


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 import save_json
def 							UpperCAmelCase_   (				__snake_case		)	->     List[Any]:







 """simple docstring"""


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


UpperCAmelCase__        =				logging.getLogger(__name__)







class  lowerCamelCase__  (			pl.Callback):





     def 	__A   (self						,	UpperCAmelCase						,	UpperCAmelCase			)	->  str:
      _lowercase				 ={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						,	UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase=True			)	->  None:
      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=__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
        _lowercase				 =metrics[key]
        if isinstance(__lowerCamelCase						,	torch.Tensor			):
         _lowercase				 =val.item()
        _lowercase				 =f"{key}: {val:.6f}\n"
        writer.write(__lowerCamelCase			)

      if not save_generations:
       return

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





     @rank_zero_only
     def 	__A   (self						,	UpperCAmelCase						,	UpperCAmelCase			)	->  Optional[Any]:
      try:
       _lowercase				 =pl_module.model.model.num_parameters()
      except AttributeError:
       _lowercase				 =pl_module.model.num_parameters()

      _lowercase				 =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						,	UpperCAmelCase						,	UpperCAmelCase			)	->  Union[str, Any]:
      save_json(pl_module.metrics						,	pl_module.metrics_save_path			)
      return self._write_logs(__lowerCamelCase						,	__lowerCamelCase						,	'''test'''			)





     @rank_zero_only
     def 	__A   (self						,	UpperCAmelCase						,	UpperCAmelCase			)	->  List[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 							UpperCAmelCase_   (				__snake_case	,		__snake_case	,		__snake_case=1	,		__snake_case=False		)	->     Tuple:







 """simple docstring"""


 if metric == "rouge2":
  _lowercase				 ="{val_avg_rouge2:.4f}-{step_count}"
 elif metric == "bleu":
  _lowercase				 ="{val_avg_bleu:.4f}-{step_count}"
 elif metric == "loss":
  _lowercase				 ="{val_avg_loss:.4f}-{step_count}"
 else:
  raise NotImplementedError(
      F"seq2seq callbacks only support rouge2, bleu and loss, got {metric}, You can make your own by adding to"
      ''' this function.'''		)

 _lowercase				 =ModelCheckpoint(
     dirpath=_lowerCamelCase	,		filename=_lowerCamelCase	,		monitor=F"val_{metric}"	,		mode='''min''' if '''loss''' in metric else '''max'''	,		save_top_k=_lowerCamelCase	,		)
 return checkpoint_callback



def 							UpperCAmelCase_   (				__snake_case	,		__snake_case		)	->     Optional[int]:







 """simple docstring"""


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






def 						UpperCAmelCase (    _lowerCamelCase     ):
			return "".join(chr(ord(_lowerCamelCase     ) + 32     ) if "A" <= char <= "Z" else char for char in word     )


if __name__ == "__main__":
							from doctest import testmod

							testmod()