






'''simple docstring'''







import math





def  SCREAMING_SNAKE_CASE_       (UpperCamelCase      )      ->       list[int]:
							lowerCamelCase__					:	Tuple         =       []
							lowerCamelCase__					:	int         =       2
							lowerCamelCase__					:	str         =       int(math.sqrt(UpperCamelCase      )      )  # Size of every segment
							lowerCamelCase__					:	Optional[int]         =       [True] * (end + 1)
							lowerCamelCase__					:	List[str]         =       []

							while start <= end:
														if temp[start] is True:
																					in_prime.append(UpperCamelCase      )
																					for i in range(start * start				,			end + 1				,			UpperCamelCase      ):
																												lowerCamelCase__					:	Optional[int]         =       False
														start += 1
							prime += in_prime

							lowerCamelCase__					:	Optional[int]         =       end + 1
							lowerCamelCase__					:	Tuple         =       min(2 * end				,			UpperCamelCase      )

							while low <= n:
														lowerCamelCase__					:	Dict         =       [True] * (high - low + 1)
														for each in in_prime:
																					lowerCamelCase__					:	List[Any]         =       math.floor(low / each      ) * each
																					if t < low:
																												t += each

																					for j in range(UpperCamelCase				,			high + 1				,			UpperCamelCase      ):
																												lowerCamelCase__					:	Tuple         =       False

														for j in range(len(UpperCamelCase      )      ):
																					if temp[j] is True:
																												prime.append(j + low      )

														lowerCamelCase__					:	List[Any]         =       high + 1
														lowerCamelCase__					:	Any         =       min(high + end				,			UpperCamelCase      )

							return prime



print(sieve(10**6))









'''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	:			Union[str, Any]         =False

class _lowercase	(				unittest.TestCase		):
							pass



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




							def 		lowerCamelCase_				(							self:				Optional[int]     ):
														lowerCamelCase__					:	Tuple         =       VersatileDiffusionImageVariationPipeline.from_pretrained("""shi-labs/versatile-diffusion"""     )
														pipe.to(UpperCamelCase__     )
														pipe.set_progress_bar_config(disable=UpperCamelCase__     )

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

														lowerCamelCase__					:	List[str]         =       image[0, 253:256, 253:256, -1]

														assert image.shape == (1, 512, 512, 3)
														lowerCamelCase__					:	Tuple         =       np.array([0.0_441, 0.0_469, 0.0_507, 0.0_575, 0.0_632, 0.0_650, 0.0_865, 0.0_909, 0.0_945]     )

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

