






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			:		Union[str, Any]   				=    False
class  lowerCAmelCase_       (				unittest.TestCase					):
							pass



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




							def 						__snake_case					(   self     :   str	):
											lowerCAmelCase__      			=						VersatileDiffusionImageVariationPipeline.from_pretrained('''shi-labs/versatile-diffusion'''	)
											pipe.to(SCREAMING_SNAKE_CASE_	)
											pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_	)

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

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

											assert image.shape == (1, 512, 512, 3)
											lowerCAmelCase__      			=						np.array([0.0_441, 0.0_469, 0.0_507, 0.0_575, 0.0_632, 0.0_650, 0.0_865, 0.0_909, 0.0_945]	)

											assert np.abs(image_slice.flatten() - expected_slice	).max() < 1e-2













from __future__ import annotations





def 	lowerCAmelCase_		(lowercase__						:       list[int]       )		->    bool:


				'''simple docstring'''




				return len(set(lowercase__       )       ) == len(lowercase__       )


if __name__ == "__main__":
			import doctest

			doctest.testmod()





