fTh8SSKJrJrJr SSKJr SSKJr SSKJ r \(aSSK J r \R"\ 5r"SS \5rS /rg ) ) TYPE_CHECKINGListOptional)PretrainedConfig)logging)CONFIG_MAPPING)SuperPointConfigc^\rSrSrSrSrSSSS\S\\\S\\\ S \S \S \ S \ 4U4S jjjr Sr U=r $)SuperGlueConfiga This is the configuration class to store the configuration of a [`SuperGlueModel`]. It is used to instantiate a SuperGlue 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 SuperGlue [magic-leap-community/superglue_indoor](https://huggingface.co/magic-leap-community/superglue_indoor) architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: keypoint_detector_config (`Union[AutoConfig, dict]`, *optional*, defaults to `SuperPointConfig`): The config object or dictionary of the keypoint detector. hidden_size (`int`, *optional*, defaults to 256): The dimension of the descriptors. keypoint_encoder_sizes (`List[int]`, *optional*, defaults to `[32, 64, 128, 256]`): The sizes of the keypoint encoder layers. gnn_layers_types (`List[str]`, *optional*, defaults to `['self', 'cross', 'self', 'cross', 'self', 'cross', 'self', 'cross', 'self', 'cross', 'self', 'cross', 'self', 'cross', 'self', 'cross', 'self', 'cross']`): The types of the GNN layers. Must be either 'self' or 'cross'. num_attention_heads (`int`, *optional*, defaults to 4): The number of heads in the GNN layers. sinkhorn_iterations (`int`, *optional*, defaults to 100): The number of Sinkhorn iterations. matching_threshold (`float`, *optional*, defaults to 0.0): The matching threshold. initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. Examples: ```python >>> from transformers import SuperGlueConfig, SuperGlueModel >>> # Initializing a SuperGlue superglue style configuration >>> configuration = SuperGlueConfig() >>> # Initializing a model from the superglue style configuration >>> model = SuperGlueModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ``` supergluekeypoint_detector_configr hidden_sizekeypoint_encoder_sizesgnn_layers_typesnum_attention_headssinkhorn_iterationsmatching_thresholdinitializer_rangec  >UbUOSS/S-Ul[SUR55(d [S5eX%-S:wa [S5eUbUO/SQUlX lX0lX@lXPlX`lXpl[U[5(a"S U;aUS OS US '[US "S 0UD6nUc[S "5nXl Xl SUl S Ul[T U]@"S 0U D6 g) Nselfcross c3*# UH oS;v M g7f))rrN).0 layer_types m/var/www/auris/envauris/lib/python3.13/site-packages/transformers/models/superglue/configuration_superglue.py +SuperGlueConfig.__init__..Vs[EZz!22EZsz5All gnn_layers_types must be either 'self' or 'cross'rz8hidden_size % num_attention_heads is different from zero) @ model_type superpointFr)rall ValueErrorrrrrr isinstancedictr rrattention_probs_dropout_prob is_decodersuper__init__) rrrrrrrrrkwargs __class__s r r0SuperGlueConfig.__init__Hs&5E4P 0W]_fVgjkVk[TEZEZ[[[TU U  , 1WX X'=&H "N` #'&<# 0#6 #6 "4 . 5 5:FJb:b(6ht %\ 2(66N|6\'](*( $ $ +'5l'C'E $(@%!2,-) "6") r-rrrr.rrrrr)Nr&NNdgg{Gz?)__name__ __module__ __qualname____firstlineno____doc__r'intrrstrfloatr0__static_attributes__ __classcell__)r2s@r r r s(TJ8<6:04#$#&$'#'-#"4-#-#!)c 3 -# #49- -# ! -#!-#"-#!-#-#r4r N)typingrrrconfiguration_utilsrutilsrautor r(r get_loggerr7loggerr __all__rr4r rHsH103!-   H %Z#&Z#z  r4