

def        UpperCamelCase_(							_snake_case   :  int	):




			"""simple docstring"""






			if divisor % 5 == 0 or divisor % 2 == 0:
						return 0
			__a        =1
			__a        =1
			while repunit:
						__a        =(10 * repunit + 1) % divisor
						repunit_index += 1
			return repunit_index






def        UpperCamelCase_(							_snake_case   :  int = 1000000	):




			"""simple docstring"""






			__a        =limit - 1
			if divisor % 2 == 0:
						divisor += 1
			while least_divisible_repunit(_snake_case	) <= limit:
						divisor += 2
			return divisor


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









import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device


_lowerCAmelCase					:			Optional[Any]  				=			False






class 	__magic_name__    (			unittest.TestCase      ):
			pass






@slow
@require_torch_gpu
class 	__magic_name__    (			unittest.TestCase      ):





			def 		__magic_name__    (		self					)   -> Optional[int]:




						'''simple docstring'''
						__a        =VersatileDiffusionImageVariationPipeline.from_pretrained('shi-labs/versatile-diffusion'					)
						pipe.to(__snake_case					)
						pipe.set_progress_bar_config(disable=__snake_case					)

						__a        =load_image(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'					)
						__a        =torch.manual_seed(0					)
						__a        =pipe(
						    image=__snake_case					,  generator=__snake_case					,  guidance_scale=7.5					,  num_inference_steps=50					,  output_type='numpy'					,  ).images

						__a        =image[0, 253:256, 253:256, -1]

						assert image.shape == (1, 512, 512, 3)
						__a        =np.array([0.0441, 0.0469, 0.0507, 0.0575, 0.0632, 0.0650, 0.0865, 0.0909, 0.0945]					)

						assert np.abs(image_slice.flatten() - expected_slice					).max() < 1e-2






