rh!(ddlZddlmZddlmZmZmZddlmZm Z m Z m Z m Z m Z ddlmZmZe r ddlmZdd lmZe r ddlZdd lmZe r dd lmZdd lmZe j8eZeed GddeZy)N)UserDict)AnyUnionoverload)add_end_docstringsis_tf_availableis_torch_availableis_vision_availableloggingrequires_backends)Pipelinebuild_pipeline_init_args)Image) load_image)6MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING_NAMES)9TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING_NAMES)stable_softmaxT)has_image_processorcXeZdZdZdZdZdZdZfdZe de e dfde e de d e ee e ffd Ze de e e e dfde e de d e e ee e ffd Zde e e e de dfde e de d e e ee e fe e ee e ffffd Zdd Z ddZdZdZxZS)#ZeroShotImageClassificationPipelineaL 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 t|di|t|d|j|jdk(r t yt y)Nvisiontf)super__init__r check_model_type frameworkrr)selfkwargs __class__s /var/www/html/ai-insurance-compliance-backend/venv/lib/python3.12/site-packages/transformers/pipelines/zero_shot_image_classification.pyrz,ZeroShotImageClassificationPipeline.__init__HsH "6"$) ~~% F H imagez Image.Imagecandidate_labelsr"returnc yNrr!r&r'r"s r$__call__z,ZeroShotImageClassificationPipeline.__call__Rs #r%c yr*rr+s r$r,z,ZeroShotImageClassificationPipeline.__call__Ws&)r%c ld|vr|jd}| tdt| |fd|i|S)a Assign labels to the image(s) passed as inputs. Args: image (`str`, `list[str]`, `PIL.Image` or `list[PIL.Image]`): The pipeline handles three types of images: - A string containing a http link pointing to an image - A string containing a local path to an image - An image loaded in PIL directly candidate_labels (`list[str]`): The candidate labels for this image. They will be formatted using *hypothesis_template*. hypothesis_template (`str`, *optional*, defaults to `"This is a photo of {}"`): The format used in conjunction with *candidate_labels* to attempt the image classification by replacing the placeholder with the candidate_labels. Pass "{}" if *candidate_labels* are already formatted. timeout (`float`, *optional*, defaults to None): The maximum time in seconds to wait for fetching images from the web. If None, no timeout is set and the call may block forever. Return: A list of dictionaries containing one entry per proposed label. Each dictionary contains the following keys: - **label** (`str`) -- One of the suggested *candidate_labels*. - **score** (`float`) -- The score attributed by the model to that label. It is a value between 0 and 1, computed as the `softmax` of `logits_per_image`. imageszSCannot call the zero-shot-image-classification pipeline without an images argument!r')pop ValueErrorrr,)r!r&r'r"r#s r$r,z,ZeroShotImageClassificationPipeline.__call__\sHJ v JJx(E =rs swS8HSFSSr%c i}d|vr|d|d<d|vr|d|d<d|vr|d|d<|tjdt||d<|iifS)Nr'timeouthypothesis_templatez^The `tokenizer_kwargs` argument is deprecated and will be removed in version 5 of Transformerstokenizer_kwargs)warningswarn FutureWarning)r!r5r"preprocess_paramss r$_sanitize_parametersz8ZeroShotImageClassificationPipeline._sanitize_parameterss  '4:;M4N 0 1  +1)+< i ( F *7=>S7T 3 4  ' MMp 5E 0 1 "b((r%c|i}t||}|j|g|j}|jdk(r|j|j}||d<|Dcgc]}|j |}}ddi} d|j jjvr| jdd d | j||j|fd |ji| } | g|d <|Scc}w) N)r3)r/return_tensorsptr'paddingTsiglip max_length@)r>r@ truncationr< text_inputs) rimage_processorr to torch_dtypeformatmodelconfig model_typeupdate tokenizer) r!r&r'r4r3r5inputsx sequencestokenizer_default_kwargsrCs r$ preprocessz.ZeroShotImageClassificationPipeline.preprocesss  #! 5'2%%eWT^^%T >>T !YYt//0F%5!"?"&&}5 k!nh /%a.K&a.+K$**;{;l;!1.. r%c|jd}|dd}|jdk(rjd|jjjvrHt j |jd}|j}t|ts|g}n|jdk(rE|jdjd}|j}t|tsW|g}nS|jdk(r,t|d }|jj}ntd |jtt!||d Dcgc] \}}||d  }}}|Scc}}w)Nr'rSrr=r?)dimr)axiszUnsupported framework: c|d S)Nrr)rNs r$zAZeroShotImageClassificationPipeline.postprocess..s_`ab_c^cr%)key)scorelabel)r0r rHrIrJtorchsigmoidsqueezetolistrTlistsoftmaxrnumpyr1sortedzip) r!rXr'rSprobsscoresracandidate_labelresults r$ postprocessz/ZeroShotImageClassificationPipeline.postprocesssF(,,-?@x(+ >>T !h$**2C2C2N2N&NMM&)11"5E\\^Ffd+  ^^t #NNrN*2226E\\^Ffd+  ^^t #"63E[[]))+F6t~~6FGH H+1V=M1NTc*d &o 6    sE)r*)NzThis is a photo of {}.NN)__name__ __module__ __qualname____doc___load_processor_load_image_processor_load_feature_extractor_load_tokenizerrrrstrrgrdictr,r:rQrYrp __classcell__)r#s@r$rr!sj@O #O #3 -.#BFs)#WZ# d38n ##)49d=&99:)NRSVi)cf) d4S>" #)))TS$s)]D4GGH)Ts))T )T tDcN#T$tCH~*>%?? @ )TV)(4 0"r%r) r6 collectionsrtypingrrrutilsrr r r r r baserrPILr image_utilsrrcmodels.auto.modeling_autormodels.auto.modeling_tf_autortf_utilsr get_loggerrqloggerrrr%r$rs ''5(bh)   H %,FGv(vHvr%