

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    snake_case_(_UpperCamelCase  )							->			List[Any]:



  """simple docstring"""






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


__A    	=			logging.getLogger(__name__)



class 						lowercase_					(						pl.Callback  ):



      def 		UpperCamelCase_	(    self   :       Optional[int]    ,      A__   :       Optional[Any]    ,      A__   :       int	)					->  Any:
        _snake_case		=							{f"""lr_group_{i}""": param['''lr'''] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups	)}
        pl_module.logger.log_metrics(A__	)



      @rank_zero_only
      def 		UpperCamelCase_	(    self   :       str    ,      A__   :       pl.Trainer    ,      A__   :       pl.LightningModule    ,      A__   :       str    ,      A__   :       List[Any]=True	)					->  None:
        logger.info(f"""***** {type_path} results at step {trainer.global_step:05d} *****"""	)
        _snake_case		=							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
        _snake_case		=							Path(pl_module.hparams.output_dir	)
        if type_path == "test":
          _snake_case		=							od / '''test_results.txt'''
          _snake_case		=							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.
          _snake_case		=							od / f"""{type_path}_results/{trainer.global_step:05d}.txt"""
          _snake_case		=							od / f"""{type_path}_generations/{trainer.global_step:05d}.txt"""
          results_file.parent.mkdir(exist_ok=A__	)
          generations_file.parent.mkdir(exist_ok=A__	)
        with open(A__    ,      '''a+'''	) as writer:
          for key in sorted(A__	):
            if key in ["log", "progress_bar", "preds"]:
              continue
            _snake_case		=							metrics[key]
            if isinstance(A__    ,      torch.Tensor	):
              _snake_case		=							val.item()
            _snake_case		=							f"""{key}: {val:.6f}\n"""
            writer.write(A__	)

        if not save_generations:
          return

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



      @rank_zero_only
      def 		UpperCamelCase_	(    self   :       Dict    ,      A__   :       Optional[Any]    ,      A__   :       Optional[int]	)					->  Optional[int]:
        try:
          _snake_case		=							pl_module.model.model.num_parameters()
        except AttributeError:
          _snake_case		=							pl_module.model.num_parameters()

        _snake_case		=							count_trainable_parameters(A__	)
        # 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 		UpperCamelCase_	(    self   :       int    ,      A__   :       pl.Trainer    ,      A__   :       pl.LightningModule	)					->  Optional[int]:
        save_json(pl_module.metrics    ,      pl_module.metrics_save_path	)
        return self._write_logs(A__    ,      A__    ,      '''test'''	)







      @rank_zero_only
      def 		UpperCamelCase_	(    self   :       Optional[Any]    ,      A__   :       pl.Trainer    ,      A__   :       Union[str, Any]	)					->  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")



def    snake_case_(_UpperCamelCase				, _UpperCamelCase				, _UpperCamelCase=1				, _UpperCamelCase=False  )							->			Tuple:



  """simple docstring"""






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

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



def    snake_case_(_UpperCamelCase				, _UpperCamelCase  )							->			int:



  """simple docstring"""






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




from collections import namedtuple

__A    	=			namedtuple('''from_to''', '''from_ to''')

__A    	=			{
    '''atm''': from_to(1, 1),
    '''pascal''': from_to(0.000_0098, 10_13_25),
    '''bar''': from_to(0.98_6923, 1.0_1325),
    '''kilopascal''': from_to(0.0098_6923, 101.325),
    '''megapascal''': from_to(9.8_6923, 0.10_1325),
    '''psi''': from_to(0.06_8046, 14.6959),
    '''inHg''': from_to(0.033_4211, 29.9213),
    '''torr''': from_to(0.0013_1579, 7_60),
}



def    snake_case_(_UpperCamelCase				, _UpperCamelCase				, _UpperCamelCase  )							->			float:



  """simple docstring"""






  if from_type not in PRESSURE_CONVERSION:
    raise ValueError(
        F"""Invalid 'from_type' value: {from_type!r}  Supported values are:\n"""
        + ''', '''.join(_UpperCamelCase  )  )
  if to_type not in PRESSURE_CONVERSION:
    raise ValueError(
        F"""Invalid 'to_type' value: {to_type!r}.  Supported values are:\n"""
        + ''', '''.join(_UpperCamelCase  )  )
  return (
      value * PRESSURE_CONVERSION[from_type].from_ * PRESSURE_CONVERSION[to_type].to
  )


if __name__ == "__main__":
 import doctest

 doctest.testmod()

