


"""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 import save_json





def     lowercase__	(  lowerCamelCase     :  Union[str, Any]				)					->     Union[str, Any]:
  lowerCAmelCase__    :    Optional[Any]								=					filter(lambda lowerCamelCase				: p.requires_grad				,	model.parameters()				)
  lowerCAmelCase__    :    Union[str, Any]								=					sum([np.prod(p.size()				) for p in model_parameters]				)
  return params


__UpperCAmelCase    				=      logging.getLogger(__name__)

class  lowercase_  ( pl.Callback						):







      def 				_lowerCAmelCase       (      self :							int		,  _lowercase :							Dict		,  _lowercase :							Optional[int]    ):
        lowerCAmelCase__    :    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 				_lowerCAmelCase       (      self :							str		,  _lowercase :							pl.Trainer		,  _lowercase :							pl.LightningModule		,  _lowercase :							str		,  _lowercase :							List[str]=True    ):
        logger.info(f"***** {type_path} results at step {trainer.global_step:05d} *****"    )
        lowerCAmelCase__    :    List[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__    :    List[str]								=					Path(pl_module.hparams.output_dir    )
        if type_path == "test":
          lowerCAmelCase__    :    Union[str, Any]								=					od / "test_results.txt"
          lowerCAmelCase__    :    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__    :    List[Any]								=					od / f"{type_path}_results/{trainer.global_step:05d}.txt"
          lowerCAmelCase__    :    List[str]								=					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
            lowerCAmelCase__    :    List[str]								=					metrics[key]
            if isinstance(_lowercase		,  torch.Tensor    ):
              lowerCAmelCase__    :    List[Any]								=					val.item()
            lowerCAmelCase__    :    List[Any]								=					f"{key}: {val:.6f}\n"
            writer.write(_lowercase    )

        if not save_generations:
          return

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







      @rank_zero_only
      def 				_lowerCAmelCase       (      self :							Optional[int]		,  _lowercase :							Tuple		,  _lowercase :							Any    ):
        try:
          lowerCAmelCase__    :    Dict								=					pl_module.model.model.num_parameters()
        except AttributeError:
          lowerCAmelCase__    :    List[Any]								=					pl_module.model.num_parameters()

        lowerCAmelCase__    :    int								=					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 				_lowerCAmelCase       (      self :							Optional[int]		,  _lowercase :							pl.Trainer		,  _lowercase :							pl.LightningModule    ):
        save_json(pl_module.metrics		,  pl_module.metrics_save_path    )
        return self._write_logs(_lowercase		,  _lowercase		,  "test"    )







      @rank_zero_only
      def 				_lowerCAmelCase       (      self :							Tuple		,  _lowercase :							pl.Trainer		,  _lowercase :							Optional[Any]    ):
        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     lowercase__	(  lowerCamelCase     :  Union[str, Any]				,	lowerCamelCase     :  Tuple				,	lowerCamelCase     :  Tuple=1				,	lowerCamelCase     :  int=False				)					->     List[str]:
  if metric == "rouge2":
    lowerCAmelCase__    :    Any								=					"{val_avg_rouge2:.4f}-{step_count}"
  elif metric == "bleu":
    lowerCAmelCase__    :    int								=					"{val_avg_bleu:.4f}-{step_count}"
  elif metric == "loss":
    lowerCAmelCase__    :    Union[str, Any]								=					"{val_avg_loss:.4f}-{step_count}"
  else:
    raise NotImplementedError(
        F"seq2seq callbacks only support rouge2, bleu and loss, got {metric}, You can make your own by adding to"
        " this function."				)

  lowerCAmelCase__    :    int								=					ModelCheckpoint(
      dirpath=lowerCamelCase				,	filename=lowerCamelCase				,	monitor=F"val_{metric}"				,	mode="min" if "loss" in metric else "max"				,	save_top_k=lowerCamelCase				,	)
  return checkpoint_callback







def     lowercase__	(  lowerCamelCase     :  List[str]				,	lowerCamelCase     :  str				)					->     Any:
  return EarlyStopping(
      monitor=F"val_{metric}"				,	mode="min" if "loss" in metric else "max"				,	patience=lowerCamelCase				,	verbose=lowerCamelCase				,	)




"""simple docstring"""





def     lowercase__	(  lowerCamelCase     :  int				)					->     int:
  if not isinstance(lowerCamelCase				,	lowerCamelCase				):
    raise TypeError("recursive_lucas_number accepts only integer arguments."				)
  if n_th_number == 0:
    return 2
  if n_th_number == 1:
    return 1

  return recursive_lucas_number(n_th_number - 1				) + recursive_lucas_number(
      n_th_number - 2				)





def     lowercase__	(  lowerCamelCase     :  int				)					->     int:
  if not isinstance(lowerCamelCase				,	lowerCamelCase				):
    raise TypeError("dynamic_lucas_number accepts only integer arguments."				)
  lowerCAmelCase__				,		lowerCAmelCase__    :    str								=					2, 1
  for _ in range(lowerCamelCase				):
    lowerCAmelCase__				,		lowerCAmelCase__    :    str								=					b, a + b
  return a


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

   testmod()
   __UpperCAmelCase    				=      int(input("Enter the number of terms in lucas series:\n").strip())
   print("Using recursive function to calculate lucas series:")
   print(" ".join(str(recursive_lucas_number(i)) for i in range(n)))
   print("\nUsing dynamic function to calculate lucas series:")
   print(" ".join(str(dynamic_lucas_number(i)) for i in range(n)))
