ZjdSrSSKJr SSKJr SSKJr \"SS9\"SS \555rS /rg ) z"Data2VecVision model configuration)strict)PreTrainedConfig)auto_docstringzfacebook/data2vec-vision-base) checkpointc\rSrSr%SrSrSr\\S'Sr \\S'Sr \\S'S r \\S 'S r \ \S 'S r\\-\S'S r\\-\S'Sr\\S'Sr\\S'Sr\\\-\\\4-\S'Sr\\\-\\\4-\S'Sr\\S'Sr\\S'Sr\\S'Sr\\S'Sr\\S'Sr\\S 'Sr\\-\S!'S"r\\S#'S$r \\\\S%4-\S&'S'r!\\\\S%4-\S('S"r"\\S)'S*r#\\S+'S,r$\\S-'S.r%\\S/'Sr&\\S0'S1r'\\S2'S3r(g4)5Data2VecVisionConfiga use_mask_token (`bool`, *optional*, defaults to `False`): Whether to use a mask token for masked image modeling. use_shared_relative_position_bias (`bool`, *optional*, defaults to `False`): Whether to use the same relative position embeddings across all self-attention layers of the Transformer. use_mean_pooling (`bool`, *optional*, defaults to `True`): Whether to mean pool the final hidden states of the patches instead of using the final hidden state of the CLS token, before applying the classification head. pool_scales (`tuple[int]`, *optional*, defaults to `[1, 2, 3, 6]`): Pooling scales used in Pooling Pyramid Module applied on the last feature map. use_auxiliary_head (`bool`, *optional*, defaults to `True`): Whether to use an auxiliary head during training. auxiliary_loss_weight (`float`, *optional*, defaults to 0.4): Weight of the cross-entropy loss of the auxiliary head. auxiliary_channels (`int`, *optional*, defaults to 256): Number of channels to use in the auxiliary head. auxiliary_num_convs (`int`, *optional*, defaults to 1): Number of convolutional layers to use in the auxiliary head. auxiliary_concat_input (`bool`, *optional*, defaults to `False`): Whether to concatenate the output of the auxiliary head with the input before the classification layer. Example: ```python >>> from transformers import Data2VecVisionConfig, Data2VecVisionModel >>> # Initializing a Data2VecVision data2vec_vision-base-patch16-224-in22k style configuration >>> configuration = Data2VecVisionConfig() >>> # Initializing a model (with random weights) from the data2vec_vision-base-patch16-224-in22k style configuration >>> model = Data2VecVisionModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```zdata2vec-visioni hidden_size num_hidden_layersnum_attention_headsi intermediate_sizegelu hidden_actghidden_dropout_probattention_probs_dropout_probg{Gz?initializer_rangeg-q=layer_norm_eps image_size patch_sizer num_channelsFuse_mask_token use_absolute_position_embeddingsuse_relative_position_bias!use_shared_relative_position_biasg?layer_scale_init_valuedrop_path_rateTuse_mean_pooling)r . out_indices)r pool_scalesuse_auxiliary_headg?auxiliary_loss_weightauxiliary_channelsr&auxiliary_num_convsauxiliary_concat_inputsemantic_loss_ignore_indexN))__name__ __module__ __qualname____firstlineno____doc__ model_typer int__annotations__r rrrstrrfloatrrrrlisttuplerrrboolrrrrr r!r%r)r*r+r-r.r/r1__static_attributes__r2ڋ/root/GenerationalWealth/GenerationalWealth/venv/lib/python3.13/site-packages/transformers/models/data2vec/configuration_data2vec_vision.pyr r s" H#JKs!!!s!J'**03 %#+3#u#!NE!47Jd3i%S/1746Jd3i%S/16L# ND -2$d2',,.3%t3$'E'"%NECK%!d!/rGsK).3#:;A*+A*<A*H " "rA