



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


a__    =	logging.get_logger(__name__)


a__    =	"""▁"""

a__    =	{"""vocab_file""": """spiece.model"""}

a__    =	{
    """vocab_file""": {
        """google/reformer-crime-and-punishment""": (
            """https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model"""
        )
    }
}

a__    =	{
    """google/reformer-crime-and-punishment""": 52_42_88,
}







class 		snake_case						(	SCREAMING_SNAKE_CASE_  ):



   '''simple docstring'''






   snake_case_					: List[str]          =		VOCAB_FILES_NAMES
   snake_case_					: List[str]          =		PRETRAINED_VOCAB_FILES_MAP
   snake_case_					: str          =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   snake_case_					: Tuple          =		["""input_ids""", """attention_mask"""]

   def __init__(					self							:			Any				,		lowerCAmelCase							:			Dict				,		lowerCAmelCase							:			Tuple="</s>"				,		lowerCAmelCase							:			Union[str, Any]="<unk>"				,		lowerCAmelCase							:			Optional[Any]=[]				,		lowerCAmelCase							:			Optional[Dict[str, Any]] = None				,		**lowerCAmelCase							:			Tuple				,		)				->				None:

    """simple docstring"""







    _snake_case    :  Union[str, Any]					=					{} if sp_model_kwargs is None else sp_model_kwargs

    super().__init__(
        eos_token=lowerCAmelCase				,		unk_token=lowerCAmelCase				,		additional_special_tokens=lowerCAmelCase				,		sp_model_kwargs=self.sp_model_kwargs				,		**lowerCAmelCase				,		)

    _snake_case    :  Optional[int]					=					vocab_file
    _snake_case    :  Union[str, Any]					=					spm.SentencePieceProcessor(**self.sp_model_kwargs)
    self.sp_model.Load(lowerCAmelCase)

   @property
   def      UpperCamelCase_  (					self							:			Optional[Any])				->				Dict:

    """simple docstring"""







    return self.sp_model.get_piece_size()

   def      UpperCamelCase_  (					self							:			List[str])				->				Dict[str, int]:

    """simple docstring"""







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

   def __getstate__(					self							:			Optional[Any])				->				Dict:

    """simple docstring"""







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

   def __setstate__(					self							:			str				,		lowerCAmelCase							:			Optional[int])				->				List[Any]:

    """simple docstring"""







    _snake_case    :  List[str]					=					d

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

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

   def      UpperCamelCase_  (					self							:			str				,		lowerCAmelCase							:			str)				->				List[str]:

    """simple docstring"""







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

   def      UpperCamelCase_  (					self							:			int				,		lowerCAmelCase							:			Union[str, Any])				->				str:

    """simple docstring"""







    return self.sp_model.piece_to_id(lowerCAmelCase)

   def      UpperCamelCase_  (					self							:			int				,		lowerCAmelCase							:			str)				->				Tuple:

    """simple docstring"""







    if index < self.sp_model.get_piece_size():
     _snake_case    :  List[str]					=					self.sp_model.IdToPiece(lowerCAmelCase)
    return token

   def      UpperCamelCase_  (					self							:			List[Any]				,		lowerCAmelCase							:			Dict)				->				str:

    """simple docstring"""







    _snake_case    :  Union[str, Any]					=					[]
    _snake_case    :  Tuple					=					""""""
    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(lowerCAmelCase) + token
      _snake_case    :  Optional[int]					=					[]
     else:
      current_sub_tokens.append(lowerCAmelCase)
    out_string += self.sp_model.decode(lowerCAmelCase)
    return out_string.strip()


   def      UpperCamelCase_  (					self							:			List[str]				,		lowerCAmelCase							:			str				,		lowerCAmelCase							:			Optional[str] = None)				->				Tuple[str]:

    """simple docstring"""







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

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

    return (out_vocab_file,)





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

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
  from .tokenization_camembert import CamembertTokenizer
else:
  a__    =	None


a__    =	logging.get_logger(__name__)

a__    =	{"""vocab_file""": """sentencepiece.bpe.model""", """tokenizer_file""": """tokenizer.json"""}

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

a__    =	{
    """camembert-base""": 5_12,
}

a__    =	"""▁"""







class 		snake_case						(	SCREAMING_SNAKE_CASE_  ):



   '''simple docstring'''






   snake_case_					: List[str]          =		VOCAB_FILES_NAMES
   snake_case_					: str          =		PRETRAINED_VOCAB_FILES_MAP
   snake_case_					: Optional[Any]          =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   snake_case_					: List[Any]          =		["""input_ids""", """attention_mask"""]
   snake_case_					: int          =		CamembertTokenizer

   def __init__(					self							:			Optional[int]				,		lowerCAmelCase							:			Optional[Any]=None				,		lowerCAmelCase							:			int=None				,		lowerCAmelCase							:			List[Any]="<s>"				,		lowerCAmelCase							:			List[str]="</s>"				,		lowerCAmelCase							:			Union[str, Any]="</s>"				,		lowerCAmelCase							:			Dict="<s>"				,		lowerCAmelCase							:			Dict="<unk>"				,		lowerCAmelCase							:			Any="<pad>"				,		lowerCAmelCase							:			List[str]="<mask>"				,		lowerCAmelCase							:			List[str]=["<s>NOTUSED", "</s>NOTUSED"]				,		**lowerCAmelCase							:			Dict				,		)				->				Tuple:

    """simple docstring"""







    _snake_case    :  Dict					=					AddedToken(lowerCAmelCase				,		lstrip=lowerCAmelCase				,		rstrip=lowerCAmelCase) if isinstance(lowerCAmelCase				,		lowerCAmelCase) else mask_token

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

    _snake_case    :  List[str]					=					vocab_file
    _snake_case    :  Optional[Any]					=					False if not self.vocab_file else True

   def      UpperCamelCase_  (					self							:			Optional[int]				,		lowerCAmelCase							:			List[int]				,		lowerCAmelCase							:			Optional[List[int]] = None)				->				List[int]:

    """simple docstring"""







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

   def      UpperCamelCase_  (					self							:			str				,		lowerCAmelCase							:			List[int]				,		lowerCAmelCase							:			Optional[List[int]] = None)				->				List[int]:

    """simple docstring"""







    _snake_case    :  str					=					[self.sep_token_id]
    _snake_case    :  List[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]


   def      UpperCamelCase_  (					self							:			str				,		lowerCAmelCase							:			str				,		lowerCAmelCase							:			Optional[str] = None)				->				Tuple[str]:

    """simple docstring"""







    if not self.can_save_slow_tokenizer:
     raise ValueError(
         """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
         """tokenizer.""")

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

    if os.path.abspath(self.vocab_file) != os.path.abspath(lowerCAmelCase):
     copyfile(self.vocab_file				,		lowerCAmelCase)

    return (out_vocab_file,)
