


import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging


__UpperCamelCase       :       Optional[Any]						       =				logging.get_logger(__name__)

class       __magic_name__	(     __lowerCAmelCase):
     A:		Union[str, Any]			=      "encoder-decoder"
     A:		List[str]			=      True


     def __init__(      self							:     Any							,	**lowerCamelCase__							:     Optional[int]			)  ->      Dict:





      '''simple docstring'''






      super().__init__(**lowerCamelCase__			)
      assert (
          "encoder" in kwargs and "decoder" in kwargs
      ), "Config has to be initialized with encoder and decoder config"
      UpperCamelCase__					:    str          =   kwargs.pop('''encoder'''			)
      UpperCamelCase__					:    Optional[int]          =   encoder_config.pop('''model_type'''			)
      UpperCamelCase__					:    Dict          =   kwargs.pop('''decoder'''			)
      UpperCamelCase__					:    Optional[Any]          =   decoder_config.pop('''model_type'''			)

      from ..auto.configuration_auto import AutoConfig

      UpperCamelCase__					:    Any          =   AutoConfig.for_model(lowerCamelCase__							,	**lowerCamelCase__			)
      UpperCamelCase__					:    Optional[int]          =   AutoConfig.for_model(lowerCamelCase__							,	**lowerCamelCase__			)
      UpperCamelCase__					:    Dict          =   True


     @classmethod
     def      UpperCAmelCase__	(      cls							:     Optional[int]							,	lowerCamelCase__							:     PretrainedConfig							,	lowerCamelCase__							:     PretrainedConfig							,	**lowerCamelCase__							:     Union[str, Any]			)  ->      PretrainedConfig:





      '''simple docstring'''






      logger.info('''Set `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config'''			)
      UpperCamelCase__					:    Any          =   True
      UpperCamelCase__					:    Any          =   True

      return cls(encoder=encoder_config.to_dict()							,	decoder=decoder_config.to_dict()							,	**lowerCamelCase__			)


     def      UpperCAmelCase__	(      self							:     List[Any]			)  ->      Any:





      '''simple docstring'''






      UpperCamelCase__					:    List[Any]          =   copy.deepcopy(self.__dict__			)
      UpperCamelCase__					:    Dict          =   self.encoder.to_dict()
      UpperCamelCase__					:    int          =   self.decoder.to_dict()
      UpperCamelCase__					:    Optional[int]          =   self.__class__.model_type
      return output






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:
       __UpperCamelCase       :       Tuple						       =				None


__UpperCamelCase       :       Optional[Any]						       =				logging.get_logger(__name__)

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

__UpperCamelCase       :       Tuple						       =				{
    "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",
    },
}

__UpperCamelCase       :       Optional[Any]						       =				{
    "camembert-base": 512,
}

__UpperCamelCase       :       Optional[Any]						       =				"▁"

class       __magic_name__	(     __lowerCAmelCase):
     A:		int			=      VOCAB_FILES_NAMES
     A:		Union[str, Any]			=      PRETRAINED_VOCAB_FILES_MAP
     A:		Optional[int]			=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     A:		List[str]			=      ["input_ids", "attention_mask"]
     A:		Dict			=      CamembertTokenizer


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





      '''simple docstring'''






      UpperCamelCase__					:    List[str]          =   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__							,	)

      UpperCamelCase__					:    Tuple          =   vocab_file
      UpperCamelCase__					:    Optional[Any]          =   False if not self.vocab_file else True


     def      UpperCAmelCase__	(      self							:     Any							,	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]
      UpperCamelCase__					:    Any          =   [self.cls_token_id]
      UpperCamelCase__					:    Dict          =   [self.sep_token_id]
      return cls + token_ids_a + sep + sep + token_ids_a + sep


     def      UpperCAmelCase__	(      self							:     Optional[int]							,	lowerCamelCase__							:     List[int]							,	lowerCamelCase__							:     Optional[List[int]] = None			)  ->      List[int]:





      '''simple docstring'''






      UpperCamelCase__					:    Optional[Any]          =   [self.sep_token_id]
      UpperCamelCase__					:    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]


     def      UpperCAmelCase__	(      self							:     Optional[int]							,	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
      UpperCamelCase__					:    List[str]          =   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,)


