






"""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							(       __UpperCamelCase					):




  """simple docstring"""







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


lowercase_								=	logging.getLogger(__name__)


class    snake_case						(				pl.Callback		):


   '''simple docstring'''

   def 							_SCREAMING_SNAKE_CASE							(      self					:	str,			_lowerCamelCase					:	Any,			_lowerCamelCase					:	Union[str, Any]    ):

     '''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(_lowerCamelCase    )


   @rank_zero_only
   def 							_SCREAMING_SNAKE_CASE							(      self					:	Optional[int],			_lowerCamelCase					:	pl.Trainer,			_lowerCamelCase					:	pl.LightningModule,			_lowerCamelCase					:	str,			_lowerCamelCase					:	Dict=True    ):

     '''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=_lowerCamelCase    )
       generations_file.parent.mkdir(exist_ok=_lowerCamelCase    )
     with open(_lowerCamelCase,			'''a+'''    ) as writer:
       for key in sorted(_lowerCamelCase    ):
         if key in ["log", "progress_bar", "preds"]:
           continue
         __A		     =					metrics[key]
         if isinstance(_lowerCamelCase,			torch.Tensor    ):
           __A		     =					val.item()
         __A		     =					f'{key}: {val:.6f}\n'
         writer.write(_lowerCamelCase    )

     if not save_generations:
       return

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


   @rank_zero_only
   def 							_SCREAMING_SNAKE_CASE							(      self					:	Tuple,			_lowerCamelCase					:	Union[str, Any],			_lowerCamelCase					:	Union[str, Any]    ):

     '''simple docstring'''
     try:
       __A		     =					pl_module.model.model.num_parameters()
     except AttributeError:
       __A		     =					pl_module.model.num_parameters()

     __A		     =					count_trainable_parameters(_lowerCamelCase    )
     # 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					:	Any,			_lowerCamelCase					:	pl.Trainer,			_lowerCamelCase					:	pl.LightningModule    ):

     '''simple docstring'''
     save_json(pl_module.metrics,			pl_module.metrics_save_path    )
     return self._write_logs(_lowerCamelCase,			_lowerCamelCase,			'''test'''    )






   @rank_zero_only
   def 							_SCREAMING_SNAKE_CASE							(      self					:	str,			_lowerCamelCase					:	pl.Trainer,			_lowerCamelCase					:	str    ):

     '''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							(       __UpperCamelCase						,			__UpperCamelCase						,			__UpperCamelCase=1						,			__UpperCamelCase=False					):




  """simple docstring"""







  if metric == "rouge2":
    __A		     =					'''{val_avg_rouge2:.4f}-{step_count}'''
  elif metric == "bleu":
    __A		     =					'''{val_avg_bleu:.4f}-{step_count}'''
  elif metric == "loss":
    __A		     =					'''{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		     =					ModelCheckpoint(
      dirpath=__UpperCamelCase						,			filename=__UpperCamelCase						,			monitor=f'val_{metric}'						,			mode='''min''' if '''loss''' in metric else '''max'''						,			save_top_k=__UpperCamelCase						,			)
  return checkpoint_callback





def    lowerCAmelCase							(       __UpperCamelCase						,			__UpperCamelCase					):




  """simple docstring"""







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










from string import ascii_uppercase

def       lowerCAmelCase_      (	lowerCamelCase:							str  ,					lowerCamelCase:							str  ,					lowerCamelCase:							bool = False  ,					lowerCamelCase:							str = ascii_uppercase    )       ->      str:


      '''simple docstring'''




      lowerCamelCase__   	=					keyword.upper()
      lowerCamelCase__   	=					plaintext.upper()
      lowerCamelCase__   	=					set(lowerCamelCase    )

      # create a list of unique characters in the keyword - their order matters
      # it determines how we will map plaintext characters to the ciphertext
      lowerCamelCase__   	=					[]
      for char in keyword:
            if char in alphabet_set and char not in unique_chars:
                  unique_chars.append(lowerCamelCase    )
    # the number of those unique characters will determine the number of rows
      lowerCamelCase__   	=					len(lowerCamelCase    )

      # create a shifted version of the alphabet
      lowerCamelCase__   	=					unique_chars + [
          char for char in alphabet if char not in unique_chars
      ]

      # create a modified alphabet by splitting the shifted alphabet into rows
      lowerCamelCase__   	=					[
          shifted_alphabet[k : k + num_unique_chars_in_keyword]
          for k in range(0  ,					26  ,					lowerCamelCase    )
      ]

      # map the alphabet characters to the modified alphabet characters
      # going 'vertically' through the modified alphabet - consider columns first
      lowerCamelCase__   	=					{}
      lowerCamelCase__   	=					0
      for column in range(lowerCamelCase    ):
            for row in modified_alphabet:
                  # if current row (the last one) is too short, break out of loop
                  if len(lowerCamelCase    ) <= column:
                        break

                  # map current letter to letter in modified alphabet
                  lowerCamelCase__   	=					row[column]
                  letter_index += 1

      if verbose:
            print(lowerCamelCase    )
      # create the encrypted text by mapping the plaintext to the modified alphabet
      return "".join(mapping[char] if char in mapping else char for char in plaintext    )


if __name__ == "__main__":
      # example use
      print(mixed_keyword("""college""", """UNIVERSITY"""))