# coding=utf-8 # Copyright 2025 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """VJEPA 2 model configuration""" from ...configuration_utils import PretrainedConfig class VJEPA2Config(PretrainedConfig): r""" 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 ```""" model_type = "vjepa2" def __init__( self, patch_size=16, crop_size=256, frames_per_clip=64, tubelet_size=2, hidden_size=1024, in_chans=3, num_attention_heads=16, num_hidden_layers=24, drop_path_rate=0.0, mlp_ratio=4.0, layer_norm_eps=1e-6, qkv_bias=True, attention_probs_dropout_prob=0.0, hidden_act="gelu", initializer_range=0.02, attention_dropout=0.0, num_pooler_layers=3, # predictor params pred_hidden_size=384, pred_num_attention_heads=12, pred_num_hidden_layers=12, pred_num_mask_tokens=10, pred_zero_init_mask_tokens=True, pred_mlp_ratio=4.0, **kwargs, ): super().__init__(**kwargs) self.crop_size = crop_size self.frames_per_clip = frames_per_clip self.patch_size = patch_size self.tubelet_size = tubelet_size self.hidden_size = hidden_size self.in_chans = in_chans self.num_attention_heads = num_attention_heads self.num_hidden_layers = num_hidden_layers self.drop_path_rate = drop_path_rate self.mlp_ratio = mlp_ratio self.layer_norm_eps = layer_norm_eps self.qkv_bias = qkv_bias self.attention_probs_dropout_prob = attention_probs_dropout_prob self.hidden_act = hidden_act self.initializer_range = initializer_range self.image_size = crop_size self.attention_dropout = attention_dropout self.num_pooler_layers = num_pooler_layers # predictor params self.pred_hidden_size = pred_hidden_size self.pred_num_attention_heads = pred_num_attention_heads self.pred_num_hidden_layers = pred_num_hidden_layers self.pred_num_mask_tokens = pred_num_mask_tokens self.pred_zero_init_mask_tokens = pred_zero_init_mask_tokens self.pred_mlp_ratio = pred_mlp_ratio __all__ = ["VJEPA2Config"]