'''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 (	__lowerCamelCase			:						List[str]				)	->							Tuple:
      '''simple docstring'''


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


lowerCAmelCase_ : List[str]						  =      logging.getLogger(__name__)


def __a (	__lowerCamelCase			:						Union[str, Any] ,		__lowerCamelCase			:						int				)	->							Any:
      '''simple docstring'''


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

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


def __a (	__lowerCamelCase			:						str ,		__lowerCamelCase			:						Dict				)	->							str:
      '''simple docstring'''


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






class 							lowercase						(						pl.Callback ):


   def __UpperCAmelCase       (   self				:	List[Any]   ,		__lowerCAmelCase				:	Optional[int]   ,		__lowerCAmelCase				:	str)      ->					Optional[Any]:
         lowercase_     =      {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 __UpperCAmelCase       (   self				:	Optional[Any]   ,		__lowerCAmelCase				:	pl.Trainer   ,		__lowerCAmelCase				:	pl.LightningModule   ,		__lowerCAmelCase				:	str   ,		__lowerCAmelCase				:	List[str]=True)      ->					None:
         logger.info(F'***** {type_path} results at step {trainer.global_step:05d} *****')
         lowercase_     =      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
         lowercase_     =      Path(pl_module.hparams.output_dir)
         if type_path == "test":
               lowercase_     =      od / "test_results.txt"
               lowercase_     =      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.
               lowercase_     =      od / F'{type_path}_results/{trainer.global_step:05d}.txt'
               lowercase_     =      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
                     lowercase_     =      metrics[key]
                     if isinstance(__lowerCAmelCase   ,		torch.Tensor):
                           lowercase_     =      val.item()
                     lowercase_     =      F'{key}: {val:.6f}\n'
                     writer.write(__lowerCAmelCase)

         if not save_generations:
               return

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


   @rank_zero_only
   def __UpperCAmelCase       (   self				:	Tuple   ,		__lowerCAmelCase				:	List[Any]   ,		__lowerCAmelCase				:	List[str])      ->					Any:
         try:
               lowercase_     =      pl_module.model.model.num_parameters()
         except AttributeError:
               lowercase_     =      pl_module.model.num_parameters()

         lowercase_     =      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 __UpperCAmelCase       (   self				:	List[str]   ,		__lowerCAmelCase				:	pl.Trainer   ,		__lowerCAmelCase				:	pl.LightningModule)      ->					Union[str, Any]:
         save_json(pl_module.metrics   ,		pl_module.metrics_save_path)
         return self._write_logs(__lowerCAmelCase   ,		__lowerCAmelCase   ,		"test")


   @rank_zero_only
   def __UpperCAmelCase       (   self				:	int   ,		__lowerCAmelCase				:	pl.Trainer   ,		__lowerCAmelCase				:	Dict)      ->					str:
         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'''

import functools
from typing import Any


def __a (	__lowerCamelCase			:						str ,		__lowerCamelCase			:						list[str]				)	->							bool:
      '''simple docstring'''


      if not isinstance(__lowerCamelCase ,		__lowerCamelCase				) or len(__lowerCamelCase				) == 0:
            raise ValueError("the string should be not empty string"				)

      if not isinstance(__lowerCamelCase ,		__lowerCamelCase				) or not all(
          isinstance(__lowerCamelCase ,		__lowerCamelCase				) and len(__lowerCamelCase				) > 0 for item in words				):
            raise ValueError("the words should be a list of non-empty strings"				)

      # Build trie
      lowercase_     =      {}
      lowercase_     =      "WORD_KEEPER"

      for word in words:
            lowercase_     =      trie
            for c in word:
                  if c not in trie_node:
                        lowercase_     =      {}

                  lowercase_     =      trie_node[c]

            lowercase_     =      True

      lowercase_     =      len(__lowerCamelCase				)

      # Dynamic programming method
      @functools.cache
      def is_breakable(__lowerCamelCase			:						int				) -> bool:
            if index == len_string:
                  return True

            lowercase_     =      trie
            for i in range(__lowerCamelCase ,		__lowerCamelCase				):
                  lowercase_     =      trie_node.get(string[i] ,		__lowerCamelCase				)

                  if trie_node is None:
                        return False

                  if trie_node.get(__lowerCamelCase ,		__lowerCamelCase				) and is_breakable(i + 1				):
                        return True

            return False

      return is_breakable(0				)


if __name__ == "__main__":
       import doctest

       doctest.testmod()






