rh0dZddlmZGddeZdgZy)zVJEPA 2 model configuration)PretrainedConfigcVeZdZdZdZ dfd ZxZS) VJEPA2Configa This is the configuration class to store the configuration of a [`VJEPA2Model`]. It is used to instantiate an VJEPA2 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 VJEPA2 [facebook/vjepa2-vitl-fpc64-256](https://huggingface.co/facebook/vjepa2-vitl-fpc64-256) architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: patch_size (`int`, *optional*, defaults to 16): The size (resolution) of each patch. 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. hidden_size (`int`, *optional*, defaults to 1024): Dimensionality of the encoder layers in_chans (`int`, *optional*, defaults to 3): The number of input channels num_attention_heads (`int`, *optional*, defaults to 16): Number of attention heads for each attention layer in the Encoder num_hidden_layers (`int`, *optional*, defaults to 24): The number of hidden layers drop_path_rate (`float`, *optional*, defaults to 0.0): Stochastic depth rate per sample (when applied in the main path of residual layers). mlp_ratio (`float`, *optional*, defaults to 4.0): Ratio of the hidden size of the MLPs used in Encoder relative to the `hidden_size`. layer_norm_eps (`float`, *optional*, defaults to 1e-06): The epsilon used by the layer normalization layers. qkv_bias (`bool`, *optional*, defaults to `True`): Whether to add a bias to the queries, keys and values. attention_probs_dropout_prob (`float`, *optional*, defaults to 0.0): The dropout probability for attentions. The dropout probability for all fully connected layers. hidden_act (`str`, *optional*, defaults to `"gelu"`): The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, `"relu"`, `"selu"` and `"gelu_new"` are supported. initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. attention_dropout (`float`, *optional*, defaults to 0.0): The dropout probability for attentions. 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 ```vjepa2c tt|di|||_||_||_||_||_||_||_||_ | |_ | |_ | |_ | |_ | |_||_||_||_||_||_||_||_||_||_||_||_y)N)super__init__ crop_sizeframes_per_clip patch_size tubelet_size hidden_sizein_chansnum_attention_headsnum_hidden_layersdrop_path_rate mlp_ratiolayer_norm_epsqkv_biasattention_probs_dropout_prob hidden_actinitializer_range image_sizeattention_dropoutnum_pooler_layerspred_hidden_sizepred_num_attention_headspred_num_hidden_layerspred_num_mask_tokenspred_zero_init_mask_tokenspred_mlp_ratio)selfr r r rrrrrrrrrrrrrrrrrr r!r"kwargs __class__s /var/www/html/ai-insurance-compliance-backend/venv/lib/python3.12/site-packages/transformers/models/vjepa2/configuration_vjepa2.pyr zVJEPA2Config.__init__as8 "6"".$(&  #6 !2,",  ,H)$!2#!2!2 0(@%&<#$8!*D',)@irr(@gư>Tr-gelug{Gz?r-ri r0 Tr.)__name__ __module__ __qualname____doc__ model_typer __classcell__)r%s@r&rrsbH TJ%(!#!#'36-6-r'rN)r5configuration_utilsrr__all__rr'r&r:s'"3C-#C-L  r'