





'''simple docstring'''




from typing import TYPE_CHECKING

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


lowercase__       : str         =       {
    '''configuration_convbert''': ['''CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ConvBertConfig''', '''ConvBertOnnxConfig'''],
    '''tokenization_convbert''': ['''ConvBertTokenizer'''],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase__       : Tuple         =       ['''ConvBertTokenizerFast''']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase__       : int         =       [
      '''CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''ConvBertForMaskedLM''',
      '''ConvBertForMultipleChoice''',
      '''ConvBertForQuestionAnswering''',
      '''ConvBertForSequenceClassification''',
      '''ConvBertForTokenClassification''',
      '''ConvBertLayer''',
      '''ConvBertModel''',
      '''ConvBertPreTrainedModel''',
      '''load_tf_weights_in_convbert''',
  ]


try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase__       : List[Any]         =       [
      '''TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''TFConvBertForMaskedLM''',
      '''TFConvBertForMultipleChoice''',
      '''TFConvBertForQuestionAnswering''',
      '''TFConvBertForSequenceClassification''',
      '''TFConvBertForTokenClassification''',
      '''TFConvBertLayer''',
      '''TFConvBertModel''',
      '''TFConvBertPreTrainedModel''',
  ]


if TYPE_CHECKING:
  from .configuration_convbert import CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvBertConfig, ConvBertOnnxConfig
  from .tokenization_convbert import ConvBertTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_convbert_fast import ConvBertTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_convbert import (
        CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        ConvBertForMaskedLM,
        ConvBertForMultipleChoice,
        ConvBertForQuestionAnswering,
        ConvBertForSequenceClassification,
        ConvBertForTokenClassification,
        ConvBertLayer,
        ConvBertModel,
        ConvBertPreTrainedModel,
        load_tf_weights_in_convbert,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_convbert import (
        TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFConvBertForMaskedLM,
        TFConvBertForMultipleChoice,
        TFConvBertForQuestionAnswering,
        TFConvBertForSequenceClassification,
        TFConvBertForTokenClassification,
        TFConvBertLayer,
        TFConvBertModel,
        TFConvBertPreTrainedModel,
    )


else:
  import sys

  lowercase__       : Union[str, Any]         =       _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






'''simple docstring'''




def 			_lowerCAmelCase     (    __snake_case    :					int     )	->	bool:
     return sum(i for i in range(1					,				number // 2 + 1     ) if number % i == 0     ) == number


if __name__ == "__main__":
  print('''Program to check whether a number is a Perfect number or not...''')
  lowercase__       : int         =       int(input('''Enter number: ''').strip())
  print(f"""{number} is {"" if perfect(number) else "not "}a Perfect Number.""")