






"""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        _lowerCamelCase		(  __A      :							Union[str, Any]	) ->   Any:
    _UpperCAmelCase			: Dict     					=			filter(lambda __A	: p.requires_grad						,						model.parameters()	)
    _UpperCAmelCase			: Union[str, Any]     					=			sum([np.prod(p.size()	) for p in model_parameters]	)
    return params


SCREAMING_SNAKE_CASE								=  logging.getLogger(__name__)
class 							A_      (  pl.Callback     ):






 '''simple docstring'''




 def 	snake_case__			(		self							,					_A							,					_A)	->						Tuple:

     """simple docstring"""



     _UpperCAmelCase			: List[str]     					=			{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 	snake_case__			(		self							,					_A							,					_A							,					_A							,					_A=True)	->						None:

     """simple docstring"""



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

     if not save_generations:
         return

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


 @rank_zero_only
 def 	snake_case__			(		self							,					_A							,					_A)	->						List[str]:

     """simple docstring"""



     try:
         _UpperCAmelCase			: str     					=			pl_module.model.model.num_parameters()
     except AttributeError:
         _UpperCAmelCase			: str     					=			pl_module.model.num_parameters()

     _UpperCAmelCase			: Optional[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 	snake_case__			(		self							,					_A							,					_A)	->						Optional[Any]:

     """simple docstring"""



     save_json(pl_module.metrics							,					pl_module.metrics_save_path)
     return self._write_logs(_UpperCAmelCase							,					_UpperCAmelCase							,					'''test''')


 @rank_zero_only
 def 	snake_case__			(		self							,					_A							,					_A)	->						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        _lowerCamelCase		(  __A      :							Any						,						__A      :							Any						,						__A      :							Optional[int]=1						,						__A      :							Any=False	) ->   List[str]:
    if metric == "rouge2":
        _UpperCAmelCase			: List[Any]     					=			"{val_avg_rouge2:.4f}-{step_count}"
    elif metric == "bleu":
        _UpperCAmelCase			: List[Any]     					=			"{val_avg_bleu:.4f}-{step_count}"
    elif metric == "loss":
        _UpperCAmelCase			: 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.'''	)

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


def        _lowerCamelCase		(  __A      :							List[Any]						,						__A      :							str	) ->   List[Any]:
    return EarlyStopping(
        monitor=f'''val_{metric}'''						,						mode='''min''' if '''loss''' in metric else '''max'''						,						patience=snake_case__						,						verbose=snake_case__						,						)












def        _lowerCamelCase		(  __A      :							list	) ->   list:
    _UpperCAmelCase			: Dict     					=			1.3
    _UpperCAmelCase			: Any     					=			len(__A	)
    _UpperCAmelCase			: List[Any]     					=			False

    while not completed:
        # Update the gap value for a next comb
        _UpperCAmelCase			: Optional[Any]     					=			int(gap / shrink_factor	)
        if gap <= 1:
            _UpperCAmelCase			: Any     					=			True

        _UpperCAmelCase			: Optional[Any]     					=			0
        while index + gap < len(__A	):
            if data[index] > data[index + gap]:
                # Swap values
                _UpperCAmelCase					,	_UpperCAmelCase			: Union[str, Any]     					=			data[index + gap], data[index]
                _UpperCAmelCase			: Any     					=			False
            index += 1

    return data


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  SCREAMING_SNAKE_CASE								=  input('Enter numbers separated by a comma:\n').strip()
  SCREAMING_SNAKE_CASE								=  [int(item) for item in user_input.split(',')]
  print(comb_sort(unsorted))




