


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 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		int	):





			"""simple docstring"""




			assert (
			    isinstance(SCREAMING_SNAKE_CASE_       ,  SCREAMING_SNAKE_CASE_	) and number_of_steps > 0
			), f'number_of_steps needs to be positive integer, your input {number_of_steps}'
			if number_of_steps == 1:
						return 1
			UpperCamelCase  ,  UpperCamelCase         =					1, 1
			for _ in range(number_of_steps - 1	):
						UpperCamelCase  ,  UpperCamelCase         =					current + previous, current
			return current


if __name__ == "__main__":
			import doctest

			doctest.testmod()




