





"""simple docstring"""



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


a_					=     {
    """configuration_roberta_prelayernorm""": [
        """ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """RobertaPreLayerNormConfig""",
        """RobertaPreLayerNormOnnxConfig""",
    ],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a_					=     [
           """ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """RobertaPreLayerNormForCausalLM""",
           """RobertaPreLayerNormForMaskedLM""",
           """RobertaPreLayerNormForMultipleChoice""",
           """RobertaPreLayerNormForQuestionAnswering""",
           """RobertaPreLayerNormForSequenceClassification""",
           """RobertaPreLayerNormForTokenClassification""",
           """RobertaPreLayerNormModel""",
           """RobertaPreLayerNormPreTrainedModel""",
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a_					=     [
           """TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """TFRobertaPreLayerNormForCausalLM""",
           """TFRobertaPreLayerNormForMaskedLM""",
           """TFRobertaPreLayerNormForMultipleChoice""",
           """TFRobertaPreLayerNormForQuestionAnswering""",
           """TFRobertaPreLayerNormForSequenceClassification""",
           """TFRobertaPreLayerNormForTokenClassification""",
           """TFRobertaPreLayerNormMainLayer""",
           """TFRobertaPreLayerNormModel""",
           """TFRobertaPreLayerNormPreTrainedModel""",
       ]

try:
       if not is_flax_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a_					=     [
           """FlaxRobertaPreLayerNormForCausalLM""",
           """FlaxRobertaPreLayerNormForMaskedLM""",
           """FlaxRobertaPreLayerNormForMultipleChoice""",
           """FlaxRobertaPreLayerNormForQuestionAnswering""",
           """FlaxRobertaPreLayerNormForSequenceClassification""",
           """FlaxRobertaPreLayerNormForTokenClassification""",
           """FlaxRobertaPreLayerNormModel""",
           """FlaxRobertaPreLayerNormPreTrainedModel""",
       ]


if TYPE_CHECKING:
       from .configuration_roberta_prelayernorm import (
           ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP,
           RobertaPreLayerNormConfig,
           RobertaPreLayerNormOnnxConfig,
       )

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_roberta_prelayernorm import (
                  ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
                  RobertaPreLayerNormForCausalLM,
                  RobertaPreLayerNormForMaskedLM,
                  RobertaPreLayerNormForMultipleChoice,
                  RobertaPreLayerNormForQuestionAnswering,
                  RobertaPreLayerNormForSequenceClassification,
                  RobertaPreLayerNormForTokenClassification,
                  RobertaPreLayerNormModel,
                  RobertaPreLayerNormPreTrainedModel,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_roberta_prelayernorm import (
                  TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFRobertaPreLayerNormForCausalLM,
                  TFRobertaPreLayerNormForMaskedLM,
                  TFRobertaPreLayerNormForMultipleChoice,
                  TFRobertaPreLayerNormForQuestionAnswering,
                  TFRobertaPreLayerNormForSequenceClassification,
                  TFRobertaPreLayerNormForTokenClassification,
                  TFRobertaPreLayerNormMainLayer,
                  TFRobertaPreLayerNormModel,
                  TFRobertaPreLayerNormPreTrainedModel,
              )

       try:
              if not is_flax_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_flax_roberta_prelayernorm import (
                  FlaxRobertaPreLayerNormForCausalLM,
                  FlaxRobertaPreLayerNormForMaskedLM,
                  FlaxRobertaPreLayerNormForMultipleChoice,
                  FlaxRobertaPreLayerNormForQuestionAnswering,
                  FlaxRobertaPreLayerNormForSequenceClassification,
                  FlaxRobertaPreLayerNormForTokenClassification,
                  FlaxRobertaPreLayerNormModel,
                  FlaxRobertaPreLayerNormPreTrainedModel,
              )

else:
       import sys

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











"""simple docstring"""



def 						__lowercase	(  snake_case_    :    int = 2000000		)  ->int:



    '''simple docstring'''







    __A       :		List[Any]							=	[0 for i in range(n + 1		)]
    __A       :		str							=	1
    __A       :		Dict							=	1

    for i in range(2							,int(n**0.5		) + 1		):
        if primality_list[i] == 0:
            for j in range(i * i							,n + 1							,snake_case_		):
                __A       :		List[Any]							=	1
    __A       :		Union[str, Any]							=	0
    for i in range(snake_case_		):
        if primality_list[i] == 0:
            sum_of_primes += i
    return sum_of_primes


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




