o ei@shddlmZddlmZddlmZddlmZmZer"ddl m Z e e Z Gdd d eZd gZd S) ) TYPE_CHECKING)PreTrainedConfig)logging)CONFIG_MAPPING AutoConfig)SuperPointConfigcsreZdZdZdZdeiZ        ddd d ed eedBdee dBdedede de ffdd Z Z S)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. is_decoder (`bool`, *optional*, defaults to `False`): Whether to only use the decoder in an encoder-decoder architecture, otherwise it has no effect on decoder-only or encoder-only architectures. 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_configNd{Gz?Fr hidden_sizekeypoint_encoder_sizesgnn_layers_typesnum_attention_headssinkhorn_iterationsmatching_thresholdinitializer_rangec  s|dur|nddgd|_tdd|jDstd||dkr%td|dur+|ngd |_||_||_||_||_||_||_t|t rZ| d d |d <t |d d i|}|durct d }||_ ||_ d|_d |_tjd i| dS)Nselfcross css|]}|dvVqdS))rrN).0 layer_typerrs/home/ubuntu/transcripts/venv/lib/python3.10/site-packages/transformers/models/superglue/configuration_superglue.py [sz+SuperGlueConfig.__init__..z5All gnn_layers_types must be either 'self' or 'cross'rz8hidden_size % num_attention_heads is different from zero) @r model_type superpointFr)rall ValueErrorrrrrr isinstancedictgetrr rattention_probs_dropout_prob is_decodersuper__init__) rr rrrrrrrr,kwargs __class__rrr.Ls2    zSuperGlueConfig.__init__) Nr NNrrrrF) __name__ __module__ __qualname____doc__r$r sub_configsintliststrfloatr. __classcell__rrr0rr s<-   r N)typingrconfiguration_utilsrutilsrautorrr%r get_loggerr2loggerr __all__rrrrs     `