


from __future__ import annotations



def  _a			(						__UpperCamelCase  ,  __UpperCamelCase ):
     a_    :     Optional[int]										=    set(__UpperCamelCase ), [start]

     while stack:
          a_    :     Any										=    stack.pop()
          explored.add(__UpperCamelCase )
          # Differences from BFS:
          # 1) pop last element instead of first one
          # 2) add adjacent elements to stack without exploring them
          for adj in reversed(graph[v] ):
               if adj not in explored:
                    stack.append(__UpperCamelCase )
     return explored


__lowerCamelCase        =     {
    '''A''': ['''B''', '''C''', '''D'''],
    '''B''': ['''A''', '''D''', '''E'''],
    '''C''': ['''A''', '''F'''],
    '''D''': ['''B''', '''D'''],
    '''E''': ['''B''', '''F'''],
    '''F''': ['''C''', '''E''', '''G'''],
    '''G''': ['''F'''],
}

if __name__ == "__main__":
       import doctest

       doctest.testmod()
       print(depth_first_search(G, '''A'''))







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


__lowerCamelCase        =     logging.getLogger(__name__)


class   a__	(						lowerCAmelCase_    ):
       lowerCamelCase__:     str							    =     """summarization"""
       lowerCamelCase__:     List[str]							    =     ["""loss"""]
       lowerCamelCase__:     List[str]							    =     ROUGE_KEYS
       lowerCamelCase__:     Union[str, Any]							    =     """rouge2"""

       def __init__(   self      :     Union[str, Any]      ,							lowerCamelCase_      :     Tuple      ,							**lowerCamelCase_      :     List[str]    ):
            if hparams.sortish_sampler and hparams.gpus > 1:
                 a_    :     Any										=    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    )
            a_    :     Any										=    Path(self.output_dir    ) / """metrics.json"""
            a_    :     Tuple										=    Path(self.output_dir    ) / """hparams.pkl"""
            pickle_save(self.hparams      ,							self.hparams_save_path    )
            a_    :     Dict										=    0
            a_    :     Optional[Any]										=    defaultdict(lowerCamelCase_    )
            a_    :     str										=    self.config.model_type
            a_    :     Union[str, Any]										=    self.config.tgt_vocab_size if self.model_type == """fsmt""" else self.config.vocab_size

            a_    :     dict										=    {
                "data_dir": self.hparams.data_dir,
                "max_source_length": self.hparams.max_source_length,
                "prefix": self.model.config.prefix or "",
            }
            a_    :     int										=    {
                """train""": self.hparams.n_train,
                """val""": self.hparams.n_val,
                """test""": self.hparams.n_test,
            }
            a_    :     List[str]										=    {k: v if v >= 0 else None for k, v in n_observations_per_split.items()}

            a_    :     Optional[Any]										=    {
                """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_    :     int										=    get_git_info()["""repo_sha"""]
            a_    :     Union[str, Any]										=    hparams.num_workers
            a_    :     Tuple										=    None  # default to config
            if self.model.config.decoder_start_token_id is None and isinstance(self.tokenizer      ,							lowerCamelCase_    ):
                 a_    :     Dict										=    self.tokenizer.lang_code_to_id[hparams.tgt_lang]
                 a_    :     List[str]										=    self.decoder_start_token_id
            a_    :     Union[str, Any]										=    (
                SeqaSeqDataset if hasattr(self.tokenizer      ,							"""prepare_seq2seq_batch"""    ) else LegacySeqaSeqDataset
            )
            a_    :     Union[str, Any]										=    False
            a_    :     Any										=    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_    :     Union[str, Any]										=    self.hparams.eval_max_gen_length
            else:
                 a_    :     Optional[Any]										=    self.model.config.max_length
            a_    :     Optional[int]										=    self.default_val_metric if self.hparams.val_metric is None else self.hparams.val_metric

       def 						UpperCAmelCase(   self      :     List[Any]      ,							lowerCamelCase_      :     Dict[str, torch.Tensor]    ):
            a_    :     Optional[Any]										=    {
                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"""    )

            a_    :     List[Any]										=    True
            return readable_batch

       def 						UpperCAmelCase(   self      :     Union[str, Any]      ,							lowerCamelCase_      :     str      ,							**lowerCamelCase_      :     Tuple    ):
            return self.model(lowerCamelCase_      ,							**lowerCamelCase_    )

       def 						UpperCAmelCase(   self      :     Tuple      ,							lowerCamelCase_      :     List[int]    ):
            a_    :     str										=    self.tokenizer.batch_decode(
                lowerCamelCase_      ,							skip_special_tokens=lowerCamelCase_      ,							clean_up_tokenization_spaces=lowerCamelCase_    )
            return lmap(str.strip      ,							lowerCamelCase_    )

       def 						UpperCAmelCase(   self      :     Optional[int]      ,							lowerCamelCase_      :     dict    ):
            a_    :     int										=    self.tokenizer.pad_token_id
            a_			,			a_    :     Optional[int]										=    batch["""input_ids"""], batch["""attention_mask"""]
            a_    :     Optional[Any]										=    batch["""labels"""]
            if isinstance(self.model      ,							lowerCamelCase_    ):
                 a_    :     List[Any]										=    self.model._shift_right(lowerCamelCase_    )
            else:
                 a_    :     Dict										=    shift_tokens_right(lowerCamelCase_      ,							lowerCamelCase_    )
            if not self.already_saved_batch:  # This would be slightly better if it only happened on rank zero
                 a_    :     int										=    decoder_input_ids
                 self.save_readable_batch(lowerCamelCase_    )

            a_    :     str										=    self(lowerCamelCase_      ,							attention_mask=lowerCamelCase_      ,							decoder_input_ids=lowerCamelCase_      ,							use_cache=lowerCamelCase_    )
            a_    :     int										=    outputs["""logits"""]
            if self.hparams.label_smoothing == 0:
                 # Same behavior as modeling_bart.py, besides ignoring pad_token_id
                 a_    :     Any										=    nn.CrossEntropyLoss(ignore_index=lowerCamelCase_    )

                 assert lm_logits.shape[-1] == self.vocab_size
                 a_    :     Union[str, Any]										=    ce_loss_fct(lm_logits.view(-1      ,							lm_logits.shape[-1]    )      ,							tgt_ids.view(-1    )    )
            else:
                 a_    :     int										=    nn.functional.log_softmax(lowerCamelCase_      ,							dim=-1    )
                 a_			,			a_    :     str										=    label_smoothed_nll_loss(
                     lowerCamelCase_      ,							lowerCamelCase_      ,							self.hparams.label_smoothing      ,							ignore_index=lowerCamelCase_    )
            return (loss,)

       @property
       def 						UpperCAmelCase(   self      :     Union[str, Any]    ):
            return self.tokenizer.pad_token_id

       def 						UpperCAmelCase(   self      :     str      ,							lowerCamelCase_      :     Optional[Any]      ,							lowerCamelCase_      :     List[Any]    ):
            a_    :     Dict										=    self._step(lowerCamelCase_    )

            a_    :     Optional[int]										=    dict(zip(self.loss_names      ,							lowerCamelCase_    )    )
            # tokens per batch
            a_    :     str										=    batch["""input_ids"""].ne(self.pad    ).sum() + batch["""labels"""].ne(self.pad    ).sum()
            a_    :     Optional[int]										=    batch["""input_ids"""].shape[0]
            a_    :     int										=    batch["""input_ids"""].eq(self.pad    ).sum()
            a_    :     str										=    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 						UpperCAmelCase(   self      :     int      ,							lowerCamelCase_      :     List[Any]      ,							lowerCamelCase_      :     Tuple    ):
            return self._generative_step(lowerCamelCase_    )

       def 						UpperCAmelCase(   self      :     Any      ,							lowerCamelCase_      :     List[str]      ,							lowerCamelCase_      :     Optional[int]="val"    ):
            self.step_count += 1
            a_    :     Optional[Any]										=    {k: torch.stack([x[k] for x in outputs]    ).mean() for k in self.loss_names}
            a_    :     Tuple										=    losses["""loss"""]
            a_    :     Optional[int]										=    {
                k: np.array([x[k] for x in outputs]    ).mean() for k in self.metric_names + ["""gen_time""", """gen_len"""]
            }
            a_    :     List[Any]										=    (
                generative_metrics[self.val_metric] if self.val_metric in generative_metrics else losses[self.val_metric]
            )
            a_    :     torch.FloatTensor										=    torch.tensor(lowerCamelCase_    ).type_as(lowerCamelCase_    )
            generative_metrics.update({k: v.item() for k, v in losses.items()}    )
            losses.update(lowerCamelCase_    )
            a_    :     str										=    {F'''{prefix}_avg_{k}''': x for k, x in losses.items()}
            a_    :     Union[str, Any]										=    self.step_count
            self.metrics[prefix].append(lowerCamelCase_    )  # callback writes this to self.metrics_save_path
            a_    :     List[str]										=    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 						UpperCAmelCase(   self      :     Any      ,							lowerCamelCase_      :     Optional[int]      ,							lowerCamelCase_      :     Optional[Any]    ):
            return calculate_rouge(lowerCamelCase_      ,							lowerCamelCase_    )

       def 						UpperCAmelCase(   self      :     int      ,							lowerCamelCase_      :     dict    ):
            a_    :     Dict										=    time.time()

            # parser.add_argument('--eval_max_gen_length', type=int, default=None, help='never generate more than n tokens')
            a_    :     Any										=    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      ,							)
            a_    :     Optional[int]										=    (time.time() - ta) / batch["""input_ids"""].shape[0]
            a_    :     List[str]										=    self.ids_to_clean_text(lowerCamelCase_    )
            a_    :     List[str]										=    self.ids_to_clean_text(batch["""labels"""]    )
            a_    :     Tuple										=    self._step(lowerCamelCase_    )
            a_    :     List[str]										=    dict(zip(self.loss_names      ,							lowerCamelCase_    )    )
            a_    :     Dict										=    self.calc_generative_metrics(lowerCamelCase_      ,							lowerCamelCase_    )
            a_    :     Optional[int]										=    np.mean(lmap(lowerCamelCase_      ,							lowerCamelCase_    )    )
            base_metrics.update(gen_time=lowerCamelCase_      ,							gen_len=lowerCamelCase_      ,							preds=lowerCamelCase_      ,							target=lowerCamelCase_      ,							**lowerCamelCase_    )
            return base_metrics

       def 						UpperCAmelCase(   self      :     int      ,							lowerCamelCase_      :     str      ,							lowerCamelCase_      :     Optional[int]    ):
            return self._generative_step(lowerCamelCase_    )

       def 						UpperCAmelCase(   self      :     str      ,							lowerCamelCase_      :     Any    ):
            return self.validation_epoch_end(lowerCamelCase_      ,							prefix="""test"""    )

       def 						UpperCAmelCase(   self      :     Any      ,							lowerCamelCase_      :     Any    ):
            a_    :     List[str]										=    self.n_obs[type_path]
            a_    :     Dict										=    self.target_lens[type_path]
            a_    :     Optional[Any]										=    self.dataset_class(
                self.tokenizer      ,							type_path=lowerCamelCase_      ,							n_obs=lowerCamelCase_      ,							max_target_length=lowerCamelCase_      ,							**self.dataset_kwargs      ,							)
            return dataset

       def 						UpperCAmelCase(   self      :     Union[str, Any]      ,							lowerCamelCase_      :     str      ,							lowerCamelCase_      :     int      ,							lowerCamelCase_      :     bool = False    ):
            a_    :     List[str]										=    self.get_dataset(lowerCamelCase_    )

            if self.hparams.sortish_sampler and type_path != "test" and type_path != "val":
                 a_    :     List[str]										=    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":
                 a_    :     int										=    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 						UpperCAmelCase(   self      :     Any    ):
            a_    :     int										=    self.get_dataloader("""train"""      ,							batch_size=self.hparams.train_batch_size      ,							shuffle=lowerCamelCase_    )
            return dataloader

       def 						UpperCAmelCase(   self      :     Dict    ):
            return self.get_dataloader("""val"""      ,							batch_size=self.hparams.eval_batch_size    )

       def 						UpperCAmelCase(   self      :     List[Any]    ):
            return self.get_dataloader("""test"""      ,							batch_size=self.hparams.eval_batch_size    )

       @staticmethod
       def 						UpperCAmelCase(   lowerCamelCase_      :     Dict      ,							lowerCamelCase_      :     Optional[Any]    ):
            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   a__	(						lowerCAmelCase_    ):
       lowerCamelCase__:     List[Any]							    =     """translation"""
       lowerCamelCase__:     int							    =     ["""loss"""]
       lowerCamelCase__:     List[str]							    =     ["""bleu"""]
       lowerCamelCase__:     Optional[Any]							    =     """bleu"""

       def __init__(   self      :     Optional[Any]      ,							lowerCamelCase_      :     Optional[Any]      ,							**lowerCamelCase_      :     Tuple    ):
            super().__init__(lowerCamelCase_      ,							**lowerCamelCase_    )
            a_    :     Union[str, Any]										=    hparams.src_lang
            a_    :     Optional[int]										=    hparams.tgt_lang

       def 						UpperCAmelCase(   self      :     Optional[Any]      ,							lowerCamelCase_      :     Tuple      ,							lowerCamelCase_      :     int    ):
            return calculate_bleu(lowerCamelCase_      ,							lowerCamelCase_    )



def  _a			(						__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										=    SummarizationModule(__UpperCamelCase )
          else:
               a_    :     SummarizationModule										=    TranslationModule(__UpperCamelCase )
     a_    :     Dict										=    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_    :     Union[str, Any]										=    True  # don't pollute wandb logs unnecessarily
     elif args.logger_name == "wandb":
          from pytorch_lightning.loggers import WandbLogger

          a_    :     Optional[int]										=    os.environ.get("""WANDB_PROJECT"""  ,  __UpperCamelCase )
          a_    :     str										=    WandbLogger(name=model.output_dir.name  ,  project=__UpperCamelCase )

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

          a_    :     Optional[int]										=    WandbLogger(name=model.output_dir.name  ,  project=F'''hf_{dataset}''' )

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

     a_    :     Tuple										=    args.val_metric == """loss"""
     a_    :     pl.Trainer										=    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_    :     Union[str, Any]										=    """"""
     a_    :     int										=    sorted(glob.glob(os.path.join(args.output_dir  ,  """*.ckpt""" )  ,  recursive=__UpperCamelCase ) )
     if checkpoints:
          a_    :     Tuple										=    checkpoints[-1]
          a_    :     Tuple										=    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__":
       __lowerCamelCase        =     argparse.ArgumentParser()
       __lowerCamelCase        =     pl.Trainer.add_argparse_args(parser)
       __lowerCamelCase        =     SummarizationModule.add_model_specific_args(parser, os.getcwd())

       __lowerCamelCase        =     parser.parse_args()

       main(args)



