
"""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     :    Dict       =			False

class __lowerCAmelCase		(				unittest.TestCase      ):
     pass


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





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      __UpperCamelCase      =	VersatileDiffusionImageVariationPipeline.from_pretrained('shi-labs/versatile-diffusion'				)
      pipe.to(__UpperCAmelCase				)
      pipe.set_progress_bar_config(disable=__UpperCAmelCase				)

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

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

      assert image.shape == (1, 512, 512, 3)
      __UpperCamelCase      =	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 							A						(    snake_case				:int		)				->     bool:
 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     :    Union[str, Any]       =			int(input("Enter number: ").strip())
      print(f'''{number} is {"" if perfect(number) else "not "}a Perfect Number.''')



