





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


UpperCAmelCase_											=  logging.get_logger(__name__)

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

UpperCAmelCase_											=  {
    "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"
        ),
    },
}

UpperCAmelCase_											=  {
    "microsoft/deberta-base": 5_12,
    "microsoft/deberta-large": 5_12,
    "microsoft/deberta-xlarge": 5_12,
    "microsoft/deberta-base-mnli": 5_12,
    "microsoft/deberta-large-mnli": 5_12,
    "microsoft/deberta-xlarge-mnli": 5_12,
}

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




class  __UpperCamelCase						(       A__						):
     __A  :     Optional[Any]            =      VOCAB_FILES_NAMES
     __A  :     List[str]            =      PRETRAINED_VOCAB_FILES_MAP
     __A  :     Dict            =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     __A  :     Optional[int]            =      ["""input_ids""", """attention_mask""", """token_type_ids"""]
     __A  :     List[Any]            =      DebertaTokenizer





     def __init__(    self							,       _UpperCamelCase=None							,       _UpperCamelCase=None							,       _UpperCamelCase=None							,       _UpperCamelCase="replace"							,       _UpperCamelCase="[CLS]"							,       _UpperCamelCase="[SEP]"							,       _UpperCamelCase="[SEP]"							,       _UpperCamelCase="[CLS]"							,       _UpperCamelCase="[UNK]"							,       _UpperCamelCase="[PAD]"							,       _UpperCamelCase="[MASK]"							,       _UpperCamelCase=False							,       **_UpperCamelCase							,       ):
        super().__init__(
            _UpperCamelCase							,       _UpperCamelCase							,       tokenizer_file=_UpperCamelCase							,       errors=_UpperCamelCase							,       bos_token=_UpperCamelCase							,       eos_token=_UpperCamelCase							,       unk_token=_UpperCamelCase							,       sep_token=_UpperCamelCase							,       cls_token=_UpperCamelCase							,       pad_token=_UpperCamelCase							,       mask_token=_UpperCamelCase							,       add_prefix_space=_UpperCamelCase							,       **_UpperCamelCase							,       )
        _UpperCAmelCase											=      kwargs.pop('''add_bos_token'''							,       _UpperCamelCase				)

        _UpperCAmelCase											=      json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()				)
        if pre_tok_state.get('''add_prefix_space'''							,       _UpperCamelCase				) != add_prefix_space:
           _UpperCAmelCase											=      getattr(_UpperCamelCase							,       pre_tok_state.pop('''type'''				)				)
           _UpperCAmelCase											=      add_prefix_space
           _UpperCAmelCase											=      pre_tok_class(**_UpperCamelCase				)

        _UpperCAmelCase											=      add_prefix_space





     @property
     def 		UpperCamelCase(    self				):
        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							,       _UpperCamelCase				):
        _UpperCAmelCase											=      AddedToken(_UpperCamelCase							,       lstrip=_UpperCamelCase							,       rstrip=_UpperCamelCase				) if isinstance(_UpperCamelCase							,       _UpperCamelCase				) else value
        _UpperCAmelCase											=      value





     def 		UpperCamelCase(    self							,       _UpperCamelCase							,       _UpperCamelCase = None				):
        if token_ids_a is None:
           return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
        _UpperCAmelCase											=      [self.cls_token_id]
        _UpperCAmelCase											=      [self.sep_token_id]
        return cls + token_ids_a + sep + token_ids_a + sep





     def 		UpperCamelCase(    self							,       _UpperCamelCase							,       _UpperCamelCase = None				):
        _UpperCAmelCase											=      [self.sep_token_id]
        _UpperCAmelCase											=      [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							,       *_UpperCamelCase							,       **_UpperCamelCase				):
        _UpperCAmelCase											=      kwargs.get('''is_split_into_words'''							,       _UpperCamelCase				)
        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(*_UpperCamelCase							,       **_UpperCamelCase				)





     def 		UpperCamelCase(    self							,       *_UpperCamelCase							,       **_UpperCamelCase				):
        _UpperCAmelCase											=      kwargs.get('''is_split_into_words'''							,       _UpperCamelCase				)

        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(*_UpperCamelCase							,       **_UpperCamelCase				)





     def 		UpperCamelCase(    self							,       _UpperCamelCase							,       _UpperCamelCase = None				):
        _UpperCAmelCase											=      self._tokenizer.model.save(_UpperCamelCase							,       name=_UpperCamelCase				)
        return tuple(_UpperCamelCase				)





     def 		UpperCamelCase(    self							,       _UpperCamelCase				):
        _UpperCAmelCase											=      []
        for is_user, text in conversation.iter_texts():
           input_ids.extend(self.encode(_UpperCamelCase							,       add_special_tokens=_UpperCamelCase				) + [self.eos_token_id]				)

        if len(_UpperCamelCase				) > self.model_max_length:
           _UpperCAmelCase											=      input_ids[-self.model_max_length :]
        return input_ids






from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


if TYPE_CHECKING:
   from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType


UpperCAmelCase_											=  logging.get_logger(__name__)

UpperCAmelCase_											=  {
    "microsoft/deberta-base": "https://huggingface.co/microsoft/deberta-base/resolve/main/config.json",
    "microsoft/deberta-large": "https://huggingface.co/microsoft/deberta-large/resolve/main/config.json",
    "microsoft/deberta-xlarge": "https://huggingface.co/microsoft/deberta-xlarge/resolve/main/config.json",
    "microsoft/deberta-base-mnli": "https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/config.json",
    "microsoft/deberta-large-mnli": "https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/config.json",
    "microsoft/deberta-xlarge-mnli": "https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/config.json",
}




class  __UpperCamelCase						(       A__						):
     __A  :     Dict            =      """deberta"""





     def __init__(    self							,       _UpperCamelCase=50265							,       _UpperCamelCase=768							,       _UpperCamelCase=12							,       _UpperCamelCase=12							,       _UpperCamelCase=3072							,       _UpperCamelCase="gelu"							,       _UpperCamelCase=0.1							,       _UpperCamelCase=0.1							,       _UpperCamelCase=512							,       _UpperCamelCase=0							,       _UpperCamelCase=0.02							,       _UpperCamelCase=1e-7							,       _UpperCamelCase=False							,       _UpperCamelCase=-1							,       _UpperCamelCase=0							,       _UpperCamelCase=True							,       _UpperCamelCase=None							,       _UpperCamelCase=0							,       _UpperCamelCase="gelu"							,       **_UpperCamelCase							,       ):
        super().__init__(**_UpperCamelCase				)

        _UpperCAmelCase											=      hidden_size
        _UpperCAmelCase											=      num_hidden_layers
        _UpperCAmelCase											=      num_attention_heads
        _UpperCAmelCase											=      intermediate_size
        _UpperCAmelCase											=      hidden_act
        _UpperCAmelCase											=      hidden_dropout_prob
        _UpperCAmelCase											=      attention_probs_dropout_prob
        _UpperCAmelCase											=      max_position_embeddings
        _UpperCAmelCase											=      type_vocab_size
        _UpperCAmelCase											=      initializer_range
        _UpperCAmelCase											=      relative_attention
        _UpperCAmelCase											=      max_relative_positions
        _UpperCAmelCase											=      pad_token_id
        _UpperCAmelCase											=      position_biased_input

        # Backwards compatibility
        if type(_UpperCamelCase				) == str:
           _UpperCAmelCase											=      [x.strip() for x in pos_att_type.lower().split('''|'''				)]

        _UpperCAmelCase											=      pos_att_type
        _UpperCAmelCase											=      vocab_size
        _UpperCAmelCase											=      layer_norm_eps

        _UpperCAmelCase											=      kwargs.get('''pooler_hidden_size'''							,       _UpperCamelCase				)
        _UpperCAmelCase											=      pooler_dropout
        _UpperCAmelCase											=      pooler_hidden_act




class  __UpperCamelCase						(       A__						):





     @property
     def 		UpperCamelCase(    self				):
        if self.task == "multiple-choice":
           _UpperCAmelCase											=      {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
        else:
           _UpperCAmelCase											=      {0: '''batch''', 1: '''sequence'''}
        if self._config.type_vocab_size > 0:
           return OrderedDict(
               [('''input_ids''', dynamic_axis), ('''attention_mask''', dynamic_axis), ('''token_type_ids''', dynamic_axis)]				)
        else:
           return OrderedDict([('''input_ids''', dynamic_axis), ('''attention_mask''', dynamic_axis)]				)





     @property
     def 		UpperCamelCase(    self				):
        return 12





     def 		UpperCamelCase(    self							,       _UpperCamelCase							,       _UpperCamelCase = -1							,       _UpperCamelCase = -1							,       _UpperCamelCase = -1							,       _UpperCamelCase = False							,       _UpperCamelCase = None							,       _UpperCamelCase = 3							,       _UpperCamelCase = 40							,       _UpperCamelCase = 40							,       _UpperCamelCase = None							,       ):
        _UpperCAmelCase											=      super().generate_dummy_inputs(preprocessor=_UpperCamelCase							,       framework=_UpperCamelCase				)
        if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
           del dummy_inputs["token_type_ids"]
        return dummy_inputs