
import torch

from diffusers import StableDiffusionPipeline


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

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

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




from math import factorial

__lowerCAmelCase					:			Dict			      =		{str(d): factorial(d) for d in range(10)}


def     UpperCAmelCase_  (						__lowerCAmelCase	) ->						int:
    return sum(DIGIT_FACTORIAL[d] for d in str(__lowerCAmelCase	)	)


def     UpperCAmelCase_  (						) ->						int:
    __lowercase		:			int	    =       7 * factorial(9	) + 1
    return sum(i for i in range(3	,					__lowerCAmelCase	) if sum_of_digit_factorial(__lowerCAmelCase	) == i	)


if __name__ == "__main__":
 print(F'{solution() = }')


