
    Z j                     d    S 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SqueezeBERT model configuration    )strict   )PreTrainedConfig)auto_docstringzsqueezebert/squeezebert-uncased)
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\\S'   Sr\\S'   Sr\\S'   Sr\S-  \S'   Sr\S-  \S'   Sr\\\   -  S-  \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#g)+SqueezeBertConfig   a  
q_groups (`int`, *optional*, defaults to 4):
    The number of groups in Q layer.
k_groups (`int`, *optional*, defaults to 4):
    The number of groups in K layer.
v_groups (`int`, *optional*, defaults to 4):
    The number of groups in V layer.
post_attention_groups (`int`, *optional*, defaults to 1):
    The number of groups in the first feed forward network layer.
intermediate_groups (`int`, *optional*, defaults to 4):
    The number of groups in the second feed forward network layer.
output_groups (`int`, *optional*, defaults to 4):
    The number of groups in the third feed forward network layer.

Examples:

```python
>>> from transformers import SqueezeBertConfig, SqueezeBertModel

>>> # Initializing a SqueezeBERT configuration
>>> configuration = SqueezeBertConfig()

>>> # Initializing a model (with random weights) from the configuration above
>>> model = SqueezeBertModel(configuration)

>>> # Accessing the model configuration
>>> configuration = model.config
```
squeezeberti:w  
vocab_sizei   hidden_size   num_hidden_layersnum_attention_headsi   intermediate_sizegelu
hidden_actg?hidden_dropout_probattention_probs_dropout_probi   max_position_embeddings   type_vocab_sizeg{Gz?initializer_rangeg-q=layer_norm_epsr   Npad_token_idbos_token_ideos_token_idembedding_size   q_groupsk_groupsv_groups   post_attention_groupsintermediate_groupsoutput_groupsTtie_word_embeddings )$__name__
__module____qualname____firstlineno____doc__
model_typer   int__annotations__r   r   r   r   r   strr   floatr   r   r   r   r   r   r   r   listr   r    r!   r"   r$   r%   r&   r'   bool__static_attributes__r(       ڊ/root/GenerationalWealth/GenerationalWealth/venv/lib/python3.13/site-packages/transformers/models/squeezebert/configuration_squeezebert.pyr	   r	      s   < JJKs!!!s!J'**03 %#+3#&S&OS#u#!NE! L#* #L#*#+/L#S	/D(/NCHcHcHc!"3"  M3 $$r6   r	   N)	r-   huggingface_hub.dataclassesr   configuration_utilsr   utilsr   r	   __all__r(   r6   r7   <module>r<      sH    & . 3 # <=7%( 7%  >7%t 
r6   