
    i                     j    d Z ddlmZ ddlmZ ddlmZ  ed      e G d d	e                    Zd	gZy
)zVJEPA 2 model configuration    )strict   )PreTrainedConfig)auto_docstringzfacebook/vjepa2-vitl-fpc64-256)
checkpointc                      e Zd ZU dZdZdZeee   z  eeef   z  e	d<   dZ
ee	d<   dZee	d<   d	Zee	d
<   dZee	d<   dZee	d<   dZee	d<   dZee	d<   dZeez  e	d<   dZeez  e	d<   dZee	d<   dZee	d<   dZeez  e	d<   dZee	d<   dZee	d<   dZeez  e	d<   dZee	d <   d!Zee	d"<   d#Zee	d$<   d#Zee	d%<   d&Z ee	d'<   dZ!ee	d(<   dZ"eez  e	d)<   y*)+VJEPA2Configa  
    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_sizer   in_chansnum_attention_heads   num_hidden_layersg        drop_path_rateg      @	mlp_ratiogư>layer_norm_epsTqkv_biasattention_probs_dropout_probgelu
hidden_actg{Gz?initializer_rangeattention_dropoutnum_pooler_layersi  pred_hidden_size   pred_num_attention_headspred_num_hidden_layers
   pred_num_mask_tokenspred_zero_init_mask_tokenspred_mlp_ratioN)#__name__
__module____qualname____doc__
model_typer   intlisttuple__annotations__r   r   r   r   r   r   r   r   floatr   r   r   boolr   r   strr   r    r!   r"   r$   r%   r'   r(   r)        /var/www/vps2.regionflexible.com/Desarrollo/venv/lib/python3.12/site-packages/transformers/models/vjepa2/configuration_vjepa2.pyr	   r	      s)   #J J46Jd3i%S/16IsOSL#KHc!!s"%NECK% IsU{  NE Hd03 %#+3J#u#%(us{(sc$&c&"$C$ "#"'++"%NC%K%r7   r	   N)	r-   huggingface_hub.dataclassesr   configuration_utilsr   utilsr   r	   __all__r6   r7   r8   <module>r=      sH    " . 3 # ;<>&# >&  =>&B 
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