



from typing import TYPE_CHECKING

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


a_      :      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_      :      str    =     [
         '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_      :      Optional[Any]    =     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)










import math
from collections.abc import Iterator
from itertools import takewhile






def     __a					(		__UpperCAmelCase       ):

 if 1 < number < 4:
  # 2 and 3 are primes
  return True
 elif number < 2 or number % 2 == 0 or number % 3 == 0:
  # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
  return False

 # All primes number are in format of 6k +/- 1
 for i in range(5							,  int(math.sqrt(__UpperCAmelCase       ) + 1       )							,  6       ):
  if number % i == 0 or number % (i + 2) == 0:
   return False
 return True






def     __a					(		):
 a__												= 2
 while True:
  if is_prime(__UpperCAmelCase       ):
   yield num
  num += 1






def     __a					(		__UpperCAmelCase = 200_0000       ):
 return sum(takewhile(lambda __UpperCAmelCase       : x < n							,  prime_generator()       )       )


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





