





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 							__snake_case							(    lowerCAmelCase_	)				->						List[str]:
    SCREAMING_SNAKE_CASE__											=			filter(lambda lowerCAmelCase_	: p.requires_grad ,							model.parameters()	)
    SCREAMING_SNAKE_CASE__											=			sum([np.prod(p.size()	) for p in model_parameters]	)
    return params


_A					:				int				=			logging.getLogger(__name__)


def 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_	)				->						Union[str, Any]:




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

    SCREAMING_SNAKE_CASE__											=			ModelCheckpoint(
        dirpath=_UpperCamelCase ,							filename=_UpperCamelCase ,							monitor=f'''val_{metric}''' ,							mode='''max''' ,							save_top_k=3 ,							every_n_epochs=1 ,							)
    return checkpoint_callback


def 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_	)				->						Union[str, Any]:
    return EarlyStopping(
        monitor=f'''val_{metric}''' ,							mode='''min''' if '''loss''' in metric else '''max''' ,							patience=_UpperCamelCase ,							verbose=_UpperCamelCase ,							)



class 					__snake_case	(  pl.Callback    ):


    '''simple docstring'''







    def  lowercase_					(       self   ,				A_   ,				A_     ):





        '''simple docstring'''

        SCREAMING_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  lowercase_					(       self   ,				A_   ,				A_   ,				A_   ,				A_=True     ):





        '''simple docstring'''

        logger.info(f'''***** {type_path} results at step {trainer.global_step:05d} *****'''     )
        SCREAMING_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
        SCREAMING_SNAKE_CASE__											=			Path(pl_module.hparams.output_dir     )
        if type_path == "test":
            SCREAMING_SNAKE_CASE__											=			od / "test_results.txt"
            SCREAMING_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.
            SCREAMING_SNAKE_CASE__											=			od / f'''{type_path}_results/{trainer.global_step:05d}.txt'''
            SCREAMING_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
                SCREAMING_SNAKE_CASE__											=			metrics[key]
                if isinstance(A_   ,				torch.Tensor     ):
                    SCREAMING_SNAKE_CASE__											=			val.item()
                SCREAMING_SNAKE_CASE__											=			f'''{key}: {val:.6f}\n'''
                writer.write(A_     )

        if not save_generations:
            return

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



    @rank_zero_only
    def  lowercase_					(       self   ,				A_   ,				A_     ):





        '''simple docstring'''

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

        SCREAMING_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  lowercase_					(       self   ,				A_   ,				A_     ):





        '''simple docstring'''

        save_json(pl_module.metrics   ,				pl_module.metrics_save_path     )
        return self._write_logs(A_   ,				A_   ,				'''test'''     )




    @rank_zero_only
    def  lowercase_					(       self   ,				A_   ,				A_     ):





        '''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")








'''simple docstring'''
def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase = 100				):


   """simple docstring"""




   lowercase_						:     Dict							     =  n * (n + 1) * (2 * n + 1) / 6
   lowercase_						:     int							     =  (n * (n + 1) / 2) ** 2
   return int(square_of_sum - sum_of_squares				)


if __name__ == "__main__":
 print(f"""{solution() = }""")




