+
    œ°³jù
  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RWKV configuration)Ústrict)ÚPreTrainedConfig)Úauto_docstringzRWKV/rwkv-4-169m-pile)Ú
checkpointc                   ó~   a a€ ] tR t^t oRtRtRR/tRtRtRt	^ t
RtRtR	t^ t^ t^tR
tRtV 3R ltV3R ltRtVtV ;t# )Ú
RwkvConfiga‡  
context_length (`int`, *optional*, defaults to 1024):
    The maximum sequence length that this model can be used with in a single forward (using it in RNN mode
    lets use any sequence length).
attention_hidden_size (`int`, *optional*):
    Dimensionality of the attention hidden states. Will default to `hidden_size` if unset.
rescale_every (`int`, *optional*, defaults to 6):
    At inference, the hidden states (and weights of the corresponding output layers) are divided by 2 every
    `rescale_every` layer. If set to 0 or a negative number, no rescale is done.

Example:

```python
>>> from transformers import RwkvConfig, RwkvModel

>>> # Initializing a Rwkv configuration
>>> configuration = RwkvConfig()

>>> # Initializing a model (with random weights) from the configuration
>>> model = RwkvModel(configuration)

>>> # Accessing the model configuration
>>> configuration = model.config
```ÚrwkvÚmax_position_embeddingsÚcontext_lengthieÄ  i   i   Ngñhãˆµøä>FTc                óâ   <€ V P                   e   V P                   MV P                  V n         V P                  e   V P                  M^V P                  ,          V n        \        SV `  ! R/ VB  R # )N© )Úattention_hidden_sizeÚhidden_sizeÚintermediate_sizeÚsuperÚ__post_init__)ÚselfÚkwargsÚ	__class__s   &,€Úl/home/luca/screen-checking/.venv/lib/python3.14/site-packages/transformers/models/rwkv/configuration_rwkv.pyr   ÚRwkvConfig.__post_init__C   sc   ø€ à*.×*DÑ*DÒ*PˆD×&Ò&ÐVZ×VfÑVfð 	Ô"ð <@×;QÑ;QÒ;] ×!7Ò!7ÐcdÐgk×gwÑgwÕcwˆÔä‰ÒÑ' Ô'ó    c                ó  <€ V ^8„  d   Qh/ S[ ;R&   S[ ;R&   S[ ;R&   S[ ;R&   S[ R,          ;R&   S[ R,          ;R&   S[;R&   S[ R,          ;R	&   S[ S[S[ ,          ,          R,          ;R
&   S[ ;R&   S[;R&   S[;R&   # )é   Ú
vocab_sizer
   r   Únum_hidden_layersNr   r   Úlayer_norm_epsilonÚbos_token_idÚeos_token_idÚrescale_everyÚtie_word_embeddingsÚ	use_cache)ÚintÚfloatÚlistÚbool)ÚformatÚ__classdict__s   "€r   Ú__annotate__ÚRwkvConfig.__annotate__   sÐ   ø‡ ‚ ñ> Ññ? ñ@ ÑñA ñB ÑñC ñD ÑñE ñF  �:Ñ,ñG ñH ˜T•zÑ(ñI ñJ Ñ$ñK ñL ˜•*Ñ ñM ñN ™™S�	•/ DÕ(Ñ,ñO ñP ÑñQ ñR Ñ%ñS ñT ÑòU r   )r   r   )Ú__name__Ú
__module__Ú__qualname__Ú__firstlineno__Ú__doc__Ú
model_typeÚattribute_mapr   r
   r   r   r   r   r   r   r   r   r    r!   r   Ú__annotate_func__Ú__static_attributes__Ú__classdictcell__Ú__classcell__)r   r'   s   @@r   r   r      sj   ù‡ € ñð2 €JØ.Ð0@ÐA€Mà€JØ€NØ€KØÐØ(,ÐØ$(ÐØ $ÐØ €LØ+,€LØ€MØ %ÐØ€Iõ(÷Y … r   r   N)	r.   Úhuggingface_hub.dataclassesr   Úconfiguration_utilsr   Úutilsr   r   Ú__all__r   r   r   Ú<module>r9      sG   ðñ å .å 3Ý #ñ Ð2Ô3Øô0(Ð!ó 0(ó ó 4ð0(ðf ˆ.‚r   