




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_rag import save_json







def SCREAMING_SNAKE_CASE							(			lowercase_		)  ->			str:



       """simple docstring"""

       A__ 					=       filter(lambda lowercase_		: p.requires_grad					,							model.parameters()		)
       A__ 					=       sum([np.prod(p.size()		) for p in model_parameters]		)
       return params


_lowerCamelCase      :						Any							=  logging.getLogger(__name__)







def SCREAMING_SNAKE_CASE							(			lowercase_					,							lowercase_		)  ->			Any:



       """simple docstring"""

       if metric == "rouge2":
              A__ 					=       '''{val_avg_rouge2:.4f}-{step_count}'''
       elif metric == "bleu":
              A__ 					=       '''{val_avg_bleu:.4f}-{step_count}'''
       elif metric == "em":
              A__ 					=       '''{val_avg_em:.4f}-{step_count}'''
       else:
              raise NotImplementedError(
                  f"""seq2seq callbacks only support rouge2 and bleu, got {metric}, You can make your own by adding to this"""
                  ''' function.'''		)

       A__ 					=       ModelCheckpoint(
           dirpath=lowercase_					,							filename=lowercase_					,							monitor=f"""val_{metric}"""					,							mode='''max'''					,							save_top_k=3					,							every_n_epochs=1					,							)
       return checkpoint_callback







def SCREAMING_SNAKE_CASE							(			lowercase_					,							lowercase_		)  ->			List[Any]:



       """simple docstring"""

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


class 							UpperCamelCase_       (					pl.Callback	):

    '''simple docstring'''






    def 			SCREAMING_SNAKE_CASE				(     self :      List[str]   ,					UpperCAmelCase__ :      str   ,					UpperCAmelCase__ :      Dict)		->Optional[int]:





           '''simple docstring'''

           A__ 					=       {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 			SCREAMING_SNAKE_CASE				(     self :      str   ,					UpperCAmelCase__ :      pl.Trainer   ,					UpperCAmelCase__ :      pl.LightningModule   ,					UpperCAmelCase__ :      str   ,					UpperCAmelCase__ :      int=True)		->None:





           '''simple docstring'''

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

           if not save_generations:
                  return

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


    @rank_zero_only
    def 			SCREAMING_SNAKE_CASE				(     self :      Tuple   ,					UpperCAmelCase__ :      int   ,					UpperCAmelCase__ :      Any)		->Optional[int]:





           '''simple docstring'''

           try:
                  A__ 					=       pl_module.model.model.num_parameters()
           except AttributeError:
                  A__ 					=       pl_module.model.num_parameters()

           A__ 					=       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 			SCREAMING_SNAKE_CASE				(     self :      Optional[int]   ,					UpperCAmelCase__ :      pl.Trainer   ,					UpperCAmelCase__ :      pl.LightningModule)		->List[str]:





           '''simple docstring'''

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





    @rank_zero_only
    def 			SCREAMING_SNAKE_CASE				(     self :      Optional[int]   ,					UpperCAmelCase__ :      pl.Trainer   ,					UpperCAmelCase__ :      List[str])		->Tuple:





           '''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")



'''simple docstring'''


import math

def   A_      (       SCREAMING_SNAKE_CASE_						)			->int:

  if not isinstance(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						):
    lowercase_     =       f"""Input value of [number={number}] must be an integer"""
    raise TypeError(SCREAMING_SNAKE_CASE_						)

  if number < 1:
    lowercase_     =       f"""Input value of [number={number}] must be > 0"""
    raise ValueError(SCREAMING_SNAKE_CASE_						)
  elif number == 1:
    return 3
  elif number == 2:
    return 5
  else:
    lowercase_     =       int(math.log(number // 3 ,	2						)						) + 2

    lowercase_     =       [3, 5]
    lowercase_     =       2
    lowercase_     =       3
    for block in range(1 ,	SCREAMING_SNAKE_CASE_						):
      for _ in range(SCREAMING_SNAKE_CASE_						):
        proth_list.append(2 ** (block + 1) + proth_list[proth_index - 1]						)
        proth_index += 1
      increment *= 2

  return proth_list[number - 1]


if __name__ == "__main__":
       import doctest

       doctest.testmod()

       for number in range(11):
              __snake_case						  =		0
              try:
                     __snake_case						  =		proth(number)
              except ValueError:
                     print(f'''ValueError: there is no {number}th Proth number''')
                     continue

              print(f'''The {number}th Proth number: {value}''')

