o ZŽhKã@s@dZddlmZddlmZe e¡ZGdd„deƒZdgZ dS)zVAN model configurationé)ÚPretrainedConfig)Úloggingc sTeZdZdZdZddgd¢gd¢gd¢gd¢gd ¢d d d d ddf ‡fdd„ Z‡ZS)Ú VanConfigaÜ 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 ```Zvanéàé)érrr)rérr)é@é€i@i)rré r)ér rrZgelug{®Gáz”?gíµ ÷Æ°>g{®Gáz„?gc sdtƒjdi|¤Ž||_||_||_||_||_||_||_||_ | |_ | |_ | |_ | |_ | |_dS)N©)ÚsuperÚ__init__Ú image_sizeÚ num_channelsÚ patch_sizesÚstridesÚ hidden_sizesÚdepthsÚ mlp_ratiosÚ hidden_actÚinitializer_rangeÚlayer_norm_epsÚlayer_scale_init_valueÚdrop_path_rateÚ dropout_rate)ÚselfrrrrrrrrrrrrrÚkwargs©Ú __class__r úc/var/www/auris/lib/python3.10/site-packages/transformers/models/deprecated/van/configuration_van.pyrMs zVanConfig.__init__)Ú__name__Ú __module__Ú __qualname__Ú__doc__Z model_typerÚ __classcell__r r rr!rs"2òrN) r%Zconfiguration_utilsrÚutilsrZ get_loggerr"ÚloggerrÚ__all__r r r r!Ús     V