Zj dSrSSKJr SSKJr SSKJr \"SS9\"SS \555rS /rg ) zVJEPA 2 model configuration)strict)PreTrainedConfig)auto_docstringzfacebook/vjepa2-vitl-fpc64-256) checkpointc\rSrSr%SrSrSr\\\-\ \\4-\ S'Sr \\ S'Sr \\ S 'S r \\ S 'S r\\ S 'Sr\\ S'Sr\\ S'Sr\\ S'Sr\\-\ S'Sr\\-\ S'Sr\\ S'Sr\\ S'Sr\\-\ S'Sr\\ S'Sr\\ S'Sr\\-\ S 'Sr\\ S!'S"r\\ S#'S$r\\ S%'S$r \\ S&'S'r!\\ S('Sr"\\ S)'Sr#\\-\ S*'S+r$g,)- VJEPA2ConfigaG crop_size (`int`, *optional*, defaults to 256): Input resolution of the model frames_per_clip (`int`, *optional*, defaults to 64): The number of frames the model has been pretrained with. Does not impact inference. tubelet_size (`int`, *optional*, defaults to 2): The number of temporal frames used for a single rastor, check paper for more information. num_pooler_layers (`int`, *optional*, defaults to 3): The number of self-attention layers in the pooler. pred_hidden_size (`int`, *optional*, defaults to 384): Dimensionality of the predictor layers pred_num_attention_heads (`int`, *optional*, defaults to 12): Number of attention heads for each attention layer in the Predictor pred_num_hidden_layers (`int`, *optional*, defaults to 12): Number of hidden layers in the Predictor pred_num_mask_tokens (`int`, *optional*, defaults to 10): Define the number of mask tokens to use in the Predictor pred_zero_init_mask_tokens (`bool`, *optional*, defaults to `True`): Initialize the mask tokens in the predictor with 0. pred_mlp_ratio (`float`, *optional*, defaults to 4.0): Ratio of the hidden size of the MLPs used in Predictor relative to the `pred_hidden_size`. Example: ```python >>> from transformers import VJEPA2Config, VJEPA2Model >>> # Initializing a VJEPA2 vjepa2-vitl-fpc64-256 style configuration >>> configuration = VJEPA2Config() >>> # Initializing a model (with random weights) from the vjepa2-vitl-fpc64-256 style configuration >>> model = VJEPA2Model(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```vjepa2 patch_size crop_size@frames_per_clip tubelet_sizei hidden_sizerin_chansnum_attention_headsnum_hidden_layersgdrop_path_rateg@ mlp_ratiogư>layer_norm_epsTqkv_biasattention_probs_dropout_probgelu hidden_actg{Gz?initializer_rangeattention_dropoutnum_pooler_layersipred_hidden_size pred_num_attention_headspred_num_hidden_layers pred_num_mask_tokenspred_zero_init_mask_tokenspred_mlp_ratioN)%__name__ __module__ __qualname____firstlineno____doc__ model_typer intlisttuple__annotations__rrrrrrrrfloatrrrboolrrstrr r!r"r#r%r&r(r)r*__static_attributes__r+ڀ/root/GenerationalWealth/GenerationalWealth/venv/lib/python3.13/site-packages/transformers/models/vjepa2/configuration_vjepa2.pyr r s)# JJ46Jd3i%S/16IsOSL#KHc!!s"%NECK% IsU{  NE Hd03 %#+3J#u#%(us{(sc$&c&"$C$ "#"'++"%NC%K%r:r N) r0huggingface_hub.dataclassesrconfiguration_utilsrutilsrr __all__r+r:r;r@sH".3#;<>&#>&=>&B  r: