
    sg                     l    d Z ddlmZ ddlmZ ddlmZmZ  ej                  e	      Z
 G d dee      Zy)z>Dilated Neighborhood Attention Transformer model configuration   )PretrainedConfig)logging)BackboneConfigMixin*get_aligned_output_features_output_indicesc                   v     e Zd ZdZdZdddZdddg d	g d
dg dg dg dg dgdddddddddddf fd	Z xZS )DinatConfiga  
    This is the configuration class to store the configuration of a [`DinatModel`]. It is used to instantiate a Dinat
    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 Dinat
    [shi-labs/dinat-mini-in1k-224](https://huggingface.co/shi-labs/dinat-mini-in1k-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:
        patch_size (`int`, *optional*, defaults to 4):
            The size (resolution) of each patch. NOTE: Only patch size of 4 is supported at the moment.
        num_channels (`int`, *optional*, defaults to 3):
            The number of input channels.
        embed_dim (`int`, *optional*, defaults to 64):
            Dimensionality of patch embedding.
        depths (`List[int]`, *optional*, defaults to `[3, 4, 6, 5]`):
            Number of layers in each level of the encoder.
        num_heads (`List[int]`, *optional*, defaults to `[2, 4, 8, 16]`):
            Number of attention heads in each layer of the Transformer encoder.
        kernel_size (`int`, *optional*, defaults to 7):
            Neighborhood Attention kernel size.
        dilations (`List[List[int]]`, *optional*, defaults to `[[1, 8, 1], [1, 4, 1, 4], [1, 2, 1, 2, 1, 2], [1, 1, 1, 1, 1]]`):
            Dilation value of each NA layer in the Transformer encoder.
        mlp_ratio (`float`, *optional*, defaults to 3.0):
            Ratio of MLP hidden dimensionality to embedding dimensionality.
        qkv_bias (`bool`, *optional*, defaults to `True`):
            Whether or not a learnable bias should be added to the queries, keys and values.
        hidden_dropout_prob (`float`, *optional*, defaults to 0.0):
            The dropout probability for all fully connected layers in the embeddings and encoder.
        attention_probs_dropout_prob (`float`, *optional*, defaults to 0.0):
            The dropout ratio for the attention probabilities.
        drop_path_rate (`float`, *optional*, defaults to 0.1):
            Stochastic depth rate.
        hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
            The non-linear activation function (function or string) in the encoder. 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.
        layer_norm_eps (`float`, *optional*, defaults to 1e-05):
            The epsilon used by the layer normalization layers.
        layer_scale_init_value (`float`, *optional*, defaults to 0.0):
            The initial value for the layer scale. Disabled if <=0.
        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.

    Example:

    ```python
    >>> from transformers import DinatConfig, DinatModel

    >>> # Initializing a Dinat shi-labs/dinat-mini-in1k-224 style configuration
    >>> configuration = DinatConfig()

    >>> # Initializing a model (with random weights) from the shi-labs/dinat-mini-in1k-224 style configuration
    >>> model = DinatModel(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```dinat	num_heads
num_layers)num_attention_headsnum_hidden_layers   r   @   )r   r         )   r            )   r   r   )r   r   r   r   )r   r   r   r   r   r   )r   r   r   r   r   g      @Tg        g?gelug{Gz?gh㈵>Nc                     t        |   di | || _        || _        || _        || _        t        |      | _        || _        || _	        || _
        || _        |	| _        |
| _        || _        || _        || _        || _        || _        t'        |dt        |      dz
  z  z        | _        || _        dgt-        dt        |      dz         D cg c]  }d| 	 c}z   | _        t1        ||| j.                        \  | _        | _        y c c}w )Nr   r   stemstage)out_featuresout_indicesstage_names )super__init__
patch_sizenum_channels	embed_dimdepthslenr   r
   kernel_size	dilations	mlp_ratioqkv_biashidden_dropout_probattention_probs_dropout_probdrop_path_rate
hidden_actlayer_norm_epsinitializer_rangeinthidden_sizelayer_scale_init_valueranger   r   _out_features_out_indices)selfr!   r"   r#   r$   r
   r&   r'   r(   r)   r*   r+   r,   r-   r/   r.   r2   r   r   kwargsidx	__class__s                        `/var/www/html/venv/lib/python3.12/site-packages/transformers/models/dinat/configuration_dinat.pyr    zDinatConfig.__init__f   s   , 	"6"$("f+"&"" #6 ,H),$,!2 y1Vq+AAB&<#"8aVWX@Y&Zse}&ZZ0Z%;DL\L\1
-D- '[s   D)__name__
__module____qualname____doc__
model_typeattribute_mapr    __classcell__)r9   s   @r:   r   r      sl    CJ J  +)M l,>P%("'/
 /
    r   N)r>   configuration_utilsr   utilsr   utils.backbone_utilsr   r   
get_loggerr;   loggerr   r   rB   r:   <module>rH      s:    E 3  c 
		H	%|
%'7 |
rB   