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


A     :  Any					     =       False


class   lowerCamelCase					(unittest.TestCase					):
  """simple docstring"""







  pass



@slow
@require_torch_gpu
class   lowerCamelCase					(unittest.TestCase					):
  """simple docstring"""







  def    __A     (							self :       List[Any]   )  ->	List[Any]:
        SCREAMING_SNAKE_CASE_          =  VersatileDiffusionImageVariationPipeline.from_pretrained("shi-labs/versatile-diffusion"   )
        pipe.to(__UpperCamelCase   )
        pipe.set_progress_bar_config(disable=__UpperCamelCase   )

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

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

        assert image.shape == (1, 512, 512, 3)
        SCREAMING_SNAKE_CASE_          =  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



from math import factorial


def      a__ (		__UpperCamelCase = 1_0_0				):
      return sum(int(__UpperCamelCase				) for x in str(factorial(__UpperCamelCase				)				)				)


if __name__ == "__main__":
      print(solution(int(input("Enter the Number: ").strip())))


