






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


UpperCamelCase    							=      logging.getLogger(__name__)







class 		_A						( UpperCAmelCase_ ):
  lowercase_						:			Dict						=					'''summarization'''
  lowercase_						:			Union[str, Any]						=					['''loss''']
  lowercase_						:			List[Any]						=					ROUGE_KEYS
  lowercase_						:			int						=					'''rouge2'''

  def __init__(    self						:	int   ,				lowerCamelCase__						:	str   ,				**lowerCamelCase__						:	int			):
     """simple docstring"""

     if hparams.sortish_sampler and hparams.gpus > 1:
        __UpperCamelCase	:			Optional[int]					=					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			)
     __UpperCamelCase	:			Union[str, Any]					=					Path(self.output_dir			) / """metrics.json"""
     __UpperCamelCase	:			Union[str, Any]					=					Path(self.output_dir			) / """hparams.pkl"""
     pickle_save(self.hparams   ,				self.hparams_save_path			)
     __UpperCamelCase	:			Union[str, Any]					=					0
     __UpperCamelCase	:			int					=					defaultdict(lowerCamelCase__			)
     __UpperCamelCase	:			Dict					=					self.config.model_type
     __UpperCamelCase	:			Optional[Any]					=					self.config.tgt_vocab_size if self.model_type == """fsmt""" else self.config.vocab_size

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

     __UpperCamelCase	:			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()			)

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

  def    a					(    self						:	List[Any]   ,				lowerCamelCase__						:	Dict[str, torch.Tensor]			):
     """simple docstring"""

     __UpperCamelCase	:			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"""			)

     __UpperCamelCase	:			Optional[int]					=					True
     return readable_batch

  def    a					(    self						:	Optional[int]   ,				lowerCamelCase__						:	Optional[int]   ,				**lowerCamelCase__						:	List[Any]			):
     """simple docstring"""

     return self.model(lowerCamelCase__   ,				**lowerCamelCase__			)

  def    a					(    self						:	Tuple   ,				lowerCamelCase__						:	List[int]			):
     """simple docstring"""

     __UpperCamelCase	:			List[Any]					=					self.tokenizer.batch_decode(
         lowerCamelCase__   ,				skip_special_tokens=lowerCamelCase__   ,				clean_up_tokenization_spaces=lowerCamelCase__			)
     return lmap(str.strip   ,				lowerCamelCase__			)

  def    a					(    self						:	str   ,				lowerCamelCase__						:	dict			):
     """simple docstring"""

     __UpperCamelCase	:			Tuple					=					self.tokenizer.pad_token_id
     __UpperCamelCase	,     __UpperCamelCase	:			Dict					=					batch["""input_ids"""], batch["""attention_mask"""]
     __UpperCamelCase	:			Tuple					=					batch["""labels"""]
     if isinstance(self.model   ,				lowerCamelCase__			):
        __UpperCamelCase	:			int					=					self.model._shift_right(lowerCamelCase__			)
     else:
        __UpperCamelCase	:			Optional[int]					=					shift_tokens_right(lowerCamelCase__   ,				lowerCamelCase__			)
     if not self.already_saved_batch:  # This would be slightly better if it only happened on rank zero
        __UpperCamelCase	:			Union[str, Any]					=					decoder_input_ids
        self.save_readable_batch(lowerCamelCase__			)

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

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

  @property
  def    a					(    self						:	Optional[Any]			):
     """simple docstring"""

     return self.tokenizer.pad_token_id

  def    a					(    self						:	List[Any]   ,				lowerCamelCase__						:	str   ,				lowerCamelCase__						:	Tuple			):
     """simple docstring"""

     __UpperCamelCase	:			Dict					=					self._step(lowerCamelCase__			)

     __UpperCamelCase	:			str					=					dict(zip(self.loss_names   ,				lowerCamelCase__			)			)
     # tokens per batch
     __UpperCamelCase	:			Any					=					batch["""input_ids"""].ne(self.pad			).sum() + batch["""labels"""].ne(self.pad			).sum()
     __UpperCamelCase	:			Union[str, Any]					=					batch["""input_ids"""].shape[0]
     __UpperCamelCase	:			Any					=					batch["""input_ids"""].eq(self.pad			).sum()
     __UpperCamelCase	:			List[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    a					(    self						:	Union[str, Any]   ,				lowerCamelCase__						:	Optional[Any]   ,				lowerCamelCase__						:	List[Any]			):
     """simple docstring"""

     return self._generative_step(lowerCamelCase__			)

  def    a					(    self						:	Any   ,				lowerCamelCase__						:	Any   ,				lowerCamelCase__						:	Dict="val"			):
     """simple docstring"""

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

     return calculate_rouge(lowerCamelCase__   ,				lowerCamelCase__			)

  def    a					(    self						:	Any   ,				lowerCamelCase__						:	dict			):
     """simple docstring"""

     __UpperCamelCase	:			List[Any]					=					time.time()

     # parser.add_argument('--eval_max_gen_length', type=int, default=None, help='never generate more than n tokens')
     __UpperCamelCase	:			Union[str, 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   ,				)
     __UpperCamelCase	:			List[Any]					=					(time.time() - ta) / batch["""input_ids"""].shape[0]
     __UpperCamelCase	:			List[str]					=					self.ids_to_clean_text(lowerCamelCase__			)
     __UpperCamelCase	:			List[str]					=					self.ids_to_clean_text(batch["""labels"""]			)
     __UpperCamelCase	:			Dict					=					self._step(lowerCamelCase__			)
     __UpperCamelCase	:			Tuple					=					dict(zip(self.loss_names   ,				lowerCamelCase__			)			)
     __UpperCamelCase	:			Dict					=					self.calc_generative_metrics(lowerCamelCase__   ,				lowerCamelCase__			)
     __UpperCamelCase	:			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    a					(    self						:	Any   ,				lowerCamelCase__						:	str   ,				lowerCamelCase__						:	Optional[int]			):
     """simple docstring"""

     return self._generative_step(lowerCamelCase__			)

  def    a					(    self						:	int   ,				lowerCamelCase__						:	Optional[int]			):
     """simple docstring"""

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

  def    a					(    self						:	Tuple   ,				lowerCamelCase__						:	Tuple			):
     """simple docstring"""

     __UpperCamelCase	:			Optional[Any]					=					self.n_obs[type_path]
     __UpperCamelCase	:			Dict					=					self.target_lens[type_path]
     __UpperCamelCase	:			str					=					self.dataset_class(
         self.tokenizer   ,				type_path=lowerCamelCase__   ,				n_obs=lowerCamelCase__   ,				max_target_length=lowerCamelCase__   ,				**self.dataset_kwargs   ,				)
     return dataset

  def    a					(    self						:	Dict   ,				lowerCamelCase__						:	str   ,				lowerCamelCase__						:	int   ,				lowerCamelCase__						:	bool = False			):
     """simple docstring"""

     __UpperCamelCase	:			Union[str, Any]					=					self.get_dataset(lowerCamelCase__			)

     if self.hparams.sortish_sampler and type_path != "test" and type_path != "val":
        __UpperCamelCase	:			Optional[Any]					=					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":
        __UpperCamelCase	:			List[Any]					=					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    a					(    self						:	List[str]			):
     """simple docstring"""

     __UpperCamelCase	:			Optional[Any]					=					self.get_dataloader("""train"""   ,				batch_size=self.hparams.train_batch_size   ,				shuffle=lowerCamelCase__			)
     return dataloader

  def    a					(    self						:	Tuple			):
     """simple docstring"""

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

  def    a					(    self						:	Union[str, Any]			):
     """simple docstring"""

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

  @staticmethod
  def    a					(    lowerCamelCase__						:	int   ,				lowerCamelCase__						:	Union[str, Any]			):
     """simple docstring"""

     BaseTransformer.add_model_specific_args(lowerCamelCase__   ,				lowerCamelCase__			)
     add_generic_args(lowerCamelCase__   ,				lowerCamelCase__			)
     parser.add_argument(
         """--max_source_length"""   ,				default=10_24   ,				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=56   ,				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_42   ,				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_42   ,				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_00   ,				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						( UpperCAmelCase_ ):
  lowercase_						:			str						=					'''translation'''
  lowercase_						:			Optional[Any]						=					['''loss''']
  lowercase_						:			List[Any]						=					['''bleu''']
  lowercase_						:			List[Any]						=					'''bleu'''

  def __init__(    self						:	int   ,				lowerCamelCase__						:	Union[str, Any]   ,				**lowerCamelCase__						:	Optional[int]			):
     """simple docstring"""

     super().__init__(lowerCamelCase__   ,				**lowerCamelCase__			)
     __UpperCamelCase	:			List[Any]					=					hparams.src_lang
     __UpperCamelCase	:			Tuple					=					hparams.tgt_lang

  def    a					(    self						:	str   ,				lowerCamelCase__						:	str   ,				lowerCamelCase__						:	str			):
     """simple docstring"""

     return calculate_bleu(lowerCamelCase__   ,				lowerCamelCase__			)







def 				__lowerCamelCase     (							__lowerCAmelCase							:					Tuple					,							__lowerCAmelCase							:					str=None							)     ->  SummarizationModule:
   Path(args.output_dir							).mkdir(exist_ok=__lowerCAmelCase							)
   check_output_dir(__lowerCAmelCase					,							expected_items=3							)

   if model is None:
      if "summarization" in args.task:
         __UpperCamelCase	:			SummarizationModule					=					SummarizationModule(__lowerCAmelCase							)
      else:
         __UpperCamelCase	:			SummarizationModule					=					TranslationModule(__lowerCAmelCase							)
   __UpperCamelCase	:			List[Any]					=					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"""							)
   ):
      __UpperCamelCase	:			Dict					=					True  # don't pollute wandb logs unnecessarily
   elif args.logger_name == "wandb":
      from pytorch_lightning.loggers import WandbLogger

      __UpperCamelCase	:			List[Any]					=					os.environ.get("""WANDB_PROJECT"""					,							__lowerCAmelCase							)
      __UpperCamelCase	:			Dict					=					WandbLogger(name=model.output_dir.name					,							project=__lowerCAmelCase							)

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

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

   if args.early_stopping_patience >= 0:
      __UpperCamelCase	:			List[Any]					=					get_early_stopping_callback(model.val_metric					,							args.early_stopping_patience							)
   else:
      __UpperCamelCase	:			List[Any]					=					False

   __UpperCamelCase	:			Tuple					=					args.val_metric == """loss"""
   __UpperCamelCase	:			pl.Trainer					=					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

   __UpperCamelCase	:			List[str]					=					""""""
   __UpperCamelCase	:			List[str]					=					sorted(glob.glob(os.path.join(args.output_dir					,							"""*.ckpt"""							)					,							recursive=__lowerCAmelCase							)							)
   if checkpoints:
      __UpperCamelCase	:			Tuple					=					checkpoints[-1]
      __UpperCamelCase	:			Optional[Any]					=					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__":
       UpperCamelCase    							=      argparse.ArgumentParser()
       UpperCamelCase    							=      pl.Trainer.add_argparse_args(parser)
       UpperCamelCase    							=      SummarizationModule.add_model_specific_args(parser, os.getcwd())

       UpperCamelCase    							=      parser.parse_args()

       main(args)











from __future__ import annotations







class 		_A						:

  def __init__(    self						:	List[str]   ,				lowerCamelCase__						:	Any=None			):
     """simple docstring"""

     __UpperCamelCase	:			Union[str, Any]					=					data
     __UpperCamelCase	:			Union[str, Any]					=					None

  def __repr__(    self						:	int			):
     """simple docstring"""

     __UpperCamelCase	:			Union[str, Any]					=					[]
     __UpperCamelCase	:			Optional[int]					=					self
     while temp:
        string_rep.append(f'{temp.data}'			)
        __UpperCamelCase	:			Dict					=					temp.next
     return "->".join(lowerCamelCase__			)







def 				__lowerCamelCase     (							__lowerCAmelCase							:					list							)     ->  Tuple:
   if not elements_list:
      raise Exception("""The Elements List is empty"""							)

   __UpperCamelCase	:			Dict					=					Node(elements_list[0]							)
   for i in range(1					,							len(__lowerCAmelCase							)							):
      __UpperCamelCase	:			List[str]					=					Node(elements_list[i]							)
      __UpperCamelCase	:			Optional[int]					=					current.next
   return head


def 				__lowerCamelCase     (							__lowerCAmelCase							:					Node							)     ->  None:
   if head_node is not None and isinstance(__lowerCAmelCase					,							__lowerCAmelCase							):
      print_reverse(head_node.next							)
      print(head_node.data							)


def 				__lowerCamelCase     (							)     ->  Union[str, Any]:
   from doctest import testmod

   testmod()

   __UpperCamelCase	:			Any					=					make_linked_list([14, 52, 14, 12, 43]							)
   print("""Linked List:"""							)
   print(__lowerCAmelCase							)
   print("""Elements in Reverse:"""							)
   print_reverse(__lowerCAmelCase							)


if __name__ == "__main__":
       main()



