

from ...utils import is_torch_available, is_transformers_available


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





def 							a		(				snake_case__:   int   ):


				'''simple docstring'''
				if not isinstance(snake_case__		,  snake_case__   ):
								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()




