





'''simple docstring'''



from __future__ import annotations




def 				_lowerCAmelCase  (			lowerCamelCase_     :					int			):
			__lowercase			  =							[True] * limit
			__lowercase			  =							False
			__lowercase			  =							False
			__lowercase			  =							True

			for i in range(3						,    int(limit**0.5 + 1			)						,    2			):
						__lowercase			  =							i * 2
						while index < limit:
									__lowercase			  =							False
									__lowercase			  =							index + i

			__lowercase			  =							[2]

			for i in range(3						,    lowerCamelCase_						,    2			):
						if is_prime[i]:
									primes.append(lowerCamelCase_			)

			return primes




def 				_lowerCAmelCase  (			lowerCamelCase_     :					int = 1_0_0_0_0_0_0			):
			__lowercase			  =							prime_sieve(lowerCamelCase_			)
			__lowercase			  =							0
			__lowercase			  =							0

			for i in range(len(lowerCamelCase_			)			):
						for j in range(i + length						,    len(lowerCamelCase_			)			):
									__lowercase			  =							sum(primes[i:j]			)
									if sol >= ceiling:
												break

									if sol in primes:
												__lowercase			  =							j - i
												__lowercase			  =							sol

			return largest


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












'''simple docstring'''



from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import BaseOutput, is_torch_available, is_transformers_available






@dataclass
class 							__lowercase    ( lowerCAmelCase__  ):







	'''simple docstring'''

	a      :			Union[List[PIL.Image.Image], np.ndarray]
	a      :			Optional[List[bool]]


if is_transformers_available() and is_torch_available():
	from .pipeline_semantic_stable_diffusion import SemanticStableDiffusionPipeline





