import{s as Ls,o as Rs,n as Ps}from"../chunks/scheduler.d586627e.js";import{S as Ss,i as Xs,g as l,s as i,u,A as As,h as n,f as s,c as o,j as d,v as h,y as c,k as r,o as p,a as t,w as f,d as g,t as M,x as y}from"../chunks/index.b757c891.js";import{T as Ns}from"../chunks/Tip.4380e997.js";import{I as we}from"../chunks/IconCopyLink.c074b82b.js";import{C as v}from"../chunks/CodeBlock.aa8a1a9b.js";function Ys($e){let m,W="Acesse a documentação do <code>pipeline()</code> para obter uma lista completa de tarefas possíveis.";return{c(){m=l("p"),m.innerHTML=W},l(j){m=n(j,"P",{"data-svelte-h":!0}),c(m)!=="svelte-18yhfdg"&&(m.innerHTML=W)},m(j,ce){t(j,m,ce)},p:Ps,d(j){j&&s(m)}}}function Fs($e){let m,W,j,ce,x,T,ge,B,is,me,ms="Pipelines para inferência",be,_,ds=`Um [pipeline] simplifica o uso dos modelos no <a href="https://huggingface.co/models" rel="nofollow">Model Hub</a> para a inferência de uma diversidade de tarefas,
como a geração de texto, a segmentação de imagens e a classificação de áudio.
Inclusive, se não tem experiência com alguma modalidade específica ou não compreende o código que forma os modelos,
pode usar eles mesmo assim com o [pipeline]! Este tutorial te ensinará a:`,Ue,H,us="<li>Utilizar um <code>pipeline()</code> para inferência.</li> <li>Utilizar um tokenizador ou model específico.</li> <li>Utilizar um <code>pipeline()</code> para tarefas de áudio e visão computacional.</li>",Te,k,ke,w,J,Me,q,os,de,hs="Uso do pipeline",Je,V,fs=`Mesmo que cada tarefa tenha um <code>pipeline()</code> associado, é mais simples usar a abstração geral do <code>pipeline()</code> que
contém todos os pipelines das tarefas mais específicas.
O <code>pipeline()</code> carrega automaticamenta um modelo predeterminado e um tokenizador com capacidade de inferência para sua
tarefa.`,Ze,E,gs="<li>Comece carregando um <code>pipeline()</code> e especifique uma tarefa de inferência:</li>",Ce,z,Ge,Z,Ms="<li>Passe seu dado de entrada, no caso um texto, ao <code>pipeline()</code>:</li>",Ie,L,We,R,ys="Se tiver mais de uma entrada, passe-a como uma lista:",Be,P,_e,S,js=`Qualquer parâmetro adicional para a sua tarefa também pode ser incluído no <code>pipeline()</code>. A tarefa <code>text-generation</code> tem um método
<code>generate()</code> com vários parâmetros para controlar a saída.
Por exemplo, se quiser gerar mais de uma saída, defina-a no parâmetro <code>num_return_sequences</code>:`,He,X,qe,$,C,ye,A,rs,ue,vs="Selecionando um modelo e um tokenizador",Ve,N,xs=`O <code>pipeline()</code> aceita qualquer modelo do <a href="https://huggingface.co/models" rel="nofollow">Model Hub</a>. Há rótulos adicionais no Model Hub
que te permitem filtrar pelo modelo que gostaria de usar para sua tarefa. Uma vez que tiver escolhido o modelo apropriado,
carregue-o com as classes <code>AutoModelFor</code> e [\`AutoTokenizer’] correspondentes. Por exemplo, carregue a classe <code>AutoModelForCausalLM</code>
para uma tarefa de modelagem de linguagem causal:`,Ee,Y,ze,F,ws="Crie uma <code>pipeline()</code> para a sua tarefa e especifíque o modelo e o tokenizador que foram carregados:",Le,Q,Re,D,$s="Passe seu texto de entrada ao <code>pipeline()</code> para gerar algum texto:",Pe,K,Se,b,G,je,O,ps,he,bs="Pipeline de audio",Xe,ee,Us=`A flexibilidade do <code>pipeline()</code> significa que também pode-se extender às tarefas de áudio.
La flexibilidad de <code>pipeline()</code> significa que también se puede extender a tareas de audio.`,Ae,se,Ts=`Por exemplo, classifiquemos a emoção de um breve fragmento do famoso discurso de John F. Kennedy /home/rzimmerdev/dev/transformers/docs/source/pt/pipeline_tutorial.md
Encontre um modelo de <a href="https://huggingface.co/models?pipeline_tag=audio-classification" rel="nofollow">audio classification</a> para
reconhecimento de emoções no Model Hub e carregue-o usando o <code>pipeline()</code>:`,Ne,ae,Ye,te,ks="Passe o arquivo de áudio ao <code>pipeline()</code>:",Fe,le,Qe,U,I,ve,ne,cs,fe,Js="Pipeline de visão computacional",De,ie,Zs=`Finalmente, utilizar um <code>pipeline()</code> para tarefas de visão é praticamente a mesma coisa.
Especifique a sua tarefa de visão e passe a sua imagem ao classificador.
A imagem pode ser um link ou uma rota local à imagem. Por exemplo, que espécie de gato está presente na imagem?`,Ke,oe,Cs='<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg" alt="pipeline-cat-chonk"/>',Oe,re,es,xe,ss;return B=new we({}),k=new Ns({props:{$$slots:{default:[Ys]},$$scope:{ctx:$e}}}),q=new we({}),z=new v({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMHBpcGVsaW5lJTBBJTBBZ2VuZXJhdG9yJTIwJTNEJTIwcGlwZWxpbmUodGFzayUzRCUyMnRleHQtZ2VuZXJhdGlvbiUyMik=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> pipeline

<span class="hljs-meta">&gt;&gt;&gt; </span>generator = pipeline(task=<span class="hljs-string">&quot;text-generation&quot;</span>)`,wrap:!1}}),L=new v({props:{code:"Z2VuZXJhdG9yKCUyMlRocmVlJTIwUmluZ3MlMjBmb3IlMjB0aGUlMjBFbHZlbi1raW5ncyUyMHVuZGVyJTIwdGhlJTIwc2t5JTJDJTIwU2V2ZW4lMjBmb3IlMjB0aGUlMjBEd2FyZi1sb3JkcyUyMGluJTIwdGhlaXIlMjBoYWxscyUyMG9mJTIwc3RvbmUlMjIp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span>generator(<span class="hljs-string">&quot;Three Rings for the Elven-kings under the sky, Seven for the Dwarf-lords in their halls of stone&quot;</span>)
[{<span class="hljs-string">&#x27;generated_text&#x27;</span>: <span class="hljs-string">&#x27;Three Rings for the Elven-kings under the sky, Seven for the Dwarf-lords in their halls of stone, Seven for the Iron-priests at the door to the east, and thirteen for the Lord Kings at the end of the mountain&#x27;</span>}]`,wrap:!1}}),P=new v({props:{code:"Z2VuZXJhdG9yKCUwQSUyMCUyMCUyMCUyMCU1QiUwQSUyMCUyMCUyMCUyMCUyMCUyMCUyMCUyMCUyMlRocmVlJTIwUmluZ3MlMjBmb3IlMjB0aGUlMjBFbHZlbi1raW5ncyUyMHVuZGVyJTIwdGhlJTIwc2t5JTJDJTIwU2V2ZW4lMjBmb3IlMjB0aGUlMjBEd2FyZi1sb3JkcyUyMGluJTIwdGhlaXIlMjBoYWxscyUyMG9mJTIwc3RvbmUlMjIlMkMlMEElMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjJOaW5lJTIwZm9yJTIwTW9ydGFsJTIwTWVuJTJDJTIwZG9vbWVkJTIwdG8lMjBkaWUlMkMlMjBPbmUlMjBmb3IlMjB0aGUlMjBEYXJrJTIwTG9yZCUyMG9uJTIwaGlzJTIwZGFyayUyMHRocm9uZSUyMiUyQyUwQSUyMCUyMCUyMCUyMCU1RCUwQSk=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span>generator(
<span class="hljs-meta">... </span>    [
<span class="hljs-meta">... </span>        <span class="hljs-string">&quot;Three Rings for the Elven-kings under the sky, Seven for the Dwarf-lords in their halls of stone&quot;</span>,
<span class="hljs-meta">... </span>        <span class="hljs-string">&quot;Nine for Mortal Men, doomed to die, One for the Dark Lord on his dark throne&quot;</span>,
<span class="hljs-meta">... </span>    ]
<span class="hljs-meta">... </span>)`,wrap:!1}}),X=new v({props:{code:"Z2VuZXJhdG9yKCUwQSUyMCUyMCUyMCUyMCUyMlRocmVlJTIwUmluZ3MlMjBmb3IlMjB0aGUlMjBFbHZlbi1raW5ncyUyMHVuZGVyJTIwdGhlJTIwc2t5JTJDJTIwU2V2ZW4lMjBmb3IlMjB0aGUlMjBEd2FyZi1sb3JkcyUyMGluJTIwdGhlaXIlMjBoYWxscyUyMG9mJTIwc3RvbmUlMjIlMkMlMEElMjAlMjAlMjAlMjBudW1fcmV0dXJuX3NlcXVlbmNlcyUzRDIlMkMlMEEp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span>generator(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;Three Rings for the Elven-kings under the sky, Seven for the Dwarf-lords in their halls of stone&quot;</span>,
<span class="hljs-meta">... </span>    num_return_sequences=<span class="hljs-number">2</span>,
<span class="hljs-meta">... </span>)`,wrap:!1}}),A=new we({}),Y=new v({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Ub2tlbml6ZXIlMkMlMjBBdXRvTW9kZWxGb3JDYXVzYWxMTSUwQSUwQXRva2VuaXplciUyMCUzRCUyMEF1dG9Ub2tlbml6ZXIuZnJvbV9wcmV0cmFpbmVkKCUyMmRpc3RpbGdwdDIlMjIpJTBBbW9kZWwlMjAlM0QlMjBBdXRvTW9kZWxGb3JDYXVzYWxMTS5mcm9tX3ByZXRyYWluZWQoJTIyZGlzdGlsZ3B0MiUyMik=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, AutoModelForCausalLM

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;distilgpt2&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForCausalLM.from_pretrained(<span class="hljs-string">&quot;distilgpt2&quot;</span>)`,wrap:!1}}),Q=new v({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMHBpcGVsaW5lJTBBJTBBZ2VuZXJhdG9yJTIwJTNEJTIwcGlwZWxpbmUodGFzayUzRCUyMnRleHQtZ2VuZXJhdGlvbiUyMiUyQyUyMG1vZGVsJTNEbW9kZWwlMkMlMjB0b2tlbml6ZXIlM0R0b2tlbml6ZXIp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> pipeline

<span class="hljs-meta">&gt;&gt;&gt; </span>generator = pipeline(task=<span class="hljs-string">&quot;text-generation&quot;</span>, model=model, tokenizer=tokenizer)`,wrap:!1}}),K=new v({props:{code:"Z2VuZXJhdG9yKCUyMlRocmVlJTIwUmluZ3MlMjBmb3IlMjB0aGUlMjBFbHZlbi1raW5ncyUyMHVuZGVyJTIwdGhlJTIwc2t5JTJDJTIwU2V2ZW4lMjBmb3IlMjB0aGUlMjBEd2FyZi1sb3JkcyUyMGluJTIwdGhlaXIlMjBoYWxscyUyMG9mJTIwc3RvbmUlMjIp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span>generator(<span class="hljs-string">&quot;Three Rings for the Elven-kings under the sky, Seven for the Dwarf-lords in their halls of stone&quot;</span>)
[{<span class="hljs-string">&#x27;generated_text&#x27;</span>: <span class="hljs-string">&#x27;Three Rings for the Elven-kings under the sky, Seven for the Dwarf-lords in their halls of stone, Seven for the Dragon-lords (for them to rule in a world ruled by their rulers, and all who live within the realm&#x27;</span>}]`,wrap:!1}}),O=new we({}),ae=new v({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMHBpcGVsaW5lJTBBJTBBYXVkaW9fY2xhc3NpZmllciUyMCUzRCUyMHBpcGVsaW5lKCUwQSUyMCUyMCUyMCUyMHRhc2slM0QlMjJhdWRpby1jbGFzc2lmaWNhdGlvbiUyMiUyQyUyMG1vZGVsJTNEJTIyZWhjYWxhYnJlcyUyRndhdjJ2ZWMyLWxnLXhsc3ItZW4tc3BlZWNoLWVtb3Rpb24tcmVjb2duaXRpb24lMjIlMEEp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> pipeline

<span class="hljs-meta">&gt;&gt;&gt; </span>audio_classifier = pipeline(
<span class="hljs-meta">... </span>    task=<span class="hljs-string">&quot;audio-classification&quot;</span>, model=<span class="hljs-string">&quot;ehcalabres/wav2vec2-lg-xlsr-en-speech-emotion-recognition&quot;</span>
<span class="hljs-meta">... </span>)`,wrap:!1}}),le=new v({props:{code:"YXVkaW9fY2xhc3NpZmllciglMjJqZmtfbW9vbl9zcGVlY2gud2F2JTIyKQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span>audio_classifier(<span class="hljs-string">&quot;jfk_moon_speech.wav&quot;</span>)
[{<span class="hljs-string">&#x27;label&#x27;</span>: <span class="hljs-string">&#x27;calm&#x27;</span>, <span class="hljs-string">&#x27;score&#x27;</span>: <span class="hljs-number">0.13856211304664612</span>},
 {<span class="hljs-string">&#x27;label&#x27;</span>: <span class="hljs-string">&#x27;disgust&#x27;</span>, <span class="hljs-string">&#x27;score&#x27;</span>: <span class="hljs-number">0.13148026168346405</span>},
 {<span class="hljs-string">&#x27;label&#x27;</span>: <span class="hljs-string">&#x27;happy&#x27;</span>, <span class="hljs-string">&#x27;score&#x27;</span>: <span class="hljs-number">0.12635163962841034</span>},
 {<span class="hljs-string">&#x27;label&#x27;</span>: <span class="hljs-string">&#x27;angry&#x27;</span>, <span class="hljs-string">&#x27;score&#x27;</span>: <span class="hljs-number">0.12439591437578201</span>},
 {<span class="hljs-string">&#x27;label&#x27;</span>: <span class="hljs-string">&#x27;fearful&#x27;</span>, <span class="hljs-string">&#x27;score&#x27;</span>: <span class="hljs-number">0.12404385954141617</span>}]`,wrap:!1}}),ne=new we({}),re=new v({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMHBpcGVsaW5lJTBBJTBBdmlzaW9uX2NsYXNzaWZpZXIlMjAlM0QlMjBwaXBlbGluZSh0YXNrJTNEJTIyaW1hZ2UtY2xhc3NpZmljYXRpb24lMjIpJTBBdmlzaW9uX2NsYXNzaWZpZXIoJTBBJTIwJTIwJTIwJTIwaW1hZ2VzJTNEJTIyaHR0cHMlM0ElMkYlMkZodWdnaW5nZmFjZS5jbyUyRmRhdGFzZXRzJTJGaHVnZ2luZ2ZhY2UlMkZkb2N1bWVudGF0aW9uLWltYWdlcyUyRnJlc29sdmUlMkZtYWluJTJGcGlwZWxpbmUtY2F0LWNob25rLmpwZWclMjIlMEEp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> pipeline

<span class="hljs-meta">&gt;&gt;&gt; </span>vision_classifier = pipeline(task=<span class="hljs-string">&quot;image-classification&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>vision_classifier(
<span class="hljs-meta">... </span>    images=<span class="hljs-string">&quot;https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg&quot;</span>
<span class="hljs-meta">... </span>)
[{<span class="hljs-string">&#x27;label&#x27;</span>: <span class="hljs-string">&#x27;lynx, catamount&#x27;</span>, <span class="hljs-string">&#x27;score&#x27;</span>: <span class="hljs-number">0.4403027892112732</span>},
 {<span class="hljs-string">&#x27;label&#x27;</span>: <span class="hljs-string">&#x27;cougar, puma, catamount, mountain lion, painter, panther, Felis concolor&#x27;</span>,
  <span class="hljs-string">&#x27;score&#x27;</span>: <span class="hljs-number">0.03433405980467796</span>},
 {<span class="hljs-string">&#x27;label&#x27;</span>: <span class="hljs-string">&#x27;snow leopard, ounce, Panthera uncia&#x27;</span>,
  <span class="hljs-string">&#x27;score&#x27;</span>: <span class="hljs-number">0.032148055732250214</span>},
 {<span class="hljs-string">&#x27;label&#x27;</span>: <span class="hljs-string">&#x27;Egyptian cat&#x27;</span>, <span class="hljs-string">&#x27;score&#x27;</span>: <span class="hljs-number">0.02353910356760025</span>},
 {<span class="hljs-string">&#x27;label&#x27;</span>: <span class="hljs-string">&#x27;tiger cat&#x27;</span>, <span class="hljs-string">&#x27;score&#x27;</span>: <span class="hljs-number">0.023034192621707916</span>}]`,wrap:!1}}),{c(){m=l("meta"),W=i(),j=l("p"),ce=i(),x=l("h1"),T=l("a"),ge=l("span"),u(B.$$.fragment),is=i(),me=l("span"),me.textContent=ms,be=i(),_=l("p"),_.innerHTML=ds,Ue=i(),H=l("ul"),H.innerHTML=us,Te=i(),u(k.$$.fragment),ke=i(),w=l("h2"),J=l("a"),Me=l("span"),u(q.$$.fragment),os=i(),de=l("span"),de.textContent=hs,Je=i(),V=l("p"),V.innerHTML=fs,Ze=i(),E=l("ol"),E.innerHTML=gs,Ce=i(),u(z.$$.fragment),Ge=i(),Z=l("ol"),Z.innerHTML=Ms,Ie=i(),u(L.$$.fragment),We=i(),R=l("p"),R.textContent=ys,Be=i(),u(P.$$.fragment),_e=i(),S=l("p"),S.innerHTML=js,He=i(),u(X.$$.fragment),qe=i(),$=l("h3"),C=l("a"),ye=l("span"),u(A.$$.fragment),rs=i(),ue=l("span"),ue.textContent=vs,Ve=i(),N=l("p"),N.innerHTML=xs,Ee=i(),u(Y.$$.fragment),ze=i(),F=l("p"),F.innerHTML=ws,Le=i(),u(Q.$$.fragment),Re=i(),D=l("p"),D.innerHTML=$s,Pe=i(),u(K.$$.fragment),Se=i(),b=l("h2"),G=l("a"),je=l("span"),u(O.$$.fragment),ps=i(),he=l("span"),he.textContent=bs,Xe=i(),ee=l("p"),ee.innerHTML=Us,Ae=i(),se=l("p"),se.innerHTML=Ts,Ne=i(),u(ae.$$.fragment),Ye=i(),te=l("p"),te.innerHTML=ks,Fe=i(),u(le.$$.fragment),Qe=i(),U=l("h2"),I=l("a"),ve=l("span"),u(ne.$$.fragment),cs=i(),fe=l("span"),fe.textContent=Js,De=i(),ie=l("p"),ie.innerHTML=Zs,Ke=i(),oe=l("p"),oe.innerHTML=Cs,Oe=i(),u(re.$$.fragment),es=i(),xe=l("p"),this.h()},l(e){const a=As("svelte-1phssyn",document.head);m=n(a,"META",{name:!0,content:!0}),a.forEach(s),W=o(e),j=n(e,"P",{}),d(j).forEach(s),ce=o(e),x=n(e,"H1",{class:!0});var pe=d(x);T=n(pe,"A",{id:!0,class:!0,href:!0});var Gs=d(T);ge=n(Gs,"SPAN",{});var Is=d(ge);h(B.$$.fragment,Is),Is.forEach(s),Gs.forEach(s),is=o(pe),me=n(pe,"SPAN",{"data-svelte-h":!0}),c(me)!=="svelte-kpynd1"&&(me.textContent=ms),pe.forEach(s),be=o(e),_=n(e,"P",{"data-svelte-h":!0}),c(_)!=="svelte-19oh8z4"&&(_.innerHTML=ds),Ue=o(e),H=n(e,"UL",{"data-svelte-h":!0}),c(H)!=="svelte-18jsh6a"&&(H.innerHTML=us),Te=o(e),h(k.$$.fragment,e),ke=o(e),w=n(e,"H2",{class:!0});var as=d(w);J=n(as,"A",{id:!0,class:!0,href:!0});var Ws=d(J);Me=n(Ws,"SPAN",{});var Bs=d(Me);h(q.$$.fragment,Bs),Bs.forEach(s),Ws.forEach(s),os=o(as),de=n(as,"SPAN",{"data-svelte-h":!0}),c(de)!=="svelte-26tiiz"&&(de.textContent=hs),as.forEach(s),Je=o(e),V=n(e,"P",{"data-svelte-h":!0}),c(V)!=="svelte-135pr0r"&&(V.innerHTML=fs),Ze=o(e),E=n(e,"OL",{"data-svelte-h":!0}),c(E)!=="svelte-157sn41"&&(E.innerHTML=gs),Ce=o(e),h(z.$$.fragment,e),Ge=o(e),Z=n(e,"OL",{start:!0,"data-svelte-h":!0}),c(Z)!=="svelte-1ouxpvk"&&(Z.innerHTML=Ms),Ie=o(e),h(L.$$.fragment,e),We=o(e),R=n(e,"P",{"data-svelte-h":!0}),c(R)!=="svelte-5nw5od"&&(R.textContent=ys),Be=o(e),h(P.$$.fragment,e),_e=o(e),S=n(e,"P",{"data-svelte-h":!0}),c(S)!=="svelte-1w6glba"&&(S.innerHTML=js),He=o(e),h(X.$$.fragment,e),qe=o(e),$=n(e,"H3",{class:!0});var ts=d($);C=n(ts,"A",{id:!0,class:!0,href:!0});var _s=d(C);ye=n(_s,"SPAN",{});var Hs=d(ye);h(A.$$.fragment,Hs),Hs.forEach(s),_s.forEach(s),rs=o(ts),ue=n(ts,"SPAN",{"data-svelte-h":!0}),c(ue)!=="svelte-2rk9tu"&&(ue.textContent=vs),ts.forEach(s),Ve=o(e),N=n(e,"P",{"data-svelte-h":!0}),c(N)!=="svelte-1bmpmlw"&&(N.innerHTML=xs),Ee=o(e),h(Y.$$.fragment,e),ze=o(e),F=n(e,"P",{"data-svelte-h":!0}),c(F)!=="svelte-w3put1"&&(F.innerHTML=ws),Le=o(e),h(Q.$$.fragment,e),Re=o(e),D=n(e,"P",{"data-svelte-h":!0}),c(D)!=="svelte-qsn026"&&(D.innerHTML=$s),Pe=o(e),h(K.$$.fragment,e),Se=o(e),b=n(e,"H2",{class:!0});var ls=d(b);G=n(ls,"A",{id:!0,class:!0,href:!0});var qs=d(G);je=n(qs,"SPAN",{});var Vs=d(je);h(O.$$.fragment,Vs),Vs.forEach(s),qs.forEach(s),ps=o(ls),he=n(ls,"SPAN",{"data-svelte-h":!0}),c(he)!=="svelte-ve5nm8"&&(he.textContent=bs),ls.forEach(s),Xe=o(e),ee=n(e,"P",{"data-svelte-h":!0}),c(ee)!=="svelte-12hxoj"&&(ee.innerHTML=Us),Ae=o(e),se=n(e,"P",{"data-svelte-h":!0}),c(se)!=="svelte-17cq1h3"&&(se.innerHTML=Ts),Ne=o(e),h(ae.$$.fragment,e),Ye=o(e),te=n(e,"P",{"data-svelte-h":!0}),c(te)!=="svelte-1khitrh"&&(te.innerHTML=ks),Fe=o(e),h(le.$$.fragment,e),Qe=o(e),U=n(e,"H2",{class:!0});var ns=d(U);I=n(ns,"A",{id:!0,class:!0,href:!0});var Es=d(I);ve=n(Es,"SPAN",{});var zs=d(ve);h(ne.$$.fragment,zs),zs.forEach(s),Es.forEach(s),cs=o(ns),fe=n(ns,"SPAN",{"data-svelte-h":!0}),c(fe)!=="svelte-1m4339j"&&(fe.textContent=Js),ns.forEach(s),De=o(e),ie=n(e,"P",{"data-svelte-h":!0}),c(ie)!=="svelte-bzcqm2"&&(ie.innerHTML=Zs),Ke=o(e),oe=n(e,"P",{"data-svelte-h":!0}),c(oe)!=="svelte-1bks2jw"&&(oe.innerHTML=Cs),Oe=o(e),h(re.$$.fragment,e),es=o(e),xe=n(e,"P",{}),d(xe).forEach(s),this.h()},h(){r(m,"name","hf:doc:metadata"),r(m,"content",JSON.stringify(Qs)),r(T,"id","pipelines-para-inferncia"),r(T,"class","header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full"),r(T,"href","#pipelines-para-inferncia"),r(x,"class","relative group"),r(J,"id","uso-do-pipeline"),r(J,"class","header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full"),r(J,"href","#uso-do-pipeline"),r(w,"class","relative group"),r(Z,"start","2"),r(C,"id","selecionando-um-modelo-e-um-tokenizador"),r(C,"class","header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full"),r(C,"href","#selecionando-um-modelo-e-um-tokenizador"),r($,"class","relative group"),r(G,"id","pipeline-de-audio"),r(G,"class","header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full"),r(G,"href","#pipeline-de-audio"),r(b,"class","relative group"),r(I,"id","pipeline-de-viso-computacional"),r(I,"class","header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full"),r(I,"href","#pipeline-de-viso-computacional"),r(U,"class","relative group")},m(e,a){p(document.head,m),t(e,W,a),t(e,j,a),t(e,ce,a),t(e,x,a),p(x,T),p(T,ge),f(B,ge,null),p(x,is),p(x,me),t(e,be,a),t(e,_,a),t(e,Ue,a),t(e,H,a),t(e,Te,a),f(k,e,a),t(e,ke,a),t(e,w,a),p(w,J),p(J,Me),f(q,Me,null),p(w,os),p(w,de),t(e,Je,a),t(e,V,a),t(e,Ze,a),t(e,E,a),t(e,Ce,a),f(z,e,a),t(e,Ge,a),t(e,Z,a),t(e,Ie,a),f(L,e,a),t(e,We,a),t(e,R,a),t(e,Be,a),f(P,e,a),t(e,_e,a),t(e,S,a),t(e,He,a),f(X,e,a),t(e,qe,a),t(e,$,a),p($,C),p(C,ye),f(A,ye,null),p($,rs),p($,ue),t(e,Ve,a),t(e,N,a),t(e,Ee,a),f(Y,e,a),t(e,ze,a),t(e,F,a),t(e,Le,a),f(Q,e,a),t(e,Re,a),t(e,D,a),t(e,Pe,a),f(K,e,a),t(e,Se,a),t(e,b,a),p(b,G),p(G,je),f(O,je,null),p(b,ps),p(b,he),t(e,Xe,a),t(e,ee,a),t(e,Ae,a),t(e,se,a),t(e,Ne,a),f(ae,e,a),t(e,Ye,a),t(e,te,a),t(e,Fe,a),f(le,e,a),t(e,Qe,a),t(e,U,a),p(U,I),p(I,ve),f(ne,ve,null),p(U,cs),p(U,fe),t(e,De,a),t(e,ie,a),t(e,Ke,a),t(e,oe,a),t(e,Oe,a),f(re,e,a),t(e,es,a),t(e,xe,a),ss=!0},p(e,[a]){const pe={};a&2&&(pe.$$scope={dirty:a,ctx:e}),k.$set(pe)},i(e){ss||(g(B.$$.fragment,e),g(k.$$.fragment,e),g(q.$$.fragment,e),g(z.$$.fragment,e),g(L.$$.fragment,e),g(P.$$.fragment,e),g(X.$$.fragment,e),g(A.$$.fragment,e),g(Y.$$.fragment,e),g(Q.$$.fragment,e),g(K.$$.fragment,e),g(O.$$.fragment,e),g(ae.$$.fragment,e),g(le.$$.fragment,e),g(ne.$$.fragment,e),g(re.$$.fragment,e),ss=!0)},o(e){M(B.$$.fragment,e),M(k.$$.fragment,e),M(q.$$.fragment,e),M(z.$$.fragment,e),M(L.$$.fragment,e),M(P.$$.fragment,e),M(X.$$.fragment,e),M(A.$$.fragment,e),M(Y.$$.fragment,e),M(Q.$$.fragment,e),M(K.$$.fragment,e),M(O.$$.fragment,e),M(ae.$$.fragment,e),M(le.$$.fragment,e),M(ne.$$.fragment,e),M(re.$$.fragment,e),ss=!1},d(e){e&&(s(W),s(j),s(ce),s(x),s(be),s(_),s(Ue),s(H),s(Te),s(ke),s(w),s(Je),s(V),s(Ze),s(E),s(Ce),s(Ge),s(Z),s(Ie),s(We),s(R),s(Be),s(_e),s(S),s(He),s(qe),s($),s(Ve),s(N),s(Ee),s(ze),s(F),s(Le),s(Re),s(D),s(Pe),s(Se),s(b),s(Xe),s(ee),s(Ae),s(se),s(Ne),s(Ye),s(te),s(Fe),s(Qe),s(U),s(De),s(ie),s(Ke),s(oe),s(Oe),s(es),s(xe)),s(m),y(B),y(k,e),y(q),y(z,e),y(L,e),y(P,e),y(X,e),y(A),y(Y,e),y(Q,e),y(K,e),y(O),y(ae,e),y(le,e),y(ne),y(re,e)}}}const Qs={local:"pipelines-para-inferncia",sections:[{local:"uso-do-pipeline",sections:[{local:"selecionando-um-modelo-e-um-tokenizador",title:"Selecionando um modelo e um tokenizador"}],title:"Uso do pipeline"},{local:"pipeline-de-audio",title:"Pipeline de audio"},{local:"pipeline-de-viso-computacional",title:"Pipeline de visão computacional"}],title:"Pipelines para inferência"};function Ds($e){return Rs(()=>{new URLSearchParams(window.location.search).get("fw")}),[]}class ta extends Ss{constructor(m){super(),Xs(this,m,Ds,Fs,Ls,{})}}export{ta as component};
