






'''simple docstring'''




from typing import TYPE_CHECKING

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


lowerCamelCase_				   =   {
    '''configuration_xlm''': ['''XLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''XLMConfig''', '''XLMOnnxConfig'''],
    '''tokenization_xlm''': ['''XLMTokenizer'''],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCamelCase_				   =   [
        '''XLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''XLMForMultipleChoice''',
        '''XLMForQuestionAnswering''',
        '''XLMForQuestionAnsweringSimple''',
        '''XLMForSequenceClassification''',
        '''XLMForTokenClassification''',
        '''XLMModel''',
        '''XLMPreTrainedModel''',
        '''XLMWithLMHeadModel''',
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCamelCase_				   =   [
        '''TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''TFXLMForMultipleChoice''',
        '''TFXLMForQuestionAnsweringSimple''',
        '''TFXLMForSequenceClassification''',
        '''TFXLMForTokenClassification''',
        '''TFXLMMainLayer''',
        '''TFXLMModel''',
        '''TFXLMPreTrainedModel''',
        '''TFXLMWithLMHeadModel''',
    ]


if TYPE_CHECKING:
    from .configuration_xlm import XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMConfig, XLMOnnxConfig
    from .tokenization_xlm import XLMTokenizer

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_xlm import (
            XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
            XLMForMultipleChoice,
            XLMForQuestionAnswering,
            XLMForQuestionAnsweringSimple,
            XLMForSequenceClassification,
            XLMForTokenClassification,
            XLMModel,
            XLMPreTrainedModel,
            XLMWithLMHeadModel,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_xlm import (
            TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFXLMForMultipleChoice,
            TFXLMForQuestionAnsweringSimple,
            TFXLMForSequenceClassification,
            TFXLMForTokenClassification,
            TFXLMMainLayer,
            TFXLMModel,
            TFXLMPreTrainedModel,
            TFXLMWithLMHeadModel,
        )

else:
    import sys

    lowerCamelCase_				   =   _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.""")