



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		:str   	=  logging.getLogger(__name__)

class 					_A (			__UpperCAmelCase					):
       UpperCamelCase__						:					Union[str, Any]		      =		'''summarization'''
       UpperCamelCase__						:					List[Any]		      =		['''loss''']
       UpperCamelCase__						:					str		      =		ROUGE_KEYS
       UpperCamelCase__						:					List[str]		      =		'''rouge2'''



       def __init__(	self							:  List[Any]  ,	__SCREAMING_SNAKE_CASE							:  Dict  ,	**__SCREAMING_SNAKE_CASE							:  Any):


           '''simple docstring'''

           if hparams.sortish_sampler and hparams.gpus > 1:
               __a		 =       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__(__SCREAMING_SNAKE_CASE  ,	num_labels=__SCREAMING_SNAKE_CASE  ,	mode=self.mode  ,	**__SCREAMING_SNAKE_CASE)
           use_task_specific_params(self.model  ,	'''summarization''')
           save_git_info(self.hparams.output_dir)
           __a		 =       Path(self.output_dir) / '''metrics.json'''
           __a		 =       Path(self.output_dir) / '''hparams.pkl'''
           pickle_save(self.hparams  ,	self.hparams_save_path)
           __a		 =       0
           __a		 =       defaultdict(__SCREAMING_SNAKE_CASE)
           __a		 =       self.config.model_type
           __a		 =       self.config.tgt_vocab_size if self.model_type == '''fsmt''' else self.config.vocab_size

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

           __a		 =       {
               '''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())

           __a		 =       get_git_info()['''repo_sha''']
           __a		 =       hparams.num_workers
           __a		 =       None  # default to config
           if self.model.config.decoder_start_token_id is None and isinstance(self.tokenizer  ,	__SCREAMING_SNAKE_CASE):
               __a		 =       self.tokenizer.lang_code_to_id[hparams.tgt_lang]
               __a		 =       self.decoder_start_token_id
           __a		 =       (
               SeqaSeqDataset if hasattr(self.tokenizer  ,	'''prepare_seq2seq_batch''') else LegacySeqaSeqDataset
           )
           __a		 =       False
           __a		 =       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:
               __a		 =       self.hparams.eval_max_gen_length
           else:
               __a		 =       self.model.config.max_length
           __a		 =       self.default_val_metric if self.hparams.val_metric is None else self.hparams.val_metric



       def 	_lowerCamelCase    (	self							:  List[str]  ,	__SCREAMING_SNAKE_CASE							:  Dict[str, torch.Tensor]):


           '''simple docstring'''

           __a		 =       {
               k: self.tokenizer.batch_decode(v.tolist()) if '''mask''' not in k else v.shape for k, v in batch.items()
           }
           save_json(__SCREAMING_SNAKE_CASE  ,	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''')

           __a		 =       True
           return readable_batch



       def 	_lowerCamelCase    (	self							:  List[str]  ,	__SCREAMING_SNAKE_CASE							:  Optional[Any]  ,	**__SCREAMING_SNAKE_CASE							:  List[str]):


           '''simple docstring'''

           return self.model(__SCREAMING_SNAKE_CASE  ,	**__SCREAMING_SNAKE_CASE)



       def 	_lowerCamelCase    (	self							:  str  ,	__SCREAMING_SNAKE_CASE							:  List[int]):


           '''simple docstring'''

           __a		 =       self.tokenizer.batch_decode(
               __SCREAMING_SNAKE_CASE  ,	skip_special_tokens=__SCREAMING_SNAKE_CASE  ,	clean_up_tokenization_spaces=__SCREAMING_SNAKE_CASE)
           return lmap(str.strip  ,	__SCREAMING_SNAKE_CASE)



       def 	_lowerCamelCase    (	self							:  int  ,	__SCREAMING_SNAKE_CASE							:  dict):


           '''simple docstring'''

           __a		 =       self.tokenizer.pad_token_id
           __a ,     __a		 =       batch['''input_ids'''], batch['''attention_mask''']
           __a		 =       batch['''labels''']
           if isinstance(self.model  ,	__SCREAMING_SNAKE_CASE):
               __a		 =       self.model._shift_right(__SCREAMING_SNAKE_CASE)
           else:
               __a		 =       shift_tokens_right(__SCREAMING_SNAKE_CASE  ,	__SCREAMING_SNAKE_CASE)
           if not self.already_saved_batch:  # This would be slightly better if it only happened on rank zero
               __a		 =       decoder_input_ids
               self.save_readable_batch(__SCREAMING_SNAKE_CASE)

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

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



       @property
       def 	_lowerCamelCase    (	self							:  Optional[int]):


           '''simple docstring'''

           return self.tokenizer.pad_token_id



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


           '''simple docstring'''

           __a		 =       self._step(__SCREAMING_SNAKE_CASE)

           __a		 =       dict(zip(self.loss_names  ,	__SCREAMING_SNAKE_CASE))
           # tokens per batch
           __a		 =       batch['''input_ids'''].ne(self.pad).sum() + batch['''labels'''].ne(self.pad).sum()
           __a		 =       batch['''input_ids'''].shape[0]
           __a		 =       batch['''input_ids'''].eq(self.pad).sum()
           __a		 =       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 	_lowerCamelCase    (	self							:  int  ,	__SCREAMING_SNAKE_CASE							:  List[str]  ,	__SCREAMING_SNAKE_CASE							:  Union[str, Any]):


           '''simple docstring'''

           return self._generative_step(__SCREAMING_SNAKE_CASE)



       def 	_lowerCamelCase    (	self							:  Dict  ,	__SCREAMING_SNAKE_CASE							:  List[str]  ,	__SCREAMING_SNAKE_CASE							:  Optional[Any]="val"):


           '''simple docstring'''

           self.step_count += 1
           __a		 =       {k: torch.stack([x[k] for x in outputs]).mean() for k in self.loss_names}
           __a		 =       losses['''loss''']
           __a		 =       {
               k: np.array([x[k] for x in outputs]).mean() for k in self.metric_names + ['''gen_time''', '''gen_len''']
           }
           __a		 =       (
               generative_metrics[self.val_metric] if self.val_metric in generative_metrics else losses[self.val_metric]
           )
           __a		 =       torch.tensor(__SCREAMING_SNAKE_CASE).type_as(__SCREAMING_SNAKE_CASE)
           generative_metrics.update({k: v.item() for k, v in losses.items()})
           losses.update(__SCREAMING_SNAKE_CASE)
           __a		 =       {F'{prefix}_avg_{k}': x for k, x in losses.items()}
           __a		 =       self.step_count
           self.metrics[prefix].append(__SCREAMING_SNAKE_CASE)  # callback writes this to self.metrics_save_path
           __a		 =       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 	_lowerCamelCase    (	self							:  List[Any]  ,	__SCREAMING_SNAKE_CASE							:  int  ,	__SCREAMING_SNAKE_CASE							:  Dict):


           '''simple docstring'''

           return calculate_rouge(__SCREAMING_SNAKE_CASE  ,	__SCREAMING_SNAKE_CASE)



       def 	_lowerCamelCase    (	self							:  Any  ,	__SCREAMING_SNAKE_CASE							:  dict):


           '''simple docstring'''

           __a		 =       time.time()

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



       def 	_lowerCamelCase    (	self							:  int  ,	__SCREAMING_SNAKE_CASE							:  Optional[int]  ,	__SCREAMING_SNAKE_CASE							:  Dict):


           '''simple docstring'''

           return self._generative_step(__SCREAMING_SNAKE_CASE)



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


           '''simple docstring'''

           return self.validation_epoch_end(__SCREAMING_SNAKE_CASE  ,	prefix='''test''')



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


           '''simple docstring'''

           __a		 =       self.n_obs[type_path]
           __a		 =       self.target_lens[type_path]
           __a		 =       self.dataset_class(
               self.tokenizer  ,	type_path=__SCREAMING_SNAKE_CASE  ,	n_obs=__SCREAMING_SNAKE_CASE  ,	max_target_length=__SCREAMING_SNAKE_CASE  ,	**self.dataset_kwargs  ,	)
           return dataset



       def 	_lowerCamelCase    (	self							:  Optional[Any]  ,	__SCREAMING_SNAKE_CASE							:  str  ,	__SCREAMING_SNAKE_CASE							:  int  ,	__SCREAMING_SNAKE_CASE							:  bool = False):


           '''simple docstring'''

           __a		 =       self.get_dataset(__SCREAMING_SNAKE_CASE)

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

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



       def 	_lowerCamelCase    (	self							:  int):


           '''simple docstring'''

           __a		 =       self.get_dataloader('''train'''  ,	batch_size=self.hparams.train_batch_size  ,	shuffle=__SCREAMING_SNAKE_CASE)
           return dataloader



       def 	_lowerCamelCase    (	self							:  int):


           '''simple docstring'''

           return self.get_dataloader('''val'''  ,	batch_size=self.hparams.eval_batch_size)



       def 	_lowerCamelCase    (	self							:  int):


           '''simple docstring'''

           return self.get_dataloader('''test'''  ,	batch_size=self.hparams.eval_batch_size)





       @staticmethod
       def 	_lowerCamelCase    (	__SCREAMING_SNAKE_CASE							:  Optional[int]  ,	__SCREAMING_SNAKE_CASE							:  List[Any]):


           '''simple docstring'''

           BaseTransformer.add_model_specific_args(__SCREAMING_SNAKE_CASE  ,	__SCREAMING_SNAKE_CASE)
           add_generic_args(__SCREAMING_SNAKE_CASE  ,	__SCREAMING_SNAKE_CASE)
           parser.add_argument(
               '''--max_source_length'''  ,	default=1_024  ,	type=__SCREAMING_SNAKE_CASE  ,	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=56  ,	type=__SCREAMING_SNAKE_CASE  ,	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=142  ,	type=__SCREAMING_SNAKE_CASE  ,	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=142  ,	type=__SCREAMING_SNAKE_CASE  ,	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=__SCREAMING_SNAKE_CASE)
           parser.add_argument('''--overwrite_output_dir'''  ,	action='''store_true'''  ,	default=__SCREAMING_SNAKE_CASE)
           parser.add_argument('''--max_tokens_per_batch'''  ,	type=__SCREAMING_SNAKE_CASE  ,	default=__SCREAMING_SNAKE_CASE)
           parser.add_argument('''--logger_name'''  ,	type=__SCREAMING_SNAKE_CASE  ,	choices=['''default''', '''wandb''', '''wandb_shared''']  ,	default='''default''')
           parser.add_argument('''--n_train'''  ,	type=__SCREAMING_SNAKE_CASE  ,	default=-1  ,	required=__SCREAMING_SNAKE_CASE  ,	help='''# examples. -1 means use all.''')
           parser.add_argument('''--n_val'''  ,	type=__SCREAMING_SNAKE_CASE  ,	default=500  ,	required=__SCREAMING_SNAKE_CASE  ,	help='''# examples. -1 means use all.''')
           parser.add_argument('''--n_test'''  ,	type=__SCREAMING_SNAKE_CASE  ,	default=-1  ,	required=__SCREAMING_SNAKE_CASE  ,	help='''# examples. -1 means use all.''')
           parser.add_argument(
               '''--task'''  ,	type=__SCREAMING_SNAKE_CASE  ,	default='''summarization'''  ,	required=__SCREAMING_SNAKE_CASE  ,	help='''# examples. -1 means use all.''')
           parser.add_argument('''--label_smoothing'''  ,	type=__SCREAMING_SNAKE_CASE  ,	default=0.0  ,	required=__SCREAMING_SNAKE_CASE)
           parser.add_argument('''--src_lang'''  ,	type=__SCREAMING_SNAKE_CASE  ,	default=''''''  ,	required=__SCREAMING_SNAKE_CASE)
           parser.add_argument('''--tgt_lang'''  ,	type=__SCREAMING_SNAKE_CASE  ,	default=''''''  ,	required=__SCREAMING_SNAKE_CASE)
           parser.add_argument('''--eval_beams'''  ,	type=__SCREAMING_SNAKE_CASE  ,	default=__SCREAMING_SNAKE_CASE  ,	required=__SCREAMING_SNAKE_CASE)
           parser.add_argument(
               '''--val_metric'''  ,	type=__SCREAMING_SNAKE_CASE  ,	default=__SCREAMING_SNAKE_CASE  ,	required=__SCREAMING_SNAKE_CASE  ,	choices=['''bleu''', '''rouge2''', '''loss''', None])
           parser.add_argument('''--eval_max_gen_length'''  ,	type=__SCREAMING_SNAKE_CASE  ,	default=__SCREAMING_SNAKE_CASE  ,	help='''never generate more than n tokens''')
           parser.add_argument('''--save_top_k'''  ,	type=__SCREAMING_SNAKE_CASE  ,	default=1  ,	required=__SCREAMING_SNAKE_CASE  ,	help='''How many checkpoints to save''')
           parser.add_argument(
               '''--early_stopping_patience'''  ,	type=__SCREAMING_SNAKE_CASE  ,	default=-1  ,	required=__SCREAMING_SNAKE_CASE  ,	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 					_A (			__UpperCAmelCase					):
       UpperCamelCase__						:					Union[str, Any]		      =		'''translation'''
       UpperCamelCase__						:					int		      =		['''loss''']
       UpperCamelCase__						:					Optional[Any]		      =		['''bleu''']
       UpperCamelCase__						:					Union[str, Any]		      =		'''bleu'''



       def __init__(	self							:  int  ,	__SCREAMING_SNAKE_CASE							:  List[Any]  ,	**__SCREAMING_SNAKE_CASE							:  List[Any]):


           '''simple docstring'''

           super().__init__(__SCREAMING_SNAKE_CASE  ,	**__SCREAMING_SNAKE_CASE)
           __a		 =       hparams.src_lang
           __a		 =       hparams.tgt_lang





       def 	_lowerCamelCase    (	self							:  int  ,	__SCREAMING_SNAKE_CASE							:  Union[str, Any]  ,	__SCREAMING_SNAKE_CASE							:  Optional[int]):


           '''simple docstring'''

           return calculate_bleu(__SCREAMING_SNAKE_CASE  ,	__SCREAMING_SNAKE_CASE)





def   __snake_case			(    _UpperCAmelCase     ,				_UpperCAmelCase=None       ):
    Path(args.output_dir       ).mkdir(exist_ok=_UpperCAmelCase       )
    check_output_dir(_UpperCAmelCase     ,				expected_items=3       )

    if model is None:
        if "summarization" in args.task:
            __a		 =       SummarizationModule(_UpperCAmelCase       )
        else:
            __a		 =       TranslationModule(_UpperCAmelCase       )
    __a		 =       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'''       )
    ):
        __a		 =       True  # don't pollute wandb logs unnecessarily
    elif args.logger_name == "wandb":
        from pytorch_lightning.loggers import WandbLogger

        __a		 =       os.environ.get('''WANDB_PROJECT'''     ,				_UpperCAmelCase       )
        __a		 =       WandbLogger(name=model.output_dir.name     ,				project=_UpperCAmelCase       )

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

        __a		 =       WandbLogger(name=model.output_dir.name     ,				project=f'hf_{dataset}'       )

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

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

    __a		 =       ''''''
    __a		 =       sorted(glob.glob(os.path.join(args.output_dir     ,				'''*.ckpt'''       )     ,				recursive=_UpperCAmelCase       )       )
    if checkpoints:
        __a		 =       checkpoints[-1]
        __a		 =       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		:Optional[int]   	=  argparse.ArgumentParser()
 __snake_case		:Any   	=  pl.Trainer.add_argparse_args(parser)
 __snake_case		:Dict   	=  SummarizationModule.add_model_specific_args(parser, os.getcwd())

 __snake_case		:Dict   	=  parser.parse_args()

 main(args)










def   __snake_case			(    _UpperCAmelCase       ):
    __a		 =       ''''''
    for ch in key:
        if ch == " " or ch not in key_no_dups and ch.isalpha():
            key_no_dups += ch
    return key_no_dups





def   __snake_case			(    _UpperCAmelCase       ):
    __a		 =       [chr(i + 65       ) for i in range(26       )]
    # Remove duplicate characters from key
    __a		 =       remove_duplicates(key.upper()       )
    __a		 =       len(_UpperCAmelCase       )
    # First fill cipher with key characters
    __a		 =       {alphabet[i]: char for i, char in enumerate(_UpperCAmelCase       )}
    # Then map remaining characters in alphabet to
    # the alphabet from the beginning
    for i in range(len(_UpperCAmelCase       )     ,				26       ):
        __a		 =       alphabet[i - offset]
        # Ensure we are not mapping letters to letters previously mapped
        while char in key:
            offset -= 1
            __a		 =       alphabet[i - offset]
        __a		 =       char
    return cipher_alphabet





def   __snake_case			(    _UpperCAmelCase     ,				_UpperCAmelCase       ):
    return "".join(cipher_map.get(_UpperCAmelCase     ,				_UpperCAmelCase       ) for ch in message.upper()       )





def   __snake_case			(    _UpperCAmelCase     ,				_UpperCAmelCase       ):
    __a		 =       {v: k for k, v in cipher_map.items()}
    return "".join(rev_cipher_map.get(_UpperCAmelCase     ,				_UpperCAmelCase       ) for ch in message.upper()       )





def   __snake_case			(    ):
    __a		 =       input('''Enter message to encode or decode: '''       ).strip()
    __a		 =       input('''Enter keyword: '''       ).strip()
    __a		 =       input('''Encipher or decipher? E/D:'''       ).strip()[0].lower()
    try:
        __a		 =       {'''e''': encipher, '''d''': decipher}[option]
    except KeyError:
        raise KeyError('''invalid input option'''       )
    __a		 =       create_cipher_map(_UpperCAmelCase       )
    print(func(_UpperCAmelCase     ,				_UpperCAmelCase       )       )


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 main()





