import{s as al,c as ll,u as il,g as cl,d as ml,o as dl,n as tn}from"../chunks/scheduler.bdbef820.js";import{S as nl,i as ol,r as g,u as f,v as h,d,t as p,w as u,g as n,m as el,s as r,h as o,j as v,n as tl,f as s,c as a,k,a as c,y as t,o as pl,A as gl,x as i}from"../chunks/index.33f81d56.js";import{T as sl}from"../chunks/Tip.34194030.js";import{D as C}from"../chunks/Docstring.64554317.js";import{C as en}from"../chunks/CodeBlock.362b34a4.js";import{E as rl}from"../chunks/ExampleCodeBlock.4f2252c6.js";import{H as Qr,E as fl}from"../chunks/EditOnGithub.a9246e21.js";function hl(L){let m,M,b,w,x;const T=L[1].default,y=ll(T,L,L[2],null);return{c(){m=n("p"),M=el("Deprecated in "),b=el(L[0]),w=r(),y&&y.c(),this.h()},l(J){m=o(J,"P",{class:!0});var U=v(m);M=tl(U,"Deprecated in "),b=tl(U,L[0]),U.forEach(s),w=a(J),y&&y.l(J),this.h()},h(){k(m,"class","font-medium")},m(J,U){c(J,m,U),t(m,M),t(m,b),c(J,w,U),y&&y.m(J,U),x=!0},p(J,U){(!x||U&1)&&pl(b,J[0]),y&&y.p&&(!x||U&4)&&il(y,T,J,J[2],x?ml(T,J[2],U,null):cl(J[2]),null)},i(J){x||(d(y,J),x=!0)},o(J){p(y,J),x=!1},d(J){J&&(s(m),s(w)),y&&y.d(J)}}}function ul(L){let m,M;return m=new sl({props:{warning:!0,$$slots:{default:[hl]},$$scope:{ctx:L}}}),{c(){g(m.$$.fragment)},l(b){f(m.$$.fragment,b)},m(b,w){h(m,b,w),M=!0},p(b,[w]){const x={};w&5&&(x.$$scope={dirty:w,ctx:b}),m.$set(x)},i(b){M||(d(m.$$.fragment,b),M=!0)},o(b){p(m.$$.fragment,b),M=!1},d(b){u(m,b)}}}function bl(L,m,M){let{$$slots:b={},$$scope:w}=m,{version:x}=m;return L.$$set=T=>{"version"in T&&M(0,x=T.version),"$$scope"in T&&M(2,w=T.$$scope)},[x,b,w]}class _l extends nl{constructor(m){super(),ol(this,m,bl,ul,al,{version:0})}}function vl(L){let m,M="Setting <code>WANDB_LOG_MODEL</code> as <code>bool</code> will be deprecated in version 5 of 🤗 Transformers.";return{c(){m=n("p"),m.innerHTML=M},l(b){m=o(b,"P",{"data-svelte-h":!0}),i(m)!=="svelte-fxlq1n"&&(m.innerHTML=M)},m(b,w){c(b,m,w)},p:tn,d(b){b&&s(m)}}}function Tl(L){let m,M="Example:",b,w,x;return w=new en({props:{code:"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",highlighted:`<span class="hljs-comment"># Note: This example skips over some setup steps for brevity.</span>
<span class="hljs-keyword">from</span> flytekit <span class="hljs-keyword">import</span> current_context, task


<span class="hljs-meta">@task</span>
<span class="hljs-keyword">def</span> <span class="hljs-title function_">train_hf_transformer</span>():
    cp = current_context().checkpoint
    trainer = Trainer(..., callbacks=[FlyteCallback()])
    output = trainer.train(resume_from_checkpoint=cp.restore())`,wrap:!1}}),{c(){m=n("p"),m.textContent=M,b=r(),g(w.$$.fragment)},l(T){m=o(T,"P",{"data-svelte-h":!0}),i(m)!=="svelte-11lpom8"&&(m.textContent=M),b=a(T),f(w.$$.fragment,T)},m(T,y){c(T,m,y),c(T,b,y),h(w,T,y),x=!0},p:tn,i(T){x||(d(w.$$.fragment,T),x=!0)},o(T){p(w.$$.fragment,T),x=!1},d(T){T&&(s(m),s(b)),u(w,T)}}}function kl(L){let m,M="Example:",b,w,x;return w=new en({props:{code:"Y2xhc3MlMjBQcmludGVyQ2FsbGJhY2soVHJhaW5lckNhbGxiYWNrKSUzQSUwQSUyMCUyMCUyMCUyMGRlZiUyMG9uX2xvZyhzZWxmJTJDJTIwYXJncyUyQyUyMHN0YXRlJTJDJTIwY29udHJvbCUyQyUyMGxvZ3MlM0ROb25lJTJDJTIwKiprd2FyZ3MpJTNBJTBBJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwXyUyMCUzRCUyMGxvZ3MucG9wKCUyMnRvdGFsX2Zsb3MlMjIlMkMlMjBOb25lKSUwQSUyMCUyMCUyMCUyMCUyMCUyMCUyMCUyMGlmJTIwc3RhdGUuaXNfbG9jYWxfcHJvY2Vzc196ZXJvJTNBJTBBJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwcHJpbnQobG9ncyk=",highlighted:`<span class="hljs-keyword">class</span> <span class="hljs-title class_">PrinterCallback</span>(<span class="hljs-title class_ inherited__">TrainerCallback</span>):
    <span class="hljs-keyword">def</span> <span class="hljs-title function_">on_log</span>(<span class="hljs-params">self, args, state, control, logs=<span class="hljs-literal">None</span>, **kwargs</span>):
        _ = logs.pop(<span class="hljs-string">&quot;total_flos&quot;</span>, <span class="hljs-literal">None</span>)
        <span class="hljs-keyword">if</span> state.is_local_process_zero:
            <span class="hljs-built_in">print</span>(logs)`,wrap:!1}}),{c(){m=n("p"),m.textContent=M,b=r(),g(w.$$.fragment)},l(T){m=o(T,"P",{"data-svelte-h":!0}),i(m)!=="svelte-11lpom8"&&(m.textContent=M),b=a(T),f(w.$$.fragment,T)},m(T,y){c(T,m,y),c(T,b,y),h(w,T,y),x=!0},p:tn,i(T){x||(d(w.$$.fragment,T),x=!0)},o(T){p(w.$$.fragment,T),x=!1},d(T){T&&(s(m),s(b)),u(w,T)}}}function $l(L){let m,M=`In all this class, one step is to be understood as one update step. When using gradient accumulation, one update
step may require several forward and backward passes: if you use <code>gradient_accumulation_steps=n</code>, then one update
step requires going through <em>n</em> batches.`;return{c(){m=n("p"),m.innerHTML=M},l(b){m=o(b,"P",{"data-svelte-h":!0}),i(m)!=="svelte-rhwh6p"&&(m.innerHTML=M)},m(b,w){c(b,m,w)},p:tn,d(b){b&&s(m)}}}function Cl(L){let m,M,b,w,x,T,y,J=`콜백은 PyTorch <a href="/docs/transformers/pr_22105/ko/main_classes/trainer#transformers.Trainer">Trainer</a>의 반복 학습 동작을 사용자 정의할 수 있는 객체입니다
(이 기능은 TensorFlow에서는 아직 구현되지 않았습니다). 콜백은 반복 학습의 상태를
검사하여 (진행 상황 보고, TensorBoard 또는 기타 머신 러닝 플랫폼에 로그 남기기 등)
결정(예: 조기 종료)을 내릴 수 있습니다.`,U,Ee,No=`콜백은 <a href="/docs/transformers/pr_22105/ko/main_classes/callback#transformers.TrainerControl">TrainerControl</a> 객체를 반환하는 것 외에는 반복 학습에서 어떤 것도 변경할 수 없는
“읽기 전용” 코드 조각입니다. 반복 학습에 변경이 필요한 사용자 정의 작업이 필요한 경우,
<a href="/docs/transformers/pr_22105/ko/main_classes/trainer#transformers.Trainer">Trainer</a>를 서브클래스로 만들어 필요한 메소드들을 오버라이드해야 합니다 (예시는 <a href="trainer">trainer</a>를 참조하세요).`,Xr,Ie,So='기본적으로 <code>TrainingArguments.report_to</code>는 <code>&quot;all&quot;</code>로 설정되어 있으므로, <a href="/docs/transformers/pr_22105/ko/main_classes/trainer#transformers.Trainer">Trainer</a>는 다음 콜백을 사용합니다.',Zr,De,Po=`<li><a href="/docs/transformers/pr_22105/ko/main_classes/callback#transformers.DefaultFlowCallback">DefaultFlowCallback</a>는 로그, 저장, 평가에 대한 기본 동작을 처리합니다.</li> <li><a href="/docs/transformers/pr_22105/ko/main_classes/callback#transformers.PrinterCallback">PrinterCallback</a> 또는 <a href="/docs/transformers/pr_22105/ko/main_classes/callback#transformers.ProgressCallback">ProgressCallback</a>는 진행 상황을 표시하고 로그를 출력합니다
(<a href="/docs/transformers/pr_22105/ko/main_classes/trainer#transformers.TrainingArguments">TrainingArguments</a>를 통해 tqdm을 비활성화하면 첫 번째 콜백이 사용되고, 그렇지 않으면 두 번째가 사용됩니다).</li> <li><a href="/docs/transformers/pr_22105/ko/main_classes/callback#transformers.integrations.TensorBoardCallback">TensorBoardCallback</a>는 TensorBoard가 (PyTorch &gt;= 1.4
또는 tensorboardX를 통해) 접근 가능하면 사용됩니다.</li> <li><a href="/docs/transformers/pr_22105/ko/main_classes/callback#transformers.integrations.WandbCallback">WandbCallback</a>는 <a href="https://www.wandb.com/" rel="nofollow">wandb</a>가 설치되어 있으면
사용됩니다.</li> <li><a href="/docs/transformers/pr_22105/ko/main_classes/callback#transformers.integrations.CometCallback">CometCallback</a>는 <a href="https://www.comet.com/site/" rel="nofollow">comet_ml</a>이 설치되어 있으면 사용됩니다.</li> <li><a href="/docs/transformers/pr_22105/ko/main_classes/callback#transformers.integrations.MLflowCallback">MLflowCallback</a>는 <a href="https://www.mlflow.org/" rel="nofollow">mlflow</a>가 설치되어 있으면 사용됩니다.</li> <li><a href="/docs/transformers/pr_22105/ko/main_classes/callback#transformers.integrations.NeptuneCallback">NeptuneCallback</a>는 <a href="https://neptune.ai/" rel="nofollow">neptune</a>이 설치되어 있으면 사용됩니다.</li> <li><a href="/docs/transformers/pr_22105/ko/main_classes/callback#transformers.integrations.AzureMLCallback">AzureMLCallback</a>는 <a href="https://pypi.org/project/azureml-sdk/" rel="nofollow">azureml-sdk</a>가 설치되어
있으면 사용됩니다.</li> <li><a href="/docs/transformers/pr_22105/ko/main_classes/callback#transformers.integrations.CodeCarbonCallback">CodeCarbonCallback</a>는 <a href="https://pypi.org/project/codecarbon/" rel="nofollow">codecarbon</a>이 설치되어
있으면 사용됩니다.</li> <li><a href="/docs/transformers/pr_22105/ko/main_classes/callback#transformers.integrations.ClearMLCallback">ClearMLCallback</a>는 <a href="https://github.com/allegroai/clearml" rel="nofollow">clearml</a>이 설치되어 있으면 사용됩니다.</li> <li><a href="/docs/transformers/pr_22105/ko/main_classes/callback#transformers.integrations.DagsHubCallback">DagsHubCallback</a>는 <a href="https://dagshub.com/" rel="nofollow">dagshub</a>이 설치되어 있으면 사용됩니다.</li> <li><a href="/docs/transformers/pr_22105/ko/main_classes/callback#transformers.integrations.FlyteCallback">FlyteCallback</a>는 <a href="https://flyte.org/" rel="nofollow">flyte</a>가 설치되어 있으면 사용됩니다.</li> <li><a href="/docs/transformers/pr_22105/ko/main_classes/callback#transformers.integrations.DVCLiveCallback">DVCLiveCallback</a>는 <a href="https://dvc.org/doc/dvclive" rel="nofollow">dvclive</a>가 설치되어 있으면 사용됩니다.</li>`,Kr,He,Fo="패키지가 설치되어 있지만 해당 통합 기능을 사용하고 싶지 않다면, <code>TrainingArguments.report_to</code>를 사용하고자 하는 통합 기능 목록으로 변경할 수 있습니다 (예: <code>[&quot;azure_ml&quot;, &quot;wandb&quot;]</code>).",ea,je,Wo=`콜백을 구현하는 주요 클래스는 <a href="/docs/transformers/pr_22105/ko/main_classes/callback#transformers.TrainerCallback">TrainerCallback</a>입니다. 이 클래스는 <a href="/docs/transformers/pr_22105/ko/main_classes/trainer#transformers.Trainer">Trainer</a>를
인스턴스화하는 데 사용된 <a href="/docs/transformers/pr_22105/ko/main_classes/trainer#transformers.TrainingArguments">TrainingArguments</a>를 가져오고, 해당 Trainer의 내부 상태를
<a href="/docs/transformers/pr_22105/ko/main_classes/callback#transformers.TrainerState">TrainerState</a>를 통해 접근할 수 있으며, <a href="/docs/transformers/pr_22105/ko/main_classes/callback#transformers.TrainerControl">TrainerControl</a>을 통해 반복 학습에서 일부
작업을 수행할 수 있습니다.`,ta,Ae,ra,Ve,zo='라이브러리에서 사용 가능한 <a href="/docs/transformers/pr_22105/ko/main_classes/callback#transformers.TrainerCallback">TrainerCallback</a> 목록은 다음과 같습니다:',aa,P,Ne,rn,jt,Bo='A <a href="/docs/transformers/pr_22105/ko/main_classes/callback#transformers.TrainerCallback">TrainerCallback</a> that sends the logs to <a href="https://www.comet.com/site/" rel="nofollow">Comet ML</a>.',an,I,Se,nn,At,Ro="Setup the optional Comet integration.",on,Vt,Oo="Environment:",sn,Nt,qo=`<li><strong>COMET_MODE</strong> (<code>str</code>, <em>optional</em>, default to <code>get_or_create</code>):
Control whether to create and log to a new Comet experiment or append to an existing experiment.
It accepts the following values:<ul><li><code>get_or_create</code>: Decides automatically depending if
<code>COMET_EXPERIMENT_KEY</code> is set and whether an Experiment
with that key already exists or not.</li> <li><code>create</code>: Always create a new Comet Experiment.</li> <li><code>get</code>: Always try to append to an Existing Comet Experiment.
Requires <code>COMET_EXPERIMENT_KEY</code> to be set.</li> <li><code>ONLINE</code>: <strong>deprecated</strong>, used to create an online
Experiment. Use <code>COMET_START_ONLINE=1</code> instead.</li> <li><code>OFFLINE</code>: <strong>deprecated</strong>, used to created an offline
Experiment. Use <code>COMET_START_ONLINE=0</code> instead.</li> <li><code>DISABLED</code>: <strong>deprecated</strong>, used to disable Comet logging.
Use the <code>--report_to</code> flag to control the integrations used
for logging result instead.</li></ul></li> <li><strong>COMET_PROJECT_NAME</strong> (<code>str</code>, <em>optional</em>):
Comet project name for experiments.</li> <li><strong>COMET_LOG_ASSETS</strong> (<code>str</code>, <em>optional</em>, defaults to <code>TRUE</code>):
Whether or not to log training assets (tf event logs, checkpoints, etc), to Comet. Can be <code>TRUE</code>, or
<code>FALSE</code>.</li>`,ln,St,Go=`For a number of configurable items in the environment, see
<a href="https://www.comet.com/docs/v2/guides/experiment-management/configure-sdk/#explore-comet-configuration-options" rel="nofollow">here</a>.`,na,Q,Pe,cn,Pt,Yo='A <a href="/docs/transformers/pr_22105/ko/main_classes/callback#transformers.TrainerCallback">TrainerCallback</a> that handles the default flow of the training loop for logs, evaluation and checkpoints.',oa,X,Fe,mn,Ft,Qo='A bare <a href="/docs/transformers/pr_22105/ko/main_classes/callback#transformers.TrainerCallback">TrainerCallback</a> that just prints the logs.',sa,Z,We,dn,Wt,Xo=`A <a href="/docs/transformers/pr_22105/ko/main_classes/callback#transformers.TrainerCallback">TrainerCallback</a> that displays the progress of training or evaluation.
You can modify <code>max_str_len</code> to control how long strings are truncated when logging.`,la,F,ze,pn,zt,Zo='A <a href="/docs/transformers/pr_22105/ko/main_classes/callback#transformers.TrainerCallback">TrainerCallback</a> that handles early stopping.',gn,Bt,Ko=`This callback depends on <a href="/docs/transformers/pr_22105/ko/main_classes/trainer#transformers.TrainingArguments">TrainingArguments</a> argument <em>load_best_model_at_end</em> functionality to set best_metric
in <a href="/docs/transformers/pr_22105/ko/main_classes/callback#transformers.TrainerState">TrainerState</a>. Note that if the <a href="/docs/transformers/pr_22105/ko/main_classes/trainer#transformers.TrainingArguments">TrainingArguments</a> argument <em>save_steps</em> differs from <em>eval_steps</em>, the
early stopping will not occur until the next save step.`,ia,K,Be,fn,Rt,es='A <a href="/docs/transformers/pr_22105/ko/main_classes/callback#transformers.TrainerCallback">TrainerCallback</a> that sends the logs to <a href="https://www.tensorflow.org/tensorboard" rel="nofollow">TensorBoard</a>.',ca,W,Re,hn,Ot,ts='A <a href="/docs/transformers/pr_22105/ko/main_classes/callback#transformers.TrainerCallback">TrainerCallback</a> that logs metrics, media, model checkpoints to <a href="https://www.wandb.com/" rel="nofollow">Weight and Biases</a>.',un,D,Oe,bn,qt,rs="Setup the optional Weights &amp; Biases (<em>wandb</em>) integration.",_n,Gt,as=`One can subclass and override this method to customize the setup if needed. Find more information
<a href="https://docs.wandb.ai/guides/integrations/huggingface" rel="nofollow">here</a>. You can also override the following environment
variables:`,vn,Yt,ns="Environment:",Tn,z,qe,Qt,os=`<strong>WANDB_LOG_MODEL</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;false&quot;</code>):
Whether to log model and checkpoints during training. Can be <code>&quot;end&quot;</code>, <code>&quot;checkpoint&quot;</code> or <code>&quot;false&quot;</code>. If set
to <code>&quot;end&quot;</code>, the model will be uploaded at the end of training. If set to <code>&quot;checkpoint&quot;</code>, the checkpoint
will be uploaded every <code>args.save_steps</code> . If set to <code>&quot;false&quot;</code>, the model will not be uploaded. Use along
with <code>load_best_model_at_end()</code> to upload best model.`,kn,ne,$n,Xt,ss=`<p><strong>WANDB_WATCH</strong> (<code>str</code>, <em>optional</em> defaults to <code>&quot;false&quot;</code>):
Can be <code>&quot;gradients&quot;</code>, <code>&quot;all&quot;</code>, <code>&quot;parameters&quot;</code>, or <code>&quot;false&quot;</code>. Set to <code>&quot;all&quot;</code> to log gradients and
parameters.</p>`,Cn,Zt,ls=`<p><strong>WANDB_PROJECT</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;huggingface&quot;</code>):
Set this to a custom string to store results in a different project.</p>`,wn,Kt,is=`<p><strong>WANDB_DISABLED</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>):
Whether to disable wandb entirely. Set <code>WANDB_DISABLED=true</code> to disable.</p>`,ma,B,Ge,xn,er,cs=`A <a href="/docs/transformers/pr_22105/ko/main_classes/callback#transformers.TrainerCallback">TrainerCallback</a> that sends the logs to <a href="https://www.mlflow.org/" rel="nofollow">MLflow</a>. Can be disabled by setting
environment variable <code>DISABLE_MLFLOW_INTEGRATION = TRUE</code>.`,yn,V,Ye,Mn,tr,ms="Setup the optional MLflow integration.",Jn,rr,ds="Environment:",Ln,ar,ps=`<li><strong>HF_MLFLOW_LOG_ARTIFACTS</strong> (<code>str</code>, <em>optional</em>):
Whether to use MLflow <code>.log_artifact()</code> facility to log artifacts. This only makes sense if logging to a
remote server, e.g. s3 or GCS. If set to <code>True</code> or <em>1</em>, will copy each saved checkpoint on each save in
<a href="/docs/transformers/pr_22105/ko/main_classes/trainer#transformers.TrainingArguments">TrainingArguments</a>’s <code>output_dir</code> to the local or remote artifact storage. Using it without a remote
storage will just copy the files to your artifact location.</li> <li><strong>MLFLOW_TRACKING_URI</strong> (<code>str</code>, <em>optional</em>):
Whether to store runs at a specific path or remote server. Unset by default, which skips setting the
tracking URI entirely.</li> <li><strong>MLFLOW_EXPERIMENT_NAME</strong> (<code>str</code>, <em>optional</em>, defaults to <code>None</code>):
Whether to use an MLflow experiment_name under which to launch the run. Default to <code>None</code> which will point
to the <code>Default</code> experiment in MLflow. Otherwise, it is a case sensitive name of the experiment to be
activated. If an experiment with this name does not exist, a new experiment with this name is created.</li> <li><strong>MLFLOW_TAGS</strong> (<code>str</code>, <em>optional</em>):
A string dump of a dictionary of key/value pair to be added to the MLflow run as tags. Example:
<code>os.environ[&#39;MLFLOW_TAGS&#39;]=&#39;{&quot;release.candidate&quot;: &quot;RC1&quot;, &quot;release.version&quot;: &quot;2.2.0&quot;}&#39;</code>.</li> <li><strong>MLFLOW_NESTED_RUN</strong> (<code>str</code>, <em>optional</em>):
Whether to use MLflow nested runs. If set to <code>True</code> or <em>1</em>, will create a nested run inside the current
run.</li> <li><strong>MLFLOW_RUN_ID</strong> (<code>str</code>, <em>optional</em>):
Allow to reattach to an existing run which can be usefull when resuming training from a checkpoint. When
<code>MLFLOW_RUN_ID</code> environment variable is set, <code>start_run</code> attempts to resume a run with the specified run ID
and other parameters are ignored.</li> <li><strong>MLFLOW_FLATTEN_PARAMS</strong> (<code>str</code>, <em>optional</em>, defaults to <code>False</code>):
Whether to flatten the parameters dictionary before logging.</li> <li><strong>MLFLOW_MAX_LOG_PARAMS</strong> (<code>int</code>, <em>optional</em>):
Set the maximum number of parameters to log in the run.</li>`,da,ee,Qe,Un,nr,gs='A <a href="/docs/transformers/pr_22105/ko/main_classes/callback#transformers.TrainerCallback">TrainerCallback</a> that sends the logs to <a href="https://pypi.org/project/azureml-sdk/" rel="nofollow">AzureML</a>.',pa,te,Xe,En,or,fs='A <a href="/docs/transformers/pr_22105/ko/main_classes/callback#transformers.TrainerCallback">TrainerCallback</a> that tracks the CO2 emission of training.',ga,R,Ze,In,sr,hs='TrainerCallback that sends the logs to <a href="https://app.neptune.ai" rel="nofollow">Neptune</a>.',Dn,lr,us=`For instructions and examples, see the <a href="https://docs.neptune.ai/integrations/transformers" rel="nofollow">Transformers integration
guide</a> in the Neptune documentation.`,fa,H,Ke,Hn,ir,bs='A <a href="/docs/transformers/pr_22105/ko/main_classes/callback#transformers.TrainerCallback">TrainerCallback</a> that sends the logs to <a href="https://clear.ml/" rel="nofollow">ClearML</a>.',jn,cr,_s="Environment:",An,mr,vs=`<li><strong>CLEARML_PROJECT</strong> (<code>str</code>, <em>optional</em>, defaults to <code>HuggingFace Transformers</code>):
ClearML project name.</li> <li><strong>CLEARML_TASK</strong> (<code>str</code>, <em>optional</em>, defaults to <code>Trainer</code>):
ClearML task name.</li> <li><strong>CLEARML_LOG_MODEL</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>):
Whether to log models as artifacts during training.</li>`,ha,O,et,Vn,dr,Ts='A <a href="/docs/transformers/pr_22105/ko/main_classes/callback#transformers.TrainerCallback">TrainerCallback</a> that logs to <a href="https://dagshub.com/" rel="nofollow">DagsHub</a>. Extends <code>MLflowCallback</code>',Nn,N,tt,Sn,pr,ks="Setup the DagsHub’s Logging integration.",Pn,gr,$s="Environment:",Fn,fr,Cs=`<li><strong>HF_DAGSHUB_LOG_ARTIFACTS</strong> (<code>str</code>, <em>optional</em>):
Whether to save the data and model artifacts for the experiment. Default to <code>False</code>.</li>`,ua,q,rt,Wn,hr,ws=`A <a href="/docs/transformers/pr_22105/ko/main_classes/callback#transformers.TrainerCallback">TrainerCallback</a> that sends the logs to <a href="https://flyte.org/" rel="nofollow">Flyte</a>.
NOTE: This callback only works within a Flyte task.`,zn,oe,ba,j,at,Bn,ur,xs='A <a href="/docs/transformers/pr_22105/ko/main_classes/callback#transformers.TrainerCallback">TrainerCallback</a> that sends the logs to <a href="https://www.dvc.org/doc/dvclive" rel="nofollow">DVCLive</a>.',Rn,br,ys=`Use the environment variables below in <code>setup</code> to configure the integration. To customize this callback beyond
those environment variables, see <a href="https://dvc.org/doc/dvclive/ml-frameworks/huggingface" rel="nofollow">here</a>.`,On,S,nt,qn,_r,Ms=`Setup the optional DVCLive integration. To customize this callback beyond the environment variables below, see
<a href="https://dvc.org/doc/dvclive/ml-frameworks/huggingface" rel="nofollow">here</a>.`,Gn,vr,Js="Environment:",Yn,Tr,Ls=`<li><strong>HF_DVCLIVE_LOG_MODEL</strong> (<code>str</code>, <em>optional</em>):
Whether to use <code>dvclive.Live.log_artifact()</code> to log checkpoints created by <a href="/docs/transformers/pr_22105/ko/main_classes/trainer#transformers.Trainer">Trainer</a>. If set to <code>True</code> or
<em>1</em>, the final checkpoint is logged at the end of training. If set to <code>all</code>, the entire
<a href="/docs/transformers/pr_22105/ko/main_classes/trainer#transformers.TrainingArguments">TrainingArguments</a>’s <code>output_dir</code> is logged at each checkpoint.</li>`,_a,ot,va,_,st,Qn,kr,Us=`A class for objects that will inspect the state of the training loop at some events and take some decisions. At
each of those events the following arguments are available:`,Xn,$r,Es=`The <code>control</code> object is the only one that can be changed by the callback, in which case the event that changes it
should return the modified version.`,Zn,Cr,Is=`The argument <code>args</code>, <code>state</code> and <code>control</code> are positionals for all events, all the others are grouped in <code>kwargs</code>.
You can unpack the ones you need in the signature of the event using them. As an example, see the code of the
simple <a href="/docs/transformers/pr_22105/ko/main_classes/callback#transformers.PrinterCallback">PrinterCallback</a>.`,Kn,se,eo,le,lt,to,wr,Ds="Event called at the beginning of an epoch.",ro,ie,it,ao,xr,Hs="Event called at the end of an epoch.",no,ce,ct,oo,yr,js="Event called after an evaluation phase.",so,me,mt,lo,Mr,As='Event called at the end of the initialization of the <a href="/docs/transformers/pr_22105/ko/main_classes/trainer#transformers.Trainer">Trainer</a>.',io,de,dt,co,Jr,Vs="Event called after logging the last logs.",mo,pe,pt,po,Lr,Ns="Event called after the optimizer step but before gradients are zeroed out. Useful for monitoring gradients.",go,ge,gt,fo,Ur,Ss="Event called before the optimizer step but after gradient clipping. Useful for monitoring gradients.",ho,fe,ft,uo,Er,Ps="Event called after a successful prediction.",bo,he,ht,_o,Ir,Fs="Event called after a prediction step.",vo,ue,ut,To,Dr,Ws="Event called after a checkpoint save.",ko,be,bt,$o,Hr,zs=`Event called at the beginning of a training step. If using gradient accumulation, one training step might take
several inputs.`,Co,_e,_t,wo,jr,Bs=`Event called at the end of a training step. If using gradient accumulation, one training step might take
several inputs.`,xo,ve,vt,yo,Ar,Rs="Event called at the end of an substep during gradient accumulation.",Mo,Te,Tt,Jo,Vr,Os="Event called at the beginning of training.",Lo,ke,kt,Uo,Nr,qs="Event called at the end of training.",Ta,$t,Gs='여기 PyTorch <a href="/docs/transformers/pr_22105/ko/main_classes/trainer#transformers.Trainer">Trainer</a>와 함께 사용자 정의 콜백을 등록하는 예시가 있습니다:',ka,Ct,$a,wt,Ys="또 다른 콜백을 등록하는 방법은 <code>trainer.add_callback()</code>을 호출하는 것입니다:",Ca,xt,wa,yt,xa,E,Mt,Eo,Sr,Qs=`A class containing the <a href="/docs/transformers/pr_22105/ko/main_classes/trainer#transformers.Trainer">Trainer</a> inner state that will be saved along the model and optimizer when checkpointing
and passed to the <a href="/docs/transformers/pr_22105/ko/main_classes/callback#transformers.TrainerCallback">TrainerCallback</a>.`,Io,$e,Do,Ce,Jt,Ho,Pr,Xs="Create an instance from the content of <code>json_path</code>.",jo,we,Lt,Ao,Fr,Zs="Save the content of this instance in JSON format inside <code>json_path</code>.",ya,Ut,Ma,re,Et,Vo,Wr,Ks=`A class that handles the <a href="/docs/transformers/pr_22105/ko/main_classes/trainer#transformers.Trainer">Trainer</a> control flow. This class is used by the <a href="/docs/transformers/pr_22105/ko/main_classes/callback#transformers.TrainerCallback">TrainerCallback</a> to activate some
switches in the training loop.`,Ja,It,La,Yr,Ua;return x=new Qr({props:{title:"콜백",local:"callbacks",headingTag:"h1"}}),Ae=new Qr({props:{title:"사용 가능한 콜백",local:"available-callbacks ][ transformers.integrations.CometCallback",headingTag:"h2"}}),Ne=new C({props:{name:"class transformers.integrations.CometCallback",anchor:"transformers.integrations.CometCallback",parameters:[],source:"https://github.com/huggingface/transformers/blob/vr_22105/src/transformers/integrations/integration_utils.py#L1008"}}),Se=new C({props:{name:"setup",anchor:"transformers.integrations.CometCallback.setup",parameters:[{name:"args",val:""},{name:"state",val:""},{name:"model",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_22105/src/transformers/integrations/integration_utils.py#L1022"}}),Pe=new C({props:{name:"class transformers.DefaultFlowCallback",anchor:"transformers.DefaultFlowCallback",parameters:[],source:"https://github.com/huggingface/transformers/blob/vr_22105/src/transformers/trainer_callback.py#L537"}}),Fe=new C({props:{name:"class transformers.PrinterCallback",anchor:"transformers.PrinterCallback",parameters:[],source:"https://github.com/huggingface/transformers/blob/vr_22105/src/transformers/trainer_callback.py#L664"}}),We=new C({props:{name:"class transformers.ProgressCallback",anchor:"transformers.ProgressCallback",parameters:[{name:"max_str_len",val:": int = 100"}],source:"https://github.com/huggingface/transformers/blob/vr_22105/src/transformers/trainer_callback.py#L590"}}),ze=new C({props:{name:"class transformers.EarlyStoppingCallback",anchor:"transformers.EarlyStoppingCallback",parameters:[{name:"early_stopping_patience",val:": int = 1"},{name:"early_stopping_threshold",val:": Optional = 0.0"}],parametersDescription:[{anchor:"transformers.EarlyStoppingCallback.early_stopping_patience",description:`<strong>early_stopping_patience</strong> (<code>int</code>) &#x2014;
Use with <code>metric_for_best_model</code> to stop training when the specified metric worsens for
<code>early_stopping_patience</code> evaluation calls.`,name:"early_stopping_patience"},{anchor:"transformers.EarlyStoppingCallback.early_stopping_threshold(float,",description:`<strong>early_stopping_threshold(<code>float</code>,</strong> <em>optional</em>) &#x2014;
Use with TrainingArguments <code>metric_for_best_model</code> and <code>early_stopping_patience</code> to denote how much the
specified metric must improve to satisfy early stopping conditions. \``,name:"early_stopping_threshold(float,"}],source:"https://github.com/huggingface/transformers/blob/vr_22105/src/transformers/trainer_callback.py#L675"}}),Be=new C({props:{name:"class transformers.integrations.TensorBoardCallback",anchor:"transformers.integrations.TensorBoardCallback",parameters:[{name:"tb_writer",val:" = None"}],parametersDescription:[{anchor:"transformers.integrations.TensorBoardCallback.tb_writer",description:`<strong>tb_writer</strong> (<code>SummaryWriter</code>, <em>optional</em>) &#x2014;
The writer to use. Will instantiate one if not set.`,name:"tb_writer"}],source:"https://github.com/huggingface/transformers/blob/vr_22105/src/transformers/integrations/integration_utils.py#L629"}}),Re=new C({props:{name:"class transformers.integrations.WandbCallback",anchor:"transformers.integrations.WandbCallback",parameters:[],source:"https://github.com/huggingface/transformers/blob/vr_22105/src/transformers/integrations/integration_utils.py#L762"}}),Oe=new C({props:{name:"setup",anchor:"transformers.integrations.WandbCallback.setup",parameters:[{name:"args",val:""},{name:"state",val:""},{name:"model",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_22105/src/transformers/integrations/integration_utils.py#L778"}}),ne=new _l({props:{version:"5.0",$$slots:{default:[vl]},$$scope:{ctx:L}}}),Ge=new C({props:{name:"class transformers.integrations.MLflowCallback",anchor:"transformers.integrations.MLflowCallback",parameters:[],source:"https://github.com/huggingface/transformers/blob/vr_22105/src/transformers/integrations/integration_utils.py#L1175"}}),Ye=new C({props:{name:"setup",anchor:"transformers.integrations.MLflowCallback.setup",parameters:[{name:"args",val:""},{name:"state",val:""},{name:"model",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_22105/src/transformers/integrations/integration_utils.py#L1194"}}),Qe=new C({props:{name:"class transformers.integrations.AzureMLCallback",anchor:"transformers.integrations.AzureMLCallback",parameters:[{name:"azureml_run",val:" = None"}],source:"https://github.com/huggingface/transformers/blob/vr_22105/src/transformers/integrations/integration_utils.py#L1152"}}),Xe=new C({props:{name:"class transformers.integrations.CodeCarbonCallback",anchor:"transformers.integrations.CodeCarbonCallback",parameters:[],source:"https://github.com/huggingface/transformers/blob/vr_22105/src/transformers/integrations/integration_utils.py#L1686"}}),Ze=new C({props:{name:"class transformers.integrations.NeptuneCallback",anchor:"transformers.integrations.NeptuneCallback",parameters:[{name:"api_token",val:": Optional = None"},{name:"project",val:": Optional = None"},{name:"name",val:": Optional = None"},{name:"base_namespace",val:": str = 'finetuning'"},{name:"run",val:" = None"},{name:"log_parameters",val:": bool = True"},{name:"log_checkpoints",val:": Optional = None"},{name:"**neptune_run_kwargs",val:""}],parametersDescription:[{anchor:"transformers.integrations.NeptuneCallback.api_token",description:`<strong>api_token</strong> (<code>str</code>, <em>optional</em>) &#x2014; Neptune API token obtained upon registration.
You can leave this argument out if you have saved your token to the <code>NEPTUNE_API_TOKEN</code> environment
variable (strongly recommended). See full setup instructions in the
<a href="https://docs.neptune.ai/setup/installation" rel="nofollow">docs</a>.`,name:"api_token"},{anchor:"transformers.integrations.NeptuneCallback.project",description:`<strong>project</strong> (<code>str</code>, <em>optional</em>) &#x2014; Name of an existing Neptune project, in the form &#x201C;workspace-name/project-name&#x201D;.
You can find and copy the name in Neptune from the project settings -&gt; Properties. If None (default), the
value of the <code>NEPTUNE_PROJECT</code> environment variable is used.`,name:"project"},{anchor:"transformers.integrations.NeptuneCallback.name",description:"<strong>name</strong> (<code>str</code>, <em>optional</em>) &#x2014; Custom name for the run.",name:"name"},{anchor:"transformers.integrations.NeptuneCallback.base_namespace",description:`<strong>base_namespace</strong> (<code>str</code>, <em>optional</em>, defaults to &#x201C;finetuning&#x201D;) &#x2014; In the Neptune run, the root namespace
that will contain all of the metadata logged by the callback.`,name:"base_namespace"},{anchor:"transformers.integrations.NeptuneCallback.log_parameters",description:`<strong>log_parameters</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
If True, logs all Trainer arguments and model parameters provided by the Trainer.`,name:"log_parameters"},{anchor:"transformers.integrations.NeptuneCallback.log_checkpoints",description:`<strong>log_checkpoints</strong> (<code>str</code>, <em>optional</em>) &#x2014; If &#x201C;same&#x201D;, uploads checkpoints whenever they are saved by the Trainer.
If &#x201C;last&#x201D;, uploads only the most recently saved checkpoint. If &#x201C;best&#x201D;, uploads the best checkpoint (among
the ones saved by the Trainer). If <code>None</code>, does not upload checkpoints.`,name:"log_checkpoints"},{anchor:"transformers.integrations.NeptuneCallback.run",description:`<strong>run</strong> (<code>Run</code>, <em>optional</em>) &#x2014; Pass a Neptune run object if you want to continue logging to an existing run.
Read more about resuming runs in the <a href="https://docs.neptune.ai/logging/to_existing_object" rel="nofollow">docs</a>.`,name:"run"},{anchor:"transformers.integrations.NeptuneCallback.*neptune_run_kwargs",description:`*<strong>*neptune_run_kwargs</strong> (<em>optional</em>) &#x2014;
Additional keyword arguments to be passed directly to the
<a href="https://docs.neptune.ai/api/neptune#init_run" rel="nofollow"><code>neptune.init_run()</code></a> function when a new run is created.`,name:"*neptune_run_kwargs"}],source:"https://github.com/huggingface/transformers/blob/vr_22105/src/transformers/integrations/integration_utils.py#L1413"}}),Ke=new C({props:{name:"class transformers.integrations.ClearMLCallback",anchor:"transformers.integrations.ClearMLCallback",parameters:[],source:"https://github.com/huggingface/transformers/blob/vr_22105/src/transformers/integrations/integration_utils.py#L1720"}}),et=new C({props:{name:"class transformers.integrations.DagsHubCallback",anchor:"transformers.integrations.DagsHubCallback",parameters:[],source:"https://github.com/huggingface/transformers/blob/vr_22105/src/transformers/integrations/integration_utils.py#L1353"}}),tt=new C({props:{name:"setup",anchor:"transformers.integrations.DagsHubCallback.setup",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_22105/src/transformers/integrations/integration_utils.py#L1367"}}),rt=new C({props:{name:"class transformers.integrations.FlyteCallback",anchor:"transformers.integrations.FlyteCallback",parameters:[{name:"save_log_history",val:": bool = True"},{name:"sync_checkpoints",val:": bool = True"}],parametersDescription:[{anchor:"transformers.integrations.FlyteCallback.save_log_history",description:`<strong>save_log_history</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
When set to True, the training logs are saved as a Flyte Deck.`,name:"save_log_history"},{anchor:"transformers.integrations.FlyteCallback.sync_checkpoints",description:`<strong>sync_checkpoints</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
When set to True, checkpoints are synced with Flyte and can be used to resume training in the case of an
interruption.`,name:"sync_checkpoints"}],source:"https://github.com/huggingface/transformers/blob/vr_22105/src/transformers/integrations/integration_utils.py#L1973"}}),oe=new rl({props:{anchor:"transformers.integrations.FlyteCallback.example",$$slots:{default:[Tl]},$$scope:{ctx:L}}}),at=new C({props:{name:"class transformers.integrations.DVCLiveCallback",anchor:"transformers.integrations.DVCLiveCallback",parameters:[{name:"live",val:": Optional = None"},{name:"log_model",val:": Union = None"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.integrations.DVCLiveCallback.live",description:`<strong>live</strong> (<code>dvclive.Live</code>, <em>optional</em>, defaults to <code>None</code>) &#x2014;
Optional Live instance. If None, a new instance will be created using **kwargs.`,name:"live"},{anchor:"transformers.integrations.DVCLiveCallback.log_model",description:`<strong>log_model</strong> (Union[Literal[&#x201C;all&#x201D;], bool], <em>optional</em>, defaults to <code>None</code>) &#x2014;
Whether to use <code>dvclive.Live.log_artifact()</code> to log checkpoints created by <a href="/docs/transformers/pr_22105/ko/main_classes/trainer#transformers.Trainer">Trainer</a>. If set to <code>True</code>,
the final checkpoint is logged at the end of training. If set to <code>&quot;all&quot;</code>, the entire
<a href="/docs/transformers/pr_22105/ko/main_classes/trainer#transformers.TrainingArguments">TrainingArguments</a>&#x2019;s <code>output_dir</code> is logged at each checkpoint.`,name:"log_model"}],source:"https://github.com/huggingface/transformers/blob/vr_22105/src/transformers/integrations/integration_utils.py#L2036"}}),nt=new C({props:{name:"setup",anchor:"transformers.integrations.DVCLiveCallback.setup",parameters:[{name:"args",val:""},{name:"state",val:""},{name:"model",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_22105/src/transformers/integrations/integration_utils.py#L2077"}}),ot=new Qr({props:{title:"TrainerCallback",local:"trainercallback ][ transformers.TrainerCallback",headingTag:"h2"}}),st=new C({props:{name:"class transformers.TrainerCallback",anchor:"transformers.TrainerCallback",parameters:[],parametersDescription:[{anchor:"transformers.TrainerCallback.args",description:`<strong>args</strong> (<a href="/docs/transformers/pr_22105/ko/main_classes/trainer#transformers.TrainingArguments">TrainingArguments</a>) &#x2014;
The training arguments used to instantiate the <a href="/docs/transformers/pr_22105/ko/main_classes/trainer#transformers.Trainer">Trainer</a>.`,name:"args"},{anchor:"transformers.TrainerCallback.state",description:`<strong>state</strong> (<a href="/docs/transformers/pr_22105/ko/main_classes/callback#transformers.TrainerState">TrainerState</a>) &#x2014;
The current state of the <a href="/docs/transformers/pr_22105/ko/main_classes/trainer#transformers.Trainer">Trainer</a>.`,name:"state"},{anchor:"transformers.TrainerCallback.control",description:`<strong>control</strong> (<a href="/docs/transformers/pr_22105/ko/main_classes/callback#transformers.TrainerControl">TrainerControl</a>) &#x2014;
The object that is returned to the <a href="/docs/transformers/pr_22105/ko/main_classes/trainer#transformers.Trainer">Trainer</a> and can be used to make some decisions.`,name:"control"},{anchor:"transformers.TrainerCallback.model",description:`<strong>model</strong> (<a href="/docs/transformers/pr_22105/ko/main_classes/model#transformers.PreTrainedModel">PreTrainedModel</a> or <code>torch.nn.Module</code>) &#x2014;
The model being trained.`,name:"model"},{anchor:"transformers.TrainerCallback.tokenizer",description:`<strong>tokenizer</strong> (<code>PreTrainedTokenizer</code>) &#x2014;
The tokenizer used for encoding the data. This is deprecated in favour of <code>processing_class</code>.`,name:"tokenizer"},{anchor:"transformers.TrainerCallback.processing_class",description:`<strong>processing_class</strong> ([<code>PreTrainedTokenizer</code> or <code>BaseImageProcessor</code> or <code>ProcessorMixin</code> or <code>FeatureExtractionMixin</code>]) &#x2014;
The processing class used for encoding the data. Can be a tokenizer, a processor, an image processor or a feature extractor.`,name:"processing_class"},{anchor:"transformers.TrainerCallback.optimizer",description:`<strong>optimizer</strong> (<code>torch.optim.Optimizer</code>) &#x2014;
The optimizer used for the training steps.`,name:"optimizer"},{anchor:"transformers.TrainerCallback.lr_scheduler",description:`<strong>lr_scheduler</strong> (<code>torch.optim.lr_scheduler.LambdaLR</code>) &#x2014;
The scheduler used for setting the learning rate.`,name:"lr_scheduler"},{anchor:"transformers.TrainerCallback.train_dataloader",description:`<strong>train_dataloader</strong> (<code>torch.utils.data.DataLoader</code>, <em>optional</em>) &#x2014;
The current dataloader used for training.`,name:"train_dataloader"},{anchor:"transformers.TrainerCallback.eval_dataloader",description:`<strong>eval_dataloader</strong> (<code>torch.utils.data.DataLoader</code>, <em>optional</em>) &#x2014;
The current dataloader used for evaluation.`,name:"eval_dataloader"},{anchor:"transformers.TrainerCallback.metrics",description:`<strong>metrics</strong> (<code>Dict[str, float]</code>) &#x2014;
The metrics computed by the last evaluation phase.</p>
<p>Those are only accessible in the event <code>on_evaluate</code>.`,name:"metrics"},{anchor:"transformers.TrainerCallback.logs",description:`<strong>logs</strong>  (<code>Dict[str, float]</code>) &#x2014;
The values to log.</p>
<p>Those are only accessible in the event <code>on_log</code>.`,name:"logs"}],source:"https://github.com/huggingface/transformers/blob/vr_22105/src/transformers/trainer_callback.py#L260"}}),se=new rl({props:{anchor:"transformers.TrainerCallback.example",$$slots:{default:[kl]},$$scope:{ctx:L}}}),lt=new C({props:{name:"on_epoch_begin",anchor:"transformers.TrainerCallback.on_epoch_begin",parameters:[{name:"args",val:": TrainingArguments"},{name:"state",val:": TrainerState"},{name:"control",val:": TrainerControl"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_22105/src/transformers/trainer_callback.py#L331"}}),it=new C({props:{name:"on_epoch_end",anchor:"transformers.TrainerCallback.on_epoch_end",parameters:[{name:"args",val:": TrainingArguments"},{name:"state",val:": TrainerState"},{name:"control",val:": TrainerControl"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_22105/src/transformers/trainer_callback.py#L337"}}),ct=new C({props:{name:"on_evaluate",anchor:"transformers.TrainerCallback.on_evaluate",parameters:[{name:"args",val:": TrainingArguments"},{name:"state",val:": TrainerState"},{name:"control",val:": TrainerControl"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_22105/src/transformers/trainer_callback.py#L375"}}),mt=new C({props:{name:"on_init_end",anchor:"transformers.TrainerCallback.on_init_end",parameters:[{name:"args",val:": TrainingArguments"},{name:"state",val:": TrainerState"},{name:"control",val:": TrainerControl"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_22105/src/transformers/trainer_callback.py#L313"}}),dt=new C({props:{name:"on_log",anchor:"transformers.TrainerCallback.on_log",parameters:[{name:"args",val:": TrainingArguments"},{name:"state",val:": TrainerState"},{name:"control",val:": TrainerControl"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_22105/src/transformers/trainer_callback.py#L393"}}),pt=new C({props:{name:"on_optimizer_step",anchor:"transformers.TrainerCallback.on_optimizer_step",parameters:[{name:"args",val:": TrainingArguments"},{name:"state",val:": TrainerState"},{name:"control",val:": TrainerControl"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_22105/src/transformers/trainer_callback.py#L356"}}),gt=new C({props:{name:"on_pre_optimizer_step",anchor:"transformers.TrainerCallback.on_pre_optimizer_step",parameters:[{name:"args",val:": TrainingArguments"},{name:"state",val:": TrainerState"},{name:"control",val:": TrainerControl"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_22105/src/transformers/trainer_callback.py#L350"}}),ft=new C({props:{name:"on_predict",anchor:"transformers.TrainerCallback.on_predict",parameters:[{name:"args",val:": TrainingArguments"},{name:"state",val:": TrainerState"},{name:"control",val:": TrainerControl"},{name:"metrics",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_22105/src/transformers/trainer_callback.py#L381"}}),ht=new C({props:{name:"on_prediction_step",anchor:"transformers.TrainerCallback.on_prediction_step",parameters:[{name:"args",val:": TrainingArguments"},{name:"state",val:": TrainerState"},{name:"control",val:": TrainerControl"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_22105/src/transformers/trainer_callback.py#L399"}}),ut=new C({props:{name:"on_save",anchor:"transformers.TrainerCallback.on_save",parameters:[{name:"args",val:": TrainingArguments"},{name:"state",val:": TrainerState"},{name:"control",val:": TrainerControl"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_22105/src/transformers/trainer_callback.py#L387"}}),bt=new C({props:{name:"on_step_begin",anchor:"transformers.TrainerCallback.on_step_begin",parameters:[{name:"args",val:": TrainingArguments"},{name:"state",val:": TrainerState"},{name:"control",val:": TrainerControl"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_22105/src/transformers/trainer_callback.py#L343"}}),_t=new C({props:{name:"on_step_end",anchor:"transformers.TrainerCallback.on_step_end",parameters:[{name:"args",val:": TrainingArguments"},{name:"state",val:": TrainerState"},{name:"control",val:": TrainerControl"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_22105/src/transformers/trainer_callback.py#L368"}}),vt=new C({props:{name:"on_substep_end",anchor:"transformers.TrainerCallback.on_substep_end",parameters:[{name:"args",val:": TrainingArguments"},{name:"state",val:": TrainerState"},{name:"control",val:": TrainerControl"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_22105/src/transformers/trainer_callback.py#L362"}}),Tt=new C({props:{name:"on_train_begin",anchor:"transformers.TrainerCallback.on_train_begin",parameters:[{name:"args",val:": TrainingArguments"},{name:"state",val:": TrainerState"},{name:"control",val:": TrainerControl"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_22105/src/transformers/trainer_callback.py#L319"}}),kt=new C({props:{name:"on_train_end",anchor:"transformers.TrainerCallback.on_train_end",parameters:[{name:"args",val:": TrainingArguments"},{name:"state",val:": TrainerState"},{name:"control",val:": TrainerControl"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_22105/src/transformers/trainer_callback.py#L325"}}),Ct=new en({props:{code:"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",highlighted:`<span class="hljs-keyword">class</span> <span class="hljs-title class_">MyCallback</span>(<span class="hljs-title class_ inherited__">TrainerCallback</span>):
    <span class="hljs-string">&quot;A callback that prints a message at the beginning of training&quot;</span>

    <span class="hljs-keyword">def</span> <span class="hljs-title function_">on_train_begin</span>(<span class="hljs-params">self, args, state, control, **kwargs</span>):
        <span class="hljs-built_in">print</span>(<span class="hljs-string">&quot;Starting training&quot;</span>)


trainer = Trainer(
    model,
    args,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    callbacks=[MyCallback],  <span class="hljs-comment"># 우리는 콜백 클래스를 이 방식으로 전달하거나 그것의 인스턴스(MyCallback())를 전달할 수 있습니다</span>
)`,wrap:!1}}),xt=new en({props:{code:"dHJhaW5lciUyMCUzRCUyMFRyYWluZXIoLi4uKSUwQXRyYWluZXIuYWRkX2NhbGxiYWNrKE15Q2FsbGJhY2spJTBBJTIzJTIwJUVCJThCJUE0JUVCJUE1JUI4JTIwJUVCJUIwJUE5JUVCJUIyJTk1JUVDJTlDJUJDJUVCJUExJTlDJUVCJThBJTk0JTIwJUVDJUJEJTlDJUVCJUIwJUIxJTIwJUVEJTgxJUI0JUVCJTlFJTk4JUVDJThBJUE0JUVDJTlEJTk4JTIwJUVDJTlEJUI4JUVDJThBJUE0JUVEJTg0JUI0JUVDJThBJUE0JUVCJUE1JUJDJTIwJUVDJUEwJTg0JUVCJThCJUFDJUVEJTk1JUEwJTIwJUVDJTg4JTk4JTIwJUVDJTlFJTg4JUVDJThBJUI1JUVCJThCJTg4JUVCJThCJUE0JTBBdHJhaW5lci5hZGRfY2FsbGJhY2soTXlDYWxsYmFjaygpKQ==",highlighted:`trainer = Trainer(...)
trainer.add_callback(MyCallback)
<span class="hljs-comment"># 다른 방법으로는 콜백 클래스의 인스턴스를 전달할 수 있습니다</span>
trainer.add_callback(MyCallback())`,wrap:!1}}),yt=new Qr({props:{title:"TrainerState",local:"trainerstate ][ transformers.TrainerState",headingTag:"h2"}}),Mt=new C({props:{name:"class transformers.TrainerState",anchor:"transformers.TrainerState",parameters:[{name:"epoch",val:": Optional = None"},{name:"global_step",val:": int = 0"},{name:"max_steps",val:": int = 0"},{name:"logging_steps",val:": int = 500"},{name:"eval_steps",val:": int = 500"},{name:"save_steps",val:": int = 500"},{name:"train_batch_size",val:": int = None"},{name:"num_train_epochs",val:": int = 0"},{name:"num_input_tokens_seen",val:": int = 0"},{name:"total_flos",val:": float = 0"},{name:"log_history",val:": List = None"},{name:"best_metric",val:": Optional = None"},{name:"best_model_checkpoint",val:": Optional = None"},{name:"is_local_process_zero",val:": bool = True"},{name:"is_world_process_zero",val:": bool = True"},{name:"is_hyper_param_search",val:": bool = False"},{name:"trial_name",val:": str = None"},{name:"trial_params",val:": Dict = None"},{name:"stateful_callbacks",val:": List = None"}],parametersDescription:[{anchor:"transformers.TrainerState.epoch",description:`<strong>epoch</strong> (<code>float</code>, <em>optional</em>) &#x2014;
Only set during training, will represent the epoch the training is at (the decimal part being the
percentage of the current epoch completed).`,name:"epoch"},{anchor:"transformers.TrainerState.global_step",description:`<strong>global_step</strong> (<code>int</code>, <em>optional</em>, defaults to 0) &#x2014;
During training, represents the number of update steps completed.`,name:"global_step"},{anchor:"transformers.TrainerState.max_steps",description:`<strong>max_steps</strong> (<code>int</code>, <em>optional</em>, defaults to 0) &#x2014;
The number of update steps to do during the current training.`,name:"max_steps"},{anchor:"transformers.TrainerState.logging_steps",description:`<strong>logging_steps</strong> (<code>int</code>, <em>optional</em>, defaults to 500) &#x2014;
Log every X updates steps`,name:"logging_steps"},{anchor:"transformers.TrainerState.eval_steps",description:`<strong>eval_steps</strong> (<code>int</code>, <em>optional</em>) &#x2014;
Run an evaluation every X steps.`,name:"eval_steps"},{anchor:"transformers.TrainerState.save_steps",description:`<strong>save_steps</strong> (<code>int</code>, <em>optional</em>, defaults to 500) &#x2014;
Save checkpoint every X updates steps.`,name:"save_steps"},{anchor:"transformers.TrainerState.train_batch_size",description:`<strong>train_batch_size</strong> (<code>int</code>, <em>optional</em>) &#x2014;
The batch size for the training dataloader. Only needed when
<code>auto_find_batch_size</code> has been used.`,name:"train_batch_size"},{anchor:"transformers.TrainerState.num_input_tokens_seen",description:`<strong>num_input_tokens_seen</strong> (<code>int</code>, <em>optional</em>, defaults to 0) &#x2014;
When tracking the inputs tokens, the number of tokens seen during training (number of input tokens, not the
number of prediction tokens).`,name:"num_input_tokens_seen"},{anchor:"transformers.TrainerState.total_flos",description:`<strong>total_flos</strong> (<code>float</code>, <em>optional</em>, defaults to 0) &#x2014;
The total number of floating operations done by the model since the beginning of training (stored as floats
to avoid overflow).`,name:"total_flos"},{anchor:"transformers.TrainerState.log_history",description:`<strong>log_history</strong> (<code>List[Dict[str, float]]</code>, <em>optional</em>) &#x2014;
The list of logs done since the beginning of training.`,name:"log_history"},{anchor:"transformers.TrainerState.best_metric",description:`<strong>best_metric</strong> (<code>float</code>, <em>optional</em>) &#x2014;
When tracking the best model, the value of the best metric encountered so far.`,name:"best_metric"},{anchor:"transformers.TrainerState.best_model_checkpoint",description:`<strong>best_model_checkpoint</strong> (<code>str</code>, <em>optional</em>) &#x2014;
When tracking the best model, the value of the name of the checkpoint for the best model encountered so
far.`,name:"best_model_checkpoint"},{anchor:"transformers.TrainerState.is_local_process_zero",description:`<strong>is_local_process_zero</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether or not this process is the local (e.g., on one machine if training in a distributed fashion on
several machines) main process.`,name:"is_local_process_zero"},{anchor:"transformers.TrainerState.is_world_process_zero",description:`<strong>is_world_process_zero</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether or not this process is the global main process (when training in a distributed fashion on several
machines, this is only going to be <code>True</code> for one process).`,name:"is_world_process_zero"},{anchor:"transformers.TrainerState.is_hyper_param_search",description:`<strong>is_hyper_param_search</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether we are in the process of a hyper parameter search using Trainer.hyperparameter_search. This will
impact the way data will be logged in TensorBoard.`,name:"is_hyper_param_search"},{anchor:"transformers.TrainerState.stateful_callbacks",description:`<strong>stateful_callbacks</strong> (<code>List[StatefulTrainerCallback]</code>, <em>optional</em>) &#x2014;
Callbacks attached to the <code>Trainer</code> that should have their states be saved or restored.
Relevent callbacks should implement a <code>state</code> and <code>from_state</code> function.`,name:"stateful_callbacks"}],source:"https://github.com/huggingface/transformers/blob/vr_22105/src/transformers/trainer_callback.py#L35"}}),$e=new sl({props:{$$slots:{default:[$l]},$$scope:{ctx:L}}}),Jt=new C({props:{name:"load_from_json",anchor:"transformers.TrainerState.load_from_json",parameters:[{name:"json_path",val:": str"}],source:"https://github.com/huggingface/transformers/blob/vr_22105/src/transformers/trainer_callback.py#L146"}}),Lt=new C({props:{name:"save_to_json",anchor:"transformers.TrainerState.save_to_json",parameters:[{name:"json_path",val:": str"}],source:"https://github.com/huggingface/transformers/blob/vr_22105/src/transformers/trainer_callback.py#L140"}}),Ut=new Qr({props:{title:"TrainerControl",local:"trainercontrol ][ transformers.TrainerControl",headingTag:"h2"}}),Et=new C({props:{name:"class transformers.TrainerControl",anchor:"transformers.TrainerControl",parameters:[{name:"should_training_stop",val:": bool = False"},{name:"should_epoch_stop",val:": bool = False"},{name:"should_save",val:": bool = False"},{name:"should_evaluate",val:": bool = False"},{name:"should_log",val:": bool = False"}],parametersDescription:[{anchor:"transformers.TrainerControl.should_training_stop",description:`<strong>should_training_stop</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not the training should be interrupted.</p>
<p>If <code>True</code>, this variable will not be set back to <code>False</code>. The training will just stop.`,name:"should_training_stop"},{anchor:"transformers.TrainerControl.should_epoch_stop",description:`<strong>should_epoch_stop</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not the current epoch should be interrupted.</p>
<p>If <code>True</code>, this variable will be set back to <code>False</code> at the beginning of the next epoch.`,name:"should_epoch_stop"},{anchor:"transformers.TrainerControl.should_save",description:`<strong>should_save</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not the model should be saved at this step.</p>
<p>If <code>True</code>, this variable will be set back to <code>False</code> at the beginning of the next step.`,name:"should_save"},{anchor:"transformers.TrainerControl.should_evaluate",description:`<strong>should_evaluate</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not the model should be evaluated at this step.</p>
<p>If <code>True</code>, this variable will be set back to <code>False</code> at the beginning of the next step.`,name:"should_evaluate"},{anchor:"transformers.TrainerControl.should_log",description:`<strong>should_log</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not the logs should be reported at this step.</p>
<p>If <code>True</code>, this variable will be set back to <code>False</code> at the beginning of the next step.`,name:"should_log"}],source:"https://github.com/huggingface/transformers/blob/vr_22105/src/transformers/trainer_callback.py#L198"}}),It=new fl({props:{source:"https://github.com/huggingface/transformers/blob/main/docs/source/ko/main_classes/callback.md"}}),{c(){m=n("meta"),M=r(),b=n("p"),w=r(),g(x.$$.fragment),T=r(),y=n("p"),y.innerHTML=J,U=r(),Ee=n("p"),Ee.innerHTML=No,Xr=r(),Ie=n("p"),Ie.innerHTML=So,Zr=r(),De=n("ul"),De.innerHTML=Po,Kr=r(),He=n("p"),He.innerHTML=Fo,ea=r(),je=n("p"),je.innerHTML=Wo,ta=r(),g(Ae.$$.fragment),ra=r(),Ve=n("p"),Ve.innerHTML=zo,aa=r(),P=n("div"),g(Ne.$$.fragment),rn=r(),jt=n("p"),jt.innerHTML=Bo,an=r(),I=n("div"),g(Se.$$.fragment),nn=r(),At=n("p"),At.textContent=Ro,on=r(),Vt=n("p"),Vt.textContent=Oo,sn=r(),Nt=n("ul"),Nt.innerHTML=qo,ln=r(),St=n("p"),St.innerHTML=Go,na=r(),Q=n("div"),g(Pe.$$.fragment),cn=r(),Pt=n("p"),Pt.innerHTML=Yo,oa=r(),X=n("div"),g(Fe.$$.fragment),mn=r(),Ft=n("p"),Ft.innerHTML=Qo,sa=r(),Z=n("div"),g(We.$$.fragment),dn=r(),Wt=n("p"),Wt.innerHTML=Xo,la=r(),F=n("div"),g(ze.$$.fragment),pn=r(),zt=n("p"),zt.innerHTML=Zo,gn=r(),Bt=n("p"),Bt.innerHTML=Ko,ia=r(),K=n("div"),g(Be.$$.fragment),fn=r(),Rt=n("p"),Rt.innerHTML=es,ca=r(),W=n("div"),g(Re.$$.fragment),hn=r(),Ot=n("p"),Ot.innerHTML=ts,un=r(),D=n("div"),g(Oe.$$.fragment),bn=r(),qt=n("p"),qt.innerHTML=rs,_n=r(),Gt=n("p"),Gt.innerHTML=as,vn=r(),Yt=n("p"),Yt.textContent=ns,Tn=r(),z=n("ul"),qe=n("li"),Qt=n("p"),Qt.innerHTML=os,kn=r(),g(ne.$$.fragment),$n=r(),Xt=n("li"),Xt.innerHTML=ss,Cn=r(),Zt=n("li"),Zt.innerHTML=ls,wn=r(),Kt=n("li"),Kt.innerHTML=is,ma=r(),B=n("div"),g(Ge.$$.fragment),xn=r(),er=n("p"),er.innerHTML=cs,yn=r(),V=n("div"),g(Ye.$$.fragment),Mn=r(),tr=n("p"),tr.textContent=ms,Jn=r(),rr=n("p"),rr.textContent=ds,Ln=r(),ar=n("ul"),ar.innerHTML=ps,da=r(),ee=n("div"),g(Qe.$$.fragment),Un=r(),nr=n("p"),nr.innerHTML=gs,pa=r(),te=n("div"),g(Xe.$$.fragment),En=r(),or=n("p"),or.innerHTML=fs,ga=r(),R=n("div"),g(Ze.$$.fragment),In=r(),sr=n("p"),sr.innerHTML=hs,Dn=r(),lr=n("p"),lr.innerHTML=us,fa=r(),H=n("div"),g(Ke.$$.fragment),Hn=r(),ir=n("p"),ir.innerHTML=bs,jn=r(),cr=n("p"),cr.textContent=_s,An=r(),mr=n("ul"),mr.innerHTML=vs,ha=r(),O=n("div"),g(et.$$.fragment),Vn=r(),dr=n("p"),dr.innerHTML=Ts,Nn=r(),N=n("div"),g(tt.$$.fragment),Sn=r(),pr=n("p"),pr.textContent=ks,Pn=r(),gr=n("p"),gr.textContent=$s,Fn=r(),fr=n("ul"),fr.innerHTML=Cs,ua=r(),q=n("div"),g(rt.$$.fragment),Wn=r(),hr=n("p"),hr.innerHTML=ws,zn=r(),g(oe.$$.fragment),ba=r(),j=n("div"),g(at.$$.fragment),Bn=r(),ur=n("p"),ur.innerHTML=xs,Rn=r(),br=n("p"),br.innerHTML=ys,On=r(),S=n("div"),g(nt.$$.fragment),qn=r(),_r=n("p"),_r.innerHTML=Ms,Gn=r(),vr=n("p"),vr.textContent=Js,Yn=r(),Tr=n("ul"),Tr.innerHTML=Ls,_a=r(),g(ot.$$.fragment),va=r(),_=n("div"),g(st.$$.fragment),Qn=r(),kr=n("p"),kr.textContent=Us,Xn=r(),$r=n("p"),$r.innerHTML=Es,Zn=r(),Cr=n("p"),Cr.innerHTML=Is,Kn=r(),g(se.$$.fragment),eo=r(),le=n("div"),g(lt.$$.fragment),to=r(),wr=n("p"),wr.textContent=Ds,ro=r(),ie=n("div"),g(it.$$.fragment),ao=r(),xr=n("p"),xr.textContent=Hs,no=r(),ce=n("div"),g(ct.$$.fragment),oo=r(),yr=n("p"),yr.textContent=js,so=r(),me=n("div"),g(mt.$$.fragment),lo=r(),Mr=n("p"),Mr.innerHTML=As,io=r(),de=n("div"),g(dt.$$.fragment),co=r(),Jr=n("p"),Jr.textContent=Vs,mo=r(),pe=n("div"),g(pt.$$.fragment),po=r(),Lr=n("p"),Lr.textContent=Ns,go=r(),ge=n("div"),g(gt.$$.fragment),fo=r(),Ur=n("p"),Ur.textContent=Ss,ho=r(),fe=n("div"),g(ft.$$.fragment),uo=r(),Er=n("p"),Er.textContent=Ps,bo=r(),he=n("div"),g(ht.$$.fragment),_o=r(),Ir=n("p"),Ir.textContent=Fs,vo=r(),ue=n("div"),g(ut.$$.fragment),To=r(),Dr=n("p"),Dr.textContent=Ws,ko=r(),be=n("div"),g(bt.$$.fragment),$o=r(),Hr=n("p"),Hr.textContent=zs,Co=r(),_e=n("div"),g(_t.$$.fragment),wo=r(),jr=n("p"),jr.textContent=Bs,xo=r(),ve=n("div"),g(vt.$$.fragment),yo=r(),Ar=n("p"),Ar.textContent=Rs,Mo=r(),Te=n("div"),g(Tt.$$.fragment),Jo=r(),Vr=n("p"),Vr.textContent=Os,Lo=r(),ke=n("div"),g(kt.$$.fragment),Uo=r(),Nr=n("p"),Nr.textContent=qs,Ta=r(),$t=n("p"),$t.innerHTML=Gs,ka=r(),g(Ct.$$.fragment),$a=r(),wt=n("p"),wt.innerHTML=Ys,Ca=r(),g(xt.$$.fragment),wa=r(),g(yt.$$.fragment),xa=r(),E=n("div"),g(Mt.$$.fragment),Eo=r(),Sr=n("p"),Sr.innerHTML=Qs,Io=r(),g($e.$$.fragment),Do=r(),Ce=n("div"),g(Jt.$$.fragment),Ho=r(),Pr=n("p"),Pr.innerHTML=Xs,jo=r(),we=n("div"),g(Lt.$$.fragment),Ao=r(),Fr=n("p"),Fr.innerHTML=Zs,ya=r(),g(Ut.$$.fragment),Ma=r(),re=n("div"),g(Et.$$.fragment),Vo=r(),Wr=n("p"),Wr.innerHTML=Ks,Ja=r(),g(It.$$.fragment),La=r(),Yr=n("p"),this.h()},l(e){const l=gl("svelte-u9bgzb",document.head);m=o(l,"META",{name:!0,content:!0}),l.forEach(s),M=a(e),b=o(e,"P",{}),v(b).forEach(s),w=a(e),f(x.$$.fragment,e),T=a(e),y=o(e,"P",{"data-svelte-h":!0}),i(y)!=="svelte-16mc8p6"&&(y.innerHTML=J),U=a(e),Ee=o(e,"P",{"data-svelte-h":!0}),i(Ee)!=="svelte-19goei1"&&(Ee.innerHTML=No),Xr=a(e),Ie=o(e,"P",{"data-svelte-h":!0}),i(Ie)!=="svelte-1n9t9xd"&&(Ie.innerHTML=So),Zr=a(e),De=o(e,"UL",{"data-svelte-h":!0}),i(De)!=="svelte-14m7ja0"&&(De.innerHTML=Po),Kr=a(e),He=o(e,"P",{"data-svelte-h":!0}),i(He)!=="svelte-1jk2r5h"&&(He.innerHTML=Fo),ea=a(e),je=o(e,"P",{"data-svelte-h":!0}),i(je)!=="svelte-1m9y6fv"&&(je.innerHTML=Wo),ta=a(e),f(Ae.$$.fragment,e),ra=a(e),Ve=o(e,"P",{"data-svelte-h":!0}),i(Ve)!=="svelte-sxjc8h"&&(Ve.innerHTML=zo),aa=a(e),P=o(e,"DIV",{class:!0});var ae=v(P);f(Ne.$$.fragment,ae),rn=a(ae),jt=o(ae,"P",{"data-svelte-h":!0}),i(jt)!=="svelte-ebinlf"&&(jt.innerHTML=Bo),an=a(ae),I=o(ae,"DIV",{class:!0});var A=v(I);f(Se.$$.fragment,A),nn=a(A),At=o(A,"P",{"data-svelte-h":!0}),i(At)!=="svelte-frc23a"&&(At.textContent=Ro),on=a(A),Vt=o(A,"P",{"data-svelte-h":!0}),i(Vt)!=="svelte-1fkshtn"&&(Vt.textContent=Oo),sn=a(A),Nt=o(A,"UL",{"data-svelte-h":!0}),i(Nt)!=="svelte-rt862w"&&(Nt.innerHTML=qo),ln=a(A),St=o(A,"P",{"data-svelte-h":!0}),i(St)!=="svelte-1baxs6u"&&(St.innerHTML=Go),A.forEach(s),ae.forEach(s),na=a(e),Q=o(e,"DIV",{class:!0});var Dt=v(Q);f(Pe.$$.fragment,Dt),cn=a(Dt),Pt=o(Dt,"P",{"data-svelte-h":!0}),i(Pt)!=="svelte-19a0zhy"&&(Pt.innerHTML=Yo),Dt.forEach(s),oa=a(e),X=o(e,"DIV",{class:!0});var Ht=v(X);f(Fe.$$.fragment,Ht),mn=a(Ht),Ft=o(Ht,"P",{"data-svelte-h":!0}),i(Ft)!=="svelte-iv6k97"&&(Ft.innerHTML=Qo),Ht.forEach(s),sa=a(e),Z=o(e,"DIV",{class:!0});var Ea=v(Z);f(We.$$.fragment,Ea),dn=a(Ea),Wt=o(Ea,"P",{"data-svelte-h":!0}),i(Wt)!=="svelte-ahjgex"&&(Wt.innerHTML=Xo),Ea.forEach(s),la=a(e),F=o(e,"DIV",{class:!0});var zr=v(F);f(ze.$$.fragment,zr),pn=a(zr),zt=o(zr,"P",{"data-svelte-h":!0}),i(zt)!=="svelte-5rhi0b"&&(zt.innerHTML=Zo),gn=a(zr),Bt=o(zr,"P",{"data-svelte-h":!0}),i(Bt)!=="svelte-1p1g6z7"&&(Bt.innerHTML=Ko),zr.forEach(s),ia=a(e),K=o(e,"DIV",{class:!0});var Ia=v(K);f(Be.$$.fragment,Ia),fn=a(Ia),Rt=o(Ia,"P",{"data-svelte-h":!0}),i(Rt)!=="svelte-1dleu38"&&(Rt.innerHTML=es),Ia.forEach(s),ca=a(e),W=o(e,"DIV",{class:!0});var Br=v(W);f(Re.$$.fragment,Br),hn=a(Br),Ot=o(Br,"P",{"data-svelte-h":!0}),i(Ot)!=="svelte-ltr62n"&&(Ot.innerHTML=ts),un=a(Br),D=o(Br,"DIV",{class:!0});var G=v(D);f(Oe.$$.fragment,G),bn=a(G),qt=o(G,"P",{"data-svelte-h":!0}),i(qt)!=="svelte-op70zs"&&(qt.innerHTML=rs),_n=a(G),Gt=o(G,"P",{"data-svelte-h":!0}),i(Gt)!=="svelte-m2rt7w"&&(Gt.innerHTML=as),vn=a(G),Yt=o(G,"P",{"data-svelte-h":!0}),i(Yt)!=="svelte-1fkshtn"&&(Yt.textContent=ns),Tn=a(G),z=o(G,"UL",{});var xe=v(z);qe=o(xe,"LI",{});var Da=v(qe);Qt=o(Da,"P",{"data-svelte-h":!0}),i(Qt)!=="svelte-py3r3s"&&(Qt.innerHTML=os),kn=a(Da),f(ne.$$.fragment,Da),Da.forEach(s),$n=a(xe),Xt=o(xe,"LI",{"data-svelte-h":!0}),i(Xt)!=="svelte-dx3m30"&&(Xt.innerHTML=ss),Cn=a(xe),Zt=o(xe,"LI",{"data-svelte-h":!0}),i(Zt)!=="svelte-n1uh6c"&&(Zt.innerHTML=ls),wn=a(xe),Kt=o(xe,"LI",{"data-svelte-h":!0}),i(Kt)!=="svelte-ybo66z"&&(Kt.innerHTML=is),xe.forEach(s),G.forEach(s),Br.forEach(s),ma=a(e),B=o(e,"DIV",{class:!0});var Rr=v(B);f(Ge.$$.fragment,Rr),xn=a(Rr),er=o(Rr,"P",{"data-svelte-h":!0}),i(er)!=="svelte-1eucz73"&&(er.innerHTML=cs),yn=a(Rr),V=o(Rr,"DIV",{class:!0});var ye=v(V);f(Ye.$$.fragment,ye),Mn=a(ye),tr=o(ye,"P",{"data-svelte-h":!0}),i(tr)!=="svelte-nmg16f"&&(tr.textContent=ms),Jn=a(ye),rr=o(ye,"P",{"data-svelte-h":!0}),i(rr)!=="svelte-1fkshtn"&&(rr.textContent=ds),Ln=a(ye),ar=o(ye,"UL",{"data-svelte-h":!0}),i(ar)!=="svelte-b9bwhk"&&(ar.innerHTML=ps),ye.forEach(s),Rr.forEach(s),da=a(e),ee=o(e,"DIV",{class:!0});var Ha=v(ee);f(Qe.$$.fragment,Ha),Un=a(Ha),nr=o(Ha,"P",{"data-svelte-h":!0}),i(nr)!=="svelte-1tavnd8"&&(nr.innerHTML=gs),Ha.forEach(s),pa=a(e),te=o(e,"DIV",{class:!0});var ja=v(te);f(Xe.$$.fragment,ja),En=a(ja),or=o(ja,"P",{"data-svelte-h":!0}),i(or)!=="svelte-1vt2g74"&&(or.innerHTML=fs),ja.forEach(s),ga=a(e),R=o(e,"DIV",{class:!0});var Or=v(R);f(Ze.$$.fragment,Or),In=a(Or),sr=o(Or,"P",{"data-svelte-h":!0}),i(sr)!=="svelte-4ag9j6"&&(sr.innerHTML=hs),Dn=a(Or),lr=o(Or,"P",{"data-svelte-h":!0}),i(lr)!=="svelte-1mhjcx0"&&(lr.innerHTML=us),Or.forEach(s),fa=a(e),H=o(e,"DIV",{class:!0});var Me=v(H);f(Ke.$$.fragment,Me),Hn=a(Me),ir=o(Me,"P",{"data-svelte-h":!0}),i(ir)!=="svelte-ymoizy"&&(ir.innerHTML=bs),jn=a(Me),cr=o(Me,"P",{"data-svelte-h":!0}),i(cr)!=="svelte-1fkshtn"&&(cr.textContent=_s),An=a(Me),mr=o(Me,"UL",{"data-svelte-h":!0}),i(mr)!=="svelte-15svthu"&&(mr.innerHTML=vs),Me.forEach(s),ha=a(e),O=o(e,"DIV",{class:!0});var qr=v(O);f(et.$$.fragment,qr),Vn=a(qr),dr=o(qr,"P",{"data-svelte-h":!0}),i(dr)!=="svelte-cmjpaj"&&(dr.innerHTML=Ts),Nn=a(qr),N=o(qr,"DIV",{class:!0});var Je=v(N);f(tt.$$.fragment,Je),Sn=a(Je),pr=o(Je,"P",{"data-svelte-h":!0}),i(pr)!=="svelte-1wbmj3"&&(pr.textContent=ks),Pn=a(Je),gr=o(Je,"P",{"data-svelte-h":!0}),i(gr)!=="svelte-1fkshtn"&&(gr.textContent=$s),Fn=a(Je),fr=o(Je,"UL",{"data-svelte-h":!0}),i(fr)!=="svelte-dna85o"&&(fr.innerHTML=Cs),Je.forEach(s),qr.forEach(s),ua=a(e),q=o(e,"DIV",{class:!0});var Gr=v(q);f(rt.$$.fragment,Gr),Wn=a(Gr),hr=o(Gr,"P",{"data-svelte-h":!0}),i(hr)!=="svelte-x9qj54"&&(hr.innerHTML=ws),zn=a(Gr),f(oe.$$.fragment,Gr),Gr.forEach(s),ba=a(e),j=o(e,"DIV",{class:!0});var Le=v(j);f(at.$$.fragment,Le),Bn=a(Le),ur=o(Le,"P",{"data-svelte-h":!0}),i(ur)!=="svelte-covwqd"&&(ur.innerHTML=xs),Rn=a(Le),br=o(Le,"P",{"data-svelte-h":!0}),i(br)!=="svelte-eu3kj7"&&(br.innerHTML=ys),On=a(Le),S=o(Le,"DIV",{class:!0});var Ue=v(S);f(nt.$$.fragment,Ue),qn=a(Ue),_r=o(Ue,"P",{"data-svelte-h":!0}),i(_r)!=="svelte-jjkenj"&&(_r.innerHTML=Ms),Gn=a(Ue),vr=o(Ue,"P",{"data-svelte-h":!0}),i(vr)!=="svelte-1fkshtn"&&(vr.textContent=Js),Yn=a(Ue),Tr=o(Ue,"UL",{"data-svelte-h":!0}),i(Tr)!=="svelte-h4vkna"&&(Tr.innerHTML=Ls),Ue.forEach(s),Le.forEach(s),_a=a(e),f(ot.$$.fragment,e),va=a(e),_=o(e,"DIV",{class:!0});var $=v(_);f(st.$$.fragment,$),Qn=a($),kr=o($,"P",{"data-svelte-h":!0}),i(kr)!=="svelte-14xg00o"&&(kr.textContent=Us),Xn=a($),$r=o($,"P",{"data-svelte-h":!0}),i($r)!=="svelte-1xprdvt"&&($r.innerHTML=Es),Zn=a($),Cr=o($,"P",{"data-svelte-h":!0}),i(Cr)!=="svelte-9308xk"&&(Cr.innerHTML=Is),Kn=a($),f(se.$$.fragment,$),eo=a($),le=o($,"DIV",{class:!0});var Aa=v(le);f(lt.$$.fragment,Aa),to=a(Aa),wr=o(Aa,"P",{"data-svelte-h":!0}),i(wr)!=="svelte-106889p"&&(wr.textContent=Ds),Aa.forEach(s),ro=a($),ie=o($,"DIV",{class:!0});var Va=v(ie);f(it.$$.fragment,Va),ao=a(Va),xr=o(Va,"P",{"data-svelte-h":!0}),i(xr)!=="svelte-oshcpj"&&(xr.textContent=Hs),Va.forEach(s),no=a($),ce=o($,"DIV",{class:!0});var Na=v(ce);f(ct.$$.fragment,Na),oo=a(Na),yr=o(Na,"P",{"data-svelte-h":!0}),i(yr)!=="svelte-1o0xh73"&&(yr.textContent=js),Na.forEach(s),so=a($),me=o($,"DIV",{class:!0});var Sa=v(me);f(mt.$$.fragment,Sa),lo=a(Sa),Mr=o(Sa,"P",{"data-svelte-h":!0}),i(Mr)!=="svelte-1j3z901"&&(Mr.innerHTML=As),Sa.forEach(s),io=a($),de=o($,"DIV",{class:!0});var Pa=v(de);f(dt.$$.fragment,Pa),co=a(Pa),Jr=o(Pa,"P",{"data-svelte-h":!0}),i(Jr)!=="svelte-10eiwg0"&&(Jr.textContent=Vs),Pa.forEach(s),mo=a($),pe=o($,"DIV",{class:!0});var Fa=v(pe);f(pt.$$.fragment,Fa),po=a(Fa),Lr=o(Fa,"P",{"data-svelte-h":!0}),i(Lr)!=="svelte-1mpbj2z"&&(Lr.textContent=Ns),Fa.forEach(s),go=a($),ge=o($,"DIV",{class:!0});var Wa=v(ge);f(gt.$$.fragment,Wa),fo=a(Wa),Ur=o(Wa,"P",{"data-svelte-h":!0}),i(Ur)!=="svelte-qj3qgf"&&(Ur.textContent=Ss),Wa.forEach(s),ho=a($),fe=o($,"DIV",{class:!0});var za=v(fe);f(ft.$$.fragment,za),uo=a(za),Er=o(za,"P",{"data-svelte-h":!0}),i(Er)!=="svelte-1df7x4n"&&(Er.textContent=Ps),za.forEach(s),bo=a($),he=o($,"DIV",{class:!0});var Ba=v(he);f(ht.$$.fragment,Ba),_o=a(Ba),Ir=o(Ba,"P",{"data-svelte-h":!0}),i(Ir)!=="svelte-18swygp"&&(Ir.textContent=Fs),Ba.forEach(s),vo=a($),ue=o($,"DIV",{class:!0});var Ra=v(ue);f(ut.$$.fragment,Ra),To=a(Ra),Dr=o(Ra,"P",{"data-svelte-h":!0}),i(Dr)!=="svelte-19xp05v"&&(Dr.textContent=Ws),Ra.forEach(s),ko=a($),be=o($,"DIV",{class:!0});var Oa=v(be);f(bt.$$.fragment,Oa),$o=a(Oa),Hr=o(Oa,"P",{"data-svelte-h":!0}),i(Hr)!=="svelte-7af61p"&&(Hr.textContent=zs),Oa.forEach(s),Co=a($),_e=o($,"DIV",{class:!0});var qa=v(_e);f(_t.$$.fragment,qa),wo=a(qa),jr=o(qa,"P",{"data-svelte-h":!0}),i(jr)!=="svelte-8cdxjr"&&(jr.textContent=Bs),qa.forEach(s),xo=a($),ve=o($,"DIV",{class:!0});var Ga=v(ve);f(vt.$$.fragment,Ga),yo=a(Ga),Ar=o(Ga,"P",{"data-svelte-h":!0}),i(Ar)!=="svelte-sluvs0"&&(Ar.textContent=Rs),Ga.forEach(s),Mo=a($),Te=o($,"DIV",{class:!0});var Ya=v(Te);f(Tt.$$.fragment,Ya),Jo=a(Ya),Vr=o(Ya,"P",{"data-svelte-h":!0}),i(Vr)!=="svelte-6bvy6d"&&(Vr.textContent=Os),Ya.forEach(s),Lo=a($),ke=o($,"DIV",{class:!0});var Qa=v(ke);f(kt.$$.fragment,Qa),Uo=a(Qa),Nr=o(Qa,"P",{"data-svelte-h":!0}),i(Nr)!=="svelte-zzwxsv"&&(Nr.textContent=qs),Qa.forEach(s),$.forEach(s),Ta=a(e),$t=o(e,"P",{"data-svelte-h":!0}),i($t)!=="svelte-17144la"&&($t.innerHTML=Gs),ka=a(e),f(Ct.$$.fragment,e),$a=a(e),wt=o(e,"P",{"data-svelte-h":!0}),i(wt)!=="svelte-1n7iy5z"&&(wt.innerHTML=Ys),Ca=a(e),f(xt.$$.fragment,e),wa=a(e),f(yt.$$.fragment,e),xa=a(e),E=o(e,"DIV",{class:!0});var Y=v(E);f(Mt.$$.fragment,Y),Eo=a(Y),Sr=o(Y,"P",{"data-svelte-h":!0}),i(Sr)!=="svelte-1qkirxu"&&(Sr.innerHTML=Qs),Io=a(Y),f($e.$$.fragment,Y),Do=a(Y),Ce=o(Y,"DIV",{class:!0});var Xa=v(Ce);f(Jt.$$.fragment,Xa),Ho=a(Xa),Pr=o(Xa,"P",{"data-svelte-h":!0}),i(Pr)!=="svelte-hbs6ga"&&(Pr.innerHTML=Xs),Xa.forEach(s),jo=a(Y),we=o(Y,"DIV",{class:!0});var Za=v(we);f(Lt.$$.fragment,Za),Ao=a(Za),Fr=o(Za,"P",{"data-svelte-h":!0}),i(Fr)!=="svelte-dkslae"&&(Fr.innerHTML=Zs),Za.forEach(s),Y.forEach(s),ya=a(e),f(Ut.$$.fragment,e),Ma=a(e),re=o(e,"DIV",{class:!0});var Ka=v(re);f(Et.$$.fragment,Ka),Vo=a(Ka),Wr=o(Ka,"P",{"data-svelte-h":!0}),i(Wr)!=="svelte-6035yy"&&(Wr.innerHTML=Ks),Ka.forEach(s),Ja=a(e),f(It.$$.fragment,e),La=a(e),Yr=o(e,"P",{}),v(Yr).forEach(s),this.h()},h(){k(m,"name","hf:doc:metadata"),k(m,"content",wl),k(I,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),k(P,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),k(Q,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),k(X,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),k(Z,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),k(F,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),k(K,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),k(D,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),k(W,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),k(V,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),k(B,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),k(ee,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),k(te,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),k(R,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),k(H,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),k(N,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),k(O,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),k(q,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),k(S,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),k(j,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),k(le,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),k(ie,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),k(ce,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),k(me,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),k(de,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),k(pe,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),k(ge,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),k(fe,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),k(he,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),k(ue,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),k(be,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),k(_e,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),k(ve,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),k(Te,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),k(ke,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),k(_,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),k(Ce,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),k(we,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),k(E,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),k(re,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8")},m(e,l){t(document.head,m),c(e,M,l),c(e,b,l),c(e,w,l),h(x,e,l),c(e,T,l),c(e,y,l),c(e,U,l),c(e,Ee,l),c(e,Xr,l),c(e,Ie,l),c(e,Zr,l),c(e,De,l),c(e,Kr,l),c(e,He,l),c(e,ea,l),c(e,je,l),c(e,ta,l),h(Ae,e,l),c(e,ra,l),c(e,Ve,l),c(e,aa,l),c(e,P,l),h(Ne,P,null),t(P,rn),t(P,jt),t(P,an),t(P,I),h(Se,I,null),t(I,nn),t(I,At),t(I,on),t(I,Vt),t(I,sn),t(I,Nt),t(I,ln),t(I,St),c(e,na,l),c(e,Q,l),h(Pe,Q,null),t(Q,cn),t(Q,Pt),c(e,oa,l),c(e,X,l),h(Fe,X,null),t(X,mn),t(X,Ft),c(e,sa,l),c(e,Z,l),h(We,Z,null),t(Z,dn),t(Z,Wt),c(e,la,l),c(e,F,l),h(ze,F,null),t(F,pn),t(F,zt),t(F,gn),t(F,Bt),c(e,ia,l),c(e,K,l),h(Be,K,null),t(K,fn),t(K,Rt),c(e,ca,l),c(e,W,l),h(Re,W,null),t(W,hn),t(W,Ot),t(W,un),t(W,D),h(Oe,D,null),t(D,bn),t(D,qt),t(D,_n),t(D,Gt),t(D,vn),t(D,Yt),t(D,Tn),t(D,z),t(z,qe),t(qe,Qt),t(qe,kn),h(ne,qe,null),t(z,$n),t(z,Xt),t(z,Cn),t(z,Zt),t(z,wn),t(z,Kt),c(e,ma,l),c(e,B,l),h(Ge,B,null),t(B,xn),t(B,er),t(B,yn),t(B,V),h(Ye,V,null),t(V,Mn),t(V,tr),t(V,Jn),t(V,rr),t(V,Ln),t(V,ar),c(e,da,l),c(e,ee,l),h(Qe,ee,null),t(ee,Un),t(ee,nr),c(e,pa,l),c(e,te,l),h(Xe,te,null),t(te,En),t(te,or),c(e,ga,l),c(e,R,l),h(Ze,R,null),t(R,In),t(R,sr),t(R,Dn),t(R,lr),c(e,fa,l),c(e,H,l),h(Ke,H,null),t(H,Hn),t(H,ir),t(H,jn),t(H,cr),t(H,An),t(H,mr),c(e,ha,l),c(e,O,l),h(et,O,null),t(O,Vn),t(O,dr),t(O,Nn),t(O,N),h(tt,N,null),t(N,Sn),t(N,pr),t(N,Pn),t(N,gr),t(N,Fn),t(N,fr),c(e,ua,l),c(e,q,l),h(rt,q,null),t(q,Wn),t(q,hr),t(q,zn),h(oe,q,null),c(e,ba,l),c(e,j,l),h(at,j,null),t(j,Bn),t(j,ur),t(j,Rn),t(j,br),t(j,On),t(j,S),h(nt,S,null),t(S,qn),t(S,_r),t(S,Gn),t(S,vr),t(S,Yn),t(S,Tr),c(e,_a,l),h(ot,e,l),c(e,va,l),c(e,_,l),h(st,_,null),t(_,Qn),t(_,kr),t(_,Xn),t(_,$r),t(_,Zn),t(_,Cr),t(_,Kn),h(se,_,null),t(_,eo),t(_,le),h(lt,le,null),t(le,to),t(le,wr),t(_,ro),t(_,ie),h(it,ie,null),t(ie,ao),t(ie,xr),t(_,no),t(_,ce),h(ct,ce,null),t(ce,oo),t(ce,yr),t(_,so),t(_,me),h(mt,me,null),t(me,lo),t(me,Mr),t(_,io),t(_,de),h(dt,de,null),t(de,co),t(de,Jr),t(_,mo),t(_,pe),h(pt,pe,null),t(pe,po),t(pe,Lr),t(_,go),t(_,ge),h(gt,ge,null),t(ge,fo),t(ge,Ur),t(_,ho),t(_,fe),h(ft,fe,null),t(fe,uo),t(fe,Er),t(_,bo),t(_,he),h(ht,he,null),t(he,_o),t(he,Ir),t(_,vo),t(_,ue),h(ut,ue,null),t(ue,To),t(ue,Dr),t(_,ko),t(_,be),h(bt,be,null),t(be,$o),t(be,Hr),t(_,Co),t(_,_e),h(_t,_e,null),t(_e,wo),t(_e,jr),t(_,xo),t(_,ve),h(vt,ve,null),t(ve,yo),t(ve,Ar),t(_,Mo),t(_,Te),h(Tt,Te,null),t(Te,Jo),t(Te,Vr),t(_,Lo),t(_,ke),h(kt,ke,null),t(ke,Uo),t(ke,Nr),c(e,Ta,l),c(e,$t,l),c(e,ka,l),h(Ct,e,l),c(e,$a,l),c(e,wt,l),c(e,Ca,l),h(xt,e,l),c(e,wa,l),h(yt,e,l),c(e,xa,l),c(e,E,l),h(Mt,E,null),t(E,Eo),t(E,Sr),t(E,Io),h($e,E,null),t(E,Do),t(E,Ce),h(Jt,Ce,null),t(Ce,Ho),t(Ce,Pr),t(E,jo),t(E,we),h(Lt,we,null),t(we,Ao),t(we,Fr),c(e,ya,l),h(Ut,e,l),c(e,Ma,l),c(e,re,l),h(Et,re,null),t(re,Vo),t(re,Wr),c(e,Ja,l),h(It,e,l),c(e,La,l),c(e,Yr,l),Ua=!0},p(e,[l]){const ae={};l&2&&(ae.$$scope={dirty:l,ctx:e}),ne.$set(ae);const A={};l&2&&(A.$$scope={dirty:l,ctx:e}),oe.$set(A);const Dt={};l&2&&(Dt.$$scope={dirty:l,ctx:e}),se.$set(Dt);const Ht={};l&2&&(Ht.$$scope={dirty:l,ctx:e}),$e.$set(Ht)},i(e){Ua||(d(x.$$.fragment,e),d(Ae.$$.fragment,e),d(Ne.$$.fragment,e),d(Se.$$.fragment,e),d(Pe.$$.fragment,e),d(Fe.$$.fragment,e),d(We.$$.fragment,e),d(ze.$$.fragment,e),d(Be.$$.fragment,e),d(Re.$$.fragment,e),d(Oe.$$.fragment,e),d(ne.$$.fragment,e),d(Ge.$$.fragment,e),d(Ye.$$.fragment,e),d(Qe.$$.fragment,e),d(Xe.$$.fragment,e),d(Ze.$$.fragment,e),d(Ke.$$.fragment,e),d(et.$$.fragment,e),d(tt.$$.fragment,e),d(rt.$$.fragment,e),d(oe.$$.fragment,e),d(at.$$.fragment,e),d(nt.$$.fragment,e),d(ot.$$.fragment,e),d(st.$$.fragment,e),d(se.$$.fragment,e),d(lt.$$.fragment,e),d(it.$$.fragment,e),d(ct.$$.fragment,e),d(mt.$$.fragment,e),d(dt.$$.fragment,e),d(pt.$$.fragment,e),d(gt.$$.fragment,e),d(ft.$$.fragment,e),d(ht.$$.fragment,e),d(ut.$$.fragment,e),d(bt.$$.fragment,e),d(_t.$$.fragment,e),d(vt.$$.fragment,e),d(Tt.$$.fragment,e),d(kt.$$.fragment,e),d(Ct.$$.fragment,e),d(xt.$$.fragment,e),d(yt.$$.fragment,e),d(Mt.$$.fragment,e),d($e.$$.fragment,e),d(Jt.$$.fragment,e),d(Lt.$$.fragment,e),d(Ut.$$.fragment,e),d(Et.$$.fragment,e),d(It.$$.fragment,e),Ua=!0)},o(e){p(x.$$.fragment,e),p(Ae.$$.fragment,e),p(Ne.$$.fragment,e),p(Se.$$.fragment,e),p(Pe.$$.fragment,e),p(Fe.$$.fragment,e),p(We.$$.fragment,e),p(ze.$$.fragment,e),p(Be.$$.fragment,e),p(Re.$$.fragment,e),p(Oe.$$.fragment,e),p(ne.$$.fragment,e),p(Ge.$$.fragment,e),p(Ye.$$.fragment,e),p(Qe.$$.fragment,e),p(Xe.$$.fragment,e),p(Ze.$$.fragment,e),p(Ke.$$.fragment,e),p(et.$$.fragment,e),p(tt.$$.fragment,e),p(rt.$$.fragment,e),p(oe.$$.fragment,e),p(at.$$.fragment,e),p(nt.$$.fragment,e),p(ot.$$.fragment,e),p(st.$$.fragment,e),p(se.$$.fragment,e),p(lt.$$.fragment,e),p(it.$$.fragment,e),p(ct.$$.fragment,e),p(mt.$$.fragment,e),p(dt.$$.fragment,e),p(pt.$$.fragment,e),p(gt.$$.fragment,e),p(ft.$$.fragment,e),p(ht.$$.fragment,e),p(ut.$$.fragment,e),p(bt.$$.fragment,e),p(_t.$$.fragment,e),p(vt.$$.fragment,e),p(Tt.$$.fragment,e),p(kt.$$.fragment,e),p(Ct.$$.fragment,e),p(xt.$$.fragment,e),p(yt.$$.fragment,e),p(Mt.$$.fragment,e),p($e.$$.fragment,e),p(Jt.$$.fragment,e),p(Lt.$$.fragment,e),p(Ut.$$.fragment,e),p(Et.$$.fragment,e),p(It.$$.fragment,e),Ua=!1},d(e){e&&(s(M),s(b),s(w),s(T),s(y),s(U),s(Ee),s(Xr),s(Ie),s(Zr),s(De),s(Kr),s(He),s(ea),s(je),s(ta),s(ra),s(Ve),s(aa),s(P),s(na),s(Q),s(oa),s(X),s(sa),s(Z),s(la),s(F),s(ia),s(K),s(ca),s(W),s(ma),s(B),s(da),s(ee),s(pa),s(te),s(ga),s(R),s(fa),s(H),s(ha),s(O),s(ua),s(q),s(ba),s(j),s(_a),s(va),s(_),s(Ta),s($t),s(ka),s($a),s(wt),s(Ca),s(wa),s(xa),s(E),s(ya),s(Ma),s(re),s(Ja),s(La),s(Yr)),s(m),u(x,e),u(Ae,e),u(Ne),u(Se),u(Pe),u(Fe),u(We),u(ze),u(Be),u(Re),u(Oe),u(ne),u(Ge),u(Ye),u(Qe),u(Xe),u(Ze),u(Ke),u(et),u(tt),u(rt),u(oe),u(at),u(nt),u(ot,e),u(st),u(se),u(lt),u(it),u(ct),u(mt),u(dt),u(pt),u(gt),u(ft),u(ht),u(ut),u(bt),u(_t),u(vt),u(Tt),u(kt),u(Ct,e),u(xt,e),u(yt,e),u(Mt),u($e),u(Jt),u(Lt),u(Ut,e),u(Et),u(It,e)}}}const wl='{"title":"콜백","local":"callbacks","sections":[{"title":"사용 가능한 콜백","local":"available-callbacks ][ transformers.integrations.CometCallback","sections":[],"depth":2},{"title":"TrainerCallback","local":"trainercallback ][ transformers.TrainerCallback","sections":[],"depth":2},{"title":"TrainerState","local":"trainerstate ][ transformers.TrainerState","sections":[],"depth":2},{"title":"TrainerControl","local":"trainercontrol ][ transformers.TrainerControl","sections":[],"depth":2}],"depth":1}';function xl(L){return dl(()=>{new URLSearchParams(window.location.search).get("fw")}),[]}class Dl extends nl{constructor(m){super(),ol(this,m,xl,Cl,al,{})}}export{Dl as component};
