


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:
  SCREAMING_SNAKE_CASE							    =							None


SCREAMING_SNAKE_CASE							    =							logging.get_logger(__name__)

SCREAMING_SNAKE_CASE							    =							{'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}

SCREAMING_SNAKE_CASE							    =							{
    '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',
    },
}

SCREAMING_SNAKE_CASE							    =							{
    'camembert-base': 5_1_2,
}

SCREAMING_SNAKE_CASE							    =							'▁'


class     __UpperCAmelCase							(       __A							):


     """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  ,       __A=None  ,       __A=None  ,       __A="<s>"  ,       __A="</s>"  ,       __A="</s>"  ,       __A="<s>"  ,       __A="<unk>"  ,       __A="<pad>"  ,       __A="<mask>"  ,       __A=["<s>NOTUSED", "</s>NOTUSED"]  ,       **__A  ,       ):
        # Mask token behave like a normal word, i.e. include the space before it
        __a					=  AddedToken(__A  ,       lstrip=__A  ,       rstrip=__A    ) if isinstance(__A  ,       __A    ) else mask_token

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

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


     def 	snake_case_  (	self  ,       __A  ,       __A = None    ):

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


     def 	snake_case_  (	self  ,       __A  ,       __A = None    ):
        __a					=  [self.sep_token_id]
        __a					=  [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 	snake_case_  (	self  ,       __A  ,       __A = None    ):
        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(__A    ):
           logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''    )
           return
        __a					=  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    ):
           copyfile(self.vocab_file  ,       __A    )

        return (out_vocab_file,)







from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


SCREAMING_SNAKE_CASE							    =							{
    'configuration_autoformer': [
        'AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'AutoformerConfig',
    ],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE							    =							[
      'AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
      'AutoformerForPrediction',
      'AutoformerModel',
      'AutoformerPreTrainedModel',
  ]


if TYPE_CHECKING:
  from .configuration_autoformer import (
      AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
      AutoformerConfig,
  )

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_autoformer import (
        AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        AutoformerForPrediction,
        AutoformerModel,
        AutoformerPreTrainedModel,
    )

else:
  import sys

  SCREAMING_SNAKE_CASE							    =							_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



