





'''simple docstring'''




def 			_lowerCAmelCase     (    __snake_case    :					list     )	->	list:

     if len(__snake_case     ) <= 1:
          return [tuple(__snake_case     )]

     __A		:					Dict						=			[]

     def generate(__snake_case    :					int					,				__snake_case    :					list     ):
          __A		:					Dict						=			[0] * n
          res.append(tuple(__snake_case     )     )

          __A		:					Dict						=			0
          while i < n:
               if c[i] < i:
                    if i % 2 == 0:
                         __A					,__A		:					Any						=			arr[i], arr[0]
                    else:
                         __A					,__A		:					int						=			arr[i], arr[c[i]]
                    res.append(tuple(__snake_case     )     )
                    c[i] += 1
                    __A		:					Union[str, Any]						=			0
               else:
                    __A		:					Optional[int]						=			0
                    i += 1

     generate(len(__snake_case     )					,				__snake_case     )
     return res


if __name__ == "__main__":
  lowercase__       : Union[str, Any]         =       input('''Enter numbers separated by a comma:\n''').strip()
  lowercase__       : List[Any]         =       [int(item) for item in user_input.split(''',''')]
  print(heaps(arr))
"""simple docstring"""






import torch

from diffusers import StableDiffusionPipeline


__lowerCAmelCase       :			str   	="""path-to-your-trained-model"""
__lowerCAmelCase       :			int   	=StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.floataa).to("""cuda""")

__lowerCAmelCase       :			Optional[int]   	="""A photo of sks dog in a bucket"""
__lowerCAmelCase       :			int   	=pipe(prompt, num_inference_steps=5_0, guidance_scale=7.5).images[0]

image.save("""dog-bucket.png""")



