
    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&Swinv2 Transformer model configuration    )strict   )BackboneConfigMixin)PreTrainedConfig)auto_docstringz(microsoft/swinv2-tiny-patch4-window8-256)
checkpointc                     ^  \ rS rSr% SrSrSSS.rSr\\	\   -  \
\\4   -  \S'   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4   -  \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*r \	\   S*-  \S+'   S*r!\	\   S*-  \S,'   U 4S- jr"S.r#U =r$$ )/Swinv2Config   a  
window_size (`int`, *optional*, defaults to 7):
    Size of windows.
pretrained_window_sizes (`list(int)`, *optional*, defaults to `[0, 0, 0, 0]`):
    Size of windows during pretraining.
encoder_stride (`int`, *optional*, defaults to 32):
    Factor to increase the spatial resolution by in the decoder head for masked image modeling.

Example:

```python
>>> from transformers import Swinv2Config, Swinv2Model

>>> # Initializing a Swinv2 microsoft/swinv2-tiny-patch4-window8-256 style configuration
>>> configuration = Swinv2Config()

>>> # Initializing a model (with random weights) from the microsoft/swinv2-tiny-patch4-window8-256 style configuration
>>> model = Swinv2Model(configuration)

>>> # Accessing the model configuration
>>> configuration = model.config
```swinv2	num_heads
num_layers)num_attention_headsnum_hidden_layers   
image_size   
patch_sizer   num_channels`   	embed_dim)   r      r   .depths)r   r            window_size)r   r   r   r   pretrained_window_sizesg      @	mlp_ratioTqkv_biasg        hidden_dropout_probattention_probs_dropout_probg?drop_path_rategelu
hidden_actFuse_absolute_embeddingsg{Gz?initializer_rangegh㈵>layer_norm_eps    encoder_strideN_out_features_out_indicesc                   > [        U R                  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  [        U R                  S[        U R                  5      S-
  -  -  5      U l	        [        TU ],  " S0 UD6  g s  snf )	Nstem   stageout_indicesout_features)r2   r3   r    )lenr   r   rangestage_names"set_output_features_output_indicespopintr   hidden_sizesuper__post_init__)selfkwargsidx	__class__s      ڀ/root/GenerationalWealth/GenerationalWealth/venv/lib/python3.13/site-packages/transformers/models/swinv2/configuration_swinv2.pyr=   Swinv2Config.__post_init__M   s    dkk*"8aT[[IY\]I]@^&_@^se}@^&__//

=$7fjjQ_aeFf 	0 	

 t~~c$++6F6J0KKL'' '`s   C)r;   r   r7   )%__name__
__module____qualname____firstlineno____doc__
model_typeattribute_mapr   r:   listtuple__annotations__r   r   r   r   r   r   r   r    floatr!   boolr"   r#   r$   r&   strr'   r(   r)   r+   r,   r-   r=   __static_attributes____classcell__)rA   s   @rB   r
   r
      sr   . J  +)M
 58Jd3i%S/1745Jd3i%S/15L#Is*6FDIc3h'6-;ItCy5c?*;K;GT#YsCx8GIuHd'**03 %#+3"%NECK%J$)T)#u# NE NC&*M49t#*%)L$s)d")	( 	(    r
   N)rH   huggingface_hub.dataclassesr   backbone_utilsr   configuration_utilsr   utilsr   r
   __all__r4   rS   rB   <module>rY      sO    - . 1 3 # EF=(&(8 =(  G=(@ 
rS   