




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


a__		     =     logging.getLogger(__name__)


class    SCREAMING_SNAKE_CASE_				(       _UpperCamelCase   ):



	"""simple docstring"""


	__magic_name__   :       str      	=		'summarization'
	__magic_name__   :       int      	=		['loss']
	__magic_name__   :       List[str]      	=		ROUGE_KEYS
	__magic_name__   :       str      	=		'rouge2'






	def __init__(					self		:						Any					,     lowerCAmelCase		:						Optional[Any]					,     **lowerCAmelCase		:						List[str]	)	->       Tuple:







						"""simple docstring"""







						if hparams.sortish_sampler and hparams.gpus > 1:
											__UpperCamelCase					:	Optional[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	)
						__UpperCamelCase					:	Optional[Any]             =      Path(self.output_dir	) / """metrics.json"""
						__UpperCamelCase					:	Any             =      Path(self.output_dir	) / """hparams.pkl"""
						pickle_save(self.hparams					,     self.hparams_save_path	)
						__UpperCamelCase					:	str             =      0
						__UpperCamelCase					:	Any             =      defaultdict(lowerCAmelCase	)
						__UpperCamelCase					:	Dict             =      self.config.model_type
						__UpperCamelCase					:	Dict             =      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					:	Any             =      {
						    """train""": self.hparams.n_train,
						    """val""": self.hparams.n_val,
						    """test""": self.hparams.n_test,
						}
						__UpperCamelCase					:	List[Any]             =      {k: v if v >= 0 else None for k, v in n_observations_per_split.items()}

						__UpperCamelCase					:	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()	)

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






	def 					lowerCamelCase__     (					self		:						Any					,     lowerCAmelCase		:						Dict[str, torch.Tensor]	)	->       Dict[str, List[str]]:







						"""simple docstring"""







						__UpperCamelCase					:	Tuple             =      {
						    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					:	List[str]             =      True
						return readable_batch






	def 					lowerCamelCase__     (					self		:						int					,     lowerCAmelCase		:						Dict					,     **lowerCAmelCase		:						int	)	->       Optional[int]:







						"""simple docstring"""







						return self.model(lowerCAmelCase					,     **lowerCAmelCase	)






	def 					lowerCamelCase__     (					self		:						Union[str, Any]					,     lowerCAmelCase		:						List[int]	)	->       Union[str, Any]:







						"""simple docstring"""







						__UpperCamelCase					:	str             =      self.tokenizer.batch_decode(
						    lowerCAmelCase					,     skip_special_tokens=lowerCAmelCase					,     clean_up_tokenization_spaces=lowerCAmelCase	)
						return lmap(str.strip					,     lowerCAmelCase	)






	def 					lowerCamelCase__     (					self		:						Any					,     lowerCAmelCase		:						dict	)	->       Tuple:







						"""simple docstring"""







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

						__UpperCamelCase					:	List[Any]             =      self(lowerCAmelCase					,     attention_mask=lowerCAmelCase					,     decoder_input_ids=lowerCAmelCase					,     use_cache=lowerCAmelCase	)
						__UpperCamelCase					:	Tuple             =      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					:	List[Any]             =      ce_loss_fct(lm_logits.view(-1					,     lm_logits.shape[-1]	)					,     tgt_ids.view(-1	)	)
						else:
											__UpperCamelCase					:	str             =      nn.functional.log_softmax(lowerCAmelCase					,     dim=-1	)
											__UpperCamelCase      ,			__UpperCamelCase					:	str             =      label_smoothed_nll_loss(
											    lowerCAmelCase					,     lowerCAmelCase					,     self.hparams.label_smoothing					,     ignore_index=lowerCAmelCase	)
						return (loss,)






	@property
	def 					lowerCamelCase__     (					self		:						Optional[Any]	)	->       int:







						"""simple docstring"""







						return self.tokenizer.pad_token_id






	def 					lowerCamelCase__     (					self		:						str					,     lowerCAmelCase		:						Optional[int]					,     lowerCAmelCase		:						Optional[int]	)	->       Dict:







						"""simple docstring"""







						__UpperCamelCase					:	str             =      self._step(lowerCAmelCase	)

						__UpperCamelCase					:	Any             =      dict(zip(self.loss_names					,     lowerCAmelCase	)	)
						# tokens per batch
						__UpperCamelCase					:	Optional[Any]             =      batch["""input_ids"""].ne(self.pad	).sum() + batch["""labels"""].ne(self.pad	).sum()
						__UpperCamelCase					:	Optional[Any]             =      batch["""input_ids"""].shape[0]
						__UpperCamelCase					:	int             =      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 					lowerCamelCase__     (					self		:						List[Any]					,     lowerCAmelCase		:						int					,     lowerCAmelCase		:						str	)	->       Dict:







						"""simple docstring"""







						return self._generative_step(lowerCAmelCase	)






	def 					lowerCamelCase__     (					self		:						int					,     lowerCAmelCase		:						Optional[Any]					,     lowerCAmelCase		:						int="val"	)	->       Dict:







						"""simple docstring"""







						self.step_count += 1
						__UpperCamelCase					:	List[Any]             =      {k: torch.stack([x[k] for x in outputs]	).mean() for k in self.loss_names}
						__UpperCamelCase					:	str             =      losses["""loss"""]
						__UpperCamelCase					:	Dict             =      {
						    k: np.array([x[k] for x in outputs]	).mean() for k in self.metric_names + ["""gen_time""", """gen_len"""]
						}
						__UpperCamelCase					:	int             =      (
						    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					:	List[str]             =      {F'''{prefix}_avg_{k}''': x for k, x in losses.items()}
						__UpperCamelCase					:	Any             =      self.step_count
						self.metrics[prefix].append(lowerCAmelCase	)  # callback writes this to self.metrics_save_path
						__UpperCamelCase					:	int             =      flatten_list([x["""preds"""] for x in outputs]	)
						return {
						    "log": all_metrics,
						    "preds": preds,
						    F'''{prefix}_loss''': loss,
						    F'''{prefix}_{self.val_metric}''': metric_tensor,
						}






	def 					lowerCamelCase__     (					self		:						str					,     lowerCAmelCase		:						Optional[int]					,     lowerCAmelCase		:						Optional[int]	)	->       Dict:







						"""simple docstring"""







						return calculate_rouge(lowerCAmelCase					,     lowerCAmelCase	)






	def 					lowerCamelCase__     (					self		:						Dict					,     lowerCAmelCase		:						dict	)	->       dict:







						"""simple docstring"""







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

						# parser.add_argument('--eval_max_gen_length', type=int, default=None, help='never generate more than n tokens')
						__UpperCamelCase					:	List[str]             =      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					:	Any             =      self._step(lowerCAmelCase	)
						__UpperCamelCase					:	str             =      dict(zip(self.loss_names					,     lowerCAmelCase	)	)
						__UpperCamelCase					:	Dict             =      self.calc_generative_metrics(lowerCAmelCase					,     lowerCAmelCase	)
						__UpperCamelCase					:	List[str]             =      np.mean(lmap(lowerCAmelCase					,     lowerCAmelCase	)	)
						base_metrics.update(gen_time=lowerCAmelCase					,     gen_len=lowerCAmelCase					,     preds=lowerCAmelCase					,     target=lowerCAmelCase					,     **lowerCAmelCase	)
						return base_metrics






	def 					lowerCamelCase__     (					self		:						Union[str, Any]					,     lowerCAmelCase		:						Optional[int]					,     lowerCAmelCase		:						List[str]	)	->       Optional[int]:







						"""simple docstring"""







						return self._generative_step(lowerCAmelCase	)






	def 					lowerCamelCase__     (					self		:						Dict					,     lowerCAmelCase		:						Tuple	)	->       Any:







						"""simple docstring"""







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






	def 					lowerCamelCase__     (					self		:						Any					,     lowerCAmelCase		:						List[Any]	)	->       SeqaSeqDataset:







						"""simple docstring"""







						__UpperCamelCase					:	Tuple             =      self.n_obs[type_path]
						__UpperCamelCase					:	Optional[int]             =      self.target_lens[type_path]
						__UpperCamelCase					:	Optional[int]             =      self.dataset_class(
						    self.tokenizer					,     type_path=lowerCAmelCase					,     n_obs=lowerCAmelCase					,     max_target_length=lowerCAmelCase					,     **self.dataset_kwargs					,     )
						return dataset






	def 					lowerCamelCase__     (					self		:						Dict					,     lowerCAmelCase		:						str					,     lowerCAmelCase		:						int					,     lowerCAmelCase		:						bool = False	)	->       DataLoader:







						"""simple docstring"""







						__UpperCamelCase					:	Dict             =      self.get_dataset(lowerCAmelCase	)

						if self.hparams.sortish_sampler and type_path != "test" and type_path != "val":
											__UpperCamelCase					:	Tuple             =      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					:	Union[str, 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 					lowerCamelCase__     (					self		:						Any	)	->       DataLoader:







						"""simple docstring"""







						__UpperCamelCase					:	int             =      self.get_dataloader("""train"""					,     batch_size=self.hparams.train_batch_size					,     shuffle=lowerCAmelCase	)
						return dataloader






	def 					lowerCamelCase__     (					self		:						Union[str, Any]	)	->       DataLoader:







						"""simple docstring"""







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






	def 					lowerCamelCase__     (					self		:						Dict	)	->       DataLoader:







						"""simple docstring"""







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






	@staticmethod
	def 					lowerCamelCase__     (					lowerCAmelCase		:						Optional[Any]					,     lowerCAmelCase		:						Tuple	)	->       List[Any]:







						"""simple docstring"""







						BaseTransformer.add_model_specific_args(lowerCAmelCase					,     lowerCAmelCase	)
						add_generic_args(lowerCAmelCase					,     lowerCAmelCase	)
						parser.add_argument(
						    """--max_source_length"""					,     default=1024					,     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=142					,     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=142					,     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=500					,     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    SCREAMING_SNAKE_CASE_				(       _UpperCamelCase   ):



	"""simple docstring"""


	__magic_name__   :       Any      	=		'translation'
	__magic_name__   :       Optional[int]      	=		['loss']
	__magic_name__   :       int      	=		['bleu']
	__magic_name__   :       str      	=		'bleu'






	def __init__(					self		:						Dict					,     lowerCAmelCase		:						int					,     **lowerCAmelCase		:						List[Any]	)	->       Optional[Any]:







						"""simple docstring"""







						super().__init__(lowerCAmelCase					,     **lowerCAmelCase	)
						__UpperCamelCase					:	List[str]             =      hparams.src_lang
						__UpperCamelCase					:	Optional[int]             =      hparams.tgt_lang






	def 					lowerCamelCase__     (					self		:						str					,     lowerCAmelCase		:						Tuple					,     lowerCAmelCase		:						Optional[Any]	)	->       dict:







						"""simple docstring"""







						return calculate_bleu(lowerCAmelCase					,     lowerCAmelCase	)






def 				A__ (snake_case   :		Optional[Any]		,	snake_case   :		List[Any]=None				)     ->						SummarizationModule:
					Path(args.output_dir				).mkdir(exist_ok=snake_case				)
					check_output_dir(snake_case		,	expected_items=3				)

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

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

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

										__UpperCamelCase					:	List[Any]             =      WandbLogger(name=model.output_dir.name		,	project=F'''hf_{dataset}'''				)

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

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

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

	a__		     =     parser.parse_args()

	main(args)












import argparse
import os

import torch

from transformers.utils import WEIGHTS_NAME


a__		     =     ['''small''', '''medium''', '''large''']

a__		     =     '''lm_head.decoder.weight'''
a__		     =     '''lm_head.weight'''





def 				A__ (snake_case   :		str		,	snake_case   :		str				)     ->						Union[str, Any]:
					__UpperCamelCase					:	Union[str, Any]             =      torch.load(snake_case				)
					__UpperCamelCase					:	int             =      d.pop(snake_case				)
					os.makedirs(snake_case		,	exist_ok=snake_case				)
					torch.save(snake_case		,	os.path.join(snake_case		,	snake_case				)				)


if __name__ == "__main__":
	a__		     =     argparse.ArgumentParser()
	parser.add_argument('''--dialogpt_path''', default='''.''', type=str)
	a__		     =     parser.parse_args()
	for MODEL in DIALOGPT_MODELS:
		a__		     =     os.path.join(args.dialogpt_path, f"{MODEL}_ft.pkl")
		a__		     =     f"./DialoGPT-{MODEL}"
		convert_dialogpt_checkpoint(
		    checkpoint_path,
		    pytorch_dump_folder_path,
		)






