
from __future__ import annotations

UpperCamelCase_   	=	[True] * 1_0_0_0_0_0_1
UpperCamelCase_   	=	2
while i * i <= 1_0_0_0_0_0_0:
					if seive[i]:
										for j in range(i * i, 1_0_0_0_0_0_1, i):
															UpperCamelCase_   	=	False
					i += 1






def 		_UpperCAmelCase   (	UpperCamelCase:     int  ):






	"""simple docstring"""


	return seive[n]






def 		_UpperCAmelCase   (	UpperCamelCase:     int  ):






	"""simple docstring"""


	return any(digit in "02468" for digit in str(UpperCamelCase  )  )






def 		_UpperCAmelCase   (	UpperCamelCase:     int = 1_0_0_0_0_0_0  ):






	"""simple docstring"""


	__lowerCAmelCase         =							[2]  # result already includes the number 2.
	for num in range(3			,							limit + 1			,							2  ):
		if is_prime(UpperCamelCase  ) and not contains_an_even_digit(UpperCamelCase  ):
			__lowerCAmelCase         =							str(UpperCamelCase  )
			__lowerCAmelCase         =							[int(str_num[j:] + str_num[:j]  ) for j in range(len(UpperCamelCase  )  )]
			if all(is_prime(UpperCamelCase  ) for i in list_nums  ):
				result.append(UpperCamelCase  )
	return result






def 		_UpperCAmelCase   (	):






	"""simple docstring"""


	return len(find_circular_primes()  )


if __name__ == "__main__":
					print(f'''{len(find_circular_primes()) = }''')








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


UpperCamelCase_   	=	False

class       a		(   unittest.TestCase					):
							pass

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







							def       UpperCAmelCase__					(						self  :  Dict ):
								"""simple docstring"""







								__lowerCAmelCase         =							VersatileDiffusionImageVariationPipeline.from_pretrained("shi-labs/versatile-diffusion" )
								pipe.to(snake_case__ )
								pipe.set_progress_bar_config(disable=snake_case__ )

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

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

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






