





"""simple docstring"""


from ...utils import is_torch_available, is_transformers_available


if is_transformers_available() and is_torch_available():
		from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline








"""simple docstring"""







def       _lowerCamelCase				(							UpperCAmelCase_ :       int     )	-> int:






	"""simple docstring"""





	if not isinstance(UpperCAmelCase_,	UpperCAmelCase_     ):
		raise ValueError("Input must be an integer"     )
	if input_num <= 0:
		raise ValueError("Input must be positive"     )
	return sum(
	    divisor for divisor in range(1,	input_num // 2 + 1     ) if input_num % divisor == 0     )


if __name__ == "__main__":
			import doctest

			doctest.testmod()




