






"""simple docstring"""



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


_UpperCamelCase		:  Optional[int]						=		False

class        a     (   unittest.TestCase	):
    pass


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







    def 			UpperCamelCase_      (			self       ):
      lowercase							    =       VersatileDiffusionImageVariationPipeline.from_pretrained('shi-labs/versatile-diffusion'       )
      pipe.to(_lowerCamelCase       )
      pipe.set_progress_bar_config(disable=_lowerCamelCase       )

      lowercase							    =       load_image(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'       )
      lowercase							    =       torch.manual_seed(0       )
      lowercase							    =       pipe(
          image=_lowerCamelCase							,		generator=_lowerCamelCase							,		guidance_scale=7.5							,		num_inference_steps=5_0							,		output_type='numpy'							,		).images

      lowercase							    =       image[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

      assert image.shape == (1, 5_1_2, 5_1_2, 3)
      lowercase							    =       np.array([0.0_4_4_1, 0.0_4_6_9, 0.0_5_0_7, 0.0_5_7_5, 0.0_6_3_2, 0.0_6_5_0, 0.0_8_6_5, 0.0_9_0_9, 0.0_9_4_5]       )

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










"""simple docstring"""



def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  int    ):





  '''simple docstring'''





  return sum(i for i in range(1		,					number // 2 + 1    ) if number % i == 0    ) == number


if __name__ == "__main__":
    print('Program to check whether a number is a Perfect number or not...')
    _UpperCamelCase		:  Tuple						=		int(input('Enter number: ').strip())
    print(F'''{number} is {'' if perfect(number) else 'not '}a Perfect Number.''')


