
import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_deberta import DebertaTokenizer


if TYPE_CHECKING:
 from transformers.pipelines.conversational import Conversation


__lowerCAmelCase   :     int      =						logging.get_logger(__name__)

__lowerCAmelCase   :     str      =						{"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

__lowerCAmelCase   :     int      =						{
    "vocab_file": {
        "microsoft/deberta-base": "https://huggingface.co/microsoft/deberta-base/resolve/main/vocab.json",
        "microsoft/deberta-large": "https://huggingface.co/microsoft/deberta-large/resolve/main/vocab.json",
        "microsoft/deberta-xlarge": "https://huggingface.co/microsoft/deberta-xlarge/resolve/main/vocab.json",
        "microsoft/deberta-base-mnli": "https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/vocab.json",
        "microsoft/deberta-large-mnli": "https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/vocab.json",
        "microsoft/deberta-xlarge-mnli": (
            "https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/vocab.json"
        ),
    },
    "merges_file": {
        "microsoft/deberta-base": "https://huggingface.co/microsoft/deberta-base/resolve/main/merges.txt",
        "microsoft/deberta-large": "https://huggingface.co/microsoft/deberta-large/resolve/main/merges.txt",
        "microsoft/deberta-xlarge": "https://huggingface.co/microsoft/deberta-xlarge/resolve/main/merges.txt",
        "microsoft/deberta-base-mnli": "https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/merges.txt",
        "microsoft/deberta-large-mnli": "https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/merges.txt",
        "microsoft/deberta-xlarge-mnli": (
            "https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/merges.txt"
        ),
    },
}

__lowerCAmelCase   :     List[Any]      =						{
    "microsoft/deberta-base": 512,
    "microsoft/deberta-large": 512,
    "microsoft/deberta-xlarge": 512,
    "microsoft/deberta-base-mnli": 512,
    "microsoft/deberta-large-mnli": 512,
    "microsoft/deberta-xlarge-mnli": 512,
}

__lowerCAmelCase   :     Dict      =						{
    "microsoft/deberta-base": {"do_lower_case": False},
    "microsoft/deberta-large": {"do_lower_case": False},
}


class   __lowerCAmelCase		(		lowerCAmelCase__ ):



      """simple docstring"""







      A__		:     Any       				=      VOCAB_FILES_NAMES
      A__		:     Any       				=      PRETRAINED_VOCAB_FILES_MAP
      A__		:     List[str]       				=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      A__		:     List[str]       				=      ['''input_ids''', '''attention_mask''', '''token_type_ids''']
      A__		:     List[Any]       				=      DebertaTokenizer




      def __init__( self						:    List[str]			,    _snake_case						:    Tuple=None			,    _snake_case						:    List[Any]=None			,    _snake_case						:    Optional[int]=None			,    _snake_case						:    List[str]="replace"			,    _snake_case						:    List[Any]="[CLS]"			,    _snake_case						:    Optional[Any]="[SEP]"			,    _snake_case						:    Union[str, Any]="[SEP]"			,    _snake_case						:    List[str]="[CLS]"			,    _snake_case						:    Optional[Any]="[UNK]"			,    _snake_case						:    str="[PAD]"			,    _snake_case						:    List[str]="[MASK]"			,    _snake_case						:    Dict=False			,    **_snake_case						:    Optional[int]			,    ):
          super().__init__(
              _A			,    _A			,    tokenizer_file=_A			,    errors=_A			,    bos_token=_A			,    eos_token=_A			,    unk_token=_A			,    sep_token=_A			,    cls_token=_A			,    pad_token=_A			,    mask_token=_A			,    add_prefix_space=_A			,    **_A			,    )
          __lowercase						:     List[str]		      =    kwargs.pop('''add_bos_token'''			,    _A			)

          __lowercase						:     int		      =    json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()			)
          if pre_tok_state.get('''add_prefix_space'''			,    _A			) != add_prefix_space:
              __lowercase						:     Optional[Any]		      =    getattr(_A			,    pre_tok_state.pop('''type'''			)			)
              __lowercase						:     Dict		      =    add_prefix_space
              __lowercase						:     Any		      =    pre_tok_class(**_A			)

          __lowercase						:     Optional[Any]		      =    add_prefix_space




      @property
      def 						snake_case_     ( self						:    List[Any]			):




          if self._mask_token is None:
              if self.verbose:
                  logger.error('''Using mask_token, but it is not set yet.'''			)
              return None
          return str(self._mask_token			)




      @mask_token.setter
      def 						snake_case_     ( self						:    Any			,    _snake_case						:    Union[str, Any]			):
          __lowercase						:     Dict		      =    AddedToken(_A			,    lstrip=_A			,    rstrip=_A			) if isinstance(_A			,    _A			) else value
          __lowercase						:     Tuple		      =    value




      def 						snake_case_     ( self						:    List[Any]			,    _snake_case						:    List[int]			,    _snake_case						:    Optional[List[int]] = None			):




          if token_ids_a is None:
              return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
          __lowercase						:     List[str]		      =    [self.cls_token_id]
          __lowercase						:     List[Any]		      =    [self.sep_token_id]
          return cls + token_ids_a + sep + token_ids_a + sep




      def 						snake_case_     ( self						:    int			,    _snake_case						:    List[int]			,    _snake_case						:    Optional[List[int]] = None			):
          __lowercase						:     Tuple		      =    [self.sep_token_id]
          __lowercase						:     List[str]		      =    [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 						snake_case_     ( self						:    Tuple			,    *_snake_case						:    Optional[Any]			,    **_snake_case						:    Optional[Any]			):
          __lowercase						:     List[str]		      =    kwargs.get('''is_split_into_words'''			,    _A			)
          assert self.add_prefix_space or not is_split_into_words, (
              F'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
              "to use it with pretokenized inputs."
          )

          return super()._batch_encode_plus(*_A			,    **_A			)




      def 						snake_case_     ( self						:    Tuple			,    *_snake_case						:    Tuple			,    **_snake_case						:    int			):
          __lowercase						:     Dict		      =    kwargs.get('''is_split_into_words'''			,    _A			)

          assert self.add_prefix_space or not is_split_into_words, (
              F'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
              "to use it with pretokenized inputs."
          )

          return super()._encode_plus(*_A			,    **_A			)




      def 						snake_case_     ( self						:    Tuple			,    _snake_case						:    str			,    _snake_case						:    Optional[str] = None			):
          __lowercase						:     Optional[int]		      =    self._tokenizer.model.save(_A			,    name=_A			)
          return tuple(_A			)




      def 						snake_case_     ( self						:    Tuple			,    _snake_case						:    "Conversation"			):
          __lowercase						:     List[Any]		      =    []
          for is_user, text in conversation.iter_texts():
              input_ids.extend(self.encode(_A			,    add_special_tokens=_A			) + [self.eos_token_id]			)

          if len(_A			) > self.model_max_length:
              __lowercase						:     Any		      =    input_ids[-self.model_max_length :]
          return input_ids




__lowerCAmelCase   :     List[Any]      =						"\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n"

__lowerCAmelCase   :     Optional[Any]      =						[{"type": "code", "content": INSTALL_CONTENT}]
__lowerCAmelCase   :     List[str]      =						{
    "{processor_class}": "FakeProcessorClass",
    "{model_class}": "FakeModelClass",
    "{object_class}": "FakeObjectClass",
}


