+
    œ°³j9  ã                   ól   € R t ^ RIHt ^RIHt ^RIHt ]! RR7      ] ! R R]4      4       4       tR.tR# )	zLayoutLMv3 model configuration)Ústrict)ÚPreTrainedConfig)Úauto_docstringzmicrosoft/layoutlmv3-base)Ú
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RtRtRtRt^tR	tR
t^t^ t^tRt^€t^€tRt^ t^€t^@tRtRtRtRt^àt ^t!^t"Rt#V 3R lt$Rt%V t&R# )ÚLayoutLMv3Configa·  
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  i   i   Úgelugš™™™™™¹?i   g{®Gáz”?gñhãˆµøä>i   Té   Nc                óP  <€ V ^8„  d   Qh/ S[ ;R&   S[ ;R&   S[ ;R&   S[ ;R&   S[ ;R&   S[;R&   S[S[ ,          ;R&   S[S[ ,          ;R&   S[ ;R	&   S[ ;R
&   S[;R&   S[;R&   S[ R,          ;R&   S[ R,          ;R&   S[ S[S[ ,          ,          R,          ;R&   S[ ;R&   S[ ;R&   S[ ;R&   S[;R&   S[ ;R&   S[ ;R&   S[ ;R&   S[ ;R&   S[;R&   S[;R&   S[;R&   S[ ;R&   S[ ;R&   S[ S[S[ ,          ,          S[S[ S[ 3,          ,          ;R&   S[S[ ,          R,          ;R&   # ) é   Ú
vocab_sizeÚhidden_sizeÚnum_hidden_layersÚnum_attention_headsÚintermediate_sizeÚ
hidden_actÚhidden_dropout_probÚattention_probs_dropout_probÚmax_position_embeddingsÚtype_vocab_sizeÚinitializer_rangeÚlayer_norm_epsNÚpad_token_idÚbos_token_idÚeos_token_idÚmax_2d_position_embeddingsÚcoordinate_sizeÚ
shape_sizeÚhas_relative_attention_biasÚrel_pos_binsÚmax_rel_posÚrel_2d_pos_binsÚmax_rel_2d_posÚhas_spatial_attention_biasÚ
text_embedÚvisual_embedÚ
input_sizeÚnum_channelsÚ
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__module__Ú__qualname__Ú__firstlineno__Ú__doc__Ú
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