


'''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					       =							logging.getLogger(__name__)




class 	a__       (							a__						):





							'''simple docstring'''






							lowercase__    :     List[str]					=   "summarization"
							lowercase__    :     Optional[Any]					=   ["loss"]
							lowercase__    :     List[Any]					=   ROUGE_KEYS
							lowercase__    :     List[str]					=   "rouge2"







							def __init__(	self      ,					lowerCamelCase_      ,					**lowerCamelCase_     ) ->							Dict:
														if hparams.sortish_sampler and hparams.gpus > 1:
																					lowerCAmelCase__      =			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     )
														lowerCAmelCase__      =			Path(self.output_dir     ) / '''metrics.json'''
														lowerCAmelCase__      =			Path(self.output_dir     ) / '''hparams.pkl'''
														pickle_save(self.hparams      ,					self.hparams_save_path     )
														lowerCAmelCase__      =			0
														lowerCAmelCase__      =			defaultdict(lowerCamelCase_     )
														lowerCAmelCase__      =			self.config.model_type
														lowerCAmelCase__      =			self.config.tgt_vocab_size if self.model_type == '''fsmt''' else self.config.vocab_size

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

														lowerCAmelCase__      =			{
														    '''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()     )

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







							def 						__SCREAMING_SNAKE_CASE							(	self      ,					lowerCamelCase_     ) ->							Dict[str, List[str]]:
														lowerCAmelCase__      =			{
														    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'''     )

														lowerCAmelCase__      =			True
														return readable_batch







							def 						__SCREAMING_SNAKE_CASE							(	self      ,					lowerCamelCase_      ,					**lowerCamelCase_     ) ->							int:
														return self.model(lowerCamelCase_      ,					**lowerCamelCase_     )







							def 						__SCREAMING_SNAKE_CASE							(	self      ,					lowerCamelCase_     ) ->							Optional[Any]:
														lowerCAmelCase__      =			self.tokenizer.batch_decode(
														    lowerCamelCase_      ,					skip_special_tokens=lowerCamelCase_      ,					clean_up_tokenization_spaces=lowerCamelCase_     )
														return lmap(str.strip      ,					lowerCamelCase_     )







							def 						__SCREAMING_SNAKE_CASE							(	self      ,					lowerCamelCase_     ) ->							Tuple:
														lowerCAmelCase__      =			self.tokenizer.pad_token_id
														lowerCAmelCase__     ,      lowerCAmelCase__      =			batch['''input_ids'''], batch['''attention_mask''']
														lowerCAmelCase__      =			batch['''labels''']
														if isinstance(self.model      ,					lowerCamelCase_     ):
																					lowerCAmelCase__      =			self.model._shift_right(lowerCamelCase_     )
														else:
																					lowerCAmelCase__      =			shift_tokens_right(lowerCamelCase_      ,					lowerCamelCase_     )
														if not self.already_saved_batch:  # This would be slightly better if it only happened on rank zero
																					lowerCAmelCase__      =			decoder_input_ids
																					self.save_readable_batch(lowerCamelCase_     )

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

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







							@property
							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							int:
														return self.tokenizer.pad_token_id







							def 						__SCREAMING_SNAKE_CASE							(	self      ,					lowerCamelCase_      ,					lowerCamelCase_     ) ->							Dict:
														lowerCAmelCase__      =			self._step(lowerCamelCase_     )

														lowerCAmelCase__      =			dict(zip(self.loss_names      ,					lowerCamelCase_     )     )
														# tokens per batch
														lowerCAmelCase__      =			batch['''input_ids'''].ne(self.pad     ).sum() + batch['''labels'''].ne(self.pad     ).sum()
														lowerCAmelCase__      =			batch['''input_ids'''].shape[0]
														lowerCAmelCase__      =			batch['''input_ids'''].eq(self.pad     ).sum()
														lowerCAmelCase__      =			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 						__SCREAMING_SNAKE_CASE							(	self      ,					lowerCamelCase_      ,					lowerCamelCase_     ) ->							Dict:
														return self._generative_step(lowerCamelCase_     )







							def 						__SCREAMING_SNAKE_CASE							(	self      ,					lowerCamelCase_      ,					lowerCamelCase_="val"     ) ->							Dict:
														self.step_count += 1
														lowerCAmelCase__      =			{k: torch.stack([x[k] for x in outputs]     ).mean() for k in self.loss_names}
														lowerCAmelCase__      =			losses['''loss''']
														lowerCAmelCase__      =			{
														    k: np.array([x[k] for x in outputs]     ).mean() for k in self.metric_names + ['''gen_time''', '''gen_len''']
														}
														lowerCAmelCase__      =			(
														    generative_metrics[self.val_metric] if self.val_metric in generative_metrics else losses[self.val_metric]
														)
														lowerCAmelCase__      =			torch.tensor(lowerCamelCase_     ).type_as(lowerCamelCase_     )
														generative_metrics.update({k: v.item() for k, v in losses.items()}     )
														losses.update(lowerCamelCase_     )
														lowerCAmelCase__      =			{F"""{prefix}_avg_{k}""": x for k, x in losses.items()}
														lowerCAmelCase__      =			self.step_count
														self.metrics[prefix].append(lowerCamelCase_     )  # callback writes this to self.metrics_save_path
														lowerCAmelCase__      =			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 						__SCREAMING_SNAKE_CASE							(	self      ,					lowerCamelCase_      ,					lowerCamelCase_     ) ->							Dict:
														return calculate_rouge(lowerCamelCase_      ,					lowerCamelCase_     )







							def 						__SCREAMING_SNAKE_CASE							(	self      ,					lowerCamelCase_     ) ->							dict:
														lowerCAmelCase__      =			time.time()

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







							def 						__SCREAMING_SNAKE_CASE							(	self      ,					lowerCamelCase_      ,					lowerCamelCase_     ) ->							List[Any]:
														return self._generative_step(lowerCamelCase_     )







							def 						__SCREAMING_SNAKE_CASE							(	self      ,					lowerCamelCase_     ) ->							List[str]:
														return self.validation_epoch_end(lowerCamelCase_      ,					prefix='''test'''     )







							def 						__SCREAMING_SNAKE_CASE							(	self      ,					lowerCamelCase_     ) ->							SeqaSeqDataset:
														lowerCAmelCase__      =			self.n_obs[type_path]
														lowerCAmelCase__      =			self.target_lens[type_path]
														lowerCAmelCase__      =			self.dataset_class(
														    self.tokenizer      ,					type_path=lowerCamelCase_      ,					n_obs=lowerCamelCase_      ,					max_target_length=lowerCamelCase_      ,					**self.dataset_kwargs      ,					)
														return dataset







							def 						__SCREAMING_SNAKE_CASE							(	self      ,					lowerCamelCase_      ,					lowerCamelCase_      ,					lowerCamelCase_ = False     ) ->							DataLoader:
														lowerCAmelCase__      =			self.get_dataset(lowerCamelCase_     )

														if self.hparams.sortish_sampler and type_path != "test" and type_path != "val":
																					lowerCAmelCase__      =			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":
																					lowerCAmelCase__      =			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 						__SCREAMING_SNAKE_CASE							(	self     ) ->							DataLoader:
														lowerCAmelCase__      =			self.get_dataloader('''train'''      ,					batch_size=self.hparams.train_batch_size      ,					shuffle=lowerCamelCase_     )
														return dataloader







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







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







							@staticmethod
							def 						__SCREAMING_SNAKE_CASE							(	lowerCamelCase_      ,					lowerCamelCase_     ) ->							str:
														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 	a__       (							a__						):





							'''simple docstring'''






							lowercase__    :     Optional[int]					=   "translation"
							lowercase__    :     Tuple					=   ["loss"]
							lowercase__    :     str					=   ["bleu"]
							lowercase__    :     Optional[int]					=   "bleu"







							def __init__(	self      ,					lowerCamelCase_      ,					**lowerCamelCase_     ) ->							Union[str, Any]:
														super().__init__(lowerCamelCase_      ,					**lowerCamelCase_     )
														lowerCAmelCase__      =			hparams.src_lang
														lowerCAmelCase__      =			hparams.tgt_lang







							def 						__SCREAMING_SNAKE_CASE							(	self      ,					lowerCamelCase_      ,					lowerCamelCase_     ) ->							dict:
														return calculate_bleu(lowerCamelCase_      ,					lowerCamelCase_     )


def       _snake_case     (  A				,					A=None )		->					SummarizationModule:
							Path(args.output_dir ).mkdir(exist_ok=A )
							check_output_dir(A				,					expected_items=3 )

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

														lowerCAmelCase__      =			os.environ.get('''WANDB_PROJECT'''				,					A )
														lowerCAmelCase__      =			WandbLogger(name=model.output_dir.name				,					project=A )

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

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

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

							lowerCAmelCase__      =			args.val_metric == '''loss'''
							lowerCAmelCase__      =			generic_train(
							    A				,					A				,					logging_callback=SeqaSeqLoggingCallback()				,					checkpoint_callback=get_checkpoint_callback(
							        args.output_dir				,					model.val_metric				,					args.save_top_k				,					A )				,					early_stopping_callback=A				,					logger=A				,					)
							pickle_save(model.hparams				,					model.output_dir / '''hparams.pkl''' )
							if not args.do_predict:
														return model

							lowerCAmelCase__      =			''''''
							lowerCAmelCase__      =			sorted(glob.glob(os.path.join(args.output_dir				,					'''*.ckpt''' )				,					recursive=A ) )
							if checkpoints:
														lowerCAmelCase__      =			checkpoints[-1]
														lowerCAmelCase__      =			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					       =							argparse.ArgumentParser()
		__UpperCAmelCase					       =							pl.Trainer.add_argparse_args(parser)
		__UpperCAmelCase					       =							SummarizationModule.add_model_specific_args(parser, os.getcwd())

		__UpperCAmelCase					       =							parser.parse_args()

		main(args)



'''simple docstring'''






import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roberta import RobertaTokenizer


__UpperCAmelCase					       =							logging.get_logger(__name__)

__UpperCAmelCase					       =							{'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

__UpperCAmelCase					       =							{
    '''vocab_file''': {
        '''roberta-base''': '''https://huggingface.co/roberta-base/resolve/main/vocab.json''',
        '''roberta-large''': '''https://huggingface.co/roberta-large/resolve/main/vocab.json''',
        '''roberta-large-mnli''': '''https://huggingface.co/roberta-large-mnli/resolve/main/vocab.json''',
        '''distilroberta-base''': '''https://huggingface.co/distilroberta-base/resolve/main/vocab.json''',
        '''roberta-base-openai-detector''': '''https://huggingface.co/roberta-base-openai-detector/resolve/main/vocab.json''',
        '''roberta-large-openai-detector''': (
            '''https://huggingface.co/roberta-large-openai-detector/resolve/main/vocab.json'''
        ),
    },
    '''merges_file''': {
        '''roberta-base''': '''https://huggingface.co/roberta-base/resolve/main/merges.txt''',
        '''roberta-large''': '''https://huggingface.co/roberta-large/resolve/main/merges.txt''',
        '''roberta-large-mnli''': '''https://huggingface.co/roberta-large-mnli/resolve/main/merges.txt''',
        '''distilroberta-base''': '''https://huggingface.co/distilroberta-base/resolve/main/merges.txt''',
        '''roberta-base-openai-detector''': '''https://huggingface.co/roberta-base-openai-detector/resolve/main/merges.txt''',
        '''roberta-large-openai-detector''': (
            '''https://huggingface.co/roberta-large-openai-detector/resolve/main/merges.txt'''
        ),
    },
    '''tokenizer_file''': {
        '''roberta-base''': '''https://huggingface.co/roberta-base/resolve/main/tokenizer.json''',
        '''roberta-large''': '''https://huggingface.co/roberta-large/resolve/main/tokenizer.json''',
        '''roberta-large-mnli''': '''https://huggingface.co/roberta-large-mnli/resolve/main/tokenizer.json''',
        '''distilroberta-base''': '''https://huggingface.co/distilroberta-base/resolve/main/tokenizer.json''',
        '''roberta-base-openai-detector''': (
            '''https://huggingface.co/roberta-base-openai-detector/resolve/main/tokenizer.json'''
        ),
        '''roberta-large-openai-detector''': (
            '''https://huggingface.co/roberta-large-openai-detector/resolve/main/tokenizer.json'''
        ),
    },
}

__UpperCAmelCase					       =							{
    '''roberta-base''': 512,
    '''roberta-large''': 512,
    '''roberta-large-mnli''': 512,
    '''distilroberta-base''': 512,
    '''roberta-base-openai-detector''': 512,
    '''roberta-large-openai-detector''': 512,
}




class 	a__       (							a__						):





							'''simple docstring'''






							lowercase__    :     Optional[Any]					=   VOCAB_FILES_NAMES
							lowercase__    :     int					=   PRETRAINED_VOCAB_FILES_MAP
							lowercase__    :     Any					=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowercase__    :     List[Any]					=   ["input_ids", "attention_mask"]
							lowercase__    :     Dict					=   RobertaTokenizer







							def __init__(	self      ,					lowerCamelCase_=None      ,					lowerCamelCase_=None      ,					lowerCamelCase_=None      ,					lowerCamelCase_="replace"      ,					lowerCamelCase_="<s>"      ,					lowerCamelCase_="</s>"      ,					lowerCamelCase_="</s>"      ,					lowerCamelCase_="<s>"      ,					lowerCamelCase_="<unk>"      ,					lowerCamelCase_="<pad>"      ,					lowerCamelCase_="<mask>"      ,					lowerCamelCase_=False      ,					lowerCamelCase_=True      ,					**lowerCamelCase_      ,					) ->							Tuple:
														super().__init__(
														    lowerCamelCase_      ,					lowerCamelCase_      ,					tokenizer_file=lowerCamelCase_      ,					errors=lowerCamelCase_      ,					bos_token=lowerCamelCase_      ,					eos_token=lowerCamelCase_      ,					sep_token=lowerCamelCase_      ,					cls_token=lowerCamelCase_      ,					unk_token=lowerCamelCase_      ,					pad_token=lowerCamelCase_      ,					mask_token=lowerCamelCase_      ,					add_prefix_space=lowerCamelCase_      ,					trim_offsets=lowerCamelCase_      ,					**lowerCamelCase_      ,					)

														lowerCAmelCase__      =			json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()     )
														if pre_tok_state.get('''add_prefix_space'''      ,					lowerCamelCase_     ) != add_prefix_space:
																					lowerCAmelCase__      =			getattr(lowerCamelCase_      ,					pre_tok_state.pop('''type'''     )     )
																					lowerCAmelCase__      =			add_prefix_space
																					lowerCAmelCase__      =			pre_tok_class(**lowerCamelCase_     )

														lowerCAmelCase__      =			add_prefix_space

														lowerCAmelCase__      =			'''post_processor'''
														lowerCAmelCase__      =			getattr(self.backend_tokenizer      ,					lowerCamelCase_      ,					lowerCamelCase_     )
														if tokenizer_component_instance:
																					lowerCAmelCase__      =			json.loads(tokenizer_component_instance.__getstate__()     )

																					# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
																					if "sep" in state:
																												lowerCAmelCase__      =			tuple(state['''sep''']     )
																					if "cls" in state:
																												lowerCAmelCase__      =			tuple(state['''cls''']     )

																					lowerCAmelCase__      =			False

																					if state.get('''add_prefix_space'''      ,					lowerCamelCase_     ) != add_prefix_space:
																												lowerCAmelCase__      =			add_prefix_space
																												lowerCAmelCase__      =			True

																					if state.get('''trim_offsets'''      ,					lowerCamelCase_     ) != trim_offsets:
																												lowerCAmelCase__      =			trim_offsets
																												lowerCAmelCase__      =			True

																					if changes_to_apply:
																												lowerCAmelCase__      =			getattr(lowerCamelCase_      ,					state.pop('''type'''     )     )
																												lowerCAmelCase__      =			component_class(**lowerCamelCase_     )
																												setattr(self.backend_tokenizer      ,					lowerCamelCase_      ,					lowerCamelCase_     )







							@property
							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							str:
														if self._mask_token is None:
																					if self.verbose:
																												logger.error('''Using mask_token, but it is not set yet.'''     )
																					return None
														return str(self._mask_token     )







							@mask_token.setter
							def 						__SCREAMING_SNAKE_CASE							(	self      ,					lowerCamelCase_     ) ->							Optional[Any]:
														lowerCAmelCase__      =			AddedToken(lowerCamelCase_      ,					lstrip=lowerCamelCase_      ,					rstrip=lowerCamelCase_     ) if isinstance(lowerCamelCase_      ,					lowerCamelCase_     ) else value
														lowerCAmelCase__      =			value







							def 						__SCREAMING_SNAKE_CASE							(	self      ,					*lowerCamelCase_      ,					**lowerCamelCase_     ) ->							BatchEncoding:
														lowerCAmelCase__      =			kwargs.get('''is_split_into_words'''      ,					lowerCamelCase_     )
														assert self.add_prefix_space or not is_split_into_words, (
														    F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
														    "to use it with pretokenized inputs."
														)

														return super()._batch_encode_plus(*lowerCamelCase_      ,					**lowerCamelCase_     )







							def 						__SCREAMING_SNAKE_CASE							(	self      ,					*lowerCamelCase_      ,					**lowerCamelCase_     ) ->							BatchEncoding:
														lowerCAmelCase__      =			kwargs.get('''is_split_into_words'''      ,					lowerCamelCase_     )

														assert self.add_prefix_space or not is_split_into_words, (
														    F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
														    "to use it with pretokenized inputs."
														)

														return super()._encode_plus(*lowerCamelCase_      ,					**lowerCamelCase_     )







							def 						__SCREAMING_SNAKE_CASE							(	self      ,					lowerCamelCase_      ,					lowerCamelCase_ = None     ) ->							Tuple[str]:
														lowerCAmelCase__      =			self._tokenizer.model.save(lowerCamelCase_      ,					name=lowerCamelCase_     )
														return tuple(lowerCamelCase_     )







							def 						__SCREAMING_SNAKE_CASE							(	self      ,					lowerCamelCase_      ,					lowerCamelCase_=None     ) ->							Tuple:
														lowerCAmelCase__      =			[self.bos_token_id] + token_ids_a + [self.eos_token_id]
														if token_ids_a is None:
																					return output

														return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]







							def 						__SCREAMING_SNAKE_CASE							(	self      ,					lowerCamelCase_      ,					lowerCamelCase_ = None     ) ->							List[int]:
														lowerCAmelCase__      =			[self.sep_token_id]
														lowerCAmelCase__      =			[self.cls_token_id]

														if token_ids_a is None:
																					return len(cls + token_ids_a + sep     ) * [0]
														return len(cls + token_ids_a + sep + sep + token_ids_a + sep     ) * [0]