




def       UpperCAmelCase       (     _snake_case							):
							lowerCAmelCase   =		[1]

							lowerCAmelCase   ,     lowerCAmelCase   ,     lowerCAmelCase   =		0, 0, 0
							lowerCAmelCase   =		ugly_nums[ia] * 2
							lowerCAmelCase   =		ugly_nums[ia] * 3
							lowerCAmelCase   =		ugly_nums[ia] * 5

							for _ in range(1						, _snake_case							):
														lowerCAmelCase   =		min(_snake_case						, _snake_case						, _snake_case							)
														ugly_nums.append(_snake_case							)
														if next_num == next_a:
																					ia += 1
																					lowerCAmelCase   =		ugly_nums[ia] * 2
														if next_num == next_a:
																					ia += 1
																					lowerCAmelCase   =		ugly_nums[ia] * 3
														if next_num == next_a:
																					ia += 1
																					lowerCAmelCase   =		ugly_nums[ia] * 5
							return ugly_nums[-1]


if __name__ == "__main__":
		from doctest import testmod

		testmod(verbose=True)
		print(F'''{ugly_numbers(200) = }''')





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""")



