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    lowerCamelCase_   (  _lowercase    )							->	List[Any]:
  __A							:   Tuple	  =				filter(lambda _lowercase    : p.requires_grad ,     model.parameters()    )
  __A							:   List[Any]	  =				sum([np.prod(p.size()    ) for p in model_parameters]    )
  return params


UpperCamelCase      			=     logging.getLogger(__name__)


class _a       (	pl.Callback ):

 '''simple docstring'''



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

   if not save_generations:
     return

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

 @rank_zero_only
 def 					__UpperCAmelCase(       self							,			__UpperCAmelCase							,			__UpperCAmelCase   ):
   try:
     __A							:   Tuple	  =				pl_module.model.model.num_parameters()
   except AttributeError:
     __A							:   Union[str, Any]	  =				pl_module.model.num_parameters()

   __A							:   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 					__UpperCAmelCase(       self							,			__UpperCAmelCase							,			__UpperCAmelCase   ):
   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							,			__UpperCAmelCase							,			__UpperCAmelCase   ):
   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    lowerCamelCase_   (  _lowercase ,     _lowercase ,     _lowercase=1 ,     _lowercase=False    )							->	List[str]:
  if metric == "rouge2":
    __A							:   Union[str, Any]	  =				'''{val_avg_rouge2:.4f}-{step_count}'''
  elif metric == "bleu":
    __A							:   Any	  =				'''{val_avg_bleu:.4f}-{step_count}'''
  elif metric == "loss":
    __A							:   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."    )

  __A							:   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    lowerCamelCase_   (  _lowercase ,     _lowercase    )							->	Any:
  return EarlyStopping(
      monitor=F"val_{metric}" ,     mode="min" if "loss" in metric else "max" ,     patience=_lowerCAmelCase ,     verbose=_lowerCAmelCase ,     )






import os



def    lowerCamelCase_   (  )							->	List[str]:
		__A							:   Any	  =				0
		__A							:   Any	  =				0
		with open(os.path.dirname(_lowercase    ) + "/p022_names.txt"    ) as file:
				__A							:   int	  =				str(file.readlines()[0]    )
				__A							:   Optional[Any]	  =				name.replace("\"" ,     ""    ).split(","    )

		name.sort()
		for i in range(len(_lowercase    )    ):
				for j in name[i]:
						temp_sum += ord(_lowercase    ) - ord("A"    ) + 1
				total_sum += (i + 1) * temp_sum
				__A							:   List[Any]	  =				0
		return total_sum


if __name__ == "__main__":
					print(solution())





