
    sg                     Z    d Z ddlmZ ddlmZ  ej
                  e      Z G d de      Zy)zMPNet model configuration   )PretrainedConfig)loggingc                   F     e Zd ZdZdZ	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 d fd	Z xZS )MPNetConfiga"  
    This is the configuration class to store the configuration of a [`MPNetModel`] or a [`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
    [microsoft/mpnet-base](https://huggingface.co/microsoft/mpnet-base) 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 30527):
            Vocabulary size of the MPNet model. Defines the number of different tokens that can be represented by the
            `inputs_ids` passed when calling [`MPNetModel`] or [`TFMPNetModel`].
        hidden_size (`int`, *optional*, defaults to 768):
            Dimensionality of the encoder layers and the pooler layer.
        num_hidden_layers (`int`, *optional*, defaults to 12):
            Number of hidden layers in the Transformer encoder.
        num_attention_heads (`int`, *optional*, defaults to 12):
            Number of attention heads for each attention layer in the Transformer encoder.
        intermediate_size (`int`, *optional*, defaults to 3072):
            Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder.
        hidden_act (`str` or `Callable`, *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.
        hidden_dropout_prob (`float`, *optional*, defaults to 0.1):
            The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
        attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1):
            The dropout ratio for the attention probabilities.
        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).
        initializer_range (`float`, *optional*, defaults to 0.02):
            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
        layer_norm_eps (`float`, *optional*, defaults to 1e-12):
            The epsilon used by the layer normalization layers.
        relative_attention_num_buckets (`int`, *optional*, defaults to 32):
            The number of buckets to use for each attention layer.

    Examples:

    ```python
    >>> 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
    ```mpnetc                     t        |   d|||d| || _        || _        || _        || _        || _        || _        || _        || _	        |	| _
        |
| _        || _        || _        y )N)pad_token_idbos_token_ideos_token_id )super__init__
vocab_sizehidden_sizenum_hidden_layersnum_attention_heads
hidden_actintermediate_sizehidden_dropout_probattention_probs_dropout_probmax_position_embeddingsinitializer_rangelayer_norm_epsrelative_attention_num_buckets)selfr   r   r   r   r   r   r   r   r   r   r   r   r	   r
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__module____qualname____doc__
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