


'''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 _SCREAMING_SNAKE_CASE (A			)			->			Dict:






       """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				:  Any    		=      logging.getLogger(__name__)

def _SCREAMING_SNAKE_CASE (A    ,					A			)			->			List[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=_UpperCamelCase    ,					filename=_UpperCamelCase    ,					monitor=f"val_{metric}"    ,					mode='''max'''    ,					save_top_k=3    ,					every_n_epochs=1    ,					)
       return checkpoint_callback

def _SCREAMING_SNAKE_CASE (A    ,					A			)			->			Union[str, Any]:






       """simple docstring"""







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

class     __lowerCAmelCase		(pl.Callback	):





      '''simple docstring'''






      def  UpperCamelCase__						(self       :       List[Any]  ,		UpperCamelCase       :       int  ,		UpperCamelCase       :       List[str]					):

             '''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(snake_case__					)



      @rank_zero_only
      def  UpperCamelCase__						(self       :       Any  ,		UpperCamelCase       :       pl.Trainer  ,		UpperCamelCase       :       pl.LightningModule  ,		UpperCamelCase       :       str  ,		UpperCamelCase       :       Any=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=snake_case__					)
                    generations_file.parent.mkdir(exist_ok=snake_case__					)
             with open(snake_case__  ,		'''a+'''					) as writer:
                    for key in sorted(snake_case__					):
                           if key in ["log", "progress_bar", "preds"]:
                                  continue
                           lowercase__      =					metrics[key]
                           if isinstance(snake_case__  ,		torch.Tensor					):
                                  lowercase__      =					val.item()
                           lowercase__      =					f"{key}: {val:.6f}\n"
                           writer.write(snake_case__					)

             if not save_generations:
                    return

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



      @rank_zero_only
      def  UpperCamelCase__						(self       :       Union[str, Any]  ,		UpperCamelCase       :       Any  ,		UpperCamelCase       :       Tuple					):

             '''simple docstring'''







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

             lowercase__      =					count_trainable_parameters(snake_case__					)
             # 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]  ,		UpperCamelCase       :       pl.Trainer  ,		UpperCamelCase       :       pl.LightningModule					):

             '''simple docstring'''







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






      @rank_zero_only
      def  UpperCamelCase__						(self       :       Dict  ,		UpperCamelCase       :       pl.Trainer  ,		UpperCamelCase       :       Dict					):

             '''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")







import math


def 							__lowerCAmelCase     (							_UpperCamelCase		:	int						)		->				list[int]:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE		    =      []
 SCREAMING_SNAKE_CASE		    =      2
 SCREAMING_SNAKE_CASE		    =      int(math.sqrt(_UpperCamelCase						)						)  # Size of every segment
 SCREAMING_SNAKE_CASE		    =      [True] * (end + 1)
 SCREAMING_SNAKE_CASE		    =      []

 while start <= end:
  if temp[start] is True:
   in_prime.append(_UpperCamelCase						)
   for i in range(start * start		,				end + 1		,				_UpperCamelCase						):
    SCREAMING_SNAKE_CASE		    =      False
  start += 1
 prime += in_prime

 SCREAMING_SNAKE_CASE		    =      end + 1
 SCREAMING_SNAKE_CASE		    =      min(2 * end		,				_UpperCamelCase						)

 while low <= n:
  SCREAMING_SNAKE_CASE		    =      [True] * (high - low + 1)
  for each in in_prime:
   SCREAMING_SNAKE_CASE		    =      math.floor(low / each						) * each
   if t < low:
    t += each

   for j in range(_UpperCamelCase		,				high + 1		,				_UpperCamelCase						):
    SCREAMING_SNAKE_CASE		    =      False

  for j in range(len(_UpperCamelCase						)						):
   if temp[j] is True:
    prime.append(j + low						)

  SCREAMING_SNAKE_CASE		    =      high + 1
  SCREAMING_SNAKE_CASE		    =      min(high + end		,				_UpperCamelCase						)

 return prime




print(sieve(10**6))

