




"""simple docstring"""






def      _snake_case			(				lowercase__		):

				def merge(lowercase__							,    lowercase__		) -> list:

								def _merge():
												while left and right:
																yield (left if left[0] <= right[0] else right).pop(0		)
												yield from left
												yield from right

								return list(_merge()		)

				if len(lowercase__		) <= 1:
								return collection
				_lowerCamelCase     :			Dict    =    len(lowercase__		) // 2
				return merge(merge_sort(collection[:mid]		)							,    merge_sort(collection[mid:]		)		)


if __name__ == "__main__":
			import doctest

			doctest.testmod()
			lowercase__     						=						input("""Enter numbers separated by a comma:\n""").strip()
			lowercase__     						=						[int(item) for item in user_input.split(""",""")]
			print(*merge_sort(unsorted), sep=""",""")




'''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


UpperCamelCase__       :	str    = logging.getLogger(__name__)






class 						_UpperCamelCase  (							lowerCamelCase__    ):


			'''simple docstring'''

			_A      :		Union[str, Any]			    =		'''summarization'''
			_A      :		Optional[Any]			    =		['''loss''']
			_A      :		Tuple			    =		ROUGE_KEYS
			_A      :		int			    =		'''rouge2'''

			def __init__(							self     : int							,     lowerCAmelCase__     : Union[str, Any]							,     **lowerCAmelCase__     : List[str]						):





					"""simple docstring"""






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

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

					__SCREAMING_SNAKE_CASE       :		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()						)

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

			def UpperCamelCase__					(							self     : str							,     lowerCAmelCase__     : Dict[str, torch.Tensor]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		Union[str, 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"""						)

					__SCREAMING_SNAKE_CASE       :		Optional[int]           =		True
					return readable_batch

			def UpperCamelCase__					(							self     : List[str]							,     lowerCAmelCase__     : Any							,     **lowerCAmelCase__     : Optional[Any]						):





					"""simple docstring"""






					return self.model(lowerCAmelCase__							,     **lowerCAmelCase__						)

			def UpperCamelCase__					(							self     : Optional[int]							,     lowerCAmelCase__     : List[int]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		Tuple           =		self.tokenizer.batch_decode(
					    lowerCAmelCase__							,     skip_special_tokens=lowerCAmelCase__							,     clean_up_tokenization_spaces=lowerCAmelCase__						)
					return lmap(str.strip							,     lowerCAmelCase__						)

			def UpperCamelCase__					(							self     : Any							,     lowerCAmelCase__     : dict						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		Tuple           =		self.tokenizer.pad_token_id
					__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       :		str           =		batch["""input_ids"""], batch["""attention_mask"""]
					__SCREAMING_SNAKE_CASE       :		Tuple           =		batch["""labels"""]
					if isinstance(self.model							,     lowerCAmelCase__						):
							__SCREAMING_SNAKE_CASE       :		str           =		self.model._shift_right(lowerCAmelCase__						)
					else:
							__SCREAMING_SNAKE_CASE       :		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
							__SCREAMING_SNAKE_CASE       :		Tuple           =		decoder_input_ids
							self.save_readable_batch(lowerCAmelCase__						)

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

							assert lm_logits.shape[-1] == self.vocab_size
							__SCREAMING_SNAKE_CASE       :		List[Any]           =		ce_loss_fct(lm_logits.view(-1							,     lm_logits.shape[-1]						)							,     tgt_ids.view(-1						)						)
					else:
							__SCREAMING_SNAKE_CASE       :		List[Any]           =		nn.functional.log_softmax(lowerCAmelCase__							,     dim=-1						)
							__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       :		Tuple           =		label_smoothed_nll_loss(
							    lowerCAmelCase__							,     lowerCAmelCase__							,     self.hparams.label_smoothing							,     ignore_index=lowerCAmelCase__						)
					return (loss,)

			@property
			def UpperCamelCase__					(							self     : List[Any]						):





					"""simple docstring"""






					return self.tokenizer.pad_token_id

			def UpperCamelCase__					(							self     : Dict							,     lowerCAmelCase__     : List[str]							,     lowerCAmelCase__     : Union[str, Any]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		List[Any]           =		self._step(lowerCAmelCase__						)

					__SCREAMING_SNAKE_CASE       :		Tuple           =		dict(zip(self.loss_names							,     lowerCAmelCase__						)						)
					# tokens per batch
					__SCREAMING_SNAKE_CASE       :		Optional[int]           =		batch["""input_ids"""].ne(self.pad						).sum() + batch["""labels"""].ne(self.pad						).sum()
					__SCREAMING_SNAKE_CASE       :		str           =		batch["""input_ids"""].shape[0]
					__SCREAMING_SNAKE_CASE       :		str           =		batch["""input_ids"""].eq(self.pad						).sum()
					__SCREAMING_SNAKE_CASE       :		Optional[int]           =		batch["""input_ids"""].eq(self.pad						).float().mean()
					# TODO(SS): make a wandb summary metric for this
					return {"loss": loss_tensors[0], "log": logs}

			def UpperCamelCase__					(							self     : Dict							,     lowerCAmelCase__     : Optional[Any]							,     lowerCAmelCase__     : str						):





					"""simple docstring"""






					return self._generative_step(lowerCAmelCase__						)

			def UpperCamelCase__					(							self     : Optional[int]							,     lowerCAmelCase__     : List[str]							,     lowerCAmelCase__     : Union[str, Any]="val"						):





					"""simple docstring"""






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





					"""simple docstring"""






					return calculate_rouge(lowerCAmelCase__							,     lowerCAmelCase__						)

			def UpperCamelCase__					(							self     : Tuple							,     lowerCAmelCase__     : dict						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		time.time()

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

			def UpperCamelCase__					(							self     : Union[str, Any]							,     lowerCAmelCase__     : Optional[int]							,     lowerCAmelCase__     : List[Any]						):





					"""simple docstring"""






					return self._generative_step(lowerCAmelCase__						)

			def UpperCamelCase__					(							self     : List[Any]							,     lowerCAmelCase__     : int						):





					"""simple docstring"""






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

			def UpperCamelCase__					(							self     : Dict							,     lowerCAmelCase__     : List[str]						):





					"""simple docstring"""






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

			def UpperCamelCase__					(							self     : str							,     lowerCAmelCase__     : str							,     lowerCAmelCase__     : int							,     lowerCAmelCase__     : bool = False						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		self.get_dataset(lowerCAmelCase__						)

					if self.hparams.sortish_sampler and type_path != "test" and type_path != "val":
							__SCREAMING_SNAKE_CASE       :		Optional[int]           =		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":
							__SCREAMING_SNAKE_CASE       :		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 UpperCamelCase__					(							self     : int						):





					"""simple docstring"""






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

			def UpperCamelCase__					(							self     : Union[str, Any]						):





					"""simple docstring"""






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

			def UpperCamelCase__					(							self     : Tuple						):





					"""simple docstring"""






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



			@staticmethod
			def UpperCamelCase__					(							lowerCAmelCase__     : Optional[int]							,     lowerCAmelCase__     : List[Any]						):





					"""simple docstring"""






					BaseTransformer.add_model_specific_args(lowerCAmelCase__							,     lowerCAmelCase__						)
					add_generic_args(lowerCAmelCase__							,     lowerCAmelCase__						)
					parser.add_argument(
					    """--max_source_length"""							,     default=1_0_2_4							,     type=lowerCAmelCase__							,     help=(
					        """The maximum total input sequence length after tokenization. Sequences longer """
					        """than this will be truncated, sequences shorter will be padded."""
					    )							,     )
					parser.add_argument(
					    """--max_target_length"""							,     default=5_6							,     type=lowerCAmelCase__							,     help=(
					        """The maximum total input sequence length after tokenization. Sequences longer """
					        """than this will be truncated, sequences shorter will be padded."""
					    )							,     )
					parser.add_argument(
					    """--val_max_target_length"""							,     default=1_4_2							,     type=lowerCAmelCase__							,     help=(
					        """The maximum total input sequence length after tokenization. Sequences longer """
					        """than this will be truncated, sequences shorter will be padded."""
					    )							,     )
					parser.add_argument(
					    """--test_max_target_length"""							,     default=1_4_2							,     type=lowerCAmelCase__							,     help=(
					        """The maximum total input sequence length after tokenization. Sequences longer """
					        """than this will be truncated, sequences shorter will be padded."""
					    )							,     )
					parser.add_argument("""--freeze_encoder"""							,     action="""store_true"""						)
					parser.add_argument("""--freeze_embeds"""							,     action="""store_true"""						)
					parser.add_argument("""--sortish_sampler"""							,     action="""store_true"""							,     default=lowerCAmelCase__						)
					parser.add_argument("""--overwrite_output_dir"""							,     action="""store_true"""							,     default=lowerCAmelCase__						)
					parser.add_argument("""--max_tokens_per_batch"""							,     type=lowerCAmelCase__							,     default=lowerCAmelCase__						)
					parser.add_argument("""--logger_name"""							,     type=lowerCAmelCase__							,     choices=["""default""", """wandb""", """wandb_shared"""]							,     default="""default"""						)
					parser.add_argument("""--n_train"""							,     type=lowerCAmelCase__							,     default=-1							,     required=lowerCAmelCase__							,     help="""# examples. -1 means use all."""						)
					parser.add_argument("""--n_val"""							,     type=lowerCAmelCase__							,     default=5_0_0							,     required=lowerCAmelCase__							,     help="""# examples. -1 means use all."""						)
					parser.add_argument("""--n_test"""							,     type=lowerCAmelCase__							,     default=-1							,     required=lowerCAmelCase__							,     help="""# examples. -1 means use all."""						)
					parser.add_argument(
					    """--task"""							,     type=lowerCAmelCase__							,     default="""summarization"""							,     required=lowerCAmelCase__							,     help="""# examples. -1 means use all."""						)
					parser.add_argument("""--label_smoothing"""							,     type=lowerCAmelCase__							,     default=0.0							,     required=lowerCAmelCase__						)
					parser.add_argument("""--src_lang"""							,     type=lowerCAmelCase__							,     default=""""""							,     required=lowerCAmelCase__						)
					parser.add_argument("""--tgt_lang"""							,     type=lowerCAmelCase__							,     default=""""""							,     required=lowerCAmelCase__						)
					parser.add_argument("""--eval_beams"""							,     type=lowerCAmelCase__							,     default=lowerCAmelCase__							,     required=lowerCAmelCase__						)
					parser.add_argument(
					    """--val_metric"""							,     type=lowerCAmelCase__							,     default=lowerCAmelCase__							,     required=lowerCAmelCase__							,     choices=["""bleu""", """rouge2""", """loss""", None]						)
					parser.add_argument("""--eval_max_gen_length"""							,     type=lowerCAmelCase__							,     default=lowerCAmelCase__							,     help="""never generate more than n tokens"""						)
					parser.add_argument("""--save_top_k"""							,     type=lowerCAmelCase__							,     default=1							,     required=lowerCAmelCase__							,     help="""How many checkpoints to save"""						)
					parser.add_argument(
					    """--early_stopping_patience"""							,     type=lowerCAmelCase__							,     default=-1							,     required=lowerCAmelCase__							,     help=(
					        """-1 means never early stop. early_stopping_patience is measured in validation checks, not epochs. So"""
					        """ val_check_interval will effect it."""
					    )							,     )
					return parser






class 						_UpperCamelCase  (							lowerCamelCase__    ):


			'''simple docstring'''

			_A      :		List[Any]			    =		'''translation'''
			_A      :		int			    =		['''loss''']
			_A      :		Union[str, Any]			    =		['''bleu''']
			_A      :		Dict			    =		'''bleu'''

			def __init__(							self     : Any							,     lowerCAmelCase__     : int							,     **lowerCAmelCase__     : Any						):





					"""simple docstring"""






					super().__init__(lowerCAmelCase__							,     **lowerCAmelCase__						)
					__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		hparams.src_lang
					__SCREAMING_SNAKE_CASE       :		Dict           =		hparams.tgt_lang



			def UpperCamelCase__					(							self     : Tuple							,     lowerCAmelCase__     : str							,     lowerCAmelCase__     : List[Any]						):





					"""simple docstring"""






					return calculate_bleu(lowerCAmelCase__							,     lowerCAmelCase__						)


def 				lowerCAmelCase_   (	_lowerCamelCase:   Optional[int]       ,      _lowerCamelCase:   str=None					):
		Path(args.output_dir					).mkdir(exist_ok=_lowerCamelCase					)
		check_output_dir(_lowerCamelCase       ,      expected_items=3					)

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

				__SCREAMING_SNAKE_CASE       :		Any           =		os.environ.get("""WANDB_PROJECT"""       ,      _lowerCamelCase					)
				__SCREAMING_SNAKE_CASE       :		str           =		WandbLogger(name=model.output_dir.name       ,      project=_lowerCamelCase					)

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

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

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

		__SCREAMING_SNAKE_CASE       :		Dict           =		args.val_metric == """loss"""
		__SCREAMING_SNAKE_CASE       :		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

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

						UpperCamelCase__       :	List[str]    = parser.parse_args()

						main(args)