import{S as wo,i as Jo,s as vo,e as p,k as c,w as f,t as e,M as _o,c as r,d as l,m,a as o,x as u,h as n,b as h,N as To,G as a,g as i,y as d,L as Uo,q as j,o as y,B as M,v as Eo}from"../../chunks/vendor-hf-doc-builder.js";import{I as Ds}from"../../chunks/IconCopyLink-hf-doc-builder.js";import{C as b}from"../../chunks/CodeBlock-hf-doc-builder.js";import{D as $o}from"../../chunks/DocNotebookDropdown-hf-doc-builder.js";function Vo(Ir){let I,Qa,x,A,Wl,is,Ie,Bl,xe,Xa,os,Aa,Ks,Ge,Ra,Os,ke,Na,J,Ql,Ce,We,Xl,Be,Qe,Al,Xe,Ae,Rl,Re,Fa,sl,Ne,Ya,T,G,Fe,ll,Ye,ze,R,Nl,He,qe,Pe,Le,Fl,Se,De,Yl,Ke,za,k,N,zl,cs,Oe,Hl,sn,Ha,U,ln,ql,an,tn,Pl,en,nn,qa,al,pn,Pa,tl,rn,La,ms,Sa,el,on,Da,hs,Ka,nl,cn,Oa,fs,st,C,F,Ll,us,mn,Sl,hn,lt,E,fn,Dl,un,dn,ds,jn,yn,at,_,Y,Kl,Mn,bn,gn,js,wn,Jn,ys,vn,_n,tt,pl,Tn,et,Ms,nt,rl,Un,pt,bs,rt,il,En,it,$,ol,Ol,$n,Vn,Zn,cl,sa,In,xn,Gn,ml,la,kn,Cn,ot,hl,Wn,ct,gs,mt,z,Bn,aa,Qn,Xn,ht,fl,An,ft,ws,ut,Js,ul,xr,dt,dl,Rn,jt,H,Nn,ta,Fn,Yn,yt,jl,zn,Mt,vs,bt,yl,Hn,gt,_s,wt,W,q,ea,Ts,qn,na,Pn,Jt,P,Ln,pa,Sn,Dn,vt,Us,_t,g,Kn,ra,On,sp,ia,lp,ap,oa,tp,ep,ca,np,pp,ma,rp,ip,ha,op,cp,fa,mp,hp,ua,fp,up,Tt,L,dp,da,jp,yp,Ut,S,Mp,ja,bp,gp,Et,Es,$t,v,wp,ya,Jp,vp,Ma,_p,Tp,ba,Up,Ep,Vt,$s,Zt,D,$p,ga,Vp,Zp,It,Vs,xt,B,K,wa,Zs,Ip,Ja,xp,Gt,O,Gp,va,kp,Cp,kt,Is,Ct,Ml,Wp,Wt,bl,gl,_a,Bp,Qp,Bt,xs,Qt,Gs,ks,Xp,Ta,Ap,Rp,Xt,Cs,At,Ws,wl,Ua,Np,Fp,Rt,Bs,Nt,ss,Yp,Ea,zp,Hp,Ft,Qs,Yt,Q,ls,$a,Xs,qp,Va,Pp,zt,as,Lp,Za,Sp,Dp,Ht,As,qt,Jl,Kp,Pt,Rs,Lt,vl,Op,St,_l,sr,Dt,V,Ia,lr,ar,xa,tr,er,Ns,nr,Ga,pr,rr,Kt,Fs,Ot,X,ts,ka,Ys,ir,Ca,or,se,Z,cr,Tl,mr,hr,zs,fr,ur,le,es,dr,Wa,jr,yr,ae,Hs,te,Ul,Mr,ee,qs,ne,ns,br,Ba,gr,wr,pe,Ps,re,El,Jr,ie,Ls,oe,$l,vr,ce;return is=new Ds({}),os=new $o({props:{classNames:"absolute z-10 right-0 top-0",options:[{label:"Mixed",value:"https://colab.research.google.com/github/huggingface/notebooks/blob/main/transformers_doc/ko/visual_question_answering.ipynb"},{label:"PyTorch",value:"https://colab.research.google.com/github/huggingface/notebooks/blob/main/transformers_doc/ko/pytorch/visual_question_answering.ipynb"},{label:"TensorFlow",value:"https://colab.research.google.com/github/huggingface/notebooks/blob/main/transformers_doc/ko/tensorflow/visual_question_answering.ipynb"},{label:"Mixed",value:"https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/transformers_doc/ko/visual_question_answering.ipynb"},{label:"PyTorch",value:"https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/transformers_doc/ko/pytorch/visual_question_answering.ipynb"},{label:"TensorFlow",value:"https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/transformers_doc/ko/tensorflow/visual_question_answering.ipynb"}]}}),cs=new Ds({}),ms=new b({props:{code:"cGlwJTIwaW5zdGFsbCUyMC1xJTIwdHJhbnNmb3JtZXJzJTIwZGF0YXNldHM=",highlighted:"pip install -q transformers datasets"}}),hs=new b({props:{code:"ZnJvbSUyMGh1Z2dpbmdmYWNlX2h1YiUyMGltcG9ydCUyMG5vdGVib29rX2xvZ2luJTBBJTBBbm90ZWJvb2tfbG9naW4oKQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> huggingface_hub <span class="hljs-keyword">import</span> notebook_login

<span class="hljs-meta">&gt;&gt;&gt; </span>notebook_login()`}}),fs=new b({props:{code:"bW9kZWxfY2hlY2twb2ludCUyMCUzRCUyMCUyMmRhbmRlbGluJTJGdmlsdC1iMzItbWxtJTIy",highlighted:'<span class="hljs-meta">&gt;&gt;&gt; </span>model_checkpoint = <span class="hljs-string">&quot;dandelin/vilt-b32-mlm&quot;</span>'}}),us=new Ds({}),Ms=new b({props:{code:"ZnJvbSUyMGRhdGFzZXRzJTIwaW1wb3J0JTIwbG9hZF9kYXRhc2V0JTBBJTBBZGF0YXNldCUyMCUzRCUyMGxvYWRfZGF0YXNldCglMjJHcmFwaGNvcmUlMkZ2cWElMjIlMkMlMjBzcGxpdCUzRCUyMnZhbGlkYXRpb24lNUIlM0EyMDAlNUQlMjIpJTBBZGF0YXNldA==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> datasets <span class="hljs-keyword">import</span> load_dataset

<span class="hljs-meta">&gt;&gt;&gt; </span>dataset = load_dataset(<span class="hljs-string">&quot;Graphcore/vqa&quot;</span>, split=<span class="hljs-string">&quot;validation[:200]&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>dataset
Dataset({
    features: [<span class="hljs-string">&#x27;question&#x27;</span>, <span class="hljs-string">&#x27;question_type&#x27;</span>, <span class="hljs-string">&#x27;question_id&#x27;</span>, <span class="hljs-string">&#x27;image_id&#x27;</span>, <span class="hljs-string">&#x27;answer_type&#x27;</span>, <span class="hljs-string">&#x27;label&#x27;</span>],
    num_rows: <span class="hljs-number">200</span>
})`}}),bs=new b({props:{code:"ZGF0YXNldCU1QjAlNUQ=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span>dataset[<span class="hljs-number">0</span>]
{<span class="hljs-string">&#x27;question&#x27;</span>: <span class="hljs-string">&#x27;Where is he looking?&#x27;</span>,
 <span class="hljs-string">&#x27;question_type&#x27;</span>: <span class="hljs-string">&#x27;none of the above&#x27;</span>,
 <span class="hljs-string">&#x27;question_id&#x27;</span>: <span class="hljs-number">262148000</span>,
 <span class="hljs-string">&#x27;image_id&#x27;</span>: <span class="hljs-string">&#x27;/root/.cache/huggingface/datasets/downloads/extracted/ca733e0e000fb2d7a09fbcc94dbfe7b5a30750681d0e965f8e0a23b1c2f98c75/val2014/COCO_val2014_000000262148.jpg&#x27;</span>,
 <span class="hljs-string">&#x27;answer_type&#x27;</span>: <span class="hljs-string">&#x27;other&#x27;</span>,
 <span class="hljs-string">&#x27;label&#x27;</span>: {<span class="hljs-string">&#x27;ids&#x27;</span>: [<span class="hljs-string">&#x27;at table&#x27;</span>, <span class="hljs-string">&#x27;down&#x27;</span>, <span class="hljs-string">&#x27;skateboard&#x27;</span>, <span class="hljs-string">&#x27;table&#x27;</span>],
  <span class="hljs-string">&#x27;weights&#x27;</span>: [<span class="hljs-number">0.30000001192092896</span>,
   <span class="hljs-number">1.0</span>,
   <span class="hljs-number">0.30000001192092896</span>,
   <span class="hljs-number">0.30000001192092896</span>]}}`}}),gs=new b({props:{code:"ZGF0YXNldCUyMCUzRCUyMGRhdGFzZXQucmVtb3ZlX2NvbHVtbnMoJTVCJ3F1ZXN0aW9uX3R5cGUnJTJDJTIwJ3F1ZXN0aW9uX2lkJyUyQyUyMCdhbnN3ZXJfdHlwZSclNUQp",highlighted:'<span class="hljs-meta">&gt;&gt;&gt; </span>dataset = dataset.remove_columns([<span class="hljs-string">&#x27;question_type&#x27;</span>, <span class="hljs-string">&#x27;question_id&#x27;</span>, <span class="hljs-string">&#x27;answer_type&#x27;</span>])'}}),ws=new b({props:{code:"ZnJvbSUyMFBJTCUyMGltcG9ydCUyMEltYWdlJTBBJTBBaW1hZ2UlMjAlM0QlMjBJbWFnZS5vcGVuKGRhdGFzZXQlNUIwJTVEJTVCJ2ltYWdlX2lkJyU1RCklMEFpbWFnZQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> PIL <span class="hljs-keyword">import</span> Image

<span class="hljs-meta">&gt;&gt;&gt; </span>image = Image.<span class="hljs-built_in">open</span>(dataset[<span class="hljs-number">0</span>][<span class="hljs-string">&#x27;image_id&#x27;</span>])
<span class="hljs-meta">&gt;&gt;&gt; </span>image`}}),vs=new b({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> itertools

<span class="hljs-meta">&gt;&gt;&gt; </span>labels = [item[<span class="hljs-string">&#x27;ids&#x27;</span>] <span class="hljs-keyword">for</span> item <span class="hljs-keyword">in</span> dataset[<span class="hljs-string">&#x27;label&#x27;</span>]]
<span class="hljs-meta">&gt;&gt;&gt; </span>flattened_labels = <span class="hljs-built_in">list</span>(itertools.chain(*labels))
<span class="hljs-meta">&gt;&gt;&gt; </span>unique_labels = <span class="hljs-built_in">list</span>(<span class="hljs-built_in">set</span>(flattened_labels))

<span class="hljs-meta">&gt;&gt;&gt; </span>label2id = {label: idx <span class="hljs-keyword">for</span> idx, label <span class="hljs-keyword">in</span> <span class="hljs-built_in">enumerate</span>(unique_labels)}
<span class="hljs-meta">&gt;&gt;&gt; </span>id2label = {idx: label <span class="hljs-keyword">for</span> label, idx <span class="hljs-keyword">in</span> label2id.items()} `}}),_s=new b({props:{code:"ZGVmJTIwcmVwbGFjZV9pZHMoaW5wdXRzKSUzQSUwQSUyMCUyMGlucHV0cyU1QiUyMmxhYmVsJTIyJTVEJTVCJTIyaWRzJTIyJTVEJTIwJTNEJTIwJTVCbGFiZWwyaWQlNUJ4JTVEJTIwZm9yJTIweCUyMGluJTIwaW5wdXRzJTVCJTIybGFiZWwlMjIlNUQlNUIlMjJpZHMlMjIlNUQlNUQlMEElMjAlMjByZXR1cm4lMjBpbnB1dHMlMEElMEElMEFkYXRhc2V0JTIwJTNEJTIwZGF0YXNldC5tYXAocmVwbGFjZV9pZHMpJTBBZmxhdF9kYXRhc2V0JTIwJTNEJTIwZGF0YXNldC5mbGF0dGVuKCklMEFmbGF0X2RhdGFzZXQuZmVhdHVyZXM=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">def</span> <span class="hljs-title function_">replace_ids</span>(<span class="hljs-params">inputs</span>):
<span class="hljs-meta">... </span>  inputs[<span class="hljs-string">&quot;label&quot;</span>][<span class="hljs-string">&quot;ids&quot;</span>] = [label2id[x] <span class="hljs-keyword">for</span> x <span class="hljs-keyword">in</span> inputs[<span class="hljs-string">&quot;label&quot;</span>][<span class="hljs-string">&quot;ids&quot;</span>]]
<span class="hljs-meta">... </span>  <span class="hljs-keyword">return</span> inputs


<span class="hljs-meta">&gt;&gt;&gt; </span>dataset = dataset.<span class="hljs-built_in">map</span>(replace_ids)
<span class="hljs-meta">&gt;&gt;&gt; </span>flat_dataset = dataset.flatten()
<span class="hljs-meta">&gt;&gt;&gt; </span>flat_dataset.features
{<span class="hljs-string">&#x27;question&#x27;</span>: Value(dtype=<span class="hljs-string">&#x27;string&#x27;</span>, <span class="hljs-built_in">id</span>=<span class="hljs-literal">None</span>),
 <span class="hljs-string">&#x27;image_id&#x27;</span>: Value(dtype=<span class="hljs-string">&#x27;string&#x27;</span>, <span class="hljs-built_in">id</span>=<span class="hljs-literal">None</span>),
 <span class="hljs-string">&#x27;label.ids&#x27;</span>: <span class="hljs-type">Sequence</span>(feature=Value(dtype=<span class="hljs-string">&#x27;int64&#x27;</span>, <span class="hljs-built_in">id</span>=<span class="hljs-literal">None</span>), length=-<span class="hljs-number">1</span>, <span class="hljs-built_in">id</span>=<span class="hljs-literal">None</span>),
 <span class="hljs-string">&#x27;label.weights&#x27;</span>: <span class="hljs-type">Sequence</span>(feature=Value(dtype=<span class="hljs-string">&#x27;float64&#x27;</span>, <span class="hljs-built_in">id</span>=<span class="hljs-literal">None</span>), length=-<span class="hljs-number">1</span>, <span class="hljs-built_in">id</span>=<span class="hljs-literal">None</span>)}`}}),Ts=new Ds({}),Us=new b({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMFZpbHRQcm9jZXNzb3IlMEElMEFwcm9jZXNzb3IlMjAlM0QlMjBWaWx0UHJvY2Vzc29yLmZyb21fcHJldHJhaW5lZChtb2RlbF9jaGVja3BvaW50KQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> ViltProcessor

<span class="hljs-meta">&gt;&gt;&gt; </span>processor = ViltProcessor.from_pretrained(model_checkpoint)`}}),Es=new b({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">def</span> <span class="hljs-title function_">preprocess_data</span>(<span class="hljs-params">examples</span>):
<span class="hljs-meta">... </span>    image_paths = examples[<span class="hljs-string">&#x27;image_id&#x27;</span>]
<span class="hljs-meta">... </span>    images = [Image.<span class="hljs-built_in">open</span>(image_path) <span class="hljs-keyword">for</span> image_path <span class="hljs-keyword">in</span> image_paths]
<span class="hljs-meta">... </span>    texts = examples[<span class="hljs-string">&#x27;question&#x27;</span>]    

<span class="hljs-meta">... </span>    encoding = processor(images, texts, padding=<span class="hljs-string">&quot;max_length&quot;</span>, truncation=<span class="hljs-literal">True</span>, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>)

<span class="hljs-meta">... </span>    <span class="hljs-keyword">for</span> k, v <span class="hljs-keyword">in</span> encoding.items():
<span class="hljs-meta">... </span>          encoding[k] = v.squeeze()
    
<span class="hljs-meta">... </span>    targets = []

<span class="hljs-meta">... </span>    <span class="hljs-keyword">for</span> labels, scores <span class="hljs-keyword">in</span> <span class="hljs-built_in">zip</span>(examples[<span class="hljs-string">&#x27;label.ids&#x27;</span>], examples[<span class="hljs-string">&#x27;label.weights&#x27;</span>]):
<span class="hljs-meta">... </span>        target = torch.zeros(<span class="hljs-built_in">len</span>(id2label))

<span class="hljs-meta">... </span>        <span class="hljs-keyword">for</span> label, score <span class="hljs-keyword">in</span> <span class="hljs-built_in">zip</span>(labels, scores):
<span class="hljs-meta">... </span>            target[label] = score
      
<span class="hljs-meta">... </span>        targets.append(target)

<span class="hljs-meta">... </span>    encoding[<span class="hljs-string">&quot;labels&quot;</span>] = targets
    
<span class="hljs-meta">... </span>    <span class="hljs-keyword">return</span> encoding`}}),$s=new b({props:{code:"cHJvY2Vzc2VkX2RhdGFzZXQlMjAlM0QlMjBmbGF0X2RhdGFzZXQubWFwKHByZXByb2Nlc3NfZGF0YSUyQyUyMGJhdGNoZWQlM0RUcnVlJTJDJTIwcmVtb3ZlX2NvbHVtbnMlM0QlNUIncXVlc3Rpb24nJTJDJ3F1ZXN0aW9uX3R5cGUnJTJDJTIwJTIwJ3F1ZXN0aW9uX2lkJyUyQyUyMCdpbWFnZV9pZCclMkMlMjAnYW5zd2VyX3R5cGUnJTJDJTIwJ2xhYmVsLmlkcyclMkMlMjAnbGFiZWwud2VpZ2h0cyclNUQpJTBBcHJvY2Vzc2VkX2RhdGFzZXQ=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span>processed_dataset = flat_dataset.<span class="hljs-built_in">map</span>(preprocess_data, batched=<span class="hljs-literal">True</span>, remove_columns=[<span class="hljs-string">&#x27;question&#x27;</span>,<span class="hljs-string">&#x27;question_type&#x27;</span>,  <span class="hljs-string">&#x27;question_id&#x27;</span>, <span class="hljs-string">&#x27;image_id&#x27;</span>, <span class="hljs-string">&#x27;answer_type&#x27;</span>, <span class="hljs-string">&#x27;label.ids&#x27;</span>, <span class="hljs-string">&#x27;label.weights&#x27;</span>])
<span class="hljs-meta">&gt;&gt;&gt; </span>processed_dataset
Dataset({
    features: [<span class="hljs-string">&#x27;input_ids&#x27;</span>, <span class="hljs-string">&#x27;token_type_ids&#x27;</span>, <span class="hljs-string">&#x27;attention_mask&#x27;</span>, <span class="hljs-string">&#x27;pixel_values&#x27;</span>, <span class="hljs-string">&#x27;pixel_mask&#x27;</span>, <span class="hljs-string">&#x27;labels&#x27;</span>],
    num_rows: <span class="hljs-number">200</span>
})`}}),Vs=new b({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMERlZmF1bHREYXRhQ29sbGF0b3IlMEElMEFkYXRhX2NvbGxhdG9yJTIwJTNEJTIwRGVmYXVsdERhdGFDb2xsYXRvcigp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> DefaultDataCollator

<span class="hljs-meta">&gt;&gt;&gt; </span>data_collator = DefaultDataCollator()`}}),Zs=new Ds({}),Is=new b({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMFZpbHRGb3JRdWVzdGlvbkFuc3dlcmluZyUwQSUwQW1vZGVsJTIwJTNEJTIwVmlsdEZvclF1ZXN0aW9uQW5zd2VyaW5nLmZyb21fcHJldHJhaW5lZChtb2RlbF9jaGVja3BvaW50JTJDJTIwbnVtX2xhYmVscyUzRGxlbihpZDJsYWJlbCklMkMlMjBpZDJsYWJlbCUzRGlkMmxhYmVsJTJDJTIwbGFiZWwyaWQlM0RsYWJlbDJpZCk=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> ViltForQuestionAnswering

<span class="hljs-meta">&gt;&gt;&gt; </span>model = ViltForQuestionAnswering.from_pretrained(model_checkpoint, num_labels=<span class="hljs-built_in">len</span>(id2label), id2label=id2label, label2id=label2id)`}}),xs=new b({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> TrainingArguments

<span class="hljs-meta">&gt;&gt;&gt; </span>repo_id = <span class="hljs-string">&quot;MariaK/vilt_finetuned_200&quot;</span>

<span class="hljs-meta">&gt;&gt;&gt; </span>training_args = TrainingArguments(
<span class="hljs-meta">... </span>    output_dir=repo_id,
<span class="hljs-meta">... </span>    per_device_train_batch_size=<span class="hljs-number">4</span>,
<span class="hljs-meta">... </span>    num_train_epochs=<span class="hljs-number">20</span>,
<span class="hljs-meta">... </span>    save_steps=<span class="hljs-number">200</span>,
<span class="hljs-meta">... </span>    logging_steps=<span class="hljs-number">50</span>,
<span class="hljs-meta">... </span>    learning_rate=<span class="hljs-number">5e-5</span>,
<span class="hljs-meta">... </span>    save_total_limit=<span class="hljs-number">2</span>,
<span class="hljs-meta">... </span>    remove_unused_columns=<span class="hljs-literal">False</span>,
<span class="hljs-meta">... </span>    push_to_hub=<span class="hljs-literal">True</span>,
<span class="hljs-meta">... </span>)`}}),Cs=new b({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMFRyYWluZXIlMEElMEF0cmFpbmVyJTIwJTNEJTIwVHJhaW5lciglMEElMjAlMjAlMjAlMjBtb2RlbCUzRG1vZGVsJTJDJTBBJTIwJTIwJTIwJTIwYXJncyUzRHRyYWluaW5nX2FyZ3MlMkMlMEElMjAlMjAlMjAlMjBkYXRhX2NvbGxhdG9yJTNEZGF0YV9jb2xsYXRvciUyQyUwQSUyMCUyMCUyMCUyMHRyYWluX2RhdGFzZXQlM0Rwcm9jZXNzZWRfZGF0YXNldCUyQyUwQSUyMCUyMCUyMCUyMHRva2VuaXplciUzRHByb2Nlc3NvciUyQyUwQSk=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> Trainer

<span class="hljs-meta">&gt;&gt;&gt; </span>trainer = Trainer(
<span class="hljs-meta">... </span>    model=model,
<span class="hljs-meta">... </span>    args=training_args,
<span class="hljs-meta">... </span>    data_collator=data_collator,
<span class="hljs-meta">... </span>    train_dataset=processed_dataset,
<span class="hljs-meta">... </span>    tokenizer=processor,
<span class="hljs-meta">... </span>)`}}),Bs=new b({props:{code:"dHJhaW5lci50cmFpbigpJTIw",highlighted:'<span class="hljs-meta">&gt;&gt;&gt; </span>trainer.train() '}}),Qs=new b({props:{code:"dHJhaW5lci5wdXNoX3RvX2h1Yigp",highlighted:'<span class="hljs-meta">&gt;&gt;&gt; </span>trainer.push_to_hub()'}}),Xs=new Ds({}),As=new b({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMHBpcGVsaW5lJTBBJTBBcGlwZSUyMCUzRCUyMHBpcGVsaW5lKCUyMnZpc3VhbC1xdWVzdGlvbi1hbnN3ZXJpbmclMjIlMkMlMjBtb2RlbCUzRCUyMk1hcmlhSyUyRnZpbHRfZmluZXR1bmVkXzIwMCUyMik=",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>pipe = pipeline(<span class="hljs-string">&quot;visual-question-answering&quot;</span>, model=<span class="hljs-string">&quot;MariaK/vilt_finetuned_200&quot;</span>)`}}),Rs=new b({props:{code:"ZXhhbXBsZSUyMCUzRCUyMGRhdGFzZXQlNUIwJTVEJTBBaW1hZ2UlMjAlM0QlMjBJbWFnZS5vcGVuKGV4YW1wbGUlNUInaW1hZ2VfaWQnJTVEKSUwQXF1ZXN0aW9uJTIwJTNEJTIwZXhhbXBsZSU1QidxdWVzdGlvbiclNUQlMEFwcmludChxdWVzdGlvbiklMEFwaXBlKGltYWdlJTJDJTIwcXVlc3Rpb24lMkMlMjB0b3BfayUzRDEp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span>example = dataset[<span class="hljs-number">0</span>]
<span class="hljs-meta">&gt;&gt;&gt; </span>image = Image.<span class="hljs-built_in">open</span>(example[<span class="hljs-string">&#x27;image_id&#x27;</span>])
<span class="hljs-meta">&gt;&gt;&gt; </span>question = example[<span class="hljs-string">&#x27;question&#x27;</span>]
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-built_in">print</span>(question)
<span class="hljs-meta">&gt;&gt;&gt; </span>pipe(image, question, top_k=<span class="hljs-number">1</span>)
<span class="hljs-string">&quot;Where is he looking?&quot;</span>
[{<span class="hljs-string">&#x27;score&#x27;</span>: <span class="hljs-number">0.5498199462890625</span>, <span class="hljs-string">&#x27;answer&#x27;</span>: <span class="hljs-string">&#x27;down&#x27;</span>}]`}}),Fs=new b({props:{code:"cHJvY2Vzc29yJTIwJTNEJTIwVmlsdFByb2Nlc3Nvci5mcm9tX3ByZXRyYWluZWQoJTIyTWFyaWFLJTJGdmlsdF9maW5ldHVuZWRfMjAwJTIyKSUwQSUwQWltYWdlJTIwJTNEJTIwSW1hZ2Uub3BlbihleGFtcGxlJTVCJ2ltYWdlX2lkJyU1RCklMEFxdWVzdGlvbiUyMCUzRCUyMGV4YW1wbGUlNUIncXVlc3Rpb24nJTVEJTBBJTBBJTIzJTIwcHJlcGFyZSUyMGlucHV0cyUwQWlucHV0cyUyMCUzRCUyMHByb2Nlc3NvcihpbWFnZSUyQyUyMHF1ZXN0aW9uJTJDJTIwcmV0dXJuX3RlbnNvcnMlM0QlMjJwdCUyMiklMEElMEFtb2RlbCUyMCUzRCUyMFZpbHRGb3JRdWVzdGlvbkFuc3dlcmluZy5mcm9tX3ByZXRyYWluZWQoJTIyTWFyaWFLJTJGdmlsdF9maW5ldHVuZWRfMjAwJTIyKSUwQSUwQSUyMyUyMGZvcndhcmQlMjBwYXNzJTBBd2l0aCUyMHRvcmNoLm5vX2dyYWQoKSUzQSUwQSUyMCUyMCUyMCUyMG91dHB1dHMlMjAlM0QlMjBtb2RlbCgqKmlucHV0cyklMEElMEFsb2dpdHMlMjAlM0QlMjBvdXRwdXRzLmxvZ2l0cyUwQWlkeCUyMCUzRCUyMGxvZ2l0cy5hcmdtYXgoLTEpLml0ZW0oKSUwQXByaW50KCUyMlByZWRpY3RlZCUyMGFuc3dlciUzQSUyMiUyQyUyMG1vZGVsLmNvbmZpZy5pZDJsYWJlbCU1QmlkeCU1RCk=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span>processor = ViltProcessor.from_pretrained(<span class="hljs-string">&quot;MariaK/vilt_finetuned_200&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>image = Image.<span class="hljs-built_in">open</span>(example[<span class="hljs-string">&#x27;image_id&#x27;</span>])
<span class="hljs-meta">&gt;&gt;&gt; </span>question = example[<span class="hljs-string">&#x27;question&#x27;</span>]

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># prepare inputs</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = processor(image, question, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>model = ViltForQuestionAnswering.from_pretrained(<span class="hljs-string">&quot;MariaK/vilt_finetuned_200&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># forward pass</span>
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">with</span> torch.no_grad():
<span class="hljs-meta">... </span>    outputs = model(**inputs)

<span class="hljs-meta">&gt;&gt;&gt; </span>logits = outputs.logits
<span class="hljs-meta">&gt;&gt;&gt; </span>idx = logits.argmax(-<span class="hljs-number">1</span>).item()
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-built_in">print</span>(<span class="hljs-string">&quot;Predicted answer:&quot;</span>, model.config.id2label[idx])
Predicted answer: down`}}),Ys=new Ds({}),Hs=new b({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoProcessor, Blip2ForConditionalGeneration
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch

<span class="hljs-meta">&gt;&gt;&gt; </span>processor = AutoProcessor.from_pretrained(<span class="hljs-string">&quot;Salesforce/blip2-opt-2.7b&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = Blip2ForConditionalGeneration.from_pretrained(<span class="hljs-string">&quot;Salesforce/blip2-opt-2.7b&quot;</span>, torch_dtype=torch.float16)
<span class="hljs-meta">&gt;&gt;&gt; </span>device = <span class="hljs-string">&quot;cuda&quot;</span> <span class="hljs-keyword">if</span> torch.cuda.is_available() <span class="hljs-keyword">else</span> <span class="hljs-string">&quot;cpu&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model.to(device)`}}),qs=new b({props:{code:"ZXhhbXBsZSUyMCUzRCUyMGRhdGFzZXQlNUIwJTVEJTBBaW1hZ2UlMjAlM0QlMjBJbWFnZS5vcGVuKGV4YW1wbGUlNUInaW1hZ2VfaWQnJTVEKSUwQXF1ZXN0aW9uJTIwJTNEJTIwZXhhbXBsZSU1QidxdWVzdGlvbiclNUQ=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span>example = dataset[<span class="hljs-number">0</span>]
<span class="hljs-meta">&gt;&gt;&gt; </span>image = Image.<span class="hljs-built_in">open</span>(example[<span class="hljs-string">&#x27;image_id&#x27;</span>])
<span class="hljs-meta">&gt;&gt;&gt; </span>question = example[<span class="hljs-string">&#x27;question&#x27;</span>]`}}),Ps=new b({props:{code:"cHJvbXB0JTIwJTNEJTIwZiUyMlF1ZXN0aW9uJTNBJTIwJTdCcXVlc3Rpb24lN0QlMjBBbnN3ZXIlM0ElMjIlMjA=",highlighted:'<span class="hljs-meta">&gt;&gt;&gt; </span>prompt = <span class="hljs-string">f&quot;Question: <span class="hljs-subst">{question}</span> Answer:&quot;</span> '}}),Ls=new b({props:{code:"aW5wdXRzJTIwJTNEJTIwcHJvY2Vzc29yKGltYWdlJTJDJTIwdGV4dCUzRHByb21wdCUyQyUyMHJldHVybl90ZW5zb3JzJTNEJTIycHQlMjIpLnRvKGRldmljZSUyQyUyMHRvcmNoLmZsb2F0MTYpJTBBJTBBZ2VuZXJhdGVkX2lkcyUyMCUzRCUyMG1vZGVsLmdlbmVyYXRlKCoqaW5wdXRzJTJDJTIwbWF4X25ld190b2tlbnMlM0QxMCklMEFnZW5lcmF0ZWRfdGV4dCUyMCUzRCUyMHByb2Nlc3Nvci5iYXRjaF9kZWNvZGUoZ2VuZXJhdGVkX2lkcyUyQyUyMHNraXBfc3BlY2lhbF90b2tlbnMlM0RUcnVlKSU1QjAlNUQuc3RyaXAoKSUwQXByaW50KGdlbmVyYXRlZF90ZXh0KQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = processor(image, text=prompt, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>).to(device, torch.float16)

<span class="hljs-meta">&gt;&gt;&gt; </span>generated_ids = model.generate(**inputs, max_new_tokens=<span class="hljs-number">10</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>generated_text = processor.batch_decode(generated_ids, skip_special_tokens=<span class="hljs-literal">True</span>)[<span class="hljs-number">0</span>].strip()
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-built_in">print</span>(generated_text)
<span class="hljs-string">&quot;He is looking at the crowd&quot;</span> `}}),{c(){I=p("meta"),Qa=c(),x=p("h1"),A=p("a"),Wl=p("span"),f(is.$$.fragment),Ie=c(),Bl=p("span"),xe=e("\uC2DC\uAC01\uC801 \uC9C8\uC758\uC751\uB2F5 (Visual Question Answering)"),Xa=c(),f(os.$$.fragment),Aa=c(),Ks=p("p"),Ge=e("\uC2DC\uAC01\uC801 \uC9C8\uC758\uC751\uB2F5(VQA)\uC740 \uC774\uBBF8\uC9C0\uB97C \uAE30\uBC18\uC73C\uB85C \uAC1C\uBC29\uD615 \uC9C8\uBB38\uC5D0 \uB300\uC751\uD558\uB294 \uC791\uC5C5\uC785\uB2C8\uB2E4. \uC774 \uC791\uC5C5\uC744 \uC9C0\uC6D0\uD558\uB294 \uBAA8\uB378\uC758 \uC785\uB825\uC740 \uB300\uBD80\uBD84 \uC774\uBBF8\uC9C0\uC640 \uC9C8\uBB38\uC758 \uC870\uD569\uC774\uBA70, \uCD9C\uB825\uC740 \uC790\uC5F0\uC5B4\uB85C \uB41C \uB2F5\uBCC0\uC785\uB2C8\uB2E4."),Ra=c(),Os=p("p"),ke=e("VQA\uC758 \uC8FC\uC694 \uC0AC\uC6A9 \uC0AC\uB840\uB294 \uB2E4\uC74C\uACFC \uAC19\uC2B5\uB2C8\uB2E4:"),Na=c(),J=p("ul"),Ql=p("li"),Ce=e("\uC2DC\uAC01 \uC7A5\uC560\uC778\uC744 \uC704\uD55C \uC811\uADFC\uC131 \uC560\uD50C\uB9AC\uCF00\uC774\uC158\uC744 \uAD6C\uCD95\uD560 \uC218 \uC788\uC2B5\uB2C8\uB2E4."),We=c(),Xl=p("li"),Be=e("\uAD50\uC721: \uAC15\uC758\uB098 \uAD50\uACFC\uC11C\uC5D0 \uB098\uC628 \uC2DC\uAC01 \uC790\uB8CC\uC5D0 \uB300\uD55C \uC9C8\uBB38\uC5D0 \uB2F5\uD560 \uC218 \uC788\uC2B5\uB2C8\uB2E4. \uB610\uD55C \uCCB4\uD5D8\uD615 \uC804\uC2DC\uC640 \uC720\uC801 \uB4F1\uC5D0\uC11C\uB3C4 VQA\uB97C \uD65C\uC6A9\uD560 \uC218 \uC788\uC2B5\uB2C8\uB2E4."),Qe=c(),Al=p("li"),Xe=e("\uACE0\uAC1D \uC11C\uBE44\uC2A4 \uBC0F \uC804\uC790\uC0C1\uAC70\uB798: VQA\uB294 \uC0AC\uC6A9\uC790\uAC00 \uC81C\uD488\uC5D0 \uB300\uD574 \uC9C8\uBB38\uD560 \uC218 \uC788\uAC8C \uD568\uC73C\uB85C\uC368 \uC0AC\uC6A9\uC790 \uACBD\uD5D8\uC744 \uD5A5\uC0C1\uC2DC\uD0AC \uC218 \uC788\uC2B5\uB2C8\uB2E4."),Ae=c(),Rl=p("li"),Re=e("\uC774\uBBF8\uC9C0 \uAC80\uC0C9: VQA \uBAA8\uB378\uC744 \uC0AC\uC6A9\uD558\uC5EC \uC6D0\uD558\uB294 \uD2B9\uC131\uC744 \uAC00\uC9C4 \uC774\uBBF8\uC9C0\uB97C \uAC80\uC0C9\uD560 \uC218 \uC788\uC2B5\uB2C8\uB2E4. \uC608\uB97C \uB4E4\uC5B4 \uC0AC\uC6A9\uC790\uB294 \u201C\uAC15\uC544\uC9C0\uAC00 \uC788\uC5B4?\u201D\uB77C\uACE0 \uBB3C\uC5B4\uBD10\uC11C \uC8FC\uC5B4\uC9C4 \uC774\uBBF8\uC9C0 \uBB36\uC74C\uC5D0\uC11C \uAC15\uC544\uC9C0\uAC00 \uC788\uB294 \uBAA8\uB4E0 \uC774\uBBF8\uC9C0\uB97C \uBC1B\uC544\uBCFC \uC218 \uC788\uC2B5\uB2C8\uB2E4."),Fa=c(),sl=p("p"),Ne=e("\uC774 \uAC00\uC774\uB4DC\uC5D0\uC11C \uD559\uC2B5\uD560 \uB0B4\uC6A9\uC740 \uB2E4\uC74C\uACFC \uAC19\uC2B5\uB2C8\uB2E4:"),Ya=c(),T=p("ul"),G=p("li"),Fe=e("VQA \uBAA8\uB378 \uC911 \uD558\uB098\uC778 "),ll=p("a"),Ye=e("ViLT"),ze=e("\uB97C "),R=p("a"),Nl=p("code"),He=e("Graphcore/vqa"),qe=e(" \uB370\uC774\uD130\uC14B"),Pe=e(" \uC5D0\uC11C \uBBF8\uC138\uC870\uC815\uD558\uB294 \uBC29\uBC95"),Le=c(),Fl=p("li"),Se=e("\uBBF8\uC138\uC870\uC815\uB41C ViLT \uBAA8\uB378\uB85C \uCD94\uB860\uD558\uB294 \uBC29\uBC95"),De=c(),Yl=p("li"),Ke=e("BLIP-2 \uAC19\uC740 \uC0DD\uC131 \uBAA8\uB378\uB85C \uC81C\uB85C\uC0F7 VQA \uCD94\uB860\uC744 \uC2E4\uD589\uD558\uB294 \uBC29\uBC95"),za=c(),k=p("h2"),N=p("a"),zl=p("span"),f(cs.$$.fragment),Oe=c(),Hl=p("span"),sn=e("ViLT \uBBF8\uC138 \uC870\uC815"),Ha=c(),U=p("p"),ln=e(`ViLT\uB294 Vision Transformer (ViT) \uB0B4\uC5D0 \uD14D\uC2A4\uD2B8 \uC784\uBCA0\uB529\uC744 \uD3EC\uD568\uD558\uC5EC \uBE44\uC804/\uC790\uC5F0\uC5B4 \uC0AC\uC804\uD6C8\uB828(VLP; Vision-and-Language Pretraining)\uC744 \uC704\uD55C \uAE30\uBCF8 \uB514\uC790\uC778\uC744 \uC81C\uACF5\uD569\uB2C8\uB2E4.
ViLT \uBAA8\uB378\uC740 \uBE44\uC804 \uD2B8\uB79C\uC2A4\uD3EC\uBA38(ViT)\uC5D0 \uD14D\uC2A4\uD2B8 \uC784\uBCA0\uB529\uC744 \uB123\uC5B4 \uBE44\uC804/\uC5B8\uC5B4 \uC0AC\uC804\uD6C8\uB828(VLP; Vision-and-Language Pre-training)\uC744 \uC704\uD55C \uAE30\uBCF8\uC801\uC778 \uB514\uC790\uC778\uC744 \uAC16\uCDC4\uC2B5\uB2C8\uB2E4. \uC774 \uBAA8\uB378\uC740 \uC5EC\uB7EC \uB2E4\uC6B4\uC2A4\uD2B8\uB9BC \uC791\uC5C5\uC5D0 \uC0AC\uC6A9\uD560 \uC218 \uC788\uC2B5\uB2C8\uB2E4. VQA \uD0DC\uC2A4\uD06C\uC5D0\uC11C\uB294 (`),ql=p("code"),an=e("[CLS]"),tn=e(` \uD1A0\uD070\uC758 \uCD5C\uC885 \uC740\uB2C9 \uC0C1\uD0DC \uC704\uC5D0 \uC120\uD615 \uB808\uC774\uC5B4\uC778) \uBD84\uB958 \uD5E4\uB354\uAC00 \uC788\uC73C\uBA70 \uBB34\uC791\uC704\uB85C \uCD08\uAE30\uD654\uB429\uB2C8\uB2E4.
\uB530\uB77C\uC11C \uC5EC\uAE30\uC5D0\uC11C \uC2DC\uAC01\uC801 \uC9C8\uC758\uC751\uB2F5\uC740 `),Pl=p("strong"),en=e("\uBD84\uB958 \uBB38\uC81C"),nn=e("\uB85C \uCDE8\uAE09\uB429\uB2C8\uB2E4."),qa=c(),al=p("p"),pn=e("\uCD5C\uADFC\uC758 BLIP, BLIP-2, InstructBLIP\uC640 \uAC19\uC740 \uBAA8\uB378\uB4E4\uC740 VQA\uB97C \uC0DD\uC131\uD615 \uC791\uC5C5\uC73C\uB85C \uAC04\uC8FC\uD569\uB2C8\uB2E4. \uAC00\uC774\uB4DC\uC758 \uD6C4\uBC18\uBD80\uC5D0\uC11C\uB294 \uC774\uB7F0 \uBAA8\uB378\uB4E4\uC744 \uC0AC\uC6A9\uD558\uC5EC \uC81C\uB85C\uC0F7 VQA \uCD94\uB860\uC744 \uD558\uB294 \uBC29\uBC95\uC5D0 \uB300\uD574 \uC124\uBA85\uD558\uACA0\uC2B5\uB2C8\uB2E4."),Pa=c(),tl=p("p"),rn=e("\uC2DC\uC791\uD558\uAE30 \uC804 \uD544\uC694\uD55C \uBAA8\uB4E0 \uB77C\uC774\uBE0C\uB7EC\uB9AC\uB97C \uC124\uCE58\uD588\uB294\uC9C0 \uD655\uC778\uD558\uC138\uC694."),La=c(),f(ms.$$.fragment),Sa=c(),el=p("p"),on=e(`\uCEE4\uBBA4\uB2C8\uD2F0\uC5D0 \uBAA8\uB378\uC744 \uACF5\uC720\uD558\uB294 \uAC83\uC744 \uAD8C\uC7A5 \uB4DC\uB9BD\uB2C8\uB2E4. Hugging Face \uACC4\uC815\uC5D0 \uB85C\uADF8\uC778\uD558\uC5EC \u{1F917} Hub\uC5D0 \uC5C5\uB85C\uB4DC\uD560 \uC218 \uC788\uC2B5\uB2C8\uB2E4.
\uBA54\uC2DC\uC9C0\uAC00 \uB098\uD0C0\uB098\uBA74 \uB85C\uADF8\uC778\uD560 \uD1A0\uD070\uC744 \uC785\uB825\uD558\uC138\uC694:`),Da=c(),f(hs.$$.fragment),Ka=c(),nl=p("p"),cn=e("\uBAA8\uB378 \uCCB4\uD06C\uD3EC\uC778\uD2B8\uB97C \uC804\uC5ED \uBCC0\uC218\uB85C \uC120\uC5B8\uD558\uC138\uC694."),Oa=c(),f(fs.$$.fragment),st=c(),C=p("h2"),F=p("a"),Ll=p("span"),f(us.$$.fragment),mn=c(),Sl=p("span"),hn=e("\uB370\uC774\uD130 \uAC00\uC838\uC624\uAE30"),lt=c(),E=p("p"),fn=e("\uC774 \uAC00\uC774\uB4DC\uC5D0\uC11C\uB294 "),Dl=p("code"),un=e("Graphcore/vqa"),dn=e(" \uB370\uC774\uD130\uC138\uD2B8\uC758 \uC791\uC740 \uC0D8\uD50C\uC744 \uC0AC\uC6A9\uD569\uB2C8\uB2E4. \uC804\uCCB4 \uB370\uC774\uD130\uC138\uD2B8\uB294 "),ds=p("a"),jn=e("\u{1F917} Hub"),yn=e(" \uC5D0\uC11C \uD655\uC778\uD560 \uC218 \uC788\uC2B5\uB2C8\uB2E4."),at=c(),_=p("p"),Y=p("a"),Kl=p("code"),Mn=e("Graphcore/vqa"),bn=e(" \uB370\uC774\uD130\uC138\uD2B8"),gn=e(" \uC758 \uB300\uC548\uC73C\uB85C \uACF5\uC2DD "),js=p("a"),wn=e("VQA \uB370\uC774\uD130\uC138\uD2B8 \uD398\uC774\uC9C0"),Jn=e(" \uC5D0\uC11C \uB3D9\uC77C\uD55C \uB370\uC774\uD130\uB97C \uC218\uB3D9\uC73C\uB85C \uB2E4\uC6B4\uB85C\uB4DC\uD560 \uC218 \uC788\uC2B5\uB2C8\uB2E4. \uC9C1\uC811 \uACF5\uC218\uD55C \uB370\uC774\uD130\uB85C \uD29C\uD1A0\uB9AC\uC5BC\uC744 \uB530\uB974\uACE0 \uC2F6\uB2E4\uBA74 "),ys=p("a"),vn=e("\uC774\uBBF8\uC9C0 \uB370\uC774\uD130\uC138\uD2B8 \uB9CC\uB4E4\uAE30"),_n=e(` \uB77C\uB294
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