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'   Sr\\S'   Sr\\S'   Sr\\-  \S'   Sr\\-  \S'   Sr\\S'   Sr\\S'   Sr\\S'   Sr\\S'   Sr\S-  \S'   Sr\S-  \S'   Sr\\\   -  S-  \S'   S r\\S!'   S"r\\S#'   S"r\\S$'   S%r\\S&'   S'r\\S('   S"r \\S)'   S*r!\\S+'   S,r"\\S-'   S%r#\\S.'   S%r$\\S/'   S%r%\\S0'   S1r&\\S2'   S3r'\\S4'   S5r(\\\   -  \)\\4   -  \S6'   Sr*\\-  S-  \S7'   S8r+g)9ÚLayoutLMv3Configé   a·  
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
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text_embedÚvisual_embedéà   Ú
input_sizer   Únum_channelsé   Ú
patch_sizeÚclassifier_dropout© ),Ú__name__Ú
__module__Ú__qualname__Ú__firstlineno__Ú__doc__Ú
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