





import argparse
import glob
import logging
import os
import sys
import time
from collections import defaultdict
from pathlib import Path
from typing import Dict, List, Tuple

import numpy as np
import pytorch_lightning as pl
import torch
from callbacks import SeqaSeqLoggingCallback, get_checkpoint_callback, get_early_stopping_callback
from torch import nn
from torch.utils.data import DataLoader

from transformers import MBartTokenizer, TaForConditionalGeneration
from transformers.models.bart.modeling_bart import shift_tokens_right
from utils import (
    ROUGE_KEYS,
    LegacySeqaSeqDataset,
    SeqaSeqDataset,
    assert_all_frozen,
    calculate_bleu,
    calculate_rouge,
    check_output_dir,
    flatten_list,
    freeze_embeds,
    freeze_params,
    get_git_info,
    label_smoothed_nll_loss,
    lmap,
    pickle_save,
    save_git_info,
    save_json,
    use_task_specific_params,
)


# need the parent dir module
sys.path.insert(2, str(Path(__file__).resolve().parents[1]))
from lightning_base import BaseTransformer, add_generic_args, generic_train  # noqa


_snake_case  :    int						    =  logging.getLogger(__name__)



class 				_UpperCAmelCase    (    _UpperCamelCase    ):

  """simple docstring"""


  a_        = """summarization"""
  a_        = ["""loss"""]
  a_        = ROUGE_KEYS
  a_        = """rouge2"""






  def __init__(				self :		str					,  lowerCAmelCase_ :		Optional[Any]					,  **lowerCAmelCase_ :		Dict     )							->						List[str]:
     if hparams.sortish_sampler and hparams.gpus > 1:
        __lowerCAmelCase								=	False
     elif hparams.max_tokens_per_batch is not None:
        if hparams.gpus > 1:
           raise NotImplementedError('Dynamic Batch size does not work for multi-gpu training'     )
        if hparams.sortish_sampler:
           raise ValueError('--sortish_sampler and --max_tokens_per_batch may not be used simultaneously'     )

     super().__init__(lowerCAmelCase_					,  num_labels=lowerCAmelCase_					,  mode=self.mode					,  **lowerCAmelCase_     )
     use_task_specific_params(self.model					,  'summarization'     )
     save_git_info(self.hparams.output_dir     )
     __lowerCAmelCase								=	Path(self.output_dir     ) / 'metrics.json'
     __lowerCAmelCase								=	Path(self.output_dir     ) / 'hparams.pkl'
     pickle_save(self.hparams					,  self.hparams_save_path     )
     __lowerCAmelCase								=	0
     __lowerCAmelCase								=	defaultdict(lowerCAmelCase_     )
     __lowerCAmelCase								=	self.config.model_type
     __lowerCAmelCase								=	self.config.tgt_vocab_size if self.model_type == 'fsmt' else self.config.vocab_size

     __lowerCAmelCase								=	{
         "data_dir": self.hparams.data_dir,
         "max_source_length": self.hparams.max_source_length,
         "prefix": self.model.config.prefix or "",
     }
     __lowerCAmelCase								=	{
         'train': self.hparams.n_train,
         'val': self.hparams.n_val,
         'test': self.hparams.n_test,
     }
     __lowerCAmelCase								=	{k: v if v >= 0 else None for k, v in n_observations_per_split.items()}

     __lowerCAmelCase								=	{
         'train': self.hparams.max_target_length,
         'val': self.hparams.val_max_target_length,
         'test': self.hparams.test_max_target_length,
     }
     assert self.target_lens["train"] <= self.target_lens["val"], f"""target_lens: {self.target_lens}"""
     assert self.target_lens["train"] <= self.target_lens["test"], f"""target_lens: {self.target_lens}"""
     if self.hparams.freeze_embeds:
        freeze_embeds(self.model     )
     if self.hparams.freeze_encoder:
        freeze_params(self.model.get_encoder()     )
        assert_all_frozen(self.model.get_encoder()     )

     __lowerCAmelCase								=	get_git_info()['repo_sha']
     __lowerCAmelCase								=	hparams.num_workers
     __lowerCAmelCase								=	None  # default to config
     if self.model.config.decoder_start_token_id is None and isinstance(self.tokenizer					,  lowerCAmelCase_     ):
        __lowerCAmelCase								=	self.tokenizer.lang_code_to_id[hparams.tgt_lang]
        __lowerCAmelCase								=	self.decoder_start_token_id
     __lowerCAmelCase								=	(
         SeqaSeqDataset if hasattr(self.tokenizer					,  'prepare_seq2seq_batch'     ) else LegacySeqaSeqDataset
     )
     __lowerCAmelCase								=	False
     __lowerCAmelCase								=	self.model.config.num_beams if self.hparams.eval_beams is None else self.hparams.eval_beams
     if self.hparams.eval_max_gen_length is not None:
        __lowerCAmelCase								=	self.hparams.eval_max_gen_length
     else:
        __lowerCAmelCase								=	self.model.config.max_length
     __lowerCAmelCase								=	self.default_val_metric if self.hparams.val_metric is None else self.hparams.val_metric






  def 				lowercase		(				self :		Optional[int]					,  lowerCAmelCase_ :		Dict[str, torch.Tensor]     )							->						Dict[str, List[str]]:
     __lowerCAmelCase								=	{
         k: self.tokenizer.batch_decode(v.tolist()     ) if 'mask' not in k else v.shape for k, v in batch.items()
     }
     save_json(lowerCAmelCase_					,  Path(self.output_dir     ) / 'text_batch.json'     )
     save_json({k: v.tolist() for k, v in batch.items()}					,  Path(self.output_dir     ) / 'tok_batch.json'     )

     __lowerCAmelCase								=	True
     return readable_batch






  def 				lowercase		(				self :		str					,  lowerCAmelCase_ :		Dict					,  **lowerCAmelCase_ :		Dict     )							->						int:
     return self.model(lowerCAmelCase_					,  **lowerCAmelCase_     )






  def 				lowercase		(				self :		int					,  lowerCAmelCase_ :		List[int]     )							->						List[str]:
     __lowerCAmelCase								=	self.tokenizer.batch_decode(
         lowerCAmelCase_					,  skip_special_tokens=lowerCAmelCase_					,  clean_up_tokenization_spaces=lowerCAmelCase_     )
     return lmap(str.strip					,  lowerCAmelCase_     )






  def 				lowercase		(				self :		Dict					,  lowerCAmelCase_ :		dict     )							->						Tuple:
     __lowerCAmelCase								=	self.tokenizer.pad_token_id
     __lowerCAmelCase	,	__lowerCAmelCase								=	batch['input_ids'], batch['attention_mask']
     __lowerCAmelCase								=	batch['labels']
     if isinstance(self.model					,  lowerCAmelCase_     ):
        __lowerCAmelCase								=	self.model._shift_right(lowerCAmelCase_     )
     else:
        __lowerCAmelCase								=	shift_tokens_right(lowerCAmelCase_					,  lowerCAmelCase_     )
     if not self.already_saved_batch:  # This would be slightly better if it only happened on rank zero
        __lowerCAmelCase								=	decoder_input_ids
        self.save_readable_batch(lowerCAmelCase_     )

     __lowerCAmelCase								=	self(lowerCAmelCase_					,  attention_mask=lowerCAmelCase_					,  decoder_input_ids=lowerCAmelCase_					,  use_cache=lowerCAmelCase_     )
     __lowerCAmelCase								=	outputs['logits']
     if self.hparams.label_smoothing == 0:
        # Same behavior as modeling_bart.py, besides ignoring pad_token_id
        __lowerCAmelCase								=	nn.CrossEntropyLoss(ignore_index=lowerCAmelCase_     )

        assert lm_logits.shape[-1] == self.vocab_size
        __lowerCAmelCase								=	ce_loss_fct(lm_logits.view(-1					,  lm_logits.shape[-1]     )					,  tgt_ids.view(-1     )     )
     else:
        __lowerCAmelCase								=	nn.functional.log_softmax(lowerCAmelCase_					,  dim=-1     )
        __lowerCAmelCase	,	__lowerCAmelCase								=	label_smoothed_nll_loss(
            lowerCAmelCase_					,  lowerCAmelCase_					,  self.hparams.label_smoothing					,  ignore_index=lowerCAmelCase_     )
     return (loss,)






  @property
  def 				lowercase		(				self :		Union[str, Any]     )							->						int:
     return self.tokenizer.pad_token_id






  def 				lowercase		(				self :		Optional[int]					,  lowerCAmelCase_ :		Any					,  lowerCAmelCase_ :		List[Any]     )							->						Dict:
     __lowerCAmelCase								=	self._step(lowerCAmelCase_     )

     __lowerCAmelCase								=	dict(zip(self.loss_names					,  lowerCAmelCase_     )     )
     # tokens per batch
     __lowerCAmelCase								=	batch['input_ids'].ne(self.pad     ).sum() + batch['labels'].ne(self.pad     ).sum()
     __lowerCAmelCase								=	batch['input_ids'].shape[0]
     __lowerCAmelCase								=	batch['input_ids'].eq(self.pad     ).sum()
     __lowerCAmelCase								=	batch['input_ids'].eq(self.pad     ).float().mean()
     # TODO(SS): make a wandb summary metric for this
     return {"loss": loss_tensors[0], "log": logs}






  def 				lowercase		(				self :		str					,  lowerCAmelCase_ :		Any					,  lowerCAmelCase_ :		Optional[int]     )							->						Dict:
     return self._generative_step(lowerCAmelCase_     )






  def 				lowercase		(				self :		Optional[Any]					,  lowerCAmelCase_ :		int					,  lowerCAmelCase_ :		int="val"     )							->						Dict:
     self.step_count += 1
     __lowerCAmelCase								=	{k: torch.stack([x[k] for x in outputs]     ).mean() for k in self.loss_names}
     __lowerCAmelCase								=	losses['loss']
     __lowerCAmelCase								=	{
         k: np.array([x[k] for x in outputs]     ).mean() for k in self.metric_names + ['gen_time', 'gen_len']
     }
     __lowerCAmelCase								=	(
         generative_metrics[self.val_metric] if self.val_metric in generative_metrics else losses[self.val_metric]
     )
     __lowerCAmelCase								=	torch.tensor(lowerCAmelCase_     ).type_as(lowerCAmelCase_     )
     generative_metrics.update({k: v.item() for k, v in losses.items()}     )
     losses.update(lowerCAmelCase_     )
     __lowerCAmelCase								=	{f"""{prefix}_avg_{k}""": x for k, x in losses.items()}
     __lowerCAmelCase								=	self.step_count
     self.metrics[prefix].append(lowerCAmelCase_     )  # callback writes this to self.metrics_save_path
     __lowerCAmelCase								=	flatten_list([x['preds'] for x in outputs]     )
     return {
         "log": all_metrics,
         "preds": preds,
         f"""{prefix}_loss""": loss,
         f"""{prefix}_{self.val_metric}""": metric_tensor,
     }






  def 				lowercase		(				self :		Dict					,  lowerCAmelCase_ :		List[str]					,  lowerCAmelCase_ :		Optional[int]     )							->						Dict:
     return calculate_rouge(lowerCAmelCase_					,  lowerCAmelCase_     )






  def 				lowercase		(				self :		str					,  lowerCAmelCase_ :		dict     )							->						dict:
     __lowerCAmelCase								=	time.time()

     # parser.add_argument('--eval_max_gen_length', type=int, default=None, help='never generate more than n tokens')
     __lowerCAmelCase								=	self.model.generate(
         batch['input_ids']					,  attention_mask=batch['attention_mask']					,  use_cache=lowerCAmelCase_					,  decoder_start_token_id=self.decoder_start_token_id					,  num_beams=self.eval_beams					,  max_length=self.eval_max_length					,  )
     __lowerCAmelCase								=	(time.time() - ta) / batch['input_ids'].shape[0]
     __lowerCAmelCase								=	self.ids_to_clean_text(lowerCAmelCase_     )
     __lowerCAmelCase								=	self.ids_to_clean_text(batch['labels']     )
     __lowerCAmelCase								=	self._step(lowerCAmelCase_     )
     __lowerCAmelCase								=	dict(zip(self.loss_names					,  lowerCAmelCase_     )     )
     __lowerCAmelCase								=	self.calc_generative_metrics(lowerCAmelCase_					,  lowerCAmelCase_     )
     __lowerCAmelCase								=	np.mean(lmap(lowerCAmelCase_					,  lowerCAmelCase_     )     )
     base_metrics.update(gen_time=lowerCAmelCase_					,  gen_len=lowerCAmelCase_					,  preds=lowerCAmelCase_					,  target=lowerCAmelCase_					,  **lowerCAmelCase_     )
     return base_metrics






  def 				lowercase		(				self :		List[str]					,  lowerCAmelCase_ :		List[str]					,  lowerCAmelCase_ :		int     )							->						int:
     return self._generative_step(lowerCAmelCase_     )






  def 				lowercase		(				self :		Optional[Any]					,  lowerCAmelCase_ :		Union[str, Any]     )							->						Any:
     return self.validation_epoch_end(lowerCAmelCase_					,  prefix='test'     )






  def 				lowercase		(				self :		List[str]					,  lowerCAmelCase_ :		Tuple     )							->						SeqaSeqDataset:
     __lowerCAmelCase								=	self.n_obs[type_path]
     __lowerCAmelCase								=	self.target_lens[type_path]
     __lowerCAmelCase								=	self.dataset_class(
         self.tokenizer					,  type_path=lowerCAmelCase_					,  n_obs=lowerCAmelCase_					,  max_target_length=lowerCAmelCase_					,  **self.dataset_kwargs					,  )
     return dataset






  def 				lowercase		(				self :		Optional[int]					,  lowerCAmelCase_ :		str					,  lowerCAmelCase_ :		int					,  lowerCAmelCase_ :		bool = False     )							->						DataLoader:
     __lowerCAmelCase								=	self.get_dataset(lowerCAmelCase_     )

     if self.hparams.sortish_sampler and type_path != "test" and type_path != "val":
        __lowerCAmelCase								=	dataset.make_sortish_sampler(lowerCAmelCase_					,  distributed=self.hparams.gpus > 1     )
        return DataLoader(
            lowerCAmelCase_					,  batch_size=lowerCAmelCase_					,  collate_fn=dataset.collate_fn					,  shuffle=lowerCAmelCase_					,  num_workers=self.num_workers					,  sampler=lowerCAmelCase_					,  )

     elif self.hparams.max_tokens_per_batch is not None and type_path != "test" and type_path != "val":
        __lowerCAmelCase								=	dataset.make_dynamic_sampler(
            self.hparams.max_tokens_per_batch					,  distributed=self.hparams.gpus > 1     )
        return DataLoader(
            lowerCAmelCase_					,  batch_sampler=lowerCAmelCase_					,  collate_fn=dataset.collate_fn					,  num_workers=self.num_workers					,  )
     else:
        return DataLoader(
            lowerCAmelCase_					,  batch_size=lowerCAmelCase_					,  collate_fn=dataset.collate_fn					,  shuffle=lowerCAmelCase_					,  num_workers=self.num_workers					,  sampler=lowerCAmelCase_					,  )






  def 				lowercase		(				self :		Dict     )							->						DataLoader:
     __lowerCAmelCase								=	self.get_dataloader('train'					,  batch_size=self.hparams.train_batch_size					,  shuffle=lowerCAmelCase_     )
     return dataloader






  def 				lowercase		(				self :		Tuple     )							->						DataLoader:
     return self.get_dataloader('val'					,  batch_size=self.hparams.eval_batch_size     )






  def 				lowercase		(				self :		int     )							->						DataLoader:
     return self.get_dataloader('test'					,  batch_size=self.hparams.eval_batch_size     )






  @staticmethod
  def 				lowercase		(				lowerCAmelCase_ :		str					,  lowerCAmelCase_ :		Any     )							->						Tuple:
     BaseTransformer.add_model_specific_args(lowerCAmelCase_					,  lowerCAmelCase_     )
     add_generic_args(lowerCAmelCase_					,  lowerCAmelCase_     )
     parser.add_argument(
         '--max_source_length'					,  default=1_0_2_4					,  type=lowerCAmelCase_					,  help=(
             'The maximum total input sequence length after tokenization. Sequences longer '
             'than this will be truncated, sequences shorter will be padded.'
         )					,  )
     parser.add_argument(
         '--max_target_length'					,  default=5_6					,  type=lowerCAmelCase_					,  help=(
             'The maximum total input sequence length after tokenization. Sequences longer '
             'than this will be truncated, sequences shorter will be padded.'
         )					,  )
     parser.add_argument(
         '--val_max_target_length'					,  default=1_4_2					,  type=lowerCAmelCase_					,  help=(
             'The maximum total input sequence length after tokenization. Sequences longer '
             'than this will be truncated, sequences shorter will be padded.'
         )					,  )
     parser.add_argument(
         '--test_max_target_length'					,  default=1_4_2					,  type=lowerCAmelCase_					,  help=(
             'The maximum total input sequence length after tokenization. Sequences longer '
             'than this will be truncated, sequences shorter will be padded.'
         )					,  )
     parser.add_argument('--freeze_encoder'					,  action='store_true'     )
     parser.add_argument('--freeze_embeds'					,  action='store_true'     )
     parser.add_argument('--sortish_sampler'					,  action='store_true'					,  default=lowerCAmelCase_     )
     parser.add_argument('--overwrite_output_dir'					,  action='store_true'					,  default=lowerCAmelCase_     )
     parser.add_argument('--max_tokens_per_batch'					,  type=lowerCAmelCase_					,  default=lowerCAmelCase_     )
     parser.add_argument('--logger_name'					,  type=lowerCAmelCase_					,  choices=['default', 'wandb', 'wandb_shared']					,  default='default'     )
     parser.add_argument('--n_train'					,  type=lowerCAmelCase_					,  default=-1					,  required=lowerCAmelCase_					,  help='# examples. -1 means use all.'     )
     parser.add_argument('--n_val'					,  type=lowerCAmelCase_					,  default=5_0_0					,  required=lowerCAmelCase_					,  help='# examples. -1 means use all.'     )
     parser.add_argument('--n_test'					,  type=lowerCAmelCase_					,  default=-1					,  required=lowerCAmelCase_					,  help='# examples. -1 means use all.'     )
     parser.add_argument(
         '--task'					,  type=lowerCAmelCase_					,  default='summarization'					,  required=lowerCAmelCase_					,  help='# examples. -1 means use all.'     )
     parser.add_argument('--label_smoothing'					,  type=lowerCAmelCase_					,  default=0.0					,  required=lowerCAmelCase_     )
     parser.add_argument('--src_lang'					,  type=lowerCAmelCase_					,  default=''					,  required=lowerCAmelCase_     )
     parser.add_argument('--tgt_lang'					,  type=lowerCAmelCase_					,  default=''					,  required=lowerCAmelCase_     )
     parser.add_argument('--eval_beams'					,  type=lowerCAmelCase_					,  default=lowerCAmelCase_					,  required=lowerCAmelCase_     )
     parser.add_argument(
         '--val_metric'					,  type=lowerCAmelCase_					,  default=lowerCAmelCase_					,  required=lowerCAmelCase_					,  choices=['bleu', 'rouge2', 'loss', None]     )
     parser.add_argument('--eval_max_gen_length'					,  type=lowerCAmelCase_					,  default=lowerCAmelCase_					,  help='never generate more than n tokens'     )
     parser.add_argument('--save_top_k'					,  type=lowerCAmelCase_					,  default=1					,  required=lowerCAmelCase_					,  help='How many checkpoints to save'     )
     parser.add_argument(
         '--early_stopping_patience'					,  type=lowerCAmelCase_					,  default=-1					,  required=lowerCAmelCase_					,  help=(
             '-1 means never early stop. early_stopping_patience is measured in validation checks, not epochs. So'
             ' val_check_interval will effect it.'
         )					,  )
     return parser





class 				_UpperCAmelCase    (    _UpperCamelCase    ):

  """simple docstring"""


  a_        = """translation"""
  a_        = ["""loss"""]
  a_        = ["""bleu"""]
  a_        = """bleu"""






  def __init__(				self :		int					,  lowerCAmelCase_ :		Optional[Any]					,  **lowerCAmelCase_ :		Optional[int]     )							->						List[Any]:
     super().__init__(lowerCAmelCase_					,  **lowerCAmelCase_     )
     __lowerCAmelCase								=	hparams.src_lang
     __lowerCAmelCase								=	hparams.tgt_lang






  def 				lowercase		(				self :		int					,  lowerCAmelCase_ :		Any					,  lowerCAmelCase_ :		List[str]     )							->						dict:
     return calculate_bleu(lowerCAmelCase_					,  lowerCAmelCase_     )





def   a_    ( lowerCAmelCase_      :			Any,	lowerCAmelCase_      :			Any=None	):
   Path(args.output_dir	).mkdir(exist_ok=lowerCAmelCase_	)
   check_output_dir(lowerCAmelCase_,	expected_items=3	)

   if model is None:
      if "summarization" in args.task:
         __lowerCAmelCase								=	SummarizationModule(lowerCAmelCase_	)
      else:
         __lowerCAmelCase								=	TranslationModule(lowerCAmelCase_	)
   __lowerCAmelCase								=	Path(args.data_dir	).name
   if (
       args.logger_name == "default"
       or args.fast_dev_run
       or str(args.output_dir	).startswith('/tmp'	)
       or str(args.output_dir	).startswith('/var'	)
   ):
      __lowerCAmelCase								=	True  # don't pollute wandb logs unnecessarily
   elif args.logger_name == "wandb":
      from pytorch_lightning.loggers import WandbLogger

      __lowerCAmelCase								=	os.environ.get('WANDB_PROJECT',	lowerCAmelCase_	)
      __lowerCAmelCase								=	WandbLogger(name=model.output_dir.name,	project=lowerCAmelCase_	)

   elif args.logger_name == "wandb_shared":
      from pytorch_lightning.loggers import WandbLogger

      __lowerCAmelCase								=	WandbLogger(name=model.output_dir.name,	project=F"""hf_{dataset}"""	)

   if args.early_stopping_patience >= 0:
      __lowerCAmelCase								=	get_early_stopping_callback(model.val_metric,	args.early_stopping_patience	)
   else:
      __lowerCAmelCase								=	False

   __lowerCAmelCase								=	args.val_metric == 'loss'
   __lowerCAmelCase								=	generic_train(
       lowerCAmelCase_,	lowerCAmelCase_,	logging_callback=SeqaSeqLoggingCallback(),	checkpoint_callback=get_checkpoint_callback(
           args.output_dir,	model.val_metric,	args.save_top_k,	lowerCAmelCase_	),	early_stopping_callback=lowerCAmelCase_,	logger=lowerCAmelCase_,	)
   pickle_save(model.hparams,	model.output_dir / 'hparams.pkl'	)
   if not args.do_predict:
      return model

   __lowerCAmelCase								=	''
   __lowerCAmelCase								=	sorted(glob.glob(os.path.join(args.output_dir,	'*.ckpt'	),	recursive=lowerCAmelCase_	)	)
   if checkpoints:
      __lowerCAmelCase								=	checkpoints[-1]
      __lowerCAmelCase								=	checkpoints[-1]
   trainer.logger.log_hyperparams(model.hparams	)

   # test() without a model tests using the best checkpoint automatically
   trainer.test()
   return model


if __name__ == "__main__":
     _snake_case  :    Union[str, Any]						    =  argparse.ArgumentParser()
     _snake_case  :    Union[str, Any]						    =  pl.Trainer.add_argparse_args(parser)
     _snake_case  :    Optional[Any]						    =  SummarizationModule.add_model_specific_args(parser, os.getcwd())

     _snake_case  :    List[str]						    =  parser.parse_args()

     main(args)








from __future__ import annotations

import string
from itertools import cycle, product
from pathlib import Path

_snake_case  :    str						    =  (
    string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
_snake_case  :    list[int]						    =  [ord(letter) for letter in string.ascii_lowercase]
_snake_case  :    set[int]						    =  {ord(char) for char in VALID_CHARS}

_snake_case  :    list[str]						    =  ["the", "be", "to", "of", "and", "in", "that", "have"]

def   a_    ( lowerCAmelCase_      :			list[int],	lowerCAmelCase_      :			tuple[int, ...]	):
   __lowerCAmelCase								=	""
   __lowerCAmelCase								=	42
   __lowerCAmelCase								=	42
   __lowerCAmelCase								=	42

   for keychar, cipherchar in zip(cycle(lowerCAmelCase_	),	lowerCAmelCase_	):
      __lowerCAmelCase								=	cipherchar ^ keychar
      if decodedchar not in VALID_INTS:
         return None
      decoded += chr(lowerCAmelCase_	)

   return decoded

def   a_    ( lowerCAmelCase_      :			list[int]	):
   __lowerCAmelCase								=	[]
   for key in product(lowerCAmelCase_,	repeat=3	):
      __lowerCAmelCase								=	try_key(lowerCAmelCase_,	lowerCAmelCase_	)
      if encoded is not None:
         possibles.append(lowerCAmelCase_	)
   return possibles

def   a_    ( lowerCAmelCase_      :			list[str],	lowerCAmelCase_      :			str	):
   return [possible for possible in possibles if common_word in possible.lower()]

def   a_    ( lowerCAmelCase_      :			str = "p059_cipher.txt"	):
   __lowerCAmelCase								=	42
   __lowerCAmelCase								=	42
   __lowerCAmelCase								=	42
   __lowerCAmelCase								=	42
   __lowerCAmelCase								=	Path(lowerCAmelCase_	).parent.joinpath(lowerCAmelCase_	).read_text(encoding='utf-8'	)

   __lowerCAmelCase								=	[int(lowerCAmelCase_	) for number in data.strip().split(','	)]

   __lowerCAmelCase								=	filter_valid_chars(lowerCAmelCase_	)
   for common_word in COMMON_WORDS:
      __lowerCAmelCase								=	filter_common_word(lowerCAmelCase_,	lowerCAmelCase_	)
      if len(lowerCAmelCase_	) == 1:
         break

   __lowerCAmelCase								=	possibles[0]
   return sum(ord(lowerCAmelCase_	) for char in decoded_text	)


if __name__ == "__main__":
     print(F"""{solution() = }""")

