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  (					__UpperCamelCase    :       Optional[int]	)							->					Union[str, Any]:







  """simple docstring"""





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


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



def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       Optional[int] ,     __UpperCamelCase    :       str	)							->					Optional[Any]:







  """simple docstring"""





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

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



def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       Tuple ,     __UpperCamelCase    :       List[Any]	)							->					str:







  """simple docstring"""





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







class __snake_case				(						pl.Callback		):




   def 	__a						(    self			:				Any       ,	_lowercase			:				Optional[int]       ,	_lowercase			:				Optional[Any]      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      {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 	__a						(    self			:				List[Any]       ,	_lowercase			:				Tuple       ,	_lowercase			:				str       ,	_lowercase			:				str       ,	_lowercase			:				Any=True      ):



     """simple docstring"""






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

     if not save_generations:
       return

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




   @rank_zero_only
   def 	__a						(    self			:				Optional[Any]       ,	_lowercase			:				str       ,	_lowercase			:				Any      ):



     """simple docstring"""






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

     SCREAMING_SNAKE_CASE__										=      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 	__a						(    self			:				int       ,	_lowercase			:				List[Any]       ,	_lowercase			:				List[str]      ):



     """simple docstring"""






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




   @rank_zero_only
   def 	__a						(    self			:				Dict       ,	_lowercase			:				int       ,	_lowercase			:				int      ):



     """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 __future__ import annotations

import math





def lowerCamelCase						(     __lowerCamelCase :    int    )						->list[int]:

    if num <= 0:
        _SCREAMING_SNAKE_CASE									=			F'{num}: Invalid input, please enter a positive integer.'
        raise ValueError(__lowerCamelCase    )

    _SCREAMING_SNAKE_CASE									=			[True] * (num + 1)
    _SCREAMING_SNAKE_CASE									=			[]
    _SCREAMING_SNAKE_CASE									=			2
    _SCREAMING_SNAKE_CASE									=			int(math.sqrt(__lowerCamelCase    )    )

    while start <= end:
        # If start is a prime
        if sieve[start] is True:
            prime.append(__lowerCamelCase    )

            # Set multiples of start be False
            for i in range(start * start					,       num + 1					,       __lowerCamelCase    ):
                if sieve[i] is True:
                    _SCREAMING_SNAKE_CASE									=			False

        start += 1

    for j in range(end + 1					,       num + 1    ):
        if sieve[j] is True:
            prime.append(__lowerCamelCase    )

    return prime


if __name__ == "__main__":
      print(prime_sieve(int(input("""Enter a positive integer: """).strip())))



