+
    œ°³jt
  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ALBERT model configuration)Ústrict)ÚPreTrainedConfig)Úauto_docstringzalbert/albert-xxlarge-v2)Ú
checkpointc                   ó„   a € ] tR t^t o RtRtRt^€tRt^t	^t
^@tRt^tRtRtRtRt^tR	tR
tRt^ t^t^tRtV 3R ltRtV tR# )ÚAlbertConfigaT  
num_hidden_groups (`int`, *optional*, defaults to 1):
    Number of groups for the hidden layers, parameters in the same group are shared.
inner_group_num (`int`, *optional*, defaults to 1):
    The number of inner repetition of attention and ffn.

Examples:

```python
>>> from transformers import AlbertConfig, AlbertModel

>>> # Initializing an ALBERT-xxlarge style configuration
>>> albert_xxlarge_configuration = AlbertConfig()

>>> # Initializing an ALBERT-base style configuration
>>> albert_base_configuration = AlbertConfig(
...     hidden_size=768,
...     num_attention_heads=12,
...     intermediate_size=3072,
... )

>>> # Initializing a model (with random weights) from the ALBERT-base style configuration
>>> model = AlbertModel(albert_xxlarge_configuration)

>>> # Accessing the model configuration
>>> configuration = model.config
```Úalberti0u  i   i @  Úgelu_newg        i   g{®Gáz”?gê-�™—q=gš™™™™™¹?Tc                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[ S[,          ;R
&   S[ S[,          ;R&   S[ ;R&   S[ ;R&   S[;R&   S[;R&   S[ S[,          ;R&   S[ R,          ;R&   S[ R,          ;R&   S[ S[S[ ,          ,          R,          ;R&   S[;R&   # )é   Ú
vocab_sizeÚembedding_sizeÚhidden_sizeÚnum_hidden_layersÚnum_hidden_groupsÚnum_attention_headsÚintermediate_sizeÚinner_group_numÚ
hidden_actÚhidden_dropout_probÚattention_probs_dropout_probÚmax_position_embeddingsÚtype_vocab_sizeÚinitializer_rangeÚlayer_norm_epsÚclassifier_dropout_probNÚpad_token_idÚbos_token_idÚeos_token_idÚtie_word_embeddings)ÚintÚstrÚfloatÚlistÚbool)ÚformatÚ__classdict__s   "€Úp/home/luca/screen-checking/.venv/lib/python3.14/site-packages/transformers/models/albert/configuration_albert.pyÚ__annotate__ÚAlbertConfig.__annotate__   sR  ø‡ ‚ ñB ÑñC ñD ÑñE ñF ÑñG ñH ÑñI ñJ ÑñK ñL Ñ!ñM ñN Ñ"ñO ñP ÑñQ ñR Ñ ñS ñT ™u�Ñ*ñU ñV #&©¥+Ñ3ñW ñX !Ñ&ñY ñZ Ññ[ ñ\ Ñ#ñ] ñ^ Ñ!ñ_ ñ` !¡5�[Ñ.ña ñb ˜•*Ñ ñc ñd ˜•*Ñ ñe ñf ™™S�	•/ DÕ(Ñ,ñg ñh Ñ$òi ó    © N)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   r   r   r   Ú__annotate_func__Ú__static_attributes__Ú__classdictcell__)r&   s   @r'   r   r      s‡   ø‡ € ñð8 €Jà€JØ€NØ€KØÐØÐØ!ÐØ"ÐØ€OØ €JØ'*ÐØ03Ð Ø#&ÐØ€OØ#ÐØ!€NØ+.ÐØ €LØ €LØ+,€LØ $Ð÷i ƒ r*   r   N)	r0   Úhuggingface_hub.dataclassesr   Úconfiguration_utilsr   Úutilsr   r   Ú__all__r+   r*   r'   Ú<module>r9      sH   ðñ !å .å 3Ý #ñ Ð5Ô6Øô2%Ð#ó 2%ó ó 7ð2%ðj Ð
‚r*   