
def a__    (       A_,     A_					):






  '''simple docstring'''





  return base * power(A_,     (exponent - 1)					) if exponent else 1


if __name__ == "__main__":
    print('Raise base to the power of exponent using recursion...')
    __lowerCAmelCase			:     int           =   int(input('Enter the base: ').strip())
    __lowerCAmelCase			:     Optional[int]           =   int(input('Enter the exponent: ').strip())
    __lowerCAmelCase			:     Dict           =   power(base, abs(exponent))
    if exponent < 0:  # power() does not properly deal w/ negative exponents
        __lowerCAmelCase			:     Union[str, Any]           =   1 / result
    print(F'''{base} to the power of {exponent} is {result}''')



from typing import TYPE_CHECKING

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


__lowerCAmelCase			:     List[str]           =   {
    '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			:     str           =   [
        '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			:     Dict           =   [
        '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			:     Any           =   _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

