
    siS                         d dl mZ d dlmZ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 )
    )Enum)AnyDictIterableN)Tensornn)SentenceTransformerc                   $    e Zd ZdZd Zd Zd ZdS )SiameseDistanceMetricz#The metric for the contrastive lossc                 0    t          j        | |d          S )N   pFpairwise_distancexys     _/var/www/icac/venv/lib/python3.11/site-packages/sentence_transformers/losses/ContrastiveLoss.py<lambda>zSiameseDistanceMetric.<lambda>       Q0A;;;     c                 0    t          j        | |d          S )N   r   r   r   s     r   r   zSiameseDistanceMetric.<lambda>   r   r   c                 2    dt          j        | |          z
  S )Nr   )r   cosine_similarityr   s     r   r   zSiameseDistanceMetric.<lambda>   s    1q':1a'@'@#@ r   N)__name__
__module____qualname____doc__	EUCLIDEAN	MANHATTANCOSINE_DISTANCE r   r   r   r   
   s,        --;;I;;I@@OOOr   r   c            	            e Zd Zej        ddfdedededdf fdZde	e
ef         fd	Zd
ee	e
ef                  dedefdZede
fd            Z xZS )ContrastiveLoss      ?Tmodelmarginsize_averagereturnNc                     t          t          |                                            || _        || _        || _        || _        dS )a	  
        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.

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

        References:
            * Further information: http://yann.lecun.com/exdb/publis/pdf/hadsell-chopra-lecun-06.pdf
            * `Training Examples > Quora Duplicate Questions <../../examples/training/quora_duplicate_questions/README.html>`_

        Requirements:
            1. (anchor, positive/negative) pairs

        Relations:
            - :class:`OnlineContrastiveLoss` is similar, but uses hard positive and hard negative pairs.
            It often yields better results.

        Inputs:
            +-----------------------------------------------+------------------------------+
            | Texts                                         | Labels                       |
            +===============================================+==============================+
            | (anchor, positive/negative) pairs             | 1 if positive, 0 if negative |
            +-----------------------------------------------+------------------------------+

        Example:
            ::

                from sentence_transformers import SentenceTransformer, SentenceTransformerTrainer, losses
                from datasets import Dataset

                model = SentenceTransformer("microsoft/mpnet-base")
                train_dataset = Dataset.from_dict({
                    "sentence1": ["It's nice weather outside today.", "He drove to work."],
                    "sentence2": ["It's so sunny.", "She walked to the store."],
                    "label": [1, 0],
                })
                loss = losses.ContrastiveLoss(model)

                trainer = SentenceTransformerTrainer(
                    model=model,
                    train_dataset=train_dataset,
                    loss=loss,
                )
                trainer.train()
        N)superr'   __init__distance_metricr*   r)   r+   )selfr)   r0   r*   r+   	__class__s        r   r/   zContrastiveLoss.__init__   sF    v 	ot$$--///.
(r   c                     | j         j        }t          t                                                    D ]'\  }}|| j         k    rd                    |          } n(|| j        | j        dS )NzSiameseDistanceMetric.{})r0   r*   r+   )r0   r   varsr   itemsformatr*   r+   )r1   distance_metric_namenamevalues       r   get_config_dictzContrastiveLoss.get_config_dictT   sz    #3< 566<<>> 	 	KD%,,,'A'H'H'N'N$ - $84;`d`qrrrr   sentence_featureslabelsc                      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                 F    g | ]}                     |          d          S )sentence_embedding)r)   ).0sentence_featurer1   s     r   
<listcomp>z+ContrastiveLoss.forward.<locals>.<listcomp>^   s-    mmmGW

+,,-ABmmmr   r   r(   r   )
lenr0   floatpowr   relur*   r+   meansum)r1   r;   r<   reps
rep_anchor	rep_other	distanceslossess   `       r   forwardzContrastiveLoss.forward]   s    mmmm[lmmm4yyA~~~~ $
I((Y??	LLNNY]]1---V0B0B0D0Dqvdk\eNeGfGfGjGjklGmGm0mm
 !% 1Cv{{}}}vzz||Cr   c                     dS )Na  
@inproceedings{hadsell2006dimensionality,
    author={Hadsell, R. and Chopra, S. and LeCun, Y.},
    booktitle={2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06)}, 
    title={Dimensionality Reduction by Learning an Invariant Mapping}, 
    year={2006},
    volume={2},
    number={},
    pages={1735-1742},
    doi={10.1109/CVPR.2006.100}
}
r%   )r1   s    r   citationzContrastiveLoss.citationg   s     r   )r   r   r    r   r$   r	   rD   boolr/   r   strr   r:   r   r   rN   propertyrP   __classcell__)r2   s   @r   r'   r'      s        .=!?) ?)"?) 	?)
 ?) 
?) ?) ?) ?) ?) ?)Bsc3h s s s sD$sF{2C)D Df DY_ D D D D #    X    r   r'   )enumr   typingr   r   r   torch.nn.functionalr   
functionalr   torchr   )sentence_transformers.SentenceTransformerr	   r   Moduler'   r%   r   r   <module>r\      s          & & & & & & & & & &                  I I I I I IA A A A AD A A Ab b b b bbi b b b b br   