class_docstr: This is the configuration class to store the configuration of a [`XLMModel`] or a [`TFXLMModel`]. It is used to
  instantiate a XLM 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
  [xlm-mlm-en-2048](https://huggingface.co/xlm-mlm-en-2048) architecture.
  
  Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
  documentation from [`PretrainedConfig`] for more information.
  
  Args:
      vocab_size (`int`, *optional*, defaults to 30145):
          Vocabulary size of the BERT model. Defines the number of different tokens that can be represented by the
          `inputs_ids` passed when calling [`XLMModel`] or [`TFXLMModel`].
      emb_dim (`int`, *optional*, defaults to 2048):
          Dimensionality of the encoder layers and the pooler layer.
      n_layer (`int`, *optional*, defaults to 12):
          Number of hidden layers in the Transformer encoder.
      n_head (`int`, *optional*, defaults to 16):
          Number of attention heads for each attention layer in the Transformer encoder.
      dropout (`float`, *optional*, defaults to 0.1):
          The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
      attention_dropout (`float`, *optional*, defaults to 0.1):
          The dropout probability for the attention mechanism
      gelu_activation (`bool`, *optional*, defaults to `True`):
          Whether or not to use *gelu* for the activations instead of *relu*.
      sinusoidal_embeddings (`bool`, *optional*, defaults to `False`):
          Whether or not to use sinusoidal positional embeddings instead of absolute positional embeddings.
      causal (`bool`, *optional*, defaults to `False`):
          Whether or not the model should behave in a causal manner. Causal models use a triangular attention mask in
          order to only attend to the left-side context instead if a bidirectional context.
      asm (`bool`, *optional*, defaults to `False`):
          Whether or not to use an adaptive log softmax projection layer instead of a linear layer for the prediction
          layer.
      n_langs (`int`, *optional*, defaults to 1):
          The number of languages the model handles. Set to 1 for monolingual models.
      use_lang_emb (`bool`, *optional*, defaults to `True`)
          Whether to use language embeddings. Some models use additional language embeddings, see [the multilingual
          models page](http://huggingface.co/transformers/multilingual.html#xlm-language-embeddings) for information
          on how to use them.
      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).
      embed_init_std (`float`, *optional*, defaults to 2048^-0.5):
          The standard deviation of the truncated_normal_initializer for initializing the embedding matrices.
      init_std (`int`, *optional*, defaults to 50257):
          The standard deviation of the truncated_normal_initializer for initializing all weight matrices except the
          embedding matrices.
      layer_norm_eps (`float`, *optional*, defaults to 1e-12):
          The epsilon used by the layer normalization layers.
      bos_index (`int`, *optional*, defaults to 0):
          The index of the beginning of sentence token in the vocabulary.
      eos_index (`int`, *optional*, defaults to 1):
          The index of the end of sentence token in the vocabulary.
      pad_index (`int`, *optional*, defaults to 2):
          The index of the padding token in the vocabulary.
      unk_index (`int`, *optional*, defaults to 3):
          The index of the unknown token in the vocabulary.
      mask_index (`int`, *optional*, defaults to 5):
          The index of the masking token in the vocabulary.
      is_encoder(`bool`, *optional*, defaults to `True`):
          Whether or not the initialized model should be a transformer encoder or decoder as seen in Vaswani et al.
      summary_type (`string`, *optional*, defaults to "first"):
          Argument used when doing sequence summary. Used in the sequence classification and multiple choice models.
  
          Has to be 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 (like GPT/GPT-2).
              - `"attn"`: Not implemented now, use multi-head attention.
      summary_use_proj (`bool`, *optional*, defaults to `True`):
          Argument used when doing sequence summary. Used in the sequence classification and multiple choice models.
  
          Whether or not to add a projection after the vector extraction.
      summary_activation (`str`, *optional*):
          Argument used when doing sequence summary. Used in the sequence classification and multiple choice models.
  
          Pass `"tanh"` for a tanh activation to the output, any other value will result in no activation.
      summary_proj_to_labels (`bool`, *optional*, defaults to `True`):
          Used in the sequence classification and multiple choice models.
  
          Whether the projection outputs should have `config.num_labels` or `config.hidden_size` classes.
      summary_first_dropout (`float`, *optional*, defaults to 0.1):
          Used in the sequence classification and multiple choice models.
  
          The dropout ratio to be used after the projection and activation.
      start_n_top (`int`, *optional*, defaults to 5):
          Used in the SQuAD evaluation script.
      end_n_top (`int`, *optional*, defaults to 5):
          Used in the SQuAD evaluation script.
      mask_token_id (`int`, *optional*, defaults to 0):
          Model agnostic parameter to identify masked tokens when generating text in an MLM context.
      lang_id (`int`, *optional*, defaults to 1):
          The ID of the language used by the model. This parameter is used when generating text in a given language.
  
  Examples:
  
  ```python
  >>> from transformers import XLMConfig, XLMModel
  
  >>> # Initializing a XLM configuration
  >>> configuration = XLMConfig()
  
  >>> # Initializing a model (with random weights) from the configuration
  >>> model = XLMModel(configuration)
  
  >>> # Accessing the model configuration
  >>> configuration = model.config
  ```
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docstr: Constructs XLMConfig.
docstr_tok: ["Constructs","XLMConfig","."]
code: def __init__(
      self,
      vocab_size=30145,
      emb_dim=2048,
      n_layers=12,
      n_heads=16,
      dropout=0.1,
      attention_dropout=0.1,
      gelu_activation=True,
      sinusoidal_embeddings=False,
      causal=False,
      asm=False,
      n_langs=1,
      use_lang_emb=True,
      max_position_embeddings=512,
      embed_init_std=2048**-0.5,
      layer_norm_eps=1e-12,
      init_std=0.02,
      bos_index=0,
      eos_index=1,
      pad_index=2,
      unk_index=3,
      mask_index=5,
      is_encoder=True,
      summary_type="first",
      summary_use_proj=True,
      summary_activation=None,
      summary_proj_to_labels=True,
      summary_first_dropout=0.1,
      start_n_top=5,
      end_n_top=5,
      mask_token_id=0,
      lang_id=0,
      pad_token_id=2,
      bos_token_id=0,
      **kwargs,
  ):
      """Constructs XLMConfig."""
      self.vocab_size = vocab_size
      self.emb_dim = emb_dim
      self.n_layers = n_layers
      self.n_heads = n_heads
      self.dropout = dropout
      self.attention_dropout = attention_dropout
      self.gelu_activation = gelu_activation
      self.sinusoidal_embeddings = sinusoidal_embeddings
      self.causal = causal
      self.asm = asm
      self.n_langs = n_langs
      self.use_lang_emb = use_lang_emb
      self.layer_norm_eps = layer_norm_eps
      self.bos_index = bos_index
      self.eos_index = eos_index
      self.pad_index = pad_index
      self.unk_index = unk_index
      self.mask_index = mask_index
      self.is_encoder = is_encoder
      self.max_position_embeddings = max_position_embeddings
      self.embed_init_std = embed_init_std
      self.init_std = init_std
      self.summary_type = summary_type
      self.summary_use_proj = summary_use_proj
      self.summary_activation = summary_activation
      self.summary_proj_to_labels = summary_proj_to_labels
      self.summary_first_dropout = summary_first_dropout
      self.start_n_top = start_n_top
      self.end_n_top = end_n_top
      self.mask_token_id = mask_token_id
      self.lang_id = lang_id
  
      if "n_words" in kwargs:
          self.n_words = kwargs["n_words"]
  
      super().__init__(
          pad_token_id=pad_token_id, bos_token_id=bos_token_id, **kwargs
      )
  
file_path: H2O/h2o_flexgen/benchmark/third_party/transformers/src/transformers/models/xlm/configuration_xlm.py
imports: from collections import OrderedDict
  from typing import Mapping
  from ...configuration_utils import PretrainedConfig
  from ...onnx import OnnxConfig
  from ...utils import logging
label_desc: Use image node_id 1 to create a new XLMConfig object from inherited base classes: PretrainedConfig with example: obj = XLMConfig(vocab_size, emb_dim, n_layers, n_heads, dropout, attention_dropout, gelu_activation, sinusoidal_embeddings, causal, asm, n_langs, use_lang_emb, max_position_embeddings, embed_init_std, layer_norm_eps, init_std, bos_index, eos_index, pad_index, unk_index, mask_index, is_encoder, summary_type, summary_use_proj, summary_activation, summary_proj_to_labels, summary_first_dropout, start_n_top, end_n_top, mask_token_id, lang_id, pad_token_id, bos_token_id)