class_docstr: This is the configuration class to store the configuration of a [`ProphetNetModel`]. It is used to instantiate a
  ProphetNet 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 ProphetNet
  [microsoft/prophetnet-large-uncased](https://huggingface.co/microsoft/prophetnet-large-uncased) architecture.
  
  Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
  documentation from [`PretrainedConfig`] for more information.
  
  Args:
      activation_dropout (`float`, *optional*, defaults to 0.1):
          The dropout ratio for activations inside the fully connected layer.
      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.
      vocab_size (`int`, *optional*, defaults to 30522):
          Vocabulary size of the ProphetNET model. Defines the number of different tokens that can be represented by
          the `inputs_ids` passed when calling [`ProphetNetModel`].
      hidden_size (`int`, *optional*, defaults to 1024):
          Dimensionality of the layers and the pooler layer.
      encoder_ffn_dim (`int`, *optional*, defaults to 4096):
          Dimensionality of the "intermediate" (often named feed-forward) layer in decoder.
      num_encoder_layers (`int`, *optional*, defaults to 12):
          Number of encoder layers.
      num_encoder_attention_heads (`int`, *optional*, defaults to 16):
          Number of attention heads for each attention layer in the Transformer encoder.
      decoder_ffn_dim (`int`, *optional*, defaults to 4096):
          Dimensionality of the `intermediate` (often named feed-forward) layer in decoder.
      num_decoder_layers (`int`, *optional*, defaults to 12):
          Number of decoder layers.
      num_decoder_attention_heads (`int`, *optional*, defaults to 16):
          Number of attention heads for each attention layer in the Transformer decoder.
      attention_dropout (`float`, *optional*, defaults to 0.1):
          The dropout ratio for the attention probabilities.
      dropout (`float`, *optional*, defaults to 0.1):
          The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
      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).
      init_std (`float`, *optional*, defaults to 0.02):
          The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
      add_cross_attention (`bool`, *optional*, defaults to `True`):
          Whether cross-attention layers should be added to the model.
      is_encoder_decoder (`bool`, *optional*, defaults to `True`):
          Whether this is an encoder/decoder model.
      pad_token_id (`int`, *optional*, defaults to 1)
          Padding token id.
      bos_token_id (`int`, *optional*, defaults to 0)
          Beginning of stream token id.
      eos_token_id (`int`, *optional*, defaults to 2)
          End of stream token id.
      ngram (`int`, *optional*, defaults to 2)
          Number of future tokens to predict. Set to 1 to be same as traditional Language model to predict next first
          token.
      num_buckets (`int`, *optional*, defaults to 32)
          The number of buckets to use for each attention layer. This is for relative position calculation. See the
          [T5 paper](see https://arxiv.org/abs/1910.10683) for more details.
      relative_max_distance (`int`, *optional*, defaults to 128)
          Relative distances greater than this number will be put into the last same bucket. This is for relative
          position calculation. See the [T5 paper](see https://arxiv.org/abs/1910.10683) for more details.
      disable_ngram_loss (`bool`, *optional*, defaults to `False`):
          Whether be trained predicting only the next first token.
      eps (`float`, *optional*, defaults to 0.0):
          Controls the `epsilon` parameter value for label smoothing in the loss calculation. If set to 0, no label
          smoothing is performed.
      use_cache (`bool`, *optional*, defaults to `True`):
          Whether or not the model should return the last key/values attentions (not used by all models).
code: def __init__(
      self,
      activation_dropout: Optional[float] = 0.1,
      activation_function: Optional[Union[str, Callable]] = "gelu",
      vocab_size: Optional[int] = 30522,
      hidden_size: Optional[int] = 1024,
      encoder_ffn_dim: Optional[int] = 4096,
      num_encoder_layers: Optional[int] = 12,
      num_encoder_attention_heads: Optional[int] = 16,
      decoder_ffn_dim: Optional[int] = 4096,
      num_decoder_layers: Optional[int] = 12,
      num_decoder_attention_heads: Optional[int] = 16,
      attention_dropout: Optional[float] = 0.1,
      dropout: Optional[float] = 0.1,
      max_position_embeddings: Optional[int] = 512,
      init_std: Optional[float] = 0.02,
      is_encoder_decoder: Optional[bool] = True,
      add_cross_attention: Optional[bool] = True,
      decoder_start_token_id: Optional[int] = 0,
      ngram: Optional[int] = 2,
      num_buckets: Optional[int] = 32,
      relative_max_distance: Optional[int] = 128,
      disable_ngram_loss: Optional[bool] = False,
      eps: Optional[float] = 0.0,
      use_cache: Optional[bool] = True,
      pad_token_id: Optional[int] = 0,
      bos_token_id: Optional[int] = 1,
      eos_token_id: Optional[int] = 2,
      **kwargs,
  ):
      self.vocab_size = vocab_size
      self.hidden_size = hidden_size
      self.encoder_ffn_dim = encoder_ffn_dim
      self.num_encoder_layers = num_encoder_layers
      self.num_encoder_attention_heads = num_encoder_attention_heads
      self.decoder_ffn_dim = decoder_ffn_dim
      self.num_decoder_layers = num_decoder_layers
      self.num_decoder_attention_heads = num_decoder_attention_heads
      self.max_position_embeddings = max_position_embeddings
      self.init_std = init_std  # Normal(0, this parameter)
      self.activation_function = activation_function
  
      # parameters for prophetnet
      self.ngram = ngram
      self.num_buckets = num_buckets
      self.relative_max_distance = relative_max_distance
      self.disable_ngram_loss = disable_ngram_loss
      self.eps = eps
  
      # 3 Types of Dropout
      self.attention_dropout = attention_dropout
      self.activation_dropout = activation_dropout
      self.dropout = dropout
  
      self.use_cache = use_cache
  
      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,
          add_cross_attention=add_cross_attention,
          decoder_start_token_id=decoder_start_token_id,
          **kwargs,
      )
  
file_path: H2O/h2o_flexgen/benchmark/third_party/transformers/src/transformers/models/prophetnet/configuration_prophetnet.py
imports: from typing import Callable, Optional, Union
  from ...configuration_utils import PretrainedConfig
  from ...utils import logging
label_desc: Use image node_id 1 to create a new ProphetNetConfig object from inherited base classes: PretrainedConfig with example: obj = ProphetNetConfig(activation_dropout, activation_function, vocab_size, hidden_size, encoder_ffn_dim, num_encoder_layers, num_encoder_attention_heads, decoder_ffn_dim, num_decoder_layers, num_decoder_attention_heads, attention_dropout, dropout, max_position_embeddings, init_std, is_encoder_decoder, add_cross_attention, decoder_start_token_id, ngram, num_buckets, relative_max_distance, disable_ngram_loss, eps, use_cache, pad_token_id, bos_token_id, eos_token_id)