qiddlmZddlmZddlmZddlmZmZerddl m Z eje Z Gdd eZd gZy ) ) TYPE_CHECKING)PreTrainedConfig)logging)CONFIG_MAPPING AutoConfig)SuperPointConfigc|eZdZdZdZdeiZ ddddedeedzdee dzd ed ed e d e ffd Z xZ 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_configNr hidden_sizekeypoint_encoder_sizesgnn_layers_typesnum_attention_headssinkhorn_iterationsmatching_thresholdinitializer_rangec  ||nddgdz|_td|jDs td||zdk7r td||ngd|_||_||_||_||_||_||_t|tr&|jd d |d <t|d d i|}| td }||_ ||_ d|_d |_t! |Dd i| y) Nselfcross c3$K|]}|dv yw))rrN).0 layer_types g/opt/pipecat/venv/lib/python3.12/site-packages/transformers/models/superglue/configuration_superglue.py z+SuperGlueConfig.__init__..[s[z:!22[sz5All gnn_layers_types must be either 'self' or 'cross'rz8hidden_size % num_attention_heads is different from zero) @ model_type superpointFr)rall ValueErrorrrrrr isinstancedictgetrrrattention_probs_dropout_prob is_decodersuper__init__) rrrrrrrrrr,kwargs __class__s rr.zSuperGlueConfig.__init__Ls-5E4P 0W]_fVgjkVk[TEZEZ[[TU U , , 1WX X'=&H "N` #'&<# 0#6 #6 "4 . 55M5Q5QR^`l5m $\ 2'56N|6\'](*( $ $ +'5l'C'E $(@%!2,-) "6") Nr#NNdgg{Gz?F) __name__ __module__ __qualname____doc__r$r sub_configsintliststrfloatr. __classcell__)r0s@rr r s+ZJ-z:K8<37-1#$#&$'#',#"4,#,#!%S D 0 ,# s)d* ,# ! ,#!,#",#!,#,#r1r N)typingrconfiguration_utilsrutilsrautorr r%r get_loggerr4loggerr __all__rr1rrEsG!3--   H %]#&]#@  r1