




'''simple docstring'''





from __future__ import annotations


def 					UpperCamelCase_(     snake_case							:	list[int]					):

       '''simple docstring'''






       if not nums:
              return 0
       snake_case_      =				nums[0]
       snake_case_      =				0
       for num in nums[1:]:
              snake_case_ ,				snake_case_      =				(
                  max_excluding + num,
                  max(snake_case							,    snake_case					),
              )
       return max(snake_case							,    snake_case					)


if __name__ == "__main__":
  import doctest

  doctest.testmod()










'''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


_SCREAMING_SNAKE_CASE							:       Any						=			False




class 			_snake_case		(     unittest.TestCase							):
			pass




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




			def        lowerCAmelCase__    ( self							)    ->    List[Any]:

										'''simple docstring'''




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

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

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

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




