+
    œ°³js  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MobileBERT model configuration)Ústrict)ÚPreTrainedConfig)Úauto_docstringzgoogle/mobilebert-uncased)Ú
checkpointc                   ó¦   a a€ ] tR t^t oRtRtRtRt^t^t	Rt
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tV 3R ltV3R ltRt Vt!V ;t"# )ÚMobileBertConfigaê  
embedding_size (`int`, *optional*, defaults to 128):
    The dimension of the word embedding vectors.
trigram_input (`bool`, *optional*, defaults to `True`):
    Use a convolution of trigram as input.
use_bottleneck (`bool`, *optional*, defaults to `True`):
    Whether to use bottleneck in BERT.
intra_bottleneck_size (`int`, *optional*, defaults to 128):
    Size of bottleneck layer output.
use_bottleneck_attention (`bool`, *optional*, defaults to `False`):
    Whether to use attention inputs from the bottleneck transformation.
key_query_shared_bottleneck (`bool`, *optional*, defaults to `True`):
    Whether to use the same linear transformation for query&key in the bottleneck.
num_feedforward_networks (`int`, *optional*, defaults to 4):
    Number of FFNs in a block.
normalization_type (`str`, *optional*, defaults to `"no_norm"`):
    The normalization type in MobileBERT.

Examples:

```python
>>> from transformers import MobileBertConfig, MobileBertModel

>>> # Initializing a MobileBERT configuration
>>> configuration = MobileBertConfig()

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

>>> # Accessing the model configuration
>>> configuration = model.config
```
Ú
mobileberti:w  i   Úrelug        gš™™™™™¹?g{®Gáz”?gê-�™—q=TFÚno_normNc                ó’   <€ V P                   '       d   V P                  V n        MV P                  V n        \        SV `  ! R/ VB  R # )N© )Úuse_bottleneckÚintra_bottleneck_sizeÚtrue_hidden_sizeÚhidden_sizeÚsuperÚ__post_init__)ÚselfÚkwargsÚ	__class__s   &,€Úx/home/luca/screen-checking/.venv/lib/python3.14/site-packages/transformers/models/mobilebert/configuration_mobilebert.pyr   ÚMobileBertConfig.__post_init__V   s;   ø€ Ø××ÐØ$(×$>Ñ$>ˆDÕ!à$(×$4Ñ$4ˆDÔ!Ü‰ÒÑ' Ô'ó    c                ó†  <€ V ^8„  d   Qh/ S[ ;R&   S[ ;R&   S[ ;R&   S[ ;R&   S[ ;R&   S[;R&   S[S[ ,          ;R&   S[S[ ,          ;R&   S[ ;R	&   S[ ;R
&   S[;R&   S[;R&   S[ R,          ;R&   S[ ;R&   S[;R&   S[;R&   S[ ;R&   S[;R&   S[;R&   S[ ;R&   S[;R&   S[;R&   S[S[ ,          R,          ;R&   S[;R&   # )é   Ú
vocab_sizer   Únum_hidden_layersÚnum_attention_headsÚintermediate_sizeÚ
hidden_actÚhidden_dropout_probÚattention_probs_dropout_probÚmax_position_embeddingsÚtype_vocab_sizeÚinitializer_rangeÚlayer_norm_epsNÚpad_token_idÚembedding_sizeÚtrigram_inputr   r   Úuse_bottleneck_attentionÚkey_query_shared_bottleneckÚnum_feedforward_networksÚnormalization_typeÚclassifier_activationÚclassifier_dropoutÚtie_word_embeddings)ÚintÚstrÚfloatÚbool)ÚformatÚ__classdict__s   "€r   Ú__annotate__ÚMobileBertConfig.__annotate__   s‚  ø‡ ‚ ñN ÑñO ñP ÑñQ ñR ÑñS ñT Ñ ñU ñV Ñ ñW ñX ÑñY ñZ ¡�Ñ*ñ[ ñ\ #(©#¥+Ñ3ñ] ñ^ !Ñ&ñ_ ñ` Ñña ñb Ñ#ñc ñd Ñ!ñe ñf ˜•*Ñ ñg ñh Ññi ñj Ññk ñl Ññm ñn Ñ$ño ñp #Ñ*ñq ñr "&Ñ,ñs ñt "Ñ%ñu ñv Ñ'ñw ñx  Ñ&ñy ñz ¡� dÕ*Ñ1ñ{ ñ| Ñ$ò} r   )r   )#Ú__name__Ú
__module__Ú__qualname__Ú__firstlineno__Ú__doc__Ú
model_typer   r   r   r   r   r   r    r!   r"   r#   r$   r%   r&   r'   r(   r   r   r)   r*   r+   r,   r-   r.   r/   r   Ú__annotate_func__Ú__static_attributes__Ú__classdictcell__Ú__classcell__)r   r5   s   @@r   r   r      s¦   ù‡ € ñ ðD €Jà€JØ€KØÐØ ÐØ ÐØ€JØ'*ÐØ03Ð Ø#&ÐØ€OØ#ÐØ!€NØ €LØ€NØ€MØ€NØ!$ÐØ%*ÐØ(,ÐØ$%ÐØ'ÐØ"&ÐØ-1ÐØ $Ðõ(÷A … r   r   N)	r<   Úhuggingface_hub.dataclassesr   Úconfiguration_utilsr   Úutilsr   r   Ú__all__r   r   r   Ú<module>rF      sK   ðñ %å .å 3Ý #ñ Ð6Ô7ØôC(Ð'ó C(ó ó 8ðC(ðL Ð
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