
    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TextNet model configuration    )strict   )BackboneConfigMixin)PreTrainedConfig)auto_docstringzczczup/textnet-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\\   \\\4   -  \-  \S'   Sr\S-  \S'   Sr\S-  \S'   Sr\\   \\S4   -  \S'   Sr\\S'   Sr\\S'   Sr\\   S-  \S'   Sr\\   S-  \S'   U 4S jrSrU =r$ )TextNetConfig   a/  
stem_kernel_size (`int`, *optional*, defaults to 3):
    The kernel size for the initial convolution layer.
stem_stride (`int`, *optional*, defaults to 2):
    The stride for the initial convolution layer.
stem_num_channels (`int`, *optional*, defaults to 3):
    The num of channels in input for the initial convolution layer.
stem_out_channels (`int`, *optional*, defaults to 64):
    The num of channels in out for the initial convolution layer.
stem_act_func (`str`, *optional*, defaults to `"relu"`):
    The activation function for the initial convolution layer.
conv_layer_kernel_sizes (`list[list[list[int]]]`, *optional*):
    A list of stage-wise kernel sizes. If `None`, defaults to:
    `[[[3, 3], [3, 3], [3, 3]], [[3, 3], [1, 3], [3, 3], [3, 1]], [[3, 3], [3, 3], [3, 1], [1, 3]], [[3, 3], [3, 1], [1, 3], [3, 3]]]`.
conv_layer_strides (`list[list[int]]`, *optional*):
    A list of stage-wise strides. If `None`, defaults to:
    `[[1, 2, 1], [2, 1, 1, 1], [2, 1, 1, 1], [2, 1, 1, 1]]`.

Examples:

```python
>>> from transformers import TextNetConfig, TextNetBackbone

>>> # Initializing a TextNetConfig
>>> configuration = TextNetConfig()

>>> # Initializing a model (with random weights)
>>> model = TextNetBackbone(configuration)

>>> # Accessing the model configuration
>>> configuration = model.config
```textnetr   stem_kernel_size   stem_stridestem_num_channels@   stem_out_channelsrelustem_act_func)  r   
image_sizeNconv_layer_kernel_sizesconv_layer_strides)r   r         i   .hidden_sizesgh㈵>batch_norm_epsg{Gz?initializer_range_out_features_out_indicesc                   > U R                   c8  SS/SS/SS//SS/SS/SS/SS//SS/SS/SS/SS//SS/SS/SS/SS///U l         U R                  c  / SQ/ SQ/ SQ/ SQ/U l        U R                    Vs/ s H  n[        U5      PM     snU l        S/[	        S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 s  snf )Nr      )r!   r   r!   )r   r!   r!   r!   stem   stageout_indicesout_features)r%   r&    )
r   r   lendepthsrangestage_names"set_output_features_output_indicespopsuper__post_init__)selfkwargslayeridx	__class__s       ڂ/root/GenerationalWealth/GenerationalWealth/venv/lib/python3.13/site-packages/transformers/models/textnet/configuration_textnet.pyr/   TextNetConfig.__post_init__K   s9   ''/Q!Q!Q(Q!Q!Q!Q0Q!Q!Q!Q0Q!Q!Q!Q0	,D( ""*'0,l&[D#/3/K/KL/Kes5z/KL"8a&Lse}&LL//

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 	'' M&Ls   5C<"D)r   r   r)   r+   )__name__
__module____qualname____firstlineno____doc__
model_typer   int__annotations__r   r   r   r   strr   listtupler   r   r   r   floatr   r   r   r/   __static_attributes____classcell__)r4   s   @r5   r
   r
      s    B JcKssM34>JS	E#s(O+c1>+/TD[/&*t*0GL$s)eCHo-G NE #u#&*M49t#*%)L$s)d")( (    r
   N)r;   huggingface_hub.dataclassesr   backbone_utilsr   configuration_utilsr   utilsr   r
   __all__r'   rE   r5   <module>rK      sQ    " . 1 3 # 01B(')9 B(  2B(J 
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