
    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LiLT configuration   )PretrainedConfig)loggingc                   J     e Zd ZdZdZ	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 d fd	Z xZS )
LiltConfiga  
    This is the configuration class to store the configuration of a [`LiltModel`]. It is used to instantiate a LiLT
    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 LiLT
    [SCUT-DLVCLab/lilt-roberta-en-base](https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-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 30522):
            Vocabulary size of the LiLT model. Defines the number of different tokens that can be represented by the
            `inputs_ids` passed when calling [`LiltModel`].
        hidden_size (`int`, *optional*, defaults to 768):
            Dimensionality of the encoder layers and the pooler layer. Should be a multiple of 24.
        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).
        type_vocab_size (`int`, *optional*, defaults to 2):
            The vocabulary size of the `token_type_ids` passed when calling [`LiltModel`].
        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.
        position_embedding_type (`str`, *optional*, defaults to `"absolute"`):
            Type of position embedding. Choose one of `"absolute"`, `"relative_key"`, `"relative_key_query"`. For
            positional embeddings use `"absolute"`. For more information on `"relative_key"`, please refer to
            [Self-Attention with Relative Position Representations (Shaw et al.)](https://arxiv.org/abs/1803.02155).
            For more information on `"relative_key_query"`, please refer to *Method 4* in [Improve Transformer Models
            with Better Relative Position Embeddings (Huang et al.)](https://arxiv.org/abs/2009.13658).
        classifier_dropout (`float`, *optional*):
            The dropout ratio for the classification head.
        channel_shrink_ratio (`int`, *optional*, defaults to 4):
            The shrink ratio compared to the `hidden_size` for the channel dimension of the layout embeddings.
        max_2d_position_embeddings (`int`, *optional*, defaults to 1024):
            The maximum value that the 2D position embedding might ever be used with. Typically set this to something
            large just in case (e.g., 1024).

    Examples:

    ```python
    >>> from transformers import LiltConfig, LiltModel

    >>> # Initializing a LiLT SCUT-DLVCLab/lilt-roberta-en-base style configuration
    >>> configuration = LiltConfig()
    >>> # Randomly initializing a model from the SCUT-DLVCLab/lilt-roberta-en-base style configuration
    >>> model = LiltModel(configuration)
    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```liltc                    t        |   dd|i| || _        || _        || _        || _        || _        || _        || _        || _	        |	| _
        |
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vocab_sizehidden_sizenum_hidden_layersnum_attention_heads
hidden_actintermediate_sizehidden_dropout_probattention_probs_dropout_probmax_position_embeddingstype_vocab_sizeinitializer_rangelayer_norm_epsposition_embedding_typeclassifier_dropoutchannel_shrink_ratiomax_2d_position_embeddings)selfr   r   r   r   r   r   r   r   r   r   r   r   r	   r   r   r   r   kwargs	__class__s                      ^/var/www/html/venv/lib/python3.12/site-packages/transformers/models/lilt/configuration_lilt.pyr   zLiltConfig.__init__Z   s    * 	=l=f=$&!2#6 $!2#6 ,H)'>$.!2,'>$"4$8!*D'    )i:w  i      r"   i   gelu皙?r$   i      g{Gz?g-q=    absoluteN   i   )__name__
__module____qualname____doc__
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