o Zh@sRdZddlmZddlmZddlmZmZee Z GdddeeZ dgZ dS)zConvNeXTV2 model configuration)PretrainedConfig)logging)BackboneConfigMixin*get_aligned_output_features_output_indicescs>eZdZdZdZ          d fd d ZZS)ConvNextV2Configa This is the configuration class to store the configuration of a [`ConvNextV2Model`]. It is used to instantiate an ConvNeXTV2 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 ConvNeXTV2 [facebook/convnextv2-tiny-1k-224](https://huggingface.co/facebook/convnextv2-tiny-1k-224) 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. patch_size (`int`, *optional*, defaults to 4): Patch size to use in the patch embedding layer. num_stages (`int`, *optional*, defaults to 4): The number of stages in the model. hidden_sizes (`List[int]`, *optional*, defaults to `[96, 192, 384, 768]`): Dimensionality (hidden size) at each stage. depths (`List[int]`, *optional*, defaults to `[3, 3, 9, 3]`): Depth (number of blocks) for each stage. hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`): The non-linear activation function (function or string) in each block. 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-12): The epsilon used by the layer normalization layers. drop_path_rate (`float`, *optional*, defaults to 0.0): The drop rate for stochastic depth. image_size (`int`, *optional*, defaults to 224): The size (resolution) of each image. out_features (`List[str]`, *optional*): If used as backbone, list of features to output. Can be any of `"stem"`, `"stage1"`, `"stage2"`, etc. (depending on how many stages the model has). If unset and `out_indices` is set, will default to the corresponding stages. If unset and `out_indices` is unset, will default to the last stage. Must be in the same order as defined in the `stage_names` attribute. out_indices (`List[int]`, *optional*): If used as backbone, list of indices of features to output. Can be any of 0, 1, 2, etc. (depending on how many stages the model has). If unset and `out_features` is set, will default to the corresponding stages. If unset and `out_features` is unset, will default to the last stage. Must be in the same order as defined in the `stage_names` attribute. Example: ```python >>> from transformers import ConvNeXTV2Config, ConvNextV2Model >>> # Initializing a ConvNeXTV2 convnextv2-tiny-1k-224 style configuration >>> configuration = ConvNeXTV2Config() >>> # Initializing a model (with random weights) from the convnextv2-tiny-1k-224 style configuration >>> model = ConvNextV2Model(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```Z convnextv2rNgelu{Gz?-q=c  stjdi| ||_||_||_|durgdn||_|dur%gdn||_||_||_||_ | |_ | |_ dgddt dt |jdD|_t| | |jd\|_|_dS) N)`ii)rr rstemcSsg|]}d|qS)Zstage).0idxrrf/var/www/auris/lib/python3.10/site-packages/transformers/models/convnextv2/configuration_convnextv2.py psz-ConvNextV2Config.__init__..) out_features out_indices stage_namesr)super__init__ num_channels patch_size num_stages hidden_sizesdepths hidden_actinitializer_rangelayer_norm_epsdrop_path_rate image_sizerangelenrrZ _out_featuresZ _out_indices)selfrrrrr r!r"r#r$r%rrkwargs __class__rrrTs&zConvNextV2Config.__init__) rrrNNrr r r r NN)__name__ __module__ __qualname____doc__Z model_typer __classcell__rrr*rrs 8rN) r/Zconfiguration_utilsrutilsrZutils.backbone_utilsrrZ get_loggerr,loggerr__all__rrrrs    ]