
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__							:				Optional[int]  ):
 __a :  str						=  filter(lambda lowerCAmelCase__  : p.requires_grad  ,   model.parameters()  )
 __a :  Any						=  sum([np.prod(p.size()  ) for p in model_parameters]  )
 return params


lowercase__       =logging.getLogger(__name__)




def    __UpperCamelCase     (				lowerCAmelCase__							:				Optional[int]  ,   lowerCAmelCase__							:				Optional[int]  ):
 if metric == "rouge2":
  __a :  int						=  '''{val_avg_rouge2:.4f}-{step_count}'''
 elif metric == "bleu":
  __a :  Optional[Any]						=  '''{val_avg_bleu:.4f}-{step_count}'''
 elif metric == "em":
  __a :  int						=  '''{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.'''  )

 __a :  int						=  ModelCheckpoint(
     dirpath=lowerCAmelCase_  ,   filename=lowerCAmelCase_  ,   monitor=f"val_{metric}"  ,   mode='''max'''  ,   save_top_k=3  ,   every_n_epochs=1  ,   )
 return checkpoint_callback




def    __UpperCamelCase     (				lowerCAmelCase__							:				Union[str, Any]  ,   lowerCAmelCase__							:				Any  ):
 return EarlyStopping(
     monitor=f"val_{metric}"  ,   mode='''min''' if '''loss''' in metric else '''max'''  ,   patience=lowerCAmelCase_  ,   verbose=lowerCAmelCase_  ,   )



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

    if not save_generations:
     return

    if "preds" in metrics:
     __a :  int						=  '''\n'''.join(metrics['''preds''']   )
     generations_file.open('''w+'''   ).write(lowerCAmelCase_   )
   @rank_zero_only
   def       lowerCAmelCase			(self :					Union[str, Any]					,				snake_case_ :					Union[str, Any]					,				snake_case_ :					List[str]   ):
    try:
     __a :  Tuple						=  pl_module.model.model.num_parameters()
    except AttributeError:
     __a :  int						=  pl_module.model.num_parameters()

    __a :  Any						=  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       lowerCAmelCase			(self :					int					,				snake_case_ :					pl.Trainer					,				snake_case_ :					pl.LightningModule   ):
    save_json(pl_module.metrics					,				pl_module.metrics_save_path   )
    return self._write_logs(lowerCAmelCase_					,				lowerCAmelCase_					,				'''test'''   )







   @rank_zero_only
   def       lowerCAmelCase			(self :					List[Any]					,				snake_case_ :					pl.Trainer					,				snake_case_ :					Any   ):
    save_json(pl_module.metrics					,				pl_module.metrics_save_path   )
    # Uncommenting this will save val generations
    # return self._write_logs(trainer, pl_module, "valid")







def   a_    ( lowerCAmelCase_      :			int	):

   if p < 2:
      raise ValueError('p should not be less than 2!'	)
   elif p == 2:
      return True

   __lowerCAmelCase								=	4
   __lowerCAmelCase								=	(1 << p) - 1
   for _ in range(p - 2	):
      __lowerCAmelCase								=	((s * s) - 2) % m
   return s == 0


if __name__ == "__main__":
     print(lucas_lehmer_test(7))
     print(lucas_lehmer_test(11))

