
"""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 import save_json


def   __magic_name__     (				__snake_case    :	Union[str, Any]  )			->    List[Any]:
							lowercase   :    Tuple			=							filter(lambda __snake_case  : p.requires_grad					,			model.parameters()  )
							lowercase   :    List[Any]			=							sum([np.prod(p.size()  ) for p in model_parameters]  )
							return params


_A      :	Optional[int]       		=	logging.getLogger(__name__)

class 		a__						(				pl.Callback						):
			def       __magic_name__      (      self     ,   _a     ,   _a    ):
										lowercase   :    Optional[Any]			=							{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       __magic_name__      (      self     ,   _a     ,   _a     ,   _a     ,   _a=True    ):
										logger.info(f"""***** {type_path} results at step {trainer.global_step:05d} *****"""    )
										lowercase   :    Union[str, 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   :    Dict			=							Path(pl_module.hparams.output_dir    )
										if type_path == "test":
																	lowercase   :    List[str]			=							od / '''test_results.txt'''
																	lowercase   :    Union[str, Any]			=							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   :    Union[str, Any]			=							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   :    int			=							metrics[key]
																								if isinstance(__snake_case     ,   torch.Tensor    ):
																															lowercase   :    Union[str, Any]			=							val.item()
																								lowercase   :    List[Any]			=							f"""{key}: {val:.6f}\n"""
																								writer.write(__snake_case    )

										if not save_generations:
																	return

										if "preds" in metrics:
																	lowercase   :    Dict			=							'''\n'''.join(metrics["preds"]    )
																	generations_file.open("w+"    ).write(__snake_case    )
			@rank_zero_only
			def       __magic_name__      (      self     ,   _a     ,   _a    ):
										try:
																	lowercase   :    Tuple			=							pl_module.model.model.num_parameters()
										except AttributeError:
																	lowercase   :    Union[str, Any]			=							pl_module.model.num_parameters()

										lowercase   :    str			=							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       __magic_name__      (      self     ,   _a     ,   _a    ):
										save_json(pl_module.metrics     ,   pl_module.metrics_save_path    )
										return self._write_logs(__snake_case     ,   __snake_case     ,   "test"    )

			@rank_zero_only
			def       __magic_name__      (      self     ,   _a     ,   _a    ):
										save_json(pl_module.metrics     ,   pl_module.metrics_save_path    )
										# Uncommenting this will save val generations
										# return self._write_logs(trainer, pl_module, "valid")


def   __magic_name__     (				__snake_case    :	Dict					,			__snake_case    :	int					,			__snake_case    :	Tuple=1					,			__snake_case    :	List[Any]=False  )			->    List[str]:
							if metric == "rouge2":
														lowercase   :    Union[str, Any]			=							'''{val_avg_rouge2:.4f}-{step_count}'''
							elif metric == "bleu":
														lowercase   :    Any			=							'''{val_avg_bleu:.4f}-{step_count}'''
							elif metric == "loss":
														lowercase   :    Union[str, Any]			=							'''{val_avg_loss:.4f}-{step_count}'''
							else:
														raise NotImplementedError(
														    f"""seq2seq callbacks only support rouge2, bleu and loss, got {metric}, You can make your own by adding to"""
														    " this function."  )

							lowercase   :    Dict			=							ModelCheckpoint(
							    dirpath=_lowerCAmelCase					,			filename=_lowerCAmelCase					,			monitor=f"""val_{metric}"""					,			mode="min" if "loss" in metric else "max"					,			save_top_k=_lowerCAmelCase					,			)
							return checkpoint_callback


def   __magic_name__     (				__snake_case    :	Optional[Any]					,			__snake_case    :	Optional[Any]  )			->    Any:
							return EarlyStopping(
							    monitor=f"""val_{metric}"""					,			mode="min" if "loss" in metric else "max"					,			patience=_lowerCAmelCase					,			verbose=_lowerCAmelCase					,			)




"""simple docstring"""




import math
from collections.abc import Generator


def   __magic_name__     (				__snake_case    :	int  )			->    Generator[int, None, None]:
       lowercase   :    Generator			=							(i for i in range(1					,			(max_n + 1)  ))
       for i in (n for n in numbers if n > 1):
              for j in range(2					,			__snake_case  ):
                     if (i % j) == 0:
                            break
              else:
                     yield i


def   __magic_name__     (				__snake_case    :	int  )			->    Generator[int, None, None]:
       lowercase   :    Generator			=							(i for i in range(1					,			(max_n + 1)  ))
       for i in (n for n in numbers if n > 1):
              # only need to check for factors up to sqrt(i)
              lowercase   :    Dict			=							int(math.sqrt(__snake_case  )  ) + 1
              for j in range(2					,			__snake_case  ):
                     if (i % j) == 0:
                            break
              else:
                     yield i


def   __magic_name__     (				__snake_case    :	int  )			->    Generator[int, None, None]:
       lowercase   :    Generator			=							(i for i in range(1					,			(max_n + 1)					,			2  ))
       # It's useless to test even numbers as they will not be prime
       if max_n > 2:
              yield 2  # Because 2 will not be tested, it's necessary to yield it now
       for i in (n for n in numbers if n > 1):
              lowercase   :    int			=							int(math.sqrt(__snake_case  )  ) + 1
              for j in range(3					,			__snake_case					,			2  ):
                     # As we removed the even numbers, we don't need them now
                     if (i % j) == 0:
                            break
              else:
                     yield i


def   __magic_name__     (				)			->    Tuple:
       from timeit import timeit

       lowercase   :    List[str]			=							"from __main__ import slow_primes, primes, fast_primes"
       print(timeit("slow_primes(1_000_000_000_000)"					,			setup=__snake_case					,			number=100_0000  )  )
       print(timeit("primes(1_000_000_000_000)"					,			setup=__snake_case					,			number=100_0000  )  )
       print(timeit("fast_primes(1_000_000_000_000)"					,			setup=__snake_case					,			number=100_0000  )  )


if __name__ == "__main__":
   _A      :	List[Any]       		=	int(input("""Calculate primes up to:\n>> """).strip())
   for ret in primes(number):
      print(ret)
   benchmark()


