"""simple docstring"""




from typing import TYPE_CHECKING

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


_a   :	List[str] 					=     {
    'configuration_tapas': ['TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TapasConfig'],
    'tokenization_tapas': ['TapasTokenizer'],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _a   :	str 					=     [
         'TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST',
         'TapasForMaskedLM',
         'TapasForQuestionAnswering',
         'TapasForSequenceClassification',
         'TapasModel',
         'TapasPreTrainedModel',
         'load_tf_weights_in_tapas',
     ]
try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _a   :	Dict 					=     [
         'TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST',
         'TFTapasForMaskedLM',
         'TFTapasForQuestionAnswering',
         'TFTapasForSequenceClassification',
         'TFTapasModel',
         'TFTapasPreTrainedModel',
     ]


if TYPE_CHECKING:
     from .configuration_tapas import TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP, TapasConfig
     from .tokenization_tapas import TapasTokenizer

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tapas import (
              TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
              TapasForMaskedLM,
              TapasForQuestionAnswering,
              TapasForSequenceClassification,
              TapasModel,
              TapasPreTrainedModel,
              load_tf_weights_in_tapas,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_tapas import (
              TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFTapasForMaskedLM,
              TFTapasForQuestionAnswering,
              TFTapasForSequenceClassification,
              TFTapasModel,
              TFTapasPreTrainedModel,
          )


else:
     import sys

     _a   :	List[Any] 					=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





"""simple docstring"""




def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: int = 1000000			) ->   int:
  _lowerCAmelCase		: List[str]						    =  [i - 1 for i in range(limit + 1			)]

  for i in range(2    ,limit + 1			):
    if phi[i] == i - 1:
      for j in range(2 * i    ,limit + 1    ,_lowerCamelCase			):
        phi[j] -= phi[j] // i

  return sum(phi[2 : limit + 1]			)


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




