import{s as Xo,c as el,u as tl,g as rl,d as al,o as nl,n as Za}from"../chunks/scheduler.9bc65507.js";import{S as Zo,i as Qo,r as f,u as g,v as h,d,t as p,w as u,g as s,m as qo,s as r,h as o,j as _,n as Oo,f as n,c as a,k as $,a as c,y as t,o as sl,A as ol,x as i}from"../chunks/index.707bf1b6.js";import{T as Ko}from"../chunks/Tip.c2ecdbf4.js";import{D as C}from"../chunks/Docstring.86474e80.js";import{C as Xa}from"../chunks/CodeBlock.54a9f38d.js";import{E as Yo}from"../chunks/ExampleCodeBlock.034f3a73.js";import{H as qr,E as ll}from"../chunks/EditOnGithub.922df6ba.js";function il(j){let m,x,b,w,y;const v=j[1].default,M=el(v,j,j[2],null);return{c(){m=s("p"),x=qo("Deprecated in "),b=qo(j[0]),w=r(),M&&M.c(),this.h()},l(L){m=o(L,"P",{class:!0});var I=_(m);x=Oo(I,"Deprecated in "),b=Oo(I,j[0]),I.forEach(n),w=a(L),M&&M.l(L),this.h()},h(){$(m,"class","font-medium")},m(L,I){c(L,m,I),t(m,x),t(m,b),c(L,w,I),M&&M.m(L,I),y=!0},p(L,I){(!y||I&1)&&sl(b,L[0]),M&&M.p&&(!y||I&4)&&tl(M,v,L,L[2],y?al(v,L[2],I,null):rl(L[2]),null)},i(L){y||(d(M,L),y=!0)},o(L){p(M,L),y=!1},d(L){L&&(n(m),n(w)),M&&M.d(L)}}}function cl(j){let m,x;return m=new Ko({props:{warning:!0,$$slots:{default:[il]},$$scope:{ctx:j}}}),{c(){f(m.$$.fragment)},l(b){g(m.$$.fragment,b)},m(b,w){h(m,b,w),x=!0},p(b,[w]){const y={};w&5&&(y.$$scope={dirty:w,ctx:b}),m.$set(y)},i(b){x||(d(m.$$.fragment,b),x=!0)},o(b){p(m.$$.fragment,b),x=!1},d(b){u(m,b)}}}function ml(j,m,x){let{$$slots:b={},$$scope:w}=m,{version:y}=m;return j.$$set=v=>{"version"in v&&x(0,y=v.version),"$$scope"in v&&x(2,w=v.$$scope)},[y,b,w]}class dl extends Zo{constructor(m){super(),Qo(this,m,ml,cl,Xo,{version:0})}}function pl(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}),i(m)!=="svelte-fxlq1n"&&(m.innerHTML=x)},m(b,w){c(b,m,w)},p:Za,d(b){b&&n(m)}}}function fl(j){let m,x="Example:",b,w,y;return w=new Xa({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(),f(w.$$.fragment)},l(v){m=o(v,"P",{"data-svelte-h":!0}),i(m)!=="svelte-11lpom8"&&(m.textContent=x),b=a(v),g(w.$$.fragment,v)},m(v,M){c(v,m,M),c(v,b,M),h(w,v,M),y=!0},p:Za,i(v){y||(d(w.$$.fragment,v),y=!0)},o(v){p(w.$$.fragment,v),y=!1},d(v){v&&(n(m),n(b)),u(w,v)}}}function gl(j){let m,x="Example:",b,w,y;return w=new Xa({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(),f(w.$$.fragment)},l(v){m=o(v,"P",{"data-svelte-h":!0}),i(m)!=="svelte-11lpom8"&&(m.textContent=x),b=a(v),g(w.$$.fragment,v)},m(v,M){c(v,m,M),c(v,b,M),h(w,v,M),y=!0},p:Za,i(v){y||(d(w.$$.fragment,v),y=!0)},o(v){p(w.$$.fragment,v),y=!1},d(v){v&&(n(m),n(b)),u(w,v)}}}function hl(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}),i(m)!=="svelte-rhwh6p"&&(m.innerHTML=x)},m(b,w){c(b,m,w)},p:Za,d(b){b&&n(m)}}}function ul(j){let m,x,b,w,y,v,M,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/pr_31161/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,Ie,Es=`コールバックは、返される <a href="/docs/transformers/pr_31161/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/pr_31161/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)。`,Or,Ee,Hs='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/pr_31161/ja/main_classes/trainer#transformers.Trainer">Trainer</a> は次のコールバックを使用します。',Yr,He,Js=`<li><a href="/docs/transformers/pr_31161/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/pr_31161/ja/main_classes/callback#transformers.PrinterCallback">PrinterCallback</a> または <a href="/docs/transformers/pr_31161/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/pr_31161/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/pr_31161/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/pr_31161/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/pr_31161/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/pr_31161/ja/main_classes/callback#transformers.integrations.MLflowCallback">MLflowCallback</a>。</li> <li><a href="/docs/transformers/pr_31161/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/pr_31161/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/pr_31161/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/pr_31161/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/pr_31161/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/pr_31161/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/pr_31161/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>`,Xr,Je,Ds="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>) 。",Zr,De,Us=`コールバックを実装するメインクラスは <a href="/docs/transformers/pr_31161/ja/main_classes/callback#transformers.TrainerCallback">TrainerCallback</a> です。それは、
<a href="/docs/transformers/pr_31161/ja/main_classes/trainer#transformers.TrainingArguments">TrainingArguments</a> は <a href="/docs/transformers/pr_31161/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/pr_31161/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/pr_31161/ja/main_classes/callback#transformers.TrainerControl">TrainerControl</a>。`,Qr,Ue,Kr,Ae,As='ライブラリで利用可能な <a href="/docs/transformers/pr_31161/ja/main_classes/callback#transformers.TrainerCallback">TrainerCallback</a> のリストは次のとおりです。',ea,P,Ne,Qa,Jt,Ns='A <a href="/docs/transformers/pr_31161/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>.',Ka,H,Se,en,Dt,Ss="Setup the optional Comet.ml integration.",tn,Ut,Fs="Environment:",rn,At,Ps=`<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>`,an,Nt,Ws=`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>.`,ta,X,Fe,nn,St,zs='A <a href="/docs/transformers/pr_31161/ja/main_classes/callback#transformers.TrainerCallback">TrainerCallback</a> that handles the default flow of the training loop for logs, evaluation and checkpoints.',ra,Z,Pe,sn,Ft,Bs='A bare <a href="/docs/transformers/pr_31161/ja/main_classes/callback#transformers.TrainerCallback">TrainerCallback</a> that just prints the logs.',aa,Q,We,on,Pt,Rs='A <a href="/docs/transformers/pr_31161/ja/main_classes/callback#transformers.TrainerCallback">TrainerCallback</a> that displays the progress of training or evaluation.',na,W,ze,ln,Wt,Vs='A <a href="/docs/transformers/pr_31161/ja/main_classes/callback#transformers.TrainerCallback">TrainerCallback</a> that handles early stopping.',cn,zt,Gs=`This callback depends on <a href="/docs/transformers/pr_31161/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/pr_31161/ja/main_classes/callback#transformers.TrainerState">TrainerState</a>. Note that if the <a href="/docs/transformers/pr_31161/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.`,sa,K,Be,mn,Bt,qs='A <a href="/docs/transformers/pr_31161/ja/main_classes/callback#transformers.TrainerCallback">TrainerCallback</a> that sends the logs to <a href="https://www.tensorflow.org/tensorboard" rel="nofollow">TensorBoard</a>.',oa,z,Re,dn,Rt,Os='A <a href="/docs/transformers/pr_31161/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>.',pn,J,Ve,fn,Vt,Ys="Setup the optional Weights &amp; Biases (<em>wandb</em>) integration.",gn,Gt,Xs=`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:`,hn,qt,Zs="Environment:",un,B,Ge,Ot,Qs=`<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.`,bn,ne,_n,Yt,Ks=`<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>`,vn,Xt,eo=`<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>`,Tn,Zt,to=`<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>`,la,R,qe,$n,Qt,ro=`A <a href="/docs/transformers/pr_31161/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>.`,Cn,N,Oe,kn,Kt,ao="Setup the optional MLflow integration.",wn,er,no="Environment:",yn,tr,so=`<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_31161/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>`,ia,ee,Ye,Mn,rr,oo='A <a href="/docs/transformers/pr_31161/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>.',ca,te,Xe,xn,ar,lo='A <a href="/docs/transformers/pr_31161/ja/main_classes/callback#transformers.TrainerCallback">TrainerCallback</a> that tracks the CO2 emission of training.',ma,V,Ze,Ln,nr,io='TrainerCallback that sends the logs to <a href="https://app.neptune.ai" rel="nofollow">Neptune</a>.',jn,sr,co=`For instructions and examples, see the <a href="https://docs.neptune.ai/integrations/transformers" rel="nofollow">Transformers integration
guide</a> in the Neptune documentation.`,da,D,Qe,In,or,mo='A <a href="/docs/transformers/pr_31161/ja/main_classes/callback#transformers.TrainerCallback">TrainerCallback</a> that sends the logs to <a href="https://clear.ml/" rel="nofollow">ClearML</a>.',En,lr,po="Environment:",Hn,ir,fo=`<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>`,pa,G,Ke,Jn,cr,go='A <a href="/docs/transformers/pr_31161/ja/main_classes/callback#transformers.TrainerCallback">TrainerCallback</a> that logs to <a href="https://dagshub.com/" rel="nofollow">DagsHub</a>. Extends <code>MLflowCallback</code>',Dn,S,et,Un,mr,ho="Setup the DagsHub’s Logging integration.",An,dr,uo="Environment:",Nn,pr,bo=`<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>`,fa,q,tt,Sn,fr,_o=`A <a href="/docs/transformers/pr_31161/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.`,Fn,se,ga,U,rt,Pn,gr,vo='A <a href="/docs/transformers/pr_31161/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>.',Wn,hr,To=`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>.`,zn,F,at,Bn,ur,$o=`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>.`,Rn,br,Co="Environment:",Vn,_r,ko=`<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_31161/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/pr_31161/ja/main_classes/trainer#transformers.TrainingArguments">TrainingArguments</a>’s <code>output_dir</code> is logged at each checkpoint.</li>`,ha,nt,ua,T,st,Gn,vr,wo=`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:`,qn,Tr,yo=`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.`,On,$r,Mo=`The argument <code>args</code>, <code>state</code> and <code>control</code> are positionals for all events, all the others are grouped in <code>kwargs</code>.
You can unpack the ones you need in the signature of the event using them. As an example, see the code of the
simple <a href="/docs/transformers/pr_31161/ja/main_classes/callback#transformers.PrinterCallback">PrinterCallback</a>.`,Yn,oe,Xn,le,ot,Zn,Cr,xo="Event called at the beginning of an epoch.",Qn,ie,lt,Kn,kr,Lo="Event called at the end of an epoch.",es,ce,it,ts,wr,jo="Event called after an evaluation phase.",rs,me,ct,as,yr,Io='Event called at the end of the initialization of the <a href="/docs/transformers/pr_31161/ja/main_classes/trainer#transformers.Trainer">Trainer</a>.',ns,de,mt,ss,Mr,Eo="Event called after logging the last logs.",os,pe,dt,ls,xr,Ho="Event called after the optimizer step but before gradients are zeroed out. Useful for monitoring gradients.",is,fe,pt,cs,Lr,Jo="Event called after a successful prediction.",ms,ge,ft,ds,jr,Do="Event called after a prediction step.",ps,he,gt,fs,Ir,Uo="Event called after a checkpoint save.",gs,ue,ht,hs,Er,Ao=`Event called at the beginning of a training step. If using gradient accumulation, one training step might take
several inputs.`,us,be,ut,bs,Hr,No=`Event called at the end of a training step. If using gradient accumulation, one training step might take
several inputs.`,_s,_e,bt,vs,Jr,So="Event called at the end of an substep during gradient accumulation.",Ts,ve,_t,$s,Dr,Fo="Event called at the beginning of training.",Cs,Te,vt,ks,Ur,Po="Event called at the end of training.",ba,Tt,Wo='以下は、カスタム コールバックを PyTorch <a href="/docs/transformers/pr_31161/ja/main_classes/trainer#transformers.Trainer">Trainer</a> に登録する方法の例です。',_a,$t,va,Ct,zo="コールバックを登録する別の方法は、次のように <code>trainer.add_callback()</code> を呼び出すことです。",Ta,kt,$a,wt,Ca,E,yt,ws,Ar,Bo=`A class containing the <a href="/docs/transformers/pr_31161/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/pr_31161/ja/main_classes/callback#transformers.TrainerCallback">TrainerCallback</a>.`,ys,$e,Ms,Ce,Mt,xs,Nr,Ro="Create an instance from the content of <code>json_path</code>.",Ls,ke,xt,js,Sr,Vo="Save the content of this instance in JSON format inside <code>json_path</code>.",ka,Lt,wa,re,jt,Is,Fr,Go=`A class that handles the <a href="/docs/transformers/pr_31161/ja/main_classes/trainer#transformers.Trainer">Trainer</a> control flow. This class is used by the <a href="/docs/transformers/pr_31161/ja/main_classes/callback#transformers.TrainerCallback">TrainerCallback</a> to activate some
switches in the training loop.`,ya,It,Ma,Gr,xa;return y=new qr({props:{title:"コールバック数",local:"コールバック数",headingTag:"h1"}}),Ue=new qr({props:{title:"利用可能なコールバック",local:"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_31161/src/transformers/integrations/integration_utils.py#L937"}}),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_31161/src/transformers/integrations/integration_utils.py#L948"}}),Fe=new C({props:{name:"class transformers.DefaultFlowCallback",anchor:"transformers.DefaultFlowCallback",parameters:[],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/trainer_callback.py#L526"}}),Pe=new C({props:{name:"class transformers.PrinterCallback",anchor:"transformers.PrinterCallback",parameters:[],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/trainer_callback.py#L634"}}),We=new C({props:{name:"class transformers.ProgressCallback",anchor:"transformers.ProgressCallback",parameters:[],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/trainer_callback.py#L579"}}),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_31161/src/transformers/trainer_callback.py#L645"}}),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_31161/src/transformers/integrations/integration_utils.py#L591"}}),Re=new C({props:{name:"class transformers.integrations.WandbCallback",anchor:"transformers.integrations.WandbCallback",parameters:[],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/integrations/integration_utils.py#L693"}}),Ve=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_31161/src/transformers/integrations/integration_utils.py#L718"}}),ne=new dl({props:{version:"5.0",$$slots:{default:[pl]},$$scope:{ctx:j}}}),qe=new C({props:{name:"class transformers.integrations.MLflowCallback",anchor:"transformers.integrations.MLflowCallback",parameters:[],source:"https://github.com/huggingface/transformers/blob/vr_31161/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/vr_31161/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/vr_31161/src/transformers/integrations/integration_utils.py#L1014"}}),Xe=new C({props:{name:"class transformers.integrations.CodeCarbonCallback",anchor:"transformers.integrations.CodeCarbonCallback",parameters:[],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/integrations/integration_utils.py#L1538"}}),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>, 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/vr_31161/src/transformers/integrations/integration_utils.py#L1265"}}),Qe=new C({props:{name:"class transformers.integrations.ClearMLCallback",anchor:"transformers.integrations.ClearMLCallback",parameters:[],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/integrations/integration_utils.py#L1572"}}),Ke=new C({props:{name:"class transformers.integrations.DagsHubCallback",anchor:"transformers.integrations.DagsHubCallback",parameters:[],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/integrations/integration_utils.py#L1205"}}),et=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_31161/src/transformers/integrations/integration_utils.py#L1219"}}),tt=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_31161/src/transformers/integrations/integration_utils.py#L1825"}}),se=new Yo({props:{anchor:"transformers.integrations.FlyteCallback.example",$$slots:{default:[fl]},$$scope:{ctx:j}}}),rt=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_31161/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/pr_31161/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/vr_31161/src/transformers/integrations/integration_utils.py#L1888"}}),at=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_31161/src/transformers/integrations/integration_utils.py#L1929"}}),nt=new qr({props:{title:"TrainerCallback",local:"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_31161/ja/main_classes/trainer#transformers.TrainingArguments">TrainingArguments</a>) &#x2014;
The training arguments used to instantiate the <a href="/docs/transformers/pr_31161/ja/main_classes/trainer#transformers.Trainer">Trainer</a>.`,name:"args"},{anchor:"transformers.TrainerCallback.state",description:`<strong>state</strong> (<a href="/docs/transformers/pr_31161/ja/main_classes/callback#transformers.TrainerState">TrainerState</a>) &#x2014;
The current state of the <a href="/docs/transformers/pr_31161/ja/main_classes/trainer#transformers.Trainer">Trainer</a>.`,name:"state"},{anchor:"transformers.TrainerCallback.control",description:`<strong>control</strong> (<a href="/docs/transformers/pr_31161/ja/main_classes/callback#transformers.TrainerControl">TrainerControl</a>) &#x2014;
The object that is returned to the <a href="/docs/transformers/pr_31161/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/pr_31161/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/pr_31161/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/vr_31161/src/transformers/trainer_callback.py#L260"}}),oe=new Yo({props:{anchor:"transformers.TrainerCallback.example",$$slots:{default:[gl]},$$scope:{ctx:j}}}),ot=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_31161/src/transformers/trainer_callback.py#L329"}}),lt=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_31161/src/transformers/trainer_callback.py#L335"}}),it=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_31161/src/transformers/trainer_callback.py#L367"}}),ct=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_31161/src/transformers/trainer_callback.py#L311"}}),mt=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_31161/src/transformers/trainer_callback.py#L385"}}),dt=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_31161/src/transformers/trainer_callback.py#L348"}}),pt=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_31161/src/transformers/trainer_callback.py#L373"}}),ft=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_31161/src/transformers/trainer_callback.py#L391"}}),gt=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_31161/src/transformers/trainer_callback.py#L379"}}),ht=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_31161/src/transformers/trainer_callback.py#L341"}}),ut=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_31161/src/transformers/trainer_callback.py#L360"}}),bt=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_31161/src/transformers/trainer_callback.py#L354"}}),_t=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_31161/src/transformers/trainer_callback.py#L317"}}),vt=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_31161/src/transformers/trainer_callback.py#L323"}}),$t=new Xa({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}}),kt=new Xa({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}}),wt=new qr({props:{title:"TrainerState",local:"transformers.TrainerState",headingTag:"h2"}}),yt=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/vr_31161/src/transformers/trainer_callback.py#L36"}}),$e=new Ko({props:{$$slots:{default:[hl]},$$scope:{ctx:j}}}),Mt=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_31161/src/transformers/trainer_callback.py#L146"}}),xt=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_31161/src/transformers/trainer_callback.py#L140"}}),Lt=new qr({props:{title:"TrainerControl",local:"transformers.TrainerControl",headingTag:"h2"}}),jt=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_31161/src/transformers/trainer_callback.py#L198"}}),It=new ll({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"),w=r(),f(y.$$.fragment),v=r(),M=s("p"),M.innerHTML=L,I=r(),Ie=s("p"),Ie.innerHTML=Es,Or=r(),Ee=s("p"),Ee.innerHTML=Hs,Yr=r(),He=s("ul"),He.innerHTML=Js,Xr=r(),Je=s("p"),Je.innerHTML=Ds,Zr=r(),De=s("p"),De.innerHTML=Us,Qr=r(),f(Ue.$$.fragment),Kr=r(),Ae=s("p"),Ae.innerHTML=As,ea=r(),P=s("div"),f(Ne.$$.fragment),Qa=r(),Jt=s("p"),Jt.innerHTML=Ns,Ka=r(),H=s("div"),f(Se.$$.fragment),en=r(),Dt=s("p"),Dt.textContent=Ss,tn=r(),Ut=s("p"),Ut.textContent=Fs,rn=r(),At=s("ul"),At.innerHTML=Ps,an=r(),Nt=s("p"),Nt.innerHTML=Ws,ta=r(),X=s("div"),f(Fe.$$.fragment),nn=r(),St=s("p"),St.innerHTML=zs,ra=r(),Z=s("div"),f(Pe.$$.fragment),sn=r(),Ft=s("p"),Ft.innerHTML=Bs,aa=r(),Q=s("div"),f(We.$$.fragment),on=r(),Pt=s("p"),Pt.innerHTML=Rs,na=r(),W=s("div"),f(ze.$$.fragment),ln=r(),Wt=s("p"),Wt.innerHTML=Vs,cn=r(),zt=s("p"),zt.innerHTML=Gs,sa=r(),K=s("div"),f(Be.$$.fragment),mn=r(),Bt=s("p"),Bt.innerHTML=qs,oa=r(),z=s("div"),f(Re.$$.fragment),dn=r(),Rt=s("p"),Rt.innerHTML=Os,pn=r(),J=s("div"),f(Ve.$$.fragment),fn=r(),Vt=s("p"),Vt.innerHTML=Ys,gn=r(),Gt=s("p"),Gt.innerHTML=Xs,hn=r(),qt=s("p"),qt.textContent=Zs,un=r(),B=s("ul"),Ge=s("li"),Ot=s("p"),Ot.innerHTML=Qs,bn=r(),f(ne.$$.fragment),_n=r(),Yt=s("li"),Yt.innerHTML=Ks,vn=r(),Xt=s("li"),Xt.innerHTML=eo,Tn=r(),Zt=s("li"),Zt.innerHTML=to,la=r(),R=s("div"),f(qe.$$.fragment),$n=r(),Qt=s("p"),Qt.innerHTML=ro,Cn=r(),N=s("div"),f(Oe.$$.fragment),kn=r(),Kt=s("p"),Kt.textContent=ao,wn=r(),er=s("p"),er.textContent=no,yn=r(),tr=s("ul"),tr.innerHTML=so,ia=r(),ee=s("div"),f(Ye.$$.fragment),Mn=r(),rr=s("p"),rr.innerHTML=oo,ca=r(),te=s("div"),f(Xe.$$.fragment),xn=r(),ar=s("p"),ar.innerHTML=lo,ma=r(),V=s("div"),f(Ze.$$.fragment),Ln=r(),nr=s("p"),nr.innerHTML=io,jn=r(),sr=s("p"),sr.innerHTML=co,da=r(),D=s("div"),f(Qe.$$.fragment),In=r(),or=s("p"),or.innerHTML=mo,En=r(),lr=s("p"),lr.textContent=po,Hn=r(),ir=s("ul"),ir.innerHTML=fo,pa=r(),G=s("div"),f(Ke.$$.fragment),Jn=r(),cr=s("p"),cr.innerHTML=go,Dn=r(),S=s("div"),f(et.$$.fragment),Un=r(),mr=s("p"),mr.textContent=ho,An=r(),dr=s("p"),dr.textContent=uo,Nn=r(),pr=s("ul"),pr.innerHTML=bo,fa=r(),q=s("div"),f(tt.$$.fragment),Sn=r(),fr=s("p"),fr.innerHTML=_o,Fn=r(),f(se.$$.fragment),ga=r(),U=s("div"),f(rt.$$.fragment),Pn=r(),gr=s("p"),gr.innerHTML=vo,Wn=r(),hr=s("p"),hr.innerHTML=To,zn=r(),F=s("div"),f(at.$$.fragment),Bn=r(),ur=s("p"),ur.innerHTML=$o,Rn=r(),br=s("p"),br.textContent=Co,Vn=r(),_r=s("ul"),_r.innerHTML=ko,ha=r(),f(nt.$$.fragment),ua=r(),T=s("div"),f(st.$$.fragment),Gn=r(),vr=s("p"),vr.textContent=wo,qn=r(),Tr=s("p"),Tr.innerHTML=yo,On=r(),$r=s("p"),$r.innerHTML=Mo,Yn=r(),f(oe.$$.fragment),Xn=r(),le=s("div"),f(ot.$$.fragment),Zn=r(),Cr=s("p"),Cr.textContent=xo,Qn=r(),ie=s("div"),f(lt.$$.fragment),Kn=r(),kr=s("p"),kr.textContent=Lo,es=r(),ce=s("div"),f(it.$$.fragment),ts=r(),wr=s("p"),wr.textContent=jo,rs=r(),me=s("div"),f(ct.$$.fragment),as=r(),yr=s("p"),yr.innerHTML=Io,ns=r(),de=s("div"),f(mt.$$.fragment),ss=r(),Mr=s("p"),Mr.textContent=Eo,os=r(),pe=s("div"),f(dt.$$.fragment),ls=r(),xr=s("p"),xr.textContent=Ho,is=r(),fe=s("div"),f(pt.$$.fragment),cs=r(),Lr=s("p"),Lr.textContent=Jo,ms=r(),ge=s("div"),f(ft.$$.fragment),ds=r(),jr=s("p"),jr.textContent=Do,ps=r(),he=s("div"),f(gt.$$.fragment),fs=r(),Ir=s("p"),Ir.textContent=Uo,gs=r(),ue=s("div"),f(ht.$$.fragment),hs=r(),Er=s("p"),Er.textContent=Ao,us=r(),be=s("div"),f(ut.$$.fragment),bs=r(),Hr=s("p"),Hr.textContent=No,_s=r(),_e=s("div"),f(bt.$$.fragment),vs=r(),Jr=s("p"),Jr.textContent=So,Ts=r(),ve=s("div"),f(_t.$$.fragment),$s=r(),Dr=s("p"),Dr.textContent=Fo,Cs=r(),Te=s("div"),f(vt.$$.fragment),ks=r(),Ur=s("p"),Ur.textContent=Po,ba=r(),Tt=s("p"),Tt.innerHTML=Wo,_a=r(),f($t.$$.fragment),va=r(),Ct=s("p"),Ct.innerHTML=zo,Ta=r(),f(kt.$$.fragment),$a=r(),f(wt.$$.fragment),Ca=r(),E=s("div"),f(yt.$$.fragment),ws=r(),Ar=s("p"),Ar.innerHTML=Bo,ys=r(),f($e.$$.fragment),Ms=r(),Ce=s("div"),f(Mt.$$.fragment),xs=r(),Nr=s("p"),Nr.innerHTML=Ro,Ls=r(),ke=s("div"),f(xt.$$.fragment),js=r(),Sr=s("p"),Sr.innerHTML=Vo,ka=r(),f(Lt.$$.fragment),wa=r(),re=s("div"),f(jt.$$.fragment),Is=r(),Fr=s("p"),Fr.innerHTML=Go,ya=r(),f(It.$$.fragment),Ma=r(),Gr=s("p"),this.h()},l(e){const l=ol("svelte-u9bgzb",document.head);m=o(l,"META",{name:!0,content:!0}),l.forEach(n),x=a(e),b=o(e,"P",{}),_(b).forEach(n),w=a(e),g(y.$$.fragment,e),v=a(e),M=o(e,"P",{"data-svelte-h":!0}),i(M)!=="svelte-1g8myoi"&&(M.innerHTML=L),I=a(e),Ie=o(e,"P",{"data-svelte-h":!0}),i(Ie)!=="svelte-1igix0v"&&(Ie.innerHTML=Es),Or=a(e),Ee=o(e,"P",{"data-svelte-h":!0}),i(Ee)!=="svelte-1slrj6p"&&(Ee.innerHTML=Hs),Yr=a(e),He=o(e,"UL",{"data-svelte-h":!0}),i(He)!=="svelte-1on09vu"&&(He.innerHTML=Js),Xr=a(e),Je=o(e,"P",{"data-svelte-h":!0}),i(Je)!=="svelte-1b834xs"&&(Je.innerHTML=Ds),Zr=a(e),De=o(e,"P",{"data-svelte-h":!0}),i(De)!=="svelte-1pboaft"&&(De.innerHTML=Us),Qr=a(e),g(Ue.$$.fragment,e),Kr=a(e),Ae=o(e,"P",{"data-svelte-h":!0}),i(Ae)!=="svelte-1ajn2r7"&&(Ae.innerHTML=As),ea=a(e),P=o(e,"DIV",{class:!0});var ae=_(P);g(Ne.$$.fragment,ae),Qa=a(ae),Jt=o(ae,"P",{"data-svelte-h":!0}),i(Jt)!=="svelte-g95jeg"&&(Jt.innerHTML=Ns),Ka=a(ae),H=o(ae,"DIV",{class:!0});var A=_(H);g(Se.$$.fragment,A),en=a(A),Dt=o(A,"P",{"data-svelte-h":!0}),i(Dt)!=="svelte-1bfkm9x"&&(Dt.textContent=Ss),tn=a(A),Ut=o(A,"P",{"data-svelte-h":!0}),i(Ut)!=="svelte-1fkshtn"&&(Ut.textContent=Fs),rn=a(A),At=o(A,"UL",{"data-svelte-h":!0}),i(At)!=="svelte-12drq5x"&&(At.innerHTML=Ps),an=a(A),Nt=o(A,"P",{"data-svelte-h":!0}),i(Nt)!=="svelte-1faq0a7"&&(Nt.innerHTML=Ws),A.forEach(n),ae.forEach(n),ta=a(e),X=o(e,"DIV",{class:!0});var Et=_(X);g(Fe.$$.fragment,Et),nn=a(Et),St=o(Et,"P",{"data-svelte-h":!0}),i(St)!=="svelte-k0cpcr"&&(St.innerHTML=zs),Et.forEach(n),ra=a(e),Z=o(e,"DIV",{class:!0});var Ht=_(Z);g(Pe.$$.fragment,Ht),sn=a(Ht),Ft=o(Ht,"P",{"data-svelte-h":!0}),i(Ft)!=="svelte-1rbaedc"&&(Ft.innerHTML=Bs),Ht.forEach(n),aa=a(e),Q=o(e,"DIV",{class:!0});var La=_(Q);g(We.$$.fragment,La),on=a(La),Pt=o(La,"P",{"data-svelte-h":!0}),i(Pt)!=="svelte-1x9ywh5"&&(Pt.innerHTML=Rs),La.forEach(n),na=a(e),W=o(e,"DIV",{class:!0});var Pr=_(W);g(ze.$$.fragment,Pr),ln=a(Pr),Wt=o(Pr,"P",{"data-svelte-h":!0}),i(Wt)!=="svelte-1dje0e"&&(Wt.innerHTML=Vs),cn=a(Pr),zt=o(Pr,"P",{"data-svelte-h":!0}),i(zt)!=="svelte-ru7s66"&&(zt.innerHTML=Gs),Pr.forEach(n),sa=a(e),K=o(e,"DIV",{class:!0});var ja=_(K);g(Be.$$.fragment,ja),mn=a(ja),Bt=o(ja,"P",{"data-svelte-h":!0}),i(Bt)!=="svelte-1vclzy5"&&(Bt.innerHTML=qs),ja.forEach(n),oa=a(e),z=o(e,"DIV",{class:!0});var Wr=_(z);g(Re.$$.fragment,Wr),dn=a(Wr),Rt=o(Wr,"P",{"data-svelte-h":!0}),i(Rt)!=="svelte-1fsyja2"&&(Rt.innerHTML=Os),pn=a(Wr),J=o(Wr,"DIV",{class:!0});var O=_(J);g(Ve.$$.fragment,O),fn=a(O),Vt=o(O,"P",{"data-svelte-h":!0}),i(Vt)!=="svelte-op70zs"&&(Vt.innerHTML=Ys),gn=a(O),Gt=o(O,"P",{"data-svelte-h":!0}),i(Gt)!=="svelte-m2rt7w"&&(Gt.innerHTML=Xs),hn=a(O),qt=o(O,"P",{"data-svelte-h":!0}),i(qt)!=="svelte-1fkshtn"&&(qt.textContent=Zs),un=a(O),B=o(O,"UL",{});var we=_(B);Ge=o(we,"LI",{});var Ia=_(Ge);Ot=o(Ia,"P",{"data-svelte-h":!0}),i(Ot)!=="svelte-py3r3s"&&(Ot.innerHTML=Qs),bn=a(Ia),g(ne.$$.fragment,Ia),Ia.forEach(n),_n=a(we),Yt=o(we,"LI",{"data-svelte-h":!0}),i(Yt)!=="svelte-dx3m30"&&(Yt.innerHTML=Ks),vn=a(we),Xt=o(we,"LI",{"data-svelte-h":!0}),i(Xt)!=="svelte-n1uh6c"&&(Xt.innerHTML=eo),Tn=a(we),Zt=o(we,"LI",{"data-svelte-h":!0}),i(Zt)!=="svelte-ybo66z"&&(Zt.innerHTML=to),we.forEach(n),O.forEach(n),Wr.forEach(n),la=a(e),R=o(e,"DIV",{class:!0});var zr=_(R);g(qe.$$.fragment,zr),$n=a(zr),Qt=o(zr,"P",{"data-svelte-h":!0}),i(Qt)!=="svelte-vwpccq"&&(Qt.innerHTML=ro),Cn=a(zr),N=o(zr,"DIV",{class:!0});var ye=_(N);g(Oe.$$.fragment,ye),kn=a(ye),Kt=o(ye,"P",{"data-svelte-h":!0}),i(Kt)!=="svelte-nmg16f"&&(Kt.textContent=ao),wn=a(ye),er=o(ye,"P",{"data-svelte-h":!0}),i(er)!=="svelte-1fkshtn"&&(er.textContent=no),yn=a(ye),tr=o(ye,"UL",{"data-svelte-h":!0}),i(tr)!=="svelte-wy5a6y"&&(tr.innerHTML=so),ye.forEach(n),zr.forEach(n),ia=a(e),ee=o(e,"DIV",{class:!0});var Ea=_(ee);g(Ye.$$.fragment,Ea),Mn=a(Ea),rr=o(Ea,"P",{"data-svelte-h":!0}),i(rr)!=="svelte-14xd9ph"&&(rr.innerHTML=oo),Ea.forEach(n),ca=a(e),te=o(e,"DIV",{class:!0});var Ha=_(te);g(Xe.$$.fragment,Ha),xn=a(Ha),ar=o(Ha,"P",{"data-svelte-h":!0}),i(ar)!=="svelte-hz3plt"&&(ar.innerHTML=lo),Ha.forEach(n),ma=a(e),V=o(e,"DIV",{class:!0});var Br=_(V);g(Ze.$$.fragment,Br),Ln=a(Br),nr=o(Br,"P",{"data-svelte-h":!0}),i(nr)!=="svelte-4ag9j6"&&(nr.innerHTML=io),jn=a(Br),sr=o(Br,"P",{"data-svelte-h":!0}),i(sr)!=="svelte-1mhjcx0"&&(sr.innerHTML=co),Br.forEach(n),da=a(e),D=o(e,"DIV",{class:!0});var Me=_(D);g(Qe.$$.fragment,Me),In=a(Me),or=o(Me,"P",{"data-svelte-h":!0}),i(or)!=="svelte-eprvcz"&&(or.innerHTML=mo),En=a(Me),lr=o(Me,"P",{"data-svelte-h":!0}),i(lr)!=="svelte-1fkshtn"&&(lr.textContent=po),Hn=a(Me),ir=o(Me,"UL",{"data-svelte-h":!0}),i(ir)!=="svelte-15svthu"&&(ir.innerHTML=fo),Me.forEach(n),pa=a(e),G=o(e,"DIV",{class:!0});var Rr=_(G);g(Ke.$$.fragment,Rr),Jn=a(Rr),cr=o(Rr,"P",{"data-svelte-h":!0}),i(cr)!=="svelte-lw7dou"&&(cr.innerHTML=go),Dn=a(Rr),S=o(Rr,"DIV",{class:!0});var xe=_(S);g(et.$$.fragment,xe),Un=a(xe),mr=o(xe,"P",{"data-svelte-h":!0}),i(mr)!=="svelte-1wbmj3"&&(mr.textContent=ho),An=a(xe),dr=o(xe,"P",{"data-svelte-h":!0}),i(dr)!=="svelte-1fkshtn"&&(dr.textContent=uo),Nn=a(xe),pr=o(xe,"UL",{"data-svelte-h":!0}),i(pr)!=="svelte-dna85o"&&(pr.innerHTML=bo),xe.forEach(n),Rr.forEach(n),fa=a(e),q=o(e,"DIV",{class:!0});var Vr=_(q);g(tt.$$.fragment,Vr),Sn=a(Vr),fr=o(Vr,"P",{"data-svelte-h":!0}),i(fr)!=="svelte-16gduux"&&(fr.innerHTML=_o),Fn=a(Vr),g(se.$$.fragment,Vr),Vr.forEach(n),ga=a(e),U=o(e,"DIV",{class:!0});var Le=_(U);g(rt.$$.fragment,Le),Pn=a(Le),gr=o(Le,"P",{"data-svelte-h":!0}),i(gr)!=="svelte-1vdggz0"&&(gr.innerHTML=vo),Wn=a(Le),hr=o(Le,"P",{"data-svelte-h":!0}),i(hr)!=="svelte-eu3kj7"&&(hr.innerHTML=To),zn=a(Le),F=o(Le,"DIV",{class:!0});var je=_(F);g(at.$$.fragment,je),Bn=a(je),ur=o(je,"P",{"data-svelte-h":!0}),i(ur)!=="svelte-jjkenj"&&(ur.innerHTML=$o),Rn=a(je),br=o(je,"P",{"data-svelte-h":!0}),i(br)!=="svelte-1fkshtn"&&(br.textContent=Co),Vn=a(je),_r=o(je,"UL",{"data-svelte-h":!0}),i(_r)!=="svelte-q7cr6m"&&(_r.innerHTML=ko),je.forEach(n),Le.forEach(n),ha=a(e),g(nt.$$.fragment,e),ua=a(e),T=o(e,"DIV",{class:!0});var k=_(T);g(st.$$.fragment,k),Gn=a(k),vr=o(k,"P",{"data-svelte-h":!0}),i(vr)!=="svelte-14xg00o"&&(vr.textContent=wo),qn=a(k),Tr=o(k,"P",{"data-svelte-h":!0}),i(Tr)!=="svelte-1xprdvt"&&(Tr.innerHTML=yo),On=a(k),$r=o(k,"P",{"data-svelte-h":!0}),i($r)!=="svelte-i1jt7d"&&($r.innerHTML=Mo),Yn=a(k),g(oe.$$.fragment,k),Xn=a(k),le=o(k,"DIV",{class:!0});var Ja=_(le);g(ot.$$.fragment,Ja),Zn=a(Ja),Cr=o(Ja,"P",{"data-svelte-h":!0}),i(Cr)!=="svelte-106889p"&&(Cr.textContent=xo),Ja.forEach(n),Qn=a(k),ie=o(k,"DIV",{class:!0});var Da=_(ie);g(lt.$$.fragment,Da),Kn=a(Da),kr=o(Da,"P",{"data-svelte-h":!0}),i(kr)!=="svelte-oshcpj"&&(kr.textContent=Lo),Da.forEach(n),es=a(k),ce=o(k,"DIV",{class:!0});var Ua=_(ce);g(it.$$.fragment,Ua),ts=a(Ua),wr=o(Ua,"P",{"data-svelte-h":!0}),i(wr)!=="svelte-1o0xh73"&&(wr.textContent=jo),Ua.forEach(n),rs=a(k),me=o(k,"DIV",{class:!0});var Aa=_(me);g(ct.$$.fragment,Aa),as=a(Aa),yr=o(Aa,"P",{"data-svelte-h":!0}),i(yr)!=="svelte-35coom"&&(yr.innerHTML=Io),Aa.forEach(n),ns=a(k),de=o(k,"DIV",{class:!0});var Na=_(de);g(mt.$$.fragment,Na),ss=a(Na),Mr=o(Na,"P",{"data-svelte-h":!0}),i(Mr)!=="svelte-10eiwg0"&&(Mr.textContent=Eo),Na.forEach(n),os=a(k),pe=o(k,"DIV",{class:!0});var Sa=_(pe);g(dt.$$.fragment,Sa),ls=a(Sa),xr=o(Sa,"P",{"data-svelte-h":!0}),i(xr)!=="svelte-1mpbj2z"&&(xr.textContent=Ho),Sa.forEach(n),is=a(k),fe=o(k,"DIV",{class:!0});var Fa=_(fe);g(pt.$$.fragment,Fa),cs=a(Fa),Lr=o(Fa,"P",{"data-svelte-h":!0}),i(Lr)!=="svelte-1df7x4n"&&(Lr.textContent=Jo),Fa.forEach(n),ms=a(k),ge=o(k,"DIV",{class:!0});var Pa=_(ge);g(ft.$$.fragment,Pa),ds=a(Pa),jr=o(Pa,"P",{"data-svelte-h":!0}),i(jr)!=="svelte-18swygp"&&(jr.textContent=Do),Pa.forEach(n),ps=a(k),he=o(k,"DIV",{class:!0});var Wa=_(he);g(gt.$$.fragment,Wa),fs=a(Wa),Ir=o(Wa,"P",{"data-svelte-h":!0}),i(Ir)!=="svelte-19xp05v"&&(Ir.textContent=Uo),Wa.forEach(n),gs=a(k),ue=o(k,"DIV",{class:!0});var za=_(ue);g(ht.$$.fragment,za),hs=a(za),Er=o(za,"P",{"data-svelte-h":!0}),i(Er)!=="svelte-7af61p"&&(Er.textContent=Ao),za.forEach(n),us=a(k),be=o(k,"DIV",{class:!0});var Ba=_(be);g(ut.$$.fragment,Ba),bs=a(Ba),Hr=o(Ba,"P",{"data-svelte-h":!0}),i(Hr)!=="svelte-8cdxjr"&&(Hr.textContent=No),Ba.forEach(n),_s=a(k),_e=o(k,"DIV",{class:!0});var Ra=_(_e);g(bt.$$.fragment,Ra),vs=a(Ra),Jr=o(Ra,"P",{"data-svelte-h":!0}),i(Jr)!=="svelte-sluvs0"&&(Jr.textContent=So),Ra.forEach(n),Ts=a(k),ve=o(k,"DIV",{class:!0});var Va=_(ve);g(_t.$$.fragment,Va),$s=a(Va),Dr=o(Va,"P",{"data-svelte-h":!0}),i(Dr)!=="svelte-6bvy6d"&&(Dr.textContent=Fo),Va.forEach(n),Cs=a(k),Te=o(k,"DIV",{class:!0});var Ga=_(Te);g(vt.$$.fragment,Ga),ks=a(Ga),Ur=o(Ga,"P",{"data-svelte-h":!0}),i(Ur)!=="svelte-zzwxsv"&&(Ur.textContent=Po),Ga.forEach(n),k.forEach(n),ba=a(e),Tt=o(e,"P",{"data-svelte-h":!0}),i(Tt)!=="svelte-10sewo9"&&(Tt.innerHTML=Wo),_a=a(e),g($t.$$.fragment,e),va=a(e),Ct=o(e,"P",{"data-svelte-h":!0}),i(Ct)!=="svelte-183oh1l"&&(Ct.innerHTML=zo),Ta=a(e),g(kt.$$.fragment,e),$a=a(e),g(wt.$$.fragment,e),Ca=a(e),E=o(e,"DIV",{class:!0});var Y=_(E);g(yt.$$.fragment,Y),ws=a(Y),Ar=o(Y,"P",{"data-svelte-h":!0}),i(Ar)!=="svelte-a5n1mi"&&(Ar.innerHTML=Bo),ys=a(Y),g($e.$$.fragment,Y),Ms=a(Y),Ce=o(Y,"DIV",{class:!0});var qa=_(Ce);g(Mt.$$.fragment,qa),xs=a(qa),Nr=o(qa,"P",{"data-svelte-h":!0}),i(Nr)!=="svelte-hbs6ga"&&(Nr.innerHTML=Ro),qa.forEach(n),Ls=a(Y),ke=o(Y,"DIV",{class:!0});var Oa=_(ke);g(xt.$$.fragment,Oa),js=a(Oa),Sr=o(Oa,"P",{"data-svelte-h":!0}),i(Sr)!=="svelte-dkslae"&&(Sr.innerHTML=Vo),Oa.forEach(n),Y.forEach(n),ka=a(e),g(Lt.$$.fragment,e),wa=a(e),re=o(e,"DIV",{class:!0});var Ya=_(re);g(jt.$$.fragment,Ya),Is=a(Ya),Fr=o(Ya,"P",{"data-svelte-h":!0}),i(Fr)!=="svelte-9p8qn2"&&(Fr.innerHTML=Go),Ya.forEach(n),ya=a(e),g(It.$$.fragment,e),Ma=a(e),Gr=o(e,"P",{}),_(Gr).forEach(n),this.h()},h(){$(m,"name","hf:doc:metadata"),$(m,"content",bl),$(H,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),$(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"),$(G,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),$(q,"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"),$(de,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),$(pe,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),$(fe,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),$(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"),$(ve,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),$(Te,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),$(T,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),$(Ce,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),$(ke,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),$(E,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),$(re,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8")},m(e,l){t(document.head,m),c(e,x,l),c(e,b,l),c(e,w,l),h(y,e,l),c(e,v,l),c(e,M,l),c(e,I,l),c(e,Ie,l),c(e,Or,l),c(e,Ee,l),c(e,Yr,l),c(e,He,l),c(e,Xr,l),c(e,Je,l),c(e,Zr,l),c(e,De,l),c(e,Qr,l),h(Ue,e,l),c(e,Kr,l),c(e,Ae,l),c(e,ea,l),c(e,P,l),h(Ne,P,null),t(P,Qa),t(P,Jt),t(P,Ka),t(P,H),h(Se,H,null),t(H,en),t(H,Dt),t(H,tn),t(H,Ut),t(H,rn),t(H,At),t(H,an),t(H,Nt),c(e,ta,l),c(e,X,l),h(Fe,X,null),t(X,nn),t(X,St),c(e,ra,l),c(e,Z,l),h(Pe,Z,null),t(Z,sn),t(Z,Ft),c(e,aa,l),c(e,Q,l),h(We,Q,null),t(Q,on),t(Q,Pt),c(e,na,l),c(e,W,l),h(ze,W,null),t(W,ln),t(W,Wt),t(W,cn),t(W,zt),c(e,sa,l),c(e,K,l),h(Be,K,null),t(K,mn),t(K,Bt),c(e,oa,l),c(e,z,l),h(Re,z,null),t(z,dn),t(z,Rt),t(z,pn),t(z,J),h(Ve,J,null),t(J,fn),t(J,Vt),t(J,gn),t(J,Gt),t(J,hn),t(J,qt),t(J,un),t(J,B),t(B,Ge),t(Ge,Ot),t(Ge,bn),h(ne,Ge,null),t(B,_n),t(B,Yt),t(B,vn),t(B,Xt),t(B,Tn),t(B,Zt),c(e,la,l),c(e,R,l),h(qe,R,null),t(R,$n),t(R,Qt),t(R,Cn),t(R,N),h(Oe,N,null),t(N,kn),t(N,Kt),t(N,wn),t(N,er),t(N,yn),t(N,tr),c(e,ia,l),c(e,ee,l),h(Ye,ee,null),t(ee,Mn),t(ee,rr),c(e,ca,l),c(e,te,l),h(Xe,te,null),t(te,xn),t(te,ar),c(e,ma,l),c(e,V,l),h(Ze,V,null),t(V,Ln),t(V,nr),t(V,jn),t(V,sr),c(e,da,l),c(e,D,l),h(Qe,D,null),t(D,In),t(D,or),t(D,En),t(D,lr),t(D,Hn),t(D,ir),c(e,pa,l),c(e,G,l),h(Ke,G,null),t(G,Jn),t(G,cr),t(G,Dn),t(G,S),h(et,S,null),t(S,Un),t(S,mr),t(S,An),t(S,dr),t(S,Nn),t(S,pr),c(e,fa,l),c(e,q,l),h(tt,q,null),t(q,Sn),t(q,fr),t(q,Fn),h(se,q,null),c(e,ga,l),c(e,U,l),h(rt,U,null),t(U,Pn),t(U,gr),t(U,Wn),t(U,hr),t(U,zn),t(U,F),h(at,F,null),t(F,Bn),t(F,ur),t(F,Rn),t(F,br),t(F,Vn),t(F,_r),c(e,ha,l),h(nt,e,l),c(e,ua,l),c(e,T,l),h(st,T,null),t(T,Gn),t(T,vr),t(T,qn),t(T,Tr),t(T,On),t(T,$r),t(T,Yn),h(oe,T,null),t(T,Xn),t(T,le),h(ot,le,null),t(le,Zn),t(le,Cr),t(T,Qn),t(T,ie),h(lt,ie,null),t(ie,Kn),t(ie,kr),t(T,es),t(T,ce),h(it,ce,null),t(ce,ts),t(ce,wr),t(T,rs),t(T,me),h(ct,me,null),t(me,as),t(me,yr),t(T,ns),t(T,de),h(mt,de,null),t(de,ss),t(de,Mr),t(T,os),t(T,pe),h(dt,pe,null),t(pe,ls),t(pe,xr),t(T,is),t(T,fe),h(pt,fe,null),t(fe,cs),t(fe,Lr),t(T,ms),t(T,ge),h(ft,ge,null),t(ge,ds),t(ge,jr),t(T,ps),t(T,he),h(gt,he,null),t(he,fs),t(he,Ir),t(T,gs),t(T,ue),h(ht,ue,null),t(ue,hs),t(ue,Er),t(T,us),t(T,be),h(ut,be,null),t(be,bs),t(be,Hr),t(T,_s),t(T,_e),h(bt,_e,null),t(_e,vs),t(_e,Jr),t(T,Ts),t(T,ve),h(_t,ve,null),t(ve,$s),t(ve,Dr),t(T,Cs),t(T,Te),h(vt,Te,null),t(Te,ks),t(Te,Ur),c(e,ba,l),c(e,Tt,l),c(e,_a,l),h($t,e,l),c(e,va,l),c(e,Ct,l),c(e,Ta,l),h(kt,e,l),c(e,$a,l),h(wt,e,l),c(e,Ca,l),c(e,E,l),h(yt,E,null),t(E,ws),t(E,Ar),t(E,ys),h($e,E,null),t(E,Ms),t(E,Ce),h(Mt,Ce,null),t(Ce,xs),t(Ce,Nr),t(E,Ls),t(E,ke),h(xt,ke,null),t(ke,js),t(ke,Sr),c(e,ka,l),h(Lt,e,l),c(e,wa,l),c(e,re,l),h(jt,re,null),t(re,Is),t(re,Fr),c(e,ya,l),h(It,e,l),c(e,Ma,l),c(e,Gr,l),xa=!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 Et={};l&2&&(Et.$$scope={dirty:l,ctx:e}),oe.$set(Et);const Ht={};l&2&&(Ht.$$scope={dirty:l,ctx:e}),$e.$set(Ht)},i(e){xa||(d(y.$$.fragment,e),d(Ue.$$.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(Ve.$$.fragment,e),d(ne.$$.fragment,e),d(qe.$$.fragment,e),d(Oe.$$.fragment,e),d(Ye.$$.fragment,e),d(Xe.$$.fragment,e),d(Ze.$$.fragment,e),d(Qe.$$.fragment,e),d(Ke.$$.fragment,e),d(et.$$.fragment,e),d(tt.$$.fragment,e),d(se.$$.fragment,e),d(rt.$$.fragment,e),d(at.$$.fragment,e),d(nt.$$.fragment,e),d(st.$$.fragment,e),d(oe.$$.fragment,e),d(ot.$$.fragment,e),d(lt.$$.fragment,e),d(it.$$.fragment,e),d(ct.$$.fragment,e),d(mt.$$.fragment,e),d(dt.$$.fragment,e),d(pt.$$.fragment,e),d(ft.$$.fragment,e),d(gt.$$.fragment,e),d(ht.$$.fragment,e),d(ut.$$.fragment,e),d(bt.$$.fragment,e),d(_t.$$.fragment,e),d(vt.$$.fragment,e),d($t.$$.fragment,e),d(kt.$$.fragment,e),d(wt.$$.fragment,e),d(yt.$$.fragment,e),d($e.$$.fragment,e),d(Mt.$$.fragment,e),d(xt.$$.fragment,e),d(Lt.$$.fragment,e),d(jt.$$.fragment,e),d(It.$$.fragment,e),xa=!0)},o(e){p(y.$$.fragment,e),p(Ue.$$.fragment,e),p(Ne.$$.fragment,e),p(Se.$$.fragment,e),p(Fe.$$.fragment,e),p(Pe.$$.fragment,e),p(We.$$.fragment,e),p(ze.$$.fragment,e),p(Be.$$.fragment,e),p(Re.$$.fragment,e),p(Ve.$$.fragment,e),p(ne.$$.fragment,e),p(qe.$$.fragment,e),p(Oe.$$.fragment,e),p(Ye.$$.fragment,e),p(Xe.$$.fragment,e),p(Ze.$$.fragment,e),p(Qe.$$.fragment,e),p(Ke.$$.fragment,e),p(et.$$.fragment,e),p(tt.$$.fragment,e),p(se.$$.fragment,e),p(rt.$$.fragment,e),p(at.$$.fragment,e),p(nt.$$.fragment,e),p(st.$$.fragment,e),p(oe.$$.fragment,e),p(ot.$$.fragment,e),p(lt.$$.fragment,e),p(it.$$.fragment,e),p(ct.$$.fragment,e),p(mt.$$.fragment,e),p(dt.$$.fragment,e),p(pt.$$.fragment,e),p(ft.$$.fragment,e),p(gt.$$.fragment,e),p(ht.$$.fragment,e),p(ut.$$.fragment,e),p(bt.$$.fragment,e),p(_t.$$.fragment,e),p(vt.$$.fragment,e),p($t.$$.fragment,e),p(kt.$$.fragment,e),p(wt.$$.fragment,e),p(yt.$$.fragment,e),p($e.$$.fragment,e),p(Mt.$$.fragment,e),p(xt.$$.fragment,e),p(Lt.$$.fragment,e),p(jt.$$.fragment,e),p(It.$$.fragment,e),xa=!1},d(e){e&&(n(x),n(b),n(w),n(v),n(M),n(I),n(Ie),n(Or),n(Ee),n(Yr),n(He),n(Xr),n(Je),n(Zr),n(De),n(Qr),n(Kr),n(Ae),n(ea),n(P),n(ta),n(X),n(ra),n(Z),n(aa),n(Q),n(na),n(W),n(sa),n(K),n(oa),n(z),n(la),n(R),n(ia),n(ee),n(ca),n(te),n(ma),n(V),n(da),n(D),n(pa),n(G),n(fa),n(q),n(ga),n(U),n(ha),n(ua),n(T),n(ba),n(Tt),n(_a),n(va),n(Ct),n(Ta),n($a),n(Ca),n(E),n(ka),n(wa),n(re),n(ya),n(Ma),n(Gr)),n(m),u(y,e),u(Ue,e),u(Ne),u(Se),u(Fe),u(Pe),u(We),u(ze),u(Be),u(Re),u(Ve),u(ne),u(qe),u(Oe),u(Ye),u(Xe),u(Ze),u(Qe),u(Ke),u(et),u(tt),u(se),u(rt),u(at),u(nt,e),u(st),u(oe),u(ot),u(lt),u(it),u(ct),u(mt),u(dt),u(pt),u(ft),u(gt),u(ht),u(ut),u(bt),u(_t),u(vt),u($t,e),u(kt,e),u(wt,e),u(yt),u($e),u(Mt),u(xt),u(Lt,e),u(jt),u(It,e)}}}const bl='{"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 _l(j){return nl(()=>{new URLSearchParams(window.location.search).get("fw")}),[]}class Ml extends Zo{constructor(m){super(),Qo(this,m,_l,ul,Xo,{})}}export{Ml as component};
