






'''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_     (    lowercase__):
 lowerCamelCase__  =     filter(lambda lowercase__: p.requires_grad				,     model.parameters())
 lowerCamelCase__  =     sum([np.prod(p.size()) for p in model_parameters])
 return params


__A							:							Dict  							=		logging.getLogger(__name__)






def    lowerCamelCase_     (    lowercase__				,     lowercase__):
 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    lowerCamelCase_     (    lowercase__				,     lowercase__):
 return EarlyStopping(
     monitor=F'''val_{metric}'''				,     mode="min" if "loss" in metric else "max"				,     patience=lowercase__				,     verbose=lowercase__				,     )
class 				lowercase       (							pl.Callback      ):


    '''simple docstring'''

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

     '''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(__lowerCamelCase     )

    @rank_zero_only
    def    a__				(					self     :     str				,     __lowerCamelCase     :     pl.Trainer				,     __lowerCamelCase     :     pl.LightningModule				,     __lowerCamelCase     :     str				,     __lowerCamelCase     :     Tuple=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=__lowerCamelCase     )
      generations_file.parent.mkdir(exist_ok=__lowerCamelCase     )
     with open(__lowerCamelCase				,     "a+"     ) as writer:
      for key in sorted(__lowerCamelCase     ):
       if key in ["log", "progress_bar", "preds"]:
        continue
       lowerCamelCase__  =     metrics[key]
       if isinstance(__lowerCamelCase				,     torch.Tensor     ):
        lowerCamelCase__  =     val.item()
       lowerCamelCase__  =     f'''{key}: {val:.6f}\n'''
       writer.write(__lowerCamelCase     )

     if not save_generations:
      return

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

    @rank_zero_only
    def    a__				(					self     :     str				,     __lowerCamelCase     :     Union[str, Any]				,     __lowerCamelCase     :     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(__lowerCamelCase     )
     # 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     :     Any				,     __lowerCamelCase     :     pl.Trainer				,     __lowerCamelCase     :     pl.LightningModule     )							->							Any:

     '''simple docstring'''




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




    @rank_zero_only
    def    a__				(					self     :     int				,     __lowerCamelCase     :     pl.Trainer				,     __lowerCamelCase     :     List[Any]     )							->							Union[str, Any]:

     '''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 __future__ import annotations






def    lowerCamelCase_     (    lowercase__				,     lowercase__				,     lowercase__):
 lowerCamelCase__  =     list(range(len(lowercase__)))
 lowerCamelCase__  =     [v / w for v, w in zip(lowercase__				,     lowercase__)]
 index.sort(key=lambda lowercase__: ratio[i]				,     reverse=lowercase__)

 lowerCamelCase__  =     0
 lowerCamelCase__  =     [0] * len(lowercase__)
 for i in index:
  if weight[i] <= capacity:
   lowerCamelCase__  =     1
   max_value += value[i]
   capacity -= weight[i]
  else:
   lowerCamelCase__  =     capacity / weight[i]
   max_value += value[i] * capacity / weight[i]
   break

 return max_value, fractions


if __name__ == "__main__":
 import doctest

 doctest.testmod()

