+
    œ°³j¶  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CANINE model configuration)Ústrict)ÚPreTrainedConfig)Úauto_docstringzgoogle/canine-s)Ú
checkpointc                   ó€   a € ] tR t^t o RtRtRt^t^tRt	Rt
RtRtRt^tRtR	t^ tR
tRt^t^t^tRt^€tV 3R ltRtV tR# )ÚCanineConfigaÒ  
downsampling_rate (`int`, *optional*, defaults to 4):
    The rate at which to downsample the original character sequence length before applying the deep Transformer
    encoder.
upsampling_kernel_size (`int`, *optional*, defaults to 4):
    The kernel size (i.e. the number of characters in each window) of the convolutional projection layer when
    projecting back from `hidden_size`*2 to `hidden_size`.
num_hash_functions (`int`, *optional*, defaults to 8):
    The number of hash functions to use. Each hash function has its own embedding matrix.
num_hash_buckets (`int`, *optional*, defaults to 16384):
    The number of hash buckets to use.
local_transformer_stride (`int`, *optional*, defaults to 128):
    The stride of the local attention of the first shallow Transformer encoder. Defaults to 128 for good
    TPU/XLA memory alignment.

Example:

```python
>>> from transformers import CanineConfig, CanineModel

>>> # Initializing a CANINE google/canine-s style configuration
>>> configuration = CanineConfig()

>>> # Initializing a model (with random weights) from the google/canine-s style configuration
>>> model = CanineModel(configuration)

>>> # Accessing the model configuration
>>> configuration = model.config
```Úcaninei   i   Úgelugš™™™™™¹?i @  g{®Gáz”?gê-�™—q=i à  ià  c                óh  <€ V ^8„  d   Qh/ 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,          ;R&   S[ R,          ;R&   S[ S[S[ ,          ,          R,          ;R&   S[ ;R&   S[ ;R&   S[ ;R&   S[ ;R&   S[ ;R&   # )é   Úhidden_sizeÚ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_epsNÚpad_token_idÚbos_token_idÚeos_token_idÚdownsampling_rateÚupsampling_kernel_sizeÚnum_hash_functionsÚnum_hash_bucketsÚlocal_transformer_stride)ÚintÚstrÚfloatÚlist)ÚformatÚ__classdict__s   "€Úp/home/luca/screen-checking/.venv/lib/python3.14/site-packages/transformers/models/canine/configuration_canine.pyÚ__annotate__ÚCanineConfig.__annotate__   s?  ø‡ ‚ ñF ÑñG ñH ÑñI ñJ Ñ!ñK ñL Ñ!ñM ñN ÑñO ñP ¡�Ñ*ñQ ñR #(©#¥+Ñ3ñS ñT !Ñ(ñU ñV ÑñW ñX Ñ#ñY ñZ Ñ!ñ[ ñ\ ˜•*Ñ ñ] ñ^ ˜•*Ñ%ñ_ ñ` ™™S�	•/ DÕ(Ñ1ña ñb Ññc ñd  Ñ#ñe ñf Ññg ñh Ñ!ñi ñj "Ñ'òk 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   Ú__annotate_func__Ú__static_attributes__Ú__classdictcell__)r$   s   @r%   r   r      s„   ø‡ € ñð< €Jà€KØÐØ!ÐØ!ÐØ€JØ'*ÐØ03Ð Ø#(ÐØ€OØ#ÐØ!€NØ €LØ%€LØ+1€LØÐØ"#ÐØÐØ!ÐØ$'Ð÷k ƒ r(   r   N)	r.   Úhuggingface_hub.dataclassesr   Úconfiguration_utilsr   Úutilsr   r   Ú__all__r)   r(   r%   Ú<module>r7      sH   ðñ !å .å 3Ý #ñ Ð,Ô-Øô3(Ð#ó 3(ó ó .ð3(ðl Ð
‚r(   