






'''simple docstring'''
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


a_					= logging.get_logger(__name__)

a_					= {
    """microsoft/deberta-v2-xlarge""": """https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/config.json""",
    """microsoft/deberta-v2-xxlarge""": """https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/config.json""",
    """microsoft/deberta-v2-xlarge-mnli""": (
        """https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/config.json"""
    ),
    """microsoft/deberta-v2-xxlarge-mnli""": (
        """https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/config.json"""
    ),
}






class      __SCREAMING_SNAKE_CASE      (			UpperCamelCase_				):
 snake_case_  							=							"""deberta-v2"""




 def __init__(      self	:							Dict					,      __lowercase	:							Dict=12_81_00					,      __lowercase	:							Any=15_36					,      __lowercase	:							Union[str, Any]=24					,      __lowercase	:							Optional[int]=24					,      __lowercase	:							Any=61_44					,      __lowercase	:							List[str]="gelu"					,      __lowercase	:							List[str]=0.1					,      __lowercase	:							int=0.1					,      __lowercase	:							Dict=5_12					,      __lowercase	:							Optional[int]=0					,      __lowercase	:							Union[str, Any]=0.02					,      __lowercase	:							str=1e-7					,      __lowercase	:							Union[str, Any]=False					,      __lowercase	:							str=-1					,      __lowercase	:							str=0					,      __lowercase	:							Optional[Any]=True					,      __lowercase	:							Union[str, Any]=None					,      __lowercase	:							Any=0					,      __lowercase	:							Any="gelu"					,      **__lowercase	:							Optional[int]					,      )	->						Optional[Any]:
  super().__init__(**__A      )

  SCREAMING_SNAKE_CASE__     :				Optional[int]									=hidden_size
  SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=num_hidden_layers
  SCREAMING_SNAKE_CASE__     :				Optional[int]									=num_attention_heads
  SCREAMING_SNAKE_CASE__     :				Tuple									=intermediate_size
  SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=hidden_act
  SCREAMING_SNAKE_CASE__     :				Any									=hidden_dropout_prob
  SCREAMING_SNAKE_CASE__     :				Tuple									=attention_probs_dropout_prob
  SCREAMING_SNAKE_CASE__     :				Tuple									=max_position_embeddings
  SCREAMING_SNAKE_CASE__     :				List[Any]									=type_vocab_size
  SCREAMING_SNAKE_CASE__     :				int									=initializer_range
  SCREAMING_SNAKE_CASE__     :				List[Any]									=relative_attention
  SCREAMING_SNAKE_CASE__     :				Dict									=max_relative_positions
  SCREAMING_SNAKE_CASE__     :				Any									=pad_token_id
  SCREAMING_SNAKE_CASE__     :				Any									=position_biased_input

  # Backwards compatibility
  if type(__A      ) == str:
   SCREAMING_SNAKE_CASE__     :				List[Any]									=[x.strip() for x in pos_att_type.lower().split('''|'''      )]

  SCREAMING_SNAKE_CASE__     :				List[str]									=pos_att_type
  SCREAMING_SNAKE_CASE__     :				Tuple									=vocab_size
  SCREAMING_SNAKE_CASE__     :				List[str]									=layer_norm_eps

  SCREAMING_SNAKE_CASE__     :				List[str]									=kwargs.get('''pooler_hidden_size'''					,      __A      )
  SCREAMING_SNAKE_CASE__     :				Tuple									=pooler_dropout
  SCREAMING_SNAKE_CASE__     :				str									=pooler_hidden_act






class      __SCREAMING_SNAKE_CASE      (			UpperCamelCase_				):




 @property
 def      __magic_name__	(      self	:							str      )	->						Tuple:
  if self.task == "multiple-choice":
   SCREAMING_SNAKE_CASE__     :				int									={0: "batch", 1: "choice", 2: "sequence"}
  else:
   SCREAMING_SNAKE_CASE__     :				List[Any]									={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      __magic_name__	(      self	:							Dict      )	->						Dict:
  return 12




 def      __magic_name__	(      self	:							List[Any]					,      __lowercase	:							Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"]					,      __lowercase	:							int = -1					,      __lowercase	:							int = -1					,      __lowercase	:							int = -1					,      __lowercase	:							bool = False					,      __lowercase	:							Optional["TensorType"] = None					,      __lowercase	:							int = 3					,      __lowercase	:							int = 40					,      __lowercase	:							int = 40					,      __lowercase	:							"PreTrainedTokenizerBase" = None					,      )	->						Tuple:
  SCREAMING_SNAKE_CASE__     :				int									=super().generate_dummy_inputs(preprocessor=__A					,      framework=__A      )
  if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
   del dummy_inputs["token_type_ids"]
  return dummy_inputs







'''simple docstring'''
import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bert import BertTokenizer


a_					= logging.get_logger(__name__)

a_					= {'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

a_					= {
    'vocab_file': {
        'bert-base-uncased': 'https://huggingface.co/bert-base-uncased/resolve/main/vocab.txt',
        'bert-large-uncased': 'https://huggingface.co/bert-large-uncased/resolve/main/vocab.txt',
        'bert-base-cased': 'https://huggingface.co/bert-base-cased/resolve/main/vocab.txt',
        'bert-large-cased': 'https://huggingface.co/bert-large-cased/resolve/main/vocab.txt',
        'bert-base-multilingual-uncased': (
            'https://huggingface.co/bert-base-multilingual-uncased/resolve/main/vocab.txt'
        ),
        'bert-base-multilingual-cased': 'https://huggingface.co/bert-base-multilingual-cased/resolve/main/vocab.txt',
        'bert-base-chinese': 'https://huggingface.co/bert-base-chinese/resolve/main/vocab.txt',
        'bert-base-german-cased': 'https://huggingface.co/bert-base-german-cased/resolve/main/vocab.txt',
        'bert-large-uncased-whole-word-masking': (
            'https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/vocab.txt'
        ),
        'bert-large-cased-whole-word-masking': (
            'https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/vocab.txt'
        ),
        'bert-large-uncased-whole-word-masking-finetuned-squad': (
            'https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/vocab.txt'
        ),
        'bert-large-cased-whole-word-masking-finetuned-squad': (
            'https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/vocab.txt'
        ),
        'bert-base-cased-finetuned-mrpc': (
            'https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/vocab.txt'
        ),
        'bert-base-german-dbmdz-cased': 'https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/vocab.txt',
        'bert-base-german-dbmdz-uncased': (
            'https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/vocab.txt'
        ),
        'TurkuNLP/bert-base-finnish-cased-v1': (
            'https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/vocab.txt'
        ),
        'TurkuNLP/bert-base-finnish-uncased-v1': (
            'https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/vocab.txt'
        ),
        'wietsedv/bert-base-dutch-cased': (
            'https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/vocab.txt'
        ),
    },
    'tokenizer_file': {
        'bert-base-uncased': 'https://huggingface.co/bert-base-uncased/resolve/main/tokenizer.json',
        'bert-large-uncased': 'https://huggingface.co/bert-large-uncased/resolve/main/tokenizer.json',
        'bert-base-cased': 'https://huggingface.co/bert-base-cased/resolve/main/tokenizer.json',
        'bert-large-cased': 'https://huggingface.co/bert-large-cased/resolve/main/tokenizer.json',
        'bert-base-multilingual-uncased': (
            'https://huggingface.co/bert-base-multilingual-uncased/resolve/main/tokenizer.json'
        ),
        'bert-base-multilingual-cased': (
            'https://huggingface.co/bert-base-multilingual-cased/resolve/main/tokenizer.json'
        ),
        'bert-base-chinese': 'https://huggingface.co/bert-base-chinese/resolve/main/tokenizer.json',
        'bert-base-german-cased': 'https://huggingface.co/bert-base-german-cased/resolve/main/tokenizer.json',
        'bert-large-uncased-whole-word-masking': (
            'https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/tokenizer.json'
        ),
        'bert-large-cased-whole-word-masking': (
            'https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/tokenizer.json'
        ),
        'bert-large-uncased-whole-word-masking-finetuned-squad': (
            'https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/tokenizer.json'
        ),
        'bert-large-cased-whole-word-masking-finetuned-squad': (
            'https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/tokenizer.json'
        ),
        'bert-base-cased-finetuned-mrpc': (
            'https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/tokenizer.json'
        ),
        'bert-base-german-dbmdz-cased': (
            'https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/tokenizer.json'
        ),
        'bert-base-german-dbmdz-uncased': (
            'https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/tokenizer.json'
        ),
        'TurkuNLP/bert-base-finnish-cased-v1': (
            'https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/tokenizer.json'
        ),
        'TurkuNLP/bert-base-finnish-uncased-v1': (
            'https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/tokenizer.json'
        ),
        'wietsedv/bert-base-dutch-cased': (
            'https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/tokenizer.json'
        ),
    },
}

a_					= {
    'bert-base-uncased': 5_1_2,
    'bert-large-uncased': 5_1_2,
    'bert-base-cased': 5_1_2,
    'bert-large-cased': 5_1_2,
    'bert-base-multilingual-uncased': 5_1_2,
    'bert-base-multilingual-cased': 5_1_2,
    'bert-base-chinese': 5_1_2,
    'bert-base-german-cased': 5_1_2,
    'bert-large-uncased-whole-word-masking': 5_1_2,
    'bert-large-cased-whole-word-masking': 5_1_2,
    'bert-large-uncased-whole-word-masking-finetuned-squad': 5_1_2,
    'bert-large-cased-whole-word-masking-finetuned-squad': 5_1_2,
    'bert-base-cased-finetuned-mrpc': 5_1_2,
    'bert-base-german-dbmdz-cased': 5_1_2,
    'bert-base-german-dbmdz-uncased': 5_1_2,
    'TurkuNLP/bert-base-finnish-cased-v1': 5_1_2,
    'TurkuNLP/bert-base-finnish-uncased-v1': 5_1_2,
    'wietsedv/bert-base-dutch-cased': 5_1_2,
}

a_					= {
    'bert-base-uncased': {'do_lower_case': True},
    'bert-large-uncased': {'do_lower_case': True},
    'bert-base-cased': {'do_lower_case': False},
    'bert-large-cased': {'do_lower_case': False},
    'bert-base-multilingual-uncased': {'do_lower_case': True},
    'bert-base-multilingual-cased': {'do_lower_case': False},
    'bert-base-chinese': {'do_lower_case': False},
    'bert-base-german-cased': {'do_lower_case': False},
    'bert-large-uncased-whole-word-masking': {'do_lower_case': True},
    'bert-large-cased-whole-word-masking': {'do_lower_case': False},
    'bert-large-uncased-whole-word-masking-finetuned-squad': {'do_lower_case': True},
    'bert-large-cased-whole-word-masking-finetuned-squad': {'do_lower_case': False},
    'bert-base-cased-finetuned-mrpc': {'do_lower_case': False},
    'bert-base-german-dbmdz-cased': {'do_lower_case': False},
    'bert-base-german-dbmdz-uncased': {'do_lower_case': True},
    'TurkuNLP/bert-base-finnish-cased-v1': {'do_lower_case': False},
    'TurkuNLP/bert-base-finnish-uncased-v1': {'do_lower_case': True},
    'wietsedv/bert-base-dutch-cased': {'do_lower_case': False},
}






class      __SCREAMING_SNAKE_CASE      (			lowerCamelCase				):
 snake_case_  							=							VOCAB_FILES_NAMES
 snake_case_  							=							PRETRAINED_VOCAB_FILES_MAP
 snake_case_  							=							PRETRAINED_INIT_CONFIGURATION
 snake_case_  							=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 snake_case_  							=							BertTokenizer




 def __init__(      self	:							int					,      __lowercase	:							Union[str, Any]=None					,      __lowercase	:							Tuple=None					,      __lowercase	:							str=True					,      __lowercase	:							Optional[Any]="[UNK]"					,      __lowercase	:							Tuple="[SEP]"					,      __lowercase	:							Any="[PAD]"					,      __lowercase	:							List[Any]="[CLS]"					,      __lowercase	:							Union[str, Any]="[MASK]"					,      __lowercase	:							Tuple=True					,      __lowercase	:							str=None					,      **__lowercase	:							Any					,      )	->						Optional[Any]:
       super().__init__(
           __lowercase					,      tokenizer_file=__lowercase					,      do_lower_case=__lowercase					,      unk_token=__lowercase					,      sep_token=__lowercase					,      pad_token=__lowercase					,      cls_token=__lowercase					,      mask_token=__lowercase					,      tokenize_chinese_chars=__lowercase					,      strip_accents=__lowercase					,      **__lowercase					,      )

       SCREAMING_SNAKE_CASE__     :				str									=json.loads(self.backend_tokenizer.normalizer.__getstate__()      )
       if (
           normalizer_state.get('''lowercase'''					,      __lowercase      ) != do_lower_case
           or normalizer_state.get('''strip_accents'''					,      __lowercase      ) != strip_accents
           or normalizer_state.get('''handle_chinese_chars'''					,      __lowercase      ) != tokenize_chinese_chars
       ):
             SCREAMING_SNAKE_CASE__     :				int									=getattr(__lowercase					,      normalizer_state.pop('''type'''      )      )
             SCREAMING_SNAKE_CASE__     :				Any									=do_lower_case
             SCREAMING_SNAKE_CASE__     :				Any									=strip_accents
             SCREAMING_SNAKE_CASE__     :				Dict									=tokenize_chinese_chars
             SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=normalizer_class(**__lowercase      )

       SCREAMING_SNAKE_CASE__     :				Optional[int]									=do_lower_case




 def      __magic_name__	(      self	:							int					,      __lowercase	:							Optional[Any]					,      __lowercase	:							Union[str, Any]=None      )	->						int:
       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=[self.cls_token_id] + token_ids_a + [self.sep_token_id]

       if token_ids_a:
             output += token_ids_a + [self.sep_token_id]

       return output




 def      __magic_name__	(      self	:							Union[str, Any]					,      __lowercase	:							List[int]					,      __lowercase	:							Optional[List[int]] = None      )	->						List[int]:
       SCREAMING_SNAKE_CASE__     :				List[str]									=[self.sep_token_id]
       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=[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      __magic_name__	(      self	:							List[Any]					,      __lowercase	:							str					,      __lowercase	:							Optional[str] = None      )	->						Tuple[str]:
       SCREAMING_SNAKE_CASE__     :				Optional[int]									=self._tokenizer.model.save(__lowercase					,      name=__lowercase      )
       return tuple(__lowercase      )