"""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


SCREAMING_SNAKE_CASE__      						=  logging.getLogger(__name__)



class   lowerCAmelCase_    (			lowerCAmelCase					):



				"""simple docstring"""
				_lowerCAmelCase     :    List[str]			  =						"""summarization"""
				_lowerCAmelCase     :    str			  =						["""loss"""]
				_lowerCAmelCase     :    str			  =						ROUGE_KEYS
				_lowerCAmelCase     :    List[Any]			  =						"""rouge2"""

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






										"""simple docstring"""

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

										super().__init__(lowerCAmelCase		,    num_labels=lowerCAmelCase		,    mode=self.mode		,    **lowerCAmelCase    )
										use_task_specific_params(self.model		,    'summarization'    )
										save_git_info(self.hparams.output_dir    )
										snake_case								=			Path(self.output_dir    ) / 'metrics.json'
										snake_case								=			Path(self.output_dir    ) / 'hparams.pkl'
										pickle_save(self.hparams		,    self.hparams_save_path    )
										snake_case								=			0
										snake_case								=			defaultdict(lowerCAmelCase    )
										snake_case								=			self.config.model_type
										snake_case								=			self.config.tgt_vocab_size if self.model_type == 'fsmt' else self.config.vocab_size

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

										snake_case								=			{
										    'train': self.hparams.max_target_length,
										    'val': self.hparams.val_max_target_length,
										    'test': self.hparams.test_max_target_length,
										}
										assert self.target_lens["train"] <= self.target_lens["val"], F"""target_lens: {self.target_lens}"""
										assert self.target_lens["train"] <= self.target_lens["test"], F"""target_lens: {self.target_lens}"""
										if self.hparams.freeze_embeds:
																freeze_embeds(self.model    )
										if self.hparams.freeze_encoder:
																freeze_params(self.model.get_encoder()    )
																assert_all_frozen(self.model.get_encoder()    )

										snake_case								=			get_git_info()['repo_sha']
										snake_case								=			hparams.num_workers
										snake_case								=			None  # default to config
										if self.model.config.decoder_start_token_id is None and isinstance(self.tokenizer		,    lowerCAmelCase    ):
																snake_case								=			self.tokenizer.lang_code_to_id[hparams.tgt_lang]
																snake_case								=			self.decoder_start_token_id
										snake_case								=			(
										    SeqaSeqDataset if hasattr(self.tokenizer		,    'prepare_seq2seq_batch'    ) else LegacySeqaSeqDataset
										)
										snake_case								=			False
										snake_case								=			self.model.config.num_beams if self.hparams.eval_beams is None else self.hparams.eval_beams
										if self.hparams.eval_max_gen_length is not None:
																snake_case								=			self.hparams.eval_max_gen_length
										else:
																snake_case								=			self.model.config.max_length
										snake_case								=			self.default_val_metric if self.hparams.val_metric is None else self.hparams.val_metric

				def 		snake_case (				self		,    lowerCAmelCase    ):






										"""simple docstring"""

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

										snake_case								=			True
										return readable_batch

				def 		snake_case (				self		,    lowerCAmelCase		,    **lowerCAmelCase    ):






										"""simple docstring"""

										return self.model(lowerCAmelCase		,    **lowerCAmelCase    )

				def 		snake_case (				self		,    lowerCAmelCase    ):






										"""simple docstring"""

										snake_case								=			self.tokenizer.batch_decode(
										    lowerCAmelCase		,    skip_special_tokens=lowerCAmelCase		,    clean_up_tokenization_spaces=lowerCAmelCase    )
										return lmap(str.strip		,    lowerCAmelCase    )

				def 		snake_case (				self		,    lowerCAmelCase    ):






										"""simple docstring"""

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

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

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

				@property
				def 		snake_case (				self    ):






										"""simple docstring"""

										return self.tokenizer.pad_token_id

				def 		snake_case (				self		,    lowerCAmelCase		,    lowerCAmelCase    ):






										"""simple docstring"""

										snake_case								=			self._step(lowerCAmelCase    )

										snake_case								=			dict(zip(self.loss_names		,    lowerCAmelCase    )    )
										# tokens per batch
										snake_case								=			batch['input_ids'].ne(self.pad    ).sum() + batch['labels'].ne(self.pad    ).sum()
										snake_case								=			batch['input_ids'].shape[0]
										snake_case								=			batch['input_ids'].eq(self.pad    ).sum()
										snake_case								=			batch['input_ids'].eq(self.pad    ).float().mean()
										# TODO(SS): make a wandb summary metric for this
										return {"loss": loss_tensors[0], "log": logs}

				def 		snake_case (				self		,    lowerCAmelCase		,    lowerCAmelCase    ):






										"""simple docstring"""

										return self._generative_step(lowerCAmelCase    )

				def 		snake_case (				self		,    lowerCAmelCase		,    lowerCAmelCase="val"    ):






										"""simple docstring"""

										self.step_count += 1
										snake_case								=			{k: torch.stack([x[k] for x in outputs]    ).mean() for k in self.loss_names}
										snake_case								=			losses['loss']
										snake_case								=			{
										    k: np.array([x[k] for x in outputs]    ).mean() for k in self.metric_names + ['gen_time', 'gen_len']
										}
										snake_case								=			(
										    generative_metrics[self.val_metric] if self.val_metric in generative_metrics else losses[self.val_metric]
										)
										snake_case								=			torch.tensor(lowerCAmelCase    ).type_as(lowerCAmelCase    )
										generative_metrics.update({k: v.item() for k, v in losses.items()}    )
										losses.update(lowerCAmelCase    )
										snake_case								=			{F"""{prefix}_avg_{k}""": x for k, x in losses.items()}
										snake_case								=			self.step_count
										self.metrics[prefix].append(lowerCAmelCase    )  # callback writes this to self.metrics_save_path
										snake_case								=			flatten_list([x['preds'] for x in outputs]    )
										return {
										    "log": all_metrics,
										    "preds": preds,
										    F"""{prefix}_loss""": loss,
										    F"""{prefix}_{self.val_metric}""": metric_tensor,
										}

				def 		snake_case (				self		,    lowerCAmelCase		,    lowerCAmelCase    ):






										"""simple docstring"""

										return calculate_rouge(lowerCAmelCase		,    lowerCAmelCase    )

				def 		snake_case (				self		,    lowerCAmelCase    ):






										"""simple docstring"""

										snake_case								=			time.time()

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

				def 		snake_case (				self		,    lowerCAmelCase		,    lowerCAmelCase    ):






										"""simple docstring"""

										return self._generative_step(lowerCAmelCase    )

				def 		snake_case (				self		,    lowerCAmelCase    ):






										"""simple docstring"""

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

				def 		snake_case (				self		,    lowerCAmelCase    ):






										"""simple docstring"""

										snake_case								=			self.n_obs[type_path]
										snake_case								=			self.target_lens[type_path]
										snake_case								=			self.dataset_class(
										    self.tokenizer		,    type_path=lowerCAmelCase		,    n_obs=lowerCAmelCase		,    max_target_length=lowerCAmelCase		,    **self.dataset_kwargs		,    )
										return dataset

				def 		snake_case (				self		,    lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase = False    ):






										"""simple docstring"""

										snake_case								=			self.get_dataset(lowerCAmelCase    )

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

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

				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			self.get_dataloader('train'		,    batch_size=self.hparams.train_batch_size		,    shuffle=lowerCAmelCase    )
										return dataloader

				def 		snake_case (				self    ):






										"""simple docstring"""

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

				def 		snake_case (				self    ):






										"""simple docstring"""

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

				@staticmethod
				def 		snake_case (				lowerCAmelCase		,    lowerCAmelCase    ):






										"""simple docstring"""

										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   lowerCAmelCase_    (			lowerCAmelCase					):



				"""simple docstring"""
				_lowerCAmelCase     :    Tuple			  =						"""translation"""
				_lowerCAmelCase     :    Tuple			  =						["""loss"""]
				_lowerCAmelCase     :    Tuple			  =						["""bleu"""]
				_lowerCAmelCase     :    Optional[int]			  =						"""bleu"""

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






										"""simple docstring"""

										super().__init__(lowerCAmelCase		,    **lowerCAmelCase    )
										snake_case								=			hparams.src_lang
										snake_case								=			hparams.tgt_lang

				def 		snake_case (				self		,    lowerCAmelCase		,    lowerCAmelCase    ):






										"""simple docstring"""

										return calculate_bleu(lowerCAmelCase		,    lowerCAmelCase    )





def    lowerCAmelCase__    (			_UpperCamelCase       :				Optional[Any] ,			_UpperCamelCase       :				str=None					)					->				SummarizationModule:





						"""simple docstring"""






						Path(args.output_dir					).mkdir(exist_ok=_UpperCamelCase					)
						check_output_dir(_UpperCamelCase ,			expected_items=3					)

						if model is None:
												if "summarization" in args.task:
																		snake_case								=			SummarizationModule(_UpperCamelCase					)
												else:
																		snake_case								=			TranslationModule(_UpperCamelCase					)
						snake_case								=			Path(args.data_dir					).name
						if (
						    args.logger_name == "default"
						    or args.fast_dev_run
						    or str(args.output_dir					).startswith('/tmp'					)
						    or str(args.output_dir					).startswith('/var'					)
						):
												snake_case								=			True  # don't pollute wandb logs unnecessarily
						elif args.logger_name == "wandb":
												from pytorch_lightning.loggers import WandbLogger

												snake_case								=			os.environ.get('WANDB_PROJECT' ,			_UpperCamelCase					)
												snake_case								=			WandbLogger(name=model.output_dir.name ,			project=_UpperCamelCase					)

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

												snake_case								=			WandbLogger(name=model.output_dir.name ,			project=f"""hf_{dataset}"""					)

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

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

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

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


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

					SCREAMING_SNAKE_CASE__      						=  parser.parse_args()

					main(args)



"""simple docstring"""
import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple

import regex as re

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


SCREAMING_SNAKE_CASE__      						=  logging.get_logger(__name__)


SCREAMING_SNAKE_CASE__      						=  {"vocab_file": "vocab.json", "merges_file": "merges.txt"}

# See all BART models at https://huggingface.co/models?filter=bart
SCREAMING_SNAKE_CASE__      						=  {
    "vocab_file": {
        "facebook/bart-base": "https://huggingface.co/facebook/bart-base/resolve/main/vocab.json",
        "facebook/bart-large": "https://huggingface.co/facebook/bart-large/resolve/main/vocab.json",
        "facebook/bart-large-mnli": "https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json",
        "facebook/bart-large-cnn": "https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json",
        "facebook/bart-large-xsum": "https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json",
        "yjernite/bart_eli5": "https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json",
    },
    "merges_file": {
        "facebook/bart-base": "https://huggingface.co/facebook/bart-base/resolve/main/merges.txt",
        "facebook/bart-large": "https://huggingface.co/facebook/bart-large/resolve/main/merges.txt",
        "facebook/bart-large-mnli": "https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt",
        "facebook/bart-large-cnn": "https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt",
        "facebook/bart-large-xsum": "https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt",
        "yjernite/bart_eli5": "https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt",
    },
}

SCREAMING_SNAKE_CASE__      						=  {
    "facebook/bart-base": 1_024,
    "facebook/bart-large": 1_024,
    "facebook/bart-large-mnli": 1_024,
    "facebook/bart-large-cnn": 1_024,
    "facebook/bart-large-xsum": 1_024,
    "yjernite/bart_eli5": 1_024,
}





@lru_cache()
def    lowerCAmelCase__    (			)					->				Union[str, Any]:





						"""simple docstring"""






						snake_case								=			(
						    list(range(ord('!'					) ,			ord('~'					) + 1					)					) + list(range(ord('¡'					) ,			ord('¬'					) + 1					)					) + list(range(ord('®'					) ,			ord('ÿ'					) + 1					)					)
						)
						snake_case								=			bs[:]
						snake_case								=			0
						for b in range(2**8					):
												if b not in bs:
																		bs.append(_UpperCamelCase					)
																		cs.append(2**8 + n					)
																		n += 1
						snake_case								=			[chr(_UpperCamelCase					) for n in cs]
						return dict(zip(_UpperCamelCase ,			_UpperCamelCase					)					)





def    lowerCAmelCase__    (			_UpperCamelCase       :				List[Any]					)					->				List[str]:





						"""simple docstring"""






						snake_case								=			set()
						snake_case								=			word[0]
						for char in word[1:]:
												pairs.add((prev_char, char)					)
												snake_case								=			char
						return pairs



class   lowerCAmelCase_    (			lowerCAmelCase					):



				"""simple docstring"""
				_lowerCAmelCase     :    Union[str, Any]			  =						VOCAB_FILES_NAMES
				_lowerCAmelCase     :    str			  =						PRETRAINED_VOCAB_FILES_MAP
				_lowerCAmelCase     :    int			  =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				_lowerCAmelCase     :    str			  =						["""input_ids""", """attention_mask"""]

				def __init__(				self		,    lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase="replace"		,    lowerCAmelCase="<s>"		,    lowerCAmelCase="</s>"		,    lowerCAmelCase="</s>"		,    lowerCAmelCase="<s>"		,    lowerCAmelCase="<unk>"		,    lowerCAmelCase="<pad>"		,    lowerCAmelCase="<mask>"		,    lowerCAmelCase=False		,    **lowerCAmelCase		,    ):






										"""simple docstring"""

										snake_case								=			AddedToken(lowerCAmelCase		,    lstrip=lowerCAmelCase		,    rstrip=lowerCAmelCase    ) if isinstance(lowerCAmelCase		,    lowerCAmelCase    ) else bos_token
										snake_case								=			AddedToken(lowerCAmelCase		,    lstrip=lowerCAmelCase		,    rstrip=lowerCAmelCase    ) if isinstance(lowerCAmelCase		,    lowerCAmelCase    ) else eos_token
										snake_case								=			AddedToken(lowerCAmelCase		,    lstrip=lowerCAmelCase		,    rstrip=lowerCAmelCase    ) if isinstance(lowerCAmelCase		,    lowerCAmelCase    ) else sep_token
										snake_case								=			AddedToken(lowerCAmelCase		,    lstrip=lowerCAmelCase		,    rstrip=lowerCAmelCase    ) if isinstance(lowerCAmelCase		,    lowerCAmelCase    ) else cls_token
										snake_case								=			AddedToken(lowerCAmelCase		,    lstrip=lowerCAmelCase		,    rstrip=lowerCAmelCase    ) if isinstance(lowerCAmelCase		,    lowerCAmelCase    ) else unk_token
										snake_case								=			AddedToken(lowerCAmelCase		,    lstrip=lowerCAmelCase		,    rstrip=lowerCAmelCase    ) if isinstance(lowerCAmelCase		,    lowerCAmelCase    ) else pad_token

										# Mask token behave like a normal word, i.e. include the space before it
										snake_case								=			AddedToken(lowerCAmelCase		,    lstrip=lowerCAmelCase		,    rstrip=lowerCAmelCase    ) if isinstance(lowerCAmelCase		,    lowerCAmelCase    ) else mask_token

										super().__init__(
										    errors=lowerCAmelCase		,    bos_token=lowerCAmelCase		,    eos_token=lowerCAmelCase		,    unk_token=lowerCAmelCase		,    sep_token=lowerCAmelCase		,    cls_token=lowerCAmelCase		,    pad_token=lowerCAmelCase		,    mask_token=lowerCAmelCase		,    add_prefix_space=lowerCAmelCase		,    **lowerCAmelCase		,    )

										with open(lowerCAmelCase		,    encoding='utf-8'    ) as vocab_handle:
																snake_case								=			json.load(lowerCAmelCase    )
										snake_case								=			{v: k for k, v in self.encoder.items()}
										snake_case								=			errors  # how to handle errors in decoding
										snake_case								=			bytes_to_unicode()
										snake_case								=			{v: k for k, v in self.byte_encoder.items()}
										with open(lowerCAmelCase		,    encoding='utf-8'    ) as merges_handle:
																snake_case								=			merges_handle.read().split('\n'    )[1:-1]
										snake_case								=			[tuple(merge.split()    ) for merge in bpe_merges]
										snake_case								=			dict(zip(lowerCAmelCase		,    range(len(lowerCAmelCase    )    )    )    )
										snake_case								=			{}
										snake_case								=			add_prefix_space

										# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
										snake_case								=			re.compile(R'\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+'    )

				@property
				def 		snake_case (				self    ):






										"""simple docstring"""

										return len(self.encoder    )

				def 		snake_case (				self    ):






										"""simple docstring"""

										return dict(self.encoder		,    **self.added_tokens_encoder    )

				def 		snake_case (				self		,    lowerCAmelCase    ):






										"""simple docstring"""

										if token in self.cache:
																return self.cache[token]
										snake_case								=			tuple(lowerCAmelCase    )
										snake_case								=			get_pairs(lowerCAmelCase    )

										if not pairs:
																return token

										while True:
																snake_case								=			min(lowerCAmelCase		,    key=lambda lowerCAmelCase    : self.bpe_ranks.get(lowerCAmelCase		,    float('inf'    )    )    )
																if bigram not in self.bpe_ranks:
																						break
																snake_case					,snake_case								=			bigram
																snake_case								=			[]
																snake_case								=			0
																while i < len(lowerCAmelCase    ):
																						try:
																												snake_case								=			word.index(lowerCAmelCase		,    lowerCAmelCase    )
																						except ValueError:
																												new_word.extend(word[i:]    )
																												break
																						else:
																												new_word.extend(word[i:j]    )
																												snake_case								=			j

																						if word[i] == first and i < len(lowerCAmelCase    ) - 1 and word[i + 1] == second:
																												new_word.append(first + second    )
																												i += 2
																						else:
																												new_word.append(word[i]    )
																												i += 1
																snake_case								=			tuple(lowerCAmelCase    )
																snake_case								=			new_word
																if len(lowerCAmelCase    ) == 1:
																						break
																else:
																						snake_case								=			get_pairs(lowerCAmelCase    )
										snake_case								=			' '.join(lowerCAmelCase    )
										snake_case								=			word
										return word

				def 		snake_case (				self		,    lowerCAmelCase    ):






										"""simple docstring"""

										snake_case								=			[]
										for token in re.findall(self.pat		,    lowerCAmelCase    ):
																snake_case								=			''.join(
																    self.byte_encoder[b] for b in token.encode('utf-8'    )    )  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
																bpe_tokens.extend(bpe_token for bpe_token in self.bpe(lowerCAmelCase    ).split(' '    )    )
										return bpe_tokens

				def 		snake_case (				self		,    lowerCAmelCase    ):






										"""simple docstring"""

										return self.encoder.get(lowerCAmelCase		,    self.encoder.get(self.unk_token    )    )

				def 		snake_case (				self		,    lowerCAmelCase    ):






										"""simple docstring"""

										return self.decoder.get(lowerCAmelCase    )

				def 		snake_case (				self		,    lowerCAmelCase    ):






										"""simple docstring"""

										snake_case								=			''.join(lowerCAmelCase    )
										snake_case								=			bytearray([self.byte_decoder[c] for c in text]    ).decode('utf-8'		,    errors=self.errors    )
										return text

				def 		snake_case (				self		,    lowerCAmelCase		,    lowerCAmelCase = None    ):






										"""simple docstring"""

										if not os.path.isdir(lowerCAmelCase    ):
																logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""    )
																return
										snake_case								=			os.path.join(
										    lowerCAmelCase		,    (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']    )
										snake_case								=			os.path.join(
										    lowerCAmelCase		,    (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file']    )

										with open(lowerCAmelCase		,    'w'		,    encoding='utf-8'    ) as f:
																f.write(json.dumps(self.encoder		,    indent=2		,    sort_keys=lowerCAmelCase		,    ensure_ascii=lowerCAmelCase    ) + '\n'    )

										snake_case								=			0
										with open(lowerCAmelCase		,    'w'		,    encoding='utf-8'    ) as writer:
																writer.write('#version: 0.2\n'    )
																for bpe_tokens, token_index in sorted(self.bpe_ranks.items()		,    key=lambda lowerCAmelCase    : kv[1]    ):
																						if index != token_index:
																												logger.warning(
																												    F"""Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."""
																												    ' Please check that the tokenizer is not corrupted!'    )
																												snake_case								=			token_index
																						writer.write(' '.join(lowerCAmelCase    ) + '\n'    )
																						index += 1

										return vocab_file, merge_file

				def 		snake_case (				self		,    lowerCAmelCase		,    lowerCAmelCase = None    ):






										"""simple docstring"""

										if token_ids_a is None:
																return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
										snake_case								=			[self.cls_token_id]
										snake_case								=			[self.sep_token_id]
										return cls + token_ids_a + sep + sep + token_ids_a + sep

				def 		snake_case (				self		,    lowerCAmelCase		,    lowerCAmelCase = None		,    lowerCAmelCase = False    ):






										"""simple docstring"""

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

										if token_ids_a is None:
																return [1] + ([0] * len(lowerCAmelCase    )) + [1]
										return [1] + ([0] * len(lowerCAmelCase    )) + [1, 1] + ([0] * len(lowerCAmelCase    )) + [1]

				def 		snake_case (				self		,    lowerCAmelCase		,    lowerCAmelCase = None    ):






										"""simple docstring"""

										snake_case								=			[self.sep_token_id]
										snake_case								=			[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]

				def 		snake_case (				self		,    lowerCAmelCase		,    lowerCAmelCase=False		,    **lowerCAmelCase    ):






										"""simple docstring"""

										snake_case								=			kwargs.pop('add_prefix_space'		,    self.add_prefix_space    )
										if (is_split_into_words or add_prefix_space) and (len(lowerCAmelCase    ) > 0 and not text[0].isspace()):
																snake_case								=			' ' + text
										return (text, kwargs)


