

import math


def        __lowercase    (  UpperCAmelCase__      ):

					"""simple docstring"""






					__lowerCAmelCase					=							[]
					__lowerCAmelCase					=							2
					__lowerCAmelCase					=							int(math.sqrt(UpperCAmelCase__      )      )  # Size of every segment
					__lowerCAmelCase					=							[True] * (end + 1)
					__lowerCAmelCase					=							[]

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

					__lowerCAmelCase					=							end + 1
					__lowerCAmelCase					=							min(2 * end		,					UpperCAmelCase__      )

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

															for j in range(UpperCAmelCase__		,					high + 1		,					UpperCAmelCase__      ):
																				__lowerCAmelCase					=							False

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

										__lowerCAmelCase					=							high + 1
										__lowerCAmelCase					=							min(high + end		,					UpperCAmelCase__      )

					return prime
print(sieve(1_0**6))









import warnings

from ...utils import logging
from .image_processing_poolformer import PoolFormerImageProcessor


lowerCamelCase      				=     logging.get_logger(__name__)





class     snake_case_			(				_a							):


							"""simple docstring"""





							def __init__( self					,						*_A					,						**_A							):
												warnings.warn(
												    'The class PoolFormerFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
												    ' Please use PoolFormerImageProcessor instead.'					,						_A					,						)
												super().__init__(*_A					,						**_A							)






