

import warnings

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




class      SCREAMING_SNAKE_CASE__  (			lowercase__      ):





	'''simple docstring'''



	_UpperCAmelCase :	List[str]					     =      ["""image_processor""", """tokenizer"""]
	_UpperCAmelCase :	Dict					     =      """VideoMAEImageProcessor"""
	_UpperCAmelCase :	List[Any]					     =      ("""CLIPTokenizer""", """CLIPTokenizerFast""")



	def __init__(						self			:					Dict    ,						lowercase			:					List[Any]=None    ,						lowercase			:					int=None    ,						**lowercase			:					int			):


				'''simple docstring'''


				_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.'    ,						_A    ,						)
							_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__(_A    ,						_A			)
				_snake_case						=    self.image_processor



	def __call__(						self			:					Optional[Any]    ,						lowercase			:					List[str]=None    ,						lowercase			:					Tuple=None    ,						lowercase			:					Any=None    ,						**lowercase			:					Dict			):


				'''simple docstring'''


				if text is None and videos is None:
							raise ValueError('You have to specify either text or videos. Both cannot be none.'			)

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

				if videos is not None:
							_snake_case						=    self.image_processor(_A    ,						return_tensors=_A    ,						**_A			)

				if text is not None and videos is not None:
							_snake_case						=    image_features.pixel_values
							return encoding
				elif text is not None:
							return encoding
				else:
							return BatchEncoding(data=dict(**_A			)    ,						tensor_type=_A			)



	def 				A							(						self			:					Dict    ,						*lowercase			:					Union[str, Any]    ,						**lowercase			:					Union[str, Any]			):


				'''simple docstring'''


				return self.tokenizer.batch_decode(*_A    ,						**_A			)



	def 				A							(						self			:					List[str]    ,						*lowercase			:					Union[str, Any]    ,						**lowercase			:					Dict			):


				'''simple docstring'''


				return self.tokenizer.decode(*_A    ,						**_A			)



	@property
	def 				A							(						self			:					int			):


				'''simple docstring'''


				return ["input_ids", "attention_mask", "position_ids", "pixel_values"]



	@property
	def 				A							(						self			:					Optional[Any]			):


				'''simple docstring'''


				warnings.warn(
				    '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'    ,						_A    ,						)
				return self.image_processor_class





	@property
	def 				A							(						self			:					Dict			):


				'''simple docstring'''


				warnings.warn(
				    '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'    ,						_A    ,						)
				return self.image_processor

def  a_	( __lowercase     :			int				)				->						bool:
			_snake_case						=    9

			while num > 0:
						if num % 10 != digit:
									return False
						num //= 100
						digit -= 1

			return True






def  a_	( )				->						int:
			_snake_case						=    138_902_663

			while not is_square_form(num * num				):
						if num % 10 == 3:
									num -= 6  # (3 - 6) % 10 = 7
						else:
									num -= 4  # (7 - 4) % 10 = 3

			return num * 10


if __name__ == "__main__":
			print(F'{solution() = }')