
import torch

from diffusers import DiffusionPipeline



class    a_ (			_a    ):






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

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






			def __call__(      self  ):
									_lowercase					       =					torch.randn(
									    (1, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)				,			)
									_lowercase					       =					1

									_lowercase					       =					self.unet(lowerCAmelCase__				,			lowerCAmelCase__  ).sample
									_lowercase					       =					self.scheduler.step(lowerCAmelCase__				,			lowerCAmelCase__				,			lowerCAmelCase__  ).prev_sample

									_lowercase					       =					scheduler_output - scheduler_output + torch.ones_like(lowerCAmelCase__  )

									return result






'''simple docstring'''

from __future__ import annotations







def    a__							(							lowercase   :	list,   lowercase   :	int )						->						List[Any]:





     """simple docstring"""

     if len(lowercase ) <= 1 or n <= 1:
          return

     insert_next(lowercase,   n - 1 )
     rec_insertion_sort(lowercase,   n - 1 )







def    a__							(							lowercase   :	list,   lowercase   :	int )						->						Optional[Any]:





     """simple docstring"""

     if index >= len(lowercase ) or collection[index - 1] <= collection[index]:
          return

     # Swaps adjacent elements since they are not in ascending order
     _UpperCamelCase       ,							_UpperCamelCase							       =			(
         collection[index],
         collection[index - 1],
     )

     insert_next(lowercase,   index + 1 )


if __name__ == "__main__":
   lowercase__		:     str			=							input('Enter integers separated by spaces: ')
   lowercase__		:     list[int]			=							[int(num) for num in numbers.split()]
   rec_insertion_sort(number_list, len(number_list))
   print(number_list)



