'''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 	lowerCAmelCase_				(					snake_case_       : str			)    ->      Optional[Any]:


     '''simple docstring'''




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


SCREAMING_SNAKE_CASE_:     Union[str, Any]  =logging.getLogger(__name__)





def 	lowerCAmelCase_				(					snake_case_       : Optional[int]					,    snake_case_       : List[str]			)    ->      List[str]:


     '''simple docstring'''




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

     UpperCAmelCase_           =   ModelCheckpoint(
         dirpath=snake_case_					,    filename=snake_case_					,    monitor=f"""val_{metric}"""					,    mode="max"					,    save_top_k=1					,    every_n_epochs=1					,    )

     return checkpoint_callback





def 	lowerCAmelCase_				(					snake_case_       : Any					,    snake_case_       : Tuple			)    ->      int:


     '''simple docstring'''




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






class        __A      ( pl.Callback	):



    def 					_lowercase						(self    :    Optional[int]   ,			__a    :    Tuple   ,			__a    :    Optional[Any] ):
         UpperCAmelCase_           =   {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 					_lowercase						(self    :    int   ,			__a    :    pl.Trainer   ,			__a    :    pl.LightningModule   ,			__a    :    str   ,			__a    :    List[Any]=True ):
         logger.info(f"""***** {type_path} results at step {trainer.global_step:05d} *****""" )
         UpperCAmelCase_           =   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_           =   Path(pl_module.hparams.output_dir )
         if type_path == "test":
              UpperCAmelCase_           =   od / "test_results.txt"
              UpperCAmelCase_           =   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_           =   od / f"""{type_path}_results/{trainer.global_step:05d}.txt"""
              UpperCAmelCase_           =   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
                   UpperCAmelCase_           =   metrics[key]
                   if isinstance(__a   ,			torch.Tensor ):
                        UpperCAmelCase_           =   val.item()
                   UpperCAmelCase_           =   f"""{key}: {val:.6f}\n"""
                   writer.write(__a )

         if not save_generations:
              return

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



    @rank_zero_only
    def 					_lowercase						(self    :    Optional[Any]   ,			__a    :    Optional[Any]   ,			__a    :    Union[str, Any] ):
         try:
              UpperCAmelCase_           =   pl_module.model.model.num_parameters()
         except AttributeError:
              UpperCAmelCase_           =   pl_module.model.num_parameters()

         UpperCAmelCase_           =   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 					_lowercase						(self    :    List[str]   ,			__a    :    pl.Trainer   ,			__a    :    pl.LightningModule ):
         save_json(pl_module.metrics   ,			pl_module.metrics_save_path )
         return self._write_logs(__a   ,			__a   ,			"test" )






    @rank_zero_only
    def 					_lowercase						(self    :    List[Any]   ,			__a    :    pl.Trainer   ,			__a    :    Dict ):
         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'''





def 	lowerCAmelCase_				(					snake_case_       : int					,    snake_case_       : int			)    ->      int:


     '''simple docstring'''




     return x if y == 0 else greatest_common_divisor(snake_case_					,    x % y			)





def 	lowerCAmelCase_				(					snake_case_       : int					,    snake_case_       : int			)    ->      int:


     '''simple docstring'''




     return (x * y) // greatest_common_divisor(snake_case_					,    snake_case_			)





def 	lowerCAmelCase_				(					snake_case_       : int = 20			)    ->      int:


     '''simple docstring'''




     UpperCAmelCase_           =   1
     for i in range(1					,    n + 1			):
          UpperCAmelCase_           =   lcm(snake_case_					,    snake_case_			)
     return g


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




