

from typing import TYPE_CHECKING

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


__UpperCAmelCase								=			{
    """configuration_mobilebert""": [
        """MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """MobileBertConfig""",
        """MobileBertOnnxConfig""",
    ],
    """tokenization_mobilebert""": ["""MobileBertTokenizer"""],
}

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __UpperCAmelCase								=			["""MobileBertTokenizerFast"""]

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __UpperCAmelCase								=			[
         """MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """MobileBertForMaskedLM""",
         """MobileBertForMultipleChoice""",
         """MobileBertForNextSentencePrediction""",
         """MobileBertForPreTraining""",
         """MobileBertForQuestionAnswering""",
         """MobileBertForSequenceClassification""",
         """MobileBertForTokenClassification""",
         """MobileBertLayer""",
         """MobileBertModel""",
         """MobileBertPreTrainedModel""",
         """load_tf_weights_in_mobilebert""",
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __UpperCAmelCase								=			[
         """TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """TFMobileBertForMaskedLM""",
         """TFMobileBertForMultipleChoice""",
         """TFMobileBertForNextSentencePrediction""",
         """TFMobileBertForPreTraining""",
         """TFMobileBertForQuestionAnswering""",
         """TFMobileBertForSequenceClassification""",
         """TFMobileBertForTokenClassification""",
         """TFMobileBertMainLayer""",
         """TFMobileBertModel""",
         """TFMobileBertPreTrainedModel""",
     ]


if TYPE_CHECKING:
     from .configuration_mobilebert import (
         MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
         MobileBertConfig,
         MobileBertOnnxConfig,
     )
     from .tokenization_mobilebert import MobileBertTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_mobilebert_fast import MobileBertTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_mobilebert import (
              MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
              MobileBertForMaskedLM,
              MobileBertForMultipleChoice,
              MobileBertForNextSentencePrediction,
              MobileBertForPreTraining,
              MobileBertForQuestionAnswering,
              MobileBertForSequenceClassification,
              MobileBertForTokenClassification,
              MobileBertLayer,
              MobileBertModel,
              MobileBertPreTrainedModel,
              load_tf_weights_in_mobilebert,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_mobilebert import (
              TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFMobileBertForMaskedLM,
              TFMobileBertForMultipleChoice,
              TFMobileBertForNextSentencePrediction,
              TFMobileBertForPreTraining,
              TFMobileBertForQuestionAnswering,
              TFMobileBertForSequenceClassification,
              TFMobileBertForTokenClassification,
              TFMobileBertMainLayer,
              TFMobileBertModel,
              TFMobileBertPreTrainedModel,
          )

else:
     import sys

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





def      snake_case_				()      ->     List[Any]:
   for n in range(1 ,       1_0_0_0_0_0_0					):
      yield n * (n + 1) // 2





def      snake_case_				(__A					:						Dict					)      ->     Tuple:
   __lowerCAmelCase  :      Optional[int]    =						1
   __lowerCAmelCase  :      Optional[int]    =						2
   while i * i <= n:
      __lowerCAmelCase  :      Optional[int]    =						0
      while n % i == 0:
         n //= i
         multiplicity += 1
      divisors_count *= multiplicity + 1
      i += 1
   if n > 1:
      divisors_count *= 2
   return divisors_count





def      snake_case_				()      ->     Dict:
   return next(i for i in triangle_number_generator() if count_divisors(__A					) > 5_0_0					)


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


