import{s as Qe,n as Ne,o as Re}from"../chunks/scheduler.25b97de1.js";import{S as Be,i as Ze,g as s,s as n,r as d,A as Ge,h as i,f as r,c as a,j as v,u as c,x as l,k as $,y as e,a as p,v as u,d as f,t as g,w as h}from"../chunks/index.d9030fc9.js";import{D as _}from"../chunks/Docstring.ffac8efa.js";import{H as Yt,E as Ke}from"../chunks/EditOnGithub.91d95064.js";function Xe(ye){let w,Ft,yt,Ct,S,Tt,I,we="This page lists all the utility functions the library provides for pipelines.",Lt,V,Fe="Most of those are only useful if you are studying the code of the models in the library.",Ht,W,Et,F,q,te,nt,Ce='Base interface for handling arguments for each <a href="/docs/transformers/pr_30530/en/main_classes/pipelines#transformers.Pipeline">Pipeline</a>.',kt,C,j,ee,at,Te=`Handles arguments for zero-shot for text classification by turning each possible label into an NLI
premise/hypothesis pair.`,Mt,P,J,re,ot,Le=`QuestionAnsweringPipeline requires the user to provide multiple arguments (i.e. question &amp; context) to be mapped to
internal <code>SquadExample</code>.`,ne,st,He=`QuestionAnsweringArgumentHandler manages all the possible to create a <code>SquadExample</code> from the command-line
supplied arguments.`,At,U,Ot,m,z,ae,it,Ee=`Base class for all the pipeline supported data format both for reading and writing. Supported data formats
currently includes:`,oe,pt,ke="<li>JSON</li> <li>CSV</li> <li>stdin/stdout (pipe)</li>",se,lt,Me=`<code>PipelineDataFormat</code> also includes some utilities to work with multi-columns like mapping from datasets columns to
pipelines keyword arguments through the <code>dataset_kwarg_1=dataset_column_1</code> format.`,ie,L,Q,pe,mt,Ae='Creates an instance of the right subclass of <a href="/docs/transformers/pr_30530/en/internal/pipelines_utils#transformers.PipelineDataFormat">PipelineDataFormat</a> depending on <code>format</code>.',le,H,N,me,dt,Oe='Save the provided data object with the representation for the current <a href="/docs/transformers/pr_30530/en/internal/pipelines_utils#transformers.PipelineDataFormat">PipelineDataFormat</a>.',de,E,R,ce,ct,Se="Save the provided data object as a pickle-formatted binary data on the disk.",St,D,B,ue,ut,Ie="Support for pipelines using CSV data format.",fe,k,Z,ge,ft,Ve='Save the provided data object with the representation for the current <a href="/docs/transformers/pr_30530/en/internal/pipelines_utils#transformers.PipelineDataFormat">PipelineDataFormat</a>.',It,y,G,he,gt,We="Support for pipelines using JSON file format.",ve,M,K,$e,ht,qe="Save the provided data object in a json file.",Vt,x,X,be,vt,je="Read data from piped input to the python process. For multi columns data, columns should separated by",_e,$t,Je="If columns are provided, then the output will be a dictionary with {column_x: value_x}",xe,A,Y,Pe,bt,Ue="Print the data.",Wt,tt,qt,T,et,De,_t,ze='Raised by a <a href="/docs/transformers/pr_30530/en/main_classes/pipelines#transformers.Pipeline">Pipeline</a> when handling <strong>call</strong>.',jt,rt,Jt,wt,Ut;return S=new Yt({props:{title:"Utilities for pipelines",local:"utilities-for-pipelines",headingTag:"h1"}}),W=new Yt({props:{title:"Argument handling",local:"transformers.pipelines.ArgumentHandler",headingTag:"h2"}}),q=new _({props:{name:"class transformers.pipelines.ArgumentHandler",anchor:"transformers.pipelines.ArgumentHandler",parameters:[],source:"https://github.com/huggingface/transformers/blob/vr_30530/src/transformers/pipelines/base.py#L445"}}),j=new _({props:{name:"class transformers.pipelines.ZeroShotClassificationArgumentHandler",anchor:"transformers.pipelines.ZeroShotClassificationArgumentHandler",parameters:[],source:"https://github.com/huggingface/transformers/blob/vr_30530/src/transformers/pipelines/zero_shot_classification.py#L14"}}),J=new _({props:{name:"class transformers.pipelines.QuestionAnsweringArgumentHandler",anchor:"transformers.pipelines.QuestionAnsweringArgumentHandler",parameters:[],source:"https://github.com/huggingface/transformers/blob/vr_30530/src/transformers/pipelines/question_answering.py#L150"}}),U=new Yt({props:{title:"Data format",local:"transformers.PipelineDataFormat",headingTag:"h2"}}),z=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>) &#x2014; Where to save the outgoing data.",name:"output_path"},{anchor:"transformers.PipelineDataFormat.input_path",description:"<strong>input_path</strong> (<code>str</code>) &#x2014; Where to look for the input data.",name:"input_path"},{anchor:"transformers.PipelineDataFormat.column",description:"<strong>column</strong> (<code>str</code>) &#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;
Whether or not to overwrite the <code>output_path</code>.`,name:"overwrite"}],source:"https://github.com/huggingface/transformers/blob/vr_30530/src/transformers/pipelines/base.py#L455"}}),Q=new _({props:{name:"from_str",anchor:"transformers.PipelineDataFormat.from_str",parameters:[{name:"format",val:": str"},{name:"output_path",val:": typing.Optional[str]"},{name:"input_path",val:": typing.Optional[str]"},{name:"column",val:": typing.Optional[str]"},{name:"overwrite",val:" = False"}],parametersDescription:[{anchor:"transformers.PipelineDataFormat.from_str.format",description:`<strong>format</strong> (<code>str</code>) &#x2014;
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Where to save the outgoing data.`,name:"output_path"},{anchor:"transformers.PipelineDataFormat.from_str.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.from_str.column",description:`<strong>column</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The column to read.`,name:"column"},{anchor:"transformers.PipelineDataFormat.from_str.overwrite",description:`<strong>overwrite</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to overwrite the <code>output_path</code>.`,name:"overwrite"}],source:"https://github.com/huggingface/transformers/blob/vr_30530/src/transformers/pipelines/base.py#L532",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>The proper data format.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><a
  href="/docs/transformers/pr_30530/en/internal/pipelines_utils#transformers.PipelineDataFormat"
>PipelineDataFormat</a></p>
`}}),N=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/vr_30530/src/transformers/pipelines/base.py#L504"}}),R=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/vr_30530/src/transformers/pipelines/base.py#L514",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>Path where the data has been saved.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>str</code></p>
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