cCiK`SrSSKJr SSKJr \R "\5r"SS\5rS/r g)zVAN model configuration)PretrainedConfig)loggingc b^\rSrSrSrSrSS/SQ/SQ/SQ/S Q/S QS S S SSS4 U4SjjrSrU=r$) VanConfiga, This is the configuration class to store the configuration of a [`VanModel`]. It is used to instantiate a VAN 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 VAN [Visual-Attention-Network/van-base](https://huggingface.co/Visual-Attention-Network/van-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: image_size (`int`, *optional*, defaults to 224): The size (resolution) of each image. num_channels (`int`, *optional*, defaults to 3): The number of input channels. patch_sizes (`list[int]`, *optional*, defaults to `[7, 3, 3, 3]`): Patch size to use in each stage's embedding layer. strides (`list[int]`, *optional*, defaults to `[4, 2, 2, 2]`): Stride size to use in each stage's embedding layer to downsample the input. hidden_sizes (`list[int]`, *optional*, defaults to `[64, 128, 320, 512]`): Dimensionality (hidden size) at each stage. depths (`list[int]`, *optional*, defaults to `[3, 3, 12, 3]`): Depth (number of layers) for each stage. mlp_ratios (`list[int]`, *optional*, defaults to `[8, 8, 4, 4]`): The expansion ratio for mlp layer at each stage. hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`): The non-linear activation function (function or string) in each layer. If string, `"gelu"`, `"relu"`, `"selu"` and `"gelu_new"` are supported. 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-06): The epsilon used by the layer normalization layers. layer_scale_init_value (`float`, *optional*, defaults to 0.01): The initial value for layer scaling. drop_path_rate (`float`, *optional*, defaults to 0.0): The dropout probability for stochastic depth. dropout_rate (`float`, *optional*, defaults to 0.0): The dropout probability for dropout. Example: ```python >>> from transformers import VanModel, VanConfig >>> # Initializing a VAN van-base style configuration >>> configuration = VanConfig() >>> # Initializing a model from the van-base style configuration >>> model = VanModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```van)r r r )rr r )@i@i)r r r )rrrgelug{Gz?gư>g{Gz?gc >[TU]"S0UD6 XlX lX0lX@lXPlX`lXplXl Xl Xl Xl Xl Xlg)N)super__init__ image_size num_channels patch_sizesstrides hidden_sizesdepths mlp_ratios hidden_actinitializer_rangelayer_norm_epslayer_scale_init_valuedrop_path_rate dropout_rate)selfrrrrrrrrrrr r!r"kwargs __class__s n/home/james-whalen/.local/lib/python3.13/site-packages/transformers/models/deprecated/van/configuration_van.pyrVanConfig.__init__Ms^" "6"$(& ( $$!2,&<#,() rr!r"rrrrrr rrrr) __name__ __module__ __qualname____firstlineno____doc__ model_typer__static_attributes__ __classcell__)r%s@r&rrsC0 dJ (#))r(rN) r-configuration_utilsrutilsr get_loggerr)loggerr__all__rr(r&r6s;4   H %S) S)l -r(