+
    œ°³jþ  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UDOP model configuration)Ústrict)ÚPreTrainedConfig)Úauto_docstringzmicrosoft/udop-large)Ú
checkpointc                   óÆ   a a€ ] tR t^t oRtRtR.tRRRRRR	/tR
tRt	^@t
Rt^tRt^t^ t^€tRtRtRtRtRtRtRt^ t^tRt^àt^t^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
UdopConfigac  
relative_attention_num_buckets (`int`, *optional*, defaults to 32):
    The number of buckets to use for each attention layer.
relative_attention_max_distance (`int`, *optional*, defaults to 128):
    The maximum distance of the longer sequences for the bucket separation.
relative_bias_args (`list[dict]`, *optional*, defaults to `[{'type': '1d'}, {'type': 'horizontal'}, {'type': 'vertical'}]`):
    A list of dictionaries containing the arguments for the relative bias layers.
feed_forward_proj (`string`, *optional*, defaults to `"relu"`):
    Type of feed forward layer to be used. Should be one of `"relu"` or `"gated-gelu"`. Udopv1.1 uses the
    `"gated-gelu"` feed forward projection. Original Udop uses `"relu"`.
max_2d_position_embeddings (`int`, *optional*, defaults to 1024):
    The maximum absolute position embeddings for relative position encoding.
ÚudopÚpast_key_valuesÚhidden_sizeÚd_modelÚnum_attention_headsÚ	num_headsÚnum_hidden_layersÚ
num_layersi±�  i   i   Ngš™™™™™¹?g�íµ ÷Æ°>g      ð?ÚreluTFc                ó`  <€ V P                   f   RR/RR/RR/.V n         V P                  e   V P                  MV P                  V n        V P                  P	                  R4      pVR	,          V n        V^ ,          R8H  V n        VP                  RR 4       RV n        \        SV `(  ! R
/ VB  R # )NÚtypeÚ1dÚ
horizontalÚverticalÚ-ÚgatedÚtie_word_embeddingsTéÿÿÿÿ© )Úrelative_bias_argsÚnum_decoder_layersr   Úfeed_forward_projÚsplitÚdense_act_fnÚis_gated_actÚpopr   ÚsuperÚ__post_init__)ÚselfÚkwargsÚact_infoÚ	__class__s   &, €Úl/home/luca/screen-checking/.venv/lib/python3.14/site-packages/transformers/models/udop/configuration_udop.pyr#   ÚUdopConfig.__post_init__E   s±   ø€ Ø×"Ñ"Ò*Ø(.° ~¸ÀÐ7MÐPVÐXbÐOcÐ&dˆDÔ#ð (,×'>Ñ'>Ò'JˆD×#Ò#ÐPT×P_ÑP_ð 	Ôð ×)Ñ)×/Ñ/°Ó4ˆØ$ R�LˆÔØ$ Q�K¨7Ñ2ˆÔà�
‰
Ð(¨$Ô/Ø#'ˆÔ Ü‰ÒÑ' Ô'ó    c                óÊ   € V P                   P                  R4      p\        V4      ^8”  d   V^ ,          R8w  g   \        V4      ^8”  d   \        RV P                    R24      hR# )zOPart of `@strict`-powered validation. Validates the architecture of the config.r   r   z`feed_forward_proj`: z¨ is not a valid activation function of the dense layer.Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. 'gated-gelu' or 'relu'N)r   r   ÚlenÚ
ValueError)r$   r&   s   & r(   Úvalidate_architectureÚ UdopConfig.validate_architectureU   sf   € à×)Ñ)×/Ñ/°Ó4ˆÜˆx‹=˜1Ô ¨!¥°Ô!7¼3¸x»=È1Ô;LÜØ'¨×(>Ñ(>Ð'?ð @)ð )óð ñ <Mr*   c                óJ  <€ V ^8„  d   Qh/ S[ ;R&   S[ ;R&   S[ ;R&   S[ ;R&   S[ ;R&   S[ R,          ;R&   S[ ;R&   S[ ;R	&   S[ ;R
&   S[S[,          R,          ;R&   S[S[ ,          ;R&   S[;R&   S[;R&   S[;R&   S[;R&   S[;R&   S[ R,          ;R&   S[ S[S[ ,          ,          R,          ;R&   S[ ;R&   S[ S[S[ ,          ,          S[S[ S[ 3,          ,          ;R&   S[ S[S[ ,          ,          S[S[ S[ 3,          ,          ;R&   S[ ;R&   S[;R&   S[;R&   S[;R&   # )é   Ú
vocab_sizer   Úd_kvÚd_ffr   Nr   r   Úrelative_attention_num_bucketsÚrelative_attention_max_distancer   Údropout_rateÚlayer_norm_epsilonÚinitializer_factorr   Úis_encoder_decoderÚ	use_cacheÚpad_token_idÚeos_token_idÚmax_2d_position_embeddingsÚ
image_sizeÚ
patch_sizeÚnum_channelsÚ
is_decoderÚadd_cross_attentionr   )ÚintÚlistÚdictÚfloatÚstrÚboolÚtuple)ÚformatÚ__classdict__s   "€r(   Ú__annotate__ÚUdopConfig.__annotate__   s±  ø‡ ‚ ñ* Ññ+ ñ, Ññ- ñ. �Nñ/ ñ0 Ññ1 ñ2 Ññ3 ñ4 ˜d�
Ñ)ñ5 ñ6 Ññ7 ñ8 %(Ñ,ñ9 ñ: &)Ñ.ñ; ñ< ™T�
 TÕ)Ñ0ñ= ñ> ™#•+Ñ#ñ? ñ@ Ñ$ñA ñB Ñ#ñC ñD Ñ#ñE ñF Ñ#ñG ñH ÑñI ñJ ˜•*Ñ ñK ñL ™™S�	•/ DÕ(Ñ,ñM ñN !$Ñ*ñO ñP ‘d™3•i•¡%©©S¨¥/Õ1Ñ7ñQ ñR ‘d™3•i•¡%©©S¨¥/Õ1Ñ6ñS ñT ÑñU ñV ÑñW ñX Ñ%ñY ñZ Ñ$ò[ r*   )r   r    r   r   r   )'Ú__name__Ú
__module__Ú__qualname__Ú__firstlineno__Ú__doc__Ú
model_typeÚkeys_to_ignore_at_inferenceÚattribute_mapr2   r   r3   r4   r   r   r   r5   r6   r   r7   r8   r9   r   r:   r;   r<   r=   r>   r?   r@   rA   rB   rC   r   r#   r.   Ú__annotate_func__Ú__static_attributes__Ú__classdictcell__Ú__classcell__)r'   rL   s   @@r(   r   r      sÉ   ù‡ € ñð €JØ#4Ð"5ÐØ" IÐ/DÀkÐSfÐhtÐu€Mà€JØ€GØ€DØ€DØ€JØ%)ÐØ€IØ*,Ð"Ø+.Ð#Ø,0ÐØ #€LØ $ÐØ #ÐØ#ÐØ#ÐØ€IØ €LØ+,€LØ&*ÐØ47€JØ46€JØ€LØ€JØ %ÐØ $Ðõ(ò ÷ … r*   r   N)	rS   Úhuggingface_hub.dataclassesr   Úconfiguration_utilsr   Úutilsr   r   Ú__all__r   r*   r(   Ú<module>r_      sJ   ðñ å .å 3Ý #ñ Ð1Ô2ØôEÐ!ó Eó ó 3ðEðP ˆ.‚r*   