






'''simple docstring'''

from __future__ import annotations



def 							snake_case					(     a_							: list[int]	)       ->				int:





      """simple docstring"""

      if not nums:
            return 0
      UpperCamelCase_				:   List[str]					    =							nums[0]
      UpperCamelCase_				:   Union[str, Any]					    =							0
      for num in nums[1:]:
            UpperCamelCase_				:   List[str]					    =							(
                max_excluding + num,
                max(a_    ,   a_	),
            )
      return max(a_    ,   a_	)


if __name__ == "__main__":
      import doctest

      doctest.testmod()














'''simple docstring'''

import torch

from diffusers import StableDiffusionPipeline


UpperCamelCase							="path-to-your-trained-model"
UpperCamelCase							=StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.floataa).to("cuda")

UpperCamelCase							="A photo of sks dog in a bucket"
UpperCamelCase							=pipe(prompt, num_inference_steps=50, guidance_scale=7.5).images[0]

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






