fThZMXSSKJrJrJrJr SSKJr SSKJrJ r J r SSK J r J r Jr SSKJrJrJr SSKJr \"5(aSSKr"S S \ S S 9rS r\"S5Vs/sH nSUSS3PM sn\"S5Vs/sH nSUSS3PM sn-rSr"SS\ 5rS/rgs snfs snf))ClassVarListOptionalUnion) BatchFeature) ImageInputis_valid_imagemake_flat_list_of_images)ProcessingKwargsProcessorMixinUnpack) AddedTokenPreTokenizedInput TextInput)is_torch_availableNc.\rSrSrSS0SSS.SS0S .rS rg ) ColPaliProcessorKwargs$paddinglongestchannels_firstT) data_formatdo_convert_rgbreturn_tensorspt) text_kwargs images_kwargs common_kwargsN)__name__ __module__ __qualname____firstlineno__ _defaults__static_attributes__r f/var/www/auris/envauris/lib/python3.13/site-packages/transformers/models/colpali/processing_colpali.pyrr$s, y ," +D1 Ir'rF)totalziz4>z3cX2-U-UUS3$)a Builds a string from the input prompt and image tokens. For example, for the call: build_string_from_input( prompt="Prefix str" bos_token="", image_seq_len=3, image_token="", ) The output will be: "Initial str" Args: prompt (`List[Union[str, ImageInput]]`): The input prompt. bos_token (`str`): The beginning of sentence token. image_seq_len (`int`): The length of the image sequence. image_token (`str`): The image token. num_images (`int`): Number of images in the prompt.  r prompt bos_token image_seq_len image_token num_imagess r(build_string_from_inputr45s"&)J6 7 {6(" MMr'c ^\rSrSr%SrSS/rS/rSrSrSr \ \ \ S 'S r \ \ \ S 'S"U4S jjrS#S \S\\\\\\\4S\\S\4SjjrSrSr\S5r\S\ 4Sj5rS$S \S\\S\4SjjrS\\\\4S\\S\4SjrS%S\S\S4S\S\S4S\S\ SS\S\ 4SS4 S jjr!S!r"U=r#$)&ColPaliProcessorKa Constructs a ColPali processor which wraps a PaliGemmaProcessor and special methods to process images and queries, as well as to compute the late-interaction retrieval score. [`ColPaliProcessor`] offers all the functionalities of [`PaliGemmaProcessor`]. See the [`~PaliGemmaProcessor.__call__`] for more information. Args: image_processor ([`SiglipImageProcessor`], *optional*): The image processor is a required input. tokenizer ([`LlamaTokenizerFast`], *optional*): The tokenizer is a required input. chat_template (`str`, *optional*): A Jinja template which will be used to convert lists of messages in a chat into a tokenizable string. image_processor tokenizer chat_template)SiglipImageProcessorSiglipImageProcessorFast)GemmaTokenizerGemmaTokenizerFastzDescribe the image.visual_prompt_prefixz Question: query_prefixc >Uc [S5eUc [S5e[US5(d [S5eURUl[US5(dK[[SSS9nS U/0nUR U5 UR [5Ul[UlO"URUlURUlUR[5 SUl SUl [TU]9XUS 9 g) Nz)You need to specify an `image_processor`.z"You need to specify a `tokenizer`.image_seq_lengthz;Image processor is missing an `image_seq_length` attribute.r2FT) normalizedspecialadditional_special_tokens)r:) ValueErrorhasattrrBr IMAGE_TOKENadd_special_tokensconvert_tokens_to_idsimage_token_idr2 add_tokens EXTRA_TOKENS add_bos_token add_eos_tokensuper__init__)selfr8r9r:kwargsr2 tokens_to_add __class__s r(rQColPaliProcessor.__init__ds  "HI I  AB B(:;;Z[ [ / @ @y-00$[UDQK8;-HM  ( ( 7"+"A"A+"ND *D "+":":D (44D \*"' "'  =Qr'imagestextrSreturnc UR"[4SURR0UD6nUSR SS5nUbSOSnUcUc [ S5eUbUb [ S5eUGb[ U5(aU/nOw[U[5(a[ US 5(aON[U[5(a.[US [5(a[ US S 5(d [ S 5eUR/[U5-n UV s/sHoRS 5PM nn [X5V V s/sHTup[U URRUR[ [U [5(a [U 5OS S 9PMV n n n [#U5nUR$"U40USD6SnUSR'SS5bUSS==UR- ss'UR"U 4SS0USD6n0UESU0EnU(a.USR)USS :HS5nUR+SU05 [-US9$Ub[U[.5(aU/nO8[U[5(a[US [.5(d [ S5eUcUR0S-n/nUHDnURRUR2-U-nUU- nUS- nUR5U5 MF USR'SS5USS'UR"U4SS0USD6nU$gs sn fs sn n f)a Main method to prepare for the model either (1) one or several texts, either (2) one or several image(s). This method is custom wrapper around the PaliGemmaProcessor's [`~PaliGemmaProcessor.__call__`] method adapted for the ColPali model. It cannot process both text and images at the same time. When preparing the text(s), this method forwards the `text` and `kwargs` arguments to LlamaTokenizerFast's [`~LlamaTokenizerFast.__call__`]. When preparing the image(s), this method forwards the `images` and `kwargs` arguments to SiglipImageProcessor's [`~SiglipImageProcessor.__call__`]. Please refer to the docstring of the above two methods for more information. Args: images (`PIL.Image.Image`, `np.ndarray`, `torch.Tensor`, `List[PIL.Image.Image]`, `List[np.ndarray]`, `List[torch.Tensor]`): The image or batch of images to be prepared. Each image can be a PIL image, NumPy array or PyTorch tensor. In case of a NumPy array/PyTorch tensor, each image should be of shape (C, H, W), where C is a number of channels, H and W are image height and width. text (`str`, `List[str]`, `List[List[str]]`): 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). return_tensors (`str` or [`~utils.TensorType`], *optional*): If set, will return tensors of a particular framework. Acceptable values are: - `'tf'`: Return TensorFlow `tf.constant` objects. - `'pt'`: Return PyTorch `torch.Tensor` objects. - `'np'`: Return NumPy `np.ndarray` objects. - `'jax'`: Return JAX `jnp.ndarray` objects. Returns: [`BatchFeature`]: A [`BatchFeature`] with the following fields: - **input_ids** -- List of token ids to be fed to a model. - **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when `return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names` and if `text` is not `None`). - **pixel_values** -- Pixel values to be fed to a model. Returned when `images` is not `None`. tokenizer_init_kwargsrsuffixNTFz&Either text or images must be providedz5Only one of text or images can be processed at a timerzAimages must be an image, list of images or list of list of imagesRGBr.r pixel_values max_lengthreturn_token_type_ids input_idstoken_type_idsilabels)dataz*Text must be a string or a list of strings r-2) _merge_kwargsrr9 init_kwargspoprFr isinstancelistr?lenconvertzipr4r0rBrHr r8get masked_fillupdaterstrquery_augmentation_tokenr@append)rRrWrXaudiovideosrS output_kwargsr\ra texts_docimager/ image_list input_stringsr_inputs return_datard texts_queryquery batch_querys r(__call__ColPaliProcessor.__call__sZ** " "&.."<"<  }-11(DA(.(: ?ummE*F?+.i*@ +A&F(!"nn66"&"7"7 +2S>SS:^^&+ .F CVB^\BK$ ,88@P9QUV9VX\]""Hf#56[1 1  $$$v t,,DGS1I1I !MNN~66;%'K0043D3DDuL ""5)  :G}9U9Y9YZfhj9kM- ( 6..&+ .K  1C@ s L:AL?c:URR"U0UD6$)z This method forwards all its arguments to GemmaTokenizerFast's [`~PreTrainedTokenizer.batch_decode`]. Please refer to the docstring of this method for more information. )r9 batch_decoderRargsrSs r(rColPaliProcessor.batch_decodes ~~**D;F;;r'c:URR"U0UD6$)z This method forwards all its arguments to GemmaTokenizerFast's [`~PreTrainedTokenizer.decode`]. Please refer to the docstring of this method for more information. )r9decoders r(rColPaliProcessor.decode s ~~$$d5f55r'cURRnURRn[[R X-55$N)r9model_input_namesr8rldictfromkeys)rRtokenizer_input_namesimage_processor_input_namess r(r"ColPaliProcessor.model_input_namess< $ @ @&*&:&:&L&L#DMM"7"UVWWr'c.URR$)zr Return the query augmentation token. Query augmentation buffers are used as reasoning buffers during inference. )r9 pad_token)rRs r(rt)ColPaliProcessor.query_augmentation_tokens~~'''r'c *UR"SSU0UD6$)a  Prepare for the model one or several image(s). This method is a wrapper around the `__call__` method of the ColPaliProcessor's [`ColPaliProcessor.__call__`]. This method forwards the `images` and `kwargs` arguments to SiglipImageProcessor's [`~SiglipImageProcessor.__call__`]. Args: images (`PIL.Image.Image`, `np.ndarray`, `torch.Tensor`, `List[PIL.Image.Image]`, `List[np.ndarray]`, `List[torch.Tensor]`): The image or batch of images to be prepared. Each image can be a PIL image, NumPy array or PyTorch tensor. In case of a NumPy array/PyTorch tensor, each image should be of shape (C, H, W), where C is a number of channels, H and W are image height and width. return_tensors (`str` or [`~utils.TensorType`], *optional*): If set, will return tensors of a particular framework. Acceptable values are: - `'tf'`: Return TensorFlow `tf.constant` objects. - `'pt'`: Return PyTorch `torch.Tensor` objects. - `'np'`: Return NumPy `np.ndarray` objects. - `'jax'`: Return JAX `jnp.ndarray` objects. Returns: [`BatchFeature`]: A [`BatchFeature`] with the following fields: - **input_ids** -- List of token ids to be fed to a model. - **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when `return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names` and if `text` is not `None`). - **pixel_values** -- Pixel values to be fed to a model. Returned when `images` is not `None`. rWr r)rRrWrSs r(process_imagesColPaliProcessor.process_images sB}}5F5f55r'c *UR"SSU0UD6$)ag Prepare for the model one or several texts. This method is a wrapper around the `__call__` method of the ColPaliProcessor's [`ColPaliProcessor.__call__`]. This method forwards the `text` and `kwargs` arguments to LlamaTokenizerFast's [`~LlamaTokenizerFast.__call__`]. Args: text (`str`, `List[str]`, `List[List[str]]`): 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). return_tensors (`str` or [`~utils.TensorType`], *optional*): If set, will return tensors of a particular framework. Acceptable values are: - `'tf'`: Return TensorFlow `tf.constant` objects. - `'pt'`: Return PyTorch `torch.Tensor` objects. - `'np'`: Return NumPy `np.ndarray` objects. - `'jax'`: Return JAX `jnp.ndarray` objects. Returns: [`BatchFeature`]: A [`BatchFeature`] with the following fields: - **input_ids** -- List of token ids to be fed to a model. - **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when `return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names` and if `text` is not `None`). rXr r)rRrXrSs r(process_queries ColPaliProcessor.process_queriesCs@}}1$1&11r'query_embeddingsz torch.Tensorpassage_embeddings batch_size output_dtypez torch.dtype output_devicez torch.devicec [U5S:Xa [S5e[U5S:Xa [S5eUSRUSR:wa [S5eUSRUSR:wa [S5eUcUSRn/n[ S[U5U5GHn/n[ R RRRXXs-SSS9n [ S[U5U5H}n [ R RRRX*X-SSS9n UR[ R"SX5RS S 9SRS S 95 M UR[ R"US S 9RU5RU55 GM [ R"USS 9$) a Compute the late-interaction/MaxSim score (ColBERT-like) for the given multi-vector query embeddings (`qs`) and passage embeddings (`ps`). For ColPali, a passage is the image of a document page. Because the embedding tensors are multi-vector and can thus have different shapes, they should be fed as: (1) a list of tensors, where the i-th tensor is of shape (sequence_length_i, embedding_dim) (2) a single tensor of shape (n_passages, max_sequence_length, embedding_dim) -> usually obtained by padding the list of tensors. Args: query_embeddings (`Union[torch.Tensor, List[torch.Tensor]`): Query embeddings. passage_embeddings (`Union[torch.Tensor, List[torch.Tensor]`): Passage embeddings. batch_size (`int`, *optional*, defaults to 128): Batch size for computing scores. output_dtype (`torch.dtype`, *optional*, defaults to `torch.float32`): The dtype of the output tensor. If `None`, the dtype of the input embeddings is used. output_device (`torch.device` or `str`, *optional*, defaults to "cpu"): The device of the output tensor. Returns: `torch.Tensor`: A tensor of shape `(n_queries, n_passages)` containing the scores. The score tensor is saved on the "cpu" device. rzNo queries providedzNo passages providedz/Queries and passages must be on the same devicez-Queries and passages must have the same dtypeT) batch_first padding_valuez bnd,csd->bcnsr)dimr^)rmrFdevicedtyperangetorchnnutilsrnn pad_sequencerueinsummaxsumcatto) rRrrrrrscoresi batch_scores batch_queriesjbatch_passagess r(score_retrieval ColPaliProcessor.score_retrievales@  A %23 3 ! "a '34 4 A  % %);A)>)E)E ENO O A  $ $(:1(=(C(C CLM M  +A.44L%'q#./:\]"A"##LL-PTTYZT[\]^bbghbi C MM%))La8;;LILL][ \=yyQ''r')rBr2rK)NNN)NNNNr)r+Ncpu)$r!r"r#r$__doc__ attributes valid_kwargsimage_processor_classtokenizer_classr?rrs__annotations__r@rQr rrrrrrrrrrpropertyrrtrrintrrr& __classcell__)rUs@r(r6r6Ks $[1J#$LP>O*?(3-?".L(3-. RD"^b }}I0$y/4HYCZZ[} /0 } }~<6XX (#(("!6!6/0!6  !6F 2ItI./ 2/0 2  2L0449 >(^0D DE>(".$~2F"FG>( >( }- >( ^S01 >( >(>(r'r6)typingrrrrfeature_extraction_utilsr image_utilsr r r processing_utilsr r rtokenization_utils_baserrrrrrrrHrrMr4r6__all__)rs0r(rs.324OOHHOO' -U  ).t5A$qgQ5RWX[R\8]R\Q4#waR\8]] N,X(~X(v  i 68]s B":B'