





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        __SCREAMING_SNAKE_CASE	(				__lowercase:		List[str]):



  '''simple docstring'''






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


__lowercase				    =      logging.getLogger(__name__)

class 	__snake_case					(      pl.Callback ):






    '''simple docstring'''





    def 						A		( self    :		Union[str, Any]      ,    snake_case    :		Optional[int]      ,    snake_case    :		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(snake_case				)





    @rank_zero_only
    def 						A		( self    :		int      ,    snake_case    :		pl.Trainer      ,    snake_case    :		pl.LightningModule      ,    snake_case    :		str      ,    snake_case    :		Dict=True				):

      """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=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
          __lowerCAmelCase								=					metrics[key]
          if isinstance(snake_case      ,    torch.Tensor				):
            __lowerCAmelCase								=					val.item()
          __lowerCAmelCase								=					f'''{key}: {val:.6f}\n'''
          writer.write(snake_case				)

      if not save_generations:
        return

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





    @rank_zero_only
    def 						A		( self    :		Tuple      ,    snake_case    :		Tuple      ,    snake_case    :		Optional[Any]				):

      """simple docstring"""





      try:
        __lowerCAmelCase								=					pl_module.model.model.num_parameters()
      except AttributeError:
        __lowerCAmelCase								=					pl_module.model.num_parameters()

      __lowerCAmelCase								=					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 						A		( self    :		Optional[int]      ,    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 						A		( self    :		int      ,    snake_case    :		pl.Trainer      ,    snake_case    :		Optional[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")


def        __SCREAMING_SNAKE_CASE	(				__lowercase:		List[str]  ,				__lowercase:		Any  ,				__lowercase:		Union[str, Any]=1  ,				__lowercase:		Dict=False):



  '''simple docstring'''






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

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




def        __SCREAMING_SNAKE_CASE	(				__lowercase:		List[Any]  ,				__lowercase:		List[str]):



  '''simple docstring'''






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







def        __SCREAMING_SNAKE_CASE	(				__lowercase:		int):



  '''simple docstring'''






  if not isinstance(__lowercase  ,				__lowercase):
    __lowerCAmelCase								=					F'''Input value of [number={number}] must be an integer'''
    raise TypeError(__lowercase)
  if number < 0 or number % 2 == 1:
    return False
  __lowerCAmelCase								=					int(number**0.5)
  return number == number_sqrt * (number_sqrt + 1)


if __name__ == "__main__":
      import doctest

      doctest.testmod()
