import{s as Bo,c as Oo,u as Yo,g as qo,d as Xo,o as Zo,n as Oa}from"../chunks/scheduler.9bc65507.js";import{S as Ro,i as Vo,r as p,u as g,v as h,d,t as f,w as u,g as s,m as Po,s as r,h as o,j as v,n as Wo,f as n,c as a,k as $,a as i,y as t,o as Qo,A as Ko,x as c}from"../chunks/index.707bf1b6.js";import{T as Go}from"../chunks/Tip.c2ecdbf4.js";import{D as C}from"../chunks/Docstring.570ddb64.js";import{C as Ga}from"../chunks/CodeBlock.54a9f38d.js";import{E as zo}from"../chunks/ExampleCodeBlock.bc2be6bc.js";import{H as Rr,E as el}from"../chunks/EditOnGithub.922df6ba.js";function tl(j){let m,x,b,k,M;const _=j[1].default,y=Oo(_,j,j[2],null);return{c(){m=s("p"),x=Po("Deprecated in "),b=Po(j[0]),k=r(),y&&y.c(),this.h()},l(L){m=o(L,"P",{class:!0});var I=v(m);x=Wo(I,"Deprecated in "),b=Wo(I,j[0]),I.forEach(n),k=a(L),y&&y.l(L),this.h()},h(){$(m,"class","font-medium")},m(L,I){i(L,m,I),t(m,x),t(m,b),i(L,k,I),y&&y.m(L,I),M=!0},p(L,I){(!M||I&1)&&Qo(b,L[0]),y&&y.p&&(!M||I&4)&&Yo(y,_,L,L[2],M?Xo(_,L[2],I,null):qo(L[2]),null)},i(L){M||(d(y,L),M=!0)},o(L){f(y,L),M=!1},d(L){L&&(n(m),n(k)),y&&y.d(L)}}}function rl(j){let m,x;return m=new Go({props:{warning:!0,$$slots:{default:[tl]},$$scope:{ctx:j}}}),{c(){p(m.$$.fragment)},l(b){g(m.$$.fragment,b)},m(b,k){h(m,b,k),x=!0},p(b,[k]){const M={};k&5&&(M.$$scope={dirty:k,ctx:b}),m.$set(M)},i(b){x||(d(m.$$.fragment,b),x=!0)},o(b){f(m.$$.fragment,b),x=!1},d(b){u(m,b)}}}function al(j,m,x){let{$$slots:b={},$$scope:k}=m,{version:M}=m;return j.$$set=_=>{"version"in _&&x(0,M=_.version),"$$scope"in _&&x(2,k=_.$$scope)},[M,b,k]}class nl extends Ro{constructor(m){super(),Vo(this,m,al,rl,Bo,{version:0})}}function sl(j){let m,x="Setting <code>WANDB_LOG_MODEL</code> as <code>bool</code> will be deprecated in version 5 of 🤗 Transformers.";return{c(){m=s("p"),m.innerHTML=x},l(b){m=o(b,"P",{"data-svelte-h":!0}),c(m)!=="svelte-fxlq1n"&&(m.innerHTML=x)},m(b,k){i(b,m,k)},p:Oa,d(b){b&&n(m)}}}function ol(j){let m,x="Example:",b,k,M;return k=new Ga({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=s("p"),m.textContent=x,b=r(),p(k.$$.fragment)},l(_){m=o(_,"P",{"data-svelte-h":!0}),c(m)!=="svelte-11lpom8"&&(m.textContent=x),b=a(_),g(k.$$.fragment,_)},m(_,y){i(_,m,y),i(_,b,y),h(k,_,y),M=!0},p:Oa,i(_){M||(d(k.$$.fragment,_),M=!0)},o(_){f(k.$$.fragment,_),M=!1},d(_){_&&(n(m),n(b)),u(k,_)}}}function ll(j){let m,x="Example:",b,k,M;return k=new Ga({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=s("p"),m.textContent=x,b=r(),p(k.$$.fragment)},l(_){m=o(_,"P",{"data-svelte-h":!0}),c(m)!=="svelte-11lpom8"&&(m.textContent=x),b=a(_),g(k.$$.fragment,_)},m(_,y){i(_,m,y),i(_,b,y),h(k,_,y),M=!0},p:Oa,i(_){M||(d(k.$$.fragment,_),M=!0)},o(_){f(k.$$.fragment,_),M=!1},d(_){_&&(n(m),n(b)),u(k,_)}}}function il(j){let m,x=`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=s("p"),m.innerHTML=x},l(b){m=o(b,"P",{"data-svelte-h":!0}),c(m)!=="svelte-rhwh6p"&&(m.innerHTML=x)},m(b,k){i(b,m,k)},p:Oa,d(b){b&&n(m)}}}function cl(j){let m,x,b,k,M,_,y,L=`コールバックは、PyTorch のトレーニング ループの動作をカスタマイズできるオブジェクトです。
トレーニング ループを検査できる <a href="/docs/transformers/main/ja/main_classes/trainer#transformers.Trainer">Trainer</a> (この機能は TensorFlow にはまだ実装されていません)
状態を確認し (進捗レポート、TensorBoard または他の ML プラットフォームへのログ記録など)、決定を下します (初期段階など)。
停止中）。`,I,je,Ms=`コールバックは、返される <a href="/docs/transformers/main/ja/main_classes/callback#transformers.TrainerControl">TrainerControl</a> オブジェクトを除けば、「読み取り専用」のコード部分です。
トレーニング ループ内では何も変更できません。トレーニング ループの変更が必要なカスタマイズの場合は、次のことを行う必要があります。
<a href="/docs/transformers/main/ja/main_classes/trainer#transformers.Trainer">Trainer</a> をサブクラス化し、必要なメソッドをオーバーライドします (例については、<a href="trainer">trainer</a> を参照してください)。`,Vr,Ie,ys='デフォルトでは、<code>TrainingArguments.report_to</code> は <code>&quot;all&quot;</code> に設定されているため、<a href="/docs/transformers/main/ja/main_classes/trainer#transformers.Trainer">Trainer</a> は次のコールバックを使用します。',Gr,Ee,xs=`<li><a href="/docs/transformers/main/ja/main_classes/callback#transformers.DefaultFlowCallback">DefaultFlowCallback</a> は、ログ記録、保存、評価のデフォルトの動作を処理します。</li> <li><a href="/docs/transformers/main/ja/main_classes/callback#transformers.PrinterCallback">PrinterCallback</a> または <a href="/docs/transformers/main/ja/main_classes/callback#transformers.ProgressCallback">ProgressCallback</a> で進行状況を表示し、
ログ (最初のログは、<a href="/docs/transformers/main/ja/main_classes/trainer#transformers.TrainingArguments">TrainingArguments</a> を通じて tqdm を非アクティブ化する場合に使用され、そうでない場合に使用されます)
2番目です)。</li> <li><a href="/docs/transformers/main/ja/main_classes/callback#transformers.integrations.TensorBoardCallback">TensorBoardCallback</a> (PyTorch &gt;= 1.4 を介して) tensorboard にアクセスできる場合
またはテンソルボードX）。</li> <li><a href="/docs/transformers/main/ja/main_classes/callback#transformers.integrations.WandbCallback">WandbCallback</a> <a href="https://www.wandb.com/" rel="nofollow">wandb</a> がインストールされている場合。</li> <li><a href="/docs/transformers/main/ja/main_classes/callback#transformers.integrations.CometCallback">CometCallback</a> <a href="https://www.comet.ml/site/" rel="nofollow">comet_ml</a> がインストールされている場合。</li> <li><a href="https://www.mlflow.org/" rel="nofollow">mlflow</a> がインストールされている場合は <a href="/docs/transformers/main/ja/main_classes/callback#transformers.integrations.MLflowCallback">MLflowCallback</a>。</li> <li><a href="/docs/transformers/main/ja/main_classes/callback#transformers.integrations.NeptuneCallback">NeptuneCallback</a> <a href="https://neptune.ai/" rel="nofollow">neptune</a> がインストールされている場合。</li> <li><a href="/docs/transformers/main/ja/main_classes/callback#transformers.integrations.AzureMLCallback">AzureMLCallback</a> <a href="https://pypi.org/project/azureml-sdk/" rel="nofollow">azureml-sdk</a> の場合
インストールされています。</li> <li><a href="/docs/transformers/main/ja/main_classes/callback#transformers.integrations.CodeCarbonCallback">CodeCarbonCallback</a> <a href="https://pypi.org/project/codecarbon/" rel="nofollow">codecarbon</a> の場合
インストールされています。</li> <li><a href="/docs/transformers/main/ja/main_classes/callback#transformers.integrations.ClearMLCallback">ClearMLCallback</a> <a href="https://github.com/allegroai/clearml" rel="nofollow">clearml</a> がインストールされている場合。</li> <li><a href="/docs/transformers/main/ja/main_classes/callback#transformers.integrations.DagsHubCallback">DagsHubCallback</a> <a href="https://dagshub.com/" rel="nofollow">dagshub</a> がインストールされている場合。</li> <li><a href="/docs/transformers/main/ja/main_classes/callback#transformers.integrations.FlyteCallback">FlyteCallback</a> <a href="https://flyte.org/" rel="nofollow">flyte</a> がインストールされている場合。</li> <li><a href="/docs/transformers/main/ja/main_classes/callback#transformers.integrations.DVCLiveCallback">DVCLiveCallback</a> <a href="https://www.dvc.org/doc/dvclive" rel="nofollow">dvclive</a> がインストールされている場合。</li>`,Or,He,Ls="パッケージがインストールされているが、付随する統合を使用したくない場合は、<code>TrainingArguments.report_to</code> を、使用したい統合のみのリストに変更できます (例: <code>[&quot;azure_ml&quot;, &quot;wandb&quot;]</code>) 。",Yr,Je,js=`コールバックを実装するメインクラスは <a href="/docs/transformers/main/ja/main_classes/callback#transformers.TrainerCallback">TrainerCallback</a> です。それは、
<a href="/docs/transformers/main/ja/main_classes/trainer#transformers.TrainingArguments">TrainingArguments</a> は <a href="/docs/transformers/main/ja/main_classes/trainer#transformers.Trainer">Trainer</a> をインスタンス化するために使用され、それにアクセスできます。
<a href="/docs/transformers/main/ja/main_classes/callback#transformers.TrainerState">TrainerState</a> を介してトレーナーの内部状態を取得し、トレーニング ループ上でいくつかのアクションを実行できます。
<a href="/docs/transformers/main/ja/main_classes/callback#transformers.TrainerControl">TrainerControl</a>。`,qr,De,Xr,Ue,Is='ライブラリで利用可能な <a href="/docs/transformers/main/ja/main_classes/callback#transformers.TrainerCallback">TrainerCallback</a> のリストは次のとおりです。',Zr,P,Ae,Ya,Et,Es='A <a href="/docs/transformers/main/ja/main_classes/callback#transformers.TrainerCallback">TrainerCallback</a> that sends the logs to <a href="https://www.comet.ml/site/" rel="nofollow">Comet ML</a>.',qa,H,Ne,Xa,Ht,Hs="Setup the optional Comet.ml integration.",Za,Jt,Js="Environment:",Qa,Dt,Ds=`<li><strong>COMET_MODE</strong> (<code>str</code>, <em>optional</em>, defaults to <code>ONLINE</code>):
Whether to create an online, offline experiment or disable Comet logging. Can be <code>OFFLINE</code>, <code>ONLINE</code>, or
<code>DISABLED</code>.</li> <li><strong>COMET_PROJECT_NAME</strong> (<code>str</code>, <em>optional</em>):
Comet project name for experiments.</li> <li><strong>COMET_OFFLINE_DIRECTORY</strong> (<code>str</code>, <em>optional</em>):
Folder to use for saving offline experiments when <code>COMET_MODE</code> is <code>OFFLINE</code>.</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>`,Ka,Ut,Us=`For a number of configurable items in the environment, see
<a href="https://www.comet.ml/docs/python-sdk/advanced/#comet-configuration-variables" rel="nofollow">here</a>.`,Qr,X,Se,en,At,As='A <a href="/docs/transformers/main/ja/main_classes/callback#transformers.TrainerCallback">TrainerCallback</a> that handles the default flow of the training loop for logs, evaluation and checkpoints.',Kr,Z,Fe,tn,Nt,Ns='A bare <a href="/docs/transformers/main/ja/main_classes/callback#transformers.TrainerCallback">TrainerCallback</a> that just prints the logs.',ea,Q,Pe,rn,St,Ss='A <a href="/docs/transformers/main/ja/main_classes/callback#transformers.TrainerCallback">TrainerCallback</a> that displays the progress of training or evaluation.',ta,W,We,an,Ft,Fs='A <a href="/docs/transformers/main/ja/main_classes/callback#transformers.TrainerCallback">TrainerCallback</a> that handles early stopping.',nn,Pt,Ps=`This callback depends on <a href="/docs/transformers/main/ja/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/main/ja/main_classes/callback#transformers.TrainerState">TrainerState</a>. Note that if the <a href="/docs/transformers/main/ja/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.`,ra,K,ze,sn,Wt,Ws='A <a href="/docs/transformers/main/ja/main_classes/callback#transformers.TrainerCallback">TrainerCallback</a> that sends the logs to <a href="https://www.tensorflow.org/tensorboard" rel="nofollow">TensorBoard</a>.',aa,z,Be,on,zt,zs='A <a href="/docs/transformers/main/ja/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>.',ln,J,Re,cn,Bt,Bs="Setup the optional Weights &amp; Biases (<em>wandb</em>) integration.",mn,Rt,Rs=`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:`,dn,Vt,Vs="Environment:",fn,B,Ve,Gt,Gs=`<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.`,pn,ne,gn,Ot,Os=`<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>`,hn,Yt,Ys=`<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>`,un,qt,qs=`<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>`,na,R,Ge,bn,Xt,Xs=`A <a href="/docs/transformers/main/ja/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>.`,_n,N,Oe,vn,Zt,Zs="Setup the optional MLflow integration.",Tn,Qt,Qs="Environment:",$n,Kt,Ks=`<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/main/ja/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>`,sa,ee,Ye,Cn,er,eo='A <a href="/docs/transformers/main/ja/main_classes/callback#transformers.TrainerCallback">TrainerCallback</a> that sends the logs to <a href="https://pypi.org/project/azureml-sdk/" rel="nofollow">AzureML</a>.',oa,te,qe,kn,tr,to='A <a href="/docs/transformers/main/ja/main_classes/callback#transformers.TrainerCallback">TrainerCallback</a> that tracks the CO2 emission of training.',la,V,Xe,wn,rr,ro='TrainerCallback that sends the logs to <a href="https://app.neptune.ai" rel="nofollow">Neptune</a>.',Mn,ar,ao=`For instructions and examples, see the <a href="https://docs.neptune.ai/integrations/transformers" rel="nofollow">Transformers integration
guide</a> in the Neptune documentation.`,ia,D,Ze,yn,nr,no='A <a href="/docs/transformers/main/ja/main_classes/callback#transformers.TrainerCallback">TrainerCallback</a> that sends the logs to <a href="https://clear.ml/" rel="nofollow">ClearML</a>.',xn,sr,so="Environment:",Ln,or,oo=`<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>`,ca,G,Qe,jn,lr,lo='A <a href="/docs/transformers/main/ja/main_classes/callback#transformers.TrainerCallback">TrainerCallback</a> that logs to <a href="https://dagshub.com/" rel="nofollow">DagsHub</a>. Extends <code>MLflowCallback</code>',In,S,Ke,En,ir,io="Setup the DagsHub’s Logging integration.",Hn,cr,co="Environment:",Jn,mr,mo=`<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>`,ma,O,et,Dn,dr,fo=`A <a href="/docs/transformers/main/ja/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.`,Un,se,da,U,tt,An,fr,po='A <a href="/docs/transformers/main/ja/main_classes/callback#transformers.TrainerCallback">TrainerCallback</a> that sends the logs to <a href="https://www.dvc.org/doc/dvclive" rel="nofollow">DVCLive</a>.',Nn,pr,go=`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>.`,Sn,F,rt,Fn,gr,ho=`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>.`,Pn,hr,uo="Environment:",Wn,ur,bo=`<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/main/ja/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/main/ja/main_classes/trainer#transformers.TrainingArguments">TrainingArguments</a>’s <code>output_dir</code> is logged at each checkpoint.</li>`,fa,at,pa,T,nt,zn,br,_o=`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:`,Bn,_r,vo=`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.`,Rn,vr,To=`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/main/ja/main_classes/callback#transformers.PrinterCallback">PrinterCallback</a>.`,Vn,oe,Gn,le,st,On,Tr,$o="Event called at the beginning of an epoch.",Yn,ie,ot,qn,$r,Co="Event called at the end of an epoch.",Xn,ce,lt,Zn,Cr,ko="Event called after an evaluation phase.",Qn,me,it,Kn,kr,wo='Event called at the end of the initialization of the <a href="/docs/transformers/main/ja/main_classes/trainer#transformers.Trainer">Trainer</a>.',es,de,ct,ts,wr,Mo="Event called after logging the last logs.",rs,fe,mt,as,Mr,yo="Event called after a successful prediction.",ns,pe,dt,ss,yr,xo="Event called after a prediction step.",os,ge,ft,ls,xr,Lo="Event called after a checkpoint save.",is,he,pt,cs,Lr,jo=`Event called at the beginning of a training step. If using gradient accumulation, one training step might take
several inputs.`,ms,ue,gt,ds,jr,Io=`Event called at the end of a training step. If using gradient accumulation, one training step might take
several inputs.`,fs,be,ht,ps,Ir,Eo="Event called at the end of an substep during gradient accumulation.",gs,_e,ut,hs,Er,Ho="Event called at the beginning of training.",us,ve,bt,bs,Hr,Jo="Event called at the end of training.",ga,_t,Do='以下は、カスタム コールバックを PyTorch <a href="/docs/transformers/main/ja/main_classes/trainer#transformers.Trainer">Trainer</a> に登録する方法の例です。',ha,vt,ua,Tt,Uo="コールバックを登録する別の方法は、次のように <code>trainer.add_callback()</code> を呼び出すことです。",ba,$t,_a,Ct,va,E,kt,_s,Jr,Ao=`A class containing the <a href="/docs/transformers/main/ja/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/main/ja/main_classes/callback#transformers.TrainerCallback">TrainerCallback</a>.`,vs,Te,Ts,$e,wt,$s,Dr,No="Create an instance from the content of <code>json_path</code>.",Cs,Ce,Mt,ks,Ur,So="Save the content of this instance in JSON format inside <code>json_path</code>.",Ta,yt,$a,re,xt,ws,Ar,Fo=`A class that handles the <a href="/docs/transformers/main/ja/main_classes/trainer#transformers.Trainer">Trainer</a> control flow. This class is used by the <a href="/docs/transformers/main/ja/main_classes/callback#transformers.TrainerCallback">TrainerCallback</a> to activate some
switches in the training loop.`,Ca,Lt,ka,Br,wa;return M=new Rr({props:{title:"コールバック数",local:"コールバック数",headingTag:"h1"}}),De=new Rr({props:{title:"利用可能なコールバック",local:"transformers.integrations.CometCallback",headingTag:"h2"}}),Ae=new C({props:{name:"class transformers.integrations.CometCallback",anchor:"transformers.integrations.CometCallback",parameters:[],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/integrations/integration_utils.py#L937"}}),Ne=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/main/src/transformers/integrations/integration_utils.py#L948"}}),Se=new C({props:{name:"class transformers.DefaultFlowCallback",anchor:"transformers.DefaultFlowCallback",parameters:[],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/trainer_callback.py#L517"}}),Fe=new C({props:{name:"class transformers.PrinterCallback",anchor:"transformers.PrinterCallback",parameters:[],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/trainer_callback.py#L625"}}),Pe=new C({props:{name:"class transformers.ProgressCallback",anchor:"transformers.ProgressCallback",parameters:[],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/trainer_callback.py#L570"}}),We=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/main/src/transformers/trainer_callback.py#L636"}}),ze=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/main/src/transformers/integrations/integration_utils.py#L591"}}),Be=new C({props:{name:"class transformers.integrations.WandbCallback",anchor:"transformers.integrations.WandbCallback",parameters:[],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/integrations/integration_utils.py#L693"}}),Re=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/main/src/transformers/integrations/integration_utils.py#L718"}}),ne=new nl({props:{version:"5.0",$$slots:{default:[sl]},$$scope:{ctx:j}}}),Ge=new C({props:{name:"class transformers.integrations.MLflowCallback",anchor:"transformers.integrations.MLflowCallback",parameters:[],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/integrations/integration_utils.py#L1037"}}),Oe=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/main/src/transformers/integrations/integration_utils.py#L1056"}}),Ye=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/main/src/transformers/integrations/integration_utils.py#L1014"}}),qe=new C({props:{name:"class transformers.integrations.CodeCarbonCallback",anchor:"transformers.integrations.CodeCarbonCallback",parameters:[],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/integrations/integration_utils.py#L1538"}}),Xe=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>, optional, 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/main/src/transformers/integrations/integration_utils.py#L1265"}}),Ze=new C({props:{name:"class transformers.integrations.ClearMLCallback",anchor:"transformers.integrations.ClearMLCallback",parameters:[],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/integrations/integration_utils.py#L1572"}}),Qe=new C({props:{name:"class transformers.integrations.DagsHubCallback",anchor:"transformers.integrations.DagsHubCallback",parameters:[],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/integrations/integration_utils.py#L1205"}}),Ke=new C({props:{name:"setup",anchor:"transformers.integrations.DagsHubCallback.setup",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/integrations/integration_utils.py#L1219"}}),et=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/main/src/transformers/integrations/integration_utils.py#L1825"}}),se=new zo({props:{anchor:"transformers.integrations.FlyteCallback.example",$$slots:{default:[ol]},$$scope:{ctx:j}}}),tt=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/main/ja/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/main/ja/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/main/src/transformers/integrations/integration_utils.py#L1888"}}),rt=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/main/src/transformers/integrations/integration_utils.py#L1929"}}),at=new Rr({props:{title:"TrainerCallback",local:"transformers.TrainerCallback",headingTag:"h2"}}),nt=new C({props:{name:"class transformers.TrainerCallback",anchor:"transformers.TrainerCallback",parameters:[],parametersDescription:[{anchor:"transformers.TrainerCallback.args",description:`<strong>args</strong> (<a href="/docs/transformers/main/ja/main_classes/trainer#transformers.TrainingArguments">TrainingArguments</a>) &#x2014;
The training arguments used to instantiate the <a href="/docs/transformers/main/ja/main_classes/trainer#transformers.Trainer">Trainer</a>.`,name:"args"},{anchor:"transformers.TrainerCallback.state",description:`<strong>state</strong> (<a href="/docs/transformers/main/ja/main_classes/callback#transformers.TrainerState">TrainerState</a>) &#x2014;
The current state of the <a href="/docs/transformers/main/ja/main_classes/trainer#transformers.Trainer">Trainer</a>.`,name:"state"},{anchor:"transformers.TrainerCallback.control",description:`<strong>control</strong> (<a href="/docs/transformers/main/ja/main_classes/callback#transformers.TrainerControl">TrainerControl</a>) &#x2014;
The object that is returned to the <a href="/docs/transformers/main/ja/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/main/ja/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> (<a href="/docs/transformers/main/ja/main_classes/tokenizer#transformers.PreTrainedTokenizer">PreTrainedTokenizer</a>) &#x2014;
The tokenizer used for encoding the data.`,name:"tokenizer"},{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/main/src/transformers/trainer_callback.py#L260"}}),oe=new zo({props:{anchor:"transformers.TrainerCallback.example",$$slots:{default:[ll]},$$scope:{ctx:j}}}),st=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/main/src/transformers/trainer_callback.py#L329"}}),ot=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/main/src/transformers/trainer_callback.py#L335"}}),lt=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/main/src/transformers/trainer_callback.py#L361"}}),it=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/main/src/transformers/trainer_callback.py#L311"}}),ct=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/main/src/transformers/trainer_callback.py#L379"}}),mt=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/main/src/transformers/trainer_callback.py#L367"}}),dt=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/main/src/transformers/trainer_callback.py#L385"}}),ft=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/main/src/transformers/trainer_callback.py#L373"}}),pt=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/main/src/transformers/trainer_callback.py#L341"}}),gt=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/main/src/transformers/trainer_callback.py#L354"}}),ht=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/main/src/transformers/trainer_callback.py#L348"}}),ut=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/main/src/transformers/trainer_callback.py#L317"}}),bt=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/main/src/transformers/trainer_callback.py#L323"}}),vt=new Ga({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"># We can either pass the callback class this way or an instance of it (MyCallback())</span>
)`,wrap:!1}}),$t=new Ga({props:{code:"dHJhaW5lciUyMCUzRCUyMFRyYWluZXIoLi4uKSUwQXRyYWluZXIuYWRkX2NhbGxiYWNrKE15Q2FsbGJhY2spJTBBJTIzJTIwQWx0ZXJuYXRpdmVseSUyQyUyMHdlJTIwY2FuJTIwcGFzcyUyMGFuJTIwaW5zdGFuY2UlMjBvZiUyMHRoZSUyMGNhbGxiYWNrJTIwY2xhc3MlMEF0cmFpbmVyLmFkZF9jYWxsYmFjayhNeUNhbGxiYWNrKCkp",highlighted:`trainer = Trainer(...)
trainer.add_callback(MyCallback)
<span class="hljs-comment"># Alternatively, we can pass an instance of the callback class</span>
trainer.add_callback(MyCallback())`,wrap:!1}}),Ct=new Rr({props:{title:"TrainerState",local:"transformers.TrainerState",headingTag:"h2"}}),kt=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;
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/main/src/transformers/trainer_callback.py#L36"}}),Te=new Go({props:{$$slots:{default:[il]},$$scope:{ctx:j}}}),wt=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/main/src/transformers/trainer_callback.py#L146"}}),Mt=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/main/src/transformers/trainer_callback.py#L140"}}),yt=new Rr({props:{title:"TrainerControl",local:"transformers.TrainerControl",headingTag:"h2"}}),xt=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/main/src/transformers/trainer_callback.py#L198"}}),Lt=new el({props:{source:"https://github.com/huggingface/transformers/blob/main/docs/source/ja/main_classes/callback.md"}}),{c(){m=s("meta"),x=r(),b=s("p"),k=r(),p(M.$$.fragment),_=r(),y=s("p"),y.innerHTML=L,I=r(),je=s("p"),je.innerHTML=Ms,Vr=r(),Ie=s("p"),Ie.innerHTML=ys,Gr=r(),Ee=s("ul"),Ee.innerHTML=xs,Or=r(),He=s("p"),He.innerHTML=Ls,Yr=r(),Je=s("p"),Je.innerHTML=js,qr=r(),p(De.$$.fragment),Xr=r(),Ue=s("p"),Ue.innerHTML=Is,Zr=r(),P=s("div"),p(Ae.$$.fragment),Ya=r(),Et=s("p"),Et.innerHTML=Es,qa=r(),H=s("div"),p(Ne.$$.fragment),Xa=r(),Ht=s("p"),Ht.textContent=Hs,Za=r(),Jt=s("p"),Jt.textContent=Js,Qa=r(),Dt=s("ul"),Dt.innerHTML=Ds,Ka=r(),Ut=s("p"),Ut.innerHTML=Us,Qr=r(),X=s("div"),p(Se.$$.fragment),en=r(),At=s("p"),At.innerHTML=As,Kr=r(),Z=s("div"),p(Fe.$$.fragment),tn=r(),Nt=s("p"),Nt.innerHTML=Ns,ea=r(),Q=s("div"),p(Pe.$$.fragment),rn=r(),St=s("p"),St.innerHTML=Ss,ta=r(),W=s("div"),p(We.$$.fragment),an=r(),Ft=s("p"),Ft.innerHTML=Fs,nn=r(),Pt=s("p"),Pt.innerHTML=Ps,ra=r(),K=s("div"),p(ze.$$.fragment),sn=r(),Wt=s("p"),Wt.innerHTML=Ws,aa=r(),z=s("div"),p(Be.$$.fragment),on=r(),zt=s("p"),zt.innerHTML=zs,ln=r(),J=s("div"),p(Re.$$.fragment),cn=r(),Bt=s("p"),Bt.innerHTML=Bs,mn=r(),Rt=s("p"),Rt.innerHTML=Rs,dn=r(),Vt=s("p"),Vt.textContent=Vs,fn=r(),B=s("ul"),Ve=s("li"),Gt=s("p"),Gt.innerHTML=Gs,pn=r(),p(ne.$$.fragment),gn=r(),Ot=s("li"),Ot.innerHTML=Os,hn=r(),Yt=s("li"),Yt.innerHTML=Ys,un=r(),qt=s("li"),qt.innerHTML=qs,na=r(),R=s("div"),p(Ge.$$.fragment),bn=r(),Xt=s("p"),Xt.innerHTML=Xs,_n=r(),N=s("div"),p(Oe.$$.fragment),vn=r(),Zt=s("p"),Zt.textContent=Zs,Tn=r(),Qt=s("p"),Qt.textContent=Qs,$n=r(),Kt=s("ul"),Kt.innerHTML=Ks,sa=r(),ee=s("div"),p(Ye.$$.fragment),Cn=r(),er=s("p"),er.innerHTML=eo,oa=r(),te=s("div"),p(qe.$$.fragment),kn=r(),tr=s("p"),tr.innerHTML=to,la=r(),V=s("div"),p(Xe.$$.fragment),wn=r(),rr=s("p"),rr.innerHTML=ro,Mn=r(),ar=s("p"),ar.innerHTML=ao,ia=r(),D=s("div"),p(Ze.$$.fragment),yn=r(),nr=s("p"),nr.innerHTML=no,xn=r(),sr=s("p"),sr.textContent=so,Ln=r(),or=s("ul"),or.innerHTML=oo,ca=r(),G=s("div"),p(Qe.$$.fragment),jn=r(),lr=s("p"),lr.innerHTML=lo,In=r(),S=s("div"),p(Ke.$$.fragment),En=r(),ir=s("p"),ir.textContent=io,Hn=r(),cr=s("p"),cr.textContent=co,Jn=r(),mr=s("ul"),mr.innerHTML=mo,ma=r(),O=s("div"),p(et.$$.fragment),Dn=r(),dr=s("p"),dr.innerHTML=fo,Un=r(),p(se.$$.fragment),da=r(),U=s("div"),p(tt.$$.fragment),An=r(),fr=s("p"),fr.innerHTML=po,Nn=r(),pr=s("p"),pr.innerHTML=go,Sn=r(),F=s("div"),p(rt.$$.fragment),Fn=r(),gr=s("p"),gr.innerHTML=ho,Pn=r(),hr=s("p"),hr.textContent=uo,Wn=r(),ur=s("ul"),ur.innerHTML=bo,fa=r(),p(at.$$.fragment),pa=r(),T=s("div"),p(nt.$$.fragment),zn=r(),br=s("p"),br.textContent=_o,Bn=r(),_r=s("p"),_r.innerHTML=vo,Rn=r(),vr=s("p"),vr.innerHTML=To,Vn=r(),p(oe.$$.fragment),Gn=r(),le=s("div"),p(st.$$.fragment),On=r(),Tr=s("p"),Tr.textContent=$o,Yn=r(),ie=s("div"),p(ot.$$.fragment),qn=r(),$r=s("p"),$r.textContent=Co,Xn=r(),ce=s("div"),p(lt.$$.fragment),Zn=r(),Cr=s("p"),Cr.textContent=ko,Qn=r(),me=s("div"),p(it.$$.fragment),Kn=r(),kr=s("p"),kr.innerHTML=wo,es=r(),de=s("div"),p(ct.$$.fragment),ts=r(),wr=s("p"),wr.textContent=Mo,rs=r(),fe=s("div"),p(mt.$$.fragment),as=r(),Mr=s("p"),Mr.textContent=yo,ns=r(),pe=s("div"),p(dt.$$.fragment),ss=r(),yr=s("p"),yr.textContent=xo,os=r(),ge=s("div"),p(ft.$$.fragment),ls=r(),xr=s("p"),xr.textContent=Lo,is=r(),he=s("div"),p(pt.$$.fragment),cs=r(),Lr=s("p"),Lr.textContent=jo,ms=r(),ue=s("div"),p(gt.$$.fragment),ds=r(),jr=s("p"),jr.textContent=Io,fs=r(),be=s("div"),p(ht.$$.fragment),ps=r(),Ir=s("p"),Ir.textContent=Eo,gs=r(),_e=s("div"),p(ut.$$.fragment),hs=r(),Er=s("p"),Er.textContent=Ho,us=r(),ve=s("div"),p(bt.$$.fragment),bs=r(),Hr=s("p"),Hr.textContent=Jo,ga=r(),_t=s("p"),_t.innerHTML=Do,ha=r(),p(vt.$$.fragment),ua=r(),Tt=s("p"),Tt.innerHTML=Uo,ba=r(),p($t.$$.fragment),_a=r(),p(Ct.$$.fragment),va=r(),E=s("div"),p(kt.$$.fragment),_s=r(),Jr=s("p"),Jr.innerHTML=Ao,vs=r(),p(Te.$$.fragment),Ts=r(),$e=s("div"),p(wt.$$.fragment),$s=r(),Dr=s("p"),Dr.innerHTML=No,Cs=r(),Ce=s("div"),p(Mt.$$.fragment),ks=r(),Ur=s("p"),Ur.innerHTML=So,Ta=r(),p(yt.$$.fragment),$a=r(),re=s("div"),p(xt.$$.fragment),ws=r(),Ar=s("p"),Ar.innerHTML=Fo,Ca=r(),p(Lt.$$.fragment),ka=r(),Br=s("p"),this.h()},l(e){const l=Ko("svelte-u9bgzb",document.head);m=o(l,"META",{name:!0,content:!0}),l.forEach(n),x=a(e),b=o(e,"P",{}),v(b).forEach(n),k=a(e),g(M.$$.fragment,e),_=a(e),y=o(e,"P",{"data-svelte-h":!0}),c(y)!=="svelte-1vobzxg"&&(y.innerHTML=L),I=a(e),je=o(e,"P",{"data-svelte-h":!0}),c(je)!=="svelte-1cfup3n"&&(je.innerHTML=Ms),Vr=a(e),Ie=o(e,"P",{"data-svelte-h":!0}),c(Ie)!=="svelte-d50wob"&&(Ie.innerHTML=ys),Gr=a(e),Ee=o(e,"UL",{"data-svelte-h":!0}),c(Ee)!=="svelte-fyjiy4"&&(Ee.innerHTML=xs),Or=a(e),He=o(e,"P",{"data-svelte-h":!0}),c(He)!=="svelte-1b834xs"&&(He.innerHTML=Ls),Yr=a(e),Je=o(e,"P",{"data-svelte-h":!0}),c(Je)!=="svelte-1ecg6pf"&&(Je.innerHTML=js),qr=a(e),g(De.$$.fragment,e),Xr=a(e),Ue=o(e,"P",{"data-svelte-h":!0}),c(Ue)!=="svelte-k1ad7d"&&(Ue.innerHTML=Is),Zr=a(e),P=o(e,"DIV",{class:!0});var ae=v(P);g(Ae.$$.fragment,ae),Ya=a(ae),Et=o(ae,"P",{"data-svelte-h":!0}),c(Et)!=="svelte-1l2y3b6"&&(Et.innerHTML=Es),qa=a(ae),H=o(ae,"DIV",{class:!0});var A=v(H);g(Ne.$$.fragment,A),Xa=a(A),Ht=o(A,"P",{"data-svelte-h":!0}),c(Ht)!=="svelte-1bfkm9x"&&(Ht.textContent=Hs),Za=a(A),Jt=o(A,"P",{"data-svelte-h":!0}),c(Jt)!=="svelte-1fkshtn"&&(Jt.textContent=Js),Qa=a(A),Dt=o(A,"UL",{"data-svelte-h":!0}),c(Dt)!=="svelte-12drq5x"&&(Dt.innerHTML=Ds),Ka=a(A),Ut=o(A,"P",{"data-svelte-h":!0}),c(Ut)!=="svelte-1faq0a7"&&(Ut.innerHTML=Us),A.forEach(n),ae.forEach(n),Qr=a(e),X=o(e,"DIV",{class:!0});var jt=v(X);g(Se.$$.fragment,jt),en=a(jt),At=o(jt,"P",{"data-svelte-h":!0}),c(At)!=="svelte-6svysx"&&(At.innerHTML=As),jt.forEach(n),Kr=a(e),Z=o(e,"DIV",{class:!0});var It=v(Z);g(Fe.$$.fragment,It),tn=a(It),Nt=o(It,"P",{"data-svelte-h":!0}),c(Nt)!=="svelte-e6fxs2"&&(Nt.innerHTML=Ns),It.forEach(n),ea=a(e),Q=o(e,"DIV",{class:!0});var Ma=v(Q);g(Pe.$$.fragment,Ma),rn=a(Ma),St=o(Ma,"P",{"data-svelte-h":!0}),c(St)!=="svelte-w3u5vn"&&(St.innerHTML=Ss),Ma.forEach(n),ta=a(e),W=o(e,"DIV",{class:!0});var Nr=v(W);g(We.$$.fragment,Nr),an=a(Nr),Ft=o(Nr,"P",{"data-svelte-h":!0}),c(Ft)!=="svelte-1u5pbjw"&&(Ft.innerHTML=Fs),nn=a(Nr),Pt=o(Nr,"P",{"data-svelte-h":!0}),c(Pt)!=="svelte-1cmgnu4"&&(Pt.innerHTML=Ps),Nr.forEach(n),ra=a(e),K=o(e,"DIV",{class:!0});var ya=v(K);g(ze.$$.fragment,ya),sn=a(ya),Wt=o(ya,"P",{"data-svelte-h":!0}),c(Wt)!=="svelte-1hfmggn"&&(Wt.innerHTML=Ws),ya.forEach(n),aa=a(e),z=o(e,"DIV",{class:!0});var Sr=v(z);g(Be.$$.fragment,Sr),on=a(Sr),zt=o(Sr,"P",{"data-svelte-h":!0}),c(zt)!=="svelte-12eqks8"&&(zt.innerHTML=zs),ln=a(Sr),J=o(Sr,"DIV",{class:!0});var Y=v(J);g(Re.$$.fragment,Y),cn=a(Y),Bt=o(Y,"P",{"data-svelte-h":!0}),c(Bt)!=="svelte-op70zs"&&(Bt.innerHTML=Bs),mn=a(Y),Rt=o(Y,"P",{"data-svelte-h":!0}),c(Rt)!=="svelte-m2rt7w"&&(Rt.innerHTML=Rs),dn=a(Y),Vt=o(Y,"P",{"data-svelte-h":!0}),c(Vt)!=="svelte-1fkshtn"&&(Vt.textContent=Vs),fn=a(Y),B=o(Y,"UL",{});var ke=v(B);Ve=o(ke,"LI",{});var xa=v(Ve);Gt=o(xa,"P",{"data-svelte-h":!0}),c(Gt)!=="svelte-py3r3s"&&(Gt.innerHTML=Gs),pn=a(xa),g(ne.$$.fragment,xa),xa.forEach(n),gn=a(ke),Ot=o(ke,"LI",{"data-svelte-h":!0}),c(Ot)!=="svelte-dx3m30"&&(Ot.innerHTML=Os),hn=a(ke),Yt=o(ke,"LI",{"data-svelte-h":!0}),c(Yt)!=="svelte-n1uh6c"&&(Yt.innerHTML=Ys),un=a(ke),qt=o(ke,"LI",{"data-svelte-h":!0}),c(qt)!=="svelte-ybo66z"&&(qt.innerHTML=qs),ke.forEach(n),Y.forEach(n),Sr.forEach(n),na=a(e),R=o(e,"DIV",{class:!0});var Fr=v(R);g(Ge.$$.fragment,Fr),bn=a(Fr),Xt=o(Fr,"P",{"data-svelte-h":!0}),c(Xt)!=="svelte-lcj2so"&&(Xt.innerHTML=Xs),_n=a(Fr),N=o(Fr,"DIV",{class:!0});var we=v(N);g(Oe.$$.fragment,we),vn=a(we),Zt=o(we,"P",{"data-svelte-h":!0}),c(Zt)!=="svelte-nmg16f"&&(Zt.textContent=Zs),Tn=a(we),Qt=o(we,"P",{"data-svelte-h":!0}),c(Qt)!=="svelte-1fkshtn"&&(Qt.textContent=Qs),$n=a(we),Kt=o(we,"UL",{"data-svelte-h":!0}),c(Kt)!=="svelte-jiwazw"&&(Kt.innerHTML=Ks),we.forEach(n),Fr.forEach(n),sa=a(e),ee=o(e,"DIV",{class:!0});var La=v(ee);g(Ye.$$.fragment,La),Cn=a(La),er=o(La,"P",{"data-svelte-h":!0}),c(er)!=="svelte-4clse7"&&(er.innerHTML=eo),La.forEach(n),oa=a(e),te=o(e,"DIV",{class:!0});var ja=v(te);g(qe.$$.fragment,ja),kn=a(ja),tr=o(ja,"P",{"data-svelte-h":!0}),c(tr)!=="svelte-1daiwkb"&&(tr.innerHTML=to),ja.forEach(n),la=a(e),V=o(e,"DIV",{class:!0});var Pr=v(V);g(Xe.$$.fragment,Pr),wn=a(Pr),rr=o(Pr,"P",{"data-svelte-h":!0}),c(rr)!=="svelte-4ag9j6"&&(rr.innerHTML=ro),Mn=a(Pr),ar=o(Pr,"P",{"data-svelte-h":!0}),c(ar)!=="svelte-1mhjcx0"&&(ar.innerHTML=ao),Pr.forEach(n),ia=a(e),D=o(e,"DIV",{class:!0});var Me=v(D);g(Ze.$$.fragment,Me),yn=a(Me),nr=o(Me,"P",{"data-svelte-h":!0}),c(nr)!=="svelte-mdwcvt"&&(nr.innerHTML=no),xn=a(Me),sr=o(Me,"P",{"data-svelte-h":!0}),c(sr)!=="svelte-1fkshtn"&&(sr.textContent=so),Ln=a(Me),or=o(Me,"UL",{"data-svelte-h":!0}),c(or)!=="svelte-15svthu"&&(or.innerHTML=oo),Me.forEach(n),ca=a(e),G=o(e,"DIV",{class:!0});var Wr=v(G);g(Qe.$$.fragment,Wr),jn=a(Wr),lr=o(Wr,"P",{"data-svelte-h":!0}),c(lr)!=="svelte-140pgpo"&&(lr.innerHTML=lo),In=a(Wr),S=o(Wr,"DIV",{class:!0});var ye=v(S);g(Ke.$$.fragment,ye),En=a(ye),ir=o(ye,"P",{"data-svelte-h":!0}),c(ir)!=="svelte-1wbmj3"&&(ir.textContent=io),Hn=a(ye),cr=o(ye,"P",{"data-svelte-h":!0}),c(cr)!=="svelte-1fkshtn"&&(cr.textContent=co),Jn=a(ye),mr=o(ye,"UL",{"data-svelte-h":!0}),c(mr)!=="svelte-dna85o"&&(mr.innerHTML=mo),ye.forEach(n),Wr.forEach(n),ma=a(e),O=o(e,"DIV",{class:!0});var zr=v(O);g(et.$$.fragment,zr),Dn=a(zr),dr=o(zr,"P",{"data-svelte-h":!0}),c(dr)!=="svelte-1ooma4z"&&(dr.innerHTML=fo),Un=a(zr),g(se.$$.fragment,zr),zr.forEach(n),da=a(e),U=o(e,"DIV",{class:!0});var xe=v(U);g(tt.$$.fragment,xe),An=a(xe),fr=o(xe,"P",{"data-svelte-h":!0}),c(fr)!=="svelte-1hfp9o6"&&(fr.innerHTML=po),Nn=a(xe),pr=o(xe,"P",{"data-svelte-h":!0}),c(pr)!=="svelte-eu3kj7"&&(pr.innerHTML=go),Sn=a(xe),F=o(xe,"DIV",{class:!0});var Le=v(F);g(rt.$$.fragment,Le),Fn=a(Le),gr=o(Le,"P",{"data-svelte-h":!0}),c(gr)!=="svelte-jjkenj"&&(gr.innerHTML=ho),Pn=a(Le),hr=o(Le,"P",{"data-svelte-h":!0}),c(hr)!=="svelte-1fkshtn"&&(hr.textContent=uo),Wn=a(Le),ur=o(Le,"UL",{"data-svelte-h":!0}),c(ur)!=="svelte-rvk2oa"&&(ur.innerHTML=bo),Le.forEach(n),xe.forEach(n),fa=a(e),g(at.$$.fragment,e),pa=a(e),T=o(e,"DIV",{class:!0});var w=v(T);g(nt.$$.fragment,w),zn=a(w),br=o(w,"P",{"data-svelte-h":!0}),c(br)!=="svelte-14xg00o"&&(br.textContent=_o),Bn=a(w),_r=o(w,"P",{"data-svelte-h":!0}),c(_r)!=="svelte-1xprdvt"&&(_r.innerHTML=vo),Rn=a(w),vr=o(w,"P",{"data-svelte-h":!0}),c(vr)!=="svelte-8ufiwj"&&(vr.innerHTML=To),Vn=a(w),g(oe.$$.fragment,w),Gn=a(w),le=o(w,"DIV",{class:!0});var Ia=v(le);g(st.$$.fragment,Ia),On=a(Ia),Tr=o(Ia,"P",{"data-svelte-h":!0}),c(Tr)!=="svelte-106889p"&&(Tr.textContent=$o),Ia.forEach(n),Yn=a(w),ie=o(w,"DIV",{class:!0});var Ea=v(ie);g(ot.$$.fragment,Ea),qn=a(Ea),$r=o(Ea,"P",{"data-svelte-h":!0}),c($r)!=="svelte-oshcpj"&&($r.textContent=Co),Ea.forEach(n),Xn=a(w),ce=o(w,"DIV",{class:!0});var Ha=v(ce);g(lt.$$.fragment,Ha),Zn=a(Ha),Cr=o(Ha,"P",{"data-svelte-h":!0}),c(Cr)!=="svelte-1o0xh73"&&(Cr.textContent=ko),Ha.forEach(n),Qn=a(w),me=o(w,"DIV",{class:!0});var Ja=v(me);g(it.$$.fragment,Ja),Kn=a(Ja),kr=o(Ja,"P",{"data-svelte-h":!0}),c(kr)!=="svelte-jt4n1g"&&(kr.innerHTML=wo),Ja.forEach(n),es=a(w),de=o(w,"DIV",{class:!0});var Da=v(de);g(ct.$$.fragment,Da),ts=a(Da),wr=o(Da,"P",{"data-svelte-h":!0}),c(wr)!=="svelte-10eiwg0"&&(wr.textContent=Mo),Da.forEach(n),rs=a(w),fe=o(w,"DIV",{class:!0});var Ua=v(fe);g(mt.$$.fragment,Ua),as=a(Ua),Mr=o(Ua,"P",{"data-svelte-h":!0}),c(Mr)!=="svelte-1df7x4n"&&(Mr.textContent=yo),Ua.forEach(n),ns=a(w),pe=o(w,"DIV",{class:!0});var Aa=v(pe);g(dt.$$.fragment,Aa),ss=a(Aa),yr=o(Aa,"P",{"data-svelte-h":!0}),c(yr)!=="svelte-18swygp"&&(yr.textContent=xo),Aa.forEach(n),os=a(w),ge=o(w,"DIV",{class:!0});var Na=v(ge);g(ft.$$.fragment,Na),ls=a(Na),xr=o(Na,"P",{"data-svelte-h":!0}),c(xr)!=="svelte-19xp05v"&&(xr.textContent=Lo),Na.forEach(n),is=a(w),he=o(w,"DIV",{class:!0});var Sa=v(he);g(pt.$$.fragment,Sa),cs=a(Sa),Lr=o(Sa,"P",{"data-svelte-h":!0}),c(Lr)!=="svelte-7af61p"&&(Lr.textContent=jo),Sa.forEach(n),ms=a(w),ue=o(w,"DIV",{class:!0});var Fa=v(ue);g(gt.$$.fragment,Fa),ds=a(Fa),jr=o(Fa,"P",{"data-svelte-h":!0}),c(jr)!=="svelte-8cdxjr"&&(jr.textContent=Io),Fa.forEach(n),fs=a(w),be=o(w,"DIV",{class:!0});var Pa=v(be);g(ht.$$.fragment,Pa),ps=a(Pa),Ir=o(Pa,"P",{"data-svelte-h":!0}),c(Ir)!=="svelte-sluvs0"&&(Ir.textContent=Eo),Pa.forEach(n),gs=a(w),_e=o(w,"DIV",{class:!0});var Wa=v(_e);g(ut.$$.fragment,Wa),hs=a(Wa),Er=o(Wa,"P",{"data-svelte-h":!0}),c(Er)!=="svelte-6bvy6d"&&(Er.textContent=Ho),Wa.forEach(n),us=a(w),ve=o(w,"DIV",{class:!0});var za=v(ve);g(bt.$$.fragment,za),bs=a(za),Hr=o(za,"P",{"data-svelte-h":!0}),c(Hr)!=="svelte-zzwxsv"&&(Hr.textContent=Jo),za.forEach(n),w.forEach(n),ga=a(e),_t=o(e,"P",{"data-svelte-h":!0}),c(_t)!=="svelte-wu4ft3"&&(_t.innerHTML=Do),ha=a(e),g(vt.$$.fragment,e),ua=a(e),Tt=o(e,"P",{"data-svelte-h":!0}),c(Tt)!=="svelte-183oh1l"&&(Tt.innerHTML=Uo),ba=a(e),g($t.$$.fragment,e),_a=a(e),g(Ct.$$.fragment,e),va=a(e),E=o(e,"DIV",{class:!0});var q=v(E);g(kt.$$.fragment,q),_s=a(q),Jr=o(q,"P",{"data-svelte-h":!0}),c(Jr)!=="svelte-1o3q9xe"&&(Jr.innerHTML=Ao),vs=a(q),g(Te.$$.fragment,q),Ts=a(q),$e=o(q,"DIV",{class:!0});var Ba=v($e);g(wt.$$.fragment,Ba),$s=a(Ba),Dr=o(Ba,"P",{"data-svelte-h":!0}),c(Dr)!=="svelte-hbs6ga"&&(Dr.innerHTML=No),Ba.forEach(n),Cs=a(q),Ce=o(q,"DIV",{class:!0});var Ra=v(Ce);g(Mt.$$.fragment,Ra),ks=a(Ra),Ur=o(Ra,"P",{"data-svelte-h":!0}),c(Ur)!=="svelte-dkslae"&&(Ur.innerHTML=So),Ra.forEach(n),q.forEach(n),Ta=a(e),g(yt.$$.fragment,e),$a=a(e),re=o(e,"DIV",{class:!0});var Va=v(re);g(xt.$$.fragment,Va),ws=a(Va),Ar=o(Va,"P",{"data-svelte-h":!0}),c(Ar)!=="svelte-t17a5m"&&(Ar.innerHTML=Fo),Va.forEach(n),Ca=a(e),g(Lt.$$.fragment,e),ka=a(e),Br=o(e,"P",{}),v(Br).forEach(n),this.h()},h(){$(m,"name","hf:doc:metadata"),$(m,"content",ml),$(H,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),$(P,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),$(X,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),$(Z,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),$(Q,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),$(W,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),$(K,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),$(J,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),$(z,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),$(N,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),$(R,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),$(ee,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),$(te,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),$(V,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),$(D,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),$(S,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),$(G,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),$(O,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),$(F,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),$(U,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),$(le,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),$(ie,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),$(ce,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),$(me,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),$(de,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),$(fe,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),$(pe,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),$(ge,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),$(he,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),$(ue,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),$(be,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),$(_e,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),$(ve,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),$(T,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),$($e,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),$(Ce,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),$(E,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),$(re,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8")},m(e,l){t(document.head,m),i(e,x,l),i(e,b,l),i(e,k,l),h(M,e,l),i(e,_,l),i(e,y,l),i(e,I,l),i(e,je,l),i(e,Vr,l),i(e,Ie,l),i(e,Gr,l),i(e,Ee,l),i(e,Or,l),i(e,He,l),i(e,Yr,l),i(e,Je,l),i(e,qr,l),h(De,e,l),i(e,Xr,l),i(e,Ue,l),i(e,Zr,l),i(e,P,l),h(Ae,P,null),t(P,Ya),t(P,Et),t(P,qa),t(P,H),h(Ne,H,null),t(H,Xa),t(H,Ht),t(H,Za),t(H,Jt),t(H,Qa),t(H,Dt),t(H,Ka),t(H,Ut),i(e,Qr,l),i(e,X,l),h(Se,X,null),t(X,en),t(X,At),i(e,Kr,l),i(e,Z,l),h(Fe,Z,null),t(Z,tn),t(Z,Nt),i(e,ea,l),i(e,Q,l),h(Pe,Q,null),t(Q,rn),t(Q,St),i(e,ta,l),i(e,W,l),h(We,W,null),t(W,an),t(W,Ft),t(W,nn),t(W,Pt),i(e,ra,l),i(e,K,l),h(ze,K,null),t(K,sn),t(K,Wt),i(e,aa,l),i(e,z,l),h(Be,z,null),t(z,on),t(z,zt),t(z,ln),t(z,J),h(Re,J,null),t(J,cn),t(J,Bt),t(J,mn),t(J,Rt),t(J,dn),t(J,Vt),t(J,fn),t(J,B),t(B,Ve),t(Ve,Gt),t(Ve,pn),h(ne,Ve,null),t(B,gn),t(B,Ot),t(B,hn),t(B,Yt),t(B,un),t(B,qt),i(e,na,l),i(e,R,l),h(Ge,R,null),t(R,bn),t(R,Xt),t(R,_n),t(R,N),h(Oe,N,null),t(N,vn),t(N,Zt),t(N,Tn),t(N,Qt),t(N,$n),t(N,Kt),i(e,sa,l),i(e,ee,l),h(Ye,ee,null),t(ee,Cn),t(ee,er),i(e,oa,l),i(e,te,l),h(qe,te,null),t(te,kn),t(te,tr),i(e,la,l),i(e,V,l),h(Xe,V,null),t(V,wn),t(V,rr),t(V,Mn),t(V,ar),i(e,ia,l),i(e,D,l),h(Ze,D,null),t(D,yn),t(D,nr),t(D,xn),t(D,sr),t(D,Ln),t(D,or),i(e,ca,l),i(e,G,l),h(Qe,G,null),t(G,jn),t(G,lr),t(G,In),t(G,S),h(Ke,S,null),t(S,En),t(S,ir),t(S,Hn),t(S,cr),t(S,Jn),t(S,mr),i(e,ma,l),i(e,O,l),h(et,O,null),t(O,Dn),t(O,dr),t(O,Un),h(se,O,null),i(e,da,l),i(e,U,l),h(tt,U,null),t(U,An),t(U,fr),t(U,Nn),t(U,pr),t(U,Sn),t(U,F),h(rt,F,null),t(F,Fn),t(F,gr),t(F,Pn),t(F,hr),t(F,Wn),t(F,ur),i(e,fa,l),h(at,e,l),i(e,pa,l),i(e,T,l),h(nt,T,null),t(T,zn),t(T,br),t(T,Bn),t(T,_r),t(T,Rn),t(T,vr),t(T,Vn),h(oe,T,null),t(T,Gn),t(T,le),h(st,le,null),t(le,On),t(le,Tr),t(T,Yn),t(T,ie),h(ot,ie,null),t(ie,qn),t(ie,$r),t(T,Xn),t(T,ce),h(lt,ce,null),t(ce,Zn),t(ce,Cr),t(T,Qn),t(T,me),h(it,me,null),t(me,Kn),t(me,kr),t(T,es),t(T,de),h(ct,de,null),t(de,ts),t(de,wr),t(T,rs),t(T,fe),h(mt,fe,null),t(fe,as),t(fe,Mr),t(T,ns),t(T,pe),h(dt,pe,null),t(pe,ss),t(pe,yr),t(T,os),t(T,ge),h(ft,ge,null),t(ge,ls),t(ge,xr),t(T,is),t(T,he),h(pt,he,null),t(he,cs),t(he,Lr),t(T,ms),t(T,ue),h(gt,ue,null),t(ue,ds),t(ue,jr),t(T,fs),t(T,be),h(ht,be,null),t(be,ps),t(be,Ir),t(T,gs),t(T,_e),h(ut,_e,null),t(_e,hs),t(_e,Er),t(T,us),t(T,ve),h(bt,ve,null),t(ve,bs),t(ve,Hr),i(e,ga,l),i(e,_t,l),i(e,ha,l),h(vt,e,l),i(e,ua,l),i(e,Tt,l),i(e,ba,l),h($t,e,l),i(e,_a,l),h(Ct,e,l),i(e,va,l),i(e,E,l),h(kt,E,null),t(E,_s),t(E,Jr),t(E,vs),h(Te,E,null),t(E,Ts),t(E,$e),h(wt,$e,null),t($e,$s),t($e,Dr),t(E,Cs),t(E,Ce),h(Mt,Ce,null),t(Ce,ks),t(Ce,Ur),i(e,Ta,l),h(yt,e,l),i(e,$a,l),i(e,re,l),h(xt,re,null),t(re,ws),t(re,Ar),i(e,Ca,l),h(Lt,e,l),i(e,ka,l),i(e,Br,l),wa=!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}),se.$set(A);const jt={};l&2&&(jt.$$scope={dirty:l,ctx:e}),oe.$set(jt);const It={};l&2&&(It.$$scope={dirty:l,ctx:e}),Te.$set(It)},i(e){wa||(d(M.$$.fragment,e),d(De.$$.fragment,e),d(Ae.$$.fragment,e),d(Ne.$$.fragment,e),d(Se.$$.fragment,e),d(Fe.$$.fragment,e),d(Pe.$$.fragment,e),d(We.$$.fragment,e),d(ze.$$.fragment,e),d(Be.$$.fragment,e),d(Re.$$.fragment,e),d(ne.$$.fragment,e),d(Ge.$$.fragment,e),d(Oe.$$.fragment,e),d(Ye.$$.fragment,e),d(qe.$$.fragment,e),d(Xe.$$.fragment,e),d(Ze.$$.fragment,e),d(Qe.$$.fragment,e),d(Ke.$$.fragment,e),d(et.$$.fragment,e),d(se.$$.fragment,e),d(tt.$$.fragment,e),d(rt.$$.fragment,e),d(at.$$.fragment,e),d(nt.$$.fragment,e),d(oe.$$.fragment,e),d(st.$$.fragment,e),d(ot.$$.fragment,e),d(lt.$$.fragment,e),d(it.$$.fragment,e),d(ct.$$.fragment,e),d(mt.$$.fragment,e),d(dt.$$.fragment,e),d(ft.$$.fragment,e),d(pt.$$.fragment,e),d(gt.$$.fragment,e),d(ht.$$.fragment,e),d(ut.$$.fragment,e),d(bt.$$.fragment,e),d(vt.$$.fragment,e),d($t.$$.fragment,e),d(Ct.$$.fragment,e),d(kt.$$.fragment,e),d(Te.$$.fragment,e),d(wt.$$.fragment,e),d(Mt.$$.fragment,e),d(yt.$$.fragment,e),d(xt.$$.fragment,e),d(Lt.$$.fragment,e),wa=!0)},o(e){f(M.$$.fragment,e),f(De.$$.fragment,e),f(Ae.$$.fragment,e),f(Ne.$$.fragment,e),f(Se.$$.fragment,e),f(Fe.$$.fragment,e),f(Pe.$$.fragment,e),f(We.$$.fragment,e),f(ze.$$.fragment,e),f(Be.$$.fragment,e),f(Re.$$.fragment,e),f(ne.$$.fragment,e),f(Ge.$$.fragment,e),f(Oe.$$.fragment,e),f(Ye.$$.fragment,e),f(qe.$$.fragment,e),f(Xe.$$.fragment,e),f(Ze.$$.fragment,e),f(Qe.$$.fragment,e),f(Ke.$$.fragment,e),f(et.$$.fragment,e),f(se.$$.fragment,e),f(tt.$$.fragment,e),f(rt.$$.fragment,e),f(at.$$.fragment,e),f(nt.$$.fragment,e),f(oe.$$.fragment,e),f(st.$$.fragment,e),f(ot.$$.fragment,e),f(lt.$$.fragment,e),f(it.$$.fragment,e),f(ct.$$.fragment,e),f(mt.$$.fragment,e),f(dt.$$.fragment,e),f(ft.$$.fragment,e),f(pt.$$.fragment,e),f(gt.$$.fragment,e),f(ht.$$.fragment,e),f(ut.$$.fragment,e),f(bt.$$.fragment,e),f(vt.$$.fragment,e),f($t.$$.fragment,e),f(Ct.$$.fragment,e),f(kt.$$.fragment,e),f(Te.$$.fragment,e),f(wt.$$.fragment,e),f(Mt.$$.fragment,e),f(yt.$$.fragment,e),f(xt.$$.fragment,e),f(Lt.$$.fragment,e),wa=!1},d(e){e&&(n(x),n(b),n(k),n(_),n(y),n(I),n(je),n(Vr),n(Ie),n(Gr),n(Ee),n(Or),n(He),n(Yr),n(Je),n(qr),n(Xr),n(Ue),n(Zr),n(P),n(Qr),n(X),n(Kr),n(Z),n(ea),n(Q),n(ta),n(W),n(ra),n(K),n(aa),n(z),n(na),n(R),n(sa),n(ee),n(oa),n(te),n(la),n(V),n(ia),n(D),n(ca),n(G),n(ma),n(O),n(da),n(U),n(fa),n(pa),n(T),n(ga),n(_t),n(ha),n(ua),n(Tt),n(ba),n(_a),n(va),n(E),n(Ta),n($a),n(re),n(Ca),n(ka),n(Br)),n(m),u(M,e),u(De,e),u(Ae),u(Ne),u(Se),u(Fe),u(Pe),u(We),u(ze),u(Be),u(Re),u(ne),u(Ge),u(Oe),u(Ye),u(qe),u(Xe),u(Ze),u(Qe),u(Ke),u(et),u(se),u(tt),u(rt),u(at,e),u(nt),u(oe),u(st),u(ot),u(lt),u(it),u(ct),u(mt),u(dt),u(ft),u(pt),u(gt),u(ht),u(ut),u(bt),u(vt,e),u($t,e),u(Ct,e),u(kt),u(Te),u(wt),u(Mt),u(yt,e),u(xt),u(Lt,e)}}}const ml='{"title":"コールバック数","local":"コールバック数","sections":[{"title":"利用可能なコールバック","local":"transformers.integrations.CometCallback","sections":[],"depth":2},{"title":"TrainerCallback","local":"transformers.TrainerCallback","sections":[],"depth":2},{"title":"TrainerState","local":"transformers.TrainerState","sections":[],"depth":2},{"title":"TrainerControl","local":"transformers.TrainerControl","sections":[],"depth":2}],"depth":1}';function dl(j){return Zo(()=>{new URLSearchParams(window.location.search).get("fw")}),[]}class vl extends Ro{constructor(m){super(),Vo(this,m,dl,cl,Bo,{})}}export{vl as component};
