



"""simple docstring"""






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 			snake_case		(		lowerCAmelCase_	)						->				List[Any]:
  _snake_case   	=      filter(lambda lowerCAmelCase_	: p.requires_grad  ,						model.parameters()	)
  _snake_case   	=      sum([np.prod(p.size()	) for p in model_parameters]	)
  return params


snake_case   					=				logging.getLogger(__name__)




def 			snake_case		(		lowerCAmelCase_  ,						lowerCAmelCase_	)						->				Optional[int]:
  if metric == "rouge2":
    _snake_case   	=      '''{val_avg_rouge2:.4f}-{step_count}'''
  elif metric == "bleu":
    _snake_case   	=      '''{val_avg_bleu:.4f}-{step_count}'''
  elif metric == "em":
    _snake_case   	=      '''{val_avg_em:.4f}-{step_count}'''
  elif metric == "loss":
    _snake_case   	=      '''{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.'''	)

  _snake_case   	=      ModelCheckpoint(
      dirpath=lowerCAmelCase_  ,						filename=lowerCAmelCase_  ,						monitor=f"""val_{metric}"""  ,						mode='''max'''  ,						save_top_k=1  ,						every_n_epochs=1  ,						)

  return checkpoint_callback




def 			snake_case		(		lowerCAmelCase_  ,						lowerCAmelCase_	)						->				Union[str, Any]:
  return EarlyStopping(
      monitor=f"""val_{metric}"""  ,						mode='''min''' if '''loss''' in metric else '''max'''  ,						patience=lowerCAmelCase_  ,						verbose=lowerCAmelCase_  ,						)







class     UpperCAmelCase    (	pl.Callback  ):



   def    __UpperCAmelCase    (       self    :	Optional[int]							,       __lowerCamelCase    :	Union[str, Any]							,       __lowerCamelCase    :	Dict				):



     """simple docstring"""
     _snake_case   	=      {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    __UpperCAmelCase    (       self    :	Optional[int]							,       __lowerCamelCase    :	pl.Trainer							,       __lowerCamelCase    :	pl.LightningModule							,       __lowerCamelCase    :	str							,       __lowerCamelCase    :	str=True				):



     """simple docstring"""
     logger.info(f"""***** {type_path} results at step {trainer.global_step:05d} *****"""				)
     _snake_case   	=      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
     _snake_case   	=      Path(pl_module.hparams.output_dir				)
     if type_path == "test":
       _snake_case   	=      od / '''test_results.txt'''
       _snake_case   	=      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.
       _snake_case   	=      od / f"""{type_path}_results/{trainer.global_step:05d}.txt"""
       _snake_case   	=      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
         _snake_case   	=      metrics[key]
         if isinstance(__lowerCamelCase							,       torch.Tensor				):
           _snake_case   	=      val.item()
         _snake_case   	=      f"""{key}: {val:.6f}\n"""
         writer.write(__lowerCamelCase				)

     if not save_generations:
       return

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



   @rank_zero_only
   def    __UpperCAmelCase    (       self    :	str							,       __lowerCamelCase    :	Optional[int]							,       __lowerCamelCase    :	str				):



     """simple docstring"""
     try:
       _snake_case   	=      pl_module.model.model.num_parameters()
     except AttributeError:
       _snake_case   	=      pl_module.model.num_parameters()

     _snake_case   	=      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    __UpperCAmelCase    (       self    :	str							,       __lowerCamelCase    :	pl.Trainer							,       __lowerCamelCase    :	pl.LightningModule				):



     """simple docstring"""
     save_json(pl_module.metrics							,       pl_module.metrics_save_path				)
     return self._write_logs(__lowerCamelCase							,       __lowerCamelCase							,       '''test'''				)





   @rank_zero_only
   def    __UpperCAmelCase    (       self    :	Any							,       __lowerCamelCase    :	pl.Trainer							,       __lowerCamelCase    :	List[Any]				):



     """simple docstring"""
     save_json(pl_module.metrics							,       pl_module.metrics_save_path				)
     # Uncommenting this will save val generations
     # return self._write_logs(trainer, pl_module, "valid")









'''simple docstring'''



from collections import defaultdict
from math import gcd



def      UpperCAmelCase__   (      UpperCAmelCase_       :							int = 1_50_00_00				)	->						int:
     __lowerCamelCase  :      defaultdict														=						defaultdict(UpperCAmelCase_				)
     __lowerCamelCase  :      Any														=						2
     while 2 * euclid_m * (euclid_m + 1) <= limit:
          for euclid_n in range((euclid_m % 2) + 1 ,       UpperCAmelCase_ ,       2				):
               if gcd(UpperCAmelCase_ ,       UpperCAmelCase_				) > 1:
                    continue
               __lowerCamelCase  :      Any														=						2 * euclid_m * (euclid_m + euclid_n)
               for perimeter in range(UpperCAmelCase_ ,       limit + 1 ,       UpperCAmelCase_				):
                    frequencies[perimeter] += 1
          euclid_m += 1
     return sum(1 for frequency in frequencies.values() if frequency == 1				)


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






