




import math



def    UpperCAmelCase__	(  lowerCamelCase ):
  return sum(int(lowerCamelCase ) for digit in str(lowerCamelCase ) )



def    UpperCAmelCase__	(  lowerCamelCase = 30 ):
  lowercase     :str   						=    []
  for digit in range(2,      100 ):
    for power in range(2,      100 ):
      lowercase     :List[str]   						=    int(math.pow(lowerCamelCase,      lowerCamelCase ) )
      if digit == digit_sum(lowerCamelCase ):
        digit_to_powers.append(lowerCamelCase )

  digit_to_powers.sort()
  return digit_to_powers[n - 1]


if __name__ == "__main__":
     print(solution())









import logging
from pathlib import Path

import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only

from utils import save_json



def    UpperCAmelCase__	(  lowerCamelCase ):
  lowercase     :Optional[Any]   						=    filter(lambda lowerCamelCase : p.requires_grad,      model.parameters() )
  lowercase     :Optional[int]   						=    sum([np.prod(p.size() ) for p in model_parameters] )
  return params


_UpperCAmelCase						:    Optional[Any]      =  logging.getLogger(__name__)




class  __lowerCAmelCase							(				pl.Callback):






       def        SCREAMING_SNAKE_CASE     ( self:       Union[str, Any]							,       _lowerCAmelCase:       List[Any]							,       _lowerCAmelCase:       Union[str, Any]					):
         lowercase     :Dict   						=    {F"lr_group_{i}": param["lr"] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups					)}
         pl_module.logger.log_metrics(_lowerCAmelCase					)






       @rank_zero_only
       def        SCREAMING_SNAKE_CASE     ( self:       Optional[Any]							,       _lowerCAmelCase:       pl.Trainer							,       _lowerCAmelCase:       pl.LightningModule							,       _lowerCAmelCase:       str							,       _lowerCAmelCase:       List[str]=True					):
         logger.info(F"***** {type_path} results at step {trainer.global_step:05d} *****"					)
         lowercase     :Tuple   						=    trainer.callback_metrics
         trainer.logger.log_metrics({k: v for k, v in metrics.items() if k not in ["log", "progress_bar", "preds"]}					)
         # Log results
         lowercase     :Optional[int]   						=    Path(pl_module.hparams.output_dir					)
         if type_path == "test":
           lowercase     :List[Any]   						=    od / "test_results.txt"
           lowercase     :Optional[int]   						=    od / "test_generations.txt"
         else:
           # this never gets hit. I prefer not to save intermediate generations, and results are in metrics.json
           # If people want this it will be easy enough to add back.
           lowercase     :int   						=    od / F"{type_path}_results/{trainer.global_step:05d}.txt"
           lowercase     :Tuple   						=    od / F"{type_path}_generations/{trainer.global_step:05d}.txt"
           results_file.parent.mkdir(exist_ok=_lowerCAmelCase					)
           generations_file.parent.mkdir(exist_ok=_lowerCAmelCase					)
         with open(_lowerCAmelCase							,       "a+"					) as writer:
           for key in sorted(_lowerCAmelCase					):
             if key in ["log", "progress_bar", "preds"]:
               continue
             lowercase     :int   						=    metrics[key]
             if isinstance(_lowerCAmelCase							,       torch.Tensor					):
               lowercase     :int   						=    val.item()
             lowercase     :Optional[Any]   						=    F"{key}: {val:.6f}\n"
             writer.write(_lowerCAmelCase					)

         if not save_generations:
           return

         if "preds" in metrics:
           lowercase     :str   						=    "\n".join(metrics["preds"]					)
           generations_file.open("w+"					).write(_lowerCAmelCase					)






       @rank_zero_only
       def        SCREAMING_SNAKE_CASE     ( self:       Optional[int]							,       _lowerCAmelCase:       Tuple							,       _lowerCAmelCase:       Tuple					):
         try:
           lowercase     :Optional[int]   						=    pl_module.model.model.num_parameters()
         except AttributeError:
           lowercase     :Dict   						=    pl_module.model.num_parameters()

         lowercase     :Any   						=    count_trainable_parameters(_lowerCAmelCase					)
         # mp stands for million parameters
         trainer.logger.log_metrics({"n_params": npars, "mp": npars / 1e6, "grad_mp": n_trainable_pars / 1e6}					)






       @rank_zero_only
       def        SCREAMING_SNAKE_CASE     ( self:       Union[str, Any]							,       _lowerCAmelCase:       pl.Trainer							,       _lowerCAmelCase:       pl.LightningModule					):
         save_json(pl_module.metrics							,       pl_module.metrics_save_path					)
         return self._write_logs(_lowerCAmelCase							,       _lowerCAmelCase							,       "test"					)






       @rank_zero_only
       def        SCREAMING_SNAKE_CASE     ( self:       List[str]							,       _lowerCAmelCase:       pl.Trainer							,       _lowerCAmelCase:       Dict					):
         save_json(pl_module.metrics							,       pl_module.metrics_save_path					)
         # Uncommenting this will save val generations
         # return self._write_logs(trainer, pl_module, "valid")







def    UpperCAmelCase__	(  lowerCamelCase,      lowerCamelCase,      lowerCamelCase=1,      lowerCamelCase=False ):
  if metric == "rouge2":
    lowercase     :str   						=    "{val_avg_rouge2:.4f}-{step_count}"
  elif metric == "bleu":
    lowercase     :int   						=    "{val_avg_bleu:.4f}-{step_count}"
  elif metric == "loss":
    lowercase     :List[str]   						=    "{val_avg_loss:.4f}-{step_count}"
  else:
    raise NotImplementedError(
        F"seq2seq callbacks only support rouge2, bleu and loss, got {metric}, You can make your own by adding to"
        " this function." )

  lowercase     :int   						=    ModelCheckpoint(
      dirpath=lowerCamelCase,      filename=lowerCamelCase,      monitor=F"val_{metric}",      mode="min" if "loss" in metric else "max",      save_top_k=lowerCamelCase,      )
  return checkpoint_callback







def    UpperCAmelCase__	(  lowerCamelCase,      lowerCamelCase ):
  return EarlyStopping(
      monitor=F"val_{metric}",      mode="min" if "loss" in metric else "max",      patience=lowerCamelCase,      verbose=lowerCamelCase,      )



