
'''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


UpperCAmelCase__ :			str											=		False






class  A  (						unittest.TestCase     ):
	pass






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





	def 				__SCREAMING_SNAKE_CASE	(       self      :							Tuple				):


				"""simple docstring"""

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

				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=__magic_name__   ,				generator=__magic_name__   ,				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([0.0441, 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










"""simple docstring"""







from ..utils import DummyObject, requires_backends

class       _a  ( metaclass=SCREAMING_SNAKE_CASE__):
			__magic_name__     					=     ["""keras_nlp"""]



			def __init__(			self							:				int   ,	*_lowercase							:				Optional[int]   ,	**_lowercase							:				Dict		)							->					List[Any]:
									requires_backends(self   ,	["keras_nlp"]		)



