+
    œ°³j  ã                   ól   € R t ^ RIHt ^RIHt ^RIHt ]! RR7      ] ! R R]4      4       4       tR.tR# )	zSqueezeBERT model configuration)Ústrict)ÚPreTrainedConfig)Úauto_docstringzsqueezebert/squeezebert-uncased)Ú
checkpointc                   ó�   a € ] tR t^t o RtRtRtRt^t^t	Rt
RtRtRtRt^tR	tR
t^ tRtRtRt^t^t^t^t^t^tRtV 3R ltRtV tR# )ÚSqueezeBertConfigaÚ  
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
```
Úsqueezeberti:w  i   i   Úgelugš™™™™™¹?i   g{®Gáz”?gê-�™—q=NTc                ó˜  <€ V ^8„  d   Qh/ S[ ;R&   S[ ;R&   S[ ;R&   S[ ;R&   S[ ;R&   S[;R&   S[S[ ,          ;R&   S[S[ ,          ;R&   S[ ;R	&   S[ ;R
&   S[;R&   S[;R&   S[ R,          ;R&   S[ R,          ;R&   S[ S[S[ ,          ,          R,          ;R&   S[ ;R&   S[ ;R&   S[ ;R&   S[ ;R&   S[ ;R&   S[ ;R&   S[ ;R&   S[;R&   # )é   Ú
vocab_sizeÚhidden_sizeÚnum_hidden_layersÚnum_attention_headsÚintermediate_sizeÚ
hidden_actÚhidden_dropout_probÚattention_probs_dropout_probÚmax_position_embeddingsÚtype_vocab_sizeÚinitializer_rangeÚlayer_norm_epsNÚpad_token_idÚbos_token_idÚeos_token_idÚembedding_sizeÚq_groupsÚk_groupsÚv_groupsÚpost_attention_groupsÚintermediate_groupsÚoutput_groupsÚtie_word_embeddings)ÚintÚstrÚfloatÚlistÚbool)ÚformatÚ__classdict__s   "€Úz/home/luca/screen-checking/.venv/lib/python3.14/site-packages/transformers/models/squeezebert/configuration_squeezebert.pyÚ__annotate__ÚSqueezeBertConfig.__annotate__   s{  ø‡ ‚ ñF ÑñG ñH ÑñI ñJ ÑñK ñL Ñ!ñM ñN Ñ!ñO ñP ÑñQ ñR ¡�Ñ*ñS ñT #(©#¥+Ñ3ñU ñV !Ñ&ñW ñX ÑñY ñZ Ñ#ñ[ ñ\ Ñ!ñ] ñ^ ˜•*Ñ ñ_ ñ` ˜•*Ñ#ña ñb ™™S�	•/ DÕ(Ñ/ñc ñd Ññe ñf Ññg ñh Ññi ñj Ññk ñl Ñ"ñm ñn Ñ ño ñp Ññq ñr Ñ$òs ó    © ) Ú__name__Ú
__module__Ú__qualname__Ú__firstlineno__Ú__doc__Ú
model_typer   r   r   r   r   r   r   r   r   r   r   r   r   r   r   r   r   r   r   r   r    r!   r"   Ú__annotate_func__Ú__static_attributes__Ú__classdictcell__)r)   s   @r*   r   r      s–   ø‡ € ñð< €Jà€JØ€KØÐØ!ÐØ!ÐØ€JØ'*ÐØ03Ð Ø#&ÐØ€OØ#ÐØ!€NØ €LØ#€LØ+/€LØ€NØ€HØ€HØ€HØ!"ÐØ ÐØ€MØ $Ð÷s ƒ r-   r   N)	r3   Úhuggingface_hub.dataclassesr   Úconfiguration_utilsr   Úutilsr   r   Ú__all__r.   r-   r*   Ú<module>r<      sH   ðñ &å .å 3Ý #ñ Ð<Ô=Øô7%Ð(ó 7%ó ó >ð7%ðt Ð
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