a  d@sddlmZmZddlmZmZmZmZmZm Z ddl m Z m Z er^ddl mZddlmZerpddlmZerdd lZdd lmZeeZee Gd d d e Zd S) )ListUnion)add_end_docstringsis_tf_availableis_torch_availableis_vision_availableloggingrequires_backends)PIPELINE_INIT_ARGSPipeline)Image) load_image)!TF_MODEL_FOR_VISION_2_SEQ_MAPPINGN)MODEL_FOR_VISION_2_SEQ_MAPPINGcsleZdZdZfddZdddZeeeededfdfd d Z dd d Z dd dZ ddZ Z S)ImageToTextPipelinea Image To Text pipeline using a `AutoModelForVision2Seq`. This pipeline predicts a caption for a given image. 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). cs8tj|i|t|d||jdkr.tntdS)NZvisiontf)super__init__r Zcheck_model_type frameworkrr)selfargskwargs __class__m/var/www/html/stable-diffusion-webui/venv/lib/python3.9/site-packages/transformers/pipelines/image_to_text.pyr6s  zImageToTextPipeline.__init__NcCsji}i}|dur||d<|dur(||d<|dur`d|vr@i|d<d|dvrTtd||dd<||ifS)Npromptgenerate_kwargsmax_new_tokenszp'max_new_tokens' is defined twice, once in 'generate_kwargs' and once as a direct parameter, please use only one) ValueError)rr rrZforward_kwargsZpreprocess_paramsrrr_sanitize_parameters=s  z(ImageToTextPipeline._sanitize_parametersz Image.Image)imagesc stj|fi|S)a Assign labels to the image(s) passed as inputs. Args: images (`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. 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. )r__call__)rr#rrrrr$QszImageToTextPipeline.__call__cCs&t|}|durt|ts.tdt|d|jjj}|dkr|j||j d}|j |ddj }|j j g|}t |d}|d|in`|d kr|j|||j d }nD|d kr|j||j d}|j ||j d }||ntd |dn|j||j d}|jjjdkr"|dur"d|d<|S)Nz&Received an invalid text input, got - zy - but expected a single string. Note also that one single text can be provided for conditional image to text generation.git)r#return_tensorsF)textZadd_special_tokensr input_idsZ pix2struct)r#Z header_textr&zvision-encoder-decoder)r&z Model type z- does not support conditional text generation)r isinstancestrr!typemodelconfig model_typeZimage_processorr tokenizerr(Z cls_token_idtorchZtensorZ unsqueezeupdate)rimagerr. model_inputsr(Z text_inputsrrr preprocessls0   zImageToTextPipeline.preprocesscCs6|dur i}||jj}|jj|fi||}|S)N)popr,Zmain_input_namegenerate)rr3rinputs model_outputsrrr_forwards zImageToTextPipeline._forwardcCs0g}|D]"}d|jj|ddi}||q|S)NZgenerated_textT)Zskip_special_tokens)r/decodeappend)rr8recordsZ output_idsrecordrrr postprocesss zImageToTextPipeline.postprocess)NNN)N)N)__name__ __module__ __qualname____doc__rr"rr*rr$r4r9r> __classcell__rrrrrs  & ' r)typingrrutilsrrrrr r baser r ZPILrZ image_utilsrZmodels.auto.modeling_tf_autorr0Zmodels.auto.modeling_autorZ get_loggerr?loggerrrrrrs