


'''simple docstring'''



from typing import TYPE_CHECKING

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


__UpperCAmelCase      :Any			    =       {
    "configuration_resnet": ["RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "ResNetConfig", "ResNetOnnxConfig"]
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase      :Optional[Any]			    =       [
          "RESNET_PRETRAINED_MODEL_ARCHIVE_LIST",
          "ResNetForImageClassification",
          "ResNetModel",
          "ResNetPreTrainedModel",
          "ResNetBackbone",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase      :Optional[int]			    =       [
          "TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST",
          "TFResNetForImageClassification",
          "TFResNetModel",
          "TFResNetPreTrainedModel",
      ]

try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase      :Optional[Any]			    =       [
          "FlaxResNetForImageClassification",
          "FlaxResNetModel",
          "FlaxResNetPreTrainedModel",
      ]

if TYPE_CHECKING:
      from .configuration_resnet import RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP, ResNetConfig, ResNetOnnxConfig

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_resnet import (
                RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
                ResNetBackbone,
                ResNetForImageClassification,
                ResNetModel,
                ResNetPreTrainedModel,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_resnet import (
                TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFResNetForImageClassification,
                TFResNetModel,
                TFResNetPreTrainedModel,
            )

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_resnet import FlaxResNetForImageClassification, FlaxResNetModel, FlaxResNetPreTrainedModel


else:
      import sys

      __UpperCAmelCase      :Any			    =       _LazyModule(__name__, globals()["__file__"], _import_structure)



'''simple docstring'''



def      _a     (						_lowercase :     List[str]  ):





     '''simple docstring'''






     __UpperCAmelCase     :			str  							=      1
     __UpperCAmelCase     :			List[str]  							=      2
     while i * i <= n:
          __UpperCAmelCase     :			Optional[Any]  							=      0
          while n % i == 0:
               n //= i
               multiplicity += 1
          n_divisors *= multiplicity + 1
          i += 1
     if n > 1:
          n_divisors *= 2
     return n_divisors




def      _a     (						):





     '''simple docstring'''






     __UpperCAmelCase     :			Optional[Any]  							=      1
     __UpperCAmelCase     :			List[Any]  							=      1

     while True:
          i += 1
          t_num += i

          if count_divisors(_lowercase  ) > 500:
               break

     return t_num


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