U 9%e@sbdZddlZddlmZddlmZddlmZddlm Z e e Z d d iZ Gd d d eZdS) z VitMatte model configurationN)List)PretrainedConfig)logging)CONFIG_MAPPINGz$hustvl/vitmatte-small-composition-1kzThttps://huggingface.co/hustvl/vitmatte-small-composition-1k/resolve/main/config.jsonc s`eZdZdZdZddddddd gd d d d gfeeeeeeeedfdd Z ddZ Z S)VitMatteConfiga This is the configuration class to store the configuration of [`VitMatteForImageMatting`]. It is used to instantiate a ViTMatte 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 ViTMatte [hustvl/vitmatte-small-composition-1k](https://huggingface.co/hustvl/vitmatte-small-composition-1k) architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: backbone_config (`PretrainedConfig` or `dict`, *optional*, defaults to `VitDetConfig()`): The configuration of the backbone model. hidden_size (`int`, *optional*, defaults to 384): The number of input channels of the decoder. batch_norm_eps (`float`, *optional*, defaults to 1e-5): The epsilon used by the batch norm layers. initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. convstream_hidden_sizes (`List[int]`, *optional*, defaults to `[48, 96, 192]`): The output channels of the ConvStream module. fusion_hidden_sizes (`List[int]`, *optional*, defaults to `[256, 128, 64, 32]`): The output channels of the Fusion blocks. Example: ```python >>> from transformers import VitMatteConfig, VitMatteForImageMatting >>> # Initializing a ViTMatte hustvl/vitmatte-small-composition-1k style configuration >>> configuration = VitMatteConfig() >>> # Initializing a model (with random weights) from the hustvl/vitmatte-small-composition-1k style configuration >>> model = VitMatteForImageMatting(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```ZvitmatteNigh㈵>g{Gz?0`@ )backbone_config hidden_sizebatch_norm_epsinitializer_rangeconvstream_hidden_sizesfusion_hidden_sizesc  stjf||dkr2tdtddgd}n&t|trX|d}t|} | |}||_ ||_ ||_ ||_ ||_ ||_dS)NzX`backbone_config` is `None`. Initializing the config with the default `VitDet` backbone.ZvitdetZstage4)Z out_features model_type)super__init__loggerinfor isinstancedictget from_dictrrrrrr) selfrrrrrrkwargsZbackbone_model_typeZ config_class __class__r/var/www/html/Darija-Ai-API/env/lib/python3.8/site-packages/transformers/models/vitmatte/configuration_vitmatte.pyrHs     zVitMatteConfig.__init__cCs*t|j}|j|d<|jj|d<|S)z Serializes this instance to a Python dictionary. Override the default [`~PretrainedConfig.to_dict`]. Returns: `Dict[str, any]`: Dictionary of all the attributes that make up this configuration instance, rr)copydeepcopy__dict__rto_dictr"r)routputr#r#r$r(cs  zVitMatteConfig.to_dict) __name__ __module__ __qualname____doc__rrintfloatrrr( __classcell__r#r#r!r$r s"% r)r-r%typingrZconfiguration_utilsrutilsrZauto.configuration_autorZ get_loggerr*rZ&VITMATTE_PRETRAINED_CONFIG_ARCHIVE_MAPrr#r#r#r$s