

from typing import TYPE_CHECKING

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


__lowercase     : Optional[int]       =						{
    '''configuration_tapas''': ['''TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''TapasConfig'''],
    '''tokenization_tapas''': ['''TapasTokenizer'''],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __lowercase     : int       =						[
         '''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:
     __lowercase     : List[Any]       =						[
         '''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

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









'''simple docstring'''



def   lowercase__(			__UpperCamelCase:					int = 1_00_00_00    ):

      """simple docstring"""






      SCREAMING_SNAKE_CASE   :      Tuple    					=						[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      ,__UpperCamelCase    ):
                        phi[j] -= phi[j] // i

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


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


