import{s as Ut,n as zt,o as Qt}from"../chunks/scheduler.9bc65507.js";import{S as Nt,i as Rt,g as o,s as a,r as d,A as Bt,h as s,f as r,c as n,j as v,u as c,x as l,k as $,y as t,a as p,v as u,d as f,t as h,w as g}from"../chunks/index.707bf1b6.js";import{D as _}from"../chunks/Docstring.17db21ae.js";import{H as Ke}from"../chunks/Heading.342b1fa6.js";function Zt(Pt){let F,Fe,De,ye,O,Ce,I,Dt="This page lists all the utility functions the library provides for pipelines.",Te,V,wt="Most of those are only useful if you are studying the code of the models in the library.",Le,W,He,y,q,Xe,re,Ft='Base interface for handling arguments for each <a href="/docs/transformers/main/en/main_classes/pipelines#transformers.Pipeline">Pipeline</a>.',Ee,C,j,Ye,ae,yt=`Handles arguments for zero-shot for text classification by turning each possible label into an NLI
premise/hypothesis pair.`,Me,P,J,et,ne,Ct=`QuestionAnsweringPipeline requires the user to provide multiple arguments (i.e. question &amp; context) to be mapped to
internal <code>SquadExample</code>.`,tt,oe,Tt=`QuestionAnsweringArgumentHandler manages all the possible to create a <code>SquadExample</code> from the command-line
supplied arguments.`,ke,U,Ae,m,z,rt,se,Lt=`Base class for all the pipeline supported data format both for reading and writing. Supported data formats
currently includes:`,at,ie,Ht="<li>JSON</li> <li>CSV</li> <li>stdin/stdout (pipe)</li>",nt,pe,Et=`<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.`,ot,L,Q,st,le,Mt='Creates an instance of the right subclass of <a href="/docs/transformers/main/en/internal/pipelines_utils#transformers.PipelineDataFormat">PipelineDataFormat</a> depending on <code>format</code>.',it,H,N,pt,me,kt='Save the provided data object with the representation for the current <a href="/docs/transformers/main/en/internal/pipelines_utils#transformers.PipelineDataFormat">PipelineDataFormat</a>.',lt,E,R,mt,de,At="Save the provided data object as a pickle-formatted binary data on the disk.",Se,D,B,dt,ce,St="Support for pipelines using CSV data format.",ct,M,Z,ut,ue,Ot='Save the provided data object with the representation for the current <a href="/docs/transformers/main/en/internal/pipelines_utils#transformers.PipelineDataFormat">PipelineDataFormat</a>.',Oe,w,G,ft,fe,It="Support for pipelines using JSON file format.",ht,k,K,gt,he,Vt="Save the provided data object in a json file.",Ie,x,X,vt,ge,Wt="Read data from piped input to the python process. For multi columns data, columns should separated by",$t,ve,qt="If columns are provided, then the output will be a dictionary with {column_x: value_x}",bt,A,Y,_t,$e,jt="Print the data.",Ve,ee,We,T,te,xt,be,Jt='Raised by a <a href="/docs/transformers/main/en/main_classes/pipelines#transformers.Pipeline">Pipeline</a> when handling <strong>call</strong>.',qe,we,je;return O=new Ke({props:{title:"Utilities for pipelines",local:"utilities-for-pipelines",headingTag:"h1"}}),W=new Ke({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/main/src/transformers/pipelines/base.py#L436"}}),j=new _({props:{name:"class transformers.pipelines.ZeroShotClassificationArgumentHandler",anchor:"transformers.pipelines.ZeroShotClassificationArgumentHandler",parameters:[],source:"https://github.com/huggingface/transformers/blob/main/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/main/src/transformers/pipelines/question_answering.py#L150"}}),U=new Ke({props:{title:"Data format",local:"transformers.PipelineDataFormat",headingTag:"h2"}}),z=new _({props:{name:"class transformers.PipelineDataFormat",anchor:"transformers.PipelineDataFormat",parameters:[{name:"output_path",val:": Optional"},{name:"input_path",val:": Optional"},{name:"column",val:": Optional"},{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/main/src/transformers/pipelines/base.py#L446"}}),Q=new _({props:{name:"from_str",anchor:"transformers.PipelineDataFormat.from_str",parameters:[{name:"format",val:": str"},{name:"output_path",val:": Optional"},{name:"input_path",val:": Optional"},{name:"column",val:": Optional"},{name:"overwrite",val:" = False"}],parametersDescription:[{anchor:"transformers.PipelineDataFormat.from_str.format",description:`<strong>format</strong> (<code>str</code>) &#x2014;
The format of the desired pipeline. Acceptable values are <code>&quot;json&quot;</code>, <code>&quot;csv&quot;</code> or <code>&quot;pipe&quot;</code>.`,name:"format"},{anchor:"transformers.PipelineDataFormat.from_str.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.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/main/src/transformers/pipelines/base.py#L523",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/main/en/internal/pipelines_utils#transformers.PipelineDataFormat"
>PipelineDataFormat</a></p>
`}}),N=new _({props:{name:"save",anchor:"transformers.PipelineDataFormat.save",parameters:[{name:"data",val:": Union"}],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/main/src/transformers/pipelines/base.py#L495"}}),R=new _({props:{name:"save_binary",anchor:"transformers.PipelineDataFormat.save_binary",parameters:[{name:"data",val:": Union"}],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/main/src/transformers/pipelines/base.py#L505",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>
`}}),B=new _({props:{name:"class transformers.CsvPipelineDataFormat",anchor:"transformers.CsvPipelineDataFormat",parameters:[{name:"output_path",val:": Optional"},{name:"input_path",val:": Optional"},{name:"column",val:": Optional"},{name:"overwrite",val:" = False"}],parametersDescription:[{anchor:"transformers.CsvPipelineDataFormat.output_path",description:"<strong>output_path</strong> (<code>str</code>) &#x2014; Where to save the outgoing data.",name:"output_path"},{anchor:"transformers.CsvPipelineDataFormat.input_path",description:"<strong>input_path</strong> (<code>str</code>) &#x2014; Where to look for the input data.",name:"input_path"},{anchor:"transformers.CsvPipelineDataFormat.column",description:"<strong>column</strong> (<code>str</code>) &#x2014; The column to read.",name:"column"},{anchor:"transformers.CsvPipelineDataFormat.overwrite",description:`<strong>overwrite</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
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