ZjSSKJr SSKJrJrJr SSKJrJrJ r J r J r SSK J r Jr \ "5(a SSKJr SSKJr \"5(a SS KrSS KJr \ R,"\5r\"\"S S 95"S S\ 55rg ))UserDict)AnyUnionoverload)add_end_docstringsis_torch_availableis_vision_availableloggingrequires_backends)Pipelinebuild_pipeline_init_args)Image) load_imageN)6MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING_NAMEST)has_image_processorc f^\rSrSrSrSrSrSrSrU4Sjr \ S\ \ S4S\ \ S \S \ \\ \44S j5r\ S\ \ \ S-S\ \ S \S \ \ \\ \44S j5rS\ \ \ \ S\ S4S\ \ S \S \ \\ \4\ \ \\ \4-4U4S jjrSSjrSSjrSrSrSrU=r$)#ZeroShotImageClassificationPipelinea Zero shot image classification pipeline using `CLIPModel`. This pipeline predicts the class of an image when you provide an image and a set of `candidate_labels`. Example: ```python >>> from transformers import pipeline >>> classifier = pipeline(model="google/siglip-so400m-patch14-384") >>> classifier( ... "https://huggingface.co/datasets/Narsil/image_dummy/raw/main/parrots.png", ... candidate_labels=["animals", "humans", "landscape"], ... ) [{'score': 0.965, 'label': 'animals'}, {'score': 0.03, 'label': 'humans'}, {'score': 0.005, 'label': 'landscape'}] >>> classifier( ... "https://huggingface.co/datasets/Narsil/image_dummy/raw/main/parrots.png", ... candidate_labels=["black and white", "photorealist", "painting"], ... ) [{'score': 0.996, 'label': 'black and white'}, {'score': 0.003, 'label': 'photorealist'}, {'score': 0.0, 'label': 'painting'}] ``` Learn more about the basics of using a pipeline in the [pipeline tutorial](../pipeline_tutorial) This image classification pipeline can currently be loaded from [`pipeline`] using the following task identifier: `"zero-shot-image-classification"`. See the list of available models on [huggingface.co/models](https://huggingface.co/models?filter=zero-shot-image-classification). FTc h>[TU]"S0UD6 [US5 UR[5 g)Nvision)super__init__r check_model_typer)selfkwargs __class__s چ/root/GenerationalWealth/GenerationalWealth/venv/lib/python3.13/site-packages/transformers/pipelines/zero_shot_image_classification.pyr,ZeroShotImageClassificationPipeline.__init__Cs, "6"$) TUimagez Image.Imagecandidate_labelsrreturnc gNrrr#r$rs r __call__,ZeroShotImageClassificationPipeline.__call__Is #r"c gr'rr(s r r)r*Ns&)r"c t>SU;aURS5nUc [S5e[TU] "U4SU0UD6$)a; Assign labels to the image(s) passed as inputs. Args: image (`str`, `list[str]`, `PIL.Image` or `list[PIL.Image]`): The pipeline handles three types of images: - A string containing a http link pointing to an image - A string containing a local path to an image - An image loaded in PIL directly candidate_labels (`list[str]`): The candidate labels for this image. They will be formatted using *hypothesis_template*. hypothesis_template (`str`, *optional*, defaults to `"This is a photo of {}"`): The format used in conjunction with *candidate_labels* to attempt the image classification by replacing the placeholder with the candidate_labels. Pass "{}" if *candidate_labels* are already formatted. timeout (`float`, *optional*, defaults to None): The maximum time in seconds to wait for fetching images from the web. If None, no timeout is set and the call may block forever. Return: A list of dictionaries containing one entry per proposed label. Each dictionary contains the following keys: - **label** (`str`) -- One of the suggested *candidate_labels*. - **score** (`float`) -- The score attributed by the model to that label. It is a value between 0 and 1, computed as the `softmax` of `logits_per_image`. imageszSCannot call the zero-shot-image-classification pipeline without an images argument!r$)pop ValueErrorrr))rr#r$rrs r r)r*SsHJ v JJx(E =rs swS8HSFSSr"c d0nSU;aUSUS'SU;aUSUS'SU;aUSUS'U004$)Nr$timeouthypothesis_templater)rtokenizer_kwargsrpreprocess_paramss r _sanitize_parameters8ZeroShotImageClassificationPipeline._sanitize_parameters~sa  '4:;M4N 0 1  +1)+< i ( F *7=>S7T 3 4 "b((r"cUc0n[XS9nURU/SS9nURUR5nX&S'UVs/sHosR U5PM nnSS0n SUR R R;aU RSS SS 9 U RU5 UR"U4S S0U D6n U /US 'U$s snf) N)r1pt)r-return_tensorsr$paddingTsiglip max_length@)r:r< truncationr9 text_inputs) rimage_processortodtypeformatmodelconfig model_typeupdate tokenizer) rr#r$r2r1r3inputsx sequencestokenizer_default_kwargsr?s r preprocess.ZeroShotImageClassificationPipeline.preprocesss  #! 52%%eWT%J4::&%5!"?"&&}5 k!nh / /%a.K&a.+K**;{;l;!1.. r"cURS5nUSSnSURRR;aN[R "U5R S5nUR5n[U[5(dU/nOFURSS9R S5nUR5n[U[5(dU/n[[XR5SS9VVs/sH upgXgS .PM nnnU$s snnf) Nr$rPrr;)dimcUS*$)Nrr)rJs r AZeroShotImageClassificationPipeline.postprocess..s_`ab_c^cr")key)scorelabel) r.rDrErFtorchsigmoidsqueezetolistrQlistsoftmaxsortedzip) rrUr$rPprobsscoresr_candidate_labelresults r postprocess/ZeroShotImageClassificationPipeline.postprocesss(,,-?@x(+ tzz((33 3MM&)11"5E\\^Ffd++ NNrN*2226E\\^Ffd++ +1V1NTc*d *d& 6*d    s)C>rr')NzThis is a photo of {}.NN)__name__ __module__ __qualname____firstlineno____doc___load_processor_load_image_processor_load_feature_extractor_load_tokenizerrrrstrrerdictr)r5rMrVrm__static_attributes__ __classcell__)rs@r rrse@O #OV #3 -.#BFs)#WZ# d38n ##)#Ym!44)HLS )]`) d4S>" #)))TS$s)]D4GGH)Ts))T )T d38n T$sCx.%9 : : )TV )4 ."r"r) collectionsrtypingrrrutilsrr r r r baserrPILr image_utilsrramodels.auto.modeling_autor get_loggerrologgerrrr"r rsy ''5(b   H %,FGf(fHfr"