T^(h1dZddlmZddlmZmZmZmZddlm Z ddl m Z m Z m Z ddlmZmZmZmZmZddlmZmZdd lmZerdd lmZej4eZd efd Zd ZGddedZ GddedZ!GddeZ"dgZ#y)z Processor class for IDEFICS2. ) accumulate) TYPE_CHECKINGListOptionalUnion) BatchFeature) ImageInputis_valid_image load_image) ImagesKwargsProcessingKwargsProcessorMixinUnpack!_validate_images_text_input_order) AddedToken TextInput)logging)PreTokenizedInputreturncHt|txr|jdS)Nhttp) isinstancestr startswith)vals n/var/www/skyplay_api_hub/venv/lib/python3.12/site-packages/transformers/models/idefics2/processing_idefics2.pyis_urlr*s c3  :CNN6$::c2t|xs t|SN)rr )elems ris_image_or_image_urlr#.s $< />$//rceZdZUeeed<y)Idefics2ImagesKwargs image_seq_lenN)__name__ __module__ __qualname__rint__annotations__rrr%r%2s C= rr%F)totalc*eZdZUeed<ddddidZy)Idefics2ProcessorKwargs images_kwargsTF)add_special_tokenspaddingis_split_into_words) text_kwargsr0N)r'r(r)r%r+ _defaultsr,rrr/r/6s$''#'#(  Irr/c eZdZdZddgZddgZdZdZ ddede e ffd Z d Z dd e eeeeeefd e ed eeed fd eedefdZdZdZedZxZS)Idefics2Processora Constructs a IDEFICS2 processor which wraps a LLama tokenizer and IDEFICS2 image processor into a single processor. [`IdeficsProcessor`] offers all the functionalities of [`Idefics2ImageProcessor`] and [`LlamaTokenizerFast`]. See the docstring of [`~IdeficsProcessor.__call__`] and [`~IdeficsProcessor.decode`] for more information. Args: image_processor (`Idefics2ImageProcessor`): An instance of [`Idefics2ImageProcessor`]. The image processor is a required input. tokenizer (`PreTrainedTokenizerBase`, *optional*): An instance of [`PreTrainedTokenizerBase`]. This should correspond with the model's text model. The tokenizer is a required input. image_seq_len (`int`, *optional*, defaults to 64): The length of the image sequence i.e. the number of tokens per image in the input. This parameter is used to build the string from the input prompt and image tokens and should match the config.perceiver_config.resampler_n_latents value for the model used. chat_template (`str`, *optional*): A Jinja template which will be used to convert lists of messages in a chat into a tokenizable string. image_processor tokenizerr& chat_templateIdefics2ImageProcessor AutoTokenizerc | td| tdt|dsRtddd|_tddd|_d |j|jgi}|j |n"|j |_|j|_td dd|_|j d |jgi||_t|)||| y) Nz)You need to specify an `image_processor`.z"You need to specify a `tokenizer`. image_tokenzFT) normalizedspecialzadditional_special_tokensz)r:) ValueErrorhasattrrfake_image_tokenr>r1image_boundary_tokenend_of_utterance_tokenr&super__init__)selfr8r9r&r:kwargs tokens_to_add __class__s rrHzIdefics2Processor.__init__\s  "HI I  AB By-0$./JW\fj$kD !))tTD 84;P;PRVRbRb:cdM  ( ( 7$-$B$BD !(44D &01ERWae&f#$$&ADD_D_C`%ab* )=Qrcg}|D]_}g}|D]E}t|r|j| t|s,|jt|G|j|a|Sr!)r appendrr )rIprompts prompt_imagespromptimagesr"s r_extract_images_from_promptsz.Idefics2Processor._extract_images_from_promptsssn  )FF 4!$'MM$'D\MM*T"23  4   ( )rrRtextrrJrc | | tdt||\}}|jtfd|jj i|}|dj dd}||n |j}g}t} |t|tr|g}n.t|tst|dts td|jj} |jj} | | |z| } |jj r| dz} g} |D]\}|j#|j%| |j'| | }|j'| | | }| j#|^|j| fi|d }| j)||t+|r|gg}nt|tt,frt+|dr|t/|t1|k7r*td  d t/|d | d t1|d dgtt3|z}t5t1|Dcgc]}|||||dz}}nO|g}nKt|tt,fs5t|dtt,fst+|dds td|Dcgc] }t1|}}|||k(std|d|d|Dcgc]}|Dcgc] }t7|c}}}}|j|fi|d}| j)|| Scc}wcc}wcc}wcc}}w)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`.tokenizer_init_kwargsr0r&rzAInvalid input text. Please provide a string, or a list of stringsr4zThe total number of zP tokens in the prompts should be the same as the number of images passed. Found  z tokens and z images.zdInvalid input images. Please provide a single image or a list of images or a list of list of images.z!The number of images in the text z and images z should be the same.)rBr _merge_kwargsr/r9 init_kwargspopr&r rrlistrDcontentr>r8do_image_splittingrNcountreplaceupdater#tuplesumlenrranger )rIrRrTaudiovideosrJ output_kwargsr&n_images_in_textinputsrDr> image_strprompt_stringssample text_inputscumsum_images_in_textin_images_in_imagesim image_inputss r__call__zIdefics2Processor.__call__sl N(N 78H7IWiVjj~ GMMF7"z"~7MFM/4//Y-:XYL MM, ' 7 "D8Ms$L/1L4# L>,L9 >L>9L>c:|jj|i|S)z This method forwards all its arguments to LlamaTokenizerFast's [`~PreTrainedTokenizer.batch_decode`]. Please refer to the docstring of this method for more information. )r9 batch_decoderIargsrJs rrwzIdefics2Processor.batch_decodes +t~~**D;F;;rc:|jj|i|S)z This method forwards all its arguments to LlamaTokenizerFast's [`~PreTrainedTokenizer.decode`]. Please refer to the docstring of this method for more information. )r9decoderxs rr{zIdefics2Processor.decodes %t~~$$d5f55rc|jj}|jj}ttj ||zSr!)r9model_input_namesr8r]dictfromkeys)rItokenizer_input_namesimage_processor_input_namess rr}z#Idefics2Processor.model_input_namess? $ @ @&*&:&:&L&L#DMM"7:U"UVWWr)N@N)NNNN)r'r(r)__doc__ attributes valid_kwargsimage_processor_classtokenizer_classr*rrrHrSrr rrrr/r rurwr{propertyr} __classcell__)rLs@rr7r7Cs&$[1J#_5L4%OhlR>ARW_`cWdR. OSbf Gj$z"2Dj9I4JJKGI2DOTJ]E^^_G 01 G GR<6XXrr7N)$r itertoolsrtypingrrrrfeature_extraction_utilsr image_utilsr r r processing_utilsr rrrrtokenization_utils_baserrutilsrr get_loggerr'loggerboolrr#r%r/r7__all__r,rrrs!774AA=<   H %;4;0!