"""simple docstring"""






from __future__ import annotations

import sys
from collections import deque
from typing import Generic, TypeVar

lowerCAmelCase__						:						Any		  =			TypeVar('T')
class        snake_case (				Generic[T]       ):




					"""simple docstring"""

					snake_case__       					=				42  # Cache store of keys
					snake_case__       					=				42  # References of the keys in cache
					snake_case__       					=				10  # Maximum capacity of cache







					def __init__(       self     :			List[Any]					,lowerCamelCase__     :			int    ):
									UpperCAmelCase__    =							deque()
									UpperCAmelCase__    =							set()
									if not n:
													UpperCAmelCase__    =							sys.maxsize
									elif n < 0:
													raise ValueError('n should be an integer greater than 0.'    )
									else:
													UpperCAmelCase__    =							n







					def 	__lowerCAmelCase		(       self     :			int					,lowerCamelCase__     :			T    ):
									if x not in self.key_reference:
													if len(self.dq_store    ) == LRUCache._MAX_CAPACITY:
																	UpperCAmelCase__    =							self.dq_store.pop()
																	self.key_reference.remove(lowerCamelCase__    )
									else:
													self.dq_store.remove(lowerCamelCase__    )

									self.dq_store.appendleft(lowerCamelCase__    )
									self.key_reference.add(lowerCamelCase__    )







					def 	__lowerCAmelCase		(       self     :			List[str]    ):
									for k in self.dq_store:
													print(lowerCamelCase__    )







					def __repr__(       self     :			List[Any]    ):
									return f'''LRUCache({self._MAX_CAPACITY}) => {list(self.dq_store    )}'''


if __name__ == "__main__":
						import doctest

						doctest.testmod()

						lowerCAmelCase__						:						LRUCache[str | int]		  =			LRUCache(4)
						lru_cache.refer('A')
						lru_cache.refer(2)
						lru_cache.refer(3)
						lru_cache.refer('A')
						lru_cache.refer(4)
						lru_cache.refer(5)
						lru_cache.display()

						print(lru_cache)
						assert str(lru_cache) == "LRUCache(4) => [5, 4, 'A', 3]"




"""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



