





def      UpperCAmelCase_    (    _UpperCAmelCase							:Any  ,						_UpperCAmelCase							:Dict				)	->				Union[str, Any]:
     '''simple docstring'''


     A_          =       [1]
     for i in range(2  ,						_UpperCAmelCase				):
          factorials.append(factorials[-1] * i				)
     assert 0 <= k < factorials[-1] * n, "k out of bounds"

     A_          =       []
     A_          =       list(range(_UpperCAmelCase				)				)

     # Find permutation
     while factorials:
          A_          =       factorials.pop()
          A_   ,    A_          =       divmod(_UpperCAmelCase  ,						_UpperCAmelCase				)
          permutation.append(elements[number]				)
          elements.remove(elements[number]				)
     permutation.append(elements[0]				)

     return permutation


if __name__ == "__main__":
      import doctest

      doctest.testmod()










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__      :     Optional[int]         =		False





class UpperCAmelCase_				( unittest.TestCase ):
  pass





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


  def 						__UpperCAmelCase     (							self ):



       """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=5_0 ,output_type='''numpy''' ,).images

       A_          =       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)
       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



