





import torch

from diffusers import DiffusionPipeline





class 						_A(					snake_case__   ):


				"""simple docstring"""


				def __init__(			self     ,					_A     ,					_A     ):
									super().__init__()

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





				def __call__(			self     ):
									__A	:						Optional[Any]       =				torch.randn(
									    (1, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)     ,					)
									__A	:						int       =				1

									__A	:						Dict       =				self.unet(_A     ,					_A     ).sample
									__A	:						Dict       =				self.scheduler.step(_A     ,					_A     ,					_A     ).prev_sample

									__A	:						int       =				scheduler_output - scheduler_output + torch.ones_like(_A     )

									return result











def  _SCREAMING_SNAKE_CASE				(   a  )      ->      Tuple:
					__A    ,				__A	:						Optional[Any]       =				[], []
					while len(a  ) > 1:
										__A    ,				__A	:						Any       =				min(a  ), max(a  )
										start.append(a  )
										end.append(a  )
										collection.remove(a  )
										collection.remove(a  )
					end.reverse()
					return start + collection + end


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




