




def 			lowerCamelCase_		(       UpperCamelCase__				:      int ,     UpperCamelCase__				:      int      )     ->       str:





				"""simple docstring"""


				if number < 0 or shift_amount < 0:
								raise ValueError('both inputs must be positive integers'      )

				__lowerCamelCase		       =    str(bin(UpperCamelCase__      )      )
				binary_number += "0" * shift_amount
				return binary_number







def 			lowerCamelCase_		(       UpperCamelCase__				:      int ,     UpperCamelCase__				:      int      )     ->       str:





				"""simple docstring"""


				if number < 0 or shift_amount < 0:
								raise ValueError('both inputs must be positive integers'      )

				__lowerCamelCase		       =    str(bin(UpperCamelCase__      )      )[2:]
				if shift_amount >= len(UpperCamelCase__      ):
								return "0b0"
				__lowerCamelCase		       =    binary_number[: len(UpperCamelCase__      ) - shift_amount]
				return "0b" + shifted_binary_number







def 			lowerCamelCase_		(       UpperCamelCase__				:      int ,     UpperCamelCase__				:      int      )     ->       str:





				"""simple docstring"""


				if number >= 0:  # Get binary representation of positive number
								__lowerCamelCase		       =    '0' + str(bin(UpperCamelCase__      )      ).strip('-'      )[2:]
				else:  # Get binary (2's complement) representation of negative number
								__lowerCamelCase		       =    len(bin(UpperCamelCase__      )[3:]      )  # Find 2's complement of number
								__lowerCamelCase		       =    bin(abs(UpperCamelCase__      ) - (1 << binary_number_length)      )[3:]
								__lowerCamelCase		       =    (
								    '1' + '0' * (binary_number_length - len(UpperCamelCase__      )) + binary_number
								)

				if shift_amount >= len(UpperCamelCase__      ):
								return "0b" + binary_number[0] * len(UpperCamelCase__      )
				return (
				    "0b"
				    + binary_number[0] * shift_amount
				    + binary_number[: len(UpperCamelCase__      ) - shift_amount]
				)


if __name__ == "__main__":
					import doctest

					doctest.testmod()





import warnings

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

class 				UpperCAmelCase_		(			__lowercase   ):
							"""simple docstring"""







							UpperCAmelCase__       :						str	    =    ["image_processor", "tokenizer"]
							UpperCAmelCase__       :						Optional[int]	    =    "CLIPImageProcessor"
							UpperCAmelCase__       :						Tuple	    =    ("XLMRobertaTokenizer", "XLMRobertaTokenizerFast")


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

												_a	:    int         =		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__(_a       ,							_a       )


							def __call__(			self       ,							_a=None       ,							_a=None       ,							_a=None       ,							**_a       )    ->    List[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:
																	_a	:    str         =		self.tokenizer(_a       ,							return_tensors=_a       ,							**_a       )

												if images is not None:
																	_a	:    Optional[Any]         =		self.image_processor(_a       ,							return_tensors=_a       ,							**_a       )

												if text is not None and images is not None:
																	_a	:    List[Any]         =		image_features.pixel_values
																	return encoding
												elif text is not None:
																	return encoding
												else:
																	return BatchEncoding(data=dict(**_a       )       ,							tensor_type=_a       )


							def 				__lowercase				(			self       ,							*_a       ,							**_a       )    ->    Optional[Any]:
												return self.tokenizer.batch_decode(*_a       ,							**_a       )


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




							@property
							def 				__lowercase				(			self       )    ->    Optional[int]:
												_a	:    Dict         =		self.tokenizer.model_input_names
												_a	:    Dict         =		self.image_processor.model_input_names
												return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names       )       )



