





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

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


UpperCAmelCase					:		Tuple		=							logging.get_logger(__name__)

UpperCAmelCase					:		Optional[Any]		=							{'vocab_file': 'spiece.model'}

UpperCAmelCase					:		Optional[Any]		=							{
    'vocab_file': {
        'bert_for_seq_generation': (
            'https://huggingface.co/google/bert_for_seq_generation_L-24_bbc_encoder/resolve/main/spiece.model'
        ),
    }
}

UpperCAmelCase					:		int		=							{'bert_for_seq_generation': 5_1_2}





class lowerCamelCase						(a__					):
  _lowercase  :	Tuple       					=				VOCAB_FILES_NAMES
  _lowercase  :	Tuple       					=				PRETRAINED_VOCAB_FILES_MAP
  _lowercase  :	Optional[int]       					=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  _lowercase  :	List[int]       					=				[]
  _lowercase  :	Any       					=				["""input_ids""", """attention_mask"""]







  def __init__(		self     ,     lowercase__     ,     lowercase__="<s>"     ,     lowercase__="</s>"     ,     lowercase__="<unk>"     ,     lowercase__="<pad>"     ,     lowercase__="<::::>"     ,     lowercase__ = None     ,     **lowercase__     ,     )		->						None:






        """simple docstring"""





        _snake_case		:	List[str]					     =     {} if sp_model_kwargs is None else sp_model_kwargs

        # Add extra_ids to the special token list
        super().__init__(
            bos_token=lowercase__     ,     eos_token=lowercase__     ,     unk_token=lowercase__     ,     pad_token=lowercase__     ,     sep_token=lowercase__     ,     sp_model_kwargs=self.sp_model_kwargs     ,     **lowercase__     ,     )

        _snake_case		:	Dict					     =     vocab_file

        _snake_case		:	List[Any]					     =     spm.SentencePieceProcessor(**self.sp_model_kwargs		)
        self.sp_model.Load(lowercase__		)







  @property
  def 				UpperCAmelCase_				(		self		)		->						Tuple:






        """simple docstring"""





        return self.sp_model.get_piece_size()







  def 				UpperCAmelCase_				(		self		)		->						Any:






        """simple docstring"""





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







  def __getstate__(		self		)		->						Dict:






        """simple docstring"""





        _snake_case		:	Tuple					     =     self.__dict__.copy()
        _snake_case		:	Union[str, Any]					     =     None
        return state







  def __setstate__(		self     ,     lowercase__		)		->						List[str]:






        """simple docstring"""





        _snake_case		:	Tuple					     =     d

        # for backward compatibility
        if not hasattr(self     ,     '''sp_model_kwargs'''		):
              _snake_case		:	List[Any]					     =     {}

        _snake_case		:	Optional[Any]					     =     spm.SentencePieceProcessor(**self.sp_model_kwargs		)
        self.sp_model.Load(self.vocab_file		)







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






        """simple docstring"""





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







  def 				UpperCAmelCase_				(		self     ,     lowercase__		)		->						str:






        """simple docstring"""





        return self.sp_model.piece_to_id(lowercase__		)







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






        """simple docstring"""





        _snake_case		:	Dict					     =     self.sp_model.IdToPiece(lowercase__		)
        return token







  def 				UpperCAmelCase_				(		self     ,     lowercase__		)		->						int:






        """simple docstring"""





        _snake_case		:	Any					     =     []
        _snake_case		:	List[Any]					     =     ''''''
        for token in tokens:
              # make sure that special tokens are not decoded using sentencepiece model
              if token in self.all_special_tokens:
                    out_string += self.sp_model.decode(lowercase__		) + token
                    _snake_case		:	str					     =     []
              else:
                    current_sub_tokens.append(lowercase__		)
        out_string += self.sp_model.decode(lowercase__		)
        return out_string.strip()







  def 				UpperCAmelCase_				(		self     ,     lowercase__     ,     lowercase__ = None		)		->						Tuple[str]:






        """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''']		)

        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		:	Union[str, Any]					     =     self.sp_model.serialized_model_proto()
                    fi.write(lowercase__		)

        return (out_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()


