code: 
  
  
  
  
  
  
  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
  
  
  lowerCamelCase			:							Any       =logging.get_logger(__name__)
  
  lowerCamelCase			:							Optional[int]       ={
      '''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     __a						(					A__ ):
   _lowerCAmelCase : Optional[int] 				=				'''deberta'''
  
  
  
  
  
  
  
   def __init__(	self      :     Tuple							,     SCREAMING_SNAKE_CASE      :     Any=5_02_65							,     SCREAMING_SNAKE_CASE      :     Optional[int]=7_68							,     SCREAMING_SNAKE_CASE      :     str=12							,     SCREAMING_SNAKE_CASE      :     Dict=12							,     SCREAMING_SNAKE_CASE      :     Dict=30_72							,     SCREAMING_SNAKE_CASE      :     List[str]="gelu"							,     SCREAMING_SNAKE_CASE      :     List[Any]=0.1							,     SCREAMING_SNAKE_CASE      :     List[str]=0.1							,     SCREAMING_SNAKE_CASE      :     str=5_12							,     SCREAMING_SNAKE_CASE      :     Any=0							,     SCREAMING_SNAKE_CASE      :     List[str]=0.0_2							,     SCREAMING_SNAKE_CASE      :     List[Any]=1e-7							,     SCREAMING_SNAKE_CASE      :     Union[str, Any]=False							,     SCREAMING_SNAKE_CASE      :     str=-1							,     SCREAMING_SNAKE_CASE      :     List[str]=0							,     SCREAMING_SNAKE_CASE      :     List[str]=True							,     SCREAMING_SNAKE_CASE      :     int=None							,     SCREAMING_SNAKE_CASE      :     Optional[int]=0							,     SCREAMING_SNAKE_CASE      :     Optional[Any]="gelu"							,     **SCREAMING_SNAKE_CASE      :     Union[str, Any]							,     ):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        super().__init__(**SCREAMING_SNAKE_CASE		)
  
        UpperCamelCase__    :     Optional[int]        =						hidden_size
        UpperCamelCase__    :     Tuple        =						num_hidden_layers
        UpperCamelCase__    :     Optional[int]        =						num_attention_heads
        UpperCamelCase__    :     Dict        =						intermediate_size
        UpperCamelCase__    :     Optional[Any]        =						hidden_act
        UpperCamelCase__    :     List[Any]        =						hidden_dropout_prob
        UpperCamelCase__    :     Union[str, Any]        =						attention_probs_dropout_prob
        UpperCamelCase__    :     List[str]        =						max_position_embeddings
        UpperCamelCase__    :     Optional[Any]        =						type_vocab_size
        UpperCamelCase__    :     Any        =						initializer_range
        UpperCamelCase__    :     Tuple        =						relative_attention
        UpperCamelCase__    :     Optional[int]        =						max_relative_positions
        UpperCamelCase__    :     List[Any]        =						pad_token_id
        UpperCamelCase__    :     Optional[int]        =						position_biased_input
  
        # Backwards compatibility
        if type(SCREAMING_SNAKE_CASE		) == str:
             UpperCamelCase__    :     List[Any]        =						[x.strip() for x in pos_att_type.lower().split("|"		)]
  
        UpperCamelCase__    :     Dict        =						pos_att_type
        UpperCamelCase__    :     Optional[Any]        =						vocab_size
        UpperCamelCase__    :     List[str]        =						layer_norm_eps
  
        UpperCamelCase__    :     List[Any]        =						kwargs.get("pooler_hidden_size"							,     SCREAMING_SNAKE_CASE		)
        UpperCamelCase__    :     str        =						pooler_dropout
        UpperCamelCase__    :     Optional[int]        =						pooler_hidden_act
  
  
  
  
  
  
  
  class     __a						(					A__ ):
  
  
  
  
  
  
  
   @property
   def       __lowercase      (	self      :     List[str]		):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        if self.task == "multiple-choice":
             UpperCamelCase__    :     Tuple        =						{0: "batch", 1: "choice", 2: "sequence"}
        else:
             UpperCamelCase__    :     List[str]        =						{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       __lowercase      (	self      :     Tuple		):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        return 12
  
  
  
  
  
  
  
   def       __lowercase      (	self      :     str							,     SCREAMING_SNAKE_CASE      :     Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"]							,     SCREAMING_SNAKE_CASE      :     int = -1							,     SCREAMING_SNAKE_CASE      :     int = -1							,     SCREAMING_SNAKE_CASE      :     int = -1							,     SCREAMING_SNAKE_CASE      :     bool = False							,     SCREAMING_SNAKE_CASE      :     Optional["TensorType"] = None							,     SCREAMING_SNAKE_CASE      :     int = 3							,     SCREAMING_SNAKE_CASE      :     int = 40							,     SCREAMING_SNAKE_CASE      :     int = 40							,     SCREAMING_SNAKE_CASE      :     "PreTrainedTokenizerBase" = None							,     ):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        UpperCamelCase__    :     Optional[int]        =						super().generate_dummy_inputs(preprocessor=SCREAMING_SNAKE_CASE							,     framework=SCREAMING_SNAKE_CASE		)
        if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
             del dummy_inputs["token_type_ids"]
        return dummy_inputs
style_context: 
  
  
  
  
  
  
  import os
  import unittest
  
  from transformers import MPNetTokenizerFast
  from transformers.models.mpnet.tokenization_mpnet import VOCAB_FILES_NAMES, MPNetTokenizer
  from transformers.testing_utils import require_tokenizers, slow
  
  from ...test_tokenization_common import TokenizerTesterMixin
  
  
  
  
  
  @require_tokenizers
  class     __a						(					A__ ,     unittest.TestCase ):
   _lowerCAmelCase : str 				=				MPNetTokenizer
   _lowerCAmelCase : List[str] 				=				MPNetTokenizerFast
   _lowerCAmelCase : List[Any] 				=				True
   _lowerCAmelCase : Union[str, Any] 				=				True
  
  
  
  
  
  
  
   def       __lowercase      (	self      :     List[Any]		):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        super().setUp()
  
        UpperCamelCase__    :     List[Any]        =						[
            "[UNK]",
            "[CLS]",
            "[SEP]",
            "[PAD]",
            "[MASK]",
            "want",
            "##want",
            "##ed",
            "wa",
            "un",
            "runn",
            "##ing",
            ",",
            "low",
            "lowest",
        ]
        UpperCamelCase__    :     int        =						os.path.join(self.tmpdirname							,     VOCAB_FILES_NAMES["vocab_file"]		)
        with open(self.vocab_file							,     "w"							,     encoding="utf-8"		) as vocab_writer:
             vocab_writer.write("".join([x + "\n" for x in vocab_tokens]		)		)
  
  
  
  
  
  
  
   def       __lowercase      (	self      :     Tuple							,     SCREAMING_SNAKE_CASE      :     List[str]		):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        UpperCamelCase__    :     List[str]        =						"UNwant\u00E9d,running"
        UpperCamelCase__    :     List[str]        =						"unwanted, running"
        return input_text, output_text
  
  
  
  
  
  
  
   def       __lowercase      (	self      :     Any		):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        UpperCamelCase__    :     str        =						self.tokenizer_class(self.vocab_file		)
  
        UpperCamelCase__    :     Any        =						tokenizer.tokenize("UNwant\u00E9d,running"		)
        self.assertListEqual(SCREAMING_SNAKE_CASE							,     ["un", "##want", "##ed", ",", "runn", "##ing"]		)
        self.assertListEqual(tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE		)							,     [9, 6, 7, 12, 10, 11]		)
  
  
  
  
  
  
  
   @slow
   def       __lowercase      (	self      :     str		):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        UpperCamelCase__    :     Union[str, Any]        =						self.tokenizer_class.from_pretrained("microsoft/mpnet-base"		)
  
        UpperCamelCase__    :     Optional[int]        =						tokenizer.encode("sequence builders"							,     add_special_tokens=SCREAMING_SNAKE_CASE		)
        UpperCamelCase__    :     Optional[int]        =						tokenizer.encode("multi-sequence build"							,     add_special_tokens=SCREAMING_SNAKE_CASE		)
  
        UpperCamelCase__    :     Tuple        =						tokenizer.build_inputs_with_special_tokens(SCREAMING_SNAKE_CASE		)
        UpperCamelCase__    :     Optional[int]        =						tokenizer.build_inputs_with_special_tokens(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE		)
  
        assert encoded_sentence == [0] + text + [2]
        assert encoded_pair == [0] + text + [2] + [2] + text_a + [2]