






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


a		:	Tuple      					=			logging.get_logger(__name__)

a		:	Dict      					=			{"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

a		:	Optional[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"
        ),
    },
}

a		:	Union[str, 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,
}

a		:	Union[str, Any]      					=			{
    "microsoft/deberta-base": {"do_lower_case": False},
    "microsoft/deberta-large": {"do_lower_case": False},
}
class    a   (			lowercase__       ):



      """simple docstring"""


      a  :			Union[str, Any]								=     VOCAB_FILES_NAMES
      a  :			Any								=     PRETRAINED_VOCAB_FILES_MAP
      a  :			Tuple								=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      a  :			Union[str, Any]								=     ['input_ids', 'attention_mask', 'token_type_ids']
      a  :			Optional[int]								=     DebertaTokenizer
      def __init__(				self :      Optional[Any]		,				__lowercase :      Optional[Any]=None		,				__lowercase :      int=None		,				__lowercase :      Optional[int]=None		,				__lowercase :      List[Any]="replace"		,				__lowercase :      List[str]="[CLS]"		,				__lowercase :      Tuple="[SEP]"		,				__lowercase :      Any="[SEP]"		,				__lowercase :      Dict="[CLS]"		,				__lowercase :      List[Any]="[UNK]"		,				__lowercase :      int="[PAD]"		,				__lowercase :      Optional[Any]="[MASK]"		,				__lowercase :      Optional[int]=False		,				**__lowercase :      List[str]		,				)							->				Union[str, Any]:
        super().__init__(
            __lowercase		,				__lowercase		,				tokenizer_file=__lowercase		,				errors=__lowercase		,				bos_token=__lowercase		,				eos_token=__lowercase		,				unk_token=__lowercase		,				sep_token=__lowercase		,				cls_token=__lowercase		,				pad_token=__lowercase		,				mask_token=__lowercase		,				add_prefix_space=__lowercase		,				**__lowercase		,				)
        __UpperCAmelCase							:     Tuple									=  kwargs.pop("""add_bos_token"""		,				__lowercase )

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

        __UpperCAmelCase							:     Optional[Any]									=  add_prefix_space
      @property
      def      UpperCAmelCase				(				self :      Dict )							->				str:
        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      UpperCAmelCase				(				self :      str		,				__lowercase :      Optional[Any] )							->				Dict:
        __UpperCAmelCase							:     List[Any]									=  AddedToken(__lowercase		,				lstrip=__lowercase		,				rstrip=__lowercase ) if isinstance(__lowercase		,				__lowercase ) else value
        __UpperCAmelCase							:     Dict									=  value
      def      UpperCAmelCase				(				self :      str		,				__lowercase :      List[int]		,				__lowercase :      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							:     List[Any]									=  [self.cls_token_id]
        __UpperCAmelCase							:     Union[str, Any]									=  [self.sep_token_id]
        return cls + token_ids_a + sep + token_ids_a + sep
      def      UpperCAmelCase				(				self :      Any		,				__lowercase :      List[int]		,				__lowercase :      Optional[List[int]] = None )							->				List[int]:
        __UpperCAmelCase							:     List[Any]									=  [self.sep_token_id]
        __UpperCAmelCase							:     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      UpperCAmelCase				(				self :      Union[str, Any]		,				*__lowercase :      Union[str, Any]		,				**__lowercase :      Optional[Any] )							->				BatchEncoding:
        __UpperCAmelCase							:     List[Any]									=  kwargs.get("""is_split_into_words"""		,				__lowercase )
        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(*__lowercase		,				**__lowercase )
      def      UpperCAmelCase				(				self :      Optional[Any]		,				*__lowercase :      List[str]		,				**__lowercase :      str )							->				BatchEncoding:
        __UpperCAmelCase							:     List[Any]									=  kwargs.get("""is_split_into_words"""		,				__lowercase )

        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(*__lowercase		,				**__lowercase )
      def      UpperCAmelCase				(				self :      int		,				__lowercase :      str		,				__lowercase :      Optional[str] = None )							->				Tuple[str]:
        __UpperCAmelCase							:     Any									=  self._tokenizer.model.save(__lowercase		,				name=__lowercase )
        return tuple(__lowercase )




      def      UpperCAmelCase				(				self :      int		,				__lowercase :      "Conversation" )							->				List[int]:
        __UpperCAmelCase							:     Union[str, Any]									=  []
        for is_user, text in conversation.iter_texts():
          input_ids.extend(self.encode(__lowercase		,				add_special_tokens=__lowercase ) + [self.eos_token_id] )

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










from typing import TYPE_CHECKING

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


a		:	Any      					=			{
    "configuration_deta": ["DETA_PRETRAINED_CONFIG_ARCHIVE_MAP", "DetaConfig"],
}

try:
       if not is_vision_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a		:	Optional[int]      					=			["DetaImageProcessor"]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a		:	Tuple      					=			[
           "DETA_PRETRAINED_MODEL_ARCHIVE_LIST",
           "DetaForObjectDetection",
           "DetaModel",
           "DetaPreTrainedModel",
       ]


if TYPE_CHECKING:
       from .configuration_deta import DETA_PRETRAINED_CONFIG_ARCHIVE_MAP, DetaConfig

       try:
              if not is_vision_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .image_processing_deta import DetaImageProcessor

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_deta import (
                  DETA_PRETRAINED_MODEL_ARCHIVE_LIST,
                  DetaForObjectDetection,
                  DetaModel,
                  DetaPreTrainedModel,
              )

else:
       import sys

       a		:	Any      					=			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


