


'''simple docstring'''



from math import sqrt




def       _a      (		_lowercase   :      int    ):





     '''simple docstring'''






     __UpperCAmelCase      :					Optional[Any]    				=     [1]
     for i in range(2     ,   int(sqrt(_lowercase    )    ) + 1     ,   1    ):
          if number % i == 0:
               values.append(_lowercase    )
               if int(number // i    ) != i:
                    values.append(int(number // i    )    )
     return sorted(_lowercase    )




def       _a      (		_lowercase   :      int    ):





     '''simple docstring'''






     return sum(factors(_lowercase    )    ) > n




def       _a      (		_lowercase   :      int    ):





     '''simple docstring'''






     __UpperCAmelCase      :					str    				=     factors(_lowercase    )
     __UpperCAmelCase      :					List[str]    				=     len(_lowercase    )
     __UpperCAmelCase      :					Optional[int]    				=     [[0 for i in range(number + 1    )] for j in range(r + 1    )]
     for i in range(r + 1    ):
          __UpperCAmelCase      :					List[str]    				=     True

     for i in range(1     ,   number + 1    ):
          __UpperCAmelCase      :					Union[str, Any]    				=     False

     for i in range(1     ,   r + 1    ):
          for j in range(1     ,   number + 1    ):
               if j < values[i - 1]:
                    __UpperCAmelCase      :					Dict    				=     subset[i - 1][j]
               else:
                    __UpperCAmelCase      :					Any    				=     subset[i - 1][j] or subset[i - 1][j - values[i - 1]]

     return subset[r][number] != 0




def       _a      (		_lowercase   :      int    ):





     '''simple docstring'''






     return abundant(_lowercase    ) and not semi_perfect(_lowercase    )


if __name__ == "__main__":
      import doctest

      doctest.testmod(verbose=True)
      for number in (6_9, 7_0, 7_1):
            print(f"""{number} is {"" if weird(number) else "not "}weird.""")



'''simple docstring'''



from typing import TYPE_CHECKING

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


__UpperCAmelCase     :Union[str, Any]												=  {
    "configuration_mpnet": ["MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "MPNetConfig"],
    "tokenization_mpnet": ["MPNetTokenizer"],
}

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

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase     :Union[str, Any]												=  [
          "MPNET_PRETRAINED_MODEL_ARCHIVE_LIST",
          "MPNetForMaskedLM",
          "MPNetForMultipleChoice",
          "MPNetForQuestionAnswering",
          "MPNetForSequenceClassification",
          "MPNetForTokenClassification",
          "MPNetLayer",
          "MPNetModel",
          "MPNetPreTrainedModel",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase     :Any												=  [
          "TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST",
          "TFMPNetEmbeddings",
          "TFMPNetForMaskedLM",
          "TFMPNetForMultipleChoice",
          "TFMPNetForQuestionAnswering",
          "TFMPNetForSequenceClassification",
          "TFMPNetForTokenClassification",
          "TFMPNetMainLayer",
          "TFMPNetModel",
          "TFMPNetPreTrainedModel",
      ]


if TYPE_CHECKING:
      from .configuration_mpnet import MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP, MPNetConfig
      from .tokenization_mpnet import MPNetTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_mpnet_fast import MPNetTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_mpnet import (
                MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
                MPNetForMaskedLM,
                MPNetForMultipleChoice,
                MPNetForQuestionAnswering,
                MPNetForSequenceClassification,
                MPNetForTokenClassification,
                MPNetLayer,
                MPNetModel,
                MPNetPreTrainedModel,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_mpnet import (
                TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFMPNetEmbeddings,
                TFMPNetForMaskedLM,
                TFMPNetForMultipleChoice,
                TFMPNetForQuestionAnswering,
                TFMPNetForSequenceClassification,
                TFMPNetForTokenClassification,
                TFMPNetMainLayer,
                TFMPNetModel,
                TFMPNetPreTrainedModel,
            )

else:
      import sys

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