text: # coding=utf-8
  # Copyright 2020, Microsoft and the HuggingFace Inc. team.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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  """ DeBERTa-v2 model configuration """
  
  from ...configuration_utils import PretrainedConfig
  from ...utils import logging
  
  
  logger = logging.get_logger(__name__)
  
  DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP = {
      "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 DebertaV2Config(PretrainedConfig):
      r"""
      This is the configuration class to store the configuration of a :class:`~transformers.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-base>`__ architecture.
  
      Configuration objects inherit from :class:`~transformers.PretrainedConfig` and can be used to control the model
      outputs. Read the documentation from :class:`~transformers.PretrainedConfig` for more information.
  
      Arguments:
          vocab_size (:obj:`int`, `optional`, defaults to 128100):
              Vocabulary size of the DeBERTa-v2 model. Defines the number of different tokens that can be represented by
              the :obj:`inputs_ids` passed when calling :class:`~transformers.DebertaV2Model`.
          hidden_size (:obj:`int`, `optional`, defaults to 1536):
              Dimensionality of the encoder layers and the pooler layer.
          num_hidden_layers (:obj:`int`, `optional`, defaults to 24):
              Number of hidden layers in the Transformer encoder.
          num_attention_heads (:obj:`int`, `optional`, defaults to 24):
              Number of attention heads for each attention layer in the Transformer encoder.
          intermediate_size (:obj:`int`, `optional`, defaults to 6144):
              Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder.
          hidden_act (:obj:`str` or :obj:`Callable`, `optional`, defaults to :obj:`"gelu"`):
              The non-linear activation function (function or string) in the encoder and pooler. If string,
              :obj:`"gelu"`, :obj:`"relu"`, :obj:`"silu"`, :obj:`"gelu"`, :obj:`"tanh"`, :obj:`"gelu_fast"`,
              :obj:`"mish"`, :obj:`"linear"`, :obj:`"sigmoid"` and :obj:`"gelu_new"` are supported.
          hidden_dropout_prob (:obj:`float`, `optional`, defaults to 0.1):
              The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
          attention_probs_dropout_prob (:obj:`float`, `optional`, defaults to 0.1):
              The dropout ratio for the attention probabilities.
          max_position_embeddings (:obj:`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 (:obj:`int`, `optional`, defaults to 0):
              The vocabulary size of the :obj:`token_type_ids` passed when calling :class:`~transformers.DebertaModel` or
              :class:`~transformers.TFDebertaModel`.
          initializer_range (:obj:`float`, `optional`, defaults to 0.02):
              The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
          layer_norm_eps (:obj:`float`, `optional`, defaults to 1e-7):
              The epsilon used by the layer normalization layers.
          relative_attention (:obj:`bool`, `optional`, defaults to :obj:`True`):
              Whether use relative position encoding.
          max_relative_positions (:obj:`int`, `optional`, defaults to -1):
              The range of relative positions :obj:`[-max_position_embeddings, max_position_embeddings]`. Use the same
              value as :obj:`max_position_embeddings`.
          pad_token_id (:obj:`int`, `optional`, defaults to 0):
              The value used to pad input_ids.
          position_biased_input (:obj:`bool`, `optional`, defaults to :obj:`False`):
              Whether add absolute position embedding to content embedding.
          pos_att_type (:obj:`List[str]`, `optional`):
              The type of relative position attention, it can be a combination of :obj:`["p2c", "c2p", "p2p"]`, e.g.
              :obj:`["p2c"]`, :obj:`["p2c", "c2p"]`, :obj:`["p2c", "c2p", 'p2p"]`.
          layer_norm_eps (:obj:`float`, optional, defaults to 1e-12):
              The epsilon used by the layer normalization layers.
          cls_dropout (:obj:`float`, `optional`):
              cls dropout.
          apply_lora (:obj:`bool`, `optional`):
              apply Lora.
          lora_alpha (:obj:`int`, `optional`):
              lora alpha.
          lora_r (:obj:`int`, `optional`):
              lora r.
          rdrop_loss_wgt (:obj:`float`, `optional`, defaults to 0):
              rdrop loss weight.
      """
      model_type = "deberta-v2"
  
      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",
          cls_dropout=None,
          apply_lora=False,
          lora_alpha=None,
          lora_r=None,
          reg_loss_wgt=0.0,
          masking_prob=0.0,
          cls_token_id=1,
          sep_token_id=2,
          unk_token_id=3,
          **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 type(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
          self.cls_dropout = cls_dropout
          self.apply_lora = apply_lora
          self.lora_alpha = lora_alpha
          self.lora_r = lora_r
          self.reg_loss_wgt = reg_loss_wgt
          self.masking_prob = masking_prob
          self.cls_token_id = cls_token_id
          self.sep_token_id = sep_token_id
          self.unk_token_id = unk_token_id
  