



'''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


A_							: List[Any]						 =	False



class 							lowercase       (  unittest.TestCase				):


 """simple docstring"""


 pass







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


 """simple docstring"""


 def _snake_case (							self							)		->   Any:
        _UpperCAmelCase  :						Optional[int]			     = VersatileDiffusionImageVariationPipeline.from_pretrained("""shi-labs/versatile-diffusion"""							)
        pipe.to(a_							)
        pipe.set_progress_bar_config(disable=a_							)

        _UpperCAmelCase  :						List[Any]			     = load_image(
            """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"""							)
        _UpperCAmelCase  :						Optional[int]			     = torch.manual_seed(0							)
        _UpperCAmelCase  :						int			     = pipe(
            image=a_  ,generator=a_  ,guidance_scale=7.5  ,num_inference_steps=50  ,output_type="""numpy"""  ,).images

        _UpperCAmelCase  :						List[Any]			     = image[0, 253:256, 253:256, -1]

        assert image.shape == (1, 512, 512, 3)
        _UpperCAmelCase  :						Optional[int]			     = 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 typing import TYPE_CHECKING

from ...utils import _LazyModule


A_							: int						 =	{"""tokenization_bertweet""": ["""BertweetTokenizer"""]}


if TYPE_CHECKING:
   from .tokenization_bertweet import BertweetTokenizer

else:
   import sys

   A_							: int						 =	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



