


import os

_A			=						{'I': 1, 'V': 5, 'X': 10, 'L': 50, 'C': 100, 'D': 500, 'M': 1000}

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : str    ):
     __UpperCamelCase   							=0

     __UpperCamelCase   							=0
     while index < len(SCREAMING_SNAKE_CASE__    ) - 1:
          __UpperCamelCase   							=SYMBOLS[numerals[index]]
          __UpperCamelCase   							=SYMBOLS[numerals[index + 1]]
          if current_value < next_value:
               total_value -= current_value
          else:
               total_value += current_value
          index += 1
     total_value += SYMBOLS[numerals[index]]

     return total_value

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : int    ):
     __UpperCamelCase   							=''

     __UpperCamelCase   							=num // 10_00
     numerals += m_count * "M"
     num %= 10_00

     __UpperCamelCase   							=num // 1_00
     if c_count == 9:
          numerals += "CM"
          c_count -= 9
     elif c_count == 4:
          numerals += "CD"
          c_count -= 4
     if c_count >= 5:
          numerals += "D"
          c_count -= 5
     numerals += c_count * "C"
     num %= 1_00

     __UpperCamelCase   							=num // 10
     if x_count == 9:
          numerals += "XC"
          x_count -= 9
     elif x_count == 4:
          numerals += "XL"
          x_count -= 4
     if x_count >= 5:
          numerals += "L"
          x_count -= 5
     numerals += x_count * "X"
     num %= 10

     if num == 9:
          numerals += "IX"
          num -= 9
     elif num == 4:
          numerals += "IV"
          num -= 4
     if num >= 5:
          numerals += "V"
          num -= 5
     numerals += num * "I"

     return numerals

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : str = "/p089_roman.txt"    ):
     __UpperCamelCase   							=0

     with open(os.path.dirname(SCREAMING_SNAKE_CASE__    ) + roman_numerals_filename    ) as filea:
          __UpperCamelCase   							=filea.readlines()

     for line in lines:
          __UpperCamelCase   							=line.strip()
          __UpperCamelCase   							=parse_roman_numerals(SCREAMING_SNAKE_CASE__    )
          __UpperCamelCase   							=generate_roman_numerals(SCREAMING_SNAKE_CASE__    )
          savings += len(SCREAMING_SNAKE_CASE__    ) - len(SCREAMING_SNAKE_CASE__    )

     return savings


if __name__ == "__main__":
   print(f"""{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_rag import save_json

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : Tuple    ):
     __UpperCamelCase   							=filter(lambda SCREAMING_SNAKE_CASE__    : p.requires_grad      ,					model.parameters()    )
     __UpperCamelCase   							=sum([np.prod(p.size()    ) for p in model_parameters]    )
     return params


_A			=						logging.getLogger(__name__)

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : Tuple      ,					SCREAMING_SNAKE_CASE__      : Optional[Any]    ):
     if metric == "rouge2":
          __UpperCamelCase   							='{val_avg_rouge2:.4f}-{step_count}'
     elif metric == "bleu":
          __UpperCamelCase   							='{val_avg_bleu:.4f}-{step_count}'
     elif metric == "em":
          __UpperCamelCase   							='{val_avg_em:.4f}-{step_count}'
     elif metric == "loss":
          __UpperCamelCase   							='{val_avg_loss:.4f}-{step_count}'
     else:
          raise NotImplementedError(
              F'seq2seq callbacks only support rouge2 and bleu, got {metric}, You can make your own by adding to this'
              ' function.'    )

     __UpperCamelCase   							=ModelCheckpoint(
         dirpath=SCREAMING_SNAKE_CASE__      ,					filename=SCREAMING_SNAKE_CASE__      ,					monitor=F'val_{metric}'      ,					mode='max'      ,					save_top_k=1      ,					every_n_epochs=1      ,					)

     return checkpoint_callback

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : int      ,					SCREAMING_SNAKE_CASE__      : Union[str, Any]    ):
     return EarlyStopping(
         monitor=F'val_{metric}'      ,					mode='min' if 'loss' in metric else 'max'      ,					patience=SCREAMING_SNAKE_CASE__      ,					verbose=SCREAMING_SNAKE_CASE__      ,					)






class    UpperCAmelCase__	( pl.Callback		):



    """simple docstring"""






    def _a (      self ,       A_ ,       A_   )  ->	int:
         __UpperCamelCase   							={f'lr_group_{i}': param['lr'] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups   )}
         pl_module.logger.log_metrics(A_   )






    @rank_zero_only
    def _a (      self ,       A_ ,       A_ ,       A_ ,       A_=True   )  ->	None:
         logger.info(f'***** {type_path} results at step {trainer.global_step:05d} *****'   )
         __UpperCamelCase   							=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
         __UpperCamelCase   							=Path(pl_module.hparams.output_dir   )
         if type_path == "test":
              __UpperCamelCase   							=od / 'test_results.txt'
              __UpperCamelCase   							=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.
              __UpperCamelCase   							=od / f'{type_path}_results/{trainer.global_step:05d}.txt'
              __UpperCamelCase   							=od / f'{type_path}_generations/{trainer.global_step:05d}.txt'
              results_file.parent.mkdir(exist_ok=A_   )
              generations_file.parent.mkdir(exist_ok=A_   )
         with open(A_ ,       'a+'   ) as writer:
              for key in sorted(A_   ):
                   if key in ["log", "progress_bar", "preds"]:
                        continue
                   __UpperCamelCase   							=metrics[key]
                   if isinstance(A_ ,       torch.Tensor   ):
                        __UpperCamelCase   							=val.item()
                   __UpperCamelCase   							=f'{key}: {val:.6f}\n'
                   writer.write(A_   )

         if not save_generations:
              return

         if "preds" in metrics:
              __UpperCamelCase   							='\n'.join(metrics['preds']   )
              generations_file.open('w+'   ).write(A_   )






    @rank_zero_only
    def _a (      self ,       A_ ,       A_   )  ->	Optional[int]:
         try:
              __UpperCamelCase   							=pl_module.model.model.num_parameters()
         except AttributeError:
              __UpperCamelCase   							=pl_module.model.num_parameters()

         __UpperCamelCase   							=count_trainable_parameters(A_   )
         # 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 _a (      self ,       A_ ,       A_   )  ->	List[str]:
         save_json(pl_module.metrics ,       pl_module.metrics_save_path   )
         return self._write_logs(A_ ,       A_ ,       'test'   )






    @rank_zero_only
    def _a (      self ,       A_ ,       A_   )  ->	List[str]:
         save_json(pl_module.metrics ,       pl_module.metrics_save_path   )
         # Uncommenting this will save val generations
         # return self._write_logs(trainer, pl_module, "valid")





