


'''simple docstring'''






import torch

from diffusers import DiffusionPipeline




class 	a__       (							a__						):





							'''simple docstring'''






							def __init__(	self      ,					lowerCamelCase_      ,					lowerCamelCase_     ) ->							Optional[int]:
														super().__init__()

														self.register_modules(unet=lowerCamelCase_      ,					scheduler=lowerCamelCase_     )







							def __call__(	self     ) ->							Dict:
														lowerCAmelCase__      =			torch.randn(
														    (1, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)      ,					)
														lowerCAmelCase__      =			1

														lowerCAmelCase__      =			self.unet(lowerCamelCase_      ,					lowerCamelCase_     ).sample
														lowerCAmelCase__      =			self.scheduler.step(lowerCamelCase_      ,					lowerCamelCase_      ,					lowerCamelCase_     ).prev_sample

														lowerCAmelCase__      =			scheduler_output - scheduler_output + torch.ones_like(lowerCamelCase_     )

														return result



'''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					       =							False




class 	a__       (							unittest.TestCase						):





							'''simple docstring'''






							pass




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





							'''simple docstring'''






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

														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=lowerCamelCase_      ,					generator=lowerCamelCase_      ,					guidance_scale=7.5      ,					num_inference_steps=50      ,					output_type='''numpy'''      ,					).images

														lowerCAmelCase__      =			image[0, 2_53:2_56, 2_53:2_56, -1]

														assert image.shape == (1, 5_12, 5_12, 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