


"""simple docstring"""







from typing import TYPE_CHECKING

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


lowerCamelCase_       :       Dict      						=  {
    """configuration_convbert""": ["""CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ConvBertConfig""", """ConvBertOnnxConfig"""],
    """tokenization_convbert""": ["""ConvBertTokenizer"""],
}

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase_       :       Any      						=  ["""ConvBertTokenizerFast"""]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase_       :       List[Any]      						=  [
           """CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """ConvBertForMaskedLM""",
           """ConvBertForMultipleChoice""",
           """ConvBertForQuestionAnswering""",
           """ConvBertForSequenceClassification""",
           """ConvBertForTokenClassification""",
           """ConvBertLayer""",
           """ConvBertModel""",
           """ConvBertPreTrainedModel""",
           """load_tf_weights_in_convbert""",
       ]


try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase_       :       int      						=  [
           """TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """TFConvBertForMaskedLM""",
           """TFConvBertForMultipleChoice""",
           """TFConvBertForQuestionAnswering""",
           """TFConvBertForSequenceClassification""",
           """TFConvBertForTokenClassification""",
           """TFConvBertLayer""",
           """TFConvBertModel""",
           """TFConvBertPreTrainedModel""",
       ]


if TYPE_CHECKING:
       from .configuration_convbert import CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvBertConfig, ConvBertOnnxConfig
       from .tokenization_convbert import ConvBertTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_convbert_fast import ConvBertTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_convbert import (
                  CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  ConvBertForMaskedLM,
                  ConvBertForMultipleChoice,
                  ConvBertForQuestionAnswering,
                  ConvBertForSequenceClassification,
                  ConvBertForTokenClassification,
                  ConvBertLayer,
                  ConvBertModel,
                  ConvBertPreTrainedModel,
                  load_tf_weights_in_convbert,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_convbert import (
                  TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFConvBertForMaskedLM,
                  TFConvBertForMultipleChoice,
                  TFConvBertForQuestionAnswering,
                  TFConvBertForSequenceClassification,
                  TFConvBertForTokenClassification,
                  TFConvBertLayer,
                  TFConvBertModel,
                  TFConvBertPreTrainedModel,
              )


else:
       import sys

       lowerCamelCase_       :       Dict      						=  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



"""simple docstring"""







def _A						(						lowercase = 2_00_00_00		):







    """simple docstring"""
    a					=[0 for i in range(n + 1		)]
    a					=1
    a					=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		):
                a					=1
    a					=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() = }')