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    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).
    coordinate_size (`int`, *optional*, defaults to `128`):
        Dimension of the coordinate embeddings.
    shape_size (`int`, *optional*, defaults to `128`):
        Dimension of the width and height embeddings.
    has_relative_attention_bias (`bool`, *optional*, defaults to `True`):
        Whether or not to use a relative attention bias in the self-attention mechanism.
    rel_pos_bins (`int`, *optional*, defaults to 32):
        The number of relative position bins to be used in the self-attention mechanism.
    max_rel_pos (`int`, *optional*, defaults to 128):
        The maximum number of relative positions to be used in the self-attention mechanism.
    rel_2d_pos_bins (`int`, *optional*, defaults to 64):
        The number of 2D relative position bins in the self-attention mechanism.
    max_rel_2d_pos (`int`, *optional*, defaults to 256):
        The maximum number of relative 2D positions in the self-attention mechanism.
    has_spatial_attention_bias (`bool`, *optional*, defaults to `True`):
        Whether or not to use a spatial attention bias in the self-attention mechanism.
    text_embed (`bool`, *optional*, defaults to `True`):
        Whether or not to add text embeddings.
    visual_embed (`bool`, *optional*, defaults to `True`):
        Whether or not to add patch embeddings.
    input_size (`int`, *optional*, defaults to `224`):
        The size (resolution) of the images.

    Example:

    ```python
    >>> from transformers import LayoutLMv3Config, LayoutLMv3Model

    >>> # Initializing a LayoutLMv3 microsoft/layoutlmv3-base style configuration
    >>> configuration = LayoutLMv3Config()

    >>> # Initializing a model (with random weights) from the microsoft/layoutlmv3-base style configuration
    >>> model = LayoutLMv3Model(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```Ú
layoutlmv3iYÄ  Ú
vocab_sizei   Úhidden_sizeé   Únum_hidden_layersÚnum_attention_headsi   Úintermediate_sizeÚgeluÚ
hidden_actgš™™™™™¹?Úhidden_dropout_probÚattention_probs_dropout_probi   Úmax_position_embeddingsé   Útype_vocab_sizeg{®Gáz”?Úinitializer_rangegñhãˆµøä>Úlayer_norm_epsé   NÚpad_token_idr   Úbos_token_idÚeos_token_idi   Úmax_2d_position_embeddingsé€   Úcoordinate_sizeÚ
shape_sizeTÚhas_relative_attention_biasé    Úrel_pos_binsÚmax_rel_posé@   Úrel_2d_pos_binsé   Úmax_rel_2d_posÚhas_spatial_attention_biasÚ
text_embedÚvisual_embedéà   Ú
input_sizer   Únum_channelsé   Ú
patch_sizeÚclassifier_dropout)*Ú__name__Ú
__module__Ú__qualname__Ú__doc__Ú
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