





from __future__ import annotations

import math






def     lowerCamelCase__			(__lowerCamelCase	):

     if num <= 0:
          _SCREAMING_SNAKE_CASE       :	Tuple						    =							f"""{num}: Invalid input, please enter a positive integer."""
          raise ValueError(__lowerCamelCase	)

     _SCREAMING_SNAKE_CASE       :	int						    =							[True] * (num + 1)
     _SCREAMING_SNAKE_CASE       :	List[str]						    =							[]
     _SCREAMING_SNAKE_CASE       :	Tuple						    =							2
     _SCREAMING_SNAKE_CASE       :	List[str]						    =							int(math.sqrt(__lowerCamelCase	)	)

     while start <= end:
          # If start is a prime
          if sieve[start] is True:
               prime.append(__lowerCamelCase	)

               # Set multiples of start be False
               for i in range(start * start,       num + 1,       __lowerCamelCase	):
                    if sieve[i] is True:
                         _SCREAMING_SNAKE_CASE       :	Dict						    =							False

          start += 1

     for j in range(end + 1,       num + 1	):
          if sieve[j] is True:
               prime.append(__lowerCamelCase	)

     return prime


if __name__ == "__main__":
       print(prime_sieve(int(input('Enter a positive integer: ').strip())))






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCamelCase__      ={
    'configuration_lilt': ['LILT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LiltConfig'],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCamelCase__      =[
           'LILT_PRETRAINED_MODEL_ARCHIVE_LIST',
           'LiltForQuestionAnswering',
           'LiltForSequenceClassification',
           'LiltForTokenClassification',
           'LiltModel',
           'LiltPreTrainedModel',
       ]

if TYPE_CHECKING:
       from .configuration_lilt import LILT_PRETRAINED_CONFIG_ARCHIVE_MAP, LiltConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_lilt import (
                  LILT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  LiltForQuestionAnswering,
                  LiltForSequenceClassification,
                  LiltForTokenClassification,
                  LiltModel,
                  LiltPreTrainedModel,
              )

else:
       import sys

       UpperCamelCase__      =_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)