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    ‚Štjš  ã                   ó„   — 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d
S )zTimesFM model configurationé    )Ústricté   )ÚPreTrainedConfig)Úauto_docstringzgoogle/timesfm-2.0-500m-pytorch)Ú
checkpointc                   óD  — e Zd ZU dZdZg 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	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         eedf         z  e	d<   dZe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&S )'ÚTimesFmConfiga(  
    patch_length (`int`, *optional*, defaults to 32):
        The length of one patch in the input sequence.
    context_length (`int`, *optional*, defaults to 512):
        The length of the input context.
    horizon_length (`int`, *optional*, defaults to 128):
        The length of the prediction horizon.
    freq_size (`int`, *optional*, defaults to 3):
        The number of frequency embeddings.
    tolerance (`float`, *optional*, defaults to 1e-06):
        The tolerance for the quantile loss.
    quantiles (`list[float]`, *optional*, defaults to `[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]`):
        The quantiles to predict.
    pad_val (`float`, *optional*, defaults to 1123581321.0):
        The value used to pad the predictions.
    attention_dropout (`float`, *optional*, defaults to 0.0):
        The dropout probability for the attention scores.
    use_positional_embedding (`bool`, *optional*, defaults to `False`):
        Whether to add positional embeddings.
    min_timescale (`int`, *optional*, defaults to 1):
        The start of the geometric positional index. Determines the periodicity of
        the added signal.
    max_timescale (`int`, *optional*, defaults to 10000):
        The end of the geometric positional index. Determines the frequency of the
        added signal.
    ÚtimesfmFé    Úpatch_lengthi   Úcontext_lengthé€   Úhorizon_lengthr   Ú	freq_sizeé2   Únum_hidden_layersi   Úhidden_sizeÚintermediate_sizeéP   Úhead_dimé   Únum_attention_headsg�íµ ÷Æ°>Ú	toleranceÚrms_norm_eps)	gš™™™™™¹?gš™™™™™É?g333333Ó?gš™™™™™Ù?g      à?g333333ã?gffffffæ?gš™™™™™é?gÍÌÌÌÌÌì?.Ú	quantilesg  @b¾ÐAÚpad_valg        Úattention_dropoutÚuse_positional_embeddingg{®Gáz”?Úinitializer_rangeé   Úmin_timescalei'  Úmax_timescaleN)Ú__name__Ú
__module__Ú__qualname__Ú__doc__Ú
model_typeÚkeys_to_ignore_at_inferenceÚis_encoder_decoderr   ÚintÚ__annotations__r   r   r   r   r   r   r   r   r   Úfloatr   r   ÚlistÚtupler   r   r   Úboolr   r!   r"   © ó    úo/var/www/html/CA-Chatbot/venv/lib/python3.11/site-packages/transformers/models/timesfm/configuration_timesfm.pyr	   r	      sk  € € € € € € ðð ð6 €JØ"$ÐØÐà€L�#ÐÐÑØ€N�CÐÐÑØ€N�CÐÐÑØ€IˆsÐÐÑØÐ�sÐÐÑØ€K�ÐÐÑØ!Ð�sÐ!Ð!Ñ!Ø€HˆcÐÐÑØ!Ð˜Ð!Ð!Ñ!Ø€IˆuÐÐÑØ€L�%ÐÐÑØ1^€Iˆt�EŒ{˜U 5¨# :Ô.Ñ.Ð^Ð^Ñ^Ø!€GˆUÐ!Ð!Ñ!Ø%(Ð�u˜s‘{Ð(Ð(Ñ(Ø%*Ð˜dÐ*Ð*Ñ*Ø#Ð�uÐ#Ð#Ñ#Ø€M�3ÐÐÑØ€M�3ÐÐÑÐÐr1   r	   N)	r&   Úhuggingface_hub.dataclassesr   Úconfiguration_utilsr   Úutilsr   r	   Ú__all__r0   r1   r2   ú<module>r7      s¢   ðð "Ð !à .Ð .Ð .Ð .Ð .Ð .à 3Ð 3Ð 3Ð 3Ð 3Ð 3Ø #Ð #Ð #Ð #Ð #Ð #ð €Ð<Ð=Ñ=Ô=Øð1 ð 1 ð 1 ð 1 ð 1 Ð$ñ 1 ô 1 ñ „ñ >Ô=ð1 ðh Ð
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