





'''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


__lowerCamelCase : Any											=			False

class       UpperCAmelCase (						unittest.TestCase  ):
		pass




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




		def 				UpperCAmelCase__   (self      :	Union[str, Any]				)					-> str:
						lowercase         = VersatileDiffusionImageVariationPipeline.from_pretrained("shi-labs/versatile-diffusion"				)
						pipe.to(A__				)
						pipe.set_progress_bar_config(disable=A__				)

						lowercase         = load_image(
						    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"				)
						lowercase         = torch.manual_seed(0				)
						lowercase         = pipe(
						    image=A__							,			generator=A__							,			guidance_scale=7.5							,			num_inference_steps=5_0							,			output_type="numpy"							,			).images

						lowercase         = image[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

						assert image.shape == (1, 5_1_2, 5_1_2, 3)
						lowercase         = 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











'''simple docstring'''

import warnings

from ...utils import logging
from .image_processing_clip import CLIPImageProcessor


__lowerCamelCase : Union[str, Any]											=			logging.get_logger(__name__)

class       UpperCAmelCase (						_lowercase  ):




		def __init__(self      :	Tuple							,			*A__      :	Union[str, Any]							,			**A__      :	Optional[Any]				)					-> None:
						warnings.warn(
						    "The class CLIPFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
						    " use CLIPImageProcessor instead."							,			A__							,			)
						super().__init__(*A__							,			**A__				)




