
    Z j{                     p    S SK Jr  SSKJr  SSKJr  SSKJrJr  \" SS9\ " S	 S
\5      5       5       r	S
/r
g)    )strict   )PreTrainedConfig)auto_docstring   )CONFIG_MAPPING
AutoConfigzgoogle/shieldgemma-2-4b-it)
checkpointc                      ^  \ rS rSr% SrSrSSSS.r\\S.rS	r	\
\-  S	-  \S
'   S	r\
\-  S	-  \S'   Sr\\S'   Sr\\S'   Sr\\S'   Sr\\S'   Sr\\S'   U 4S jrSrU =r$ )ShieldGemma2Config   a~  
tie_word_embeddings (`bool`, *optional*):
    Whether to tie the word embeddings. Defaults to the value of `text_config.tie_word_embeddings` if not set.
mm_tokens_per_image (`int`, *optional*, defaults to 256):
    The number of tokens per image embedding.
boi_token_index (`int`, *optional*, defaults to 255999):
    The begin-of-image token index to wrap the image prompt.
eoi_token_index (`int`, *optional*, defaults to 256000):
    The end-of-image token index to wrap the image prompt.

Example:

```python
>>> from transformers import ShieldGemma2ForConditionalGeneration, ShieldGemma2Config, SiglipVisionConfig, ShieldGemma2TextConfig

>>> # Initializing a Siglip-like vision config
>>> vision_config = SiglipVisionConfig()

>>> # Initializing a ShieldGemma2 Text config
>>> text_config = ShieldGemma2TextConfig()

>>> # Initializing a ShieldGemma2 gemma-3-4b style configuration
>>> configuration = ShieldGemma2Config(vision_config, text_config)

>>> # Initializing a model from the gemma-3-4b style configuration
>>> model = ShieldGemma2TextConfig(configuration)

>>> # Accessing the model configuration
>>> configuration = model.config
```shieldgemma2image_token_indexboi_token_indexeoi_token_index)image_token_idboi_token_ideoi_token_id)text_configvision_configNr   r      mm_tokens_per_imagei i  i   g{Gz?initializer_rangec                   > [        U R                  [        5      (       aU  U R                  R                  SS5      U R                  S'   [        U R                  S      " S0 U R                  D6U l        O U R                  c  [        S   " 5       U l        [        U R
                  [        5      (       aU  U R
                  R                  SS5      U R
                  S'   [        U R
                  S      " S0 U R
                  D6U l        O U R
                  c  [        S   " 5       U l        UR                  S5      c  [        U R
                  SS5      U l        [        TU ]$  " S0 UD6  g )N
model_typesiglip_vision_modelgemma3_texttie_word_embeddingsT )

isinstancer   dictgetr   r   getattrr   super__post_init__)selfkwargs	__class__s     ڌ/root/GenerationalWealth/GenerationalWealth/venv/lib/python3.13/site-packages/transformers/models/shieldgemma2/configuration_shieldgemma2.pyr%    ShieldGemma2Config.__post_init__J   s1   d(($///3/A/A/E/ElTi/jD|,!/0B0B<0P!Q!gTXTfTf!gD'!/0E!F!HDd&&---1-=-=-A-A,P]-^D\*-d.>.>|.LMaPTP`P`aD%-m<>D::+,4'.t/?/?AVX\']D$''    )r   r   r   )__name__
__module____qualname____firstlineno____doc__r   attribute_mapr	   sub_configsr   r!   r   __annotations__r   r   intr   r   r   r   floatr%   __static_attributes____classcell__)r(   s   @r)   r   r      s    >  J-))M
 #-zJK26K((4/648M4**T18"""OS""OS"$s$#u#( (r+   r   N)huggingface_hub.dataclassesr   configuration_utilsr   utilsr   autor   r	   r   __all__r   r+   r)   <module>r=      sH   " / 3 # - 78?() ?(  9?(D  
 r+   