
    Z j                     r    S r SSKJr  SSKJr  SSKJr  SSKJr  \" SS9\ " S	 S
\\5      5       5       r	S
/r
g)z>Dilated Neighborhood Attention Transformer model configuration    )strict   )BackboneConfigMixin)PreTrainedConfig)auto_docstringzshi-labs/dinat-mini-in1k-224)
checkpointc                     ^  \ rS rSr% SrSrSSS.rSr\\	\   -  \
\\4   -  \S'   S	r\\S
'   Sr\\S'   Sr\	\   \
\S4   -  \S'   Sr\	\   \
\S4   -  \S'   Sr\\S'   Sr\	\
-  S-  \S'   Sr\\S'   Sr\\S'   Sr\\-  \S'   Sr\\-  \S'   Sr\\-  \S'   Sr\\S'   S r\\S!'   S"r\\S#'   Sr\\S$'   Sr\	\   S-  \S%'   Sr\	\   S-  \S&'   U 4S' jr S(r!U =r"$ ))DinatConfig   aR  
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.

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   
patch_sizer   num_channels@   	embed_dim)r   r         .depths)   r            kernel_sizeN	dilationsg      @	mlp_ratioTqkv_biasg        hidden_dropout_probattention_probs_dropout_probg?drop_path_rategelu
hidden_actg{Gz?initializer_rangegh㈵>layer_norm_epslayer_scale_init_value_out_features_out_indicesc                   > [        U R                  5      U l        U R                  =(       d    / SQ/ SQ/ SQ/ SQ/U l        [	        U R
                  S[        U R                  5      S-
  -  -  5      U l        S/[        S[        U R                  5      S-   5       Vs/ s H  nSU 3PM
     sn-   U l        U R                  UR                  S	S 5      UR                  S
S 5      S9  [        TU ]0  " S0 UD6  g s  snf )N)   r   r,   )r,   r   r,   r   )r,   r   r,   r   r,   r   )r,   r,   r,   r,   r,   r   r,   stemstageout_indicesout_features)r/   r0    )lenr   r   r   intr   hidden_sizerangestage_names"set_output_features_output_indicespopsuper__post_init__)selfkwargsidx	__class__s      ~/root/GenerationalWealth/GenerationalWealth/venv/lib/python3.13/site-packages/transformers/models/dinat/configuration_dinat.pyr:   DinatConfig.__post_init__G   s    dkk*iI|EWYh+i t~~c$++6F6J0KKL"8aT[[IY\]I]@^&_@^se}@^&__//

=$7fjjQ_aeFf 	0 	
 	''	 '`s   C8)r   r4   r   r6   )#__name__
__module____qualname____firstlineno____doc__
model_typeattribute_mapr   r3   listtuple__annotations__r   r   r   r   r   r   r   floatr    boolr!   r"   r#   r%   strr&   r'   r(   r)   r*   r:   __static_attributes____classcell__)r>   s   @r?   r
   r
      s@   & J  +)M
 56Jd3i%S/15L#Is*6FDIc3h'6-:ItCy5c?*:K%)Ite|d")IuHd'**03 %#+3"%NECK%J#u# NE $'E'&*M49t#*%)L$s)d")( (    r
   N)rE   huggingface_hub.dataclassesr   backbone_utilsr   configuration_utilsr   utilsr   r
   __all__r1   rP   r?   <module>rV      sN    E . 1 3 # 9:9(%'7 9(  ;9(x /rP   