





import warnings
from typing import List, Optional, Union

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



class 					_a		(_UpperCamelCase       ):


					'''simple docstring'''






					UpperCAmelCase__:     Union[str, Any]													= ['''image_processor''', '''tokenizer''']
					UpperCAmelCase__:     Union[str, Any]													= '''FlavaImageProcessor'''
					UpperCAmelCase__:     Union[str, Any]													= ('''BertTokenizer''', '''BertTokenizerFast''')






					def __init__(				self					, A__=None					, A__=None					, **A__    ):
						A__							:					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."""					, _UpperCAmelCase					, )
							A__							:					Optional[Any]       =	kwargs.pop("""feature_extractor"""    )

						A__							:					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__(_UpperCAmelCase					, _UpperCAmelCase    )
						A__							:					Optional[Any]       =	self.image_processor






					def __call__(				self					, A__ = None					, A__ = None					, A__ = True					, A__ = False					, A__ = False					, A__ = None					, A__ = 0					, A__ = None					, A__ = None					, A__ = None					, A__ = None					, A__ = None					, A__ = False					, A__ = False					, A__ = False					, A__ = False					, A__ = True					, A__ = None					, **A__					, ):

						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:
							A__							:					List[str]       =	self.tokenizer(
							    text=_UpperCAmelCase					, add_special_tokens=_UpperCAmelCase					, padding=_UpperCAmelCase					, truncation=_UpperCAmelCase					, max_length=_UpperCAmelCase					, stride=_UpperCAmelCase					, pad_to_multiple_of=_UpperCAmelCase					, return_token_type_ids=_UpperCAmelCase					, return_attention_mask=_UpperCAmelCase					, return_overflowing_tokens=_UpperCAmelCase					, return_special_tokens_mask=_UpperCAmelCase					, return_offsets_mapping=_UpperCAmelCase					, return_length=_UpperCAmelCase					, verbose=_UpperCAmelCase					, return_tensors=_UpperCAmelCase					, **_UpperCAmelCase					, )
						if images is not None:
							A__							:					Any       =	self.image_processor(
							    _UpperCAmelCase					, return_image_mask=_UpperCAmelCase					, return_codebook_pixels=_UpperCAmelCase					, return_tensors=_UpperCAmelCase					, **_UpperCAmelCase					, )

						if text is not None and images is not None:
							encoding.update(_UpperCAmelCase    )
							return encoding
						elif text is not None:
							return encoding
						else:
							return BatchEncoding(data=dict(**_UpperCAmelCase    )					, tensor_type=_UpperCAmelCase    )






					def 						__A    (				self					, *A__					, **A__    ):
						return self.tokenizer.batch_decode(*_UpperCAmelCase					, **_UpperCAmelCase    )






					def 						__A    (				self					, *A__					, **A__    ):
						return self.tokenizer.decode(*_UpperCAmelCase					, **_UpperCAmelCase    )






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






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






def 		UpperCamelCase      (lowercase_:				int					,   lowercase_:				int      )  ->   int:
	while second != 0:
		A__							:					int       =	first & second
		first ^= second
		A__							:					int       =	c << 1
	return first


if __name__ == "__main__":
	import doctest

	doctest.testmod()

	A_ :				Optional[Any]						 =    int(input('Enter the first number: ').strip())
	A_ :				List[str]						 =    int(input('Enter the second number: ').strip())
	print(f'''{add(first, second) = }''')



