




'''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 import save_json

def       __lowerCAmelCase (   snake_case__						):
   __UpperCamelCase				:      Dict									=       filter(lambda snake_case__						: p.requires_grad ,				model.parameters()						)
   __UpperCamelCase				:      Union[str, Any]									=       sum([np.prod(p.size()						) for p in model_parameters]						)
   return params


_lowerCAmelCase				     = logging.getLogger(__name__)



class  A		(			pl.Callback		):




    '''simple docstring'''





    def 	a_	(self			,  _UpperCAmelCase			,  _UpperCAmelCase      )     ->	Tuple:
       __UpperCamelCase				:      List[str]									=       {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 	a_	(self			,  _UpperCAmelCase			,  _UpperCAmelCase			,  _UpperCAmelCase			,  _UpperCAmelCase=True      )     ->	None:
       logger.info(f"***** {type_path} results at step {trainer.global_step:05d} *****"      )
       __UpperCamelCase				:      Any									=       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				:      Tuple									=       Path(pl_module.hparams.output_dir      )
       if type_path == "test":
          __UpperCamelCase				:      int									=       od / "test_results.txt"
          __UpperCamelCase				:      Optional[int]									=       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				:      Union[str, Any]									=       od / f"{type_path}_results/{trainer.global_step:05d}.txt"
          __UpperCamelCase				:      Union[str, Any]									=       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
             __UpperCamelCase				:      Optional[Any]									=       metrics[key]
             if isinstance(_UpperCAmelCase			,  torch.Tensor      ):
                __UpperCamelCase				:      Optional[Any]									=       val.item()
             __UpperCamelCase				:      str									=       f"{key}: {val:.6f}\n"
             writer.write(_UpperCAmelCase      )

       if not save_generations:
          return

       if "preds" in metrics:
          __UpperCamelCase				:      List[Any]									=       "\n".join(metrics["preds"]      )
          generations_file.open("w+"      ).write(_UpperCAmelCase      )
    @rank_zero_only
    def 	a_	(self			,  _UpperCAmelCase			,  _UpperCAmelCase      )     ->	List[str]:
       try:
          __UpperCamelCase				:      str									=       pl_module.model.model.num_parameters()
       except AttributeError:
          __UpperCamelCase				:      str									=       pl_module.model.num_parameters()

       __UpperCamelCase				:      Optional[Any]									=       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 	a_	(self			,  _UpperCAmelCase			,  _UpperCAmelCase      )     ->	Optional[Any]:
       save_json(pl_module.metrics			,  pl_module.metrics_save_path      )
       return self._write_logs(_UpperCAmelCase			,  _UpperCAmelCase			,  "test"      )





    @rank_zero_only
    def 	a_	(self			,  _UpperCAmelCase			,  _UpperCAmelCase      )     ->	List[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")







def       __lowerCAmelCase (   snake_case__ ,				snake_case__ ,				snake_case__=1 ,				snake_case__=False						):
   if metric == "rouge2":
      __UpperCamelCase				:      List[Any]									=       "{val_avg_rouge2:.4f}-{step_count}"
   elif metric == "bleu":
      __UpperCamelCase				:      List[Any]									=       "{val_avg_bleu:.4f}-{step_count}"
   elif metric == "loss":
      __UpperCamelCase				:      Union[str, Any]									=       "{val_avg_loss:.4f}-{step_count}"
   else:
      raise NotImplementedError(
          F"seq2seq callbacks only support rouge2, bleu and loss, got {metric}, You can make your own by adding to"
          " this function."						)

   __UpperCamelCase				:      List[str]									=       ModelCheckpoint(
       dirpath=snake_case__ ,				filename=snake_case__ ,				monitor=F"val_{metric}" ,				mode="min" if "loss" in metric else "max" ,				save_top_k=snake_case__ ,				)
   return checkpoint_callback




def       __lowerCAmelCase (   snake_case__ ,				snake_case__						):
   return EarlyStopping(
       monitor=F"val_{metric}" ,				mode="min" if "loss" in metric else "max" ,				patience=snake_case__ ,				verbose=snake_case__ ,				)









'''simple docstring'''






def       __lowerCAmelCase (   snake_case__ = 4_000_000						):
			__UpperCamelCase				:      Tuple									=       1
			__UpperCamelCase				:      int									=       2
			__UpperCamelCase				:      str									=       0
			while j <= n:
						if j % 2 == 0:
									total += j
						__UpperCamelCase		, __UpperCamelCase				:      List[Any]									=       j, i + j

			return total


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



