



"""simple docstring"""
def      __magic_name__				(							lowercase = 200_0000 ):
   SCREAMING_SNAKE_CASE_: List[Any]     			=[0 for i in range(n + 1 )]
   SCREAMING_SNAKE_CASE_: Union[str, Any]     			=1
   SCREAMING_SNAKE_CASE_: Optional[Any]     			=1

   for i in range(2							,			int(n**0.5 ) + 1 ):
      if primality_list[i] == 0:
         for j in range(i * i							,			n + 1							,			lowercase ):
            SCREAMING_SNAKE_CASE_: Optional[int]     			=1
   SCREAMING_SNAKE_CASE_: Dict     			=0
   for i in range(lowercase ):
      if primality_list[i] == 0:
         sum_of_primes += i
   return sum_of_primes


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








"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


_UpperCAmelCase     =			{
    """configuration_xlm""": ["""XLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """XLMConfig""", """XLMOnnxConfig"""],
    """tokenization_xlm""": ["""XLMTokenizer"""],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCAmelCase     =			[
       """XLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """XLMForMultipleChoice""",
       """XLMForQuestionAnswering""",
       """XLMForQuestionAnsweringSimple""",
       """XLMForSequenceClassification""",
       """XLMForTokenClassification""",
       """XLMModel""",
       """XLMPreTrainedModel""",
       """XLMWithLMHeadModel""",
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCAmelCase     =			[
       """TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """TFXLMForMultipleChoice""",
       """TFXLMForQuestionAnsweringSimple""",
       """TFXLMForSequenceClassification""",
       """TFXLMForTokenClassification""",
       """TFXLMMainLayer""",
       """TFXLMModel""",
       """TFXLMPreTrainedModel""",
       """TFXLMWithLMHeadModel""",
   ]


if TYPE_CHECKING:
   from .configuration_xlm import XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMConfig, XLMOnnxConfig
   from .tokenization_xlm import XLMTokenizer

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_xlm import (
          XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
          XLMForMultipleChoice,
          XLMForQuestionAnswering,
          XLMForQuestionAnsweringSimple,
          XLMForSequenceClassification,
          XLMForTokenClassification,
          XLMModel,
          XLMPreTrainedModel,
          XLMWithLMHeadModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_xlm import (
          TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFXLMForMultipleChoice,
          TFXLMForQuestionAnsweringSimple,
          TFXLMForSequenceClassification,
          TFXLMForTokenClassification,
          TFXLMMainLayer,
          TFXLMModel,
          TFXLMPreTrainedModel,
          TFXLMWithLMHeadModel,
      )

else:
   import sys

   _UpperCAmelCase     =			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



