+
    œ°³jI  ã                   ót   € ^ RI H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	# )
é    )Ústrict)ÚPreTrainedConfig)ÚRopeParameters)Úauto_docstringzgoogle/gemma-7b)Ú
checkpointc                   óÔ   a € ] tR t^t o RtRtR.tRRRRRRRR	R
RRRRR	/tRR.R.3RRR.R.3RR.R.3/tRt	Rt
Rt^t^t^tRtRtRtRtRtRt^ t^t^tRtRtRtRtRtV 3R  ltR!tV tR# )"ÚGemmaConfigaÖ  
use_bidirectional_attention (`bool`, *optional*):
    If True, the model will attend to all text tokens instead of using a causal mask.

```python
>>> from transformers import GemmaModel, GemmaConfig
>>> # Initializing a Gemma gemma-7b style configuration
>>> configuration = GemmaConfig()
>>> # Initializing a model from the gemma-7b style configuration
>>> model = GemmaModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```ÚgemmaÚpast_key_valueszlayers.*.self_attn.q_projÚcolwisezlayers.*.self_attn.k_projzlayers.*.self_attn.v_projzlayers.*.self_attn.o_projÚrowwisezlayers.*.mlp.gate_projzlayers.*.mlp.up_projzlayers.*.mlp.down_projÚembed_tokensÚ	input_idsÚinputs_embedsÚlayersÚhidden_statesÚattention_maskÚnormi è i   i `  é   Úgelu_pytorch_tanhi    g{®Gáz”?g�íµ ÷Æ°>TNFg        c                ó�  <€ V ^8„  d   Qh/ S[ ;R&   S[ ;R&   S[ ;R&   S[ ;R&   S[ ;R&   S[ ;R&   S[ ;R&   S[;R&   S[ ;R	&   S[;R
&   S[;R&   S[;R&   S[ R,          ;R&   S[ S[S[ ,          ,          R,          ;R&   S[ R,          ;R&   S[;R&   S[S[,          R,          ;R&   S[;R&   S[S[ ,          ;R&   S[R,          ;R&   # )é   Ú
vocab_sizeÚhidden_sizeÚintermediate_sizeÚnum_hidden_layersÚnum_attention_headsÚnum_key_value_headsÚhead_dimÚ
hidden_actÚmax_position_embeddingsÚinitializer_rangeÚrms_norm_epsÚ	use_cacheNÚpad_token_idÚeos_token_idÚbos_token_idÚtie_word_embeddingsÚrope_parametersÚattention_biasÚattention_dropoutÚuse_bidirectional_attention)ÚintÚstrÚfloatÚboolÚlistr   Údict)ÚformatÚ__classdict__s   "€Ún/home/luca/screen-checking/.venv/lib/python3.14/site-packages/transformers/models/gemma/configuration_gemma.pyÚ__annotate__ÚGemmaConfig.__annotate__   sX  ø‡ ‚ ñD ÑñE ñF ÑñG ñH Ñ"ñI ñJ ÑñK ñL Ñ!ñM ñN Ñ!ñO ñP ÑñQ ñR Ñ)ñS ñT !Ñ'ñU ñV Ñ#ñW ñX ÑñY ñZ Ññ[ ñ\ ˜•*Ñ ñ] ñ^ ™™S�	•/ DÕ(Ñ,ñ_ ñ` ˜•*Ñ ña ñb Ñ$ñc ñd $¡dÕ*¨TÕ1Ñ8ñe ñf Ñ ñg ñh ™s•{Ñ(ñi ñj "&¨¥Ñ3òk ó    © ) Ú__name__Ú
__module__Ú__qualname__Ú__firstlineno__Ú__doc__Ú
model_typeÚkeys_to_ignore_at_inferenceÚbase_model_tp_planÚbase_model_pp_planr   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__)r4   s   @r5   r	   r	      sø   ø‡ € ñð €JØ#4Ð"5Ðà# YØ# YØ# YØ# YØ  )Ø 	Ø  )ðÐð 	˜+˜¨Ð(9Ð:Ø�OÐ%5Ð6¸Ð8IÐJØ�/Ð" _Ð$5Ð6ðÐð €JØ€KØ"ÐØÐØ!ÐØ!ÐØ€HØ)€JØ#'ÐØ#ÐØ€LØ€IØ €LØ+,€LØ €LØ $ÐØ48€OØ €NØ%(ÐØ/3Ð÷k ƒ r8   r	   N)
Úhuggingface_hub.dataclassesr   Úconfiguration_utilsr   Úmodeling_rope_utilsr   Úutilsr   r	   Ú__all__r9   r8   r5   Ú<module>rK      sG   ðõ. /å 3Ý 1Ý #ñ Ð,Ô-Øô34Ð"ó 34ó ó .ð34ðl ˆ/‚r8   