class_docstr: This is the configuration class to store the configuration of a [`M2M100Model`]. It is used to instantiate an
  M2M100 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 M2M100
  [facebook/m2m100_418M](https://huggingface.co/facebook/m2m100_418M) 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 50265):
          Vocabulary size of the M2M100 model. Defines the number of different tokens that can be represented by the
          `inputs_ids` passed when calling [`M2M100Model`] or
      d_model (`int`, *optional*, defaults to 1024):
          Dimensionality of the layers and the pooler layer.
      encoder_layers (`int`, *optional*, defaults to 12):
          Number of encoder layers.
      decoder_layers (`int`, *optional*, defaults to 12):
          Number of decoder layers.
      encoder_attention_heads (`int`, *optional*, defaults to 16):
          Number of attention heads for each attention layer in the Transformer encoder.
      decoder_attention_heads (`int`, *optional*, defaults to 16):
          Number of attention heads for each attention layer in the Transformer decoder.
      decoder_ffn_dim (`int`, *optional*, defaults to 4096):
          Dimensionality of the "intermediate" (often named feed-forward) layer in decoder.
      encoder_ffn_dim (`int`, *optional*, defaults to 4096):
          Dimensionality of the "intermediate" (often named feed-forward) layer in decoder.
      activation_function (`str` or `function`, *optional*, defaults to `"gelu"`):
          The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
          `"relu"`, `"silu"` and `"gelu_new"` are supported.
      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 0.0):
          The dropout ratio for the attention probabilities.
      activation_dropout (`float`, *optional*, defaults to 0.0):
          The dropout ratio for activations inside the fully connected layer.
      classifier_dropout (`float`, *optional*, defaults to 0.0):
          The dropout ratio for classifier.
      max_position_embeddings (`int`, *optional*, defaults to 1024):
          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).
      init_std (`float`, *optional*, defaults to 0.02):
          The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
      encoder_layerdrop (`float`, *optional*, defaults to 0.0):
          The LayerDrop probability for the encoder. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556)
          for more details.
      decoder_layerdrop (`float`, *optional*, defaults to 0.0):
          The LayerDrop probability for the decoder. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556)
          for more details.
      use_cache (`bool`, *optional*, defaults to `True`):
          Whether or not the model should return the last key/values attentions (not used by all models).
  
  Example:
  
  ```python
  >>> from transformers import M2M100Model, M2M100Config
  
  >>> # Initializing a M2M100 facebook/m2m100_418M style configuration
  >>> configuration = M2M100Config()
  
  >>> # Initializing a model from the facebook/m2m100_418M style configuration
  >>> model = M2M100Model(configuration)
  
  >>> # Accessing the model configuration
  >>> configuration = model.config
  ```
code: def __init__(
      self,
      vocab_size=128112,
      max_position_embeddings=1024,
      encoder_layers=12,
      encoder_ffn_dim=4096,
      encoder_attention_heads=16,
      decoder_layers=12,
      decoder_ffn_dim=4096,
      decoder_attention_heads=16,
      encoder_layerdrop=0.05,
      decoder_layerdrop=0.05,
      use_cache=True,
      is_encoder_decoder=True,
      activation_function="relu",
      d_model=1024,
      dropout=0.1,
      attention_dropout=0.1,
      activation_dropout=0.0,
      init_std=0.02,
      decoder_start_token_id=2,
      scale_embedding=True,
      pad_token_id=1,
      bos_token_id=0,
      eos_token_id=2,
      **kwargs,
  ):
      self.vocab_size = vocab_size
      self.max_position_embeddings = max_position_embeddings
      self.d_model = d_model
      self.encoder_ffn_dim = encoder_ffn_dim
      self.encoder_layers = encoder_layers
      self.encoder_attention_heads = encoder_attention_heads
      self.decoder_ffn_dim = decoder_ffn_dim
      self.decoder_layers = decoder_layers
      self.decoder_attention_heads = decoder_attention_heads
      self.dropout = dropout
      self.attention_dropout = attention_dropout
      self.activation_dropout = activation_dropout
      self.activation_function = activation_function
      self.init_std = init_std
      self.encoder_layerdrop = encoder_layerdrop
      self.decoder_layerdrop = decoder_layerdrop
      self.use_cache = use_cache
      self.num_hidden_layers = encoder_layers
      self.scale_embedding = (
          scale_embedding  # scale factor will be sqrt(d_model) if True
      )
  
      super().__init__(
          pad_token_id=pad_token_id,
          bos_token_id=bos_token_id,
          eos_token_id=eos_token_id,
          is_encoder_decoder=is_encoder_decoder,
          decoder_start_token_id=decoder_start_token_id,
          **kwargs,
      )
  
file_path: H2O/h2o_flexgen/benchmark/third_party/transformers/src/transformers/models/m2m_100/configuration_m2m_100.py
imports: from collections import OrderedDict
  from typing import Any, Mapping, Optional
  from ...None import PreTrainedTokenizer
  from ...configuration_utils import PretrainedConfig
  from ...onnx import OnnxConfig, OnnxSeq2SeqConfigWithPast
  from ...onnx.utils import compute_effective_axis_dimension
  from ...utils import TensorType, is_torch_available, logging
label_desc: Use image node_id 1 to create a new M2M100Config object from inherited base classes: PretrainedConfig with example: obj = M2M100Config(vocab_size, max_position_embeddings, encoder_layers, encoder_ffn_dim, encoder_attention_heads, decoder_layers, decoder_ffn_dim, decoder_attention_heads, encoder_layerdrop, decoder_layerdrop, use_cache, is_encoder_decoder, activation_function, d_model, dropout, attention_dropout, activation_dropout, init_std, decoder_start_token_id, scale_embedding, pad_token_id, bos_token_id, eos_token_id)