
    Z jF                     ~    S r SSKJ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ResNet model configuration    )ClassVar)strict   )BackboneConfigMixin)PreTrainedConfig)auto_docstringzmicrosoft/resnet-50)
checkpointc                      ^  \ rS rSr% SrSrSS/r\\\	      \
S'   Sr\\
S'   S	r\\
S
'   Sr\\   \\S4   -  S-  \
S'   Sr\\   \\S4   -  S-  \
S'   Sr\	\
S'   Sr\	\
S'   Sr\\
S'   Sr\\
S'   U 4S jrS rSrU =r$ )ResNetConfig   a  
layer_type (`str`, *optional*, defaults to `"bottleneck"`):
    The layer to use, it can be either `"basic"` (used for smaller models, like resnet-18 or resnet-34) or
    `"bottleneck"` (used for larger models like resnet-50 and above).
downsample_in_first_stage (`bool`, *optional*, defaults to `False`):
    If `True`, the first stage will downsample the inputs using a `stride` of 2.
downsample_in_bottleneck (`bool`, *optional*, defaults to `False`):
    If `True`, the first conv 1x1 in ResNetBottleNeckLayer will downsample the inputs using a `stride` of 2.

Example:
```python
>>> from transformers import ResNetConfig, ResNetModel

>>> # Initializing a ResNet resnet-50 style configuration
>>> configuration = ResNetConfig()

>>> # Initializing a model (with random weights) from the resnet-50 style configuration
>>> model = ResNetModel(configuration)

>>> # Accessing the model configuration
>>> configuration = model.config
```
resnetbasic
bottlenecklayer_typesr   num_channels@   embedding_size)   i   i   i   .Nhidden_sizes)r         r   depths
layer_typerelu
hidden_actFdownsample_in_first_stagedownsample_in_bottleneckc                 B  > 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                  5      U l        [        TU ]$  " S0 UD6  g s  snf )Nstem   stageout_indicesout_features)r"   r#    )
rangelenr   stage_names"set_output_features_output_indicespoplistr   super__post_init__)selfkwargsidx	__class__s      ڀ/root/GenerationalWealth/GenerationalWealth/venv/lib/python3.13/site-packages/transformers/models/resnet/configuration_resnet.pyr,   ResNetConfig.__post_init__@   s    "8aT[[IY\]I]@^&_@^se}@^&__//

=$7fjjQ_aeFf 	0 	
 !!2!23'' '`s   Bc                     U R                   U R                  ;  a4  [        SU R                    SSR                  U R                  5       35      eg)z.Check that `layer_types` is correctly defined.zlayer_type=z is not one of ,N)r   r   
ValueErrorjoin)r-   s    r1   validate_layer_type ResNetConfig.validate_layer_typeH   sG    ??$"2"22{4??*;?388TXTdTdKeJfghh 3    )r   r'   )__name__
__module____qualname____firstlineno____doc__
model_typer   r   r*   str__annotations__r   intr   r   tupler   r   r   r   boolr   r,   r7   __static_attributes____classcell__)r0   s   @r1   r   r      s    0 J(/'>K$s)$>L#NC7ML$s)eCHo-4M1=FDIc3h'$.="J"J&+t+%*d*(i ir9   r   N)r>   typingr   huggingface_hub.dataclassesr   backbone_utilsr   configuration_utilsr   utilsr   r   __all__r$   r9   r1   <module>rM      sT    !  . 1 3 # 010i&(8 0i  20if 
r9   