





'''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 	__a(SCREAMING_SNAKE_CASE_  :					Optional[int]    ):



    '''simple docstring'''





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


_SCREAMING_SNAKE_CASE	 =							logging.getLogger(__name__)


def 	__a(SCREAMING_SNAKE_CASE_  :					Tuple		,       SCREAMING_SNAKE_CASE_  :					List[Any]    ):



    '''simple docstring'''





    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=SCREAMING_SNAKE_CASE_		,       filename=SCREAMING_SNAKE_CASE_		,       monitor=F'''val_{metric}'''		,       mode="max"		,       save_top_k=1		,       every_n_epochs=1		,       )

    return checkpoint_callback


def 	__a(SCREAMING_SNAKE_CASE_  :					Union[str, Any]		,       SCREAMING_SNAKE_CASE_  :					Any    ):



    '''simple docstring'''





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





class    lowerCAmelCase_		(		pl.Callback		):




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




 @rank_zero_only
 def 							_snake_case   (	self			,     _lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase=True		)    ->							None:
     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=_lowerCAmelCase		)
         generations_file.parent.mkdir(exist_ok=_lowerCAmelCase		)
     with open(_lowerCAmelCase			,     "a+"		) as writer:
         for key in sorted(_lowerCAmelCase		):
             if key in ["log", "progress_bar", "preds"]:
                 continue
             _lowerCAmelCase				=      metrics[key]
             if isinstance(_lowerCAmelCase			,     torch.Tensor		):
                 _lowerCAmelCase				=      val.item()
             _lowerCAmelCase				=      f'''{key}: {val:.6f}\n'''
             writer.write(_lowerCAmelCase		)

     if not save_generations:
         return

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




 @rank_zero_only
 def 							_snake_case   (	self			,     _lowerCAmelCase			,     _lowerCAmelCase		)    ->							str:
     try:
         _lowerCAmelCase				=      pl_module.model.model.num_parameters()
     except AttributeError:
         _lowerCAmelCase				=      pl_module.model.num_parameters()

     _lowerCAmelCase				=      count_trainable_parameters(_lowerCAmelCase		)
     # 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 							_snake_case   (	self			,     _lowerCAmelCase			,     _lowerCAmelCase		)    ->							Tuple:
     save_json(pl_module.metrics			,     pl_module.metrics_save_path		)
     return self._write_logs(_lowerCAmelCase			,     _lowerCAmelCase			,     "test"		)





 @rank_zero_only
 def 							_snake_case   (	self			,     _lowerCAmelCase			,     _lowerCAmelCase		)    ->							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")









'''simple docstring'''


def 	__a(SCREAMING_SNAKE_CASE_  :					str		,       SCREAMING_SNAKE_CASE_  :					bool = False    ):



    '''simple docstring'''





    if not isinstance(SCREAMING_SNAKE_CASE_		,       SCREAMING_SNAKE_CASE_    ):
        _lowerCAmelCase				=      F'''Expected string as input, found {type(SCREAMING_SNAKE_CASE_    )}'''
        raise ValueError(SCREAMING_SNAKE_CASE_    )
    if not isinstance(SCREAMING_SNAKE_CASE_		,       SCREAMING_SNAKE_CASE_    ):
        _lowerCAmelCase				=      F'''Expected boolean as use_pascal parameter, found {type(SCREAMING_SNAKE_CASE_    )}'''
        raise ValueError(SCREAMING_SNAKE_CASE_    )

    _lowerCAmelCase				=      input_str.split("_"    )

    _lowerCAmelCase				=      0 if use_pascal else 1

    _lowerCAmelCase				=      words[start_index:]

    _lowerCAmelCase				=      [word[0].upper() + word[1:] for word in words_to_capitalize]

    _lowerCAmelCase				=      "" if use_pascal else words[0]

    return "".join([initial_word, *capitalized_words]    )


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

       testmod()


