




"""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_rag import save_json






def 						_snake_case					( _snake_case							: Optional[int]							)   -> int:






      '''simple docstring'''


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


a         =      logging.getLogger(__name__)






def 						_snake_case					( _snake_case							: Optional[int]    ,  _snake_case							: Optional[int]							)   -> List[str]:






      '''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=_snake_case    ,  filename=_snake_case    ,  monitor=F'''val_{metric}'''    ,  mode='max'    ,  save_top_k=3    ,  every_n_epochs=1    ,  )
      return checkpoint_callback






def 						_snake_case					( _snake_case							: Optional[int]    ,  _snake_case							: List[Any]							)   -> List[Any]:






      '''simple docstring'''


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







class     lowercase_	(					pl.Callback							):



 '''simple docstring'''

 def       lowerCAmelCase_							(			self	:	List[Any]   ,							_UpperCAmelCase	:	Union[str, Any]   ,							_UpperCAmelCase	:	Union[str, Any]		):
       _A        =       {F'''lr_group_{i}''': param['''lr'''] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups		)}
       pl_module.logger.log_metrics(_SCREAMING_SNAKE_CASE		)

 @rank_zero_only
 def       lowerCAmelCase_							(			self	:	Tuple   ,							_UpperCAmelCase	:	Dict   ,							_UpperCAmelCase	:	Any   ,							_UpperCAmelCase	:	Dict   ,							_UpperCAmelCase	:	Optional[int]=True		):
       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=_SCREAMING_SNAKE_CASE		)
             generations_file.parent.mkdir(exist_ok=_SCREAMING_SNAKE_CASE		)
       with open(_SCREAMING_SNAKE_CASE   ,							'a+'		) as writer:
             for key in sorted(_SCREAMING_SNAKE_CASE		):
                   if key in ["log", "progress_bar", "preds"]:
                         continue
                   _A        =       metrics[key]
                   if isinstance(_SCREAMING_SNAKE_CASE   ,							torch.Tensor		):
                         _A        =       val.item()
                   _A        =       F'''{key}: {val:.6f}\n'''
                   writer.write(_SCREAMING_SNAKE_CASE		)

       if not save_generations:
             return

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

 @rank_zero_only
 def       lowerCAmelCase_							(			self	:	List[str]   ,							_UpperCAmelCase	:	Optional[int]   ,							_UpperCAmelCase	:	int		):





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

       _A        =       count_trainable_parameters(_SCREAMING_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       lowerCAmelCase_							(			self	:	str   ,							_UpperCAmelCase	:	Dict   ,							_UpperCAmelCase	:	str		):
       save_json(pl_module.metrics   ,							pl_module.metrics_save_path		)
       return self._write_logs(_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE   ,							'test'		)



 @rank_zero_only
 def       lowerCAmelCase_							(			self	:	Dict   ,							_UpperCAmelCase	:	str   ,							_UpperCAmelCase	:	Optional[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")







"""simple docstring"""

import secrets
from random import shuffle
from string import ascii_letters, ascii_lowercase, ascii_uppercase, digits, punctuation






def 						_snake_case					( _snake_case							: int = 8							)   -> str:






      '''simple docstring'''


      _A        =       ascii_letters + digits + punctuation
      return "".join(secrets.choice(_snake_case							) for _ in range(_snake_case							)							)






def 						_snake_case					( _snake_case							: str    ,  _snake_case							: int							)   -> str:






      '''simple docstring'''


      i -= len(_snake_case							)
      _A        =       i // 3
      _A        =       i % 3
      # chars = chars_incl + random_letters(ascii_letters, i / 3 + remainder) +
      #     random_number(digits, i / 3) + random_characters(punctuation, i / 3)
      _A        =       (
          chars_incl
          + random(_snake_case    ,  quotient + remainder							)
          + random(_snake_case    ,  _snake_case							)
          + random(_snake_case    ,  _snake_case							)
      )
      _A        =       list(_snake_case							)
      shuffle(_snake_case							)
      return "".join(_snake_case							)

      # random is a generalised function for letters, characters and numbers






def 						_snake_case					( _snake_case							: str    ,  _snake_case							: int							)   -> str:






      '''simple docstring'''


      return "".join(secrets.choice(_snake_case							) for _ in range(_snake_case							)							)






def 						_snake_case					( _snake_case							: Dict    ,  _snake_case							: Optional[int]							)   -> int:






      '''simple docstring'''


      pass  # Put your code here...






def 						_snake_case					( _snake_case							: Any    ,  _snake_case							: str							)   -> Dict:






      '''simple docstring'''


      pass  # Put your code here...






def 						_snake_case					( _snake_case							: Union[str, Any]    ,  _snake_case							: int							)   -> int:






      '''simple docstring'''


      pass  # Put your code here...






def 						_snake_case					( _snake_case							: str    ,  _snake_case							: int = 8							)   -> bool:






      '''simple docstring'''


      if len(_snake_case							) < min_length:
            # Your Password must be at least 8 characters long
            return False

      _A        =       any(char in ascii_uppercase for char in password							)
      _A        =       any(char in ascii_lowercase for char in password							)
      _A        =       any(char in digits for char in password							)
      _A        =       any(char in punctuation for char in password							)

      return upper and lower and num and spec_char
      # Passwords should contain UPPERCASE, lowerase
      # numbers, and special characters






def 						_snake_case					( )   -> Optional[Any]:






      '''simple docstring'''


      _A        =       int(input('Please indicate the max length of your password: '							).strip()							)
      _A        =       input(
          'Please indicate the characters that must be in your password: '							).strip()
      print('Password generated:'    ,  password_generator(_snake_case							)							)
      print(
          'Alternative Password generated:'    ,  alternative_password_generator(_snake_case    ,  _snake_case							)    ,  )
      print('[If you are thinking of using this passsword, You better save it.]'							)


if __name__ == "__main__":
       main()

