






'''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        _SCREAMING_SNAKE_CASE  (				A  :       Any  )    -> Union[str, Any]:







   """simple docstring"""
   __snake_case				:      List[str]     							= filter(lambda A  : p.requires_grad  ,		model.parameters()  )
   __snake_case				:      int     							= sum([np.prod(p.size()  ) for p in model_parameters]  )
   return params


__A    	=   logging.getLogger(__name__)




def        _SCREAMING_SNAKE_CASE  (				A  :       List[Any]  ,		A  :       int  )    -> Any:







   """simple docstring"""
   if metric == "rouge2":
      __snake_case				:      Tuple     							= '{val_avg_rouge2:.4f}-{step_count}'
   elif metric == "bleu":
      __snake_case				:      Tuple     							= '{val_avg_bleu:.4f}-{step_count}'
   elif metric == "em":
      __snake_case				:      Optional[int]     							= '{val_avg_em:.4f}-{step_count}'
   elif metric == "loss":
      __snake_case				:      Union[str, Any]     							= '{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				:      str     							= ModelCheckpoint(
       dirpath=A  ,		filename=A  ,		monitor=F"""val_{metric}"""  ,		mode='max'  ,		save_top_k=1  ,		every_n_epochs=1  ,		)

   return checkpoint_callback




def        _SCREAMING_SNAKE_CASE  (				A  :       Optional[int]  ,		A  :       Tuple  )    -> Dict:







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





class a_    (    pl.Callback							):







   def     SCREAMING_SNAKE_CASE__					(self					,   __a					,   __a)			->      str:
      """simple docstring"""






      __snake_case				:      int     							= {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     SCREAMING_SNAKE_CASE__					(self					,   __a					,   __a					,   __a					,   __a=True)			->      None:
      """simple docstring"""






      logger.info(F"""***** {type_path} results at step {trainer.global_step:05d} *****""")
      __snake_case				:      List[str]     							= 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				:      int     							= Path(pl_module.hparams.output_dir)
      if type_path == "test":
         __snake_case				:      Any     							= od / 'test_results.txt'
         __snake_case				:      Any     							= 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				:      Optional[Any]     							= od / F"""{type_path}_results/{trainer.global_step:05d}.txt"""
         __snake_case				:      Union[str, Any]     							= 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
            __snake_case				:      Dict     							= metrics[key]
            if isinstance(__a					,   torch.Tensor):
               __snake_case				:      str     							= val.item()
            __snake_case				:      Tuple     							= F"""{key}: {val:.6f}\n"""
            writer.write(__a)

      if not save_generations:
         return

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







   @rank_zero_only
   def     SCREAMING_SNAKE_CASE__					(self					,   __a					,   __a)			->      Dict:
      """simple docstring"""






      try:
         __snake_case				:      List[Any]     							= pl_module.model.model.num_parameters()
      except AttributeError:
         __snake_case				:      Dict     							= pl_module.model.num_parameters()

      __snake_case				:      Union[str, Any]     							= 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     SCREAMING_SNAKE_CASE__					(self					,   __a					,   __a)			->      Optional[int]:
      """simple docstring"""






      save_json(pl_module.metrics					,   pl_module.metrics_save_path)
      return self._write_logs(__a					,   __a					,   'test')







   @rank_zero_only
   def     SCREAMING_SNAKE_CASE__					(self					,   __a					,   __a)			->      str:
      """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'''






__A    	=   {str(digit): digit**5 for digit in range(1_0)}




def        _SCREAMING_SNAKE_CASE  (				A  :       int  )    -> int:







   """simple docstring"""
   return sum(DIGITS_FIFTH_POWER[digit] for digit in str(A  )  )




def        _SCREAMING_SNAKE_CASE  (				)    -> int:







   """simple docstring"""
   return sum(
       number
       for number in range(10_00  ,		1_00_00_00  )
       if number == digits_fifth_powers_sum(A  )  )


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