+
    œ°³j  ã                   ó²   € ^ RI Ht ^RIHt ^RIHt ^RIHt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strict)ÚPreTrainedConfig)Úauto_docstring)ÚCONFIG_MAPPINGÚ
AutoConfigzOpenGVLab/InternVL3-1B-hf)Ú
checkpointc                   óš   a 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RtRtR	tR
tRtRtRt^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InternVLVisionConfiga  
projection_dropout (`float`, *optional*, defaults to 0.0):
    Dropout probability for the projection layer.
norm_type (`str`, *optional*, defaults to `"layer_norm"`):
    The type of normalization to use in the encoder. Can be `"layer_norm"` or `"rms_norm"`.
use_mask_token (`bool`, *optional*, defaults to `False`):
    Whether to use a mask token for masked image modeling
use_mean_pooling (`bool`, *optional*, defaults to `True`):
    Whether to mean pool the final hidden states of the patches instead of using the final hidden state of the
    CLS token, before applying the classification head.

Example:

```python
>>> from transformers import InternVLVisionConfig, InternVLVisionModel

>>> # Initializing a InternVLVisionModel OpenGVLab/InternVL3-1B-hf style configuration
>>> configuration = InternVLVisionConfig()

>>> # Initializing a model (with random weights) from the OpenGVLab/InternVL3-1B-hf configuration
>>> model = InternVLVisionModel(configuration)

>>> # Accessing the model configuration
>>> configuration = model.config
```Úinternvl_visionÚvision_configi   Fi   Úgelug        g{®Gáz”?Ú
layer_normg�íµ ÷Æ°>Tgš™™™™™¹?c                ód  <€ \        V P                  \        \        34      '       d   V P                  MV P                  V P                  3V n        \        V P                  \        \        34      '       d   V P                  MV P                  V P                  3V n        \
        SV `  ! R/ VB  R # )N© )Ú
isinstanceÚ
image_sizeÚlistÚtupleÚ
patch_sizeÚsuperÚ__post_init__©ÚselfÚkwargsÚ	__class__s   &,€Út/home/luca/screen-checking/.venv/lib/python3.14/site-packages/transformers/models/internvl/configuration_internvl.pyr   Ú"InternVLVisionConfig.__post_init__L   s…   ø€ ä)¨$¯/©/¼DÄ%¸=×IÒIˆD�OŠOÐPT×P_ÑP_Ðae×apÑapÐOqð 	Œô  *¨$¯/©/¼DÄ%¸=×IÒIˆD�OŠOÐPT×P_ÑP_Ðae×apÑapÐOqð 	Œô 	‰ÒÑ' Ô'ó    c                óÂ  <€ V ^8„  d   Qh/ S[ ;R&   S[ ;R&   S[ ;R&   S[;R&   S[;R&   S[ ;R&   S[;R&   S[S[ ,          ;R&   S[S[ ,          ;R	&   S[S[ ,          ;R
&   S[;R&   S[;R&   S[;R&   S[ S[S[ ,          ,          S[S[ R3,          ,          ;R&   S[ S[S[ ,          ,          S[S[ R3,          ,          ;R&   S[ ;R&   S[;R&   S[;R&   S[;R&   S[;R&   # )é   Úhidden_sizeÚnum_hidden_layersÚnum_attention_headsÚattention_biasÚuse_qk_normÚintermediate_sizeÚ
hidden_actÚhidden_dropout_probÚattention_dropoutÚprojection_dropoutÚinitializer_rangeÚ	norm_typeÚlayer_norm_eps.r   r   Únum_channelsÚuse_mask_tokenÚ use_absolute_position_embeddingsÚlayer_scale_init_valueÚuse_mean_pooling)ÚintÚboolÚstrÚfloatr   r   )ÚformatÚ__classdict__s   "€r   Ú__annotate__Ú!InternVLVisionConfig.__annotate__   sg  ø‡ ‚ ñ@ ÑñA ñB ÑñC ñD Ñ!ñE ñF Ñ ñG ñH ÑñI ñJ Ñ!ñK ñL ÑñM ñN ¡�Ñ*ñO ñP ™s•{Ñ(ñQ ñR ¡�Ñ)ñS ñT Ñ#ñU ñV Ñ!ñW ñX Ñ!ñY ñZ ‘d™3•i•¡%©¨S¨¥/Õ1Ñ>ñ[ ñ\ ‘d™3•i•¡%©¨S¨¥/Õ1Ñ<ñ] ñ^ Ññ_ ñ` Ñ ña ñb '+Ñ1ñc ñd "Ñ'ñe ñf Ñ!òg r   )r   r   )éÀ  r;   )é   r<   ) Ú__name__Ú
__module__Ú__qualname__Ú__firstlineno__Ú__doc__Ú
model_typeÚbase_config_keyr!   r"   r#   r$   r%   r&   r'   r(   r)   r*   r+   r,   r-   r   r   r.   r/   r0   r1   r2   r   Ú__annotate_func__Ú__static_attributes__Ú__classdictcell__Ú__classcell__©r   r8   s   @@r   r
   r
      s‘   ù‡ € ñð4 #€JØ%€Oà€KØÐØ!ÐØ €NØ€KØ!ÐØ€JØ'*ÐØ%(ÐØ&)ÐØ#ÐØ!€IØ!€NØ4>€JØ4<€JØ€LØ €NØ-1Ð$Ø$'ÐØ!Ðõ(÷k … r   r
   c                   óv   a a€ ] tR t^Vt oRtRtR]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V3R ltRtVtV ;t# )ÚInternVLConfiga  
downsample_ratio (`float`, *optional*, defaults to 0.5):
    Factor by which to downsample the image.

Example:

```python
>>> from transformers import InternVLForConditionalGeneration, InternVLConfig

>>> # Initializing a InternVL style configuration
>>> configuration = InternVLConfig()

>>> # Initializing a model (with random weights) from the OpenGVLab/InternVL3-1B-hf configuration
>>> model = InternVLForConditionalGeneration(configuration)

>>> # Accessing the model configuration
>>> configuration = model.config
```ÚinternvlÚtext_configr   NisP é   g      à?r   ÚdefaultTc                ó  <€ \        V P                  \        4      '       d   \        R/ V P                  B V n        MV P                  f   \        4       V n        \        V P                  \        4      '       d^   V P                  P                  RR4      V P                  R&   \        V P                  R,          ,          ! R/ V P                  B V n        M%V P                  f   \        R,          ! 4       V n        \        SV `   ! R/ VB  R # )NrB   Úqwen2r   )	r   r   Údictr
   rL   Úgetr   r   r   r   s   &,€r   r   ÚInternVLConfig.__post_init__y   sÍ   ø€ Ü�d×(Ñ(¬$×/Ò/Ü!5Ñ!K¸×8JÑ8JÑ!KˆDÕØ×ÑÒ'Ü!5Ó!7ˆDÔä�d×&Ñ&¬×-Ò-Ø-1×-=Ñ-=×-AÑ-AÀ,ÐPWÓ-XˆD×Ñ˜\Ñ*Ü-¨d×.>Ñ.>¸|Õ.LÖMÑaÐPT×P`ÑP`ÑaˆDÕØ×ÑÒ%Ü-¨gÖ6Ó8ˆDÔä‰ÒÑ' Ô'r   c                óâ   <€ V ^8„  d   Qh/ S[ S[,          R,          ;R&   S[ S[,          R,          ;R&   S[;R&   S[;R&   S[;R&   S[;R&   S[S[S[,          ,          ;R&   S[;R	&   S[;R
&   # )r    Nr   rL   Úimage_token_idÚimage_seq_lengthÚdownsample_ratioÚprojector_hidden_actÚvision_feature_layerÚvision_feature_select_strategyÚtie_word_embeddings)rQ   r   r3   r6   r5   r   r4   )r7   r8   s   "€r   r9   ÚInternVLConfig.__annotate__V   sœ   ø‡ ‚ ñ2 Ñ*Õ*¨TÕ1Ñ8ñ3 ñ4 Ñ(Õ(¨4Õ/Ñ6ñ5 ñ6 Ñ ñ7 ñ8 Ññ9 ñ: Ñ!ñ; ñ< Ñ&ñ= ñ> ¡¡S¥	�/Ñ.ñ? ñ@ %(Ñ3ñA ñB Ñ$òC r   )rL   r   éÿÿÿÿ)r=   r>   r?   r@   rA   rB   r   r
   Úsub_configsr   rL   rU   rV   rW   rX   rY   rZ   r[   r   rD   rE   rF   rG   rH   s   @@r   rJ   rJ   V   s`   ù‡ € ñð& €JØ  *¨oÐ?SÐT€Kà48€MØ26€KØ €NØÐØ!ÐØ &ÐØ,.ÐØ*3Ð"Ø $Ðõ(÷G … r   rJ   N)Úhuggingface_hub.dataclassesr   Úconfiguration_utilsr   Úutilsr   Úautor   r   r
   rJ   Ú__all__r   r   r   Ú<module>rd      sv   ðõ  /å 3Ý #ß -ñ Ð6Ô7Øô:(Ð+ó :(ó ó 8ð:(ñz Ð6Ô7Øô-(Ð%ó -(ó ó 8ð-(ð` "Ð#3Ð
4‚r   