






'''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    _SCREAMING_SNAKE_CASE    (			UpperCamelCase__						:				Tuple							):




     """simple docstring"""


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


lowerCAmelCase_			:     int							    =     logging.getLogger(__name__)







class     SCREAMING_SNAKE_CASE						(	pl.Callback							):






      def        lowercase_	(   self      :     Tuple	,	lowercase__      :     Union[str, Any]	,	lowercase__      :     Any					):


           '''simple docstring'''

           a_    :       Union[str, Any]    	=     {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      :     str	,	lowercase__      :     pl.Trainer	,	lowercase__      :     pl.LightningModule	,	lowercase__      :     str	,	lowercase__      :     int=True					):


           '''simple docstring'''

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

           if not save_generations:
                return

           if "preds" in metrics:
                a_    :       List[Any]    	=     '''\n'''.join(metrics["""preds"""]					)
                generations_file.open("""w+"""					).write(UpperCamelCase__					)






      @rank_zero_only
      def        lowercase_	(   self      :     List[Any]	,	lowercase__      :     Union[str, Any]	,	lowercase__      :     Union[str, Any]					):


           '''simple docstring'''

           try:
                a_    :       Optional[Any]    	=     pl_module.model.model.num_parameters()
           except AttributeError:
                a_    :       Dict    	=     pl_module.model.num_parameters()

           a_    :       Optional[int]    	=     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[Any]	,	lowercase__      :     pl.Trainer	,	lowercase__      :     pl.LightningModule					):


           '''simple docstring'''

           save_json(pl_module.metrics	,	pl_module.metrics_save_path					)
           return self._write_logs(UpperCamelCase__	,	UpperCamelCase__	,	"""test"""					)






      @rank_zero_only
      def        lowercase_	(   self      :     int	,	lowercase__      :     pl.Trainer	,	lowercase__      :     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    _SCREAMING_SNAKE_CASE    (			UpperCamelCase__						:				Tuple   ,						UpperCamelCase__						:				Union[str, Any]   ,						UpperCamelCase__						:				Optional[Any]=1   ,						UpperCamelCase__						:				Optional[Any]=False							):




     """simple docstring"""


     if metric == "rouge2":
          a_    :       List[str]    	=     '''{val_avg_rouge2:.4f}-{step_count}'''
     elif metric == "bleu":
          a_    :       Union[str, Any]    	=     '''{val_avg_bleu:.4f}-{step_count}'''
     elif metric == "loss":
          a_    :       List[str]    	=     '''{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=SCREAMING_SNAKE_CASE__   ,						filename=SCREAMING_SNAKE_CASE__   ,						monitor=F"val_{metric}"   ,						mode="""min""" if """loss""" in metric else """max"""   ,						save_top_k=SCREAMING_SNAKE_CASE__   ,						)
     return checkpoint_callback



def    _SCREAMING_SNAKE_CASE    (			UpperCamelCase__						:				Any   ,						UpperCamelCase__						:				List[str]							):




     """simple docstring"""


     return EarlyStopping(
         monitor=F"val_{metric}"   ,						mode="""min""" if """loss""" in metric else """max"""   ,						patience=SCREAMING_SNAKE_CASE__   ,						verbose=SCREAMING_SNAKE_CASE__   ,						)






'''simple docstring'''


from .stack import Stack



def    _SCREAMING_SNAKE_CASE    (			UpperCamelCase__						:				str							):




     """simple docstring"""


     a_    :       Stack[str]    	=     Stack()
     a_    :       Tuple    	=     {"""(""": """)""", """[""": """]""", """{""": """}"""}
     for bracket in parentheses:
          if bracket in bracket_pairs:
               stack.push(UpperCamelCase__							)
          elif bracket in (")", "]", "}"):
               if stack.is_empty() or bracket_pairs[stack.pop()] != bracket:
                    return False
     return stack.is_empty()


if __name__ == "__main__":
 from doctest import testmod

 testmod()

 lowerCAmelCase_			:     Any							    =     ['((()))', '((())', '(()))']
 print('Balanced parentheses demonstration:\n')
 for example in examples:
  lowerCAmelCase_			:     Optional[Any]							    =     '' if balanced_parentheses(example) else 'not '
  print(f"{example} is {not_str}balanced")
