UpperCamelCase_:							int	=						self.image_processor

			def __call__(       self :       Tuple						,	snake_case_ :       List[str]						,	snake_case_ :       Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None						,	snake_case_ :       bool = True						,	snake_case_ :       Union[bool, str, PaddingStrategy] = False						,	snake_case_ :       Union[bool, str, TruncationStrategy] = None						,	snake_case_ :       Optional[int] = None						,	snake_case_ :       int = 0						,	snake_case_ :       Optional[int] = None						,	snake_case_ :       Optional[bool] = None						,	snake_case_ :       Optional[bool] = None						,	snake_case_ :       bool = False						,	snake_case_ :       bool = False						,	snake_case_ :       bool = False						,	snake_case_ :       bool = False						,	snake_case_ :       bool = True						,	snake_case_ :       Optional[Union[str, TensorType]] = None						,	**snake_case_ :       int						,	):
							UpperCamelCase_:							Any	=						self.tokenizer(
							    text=snake_case_						,	add_special_tokens=snake_case_						,	padding=snake_case_						,	truncation=snake_case_						,	max_length=snake_case_						,	stride=snake_case_						,	pad_to_multiple_of=snake_case_						,	return_token_type_ids=snake_case_						,	return_attention_mask=snake_case_						,	return_overflowing_tokens=snake_case_						,	return_special_tokens_mask=snake_case_						,	return_offsets_mapping=snake_case_						,	return_length=snake_case_						,	verbose=snake_case_						,	return_tensors=snake_case_						,	**snake_case_						,	)
							# add pixel_values + pixel_mask
							UpperCamelCase_:							Any	=						self.image_processor(snake_case_						,	return_tensors=snake_case_      )
							encoding.update(snake_case_      )

							return encoding

			def       lowerCAmelCase__	(       self :       Optional[Any]						,	*snake_case_ :       Dict						,	**snake_case_ :       Tuple      ):
							return self.tokenizer.batch_decode(*snake_case_						,	**snake_case_      )

			def       lowerCAmelCase__	(       self :       List[Any]						,	*snake_case_ :       Optional[int]						,	**snake_case_ :       List[str]      ):
							return self.tokenizer.decode(*snake_case_						,	**snake_case_      )

			@property
			def       lowerCAmelCase__	(       self :       List[str]      ):
							UpperCamelCase_:							int	=						self.tokenizer.model_input_names
							UpperCamelCase_:							Dict	=						self.image_processor.model_input_names
							return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names      )      )

			@property
			def       lowerCAmelCase__	(       self :       List[Any]      ):
							warnings.warn(
							    """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""						,	snake_case_						,	)
							return self.image_processor_class




			@property
			def       lowerCAmelCase__	(       self :       int      ):
							warnings.warn(
							    """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""						,	snake_case_						,	)
							return self.image_processor








def        A__					(						lowerCamelCase     ,    lowerCamelCase						)  ->	int:
				while second != 0:
								UpperCamelCase_:							Optional[Any]	=						first & second
								first ^= second
								UpperCamelCase_:							Any	=						c << 1
				return first


if __name__ == "__main__":
							import doctest

							doctest.testmod()

							lowerCamelCase_						: List[Any]         =  int(input("""Enter the first number: """).strip())
							lowerCamelCase_						: Tuple         =  int(input("""Enter the second number: """).strip())
							print(F"""{add(first, second) = }""")






