


'''simple docstring'''





from typing import TYPE_CHECKING

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


UpperCamelCase :				int						    =       {
    '''configuration_deberta_v2''': ['''DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DebertaV2Config''', '''DebertaV2OnnxConfig'''],
    '''tokenization_deberta_v2''': ['''DebertaV2Tokenizer'''],
}

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCamelCase :				str						    =       ['''DebertaV2TokenizerFast''']

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCamelCase :				Optional[Any]						    =       [
					    '''TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''TFDebertaV2ForMaskedLM''',
					    '''TFDebertaV2ForQuestionAnswering''',
					    '''TFDebertaV2ForSequenceClassification''',
					    '''TFDebertaV2ForTokenClassification''',
					    '''TFDebertaV2Model''',
					    '''TFDebertaV2PreTrainedModel''',
					]

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCamelCase :				Union[str, Any]						    =       [
					    '''DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''DebertaV2ForMaskedLM''',
					    '''DebertaV2ForMultipleChoice''',
					    '''DebertaV2ForQuestionAnswering''',
					    '''DebertaV2ForSequenceClassification''',
					    '''DebertaV2ForTokenClassification''',
					    '''DebertaV2Model''',
					    '''DebertaV2PreTrainedModel''',
					]


if TYPE_CHECKING:
					from .configuration_deberta_va import (
					    DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    DebertaVaConfig,
					    DebertaVaOnnxConfig,
					)
					from .tokenization_deberta_va import DebertaVaTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_deberta_va_fast import DebertaVaTokenizerFast

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_deberta_va import (
										    TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFDebertaVaForMaskedLM,
										    TFDebertaVaForQuestionAnswering,
										    TFDebertaVaForSequenceClassification,
										    TFDebertaVaForTokenClassification,
										    TFDebertaVaModel,
										    TFDebertaVaPreTrainedModel,
										)

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_deberta_va import (
										    DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
										    DebertaVaForMaskedLM,
										    DebertaVaForMultipleChoice,
										    DebertaVaForQuestionAnswering,
										    DebertaVaForSequenceClassification,
										    DebertaVaForTokenClassification,
										    DebertaVaModel,
										    DebertaVaPreTrainedModel,
										)

else:
					import sys

					UpperCamelCase :				Dict						    =       _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





def 							UpperCamelCase_  (				__snake_case			:						int = 1_00     )->      int:




						'''simple docstring'''
						__magic_name__ 		=			1
						__magic_name__ 		=			0
						for i in range(1	,			num + 1     ):
												fact *= i

						for j in str(__snake_case     ):
												result += int(__snake_case     )

						return result


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

