a h.@sdZddlmZddlmZmZmZddlmZddl m Z m Z m Z ddl mZmZmZmZddlmZmZdd lmZerdd lmZeeZed d d ZddZGdddeddZGdddeddZGdddeZ dgZ!dS)z Processor class for IDEFICS2. ) accumulate) TYPE_CHECKINGOptionalUnion) BatchFeature) ImageInputis_valid_image load_image) ImagesKwargsProcessingKwargsProcessorMixinUnpack) AddedToken TextInput)logging)PreTokenizedInput)returncCst|to|dS)Nhttp) isinstancestr startswith)valrl/var/www/html/assistant/venv/lib/python3.9/site-packages/transformers/models/idefics2/processing_idefics2.pyis_url)srcCst|pt|SN)rr )elemrrris_image_or_image_url-src@seZdZUeeed<dS)Idefics2ImagesKwargs image_seq_lenN)__name__ __module__ __qualname__rint__annotations__rrrrr1s rF)totalc@s(eZdZUeed<ddddidZdS)Idefics2ProcessorKwargs images_kwargsTF)add_special_tokenspaddingZis_split_into_words) text_kwargsr(N)r!r"r#rr% _defaultsrrrrr'5s r'cseZdZdZddgZdZdZdeee dfd d Z d d Z de e ee eee fe ed eeed feeedddZZS)Idefics2Processora Constructs a IDEFICS2 processor which wraps a LLama tokenizer and IDEFICS2 image processor into a single processor. [`IdeficsProcessor`] offers all the functionalities of [`Idefics2ImageProcessor`] and [`LlamaTokenizerFast`]. See the docstring of [`~IdeficsProcessor.__call__`] and [`~IdeficsProcessor.decode`] for more information. Args: image_processor (`Idefics2ImageProcessor`): An instance of [`Idefics2ImageProcessor`]. The image processor is a required input. tokenizer (`PreTrainedTokenizerBase`, *optional*): An instance of [`PreTrainedTokenizerBase`]. This should correspond with the model's text model. The tokenizer is a required input. image_seq_len (`int`, *optional*, defaults to 64): The length of the image sequence i.e. the number of tokens per image in the input. This parameter is used to build the string from the input prompt and image tokens and should match the config.perceiver_config.resampler_n_latents value for the model used. chat_template (`str`, *optional*): A Jinja template which will be used to convert lists of messages in a chat into a tokenizable string. image_processor tokenizerZIdefics2ImageProcessorZ AutoTokenizerN@)r chat_templatec s|durtd|dur tdt|dsxtddddj|_tddddj|_d |j|jgi}||||j|_n|j |_|j|_|j|_td ddd|_ |d |j gi||_ t j |||d dS) Nz)You need to specify an `image_processor`.z"You need to specify a `tokenizer`. image_tokenzFT) normalizedZspecialzZadditional_special_tokensz)r1) ValueErrorhasattrrcontentfake_image_tokenr2r)Zconvert_tokens_to_idsZimage_token_idZimage_boundary_tokenZend_of_utterance_tokenr super__init__)selfr.r/r r1kwargsZ tokens_to_add __class__rrr9Zs"  zIdefics2Processor.__init__cCsTg}|D]F}g}|D].}t|r,||qt|r|t|q||q|Sr)r appendrr )r:ZpromptsZ prompt_imagespromptimagesrrrr_extract_images_from_promptsss  z.Idefics2Processor._extract_images_from_promptsr)r@textr;rc s|durdurtd|jtfd|jji|}|ddd}|durN|n|j}|ddd}g} i} |dur\t|tr|g}n t|t st|dtstd |j } |j } | | || } |j j r| d } |d 9}g}|D]@}| || || | }|| | | }||q|j|fi|d}|j||d gd | |durtrzggntt tfrtdr|durt| tkrtd | dt| d| dtd dgt t| fddtt| Dngn>tt tfsTtdt tfsTtddsTtdddD}|dur|| kstd| d|dddD|j fi|d}| |t| |dS)a Processes the input prompts and returns a BatchEncoding. Example: ```python >>> import requests >>> from transformers import Idefics2Processor >>> from transformers.image_utils import load_image >>> processor = Idefics2Processor.from_pretrained("HuggingFaceM4/idefics2-8b", image_seq_len=2) >>> processor.image_processor.do_image_splitting = False # Force as False to simplify the example >>> url1 = "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" >>> url2 = "https://cdn.britannica.com/59/94459-050-DBA42467/Skyline-Chicago.jpg" >>> image1, image2 = load_image(url1), load_image(url2) >>> images = [[image1], [image2]] >>> text = [ ... "In this image, we see", ... "bla bla bla", ... ] >>> outputs = processor(images=images, text=text, return_tensors="pt", padding=True) >>> input_ids = outputs.input_ids >>> input_tokens = processor.tokenizer.batch_decode(input_ids) >>> print(input_tokens) [' In this image, we see', ' bla bla bla'] ``` Args: images (`PIL.Image.Image`, `np.ndarray`, `torch.Tensor`, `list[PIL.Image.Image]`, `list[np.ndarray]`, `list[torch.Tensor]`, *optional*): The image or batch of images to be prepared. Each image can be a PIL image, NumPy array or PyTorch tensor. If is of type `list[ImageInput]`, it's assumed that this is for a single prompt i.e. of batch size 1. text (`Union[TextInput, PreTokenizedInput, list[TextInput], list[PreTokenizedInput]]`, *optional*): The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings (pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set `is_split_into_words=True` (to lift the ambiguity with a batch of sequences). Wherever an image token, `` is encountered it is expanded to `` + `` * `image_seq_len` * `. return_tensors (`Union[str, TensorType]`, *optional*): If set, will return tensors of a particular framework. See [`PreTrainedTokenizerFast.__call__`] for more information. Nz+You must provide either `text` or `images`.Ztokenizer_init_kwargsr(r r+return_tensorsrzAInvalid input text. Please provide a string, or a list of stringsimage)Z modalitieszThe total number of zP tokens in the prompts should be the same as the number of images passed. Found  z tokens and z images.cs$g|]}||dqS)r).0iZcumsum_images_in_textr@rr sz.Idefics2Processor.__call__..zdInvalid input images. Please provide a single image or a list of images or a list of list of images.cSsg|] }t|qSr)lenrHsamplerrrrKz!The number of images in the text z and images z should be the same.cSsg|]}dd|DqS)cSsg|] }t|qSr)r )rHZimrrrrKrOz9Idefics2Processor.__call__...rrMrrrrKrO)Z tensor_type)r4Z _merge_kwargsr'r/Z init_kwargspopr rrlistr7r2r.Zdo_image_splittingr>countreplaceZ_check_special_mm_tokensupdatertuplesumrLrranger)r:r@rBZaudioZvideosr;Z output_kwargsr rCZn_images_in_textinputsr7r2Z image_strZprompt_stringsrNZ text_inputsZn_images_in_imagesZ image_inputsrrJr__call__s6             zIdefics2Processor.__call__)Nr0N)NNNN)r!r"r#__doc__ attributesZimage_processor_classZtokenizer_classr$rrr9rArrrQrrr'rrY __classcell__rrr<rr-Bs&r-N)"rZ itertoolsrtypingrrrZfeature_extraction_utilsrZ image_utilsrr r Zprocessing_utilsr r r rZtokenization_utils_baserrutilsrrZ get_loggerr!loggerboolrrrr'r-__all__rrrrs"      I