

'''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 				A_					(	snake_case	):
  SCREAMING_SNAKE_CASE:List[str]   =   filter(lambda snake_case	: p.requires_grad ,    model.parameters()	)
  SCREAMING_SNAKE_CASE:str   =   sum([np.prod(p.size()	) for p in model_parameters]	)
  return params


A_      =							logging.getLogger(__name__)





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

     if not save_generations:
       return

     if "preds" in metrics:
       SCREAMING_SNAKE_CASE:List[Any]   =   """\n""".join(metrics["preds"]							)
       generations_file.open("w+"							).write(snake_case_							)
   @rank_zero_only
   def       __UpperCamelCase  (	self     : List[Any]       ,SCREAMING_SNAKE_CASE__     : Dict       ,SCREAMING_SNAKE_CASE__     : Union[str, Any]							):
     try:
       SCREAMING_SNAKE_CASE:List[str]   =   pl_module.model.model.num_parameters()
     except AttributeError:
       SCREAMING_SNAKE_CASE:int   =   pl_module.model.num_parameters()

     SCREAMING_SNAKE_CASE:Dict   =   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     : Tuple       ,SCREAMING_SNAKE_CASE__     : Optional[Any]       ,SCREAMING_SNAKE_CASE__     : Dict							):
     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     : Tuple       ,SCREAMING_SNAKE_CASE__     : Union[str, Any]       ,SCREAMING_SNAKE_CASE__     : 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 				A_					(	snake_case ,    snake_case ,    snake_case=1 ,    snake_case=False	):
  if metric == "rouge2":
    SCREAMING_SNAKE_CASE:Union[str, Any]   =   """{val_avg_rouge2:.4f}-{step_count}"""
  elif metric == "bleu":
    SCREAMING_SNAKE_CASE:Optional[Any]   =   """{val_avg_bleu:.4f}-{step_count}"""
  elif metric == "loss":
    SCREAMING_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."	)

  SCREAMING_SNAKE_CASE:Tuple   =   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 				A_					(	snake_case ,    snake_case	):
  return EarlyStopping(
      monitor=F'''val_{metric}''' ,    mode="min" if "loss" in metric else "max" ,    patience=_lowerCamelCase ,    verbose=_lowerCamelCase ,    )









'''simple docstring'''

from collections import Counter





def 				A_					(	snake_case	):
  return sum(socks_by_color // 2 for socks_by_color in Counter(snake_case	).values()	)


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    A_      =							[int(x) for x in input("Enter socks by color :").rstrip().split()]
    print(f'''sock_merchant({colors}) = {sock_merchant(colors)}''')






