





'''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 	A_       (  _lowerCAmelCase  )		->				Optional[int]:
 UpperCamelCase			:      str 			=     filter(lambda _lowerCAmelCase  : p.requires_grad							,     model.parameters()  )
 UpperCamelCase			:      Any 			=     sum([np.prod(p.size()  ) for p in model_parameters]  )
 return params


__lowerCamelCase :  int										=  logging.getLogger(__name__)



def 	A_       (  _lowerCAmelCase							,     _lowerCAmelCase  )		->				Any:
 if metric == "rouge2":
  UpperCamelCase			:      Dict 			=     "{val_avg_rouge2:.4f}-{step_count}"
 elif metric == "bleu":
  UpperCamelCase			:      int 			=     "{val_avg_bleu:.4f}-{step_count}"
 elif metric == "em":
  UpperCamelCase			:      Optional[int] 			=     "{val_avg_em:.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 and bleu, got {metric}, You can make your own by adding to this"""
      " function."  )

 UpperCamelCase			:      str 			=     ModelCheckpoint(
     dirpath=UpperCAmelCase__							,     filename=UpperCAmelCase__							,     monitor=F"""val_{metric}"""							,     mode="max"							,     save_top_k=1							,     every_n_epochs=1							,     )

 return checkpoint_callback



def 	A_       (  _lowerCAmelCase							,     _lowerCAmelCase  )		->				str:
 return EarlyStopping(
     monitor=F"""val_{metric}"""							,     mode="min" if "loss" in metric else "max"							,     patience=UpperCAmelCase__							,     verbose=UpperCAmelCase__							,     )



class       A__     ( pl.Callback    ):





      def  __UpperCamelCase(				self ,							A_ ,							A_     ):
       '''simple docstring'''




       UpperCamelCase			:      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  __UpperCamelCase(				self ,							A_ ,							A_ ,							A_ ,							A_=True     ):
       '''simple docstring'''




       logger.info(F"""***** {type_path} results at step {trainer.global_step:05d} *****"""     )
       UpperCamelCase			:      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
       UpperCamelCase			:      Dict 			=     Path(pl_module.hparams.output_dir     )
       if type_path == "test":
        UpperCamelCase			:      Optional[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			:      Dict 			=     od / F"""{type_path}_results/{trainer.global_step:05d}.txt"""
        UpperCamelCase			:      List[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			:      Tuple 			=     val.item()
         UpperCamelCase			:      Optional[int] 			=     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  __UpperCamelCase(				self ,							A_ ,							A_     ):
       '''simple docstring'''




       try:
        UpperCamelCase			:      Tuple 			=     pl_module.model.model.num_parameters()
       except AttributeError:
        UpperCamelCase			:      Dict 			=     pl_module.model.num_parameters()

       UpperCamelCase			:      Union[str, 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  __UpperCamelCase(				self ,							A_ ,							A_     ):
       '''simple docstring'''




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





      @rank_zero_only
      def  __UpperCamelCase(				self ,							A_ ,							A_     ):
       '''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")







from math import loga



def 	A_       (  _lowerCAmelCase  )		->				int:
 if a < 0:
  raise ValueError("Input value must be a positive integer"  )
 elif isinstance(_lowerCAmelCase							,     _lowerCAmelCase  ):
  raise TypeError("Input value must be a 'int' type"  )
 return 0 if (a == 0) else int(loga(a & -a  )  )


if __name__ == "__main__":
    import doctest

    doctest.testmod()
