





'''simple docstring'''






from typing import TYPE_CHECKING

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


__lowerCamelCase						: Tuple		=   {
    '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						: Any		=   [
          '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						: Tuple		=   _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)








'''simple docstring'''






def 					_a   (__SCREAMING_SNAKE_CASE   ):


  """simple docstring"""

  return 1 if digit in (0, 1) else (digit * factorial(digit - 1   ))


def 					_a   (__SCREAMING_SNAKE_CASE   ):


  """simple docstring"""

  _UpperCamelCase       			=0
  _UpperCamelCase       			=number
  while duplicate > 0:
    _UpperCamelCase			,     _UpperCamelCase       			=divmod(__SCREAMING_SNAKE_CASE  ,      10   )
    fact_sum += factorial(__SCREAMING_SNAKE_CASE   )
  return fact_sum == number


if __name__ == "__main__":
      print('Program to check whether a number is a Krisnamurthy Number or not.')
      __lowerCamelCase						: Optional[Any]		=   int(input('Enter number: ').strip())
      print(
          F"""{number} is {'' if krishnamurthy(number) else 'not '}a Krishnamurthy Number."""
      )






