





'''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 							UpperCAmelCase_  (						__lowerCamelCase : Union[str, Any]			):
      lowercase_      :int         =			filter(lambda __lowerCamelCase			: p.requires_grad     ,model.parameters()			)
      lowercase_      :List[Any]         =			sum([np.prod(p.size()			) for p in model_parameters]			)
      return params


lowerCAmelCase :					Optional[Any] 							=logging.getLogger(__name__)
def 							UpperCAmelCase_  (						__lowerCamelCase : Optional[int]     ,__lowerCamelCase : str			):
      if metric == "rouge2":
            lowercase_      :Union[str, Any]         =			"{val_avg_rouge2:.4f}-{step_count}"
      elif metric == "bleu":
            lowercase_      :Any         =			"{val_avg_bleu:.4f}-{step_count}"
      elif metric == "em":
            lowercase_      :Union[str, Any]         =			"{val_avg_em:.4f}-{step_count}"
      elif metric == "loss":
            lowercase_      :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."			)

      lowercase_      :Optional[Any]         =			ModelCheckpoint(
          dirpath=__lowerCamelCase     ,filename=__lowerCamelCase     ,monitor=F'val_{metric}'     ,mode="max"     ,save_top_k=1     ,every_n_epochs=1     ,)

      return checkpoint_callback
def 							UpperCAmelCase_  (						__lowerCamelCase : int     ,__lowerCamelCase : Optional[Any]			):
      return EarlyStopping(
          monitor=F'val_{metric}'     ,mode="min" if "loss" in metric else "max"     ,patience=__lowerCamelCase     ,verbose=__lowerCamelCase     ,)


class 				a_       (    pl.Callback							):





     def 							lowercase__				(			self		: str     ,				lowercase		: str     ,				lowercase		: Any						):





           """simple docstring"""




           lowercase_      :Tuple         =			{F'lr_group_{i}': param["lr"] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups						)}
           pl_module.logger.log_metrics(lowercase						)





     @rank_zero_only
     def 							lowercase__				(			self		: Optional[Any]     ,				lowercase		: pl.Trainer     ,				lowercase		: pl.LightningModule     ,				lowercase		: str     ,				lowercase		: Union[str, Any]=True						):





           """simple docstring"""




           logger.info(F'***** {type_path} results at step {trainer.global_step:05d} *****'						)
           lowercase_      :Tuple         =			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
           lowercase_      :Optional[Any]         =			Path(pl_module.hparams.output_dir						)
           if type_path == "test":
                 lowercase_      :str         =			od / "test_results.txt"
                 lowercase_      :Dict         =			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.
                 lowercase_      :Union[str, Any]         =			od / F'{type_path}_results/{trainer.global_step:05d}.txt'
                 lowercase_      :Dict         =			od / F'{type_path}_generations/{trainer.global_step:05d}.txt'
                 results_file.parent.mkdir(exist_ok=lowercase						)
                 generations_file.parent.mkdir(exist_ok=lowercase						)
           with open(lowercase     ,				"a+"						) as writer:
                 for key in sorted(lowercase						):
                       if key in ["log", "progress_bar", "preds"]:
                             continue
                       lowercase_      :List[Any]         =			metrics[key]
                       if isinstance(lowercase     ,				torch.Tensor						):
                             lowercase_      :Tuple         =			val.item()
                       lowercase_      :Union[str, Any]         =			F'{key}: {val:.6f}\n'
                       writer.write(lowercase						)

           if not save_generations:
                 return

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





     @rank_zero_only
     def 							lowercase__				(			self		: Optional[int]     ,				lowercase		: str     ,				lowercase		: Optional[int]						):





           """simple docstring"""




           try:
                 lowercase_      :List[Any]         =			pl_module.model.model.num_parameters()
           except AttributeError:
                 lowercase_      :Optional[Any]         =			pl_module.model.num_parameters()

           lowercase_      :Dict         =			count_trainable_parameters(lowercase						)
           # 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[Any]     ,				lowercase		: pl.Trainer     ,				lowercase		: pl.LightningModule						):





           """simple docstring"""




           save_json(pl_module.metrics     ,				pl_module.metrics_save_path						)
           return self._write_logs(lowercase     ,				lowercase     ,				"test"						)







     @rank_zero_only
     def 							lowercase__				(			self		: str     ,				lowercase		: pl.Trainer     ,				lowercase		: List[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'''


from abc import ABC, abstractmethod
from argparse import ArgumentParser
class 				a_       (    _lowerCAmelCase							):





     @staticmethod
     @abstractmethod
     def 							lowercase__				(			lowercase		: ArgumentParser						):





        """simple docstring"""




        raise NotImplementedError()







     @abstractmethod
     def 							lowercase__				(			self		: str						):





        """simple docstring"""




        raise NotImplementedError()


