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							:			Tuple				=logging.get_logger(__name__)
  
  __lowerCAmelCase							:			Union[str, Any]				={
      "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			( UpperCamelCase__       ):
   __lowercase     	=       """deberta"""
   def __init__(       self   :List[str]		, lowercase_   :Union[str, Any]=5_02_65		, lowercase_   :Tuple=7_68		, lowercase_   :Union[str, Any]=12		, lowercase_   :str=12		, lowercase_   :List[Any]=30_72		, lowercase_   :int="gelu"		, lowercase_   :List[Any]=0.1		, lowercase_   :List[Any]=0.1		, lowercase_   :Union[str, Any]=5_12		, lowercase_   :Optional[int]=0		, lowercase_   :Union[str, Any]=0.0_2		, lowercase_   :Dict=1E-7		, lowercase_   :int=False		, lowercase_   :str=-1		, lowercase_   :Any=0		, lowercase_   :Optional[Any]=True		, lowercase_   :int=None		, lowercase_   :Any=0		, lowercase_   :Dict="gelu"		, **lowercase_   :int		, )->							Tuple:
       super().__init__(**lowercase_							)
  
       A__       =    hidden_size
       A__       =    num_hidden_layers
       A__       =    num_attention_heads
       A__       =    intermediate_size
       A__       =    hidden_act
       A__       =    hidden_dropout_prob
       A__       =    attention_probs_dropout_prob
       A__       =    max_position_embeddings
       A__       =    type_vocab_size
       A__       =    initializer_range
       A__       =    relative_attention
       A__       =    max_relative_positions
       A__       =    pad_token_id
       A__       =    position_biased_input
  
       # Backwards compatibility
       if type(lowercase_							) == str:
           A__       =    [x.strip() for x in pos_att_type.lower().split("|"							)]
  
       A__       =    pos_att_type
       A__       =    vocab_size
       A__       =    layer_norm_eps
  
       A__       =    kwargs.get("pooler_hidden_size"		, lowercase_							)
       A__       =    pooler_dropout
       A__       =    pooler_hidden_act
  
  
  
  
  
  
  class      UpperCAmelCase			( UpperCamelCase__       ):
   @property
   def   UpperCAmelCase_							(       self   :str							)->							Mapping[str, Mapping[int, str]]:
       if self.task == "multiple-choice":
           A__       =    {0: "batch", 1: "choice", 2: "sequence"}
       else:
           A__       =    {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   :Optional[Any]							)->							int:
       return 12
  
  
  
   def   UpperCAmelCase_							(       self   :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		, )->							Mapping[str, Any]:
       A__       =    super().generate_dummy_inputs(preprocessor=lowercase_		, framework=lowercase_							)
       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: 
  
  
  
  
  
  '''simple docstring'''
  
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_tf_available,
      is_tokenizers_available,
      is_torch_available,
  )
  
  
  __lowerCAmelCase							:			int				={
      "configuration_layoutlm": ["LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "LayoutLMConfig", "LayoutLMOnnxConfig"],
      "tokenization_layoutlm": ["LayoutLMTokenizer"],
  }
  
  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    __lowerCAmelCase							:			Any				=["LayoutLMTokenizerFast"]
  
  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    __lowerCAmelCase							:			Dict				=[
        "LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST",
        "LayoutLMForMaskedLM",
        "LayoutLMForSequenceClassification",
        "LayoutLMForTokenClassification",
        "LayoutLMForQuestionAnswering",
        "LayoutLMModel",
        "LayoutLMPreTrainedModel",
    ]
  
  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    __lowerCAmelCase							:			str				=[
        "TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST",
        "TFLayoutLMForMaskedLM",
        "TFLayoutLMForSequenceClassification",
        "TFLayoutLMForTokenClassification",
        "TFLayoutLMForQuestionAnswering",
        "TFLayoutLMMainLayer",
        "TFLayoutLMModel",
        "TFLayoutLMPreTrainedModel",
    ]
  
  
  if TYPE_CHECKING:
    from .configuration_layoutlm import LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMConfig, LayoutLMOnnxConfig
    from .tokenization_layoutlm import LayoutLMTokenizer
  
    try:
      if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
      pass
    else:
      from .tokenization_layoutlm_fast import LayoutLMTokenizerFast
  
    try:
      if not is_torch_available():
        raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
      pass
    else:
      from .modeling_layoutlm import (
          LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
          LayoutLMForMaskedLM,
          LayoutLMForQuestionAnswering,
          LayoutLMForSequenceClassification,
          LayoutLMForTokenClassification,
          LayoutLMModel,
          LayoutLMPreTrainedModel,
      )
    try:
      if not is_tf_available():
        raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
      pass
    else:
      from .modeling_tf_layoutlm import (
          TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFLayoutLMForMaskedLM,
          TFLayoutLMForQuestionAnswering,
          TFLayoutLMForSequenceClassification,
          TFLayoutLMForTokenClassification,
          TFLayoutLMMainLayer,
          TFLayoutLMModel,
          TFLayoutLMPreTrainedModel,
      )
  
  else:
    import sys
  
    __lowerCAmelCase							:			int				=_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
  
  
  
  