


'''simple docstring'''







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


SCREAMING_SNAKE_CASE_      =			logging.getLogger(__name__)


class  lowerCAmelCase_						(       snake_case__      ):





  """simple docstring"""



  a_       :List[Any]												="""summarization"""
  a_       :Dict												=["""loss"""]
  a_       :Union[str, Any]												=ROUGE_KEYS
  a_       :List[str]												="""rouge2"""

  def __init__(					self     :							Union[str, Any]				,							SCREAMING_SNAKE_CASE__     :							int				,							**SCREAMING_SNAKE_CASE__     :							Union[str, 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 __a						(					self     :							Dict				,							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 __a						(					self     :							int				,							SCREAMING_SNAKE_CASE__     :							Optional[int]				,							**SCREAMING_SNAKE_CASE__     :							Union[str, Any]						):





       '''simple docstring'''

       return self.model(SCREAMING_SNAKE_CASE__				,							**SCREAMING_SNAKE_CASE__						)

  def __a						(					self     :							Optional[Any]				,							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 __a						(					self     :							Tuple				,							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 __a						(					self     :							Optional[Any]						):





       '''simple docstring'''

       return self.tokenizer.pad_token_id

  def __a						(					self     :							Optional[int]				,							SCREAMING_SNAKE_CASE__     :							List[Any]				,							SCREAMING_SNAKE_CASE__     :							int						):





       '''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 __a						(					self     :							Tuple				,							SCREAMING_SNAKE_CASE__     :							int				,							SCREAMING_SNAKE_CASE__     :							List[str]						):





       '''simple docstring'''

       return self._generative_step(SCREAMING_SNAKE_CASE__						)

  def __a						(					self     :							Union[str, Any]				,							SCREAMING_SNAKE_CASE__     :							str				,							SCREAMING_SNAKE_CASE__     :							Tuple="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 __a						(					self     :							List[str]				,							SCREAMING_SNAKE_CASE__     :							Optional[Any]				,							SCREAMING_SNAKE_CASE__     :							Union[str, Any]						):





       '''simple docstring'''

       return calculate_rouge(SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__						)

  def __a						(					self     :							Union[str, 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 __a						(					self     :							Union[str, Any]				,							SCREAMING_SNAKE_CASE__     :							List[str]				,							SCREAMING_SNAKE_CASE__     :							str						):





       '''simple docstring'''

       return self._generative_step(SCREAMING_SNAKE_CASE__						)

  def __a						(					self     :							str				,							SCREAMING_SNAKE_CASE__     :							Union[str, Any]						):





       '''simple docstring'''

       return self.validation_epoch_end(SCREAMING_SNAKE_CASE__				,							prefix="""test"""						)

  def __a						(					self     :							Union[str, Any]				,							SCREAMING_SNAKE_CASE__     :							Any						):





       '''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 __a						(					self     :							Optional[int]				,							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 __a						(					self     :							List[Any]						):





       '''simple docstring'''

       __a       =   self.get_dataloader("""train"""				,							batch_size=self.hparams.train_batch_size				,							shuffle=SCREAMING_SNAKE_CASE__						)
       return dataloader

  def __a						(					self     :							Dict						):





       '''simple docstring'''

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

  def __a						(					self     :							List[Any]						):





       '''simple docstring'''

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


  @staticmethod
  def __a						(					SCREAMING_SNAKE_CASE__     :							Optional[Any]				,							SCREAMING_SNAKE_CASE__     :							List[str]						):





       '''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_0_2_4				,							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=5_6				,							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=1_4_2				,							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=1_4_2				,							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=5_0_0				,							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  lowerCAmelCase_						(       snake_case__      ):





  """simple docstring"""



  a_       :Any												="""translation"""
  a_       :Optional[int]												=["""loss"""]
  a_       :Any												=["""bleu"""]
  a_       :Union[str, Any]												="""bleu"""

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





       '''simple docstring'''

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


  def __a						(					self     :							Tuple				,							SCREAMING_SNAKE_CASE__     :							Any				,							SCREAMING_SNAKE_CASE__     :							List[str]						):





       '''simple docstring'''

       return calculate_bleu(SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__						)




def        __lowercase						(      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE=None		)				->					SummarizationModule:
     """simple docstring"""




     Path(args.output_dir		).mkdir(exist_ok=__SCREAMING_SNAKE_CASE		)
     check_output_dir(__SCREAMING_SNAKE_CASE				,  expected_items=3		)

     if model is None:
          if "summarization" in args.task:
               __a       =   SummarizationModule(__SCREAMING_SNAKE_CASE		)
          else:
               __a       =   TranslationModule(__SCREAMING_SNAKE_CASE		)
     __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"""				,  __SCREAMING_SNAKE_CASE		)
          __a       =   WandbLogger(name=model.output_dir.name				,  project=__SCREAMING_SNAKE_CASE		)

     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(
         __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE				,  logging_callback=SeqaSeqLoggingCallback()				,  checkpoint_callback=get_checkpoint_callback(
             args.output_dir				,  model.val_metric				,  args.save_top_k				,  __SCREAMING_SNAKE_CASE		)				,  early_stopping_callback=__SCREAMING_SNAKE_CASE				,  logger=__SCREAMING_SNAKE_CASE				,  )
     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=__SCREAMING_SNAKE_CASE		)		)
     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__":
 SCREAMING_SNAKE_CASE_      =			argparse.ArgumentParser()
 SCREAMING_SNAKE_CASE_      =			pl.Trainer.add_argparse_args(parser)
 SCREAMING_SNAKE_CASE_      =			SummarizationModule.add_model_specific_args(parser, os.getcwd())

 SCREAMING_SNAKE_CASE_      =			parser.parse_args()

 main(args)










'''simple docstring'''







SCREAMING_SNAKE_CASE_      =			0  # The first color of the flag.
SCREAMING_SNAKE_CASE_      =			1  # The second color of the flag.
SCREAMING_SNAKE_CASE_      =			2  # The third color of the flag.
SCREAMING_SNAKE_CASE_      =			(red, white, blue)




def        __lowercase						(      __SCREAMING_SNAKE_CASE		)				->					list:
     """simple docstring"""




     if not sequence:
          return []
     if len(__SCREAMING_SNAKE_CASE		) == 1:
          return list(__SCREAMING_SNAKE_CASE		)
     __a       =   0
     __a       =   len(__SCREAMING_SNAKE_CASE		) - 1
     __a       =   0
     while mid <= high:
          if sequence[mid] == colors[0]:
               __a ,						__a       =   sequence[mid], sequence[low]
               low += 1
               mid += 1
          elif sequence[mid] == colors[1]:
               mid += 1
          elif sequence[mid] == colors[2]:
               __a ,						__a       =   sequence[high], sequence[mid]
               high -= 1
          else:
               __a       =   F'''The elements inside the sequence must contains only {colors} values'''
               raise ValueError(__SCREAMING_SNAKE_CASE		)
     return sequence


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 SCREAMING_SNAKE_CASE_      =			input('Enter numbers separated by commas:\n').strip()
 SCREAMING_SNAKE_CASE_      =			[int(item.strip()) for item in user_input.split(',')]
 print(f"""{dutch_national_flag_sort(unsorted)}""")






