def lowerCamelCase  (   SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







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



def lowerCamelCase  (   SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







    __UpperCamelCase     :Optional[Any]			  =		0
    while number > 0:
        __UpperCamelCase     :Optional[Any]			  =		number % 10
        sum_of_digits += last_digit
        __UpperCamelCase     :str			  =		number // 10  # Removing the last_digit from the given number
    return sum_of_digits



def lowerCamelCase  (   SCREAMING_SNAKE_CASE = 100	):
    '''simple docstring'''







    __UpperCamelCase     :Dict			  =		factorial(SCREAMING_SNAKE_CASE	)
    __UpperCamelCase     :Dict			  =		split_and_add(SCREAMING_SNAKE_CASE	)
    return result


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












"""simple docstring"""





from typing import TYPE_CHECKING

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


__A											=		{"""configuration_reformer""": ["""REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ReformerConfig"""]}

try:
  if not is_sentencepiece_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __A											=		["""ReformerTokenizer"""]

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __A											=		["""ReformerTokenizerFast"""]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __A											=		[
      """REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """ReformerAttention""",
      """ReformerForMaskedLM""",
      """ReformerForQuestionAnswering""",
      """ReformerForSequenceClassification""",
      """ReformerLayer""",
      """ReformerModel""",
      """ReformerModelWithLMHead""",
      """ReformerPreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_reformer import REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, ReformerConfig

  try:
    if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_reformer import ReformerTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_reformer_fast import ReformerTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_reformer import (
        REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        ReformerAttention,
        ReformerForMaskedLM,
        ReformerForQuestionAnswering,
        ReformerForSequenceClassification,
        ReformerLayer,
        ReformerModel,
        ReformerModelWithLMHead,
        ReformerPreTrainedModel,
    )

else:
  import sys

  __A											=		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
