'''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 __lowerCAmelCase (							UpperCamelCase__				)	->    Tuple:
    __lowerCamelCase		=  filter(lambda UpperCamelCase__				: p.requires_grad				,  model.parameters()				)
    __lowerCamelCase		=  sum([np.prod(p.size()				) for p in model_parameters]				)
    return params


__UpperCAmelCase     							=logging.getLogger(__name__)






def __lowerCAmelCase (							UpperCamelCase__				,  UpperCamelCase__				)	->    Optional[int]:
    if metric == "rouge2":
        __lowerCamelCase		=  '''{val_avg_rouge2:.4f}-{step_count}'''
    elif metric == "bleu":
        __lowerCamelCase		=  '''{val_avg_bleu:.4f}-{step_count}'''
    elif metric == "em":
        __lowerCamelCase		=  '''{val_avg_em:.4f}-{step_count}'''
    elif metric == "loss":
        __lowerCamelCase		=  '''{val_avg_loss:.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		=  ModelCheckpoint(
        dirpath=UpperCamelCase__				,  filename=UpperCamelCase__				,  monitor=f"""val_{metric}"""				,  mode='''max'''				,  save_top_k=1				,  every_n_epochs=1				,  )

    return checkpoint_callback






def __lowerCAmelCase (							UpperCamelCase__				,  UpperCamelCase__				)	->    Optional[int]:
    return EarlyStopping(
        monitor=f"""val_{metric}"""				,  mode='''min''' if '''loss''' in metric else '''max'''				,  patience=UpperCamelCase__				,  verbose=UpperCamelCase__				,  )




class     a__      (   pl.Callback ):






     def    SCREAMING_SNAKE_CASE__   ( self		:		Any  ,				a		:		str  ,				a		:		List[Any]    ):





         """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(a    )






     @rank_zero_only
     def    SCREAMING_SNAKE_CASE__   ( self		:		Dict  ,				a		:		pl.Trainer  ,				a		:		pl.LightningModule  ,				a		:		str  ,				a		:		List[Any]=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=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
                 __lowerCamelCase		=  metrics[key]
                 if isinstance(a  ,				torch.Tensor    ):
                     __lowerCamelCase		=  val.item()
                 __lowerCamelCase		=  f"""{key}: {val:.6f}\n"""
                 writer.write(a    )

         if not save_generations:
             return

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






     @rank_zero_only
     def    SCREAMING_SNAKE_CASE__   ( self		:		str  ,				a		:		Dict  ,				a		:		List[str]    ):





         """simple docstring"""



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

         __lowerCamelCase		=  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    SCREAMING_SNAKE_CASE__   ( self		:		int  ,				a		:		pl.Trainer  ,				a		:		pl.LightningModule    ):





         """simple docstring"""



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






     @rank_zero_only
     def    SCREAMING_SNAKE_CASE__   ( self		:		Tuple  ,				a		:		pl.Trainer  ,				a		:		Optional[int]    ):





         """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 				A				(					_lowerCamelCase				, _lowerCamelCase  ):





							'''simple docstring'''




							if density <= 0:
														raise ValueError("Impossible fluid density"  )
							if bulk_modulus <= 0:
														raise ValueError("Impossible bulk modulus"  )

							return (bulk_modulus / density) ** 0.5


if __name__ == "__main__":
						import doctest

						doctest.testmod()
