+
    œ°³jÁ
  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OpenAI ImageGPT configuration)Ústrict)ÚPreTrainedConfig)Úauto_docstringzopenai/imagegpt-small)Ú
checkpointc                   ó¢   a € ] tR t^t o RtRtR.tRRRRRR	R
R/tRtRt	Rt
^t^tRtRtRtRtRtRtRtRtRtRtRtRtRtRtRtRtV 3R ltRtV tR# )ÚImageGPTConfigaá  
scale_attn_by_inverse_layer_idx (`bool`, *optional*, defaults to `False`):
    Whether to additionally scale attention weights by `1 / layer_idx + 1`.
reorder_and_upcast_attn (`bool`, *optional*, defaults to `False`):
    Whether to scale keys (K) prior to computing attention (dot-product) and upcast attention
    dot-product/softmax to float() when training with mixed precision.

Example:

```python
>>> from transformers import ImageGPTConfig, ImageGPTModel

>>> # Initializing a ImageGPT configuration
>>> configuration = ImageGPTConfig()

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

>>> # Accessing the model configuration
>>> configuration = model.config
```ÚimagegptÚpast_key_valuesÚhidden_sizeÚn_embdÚmax_position_embeddingsÚn_positionsÚnum_attention_headsÚn_headÚnum_hidden_layersÚn_layeri   NÚ
quick_gelugš™™™™™¹?gñhãˆµøä>g{®Gáz”?TFc                óž  <€ V ^8„  d   Qh/ S[ ;R&   S[ ;R&   S[ ;R&   S[ ;R&   S[ ;R&   S[ R,          ;R&   S[;R&   S[S[ ,          ;R	&   S[S[ ,          ;R
&   S[S[ ,          ;R&   S[;R&   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&   # )é   Ú
vocab_sizer   r   r   r   NÚn_innerÚactivation_functionÚresid_pdropÚ
embd_pdropÚ
attn_pdropÚlayer_norm_epsilonÚinitializer_rangeÚscale_attn_weightsÚ	use_cacheÚtie_word_embeddingsÚscale_attn_by_inverse_layer_idxÚreorder_and_upcast_attnÚadd_cross_attentionÚpad_token_idÚbos_token_idÚeos_token_id)ÚintÚstrÚfloatÚboolÚlist)ÚformatÚ__classdict__s   "€Út/home/luca/screen-checking/.venv/lib/python3.14/site-packages/transformers/models/imagegpt/configuration_imagegpt.pyÚ__annotate__ÚImageGPTConfig.__annotate__   sd  ø‡ ‚ ñD ÑñE ñF ÑñG ñH ÑñI ñJ ÑñK ñL �OñM ñN �4�ZÑñO ñP Ñ+ñQ ñR ™•Ñ"ñS ñT ™•Ñ!ñU ñV ™•Ñ!ñW ñX Ñ$ñY ñZ Ñ#ñ[ ñ\ Ñ#ñ] ñ^ Ññ_ ñ` Ñ%ña ñb &*Ñ1ñc ñd "Ñ)ñe ñf Ñ%ñg ñh ˜•*Ñ#ñi ñj ˜•*Ñ#ñk ñl ™™S�	•/ DÕ(Ñ/òm ó    © i  i   ) Ú__name__Ú
__module__Ú__qualname__Ú__firstlineno__Ú__doc__Ú
model_typeÚkeys_to_ignore_at_inferenceÚattribute_mapr   r   r   r   r   r   r   r   r   r   r   r   r   r   r   r    r!   r"   r#   r$   r%   Ú__annotate_func__Ú__static_attributes__Ú__classdictcell__)r,   s   @r-   r   r      s°   ø‡ € ñð, €JØ#4Ð"5Ðà�xØ! =Ø˜xØ˜Yð	€Mð €JØ€KØ€FØ€GØ€FØ€GØ+ÐØ"€KØ!€JØ!€JØ $ÐØ#ÐØ#ÐØ€IØ %ÐØ,1Ð#Ø$)ÐØ %ÐØ#€LØ#€LØ+/€L÷m ƒ r0   r   N)	r6   Úhuggingface_hub.dataclassesr   Úconfiguration_utilsr   Úutilsr   r   Ú__all__r1   r0   r-   Ú<module>rA      sH   ðñ $å .å 3Ý #ñ Ð2Ô3Øô40Ð%ó 40ó ó 4ð40ðn Ð
‚r0   