rhe(ddlmZmZmZddlmZddlmZmZm Z m Z m Z m Z ddl mZmZe r ddlmZddlmZerdd lmZe r dd lZdd lmZe j2eZeed d GddeZy ))AnyUnionoverload)GenerationConfig)add_end_docstringsis_tf_availableis_torch_availableis_vision_availableloggingrequires_backends)Pipelinebuild_pipeline_init_args)Image) load_image)'TF_MODEL_FOR_VISION_2_SEQ_MAPPING_NAMESN)$MODEL_FOR_VISION_2_SEQ_MAPPING_NAMEST) has_tokenizerhas_image_processorc eZdZdZdZdZdZdZdZe dZ fdZ ddZ e deed fd ed eeeeffd Ze deeeed fd ed eeeeeffd Zdeeeed ed fffd ZddZdZdZxZS)ImageToTextPipelinea Image To Text pipeline using a `AutoModelForVision2Seq`. This pipeline predicts a caption for a given image. Unless the model you're using explicitly sets these generation parameters in its configuration files (`generation_config.json`), the following default values will be used: - max_new_tokens: 256 Example: ```python >>> from transformers import pipeline >>> captioner = pipeline(model="ydshieh/vit-gpt2-coco-en") >>> captioner("https://huggingface.co/datasets/Narsil/image_dummy/raw/main/parrots.png") [{'generated_text': 'two birds are standing next to each other '}] ``` Learn more about the basics of using a pipeline in the [pipeline tutorial](../pipeline_tutorial) This image to text pipeline can currently be loaded from pipeline() using the following task identifier: "image-to-text". See the list of available models on [huggingface.co/models](https://huggingface.co/models?pipeline_tag=image-to-text). TF)max_new_tokensct||i|t|d|j|jdk(r t yt y)Nvisiontf)super__init__r check_model_type frameworkrr)selfargskwargs __class__s w/var/www/html/ai-insurance-compliance-backend/venv/lib/python3.12/site-packages/transformers/pipelines/image_to_text.pyrzImageToTextPipeline.__init__TsD $)&)$) 7;~~7M 3 Sw ci}i}|||d<|||d<|||d<|"|d|vr td|j||j|j|d<|j|j|d<|j|d<||ifS)Nprompttimeoutrzp`max_new_tokens` is defined both as an argument and inside `generate_kwargs` argument, please use only 1 versionassistant_model tokenizerassistant_tokenizer) ValueErrorupdater+r-r,)r"rgenerate_kwargsr)r*forward_paramspreprocess_paramss r&_sanitize_parametersz(ImageToTextPipeline._sanitize_parameters[s  *0 h '  +2 i (  %/=N+ ,  &).>/.Q &  ! !/ 2    +040D0DN, -  # # /*...N; '484L4LN0 1 ."44r'inputsz Image.Imager$returnc yNr"r4r$s r&__call__zImageToTextPipeline.__call__vsber'c yr7r8r9s r&r:zImageToTextPipeline.__call__ystwr'c hd|vr|jd}| tdt| |fi|S)a Assign labels to the image(s) passed as inputs. Args: inputs (`str`, `list[str]`, `PIL.Image` or `list[PIL.Image]`): The pipeline handles three types of images: - A string containing a HTTP(s) link pointing to an image - A string containing a local path to an image - An image loaded in PIL directly The pipeline accepts either a single image or a batch of images. max_new_tokens (`int`, *optional*): The amount of maximum tokens to generate. By default it will use `generate` default. generate_kwargs (`Dict`, *optional*): Pass it to send all of these arguments directly to `generate` allowing full control of this function. 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 or a list of list of `dict`: Each result comes as a dictionary with the following key: - **generated_text** (`str`) -- The generated text. imageszBCannot call the image-to-text pipeline without an inputs argument!)popr.rr:)r"r4r$r%s r&r:zImageToTextPipeline.__call__|sA< v ZZ)F >ab bw1&11r'ct||}|tjdt|tst dt |d|jjj}|dk(r|j||j}|jdk(r|j|j}|j|d j}|jj g|z}t#j$|j'd }|j)d |in|d k(rI|j|||j }|jdk(r|j|j}n|dk7rv|j||j}|jdk(r|j|j}|j||j}|j)|nVt d|d|j||j}|jdk(r|j|j}|jjjdk(r|d|d <|S)N)r*uPassing `prompt` to the `image-to-text` pipeline is deprecated and will be removed in version 4.48 of 🤗 Transformers. Use the `image-text-to-text` pipeline insteadz&Received an invalid text input, got - zy - but expected a single string. Note also that one single text can be provided for conditional image to text generation.git)r=return_tensorsptF)textadd_special_tokensr input_ids pix2struct)r= header_textrAzvision-encoder-decoder)rAz Model type z- does not support conditional text generation)rlogger warning_once isinstancestrr.typemodelconfig model_typeimage_processorr!to torch_dtyper,rE cls_token_idtorchtensor unsqueezer/)r"imager)r*rO model_inputsrE text_inputss r& preprocesszImageToTextPipeline.preprocesss<5'2     W fc* >T)#/??43C3C#DL NN5NQ[[ !^^889IE !LL3==a@ ##[)$<=|+#335feieses3t >>T)#/??43C3C#DL77#335QUQ_Q_3` >>T)#/??43C3C#DL"nnVDNNnS ##K0!;zl:g!hii //uT^^/\L~~%+t/?/?@ ::   ' '5 0V^(,L %r'c d|vr-t|dtrtd|dDrd|d<d|vr|j|d<|j |j j }|j j|fi||}|S)NrEc3$K|]}|du ywr7r8).0xs r& z/ImageToTextPipeline._forward..sA!AIAsgeneration_config)rJlistallr`r>rMmain_input_namegenerate)r"rXr0r4 model_outputss r&_forwardzImageToTextPipeline._forwards < '< 4d;A|K'@AA(,L % o 5373I3IO/ 0 !!$**"<"<=+ ++FVlVoV r'cxg}|D]2}d|jj|di}|j|4|S)Ngenerated_textT)skip_special_tokens)r,decodeappend)r"rerecords output_idsrecords r& postprocesszImageToTextPipeline.postprocesssQ' #J $.."7"7(,#8#F NN6 " #r')NNNN)NN)__name__ __module__ __qualname____doc___pipeline_calls_generate_load_processor_load_image_processor_load_feature_extractor_load_tokenizerr_default_generation_configrr3rrrKrradictr:rZrfro __classcell__)r%s@r&rr.s4 $O #O!1" 56euS-%78eCeDQUVY[^V^Q_L`ee wuT#Y]0C%CDwPSwX\]abfgjlogobp]qXrww"2uS$s)]DDW%WX"2H1f, r'r)typingrrr generationrutilsrr r r r r baserrPILr image_utilsrmodels.auto.modeling_tf_autorrTmodels.auto.modeling_autor get_loggerrprHrr8r'r&rs (')5(VP   H %,4UYZ[D(D\Dr'