


'''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 			UpperCAmelCase__					(	UpperCAmelCase_    :				List[Any]							)     ->		int:
     __lowerCamelCase   :	int								=		filter(lambda UpperCAmelCase_							: p.requires_grad      ,    model.parameters()							)
     __lowerCamelCase   :	Optional[Any]								=		sum([np.prod(p.size()							) for p in model_parameters]							)
     return params


A__		:					Any												=  logging.getLogger(__name__)



def 			UpperCAmelCase__					(	UpperCAmelCase_    :				Tuple      ,    UpperCAmelCase_    :				List[str]							)     ->		List[Any]:
     if metric == "rouge2":
          __lowerCamelCase   :	Dict								=		'{val_avg_rouge2:.4f}-{step_count}'
     elif metric == "bleu":
          __lowerCamelCase   :	Optional[Any]								=		'{val_avg_bleu:.4f}-{step_count}'
     elif metric == "em":
          __lowerCamelCase   :	Optional[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.'							)

     __lowerCamelCase   :	Tuple								=		ModelCheckpoint(
         dirpath=UpperCAmelCase_      ,    filename=UpperCAmelCase_      ,    monitor=F'val_{metric}'      ,    mode='max'      ,    save_top_k=3      ,    every_n_epochs=1      ,    )
     return checkpoint_callback



def 			UpperCAmelCase__					(	UpperCAmelCase_    :				Union[str, Any]      ,    UpperCAmelCase_    :				Tuple							)     ->		int:
     return EarlyStopping(
         monitor=F'val_{metric}'      ,    mode='min' if 'loss' in metric else 'max'      ,    patience=UpperCAmelCase_      ,    verbose=UpperCAmelCase_      ,    )





class    UpperCAmelCase_  (pl.Callback  ):





    """simple docstring"""






    def       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_	)				->     str:
         __lowerCamelCase   :	Optional[int]								=		{f'lr_group_{i}': param['lr'] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups	)}
         pl_module.logger.log_metrics(SCREAMING_SNAKE_CASE_	)







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

         if not save_generations:
              return

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







    @rank_zero_only
    def       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_	)				->     Union[str, Any]:
         try:
              __lowerCamelCase   :	List[str]								=		pl_module.model.model.num_parameters()
         except AttributeError:
              __lowerCamelCase   :	Dict								=		pl_module.model.num_parameters()

         __lowerCamelCase   :	Union[str, Any]								=		count_trainable_parameters(SCREAMING_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       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_	)				->     Optional[Any]:
         save_json(pl_module.metrics			,     pl_module.metrics_save_path	)
         return self._write_logs(SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     'test'	)







    @rank_zero_only
    def       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_	)				->     str:
         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'''



A__		:					Any												=  8.3_1_4_4_6_2  # Unit - J mol-1 K-1



def 			UpperCAmelCase__					(	UpperCAmelCase_    :				float      ,    UpperCAmelCase_    :				float      ,    UpperCAmelCase_    :				float							)     ->		float:
     if moles < 0 or kelvin < 0 or volume < 0:
          raise ValueError('Invalid inputs. Enter positive value.'							)
     return moles * kelvin * UNIVERSAL_GAS_CONSTANT / volume



def 			UpperCAmelCase__					(	UpperCAmelCase_    :				float      ,    UpperCAmelCase_    :				float      ,    UpperCAmelCase_    :				float							)     ->		float:
     if moles < 0 or kelvin < 0 or pressure < 0:
          raise ValueError('Invalid inputs. Enter positive value.'							)
     return moles * kelvin * UNIVERSAL_GAS_CONSTANT / pressure


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

      testmod()






