




import argparse
import glob
import logging
import os
import sys
import time
from collections import defaultdict
from pathlib import Path
from typing import Dict, List, Tuple

import numpy as np
import pytorch_lightning as pl
import torch
from callbacks import SeqaSeqLoggingCallback, get_checkpoint_callback, get_early_stopping_callback
from torch import nn
from torch.utils.data import DataLoader

from transformers import MBartTokenizer, TaForConditionalGeneration
from transformers.models.bart.modeling_bart import shift_tokens_right
from utils import (
    ROUGE_KEYS,
    LegacySeqaSeqDataset,
    SeqaSeqDataset,
    assert_all_frozen,
    calculate_bleu,
    calculate_rouge,
    check_output_dir,
    flatten_list,
    freeze_embeds,
    freeze_params,
    get_git_info,
    label_smoothed_nll_loss,
    lmap,
    pickle_save,
    save_git_info,
    save_json,
    use_task_specific_params,
)


# need the parent dir module
sys.path.insert(2, str(Path(__file__).resolve().parents[1]))
from lightning_base import BaseTransformer, add_generic_args, generic_train  # noqa


__SCREAMING_SNAKE_CASE 						=logging.getLogger(__name__)






class  __magic_name__						(  __UpperCAmelCase):
				'''simple docstring'''







				SCREAMING_SNAKE_CASE__					:							Any	      =					"summarization"
				SCREAMING_SNAKE_CASE__					:							List[str]	      =					["loss"]
				SCREAMING_SNAKE_CASE__					:							List[str]	      =					ROUGE_KEYS
				SCREAMING_SNAKE_CASE__					:							int	      =					"rouge2"


				def __init__(							self:   List[str]		,					_lowerCamelCase:   Any		,					**_lowerCamelCase:   List[str]		):
											if hparams.sortish_sampler and hparams.gpus > 1:
																		SCREAMING_SNAKE_CASE_   	=    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_   	=    Path(self.output_dir		) / '''metrics.json'''
											SCREAMING_SNAKE_CASE_   	=    Path(self.output_dir		) / '''hparams.pkl'''
											pickle_save(self.hparams		,					self.hparams_save_path		)
											SCREAMING_SNAKE_CASE_   	=    0
											SCREAMING_SNAKE_CASE_   	=    defaultdict(_lowerCamelCase		)
											SCREAMING_SNAKE_CASE_   	=    self.config.model_type
											SCREAMING_SNAKE_CASE_   	=    self.config.tgt_vocab_size if self.model_type == '''fsmt''' else self.config.vocab_size

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

											SCREAMING_SNAKE_CASE_   	=    {
											    '''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_   	=    get_git_info()['''repo_sha''']
											SCREAMING_SNAKE_CASE_   	=    hparams.num_workers
											SCREAMING_SNAKE_CASE_   	=    None  # default to config
											if self.model.config.decoder_start_token_id is None and isinstance(self.tokenizer		,					_lowerCamelCase		):
																		SCREAMING_SNAKE_CASE_   	=    self.tokenizer.lang_code_to_id[hparams.tgt_lang]
																		SCREAMING_SNAKE_CASE_   	=    self.decoder_start_token_id
											SCREAMING_SNAKE_CASE_   	=    (
											    SeqaSeqDataset if hasattr(self.tokenizer		,					'''prepare_seq2seq_batch'''		) else LegacySeqaSeqDataset
											)
											SCREAMING_SNAKE_CASE_   	=    False
											SCREAMING_SNAKE_CASE_   	=    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_   	=    self.hparams.eval_max_gen_length
											else:
																		SCREAMING_SNAKE_CASE_   	=    self.model.config.max_length
											SCREAMING_SNAKE_CASE_   	=    self.default_val_metric if self.hparams.val_metric is None else self.hparams.val_metric


				def     _A							(							self:   Optional[Any]		,					_lowerCamelCase:   Dict[str, torch.Tensor]		):
											SCREAMING_SNAKE_CASE_   	=    {
											    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_   	=    True
											return readable_batch


				def     _A							(							self:   Union[str, Any]		,					_lowerCamelCase:   Dict		,					**_lowerCamelCase:   List[str]		):
											return self.model(_lowerCamelCase		,					**_lowerCamelCase		)


				def     _A							(							self:   Optional[Any]		,					_lowerCamelCase:   List[int]		):
											SCREAMING_SNAKE_CASE_   	=    self.tokenizer.batch_decode(
											    _lowerCamelCase		,					skip_special_tokens=_lowerCamelCase		,					clean_up_tokenization_spaces=_lowerCamelCase		)
											return lmap(str.strip		,					_lowerCamelCase		)


				def     _A							(							self:   Any		,					_lowerCamelCase:   dict		):
											SCREAMING_SNAKE_CASE_   	=    self.tokenizer.pad_token_id
											SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_   	=    batch['''input_ids'''], batch['''attention_mask''']
											SCREAMING_SNAKE_CASE_   	=    batch['''labels''']
											if isinstance(self.model		,					_lowerCamelCase		):
																		SCREAMING_SNAKE_CASE_   	=    self.model._shift_right(_lowerCamelCase		)
											else:
																		SCREAMING_SNAKE_CASE_   	=    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_   	=    decoder_input_ids
																		self.save_readable_batch(_lowerCamelCase		)

											SCREAMING_SNAKE_CASE_   	=    self(_lowerCamelCase		,					attention_mask=_lowerCamelCase		,					decoder_input_ids=_lowerCamelCase		,					use_cache=_lowerCamelCase		)
											SCREAMING_SNAKE_CASE_   	=    outputs['''logits''']
											if self.hparams.label_smoothing == 0:
																		# Same behavior as modeling_bart.py, besides ignoring pad_token_id
																		SCREAMING_SNAKE_CASE_   	=    nn.CrossEntropyLoss(ignore_index=_lowerCamelCase		)

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


				@property
				def     _A							(							self:   Tuple		):
											return self.tokenizer.pad_token_id


				def     _A							(							self:   Tuple		,					_lowerCamelCase:   int		,					_lowerCamelCase:   Optional[Any]		):
											SCREAMING_SNAKE_CASE_   	=    self._step(_lowerCamelCase		)

											SCREAMING_SNAKE_CASE_   	=    dict(zip(self.loss_names		,					_lowerCamelCase		)		)
											# tokens per batch
											SCREAMING_SNAKE_CASE_   	=    batch['''input_ids'''].ne(self.pad		).sum() + batch['''labels'''].ne(self.pad		).sum()
											SCREAMING_SNAKE_CASE_   	=    batch['''input_ids'''].shape[0]
											SCREAMING_SNAKE_CASE_   	=    batch['''input_ids'''].eq(self.pad		).sum()
											SCREAMING_SNAKE_CASE_   	=    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:   List[Any]		,					_lowerCamelCase:   Optional[int]		,					_lowerCamelCase:   Optional[int]		):
											return self._generative_step(_lowerCamelCase		)


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


				def     _A							(							self:   int		,					_lowerCamelCase:   dict		):
											SCREAMING_SNAKE_CASE_   	=    time.time()

											# parser.add_argument('--eval_max_gen_length', type=int, default=None, help='never generate more than n tokens')
											SCREAMING_SNAKE_CASE_   	=    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_   	=    (time.time() - ta) / batch['''input_ids'''].shape[0]
											SCREAMING_SNAKE_CASE_   	=    self.ids_to_clean_text(_lowerCamelCase		)
											SCREAMING_SNAKE_CASE_   	=    self.ids_to_clean_text(batch['''labels''']		)
											SCREAMING_SNAKE_CASE_   	=    self._step(_lowerCamelCase		)
											SCREAMING_SNAKE_CASE_   	=    dict(zip(self.loss_names		,					_lowerCamelCase		)		)
											SCREAMING_SNAKE_CASE_   	=    self.calc_generative_metrics(_lowerCamelCase		,					_lowerCamelCase		)
											SCREAMING_SNAKE_CASE_   	=    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:   List[str]		,					_lowerCamelCase:   Union[str, Any]		,					_lowerCamelCase:   Tuple		):
											return self._generative_step(_lowerCamelCase		)


				def     _A							(							self:   Tuple		,					_lowerCamelCase:   Optional[Any]		):
											return self.validation_epoch_end(_lowerCamelCase		,					prefix='''test'''		)


				def     _A							(							self:   Union[str, Any]		,					_lowerCamelCase:   Optional[Any]		):
											SCREAMING_SNAKE_CASE_   	=    self.n_obs[type_path]
											SCREAMING_SNAKE_CASE_   	=    self.target_lens[type_path]
											SCREAMING_SNAKE_CASE_   	=    self.dataset_class(
											    self.tokenizer		,					type_path=_lowerCamelCase		,					n_obs=_lowerCamelCase		,					max_target_length=_lowerCamelCase		,					**self.dataset_kwargs		,					)
											return dataset


				def     _A							(							self:   Optional[int]		,					_lowerCamelCase:   str		,					_lowerCamelCase:   int		,					_lowerCamelCase:   bool = False		):
											SCREAMING_SNAKE_CASE_   	=    self.get_dataset(_lowerCamelCase		)

											if self.hparams.sortish_sampler and type_path != "test" and type_path != "val":
																		SCREAMING_SNAKE_CASE_   	=    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_   	=    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:   str		):
											SCREAMING_SNAKE_CASE_   	=    self.get_dataloader('''train'''		,					batch_size=self.hparams.train_batch_size		,					shuffle=_lowerCamelCase		)
											return dataloader


				def     _A							(							self:   Dict		):
											return self.get_dataloader('''val'''		,					batch_size=self.hparams.eval_batch_size		)


				def     _A							(							self:   Optional[Any]		):
											return self.get_dataloader('''test'''		,					batch_size=self.hparams.eval_batch_size		)





				@staticmethod
				def     _A							(							_lowerCamelCase:   Dict		,					_lowerCamelCase:   Union[str, Any]		):
											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  __magic_name__						(  __UpperCAmelCase):
				'''simple docstring'''







				SCREAMING_SNAKE_CASE__					:							Dict	      =					"translation"
				SCREAMING_SNAKE_CASE__					:							Optional[Any]	      =					["loss"]
				SCREAMING_SNAKE_CASE__					:							Optional[int]	      =					["bleu"]
				SCREAMING_SNAKE_CASE__					:							int	      =					"bleu"


				def __init__(							self:   Any		,					_lowerCamelCase:   Optional[int]		,					**_lowerCamelCase:   Union[str, Any]		):
											super().__init__(_lowerCamelCase		,					**_lowerCamelCase		)
											SCREAMING_SNAKE_CASE_   	=    hparams.src_lang
											SCREAMING_SNAKE_CASE_   	=    hparams.tgt_lang





				def     _A							(							self:   Dict		,					_lowerCamelCase:   str		,					_lowerCamelCase:   Union[str, Any]		):
											return calculate_bleu(_lowerCamelCase		,					_lowerCamelCase		)






def 			a   (_lowerCAmelCase  ,    _lowerCAmelCase=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(_lowerCAmelCase     )
														else:
																					SCREAMING_SNAKE_CASE_   	=    TranslationModule(_lowerCAmelCase     )
							SCREAMING_SNAKE_CASE_   	=    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_   	=    True  # don't pollute wandb logs unnecessarily
							elif args.logger_name == "wandb":
														from pytorch_lightning.loggers import WandbLogger

														SCREAMING_SNAKE_CASE_   	=    os.environ.get('''WANDB_PROJECT'''  ,    _lowerCAmelCase     )
														SCREAMING_SNAKE_CASE_   	=    WandbLogger(name=model.output_dir.name  ,    project=_lowerCAmelCase     )

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

														SCREAMING_SNAKE_CASE_   	=    WandbLogger(name=model.output_dir.name  ,    project=F"hf_{dataset}"     )

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

							SCREAMING_SNAKE_CASE_   	=    args.val_metric == '''loss'''
							SCREAMING_SNAKE_CASE_   	=    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_   	=    ''''''
							SCREAMING_SNAKE_CASE_   	=    sorted(glob.glob(os.path.join(args.output_dir  ,    '''*.ckpt'''     )  ,    recursive=_lowerCAmelCase     )     )
							if checkpoints:
														SCREAMING_SNAKE_CASE_   	=    checkpoints[-1]
														SCREAMING_SNAKE_CASE_   	=    checkpoints[-1]
							trainer.logger.log_hyperparams(model.hparams     )

							# test() without a model tests using the best checkpoint automatically
							trainer.test()
							return model


if __name__ == "__main__":
			__SCREAMING_SNAKE_CASE 						=argparse.ArgumentParser()
			__SCREAMING_SNAKE_CASE 						=pl.Trainer.add_argparse_args(parser)
			__SCREAMING_SNAKE_CASE 						=SummarizationModule.add_model_specific_args(parser, os.getcwd())

			__SCREAMING_SNAKE_CASE 						=parser.parse_args()

			main(args)








import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
			import torch

			from transformers.generation import DisjunctiveConstraint






@require_torch
class  __magic_name__						(  unittest.TestCase):
				'''simple docstring'''







				def     _A							(							self:   str		):
											# For consistency across different places the DisjunctiveConstraint is called,
											# dc.token_ids is a list of integers. It is also initialized only by integers.

											SCREAMING_SNAKE_CASE_   	=    [[1, 2, 4], [1, 2, 3, 4]]
											SCREAMING_SNAKE_CASE_   	=    DisjunctiveConstraint(_lowerCamelCase		)
											self.assertTrue(isinstance(dc.token_ids		,					_lowerCamelCase		)		)

											with self.assertRaises(_lowerCamelCase		):
																		DisjunctiveConstraint(torch.LongTensor([[1, 2, 4], [1, 2, 3]]		)		)

											with self.assertRaises(_lowerCamelCase		):
																		DisjunctiveConstraint([torch.LongTensor([1, 2, 4]		), torch.LongTensor([1, 2, 3, 4, 5]		)]		)


				def     _A							(							self:   Union[str, Any]		):
											# We can't have constraints that are complete subsets of another. This leads to a preverse
											# interpretation of "constraint fulfillment": does generating [1,2,3] fulfill the constraint?
											# It would mean that it generated [1,2] which fulfills it, but it's in the middle of potentially
											# fulfilling [1,2,3,4]. If we believe that [1,2,3] does fulfill the constraint, then the algorithm
											# will necessarily never reach [1,2,3,4], giving users a false sense of control (better to just not allow it).
											SCREAMING_SNAKE_CASE_   	=    [[1, 2], [1, 2, 3, 4]]

											with self.assertRaises(_lowerCamelCase		):
																		DisjunctiveConstraint(_lowerCamelCase		)  # fails here


				def     _A							(							self:   Dict		):
											SCREAMING_SNAKE_CASE_   	=    [[1, 2, 3], [1, 2, 4]]

											SCREAMING_SNAKE_CASE_   	=    DisjunctiveConstraint(_lowerCamelCase		)

											SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_   	=    dc.update(1		)
											SCREAMING_SNAKE_CASE_   	=    stepped is True and completed is False and reset is False
											self.assertTrue(_lowerCamelCase		)
											self.assertTrue(not dc.completed		)
											self.assertTrue(dc.current_seq == [1]		)

											SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_   	=    dc.update(2		)
											SCREAMING_SNAKE_CASE_   	=    stepped is True and completed is False and reset is False
											self.assertTrue(_lowerCamelCase		)
											self.assertTrue(not dc.completed		)
											self.assertTrue(dc.current_seq == [1, 2]		)

											SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_   	=    dc.update(3		)
											SCREAMING_SNAKE_CASE_   	=    stepped is True and completed is True and reset is False
											self.assertTrue(_lowerCamelCase		)
											self.assertTrue(dc.completed		)  # Completed!
											self.assertTrue(dc.current_seq == [1, 2, 3]		)





				def     _A							(							self:   Optional[int]		):
											SCREAMING_SNAKE_CASE_   	=    [[1, 2, 3], [1, 2, 4, 5], [1, 2, 5]]

											SCREAMING_SNAKE_CASE_   	=    DisjunctiveConstraint(_lowerCamelCase		)

											SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_   	=    dc.update(1		)
											self.assertTrue(not dc.completed		)
											self.assertTrue(dc.current_seq == [1]		)

											SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_   	=    dc.update(2		)
											self.assertTrue(not dc.completed		)
											self.assertTrue(dc.current_seq == [1, 2]		)

											SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_   	=    dc.update(4		)
											self.assertTrue(not dc.completed		)
											self.assertTrue(dc.current_seq == [1, 2, 4]		)

											SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_   	=    dc.update(5		)
											self.assertTrue(dc.completed		)  # Completed!
											self.assertTrue(dc.current_seq == [1, 2, 4, 5]		)

											dc.reset()

											SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_   	=    dc.update(1		)
											self.assertTrue(not dc.completed		)
											self.assertTrue(dc.remaining() == 3		)
											self.assertTrue(dc.current_seq == [1]		)

											SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_   	=    dc.update(2		)
											self.assertTrue(not dc.completed		)
											self.assertTrue(dc.remaining() == 2		)
											self.assertTrue(dc.current_seq == [1, 2]		)

											SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_   	=    dc.update(5		)
											self.assertTrue(dc.completed		)  # Completed!
											self.assertTrue(dc.remaining() == 0		)
											self.assertTrue(dc.current_seq == [1, 2, 5]		)


