Ë
    ÜÓ^jm  ã                   ój   — d Z ddlmZ ddlmZ ddlmZ  ed¬«      e G d„ d	e«      «       «       Zd	gZy
)zYOSO model configurationé    )Ústricté   )ÚPreTrainedConfig)Úauto_docstringzuw-madison/yoso-4096)Ú
checkpointc                   óˆ  — e Zd ZU dZdZdZeed<   dZeed<   dZ	eed<   dZ
eed	<   d
Zeed<   dZeed<   dZeez  ed<   dZeez  ed<   dZeed<   dZeed<   dZeed<   dZeed<   dZeed<   dZeed<   dZeed<   dZedz  ed <   dZeed!<   dZeed"<   dZedz  ed#<   d$Zedz  ed%<   d&Zeee   z  dz  ed'<   d(Z eed)<   dZ!eed*<   y)+Ú
YosoConfiga±  
    use_expectation (`bool`, *optional*, defaults to `True`):
        Whether or not to use YOSO Expectation. Overrides any effect of num_hash.
    hash_code_len (`int`, *optional*, defaults to 9):
        The length of hashes generated by the hash functions.
    num_hash (`int`, *optional*, defaults to 64):
        Number of hash functions used in [`YosoSelfAttention`].
    conv_window (`int`, *optional*):
        Kernel size of depth-wise convolution.
    use_fast_hash (`bool`, *optional*, defaults to `False`):
        Whether or not to use custom cuda kernels which perform fast random projection via hadamard transform.
    lsh_backward (`bool`, *optional*, defaults to `True`):
        Whether or not to perform backpropagation using Locality Sensitive Hashing.

    Example:

    ```python
    >>> from transformers import YosoConfig, YosoModel

    >>> # Initializing a YOSO uw-madison/yoso-4096 style configuration
    >>> configuration = YosoConfig()

    >>> # Initializing a model (with random weights) from the uw-madison/yoso-4096 style configuration
    >>> model = YosoModel(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```ÚyosoiYÄ  Ú
vocab_sizei   Úhidden_sizeé   Únum_hidden_layersÚnum_attention_headsi   Úintermediate_sizeÚgeluÚ
hidden_actgš™™™™™¹?Úhidden_dropout_probÚattention_probs_dropout_probi   Úmax_position_embeddingsé   Útype_vocab_sizeg{®Gáz”?Úinitializer_rangegê-�™—q=Úlayer_norm_epsTÚuse_expectationé	   Úhash_code_lené@   Únum_hashNÚconv_windowÚuse_fast_hashÚlsh_backwardÚpad_token_idr   Úbos_token_idé   Úeos_token_idFÚadd_cross_attentionÚtie_word_embeddings)"Ú__name__Ú
__module__Ú__qualname__Ú__doc__Ú
model_typer   ÚintÚ__annotations__r   r   r   r   r   Ústrr   Úfloatr   r   r   r   r   r   Úboolr   r   r   r    r!   r"   r#   r%   Úlistr&   r'   © ó    úv/var/www/ramen.bs-engineer-server.com/venv/lib/python3.12/site-packages/transformers/models/yoso/configuration_yoso.pyr	   r	      s  … ñð: €Jà€J�ÓØ€K�ÓØÐ�sÓØ!Ð˜Ó!Ø!Ð�sÓ!Ø€J�ÓØ'*Ð˜ ™Ó*Ø03Ð  %¨#¡+Ó3Ø#'Ð˜SÓ'Ø€O�SÓØ#Ð�uÓ#Ø!€N�EÓ!Ø €O�TÓ Ø€M�3ÓØ€HˆcÓØ"€K��t‘Ó"Ø€M�4ÓØ€L�$ÓØ €L�#˜‘*Ó Ø €L�#˜‘*Ó Ø+,€L�#˜˜S™	‘/ DÑ(Ó,Ø %Ð˜Ó%Ø $Ð˜Ô$r4   r	   N)	r+   Úhuggingface_hub.dataclassesr   Úconfiguration_utilsr   Úutilsr   r	   Ú__all__r3   r4   r5   ú<module>r:      sG   ðñ å .å 3Ý #ñ Ð1Ô2Øô6%Ð!ó 6%ó ó 3ð6%ðr ˆ.�r4   