§
    æ˜ºh
  ã                   ó’   — d dl mZ d dlmZmZ d dlmc mZ d dl	mZm
Z
 d dlmZ  G d„ de¦  «        Z G d„ d	ej        ¦  «        ZdS )
é    )ÚEnum)ÚIterableÚDictN)ÚnnÚTensor)ÚSentenceTransformerc                   ó$   — e Zd ZdZd„ Zd„ Zd„ ZdS )ÚSiameseDistanceMetricz-
    The metric for the contrastive loss
    c                 ó0   — t          j        | |d¬¦  «        S )Né   ©Úp©ÚFÚpairwise_distance©ÚxÚys     út/var/www/html/services/rag-service/venv/lib/python3.11/site-packages/sentence_transformers/losses/ContrastiveLoss.pyú<lambda>zSiameseDistanceMetric.<lambda>   ó   € �QÔ0°°A¸Ð;Ñ;Ô;€ ó    c                 ó0   — t          j        | |d¬¦  «        S )Né   r   r   r   s     r   r   zSiameseDistanceMetric.<lambda>   r   r   c                 ó2   — dt          j        | |¦  «        z
  S )Nr   )r   Úcosine_similarityr   s     r   r   zSiameseDistanceMetric.<lambda>   s   €  1¥qÔ':¸1¸aÑ'@Ô'@Ñ#@€ r   N)Ú__name__Ú
__module__Ú__qualname__Ú__doc__Ú	EUCLIDEANÚ	MANHATTANÚCOSINE_DISTANCE© r   r   r
   r
      s2   € € € € € ðð ð <Ð;€IØ;Ð;€IØ@Ð@€O€O€Or   r
   c                   ót   ‡ — e Zd ZdZej        ddfdededefˆ fd„Z	d„ Z
d	eeeef                  d
efd„Zˆ xZS )ÚContrastiveLossak  
    Contrastive loss. Expects as input two texts and a label of either 0 or 1. If the label == 1, then the distance between the
    two embeddings is reduced. If the label == 0, then the distance between the embeddings is increased.

    Further information: http://yann.lecun.com/exdb/publis/pdf/hadsell-chopra-lecun-06.pdf

    :param model: SentenceTransformer model
    :param distance_metric: Function that returns a distance between two embeddings. The class SiameseDistanceMetric contains pre-defined metrices that can be used
    :param margin: Negative samples (label == 0) should have a distance of at least the margin value.
    :param size_average: Average by the size of the mini-batch.

    Example::

        from sentence_transformers import SentenceTransformer, LoggingHandler, losses, InputExample
        from torch.utils.data import DataLoader

        model = SentenceTransformer('all-MiniLM-L6-v2')
        train_examples = [
            InputExample(texts=['This is a positive pair', 'Where the distance will be minimized'], label=1),
            InputExample(texts=['This is a negative pair', 'Their distance will be increased'], label=0)]

        train_dataloader = DataLoader(train_examples, shuffle=True, batch_size=2)
        train_loss = losses.ContrastiveLoss(model=model)

        model.fit([(train_dataloader, train_loss)], show_progress_bar=True)

    ç      à?TÚmodelÚmarginÚsize_averagec                 óŽ   •— t          t          | ¦  «                             ¦   «          || _        || _        || _        || _        d S )N)Úsuperr&   Ú__init__Údistance_metricr)   r(   r*   )Úselfr(   r.   r)   r*   Ú	__class__s        €r   r-   zContrastiveLoss.__init__/   sE   ø€ õ 	�o˜tÑ$Ô$×-Ò-Ñ/Ô/Ð/Ø.ˆÔØˆŒØˆŒ
Ø(ˆÔÐÐr   c                 óÖ   — | j         j        }t          t          ¦  «                             ¦   «         D ]'\  }}|| j         k    rd                     |¦  «        } nŒ(|| j        | j        dœS )NzSiameseDistanceMetric.{})r.   r)   r*   )r.   r   Úvarsr
   ÚitemsÚformatr)   r*   )r/   Údistance_metric_nameÚnameÚvalues       r   Úget_config_dictzContrastiveLoss.get_config_dict<   sz   € Ø#Ô3Ô<ÐÝÕ 5Ñ6Ô6×<Ò<Ñ>Ô>ð 	ð 	‰KˆD�%Ø˜Ô,Ò,Ð,Ø'A×'HÒ'HÈÑ'NÔ'NÐ$Ø�ð -ð $8À4Ä;Ð`dÔ`qÐrÐrÐrr   Úsentence_featuresÚlabelsc                 óÈ  ‡ — ˆ fd„|D ¦   «         }t          |¦  «        dk    sJ ‚|\  }}‰                      ||¦  «        }d|                     ¦   «         |                     d¦  «        z  d|z
                       ¦   «         t	          j        ‰ j        |z
  ¦  «                             d¦  «        z  z   z  }‰ j        r|                     ¦   «         n| 	                    ¦   «         S )Nc                 óF   •— g | ]}‰                      |¦  «        d          ‘ŒS )Úsentence_embedding)r(   )Ú.0Úsentence_featurer/   s     €r   ú
<listcomp>z+ContrastiveLoss.forward.<locals>.<listcomp>F   s-   ø€ ÐmÐmÐmÐGW�—
’
Ð+Ñ,Ô,Ð-AÔBÐmÐmÐmr   r   r'   r   )
Úlenr.   ÚfloatÚpowr   Úrelur)   r*   ÚmeanÚsum)r/   r9   r:   ÚrepsÚ
rep_anchorÚ	rep_otherÚ	distancesÚlossess   `       r   ÚforwardzContrastiveLoss.forwardE   sÔ   ø€ ØmÐmÐmÐmÐ[lÐmÑmÔmˆÝ�4‰yŒy˜AŠ~ˆ~ˆ~ˆ~Ø $Ñˆ
�IØ×(Ò(¨°YÑ?Ô?ˆ	ØØ�LŠL‰NŒN˜YŸ]š]¨1Ñ-Ô-Ñ-°°V±×0BÒ0BÑ0DÔ0DÅqÄvÈdÌkÐ\eÑNeÑGfÔGf×GjÒGjÐklÑGmÔGmÑ0mÑmñ
ˆð !%Ô 1ÐCˆv�{Š{‰}Œ}ˆ}°v·z²z±|´|ÐCr   )r   r   r   r    r
   r#   r   rB   Úboolr-   r8   r   r   Ústrr   rL   Ú__classcell__)r0   s   @r   r&   r&      sÂ   ø€ € € € € ðð ð> .Ô=ØØ!ð)ð )à"ð)ð ð	)ð
 ð)ð )ð )ð )ð )ð )ðsð sð sðD¨°$°s¸F°{Ô2CÔ)Dð DÈfð Dð Dð Dð Dð Dð Dð Dð Dr   r&   )Úenumr   Útypingr   r   Útorch.nn.functionalr   Ú
functionalr   Útorchr   Ú)sentence_transformers.SentenceTransformerr   r
   ÚModuler&   r$   r   r   ú<module>rW      sæ   ðØ Ð Ð Ð Ð Ð Ø !Ð !Ð !Ð !Ð !Ð !Ð !Ð !Ø Ð Ð Ð Ð Ð Ð Ð Ð Ø Ð Ð Ð Ð Ð Ð Ð Ø IÐ IÐ IÐ IÐ IÐ IðAð Að Að Að A˜Dñ Aô Að Að;Dð ;Dð ;Dð ;Dð ;D�b”iñ ;Dô ;Dð ;Dð ;Dð ;Dr   