





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_rembert import RemBertTokenizer
else:
     _snake_case  :    str						    =  None

_snake_case  :    Dict						    =  logging.get_logger(__name__)
_snake_case  :    List[str]						    =  {'vocab_file': 'sentencepiece.model', 'tokenizer_file': 'tokenizer.json'}

_snake_case  :    List[Any]						    =  {
    'vocab_file': {
        'google/rembert': 'https://huggingface.co/google/rembert/resolve/main/sentencepiece.model',
    },
    'tokenizer_file': {
        'google/rembert': 'https://huggingface.co/google/rembert/resolve/main/tokenizer.json',
    },
}

_snake_case  :    str						    =  {
    'google/rembert': 256,
}

_snake_case  :    Optional[int]						    =  '▁'



class 				_UpperCAmelCase    (    _UpperCamelCase    ):

  """simple docstring"""


  a_        = VOCAB_FILES_NAMES
  a_        = PRETRAINED_VOCAB_FILES_MAP
  a_        = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  a_        = RemBertTokenizer






  def __init__(				self :		int					,  lowerCAmelCase_ :		List[str]=None					,  lowerCAmelCase_ :		int=None					,  lowerCAmelCase_ :		Dict=True					,  lowerCAmelCase_ :		Any=True					,  lowerCAmelCase_ :		int=False					,  lowerCAmelCase_ :		Any="[CLS]"					,  lowerCAmelCase_ :		Dict="[SEP]"					,  lowerCAmelCase_ :		Dict="<unk>"					,  lowerCAmelCase_ :		str="[SEP]"					,  lowerCAmelCase_ :		Tuple="<pad>"					,  lowerCAmelCase_ :		Optional[int]="[CLS]"					,  lowerCAmelCase_ :		int="[MASK]"					,  **lowerCAmelCase_ :		Optional[Any]					,  )							->						Tuple:
     # Mask token behave like a normal word, i.e. include the space before it
     __lowerCAmelCase								=	AddedToken(lowerCAmelCase_					,  lstrip=lowerCAmelCase_					,  rstrip=lowerCAmelCase_     ) if isinstance(lowerCAmelCase_					,  lowerCAmelCase_     ) else mask_token

     super().__init__(
         lowerCAmelCase_					,  tokenizer_file=lowerCAmelCase_					,  do_lower_case=lowerCAmelCase_					,  remove_space=lowerCAmelCase_					,  keep_accents=lowerCAmelCase_					,  bos_token=lowerCAmelCase_					,  eos_token=lowerCAmelCase_					,  unk_token=lowerCAmelCase_					,  sep_token=lowerCAmelCase_					,  pad_token=lowerCAmelCase_					,  cls_token=lowerCAmelCase_					,  mask_token=lowerCAmelCase_					,  **lowerCAmelCase_					,  )

     __lowerCAmelCase								=	do_lower_case
     __lowerCAmelCase								=	remove_space
     __lowerCAmelCase								=	keep_accents
     __lowerCAmelCase								=	vocab_file
     __lowerCAmelCase								=	False if not self.vocab_file else True






  def 				lowercase		(				self :		Dict					,  lowerCAmelCase_ :		List[int]					,  lowerCAmelCase_ :		Optional[List[int]] = None     )							->						List[int]:
     __lowerCAmelCase								=	[self.sep_token_id]
     __lowerCAmelCase								=	[self.cls_token_id]
     if token_ids_a is None:
        return cls + token_ids_a + sep
     return cls + token_ids_a + sep + token_ids_a + sep






  def 				lowercase		(				self :		Tuple					,  lowerCAmelCase_ :		List[int]					,  lowerCAmelCase_ :		Optional[List[int]] = None					,  lowerCAmelCase_ :		bool = False     )							->						List[int]:

     if already_has_special_tokens:
        if token_ids_a is not None:
           raise ValueError(
               'You should not supply a second sequence if the provided sequence of '
               'ids is already formatted with special tokens for the model.'     )
        return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

     if token_ids_a is not None:
        return [1] + ([0] * len(lowerCAmelCase_     )) + [1] + ([0] * len(lowerCAmelCase_     )) + [1]
     return [1] + ([0] * len(lowerCAmelCase_     )) + [1]






  def 				lowercase		(				self :		Any					,  lowerCAmelCase_ :		List[int]					,  lowerCAmelCase_ :		Optional[List[int]] = None     )							->						List[int]:
     __lowerCAmelCase								=	[self.sep_token_id]
     __lowerCAmelCase								=	[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     ) * [0] + len(token_ids_a + sep     ) * [1]






  def 				lowercase		(				self :		int					,  lowerCAmelCase_ :		str					,  lowerCAmelCase_ :		Optional[str] = None     )							->						Tuple[str]:
     if not os.path.isdir(lowerCAmelCase_     ):
        logger.error('Vocabulary path ({}) should be a directory'.format(lowerCAmelCase_     )     )
        return
     __lowerCAmelCase								=	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,)








from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


_snake_case  :    List[Any]						    =  {
    'configuration_biogpt': ['BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BioGptConfig'],
    'tokenization_biogpt': ['BioGptTokenizer'],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _snake_case  :    Optional[int]						    =  [
         'BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST',
         'BioGptForCausalLM',
         'BioGptForTokenClassification',
         'BioGptForSequenceClassification',
         'BioGptModel',
         'BioGptPreTrainedModel',
     ]


if TYPE_CHECKING:
     from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
     from .tokenization_biogpt import BioGptTokenizer

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_biogpt import (
              BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
              BioGptForCausalLM,
              BioGptForSequenceClassification,
              BioGptForTokenClassification,
              BioGptModel,
              BioGptPreTrainedModel,
          )


else:
     import sys

     _snake_case  :    List[str]						    =  _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

