fTh`SrSSKJr SSKJr \R "\5r"SS\5rS/r g)zRegNet model configuration)PretrainedConfig)loggingcR^\rSrSrSrSrSS/rSS/SQ/S QS SS 4U4S jjrS rU=r $) RegNetConfiga& This is the configuration class to store the configuration of a [`RegNetModel`]. It is used to instantiate a RegNet 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 RegNet [facebook/regnet-y-040](https://huggingface.co/facebook/regnet-y-040) architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: num_channels (`int`, *optional*, defaults to 3): The number of input channels. embedding_size (`int`, *optional*, defaults to 64): Dimensionality (hidden size) for the embedding layer. hidden_sizes (`List[int]`, *optional*, defaults to `[256, 512, 1024, 2048]`): Dimensionality (hidden size) at each stage. depths (`List[int]`, *optional*, defaults to `[3, 4, 6, 3]`): Depth (number of layers) for each stage. layer_type (`str`, *optional*, defaults to `"y"`): The layer to use, it can be either `"x" or `"y"`. An `x` layer is a ResNet's BottleNeck layer with `reduction` fixed to `1`. While a `y` layer is a `x` but with squeeze and excitation. Please refer to the paper for a detailed explanation of how these layers were constructed. hidden_act (`str`, *optional*, defaults to `"relu"`): The non-linear activation function in each block. If string, `"gelu"`, `"relu"`, `"selu"` and `"gelu_new"` are supported. downsample_in_first_stage (`bool`, *optional*, defaults to `False`): If `True`, the first stage will downsample the inputs using a `stride` of 2. Example: ```python >>> from transformers import RegNetConfig, RegNetModel >>> # Initializing a RegNet regnet-y-40 style configuration >>> configuration = RegNetConfig() >>> # Initializing a model from the regnet-y-40 style configuration >>> model = RegNetModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ``` regnetxyr )ii@) r@reluc >[T U]"S0UD6 X`R;a*[SUSSR UR535eXlX lX0lX@lXPl X`l Xpl SUl g)Nz layer_type=z is not one of ,T) super__init__ layer_types ValueErrorjoin num_channelsembedding_size hidden_sizesdepths groups_width layer_type hidden_actdownsample_in_first_stage) selfrrrrrr r!kwargs __class__s g/var/www/auris/envauris/lib/python3.13/site-packages/transformers/models/regnet/configuration_regnet.pyrRegNetConfig.__init__Esx "6" -- -{:,ochhtO_O_F`Eabc c(,( ($$)-&)rr"rrr!rr r) __name__ __module__ __qualname____firstlineno____doc__ model_typerr__static_attributes__ __classcell__)r%s@r&rrs:'RJ*K*..r(rN) r-configuration_utilsrutilsr get_loggerr)loggerr__all__rr(r&r6s<!3   H %C.#C.L  r(