
    Z j                     n    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
)    )strict   )BackboneConfigMixin)PreTrainedConfig)auto_docstringzfacebook/pixio-huge)
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'   Sr\\   S-  \S'   Sr\\S '   Sr\\S!'   S"r\\S#'   U 4S$ jr S%r!U =r"$ )&PixioConfig   a  
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)`.
n_cls_tokens (`int`, *optional*, defaults to 8):
    Number of class tokens in the Transformer encoder.

Example:

```python
>>> from transformers import PixioConfig, PixioModel

>>> # Initializing a Pixio pixio-huge style configuration
>>> configuration = PixioConfig()

>>> # Initializing a model (with random weights) from the pixio-huge style configuration
>>> model = PixioModel(configuration)

>>> # Accessing the model configuration
>>> configuration = model.config
```pixioi   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_biasdrop_path_rateN_out_features_out_indicesapply_layernormreshape_hidden_states   n_cls_tokensc                    > 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      ~/root/GenerationalWealth/GenerationalWealth/venv/lib/python3.13/site-packages/transformers/models/pixio/configuration_pixio.pyr2   PixioConfig.__post_init__M   s    "8aI_I_bcIc@d&e@dse}@d&ee//

=$7fjjQ_aeFf 	0 	
 	''	 'fs   A9)r.   )#__name__
__module____qualname____firstlineno____doc__
model_typer   int__annotations__r   r   r   r   strr   floatr   r   r   r   listtupler   r   r   boolr   r    r!   r"   r#   r%   r2   __static_attributes____classcell__)r6   s   @r7   r
   r
      s%   2 JKs!!IsJ'**03 %#+3#u# NE 47Jd3i%S/1746Jd3i%S/16L#Hd"%NECK%&*M49t#*%)L$s)d") OT "&4&L#( (    r
   N)
huggingface_hub.dataclassesr   backbone_utilsr   configuration_utilsr   utilsr   r
   __all__r,   rH   r7   <module>rN      sJ   ( / 1 3 # 015(%'7 5(  25(p /rH   