'''simple docstring'''
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:
      lowercase__  :      List[Any]        =					None


lowercase__  :      str        =					logging.get_logger(__name__)

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

lowercase__  :      str        =					{
    "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",
    },
}

lowercase__  :      str        =					{
    "camembert-base": 512,
}

lowercase__  :      int        =					"▁"







class lowerCamelCase							(	lowerCamelCase					):




  '''simple docstring'''



  lowerCAmelCase__										=	VOCAB_FILES_NAMES
  lowerCAmelCase__										=	PRETRAINED_VOCAB_FILES_MAP
  lowerCAmelCase__										=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  lowerCAmelCase__										=	['''input_ids''', '''attention_mask''']
  lowerCAmelCase__										=	CamembertTokenizer






  def __init__(      self			:       int  ,							UpperCAmelCase__			:       Optional[Any]=None  ,							UpperCAmelCase__			:       Optional[Any]=None  ,							UpperCAmelCase__			:       Optional[int]="<s>"  ,							UpperCAmelCase__			:       List[str]="</s>"  ,							UpperCAmelCase__			:       int="</s>"  ,							UpperCAmelCase__			:       Optional[Any]="<s>"  ,							UpperCAmelCase__			:       Optional[Any]="<unk>"  ,							UpperCAmelCase__			:       Tuple="<pad>"  ,							UpperCAmelCase__			:       Optional[int]="<mask>"  ,							UpperCAmelCase__			:       str=["<s>NOTUSED", "</s>NOTUSED"]  ,							**UpperCAmelCase__			:       int  ,							)				->Any:
        # Mask token behave like a normal word, i.e. include the space before it
        UpperCAmelCase_            =  AddedToken(UpperCAmelCase__  ,							lstrip=UpperCAmelCase__  ,							rstrip=UpperCAmelCase__  ) if isinstance(UpperCAmelCase__  ,							UpperCAmelCase__  ) else mask_token

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

        UpperCAmelCase_            =  vocab_file
        UpperCAmelCase_            =  False if not self.vocab_file else True






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

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






  def  lowerCAmelCase__ (      self			:       Tuple  ,							UpperCAmelCase__			:       List[int]  ,							UpperCAmelCase__			:       Optional[List[int]] = None  )				->List[int]:
        UpperCAmelCase_            =  [self.sep_token_id]
        UpperCAmelCase_            =  [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  lowerCAmelCase__ (      self			:       str  ,							UpperCAmelCase__			:       str  ,							UpperCAmelCase__			:       Optional[str] = None  )				->Tuple[str]:
        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(UpperCAmelCase__  ):
              logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""  )
              return
        UpperCAmelCase_            =  os.path.join(
            UpperCAmelCase__  ,							(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']  )

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

        return (out_vocab_file,)






'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


lowercase__  :      Optional[Any]        =					{
    "configuration_encodec": [
        "ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "EncodecConfig",
    ],
    "feature_extraction_encodec": ["EncodecFeatureExtractor"],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase__  :      List[str]        =					[
          "ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST",
          "EncodecModel",
          "EncodecPreTrainedModel",
      ]

if TYPE_CHECKING:
      from .configuration_encodec import (
          ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP,
          EncodecConfig,
      )
      from .feature_extraction_encodec import EncodecFeatureExtractor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_encodec import (
                ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST,
                EncodecModel,
                EncodecPreTrainedModel,
            )

else:
      import sys

      lowercase__  :      str        =					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





