




from __future__ import annotations

from fractions import Fraction



def       UpperCAmelCase       (     _snake_case						, _snake_case							):
							return (
							    num != den and num % 10 == den // 10 and (num // 10) / (den % 10) == num / den
							)



def       UpperCAmelCase       (     _snake_case							):
							lowerCAmelCase   =		[]
							lowerCAmelCase   =		11
							lowerCAmelCase   =		int('''1''' + '''0''' * digit_len							)
							for num in range(_snake_case						, _snake_case							):
														while den <= 99:
																					if (num != den) and (num % 10 == den // 10) and (den % 10 != 0):
																												if is_digit_cancelling(_snake_case						, _snake_case							):
																																			solutions.append(F"""{num}/{den}"""							)
																					den += 1
														num += 1
														lowerCAmelCase   =		10
							return solutions



def       UpperCAmelCase       (     _snake_case = 2							):
							lowerCAmelCase   =		1.0
							for fraction in fraction_list(_snake_case							):
														lowerCAmelCase   =		Fraction(_snake_case							)
														result *= frac.denominator / frac.numerator
							return int(_snake_case							)


if __name__ == "__main__":
		print(solution())









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""")



