
'''simple docstring'''







import warnings

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


class 						__UpperCAmelCase      (					_lowerCamelCase						):
							__lowercase      		=       ["""image_processor""", """tokenizer"""]
							__lowercase      		=       """ChineseCLIPImageProcessor"""
							__lowercase      		=       ("""BertTokenizer""", """BertTokenizerFast""")


							def __init__(       self    ,   lowerCAmelCase_=None    ,   lowerCAmelCase_=None    ,   **lowerCAmelCase_  ):





								"""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.'    ,   lowerCAmelCase_    ,   )
									_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__(lowerCAmelCase_    ,   lowerCAmelCase_  )
								_snake_case   						=		self.image_processor


							def __call__(       self    ,   lowerCAmelCase_=None    ,   lowerCAmelCase_=None    ,   lowerCAmelCase_=None    ,   **lowerCAmelCase_  ):





								"""simple docstring"""






								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:
									_snake_case   						=		self.tokenizer(lowerCAmelCase_    ,   return_tensors=lowerCAmelCase_    ,   **lowerCAmelCase_  )

								if images is not None:
									_snake_case   						=		self.image_processor(lowerCAmelCase_    ,   return_tensors=lowerCAmelCase_    ,   **lowerCAmelCase_  )

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


							def  lowerCamelCase				(       self    ,   *lowerCAmelCase_    ,   **lowerCAmelCase_  ):





								"""simple docstring"""






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


							def  lowerCamelCase				(       self    ,   *lowerCAmelCase_    ,   **lowerCAmelCase_  ):





								"""simple docstring"""






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


							@property
							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								_snake_case   						=		self.tokenizer.model_input_names
								_snake_case   						=		self.image_processor.model_input_names
								return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names  )  )




							@property
							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






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



'''simple docstring'''







def 				SCREAMING_SNAKE_CASE__	(   __A	)       ->	str:
	_snake_case   						=		1
	_snake_case   						=		2
	while i * i <= n:
		_snake_case   						=		0
		while n % i == 0:
			n //= i
			multiplicity += 1
		n_divisors *= multiplicity + 1
		i += 1
	if n > 1:
		n_divisors *= 2
	return n_divisors







def 				SCREAMING_SNAKE_CASE__	(   )       ->	List[str]:
	_snake_case   						=		1
	_snake_case   						=		1

	while True:
		i += 1
		t_num += i

		if count_divisors(__A	) > 500:
			break

	return t_num


if __name__ == "__main__":
					print(solution())

