


"""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__				:Dict			   =	logging.getLogger(__name__)






class      A(  lowerCamelCase__		):

		"""simple docstring"""





		A    =							"summarization"
		A    =							["loss"]
		A    =							ROUGE_KEYS
		A    =							"rouge2"
		def __init__( self					,				SCREAMING_SNAKE_CASE__					,				**SCREAMING_SNAKE_CASE__   )      ->	Optional[int]:

					"""simple docstring"""



					if hparams.sortish_sampler and hparams.gpus > 1:
								_UpperCamelCase					:Union[str, Any]					     =	False
					elif hparams.max_tokens_per_batch is not None:
								if hparams.gpus > 1:
											raise NotImplementedError('''Dynamic Batch size does not work for multi-gpu training'''   )
								if hparams.sortish_sampler:
											raise ValueError('''--sortish_sampler and --max_tokens_per_batch may not be used simultaneously'''   )

					super().__init__(SCREAMING_SNAKE_CASE__					,				num_labels=SCREAMING_SNAKE_CASE__					,				mode=self.mode					,				**SCREAMING_SNAKE_CASE__   )
					use_task_specific_params(self.model					,				'''summarization'''   )
					save_git_info(self.hparams.output_dir   )
					_UpperCamelCase					:Optional[int]					     =	Path(self.output_dir   ) / '''metrics.json'''
					_UpperCamelCase					:Tuple					     =	Path(self.output_dir   ) / '''hparams.pkl'''
					pickle_save(self.hparams					,				self.hparams_save_path   )
					_UpperCamelCase					:Optional[Any]					     =	0
					_UpperCamelCase					:List[Any]					     =	defaultdict(SCREAMING_SNAKE_CASE__   )
					_UpperCamelCase					:Optional[int]					     =	self.config.model_type
					_UpperCamelCase					:Optional[Any]					     =	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					:List[Any]					     =	{
					    '''train''': self.hparams.n_train,
					    '''val''': self.hparams.n_val,
					    '''test''': self.hparams.n_test,
					}
					_UpperCamelCase					:Any					     =	{k: v if v >= 0 else None for k, v in n_observations_per_split.items()}

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

					"""simple docstring"""



					_UpperCamelCase					:str					     =	{
					    k: self.tokenizer.batch_decode(v.tolist()   ) if '''mask''' not in k else v.shape for k, v in batch.items()
					}
					save_json(SCREAMING_SNAKE_CASE__					,				Path(self.output_dir   ) / '''text_batch.json'''   )
					save_json({k: v.tolist() for k, v in batch.items()}					,				Path(self.output_dir   ) / '''tok_batch.json'''   )

					_UpperCamelCase					:Tuple					     =	True
					return readable_batch
		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__					,				**SCREAMING_SNAKE_CASE__   )      ->	Any:

					"""simple docstring"""



					return self.model(SCREAMING_SNAKE_CASE__					,				**SCREAMING_SNAKE_CASE__   )
		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__   )      ->	Dict:

					"""simple docstring"""



					_UpperCamelCase					:Any					     =	self.tokenizer.batch_decode(
					    SCREAMING_SNAKE_CASE__					,				skip_special_tokens=SCREAMING_SNAKE_CASE__					,				clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE__   )
					return lmap(str.strip					,				SCREAMING_SNAKE_CASE__   )
		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__   )      ->	Tuple:

					"""simple docstring"""



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

					_UpperCamelCase					:str					     =	self(SCREAMING_SNAKE_CASE__					,				attention_mask=SCREAMING_SNAKE_CASE__					,				decoder_input_ids=SCREAMING_SNAKE_CASE__					,				use_cache=SCREAMING_SNAKE_CASE__   )
					_UpperCamelCase					:List[str]					     =	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=SCREAMING_SNAKE_CASE__   )

								assert lm_logits.shape[-1] == self.vocab_size
								_UpperCamelCase					:Any					     =	ce_loss_fct(lm_logits.view(-1					,				lm_logits.shape[-1]   )					,				tgt_ids.view(-1   )   )
					else:
								_UpperCamelCase					:List[str]					     =	nn.functional.log_softmax(SCREAMING_SNAKE_CASE__					,				dim=-1   )
								_UpperCamelCase     ,      _UpperCamelCase					:str					     =	label_smoothed_nll_loss(
								    SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				self.hparams.label_smoothing					,				ignore_index=SCREAMING_SNAKE_CASE__   )
					return (loss,)
		@property
		def   _UpperCamelCase( self   )      ->	int:

					"""simple docstring"""



					return self.tokenizer.pad_token_id
		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   )      ->	Dict:

					"""simple docstring"""



					_UpperCamelCase					:Optional[int]					     =	self._step(SCREAMING_SNAKE_CASE__   )

					_UpperCamelCase					:Optional[int]					     =	dict(zip(self.loss_names					,				SCREAMING_SNAKE_CASE__   )   )
					# tokens per batch
					_UpperCamelCase					:Union[str, Any]					     =	batch['''input_ids'''].ne(self.pad   ).sum() + batch['''labels'''].ne(self.pad   ).sum()
					_UpperCamelCase					:List[Any]					     =	batch['''input_ids'''].shape[0]
					_UpperCamelCase					:Any					     =	batch['''input_ids'''].eq(self.pad   ).sum()
					_UpperCamelCase					:Dict					     =	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					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   )      ->	Dict:

					"""simple docstring"""



					return self._generative_step(SCREAMING_SNAKE_CASE__   )
		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__="val"   )      ->	Dict:

					"""simple docstring"""



					self.step_count += 1
					_UpperCamelCase					:Any					     =	{k: torch.stack([x[k] for x in outputs]   ).mean() for k in self.loss_names}
					_UpperCamelCase					:int					     =	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					:Optional[Any]					     =	(
					    generative_metrics[self.val_metric] if self.val_metric in generative_metrics else losses[self.val_metric]
					)
					_UpperCamelCase					:torch.FloatTensor					     =	torch.tensor(SCREAMING_SNAKE_CASE__   ).type_as(SCREAMING_SNAKE_CASE__   )
					generative_metrics.update({k: v.item() for k, v in losses.items()}   )
					losses.update(SCREAMING_SNAKE_CASE__   )
					_UpperCamelCase					:List[Any]					     =	{f"{prefix}_avg_{k}": x for k, x in losses.items()}
					_UpperCamelCase					:str					     =	self.step_count
					self.metrics[prefix].append(SCREAMING_SNAKE_CASE__   )  # callback writes this to self.metrics_save_path
					_UpperCamelCase					:List[str]					     =	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					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   )      ->	Dict:

					"""simple docstring"""



					return calculate_rouge(SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   )
		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__   )      ->	dict:

					"""simple docstring"""



					_UpperCamelCase					:Tuple					     =	time.time()

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

					"""simple docstring"""



					return self._generative_step(SCREAMING_SNAKE_CASE__   )
		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__   )      ->	Optional[Any]:

					"""simple docstring"""



					return self.validation_epoch_end(SCREAMING_SNAKE_CASE__					,				prefix='''test'''   )
		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__   )      ->	SeqaSeqDataset:

					"""simple docstring"""



					_UpperCamelCase					:Optional[Any]					     =	self.n_obs[type_path]
					_UpperCamelCase					:Dict					     =	self.target_lens[type_path]
					_UpperCamelCase					:Dict					     =	self.dataset_class(
					    self.tokenizer					,				type_path=SCREAMING_SNAKE_CASE__					,				n_obs=SCREAMING_SNAKE_CASE__					,				max_target_length=SCREAMING_SNAKE_CASE__					,				**self.dataset_kwargs					,				)
					return dataset
		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__ = False   )      ->	DataLoader:

					"""simple docstring"""



					_UpperCamelCase					:int					     =	self.get_dataset(SCREAMING_SNAKE_CASE__   )

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

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

					"""simple docstring"""



					_UpperCamelCase					:int					     =	self.get_dataloader('''train'''					,				batch_size=self.hparams.train_batch_size					,				shuffle=SCREAMING_SNAKE_CASE__   )
					return dataloader
		def   _UpperCamelCase( self   )      ->	DataLoader:

					"""simple docstring"""



					return self.get_dataloader('''val'''					,				batch_size=self.hparams.eval_batch_size   )
		def   _UpperCamelCase( self   )      ->	DataLoader:

					"""simple docstring"""



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





		@staticmethod
		def   _UpperCamelCase( SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   )      ->	Dict:

					"""simple docstring"""



					BaseTransformer.add_model_specific_args(SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   )
					add_generic_args(SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   )
					parser.add_argument(
					    '''--max_source_length'''					,				default=10_24					,				type=SCREAMING_SNAKE_CASE__					,				help=(
					        '''The maximum total input sequence length after tokenization. Sequences longer '''
					        '''than this will be truncated, sequences shorter will be padded.'''
					    )					,				)
					parser.add_argument(
					    '''--max_target_length'''					,				default=56					,				type=SCREAMING_SNAKE_CASE__					,				help=(
					        '''The maximum total input sequence length after tokenization. Sequences longer '''
					        '''than this will be truncated, sequences shorter will be padded.'''
					    )					,				)
					parser.add_argument(
					    '''--val_max_target_length'''					,				default=1_42					,				type=SCREAMING_SNAKE_CASE__					,				help=(
					        '''The maximum total input sequence length after tokenization. Sequences longer '''
					        '''than this will be truncated, sequences shorter will be padded.'''
					    )					,				)
					parser.add_argument(
					    '''--test_max_target_length'''					,				default=1_42					,				type=SCREAMING_SNAKE_CASE__					,				help=(
					        '''The maximum total input sequence length after tokenization. Sequences longer '''
					        '''than this will be truncated, sequences shorter will be padded.'''
					    )					,				)
					parser.add_argument('''--freeze_encoder'''					,				action='''store_true'''   )
					parser.add_argument('''--freeze_embeds'''					,				action='''store_true'''   )
					parser.add_argument('''--sortish_sampler'''					,				action='''store_true'''					,				default=SCREAMING_SNAKE_CASE__   )
					parser.add_argument('''--overwrite_output_dir'''					,				action='''store_true'''					,				default=SCREAMING_SNAKE_CASE__   )
					parser.add_argument('''--max_tokens_per_batch'''					,				type=SCREAMING_SNAKE_CASE__					,				default=SCREAMING_SNAKE_CASE__   )
					parser.add_argument('''--logger_name'''					,				type=SCREAMING_SNAKE_CASE__					,				choices=['''default''', '''wandb''', '''wandb_shared''']					,				default='''default'''   )
					parser.add_argument('''--n_train'''					,				type=SCREAMING_SNAKE_CASE__					,				default=-1					,				required=SCREAMING_SNAKE_CASE__					,				help='''# examples. -1 means use all.'''   )
					parser.add_argument('''--n_val'''					,				type=SCREAMING_SNAKE_CASE__					,				default=5_00					,				required=SCREAMING_SNAKE_CASE__					,				help='''# examples. -1 means use all.'''   )
					parser.add_argument('''--n_test'''					,				type=SCREAMING_SNAKE_CASE__					,				default=-1					,				required=SCREAMING_SNAKE_CASE__					,				help='''# examples. -1 means use all.'''   )
					parser.add_argument(
					    '''--task'''					,				type=SCREAMING_SNAKE_CASE__					,				default='''summarization'''					,				required=SCREAMING_SNAKE_CASE__					,				help='''# examples. -1 means use all.'''   )
					parser.add_argument('''--label_smoothing'''					,				type=SCREAMING_SNAKE_CASE__					,				default=0.0					,				required=SCREAMING_SNAKE_CASE__   )
					parser.add_argument('''--src_lang'''					,				type=SCREAMING_SNAKE_CASE__					,				default=''''''					,				required=SCREAMING_SNAKE_CASE__   )
					parser.add_argument('''--tgt_lang'''					,				type=SCREAMING_SNAKE_CASE__					,				default=''''''					,				required=SCREAMING_SNAKE_CASE__   )
					parser.add_argument('''--eval_beams'''					,				type=SCREAMING_SNAKE_CASE__					,				default=SCREAMING_SNAKE_CASE__					,				required=SCREAMING_SNAKE_CASE__   )
					parser.add_argument(
					    '''--val_metric'''					,				type=SCREAMING_SNAKE_CASE__					,				default=SCREAMING_SNAKE_CASE__					,				required=SCREAMING_SNAKE_CASE__					,				choices=['''bleu''', '''rouge2''', '''loss''', None]   )
					parser.add_argument('''--eval_max_gen_length'''					,				type=SCREAMING_SNAKE_CASE__					,				default=SCREAMING_SNAKE_CASE__					,				help='''never generate more than n tokens'''   )
					parser.add_argument('''--save_top_k'''					,				type=SCREAMING_SNAKE_CASE__					,				default=1					,				required=SCREAMING_SNAKE_CASE__					,				help='''How many checkpoints to save'''   )
					parser.add_argument(
					    '''--early_stopping_patience'''					,				type=SCREAMING_SNAKE_CASE__					,				default=-1					,				required=SCREAMING_SNAKE_CASE__					,				help=(
					        '''-1 means never early stop. early_stopping_patience is measured in validation checks, not epochs. So'''
					        ''' val_check_interval will effect it.'''
					    )					,				)
					return parser






class      A(  lowerCamelCase__		):

		"""simple docstring"""





		A    =							"translation"
		A    =							["loss"]
		A    =							["bleu"]
		A    =							"bleu"
		def __init__( self					,				SCREAMING_SNAKE_CASE__					,				**SCREAMING_SNAKE_CASE__   )      ->	Union[str, Any]:

					"""simple docstring"""



					super().__init__(SCREAMING_SNAKE_CASE__					,				**SCREAMING_SNAKE_CASE__   )
					_UpperCamelCase					:List[str]					     =	hparams.src_lang
					_UpperCamelCase					:Union[str, Any]					     =	hparams.tgt_lang





		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   )      ->	dict:

					"""simple docstring"""



					return calculate_bleu(SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   )





def 				A_       ( snake_case__       ,					snake_case__=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					: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'''		)
			):
						_UpperCamelCase					:str					     =	True  # don't pollute wandb logs unnecessarily
			elif args.logger_name == "wandb":
						from pytorch_lightning.loggers import WandbLogger

						_UpperCamelCase					:int					     =	os.environ.get('''WANDB_PROJECT'''       ,					snake_case__		)
						_UpperCamelCase					:List[str]					     =	WandbLogger(name=model.output_dir.name       ,					project=snake_case__		)

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

						_UpperCamelCase					:Dict					     =	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					:int					     =	False

			_UpperCamelCase					:List[str]					     =	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					:Dict					     =	''''''
			_UpperCamelCase					:Optional[Any]					     =	sorted(glob.glob(os.path.join(args.output_dir       ,					'''*.ckpt'''		)       ,					recursive=snake_case__		)		)
			if checkpoints:
						_UpperCamelCase					:Optional[Any]					     =	checkpoints[-1]
						_UpperCamelCase					:Optional[Any]					     =	checkpoints[-1]
			trainer.logger.log_hyperparams(model.hparams		)

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


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

					UpperCamelCase__				:Optional[int]			   =	parser.parse_args()

					main(args)





"""simple docstring"""






import warnings
from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


UpperCamelCase__				:Dict			   =	logging.get_logger(__name__)

UpperCamelCase__				:int			   =	{
    """facebook/bart-large""": """https://huggingface.co/facebook/bart-large/resolve/main/config.json""",
    # See all BART models at https://huggingface.co/models?filter=bart
}






class      A(  lowerCamelCase__		):

		"""simple docstring"""





		A    =							"bart"
		A    =							["past_key_values"]
		A    =							{"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}
		def __init__( self					,				SCREAMING_SNAKE_CASE__=5_02_65					,				SCREAMING_SNAKE_CASE__=10_24					,				SCREAMING_SNAKE_CASE__=12					,				SCREAMING_SNAKE_CASE__=40_96					,				SCREAMING_SNAKE_CASE__=16					,				SCREAMING_SNAKE_CASE__=12					,				SCREAMING_SNAKE_CASE__=40_96					,				SCREAMING_SNAKE_CASE__=16					,				SCREAMING_SNAKE_CASE__=0.0					,				SCREAMING_SNAKE_CASE__=0.0					,				SCREAMING_SNAKE_CASE__="gelu"					,				SCREAMING_SNAKE_CASE__=10_24					,				SCREAMING_SNAKE_CASE__=0.1					,				SCREAMING_SNAKE_CASE__=0.0					,				SCREAMING_SNAKE_CASE__=0.0					,				SCREAMING_SNAKE_CASE__=0.0_2					,				SCREAMING_SNAKE_CASE__=0.0					,				SCREAMING_SNAKE_CASE__=False					,				SCREAMING_SNAKE_CASE__=True					,				SCREAMING_SNAKE_CASE__=3					,				SCREAMING_SNAKE_CASE__=1					,				SCREAMING_SNAKE_CASE__=0					,				SCREAMING_SNAKE_CASE__=2					,				SCREAMING_SNAKE_CASE__=True					,				SCREAMING_SNAKE_CASE__=2					,				SCREAMING_SNAKE_CASE__=2					,				**SCREAMING_SNAKE_CASE__					,				)      ->	Optional[Any]:

					"""simple docstring"""



					_UpperCamelCase					:Union[str, Any]					     =	vocab_size
					_UpperCamelCase					:Union[str, Any]					     =	max_position_embeddings
					_UpperCamelCase					:int					     =	d_model
					_UpperCamelCase					:Any					     =	encoder_ffn_dim
					_UpperCamelCase					:str					     =	encoder_layers
					_UpperCamelCase					:str					     =	encoder_attention_heads
					_UpperCamelCase					:Optional[int]					     =	decoder_ffn_dim
					_UpperCamelCase					:Any					     =	decoder_layers
					_UpperCamelCase					:Dict					     =	decoder_attention_heads
					_UpperCamelCase					:str					     =	dropout
					_UpperCamelCase					:Tuple					     =	attention_dropout
					_UpperCamelCase					:Optional[Any]					     =	activation_dropout
					_UpperCamelCase					:Optional[Any]					     =	activation_function
					_UpperCamelCase					:int					     =	init_std
					_UpperCamelCase					:Any					     =	encoder_layerdrop
					_UpperCamelCase					:List[Any]					     =	decoder_layerdrop
					_UpperCamelCase					:Union[str, Any]					     =	classifier_dropout
					_UpperCamelCase					:List[Any]					     =	use_cache
					_UpperCamelCase					:str					     =	encoder_layers
					_UpperCamelCase					:int					     =	scale_embedding  # scale factor will be sqrt(d_model) if True

					super().__init__(
					    num_labels=SCREAMING_SNAKE_CASE__					,				pad_token_id=SCREAMING_SNAKE_CASE__					,				bos_token_id=SCREAMING_SNAKE_CASE__					,				eos_token_id=SCREAMING_SNAKE_CASE__					,				is_encoder_decoder=SCREAMING_SNAKE_CASE__					,				decoder_start_token_id=SCREAMING_SNAKE_CASE__					,				forced_eos_token_id=SCREAMING_SNAKE_CASE__					,				**SCREAMING_SNAKE_CASE__					,				)

					# ensure backward compatibility for BART CNN models
					if self.forced_bos_token_id is None and kwargs.get('''force_bos_token_to_be_generated'''					,				SCREAMING_SNAKE_CASE__   ):
								_UpperCamelCase					:Any					     =	self.bos_token_id
								warnings.warn(
								    f"Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. "
								    '''The config can simply be saved and uploaded again to be fixed.'''   )






class      A(  lowerCamelCase__		):

		"""simple docstring"""





		@property
		def   _UpperCamelCase( self   )      ->	Mapping[str, Mapping[int, str]]:

					"""simple docstring"""



					if self.task in ["default", "seq2seq-lm"]:
								_UpperCamelCase					:List[Any]					     =	OrderedDict(
								    [
								        ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
								        ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
								    ]   )

								if self.use_past:
											_UpperCamelCase					:Optional[Any]					     =	{0: '''batch'''}
											_UpperCamelCase					:int					     =	{0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
								else:
											_UpperCamelCase					:Optional[Any]					     =	{0: '''batch''', 1: '''decoder_sequence'''}
											_UpperCamelCase					:Optional[Any]					     =	{0: '''batch''', 1: '''decoder_sequence'''}

								if self.use_past:
											self.fill_with_past_key_values_(SCREAMING_SNAKE_CASE__					,				direction='''inputs'''   )
					elif self.task == "causal-lm":
								# TODO: figure this case out.
								_UpperCamelCase					:Tuple					     =	OrderedDict(
								    [
								        ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
								        ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
								    ]   )
								if self.use_past:
											_UpperCamelCase     ,      _UpperCamelCase					:List[str]					     =	self.num_layers
											for i in range(SCREAMING_SNAKE_CASE__   ):
														_UpperCamelCase					:int					     =	{0: '''batch''', 2: '''past_sequence + sequence'''}
														_UpperCamelCase					:List[Any]					     =	{0: '''batch''', 2: '''past_sequence + sequence'''}
					else:
								_UpperCamelCase					:List[Any]					     =	OrderedDict(
								    [
								        ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
								        ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
								        ('''decoder_input_ids''', {0: '''batch''', 1: '''decoder_sequence'''}),
								        ('''decoder_attention_mask''', {0: '''batch''', 1: '''decoder_sequence'''}),
								    ]   )

					return common_inputs
		@property
		def   _UpperCamelCase( self   )      ->	Mapping[str, Mapping[int, str]]:

					"""simple docstring"""



					if self.task in ["default", "seq2seq-lm"]:
								_UpperCamelCase					:Optional[int]					     =	super().outputs
					else:
								_UpperCamelCase					:List[str]					     =	super(SCREAMING_SNAKE_CASE__					,				self   ).outputs
								if self.use_past:
											_UpperCamelCase     ,      _UpperCamelCase					:int					     =	self.num_layers
											for i in range(SCREAMING_SNAKE_CASE__   ):
														_UpperCamelCase					:Optional[Any]					     =	{0: '''batch''', 2: '''past_sequence + sequence'''}
														_UpperCamelCase					:Optional[Any]					     =	{0: '''batch''', 2: '''past_sequence + sequence'''}
					return common_outputs
		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__ = -1					,				SCREAMING_SNAKE_CASE__ = -1					,				SCREAMING_SNAKE_CASE__ = False					,				SCREAMING_SNAKE_CASE__ = None					,				)      ->	Mapping[str, Any]:

					"""simple docstring"""



					_UpperCamelCase					:str					     =	self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
					    SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   )

					# Generate decoder inputs
					_UpperCamelCase					:List[Any]					     =	seq_length if not self.use_past else 1
					_UpperCamelCase					:Tuple					     =	self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
					    SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   )
					_UpperCamelCase					:Union[str, Any]					     =	{f"decoder_{name}": tensor for name, tensor in decoder_inputs.items()}
					_UpperCamelCase					:str					     =	dict(**SCREAMING_SNAKE_CASE__					,				**SCREAMING_SNAKE_CASE__   )

					if self.use_past:
								if not is_torch_available():
											raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''   )
								else:
											import torch
								_UpperCamelCase     ,      _UpperCamelCase					:Any					     =	common_inputs['''input_ids'''].shape
								_UpperCamelCase					:Dict					     =	common_inputs['''decoder_input_ids'''].shape[1]
								_UpperCamelCase     ,      _UpperCamelCase					:Any					     =	self.num_attention_heads
								_UpperCamelCase					:Dict					     =	(
								    batch,
								    num_encoder_attention_heads,
								    encoder_seq_length,
								    self._config.hidden_size // num_encoder_attention_heads,
								)
								_UpperCamelCase					:Tuple					     =	decoder_seq_length + 3
								_UpperCamelCase					:Optional[int]					     =	(
								    batch,
								    num_decoder_attention_heads,
								    decoder_past_length,
								    self._config.hidden_size // num_decoder_attention_heads,
								)

								_UpperCamelCase					:Optional[int]					     =	torch.cat(
								    [common_inputs['''decoder_attention_mask'''], torch.ones(SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   )]					,				dim=1   )

								_UpperCamelCase					:Union[str, Any]					     =	[]
								# If the number of encoder and decoder layers are present in the model configuration, both are considered
								_UpperCamelCase     ,      _UpperCamelCase					:int					     =	self.num_layers
								_UpperCamelCase					:Any					     =	min(SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   )
								_UpperCamelCase					:Optional[int]					     =	max(SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   ) - min_num_layers
								_UpperCamelCase					:int					     =	'''encoder''' if num_encoder_layers > num_decoder_layers else '''decoder'''

								for _ in range(SCREAMING_SNAKE_CASE__   ):
											common_inputs["past_key_values"].append(
											    (
											        torch.zeros(SCREAMING_SNAKE_CASE__   ),
											        torch.zeros(SCREAMING_SNAKE_CASE__   ),
											        torch.zeros(SCREAMING_SNAKE_CASE__   ),
											        torch.zeros(SCREAMING_SNAKE_CASE__   ),
											    )   )
								# TODO: test this.
								_UpperCamelCase					:Optional[int]					     =	encoder_shape if remaining_side_name == '''encoder''' else decoder_shape
								for _ in range(SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   ):
											common_inputs["past_key_values"].append((torch.zeros(SCREAMING_SNAKE_CASE__   ), torch.zeros(SCREAMING_SNAKE_CASE__   ))   )
					return common_inputs
		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__ = -1					,				SCREAMING_SNAKE_CASE__ = -1					,				SCREAMING_SNAKE_CASE__ = False					,				SCREAMING_SNAKE_CASE__ = None					,				)      ->	Mapping[str, Any]:

					"""simple docstring"""



					_UpperCamelCase					:Dict					     =	self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
					    SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   )

					if self.use_past:
								if not is_torch_available():
											raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''   )
								else:
											import torch
								_UpperCamelCase     ,      _UpperCamelCase					:List[Any]					     =	common_inputs['''input_ids'''].shape
								# Not using the same length for past_key_values
								_UpperCamelCase					:str					     =	seqlen + 2
								_UpperCamelCase     ,      _UpperCamelCase					:Dict					     =	self.num_layers
								_UpperCamelCase     ,      _UpperCamelCase					:Union[str, Any]					     =	self.num_attention_heads
								_UpperCamelCase					:List[Any]					     =	(
								    batch,
								    num_encoder_attention_heads,
								    past_key_values_length,
								    self._config.hidden_size // num_encoder_attention_heads,
								)

								_UpperCamelCase					:Tuple					     =	common_inputs['''attention_mask'''].dtype
								_UpperCamelCase					:List[Any]					     =	torch.cat(
								    [common_inputs['''attention_mask'''], torch.ones(SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				dtype=SCREAMING_SNAKE_CASE__   )]					,				dim=1   )
								_UpperCamelCase					:int					     =	[
								    (torch.zeros(SCREAMING_SNAKE_CASE__   ), torch.zeros(SCREAMING_SNAKE_CASE__   )) for _ in range(SCREAMING_SNAKE_CASE__   )
								]
					return common_inputs
		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__ = -1					,				SCREAMING_SNAKE_CASE__ = -1					,				SCREAMING_SNAKE_CASE__ = False					,				SCREAMING_SNAKE_CASE__ = None					,				)      ->	Mapping[str, Any]:

					"""simple docstring"""



					_UpperCamelCase					:List[str]					     =	compute_effective_axis_dimension(
					    SCREAMING_SNAKE_CASE__					,				fixed_dimension=OnnxConfig.default_fixed_batch					,				num_token_to_add=0   )

					# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
					_UpperCamelCase					:str					     =	tokenizer.num_special_tokens_to_add(SCREAMING_SNAKE_CASE__   )
					_UpperCamelCase					:str					     =	compute_effective_axis_dimension(
					    SCREAMING_SNAKE_CASE__					,				fixed_dimension=OnnxConfig.default_fixed_sequence					,				num_token_to_add=SCREAMING_SNAKE_CASE__   )

					# Generate dummy inputs according to compute batch and sequence
					_UpperCamelCase					:List[Any]					     =	[''' '''.join([tokenizer.unk_token]   ) * seq_length] * batch_size
					_UpperCamelCase					:Optional[Any]					     =	dict(tokenizer(SCREAMING_SNAKE_CASE__					,				return_tensors=SCREAMING_SNAKE_CASE__   )   )
					return common_inputs
		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__ = -1					,				SCREAMING_SNAKE_CASE__ = -1					,				SCREAMING_SNAKE_CASE__ = False					,				SCREAMING_SNAKE_CASE__ = None					,				)      ->	Mapping[str, Any]:

					"""simple docstring"""



					if self.task in ["default", "seq2seq-lm"]:
								_UpperCamelCase					:str					     =	self._generate_dummy_inputs_for_default_and_seqaseq_lm(
								    SCREAMING_SNAKE_CASE__					,				batch_size=SCREAMING_SNAKE_CASE__					,				seq_length=SCREAMING_SNAKE_CASE__					,				is_pair=SCREAMING_SNAKE_CASE__					,				framework=SCREAMING_SNAKE_CASE__   )

					elif self.task == "causal-lm":
								_UpperCamelCase					:Optional[Any]					     =	self._generate_dummy_inputs_for_causal_lm(
								    SCREAMING_SNAKE_CASE__					,				batch_size=SCREAMING_SNAKE_CASE__					,				seq_length=SCREAMING_SNAKE_CASE__					,				is_pair=SCREAMING_SNAKE_CASE__					,				framework=SCREAMING_SNAKE_CASE__   )
					else:
								_UpperCamelCase					:Any					     =	self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
								    SCREAMING_SNAKE_CASE__					,				batch_size=SCREAMING_SNAKE_CASE__					,				seq_length=SCREAMING_SNAKE_CASE__					,				is_pair=SCREAMING_SNAKE_CASE__					,				framework=SCREAMING_SNAKE_CASE__   )

					return common_inputs





		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   )      ->	Dict:

					"""simple docstring"""



					if self.task in ["default", "seq2seq-lm"]:
								_UpperCamelCase					:Union[str, Any]					     =	super()._flatten_past_key_values_(SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   )
					else:
								_UpperCamelCase					:Tuple					     =	super(SCREAMING_SNAKE_CASE__					,				self   )._flatten_past_key_values_(
								    SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   )

