





from typing import TYPE_CHECKING

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


_lowerCamelCase			:	Any							 =     {
    "configuration_roberta_prelayernorm": [
        "ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "RobertaPreLayerNormConfig",
        "RobertaPreLayerNormOnnxConfig",
    ],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCamelCase			:	Union[str, Any]							 =     [
      "ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST",
      "RobertaPreLayerNormForCausalLM",
      "RobertaPreLayerNormForMaskedLM",
      "RobertaPreLayerNormForMultipleChoice",
      "RobertaPreLayerNormForQuestionAnswering",
      "RobertaPreLayerNormForSequenceClassification",
      "RobertaPreLayerNormForTokenClassification",
      "RobertaPreLayerNormModel",
      "RobertaPreLayerNormPreTrainedModel",
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCamelCase			:	Optional[Any]							 =     [
      "TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST",
      "TFRobertaPreLayerNormForCausalLM",
      "TFRobertaPreLayerNormForMaskedLM",
      "TFRobertaPreLayerNormForMultipleChoice",
      "TFRobertaPreLayerNormForQuestionAnswering",
      "TFRobertaPreLayerNormForSequenceClassification",
      "TFRobertaPreLayerNormForTokenClassification",
      "TFRobertaPreLayerNormMainLayer",
      "TFRobertaPreLayerNormModel",
      "TFRobertaPreLayerNormPreTrainedModel",
  ]

try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCamelCase			:	List[Any]							 =     [
      "FlaxRobertaPreLayerNormForCausalLM",
      "FlaxRobertaPreLayerNormForMaskedLM",
      "FlaxRobertaPreLayerNormForMultipleChoice",
      "FlaxRobertaPreLayerNormForQuestionAnswering",
      "FlaxRobertaPreLayerNormForSequenceClassification",
      "FlaxRobertaPreLayerNormForTokenClassification",
      "FlaxRobertaPreLayerNormModel",
      "FlaxRobertaPreLayerNormPreTrainedModel",
  ]


if TYPE_CHECKING:
  from .configuration_roberta_prelayernorm import (
      ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP,
      RobertaPreLayerNormConfig,
      RobertaPreLayerNormOnnxConfig,
  )

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_roberta_prelayernorm import (
        ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
        RobertaPreLayerNormForCausalLM,
        RobertaPreLayerNormForMaskedLM,
        RobertaPreLayerNormForMultipleChoice,
        RobertaPreLayerNormForQuestionAnswering,
        RobertaPreLayerNormForSequenceClassification,
        RobertaPreLayerNormForTokenClassification,
        RobertaPreLayerNormModel,
        RobertaPreLayerNormPreTrainedModel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_roberta_prelayernorm import (
        TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFRobertaPreLayerNormForCausalLM,
        TFRobertaPreLayerNormForMaskedLM,
        TFRobertaPreLayerNormForMultipleChoice,
        TFRobertaPreLayerNormForQuestionAnswering,
        TFRobertaPreLayerNormForSequenceClassification,
        TFRobertaPreLayerNormForTokenClassification,
        TFRobertaPreLayerNormMainLayer,
        TFRobertaPreLayerNormModel,
        TFRobertaPreLayerNormPreTrainedModel,
    )

  try:
    if not is_flax_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flax_roberta_prelayernorm import (
        FlaxRobertaPreLayerNormForCausalLM,
        FlaxRobertaPreLayerNormForMaskedLM,
        FlaxRobertaPreLayerNormForMultipleChoice,
        FlaxRobertaPreLayerNormForQuestionAnswering,
        FlaxRobertaPreLayerNormForSequenceClassification,
        FlaxRobertaPreLayerNormForTokenClassification,
        FlaxRobertaPreLayerNormModel,
        FlaxRobertaPreLayerNormPreTrainedModel,
    )

else:
  import sys

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








def SCREAMING_SNAKE_CASE       ( lowercase_		)     -> int:



       """simple docstring"""

       A__   				=					1
       for i in range(1				,       num + 1		):
              fact *= i
       return fact







def SCREAMING_SNAKE_CASE       ( lowercase_		)     -> int:



       """simple docstring"""

       A__   				=					0
       while number > 0:
              A__   				=					number % 10
              sum_of_digits += last_digit
              A__   				=					number // 10  # Removing the last_digit from the given number
       return sum_of_digits







def SCREAMING_SNAKE_CASE       ( lowercase_ = 100		)     -> int:



       """simple docstring"""

       A__   				=					factorial(lowercase_		)
       A__   				=					split_and_add(lowercase_		)
       return result


if __name__ == "__main__":
  print(solution(int(input("""Enter the Number: """).strip())))


