
def 			UpperCamelCase					(    __lowerCamelCase     : int = 1000    ):
       snake_case       :   List[Any]							=      2**power
       snake_case       :   Any							=      0
       while n:
              snake_case				,					snake_case       :   Tuple							=      r + n % 10, n // 10
       return r


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






import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional

import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors

import transformers
from transformers import (
    AutoConfig,
    AutoModelForMultipleChoice,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


A   :     Any       =				logging.getLogger(__name__)


def    a__       (						__UpperCamelCase   ,					__UpperCamelCase ):
      return (preds == labels).mean()


@dataclass
class      lowerCamelCase    :
  """simple docstring"""







  lowerCamelCase__   =  field(
      metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''}    )
  lowerCamelCase__   =  field(
      default=SCREAMING_SNAKE_CASE__     ,					metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}    )
  lowerCamelCase__   =  field(
      default=SCREAMING_SNAKE_CASE__     ,					metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''}    )
  lowerCamelCase__   =  field(
      default=SCREAMING_SNAKE_CASE__     ,					metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}     ,					)



@dataclass
class      lowerCamelCase    :
  """simple docstring"""







  lowerCamelCase__   =  field(metadata={'''help''': '''The name of the task to train on: ''' + ''', '''.join(processors.keys()    )}    )
  lowerCamelCase__   =  field(metadata={'''help''': '''Should contain the data files for the task.'''}    )
  lowerCamelCase__   =  field(
      default=1_2_8     ,					metadata={
          '''help''': (
              '''The maximum total input sequence length after tokenization. Sequences longer '''
              '''than this will be truncated, sequences shorter will be padded.'''
          )
      }     ,					)
  lowerCamelCase__   =  field(
      default=SCREAMING_SNAKE_CASE__     ,					metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}    )



def    a__       (						):
      # See all possible arguments in src/transformers/training_args.py
      # or by passing the --help flag to this script.
      # We now keep distinct sets of args, for a cleaner separation of concerns.

      SCREAMING_SNAKE_CASE_      =     HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
      SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_      =     parser.parse_args_into_dataclasses()

      if (
          os.path.exists(training_args.output_dir )
          and os.listdir(training_args.output_dir )
          and training_args.do_train
          and not training_args.overwrite_output_dir
      ):
            raise ValueError(
                F'''Output directory ({training_args.output_dir}) already exists and is not empty. Use'''
                " --overwrite_output_dir to overcome." )

      # Setup logging
      logging.basicConfig(
          format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"   ,					datefmt="%m/%d/%Y %H:%M:%S"   ,					level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN   ,					)
      logger.warning(
          "Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s"   ,					training_args.local_rank   ,					training_args.device   ,					training_args.n_gpu   ,					bool(training_args.local_rank != -1 )   ,					training_args.fpaa   ,					)
      # Set the verbosity to info of the Transformers logger (on main process only):
      if is_main_process(training_args.local_rank ):
            transformers.utils.logging.set_verbosity_info()
            transformers.utils.logging.enable_default_handler()
            transformers.utils.logging.enable_explicit_format()
      logger.info("Training/evaluation parameters %s"   ,					__UpperCamelCase )

      # Set seed
      set_seed(training_args.seed )

      try:
            SCREAMING_SNAKE_CASE_      =     processors[data_args.task_name]()
            SCREAMING_SNAKE_CASE_      =     processor.get_labels()
            SCREAMING_SNAKE_CASE_      =     len(__UpperCamelCase )
      except KeyError:
            raise ValueError("Task not found: %s" % (data_args.task_name) )

      # Load pretrained model and tokenizer
      #
      # Distributed training:
      # The .from_pretrained methods guarantee that only one local process can concurrently
      # download model & vocab.

      SCREAMING_SNAKE_CASE_      =     AutoConfig.from_pretrained(
          model_args.config_name if model_args.config_name else model_args.model_name_or_path   ,					num_labels=__UpperCamelCase   ,					finetuning_task=data_args.task_name   ,					cache_dir=model_args.cache_dir   ,					)
      SCREAMING_SNAKE_CASE_      =     AutoTokenizer.from_pretrained(
          model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path   ,					cache_dir=model_args.cache_dir   ,					)
      SCREAMING_SNAKE_CASE_      =     AutoModelForMultipleChoice.from_pretrained(
          model_args.model_name_or_path   ,					from_tf=bool(".ckpt" in model_args.model_name_or_path )   ,					config=__UpperCamelCase   ,					cache_dir=model_args.cache_dir   ,					)

      # Get datasets
      SCREAMING_SNAKE_CASE_      =     (
          MultipleChoiceDataset(
              data_dir=data_args.data_dir   ,					tokenizer=__UpperCamelCase   ,					task=data_args.task_name   ,					max_seq_length=data_args.max_seq_length   ,					overwrite_cache=data_args.overwrite_cache   ,					mode=Split.train   ,					)
          if training_args.do_train
          else None
      )
      SCREAMING_SNAKE_CASE_      =     (
          MultipleChoiceDataset(
              data_dir=data_args.data_dir   ,					tokenizer=__UpperCamelCase   ,					task=data_args.task_name   ,					max_seq_length=data_args.max_seq_length   ,					overwrite_cache=data_args.overwrite_cache   ,					mode=Split.dev   ,					)
          if training_args.do_eval
          else None
      )

      def compute_metrics(__UpperCamelCase ) -> Dict:
            SCREAMING_SNAKE_CASE_      =     np.argmax(p.predictions   ,					axis=1 )
            return {"acc": simple_accuracy(__UpperCamelCase   ,					p.label_ids )}

      # Data collator
      SCREAMING_SNAKE_CASE_      =     DataCollatorWithPadding(__UpperCamelCase   ,					pad_to_multiple_of=8 ) if training_args.fpaa else None

      # Initialize our Trainer
      SCREAMING_SNAKE_CASE_      =     Trainer(
          model=__UpperCamelCase   ,					args=__UpperCamelCase   ,					train_dataset=__UpperCamelCase   ,					eval_dataset=__UpperCamelCase   ,					compute_metrics=__UpperCamelCase   ,					data_collator=__UpperCamelCase   ,					)

      # Training
      if training_args.do_train:
            trainer.train(
                model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path ) else None )
            trainer.save_model()
            # For convenience, we also re-save the tokenizer to the same directory,
            # so that you can share your model easily on huggingface.co/models =)
            if trainer.is_world_master():
                  tokenizer.save_pretrained(training_args.output_dir )

    # Evaluation
      SCREAMING_SNAKE_CASE_      =     {}
      if training_args.do_eval:
            logger.info("*** Evaluate ***" )

            SCREAMING_SNAKE_CASE_      =     trainer.evaluate()

            SCREAMING_SNAKE_CASE_      =     os.path.join(training_args.output_dir   ,					"eval_results.txt" )
            if trainer.is_world_master():
                  with open(__UpperCamelCase   ,					"w" ) as writer:
                        logger.info("***** Eval results *****" )
                        for key, value in result.items():
                              logger.info("  %s = %s"   ,					__UpperCamelCase   ,					__UpperCamelCase )
                              writer.write("%s = %s\n" % (key, value) )

                        results.update(__UpperCamelCase )

      return results


def    a__       (						__UpperCamelCase ):
      # For xla_spawn (TPUs)
      main()


if __name__ == "__main__":
      main()


