"""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   (		A      ):






     '''simple docstring'''



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


lowerCAmelCase_:					str     =					logging.getLogger(__name__)







def 					__a   (		A    ,				A      ):






     '''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}"
     elif metric == "loss":
          lowercase__				 =							"{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."      )

     lowercase__				 =							ModelCheckpoint(
         dirpath=A    ,				filename=A    ,				monitor=f'''val_{metric}'''    ,				mode="max"    ,				save_top_k=1    ,				every_n_epochs=1    ,				)

     return checkpoint_callback







def 					__a   (		A    ,				A      ):






     '''simple docstring'''



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






class    a__    (   pl.Callback	):


   def 	snake_case__	(     self,				_UpperCAmelCase,				_UpperCAmelCase  ):
        '''simple docstring'''



        lowercase__				 =							{F'''lr_group_{i}''': param["lr"] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups  )}
        pl_module.logger.log_metrics(_UpperCAmelCase  )


   @rank_zero_only
   def 	snake_case__	(     self,				_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase=True  ):
        '''simple docstring'''



        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=_UpperCAmelCase  )
             generations_file.parent.mkdir(exist_ok=_UpperCAmelCase  )
        with open(_UpperCAmelCase,				"a+"  ) as writer:
             for key in sorted(_UpperCAmelCase  ):
                  if key in ["log", "progress_bar", "preds"]:
                       continue
                  lowercase__				 =							metrics[key]
                  if isinstance(_UpperCAmelCase,				torch.Tensor  ):
                       lowercase__				 =							val.item()
                  lowercase__				 =							F'''{key}: {val:.6f}\n'''
                  writer.write(_UpperCAmelCase  )

        if not save_generations:
             return

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


   @rank_zero_only
   def 	snake_case__	(     self,				_UpperCAmelCase,				_UpperCAmelCase  ):
        '''simple docstring'''



        try:
             lowercase__				 =							pl_module.model.model.num_parameters()
        except AttributeError:
             lowercase__				 =							pl_module.model.num_parameters()

        lowercase__				 =							count_trainable_parameters(_UpperCAmelCase  )
        # 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,				_UpperCAmelCase,				_UpperCAmelCase  ):
        '''simple docstring'''



        save_json(pl_module.metrics,				pl_module.metrics_save_path  )
        return self._write_logs(_UpperCAmelCase,				_UpperCAmelCase,				"test"  )


   @rank_zero_only
   def 	snake_case__	(     self,				_UpperCAmelCase,				_UpperCAmelCase  ):
        '''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"""


import itertools
import math







def 					__a   (		A      ):






     '''simple docstring'''



     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(math.sqrt(A      ) + 1      )    ,				6      ):
          if number % i == 0 or number % (i + 2) == 0:
               return False
     return True







def 					__a   (		):






     '''simple docstring'''



     lowercase__				 =							2
     while True:
          if is_prime(A      ):
               yield num
          num += 1







def 					__a   (		A = 1_00_01      ):






     '''simple docstring'''



     return next(itertools.islice(prime_generator()    ,				nth - 1    ,				A      )      )


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




