





from __future__ import annotations

from collections.abc import Generator



def _a				(	)	->						Generator[int, None, None]:







      '''simple docstring'''






      __A          =				{}
      __A          =				2
      while True:
            __A          =				factor_map.pop(lowerCamelCase					,				lowerCamelCase )
            if factor:
                  __A          =				factor + prime
                  while x in factor_map:
                        x += factor
                  __A          =				factor
            else:
                  __A          =				prime
                  yield prime
            prime += 1



def _a				(	lowerCamelCase:   float = 1e10 )	->						int:







      '''simple docstring'''






      __A          =				sieve()

      __A          =				1
      while True:
            __A          =				next(lowerCamelCase )
            if (2 * prime * n) > limit:
                  return n
            # Ignore the next prime as the reminder will be 2.
            next(lowerCamelCase )
            n += 2


if __name__ == "__main__":
       print(solution())












from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


snake_case__					:							Optional[Any]													=     {'configuration_ibert': ['IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'IBertConfig', 'IBertOnnxConfig']}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       snake_case__					:							List[str]													=     [
           'IBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
           'IBertForMaskedLM',
           'IBertForMultipleChoice',
           'IBertForQuestionAnswering',
           'IBertForSequenceClassification',
           'IBertForTokenClassification',
           'IBertModel',
           'IBertPreTrainedModel',
       ]

if TYPE_CHECKING:
       from .configuration_ibert import IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, IBertConfig, IBertOnnxConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_ibert import (
                  IBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  IBertForMaskedLM,
                  IBertForMultipleChoice,
                  IBertForQuestionAnswering,
                  IBertForSequenceClassification,
                  IBertForTokenClassification,
                  IBertModel,
                  IBertPreTrainedModel,
              )

else:
       import sys

       snake_case__					:							Dict													=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





