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


__UpperCamelCase       :    Optional[Any]         =		logging.getLogger(__name__)
class   UpperCAmelCase_	(				pl.Callback   ):
    def 		_lowerCamelCase      (	self			:       str   ,						_lowercase			:       List[str]   ,						_lowercase			:       Any		)						->    List[str]:
       _lowercase							     =       {f"""lr_group_{i}""": param["lr"] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups		)}
       pl_module.logger.log_metrics(_lowercase		)
    @rank_zero_only
    def 		_lowerCamelCase      (	self			:       Optional[int]   ,						_lowercase			:       pl.Trainer   ,						_lowercase			:       pl.LightningModule   ,						_lowercase			:       str   ,						_lowercase			:       Dict=True		)						->    None:
       logger.info(f"""***** {type_path} results at step {trainer.global_step:05d} *****"""		)
       _lowercase							     =       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
       _lowercase							     =       Path(pl_module.hparams.output_dir		)
       if type_path == "test":
          _lowercase							     =       od / "test_results.txt"
          _lowercase							     =       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.
          _lowercase							     =       od / f"""{type_path}_results/{trainer.global_step:05d}.txt"""
          _lowercase							     =       od / f"""{type_path}_generations/{trainer.global_step:05d}.txt"""
          results_file.parent.mkdir(exist_ok=_lowercase		)
          generations_file.parent.mkdir(exist_ok=_lowercase		)
       with open(_lowercase   ,						"a+"		) as writer:
          for key in sorted(_lowercase		):
             if key in ["log", "progress_bar", "preds"]:
                continue
             _lowercase							     =       metrics[key]
             if isinstance(_lowercase   ,						torch.Tensor		):
                _lowercase							     =       val.item()
             _lowercase							     =       f"""{key}: {val:.6f}\n"""
             writer.write(_lowercase		)

       if not save_generations:
          return

       if "preds" in metrics:
          _lowercase							     =       "\n".join(metrics["preds"]		)
          generations_file.open("w+"		).write(_lowercase		)
    @rank_zero_only
    def 		_lowerCamelCase      (	self			:       Tuple   ,						_lowercase			:       Optional[Any]   ,						_lowercase			:       Tuple		)						->    Dict:
       try:
          _lowercase							     =       pl_module.model.model.num_parameters()
       except AttributeError:
          _lowercase							     =       pl_module.model.num_parameters()

       _lowercase							     =       count_trainable_parameters(_lowercase		)
       # 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			:       Tuple   ,						_lowercase			:       pl.Trainer   ,						_lowercase			:       pl.LightningModule		)						->    Dict:
       save_json(pl_module.metrics   ,						pl_module.metrics_save_path		)
       return self._write_logs(_lowercase   ,						_lowercase   ,						"test"		)




    @rank_zero_only
    def 		_lowerCamelCase      (	self			:       List[Any]   ,						_lowercase			:       pl.Trainer   ,						_lowercase			:       Any		)						->    Optional[int]:
       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   __UpperCAmelCase     (	_snake_case		:       List[str],				_snake_case		:       str,				_snake_case		:       Dict=1,				_snake_case		:       Union[str, Any]=False       ):
   if metric == "rouge2":
      _lowercase							     =       "{val_avg_rouge2:.4f}-{step_count}"
   elif metric == "bleu":
      _lowercase							     =       "{val_avg_bleu:.4f}-{step_count}"
   elif metric == "loss":
      _lowercase							     =       "{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."       )

   _lowercase							     =       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   __UpperCAmelCase     (	_snake_case		:       Tuple,				_snake_case		:       Union[str, Any]       ):
   return EarlyStopping(
       monitor=f"""val_{metric}""",				mode="min" if "loss" in metric else "max",				patience=_snake_case,				verbose=_snake_case,				)
"""simple docstring"""


from typing import Literal

__UpperCamelCase       :    int         =		"ABCDEFGHIJKLMNOPQRSTUVWXYZ"


def   __UpperCAmelCase     (	_snake_case		:       str,				_snake_case		:       str,				_snake_case		:       Literal["encrypt", "decrypt"]       ):
   _lowercase							     =       LETTERS if mode == "decrypt" else key
   _lowercase							     =       key if mode == "decrypt" else LETTERS
   _lowercase							     =       ""
   # loop through each symbol in the message
   for symbol in message:
      if symbol.upper() in chars_a:
         # encrypt/decrypt the symbol
         _lowercase							     =       chars_a.find(symbol.upper()       )
         if symbol.isupper():
            translated += chars_b[sym_index].upper()
         else:
            translated += chars_b[sym_index].lower()
      else:
         # symbol is not in LETTERS, just add it
         translated += symbol
   return translated


def   __UpperCAmelCase     (	_snake_case		:       str,				_snake_case		:       str       ):
   return translate_message(_snake_case,				_snake_case,				"encrypt"       )


def   __UpperCAmelCase     (	_snake_case		:       str,				_snake_case		:       str       ):
   return translate_message(_snake_case,				_snake_case,				"decrypt"       )


def   __UpperCAmelCase     (	):
   _lowercase							     =       "Hello World"
   _lowercase							     =       "QWERTYUIOPASDFGHJKLZXCVBNM"
   _lowercase							     =       "decrypt"  # set to 'encrypt' or 'decrypt'

   if mode == "encrypt":
      _lowercase							     =       encrypt_message(_snake_case,				_snake_case       )
   elif mode == "decrypt":
      _lowercase							     =       decrypt_message(_snake_case,				_snake_case       )
   print(f"""Using the key {key}, the {mode}ed message is: {translated}"""       )


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  main()