


"""simple docstring"""





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

from ...file_utils import is_sentencepiece_available
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if is_sentencepiece_available():
   from .tokenization_deberta_va import DebertaVaTokenizer
else:
   __UpperCAmelCase    				=      None

__UpperCAmelCase    				=      logging.get_logger(__name__)

__UpperCAmelCase    				=      {"vocab_file": "spm.model", "tokenizer_file": "tokenizer.json"}

__UpperCAmelCase    				=      {
    "vocab_file": {
        "microsoft/deberta-v2-xlarge": "https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/spm.model",
        "microsoft/deberta-v2-xxlarge": "https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/spm.model",
        "microsoft/deberta-v2-xlarge-mnli": (
            "https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/spm.model"
        ),
        "microsoft/deberta-v2-xxlarge-mnli": (
            "https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/spm.model"
        ),
    }
}

__UpperCAmelCase    				=      {
    "microsoft/deberta-v2-xlarge": 512,
    "microsoft/deberta-v2-xxlarge": 512,
    "microsoft/deberta-v2-xlarge-mnli": 512,
    "microsoft/deberta-v2-xxlarge-mnli": 512,
}

__UpperCAmelCase    				=      {
    "microsoft/deberta-v2-xlarge": {"do_lower_case": False},
    "microsoft/deberta-v2-xxlarge": {"do_lower_case": False},
    "microsoft/deberta-v2-xlarge-mnli": {"do_lower_case": False},
    "microsoft/deberta-v2-xxlarge-mnli": {"do_lower_case": False},
}

class  lowercase_  ( a_						):
      __magic_name__     :							Dict          =				VOCAB_FILES_NAMES
      __magic_name__     :							Any          =				PRETRAINED_VOCAB_FILES_MAP
      __magic_name__     :							int          =				PRETRAINED_INIT_CONFIGURATION
      __magic_name__     :							str          =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      __magic_name__     :							List[Any]          =				DebertaVaTokenizer







      def __init__(      self :							List[str]		,  _lowercase :							List[str]=None		,  _lowercase :							str=None		,  _lowercase :							str=False		,  _lowercase :							Tuple=False		,  _lowercase :							str="[CLS]"		,  _lowercase :							Optional[int]="[SEP]"		,  _lowercase :							Dict="[UNK]"		,  _lowercase :							Optional[int]="[SEP]"		,  _lowercase :							Optional[int]="[PAD]"		,  _lowercase :							str="[CLS]"		,  _lowercase :							Optional[int]="[MASK]"		,  **_lowercase :							List[Any]		,  ):
        super().__init__(
            _lowercase		,  tokenizer_file=_lowercase		,  do_lower_case=_lowercase		,  bos_token=_lowercase		,  eos_token=_lowercase		,  unk_token=_lowercase		,  sep_token=_lowercase		,  pad_token=_lowercase		,  cls_token=_lowercase		,  mask_token=_lowercase		,  split_by_punct=_lowercase		,  **_lowercase		,  )

        lowerCAmelCase__    :    List[str]								=					do_lower_case
        lowerCAmelCase__    :    Union[str, Any]								=					split_by_punct
        lowerCAmelCase__    :    Dict								=					vocab_file
        lowerCAmelCase__    :    str								=					False if not self.vocab_file else True







      def 				_lowerCAmelCase       (      self :							Dict		,  _lowercase :							Optional[int]		,  _lowercase :							Dict=None    ):

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







      def 				_lowerCAmelCase       (      self :							Tuple		,  _lowercase :							str		,  _lowercase :							Dict=None		,  _lowercase :							str=False    ):

        if already_has_special_tokens:
          return super().get_special_tokens_mask(
              token_ids_a=_lowercase		,  token_ids_a=_lowercase		,  already_has_special_tokens=_lowercase    )

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







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

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

        return (out_vocab_file,)




"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__UpperCAmelCase    				=      {"configuration_sew_d": ["SEW_D_PRETRAINED_CONFIG_ARCHIVE_MAP", "SEWDConfig"]}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __UpperCAmelCase    				=      [
       "SEW_D_PRETRAINED_MODEL_ARCHIVE_LIST",
       "SEWDForCTC",
       "SEWDForSequenceClassification",
       "SEWDModel",
       "SEWDPreTrainedModel",
   ]

if TYPE_CHECKING:
   from .configuration_sew_d import SEW_D_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWDConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_sew_d import (
          SEW_D_PRETRAINED_MODEL_ARCHIVE_LIST,
          SEWDForCTC,
          SEWDForSequenceClassification,
          SEWDModel,
          SEWDPreTrainedModel,
      )


else:
   import sys

   __UpperCAmelCase    				=      _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
