text: # coding=utf-8
  # Copyright 2018 Google AI, Google Brain and Carnegie Mellon University Authors and the HuggingFace Inc. team.
  # Copyright (c) 2018, NVIDIA CORPORATION.  All rights reserved.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
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  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
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  # 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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  """ XLNet configuration """
  
  
  import logging
  
  from .configuration_utils import PretrainedConfig
  
  
  logger = logging.getLogger(__name__)
  
  XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP = {
      "xlnet-base-cased": "https://s3.amazonaws.com/models.huggingface.co/bert/xlnet-base-cased-config.json",
      "xlnet-large-cased": "https://s3.amazonaws.com/models.huggingface.co/bert/xlnet-large-cased-config.json",
  }
  
  
  class XLNetConfig(PretrainedConfig):
      """
          This is the configuration class to store the configuration of a :class:`~transformers.XLNetModel`.
          It is used to instantiate an XLNet 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 `xlnet-large-cased <https://huggingface.co/xlnet-large-cased>`__ 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 32000):
                  Vocabulary size of the XLNet model. Defines the different tokens that
                  can be represented by the `inputs_ids` passed to the forward method of :class:`~transformers.XLNetModel`.
              d_model (:obj:`int`, optional, defaults to 1024):
                  Dimensionality of the encoder layers and the pooler layer.
              n_layer (:obj:`int`, optional, defaults to 24):
                  Number of hidden layers in the Transformer encoder.
              n_head (:obj:`int`, optional, defaults to 16):
                  Number of attention heads for each attention layer in the Transformer encoder.
              d_inner (:obj:`int`, optional, defaults to 4096):
                  Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
              ff_activation (:obj:`string`, optional, defaults to "gelu"):
                  The non-linear activation function (function or string) in the
                  encoder and pooler. If string, "gelu", "relu" and "swish" are supported.
              untie_r (:obj:`boolean`, optional, defaults to :obj:`True`):
                  Untie relative position biases
              attn_type (:obj:`string`, optional, defaults to "bi"):
                  The attention type used by the model. Set 'bi' for XLNet, 'uni' for Transformer-XL.
              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.
              dropout (:obj:`float`, optional, defaults to 0.1):
                  The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
              mem_len (:obj:`int` or :obj:`None`, optional, defaults to :obj:`None`):
                  The number of tokens to cache. The key/value pairs that have already been pre-computed
                  in a previous forward pass won't be re-computed. See the
                  `quickstart <https://huggingface.co/transformers/quickstart.html#using-the-past>`__
                  for more information.
              reuse_len (:obj:`int` or :obj:`None`, optional, defaults to :obj:`None`):
                  The number of tokens in the current batch to be cached and reused in the future.
              bi_data (:obj:`boolean`, optional, defaults to :obj:`False`):
                  Whether to use bidirectional input pipeline. Usually set to `True` during
                  pretraining and `False` during finetuning.
              clamp_len (:obj:`int`, optional, defaults to -1):
                  Clamp all relative distances larger than clamp_len.
                  Setting this attribute to -1 means no clamping.
              same_length (:obj:`boolean`, optional, defaults to :obj:`False`):
                  Whether to use the same attention length for each token.
              summary_type (:obj:`string`, optional, defaults to "last"):
                  Argument used when doing sequence summary. Used in for the multiple choice head in
                  :class:transformers.XLNetForSequenceClassification` and :class:`~transformers.XLNetForMultipleChoice`.
                  Is one of the following options:
  
                  - 'last' => take the last token hidden state (like XLNet)
                  - 'first' => take the first token hidden state (like Bert)
                  - 'mean' => take the mean of all tokens hidden states
                  - 'cls_index' => supply a Tensor of classification token position (GPT/GPT-2)
                  - 'attn' => Not implemented now, use multi-head attention
              summary_use_proj (:obj:`boolean`, optional, defaults to :obj:`True`):
                  Argument used when doing sequence summary. Used in for the multiple choice head in
                  :class:`~transformers.XLNetForSequenceClassification` and :class:`~transformers.XLNetForMultipleChoice`.
                  Add a projection after the vector extraction
              summary_activation (:obj:`string` or :obj:`None`, optional, defaults to :obj:`None`):
                  Argument used when doing sequence summary. Used in for the multiple choice head in
                  :class:`~transformers.XLNetForSequenceClassification` and :class:`~transformers.XLNetForMultipleChoice`.
                  'tanh' => add a tanh activation to the output, Other => no activation.
              summary_proj_to_labels (:obj:`boolean`, optional, defaults to :obj:`True`):
                  Argument used when doing sequence summary. Used in for the multiple choice head in
                  :class:`~transformers.XLNetForSequenceClassification` and :class:`~transformers.XLNetForMultipleChoice`.
                  If True, the projection outputs to config.num_labels classes (otherwise to hidden_size). Default: False.
              summary_last_dropout (:obj:`float`, optional, defaults to 0.1):
                  Argument used when doing sequence summary. Used in for the multiple choice head in
                  :class:`~transformers.XLNetForSequenceClassification` and :class:`~transformers.XLNetForMultipleChoice`.
                  Add a dropout after the projection and activation
              start_n_top (:obj:`int`, optional, defaults to 5):
                  Used in the SQuAD evaluation script for XLM and XLNet.
              end_n_top (:obj:`int`, optional, defaults to 5):
                  Used in the SQuAD evaluation script for XLM and XLNet.
  
          Example::
  
              from transformers import XLNetConfig, XLNetModel
  
              # Initializing a XLNet configuration
              configuration = XLNetConfig()
  
              # Initializing a model from the configuration
              model = XLNetModel(configuration)
  
              # Accessing the model configuration
              configuration = model.config
      """
  
      model_type = "xlnet"
  
      def __init__(
          self,
          vocab_size=32000,
          d_model=1024,
          n_layer=24,
          n_head=16,
          d_inner=4096,
          ff_activation="gelu",
          untie_r=True,
          attn_type="bi",
          initializer_range=0.02,
          layer_norm_eps=1e-12,
          dropout=0.1,
          mem_len=None,
          reuse_len=None,
          bi_data=False,
          clamp_len=-1,
          same_length=False,
          summary_type="last",
          summary_use_proj=True,
          summary_activation="tanh",
          summary_last_dropout=0.1,
          start_n_top=5,
          end_n_top=5,
          pad_token_id=5,
          bos_token_id=1,
          eos_token_id=2,
          **kwargs
      ):
          """Constructs XLNetConfig.
          """
          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.d_model = d_model
          self.n_layer = n_layer
          self.n_head = n_head
          assert d_model % n_head == 0
          if "d_head" in kwargs:
              assert (
                  kwargs["d_head"] == d_model // n_head
              ), f"`d_head` ({kwargs['d_head']}) should be equal to `d_model // n_head` ({d_model // n_head})"
          self.d_head = d_model // n_head
          self.ff_activation = ff_activation
          self.d_inner = d_inner
          self.untie_r = untie_r
          self.attn_type = attn_type
  
          self.initializer_range = initializer_range
          self.layer_norm_eps = layer_norm_eps
  
          self.dropout = dropout
          self.mem_len = mem_len
          self.reuse_len = reuse_len
          self.bi_data = bi_data
          self.clamp_len = clamp_len
          self.same_length = same_length
  
          self.summary_type = summary_type
          self.summary_use_proj = summary_use_proj
          self.summary_activation = summary_activation
          self.summary_last_dropout = summary_last_dropout
          self.start_n_top = start_n_top
          self.end_n_top = end_n_top
  
          self.bos_token_id = bos_token_id
          self.pad_token_id = pad_token_id
          self.eos_token_id = eos_token_id
  
      @property
      def max_position_embeddings(self):
          return -1
  
      @property
      def n_token(self):  # Backward compatibility
          return self.vocab_size
  
      @n_token.setter
      def n_token(self, value):  # Backward compatibility
          self.vocab_size = value
  
      @property
      def hidden_size(self):
          return self.d_model
  
      @property
      def num_attention_heads(self):
          return self.n_head
  
      @property
      def num_hidden_layers(self):
          return self.n_layer
  