import{s as Ue,n as ze,o as Qe}from"../chunks/scheduler.9bc65507.js";import{S as Ne,i as Re,g as o,s as a,r as d,A as Be,h as s,f as r,c as n,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.707bf1b6.js";import{D as _}from"../chunks/Docstring.17db21ae.js";import{H as Kt}from"../chunks/Heading.342b1fa6.js";function Ze(Pe){let w,wt,Dt,Ft,O,Ct,j,De="このページには、ライブラリがパイプラインに提供するすべてのユーティリティ関数がリストされます。",Tt,I,ye="これらのほとんどは、ライブラリ内のモデルのコードを研究する場合にのみ役に立ちます。",Lt,V,Ht,F,W,Xt,rt,we='Base interface for handling arguments for each <a href="/docs/transformers/main/ja/main_classes/pipelines#transformers.Pipeline">Pipeline</a>.',Et,C,q,Yt,at,Fe=`Handles arguments for zero-shot for text classification by turning each possible label into an NLI
premise/hypothesis pair.`,kt,P,J,te,nt,Ce=`QuestionAnsweringPipeline requires the user to provide multiple arguments (i.e. question &amp; context) to be mapped to
internal <code>SquadExample</code>.`,ee,ot,Te=`QuestionAnsweringArgumentHandler manages all the possible to create a <code>SquadExample</code> from the command-line
supplied arguments.`,Mt,U,At,m,z,re,st,Le=`Base class for all the pipeline supported data format both for reading and writing. Supported data formats
currently includes:`,ae,it,He="<li>JSON</li> <li>CSV</li> <li>stdin/stdout (pipe)</li>",ne,pt,Ee=`<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.`,oe,L,Q,se,lt,ke='Creates an instance of the right subclass of <a href="/docs/transformers/main/ja/internal/pipelines_utils#transformers.PipelineDataFormat">PipelineDataFormat</a> depending on <code>format</code>.',ie,H,N,pe,mt,Me='Save the provided data object with the representation for the current <a href="/docs/transformers/main/ja/internal/pipelines_utils#transformers.PipelineDataFormat">PipelineDataFormat</a>.',le,E,R,me,dt,Ae="Save the provided data object as a pickle-formatted binary data on the disk.",St,D,B,de,ct,Se="Support for pipelines using CSV data format.",ce,k,Z,ue,ut,Oe='Save the provided data object with the representation for the current <a href="/docs/transformers/main/ja/internal/pipelines_utils#transformers.PipelineDataFormat">PipelineDataFormat</a>.',Ot,y,G,fe,ft,je="Support for pipelines using JSON file format.",ge,M,K,he,gt,Ie="Save the provided data object in a json file.",jt,x,X,ve,ht,Ve="Read data from piped input to the python process. For multi columns data, columns should separated by",$e,vt,We="If columns are provided, then the output will be a dictionary with {column_x: value_x}",be,A,Y,_e,$t,qe="Print the data.",It,tt,Vt,T,et,xe,bt,Je='Raised by a <a href="/docs/transformers/main/ja/main_classes/pipelines#transformers.Pipeline">Pipeline</a> when handling <strong>call</strong>.',Wt,yt,qt;return O=new Kt({props:{title:"パイプライン用のユーティリティ",local:"パイプライン用のユーティリティ",headingTag:"h1"}}),V=new Kt({props:{title:"Argument handling",local:"transformers.pipelines.ArgumentHandler",headingTag:"h2"}}),W=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#L425"}}),q=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 Kt({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/main/src/transformers/pipelines/base.py#L435"}}),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 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;
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<p>The proper data format.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><a
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`}}),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/main/src/transformers/pipelines/base.py#L484"}}),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/main/src/transformers/pipelines/base.py#L494",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>Path where the data has been saved.</p>
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<p><code>str</code></p>
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