


from typing import TYPE_CHECKING

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


UpperCamelCase :				Tuple						    =       {
    '''configuration_segformer''': ['''SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''SegformerConfig''', '''SegformerOnnxConfig''']
}

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCamelCase :				Tuple						    =       ['''SegformerFeatureExtractor''']
					UpperCamelCase :				Optional[int]						    =       ['''SegformerImageProcessor''']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCamelCase :				Optional[int]						    =       [
					    '''SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''SegformerDecodeHead''',
					    '''SegformerForImageClassification''',
					    '''SegformerForSemanticSegmentation''',
					    '''SegformerLayer''',
					    '''SegformerModel''',
					    '''SegformerPreTrainedModel''',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCamelCase :				Any						    =       [
					    '''TF_SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''TFSegformerDecodeHead''',
					    '''TFSegformerForImageClassification''',
					    '''TFSegformerForSemanticSegmentation''',
					    '''TFSegformerModel''',
					    '''TFSegformerPreTrainedModel''',
					]


if TYPE_CHECKING:
					from .configuration_segformer import SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, SegformerConfig, SegformerOnnxConfig

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .feature_extraction_segformer import SegformerFeatureExtractor
										from .image_processing_segformer import SegformerImageProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_segformer import (
										    SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    SegformerDecodeHead,
										    SegformerForImageClassification,
										    SegformerForSemanticSegmentation,
										    SegformerLayer,
										    SegformerModel,
										    SegformerPreTrainedModel,
										)
					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_segformer import (
										    TF_SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFSegformerDecodeHead,
										    TFSegformerForImageClassification,
										    TFSegformerForSemanticSegmentation,
										    TFSegformerModel,
										    TFSegformerPreTrainedModel,
										)

else:
					import sys

					UpperCamelCase :				List[str]						    =       _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())))

