



'''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


snake_case_			=     logging.getLogger(__name__)


class 	a__     (				_lowercase	):
	__magic_name__		:    Any	    =     "summarization"
	__magic_name__		:    int	    =     ["loss"]
	__magic_name__		:    Any	    =     ROUGE_KEYS
	__magic_name__		:    List[Any]	    =     "rouge2"


	def __init__(self : Optional[int],					__UpperCAmelCase : int,					**__UpperCAmelCase : Optional[Any]      )     ->			Tuple:

					"""simple docstring"""

					if hparams.sortish_sampler and hparams.gpus > 1:
									SCREAMING_SNAKE_CASE  :						Tuple      					= 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__(__UpperCAmelCase,					num_labels=__UpperCAmelCase,					mode=self.mode,					**__UpperCAmelCase      )
					use_task_specific_params(self.model,					'''summarization'''      )
					save_git_info(self.hparams.output_dir      )
					SCREAMING_SNAKE_CASE  :						Dict      					= Path(self.output_dir      ) / '''metrics.json'''
					SCREAMING_SNAKE_CASE  :						List[Any]      					= Path(self.output_dir      ) / '''hparams.pkl'''
					pickle_save(self.hparams,					self.hparams_save_path      )
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= 0
					SCREAMING_SNAKE_CASE  :						int      					= defaultdict(__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= 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  :						Optional[Any]      					= {
					    '''train''': self.hparams.n_train,
					    '''val''': self.hparams.n_val,
					    '''test''': self.hparams.n_test,
					}
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= {k: v if v >= 0 else None for k, v in n_observations_per_split.items()}

					SCREAMING_SNAKE_CASE  :						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()      )

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


	def 				lowercase__  (self : Dict,					__UpperCAmelCase : Dict[str, torch.Tensor]      )     ->			Dict[str, List[str]]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Dict      					= {
					    k: self.tokenizer.batch_decode(v.tolist()      ) if '''mask''' not in k else v.shape for k, v in batch.items()
					}
					save_json(__UpperCAmelCase,					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  :						Dict      					= True
					return readable_batch


	def 				lowercase__  (self : Optional[int],					__UpperCAmelCase : List[Any],					**__UpperCAmelCase : Any      )     ->			List[str]:

					"""simple docstring"""

					return self.model(__UpperCAmelCase,					**__UpperCAmelCase      )


	def 				lowercase__  (self : List[Any],					__UpperCAmelCase : List[int]      )     ->			Tuple:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Any      					= self.tokenizer.batch_decode(
					    __UpperCAmelCase,					skip_special_tokens=__UpperCAmelCase,					clean_up_tokenization_spaces=__UpperCAmelCase      )
					return lmap(str.strip,					__UpperCAmelCase      )


	def 				lowercase__  (self : List[Any],					__UpperCAmelCase : dict      )     ->			Tuple:

					"""simple docstring"""

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

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

									assert lm_logits.shape[-1] == self.vocab_size
									SCREAMING_SNAKE_CASE  :						Union[str, 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(__UpperCAmelCase,					dim=-1      )
									SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= label_smoothed_nll_loss(
									    __UpperCAmelCase,					__UpperCAmelCase,					self.hparams.label_smoothing,					ignore_index=__UpperCAmelCase      )
					return (loss,)


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

					"""simple docstring"""

					return self.tokenizer.pad_token_id


	def 				lowercase__  (self : Union[str, Any],					__UpperCAmelCase : Optional[Any],					__UpperCAmelCase : Dict      )     ->			Dict:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						List[Any]      					= self._step(__UpperCAmelCase      )

					SCREAMING_SNAKE_CASE  :						str      					= dict(zip(self.loss_names,					__UpperCAmelCase      )      )
					# tokens per batch
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= 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  :						Optional[Any]      					= batch['''input_ids'''].eq(self.pad      ).sum()
					SCREAMING_SNAKE_CASE  :						Optional[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 				lowercase__  (self : Optional[Any],					__UpperCAmelCase : Tuple,					__UpperCAmelCase : Any      )     ->			Dict:

					"""simple docstring"""

					return self._generative_step(__UpperCAmelCase      )


	def 				lowercase__  (self : Any,					__UpperCAmelCase : str,					__UpperCAmelCase : Union[str, Any]="val"      )     ->			Dict:

					"""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[str]      					= losses['''loss''']
					SCREAMING_SNAKE_CASE  :						Any      					= {
					    k: np.array([x[k] for x in outputs]      ).mean() for k in self.metric_names + ['''gen_time''', '''gen_len''']
					}
					SCREAMING_SNAKE_CASE  :						int      					= (
					    generative_metrics[self.val_metric] if self.val_metric in generative_metrics else losses[self.val_metric]
					)
					SCREAMING_SNAKE_CASE  :						torch.FloatTensor      					= torch.tensor(__UpperCAmelCase      ).type_as(__UpperCAmelCase      )
					generative_metrics.update({k: v.item() for k, v in losses.items()}      )
					losses.update(__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						List[str]      					= {F'''{prefix}_avg_{k}''': x for k, x in losses.items()}
					SCREAMING_SNAKE_CASE  :						List[str]      					= self.step_count
					self.metrics[prefix].append(__UpperCAmelCase      )  # callback writes this to self.metrics_save_path
					SCREAMING_SNAKE_CASE  :						Optional[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 				lowercase__  (self : Union[str, Any],					__UpperCAmelCase : int,					__UpperCAmelCase : int      )     ->			Dict:

					"""simple docstring"""

					return calculate_rouge(__UpperCAmelCase,					__UpperCAmelCase      )


	def 				lowercase__  (self : List[Any],					__UpperCAmelCase : dict      )     ->			dict:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						str      					= time.time()

					# parser.add_argument('--eval_max_gen_length', type=int, default=None, help='never generate more than n tokens')
					SCREAMING_SNAKE_CASE  :						str      					= self.model.generate(
					    batch['''input_ids'''],					attention_mask=batch['''attention_mask'''],					use_cache=__UpperCAmelCase,					decoder_start_token_id=self.decoder_start_token_id,					num_beams=self.eval_beams,					max_length=self.eval_max_length,					)
					SCREAMING_SNAKE_CASE  :						Tuple      					= (time.time() - ta) / batch['''input_ids'''].shape[0]
					SCREAMING_SNAKE_CASE  :						List[str]      					= self.ids_to_clean_text(__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						List[str]      					= self.ids_to_clean_text(batch['''labels''']      )
					SCREAMING_SNAKE_CASE  :						Any      					= self._step(__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						List[Any]      					= dict(zip(self.loss_names,					__UpperCAmelCase      )      )
					SCREAMING_SNAKE_CASE  :						Dict      					= self.calc_generative_metrics(__UpperCAmelCase,					__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= np.mean(lmap(__UpperCAmelCase,					__UpperCAmelCase      )      )
					base_metrics.update(gen_time=__UpperCAmelCase,					gen_len=__UpperCAmelCase,					preds=__UpperCAmelCase,					target=__UpperCAmelCase,					**__UpperCAmelCase      )
					return base_metrics


	def 				lowercase__  (self : int,					__UpperCAmelCase : str,					__UpperCAmelCase : List[Any]      )     ->			List[Any]:

					"""simple docstring"""

					return self._generative_step(__UpperCAmelCase      )


	def 				lowercase__  (self : List[Any],					__UpperCAmelCase : Optional[Any]      )     ->			Any:

					"""simple docstring"""

					return self.validation_epoch_end(__UpperCAmelCase,					prefix='''test'''      )


	def 				lowercase__  (self : Dict,					__UpperCAmelCase : str      )     ->			SeqaSeqDataset:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= self.n_obs[type_path]
					SCREAMING_SNAKE_CASE  :						List[Any]      					= self.target_lens[type_path]
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= self.dataset_class(
					    self.tokenizer,					type_path=__UpperCAmelCase,					n_obs=__UpperCAmelCase,					max_target_length=__UpperCAmelCase,					**self.dataset_kwargs,					)
					return dataset


	def 				lowercase__  (self : List[Any],					__UpperCAmelCase : str,					__UpperCAmelCase : int,					__UpperCAmelCase : bool = False      )     ->			DataLoader:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						List[str]      					= self.get_dataset(__UpperCAmelCase      )

					if self.hparams.sortish_sampler and type_path != "test" and type_path != "val":
									SCREAMING_SNAKE_CASE  :						Any      					= dataset.make_sortish_sampler(__UpperCAmelCase,					distributed=self.hparams.gpus > 1      )
									return DataLoader(
									    __UpperCAmelCase,					batch_size=__UpperCAmelCase,					collate_fn=dataset.collate_fn,					shuffle=__UpperCAmelCase,					num_workers=self.num_workers,					sampler=__UpperCAmelCase,					)

					elif self.hparams.max_tokens_per_batch is not None and type_path != "test" and type_path != "val":
									SCREAMING_SNAKE_CASE  :						Dict      					= dataset.make_dynamic_sampler(
									    self.hparams.max_tokens_per_batch,					distributed=self.hparams.gpus > 1      )
									return DataLoader(
									    __UpperCAmelCase,					batch_sampler=__UpperCAmelCase,					collate_fn=dataset.collate_fn,					num_workers=self.num_workers,					)
					else:
									return DataLoader(
									    __UpperCAmelCase,					batch_size=__UpperCAmelCase,					collate_fn=dataset.collate_fn,					shuffle=__UpperCAmelCase,					num_workers=self.num_workers,					sampler=__UpperCAmelCase,					)


	def 				lowercase__  (self : List[Any]      )     ->			DataLoader:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Dict      					= self.get_dataloader('''train''',					batch_size=self.hparams.train_batch_size,					shuffle=__UpperCAmelCase      )
					return dataloader


	def 				lowercase__  (self : int      )     ->			DataLoader:

					"""simple docstring"""

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


	def 				lowercase__  (self : Dict      )     ->			DataLoader:

					"""simple docstring"""

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






	@staticmethod
	def 				lowercase__  (__UpperCAmelCase : Union[str, Any],					__UpperCAmelCase : Tuple      )     ->			Optional[int]:

					"""simple docstring"""

					BaseTransformer.add_model_specific_args(__UpperCAmelCase,					__UpperCAmelCase      )
					add_generic_args(__UpperCAmelCase,					__UpperCAmelCase      )
					parser.add_argument(
					    '''--max_source_length''',					default=1024,					type=__UpperCAmelCase,					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=__UpperCAmelCase,					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=__UpperCAmelCase,					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=__UpperCAmelCase,					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=__UpperCAmelCase      )
					parser.add_argument('''--overwrite_output_dir''',					action='''store_true''',					default=__UpperCAmelCase      )
					parser.add_argument('''--max_tokens_per_batch''',					type=__UpperCAmelCase,					default=__UpperCAmelCase      )
					parser.add_argument('''--logger_name''',					type=__UpperCAmelCase,					choices=['''default''', '''wandb''', '''wandb_shared'''],					default='''default'''      )
					parser.add_argument('''--n_train''',					type=__UpperCAmelCase,					default=-1,					required=__UpperCAmelCase,					help='''# examples. -1 means use all.'''      )
					parser.add_argument('''--n_val''',					type=__UpperCAmelCase,					default=500,					required=__UpperCAmelCase,					help='''# examples. -1 means use all.'''      )
					parser.add_argument('''--n_test''',					type=__UpperCAmelCase,					default=-1,					required=__UpperCAmelCase,					help='''# examples. -1 means use all.'''      )
					parser.add_argument(
					    '''--task''',					type=__UpperCAmelCase,					default='''summarization''',					required=__UpperCAmelCase,					help='''# examples. -1 means use all.'''      )
					parser.add_argument('''--label_smoothing''',					type=__UpperCAmelCase,					default=0.0,					required=__UpperCAmelCase      )
					parser.add_argument('''--src_lang''',					type=__UpperCAmelCase,					default='''''',					required=__UpperCAmelCase      )
					parser.add_argument('''--tgt_lang''',					type=__UpperCAmelCase,					default='''''',					required=__UpperCAmelCase      )
					parser.add_argument('''--eval_beams''',					type=__UpperCAmelCase,					default=__UpperCAmelCase,					required=__UpperCAmelCase      )
					parser.add_argument(
					    '''--val_metric''',					type=__UpperCAmelCase,					default=__UpperCAmelCase,					required=__UpperCAmelCase,					choices=['''bleu''', '''rouge2''', '''loss''', None]      )
					parser.add_argument('''--eval_max_gen_length''',					type=__UpperCAmelCase,					default=__UpperCAmelCase,					help='''never generate more than n tokens'''      )
					parser.add_argument('''--save_top_k''',					type=__UpperCAmelCase,					default=1,					required=__UpperCAmelCase,					help='''How many checkpoints to save'''      )
					parser.add_argument(
					    '''--early_stopping_patience''',					type=__UpperCAmelCase,					default=-1,					required=__UpperCAmelCase,					help=(
					        '''-1 means never early stop. early_stopping_patience is measured in validation checks, not epochs. So'''
					        ''' val_check_interval will effect it.'''
					    ),					)
					return parser



class 	a__     (				_lowercase	):
	__magic_name__		:    Any	    =     "translation"
	__magic_name__		:    Tuple	    =     ["loss"]
	__magic_name__		:    List[Any]	    =     ["bleu"]
	__magic_name__		:    str	    =     "bleu"


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

					"""simple docstring"""

					super().__init__(__UpperCAmelCase,					**__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						List[str]      					= hparams.src_lang
					SCREAMING_SNAKE_CASE  :						List[str]      					= hparams.tgt_lang






	def 				lowercase__  (self : Any,					__UpperCAmelCase : List[Any],					__UpperCAmelCase : List[Any]      )     ->			dict:

					"""simple docstring"""

					return calculate_bleu(__UpperCAmelCase,					__UpperCAmelCase      )






def       __lowercase  (_SCREAMING_SNAKE_CASE      :Dict    ,	_SCREAMING_SNAKE_CASE      :Optional[int]=None       ):
				Path(args.output_dir       ).mkdir(exist_ok=_SCREAMING_SNAKE_CASE       )
				check_output_dir(_SCREAMING_SNAKE_CASE    ,	expected_items=3       )

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

								SCREAMING_SNAKE_CASE  :						int      					= os.environ.get('''WANDB_PROJECT'''    ,	_SCREAMING_SNAKE_CASE       )
								SCREAMING_SNAKE_CASE  :						str      					= WandbLogger(name=model.output_dir.name    ,	project=_SCREAMING_SNAKE_CASE       )

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

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

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

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

				SCREAMING_SNAKE_CASE  :						Tuple      					= ''''''
				SCREAMING_SNAKE_CASE  :						str      					= sorted(glob.glob(os.path.join(args.output_dir    ,	'''*.ckpt'''       )    ,	recursive=_SCREAMING_SNAKE_CASE       )       )
				if checkpoints:
								SCREAMING_SNAKE_CASE  :						Union[str, 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__":
	snake_case_			=     argparse.ArgumentParser()
	snake_case_			=     pl.Trainer.add_argparse_args(parser)
	snake_case_			=     SummarizationModule.add_model_specific_args(parser, os.getcwd())

	snake_case_			=     parser.parse_args()

	main(args)









'''simple docstring'''

from collections import deque






def       __lowercase  (_SCREAMING_SNAKE_CASE      :List[str]       ):
				SCREAMING_SNAKE_CASE  :						Optional[Any]      					= len(_SCREAMING_SNAKE_CASE       )
				SCREAMING_SNAKE_CASE  :						List[str]      					= deque()
				SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= [False for _ in range(_SCREAMING_SNAKE_CASE       )]
				SCREAMING_SNAKE_CASE  :						Any      					= [-1 for _ in range(_SCREAMING_SNAKE_CASE       )]
				SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= index_of[:]

				def strong_connect(_SCREAMING_SNAKE_CASE      :List[str]    ,	_SCREAMING_SNAKE_CASE      :Any    ,	_SCREAMING_SNAKE_CASE      :List[str]       ):
								SCREAMING_SNAKE_CASE  :						Any      					= index  # the number when this node is seen
								SCREAMING_SNAKE_CASE  :						str      					= index  # lowest rank node reachable from here
								index += 1
								stack.append(_SCREAMING_SNAKE_CASE       )
								SCREAMING_SNAKE_CASE  :						Optional[int]      					= True

								for w in g[v]:
												if index_of[w] == -1:
																SCREAMING_SNAKE_CASE  :						List[str]      					= strong_connect(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE       )
																SCREAMING_SNAKE_CASE  :						Optional[int]      					= (
																    lowlink_of[w] if lowlink_of[w] < lowlink_of[v] else lowlink_of[v]
																)
												elif on_stack[w]:
																SCREAMING_SNAKE_CASE  :						List[str]      					= (
																    lowlink_of[w] if lowlink_of[w] < lowlink_of[v] else lowlink_of[v]
																)

								if lowlink_of[v] == index_of[v]:
												SCREAMING_SNAKE_CASE  :						str      					= []
												SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= stack.pop()
												SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= False
												component.append(_SCREAMING_SNAKE_CASE       )
												while w != v:
																SCREAMING_SNAKE_CASE  :						int      					= stack.pop()
																SCREAMING_SNAKE_CASE  :						Tuple      					= False
																component.append(_SCREAMING_SNAKE_CASE       )
												components.append(_SCREAMING_SNAKE_CASE       )
								return index

				SCREAMING_SNAKE_CASE  :						int      					= []
				for v in range(_SCREAMING_SNAKE_CASE       ):
								if index_of[v] == -1:
												strong_connect(_SCREAMING_SNAKE_CASE    ,	0    ,	_SCREAMING_SNAKE_CASE       )

				return components






def       __lowercase  (_SCREAMING_SNAKE_CASE      :List[str]    ,	_SCREAMING_SNAKE_CASE      :Optional[int]       ):
				SCREAMING_SNAKE_CASE  :						Tuple      					= [[] for _ in range(_SCREAMING_SNAKE_CASE       )]
				for u, v in edges:
								g[u].append(_SCREAMING_SNAKE_CASE       )
				return g


if __name__ == "__main__":
	# Test
	snake_case_			=     7
	snake_case_			=     [0, 0, 1, 2, 3, 3, 4, 4, 6]
	snake_case_			=     [1, 3, 2, 0, 1, 4, 5, 6, 5]
	snake_case_			=     [(u, v) for u, v in zip(source, target)]
	snake_case_			=     create_graph(n_vertices, edges)

	assert [[5], [6], [4], [3, 2, 1, 0]] == tarjan(g)




