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						(__lowercase						)	->	Optional[int]:

      '''simple docstring'''



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


__SCREAMING_SNAKE_CASE						:   Tuple         =			logging.getLogger(__name__)
def  snake_case						(__lowercase ,      __lowercase						)	->	Union[str, Any]:

      '''simple docstring'''



      if metric == "rouge2":
            _snake_case						:					int    =   "{val_avg_rouge2:.4f}-{step_count}"
      elif metric == "bleu":
            _snake_case						:					Dict    =   "{val_avg_bleu:.4f}-{step_count}"
      elif metric == "em":
            _snake_case						:					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."						)

      _snake_case						:					Union[str, Any]    =   ModelCheckpoint(
          dirpath=__lowercase ,      filename=__lowercase ,      monitor=F"""val_{metric}""" ,      mode="max" ,      save_top_k=3 ,      every_n_epochs=1 ,      )
      return checkpoint_callback
def  snake_case						(__lowercase ,      __lowercase						)	->	List[Any]:

      '''simple docstring'''



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



class        lowercase_   (					pl.Callback		):



       def 	UpperCamelCase	(						self				,    lowercase_				,    lowercase_    ):
             _snake_case						:					Optional[Any]    =   {f"""lr_group_{i}""": param["lr"] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups    )}
             pl_module.logger.log_metrics(lowercase_    )



       @rank_zero_only
       def 	UpperCamelCase	(						self				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_=True    ):
             logger.info(f"""***** {type_path} results at step {trainer.global_step:05d} *****"""    )
             _snake_case						:					Optional[Any]    =   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						:					Tuple    =   Path(pl_module.hparams.output_dir    )
             if type_path == "test":
                   _snake_case						:					Tuple    =   od / "test_results.txt"
                   _snake_case						:					Tuple    =   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						:					Tuple    =   od / f"""{type_path}_results/{trainer.global_step:05d}.txt"""
                   _snake_case						:					Optional[Any]    =   od / f"""{type_path}_generations/{trainer.global_step:05d}.txt"""
                   results_file.parent.mkdir(exist_ok=lowercase_    )
                   generations_file.parent.mkdir(exist_ok=lowercase_    )
             with open(lowercase_				,    "a+"    ) as writer:
                   for key in sorted(lowercase_    ):
                         if key in ["log", "progress_bar", "preds"]:
                               continue
                         _snake_case						:					Dict    =   metrics[key]
                         if isinstance(lowercase_				,    torch.Tensor    ):
                               _snake_case						:					Any    =   val.item()
                         _snake_case						:					List[Any]    =   f"""{key}: {val:.6f}\n"""
                         writer.write(lowercase_    )

             if not save_generations:
                   return

             if "preds" in metrics:
                   _snake_case						:					Union[str, Any]    =   "\n".join(metrics["preds"]    )
                   generations_file.open("w+"    ).write(lowercase_    )



       @rank_zero_only
       def 	UpperCamelCase	(						self				,    lowercase_				,    lowercase_    ):
             try:
                   _snake_case						:					Tuple    =   pl_module.model.model.num_parameters()
             except AttributeError:
                   _snake_case						:					List[str]    =   pl_module.model.num_parameters()

             _snake_case						:					Union[str, Any]    =   count_trainable_parameters(lowercase_    )
             # 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				,    lowercase_				,    lowercase_    ):
             save_json(pl_module.metrics				,    pl_module.metrics_save_path    )
             return self._write_logs(lowercase_				,    lowercase_				,    "test"    )



       @rank_zero_only
       def 	UpperCamelCase	(						self				,    lowercase_				,    lowercase_    ):
             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						(__lowercase						)	->	list:

						'''simple docstring'''



						return [
						    txt[:a] + txt[a].upper() + txt[a + 1 :]
						    for a in range(len(__lowercase						)						)
						    if txt[a].isalpha()
						]


if __name__ == "__main__":
				__import__('doctest').testmod()