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Args: model ([`PreTrainedModel`]): The main model that will be used by the pipeline to make predictions. assistant_model (`str` or [`PreTrainedModel`], *optional*): The assistant model that will be used by the pipeline to make predictions. assistant_tokenizer ([`PreTrainedTokenizer`], *optional*): The assistant tokenizer that will be used by the pipeline to encode data for the model. Returns: Tuple: The loaded assistant model and (optionally) the loaded tokenizer. N)ru)devicerDc3V<"TFp\SV4\SV48HxK R#5iN)rp)r:tokenassistant_text_configmodel_text_configs& r/r='load_assistant_model..Ws-EE !5)W5JE-RREr@FTzkThe assistant model has a different tokenizer than the main model. 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Args: task (`str`): The task of the pipeline. model (`str`): The model used by the pipeline. reason (`str`): The error message to display. c$V^8dQhRRRRRR/#)rBrwrrreasonry)rzs"r/r{PipelineException.__annotate__ps!Scr1c ><\SV`V4WnW nR#r)super__init__rwr)selfrwrr __class__s&&&&r/rPipelineException.__init__ps    r1)rrw)r __module__ __qualname____firstlineno____doc__r__static_attributes__ __classcell__rs@r/rrfsr1rc*]tRtRtRt]R4tRtR#)ArgumentHandleriwzI Base interface for handling arguments for each [`~pipelines.Pipeline`]. c \4hrNotImplementedError)rargsrs&*,r/__call__ArgumentHandler.__call__| !##r1ryN)rrrrrrrrryr1r/rrws$$r1rc~]tRtRtRt.ROtRRRllt]R4t]RRl4t RR lt ] RR R ll4t R t R #)PipelineDataFormatia~ Base class for all the pipeline supported data format both for reading and writing. 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Acceptable values are `"json"`, `"csv"` or `"pipe"`. output_path (`str`, *optional*): Where to save the outgoing data. input_path (`str`, *optional*): Where to look for the input data. column (`str`, *optional*): The column to read. overwrite (`bool`, *optional*, defaults to `False`): Whether or not to overwrite the `output_path`. Returns: [`~pipelines.PipelineDataFormat`]: The proper data format. jsonrcsvpipezUnknown reader z% (Available reader are json/csv/pipe))JsonPipelineDataFormatCsvPipelineDataFormatPipedPipelineDataFormatKeyError)rzrrrrs&&&&&r/from_strPipelineDataFormat.from_strsW4 V )+6_ _ u_(&^ ^ v *;F` `_VH4YZ[ [r1)rrrrN)rrrF)rrrrrSUPPORTED_FORMATSrrrrr  staticmethodrrryr1r/rrsX&0J.$$$$$ \ \r1rcHa]tRtRtRtR RV3RllltRtRRltRtV;t #) riaA Support for pipelines using CSV data format. 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Args: output_path (`str`): Where to save the outgoing data. input_path (`str`): Where to look for the input data. column (`str`): The column to read. overwrite (`bool`, *optional*, defaults to `False`): Whether or not to overwrite the `output_path`. c$V^8dQhRRRRRR/#rry)rzs"r/r{#JsonPipelineDataFormat.__annotate__*s( ) ) ) ) )r1c <\SV`WW4R7\VR4;_uu_4p\P!V4VnRRR4R# +'giR#;i)rr#N)rrrrload_entries)rrrrrr%rs&&&&& r/rJsonPipelineDataFormat.__init__*sD &N *c " "a IIaLDM# " " "s A  A c# "VPF[pVP'd,VPUUu/uFwr#W!V,bK uppxK@WP^,,xK] R#uuppi5irN)r8rr)rentryr(rs& r/rJsonPipelineDataFormat.__iter__6sT]]E$$$/3{{;{tqq({{;;KKN++ #;s1A6A0/A6cV^8dQhRR/#rBrrry)rzs"r/r{r5=sr1c \VPR4;_uu_4p\P!W4RRR4R# +'giR#;i)z\ Save the provided data object in a json file. Args: data (`dict`): The data to store. r,N)rrrr)rrr%s&& r/rJsonPipelineDataFormat.save=s4$""C ( (A IId ) ( ( (s A A )r8rr2rs@r/rrs!  ) ),r1rcDa]tRtRtRtRtRRltRV3RlltRtV;t #) riHa Read data from piped input to the python process. For multi columns data, columns should separated by If columns are provided, then the output will be a dictionary with {column_x: value_x} Args: output_path (`str`): Where to save the outgoing data. input_path (`str`): Where to look for the input data. column (`str`): The column to read. overwrite (`bool`, *optional*, defaults to `False`): Whether or not to overwrite the `output_path`. c # *"\PFspRV9dfVPR4pVP'd4\ VPV4UUUu/uF wwr#qBVbK upppxK`\ V4xKoVxKu R#uupppi5i) N)sysstdinrrzipr)rlinerrls& r/r PipedPipelineDataFormat.__iter__VsrIIDt|zz$';;;;>t{{D;QR;Q&a19;QRR+%  Ss=BBB *)BcV^8dQhRR/#r?ry)rzs"r/r{$PipedPipelineDataFormat.__annotate__esr1c \V4R#)z> Print the data. Args: data (`dict`): The data to store. N)printrs&&r/rPipedPipelineDataFormat.savees  d r1c V^8dQhRRRR/#rry)rzs"r/r{rLns)) 1)c)r1c T<VPf \R4h\SV` V4#)NzWhen using piped input on pipeline outputting large object requires an output file path. Please provide such output path through --output argument.)rrrr )rrrs&&r/r #PipedPipelineDataFormat.save_binaryns4    #M  w"4((r1ry) rrrrrrrr rrrs@r/rrHs  ))r1rc:]tRtRtRt]R4t]R4tRtR#) _ScikitCompatixz9 Interface layer for the Scikit and Keras compatibility. c \4hrrrXs&&r/ transform_ScikitCompat.transform}rr1c \4hrrrVs&&r/predict_ScikitCompat.predictrr1ryN) rrrrrrrXr[rryr1r/rTrTxs/$$$$r1rTTc4V^8dQhRRRRRRRRRRRRRR /#) rB has_tokenizerrhas_feature_extractorhas_image_processorhas_video_processor has_processorsupports_binary_outputrrry)rzs"r/r{r{sN>>>>> >  > ! > >r1cRpV'd VR, pV'd VR, pV'd VR, pV'd VR, pV'd VR, pVR, pV'd VR, pV#)z Arguments: model ([`PreTrainedModel`]): The model that will be used by the pipeline to make predictions. This needs to be a model inheriting from [`PreTrainedModel`].z tokenizer ([`PreTrainedTokenizer`]): The tokenizer that will be used by the pipeline to encode data for the model. This object inherits from [`PreTrainedTokenizer`].z feature_extractor ([`SequenceFeatureExtractor`]): The feature extractor that will be used by the pipeline to encode data for the model. This object inherits from [`SequenceFeatureExtractor`].z image_processor ([`BaseImageProcessor`]): The image processor that will be used by the pipeline to encode data for the model. This object inherits from [`BaseImageProcessor`].z video_processor ([`BaseVideoProcessor`]): The video processor that will be used by the pipeline to encode video data for the model. This object inherits from [`BaseVideoProcessor`].a4 processor ([`ProcessorMixin`]): The processor that will be used by the pipeline to encode data for the model. This object inherits from [`ProcessorMixin`]. Processor is a composite object that might contain `tokenizer`, `feature_extractor`, and `image_processor`.a2 task (`str`, defaults to `""`): A task-identifier for the pipeline. num_workers (`int`, *optional*, defaults to 8): When the pipeline will use *DataLoader* (when passing a dataset, on GPU for a Pytorch model), the number of workers to be used. batch_size (`int`, *optional*, defaults to 1): When the pipeline will use *DataLoader* (when passing a dataset, on GPU for a Pytorch model), the size of the batch to use, for inference this is not always beneficial, please read [Batching with pipelines](https://huggingface.co/transformers/main_classes/pipelines.html#pipeline-batching) . args_parser ([`~pipelines.ArgumentHandler`], *optional*): Reference to the object in charge of parsing supplied pipeline parameters. device (`int`, *optional*, defaults to -1): Device ordinal for CPU/GPU supports. Setting this to -1 will leverage CPU, a positive will run the model on the associated CUDA device id. You can pass native `torch.device` or a `str` too dtype (`str` or `torch.dtype`, *optional*): Sent directly as `model_kwargs` (just a simpler shortcut) to use the available precision for this model (`torch.float16`, `torch.bfloat16`, ... or `"auto"`)z binary_output (`bool`, *optional*, defaults to `False`): Flag indicating if the output the pipeline should happen in a serialized format (i.e., pickle) or as the raw output data e.g. text.ry)r^r_r`rarbrc docstrings&&&&&& r/build_pipeline_init_argsrfs$I ( ( - - ' ' 5 5 " " DDI$. .  r1)r^r_r`rbrczdocument-question-answeringPeftModelForQuestionAnsweringzfeature-extractionPeftModelForFeatureExtraction PeftModel summarizationPeftModelForSeq2SeqLMztable-question-answeringztext-classification"PeftModelForSequenceClassificationzsentiment-analysisztext-generationPeftModelForCausalLMztoken-classificationPeftModelForTokenClassificationnerzzero-shot-classification)PipelineChunkIteratorPipelineDatasetPipelineIteratorPipelinePackIterator)r^r_r`rbcF]tRtRtRtRtRtRtRtRt Rt Rt R'RRllt Rt RR ltR tR t]R R l4t]RRl4t]R4tRtRtRRlt]R4t]RRl4t]RRl4t]RRl4tRtRtRRlt R RR!R/R"lt!R#t"R$t#R%t$R&t%R#)(Pipelineia The Pipeline class is the class from which all pipelines inherit. Refer to this class for methods shared across different pipelines. Base class implementing pipelined operations. Pipeline workflow is defined as a sequence of the following operations: Input -> Tokenization -> Model Inference -> Post-Processing (task dependent) -> Output Pipeline supports running on CPU or GPU through the device argument (see below). Some pipeline, like for instance [`FeatureExtractionPipeline`] (`'feature-extraction'`) output large tensor object as nested-lists. In order to avoid dumping such large structure as textual data we provide the `binary_output` constructor argument. If set to `True`, the output will be stored in the pickle format. NFc<V^8dQhRRRRRRRRR R R R R RRRRR/ #)rBrr(rlrrjz!PreTrainedFeatureExtractor | Noneimage_processorzBaseImageProcessor | Nonevideo_processorzBaseVideoProcessor | None processorzProcessorMixin | Nonerwrrzint | torch.device | None binary_outputrry)rzs"r/r{Pipeline.__annotate__ sob>b>b>.b>= b> 3 b> 3 b>)b>b>*b>b>r1c  V PRR4V PRR4V PRR4p WpnWnW nW0nW@nWPnW`n\VPRR4p V eVe \R4hVf*V e$\\V P444pM^pVR 8Xd/VPPeVPPp\V\P4'd[VP R8Xd\#RR7'd"VP R 8Xd\%4'g\V R 24hWn EM\V\&4'd\RV9d\#RR7'dR V9d\%4'g\V R 24h\P!V4Vn EMV^8d\P!R 4Vn EMh\)4'd!\P!R V 24Vn EM8\+4'd!\P!R V 24Vn EM\-4'd \P!RV 24Vn M\/4'd \P!RV 24Vn M\%4'd \P!RV 24Vn M{\#RR7'd \P!RV 24Vn MJ\14'd \P!RV 24Vn M\P!R 4Vn \P2P54'd@\P2P74'dVPPVn \8P;RVP 24WnVPPVP8wd[\VP\>4'dVP^8g*V f&VPPAVP4VPB'EdVPPE4'Edh\GVPV PRR4V PRR44wVn$Vn%\MVPPNR4'd!VPPNPPMRVn(\VR\S44p \MVPR4'dVPPT!R!RV /V BwrWn+V PXejVPVPXV PX8XdEVPVPZe-VPVPZ^8wdRVPVn,M/\\P^!VPPV4Vn+\VPPNRR4pVeQW9dKVPaV4pRV9dVPR4Vn(VPVPb!R!/VBVPeVVPPde>VPVPdf&VPPdVPVn2^Vn3V PRR4Vn4V PRR4Vn5VPl!R!/V BwVn7Vn8Vn9VPe\uVPRJVPRJVP RJ.4'dU\VPRR4Vn\VPRR4Vn\VPRR4VnVP fFVPe6\VP\v4'dVPVnR#R#R#R#)" args_parserN torch_dtyperD hf_device_mapzThe model has been loaded with `accelerate` and therefore cannot be moved to a specific device. Please discard the `device` argument when creating your pipeline object.xpuT) check_devicehpuz6 is not available, you should use device="cpu" insteadcpuzmlu:zmusa:zcuda:znpu:zhpu:zxpu:zmps:zDevice set to use rrprefix_default_generation_config_prepare_generation_configgeneration_configtask_specific_paramsrT num_workersrlrjrwry)<poprwrrlrjrwrxryrpr-nextitervaluesrrHrItyper rrrrrrr distributed is_availableis_initializedrdebugrzintr_pipeline_calls_generaterrrrhasattrrurrrrmax_new_tokensrUrdeepcopygetupdatero call_count _batch_size _num_workers_sanitize_parameters_preprocess_params_forward_params_postprocess_paramsrr)rrrlrjrwrxryrwrrzrrr"default_pipeline_generation_configprepared_generation_configrthis_task_paramss&&&&&&&&&&, r/rPipeline.__init__ sc**]D16::mT3RTZT^T^_fhlTma1  "!2.."  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Args: save_directory (`str` or `os.PathLike`): A path to the directory where to saved. It will be created if it doesn't exist. kwargs (`dict[str, Any]`, *optional*): Additional key word arguments passed along to the [`~utils.PushToHubMixin.push_to_hub`] method. zProvided path (z#) should be a directory, not a fileNT)exist_ok_registered_implimplrc38"TFqPxK R#5ir)r)r:rs& r/r=+Pipeline.save_pretrained..s"Bz!::zsptr)rrisfilerrmakedirsrrrr.rrrrrrrucustom_pipelinesr save_pretrainedrlrjrw) rrr pipeline_inforrwinfo module_name last_modules &&, r/rPipeline.save_pretraineds 77>>. ) ) LL?>*::]^ _  NT2 4+ , , 11668M! +113 <4>>1yy{"6l55 )//4R8 "-aV 0E0E/FGV "U"BtDz"BUU"BtDz"BBT )-&42BJJ   . t 4 "">> % NN * *> DV D  ! ! -  " " 2 2> LV L    +  0 0 J6 J ,r1c V!V4#z^ Scikit / Keras interface to transformers' pipelines. This method will forward to __call__(). ryrVs&&r/rXPipeline.transform Awr1c V!V4#rryrVs&&r/r[Pipeline.predictrr1cV^8dQhRR/#rBrztorch.dtype | Nonery)rzs"r/r{r{s22)2r1c 0\VPRR4#)z? Dtype of the model (if it's Pytorch model), `None` otherwise. rDN)rprrs&r/rDPipeline.dtypes tzz7D11r1cV^8dQhRR/#rry)rzs"r/r{r{s22/2r1c Z\PR4\VPRR4#)zE Torch dtype of the model (if it's Pytorch model), `None` otherwise. z;`torch_dtype` attribute is deprecated. Use `dtype` instead!rDN)r warning_oncerprrs&r/r~Pipeline.torch_dtypes& YZtzz7D11r1c# "VPPR8XdA\PPVP4;_uu_4RxRRR4R#VPPR8XdA\PPVP4;_uu_4RxRRR4R#VPPR8XdA\P PVP4;_uu_4RxRRR4R#VPPR8XdA\P PVP4;_uu_4RxRRR4R#RxR# +'giR#;i +'giR#;i +'giR#;i +'giR#;i5i)aO Context Manager allowing tensor allocation on the user-specified device. Returns: Context manager Examples: ```python # Explicitly ask for tensor allocation on CUDA device :0 pipe = pipeline(..., device=0) with pipe.device_placement(): # Every tensor allocation will be done on the request device output = pipe(...) ```cudaNmlumusar)rrrIrrrrrs&r/device_placementPipeline.device_placements" ;;  v %""4;;//0/ [[   &!!$++../. [[   '""4;;//0/ [[   &!!$++../. 0///..0///..siA GE5AG*F /AGF AG F1%G5 F G F  G F. ( G1 G < Gc 8VPWP4#)a6 Ensure PyTorch tensors are on the specified device. Args: inputs (keyword arguments that should be `torch.Tensor`, the rest is ignored): The tensors to place on `self.device`. Recursive on lists **only**. Return: `dict[str, torch.Tensor]`: The same as `inputs` but on the proper device. )_ensure_tensor_on_devicer)rinputss&,r/ensure_tensor_on_device Pipeline.ensure_tensor_on_device#s,,V[[AAr1c  aa\V\4'd?\VP4UUu/uFwr4VSPVS4bK upp4#\V\4'd6VP4UUu/uFwr4VSPVS4bK upp#\V\ 4'd?\ VP4UUu/uFwr4VSPVS4bK upp4#\V\ 4'd#VUu.uFpSPVS4NK up#\V\4'd5\;QJd.VV3RlV4FNK 5#!VV3RlV44#\V\P4'dVPS4#V#uuppiuuppiuuppiuupi)c3H<"TFpSPVS4xK R#5ir)r)r:r;rrs& r/r=4Pipeline._ensure_tensor_on_device..=s#XQW66tVDDQWs") rHrr.rrrrPrrIrJr)rrrnamerWr;sf&f r/r!Pipeline._ensure_tensor_on_device1sq fk * *Y_YeYeYghYgt44VVDDYgh  % %\b\h\h\jk\jLDD$77GG\jk k  ) )ekeqeqestesUaUYT4#@#@#PPestu u  % %LRSFDD11$?FS S  & &5XQWX5 X5XQWXX X  - -99V$ $MiltSsF+ F1F7 F=cV^8dQhRR/#)rBsupported_modelszlist[str] | dictry)rzs"r/r{r{Cs1Ar1c  \V\4'Eg6.pVP\9d'VP \VP,4RpVP 4FFp\V\ 4'dVP \V44K5VPV4KH \VR4'dVPPP 4F`p\V\ 4'd-TP VUu.uFqUPNK up4KEVPVP4Kb TpVPPPV9dJ\PRVPPP RVP RV R24R#R#uupi)z Check if the model class is in supported by the pipeline. Args: supported_models (`list[str]` or `dict`): The list of models supported by the pipeline, or a dictionary with model class values. N_model_mappingz The model 'z' is not supported for z. Supported models are r)rHrPrwSUPPORTED_PEFT_TASKSextendrrrrr_extra_contentrrrrr)rrsupported_models_names model_namerms&& r/check_model_typePipeline.check_model_typeCse*D11%' "yy00&--.B499.MNJ.557 j%00*11$z2BC*11*= 8 ')9::-<<KKRRTE!%//.5556Q5azz56QR.55ennE U 6  ::   ( (0@ @ LLdjj22;;<??r1c$V^8dQhRRRRRR/#)rB input_tensorsrforward_parametersrrrry)rzs"r/r{r{zs$ > >&> >VZ >_j >r1c \R4h)aL _forward will receive the prepared dictionary from `preprocess` and run it on the model. This method might involve the GPU or the CPU and should be agnostic to it. Isolating this function is the reason for `preprocess` and `postprocess` to exist, so that the hot path, this method generally can run as fast as possible. It is not meant to be called directly, `forward` is preferred. It is basically the same but contains additional code surrounding `_forward` making sure tensors and models are on the same device, disabling the training part of the code (leading to faster inference). z_forward not implementedr)rrrs&&,r/_forwardPipeline._forwardys""<==r1c$V^8dQhRRRRRR/#)rB model_outputsrpostprocess_parametersrrr ry)rzs"r/r{r{s(AAAPTAY\Ar1c \R4h)z Postprocess will receive the raw outputs of the `_forward` method, generally tensors, and reformat them into something more friendly. 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In order to maximize efficiency please use a datasetry)rrrPrtypes GeneratorTyperr)rHr#r"rrrrrrrrrrrr'r run_multiiterate ChunkPipelinerr run_single)rrrrTrrcontainer_typesr"rrr is_dataset is_generatoris_list is_iterablecan_use_iteratorroutputss&&$$*, r/rPipeline.__call__s  NN-dV4 5 (;(;<   <<     D(HHZ-H !&%*=*=> VT* ;;L;;G %@@@@ !%!2!2"~.~~fdd $$ZN <<>^ ^ m , ,%%+;L^p ??6na au9sM c  NVUu.uFqPPWRW44NK up#uupirr))rrrrrr;s&&&&& r/r&Pipeline.run_multis&iopioae\ioppps"c xVP!V3/VBpVP!V3/VBpVP!V3/VBpV#r)rrr)rrrrrrrr0s&&&&& r/r)Pipeline.run_singlesDvC1BC  \D^D ""=G4FGr1c# H"VFpVPWRW44xK R#5irr3)rrrrrrs&&&&& r/r'Pipeline.iterates"F//&^` `s ")rrrrrrrrzrrrjrrwrrryrwrlrx)NNNNNrNF)&rrrrr_load_processor_load_image_processor_load_video_processor_load_feature_extractor_load_tokenizerrdefault_input_namesrrrrXr[propertyrDr~rrrrrrrrrrrrrrr&r)r'rryr1r/rurus. *O  "O %b>H @,K\  22 22> B$B J J@@ > >AA2Hb$Hb4HbTq ar1rurpipelinez pipeline file)object object_class object_filesz.from_pretrainedrc(]tRtRtRtRRltRtR#)r(ic .pVP!V3/VBF'pVP!V3/VBpVPV4K) VP!V3/VBpV#r)rrrr) rrrrr all_outputsrrr0s &&&&& r/r)ChunkPipeline.run_singles_  OOFH6GHL LLHHM   } -I"";E2DEr1c V^8dQhRRRR/#r ry)rzs"r/r{ChunkPipeline.__annotate__s#&47r1c R\P9d)\PR4R\PR&V^8d\P R4^p\ WP V4pVPe VPM VPpV^8Xd\M\VPV4p \WrW9R7p \WPWSR7p \WP V4p V #)r r rzFor ChunkPipeline using num_workers>0 is likely to result in errors since everything is iterable, setting `num_workers=1` to guarantee correctness.rr)rrrrrrprrjrwr0rsrlr&rsrrrrrs &&&&&&& r/rChunkPipeline.get_iterators $2:: 5 KKh i3:BJJ/ 0 ? 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