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 ddlmZ dd	lmZmZ  ej                   e      Z G d
 dee      Z G d de
      Zy)zDINOv2 model configuration    OrderedDict)Mapping)version   )PretrainedConfig)
OnnxConfig)logging)BackboneConfigMixin*get_aligned_output_features_output_indicesc                   P     e Zd ZdZdZ	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 d fd	Z xZS )Dinov2Configa  
    This is the configuration class to store the configuration of a [`Dinov2Model`]. It is used to instantiate an
    Dinov2 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 Dinov2
    [google/dinov2-base-patch16-224](https://huggingface.co/google/dinov2-base-patch16-224) 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.
        mlp_ratio (`int`, *optional*, defaults to 4):
            Ratio of the hidden size of the MLPs relative to the `hidden_size`.
        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-06):
            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 14):
            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.
        layerscale_value (`float`, *optional*, defaults to 1.0):
           Initial value to use for layer scale.
        drop_path_rate (`float`, *optional*, defaults to 0.0):
            Stochastic depth rate per sample (when applied in the main path of residual layers).
        use_swiglu_ffn (`bool`, *optional*, defaults to `False`):
            Whether to use the SwiGLU feedforward neural network.
        out_features (`List[str]`, *optional*):
            If used as backbone, list of features to output. Can be any of `"stem"`, `"stage1"`, `"stage2"`, etc.
            (depending on how many stages the model has). If unset and `out_indices` is set, will default to the
            corresponding stages. If unset and `out_indices` is unset, will default to the last stage. Must be in the
            same order as defined in the `stage_names` attribute.
        out_indices (`List[int]`, *optional*):
            If used as backbone, list of indices of features to output. Can be any of 0, 1, 2, etc. (depending on how
            many stages the model has). If unset and `out_features` is set, will default to the corresponding stages.
            If unset and `out_features` is unset, will default to the last stage. Must be in the
            same order as defined in the `stage_names` attribute.
        apply_layernorm (`bool`, *optional*, defaults to `True`):
            Whether to apply layer normalization to the feature maps in case the model is used as backbone.
        reshape_hidden_states (`bool`, *optional*, defaults to `True`):
            Whether to reshape the feature maps to 4D tensors of shape `(batch_size, hidden_size, height, width)` in
            case the model is used as backbone. If `False`, the feature maps will be 3D tensors of shape `(batch_size,
            seq_len, hidden_size)`.

    Example:

    ```python
    >>> from transformers import Dinov2Config, Dinov2Model

    >>> # Initializing a Dinov2 dinov2-base-patch16-224 style configuration
    >>> configuration = Dinov2Config()

    >>> # Initializing a model (with random weights) from the dinov2-base-patch16-224 style configuration
    >>> model = Dinov2Model(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```dinov2c                    t        |   di | || _        || _        || _        || _        || _        || _        || _        || _	        |	| _
        |
| _        || _        || _        || _        || _        || _        || _        dgt%        d|dz         D cg c]  }d| 	 c}z   | _        t)        ||| j&                        \  | _        | _        || _        || _        y c c}w )Nstem   stage)out_featuresout_indicesstage_names )super__init__hidden_sizenum_hidden_layersnum_attention_heads	mlp_ratio
hidden_acthidden_dropout_probattention_probs_dropout_probinitializer_rangelayer_norm_eps
image_size
patch_sizenum_channelsqkv_biaslayerscale_valuedrop_path_rateuse_swiglu_ffnranger   r   _out_features_out_indicesapply_layernormreshape_hidden_states)selfr   r   r   r   r   r   r    r!   r"   r#   r$   r%   r&   r'   r(   r)   r   r   r-   r.   kwargsidx	__class__s                          b/var/www/html/venv/lib/python3.12/site-packages/transformers/models/dinov2/configuration_dinov2.pyr   zDinov2Config.__init__m   s    0 	"6"&!2#6 "$#6 ,H)!2,$$(  0,,"8aIZ]^I^@_&`se}&``0Z%;DL\L\1
-D-  /%:" 'as   C)i      r4      gelu        r7   g{Gz?gư>      r   Tg      ?r7   FNNTT)__name__
__module____qualname____doc__
model_typer   __classcell__)r2   s   @r3   r   r      sY    IV J %("+/; /;    r   c                   p    e Zd Z ej                  d      Zedeeee	ef   f   fd       Z
edefd       Zy)Dinov2OnnxConfigz1.11returnc                 (    t        ddddddfg      S )Npixel_valuesbatchr%   heightwidth)r   r      r   r   r/   s    r3   inputszDinov2OnnxConfig.inputs   s&    WHQX!YZ
 	
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WS#X%6 67 
 
 U  r@   rB   N)r=   collectionsr   typingr   	packagingr   configuration_utilsr   onnxr	   utilsr
   utils.backbone_utilsr   r   
get_loggerr:   loggerr   rB   r   r@   r3   <module>r]      sR    ! #   3   c 
		H	%};&(8 };@z r@   