U ×9%edã@sFdZddlmZddlmZddlmZe e¡Z Gdd„deƒZ dS) z UperNet model configurationé)ÚPretrainedConfig)Úloggingé)ÚCONFIG_MAPPINGc sDeZdZdZdZddddddd gd d d d dddf ‡fdd„ Z‡ZS)Ú UperNetConfigaÖ This is the configuration class to store the configuration of an [`UperNetForSemanticSegmentation`]. It is used to instantiate an UperNet 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 UperNet [openmmlab/upernet-convnext-tiny](https://huggingface.co/openmmlab/upernet-convnext-tiny) 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 `ResNetConfig()`): The configuration of the backbone model. hidden_size (`int`, *optional*, defaults to 512): The number of hidden units in the convolutional layers. initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. pool_scales (`Tuple[int]`, *optional*, defaults to `[1, 2, 3, 6]`): Pooling scales used in Pooling Pyramid Module applied on the last feature map. use_auxiliary_head (`bool`, *optional*, defaults to `True`): Whether to use an auxiliary head during training. auxiliary_loss_weight (`float`, *optional*, defaults to 0.4): Weight of the cross-entropy loss of the auxiliary head. auxiliary_channels (`int`, *optional*, defaults to 256): Number of channels to use in the auxiliary head. auxiliary_num_convs (`int`, *optional*, defaults to 1): Number of convolutional layers to use in the auxiliary head. auxiliary_concat_input (`bool`, *optional*, defaults to `False`): Whether to concatenate the output of the auxiliary head with the input before the classification layer. loss_ignore_index (`int`, *optional*, defaults to 255): The index that is ignored by the loss function. Examples: ```python >>> from transformers import UperNetConfig, UperNetForSemanticSegmentation >>> # Initializing a configuration >>> configuration = UperNetConfig() >>> # Initializing a model (with random weights) from the configuration >>> model = UperNetForSemanticSegmentation(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```ZupernetNig{®Gáz”?érréTgš™™™™™Ù?i€éFéÿc  s¤tƒjf| Ž|dkr8t d¡tdddddgd}n&t|tƒr^| d¡} t| }| |¡}||_ ||_ ||_ ||_ ||_ ||_||_||_| |_| |_| |_dS) NzX`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.ZresnetZstage1Zstage2Zstage3Zstage4)Z out_featuresÚ model_type)ÚsuperÚ__init__ÚloggerÚinforÚ isinstanceÚdictÚgetÚ from_dictÚbackbone_configÚ hidden_sizeÚinitializer_rangeÚ pool_scalesÚuse_auxiliary_headÚauxiliary_loss_weightÚauxiliary_in_channelsÚauxiliary_channelsÚauxiliary_num_convsÚauxiliary_concat_inputÚloss_ignore_index)ÚselfrrrrrrrrrrrÚkwargsZbackbone_model_typeZ config_class©Ú __class__©úp/var/www/html/Darija-Ai-API/env/lib/python3.8/site-packages/transformers/models/upernet/configuration_upernet.pyr Js&    zUperNetConfig.__init__)Ú__name__Ú __module__Ú __qualname__Ú__doc__r r Ú __classcell__r#r#r!r$rs- ôrN) r(Zconfiguration_utilsrÚutilsrZauto.configuration_autorZ get_loggerr%rrr#r#r#r$Ús