class_docstr: This is the configuration class to store the configuration of a [`DebertaV2Model`]. It is used to instantiate a
  DeBERTa-v2 model according to the specified arguments, defining the model architecture. Instantiating a
  configuration with the defaults will yield a similar configuration to that of the DeBERTa
  [microsoft/deberta-v2-xlarge](https://huggingface.co/microsoft/deberta-v2-xlarge) architecture.
  
  Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
  documentation from [`PretrainedConfig`] for more information.
  
  Arguments:
      vocab_size (`int`, *optional*, defaults to 128100):
          Vocabulary size of the DeBERTa-v2 model. Defines the number of different tokens that can be represented by
          the `inputs_ids` passed when calling [`DebertaV2Model`].
      hidden_size (`int`, *optional*, defaults to 1536):
          Dimensionality of the encoder layers and the pooler layer.
      num_hidden_layers (`int`, *optional*, defaults to 24):
          Number of hidden layers in the Transformer encoder.
      num_attention_heads (`int`, *optional*, defaults to 24):
          Number of attention heads for each attention layer in the Transformer encoder.
      intermediate_size (`int`, *optional*, defaults to 6144):
          Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder.
      hidden_act (`str` or `Callable`, *optional*, defaults to `"gelu"`):
          The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
          `"relu"`, `"silu"`, `"gelu"`, `"tanh"`, `"gelu_fast"`, `"mish"`, `"linear"`, `"sigmoid"` and `"gelu_new"`
          are supported.
      hidden_dropout_prob (`float`, *optional*, defaults to 0.1):
          The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
      attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1):
          The dropout ratio for the attention probabilities.
      max_position_embeddings (`int`, *optional*, defaults to 512):
          The maximum sequence length that this model might ever be used with. Typically set this to something large
          just in case (e.g., 512 or 1024 or 2048).
      type_vocab_size (`int`, *optional*, defaults to 0):
          The vocabulary size of the `token_type_ids` passed when calling [`DebertaModel`] or [`TFDebertaModel`].
      initializer_range (`float`, *optional*, defaults to 0.02):
          The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
      layer_norm_eps (`float`, *optional*, defaults to 1e-7):
          The epsilon used by the layer normalization layers.
      relative_attention (`bool`, *optional*, defaults to `True`):
          Whether use relative position encoding.
      max_relative_positions (`int`, *optional*, defaults to -1):
          The range of relative positions `[-max_position_embeddings, max_position_embeddings]`. Use the same value
          as `max_position_embeddings`.
      pad_token_id (`int`, *optional*, defaults to 0):
          The value used to pad input_ids.
      position_biased_input (`bool`, *optional*, defaults to `False`):
          Whether add absolute position embedding to content embedding.
      pos_att_type (`List[str]`, *optional*):
          The type of relative position attention, it can be a combination of `["p2c", "c2p"]`, e.g. `["p2c"]`,
          `["p2c", "c2p"]`, `["p2c", "c2p"]`.
      layer_norm_eps (`float`, optional, defaults to 1e-12):
          The epsilon used by the layer normalization layers.
  
  Example:
  
  ```python
  >>> from transformers import DebertaV2Config, DebertaV2Model
  
  >>> # Initializing a DeBERTa-v2 microsoft/deberta-v2-xlarge style configuration
  >>> configuration = DebertaV2Config()
  
  >>> # Initializing a model (with random weights) from the microsoft/deberta-v2-xlarge style configuration
  >>> model = DebertaV2Model(configuration)
  
  >>> # Accessing the model configuration
  >>> configuration = model.config
  ```
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code: def __init__(
      self,
      vocab_size=128100,
      hidden_size=1536,
      num_hidden_layers=24,
      num_attention_heads=24,
      intermediate_size=6144,
      hidden_act="gelu",
      hidden_dropout_prob=0.1,
      attention_probs_dropout_prob=0.1,
      max_position_embeddings=512,
      type_vocab_size=0,
      initializer_range=0.02,
      layer_norm_eps=1e-7,
      relative_attention=False,
      max_relative_positions=-1,
      pad_token_id=0,
      position_biased_input=True,
      pos_att_type=None,
      pooler_dropout=0,
      pooler_hidden_act="gelu",
      **kwargs,
  ):
      super().__init__(**kwargs)
  
      self.hidden_size = hidden_size
      self.num_hidden_layers = num_hidden_layers
      self.num_attention_heads = num_attention_heads
      self.intermediate_size = intermediate_size
      self.hidden_act = hidden_act
      self.hidden_dropout_prob = hidden_dropout_prob
      self.attention_probs_dropout_prob = attention_probs_dropout_prob
      self.max_position_embeddings = max_position_embeddings
      self.type_vocab_size = type_vocab_size
      self.initializer_range = initializer_range
      self.relative_attention = relative_attention
      self.max_relative_positions = max_relative_positions
      self.pad_token_id = pad_token_id
      self.position_biased_input = position_biased_input
  
      # Backwards compatibility
      if isinstance(pos_att_type, str):
          pos_att_type = [
              x.strip() for x in pos_att_type.lower().split("|")
          ]
  
      self.pos_att_type = pos_att_type
      self.vocab_size = vocab_size
      self.layer_norm_eps = layer_norm_eps
  
      self.pooler_hidden_size = kwargs.get(
          "pooler_hidden_size", hidden_size
      )
      self.pooler_dropout = pooler_dropout
      self.pooler_hidden_act = pooler_hidden_act
  
file_path: transformers/src/transformers/models/deberta_v2/configuration_deberta_v2.py
imports: 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
label_desc: Use image node_id 1 to create a new DebertaV2Config object from inherited base classes: PretrainedConfig with example: obj = DebertaV2Config(vocab_size, hidden_size, num_hidden_layers, num_attention_heads, intermediate_size, hidden_act, hidden_dropout_prob, attention_probs_dropout_prob, max_position_embeddings, type_vocab_size, initializer_range, layer_norm_eps, relative_attention, max_relative_positions, pad_token_id, position_biased_input, pos_att_type, pooler_dropout, pooler_hidden_act)