
    Z j9                     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DINOv2 model configuration    )strict   )BackboneConfigMixin)PreTrainedConfig)auto_docstringzfacebook/dinov2-base)
checkpointc                     ^  \ rS rSr% SrSr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\\\   -  \\\4   -  \S'   Sr\\\   -  \\\4   -  \S'   Sr\\S'   Sr\\S'   Sr\\S'   Sr\\-  \S'   Sr\\S '   S!r\\   S!-  \S"'   S!r\\   S!-  \S#'   Sr\\S$'   Sr \\S%'   Sr!\\S&'   U 4S' jr"S(r#U =r$$ ))Dinov2Config   a  
layerscale_value (`float`, *optional*, defaults to 1.0):
    Initial value to use for layer scale.
use_swiglu_ffn (`bool`, *optional*, defaults to `False`):
    Whether to use the SwiGLU feedforward neural network.
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)`.
use_mask_token (`bool`, *optional*, defaults to `True`):
    Whether to use mask_token in embeddings.

Example:

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

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

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

>>> # Accessing the model configuration
>>> configuration = model.config
```dinov2i   hidden_size   num_hidden_layersnum_attention_heads   	mlp_ratiogelu
hidden_actg        hidden_dropout_probattention_probs_dropout_probg{Gz?initializer_rangegư>layer_norm_eps   
image_size   
patch_sizer   num_channelsTqkv_biasg      ?layerscale_valuedrop_path_rateFuse_swiglu_ffnN_out_features_out_indicesapply_layernormreshape_hidden_statesuse_mask_tokenc                    > S/[        SU R                  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 ]  " S0 UD6  g s  snf )Nstem   stageout_indicesout_features)r+   r,    )ranger   stage_names"set_output_features_output_indicespopsuper__post_init__)selfkwargsidx	__class__s      ڀ/root/GenerationalWealth/GenerationalWealth/venv/lib/python3.13/site-packages/transformers/models/dinov2/configuration_dinov2.pyr3   Dinov2Config.__post_init__O   s    "8aI_I_bcIc@d&e@dse}@d&ee//

=$7fjjQ_aeFf 	0 	
 	''	 'fs   A9)r/   )%__name__
__module____qualname____firstlineno____doc__
model_typer   int__annotations__r   r   r   r   strr   floatr   r   r   r   listtupler   r   r   boolr   r    r!   r"   r#   r$   r%   r&   r3   __static_attributes____classcell__)r7   s   @r8   r
   r
      s:   : JKs!!IsJ'**03 %#+3#u# NE 47Jd3i%S/1746Jd3i%S/16L#Hd!e!"%NECK% ND &*M49t#*%)L$s)d") OT "&4&ND( (    r
   N)r>   huggingface_hub.dataclassesr   backbone_utilsr   configuration_utilsr   utilsr   r
   __all__r-   rI   r8   <module>rO      sN    ! . 1 3 # 12;(&(8 ;(  3;(| 
rI   