


"""simple docstring"""






class        snake_case :




					"""simple docstring"""

					def __init__(       self     :			Optional[int]					,lowerCamelCase__     :			Optional[Any]					,lowerCamelCase__     :			str					,lowerCamelCase__     :			Optional[int]    ):
									UpperCAmelCase__    =							name
									UpperCAmelCase__    =							value
									UpperCAmelCase__    =							weight







					def __repr__(       self     :			Tuple    ):
									return f'''{self.__class__.__name__}({self.name}, {self.value}, {self.weight})'''







					def 	__lowerCAmelCase		(       self     :			List[Any]    ):
									return self.value







					def 	__lowerCAmelCase		(       self     :			Optional[int]    ):
									return self.name







					def 	__lowerCAmelCase		(       self     :			Optional[Any]    ):
									return self.weight







					def 	__lowerCAmelCase		(       self     :			List[Any]    ):
									return self.value / self.weight






def 						a_		(      lowerCamelCase      ,    lowerCamelCase      ,    lowerCamelCase ):
				UpperCAmelCase__    =							[]
				for i in range(len(lowercase_ ) ):
								menu.append(Things(name[i]      ,    value[i]      ,    weight[i] ) )
				return menu



def 						a_		(      lowerCamelCase      ,    lowerCamelCase      ,    lowerCamelCase ):
				UpperCAmelCase__    =							sorted(lowercase_      ,    key=lowercase_      ,    reverse=lowercase_ )
				UpperCAmelCase__    =							[]
				UpperCAmelCase__    =							0.0, 0.0
				for i in range(len(lowercase_ ) ):
								if (total_cost + items_copy[i].get_weight()) <= max_cost:
												result.append(items_copy[i] )
												total_cost += items_copy[i].get_weight()
												total_value += items_copy[i].get_value()
				return (result, total_value)



def 						a_		(      ):
				pass



if __name__ == "__main__":
						import doctest

						doctest.testmod()




"""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__						:						Optional[int]		  =			False
class        snake_case (				unittest.TestCase       ):




					"""simple docstring"""

					pass






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




					"""simple docstring"""

					def 	__lowerCAmelCase		(       self     :			Tuple    ):
									UpperCAmelCase__    =							VersatileDiffusionImageVariationPipeline.from_pretrained('shi-labs/versatile-diffusion'    )
									pipe.to(lowerCamelCase__    )
									pipe.set_progress_bar_config(disable=lowerCamelCase__    )

									UpperCAmelCase__    =							load_image(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'    )
									UpperCAmelCase__    =							torch.manual_seed(0    )
									UpperCAmelCase__    =							pipe(
									    image=lowerCamelCase__					,generator=lowerCamelCase__					,guidance_scale=7.5					,num_inference_steps=50					,output_type='numpy'					,).images

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

									assert image.shape == (1, 512, 512, 3)
									UpperCAmelCase__    =							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



