

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


lowercase__				:      List[Any]	   = False




class       a__	( unittest.TestCase				):
     pass







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







     def       lowerCAmelCase_    (     self			)  ->   Optional[Any]:



          '''simple docstring'''



          a     =				VersatileDiffusionImageVariationPipeline.from_pretrained("shi-labs/versatile-diffusion"			)
          pipe.to(a__			)
          pipe.set_progress_bar_config(disable=a__			)

          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=a__  ,		generator=a__  ,		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([0.0_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







import math
import sys

def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase)      ->					int:
     if number != int(__UpperCamelCase):
          raise ValueError("the value of input must be a natural number")
     if number < 0:
          raise ValueError("the value of input must not be a negative number")
     if number == 0:
          return 1
     a     =				[-1] * (number + 1)
     a     =				0
     for i in range(1		, number + 1):
          a     =				sys.maxsize
          a     =				int(math.sqrt(__UpperCamelCase))
          for j in range(1		, root + 1):
               a     =				1 + answers[i - (j**2)]
               a     =				min(__UpperCamelCase		, __UpperCamelCase)
          a     =				answer
     return answers[number]


if __name__ == "__main__":
   import doctest

   doctest.testmod()

