



import torch

from diffusers import StableDiffusionPipeline


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

a_  :Union[str, Any]     = '''A photo of sks dog in a bucket'''
a_  :str     = pipe(prompt, num_inference_steps=50, guidance_scale=7.5).images[0]

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








"""simple docstring"""





def 				A_  (__a					):

					'''simple docstring'''





					A_								=    len(__a					)
					while cur > 1:
										# Find the maximum number in arr
										A_								=    arr.index(max(arr[0:cur]					)					)
										# Reverse from 0 to mi
										A_								=    arr[mi::-1] + arr[mi + 1 : len(__a					)]
										# Reverse whole list
										A_								=    arr[cur - 1 :: -1] + arr[cur : len(__a					)]
										cur -= 1
					return arr


if __name__ == "__main__":
		UpperCamelCase_       :    Optional[Any]						      =			input('''Enter numbers separated by a comma:\n''').strip()
		UpperCamelCase_       :    Any						      =			[int(item) for item in user_input.split(''',''')]
		print(pancake_sort(unsorted))




