
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		: Any								=     False



class    a_ (			unittest.TestCase    ):
			pass







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






			def    UpperCamelCase_				(      self  ):
									_lowercase					       =					VersatileDiffusionImageVariationPipeline.from_pretrained("""shi-labs/versatile-diffusion"""  )
									pipe.to(lowerCAmelCase__  )
									pipe.set_progress_bar_config(disable=lowerCAmelCase__  )

									_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=lowerCAmelCase__				,			generator=lowerCAmelCase__				,			guidance_scale=7.5				,			num_inference_steps=50				,			output_type="""numpy"""				,			).images

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

									assert image.shape == (1, 512, 512, 3)
									_lowercase					       =					np.array([0.04_41, 0.04_69, 0.05_07, 0.05_75, 0.06_32, 0.06_50, 0.08_65, 0.09_09, 0.09_45]  )

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






'''simple docstring'''

def    a__							(							lowercase   :	int,   lowercase   :	int,   lowercase   :	int )						->						float:





     """simple docstring"""

     _UpperCamelCase							       =			(num_of_terms / 2) * (2 * first_term + (num_of_terms - 1) * common_diff)
     # formula for sum of series
     return total







def    a__							(							)						->						Tuple:





     """simple docstring"""

     print(sum_of_series(1,   1,   10 ) )


if __name__ == "__main__":
   import doctest

   doctest.testmod()



