





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__  :     Any   			=logging.getLogger(__name__)

class  __A       (  lowerCamelCase__      ):






				"""simple docstring"""




				__A								=			"""summarization"""
				__A								=			["""loss"""]
				__A								=			ROUGE_KEYS
				__A								=			"""rouge2"""


				def __init__(						self				,	UpperCAmelCase_				,	**UpperCAmelCase_							):





								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__(snake_case__				,	num_labels=snake_case__				,	mode=self.mode				,	**snake_case__							)
								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(snake_case__							)
								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				,	snake_case__							):
												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 _snake_case    (						self				,	UpperCAmelCase_							):
								lowerCamelCase			={
								    k: self.tokenizer.batch_decode(v.tolist()							) if '''mask''' not in k else v.shape for k, v in batch.items()
								}
								save_json(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"""							)

								lowerCamelCase			=True
								return readable_batch


				def _snake_case    (						self				,	UpperCAmelCase_				,	**UpperCAmelCase_							):
								return self.model(snake_case__				,	**snake_case__							)


				def _snake_case    (						self				,	UpperCAmelCase_							):
								lowerCamelCase			=self.tokenizer.batch_decode(
								    snake_case__				,	skip_special_tokens=snake_case__				,	clean_up_tokenization_spaces=snake_case__							)
								return lmap(str.strip				,	snake_case__							)


				def _snake_case    (						self				,	UpperCAmelCase_							):
								lowerCamelCase			=self.tokenizer.pad_token_id
								lowerCamelCase			=batch['''input_ids'''], batch['''attention_mask''']
								lowerCamelCase			=batch['''labels''']
								if isinstance(self.model				,	snake_case__							):
												lowerCamelCase			=self.model._shift_right(snake_case__							)
								else:
												lowerCamelCase			=shift_tokens_right(snake_case__				,	snake_case__							)
								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(snake_case__							)

								lowerCamelCase			=self(snake_case__				,	attention_mask=snake_case__				,	decoder_input_ids=snake_case__				,	use_cache=snake_case__							)
								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=snake_case__							)

												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(snake_case__				,	dim=-1							)
												lowerCamelCase			=label_smoothed_nll_loss(
												    snake_case__				,	snake_case__				,	self.hparams.label_smoothing				,	ignore_index=snake_case__							)
								return (loss,)


				@property
				def _snake_case    (						self							):
								return self.tokenizer.pad_token_id


				def _snake_case    (						self				,	UpperCAmelCase_				,	UpperCAmelCase_							):
								lowerCamelCase			=self._step(snake_case__							)

								lowerCamelCase			=dict(zip(self.loss_names				,	snake_case__							)							)
								# 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 _snake_case    (						self				,	UpperCAmelCase_				,	UpperCAmelCase_							):
								return self._generative_step(snake_case__							)


				def _snake_case    (						self				,	UpperCAmelCase_				,	UpperCAmelCase_="val"							):
								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(snake_case__							).type_as(snake_case__							)
								generative_metrics.update({k: v.item() for k, v in losses.items()}							)
								losses.update(snake_case__							)
								lowerCamelCase			={f"""{prefix}_avg_{k}""": x for k, x in losses.items()}
								lowerCamelCase			=self.step_count
								self.metrics[prefix].append(snake_case__							)  # 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 _snake_case    (						self				,	UpperCAmelCase_				,	UpperCAmelCase_							):
								return calculate_rouge(snake_case__				,	snake_case__							)


				def _snake_case    (						self				,	UpperCAmelCase_							):
								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=snake_case__				,	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(snake_case__							)
								lowerCamelCase			=self.ids_to_clean_text(batch["""labels"""]							)
								lowerCamelCase			=self._step(snake_case__							)
								lowerCamelCase			=dict(zip(self.loss_names				,	snake_case__							)							)
								lowerCamelCase			=self.calc_generative_metrics(snake_case__				,	snake_case__							)
								lowerCamelCase			=np.mean(lmap(snake_case__				,	snake_case__							)							)
								base_metrics.update(gen_time=snake_case__				,	gen_len=snake_case__				,	preds=snake_case__				,	target=snake_case__				,	**snake_case__							)
								return base_metrics


				def _snake_case    (						self				,	UpperCAmelCase_				,	UpperCAmelCase_							):
								return self._generative_step(snake_case__							)


				def _snake_case    (						self				,	UpperCAmelCase_							):
								return self.validation_epoch_end(snake_case__				,	prefix="""test"""							)


				def _snake_case    (						self				,	UpperCAmelCase_							):
								lowerCamelCase			=self.n_obs[type_path]
								lowerCamelCase			=self.target_lens[type_path]
								lowerCamelCase			=self.dataset_class(
								    self.tokenizer				,	type_path=snake_case__				,	n_obs=snake_case__				,	max_target_length=snake_case__				,	**self.dataset_kwargs				,	)
								return dataset


				def _snake_case    (						self				,	UpperCAmelCase_				,	UpperCAmelCase_				,	UpperCAmelCase_ = False							):
								lowerCamelCase			=self.get_dataset(snake_case__							)

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

								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(
												    snake_case__				,	batch_sampler=snake_case__				,	collate_fn=dataset.collate_fn				,	num_workers=self.num_workers				,	)
								else:
												return DataLoader(
												    snake_case__				,	batch_size=snake_case__				,	collate_fn=dataset.collate_fn				,	shuffle=snake_case__				,	num_workers=self.num_workers				,	sampler=snake_case__				,	)


				def _snake_case    (						self							):
								lowerCamelCase			=self.get_dataloader("""train"""				,	batch_size=self.hparams.train_batch_size				,	shuffle=snake_case__							)
								return dataloader


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


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


				@staticmethod
				def _snake_case    (						UpperCAmelCase_				,	UpperCAmelCase_							):
								BaseTransformer.add_model_specific_args(snake_case__				,	snake_case__							)
								add_generic_args(snake_case__				,	snake_case__							)
								parser.add_argument(
								    """--max_source_length"""				,	default=1024				,	type=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=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=142				,	type=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=142				,	type=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=snake_case__							)
								parser.add_argument("""--overwrite_output_dir"""				,	action="""store_true"""				,	default=snake_case__							)
								parser.add_argument("""--max_tokens_per_batch"""				,	type=snake_case__				,	default=snake_case__							)
								parser.add_argument("""--logger_name"""				,	type=snake_case__				,	choices=["""default""", """wandb""", """wandb_shared"""]				,	default="""default"""							)
								parser.add_argument("""--n_train"""				,	type=snake_case__				,	default=-1				,	required=snake_case__				,	help="""# examples. -1 means use all."""							)
								parser.add_argument("""--n_val"""				,	type=snake_case__				,	default=500				,	required=snake_case__				,	help="""# examples. -1 means use all."""							)
								parser.add_argument("""--n_test"""				,	type=snake_case__				,	default=-1				,	required=snake_case__				,	help="""# examples. -1 means use all."""							)
								parser.add_argument(
								    """--task"""				,	type=snake_case__				,	default="""summarization"""				,	required=snake_case__				,	help="""# examples. -1 means use all."""							)
								parser.add_argument("""--label_smoothing"""				,	type=snake_case__				,	default=0.0				,	required=snake_case__							)
								parser.add_argument("""--src_lang"""				,	type=snake_case__				,	default=""""""				,	required=snake_case__							)
								parser.add_argument("""--tgt_lang"""				,	type=snake_case__				,	default=""""""				,	required=snake_case__							)
								parser.add_argument("""--eval_beams"""				,	type=snake_case__				,	default=snake_case__				,	required=snake_case__							)
								parser.add_argument(
								    """--val_metric"""				,	type=snake_case__				,	default=snake_case__				,	required=snake_case__				,	choices=["""bleu""", """rouge2""", """loss""", None]							)
								parser.add_argument("""--eval_max_gen_length"""				,	type=snake_case__				,	default=snake_case__				,	help="""never generate more than n tokens"""							)
								parser.add_argument("""--save_top_k"""				,	type=snake_case__				,	default=1				,	required=snake_case__				,	help="""How many checkpoints to save"""							)
								parser.add_argument(
								    """--early_stopping_patience"""				,	type=snake_case__				,	default=-1				,	required=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				,	UpperCAmelCase_				,	**UpperCAmelCase_							):
								super().__init__(snake_case__				,	**snake_case__							)
								lowerCamelCase			=hparams.src_lang
								lowerCamelCase			=hparams.tgt_lang


				def _snake_case    (						self				,	UpperCAmelCase_				,	UpperCAmelCase_							):
								return calculate_bleu(snake_case__				,	snake_case__							)





def        _lowercase   (  _UpperCAmelCase     , _UpperCAmelCase=None			)  ->					SummarizationModule:
				Path(args.output_dir			).mkdir(exist_ok=__lowerCAmelCase			)
				check_output_dir(__lowerCAmelCase     , expected_items=3			)

				if model is None:
								if "summarization" in args.task:
												lowerCamelCase			=SummarizationModule(__lowerCAmelCase			)
								else:
												lowerCamelCase			=TranslationModule(__lowerCAmelCase			)
				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"""     , __lowerCAmelCase			)
								lowerCamelCase			=WandbLogger(name=model.output_dir.name     , project=__lowerCAmelCase			)

				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(
				    __lowerCAmelCase     , __lowerCAmelCase     , logging_callback=SeqaSeqLoggingCallback()     , checkpoint_callback=get_checkpoint_callback(
				        args.output_dir     , model.val_metric     , args.save_top_k     , __lowerCAmelCase			)     , early_stopping_callback=__lowerCAmelCase     , logger=__lowerCAmelCase     , )
				pickle_save(model.hparams     , model.output_dir / """hparams.pkl"""			)
				if not args.do_predict:
								return model

				lowerCamelCase			=''''''
				lowerCamelCase			=sorted(glob.glob(os.path.join(args.output_dir     , """*.ckpt"""			)     , recursive=__lowerCAmelCase			)			)
				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__  :     List[str]   			=argparse.ArgumentParser()
							UpperCAmelCase__  :     List[Any]   			=pl.Trainer.add_argparse_args(parser)
							UpperCAmelCase__  :     Any   			=SummarizationModule.add_model_specific_args(parser, os.getcwd())

							UpperCAmelCase__  :     Any   			=parser.parse_args()

							main(args)












import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import SPIECE_UNDERLINE, logging


UpperCAmelCase__  :     List[Any]   			=logging.get_logger(__name__)

UpperCAmelCase__  :     Dict   			={'''vocab_file''': '''spiece.model'''}

UpperCAmelCase__  :     Dict   			={
    '''vocab_file''': {
        '''xlnet-base-cased''': '''https://huggingface.co/xlnet-base-cased/resolve/main/spiece.model''',
        '''xlnet-large-cased''': '''https://huggingface.co/xlnet-large-cased/resolve/main/spiece.model''',
    }
}

UpperCAmelCase__  :     List[str]   			={
    '''xlnet-base-cased''': None,
    '''xlnet-large-cased''': None,
}

# Segments (not really needed)
UpperCAmelCase__  :     Any   			=0
UpperCAmelCase__  :     List[Any]   			=1
UpperCAmelCase__  :     Union[str, Any]   			=2
UpperCAmelCase__  :     Tuple   			=3
UpperCAmelCase__  :     int   			=4

class  __A       (  a      ):
				__A								=			VOCAB_FILES_NAMES
				__A								=			PRETRAINED_VOCAB_FILES_MAP
				__A								=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				__A								=			"""left"""


				def __init__(						self				,	UpperCAmelCase_				,	UpperCAmelCase_=False				,	UpperCAmelCase_=True				,	UpperCAmelCase_=False				,	UpperCAmelCase_="<s>"				,	UpperCAmelCase_="</s>"				,	UpperCAmelCase_="<unk>"				,	UpperCAmelCase_="<sep>"				,	UpperCAmelCase_="<pad>"				,	UpperCAmelCase_="<cls>"				,	UpperCAmelCase_="<mask>"				,	UpperCAmelCase_=["<eop>", "<eod>"]				,	UpperCAmelCase_ = None				,	**UpperCAmelCase_				,	):
								# Mask token behave like a normal word, i.e. include the space before it
								lowerCamelCase			=AddedToken(UpperCAmelCase_				,	lstrip=UpperCAmelCase_				,	rstrip=UpperCAmelCase_							) if isinstance(UpperCAmelCase_				,	UpperCAmelCase_							) else mask_token

								lowerCamelCase			={} if sp_model_kwargs is None else sp_model_kwargs

								super().__init__(
								    do_lower_case=UpperCAmelCase_				,	remove_space=UpperCAmelCase_				,	keep_accents=UpperCAmelCase_				,	bos_token=UpperCAmelCase_				,	eos_token=UpperCAmelCase_				,	unk_token=UpperCAmelCase_				,	sep_token=UpperCAmelCase_				,	pad_token=UpperCAmelCase_				,	cls_token=UpperCAmelCase_				,	mask_token=UpperCAmelCase_				,	additional_special_tokens=UpperCAmelCase_				,	sp_model_kwargs=self.sp_model_kwargs				,	**UpperCAmelCase_				,	)

								lowerCamelCase			=3

								lowerCamelCase			=do_lower_case
								lowerCamelCase			=remove_space
								lowerCamelCase			=keep_accents
								lowerCamelCase			=vocab_file

								lowerCamelCase			=spm.SentencePieceProcessor(**self.sp_model_kwargs							)
								self.sp_model.Load(UpperCAmelCase_							)


				@property
				def _snake_case    (						self							):
								return len(self.sp_model							)


				def _snake_case    (						self							):
								lowerCamelCase			={self.convert_ids_to_tokens(UpperCAmelCase_							): i for i in range(self.vocab_size							)}
								vocab.update(self.added_tokens_encoder							)
								return vocab


				def __getstate__(						self							):
								lowerCamelCase			=self.__dict__.copy()
								lowerCamelCase			=None
								return state


				def __setstate__(						self				,	UpperCAmelCase_							):
								lowerCamelCase			=d

								# for backward compatibility
								if not hasattr(self				,	"""sp_model_kwargs"""							):
												lowerCamelCase			={}

								lowerCamelCase			=spm.SentencePieceProcessor(**self.sp_model_kwargs							)
								self.sp_model.Load(self.vocab_file							)


				def _snake_case    (						self				,	UpperCAmelCase_							):
								if self.remove_space:
												lowerCamelCase			=""" """.join(inputs.strip().split()							)
								else:
												lowerCamelCase			=inputs
								lowerCamelCase			=outputs.replace("""``"""				,	"""\""""							).replace("""''"""				,	"""\""""							)

								if not self.keep_accents:
												lowerCamelCase			=unicodedata.normalize("""NFKD"""				,	UpperCAmelCase_							)
												lowerCamelCase			="""""".join([c for c in outputs if not unicodedata.combining(UpperCAmelCase_							)]							)
								if self.do_lower_case:
												lowerCamelCase			=outputs.lower()

								return outputs


				def _snake_case    (						self				,	UpperCAmelCase_							):
								lowerCamelCase			=self.preprocess_text(UpperCAmelCase_							)
								lowerCamelCase			=self.sp_model.encode(UpperCAmelCase_				,	out_type=UpperCAmelCase_							)
								lowerCamelCase			=[]
								for piece in pieces:
												if len(UpperCAmelCase_							) > 1 and piece[-1] == str(""","""							) and piece[-2].isdigit():
																lowerCamelCase			=self.sp_model.EncodeAsPieces(piece[:-1].replace(UpperCAmelCase_				,	""""""							)							)
																if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
																				if len(cur_pieces[0]							) == 1:
																								lowerCamelCase			=cur_pieces[1:]
																				else:
																								lowerCamelCase			=cur_pieces[0][1:]
																cur_pieces.append(piece[-1]							)
																new_pieces.extend(UpperCAmelCase_							)
												else:
																new_pieces.append(UpperCAmelCase_							)

								return new_pieces


				def _snake_case    (						self				,	UpperCAmelCase_							):
								return self.sp_model.PieceToId(UpperCAmelCase_							)


				def _snake_case    (						self				,	UpperCAmelCase_							):
								return self.sp_model.IdToPiece(UpperCAmelCase_							)


				def _snake_case    (						self				,	UpperCAmelCase_							):
								lowerCamelCase			="""""".join(UpperCAmelCase_							).replace(UpperCAmelCase_				,	""" """							).strip()
								return out_string


				def _snake_case    (						self				,	UpperCAmelCase_				,	UpperCAmelCase_ = False				,	UpperCAmelCase_ = None				,	UpperCAmelCase_ = True				,	**UpperCAmelCase_				,	):
								lowerCamelCase			=kwargs.pop("""use_source_tokenizer"""				,	UpperCAmelCase_							)

								lowerCamelCase			=self.convert_ids_to_tokens(UpperCAmelCase_				,	skip_special_tokens=UpperCAmelCase_							)

								# To avoid mixing byte-level and unicode for byte-level BPT
								# we need to build string separately for added tokens and byte-level tokens
								# cf. https://github.com/huggingface/transformers/issues/1133
								lowerCamelCase			=[]
								lowerCamelCase			=[]
								for token in filtered_tokens:
												if skip_special_tokens and token in self.all_special_ids:
																continue
												if token in self.added_tokens_encoder:
																if current_sub_text:
																				sub_texts.append(self.convert_tokens_to_string(UpperCAmelCase_							)							)
																				lowerCamelCase			=[]
																sub_texts.append(UpperCAmelCase_							)
												else:
																current_sub_text.append(UpperCAmelCase_							)
								if current_sub_text:
												sub_texts.append(self.convert_tokens_to_string(UpperCAmelCase_							)							)

								# Mimic the behavior of the Rust tokenizer:
								# By default, there are no spaces between special tokens
								lowerCamelCase			="""""".join(UpperCAmelCase_							)

								lowerCamelCase			=(
								    clean_up_tokenization_spaces
								    if clean_up_tokenization_spaces is not None
								    else self.clean_up_tokenization_spaces
								)
								if clean_up_tokenization_spaces:
												lowerCamelCase			=self.clean_up_tokenization(UpperCAmelCase_							)
												return clean_text
								else:
												return text


				def _snake_case    (						self				,	UpperCAmelCase_				,	UpperCAmelCase_ = None							):
								lowerCamelCase			=[self.sep_token_id]
								lowerCamelCase			=[self.cls_token_id]
								if token_ids_a is None:
												return token_ids_a + sep + cls
								return token_ids_a + sep + token_ids_a + sep + cls


				def _snake_case    (						self				,	UpperCAmelCase_				,	UpperCAmelCase_ = None				,	UpperCAmelCase_ = False							):

								if already_has_special_tokens:
												return super().get_special_tokens_mask(
												    token_ids_a=UpperCAmelCase_				,	token_ids_a=UpperCAmelCase_				,	already_has_special_tokens=UpperCAmelCase_							)

								if token_ids_a is not None:
												return ([0] * len(UpperCAmelCase_							)) + [1] + ([0] * len(UpperCAmelCase_							)) + [1, 1]
								return ([0] * len(UpperCAmelCase_							)) + [1, 1]


				def _snake_case    (						self				,	UpperCAmelCase_				,	UpperCAmelCase_ = None							):
								lowerCamelCase			=[self.sep_token_id]
								lowerCamelCase			=[2]

								if token_ids_a is None:
												return len(token_ids_a + sep							) * [0] + cls_segment_id
								return len(token_ids_a + sep							) * [0] + len(token_ids_a + sep							) * [1] + cls_segment_id


				def _snake_case    (						self				,	UpperCAmelCase_				,	UpperCAmelCase_ = None							):
								if not os.path.isdir(UpperCAmelCase_							):
												logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""							)
												return
								lowerCamelCase			=os.path.join(
								    UpperCAmelCase_				,	(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]							)

								if os.path.abspath(self.vocab_file							) != os.path.abspath(UpperCAmelCase_							) and os.path.isfile(self.vocab_file							):
												copyfile(self.vocab_file				,	UpperCAmelCase_							)
								elif not os.path.isfile(self.vocab_file							):
												with open(UpperCAmelCase_				,	"""wb"""							) as fi:
																lowerCamelCase			=self.sp_model.serialized_model_proto()
																fi.write(UpperCAmelCase_							)

								return (out_vocab_file,)





