import{s as Dr,n as Cr,o as Fr}from"../chunks/scheduler.9bc65507.js";import{S as Er,i as Lr,g as a,s as o,u,A as Tr,h as n,f as r,c as s,j as m,v as h,y as d,k as l,o as t,a as p,w as f,d as g,t as v,x as b}from"../chunks/index.78c82d43.js";import{D as _}from"../chunks/Docstring.f3d04bd2.js";import{I as vt}from"../chunks/IconCopyLink.bedaa44d.js";function kr(Qt){let D,Ne,Me,Je,C,A,Ie,U,bt,me,Rt="Utilities for pipelines",je,z,Bt="This page lists all the utility functions the library provides for pipelines.",Ue,Q,Zt="Most of those are only useful if you are studying the code of the models in the library.",ze,F,S,Oe,R,$t,de,Gt="Argument handling",Qe,E,B,_t,ce,Kt='Base interface for handling arguments for each <a href="/docs/transformers/v4.33.3/en/main_classes/pipelines#transformers.Pipeline">Pipeline</a>.',Re,L,Z,xt,ue,Xt=`Handles arguments for zero-shot for text classification by turning each possible label into an NLI
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pipelines keyword arguments through the <code>dataset_kwarg_1=dataset_column_1</code> format.`,Et,I,Y,Lt,_e,or='Creates an instance of the right subclass of <a href="/docs/transformers/v4.33.3/en/internal/pipelines_utils#transformers.PipelineDataFormat">PipelineDataFormat</a> depending on <code>format</code>.',Tt,O,ee,kt,xe,sr='Save the provided data object with the representation for the current <a href="/docs/transformers/v4.33.3/en/internal/pipelines_utils#transformers.PipelineDataFormat">PipelineDataFormat</a>.',Ht,V,te,At,Pe,ir="Save the provided data object as a pickle-formatted binary data on the disk.",Ke,y,re,St,ye,lr="Support for pipelines using CSV data format.",Mt,W,ae,It,we,pr='Save the provided data object with the representation for the current <a href="/docs/transformers/v4.33.3/en/internal/pipelines_utils#transformers.PipelineDataFormat">PipelineDataFormat</a>.',Xe,w,ne,Ot,De,mr="Support for pipelines using JSON file format.",Vt,q,oe,Wt,Ce,dr="Save the provided data object in a json file.",Ye,x,se,qt,Fe,cr="Read data from piped input to the python process. For multi columns data, columns should separated by",Nt,Ee,ur="If columns are provided, then the output will be a dictionary with {column_x: value_x}",Jt,N,ie,jt,Le,hr="Print the data.",et,k,J,We,le,Ut,Te,fr="Utilities",tt,H,pe,zt,ke,gr='Raised by a <a href="/docs/transformers/v4.33.3/en/main_classes/pipelines#transformers.Pipeline">Pipeline</a> when handling <strong>call</strong>.',rt,qe,at;return U=new vt({}),R=new vt({}),B=new _({props:{name:"class transformers.pipelines.ArgumentHandler",anchor:"transformers.pipelines.ArgumentHandler",parameters:[],source:"https://github.com/huggingface/transformers/blob/v4.33.3/src/transformers/pipelines/base.py#L425"}}),Z=new _({props:{name:"class transformers.pipelines.ZeroShotClassificationArgumentHandler",anchor:"transformers.pipelines.ZeroShotClassificationArgumentHandler",parameters:[],source:"https://github.com/huggingface/transformers/blob/v4.33.3/src/transformers/pipelines/zero_shot_classification.py#L14"}}),G=new _({props:{name:"class transformers.pipelines.QuestionAnsweringArgumentHandler",anchor:"transformers.pipelines.QuestionAnsweringArgumentHandler",parameters:[],source:"https://github.com/huggingface/transformers/blob/v4.33.3/src/transformers/pipelines/question_answering.py#L150"}}),K=new vt({}),X=new _({props:{name:"class transformers.PipelineDataFormat",anchor:"transformers.PipelineDataFormat",parameters:[{name:"output_path",val:": typing.Optional[str]"},{name:"input_path",val:": typing.Optional[str]"},{name:"column",val:": typing.Optional[str]"},{name:"overwrite",val:": bool = False"}],parametersDescription:[{anchor:"transformers.PipelineDataFormat.output_path",description:"<strong>output_path</strong> (<code>str</code>, <em>optional</em>) &#x2014; Where to save the outgoing data.",name:"output_path"},{anchor:"transformers.PipelineDataFormat.input_path",description:"<strong>input_path</strong> (<code>str</code>, <em>optional</em>) &#x2014; Where to look for the input data.",name:"input_path"},{anchor:"transformers.PipelineDataFormat.column",description:"<strong>column</strong> (<code>str</code>, <em>optional</em>) &#x2014; The column to read.",name:"column"},{anchor:"transformers.PipelineDataFormat.overwrite",description:`<strong>overwrite</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
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Where to look for the input data.`,name:"input_path"},{anchor:"transformers.PipelineDataFormat.from_str.column",description:`<strong>column</strong> (<code>str</code>, <em>optional</em>) &#x2014;
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`,returnType:`
<p><a
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`}}),ee=new _({props:{name:"save",anchor:"transformers.PipelineDataFormat.save",parameters:[{name:"data",val:": typing.Union[dict, typing.List[dict]]"}],parametersDescription:[{anchor:"transformers.PipelineDataFormat.save.data",description:"<strong>data</strong> (<code>dict</code> or list of <code>dict</code>) &#x2014; The data to store.",name:"data"}],source:"https://github.com/huggingface/transformers/blob/v4.33.3/src/transformers/pipelines/base.py#L484"}}),te=new _({props:{name:"save_binary",anchor:"transformers.PipelineDataFormat.save_binary",parameters:[{name:"data",val:": typing.Union[dict, typing.List[dict]]"}],parametersDescription:[{anchor:"transformers.PipelineDataFormat.save_binary.data",description:"<strong>data</strong> (<code>dict</code> or list of <code>dict</code>) &#x2014; The data to store.",name:"data"}],source:"https://github.com/huggingface/transformers/blob/v4.33.3/src/transformers/pipelines/base.py#L494",returnDescription:`
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