code: # coding=utf-8
  # Copyright 2018 The HuggingFace Inc. team, Microsoft Corporation.
  # Copyright (c) 2018, NVIDIA CORPORATION.  All rights reserved.
  #
  # 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.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  """ MPNet model configuration """
  
  from ...configuration_utils import PretrainedConfig
  from ...utils import logging
  
  
  logger = logging.get_logger(__name__)
  
  MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP = {
      "microsoft/mpnet-base": "https://huggingface.co/microsoft/mpnet-base/resolve/main/config.json",
  }
  
  
  class MPNetConfig(PretrainedConfig):
      r"""
      This is the configuration class to store the configuration of a :class:`~transformers.MPNetModel` or a
      :class:`~transformers.TFMPNetModel`. It is used to instantiate a MPNet 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 MPNet `mpnet-base <https://huggingface.co/mpnet-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.
  
      Args:
          vocab_size (:obj:`int`, `optional`, defaults to 30527):
              Vocabulary size of the MPNet model. Defines the number of different tokens that can be represented by the
              :obj:`inputs_ids` passed when calling :class:`~transformers.MPNetModel` or
              :class:`~transformers.TFMPNetModel`.
          hidden_size (:obj:`int`, `optional`, defaults to 768):
              Dimensionality of the encoder layers and the pooler layer.
          num_hidden_layers (:obj:`int`, `optional`, defaults to 12):
              Number of hidden layers in the Transformer encoder.
          num_attention_heads (:obj:`int`, `optional`, defaults to 12):
              Number of attention heads for each attention layer in the Transformer encoder.
          intermediate_size (:obj:`int`, `optional`, defaults to 3072):
              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"` 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).
          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-12):
              The epsilon used by the layer normalization layers.
          relative_attention_num_buckets (:obj:`int`, `optional`, defaults to 32):
              The number of buckets to use for each attention layer.
  
      Examples::
  
          >>> from transformers import MPNetModel, MPNetConfig
  
          >>> # Initializing a MPNet mpnet-base style configuration
          >>> configuration = MPNetConfig()
  
          >>> # Initializing a model from the mpnet-base style configuration
          >>> model = MPNetModel(configuration)
  
          >>> # Accessing the model configuration
          >>> configuration = model.config
      """
      model_type = "mpnet"
  
      def __init__(
          self,
          vocab_size=30527,
          hidden_size=768,
          num_hidden_layers=12,
          num_attention_heads=12,
          intermediate_size=3072,
          hidden_act="gelu",
          hidden_dropout_prob=0.1,
          attention_probs_dropout_prob=0.1,
          max_position_embeddings=512,
          initializer_range=0.02,
          layer_norm_eps=1e-12,
          relative_attention_num_buckets=32,
          pad_token_id=1,
          bos_token_id=0,
          eos_token_id=2,
          **kwargs,
      ):
          super().__init__(pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs)
  
          self.vocab_size = vocab_size
          self.hidden_size = hidden_size
          self.num_hidden_layers = num_hidden_layers
          self.num_attention_heads = num_attention_heads
          self.hidden_act = hidden_act
          self.intermediate_size = intermediate_size
          self.hidden_dropout_prob = hidden_dropout_prob
          self.attention_probs_dropout_prob = attention_probs_dropout_prob
          self.max_position_embeddings = max_position_embeddings
          self.initializer_range = initializer_range
          self.layer_norm_eps = layer_norm_eps
          self.relative_attention_num_buckets = relative_attention_num_buckets
  