+
    œ°³jE  ã                   óº   € ^ RI Ht ^ 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R7      ] ! R
 R]4      4       4       tRR	.tR# )é    )ÚAny)Ústrict)ÚPreTrainedConfig)ÚRopeParameters)Úauto_docstringzgoogle/t5_gemma_module-7b)Ú
checkpointc                   ó   a 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R tRtR!tR"t Rt!V 3R# lt"R$ t#V3R% lt$R&t%Vt&V ;t'# )'ÚT5GemmaModuleConfiga  
query_pre_attn_scalar (`float`, *optional*, defaults to 256):
    scaling factor used on the attention scores
final_logit_softcapping (`float`, *optional*, defaults to 30.0):
    scaling factor when applying tanh softcapping on the logits.
attn_logit_softcapping (`float`, *optional*, defaults to 50.0):
    scaling factor when applying tanh softcapping on the attention scores.

```python
>>> from transformers import T5GemmaModuleModel, T5GemmaModuleConfig
>>> # Initializing a T5GemmaModule t5_gemma_module-7b style configuration
>>> configuration = T5GemmaModuleConfig()
>>> # Initializing a model from the t5_gemma_module-7b style configuration
>>> model = T5GemmaModuleModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```Út5_gemma_moduleÚ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normé è i 	  i $  é   Úgelu_pytorch_tanhi    g{®Gáz”?g�íµ ÷Æ°>TNFç        i   g      >@g      I@c                óä   <€ V P                   fL   \        V P                  4       Uu. uF&  p\        V^,           ^,          4      '       d   RMRNK(  	  upV n         \        SV `  ! R/ VB  R # u upi )NÚsliding_attentionÚfull_attention© )Úlayer_typesÚrangeÚnum_hidden_layersÚboolÚsuperÚ__post_init__)ÚselfÚkwargsÚiÚ	__class__s   &, €Úr/home/luca/screen-checking/.venv/lib/python3.14/site-packages/transformers/models/t5gemma/configuration_t5gemma.pyr#   Ú!T5GemmaModuleConfig.__post_init___   si   ø€ Ø×ÑÒ#äX]Ð^b×^tÑ^tÔXuó ÙXuÐST¤t¨Q°­U°a­K×'8Ò'8Ñ#Ð>NÒNÑXuñ ˆDÔô 	‰ÒÑ' Ô'ùò	 s   §,A-c                ó–   € V P                   V P                  ,          ^ 8w  d'   \        RV P                    RV P                   R24      hR# )zOPart of `@strict`-powered validation. Validates the architecture of the config.zThe hidden size (z6) is not a multiple of the number of attention heads (z).N)Úhidden_sizeÚnum_attention_headsÚ
ValueError)r$   s   &r(   Úvalidate_architectureÚ)T5GemmaModuleConfig.validate_architectureg   sS   € à×Ñ˜d×6Ñ6Õ6¸!Ô;ÜØ# D×$4Ñ$4Ð#5ð 6Ø×2Ñ2Ð3°2ð7óð ñ <ó    c                ó  <€ V ^8„  d   Qh/ S[ ;R&   S[ ;R&   S[ ;R&   S[ ;R&   S[ ;R&   S[ ;R&   S[ ;R&   S[;R&   S[ ;R	&   S[;R
&   S[;R&   S[;R&   S[ R,          ;R&   S[ S[S[ ,          ,          R,          ;R&   S[ R,          ;R&   S[;R&   S[S[,          R,          ;R&   S[;R&   S[ S[,          R,          ;R&   S[ ;R&   S[ R,          ;R&   S[S[,          R,          ;R&   S[R,          ;R&   S[R,          ;R&   S[;R&   # )é   Ú
vocab_sizer+   Úintermediate_sizer    r,   Únum_key_value_headsÚhead_dimÚhidden_activationÚmax_position_embeddingsÚinitializer_rangeÚrms_norm_epsÚ	use_cacheNÚpad_token_idÚeos_token_idÚbos_token_idÚtie_word_embeddingsÚrope_parametersÚattention_biasÚattention_dropoutÚquery_pre_attn_scalarÚsliding_windowr   Úfinal_logit_softcappingÚattn_logit_softcappingÚ
is_decoder)ÚintÚstrÚfloatr!   Úlistr   Údict)ÚformatÚ__classdict__s   "€r(   Ú__annotate__Ú T5GemmaModuleConfig.__annotate__   s¹  ø‡ ‚ ñL ÑñM ñN ÑñO ñP Ñ!ñQ ñR ÑñS ñT Ñ ñU ñV Ñ ñW ñX ÑñY ñZ Ñ0ñ[ ñ\ !Ñ'ñ] ñ^ Ñ#ñ_ ñ` Ñña ñb Ññc ñd ˜•*Ñ ñe ñf ™™S�	•/ DÕ(Ñ,ñg ñh ˜•*Ñ ñi ñj Ñ$ñk ñl $¡dÕ*¨TÕ1Ñ8ñm ñn Ñ ño ñp ™U•{ TÕ)Ñ/ñq ñr Ñ$ñs ñt ˜$•JÑ%ñu ñv ‘c•˜TÕ!Ñ(ñw ñx # T�\Ñ0ñy ñz " D�LÑ/ñ{ ñ~ Ñò r0   )r   )(Ú__name__Ú
__module__Ú__qualname__Ú__firstlineno__Ú__doc__Ú
model_typeÚkeys_to_ignore_at_inferenceÚbase_model_tp_planÚbase_model_pp_planr3   r+   r4   r    r,   r5   r6   r7   r8   r9   r:   r;   r<   r=   r>   r?   r@   rA   rB   rC   rD   r   rE   rF   rG   r#   r.   Ú__annotate_func__Ú__static_attributes__Ú__classdictcell__Ú__classcell__©r'   rN   s   @@r(   r
   r
      s  ù‡ € ñð$ #€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0ÐØ#'ÐØ#ÐØ€LØ€IØ €LØ+,€LØ €LØ $ÐØ48€OØ €NØ,/ÐØ!$ÐØ!%€NØ$(€KØ,0ÐØ+/Ðà€Jõ(ò÷S … r0   r
   c                   óx   a a€ ] tR t^pt oRtRtR.tR]R]/tRt	Rt
RtRtRtRtRtR	tV 3R
 ltV3R ltRtVtV ;t# )ÚT5GemmaConfiga   
encoder (`Union[T5GemmaModuleConfig, dict]`, optional, *optional*):
    Configuration for the encoder.
decoder (`Union[T5GemmaModuleConfig, dict]`, optional, *optional*):
    Configuration for the decoder.

Example:

```python
>>> from transformers import T5GemmaConfig, T5GemmaModel
>>> t5gemma_config = T5GemmaConfig.from_pretrained("google/t5gemma-2b-2b-prefixlm-it")
>>> model = T5GemmaModel(t5gemma_config)
```Út5gemmar   ÚencoderÚdecoderNTr   r   c                ó¸  <€ \        V P                  \        4      '       d   \        R/ V P                  B V n        MV P                  f   \        4       V n        \        V P                  \        4      '       d   \        R/ V P                  B V n        MV P                  f   \        4       V n        RV P                  n        V P                  V P                  n        V P                  V P                  n        RV P                  n        RV P                  n        V P                  V P                  n        V P                  V P                  n        V P                  P                  V P                  n
        VP                  RV P                  P                  4      V n        R F#  pW!9  g   K  \        V P                  V4      W&   K%  	  \        SV `<  ! R/ VB  R # )NFTr9   r   )r>   r<   r=   )Ú
isinstancerb   rL   r
   rc   rG   Údropout_raterB   r;   r+   Úcross_attention_hidden_sizeÚpopr9   Úgetattrr"   r#   )r$   r%   Úspecial_token_keyr'   s   &, €r(   r#   ÚT5GemmaConfig.__post_init__Ž   sK  ø€ Ü�d—l‘l¤D×)Ò)Ü.Ñ>°·±Ñ>ˆD�LØ�\‰\Ò!Ü.Ó0ˆDŒLä�d—l‘l¤D×)Ò)Ü.Ñ>°·±Ñ>ˆD�LØ�\‰\Ò!Ü.Ó0ˆDŒLà"'ˆ�‰ÔØ$(×$5Ñ$5ˆ�‰Ô!Ø)-×)?Ñ)?ˆ�‰Ô&à"&ˆ�‰ÔØ!%ˆ�‰ÔØ$(×$5Ñ$5ˆ�‰Ô!Ø)-×)?Ñ)?ˆ�‰Ô&Ø37·<±<×3KÑ3Kˆ�‰Ô0à!'§¡Ð,?ÀÇÁ×A_ÑA_Ó!`ˆÔã!QÐØ Ö.Ü,3°D·L±LÐBSÓ,T�Ó)ñ "Rô 	‰ÒÑ' Ô'r0   c                ó  <€ V ^8„  d   Qh/ S[ S[S[S[3,          ,          R,          ;R&   S[ S[S[S[3,          ,          R,          ;R&   S[;R&   S[S[,          ;R&   S[S[,          ;R&   S[S[,          ;R&   S[;R&   S[;R	&   # )
r2   Nrb   rc   Úis_encoder_decoderrf   Úclassifier_dropout_raterB   r?   r3   )r
   rL   r   r!   rH   rJ   )rM   rN   s   "€r(   rO   ÚT5GemmaConfig.__annotate__p   s�   ø‡ ‚ ñ* !¡4©©S¨¥>Õ1°DÕ8Ñ?ñ+ ñ, !¡4©©S¨¥>Õ1°DÕ8Ñ?ñ- ñ. Ñ#ñ/ ñ0 ™•+Ñ#ñ1 ñ2 !¡5�[Ñ.ñ3 ñ4 ™s•{Ñ(ñ5 ñ6 Ñ$ñ7 ñ8 Ñò9 r0   )rc   rb   r9   )rQ   rR   rS   rT   rU   rV   rW   r
   Úsub_configsrb   rc   rm   rf   rn   rB   r?   r3   r#   rZ   r[   r\   r]   r^   s   @@r(   r`   r`   p   sb   ù‡ € ñð €JØ#4Ð"5ÐØÐ1°9Ð>QÐR€Kà;?€GØ;?€GØ#ÐØ #€LØ+.ÐØ%(ÐØ $ÐØ€Jõ(÷= … r0   r`   N)Útypingr   Úhuggingface_hub.dataclassesr   Úconfiguration_utilsr   Úmodeling_rope_utilsr   Úutilsr   r
   r`   Ú__all__r   r0   r(   Ú<module>rw      s|   ðõ* å .å 3Ý 1Ý #ñ Ð6Ô7ØôMÐ*ó Mó ó 8ðMñ` Ð6Ô7Øô7(Ð$ó 7(ó ó 8ð7(ðt Ð1Ð
2‚r0   