rhHddlmZddlmZmZddlmZGddeZdgZy))PretrainedConfig)CONFIG_MAPPING AutoConfig)SuperPointConfigcteZdZdZdZdeiZ ddedededede de d e d e d e d e ffd Z xZ S)LightGlueConfiga This is the configuration class to store the configuration of a [`LightGlueForKeypointMatching`]. It is used to instantiate a LightGlue 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 LightGlue [ETH-CVG/lightglue_superpoint](https://huggingface.co/ETH-CVG/lightglue_superpoint) 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. descriptor_dim (`int`, *optional*, defaults to 256): The dimension of the descriptors. num_hidden_layers (`int`, *optional*, defaults to 9): The number of self and cross attention layers. num_attention_heads (`int`, *optional*, defaults to 4): The number of heads in the multi-head attention. num_key_value_heads (`int`, *optional*): This is the number of key_value heads that should be used to implement Grouped Query Attention. If `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if `num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed by meanpooling all the original heads within that group. For more details checkout [this paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to `num_attention_heads`. depth_confidence (`float`, *optional*, defaults to 0.95): The confidence threshold used to perform early stopping width_confidence (`float`, *optional*, defaults to 0.99): The confidence threshold used to prune points filter_threshold (`float`, *optional*, defaults to 0.1): The confidence threshold used to filter matches initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. hidden_act (`str`, *optional*, defaults to `"gelu"`): The activation function to be used in the hidden layers. attention_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for the attention probabilities. attention_bias (`bool`, *optional*, defaults to `True`): Whether to use a bias in the query, key, value and output projection layers during self-attention. trust_remote_code (`bool`, *optional*, defaults to `False`): Whether to trust remote code when using other models than SuperPoint as keypoint detector. Examples: ```python >>> from transformers import LightGlueConfig, LightGlueForKeypointMatching >>> # Initializing a LightGlue style configuration >>> configuration = LightGlueConfig() >>> # Initializing a model from the LightGlue style configuration >>> model = LightGlueForKeypointMatching(configuration) >>> # Accessing the model configuration >>> configuration = model.config ``` lightgluekeypoint_detector_configdescriptor_dimnum_hidden_layersnum_attention_headsdepth_confidencewidth_confidencefilter_thresholdinitializer_range hidden_acttrust_remote_codec | |_||zdk7r td||_||_||_||}||_||_||_||_| |_ t|trZ|jdd|d<|dtvr%tj|d|j}nt|dd i|ddi}|tdd }||_||_|d z|_| |_| |_| |_t-|\d i|y) Nz1descriptor_dim % num_heads is different from zero model_type superpoint _name_or_path)rattn_implementationeager)rr)r ValueErrorr r rnum_key_value_headsrrrr isinstancedictgetrrfrom_pretrainedr hidden_sizeintermediate_sizerattention_dropoutattention_biassuper__init__)selfr r r rrrrrrrr%r&rkwargs __class__s /var/www/html/ai-insurance-compliance-backend/venv/lib/python3.12/site-packages/transformers/models/lightglue/configuration_lightglue.pyr(zLightGlueConfig.__init__YsQ("3 / /1 4PQ Q,!2#6   &"5 #6 0 0 0!2 . 55M5Q5QR^`l5m $\ 2' 5^K+5+E+E,_=QUQgQg,(,::RS_:`+a,.,DK,( $ +'5l'CX_'` $(@%)!/!!3$!2, "6") N Ngffffff?gGz?g?g{Gz?gelugTF)__name__ __module__ __qualname____doc__rr sub_configsrintfloatstrboolr( __classcell__)r+s@r,r r s8tJ-z:K6:!!"#$ "&"&"%#' "'?#"2?#?# ?# ! ?# ?# ?# ?#!?#?# ?#?#r-r N) configuration_utilsrautorrrrr __all__rr-r,r?s*,4-)}#&}#@  r-