+
    œ°³j0  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DeBERTa model configuration)Ústrict)ÚPreTrainedConfig)Úauto_docstringzmicrosoft/deberta-base)Ú
checkpointc                   ó¢   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^ tR	tR
tRtRt^ 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DebertaConfiga�  
relative_attention (`bool`, *optional*, defaults to `False`):
    Whether use relative position encoding.
max_relative_positions (`int`, *optional*, defaults to -1):
    The range of relative positions `[-max_position_embeddings, max_position_embeddings]`. Use the same value
    as `max_position_embeddings`.
position_biased_input (`bool`, *optional*, defaults to `True`):
    Whether add absolute position embedding to content embedding.
pos_att_type (`list[str]`, *optional*):
    The type of relative position attention, it can be a combination of `["p2c", "c2p"]`, e.g. `["p2c"]`,
    `["p2c", "c2p"]`.
pooler_dropout (`float`, *optional*, defaults to `0`):
    Dropout rate in the pooler module.
pooler_hidden_act (`str`, *optional*, defaults to `"gelu"`):
    Activation function used in the dropout module.
legacy (`bool`, *optional*, defaults to `True`):
    Whether or not the model should use the legacy `LegacyDebertaOnlyMLMHead`, which does not work properly
    for mask infilling tasks.

Example:

```python
>>> from transformers import DebertaConfig, DebertaModel

>>> # Initializing a DeBERTa microsoft/deberta-base style configuration
>>> configuration = DebertaConfig()

>>> # Initializing a model (with random weights) from the microsoft/deberta-base style configuration
>>> model = DebertaModel(configuration)

>>> # Accessing the model configuration
>>> configuration = model.config
```ÚdebertaiYÄ  i   i   Úgelugš™™™™™¹?i   g{®Gáz”?gH¯¼šò×z>FNTg        c                óJ  <€ \        V P                  \        4      '       dL   V P                  P                  4       P	                  R 4       Uu. uF  q"P                  4       NK  	  upV n        VP                  RV P                  4      V n        \        SV `(  ! R/ VB  R# u upi )Ú|Úpooler_hidden_sizeN© )Ú
isinstanceÚpos_att_typeÚstrÚlowerÚsplitÚstripÚgetÚhidden_sizer   ÚsuperÚ__post_init__)ÚselfÚkwargsÚxÚ	__class__s   &, €Úr/home/luca/screen-checking/.venv/lib/python3.14/site-packages/transformers/models/deberta/configuration_deberta.pyr   ÚDebertaConfig.__post_init__U   s~   ø€ ä�d×'Ñ'¬×-Ò-Ø48×4EÑ4E×4KÑ4KÓ4M×4SÑ4SÐTWÔ4XÓ YÑ4X¨q§¡¦Ñ4XÑ YˆDÔà"(§*¡*Ð-AÀ4×CSÑCSÓ"TˆÔÜ‰ÒÑ' Ô'ùò !Zs   ÁB c                óÖ  <€ V ^8„  d   Qh/ 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[;R&   S[;R&   S[ ;R&   S[ R,          ;R&   S[ R,          ;R&   S[ S[S[ ,          ,          R,          ;R&   S[;R&   S[S[S[,          ,          R,          ;R&   S[S[ ,          ;R&   S[;R&   S[;R&   S[;R&   # )é   Ú
vocab_sizer   Únum_hidden_layersÚnum_attention_headsÚintermediate_sizeÚ
hidden_actÚhidden_dropout_probÚattention_probs_dropout_probÚmax_position_embeddingsÚtype_vocab_sizeÚinitializer_rangeÚlayer_norm_epsÚrelative_attentionÚmax_relative_positionsNÚpad_token_idÚbos_token_idÚeos_token_idÚposition_biased_inputr   Úpooler_dropoutÚpooler_hidden_actÚlegacyÚtie_word_embeddings)Úintr   ÚfloatÚboolÚlist)ÚformatÚ__classdict__s   "€r   Ú__annotate__ÚDebertaConfig.__annotate__   sŒ  ø‡ ‚ ñN ÑñO ñP ÑñQ ñR ÑñS ñT Ñ!ñU ñV Ñ!ñW ñX ÑñY ñZ ¡�Ñ*ñ[ ñ\ #(©#¥+Ñ3ñ] ñ^ !Ñ&ñ_ ñ` Ñña ñb Ñ#ñc ñd Ñ ñe ñf Ñ$ñg ñh  Ñ$ñi ñj ˜•*Ñ ñk ñl ˜•*Ñ#ñm ñn ™™S�	•/ DÕ(Ñ/ño ñp  Ñ&ñq ñr ™™S�	•/ DÕ(Ñ/ñs ñt ™C•KÑ%ñu ñv Ñ#ñw ñx Ññy ñz Ñ$ò{ ó    )r   r   éÿÿÿÿ)"Ú__name__Ú
__module__Ú__qualname__Ú__firstlineno__Ú__doc__Ú
model_typer    r   r!   r"   r#   r$   r%   r&   r'   r(   r)   r*   r+   r,   r-   r.   r/   r0   r   r1   r2   r3   r4   r   Ú__annotate_func__Ú__static_attributes__Ú__classdictcell__Ú__classcell__)r   r:   s   @@r   r   r      sž   ù‡ € ñ ðD €Jà€JØ€KØÐØ!ÐØ!ÐØ€JØ'*ÐØ03Ð Ø#&ÐØ€OØ#ÐØ €NØ$ÐØ"$ÐØ €LØ#€LØ+/€LØ"&ÐØ+/€LØ"%€NØ#ÐØ€FØ $Ðõ(÷ … r=   r   N)	rC   Úhuggingface_hub.dataclassesr   Úconfiguration_utilsr   Úutilsr   r   Ú__all__r   r=   r   Ú<module>rM      sK   ðñ "å .å 3Ý #ñ Ð3Ô4ØôC(Ð$ó C(ó ó 5ðC(ðL Ð
‚r=   