

"""simple docstring"""






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


_lowerCAmelCase						=		logging.get_logger(__name__)

_lowerCAmelCase						=		{"""vocab_file""": """sentencepiece.bpe.model"""}

_lowerCAmelCase						=		{
    """vocab_file""": {
        """camembert-base""": """https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model""",
    }
}

_lowerCAmelCase						=		{
    """camembert-base""": 5_1_2,
}

_lowerCAmelCase						=		"""▁"""







class 	__UpperCamelCase    (    a__						):
  _UpperCAmelCase          =		VOCAB_FILES_NAMES
  _UpperCAmelCase          =		PRETRAINED_VOCAB_FILES_MAP
  _UpperCAmelCase          =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  _UpperCAmelCase          =		["input_ids", "attention_mask"]

  def __init__(		self    ,_A    ,_A="<s>"    ,_A="</s>"    ,_A="</s>"    ,_A="<s>"    ,_A="<unk>"    ,_A="<pad>"    ,_A="<mask>"    ,_A=["<s>NOTUSED", "</s>NOTUSED"]    ,_A = None    ,**_A    ,):
      '''simple docstring'''

      _lowerCAmelCase       :   int  =    AddedToken(_A    ,lstrip=_A    ,rstrip=_A      ) if isinstance(_A    ,_A      ) else mask_token

      _lowerCAmelCase       :   Dict  =    {} if sp_model_kwargs is None else sp_model_kwargs

      super().__init__(
          bos_token=_A    ,eos_token=_A    ,unk_token=_A    ,sep_token=_A    ,cls_token=_A    ,pad_token=_A    ,mask_token=_A    ,additional_special_tokens=_A    ,sp_model_kwargs=self.sp_model_kwargs    ,**_A    ,)
      _lowerCAmelCase       :   Union[str, Any]  =    spm.SentencePieceProcessor(**self.sp_model_kwargs      )
      self.sp_model.Load(str(_A      )      )
      _lowerCAmelCase       :   Tuple  =    vocab_file
      # HACK: These tokens were added by fairseq but don't seem to be actually used when duplicated in the actual
      # sentencepiece vocabulary (this is the case for <s> and </s>
      _lowerCAmelCase       :   Optional[int]  =    {'<s>NOTUSED': 0, '<pad>': 1, '</s>NOTUSED': 2, '<unk>': 3}
      _lowerCAmelCase       :   List[str]  =    len(self.fairseq_tokens_to_ids      )
      _lowerCAmelCase       :   Union[str, Any]  =    len(self.sp_model      ) + len(self.fairseq_tokens_to_ids      )
      _lowerCAmelCase       :   Optional[int]  =    {v: k for k, v in self.fairseq_tokens_to_ids.items()}

  def __lowerCamelCase		(		self    ,_A    ,_A = None      ):
      '''simple docstring'''

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

  def __lowerCamelCase		(		self    ,_A    ,_A = None    ,_A = False      ):
      '''simple docstring'''

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

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

  def __lowerCamelCase		(		self    ,_A    ,_A = None      ):
      '''simple docstring'''

      _lowerCAmelCase       :   int  =    [self.sep_token_id]
      _lowerCAmelCase       :   str  =    [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 __lowerCamelCase		(		self      ):
      '''simple docstring'''

      return len(self.fairseq_tokens_to_ids      ) + len(self.sp_model      )

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Optional[int]  =    {self.convert_ids_to_tokens(_A      ): i for i in range(self.vocab_size      )}
      vocab.update(self.added_tokens_encoder      )
      return vocab

  def __lowerCamelCase		(		self    ,_A      ):
      '''simple docstring'''

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

  def __lowerCamelCase		(		self    ,_A      ):
      '''simple docstring'''

      if token in self.fairseq_tokens_to_ids:
          return self.fairseq_tokens_to_ids[token]
      elif self.sp_model.PieceToId(_A      ) == 0:
          # Convert sentence piece unk token to fairseq unk token index
          return self.unk_token_id
      return self.fairseq_offset + self.sp_model.PieceToId(_A      )

  def __lowerCamelCase		(		self    ,_A      ):
      '''simple docstring'''

      if index in self.fairseq_ids_to_tokens:
          return self.fairseq_ids_to_tokens[index]
      return self.sp_model.IdToPiece(index - self.fairseq_offset      )

  def __lowerCamelCase		(		self    ,_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Tuple  =    []
      _lowerCAmelCase       :   Optional[int]  =    ''
      _lowerCAmelCase       :   Dict  =    False
      for token in tokens:
          # make sure that special tokens are not decoded using sentencepiece model
          if token in self.all_special_tokens:
              if not prev_is_special:
                  out_string += " "
              out_string += self.sp_model.decode(_A      ) + token
              _lowerCAmelCase       :   str  =    True
              _lowerCAmelCase       :   Optional[int]  =    []
          else:
              current_sub_tokens.append(_A      )
              _lowerCAmelCase       :   Union[str, Any]  =    False
      out_string += self.sp_model.decode(_A      )
      return out_string.strip()

  def __getstate__(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   int  =    self.__dict__.copy()
      _lowerCAmelCase       :   str  =    None
      return state

  def __setstate__(		self    ,_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Union[str, Any]  =    d

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

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







  def __lowerCamelCase		(		self    ,_A    ,_A = None      ):
      '''simple docstring'''

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

      if os.path.abspath(self.vocab_file      ) != os.path.abspath(_A      ) and os.path.isfile(self.vocab_file      ):
          copyfile(self.vocab_file    ,_A      )
      elif not os.path.isfile(self.vocab_file      ):
          with open(_A    ,'wb'      ) as fi:
              _lowerCAmelCase       :   str  =    self.sp_model.serialized_model_proto()
              fi.write(_A      )

      return (out_vocab_file,)








"""simple docstring"""






from __future__ import annotations

def 		lowerCamelCase__		(					_lowerCamelCase   ):

    '''simple docstring'''



    create_state_space_tree(_lowerCamelCase ,   [] ,   0 ,   [0 for i in range(len(_lowerCamelCase   )   )]   )

def 		lowerCamelCase__		(					_lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase ,   ):

    '''simple docstring'''



    if index == len(_lowerCamelCase   ):
        print(_lowerCamelCase   )
        return

    for i in range(len(_lowerCamelCase   )   ):
        if not index_used[i]:
            current_sequence.append(sequence[i]   )
            _lowerCAmelCase       :   List[str]  =    True
            create_state_space_tree(_lowerCamelCase ,   _lowerCamelCase ,   index + 1 ,   _lowerCamelCase   )
            current_sequence.pop()
            _lowerCAmelCase       :   int  =    False
_lowerCAmelCase						=		[3, 1, 2, 4]
generate_all_permutations(sequence)

_lowerCAmelCase						=		["A", "B", "C"]

generate_all_permutations(sequence_a)





