


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


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




class  snake_case__						(	pl.Callback					):






     """simple docstring"""


     def        lowercase_       (	self  :  List[Any],			_snake_case  :  Any,			_snake_case  :  Optional[Any] )       ->Tuple:
            snake_case__							:     Tuple            =     {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        lowercase_       (	self  :  Union[str, Any],			_snake_case  :  pl.Trainer,			_snake_case  :  pl.LightningModule,			_snake_case  :  str,			_snake_case  :  List[Any]=True )       ->Optional[Any]:
            logger.info(F'''***** {type_path} results at step {trainer.global_step:05d} *****''' )
            snake_case__							:     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
            snake_case__							:     str            =     Path(pl_module.hparams.output_dir )
            if type_path == "test":
                   snake_case__							:     List[Any]            =     od / 'test_results.txt'
                   snake_case__							:     List[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.
                   snake_case__							:     str            =     od / F'''{type_path}_results/{trainer.global_step:05d}.txt'''
                   snake_case__							:     Optional[Any]            =     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
                          snake_case__							:     Any            =     metrics[key]
                          if isinstance(UpperCamelCase_,			torch.Tensor ):
                                 snake_case__							:     Optional[int]            =     val.item()
                          snake_case__							:     Optional[int]            =     F'''{key}: {val:.6f}\n'''
                          writer.write(UpperCamelCase_ )

            if not save_generations:
                   return

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






     @rank_zero_only
     def        lowercase_       (	self  :  Optional[Any],			_snake_case  :  Optional[int],			_snake_case  :  Tuple )       ->Any:



            try:
                   snake_case__							:     Optional[Any]            =     pl_module.model.model.num_parameters()
            except AttributeError:
                   snake_case__							:     Tuple            =     pl_module.model.num_parameters()

            snake_case__							:     List[Any]            =     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        lowercase_       (	self  :  List[str],			_snake_case  :  pl.Trainer,			_snake_case  :  pl.LightningModule )       ->Dict:
            save_json(pl_module.metrics,			pl_module.metrics_save_path )
            return self._write_logs(UpperCamelCase_,			UpperCamelCase_,			'test' )






     @rank_zero_only
     def        lowercase_       (	self  :  Tuple,			_snake_case  :  pl.Trainer,			_snake_case  :  List[Any] )       ->List[Any]:
            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 	lowercase_							(A			:			List[Any]		, A			:			List[str]		, A			:			Optional[int]=1		, A			:			Optional[Any]=False				):

       if metric == "rouge2":
              snake_case__							:     List[str]            =     '{val_avg_rouge2:.4f}-{step_count}'
       elif metric == "bleu":
              snake_case__							:     Dict            =     '{val_avg_bleu:.4f}-{step_count}'
       elif metric == "loss":
              snake_case__							:     Tuple            =     '{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.'				)

       snake_case__							:     List[Any]            =     ModelCheckpoint(
           dirpath=__lowercase		, filename=__lowercase		, monitor=F'''val_{metric}'''		, mode='min' if 'loss' in metric else 'max'		, save_top_k=__lowercase		, )
       return checkpoint_callback





def 	lowercase_							(A			:			Tuple		, A			:			Optional[Any]				):
       return EarlyStopping(
           monitor=F'''val_{metric}'''		, mode='min' if 'loss' in metric else 'max'		, patience=__lowercase		, verbose=__lowercase		, )











import math
def 	lowercase_							(A			:			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(math.sqrt(A				) + 1				)		, 6				):
              if number % i == 0 or number % (i + 2) == 0:
                     return False
       return True
def 	lowercase_							(A			:			int = 1_0_0_0		, A			:			int = 1_0_0_0				):
       snake_case__							:     str            =     [0, 0, 0]  # length, a, b
       for a in range((a_limit * -1) + 1		, A				):
              for b in range(2		, A				):
                     if is_prime(A				):
                            snake_case__							:     Optional[Any]            =     0
                            snake_case__							:     Optional[int]            =     0
                            while is_prime((n**2) + (a * n) + b				):
                                   count += 1
                                   n += 1
                            if count > longest[0]:
                                   snake_case__							:     Union[str, Any]            =     [count, a, b]
       snake_case__							:     List[str]            =     longest[1] * longest[2]
       return ans


if __name__ == "__main__":
 print(solution(1_000, 1_000))






