





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 _a				(	lowerCamelCase:   int )	->						int:







      '''simple docstring'''






      __A          =				filter(lambda lowerCamelCase : p.requires_grad					,				model.parameters() )
      __A          =				sum([np.prod(p.size() ) for p in model_parameters] )
      return params


snake_case__					:							List[Any]													=     logging.getLogger(__name__)



def _a				(	lowerCamelCase:   str					,				lowerCamelCase:   List[Any] )	->						Tuple:







      '''simple docstring'''






      if metric == "rouge2":
            __A          =				'''{val_avg_rouge2:.4f}-{step_count}'''
      elif metric == "bleu":
            __A          =				'''{val_avg_bleu:.4f}-{step_count}'''
      elif metric == "em":
            __A          =				'''{val_avg_em:.4f}-{step_count}'''
      elif metric == "loss":
            __A          =				'''{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.''' )

      __A          =				ModelCheckpoint(
          dirpath=lowerCamelCase					,				filename=lowerCamelCase					,				monitor=F"""val_{metric}"""					,				mode='''max'''					,				save_top_k=1					,				every_n_epochs=1					,				)

      return checkpoint_callback



def _a				(	lowerCamelCase:   Any					,				lowerCamelCase:   List[Any] )	->						Tuple:







      '''simple docstring'''






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







class A_					(			pl.Callback   ):




   def _lowerCAmelCase      (self	:Any    ,   _UpperCamelCase	:Dict    ,   _UpperCamelCase	:Optional[int] )->					List[str]:
         __A          =				{f"""lr_group_{i}""": param['''lr'''] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups )}
         pl_module.logger.log_metrics(_UpperCamelCase )




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

         if not save_generations:
               return

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




   @rank_zero_only
   def _lowerCAmelCase      (self	:Optional[Any]    ,   _UpperCamelCase	:str    ,   _UpperCamelCase	:List[Any] )->					List[Any]:
         try:
               __A          =				pl_module.model.model.num_parameters()
         except AttributeError:
               __A          =				pl_module.model.num_parameters()

         __A          =				count_trainable_parameters(_UpperCamelCase )
         # 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 _lowerCAmelCase      (self	:List[str]    ,   _UpperCamelCase	:pl.Trainer    ,   _UpperCamelCase	:pl.LightningModule )->					Any:
         save_json(pl_module.metrics    ,   pl_module.metrics_save_path )
         return self._write_logs(_UpperCamelCase    ,   _UpperCamelCase    ,   '''test''' )






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












import os

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



def _a				(	lowerCamelCase:   str )	->						int:







      '''simple docstring'''






      __A          =				0

      __A          =				0
      while index < len(lowerCamelCase ) - 1:
            __A          =				SYMBOLS[numerals[index]]
            __A          =				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 _a				(	lowerCamelCase:   int )	->						str:







      '''simple docstring'''






      __A          =				''''''

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

      __A          =				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

      __A          =				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 _a				(	lowerCamelCase:   str = "/p089_roman.txt" )	->						int:







      '''simple docstring'''






      __A          =				0

      with open(os.path.dirname(lowerCamelCase ) + roman_numerals_filename ) as filea:
            __A          =				filea.readlines()

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

      return savings


if __name__ == "__main__":
       print(f'{solution() = }')





