






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


__magic_name__:					int							=     logging.getLogger(__name__)





class 	snake_case__ (							lowerCAmelCase_				):
	lowercase__						:					Optional[int]											=   """summarization"""
	lowercase__						:					List[str]											=   ["""loss"""]
	lowercase__						:					List[Any]											=   ROUGE_KEYS
	lowercase__						:					Tuple											=   """rouge2"""
	def __init__(    self      ,       lowerCAmelCase__      ,       **lowerCAmelCase__			)							->				Optional[int]:

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

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

							__magic_name__      :       List[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()			)

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

							__magic_name__      :       str       						= True
							return readable_batch
	def       __magic_name__    (    self      ,       lowerCAmelCase__      ,       **lowerCAmelCase__			)							->				int:
							return self.model(__SCREAMING_SNAKE_CASE      ,       **__SCREAMING_SNAKE_CASE			)
	def       __magic_name__    (    self      ,       lowerCAmelCase__			)							->				Any:
							__magic_name__      :       Tuple       						= 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       __magic_name__    (    self      ,       lowerCAmelCase__			)							->				Union[str, Any]:
							__magic_name__      :       Tuple       						= self.tokenizer.pad_token_id
							__magic_name__						,__magic_name__      :       List[str]       						= batch["""input_ids"""], batch["""attention_mask"""]
							__magic_name__      :       Optional[int]       						= batch["""labels"""]
							if isinstance(self.model      ,       __SCREAMING_SNAKE_CASE			):
													__magic_name__      :       Union[str, Any]       						= self.model._shift_right(__SCREAMING_SNAKE_CASE			)
							else:
													__magic_name__      :       List[Any]       						= 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
													__magic_name__      :       Optional[int]       						= decoder_input_ids
													self.save_readable_batch(__SCREAMING_SNAKE_CASE			)

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

													assert lm_logits.shape[-1] == self.vocab_size
													__magic_name__      :       Union[str, Any]       						= ce_loss_fct(lm_logits.view(-1      ,       lm_logits.shape[-1]			)      ,       tgt_ids.view(-1			)			)
							else:
													__magic_name__      :       Tuple       						= nn.functional.log_softmax(__SCREAMING_SNAKE_CASE      ,       dim=-1			)
													__magic_name__						,__magic_name__      :       Dict       						= label_smoothed_nll_loss(
													    __SCREAMING_SNAKE_CASE      ,       __SCREAMING_SNAKE_CASE      ,       self.hparams.label_smoothing      ,       ignore_index=__SCREAMING_SNAKE_CASE			)
							return (loss,)
	@property
	def       __magic_name__    (    self			)							->				List[Any]:
							return self.tokenizer.pad_token_id
	def       __magic_name__    (    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__			)							->				int:
							__magic_name__      :       List[str]       						= self._step(__SCREAMING_SNAKE_CASE			)

							__magic_name__      :       Dict       						= dict(zip(self.loss_names      ,       __SCREAMING_SNAKE_CASE			)			)
							# tokens per batch
							__magic_name__      :       Any       						= batch["""input_ids"""].ne(self.pad			).sum() + batch["""labels"""].ne(self.pad			).sum()
							__magic_name__      :       str       						= batch["""input_ids"""].shape[0]
							__magic_name__      :       Any       						= batch["""input_ids"""].eq(self.pad			).sum()
							__magic_name__      :       List[Any]       						= 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       __magic_name__    (    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__			)							->				Optional[Any]:
							return self._generative_step(__SCREAMING_SNAKE_CASE			)
	def       __magic_name__    (    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__="val"			)							->				Optional[int]:
							self.step_count += 1
							__magic_name__      :       int       						= {k: torch.stack([x[k] for x in outputs]			).mean() for k in self.loss_names}
							__magic_name__      :       Tuple       						= losses["""loss"""]
							__magic_name__      :       List[Any]       						= {
							    k: np.array([x[k] for x in outputs]			).mean() for k in self.metric_names + ["""gen_time""", """gen_len"""]
							}
							__magic_name__      :       Dict       						= (
							    generative_metrics[self.val_metric] if self.val_metric in generative_metrics else losses[self.val_metric]
							)
							__magic_name__      :       Optional[int]       						= 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			)
							__magic_name__      :       Optional[Any]       						= {F'{prefix}_avg_{k}': x for k, x in losses.items()}
							__magic_name__      :       str       						= self.step_count
							self.metrics[prefix].append(__SCREAMING_SNAKE_CASE			)  # callback writes this to self.metrics_save_path
							__magic_name__      :       List[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       __magic_name__    (    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__			)							->				Tuple:
							return calculate_rouge(__SCREAMING_SNAKE_CASE      ,       __SCREAMING_SNAKE_CASE			)
	def       __magic_name__    (    self      ,       lowerCAmelCase__			)							->				Dict:
							__magic_name__      :       Any       						= time.time()

							# parser.add_argument('--eval_max_gen_length', type=int, default=None, help='never generate more than n tokens')
							__magic_name__      :       Dict       						= 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      ,       )
							__magic_name__      :       Dict       						= (time.time() - ta) / batch["""input_ids"""].shape[0]
							__magic_name__      :       Optional[int]       						= self.ids_to_clean_text(__SCREAMING_SNAKE_CASE			)
							__magic_name__      :       Union[str, Any]       						= self.ids_to_clean_text(batch["""labels"""]			)
							__magic_name__      :       List[Any]       						= self._step(__SCREAMING_SNAKE_CASE			)
							__magic_name__      :       Optional[int]       						= dict(zip(self.loss_names      ,       __SCREAMING_SNAKE_CASE			)			)
							__magic_name__      :       List[str]       						= self.calc_generative_metrics(__SCREAMING_SNAKE_CASE      ,       __SCREAMING_SNAKE_CASE			)
							__magic_name__      :       Any       						= 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       __magic_name__    (    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__			)							->				Optional[Any]:
							return self._generative_step(__SCREAMING_SNAKE_CASE			)
	def       __magic_name__    (    self      ,       lowerCAmelCase__			)							->				Optional[int]:
							return self.validation_epoch_end(__SCREAMING_SNAKE_CASE      ,       prefix="""test"""			)
	def       __magic_name__    (    self      ,       lowerCAmelCase__			)							->				List[str]:
							__magic_name__      :       Union[str, Any]       						= self.n_obs[type_path]
							__magic_name__      :       Dict       						= self.target_lens[type_path]
							__magic_name__      :       Tuple       						= 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       __magic_name__    (    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__ = False			)							->				str:
							__magic_name__      :       Union[str, Any]       						= self.get_dataset(__SCREAMING_SNAKE_CASE			)

							if self.hparams.sortish_sampler and type_path != "test" and type_path != "val":
													__magic_name__      :       List[str]       						= 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":
													__magic_name__      :       str       						= 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       __magic_name__    (    self			)							->				Union[str, Any]:
							__magic_name__      :       Dict       						= self.get_dataloader("""train"""      ,       batch_size=self.hparams.train_batch_size      ,       shuffle=__SCREAMING_SNAKE_CASE			)
							return dataloader
	def       __magic_name__    (    self			)							->				List[Any]:
							return self.get_dataloader("""val"""      ,       batch_size=self.hparams.eval_batch_size			)
	def       __magic_name__    (    self			)							->				int:
							return self.get_dataloader("""test"""      ,       batch_size=self.hparams.eval_batch_size			)







	@staticmethod
	def       __magic_name__    (    lowerCAmelCase__      ,       lowerCAmelCase__			)							->				int:
							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=10_24      ,       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=1_42      ,       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_42      ,       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_00      ,       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 	snake_case__ (							lowerCAmelCase_				):
	lowercase__						:					int											=   """translation"""
	lowercase__						:					Optional[Any]											=   ["""loss"""]
	lowercase__						:					Optional[Any]											=   ["""bleu"""]
	lowercase__						:					Dict											=   """bleu"""
	def __init__(    self      ,       lowerCAmelCase__      ,       **lowerCAmelCase__			)							->				List[Any]:
							super().__init__(__SCREAMING_SNAKE_CASE      ,       **__SCREAMING_SNAKE_CASE			)
							__magic_name__      :       str       						= hparams.src_lang
							__magic_name__      :       Tuple       						= hparams.tgt_lang







	def       __magic_name__    (    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__			)							->				int:
							return calculate_bleu(__SCREAMING_SNAKE_CASE      ,       __SCREAMING_SNAKE_CASE			)



def    UpperCamelCase						(	_A,   _A=None			):






						"""simple docstring"""







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

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

												__magic_name__      :       Union[str, Any]       						= os.environ.get("""WANDB_PROJECT""",   _A			)
												__magic_name__      :       Any       						= WandbLogger(name=model.output_dir.name,   project=_A			)

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

												__magic_name__      :       Union[str, Any]       						= WandbLogger(name=model.output_dir.name,   project=f'hf_{dataset}'			)

						if args.early_stopping_patience >= 0:
												__magic_name__      :       Dict       						= get_early_stopping_callback(model.val_metric,   args.early_stopping_patience			)
						else:
												__magic_name__      :       Union[str, Any]       						= False

						__magic_name__      :       Optional[Any]       						= args.val_metric == """loss"""
						__magic_name__      :       Any       						= generic_train(
						    _A,   _A,   logging_callback=SeqaSeqLoggingCallback(),   checkpoint_callback=get_checkpoint_callback(
						        args.output_dir,   model.val_metric,   args.save_top_k,   _A			),   early_stopping_callback=_A,   logger=_A,   )
						pickle_save(model.hparams,   model.output_dir / """hparams.pkl"""			)
						if not args.do_predict:
												return model

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

			__magic_name__:					Tuple							=     parser.parse_args()

			main(args)








__magic_name__:					List[Any]							=     "\n# Transformers 설치 방법\n! pip install transformers datasets\n# 마지막 릴리스 대신 소스에서 설치하려면, 위 명령을 주석으로 바꾸고 아래 명령을 해제하세요.\n# ! pip install git+https://github.com/huggingface/transformers.git\n"

__magic_name__:					Dict							=     [{"type": "code", "content": INSTALL_CONTENT}]
__magic_name__:					Optional[Any]							=     {
    "{processor_class}": "FakeProcessorClass",
    "{model_class}": "FakeModelClass",
    "{object_class}": "FakeObjectClass",
}
