+
    œ°³jÔ  ã                   ó„   € R t ^ 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	.tR
# )zDETR model configuration)Ústrict)Ú%consolidate_backbone_kwargs_to_config)ÚPreTrainedConfig)Úauto_docstring)Ú
AutoConfigzfacebook/detr-resnet-50)Ú
checkpointc                   óÜ   a a€ ] tR t^t oRtRtR]/tR.tRRRRR	R
/t	Rt
^t^dt^tRt^t^tRt^tRtRtRtRtRtRtRtRtRtRtRtRtRt^t ^t!^t"^t#^t$^t%^t&Rt'V 3R lt(V3R lt)Rt*Vt+V ;t,# )Ú
DetrConfigaß  
num_queries (`int`, *optional*, defaults to 100):
    Number of object queries, i.e. detection slots. This is the maximal number of objects
    [`ConditionalDetrModel`] can detect in a single image. For COCO, we recommend 100 queries.
position_embedding_type (`str`, *optional*, defaults to `"sine"`):
    Type of position embeddings to be used on top of the image features. One of `"sine"` or `"learned"`.
dilation (`bool`, *optional*, defaults to `False`):
    Whether to replace stride with dilation in the last convolutional block (DC5). Only supported when
    `use_timm_backbone` = `True`.

Examples:

```python
>>> from transformers import DetrConfig, DetrModel

>>> # Initializing a DETR facebook/detr-resnet-50 style configuration
>>> configuration = DetrConfig()

>>> # Initializing a model (with random weights) from the facebook/detr-resnet-50 style configuration
>>> model = DetrModel(configuration)

>>> # Accessing the model configuration
>>> configuration = model.config
```ÚdetrÚbackbone_configÚpast_key_valuesÚhidden_sizeÚd_modelÚnum_attention_headsÚencoder_attention_headsÚnum_hidden_layersÚencoder_layersNi   g        TÚrelué   gš™™™™™¹?g{®Gáz”?g      ð?FÚsinec                óZ  <€ VP                  R / 4      pRVP                  RV P                  4      RRRRRVP                  R. RO4      /pV P                  '       d   VP                  R^4      VR&   \        RRV P                  R	R
RRRRR./RV/VB w  V n        p\
        SV `  ! R/ VB  R# )Úbackbone_kwargsÚnum_channelsÚfeatures_onlyTÚuse_pretrained_backboneFÚout_indicesÚoutput_strider   Údefault_backboneÚresnet50Údefault_config_typeÚresnetÚdefault_config_kwargsÚout_featuresÚstage4Útimm_default_kwargsN)é   é   é   é   © )Úgetr   Údilationr   r   ÚsuperÚ__post_init__)ÚselfÚkwargsr   r$   Ú	__class__s   &,  €Úl/home/luca/screen-checking/.venv/lib/python3.14/site-packages/transformers/models/detr/configuration_detr.pyr-   ÚDetrConfig.__post_init__\   sÝ   ø€ Ø Ÿ*™*Ð%6¸Ó;ˆà˜O×/Ñ/°À×@QÑ@QÓRØ˜TØ% uØ˜?×.Ñ.¨}ºlÓKð	
Ðð �=�=ˆ=Ø3B×3FÑ3FÀÐXZÓ3[Ð Ñ0ä'Lñ (
Ø ×0Ñ0ð(
à'ð(
ð !)ð(
ð $2°H°:Ð">ð	(
ð
 !4ð(
ð ñ(
Ñ$ˆÔ˜fô 	‰ÒÑ' Ô'ó    c                óð  <€ V ^8„  d   Qh/ S[ S[,          R,          ;R&   S[;R&   S[;R&   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[S[,          ;R&   S[S[,          ;R&   S[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&   S[;R&   # ) r&   Nr   r   Únum_queriesr   Úencoder_ffn_dimr   Údecoder_layersÚdecoder_ffn_dimÚdecoder_attention_headsÚencoder_layerdropÚdecoder_layerdropÚis_encoder_decoderÚactivation_functionr   ÚdropoutÚattention_dropoutÚactivation_dropoutÚinit_stdÚinit_xavier_stdÚauxiliary_lossÚposition_embedding_typer+   Ú
class_costÚ	bbox_costÚ	giou_costÚmask_loss_coefficientÚdice_loss_coefficientÚbbox_loss_coefficientÚgiou_loss_coefficientÚeos_coefficient)Údictr   ÚintÚfloatÚboolÚstr)ÚformatÚ__classdict__s   "€r1   Ú__annotate__ÚDetrConfig.__annotate__   sæ  ø‡ ‚ ñJ Ñ,Õ,¨tÕ3Ñ: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\ ™s•{Ñ(ñ] ñ^ ™s•{Ñ(ñ_ ñ` Ñ#ña ñb Ñ%ñc ñd Ññe ñf ‘S�[Ññg ñh ™s•{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~ Ñ"ñ ñ@ Ñ"ñA ñB Ñ"ñC ñD Ñ òE r3   )r   )-Ú__name__Ú
__module__Ú__qualname__Ú__firstlineno__Ú__doc__Ú
model_typer   Úsub_configsÚkeys_to_ignore_at_inferenceÚattribute_mapr   r   r5   r   r6   r   r7   r8   r9   r:   r;   r<   r=   r   r>   r?   r@   rA   rB   rC   rD   r+   rE   rF   rG   rH   rI   rJ   rK   rL   r-   Ú__annotate_func__Ú__static_attributes__Ú__classdictcell__Ú__classcell__)r0   rS   s   @@r1   r	   r	      sî   ù‡ € ñð2 €JØ$ jÐ1€KØ#4Ð"5Ðà�yØÐ8ØÐ-ð€Mð 7;€OØ€LØ€KØ€NØ€OØ#$ÐØ€NØ€OØ#$ÐØ%(ÐØ%(ÐØ#ÐØ%ÐØ€GØ€GØ%(ÐØ&)ÐØ€HØ €OØ €NØ#)ÐØ€HØ€JØ€IØ€IØ!"ÐØ!"ÐØ!"ÐØ!"ÐØ €Oõ(÷I … r3   r	   N)rZ   Úhuggingface_hub.dataclassesr   Úbackbone_utilsr   Úconfiguration_utilsr   Úutilsr   Úautor   r	   Ú__all__r)   r3   r1   Ú<module>ri      sP   ðñ å .å CÝ 3Ý #Ý ñ Ð4Ô5ØôU(Ð!ó U(ó ó 6ðU(ðp ˆ.‚r3   