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  (		lowerCamelCase      :  Tuple			):
 UpperCamelCase_      :		Dict				=      filter(lambda lowerCamelCase			: p.requires_grad						,						model.parameters()			)
 UpperCamelCase_      :		Tuple				=      sum([np.prod(p.size()			) for p in model_parameters]			)
 return params


a_					      = logging.getLogger(__name__)





class 	_lowercase		(      pl.Callback ):

 def 		SCREAMING_SNAKE_CASE__     (				self			:							Dict     ,   snake_case			:							str     ,   snake_case			:							Dict    )     ->		List[Any]:

  """simple docstring"""





  UpperCamelCase_      :		List[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 		SCREAMING_SNAKE_CASE__     (				self			:							List[Any]     ,   snake_case			:							pl.Trainer     ,   snake_case			:							pl.LightningModule     ,   snake_case			:							str     ,   snake_case			:							Optional[Any]=True    )     ->		None:

  """simple docstring"""





  logger.info(f"***** {type_path} results at step {trainer.global_step:05d} *****"    )
  UpperCamelCase_      :		Optional[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
  UpperCamelCase_      :		Dict				=      Path(pl_module.hparams.output_dir    )
  if type_path == "test":
   UpperCamelCase_      :		List[Any]				=      od / 'test_results.txt'
   UpperCamelCase_      :		Dict				=      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.
   UpperCamelCase_      :		List[Any]				=      od / f"{type_path}_results/{trainer.global_step:05d}.txt"
   UpperCamelCase_      :		int				=      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
    UpperCamelCase_      :		List[Any]				=      metrics[key]
    if isinstance(snake_case     ,   torch.Tensor    ):
     UpperCamelCase_      :		List[str]				=      val.item()
    UpperCamelCase_      :		Union[str, Any]				=      f"{key}: {val:.6f}\n"
    writer.write(snake_case    )

  if not save_generations:
   return

  if "preds" in metrics:
   UpperCamelCase_      :		Union[str, Any]				=      '\n'.join(metrics['preds']    )
   generations_file.open('w+'    ).write(snake_case    )

 @rank_zero_only
 def 		SCREAMING_SNAKE_CASE__     (				self			:							List[Any]     ,   snake_case			:							str     ,   snake_case			:							List[str]    )     ->		List[str]:

  """simple docstring"""





  try:
   UpperCamelCase_      :		Dict				=      pl_module.model.model.num_parameters()
  except AttributeError:
   UpperCamelCase_      :		int				=      pl_module.model.num_parameters()

  UpperCamelCase_      :		Optional[int]				=      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 		SCREAMING_SNAKE_CASE__     (				self			:							Union[str, Any]     ,   snake_case			:							pl.Trainer     ,   snake_case			:							pl.LightningModule    )     ->		int:

  """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 		SCREAMING_SNAKE_CASE__     (				self			:							str     ,   snake_case			:							pl.Trainer     ,   snake_case			:							Optional[int]    )     ->		List[Any]:

  """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")





def      __lowercase  (		lowerCamelCase      :  Union[str, Any]						,						lowerCamelCase      :  List[str]						,						lowerCamelCase      :  Union[str, Any]=1						,						lowerCamelCase      :  Optional[int]=False			):
 if metric == "rouge2":
  UpperCamelCase_      :		Union[str, Any]				=      '{val_avg_rouge2:.4f}-{step_count}'
 elif metric == "bleu":
  UpperCamelCase_      :		Any				=      '{val_avg_bleu:.4f}-{step_count}'
 elif metric == "loss":
  UpperCamelCase_      :		Dict				=      '{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.'			)

 UpperCamelCase_      :		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      __lowercase  (		lowerCamelCase      :  List[Any]						,						lowerCamelCase      :  Tuple			):
 return EarlyStopping(
     monitor=F"val_{metric}"						,						mode='min' if 'loss' in metric else 'max'						,						patience=lowerCamelCase						,						verbose=lowerCamelCase						,						)




def      __lowercase  (		lowerCamelCase      :  int = 998001			):
 UpperCamelCase_      :		int				=      0
 for i in range(999						,						99						,						-1			):  # 3 digit numbers range from 999 down to 100
  for j in range(999						,						99						,						-1			):
   UpperCamelCase_      :		str				=      str(i * j			)
   if product_string == product_string[::-1] and i * j < n:
    UpperCamelCase_      :		str				=      max(lowerCamelCase						,						i * j			)
 return answer


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



