
    %	&hE                     0    d Z ddlmZ  G d de      ZdgZy)zI-JEPA model configuration   )PretrainedConfigc                   F     e Zd ZdZdZ	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 d fd	Z xZS )IJepaConfiga#  
    This is the configuration class to store the configuration of a [`IJepaModel`]. It is used to instantiate an IJEPA
    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 I-JEPA
    [facebook/ijepa_vith14_1k](https://huggingface.co/facebook/ijepa_vith14_1k) architecture.

    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
    documentation from [`PretrainedConfig`] for more information.


    Args:
        hidden_size (`int`, *optional*, defaults to 768):
            Dimensionality of the encoder layers and the pooler layer.
        num_hidden_layers (`int`, *optional*, defaults to 12):
            Number of hidden layers in the Transformer encoder.
        num_attention_heads (`int`, *optional*, defaults to 12):
            Number of attention heads for each attention layer in the Transformer encoder.
        intermediate_size (`int`, *optional*, defaults to 3072):
            Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
        hidden_act (`str` or `function`, *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.
        hidden_dropout_prob (`float`, *optional*, defaults to 0.0):
            The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
        attention_probs_dropout_prob (`float`, *optional*, defaults to 0.0):
            The dropout ratio for the attention probabilities.
        initializer_range (`float`, *optional*, defaults to 0.02):
            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
        layer_norm_eps (`float`, *optional*, defaults to 1e-12):
            The epsilon used by the layer normalization layers.
        image_size (`int`, *optional*, defaults to 224):
            The size (resolution) of each image.
        patch_size (`int`, *optional*, defaults to 16):
            The size (resolution) of each patch.
        num_channels (`int`, *optional*, defaults to 3):
            The number of input channels.
        qkv_bias (`bool`, *optional*, defaults to `True`):
            Whether to add a bias to the queries, keys and values.
        pooler_output_size (`int`, *optional*):
           Dimensionality of the pooler layer. If None, defaults to `hidden_size`.
        pooler_act (`str`, *optional*, defaults to `"tanh"`):
           The activation function to be used by the pooler. Keys of ACT2FN are supported for Flax and
           Pytorch, and elements of https://www.tensorflow.org/api_docs/python/tf/keras/activations are
           supported for Tensorflow.

    Example:

    ```python
    >>> from transformers import IJepaConfig, IJepaModel

    >>> # Initializing a IJEPA ijepa-base-patch16-224 style configuration
    >>> configuration = IJepaConfig()

    >>> # Initializing a model (with random weights) from the ijepa-base-patch16-224 style configuration
    >>> model = IJepaModel(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```ijepac                     t        |   di | || _        || _        || _        || _        || _        || _        || _        || _	        |	| _
        |
| _        || _        || _        || _        |r|n|| _        || _        y )N )super__init__hidden_sizenum_hidden_layersnum_attention_headsintermediate_size
hidden_acthidden_dropout_probattention_probs_dropout_probinitializer_rangelayer_norm_eps
image_size
patch_sizenum_channelsqkv_biaspooler_output_size
pooler_act)selfr   r   r   r   r   r   r   r   r   r   r   r   r   r   r   kwargs	__class__s                    /var/www/pru.catia.catastroantioquia-mas.com/valormas/lib/python3.12/site-packages/transformers/models/ijepa/configuration_ijepa.pyr
   zIJepaConfig.__init__S   s    & 	"6"&!2#6 !2$#6 ,H)!2,$$( 8J"4P[$    )i      r   i   gelu        r!   g{Gz?g-q=      r   TNtanh)__name__
__module____qualname____doc__
model_typer
   __classcell__)r   s   @r   r   r      sI    :x J %(!#% #%r   r   N)r(   configuration_utilsr   r   __all__r   r   r   <module>r-      s&    ! 3b%" b%J /r   