^NjddlmZmZmZmZddlmZddlmZm Z ddl m Z m Z ddl mZddlmZddlmZGdd eZy ) )AnyIterableSequenceType)asdict) NumpyArrayDevice) ImageInput OnnxProvider)ImageEmbeddingBase)OnnxImageEmbedding)DenseModelDescriptioncVeZdZUegZeeeed<e dee e e ffdZ e deefdZdddej"ddfde de dzd edzd eedzd eezd eedzd ede ffd ZedefdZe de defdZ ddeeezdededzde deef dZxZS)ImageEmbeddingEMBEDDINGS_REGISTRYreturncZ|jDcgc] }t|c}Scc}w)a Lists the supported models. Returns: list[dict[str, Any]]: A list of dictionaries containing the model information. Example: ``` [ { "model": "Qdrant/clip-ViT-B-32-vision", "dim": 512, "description": "CLIP vision encoder based on ViT-B/32", "license": "mit", "size_in_GB": 0.33, "sources": { "hf": "Qdrant/clip-ViT-B-32-vision", }, "model_file": "model.onnx", } ] ``` )_list_supported_modelsr)clsmodels p/Users/ahmed/devFolder/Ultron/claude-voice/.venv/lib/python3.12/site-packages/fastembed/image/image_embedding.pylist_supported_modelsz$ImageEmbedding.list_supported_modelss+2,/+E+E+GH+G%u +GHHHs(cjg}|jD]!}|j|j#|SN)rextendr)rresult embeddings rrz%ImageEmbedding._list_supported_models)s1.000I MM)::< =1 NF model_name cache_dirthreads providerscuda device_ids lazy_loadkwargsc t |||fi||jD]=} | j} t fd| Ds(| |f|||||d||_yt dd)Nc3tK|]/}j|jjk(1ywr)lowerr).0rrs r z*ImageEmbedding.__init__..>s/[JZ:##%):):)<>!,(  !@LM ;,, KM+J<8**58  NsBimages batch_sizeparallelc+^K|jj|||fi|Ed{y7w)aa Encode a list of images into list of embeddings. Args: images: Iterator of image paths or single image path to embed batch_size: Batch size for encoding -- higher values will use more memory, but be faster parallel: If > 1, data-parallel encoding will be used, recommended for offline encoding of large datasets. If 0, use all available cores. If None, don't use data-parallel processing, use default onnxruntime threading instead. Returns: List of embeddings, one per document N)rembed)r0r=r>r?r&s rrAzImageEmbedding.embedrs+*$4::##FJKFKKKs #-+-)N)__name__ __module__ __qualname__r rlistrr __annotations__ classmethoddictstrrrrrr AUTOintrr boolr-propertyr7r6r rrrA __classcell__)r3s@rrr s;M:Nd#567NId4S>&:II4t,A'B!%"37$kk'+  : t  L)D0  Vm  I$   @$$$ CC:# LXj11LL* L  L *  LrrN)typingrrrr dataclassesrfastembed.common.typesrr fastembed.commonr r $fastembed.image.image_embedding_baser fastembed.image.onnx_embeddingr "fastembed.common.model_descriptionrrrrrXs.0055C=D|L'|Lr