





import torch

from diffusers import DiffusionPipeline





class  __magic_name__   (							A__						):






		def __init__(						self      :     Any				,		UpperCamelCase__      :     Optional[Any]				,		UpperCamelCase__      :     Union[str, Any]      )				->					Optional[Any]:
								'''simple docstring'''







								super().__init__()

								self.register_modules(unet=UpperCamelCase__				,		scheduler=UpperCamelCase__      )







		def __call__(						self      :     int      )				->					List[str]:
								'''simple docstring'''







								UpperCAmelCase         =     torch.randn(
								    (1, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)				,		)
								UpperCAmelCase         =     1

								UpperCAmelCase         =     self.unet(UpperCamelCase__				,		UpperCamelCase__      ).sample
								UpperCAmelCase         =     self.scheduler.step(UpperCamelCase__				,		UpperCamelCase__				,		UpperCamelCase__      ).prev_sample

								UpperCAmelCase         =     scheduler_output - scheduler_output + torch.ones_like(UpperCamelCase__      )

								return result








def    lowerCamelCase_(lowerCamelCase_    ) ->       list[int]:
						UpperCAmelCase         =     len(lowerCamelCase_    )
						for i in range(lowerCamelCase_    ):
												for j in range(i + 1    ,   lowerCamelCase_    ):
																		if numbers[j] < numbers[i]:
																								UpperCAmelCase       ,		UpperCAmelCase         =     numbers[j], numbers[i]
						return numbers


if __name__ == "__main__":
		__lowerCamelCase     :							int  							=				input("Enter numbers separated by a comma:\n").strip()
		__lowerCamelCase     :							Dict  							=				[int(item) for item in user_input.split(",")]
		print(exchange_sort(unsorted))



