






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







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]