


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


__UpperCamelCase				:  Dict      					=							logging.getLogger(__name__)
def      snake_case_						( __lowercase			,       __lowercase       ):
   if metric == "rouge2":
      UpperCAmelCase_							:      List[Any]						=   '''{val_avg_rouge2:.4f}-{step_count}'''
   elif metric == "bleu":
      UpperCAmelCase_							:      str						=   '''{val_avg_bleu:.4f}-{step_count}'''
   elif metric == "em":
      UpperCAmelCase_							:      Optional[Any]						=   '''{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_							:      Optional[int]						=   ModelCheckpoint(
       dirpath=__lowercase			,       filename=__lowercase			,       monitor=F'''val_{metric}'''			,       mode='''max'''			,       save_top_k=1			,       every_n_epochs=1			,       )

   return checkpoint_callback
def      snake_case_						( __lowercase			,       __lowercase       ):
   return EarlyStopping(
       monitor=F'''val_{metric}'''			,       mode='''min''' if '''loss''' in metric else '''max'''			,       patience=__lowercase			,       verbose=__lowercase			,       )





class  lowerCAmelCase__(    pl.Callback						):






    '''simple docstring'''





    def 					_lowerCamelCase (	self				:   Dict       ,   __snake_case				:   Optional[int]       ,   __snake_case				:   int	):





       '''simple docstring'''
       UpperCAmelCase_							:      int						=   {f'''lr_group_{i}''': param['''lr'''] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups	)}
       pl_module.logger.log_metrics(__snake_case	)





    @rank_zero_only
    def 					_lowerCamelCase (	self				:   Tuple       ,   __snake_case				:   pl.Trainer       ,   __snake_case				:   pl.LightningModule       ,   __snake_case				:   str       ,   __snake_case				:   Optional[Any]=True	):





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

       if not save_generations:
          return

       if "preds" in metrics:
          UpperCAmelCase_							:      Union[str, Any]						=   '''\n'''.join(metrics['''preds''']	)
          generations_file.open('''w+'''	).write(__snake_case	)





    @rank_zero_only
    def 					_lowerCamelCase (	self				:   Any       ,   __snake_case				:   Tuple       ,   __snake_case				:   List[str]	):





       '''simple docstring'''
       try:
          UpperCAmelCase_							:      List[str]						=   pl_module.model.model.num_parameters()
       except AttributeError:
          UpperCAmelCase_							:      Union[str, Any]						=   pl_module.model.num_parameters()

       UpperCAmelCase_							:      Tuple						=   count_trainable_parameters(__snake_case	)
       # 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 					_lowerCamelCase (	self				:   List[str]       ,   __snake_case				:   pl.Trainer       ,   __snake_case				:   pl.LightningModule	):





       '''simple docstring'''
       save_json(pl_module.metrics       ,   pl_module.metrics_save_path	)
       return self._write_logs(__snake_case       ,   __snake_case       ,   '''test'''	)





    @rank_zero_only
    def 					_lowerCamelCase (	self				:   Any       ,   __snake_case				:   pl.Trainer       ,   __snake_case				:   Optional[int]	):





       '''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")







import fire

from utils import calculate_rouge, save_json
def      snake_case_						( __lowercase			,       __lowercase			,       __lowercase=None			,       **__lowercase       ):
   UpperCAmelCase_							:      Tuple						=   [x.strip() for x in open(__lowercase       ).readlines()]
   UpperCAmelCase_							:      Dict						=   [x.strip() for x in open(__lowercase       ).readlines()][: len(__lowercase       )]
   UpperCAmelCase_							:      int						=   calculate_rouge(__lowercase			,       __lowercase			,       **__lowercase       )
   if save_path is not None:
      save_json(__lowercase			,       __lowercase			,       indent=__lowercase       )
   return metrics  # these print nicely


if __name__ == "__main__":
       fire.Fire(calculate_rouge_path)