+
    œ°³jŽ  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LED model configuration)Ústrict)ÚPreTrainedConfig)Úauto_docstringzallenai/led-base-16384)Ú
checkpointc            
       ó´   a € ] tR t^t o RtRtRRRRRRR	R
RR/tRtRtRt	^t
Rt^t^tRt^tRtRtRtRtRtRtRtRtRtRt^tRt^t^ t^tRtRt V 3R lt!Rt"V t#R# )Ú	LEDConfiga¶  
max_encoder_position_embeddings (`int`, *optional*, defaults to 16384):
    The maximum sequence length that the encoder might ever be used with.
max_decoder_position_embeddings (`int`, *optional*, defaults to 16384):
    The maximum sequence length that the decoder might ever be used with.
attention_window (`int` or `list[int]`, *optional*, defaults to 512):
    Size of an attention window around each token. If an `int`, use the same size for all layers. To specify a
    different window size for each layer, use a `list[int]` where `len(attention_window) == num_hidden_layers`.

Example:

```python
>>> from transformers import LEDModel, LEDConfig

>>> # Initializing a LED allenai/led-base-16384 style configuration
>>> configuration = LEDConfig()

>>> # Initializing a model from the allenai/led-base-16384 style configuration
>>> model = LEDModel(configuration)

>>> # Accessing the model configuration
>>> configuration = model.config
```ÚledÚnum_attention_headsÚencoder_attention_headsÚhidden_sizeÚd_modelÚattention_probs_dropout_probÚattention_dropoutÚinitializer_rangeÚinit_stdÚnum_hidden_layersÚencoder_layersiYÄ  i @  i   i   g        TÚgelugš™™™™™¹?g{®Gáz”?i   c                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[S[ ,          ;R&   S[S[ ,          ;R&   S[;R&   S[ ;R&   S[S[ ,          ;R&   S[ R,          ;R&   S[ R,          ;R&   S[ S[S[ ,          ,          R,          ;R&   S[S[ ,          S[ ,          ;R&   S[;R&   # )é   Ú
vocab_sizeÚmax_encoder_position_embeddingsÚmax_decoder_position_embeddingsr   Úencoder_ffn_dimr
   Údecoder_layersÚdecoder_ffn_dimÚdecoder_attention_headsÚencoder_layerdropÚdecoder_layerdropÚ	use_cacheÚis_encoder_decoderÚactivation_functionr   Údropoutr   Úactivation_dropoutr   Údecoder_start_token_idÚclassifier_dropoutNÚpad_token_idÚbos_token_idÚeos_token_idÚattention_windowÚtie_word_embeddings)ÚintÚfloatÚboolÚstrÚlist)ÚformatÚ__classdict__s   "€Új/home/luca/screen-checking/.venv/lib/python3.14/site-packages/transformers/models/led/configuration_led.pyÚ__annotate__ÚLEDConfig.__annotate__   sÀ  ø‡ ‚ ñH ÑñI ñJ &)Ñ0ñK ñL &)Ñ/ñM ñN ÑñO ñP ÑñQ ñR !Ñ%ñS ñT ÑñU ñV ÑñW ñX !Ñ%ñY ñZ ™s•{Ñ(ñ[ ñ\ ™s•{Ñ(ñ] ñ^ Ññ_ ñ` Ñ#ña ñb Ñ%ñc ñd Ññe ñf ‘S�[Ññg ñh ™s•{Ñ(ñi ñj ¡�Ñ)ñk ñl Ññm ñn  Ñ#ño ñp ¡�Ñ)ñq ñr ˜•*Ñ ñs ñt ˜•*Ñ ñu ñv ™™S�	•/ DÕ(Ñ,ñw ñx ™3•i¡#•oÑ+ñy ñz Ñ$ò{ ó    © N)$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    r!   r   r"   r   r#   r   r$   r%   r&   r'   r(   r)   r*   Ú__annotate_func__Ú__static_attributes__Ú__classdictcell__)r1   s   @r2   r   r      sÎ   ø‡ € ñð0 €JàÐ8Ø�yØ&Ð(;Ø˜ZØÐ-ð€Mð €JØ+0Ð#Ø+/Ð#Ø€NØ€OØ#%ÐØ€NØ€OØ#%ÐØ%(ÐØ%(ÐØ€IØ#ÐØ%ÐØ€GØ€GØ%(ÐØ&)ÐØ€HØ"#ÐØ&)ÐØ €LØ €LØ+,€LØ(+ÐØ $Ð÷{ ƒ r5   r   N)	r;   Úhuggingface_hub.dataclassesr   Úconfiguration_utilsr   Úutilsr   r   Ú__all__r6   r5   r2   Ú<module>rE      sG   ðñ å .å 3Ý #ñ Ð3Ô4Øô;%Ð ó ;%ó ó 5ð;%ð| ˆ-‚r5   