





from ..utils import is_flax_available, is_torch_available


if is_torch_available():
       from .autoencoder_kl import AutoencoderKL
       from .controlnet import ControlNetModel
       from .dual_transformer_ad import DualTransformeraDModel
       from .modeling_utils import ModelMixin
       from .prior_transformer import PriorTransformer
       from .ta_film_transformer import TaFilmDecoder
       from .transformer_ad import TransformeraDModel
       from .unet_ad import UNetaDModel
       from .unet_ad import UNetaDModel
       from .unet_ad_condition import UNetaDConditionModel
       from .unet_ad_condition import UNetaDConditionModel
       from .vq_model import VQModel

if is_flax_available():
       from .controlnet_flax import FlaxControlNetModel
       from .unet_ad_condition_flax import FlaxUNetaDConditionModel
       from .vae_flax import FlaxAutoencoderKL








def    __UpperCAmelCase(      lowercase_ = 10_00      ):
       _lowerCamelCase							:     Optional[int]	=		2**power
       _lowerCamelCase							:     Optional[int]	=		0
       while n:
              _lowerCamelCase,  _lowerCamelCase							:     Optional[Any]	=		r + n % 10, n // 10
       return r


if __name__ == "__main__":
       print(solution(int(str(input()).strip())))

