import{s as ea,n as ta,o as ra}from"../chunks/scheduler.56725da7.js";import{S as na,i as aa,e as a,s as r,c as u,h as oa,a as o,d as l,b as n,f,g as p,j as m,k as h,l as e,m as T,n as d,t as c,o as g,p as _}from"../chunks/index.18a26576.js";import{C as sa}from"../chunks/CopyLLMTxtMenu.a8c0e768.js";import{D as b}from"../chunks/Docstring.5e042fbb.js";import{H as gn}from"../chunks/MermaidChart.svelte_svelte_type_style_lang.c3d650b8.js";function ia(_n){let D,Nt,Tt,Ct,ee,kt,te,wt,re,fn="Training classes for AWS Trainium accelerators.",Lt,ne,zt,$,ae,rr,x,oe,nr,He,hn=`Returns the log level to be used depending on whether this process is the main process of node 0, main process
of node non-0, or a non-main process.`,ar,Ae,bn=`For the main process the log level defaults to the logging level set (<code>logging.WARNING</code> if you didn’t do
anything) unless overridden by <code>log_level</code> argument.`,or,Ve,vn=`For the replica processes the log level defaults to <code>logging.WARNING</code> unless overridden by <code>log_level_replica</code>
argument.`,sr,Se,$n="The choice between the main and replica process settings is made according to the return value of <code>should_log</code>.",ir,P,se,lr,Fe,xn="Get number of steps used for a linear warmup.",mr,I,ie,ur,Re,Tn=`Serializes this instance while replace <code>Enum</code> by their values (for JSON serialization support). It obfuscates
the token values by removing their value.`,pr,M,le,dr,je,yn="Serializes this instance to a JSON string.",cr,E,me,gr,qe,Nn="Sanitized serialization to use with TensorBoard’s hparams",Dt,ue,Pt,s,pe,_r,H,de,fr,Oe,Cn="Add a callback to the current list of <code>TrainerCallback</code>.",hr,A,ce,br,We,kn=`A helper wrapper that creates an appropriate context manager for <code>autocast</code> while feeding it the desired
arguments, depending on the situation.`,vr,V,ge,$r,Be,wn="Creates NeuronAccelerator instance and prepares model for distributed training.",xr,C,_e,Tr,Ge,Ln="Setup the optimizer.",yr,Ue,zn=`We provide a reasonable default that works well. If you want to use something else, you can pass a tuple in the
NeuronTrainer’s init through <code>optimizers</code>, or subclass and override this method in a subclass.`,Nr,k,fe,Cr,Je,Dn="Setup the optimizer and the learning rate scheduler.",kr,Ye,Pn=`We provide a reasonable default that works well. If you want to use something else, you can pass a tuple in the
NeuronTrainer’s init through <code>optimizers</code>, or subclass and override this method (or <code>create_optimizer</code> and/or
<code>create_scheduler</code>) in a subclass.`,wr,S,he,Lr,Ke,In=`Setup the scheduler. The optimizer of the trainer must have been set up either before this method is called or
passed as an argument.`,zr,y,be,Dr,Qe,Mn="Get all parameter names that weight decay will be applied to.",Pr,Xe,En="This function filters out parameters in two ways:",Ir,Ze,Hn="<li>By layer type (instances of layers specified in ALL_LAYERNORM_LAYERS)</li> <li>By parameter name patterns (containing ‘bias’, ‘layernorm’, or ‘rmsnorm’)</li>",Mr,F,ve,Er,et,An="Returns the learning rate of each parameter from self.optimizer.",Hr,R,$e,Ar,tt,Vn="Get the number of trainable parameters.",Vr,j,xe,Sr,rt,Sn="Returns the optimizer class and optimizer parameters based on the training arguments.",Fr,q,Te,Rr,nt,Fn="Returns optimizer group for a parameter if given, else returns all optimizer groups for params.",jr,O,ye,qr,at,Rn="Returns the training DataLoader with appropriate sampler and batch size.",Or,W,Ne,Wr,ot,jn=`Whether or not this process is the local (e.g., on one machine if training in a distributed fashion on several
machines) main process.`,Br,B,Ce,Gr,st,qn=`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).`,Ur,G,ke,Jr,it,On="Log training metrics to the state history and callbacks.",Yr,U,we,Kr,lt,Wn="Log training step metrics if logging is due.",Qr,J,Le,Xr,mt,Bn="Save checkpoint if saving is due.",Zr,Y,ze,en,ut,Gn=`Helper to get number of samples in a <code>~torch.utils.data.DataLoader</code> by accessing its dataset. When
dataloader.dataset does not exist or has no length, estimates as best it can`,tn,K,De,rn,pt,Un="Helper to get number of tokens in a <code>~torch.utils.data.DataLoader</code> by enumerating dataloader.",nn,w,Pe,an,dt,Jn="Remove a callback from the current list of <code>TrainerCallback</code> and returns it.",on,ct,Yn="If the callback is not found, returns <code>None</code> (and no error is raised).",sn,Q,Ie,ln,gt,Kn="Remove a callback from the current list of <code>TrainerCallback</code>.",mn,L,Me,un,_t,Qn="Calculates and returns the following values:",pn,ft,Xn="<li><code>num_train_epochs</code></li> <li><code>num_update_steps_per_epoch</code></li> <li><code>num_examples</code></li> <li><code>num_train_samples</code></li> <li><code>epoch_based</code></li> <li><code>len_dataloader</code></li> <li><code>max_steps</code></li>",dn,X,Ee,cn,ht,Zn=`Setup everything to prepare for the training loop.
This methods does not return anything but initializes many attributes of the class for training.`,It,yt,Mt;return ee=new sa({props:{containerStyle:"float: right; margin-left: 10px; display: inline-flex; position: relative; z-index: 10;"}}),te=new gn({props:{title:"NeuronTrainer",local:"neurontrainer",headingTag:"h1"}}),ne=new gn({props:{title:"NeuronTrainingArguments",local:"optimum.neuron.NeuronTrainingArguments",headingTag:"h2"}}),ae=new b({props:{name:"class optimum.neuron.NeuronTrainingArguments",anchor:"optimum.neuron.NeuronTrainingArguments",parameters:[{name:"output_dir",val:": str | None = None"},{name:"overwrite_output_dir",val:": bool = False"},{name:"do_train",val:": bool = False"},{name:"do_eval",val:": bool = False"},{name:"eval_strategy",val:": transformers.trainer_utils.IntervalStrategy | str = 'no'"},{name:"per_device_train_batch_size",val:": int = 1"},{name:"per_device_eval_batch_size",val:": int = 1"},{name:"gradient_accumulation_steps",val:": int = 1"},{name:"learning_rate",val:": float = 5e-05"},{name:"weight_decay",val:": float = 0.0"},{name:"adam_beta1",val:": float = 0.9"},{name:"adam_beta2",val:": float = 0.999"},{name:"adam_epsilon",val:": float = 1e-08"},{name:"max_grad_norm",val:": float = 1.0"},{name:"num_train_epochs",val:": float = 3.0"},{name:"max_steps",val:": int = -1"},{name:"lr_scheduler_type",val:": transformers.trainer_utils.SchedulerType | str = 'linear'"},{name:"lr_scheduler_kwargs",val:": dict[str, typing.Any] | str | None = <factory>"},{name:"warmup_ratio",val:": float = 0.0"},{name:"warmup_steps",val:": int = 0"},{name:"log_level",val:": str = 'info'"},{name:"log_level_replica",val:": str = 'silent'"},{name:"logging_dir",val:": str | None = None"},{name:"logging_strategy",val:": transformers.trainer_utils.IntervalStrategy | str = 'steps'"},{name:"logging_first_step",val:": bool = False"},{name:"logging_steps",val:": float = 500"},{name:"save_strategy",val:": transformers.trainer_utils.SaveStrategy | str = 'steps'"},{name:"save_steps",val:": float = 500"},{name:"save_total_limit",val:": int | None = None"},{name:"save_only_model",val:": bool = False"},{name:"restore_callback_states_from_checkpoint",val:": bool = False"},{name:"seed",val:": int = 42"},{name:"bf16",val:": bool = False"},{name:"dataloader_drop_last",val:": bool = False"},{name:"eval_steps",val:": float | None = None"},{name:"dataloader_num_workers",val:": int = 0"},{name:"dataloader_prefetch_factor",val:": int | None = None"},{name:"run_name",val:": str | None = None"},{name:"disable_tqdm",val:": bool | None = None"},{name:"remove_unused_columns",val:": bool | None = True"},{name:"label_names",val:": list[str] | None = None"},{name:"accelerator_config",val:": dict | str | None = None"},{name:"label_smoothing_factor",val:": float = 0.0"},{name:"optim",val:": transformers.training_args.OptimizerNames | str = 'adamw_torch'"},{name:"optim_args",val:": str | None = None"},{name:"report_to",val:": None | str | list[str] = None"},{name:"resume_from_checkpoint",val:": str | None = None"},{name:"gradient_checkpointing",val:": bool = False"},{name:"gradient_checkpointing_kwargs",val:": dict[str, typing.Any] | str | None = None"},{name:"use_liger_kernel",val:": bool | None = False"},{name:"average_tokens_across_devices",val:": bool | None = False"},{name:"dataloader_prefetch_size",val:": int = None"},{name:"skip_cache_push",val:": bool = False"},{name:"use_autocast",val:": bool = False"},{name:"zero_1",val:": bool = True"},{name:"stochastic_rounding_enabled",val:": bool = True"},{name:"optimizer_use_master_weights",val:": bool = True"},{name:"optimizer_use_fp32_grad_acc",val:": bool = True"},{name:"optimizer_save_master_weights_in_ckpt",val:": bool = False"},{name:"tensor_parallel_size",val:": int = 1"},{name:"disable_sequence_parallel",val:": bool = False"},{name:"pipeline_parallel_size",val:": int = 1"},{name:"pipeline_parallel_num_microbatches",val:": int = -1"},{name:"kv_size_multiplier",val:": int | None = None"},{name:"num_local_ranks_per_step",val:": int = 8"},{name:"use_xser",val:": bool = True"},{name:"async_save",val:": bool = False"},{name:"fuse_qkv",val:": bool = False"},{name:"recompute_causal_mask",val:": bool = True"}],source:"https://github.com/huggingface/optimum-neuron/blob/main/optimum/neuron/trainers/training_args.py#L51"}}),oe=new b({props:{name:"get_process_log_level",anchor:"optimum.neuron.NeuronTrainingArguments.get_process_log_level",parameters:[],source:"https://github.com/huggingface/optimum-neuron/blob/main/optimum/neuron/trainers/training_args.py#L726"}}),se=new b({props:{name:"get_warmup_steps",anchor:"optimum.neuron.NeuronTrainingArguments.get_warmup_steps",parameters:[{name:"num_training_steps",val:": int"}],source:"https://github.com/huggingface/optimum-neuron/blob/main/optimum/neuron/trainers/training_args.py#L748"}}),ie=new b({props:{name:"to_dict",anchor:"optimum.neuron.NeuronTrainingArguments.to_dict",parameters:[],source:"https://github.com/huggingface/optimum-neuron/blob/main/optimum/neuron/trainers/training_args.py#L768"}}),le=new b({props:{name:"to_json_string",anchor:"optimum.neuron.NeuronTrainingArguments.to_json_string",parameters:[],source:"https://github.com/huggingface/optimum-neuron/blob/main/optimum/neuron/trainers/training_args.py#L795"}}),me=new b({props:{name:"to_sanitized_dict",anchor:"optimum.neuron.NeuronTrainingArguments.to_sanitized_dict",parameters:[],source:"https://github.com/huggingface/optimum-neuron/blob/main/optimum/neuron/trainers/training_args.py#L801"}}),ue=new gn({props:{title:"NeuronTrainer",local:"optimum.neuron.NeuronTrainer",headingTag:"h2"}}),pe=new b({props:{name:"class optimum.neuron.NeuronTrainer",anchor:"optimum.neuron.NeuronTrainer",parameters:[{name:"model",val:": transformers.modeling_utils.PreTrainedModel | torch.nn.modules.module.Module"},{name:"args",val:": NeuronTrainingArguments"},{name:"data_collator",val:": typing.Optional[transformers.data.data_collator.DataCollator] = None"},{name:"train_dataset",val:": Dataset | IterableDataset | datasets.Dataset | None = None"},{name:"eval_dataset",val:": Dataset | dict[str, Dataset] | datasets.Dataset | None = None"},{name:"processing_class",val:": transformers.tokenization_utils_base.PreTrainedTokenizerBase | transformers.image_processing_utils.BaseImageProcessor | transformers.feature_extraction_utils.FeatureExtractionMixin | transformers.processing_utils.ProcessorMixin | None = None"},{name:"callbacks",val:": list[transformers.trainer_callback.TrainerCallback] | None = None"},{name:"optimizers",val:": tuple[torch.optim.optimizer.Optimizer | None, torch.optim.lr_scheduler.LambdaLR | None] = (None, None)"},{name:"optimizer_cls_and_kwargs",val:": tuple[type[torch.optim.optimizer.Optimizer], dict[str, typing.Any]] | None = None"},{name:"tokenizer",val:": transformers.tokenization_utils_base.PreTrainedTokenizerBase | None = None"}],source:"https://github.com/huggingface/optimum-neuron/blob/main/optimum/neuron/trainers/transformers.py#L114"}}),de=new b({props:{name:"add_callback",anchor:"optimum.neuron.NeuronTrainer.add_callback",parameters:[{name:"callback",val:": typing.Union[typing.Type[transformers.trainer_callback.TrainerCallback], transformers.trainer_callback.TrainerCallback]"}],parametersDescription:[{anchor:"optimum.neuron.NeuronTrainer.add_callback.callback",description:`<strong>callback</strong> (<code>Type[TrainerCallback] | TrainerCallback</code>) &#x2014;
A <code>TrainerCallback</code> class or an instance of a <code>TrainerCallback</code>. In the
first case, will instantiate a member of that class.`,name:"callback"}],source:"https://github.com/huggingface/optimum-neuron/blob/main/optimum/neuron/trainers/transformers.py#L367"}}),ce=new b({props:{name:"autocast_smart_context_manager",anchor:"optimum.neuron.NeuronTrainer.autocast_smart_context_manager",parameters:[{name:"cache_enabled",val:": bool | None = True"}],source:"https://github.com/huggingface/optimum-neuron/blob/main/optimum/neuron/trainers/transformers.py#L727"}}),ge=new b({props:{name:"create_accelerator_and_postprocess",anchor:"optimum.neuron.NeuronTrainer.create_accelerator_and_postprocess",parameters:[],source:"https://github.com/huggingface/optimum-neuron/blob/main/optimum/neuron/trainers/transformers.py#L283"}}),_e=new b({props:{name:"create_optimizer",anchor:"optimum.neuron.NeuronTrainer.create_optimizer",parameters:[],source:"https://github.com/huggingface/optimum-neuron/blob/main/optimum/neuron/trainers/transformers.py#L561"}}),fe=new b({props:{name:"create_optimizer_and_scheduler",anchor:"optimum.neuron.NeuronTrainer.create_optimizer_and_scheduler",parameters:[{name:"num_training_steps",val:": int"}],source:"https://github.com/huggingface/optimum-neuron/blob/main/optimum/neuron/trainers/transformers.py#L539"}}),he=new b({props:{name:"create_scheduler",anchor:"optimum.neuron.NeuronTrainer.create_scheduler",parameters:[{name:"num_training_steps",val:": int"},{name:"optimizer",val:": torch.optim.optimizer.Optimizer | None = None"}],parametersDescription:[{anchor:"optimum.neuron.NeuronTrainer.create_scheduler.num_training_steps",description:"<strong>num_training_steps</strong> (int) &#x2014; The number of training steps to do.",name:"num_training_steps"}],source:"https://github.com/huggingface/optimum-neuron/blob/main/optimum/neuron/trainers/transformers.py#L675"}}),be=new b({props:{name:"get_decay_parameter_names",anchor:"optimum.neuron.NeuronTrainer.get_decay_parameter_names",parameters:[{name:"model",val:""}],source:"https://github.com/huggingface/optimum-neuron/blob/main/optimum/neuron/trainers/transformers.py#L550"}}),ve=new b({props:{name:"get_learning_rates",anchor:"optimum.neuron.NeuronTrainer.get_learning_rates",parameters:[],source:"https://github.com/huggingface/optimum-neuron/blob/main/optimum/neuron/trainers/transformers.py#L618"}}),$e=new b({props:{name:"get_num_trainable_parameters",anchor:"optimum.neuron.NeuronTrainer.get_num_trainable_parameters",parameters:[],source:"https://github.com/huggingface/optimum-neuron/blob/main/optimum/neuron/trainers/transformers.py#L612"}}),xe=new b({props:{name:"get_optimizer_cls_and_kwargs",anchor:"optimum.neuron.NeuronTrainer.get_optimizer_cls_and_kwargs",parameters:[{name:"args",val:": TrainingArguments"},{name:"model",val:": transformers.modeling_utils.PreTrainedModel | None = None"}],parametersDescription:[{anchor:"optimum.neuron.NeuronTrainer.get_optimizer_cls_and_kwargs.args",description:`<strong>args</strong> (<code>transformers.training_args.TrainingArguments</code>) &#x2014;
The training arguments for the training session.`,name:"args"}],source:"https://github.com/huggingface/optimum-neuron/blob/main/optimum/neuron/trainers/transformers.py#L642"}}),Te=new b({props:{name:"get_optimizer_group",anchor:"optimum.neuron.NeuronTrainer.get_optimizer_group",parameters:[{name:"param",val:": str | torch.nn.parameter.Parameter | None = None"}],parametersDescription:[{anchor:"optimum.neuron.NeuronTrainer.get_optimizer_group.param",description:`<strong>param</strong> (<code>str | torch.nn.parameter.Parameter | None</code>, defaults to <code>None</code>) &#x2014;
The parameter for which optimizer group needs to be returned.`,name:"param"}],source:"https://github.com/huggingface/optimum-neuron/blob/main/optimum/neuron/trainers/transformers.py#L626"}}),ye=new b({props:{name:"get_train_dataloader",anchor:"optimum.neuron.NeuronTrainer.get_train_dataloader",parameters:[],source:"https://github.com/huggingface/optimum-neuron/blob/main/optimum/neuron/trainers/transformers.py#L520"}}),Ne=new b({props:{name:"is_local_process_zero",anchor:"optimum.neuron.NeuronTrainer.is_local_process_zero",parameters:[],source:"https://github.com/huggingface/optimum-neuron/blob/main/optimum/neuron/trainers/transformers.py#L1166"}}),Ce=new b({props:{name:"is_world_process_zero",anchor:"optimum.neuron.NeuronTrainer.is_world_process_zero",parameters:[],source:"https://github.com/huggingface/optimum-neuron/blob/main/optimum/neuron/trainers/transformers.py#L1173"}}),ke=new b({props:{name:"log",anchor:"optimum.neuron.NeuronTrainer.log",parameters:[{name:"logs",val:": dict[str, float]"}],source:"https://github.com/huggingface/optimum-neuron/blob/main/optimum/neuron/trainers/transformers.py#L1233"}}),we=new b({props:{name:"maybe_log_train_step_metrics",anchor:"optimum.neuron.NeuronTrainer.maybe_log_train_step_metrics",parameters:[],source:"https://github.com/huggingface/optimum-neuron/blob/main/optimum/neuron/trainers/transformers.py#L989"}}),Le=new b({props:{name:"maybe_save_checkpoint",anchor:"optimum.neuron.NeuronTrainer.maybe_save_checkpoint",parameters:[],source:"https://github.com/huggingface/optimum-neuron/blob/main/optimum/neuron/trainers/transformers.py#L1025"}}),ze=new b({props:{name:"num_examples",anchor:"optimum.neuron.NeuronTrainer.num_examples",parameters:[{name:"dataloader",val:": DataLoader"}],source:"https://github.com/huggingface/optimum-neuron/blob/main/optimum/neuron/trainers/transformers.py#L695"}}),De=new b({props:{name:"num_tokens",anchor:"optimum.neuron.NeuronTrainer.num_tokens",parameters:[{name:"train_dl",val:": DataLoader"},{name:"max_steps",val:": int | None = None"}],source:"https://github.com/huggingface/optimum-neuron/blob/main/optimum/neuron/trainers/transformers.py#L709"}}),Pe=new b({props:{name:"pop_callback",anchor:"optimum.neuron.NeuronTrainer.pop_callback",parameters:[{name:"callback",val:": typing.Union[typing.Type[transformers.trainer_callback.TrainerCallback], transformers.trainer_callback.TrainerCallback]"}],parametersDescription:[{anchor:"optimum.neuron.NeuronTrainer.pop_callback.callback",description:`<strong>callback</strong> (<code>Type[TrainerCallback] | TrainerCallback</code>) &#x2014;
A <code>TrainerCallback</code> class or an instance of a <code>TrainerCallback</code>. In the
first case, will pop the first member of that class found in the list of callbacks.`,name:"callback"}],source:"https://github.com/huggingface/optimum-neuron/blob/main/optimum/neuron/trainers/transformers.py#L378",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>The callback removed, if found.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>TrainerCallback | None</code></p>
`}}),Ie=new b({props:{name:"remove_callback",anchor:"optimum.neuron.NeuronTrainer.remove_callback",parameters:[{name:"callback",val:": typing.Union[typing.Type[transformers.trainer_callback.TrainerCallback], transformers.trainer_callback.TrainerCallback]"}],parametersDescription:[{anchor:"optimum.neuron.NeuronTrainer.remove_callback.callback",description:`<strong>callback</strong> (<code>Type[TrainerCallback] | TrainerCallback</code>) &#x2014;
A <code>TrainerCallback</code> class or an instance of a <code>TrainerCallback</code>. In the
first case, will remove the first member of that class found in the list of callbacks.`,name:"callback"}],source:"https://github.com/huggingface/optimum-neuron/blob/main/optimum/neuron/trainers/transformers.py#L394"}}),Me=new b({props:{name:"set_initial_training_values",anchor:"optimum.neuron.NeuronTrainer.set_initial_training_values",parameters:[{name:"args",val:": NeuronTrainingArguments"},{name:"dataloader",val:": DataLoader"},{name:"total_train_batch_size",val:": int"}],source:"https://github.com/huggingface/optimum-neuron/blob/main/optimum/neuron/trainers/transformers.py#L738"}}),Ee=new b({props:{name:"setup_training",anchor:"optimum.neuron.NeuronTrainer.setup_training",parameters:[{name:"train_dataloader",val:": DataLoader"},{name:"max_steps",val:": int"},{name:"num_train_epochs",val:": int"},{name:"num_examples",val:": int"},{name:"total_train_batch_size",val:": int"}],source:"https://github.com/huggingface/optimum-neuron/blob/main/optimum/neuron/trainers/transformers.py#L802"}}),{c(){D=a("meta"),Nt=r(),Tt=a("p"),Ct=r(),u(ee.$$.fragment),kt=r(),u(te.$$.fragment),wt=r(),re=a("p"),re.textContent=fn,Lt=r(),u(ne.$$.fragment),zt=r(),$=a("div"),u(ae.$$.fragment),rr=r(),x=a("div"),u(oe.$$.fragment),nr=r(),He=a("p"),He.textContent=hn,ar=r(),Ae=a("p"),Ae.innerHTML=bn,or=r(),Ve=a("p"),Ve.innerHTML=vn,sr=r(),Se=a("p"),Se.innerHTML=$n,ir=r(),P=a("div"),u(se.$$.fragment),lr=r(),Fe=a("p"),Fe.textContent=xn,mr=r(),I=a("div"),u(ie.$$.fragment),ur=r(),Re=a("p"),Re.innerHTML=Tn,pr=r(),M=a("div"),u(le.$$.fragment),dr=r(),je=a("p"),je.textContent=yn,cr=r(),E=a("div"),u(me.$$.fragment),gr=r(),qe=a("p"),qe.textContent=Nn,Dt=r(),u(ue.$$.fragment),Pt=r(),s=a("div"),u(pe.$$.fragment),_r=r(),H=a("div"),u(de.$$.fragment),fr=r(),Oe=a("p"),Oe.innerHTML=Cn,hr=r(),A=a("div"),u(ce.$$.fragment),br=r(),We=a("p"),We.innerHTML=kn,vr=r(),V=a("div"),u(ge.$$.fragment),$r=r(),Be=a("p"),Be.textContent=wn,xr=r(),C=a("div"),u(_e.$$.fragment),Tr=r(),Ge=a("p"),Ge.textContent=Ln,yr=r(),Ue=a("p"),Ue.innerHTML=zn,Nr=r(),k=a("div"),u(fe.$$.fragment),Cr=r(),Je=a("p"),Je.textContent=Dn,kr=r(),Ye=a("p"),Ye.innerHTML=Pn,wr=r(),S=a("div"),u(he.$$.fragment),Lr=r(),Ke=a("p"),Ke.textContent=In,zr=r(),y=a("div"),u(be.$$.fragment),Dr=r(),Qe=a("p"),Qe.textContent=Mn,Pr=r(),Xe=a("p"),Xe.textContent=En,Ir=r(),Ze=a("ol"),Ze.innerHTML=Hn,Mr=r(),F=a("div"),u(ve.$$.fragment),Er=r(),et=a("p"),et.textContent=An,Hr=r(),R=a("div"),u($e.$$.fragment),Ar=r(),tt=a("p"),tt.textContent=Vn,Vr=r(),j=a("div"),u(xe.$$.fragment),Sr=r(),rt=a("p"),rt.textContent=Sn,Fr=r(),q=a("div"),u(Te.$$.fragment),Rr=r(),nt=a("p"),nt.textContent=Fn,jr=r(),O=a("div"),u(ye.$$.fragment),qr=r(),at=a("p"),at.textContent=Rn,Or=r(),W=a("div"),u(Ne.$$.fragment),Wr=r(),ot=a("p"),ot.textContent=jn,Br=r(),B=a("div"),u(Ce.$$.fragment),Gr=r(),st=a("p"),st.innerHTML=qn,Ur=r(),G=a("div"),u(ke.$$.fragment),Jr=r(),it=a("p"),it.textContent=On,Yr=r(),U=a("div"),u(we.$$.fragment),Kr=r(),lt=a("p"),lt.textContent=Wn,Qr=r(),J=a("div"),u(Le.$$.fragment),Xr=r(),mt=a("p"),mt.textContent=Bn,Zr=r(),Y=a("div"),u(ze.$$.fragment),en=r(),ut=a("p"),ut.innerHTML=Gn,tn=r(),K=a("div"),u(De.$$.fragment),rn=r(),pt=a("p"),pt.innerHTML=Un,nn=r(),w=a("div"),u(Pe.$$.fragment),an=r(),dt=a("p"),dt.innerHTML=Jn,on=r(),ct=a("p"),ct.innerHTML=Yn,sn=r(),Q=a("div"),u(Ie.$$.fragment),ln=r(),gt=a("p"),gt.innerHTML=Kn,mn=r(),L=a("div"),u(Me.$$.fragment),un=r(),_t=a("p"),_t.textContent=Qn,pn=r(),ft=a("ul"),ft.innerHTML=Xn,dn=r(),X=a("div"),u(Ee.$$.fragment),cn=r(),ht=a("p"),ht.textContent=Zn,It=r(),yt=a("p"),this.h()},l(t){const v=oa("svelte-u9bgzb",document.head);D=o(v,"META",{name:!0,content:!0}),v.forEach(l),Nt=n(t),Tt=o(t,"P",{}),f(Tt).forEach(l),Ct=n(t),p(ee.$$.fragment,t),kt=n(t),p(te.$$.fragment,t),wt=n(t),re=o(t,"P",{"data-svelte-h":!0}),m(re)!=="svelte-8btytl"&&(re.textContent=fn),Lt=n(t),p(ne.$$.fragment,t),zt=n(t),$=o(t,"DIV",{class:!0});var N=f($);p(ae.$$.fragment,N),rr=n(N),x=o(N,"DIV",{class:!0});var z=f(x);p(oe.$$.fragment,z),nr=n(z),He=o(z,"P",{"data-svelte-h":!0}),m(He)!=="svelte-hj8f41"&&(He.textContent=hn),ar=n(z),Ae=o(z,"P",{"data-svelte-h":!0}),m(Ae)!=="svelte-1iq1lj0"&&(Ae.innerHTML=bn),or=n(z),Ve=o(z,"P",{"data-svelte-h":!0}),m(Ve)!=="svelte-18bal86"&&(Ve.innerHTML=vn),sr=n(z),Se=o(z,"P",{"data-svelte-h":!0}),m(Se)!=="svelte-oacj0p"&&(Se.innerHTML=$n),z.forEach(l),ir=n(N),P=o(N,"DIV",{class:!0});var Et=f(P);p(se.$$.fragment,Et),lr=n(Et),Fe=o(Et,"P",{"data-svelte-h":!0}),m(Fe)!=="svelte-9pbc1"&&(Fe.textContent=xn),Et.forEach(l),mr=n(N),I=o(N,"DIV",{class:!0});var Ht=f(I);p(ie.$$.fragment,Ht),ur=n(Ht),Re=o(Ht,"P",{"data-svelte-h":!0}),m(Re)!=="svelte-1ciob6u"&&(Re.innerHTML=Tn),Ht.forEach(l),pr=n(N),M=o(N,"DIV",{class:!0});var At=f(M);p(le.$$.fragment,At),dr=n(At),je=o(At,"P",{"data-svelte-h":!0}),m(je)!=="svelte-5ayq1f"&&(je.textContent=yn),At.forEach(l),cr=n(N),E=o(N,"DIV",{class:!0});var Vt=f(E);p(me.$$.fragment,Vt),gr=n(Vt),qe=o(Vt,"P",{"data-svelte-h":!0}),m(qe)!=="svelte-lg45ly"&&(qe.textContent=Nn),Vt.forEach(l),N.forEach(l),Dt=n(t),p(ue.$$.fragment,t),Pt=n(t),s=o(t,"DIV",{class:!0});var i=f(s);p(pe.$$.fragment,i),_r=n(i),H=o(i,"DIV",{class:!0});var St=f(H);p(de.$$.fragment,St),fr=n(St),Oe=o(St,"P",{"data-svelte-h":!0}),m(Oe)!=="svelte-erlepi"&&(Oe.innerHTML=Cn),St.forEach(l),hr=n(i),A=o(i,"DIV",{class:!0});var Ft=f(A);p(ce.$$.fragment,Ft),br=n(Ft),We=o(Ft,"P",{"data-svelte-h":!0}),m(We)!=="svelte-9kwitf"&&(We.innerHTML=kn),Ft.forEach(l),vr=n(i),V=o(i,"DIV",{class:!0});var Rt=f(V);p(ge.$$.fragment,Rt),$r=n(Rt),Be=o(Rt,"P",{"data-svelte-h":!0}),m(Be)!=="svelte-1wko3le"&&(Be.textContent=wn),Rt.forEach(l),xr=n(i),C=o(i,"DIV",{class:!0});var bt=f(C);p(_e.$$.fragment,bt),Tr=n(bt),Ge=o(bt,"P",{"data-svelte-h":!0}),m(Ge)!=="svelte-1w438iv"&&(Ge.textContent=Ln),yr=n(bt),Ue=o(bt,"P",{"data-svelte-h":!0}),m(Ue)!=="svelte-17qjzpp"&&(Ue.innerHTML=zn),bt.forEach(l),Nr=n(i),k=o(i,"DIV",{class:!0});var vt=f(k);p(fe.$$.fragment,vt),Cr=n(vt),Je=o(vt,"P",{"data-svelte-h":!0}),m(Je)!=="svelte-wdco14"&&(Je.textContent=Dn),kr=n(vt),Ye=o(vt,"P",{"data-svelte-h":!0}),m(Ye)!=="svelte-x1lyka"&&(Ye.innerHTML=Pn),vt.forEach(l),wr=n(i),S=o(i,"DIV",{class:!0});var jt=f(S);p(he.$$.fragment,jt),Lr=n(jt),Ke=o(jt,"P",{"data-svelte-h":!0}),m(Ke)!=="svelte-qi4mtl"&&(Ke.textContent=In),jt.forEach(l),zr=n(i),y=o(i,"DIV",{class:!0});var Z=f(y);p(be.$$.fragment,Z),Dr=n(Z),Qe=o(Z,"P",{"data-svelte-h":!0}),m(Qe)!=="svelte-c1jesw"&&(Qe.textContent=Mn),Pr=n(Z),Xe=o(Z,"P",{"data-svelte-h":!0}),m(Xe)!=="svelte-25ysg0"&&(Xe.textContent=En),Ir=n(Z),Ze=o(Z,"OL",{"data-svelte-h":!0}),m(Ze)!=="svelte-1jln558"&&(Ze.innerHTML=Hn),Z.forEach(l),Mr=n(i),F=o(i,"DIV",{class:!0});var qt=f(F);p(ve.$$.fragment,qt),Er=n(qt),et=o(qt,"P",{"data-svelte-h":!0}),m(et)!=="svelte-fo0w3k"&&(et.textContent=An),qt.forEach(l),Hr=n(i),R=o(i,"DIV",{class:!0});var Ot=f(R);p($e.$$.fragment,Ot),Ar=n(Ot),tt=o(Ot,"P",{"data-svelte-h":!0}),m(tt)!=="svelte-n48d75"&&(tt.textContent=Vn),Ot.forEach(l),Vr=n(i),j=o(i,"DIV",{class:!0});var Wt=f(j);p(xe.$$.fragment,Wt),Sr=n(Wt),rt=o(Wt,"P",{"data-svelte-h":!0}),m(rt)!=="svelte-1enq7q"&&(rt.textContent=Sn),Wt.forEach(l),Fr=n(i),q=o(i,"DIV",{class:!0});var Bt=f(q);p(Te.$$.fragment,Bt),Rr=n(Bt),nt=o(Bt,"P",{"data-svelte-h":!0}),m(nt)!=="svelte-4yzrvz"&&(nt.textContent=Fn),Bt.forEach(l),jr=n(i),O=o(i,"DIV",{class:!0});var Gt=f(O);p(ye.$$.fragment,Gt),qr=n(Gt),at=o(Gt,"P",{"data-svelte-h":!0}),m(at)!=="svelte-3lk00k"&&(at.textContent=Rn),Gt.forEach(l),Or=n(i),W=o(i,"DIV",{class:!0});var Ut=f(W);p(Ne.$$.fragment,Ut),Wr=n(Ut),ot=o(Ut,"P",{"data-svelte-h":!0}),m(ot)!=="svelte-16imdke"&&(ot.textContent=jn),Ut.forEach(l),Br=n(i),B=o(i,"DIV",{class:!0});var Jt=f(B);p(Ce.$$.fragment,Jt),Gr=n(Jt),st=o(Jt,"P",{"data-svelte-h":!0}),m(st)!=="svelte-1s7jdq8"&&(st.innerHTML=qn),Jt.forEach(l),Ur=n(i),G=o(i,"DIV",{class:!0});var Yt=f(G);p(ke.$$.fragment,Yt),Jr=n(Yt),it=o(Yt,"P",{"data-svelte-h":!0}),m(it)!=="svelte-1ivw6sf"&&(it.textContent=On),Yt.forEach(l),Yr=n(i),U=o(i,"DIV",{class:!0});var Kt=f(U);p(we.$$.fragment,Kt),Kr=n(Kt),lt=o(Kt,"P",{"data-svelte-h":!0}),m(lt)!=="svelte-1mzc3sd"&&(lt.textContent=Wn),Kt.forEach(l),Qr=n(i),J=o(i,"DIV",{class:!0});var Qt=f(J);p(Le.$$.fragment,Qt),Xr=n(Qt),mt=o(Qt,"P",{"data-svelte-h":!0}),m(mt)!=="svelte-5fsriy"&&(mt.textContent=Bn),Qt.forEach(l),Zr=n(i),Y=o(i,"DIV",{class:!0});var Xt=f(Y);p(ze.$$.fragment,Xt),en=n(Xt),ut=o(Xt,"P",{"data-svelte-h":!0}),m(ut)!=="svelte-19inmpu"&&(ut.innerHTML=Gn),Xt.forEach(l),tn=n(i),K=o(i,"DIV",{class:!0});var Zt=f(K);p(De.$$.fragment,Zt),rn=n(Zt),pt=o(Zt,"P",{"data-svelte-h":!0}),m(pt)!=="svelte-tkw811"&&(pt.innerHTML=Un),Zt.forEach(l),nn=n(i),w=o(i,"DIV",{class:!0});var $t=f(w);p(Pe.$$.fragment,$t),an=n($t),dt=o($t,"P",{"data-svelte-h":!0}),m(dt)!=="svelte-11dvmkl"&&(dt.innerHTML=Jn),on=n($t),ct=o($t,"P",{"data-svelte-h":!0}),m(ct)!=="svelte-1ojzwr1"&&(ct.innerHTML=Yn),$t.forEach(l),sn=n(i),Q=o(i,"DIV",{class:!0});var er=f(Q);p(Ie.$$.fragment,er),ln=n(er),gt=o(er,"P",{"data-svelte-h":!0}),m(gt)!=="svelte-1hz0n8s"&&(gt.innerHTML=Kn),er.forEach(l),mn=n(i),L=o(i,"DIV",{class:!0});var xt=f(L);p(Me.$$.fragment,xt),un=n(xt),_t=o(xt,"P",{"data-svelte-h":!0}),m(_t)!=="svelte-130oxb1"&&(_t.textContent=Qn),pn=n(xt),ft=o(xt,"UL",{"data-svelte-h":!0}),m(ft)!=="svelte-39b8ej"&&(ft.innerHTML=Xn),xt.forEach(l),dn=n(i),X=o(i,"DIV",{class:!0});var tr=f(X);p(Ee.$$.fragment,tr),cn=n(tr),ht=o(tr,"P",{"data-svelte-h":!0}),m(ht)!=="svelte-9r3hwj"&&(ht.textContent=Zn),tr.forEach(l),i.forEach(l),It=n(t),yt=o(t,"P",{}),f(yt).forEach(l),this.h()},h(){h(D,"name","hf:doc:metadata"),h(D,"content",la),h(x,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),h(P,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),h(I,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),h(M,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),h(E,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),h($,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),h(H,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),h(A,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),h(V,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),h(C,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),h(k,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),h(S,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),h(y,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),h(F,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),h(R,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),h(j,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),h(q,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),h(O,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),h(W,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),h(B,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),h(G,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),h(U,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),h(J,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),h(Y,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),h(K,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),h(w,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),h(Q,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),h(L,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),h(X,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),h(s,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8")},m(t,v){e(document.head,D),T(t,Nt,v),T(t,Tt,v),T(t,Ct,v),d(ee,t,v),T(t,kt,v),d(te,t,v),T(t,wt,v),T(t,re,v),T(t,Lt,v),d(ne,t,v),T(t,zt,v),T(t,$,v),d(ae,$,null),e($,rr),e($,x),d(oe,x,null),e(x,nr),e(x,He),e(x,ar),e(x,Ae),e(x,or),e(x,Ve),e(x,sr),e(x,Se),e($,ir),e($,P),d(se,P,null),e(P,lr),e(P,Fe),e($,mr),e($,I),d(ie,I,null),e(I,ur),e(I,Re),e($,pr),e($,M),d(le,M,null),e(M,dr),e(M,je),e($,cr),e($,E),d(me,E,null),e(E,gr),e(E,qe),T(t,Dt,v),d(ue,t,v),T(t,Pt,v),T(t,s,v),d(pe,s,null),e(s,_r),e(s,H),d(de,H,null),e(H,fr),e(H,Oe),e(s,hr),e(s,A),d(ce,A,null),e(A,br),e(A,We),e(s,vr),e(s,V),d(ge,V,null),e(V,$r),e(V,Be),e(s,xr),e(s,C),d(_e,C,null),e(C,Tr),e(C,Ge),e(C,yr),e(C,Ue),e(s,Nr),e(s,k),d(fe,k,null),e(k,Cr),e(k,Je),e(k,kr),e(k,Ye),e(s,wr),e(s,S),d(he,S,null),e(S,Lr),e(S,Ke),e(s,zr),e(s,y),d(be,y,null),e(y,Dr),e(y,Qe),e(y,Pr),e(y,Xe),e(y,Ir),e(y,Ze),e(s,Mr),e(s,F),d(ve,F,null),e(F,Er),e(F,et),e(s,Hr),e(s,R),d($e,R,null),e(R,Ar),e(R,tt),e(s,Vr),e(s,j),d(xe,j,null),e(j,Sr),e(j,rt),e(s,Fr),e(s,q),d(Te,q,null),e(q,Rr),e(q,nt),e(s,jr),e(s,O),d(ye,O,null),e(O,qr),e(O,at),e(s,Or),e(s,W),d(Ne,W,null),e(W,Wr),e(W,ot),e(s,Br),e(s,B),d(Ce,B,null),e(B,Gr),e(B,st),e(s,Ur),e(s,G),d(ke,G,null),e(G,Jr),e(G,it),e(s,Yr),e(s,U),d(we,U,null),e(U,Kr),e(U,lt),e(s,Qr),e(s,J),d(Le,J,null),e(J,Xr),e(J,mt),e(s,Zr),e(s,Y),d(ze,Y,null),e(Y,en),e(Y,ut),e(s,tn),e(s,K),d(De,K,null),e(K,rn),e(K,pt),e(s,nn),e(s,w),d(Pe,w,null),e(w,an),e(w,dt),e(w,on),e(w,ct),e(s,sn),e(s,Q),d(Ie,Q,null),e(Q,ln),e(Q,gt),e(s,mn),e(s,L),d(Me,L,null),e(L,un),e(L,_t),e(L,pn),e(L,ft),e(s,dn),e(s,X),d(Ee,X,null),e(X,cn),e(X,ht),T(t,It,v),T(t,yt,v),Mt=!0},p:ta,i(t){Mt||(c(ee.$$.fragment,t),c(te.$$.fragment,t),c(ne.$$.fragment,t),c(ae.$$.fragment,t),c(oe.$$.fragment,t),c(se.$$.fragment,t),c(ie.$$.fragment,t),c(le.$$.fragment,t),c(me.$$.fragment,t),c(ue.$$.fragment,t),c(pe.$$.fragment,t),c(de.$$.fragment,t),c(ce.$$.fragment,t),c(ge.$$.fragment,t),c(_e.$$.fragment,t),c(fe.$$.fragment,t),c(he.$$.fragment,t),c(be.$$.fragment,t),c(ve.$$.fragment,t),c($e.$$.fragment,t),c(xe.$$.fragment,t),c(Te.$$.fragment,t),c(ye.$$.fragment,t),c(Ne.$$.fragment,t),c(Ce.$$.fragment,t),c(ke.$$.fragment,t),c(we.$$.fragment,t),c(Le.$$.fragment,t),c(ze.$$.fragment,t),c(De.$$.fragment,t),c(Pe.$$.fragment,t),c(Ie.$$.fragment,t),c(Me.$$.fragment,t),c(Ee.$$.fragment,t),Mt=!0)},o(t){g(ee.$$.fragment,t),g(te.$$.fragment,t),g(ne.$$.fragment,t),g(ae.$$.fragment,t),g(oe.$$.fragment,t),g(se.$$.fragment,t),g(ie.$$.fragment,t),g(le.$$.fragment,t),g(me.$$.fragment,t),g(ue.$$.fragment,t),g(pe.$$.fragment,t),g(de.$$.fragment,t),g(ce.$$.fragment,t),g(ge.$$.fragment,t),g(_e.$$.fragment,t),g(fe.$$.fragment,t),g(he.$$.fragment,t),g(be.$$.fragment,t),g(ve.$$.fragment,t),g($e.$$.fragment,t),g(xe.$$.fragment,t),g(Te.$$.fragment,t),g(ye.$$.fragment,t),g(Ne.$$.fragment,t),g(Ce.$$.fragment,t),g(ke.$$.fragment,t),g(we.$$.fragment,t),g(Le.$$.fragment,t),g(ze.$$.fragment,t),g(De.$$.fragment,t),g(Pe.$$.fragment,t),g(Ie.$$.fragment,t),g(Me.$$.fragment,t),g(Ee.$$.fragment,t),Mt=!1},d(t){t&&(l(Nt),l(Tt),l(Ct),l(kt),l(wt),l(re),l(Lt),l(zt),l($),l(Dt),l(Pt),l(s),l(It),l(yt)),l(D),_(ee,t),_(te,t),_(ne,t),_(ae),_(oe),_(se),_(ie),_(le),_(me),_(ue,t),_(pe),_(de),_(ce),_(ge),_(_e),_(fe),_(he),_(be),_(ve),_($e),_(xe),_(Te),_(ye),_(Ne),_(Ce),_(ke),_(we),_(Le),_(ze),_(De),_(Pe),_(Ie),_(Me),_(Ee)}}}const la='{"title":"NeuronTrainer","local":"neurontrainer","sections":[{"title":"NeuronTrainingArguments","local":"optimum.neuron.NeuronTrainingArguments","sections":[],"depth":2},{"title":"NeuronTrainer","local":"optimum.neuron.NeuronTrainer","sections":[],"depth":2}],"depth":1}';function ma(_n){return ra(()=>{new URLSearchParams(window.location.search).get("fw")}),[]}class _a extends na{constructor(D){super(),aa(this,D,ma,ia,ea,{})}}export{_a as component};
