




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    (							lowercase			: Optional[int]  ):


      '''simple docstring'''



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


lowerCamelCase       :	int									=     logging.getLogger(__name__)

def     _SCREAMING_SNAKE_CASE    (							lowercase			: List[Any]			,							lowercase			: Any  ):


      '''simple docstring'''



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

      lowerCamelCase_	  =					ModelCheckpoint(
          dirpath=lowercase			,							filename=lowercase			,							monitor=f"""val_{metric}"""			,							mode='max'			,							save_top_k=3			,							every_n_epochs=1			,							)
      return checkpoint_callback

def     _SCREAMING_SNAKE_CASE    (							lowercase			: Optional[int]			,							lowercase			: Optional[int]  ):


      '''simple docstring'''



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







class 						A(		pl.Callback     ):



 '''simple docstring'''


 def        a__				(       self      :						Optional[int]		,							A_      :						List[Any]		,							A_      :						List[str]						)     ->			List[Any]:

       """simple docstring"""






       lowerCamelCase_	  =					{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      :						Optional[Any]		,							A_      :						pl.Trainer		,							A_      :						pl.LightningModule		,							A_      :						str		,							A_      :						Dict=True						)     ->			None:

       """simple docstring"""






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

       if not save_generations:
             return

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

 @rank_zero_only
 def        a__				(       self      :						Union[str, Any]		,							A_      :						Optional[int]		,							A_      :						Union[str, Any]						)     ->			Union[str, Any]:

       """simple docstring"""






       try:
             lowerCamelCase_	  =					pl_module.model.model.num_parameters()
       except AttributeError:
             lowerCamelCase_	  =					pl_module.model.num_parameters()

       lowerCamelCase_	  =					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      :						Optional[int]		,							A_      :						pl.Trainer		,							A_      :						pl.LightningModule						)     ->			List[Any]:

       """simple docstring"""






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





 @rank_zero_only
 def        a__				(       self      :						Optional[Any]		,							A_      :						pl.Trainer		,							A_      :						Optional[int]						)     ->			Tuple:

       """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")









def     _SCREAMING_SNAKE_CASE    (							):


      '''simple docstring'''



      lowerCamelCase_	  =					0
      for i in range(1			,							10_01  ):
            total += i**i
      return str(lowercase  )[-10:]


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



