import{S as eo,i as ao,s as oo,e as l,k as p,w as T,t as o,M as so,c as r,d as a,m as u,a as n,x as J,h as s,b as h,G as e,g as d,y as j,q as E,o as $,B as k,v as lo}from"../../chunks/vendor-hf-doc-builder.js";import{T as to}from"../../chunks/Tip-hf-doc-builder.js";import{I as Me}from"../../chunks/IconCopyLink-hf-doc-builder.js";import{C as Yt}from"../../chunks/CodeBlock-hf-doc-builder.js";function ro(nt){let f,w,c,y,g;return{c(){f=l("p"),w=o("\u{1F4A1} Learn more about how to create an image dataset for training in the "),c=l("a"),y=o("Create an image dataset"),g=o(" guide."),this.h()},l(m){f=r(m,"P",{});var M=n(f);w=s(M,"\u{1F4A1} Learn more about how to create an image dataset for training in the "),c=r(M,"A",{href:!0,rel:!0});var b=n(c);y=s(b,"Create an image dataset"),b.forEach(a),g=s(M," guide."),M.forEach(a),this.h()},h(){h(c,"href","https://huggingface.co/docs/datasets/image_dataset"),h(c,"rel","nofollow")},m(m,M){d(m,f,M),e(f,w),e(f,c),e(c,y),e(f,g)},d(m){m&&a(f)}}}function no(nt){let f,w,c,y,g;return{c(){f=l("p"),w=o("\u{1F4A1} For more details and context about creating and uploading a dataset to the Hub, take a look at the "),c=l("a"),y=o("Image search with \u{1F917} Datasets"),g=o(" post."),this.h()},l(m){f=r(m,"P",{});var M=n(f);w=s(M,"\u{1F4A1} For more details and context about creating and uploading a dataset to the Hub, take a look at the "),c=r(M,"A",{href:!0,rel:!0});var b=n(c);y=s(b,"Image search with \u{1F917} Datasets"),b.forEach(a),g=s(M," post."),M.forEach(a),this.h()},h(){h(c,"href","https://huggingface.co/blog/image-search-datasets"),h(c,"rel","nofollow")},m(m,M){d(m,f,M),e(f,w),e(f,c),e(c,y),e(f,g)},d(m){m&&a(f)}}}function io(nt){let f,w,c,y,g,m,M,b,ye,Ct,Z,_e,D,ge,we,it,ve,be,Ft,dt,Te,At,x,S,Je,Mt,je,Ee,$e,Q,ke,yt,Ze,Ie,Wt,Y,Nt,X,C,_t,V,Ue,gt,Ge,zt,F,Xe,P,wt,Be,Re,Ht,O,qt,A,xe,vt,Ye,Ce,Dt,L,St,B,W,bt,K,Fe,Tt,Ae,Qt,N,Vt,I,We,tt,Jt,Ne,ze,jt,He,qe,Pt,_,De,Et,Se,Qe,$t,Ve,Pe,kt,Oe,Le,Zt,Ke,ta,It,ea,aa,Ot,et,Lt,z,oa,at,sa,la,Kt,ot,te,H,ra,Ut,na,ia,ee,st,ae,R,q,Gt,lt,da,Xt,ha,oe,U,fa,Bt,pa,ua,Rt,ca,ma,se,G,Ma,ht,ya,_a,ft,ga,wa,le;return m=new Me({}),Y=new to({props:{$$slots:{default:[ro]},$$scope:{ctx:nt}}}),V=new Me({}),O=new Yt({props:{code:"ZGF0YV9kaXIlMkZ4eHgucG5nJTBBZGF0YV9kaXIlMkZ4eHkucG5nJTBBZGF0YV9kaXIlMkYlNUIuLi4lNUQlMkZ4eHoucG5n",highlighted:`data_dir/xxx.png
data_dir/xxy.png
data_dir/[...]/xxz.png`}}),L=new Yt({props:{code:"YWNjZWxlcmF0ZSUyMGxhdW5jaCUyMHRyYWluX3VuY29uZGl0aW9uYWwucHklMjAlNUMlMEElMjAlMjAlMjAlMjAtLXRyYWluX2RhdGFfZGlyJTIwJTNDcGF0aC10by10cmFpbi1kaXJlY3RvcnklM0UlMjAlNUMlMEElMjAlMjAlMjAlMjAlM0NvdGhlci1hcmd1bWVudHMlM0U=",highlighted:`accelerate launch train_unconditional.py \\
    --train_data_dir &lt;path-to-train-directory&gt; \\
    &lt;other-arguments&gt;`}}),K=new Me({}),N=new to({props:{$$slots:{default:[no]},$$scope:{ctx:nt}}}),et=new Yt({props:{code:"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",highlighted:`<span class="hljs-keyword">from</span> datasets <span class="hljs-keyword">import</span> load_dataset

<span class="hljs-comment"># example 1: local folder</span>
dataset = load_dataset(<span class="hljs-string">&quot;imagefolder&quot;</span>, data_dir=<span class="hljs-string">&quot;path_to_your_folder&quot;</span>)

<span class="hljs-comment"># example 2: local files (supported formats are tar, gzip, zip, xz, rar, zstd)</span>
dataset = load_dataset(<span class="hljs-string">&quot;imagefolder&quot;</span>, data_files=<span class="hljs-string">&quot;path_to_zip_file&quot;</span>)

<span class="hljs-comment"># example 3: remote files (supported formats are tar, gzip, zip, xz, rar, zstd)</span>
dataset = load_dataset(
    <span class="hljs-string">&quot;imagefolder&quot;</span>,
    data_files=<span class="hljs-string">&quot;https://download.microsoft.com/download/3/E/1/3E1C3F21-ECDB-4869-8368-6DEBA77B919F/kagglecatsanddogs_3367a.zip&quot;</span>,
)

<span class="hljs-comment"># example 4: providing several splits</span>
dataset = load_dataset(
    <span class="hljs-string">&quot;imagefolder&quot;</span>, data_files={<span class="hljs-string">&quot;train&quot;</span>: [<span class="hljs-string">&quot;path/to/file1&quot;</span>, <span class="hljs-string">&quot;path/to/file2&quot;</span>], <span class="hljs-string">&quot;test&quot;</span>: [<span class="hljs-string">&quot;path/to/file3&quot;</span>, <span class="hljs-string">&quot;path/to/file4&quot;</span>]}
)`}}),ot=new Yt({props:{code:"JTIzJTIwYXNzdW1pbmclMjB5b3UlMjBoYXZlJTIwcmFuJTIwdGhlJTIwaHVnZ2luZ2ZhY2UtY2xpJTIwbG9naW4lMjBjb21tYW5kJTIwaW4lMjBhJTIwdGVybWluYWwlMEFkYXRhc2V0LnB1c2hfdG9faHViKCUyMm5hbWVfb2ZfeW91cl9kYXRhc2V0JTIyKSUwQSUwQSUyMyUyMGlmJTIweW91JTIwd2FudCUyMHRvJTIwcHVzaCUyMHRvJTIwYSUyMHByaXZhdGUlMjByZXBvJTJDJTIwc2ltcGx5JTIwcGFzcyUyMHByaXZhdGUlM0RUcnVlJTNBJTBBZGF0YXNldC5wdXNoX3RvX2h1YiglMjJuYW1lX29mX3lvdXJfZGF0YXNldCUyMiUyQyUyMHByaXZhdGUlM0RUcnVlKQ==",highlighted:`<span class="hljs-comment"># assuming you have ran the huggingface-cli login command in a terminal</span>
dataset.push_to_hub(<span class="hljs-string">&quot;name_of_your_dataset&quot;</span>)

<span class="hljs-comment"># if you want to push to a private repo, simply pass private=True:</span>
dataset.push_to_hub(<span class="hljs-string">&quot;name_of_your_dataset&quot;</span>, private=<span class="hljs-literal">True</span>)`}}),st=new Yt({props:{code:"YWNjZWxlcmF0ZSUyMGxhdW5jaCUyMC0tbWl4ZWRfcHJlY2lzaW9uJTNEJTIyZnAxNiUyMiUyMCUyMHRyYWluX3RleHRfdG9faW1hZ2UucHklMjAlNUMlMEElMjAlMjAtLXByZXRyYWluZWRfbW9kZWxfbmFtZV9vcl9wYXRoJTNEJTIycnVud2F5bWwlMkZzdGFibGUtZGlmZnVzaW9uLXYxLTUlMjIlMjAlNUMlMEElMjAlMjAtLWRhdGFzZXRfbmFtZSUzRCUyMm5hbWVfb2ZfeW91cl9kYXRhc2V0JTIyJTIwJTVDJTBBJTIwJTIwJTNDb3RoZXItYXJndW1lbnRzJTNF",highlighted:`accelerate launch --mixed_precision=<span class="hljs-string">&quot;fp16&quot;</span>  train_text_to_image.py \\
  --pretrained_model_name_or_path=<span class="hljs-string">&quot;runwayml/stable-diffusion-v1-5&quot;</span> \\
  --dataset_name=<span class="hljs-string">&quot;name_of_your_dataset&quot;</span> \\
  &lt;other-arguments&gt;`}}),lt=new Me({}),{c(){f=l("meta"),w=p(),c=l("h1"),y=l("a"),g=l("span"),T(m.$$.fragment),M=p(),b=l("span"),ye=o("Create a dataset for training"),Ct=p(),Z=l("p"),_e=o("There are many datasets on the "),D=l("a"),ge=o("Hub"),we=o(" to train a model on, but if you can\u2019t find one you\u2019re interested in or want to use your own, you can create a dataset with the \u{1F917} "),it=l("a"),ve=o("Datasets"),be=o(" library. The dataset structure depends on the task you want to train your model on. The most basic dataset structure is a directory of images for tasks like unconditional image generation. Another dataset structure may be a directory of images and a text file containing their corresponding text captions for tasks like text-to-image generation."),Ft=p(),dt=l("p"),Te=o("This guide will show you two ways to create a dataset to finetune on:"),At=p(),x=l("ul"),S=l("li"),Je=o("provide a folder of images to the "),Mt=l("code"),je=o("--train_data_dir"),Ee=o(" argument"),$e=p(),Q=l("li"),ke=o("upload a dataset to the Hub and pass the dataset repository id to the "),yt=l("code"),Ze=o("--dataset_name"),Ie=o(" argument"),Wt=p(),T(Y.$$.fragment),Nt=p(),X=l("h2"),C=l("a"),_t=l("span"),T(V.$$.fragment),Ue=p(),gt=l("span"),Ge=o("Provide a dataset as a folder"),zt=p(),F=l("p"),Xe=o("For unconditional generation, you can provide your own dataset as a folder of images. The training script uses the "),P=l("a"),wt=l("code"),Be=o("ImageFolder"),Re=o(" builder from \u{1F917} Datasets to automatically build a dataset from the folder. Your directory structure should look like:"),Ht=p(),T(O.$$.fragment),qt=p(),A=l("p"),xe=o("Pass the path to the dataset directory to the "),vt=l("code"),Ye=o("--train_data_dir"),Ce=o(" argument, and then you can start training:"),Dt=p(),T(L.$$.fragment),St=p(),B=l("h2"),W=l("a"),bt=l("span"),T(K.$$.fragment),Fe=p(),Tt=l("span"),Ae=o("Upload your data to the Hub"),Qt=p(),T(N.$$.fragment),Vt=p(),I=l("p"),We=o("Start by creating a dataset with the "),tt=l("a"),Jt=l("code"),Ne=o("ImageFolder"),ze=o(" feature, which creates an "),jt=l("code"),He=o("image"),qe=o(" column containing the PIL-encoded images."),Pt=p(),_=l("p"),De=o("You can use the "),Et=l("code"),Se=o("data_dir"),Qe=o(" or "),$t=l("code"),Ve=o("data_files"),Pe=o(" parameters to specify the location of the dataset. The "),kt=l("code"),Oe=o("data_files"),Le=o(" parameter supports mapping specific files to dataset splits like "),Zt=l("code"),Ke=o("train"),ta=o(" or "),It=l("code"),ea=o("test"),aa=o(":"),Ot=p(),T(et.$$.fragment),Lt=p(),z=l("p"),oa=o("Then use the "),at=l("a"),sa=o("push_to_hub"),la=o(" method to upload the dataset to the Hub:"),Kt=p(),T(ot.$$.fragment),te=p(),H=l("p"),ra=o("Now the dataset is available for training by passing the dataset name to the "),Ut=l("code"),na=o("--dataset_name"),ia=o(" argument:"),ee=p(),T(st.$$.fragment),ae=p(),R=l("h2"),q=l("a"),Gt=l("span"),T(lt.$$.fragment),da=p(),Xt=l("span"),ha=o("Next steps"),oe=p(),U=l("p"),fa=o("Now that you\u2019ve created a dataset, you can plug it into the "),Bt=l("code"),pa=o("train_data_dir"),ua=o(" (if your dataset is local) or "),Rt=l("code"),ca=o("dataset_name"),ma=o(" (if your dataset is on the Hub) arguments of a training script."),se=p(),G=l("p"),Ma=o("For your next steps, feel free to try and use your dataset to train a model for "),ht=l("a"),ya=o("unconditional generation"),_a=o(" or "),ft=l("a"),ga=o("text-to-image generation"),wa=o("!"),this.h()},l(t){const i=so('[data-svelte="svelte-1phssyn"]',document.head);f=r(i,"META",{name:!0,content:!0}),i.forEach(a),w=u(t),c=r(t,"H1",{class:!0});var rt=n(c);y=r(rt,"A",{id:!0,class:!0,href:!0});var xt=n(y);g=r(xt,"SPAN",{});var va=n(g);J(m.$$.fragment,va),va.forEach(a),xt.forEach(a),M=u(rt),b=r(rt,"SPAN",{});var ba=n(b);ye=s(ba,"Create a dataset for training"),ba.forEach(a),rt.forEach(a),Ct=u(t),Z=r(t,"P",{});var pt=n(Z);_e=s(pt,"There are many datasets on the "),D=r(pt,"A",{href:!0,rel:!0});var Ta=n(D);ge=s(Ta,"Hub"),Ta.forEach(a),we=s(pt," to train a model on, but if you can\u2019t find one you\u2019re interested in or want to use your own, you can create a dataset with the \u{1F917} "),it=r(pt,"A",{href:!0});var Ja=n(it);ve=s(Ja,"Datasets"),Ja.forEach(a),be=s(pt," library. The dataset structure depends on the task you want to train your model on. The most basic dataset structure is a directory of images for tasks like unconditional image generation. Another dataset structure may be a directory of images and a text file containing their corresponding text captions for tasks like text-to-image generation."),pt.forEach(a),Ft=u(t),dt=r(t,"P",{});var ja=n(dt);Te=s(ja,"This guide will show you two ways to create a dataset to finetune on:"),ja.forEach(a),At=u(t),x=r(t,"UL",{});var re=n(x);S=r(re,"LI",{});var ne=n(S);Je=s(ne,"provide a folder of images to the "),Mt=r(ne,"CODE",{});var Ea=n(Mt);je=s(Ea,"--train_data_dir"),Ea.forEach(a),Ee=s(ne," argument"),ne.forEach(a),$e=u(re),Q=r(re,"LI",{});var ie=n(Q);ke=s(ie,"upload a dataset to the Hub and pass the dataset repository id to the "),yt=r(ie,"CODE",{});var $a=n(yt);Ze=s($a,"--dataset_name"),$a.forEach(a),Ie=s(ie," argument"),ie.forEach(a),re.forEach(a),Wt=u(t),J(Y.$$.fragment,t),Nt=u(t),X=r(t,"H2",{class:!0});var de=n(X);C=r(de,"A",{id:!0,class:!0,href:!0});var ka=n(C);_t=r(ka,"SPAN",{});var Za=n(_t);J(V.$$.fragment,Za),Za.forEach(a),ka.forEach(a),Ue=u(de),gt=r(de,"SPAN",{});var Ia=n(gt);Ge=s(Ia,"Provide a dataset as a folder"),Ia.forEach(a),de.forEach(a),zt=u(t),F=r(t,"P",{});var he=n(F);Xe=s(he,"For unconditional generation, you can provide your own dataset as a folder of images. The training script uses the "),P=r(he,"A",{href:!0,rel:!0});var Ua=n(P);wt=r(Ua,"CODE",{});var Ga=n(wt);Be=s(Ga,"ImageFolder"),Ga.forEach(a),Ua.forEach(a),Re=s(he," builder from \u{1F917} Datasets to automatically build a dataset from the folder. Your directory structure should look like:"),he.forEach(a),Ht=u(t),J(O.$$.fragment,t),qt=u(t),A=r(t,"P",{});var fe=n(A);xe=s(fe,"Pass the path to the dataset directory to the "),vt=r(fe,"CODE",{});var Xa=n(vt);Ye=s(Xa,"--train_data_dir"),Xa.forEach(a),Ce=s(fe," argument, and then you can start training:"),fe.forEach(a),Dt=u(t),J(L.$$.fragment,t),St=u(t),B=r(t,"H2",{class:!0});var pe=n(B);W=r(pe,"A",{id:!0,class:!0,href:!0});var Ba=n(W);bt=r(Ba,"SPAN",{});var Ra=n(bt);J(K.$$.fragment,Ra),Ra.forEach(a),Ba.forEach(a),Fe=u(pe),Tt=r(pe,"SPAN",{});var xa=n(Tt);Ae=s(xa,"Upload your data to the Hub"),xa.forEach(a),pe.forEach(a),Qt=u(t),J(N.$$.fragment,t),Vt=u(t),I=r(t,"P",{});var ut=n(I);We=s(ut,"Start by creating a dataset with the "),tt=r(ut,"A",{href:!0,rel:!0});var Ya=n(tt);Jt=r(Ya,"CODE",{});var Ca=n(Jt);Ne=s(Ca,"ImageFolder"),Ca.forEach(a),Ya.forEach(a),ze=s(ut," feature, which creates an "),jt=r(ut,"CODE",{});var Fa=n(jt);He=s(Fa,"image"),Fa.forEach(a),qe=s(ut," column containing the PIL-encoded images."),ut.forEach(a),Pt=u(t),_=r(t,"P",{});var v=n(_);De=s(v,"You can use the "),Et=r(v,"CODE",{});var Aa=n(Et);Se=s(Aa,"data_dir"),Aa.forEach(a),Qe=s(v," or "),$t=r(v,"CODE",{});var Wa=n($t);Ve=s(Wa,"data_files"),Wa.forEach(a),Pe=s(v," parameters to specify the location of the dataset. The "),kt=r(v,"CODE",{});var Na=n(kt);Oe=s(Na,"data_files"),Na.forEach(a),Le=s(v," parameter supports mapping specific files to dataset splits like "),Zt=r(v,"CODE",{});var za=n(Zt);Ke=s(za,"train"),za.forEach(a),ta=s(v," or "),It=r(v,"CODE",{});var Ha=n(It);ea=s(Ha,"test"),Ha.forEach(a),aa=s(v,":"),v.forEach(a),Ot=u(t),J(et.$$.fragment,t),Lt=u(t),z=r(t,"P",{});var ue=n(z);oa=s(ue,"Then use the "),at=r(ue,"A",{href:!0,rel:!0});var qa=n(at);sa=s(qa,"push_to_hub"),qa.forEach(a),la=s(ue," method to upload the dataset to the Hub:"),ue.forEach(a),Kt=u(t),J(ot.$$.fragment,t),te=u(t),H=r(t,"P",{});var ce=n(H);ra=s(ce,"Now the dataset is available for training by passing the dataset name to the "),Ut=r(ce,"CODE",{});var Da=n(Ut);na=s(Da,"--dataset_name"),Da.forEach(a),ia=s(ce," argument:"),ce.forEach(a),ee=u(t),J(st.$$.fragment,t),ae=u(t),R=r(t,"H2",{class:!0});var me=n(R);q=r(me,"A",{id:!0,class:!0,href:!0});var Sa=n(q);Gt=r(Sa,"SPAN",{});var Qa=n(Gt);J(lt.$$.fragment,Qa),Qa.forEach(a),Sa.forEach(a),da=u(me),Xt=r(me,"SPAN",{});var Va=n(Xt);ha=s(Va,"Next steps"),Va.forEach(a),me.forEach(a),oe=u(t),U=r(t,"P",{});var ct=n(U);fa=s(ct,"Now that you\u2019ve created a dataset, you can plug it into the "),Bt=r(ct,"CODE",{});var Pa=n(Bt);pa=s(Pa,"train_data_dir"),Pa.forEach(a),ua=s(ct," (if your dataset is local) or "),Rt=r(ct,"CODE",{});var Oa=n(Rt);ca=s(Oa,"dataset_name"),Oa.forEach(a),ma=s(ct," (if your dataset is on the Hub) arguments of a training script."),ct.forEach(a),se=u(t),G=r(t,"P",{});var mt=n(G);Ma=s(mt,"For your next steps, feel free to try and use your dataset to train a model for "),ht=r(mt,"A",{href:!0});var La=n(ht);ya=s(La,"unconditional generation"),La.forEach(a),_a=s(mt," or "),ft=r(mt,"A",{href:!0});var Ka=n(ft);ga=s(Ka,"text-to-image generation"),Ka.forEach(a),wa=s(mt,"!"),mt.forEach(a),this.h()},h(){h(f,"name","hf:doc:metadata"),h(f,"content",JSON.stringify(ho)),h(y,"id","create-a-dataset-for-training"),h(y,"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"),h(y,"href","#create-a-dataset-for-training"),h(c,"class","relative group"),h(D,"href","https://huggingface.co/datasets?task_categories=task_categories:text-to-image&sort=downloads"),h(D,"rel","nofollow"),h(it,"href","hf.co/docs/datasets"),h(C,"id","provide-a-dataset-as-a-folder"),h(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"),h(C,"href","#provide-a-dataset-as-a-folder"),h(X,"class","relative group"),h(P,"href","https://huggingface.co/docs/datasets/en/image_dataset#imagefolder"),h(P,"rel","nofollow"),h(W,"id","upload-your-data-to-the-hub"),h(W,"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"),h(W,"href","#upload-your-data-to-the-hub"),h(B,"class","relative group"),h(tt,"href","https://huggingface.co/docs/datasets/image_load#imagefolder"),h(tt,"rel","nofollow"),h(at,"href","https://huggingface.co/docs/datasets/v2.12.0/en/package_reference/main_classes#datasets.Dataset.push_to_hub"),h(at,"rel","nofollow"),h(q,"id","next-steps"),h(q,"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"),h(q,"href","#next-steps"),h(R,"class","relative group"),h(ht,"href","uncondtional_training"),h(ft,"href","text2image")},m(t,i){e(document.head,f),d(t,w,i),d(t,c,i),e(c,y),e(y,g),j(m,g,null),e(c,M),e(c,b),e(b,ye),d(t,Ct,i),d(t,Z,i),e(Z,_e),e(Z,D),e(D,ge),e(Z,we),e(Z,it),e(it,ve),e(Z,be),d(t,Ft,i),d(t,dt,i),e(dt,Te),d(t,At,i),d(t,x,i),e(x,S),e(S,Je),e(S,Mt),e(Mt,je),e(S,Ee),e(x,$e),e(x,Q),e(Q,ke),e(Q,yt),e(yt,Ze),e(Q,Ie),d(t,Wt,i),j(Y,t,i),d(t,Nt,i),d(t,X,i),e(X,C),e(C,_t),j(V,_t,null),e(X,Ue),e(X,gt),e(gt,Ge),d(t,zt,i),d(t,F,i),e(F,Xe),e(F,P),e(P,wt),e(wt,Be),e(F,Re),d(t,Ht,i),j(O,t,i),d(t,qt,i),d(t,A,i),e(A,xe),e(A,vt),e(vt,Ye),e(A,Ce),d(t,Dt,i),j(L,t,i),d(t,St,i),d(t,B,i),e(B,W),e(W,bt),j(K,bt,null),e(B,Fe),e(B,Tt),e(Tt,Ae),d(t,Qt,i),j(N,t,i),d(t,Vt,i),d(t,I,i),e(I,We),e(I,tt),e(tt,Jt),e(Jt,Ne),e(I,ze),e(I,jt),e(jt,He),e(I,qe),d(t,Pt,i),d(t,_,i),e(_,De),e(_,Et),e(Et,Se),e(_,Qe),e(_,$t),e($t,Ve),e(_,Pe),e(_,kt),e(kt,Oe),e(_,Le),e(_,Zt),e(Zt,Ke),e(_,ta),e(_,It),e(It,ea),e(_,aa),d(t,Ot,i),j(et,t,i),d(t,Lt,i),d(t,z,i),e(z,oa),e(z,at),e(at,sa),e(z,la),d(t,Kt,i),j(ot,t,i),d(t,te,i),d(t,H,i),e(H,ra),e(H,Ut),e(Ut,na),e(H,ia),d(t,ee,i),j(st,t,i),d(t,ae,i),d(t,R,i),e(R,q),e(q,Gt),j(lt,Gt,null),e(R,da),e(R,Xt),e(Xt,ha),d(t,oe,i),d(t,U,i),e(U,fa),e(U,Bt),e(Bt,pa),e(U,ua),e(U,Rt),e(Rt,ca),e(U,ma),d(t,se,i),d(t,G,i),e(G,Ma),e(G,ht),e(ht,ya),e(G,_a),e(G,ft),e(ft,ga),e(G,wa),le=!0},p(t,[i]){const rt={};i&2&&(rt.$$scope={dirty:i,ctx:t}),Y.$set(rt);const xt={};i&2&&(xt.$$scope={dirty:i,ctx:t}),N.$set(xt)},i(t){le||(E(m.$$.fragment,t),E(Y.$$.fragment,t),E(V.$$.fragment,t),E(O.$$.fragment,t),E(L.$$.fragment,t),E(K.$$.fragment,t),E(N.$$.fragment,t),E(et.$$.fragment,t),E(ot.$$.fragment,t),E(st.$$.fragment,t),E(lt.$$.fragment,t),le=!0)},o(t){$(m.$$.fragment,t),$(Y.$$.fragment,t),$(V.$$.fragment,t),$(O.$$.fragment,t),$(L.$$.fragment,t),$(K.$$.fragment,t),$(N.$$.fragment,t),$(et.$$.fragment,t),$(ot.$$.fragment,t),$(st.$$.fragment,t),$(lt.$$.fragment,t),le=!1},d(t){a(f),t&&a(w),t&&a(c),k(m),t&&a(Ct),t&&a(Z),t&&a(Ft),t&&a(dt),t&&a(At),t&&a(x),t&&a(Wt),k(Y,t),t&&a(Nt),t&&a(X),k(V),t&&a(zt),t&&a(F),t&&a(Ht),k(O,t),t&&a(qt),t&&a(A),t&&a(Dt),k(L,t),t&&a(St),t&&a(B),k(K),t&&a(Qt),k(N,t),t&&a(Vt),t&&a(I),t&&a(Pt),t&&a(_),t&&a(Ot),k(et,t),t&&a(Lt),t&&a(z),t&&a(Kt),k(ot,t),t&&a(te),t&&a(H),t&&a(ee),k(st,t),t&&a(ae),t&&a(R),k(lt),t&&a(oe),t&&a(U),t&&a(se),t&&a(G)}}}const ho={local:"create-a-dataset-for-training",sections:[{local:"provide-a-dataset-as-a-folder",title:"Provide a dataset as a folder"},{local:"upload-your-data-to-the-hub",title:"Upload your data to the Hub"},{local:"next-steps",title:"Next steps"}],title:"Create a dataset for training"};function fo(nt){return lo(()=>{new URLSearchParams(window.location.search).get("fw")}),[]}class Mo extends eo{constructor(f){super();ao(this,f,fo,io,oo,{})}}export{Mo as default,ho as metadata};
