+ jC^RIt^RIt^RIt^RIHt^RIHtHtH t ^RI H t H t H t HtHt]!4'd ^RIt^RIHtHt!RR] 4t]!]!RR 74!R R ] 44tR#) N)GenerationConfig)add_end_docstringsis_torch_availablerequires_backends)ArgumentHandlerDatasetPipelinePipelineExceptionbuild_pipeline_init_args),MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES0MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING_NAMESc.a]tRt^toRtRRltRtVtR#)%TableQuestionAnsweringArgumentHandlerz: Handles arguments for the TableQuestionAnsweringPipeline Nc \VR4^RIpVf \R4hVEfj\V\4'd,VP R4eVP R4eV.pEM/\V\ 4'd\V4^8d\;QJdRV4F 'dK RM RM !RV44'g\RR V4 24hV^,P R4eV^,P R4eTpM\R V^,P4 R 24h\e\V\4'g!\V\P4'dV#\R \V4 R 24hRVRV/.pVFYp\VR,VP4'dK(VR,f \R4hVPVR,4VR&K[ V#)pandasNz(Keyword argument `table` cannot be None.querytablec3B"TFp\V\4xK R#5iN) isinstancedict.0ds& p/home/luca/screen-checking/.venv/lib/python3.14/site-packages/transformers/pipelines/table_question_answering.py ATableQuestionAnsweringArgumentHandler.__call__..-s>1:a..sFTz:Keyword argument `table` should be a list of dict, but is c38"TFp\V4xK R#5ir)typers& rrr/sUmglbcVZ[\V]V]glszIf keyword argument `table` is a list of dictionaries, each dictionary should have a `table` and `query` key, but only dictionary has keys z `table` and `query` keys.zZInvalid input. Keyword argument `table` should be either of type `dict` or `list`, but is )zTable cannot be None.)rr ValueErrorrrgetlistlenallkeysrtypes GeneratorTyper DataFrame)selfrrkwargspdtqa_pipeline_inputstqa_pipeline_inputs&&&, r__call__.TableQuestionAnsweringArgumentHandler.__call__s $) =GH H ]%&&599W+=+IeiiX_N`Nl',g#E4((SZ!^s>>sss>>>>$TUmglUmTno8<<(4qg9N9Z*/'$JJOPQ(--/IZZtv$E7)C)CzRWY^YlYlGmGm  u+a) %,UGU#C"D "5 092<<HH%g.6$%<==.0ll;Mg;V.W"7+ #6#")NN)__name__ __module__ __qualname____firstlineno____doc__r/__static_attributes____classdictcell__) __classdict__s@rrrs-#-#r1rT) has_tokenizercaa]tRt^MtoRtRtRtRtRtRt Rt ] !RR7t ] !43V3RlltRtR tV3R ltRR ltRR ltRR ltRtRtVtV;t#)TableQuestionAnsweringPipelineah Table Question Answering pipeline using a `ModelForTableQuestionAnswering`. This pipeline is only available in PyTorch. Unless the model you're using explicitly sets these generation parameters in its configuration files (`generation_config.json`), the following default values will be used: - max_new_tokens: 256 Example: ```python >>> from transformers import pipeline >>> oracle = pipeline(model="google/tapas-base-finetuned-wtq") >>> table = { ... "Repository": ["Transformers", "Datasets", "Tokenizers"], ... "Stars": ["36542", "4512", "3934"], ... "Contributors": ["651", "77", "34"], ... "Programming language": ["Python", "Python", "Rust, Python and NodeJS"], ... } >>> oracle(query="How many stars does the transformers repository have?", table=table) {'answer': 'AVERAGE > 36542', 'coordinates': [(0, 1)], 'cells': ['36542'], 'aggregator': 'AVERAGE'} ``` Learn more about the basics of using a pipeline in the [pipeline tutorial](../pipeline_tutorial) This tabular question answering pipeline can currently be loaded from [`pipeline`] using the following task identifier: `"table-question-answering"`. The models that this pipeline can use are models that have been fine-tuned on a tabular question answering task. See the up-to-date list of available models on [huggingface.co/models](https://huggingface.co/models?filter=table-question-answering). z table,queryTF)max_new_tokensc <\SV`!R/VBWn\P!4pVP \ 4VPV4\VPPRR4;'d"\VPPRR4Vn \VPPR4'd RVn R#RVn R#)aggregation_labelsNnum_aggregation_labelstapasr2)super__init__ _args_parserr copyupdater check_model_typegetattrmodelconfig aggregatehasattrr)r* args_parserr+mapping __class__s&&, rrE'TableQuestionAnsweringPipeline.__init__}s "6"'BGGICD g& !2!24H$O  T[ JJ  7U  'tzz'8'8:NOOG UY r1c &VP!R/VB#)Nr2)rK)r*inputss&,rbatch_inference.TableQuestionAnsweringPipeline.batch_inferenceszz#F##r1c  .p.pRpVR,P^,pVR,PVP4pVR,PVP4pVR,PVP4pRp \V4EFp VEeNV R,p \P !V P 4P44p W,p \V P^,4Fp V R ,P4V ,pV R ,P4V ,^, pV R ,P4V ,^, pV^8gKtV^8gK}V^8XgK\WOV3,4W&K \P!V 4P\P4PVP4V R&Wj,pWz,pW,p VPVP^4VP^4V P^4R7pVP pVP"'dVP%VP&4VP%V4\P(P+VR7pVP,VP\P.4PVP,P4,p\0P2!\44p\7VP94P44Fwp pV R ,P4V ,pV R ,P4V ,^, pV R ,P4V ,^, pV^8gKwV^8gKV^8XgKVVV3,P%V4K VUu/uF2pV\P:!VV,4P=4R8bK4 ppEK \P>!\AV4^4pVP"'gV3#V\P>!\AV4^43#uupi) z Inference used for models that need to process sequences in a sequential fashion, like the SQA models which handle conversational query related to a table. N input_idsattention_masktoken_type_ids)rXrYrZ)logitsg?)NNN)r\r)r\)r\)!shapetodevicerangenp zeros_likecpunumpytolistinttorch from_numpyrlongrK unsqueezer[rMappendlogits_aggregation distributions Bernoulliprobsfloat32 collections defaultdictr# enumeratesqueezearraymeancattuple)r*rT all_logitsall_aggregations prev_answers batch_sizerXrYrZtoken_type_ids_exampleindexprev_labels_example model_labelsi segment_idcol_idrow_idinput_ids_exampleattention_mask_exampleoutputsr[dist_per_token probabilitiescoords_to_probspcolrowkey logits_batchs&, rsequential_inference3TableQuestionAnsweringPipeline.sequential_inferences   K(..q1 ;'**4;;7  0144T[[A 0144T[[A!%:&E'&&|11!45A!7!=!D!D!Fq!IJ3D9@@B1EIF3D9@@B1EIF{v{zQ*-lF;K.L*M  605/?/? /M/R/RSXS]S]/^/a/abfbmbm/n&t, ) 0 %3%: "%3%: "jj+55a85??B5??B!G ^^F~~~ ''(B(BC   f %"00::&:IN*003I3N3Nu}}3]3`3`$$++4M*55d;O!-"7"7"9"@"@"BC13D9@@B1E ,T299;A>B,T299;A>B!8qZ1_#S#J/66q9 DYhhXgQTC/#*>!?!D!D!F!LLXgLhL]'`yyz!2A6 &*nnn o<SXYiSjlmIn:oo is,8Rc <VP!V/VBp\SV`!V3/VBp\V4^8Xd V^,#V#)a2 Answers queries according to a table. The pipeline accepts several types of inputs which are detailed below: - `pipeline(table, query)` - `pipeline(table, [query])` - `pipeline(table=table, query=query)` - `pipeline(table=table, query=[query])` - `pipeline({"table": table, "query": query})` - `pipeline({"table": table, "query": [query]})` - `pipeline([{"table": table, "query": query}, {"table": table, "query": query}])` The `table` argument should be a dict or a DataFrame built from that dict, containing the whole table: Example: ```python data = { "actors": ["brad pitt", "leonardo di caprio", "george clooney"], "age": ["56", "45", "59"], "number of movies": ["87", "53", "69"], "date of birth": ["7 february 1967", "10 june 1996", "28 november 1967"], } ``` This dictionary can be passed in as such, or can be converted to a pandas DataFrame: Example: ```python import pandas as pd table = pd.DataFrame.from_dict(data) ``` Args: table (`pd.DataFrame` or `Dict`): Pandas DataFrame or dictionary that will be converted to a DataFrame containing all the table values. See above for an example of dictionary. query (`str` or `list[str]`): Query or list of queries that will be sent to the model alongside the table. sequential (`bool`, *optional*, defaults to `False`): Whether to do inference sequentially or as a batch. Batching is faster, but models like SQA require the inference to be done sequentially to extract relations within sequences, given their conversational nature. padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`): Activates and controls padding. Accepts the following values: - `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single sequence if provided). - `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum acceptable input length for the model if that argument is not provided. - `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different lengths). truncation (`bool`, `str` or [`TapasTruncationStrategy`], *optional*, defaults to `False`): Activates and controls truncation. Accepts the following values: - `True` or `'drop_rows_to_fit'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum acceptable input length for the model if that argument is not provided. This will truncate row by row, removing rows from the table. - `False` or `'do_not_truncate'` (default): No truncation (i.e., can output batch with sequence lengths greater than the model maximum admissible input size). Return: A dictionary or a list of dictionaries containing results: Each result is a dictionary with the following keys: - **answer** (`str`) -- The answer of the query given the table. If there is an aggregator, the answer will be preceded by `AGGREGATOR >`. - **coordinates** (`list[tuple[int, int]]`) -- Coordinates of the cells of the answers. - **cells** (`list[str]`) -- List of strings made up of the answer cell values. - **aggregator** (`str`) -- If the model has an aggregator, this returns the aggregator. )rFrDr/r$)r*argsr+pipeline_inputsresultsrQs&*, rr/'TableQuestionAnsweringPipeline.__call__sJV++T>#/97;7M7M 34jj))LLLOLG'wyRYZ r1c VR,pVR,pVR,pVPR8XEdVP'dVR,wrVVPPW%V4pVwr\ V 4U U u/uF,wrWP P PV ,bK. p p p VP P Pp \ V 4U U u/uFwrW8wgK WV ,R,bK pp p M1V^,pVPPW%4pV^,p/p /p.p\ V4FwppVUu.uFpVPV,NK ppV PVR4pVPVR4pRVRPV4,R TR VUu.uFpVPV,NK up/pV'dVVR &VPV4K \X4^8Xd"\R VP PR 4hM.VPP!VRR7Uu.uFpRV/NK pp\V4^8dV#V^,#uup p iuup p iuupiuupiuupi)rrrrC:Nr_Nz > ranswerz, coordinatescells aggregatorzTable question answeringz Empty answerT)skip_special_tokens)rrMrconvert_logits_to_predictionsrvrKrLrAno_aggregation_label_indexiatr"joinrnr$r name_or_path batch_decode)r*rrTrrr[ logits_agg predictionsanswer_coordinates_batchagg_predictionsrpred aggregatorsno_agg_label_indexaggregators_prefixanswersrr coordinaterraggregator_prefixrs&& r postprocess*TableQuestionAnsweringPipeline.postprocessVsp~.g& * 99 ~~~%,R[""nnJJ6[ef ( %'"G&/0H&I"{ALM::..M(__UB7 $6$:$:5"$E!/$))E2BB!;kRk eii 33kR +5F<(v&'J6{a'(BDJJD[D[]kll 9=8S8ST[qu8S8vw8vf&)8vGwg,*w: :?x&N Sxs$92I) I/+I/ I5,I:? 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