





import os
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 logging


UpperCAmelCase					:		Union[str, Any]		=							logging.get_logger(__name__)

UpperCAmelCase					:		Union[str, Any]		=							"▁"

UpperCAmelCase					:		str		=							{"vocab_file": "sentencepiece.bpe.model", "monolingual_vocab_file": "dict.txt"}

UpperCAmelCase					:		Tuple		=							{
    "vocab_file": {
        "vinai/bartpho-syllable": "https://huggingface.co/vinai/bartpho-syllable/resolve/main/sentencepiece.bpe.model",
    },
    "monolingual_vocab_file": {
        "vinai/bartpho-syllable": "https://huggingface.co/vinai/bartpho-syllable/resolve/main/dict.txt",
    },
}

UpperCAmelCase					:		Optional[int]		=							{"vinai/bartpho-syllable": 1_0_2_4}





class lowerCamelCase						(_UpperCAmelCase					):
  _lowercase  :	Union[str, Any]       					=				VOCAB_FILES_NAMES
  _lowercase  :	Any       					=				PRETRAINED_VOCAB_FILES_MAP
  _lowercase  :	int       					=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  _lowercase  :	List[str]       					=				["""input_ids""", """attention_mask"""]







  def __init__(		self     ,     lowercase__     ,     lowercase__     ,     lowercase__="<s>"     ,     lowercase__="</s>"     ,     lowercase__="</s>"     ,     lowercase__="<s>"     ,     lowercase__="<unk>"     ,     lowercase__="<pad>"     ,     lowercase__="<mask>"     ,     lowercase__ = None     ,     **lowercase__     ,     )		->						List[str]:






        """simple docstring"""





        _snake_case		:	int					     =     AddedToken(lowercase_     ,     lstrip=lowercase_     ,     rstrip=lowercase_		) if isinstance(lowercase_     ,     lowercase_		) else mask_token

        _snake_case		:	Any					     =     {} if sp_model_kwargs is None else sp_model_kwargs

        super().__init__(
            bos_token=lowercase_     ,     eos_token=lowercase_     ,     unk_token=lowercase_     ,     sep_token=lowercase_     ,     cls_token=lowercase_     ,     pad_token=lowercase_     ,     mask_token=lowercase_     ,     sp_model_kwargs=self.sp_model_kwargs     ,     **lowercase_     ,     )

        _snake_case		:	Tuple					     =     vocab_file
        _snake_case		:	Any					     =     monolingual_vocab_file
        _snake_case		:	Optional[int]					     =     spm.SentencePieceProcessor(**self.sp_model_kwargs		)
        self.sp_model.Load(str(lowercase_		)		)

        # Load the reduced vocab

        # Keep order of special tokens for backward compatibility
        _snake_case		:	Dict					     =     {}
        _snake_case		:	Optional[int]					     =     0
        for token in [bos_token, pad_token, eos_token, unk_token, sep_token, cls_token]:
              if str(lowercase_		) not in self.fairseq_tokens_to_ids:
                    _snake_case		:	List[Any]					     =     cnt
                    cnt += 1
        with open(lowercase_     ,     '''r'''     ,     encoding='''utf-8'''		) as f:
              for line in f.readlines():
                    _snake_case		:	int					     =     line.strip().split()[0]
                    _snake_case		:	Dict					     =     len(self.fairseq_tokens_to_ids		)
        if str(lowercase_		) not in self.fairseq_tokens_to_ids:
              _snake_case		:	Optional[Any]					     =     len(self.fairseq_tokens_to_ids		)

        _snake_case		:	Tuple					     =     {v: k for k, v in self.fairseq_tokens_to_ids.items()}







  def __getstate__(		self		)		->						str:






        """simple docstring"""





        _snake_case		:	List[str]					     =     self.__dict__.copy()
        _snake_case		:	Optional[Any]					     =     None
        _snake_case		:	str					     =     self.sp_model.serialized_model_proto()
        return state







  def __setstate__(		self     ,     lowercase__		)		->						str:






        """simple docstring"""





        _snake_case		:	int					     =     d

        # for backward compatibility
        if not hasattr(self     ,     '''sp_model_kwargs'''		):
              _snake_case		:	int					     =     {}

        _snake_case		:	List[str]					     =     spm.SentencePieceProcessor(**self.sp_model_kwargs		)
        self.sp_model.LoadFromSerializedProto(self.sp_model_proto		)







  def 				UpperCAmelCase_				(		self     ,     lowercase__     ,     lowercase__ = None		)		->						Any:






        """simple docstring"""





        if token_ids_a is None:
              return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
        _snake_case		:	Optional[int]					     =     [self.cls_token_id]
        _snake_case		:	str					     =     [self.sep_token_id]
        return cls + token_ids_a + sep + sep + token_ids_a + sep







  def 				UpperCAmelCase_				(		self     ,     lowercase__     ,     lowercase__ = None     ,     lowercase__ = False		)		->						Optional[Any]:






        """simple docstring"""





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

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







  def 				UpperCAmelCase_				(		self     ,     lowercase__     ,     lowercase__ = None		)		->						List[Any]:






        """simple docstring"""





        _snake_case		:	Any					     =     [self.sep_token_id]
        _snake_case		:	Tuple					     =     [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]







  @property
  def 				UpperCAmelCase_				(		self		)		->						List[str]:






        """simple docstring"""





        return len(self.fairseq_ids_to_tokens		)







  def 				UpperCAmelCase_				(		self		)		->						List[str]:






        """simple docstring"""





        _snake_case		:	Dict					     =     {self.convert_ids_to_tokens(lowercase_		): i for i in range(self.vocab_size		)}
        vocab.update(self.added_tokens_encoder		)
        return vocab







  def 				UpperCAmelCase_				(		self     ,     lowercase__		)		->						Union[str, Any]:






        """simple docstring"""





        return self.sp_model.encode(lowercase_     ,     out_type=lowercase_		)







  def 				UpperCAmelCase_				(		self     ,     lowercase__		)		->						Optional[int]:






        """simple docstring"""





        if token in self.fairseq_tokens_to_ids:
              return self.fairseq_tokens_to_ids[token]
        else:
              return self.unk_token_id







  def 				UpperCAmelCase_				(		self     ,     lowercase__		)		->						str:






        """simple docstring"""





        return self.fairseq_ids_to_tokens[index]







  def 				UpperCAmelCase_				(		self     ,     lowercase__		)		->						Tuple:






        """simple docstring"""





        _snake_case		:	Union[str, Any]					     =     """""".join(lowercase_		).replace(lowercase_     ,     ''' '''		).strip()
        return out_string







  def 				UpperCAmelCase_				(		self     ,     lowercase__     ,     lowercase__ = None		)		->						Optional[Any]:






        """simple docstring"""





        if not os.path.isdir(lowercase_		):
              logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''		)
              return
        _snake_case		:	Optional[int]					     =     os.path.join(
            lowercase_     ,     (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']		)
        _snake_case		:	str					     =     os.path.join(
            lowercase_     ,     (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''monolingual_vocab_file''']     ,     )

        if os.path.abspath(self.vocab_file		) != os.path.abspath(lowercase_		) and os.path.isfile(self.vocab_file		):
              copyfile(self.vocab_file     ,     lowercase_		)
        elif not os.path.isfile(self.vocab_file		):
              with open(lowercase_     ,     '''wb'''		) as fi:
                    _snake_case		:	Optional[Any]					     =     self.sp_model.serialized_model_proto()
                    fi.write(lowercase_		)

        if os.path.abspath(self.monolingual_vocab_file		) != os.path.abspath(
            lowercase_		) and os.path.isfile(self.monolingual_vocab_file		):
              copyfile(self.monolingual_vocab_file     ,     lowercase_		)
        elif not os.path.isfile(self.monolingual_vocab_file		):
              with open(lowercase_     ,     '''w'''     ,     encoding='''utf-8'''		) as fp:
                    for token in self.fairseq_tokens_to_ids:
                          if token not in self.all_special_tokens:
                                fp.write(F'''{str(lowercase_		)} \n'''		)

        return out_vocab_file, out_monolingual_vocab_file









'''simple docstring'''

import pickle
import unittest

import torch

from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu





@require_cpu
class lowerCamelCase						(unittest.TestCase					):







  def 				UpperCAmelCase_				(		self		)		->						Union[str, Any]:






        """simple docstring"""





        _snake_case		:	Any					     =     torch.nn.Linear(10     ,     10		)
        _snake_case		:	Optional[int]					     =     torch.optim.SGD(model.parameters()     ,     0.1		)
        _snake_case		:	List[str]					     =     Accelerator()
        _snake_case		:	Optional[Any]					     =     accelerator.prepare(lowercase__		)
        try:
              pickle.loads(pickle.dumps(lowercase__		)		)
        except Exception as e:
              self.fail(F'''Accelerated optimizer pickling failed with {e}'''		)
        AcceleratorState._reset_state()


