

'''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     (    A__      ):






 """simple docstring"""



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


lowerCAmelCase__											=	logging.getLogger(__name__)


def 		_A     (    A__     ,				A__      ):






 """simple docstring"""



 if metric == "rouge2":
  __lowercase							  = '''{val_avg_rouge2:.4f}-{step_count}'''
 elif metric == "bleu":
  __lowercase							  = '''{val_avg_bleu:.4f}-{step_count}'''
 elif metric == "em":
  __lowercase							  = '''{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.'''      )

 __lowercase							  = ModelCheckpoint(
     dirpath=A__     ,				filename=A__     ,				monitor=F"val_{metric}"     ,				mode='''max'''     ,				save_top_k=3     ,				every_n_epochs=1     ,				)
 return checkpoint_callback


def 		_A     (    A__     ,				A__      ):






 """simple docstring"""



 return EarlyStopping(
     monitor=F"val_{metric}"     ,				mode='''min''' if '''loss''' in metric else '''max'''     ,				patience=A__     ,				verbose=A__     ,				)






class 			lowercase_	(pl.Callback      ):




  """simple docstring"""




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

   if not save_generations:
    return

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






  @rank_zero_only
  def 					SCREAMING_SNAKE_CASE      (      self   :     List[Any]					,lowercase__   :     int					,lowercase__   :     Dict			):
   try:
    __lowercase							  = pl_module.model.model.num_parameters()
   except AttributeError:
    __lowercase							  = pl_module.model.num_parameters()

   __lowercase							  = 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 					SCREAMING_SNAKE_CASE      (      self   :     List[Any]					,lowercase__   :     pl.Trainer					,lowercase__   :     pl.LightningModule			):
   save_json(pl_module.metrics					,pl_module.metrics_save_path			)
   return self._write_logs(lowercase__					,lowercase__					,'''test'''			)






  @rank_zero_only
  def 					SCREAMING_SNAKE_CASE      (      self   :     Union[str, Any]					,lowercase__   :     pl.Trainer					,lowercase__   :     Optional[int]			):
   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'''






lowerCAmelCase__											=	'''Input must be a string of 8 numbers plus letter'''
lowerCAmelCase__											=	'''TRWAGMYFPDXBNJZSQVHLCKE'''


def 		_A     (    A__      ):






 """simple docstring"""



 if not isinstance(A__     ,				A__      ):
  __lowercase							  = F"Expected string as input, found {type(A__      ).__name__}"
  raise TypeError(A__      )

 __lowercase							  = spanish_id.replace('''-'''     ,				''''''      ).upper()
 if len(A__      ) != 9:
  raise ValueError(A__      )

 try:
  __lowercase							  = int(spanish_id_clean[0:8]      )
  __lowercase							  = spanish_id_clean[8]
 except ValueError as ex:
  raise ValueError(A__      ) from ex

 if letter.isdigit():
  raise ValueError(A__      )

 return letter == LOOKUP_LETTERS[number % 23]


if __name__ == "__main__":
 import doctest

 doctest.testmod()

