

"""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     lowercase__( __SCREAMING_SNAKE_CASE							:  Any   ):
     lowercase_			:       Union[str, Any]									=			filter(lambda __SCREAMING_SNAKE_CASE   : p.requires_grad  ,    model.parameters()   )
     lowercase_			:       Tuple									=			sum([np.prod(p.size()   ) for p in model_parameters]   )
     return params


__SCREAMING_SNAKE_CASE								=logging.getLogger(__name__)



def     lowercase__( __SCREAMING_SNAKE_CASE							:  Any  ,    __SCREAMING_SNAKE_CASE							:  Any   ):





     if metric == "rouge2":
          lowercase_			:       str									=			'{val_avg_rouge2:.4f}-{step_count}'
     elif metric == "bleu":
          lowercase_			:       Optional[int]									=			'{val_avg_bleu:.4f}-{step_count}'
     elif metric == "em":
          lowercase_			:       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.'   )

     lowercase_			:       Optional[int]									=			ModelCheckpoint(
         dirpath=__snake_case  ,    filename=__snake_case  ,    monitor=F'''val_{metric}'''  ,    mode='max'  ,    save_top_k=3  ,    every_n_epochs=1  ,    )
     return checkpoint_callback



def     lowercase__( __SCREAMING_SNAKE_CASE							:  List[str]  ,    __SCREAMING_SNAKE_CASE							:  Tuple   ):
     return EarlyStopping(
         monitor=F'''val_{metric}'''  ,    mode='min' if 'loss' in metric else 'max'  ,    patience=__snake_case  ,    verbose=__snake_case  ,    )







class  UpperCamelCase	(    pl.Callback						):
   def     _UpperCAmelCase     (    self  ,__UpperCamelCase  ,__UpperCamelCase    )			->   int:


        '''simple docstring'''


        lowercase_			:       Union[str, Any]									=			{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     _UpperCAmelCase     (    self  ,__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase=True    )			->   None:


        '''simple docstring'''


        logger.info(f'''***** {type_path} results at step {trainer.global_step:05d} *****'''    )
        lowercase_			:       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
        lowercase_			:       Optional[Any]									=			Path(pl_module.hparams.output_dir    )
        if type_path == "test":
             lowercase_			:       List[str]									=			od / 'test_results.txt'
             lowercase_			:       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.
             lowercase_			:       int									=			od / f'''{type_path}_results/{trainer.global_step:05d}.txt'''
             lowercase_			:       List[str]									=			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
                  lowercase_			:       Optional[int]									=			metrics[key]
                  if isinstance(__a  ,torch.Tensor    ):
                       lowercase_			:       Any									=			val.item()
                  lowercase_			:       int									=			f'''{key}: {val:.6f}\n'''
                  writer.write(__a    )

        if not save_generations:
             return

        if "preds" in metrics:
             lowercase_			:       Any									=			'\n'.join(metrics['preds']    )
             generations_file.open('w+'    ).write(__a    )
   @rank_zero_only
   def     _UpperCAmelCase     (    self  ,__UpperCamelCase  ,__UpperCamelCase    )			->   List[str]:


        '''simple docstring'''


        try:
             lowercase_			:       int									=			pl_module.model.model.num_parameters()
        except AttributeError:
             lowercase_			:       str									=			pl_module.model.num_parameters()

        lowercase_			:       Dict									=			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     _UpperCAmelCase     (    self  ,__UpperCamelCase  ,__UpperCamelCase    )			->   int:


        '''simple docstring'''


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







   @rank_zero_only
   def     _UpperCAmelCase     (    self  ,__UpperCamelCase  ,__UpperCamelCase    )			->   Any:


        '''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")


"""simple docstring"""







from math import sqrt



def     lowercase__( __SCREAMING_SNAKE_CASE							:  int   ):

     if 1 < number < 4:
          # 2 and 3 are primes
          return True
     elif number < 2 or number % 2 == 0 or number % 3 == 0:
          # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
          return False

     # All primes number are in format of 6k +/- 1
     for i in range(5  ,    int(sqrt(__SCREAMING_SNAKE_CASE   ) + 1   )  ,    6   ):
          if number % i == 0 or number % (i + 2) == 0:
               return False
     return True



def     lowercase__( __SCREAMING_SNAKE_CASE							:  int = 1_00_01   ):
     lowercase_			:       str									=			0
     lowercase_			:       Optional[Any]									=			1
     while count != nth and number < 3:
          number += 1
          if is_prime(__SCREAMING_SNAKE_CASE   ):
               count += 1
     while count != nth:
          number += 2
          if is_prime(__SCREAMING_SNAKE_CASE   ):
               count += 1
     return number


if __name__ == "__main__":
       print(F"{solution() = }")

