+ j`^RIt^RIHt^RIHtHtHt^RIt^RI t ^RI H t ^RI H t Ht^RIHtHtHtHt^RIHtHtHt^RIHt^R IHt]'d^R IHt!R R ]R R7t]!RR7]!RR]444tR.tR#)N) accumulate) TYPE_CHECKINGOptionalUnion) BatchFeature) ImageInputis_valid_image)MultiModalDataProcessingKwargsProcessorMixinUnpack) AddedToken BatchEncoding TextInput)auto_docstring)requires)PreTokenizedInputc :]tRt^(tRRR/RRRRRR R/R R R //tR tR#)ColModernVBertProcessorKwargs text_kwargspaddinglongest images_kwargsreturn_row_col_infoT data_formatchannels_firstdo_convert_rgb common_kwargsreturn_tensorsptN)__name__ __module__ __qualname____firstlineno__ _defaults__static_attributes__r!}/home/luca/screen-checking/.venv/lib/python3.14/site-packages/transformers/models/colmodernvbert/processing_colmodernvbert.pyrr(s= y   !4 + d *D1 Ir(rF)total)backendscaa]tRt^6to]tRV3RlV3Rlllt]RV3RlRll4tRV3RlRllt RV3RlV3Rlllt V3R lR lt V3R lR lt RR lt RV3RlRlltV3RlRltRV3RlRlltRtVtV;t#)ColModernVBertProcessorcH<V^8dQhRS[RS[R,RS[R,/#) image_seq_lenvisual_prompt_prefixN query_prefix)intstr)format __classdict__s"r) __annotate__$ColModernVBertProcessor.__annotate__;s83D3D  3D "Dj 3DDj3Dr(c <Rp\RRRR7PVn\RRRR7PVn\RRRR7PVnRVnW@nVPVP4VnVPVP4Vn VPVP 4Vn \^4UU u.uF:p\^4F(qPR V^, R V ^, R 24NK* K< up pVn \P!R 4VnR VPVPVP./p VP!V 4VPVP4Vn\"S V`H!W3RV/VBT;'gRVP R2VnT;'gRVnVPVnR#uup pi)aJ image_seq_len (`int`, *optional*, defaults to 64): The length of the image sequence i.e. the number of tokens per image in the input. visual_prompt_prefix (`str`, *optional*): A string that gets tokenized and prepended to the image tokens. query_prefix (`str`, *optional*): A prefix to be used for the query. NzFT) normalizedspecialzzz z*(\n?\n?|\n?)+additional_special_tokens chat_templatez<|begin_of_text|>User:z0Describe the image. Assistant:)rcontentfake_image_token image_tokenend_of_utterance_tokenglobal_image_tagr0convert_tokens_to_idsimage_token_idfake_image_token_idglobal_image_token_idrange row_col_idsrecompile%_regex_to_remove_extra_special_tokensadd_special_tokenssuper__init__r1r2query_augmentation_token) selfimage_processor tokenizerr@r0r1r2kwargsij tokens_to_add __class__s &&&&&&&, r)rR ColModernVBertProcessor.__init__;s$ *+FSXbf g o o%iE4PXX&01ERWae&f&n&n# .*'==d>N>NO#,#B#B4CXCX#Y %.%D%DTEZEZ%["SXYZS[ S[aejklem`a + +eAE7%Awa,H Iem IS[  68ZZ@m5n2 (%%  ++*  $$]3'==d>N>NO [=[TZ[$8% % $T%5%5$66g h !)..B(,(C(C%1 sAG&c <V^8dQhRS[S[S[,,S[S[S[,,,RS[S[RS[S[,S[R,3,RS[R,RS[S[,RS[/#)r/imagestextrr0NrWreturn)rlistrrr3r rr)r5r6s"r)r7r8qs>^>^T*--T*5E0FF>^I2DOTJ]E^^_>^Tz >^ 67 >^  >^r(c `VP!RRVRV/VBwrVP!RRVRV/VBVP!\3RVPP /VBpVeTM VP pVR,PRR4pVR,PRR4pVR,PR R4p/;rVEe@VP!V3/VR ,BwrV PR R4V PR R4VeVPW+R 7wr,VP!V3/VR,Bp V'dWR&.p \V 4Fwwr.pVFYpVR,wppV PVV4pV PVV^, 4pVPVV, ^,4K[ V PV4Ky V'dVPV R,V 4V R&VPW*R.R7MVeVP!RRV/VR,Bp \/V CV CVR7#)z image_seq_len (`int`, *optional*): The length of the image sequence. If not provided, the default value of self.image_seq_len is used. image_seq_len should be equal to int(((image_size // patch_size) ** 2) / (scale_factor**2)) r^r_tokenizer_init_kwargsNrreturn_text_replacement_offsetsFreturn_mm_token_type_idsrrrowscols)images_replacementstext_replacement_offsetsnew_span input_idsmm_token_type_idsimage) modalities)data tensor_typer!)prepare_inputs_layoutvalidate_inputs _merge_kwargsrrV init_kwargsr0pop_process_imagesget_text_with_replacements enumerate char_to_tokenappendcreate_mm_token_type_ids_check_special_mm_tokensr)rTr^r_r0rW output_kwargsrdrer image_inputs text_inputsrhribatch_image_seq_lengthsbatch_idtext_replacement_offsetimage_seq_lensrostartend start_id_pos end_id_poss&&&&, r)__call__ ColModernVBertProcessor.__call__psa11UUdUfU  @F@@@** ) "&.."<"<  *7)B HZHZ *7 *F*J*JKlns*t'#0#?#C#CD^`e#f &}599:JDQ%''  040D0DV0n}]lOm0n -L   VT *   VT *151P1P2Q2.#nnTR]=5QR 2>V :;*,'9BC[9\5H%'N 7%)*%5 s'2'@'@5'Q %0%>%>xq%Q &--j<.G!.KL !8 ,22>B:],7;7T7T#K02I8K 34--dWI-V  ..SdSmM6RSK!@K!@Ff%%!($|#FT5M22~fQi7P7P#UY'ZUY6 T5E5E(FUY$'Z-.C$zBR7S2T,T)"'s+;'>!-8Mb8Q8S1T0U!U | ".|%XF|([$s +#E!E#c <V^8dQhRS[R,RS[S[RS[S[,S[R,3,RS[S[,/#rr)r5r6s"r)r7r8sMT!I2DOTJ]E^^_67 r(c <\SV`!W3/VBVfVf \R4hVeVUu.uFqDPVP4NK ppVeOVUu.uFp\ V4NK ppWW8wd-\RVP RV RVP RV R2 4hR#Vf9\ V4'd&\R\V4 RVP R24hR#R#R#uupiuupi) Nz+You must provide either `text` or `images`.zThe total number of zP tokens in the prompts should be the same as the number of images passed. Found  z tokens and z images per sample.zFound z. tokens in the text but no images were passed.)rQrr ValueErrorrrDranysum) rTr^r_rWrrsublistn_images_in_imagesr[s &&&, r)rr'ColModernVBertProcessor.validate_inputss 77 T R!BH%I&wc'l&"%I#9$.t/?/?.@A""2!31T5E5E4FlSeRffy{: C(8$9$9 S!1231T5E5E4FFtu%: R%Is #C%C*c,<V^8dQhRS[RS[RS[/#)r/r~ image_idxr`)dictr3r4)r5r6s"r)r7r8s#%%%%SV%r(c DVR,UUu.uF qDFqUNK K uppV,pVR,UUu.uF qwFqNK K uppV,p V^8Xd\V ^8XdUVP VP ,VP VP,,VP ,#Rp \ V4Fqp \ V 4FVp V VP RV ^, RV ^, R2,VP VP,,, p KX V R, p Ks V RVP 2VP ,VP VP,,VP ,, p V #uuppiuuppi)rfrgrAr<r=r> )rCrFrDr0rK) rTr~rrWrow_listrow image_rowscol_listcol image_colstext_split_imagesn_hn_ws &&&, r)replace_image_token+ColModernVBertProcessor.replace_image_tokens*6v*>S*>h(3c(c*>ST]^ *6v*>S*>h(3c(c*>ST]^ ?zQ(()**+-%%&$*<*<<=**+- !# Z( ,C%001!#'%ay:;!--.$2D2DDE%- "T)!) T**+,**+-%%&$*<*<<=**+-   % $5TSs FFc\<V^8dQhRS[RS[S[,RS[S[S[,,/#)r/rkrr`)rar3)r5r6s"r)r7r8 s6!!$!QUVYQZ!_cdhildm_n!r(c.p\V4FwrE\P!W,4p\P!V4p\P!W`P 8H4^,p^p VF?p V \ V48dM.W,p W,p ^W{V %\P!W4p KA VPVP44K V#)r) rxnparray zeros_likewhererIr searchsortedrztolist) rTrkrrlrX seq_lengths array_idsmm_token_typesimage_start_positionsrYseq_lenrrs &&& r)r{0ColModernVBertProcessor.create_mm_token_type_ids s'(?@NA.I]]95N$&HHY:R:R-R$STU$V !A&122-0o,-S)OO$9? '  $ $^%:%:%< =A! r(c x/pVEe%\PPR/4pVPV4VUu.uF"pVPP !.VOVN5!NK$ ppVP ^,pVP ^,p\VPRRR7R,4^, p .p .p VFVwrpW,^,pV ^8dT M^pV PVV,W,,4V PV 4KX VPRV RV /4\R/VB#uupi) a{ Computes the number of placeholder tokens needed for multimodal inputs with the given sizes. Args: image_sizes (`list[list[int]]`, *optional*): The input sizes formatted as (height, width) per each image. Returns: `MultiModalData`: A `MultiModalData` object holding number of tokens per each of the provided input modalities, along with other useful data. rz F)rPrknum_image_tokensnum_image_patchesr!) rr&getupdaterUget_number_of_image_patchesr0rrVrzr )rT image_sizesrW vision_datar image_sizenum_image_row_colsbase_image_length col_lengthextra_split_newlinerr num_patchesnum_rowsnum_cols row_length split_extras&&, r)_get_num_multimodal_tokens2ColModernVBertProcessor._get_num_multimodal_tokenssL  "9CCGGY[\M   (#.""-J$$@@\*\m\"- " !% 2 2Q 6 ++a/J #&dnnVPUn&VWb&c"dgh"h !  " 3E/ x'2Q6 5=\1q  ''(9K(G:K`(ab!((5 4F    24DFY[lm n, ,,/"s(D7cJ<V^8dQhRS[R,RS[S[,RS[/#)r/r^NrWr`)rr rr)r5r6s"r)r7r8Is9@@T!@67@  @r(c VP!\3RVPP/VBpVR,P RR4pVRJp\ V4'dV.pM\ V\4'd\ V^,4'dM]\ V\4'd=\ V^,\4'd \ V^,^,4'g \R4hVUu.uFqfPR4NK ppVPVP.\V4,VVR,VR,R7pV'd7VR ,PVR ,^8HR 4pVPR V/4V#uupi) au Prepare for the model one or several image(s). Handles input validation, RGB conversion, and prepends the `visual_prompt_prefix` to each image. Optionally computes labels from `token_type_ids` when a `suffix` is provided in `text_kwargs`. 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: - `'pt'`: Return PyTorch `torch.Tensor` objects. - `'np'`: Return NumPy `np.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`. rcrsuffixNzAimages must be an image, list of images or list of list of imagesRGBr)r_r^rrrktoken_type_idslabelsi)rsrrVrtrur rrarconvertrr1r masked_fillr) rTr^rWr}rreturn_token_type_idsrm batch_docrs &&, r)process_images&ColModernVBertProcessor.process_imagesIsj<** ) "&.."<"<  }-11(DA &d 2 & ! !XF  % %.*C*C VT**z&)T/J/J~^def^ghi^jOkOk`a a5;;F5--&F;MM++,s6{:'8%m4 "  !{+77 BR8SWX8XZ^_F   h/ 0\ V\4'd\ V^,\ 4'g \R4hVfVP^ ,pVUu.uFqPPV,V,NK ppVPVRVR,R7pV#uupi)a Prepare for the model one or several text queries. Handles input validation, prepends the `query_prefix`, and appends query augmentation tokens (used to pad query embeddings for better late-interaction retrieval performance). 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: - `'pt'`: Return PyTorch `torch.Tensor` objects. - `'np'`: Return NumPy `np.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`). rcrrNz*Text must be a string or a list of stringsF)r_rr) rsrrVrtrurr4rarrSr2r)rTr_rWr}rquery texts_query batch_querys&&, r)process_queries'ColModernVBertProcessor.process_queriess:** ) "&.."<"<  }-11(DA dC 6DT4((ZQ-E-EIJ J >22R7FSW!WRV"3"3e";f"D"DRV !Wmm"'%m4$ "Xs:"C;c <V^8dQhRS[RS[R,3,RS[RS[R,3,RS[RS[R,RS[RS[3,R R/#) r/query_embeddingsz torch.Tensorpassage_embeddings batch_size output_dtypez torch.dtype output_devicez torch.devicer`)rrar3rr4)r5r6s"r)r7r8st>(>(^0D DE>(".$~2F"FG>( >( }- >( ^S01 >( >(r(c  8\V4^8Xd \R4h\V4^8Xd \R4hV^,PV^,P8wd \R4hV^,PV^,P8wd \R4hVfV^,Pp.p\ ^\V4V4EF1p.p\ P PPPWWs,R^R7p \ ^\V4V4Fp \ P PPPW*W,R^R7p VP\ P!RW4P^R7^,P^R74K VP\ P!V^R7PV4PV44EK4 \ P!V^R7#) a Compute the late-interaction/MaxSim score (ColBERT-like) for the given multi-vector query embeddings (`qs`) and passage embeddings (`ps`). For ColQwen2, 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. zNo 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->bcns)dim)rrdevicedtyperKtorchnnutilsrnn pad_sequencerzeinsummaxrcatto) rTrrrrrscoresrX batch_scores batch_queriesrYbatch_passagess &&&&&& r)score_retrieval'ColModernVBertProcessor.score_retrievals@  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() rOrErCrIrFrJr0rDrHrSr2rLr1)NN@NN)NNN)NN)N)Ncpu)r"r#r$r%rvalid_processor_kwargsrRrrrqrrrr{rrrrr'__classdictcell__ __classcell__)r[r6s@@r)r-r-6s;3D3Dj>^>^>^@  D2%%:!!*)-V@@D77r>(>(>(r(r-)r) rM itertoolsrtypingrrrnumpyrrfeature_extraction_utilsr image_utilsrr processing_utilsr r r r tokenization_utils_baserrrrrutils.import_utilsrrrr-__all__r!r(r)rs~* 11 45XXKK#*< $4E  :J(nJ(J(Z % %r(