a ¾ Àhaã@shddlmZmZddlmZddlmZddlmZerDddl m Z e  e ¡Z Gdd „d eƒZd gZd S) é)Ú TYPE_CHECKINGÚOptionalé)ÚPretrainedConfig)Úloggingé)ÚCONFIG_MAPPING)ÚSuperPointConfigc sVeZdZdZdZdd eeeeeeeeee e d œ‡fd d „ Z e d d„ƒZ ‡Z S)ÚSuperGlueConfiga 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 ``` Z superglueNééédçç{®Gáz”?r )Úkeypoint_detector_configÚ hidden_sizeÚkeypoint_encoder_sizesÚgnn_layers_typesÚnum_attention_headsÚsinkhorn_iterationsÚmatching_thresholdÚinitializer_rangec  sô|dur |n ddgd|_tdd„|jDƒƒs6tdƒ‚||dkrJtdƒ‚|durV|ngd ¢|_||_||_||_||_||_||_t|t ƒr´|  d d ¡|d <t |d fi|¤Ž}|durÆt d ƒ}||_ ||_ d|_d |_tƒjfi| ¤ŽdS) NÚselfÚcrossé css|]}|dvVqdS))rrN©)Ú.0Z layer_typerrúq/var/www/html/assistant/venv/lib/python3.9/site-packages/transformers/models/superglue/configuration_superglue.pyÚ Vóz+SuperGlueConfig.__init__..z5All gnn_layers_types must be either 'self' or 'cross'rz8hidden_size % num_attention_heads is different from zero)é é@é€r Ú model_typeÚ superpointF)rÚallÚ ValueErrorrrrrrÚ isinstanceÚdictÚgetrrrZattention_probs_dropout_probZ is_decoderÚsuperÚ__init__) rrrrrrrrrÚkwargs©Ú __class__rrr+Hs2  ÿ ÿ zSuperGlueConfig.__init__cCsdt|jƒiS)Nr)Útyper)rrrrÚ sub_configsuszSuperGlueConfig.sub_configs)Nr NNr r rr)Ú__name__Ú __module__Ú __qualname__Ú__doc__r#ÚintrÚlistÚstrÚfloatr+Úpropertyr0Ú __classcell__rrr-rr s,*÷  ÷-r N)ÚtypingrrZconfiguration_utilsrÚutilsrÚautorr$r Z get_loggerr1Úloggerr Ú__all__rrrrÚs     _