import{s as cn,n as gn,o as un}from"../chunks/scheduler.bdbef820.js";import{S as hn,i as xn,g as s,s as r,r as f,A as _n,h as i,f as n,c as o,j as _,u as l,x as u,k as v,y as t,a as m,v as d,d as p,t as c,w as g}from"../chunks/index.33f81d56.js";import{D as $}from"../chunks/Docstring.64554317.js";import{H as he,E as vn}from"../chunks/EditOnGithub.a9246e21.js";function $n(At){let O,Ee,Le,Ie,z,We,V,jt="🤗 트랜스포머는 <code>transformers.onnx</code> 패키지를 제공하며, 이 패키지는 설정 객체를 활용하여 모델 체크포인트를 ONNX 그래프로 변환할 수 있게 합니다.",ze,X,Rt='🤗 Transformers에 대한 자세한 내용은 <a href="../serialization">이 가이드</a>를 참조하세요.',Ve,H,Xe,A,Bt="내보내려는(export) 모델 아키텍처의 유형에 따라 상속받아야 할 세 가지 추상 클래스를 제공합니다:",He,j,Gt='<li>인코더 기반 모델은 <a href="/docs/transformers/pr_22105/ko/main_classes/onnx#transformers.onnx.OnnxConfig">OnnxConfig</a>을 상속받습니다.</li> <li>디코더 기반 모델은 <a href="/docs/transformers/pr_22105/ko/main_classes/onnx#transformers.onnx.OnnxConfigWithPast">OnnxConfigWithPast</a>을 상속받습니다.</li> <li>인코더-디코더 기반 모델은 <a href="/docs/transformers/pr_22105/ko/main_classes/onnx#transformers.onnx.OnnxSeq2SeqConfigWithPast">OnnxSeq2SeqConfigWithPast</a>을 상속받습니다.</li>',Ae,R,je,h,B,gt,xe,Ut="Base class for ONNX exportable model describing metadata on how to export the model through the ONNX format.",ut,T,G,ht,_e,Jt=`Flatten any potential nested structure expanding the name of the field with the index of the element within the
structure.`,xt,k,U,_t,ve,Kt="Instantiate a OnnxConfig for a specific model",vt,P,J,$t,$e,Qt="Generate inputs to provide to the ONNX exporter for the specific framework",bt,M,K,yt,be,Yt=`Generate inputs for ONNX Runtime using the reference model inputs. Override this to run inference with seq2seq
models which have the encoder and decoder exported as separate ONNX files.`,Ct,D,Q,wt,ye,Zt="Flag indicating if the model requires using external data format",Re,Y,Be,w,Z,Ot,F,ee,Tt,Ce,en="Fill the input_or_outputs mapping with past_key_values dynamic axes considering.",kt,q,te,Pt,we,tn="Instantiate a OnnxConfig with <code>use_past</code> attribute set to True",Ge,ne,Ue,re,oe,Je,ae,Ke,se,nn="각 ONNX 설정은 다양한 유형의 토폴로지나 작업에 대해 모델을 내보낼 수 있게(exporting) 해주는 <em>features</em> 세트와 연관되어 있습니다.",Qe,ie,Ye,x,me,Mt,N,fe,Dt,Oe,rn="Check whether or not the model has the requested features.",Ft,C,le,qt,Te,on="Determines the framework to use for the export.",Nt,ke,an="The priority is in the following order:",Lt,Pe,sn="<li>User input via <code>framework</code>.</li> <li>If local checkpoint is provided, use the same framework as the checkpoint.</li> <li>Available framework in environment, with priority given to PyTorch</li>",St,L,de,Et,Me,mn="Gets the OnnxConfig for a model_type and feature combination.",It,S,pe,Wt,De,fn="Attempts to retrieve an AutoModel class from a feature name.",zt,E,ce,Vt,Fe,ln="Attempts to retrieve a model from a model’s name and the feature to be enabled.",Xt,I,ge,Ht,qe,dn="Tries to retrieve the feature -> OnnxConfig constructor map from the model type.",Ze,ue,et,Se,tt;return z=new he({props:{title:"🤗 Transformers 모델을 ONNX로 내보내기",local:"exporting—transformers-models-to-onnx",headingTag:"h1"}}),H=new he({props:{title:"ONNX 설정",local:"onnx-configurations",headingTag:"h2"}}),R=new he({props:{title:"OnnxConfig",local:"transformers.onnx.OnnxConfig ][ transformers.onnx.OnnxConfig",headingTag:"h3"}}),B=new $({props:{name:"class transformers.onnx.OnnxConfig",anchor:"transformers.onnx.OnnxConfig",parameters:[{name:"config",val:": PretrainedConfig"},{name:"task",val:": str = 'default'"},{name:"patching_specs",val:": typing.List[transformers.onnx.config.PatchingSpec] = None"}],source:"https://github.com/huggingface/transformers/blob/vr_22105/src/transformers/onnx/config.py#L68"}}),G=new $({props:{name:"flatten_output_collection_property",anchor:"transformers.onnx.OnnxConfig.flatten_output_collection_property",parameters:[{name:"name",val:": str"},{name:"field",val:": typing.Iterable[typing.Any]"}],parametersDescription:[{anchor:"transformers.onnx.OnnxConfig.flatten_output_collection_property.name",description:"<strong>name</strong> &#x2014; The name of the nested structure",name:"name"},{anchor:"transformers.onnx.OnnxConfig.flatten_output_collection_property.field",description:"<strong>field</strong> &#x2014; The structure to, potentially, be flattened",name:"field"}],source:"https://github.com/huggingface/transformers/blob/vr_22105/src/transformers/onnx/config.py#L424",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>Outputs with flattened structure and key mapping this new structure.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p>(Dict[str, Any])</p>
`}}),U=new $({props:{name:"from_model_config",anchor:"transformers.onnx.OnnxConfig.from_model_config",parameters:[{name:"config",val:": PretrainedConfig"},{name:"task",val:": str = 'default'"}],parametersDescription:[{anchor:"transformers.onnx.OnnxConfig.from_model_config.config",description:"<strong>config</strong> &#x2014; The model&#x2019;s configuration to use when exporting to ONNX",name:"config"}],source:"https://github.com/huggingface/transformers/blob/vr_22105/src/transformers/onnx/config.py#L127",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>OnnxConfig for this model</p>
`}}),J=new $({props:{name:"generate_dummy_inputs",anchor:"transformers.onnx.OnnxConfig.generate_dummy_inputs",parameters:[{name:"preprocessor",val:": typing.Union[ForwardRef('PreTrainedTokenizerBase'), ForwardRef('FeatureExtractionMixin'), ForwardRef('ImageProcessingMixin')]"},{name:"batch_size",val:": int = -1"},{name:"seq_length",val:": int = -1"},{name:"num_choices",val:": int = -1"},{name:"is_pair",val:": bool = False"},{name:"framework",val:": typing.Optional[transformers.utils.generic.TensorType] = None"},{name:"num_channels",val:": int = 3"},{name:"image_width",val:": int = 40"},{name:"image_height",val:": int = 40"},{name:"sampling_rate",val:": int = 22050"},{name:"time_duration",val:": float = 5.0"},{name:"frequency",val:": int = 220"},{name:"tokenizer",val:": PreTrainedTokenizerBase = None"}],parametersDescription:[{anchor:"transformers.onnx.OnnxConfig.generate_dummy_inputs.preprocessor",description:`<strong>preprocessor</strong> &#x2014; (<a href="/docs/transformers/pr_22105/ko/internal/tokenization_utils#transformers.PreTrainedTokenizerBase">PreTrainedTokenizerBase</a>, <a href="/docs/transformers/pr_22105/ko/main_classes/feature_extractor#transformers.FeatureExtractionMixin">FeatureExtractionMixin</a>, or <a href="/docs/transformers/pr_22105/ko/internal/image_processing_utils#transformers.ImageProcessingMixin">ImageProcessingMixin</a>):
The preprocessor associated with this model configuration.`,name:"preprocessor"},{anchor:"transformers.onnx.OnnxConfig.generate_dummy_inputs.batch_size",description:`<strong>batch_size</strong> (<code>int</code>, <em>optional</em>, defaults to -1) &#x2014;
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The sequence length to export the model for (-1 means dynamic axis).`,name:"seq_length"},{anchor:"transformers.onnx.OnnxConfig.generate_dummy_inputs.is_pair",description:`<strong>is_pair</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
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The framework (PyTorch or TensorFlow) that the tokenizer will generate tensors for.`,name:"framework"},{anchor:"transformers.onnx.OnnxConfig.generate_dummy_inputs.num_channels",description:`<strong>num_channels</strong> (<code>int</code>, <em>optional</em>, defaults to 3) &#x2014;
The number of channels of the generated images.`,name:"num_channels"},{anchor:"transformers.onnx.OnnxConfig.generate_dummy_inputs.image_width",description:`<strong>image_width</strong> (<code>int</code>, <em>optional</em>, defaults to 40) &#x2014;
The width of the generated images.`,name:"image_width"},{anchor:"transformers.onnx.OnnxConfig.generate_dummy_inputs.image_height",description:`<strong>image_height</strong> (<code>int</code>, <em>optional</em>, defaults to 40) &#x2014;
The height of the generated images.`,name:"image_height"},{anchor:"transformers.onnx.OnnxConfig.generate_dummy_inputs.sampling_rate",description:`<strong>sampling_rate</strong> (<code>int</code>, <em>optional</em> defaults to 22050) &#x2014;
The sampling rate for audio data generation.`,name:"sampling_rate"},{anchor:"transformers.onnx.OnnxConfig.generate_dummy_inputs.time_duration",description:`<strong>time_duration</strong> (<code>float</code>, <em>optional</em> defaults to 5.0) &#x2014;
Total seconds of sampling for audio data generation.`,name:"time_duration"},{anchor:"transformers.onnx.OnnxConfig.generate_dummy_inputs.frequency",description:`<strong>frequency</strong> (<code>int</code>, <em>optional</em> defaults to 220) &#x2014;
The desired natural frequency of generated audio.`,name:"frequency"}],source:"https://github.com/huggingface/transformers/blob/vr_22105/src/transformers/onnx/config.py#L280",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>Mapping[str, Tensor] holding the kwargs to provide to the model’s forward function</p>
`}}),K=new $({props:{name:"generate_dummy_inputs_onnxruntime",anchor:"transformers.onnx.OnnxConfig.generate_dummy_inputs_onnxruntime",parameters:[{name:"reference_model_inputs",val:": typing.Mapping[str, typing.Any]"}],parametersDescription:[{anchor:"transformers.onnx.OnnxConfig.generate_dummy_inputs_onnxruntime.reference_model_inputs",description:`<strong>reference_model_inputs</strong> ([<code>Mapping[str, Tensor]</code>) &#x2014;
Reference inputs for the model.`,name:"reference_model_inputs"}],source:"https://github.com/huggingface/transformers/blob/vr_22105/src/transformers/onnx/config.py#L400",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>The mapping holding the kwargs to provide to the model’s forward function</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>Mapping[str, Tensor]</code></p>
`}}),Q=new $({props:{name:"use_external_data_format",anchor:"transformers.onnx.OnnxConfig.use_external_data_format",parameters:[{name:"num_parameters",val:": int"}],parametersDescription:[{anchor:"transformers.onnx.OnnxConfig.use_external_data_format.num_parameters",description:"<strong>num_parameters</strong> &#x2014; Number of parameter on the model",name:"num_parameters"}],source:"https://github.com/huggingface/transformers/blob/vr_22105/src/transformers/onnx/config.py#L241",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>True if model.num_parameters() * size_of(float32) >= 2Gb False otherwise</p>
`}}),Y=new he({props:{title:"OnnxConfigWithPast",local:"transformers.onnx.OnnxConfigWithPast ][ transformers.onnx.OnnxConfigWithPast",headingTag:"h3"}}),Z=new $({props:{name:"class transformers.onnx.OnnxConfigWithPast",anchor:"transformers.onnx.OnnxConfigWithPast",parameters:[{name:"config",val:": PretrainedConfig"},{name:"task",val:": str = 'default'"},{name:"patching_specs",val:": typing.List[transformers.onnx.config.PatchingSpec] = None"},{name:"use_past",val:": bool = False"}],source:"https://github.com/huggingface/transformers/blob/vr_22105/src/transformers/onnx/config.py#L443"}}),ee=new $({props:{name:"fill_with_past_key_values_",anchor:"transformers.onnx.OnnxConfigWithPast.fill_with_past_key_values_",parameters:[{name:"inputs_or_outputs",val:": typing.Mapping[str, typing.Mapping[int, str]]"},{name:"direction",val:": str"},{name:"inverted_values_shape",val:": bool = False"}],parametersDescription:[{anchor:"transformers.onnx.OnnxConfigWithPast.fill_with_past_key_values_.inputs_or_outputs",description:"<strong>inputs_or_outputs</strong> &#x2014; The mapping to fill.",name:"inputs_or_outputs"},{anchor:"transformers.onnx.OnnxConfigWithPast.fill_with_past_key_values_.direction",description:`<strong>direction</strong> &#x2014; either &#x201C;inputs&#x201D; or &#x201C;outputs&#x201D;, it specifies whether input_or_outputs is the input mapping or the
output mapping, this is important for axes naming.`,name:"direction"},{anchor:"transformers.onnx.OnnxConfigWithPast.fill_with_past_key_values_.inverted_values_shape",description:`<strong>inverted_values_shape</strong> &#x2014;
If <code>True</code>, store values on dynamic axis 1, else on axis 2.`,name:"inverted_values_shape"}],source:"https://github.com/huggingface/transformers/blob/vr_22105/src/transformers/onnx/config.py#L550"}}),te=new $({props:{name:"with_past",anchor:"transformers.onnx.OnnxConfigWithPast.with_past",parameters:[{name:"config",val:": PretrainedConfig"},{name:"task",val:": str = 'default'"}],parametersDescription:[{anchor:"transformers.onnx.OnnxConfigWithPast.with_past.config",description:"<strong>config</strong> &#x2014; The underlying model&#x2019;s config to use when exporting to ONNX",name:"config"}],source:"https://github.com/huggingface/transformers/blob/vr_22105/src/transformers/onnx/config.py#L454",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>OnnxConfig with <code>.use_past = True</code></p>
`}}),ne=new he({props:{title:"OnnxSeq2SeqConfigWithPast",local:"OnnxSeq2SeqConfigWithPast ][ transformers.onnx.OnnxSeq2SeqConfigWithPast",headingTag:"h3"}}),oe=new $({props:{name:"class transformers.onnx.OnnxSeq2SeqConfigWithPast",anchor:"transformers.onnx.OnnxSeq2SeqConfigWithPast",parameters:[{name:"config",val:": PretrainedConfig"},{name:"task",val:": str = 'default'"},{name:"patching_specs",val:": typing.List[transformers.onnx.config.PatchingSpec] = None"},{name:"use_past",val:": bool = False"}],source:"https://github.com/huggingface/transformers/blob/vr_22105/src/transformers/onnx/config.py#L590"}}),ae=new he({props:{title:"ONNX 특징",local:"onnx-features",headingTag:"h2"}}),ie=new he({props:{title:"FeaturesManager",local:"transformers.onnx.FeaturesManager ][ transformers.onnx.FeaturesManager",headingTag:"h3"}}),me=new $({props:{name:"class transformers.onnx.FeaturesManager",anchor:"transformers.onnx.FeaturesManager",parameters:[],source:"https://github.com/huggingface/transformers/blob/vr_22105/src/transformers/onnx/features.py#L85"}}),fe=new $({props:{name:"check_supported_model_or_raise",anchor:"transformers.onnx.FeaturesManager.check_supported_model_or_raise",parameters:[{name:"model",val:": typing.Union[ForwardRef('PreTrainedModel'), ForwardRef('TFPreTrainedModel')]"},{name:"feature",val:": str = 'default'"}],parametersDescription:[{anchor:"transformers.onnx.FeaturesManager.check_supported_model_or_raise.model",description:"<strong>model</strong> &#x2014; The model to export.",name:"model"},{anchor:"transformers.onnx.FeaturesManager.check_supported_model_or_raise.feature",description:"<strong>feature</strong> &#x2014; The name of the feature to check if it is available.",name:"feature"}],source:"https://github.com/huggingface/transformers/blob/vr_22105/src/transformers/onnx/features.py#L711",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>(str) The type of the model (OnnxConfig) The OnnxConfig instance holding the model export properties.</p>
`}}),le=new $({props:{name:"determine_framework",anchor:"transformers.onnx.FeaturesManager.determine_framework",parameters:[{name:"model",val:": str"},{name:"framework",val:": str = None"}],parametersDescription:[{anchor:"transformers.onnx.FeaturesManager.determine_framework.model",description:`<strong>model</strong> (<code>str</code>) &#x2014;
The name of the model to export.`,name:"model"},{anchor:"transformers.onnx.FeaturesManager.determine_framework.framework",description:`<strong>framework</strong> (<code>str</code>, <em>optional</em>, defaults to <code>None</code>) &#x2014;
The framework to use for the export. See above for priority if none provided.`,name:"framework"}],source:"https://github.com/huggingface/transformers/blob/vr_22105/src/transformers/onnx/features.py#L628",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>The framework to use for the export.</p>
`}}),de=new $({props:{name:"get_config",anchor:"transformers.onnx.FeaturesManager.get_config",parameters:[{name:"model_type",val:": str"},{name:"feature",val:": str"}],parametersDescription:[{anchor:"transformers.onnx.FeaturesManager.get_config.model_type",description:`<strong>model_type</strong> (<code>str</code>) &#x2014;
The model type to retrieve the config for.`,name:"model_type"},{anchor:"transformers.onnx.FeaturesManager.get_config.feature",description:`<strong>feature</strong> (<code>str</code>) &#x2014;
The feature to retrieve the config for.`,name:"feature"}],source:"https://github.com/huggingface/transformers/blob/vr_22105/src/transformers/onnx/features.py#L736",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>config for the combination</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>OnnxConfig</code></p>
`}}),pe=new $({props:{name:"get_model_class_for_feature",anchor:"transformers.onnx.FeaturesManager.get_model_class_for_feature",parameters:[{name:"feature",val:": str"},{name:"framework",val:": str = 'pt'"}],parametersDescription:[{anchor:"transformers.onnx.FeaturesManager.get_model_class_for_feature.feature",description:`<strong>feature</strong> (<code>str</code>) &#x2014;
The feature required.`,name:"feature"},{anchor:"transformers.onnx.FeaturesManager.get_model_class_for_feature.framework",description:`<strong>framework</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;pt&quot;</code>) &#x2014;
The framework to use for the export.`,name:"framework"}],source:"https://github.com/huggingface/transformers/blob/vr_22105/src/transformers/onnx/features.py#L601",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>The AutoModel class corresponding to the feature.</p>
`}}),ce=new $({props:{name:"get_model_from_feature",anchor:"transformers.onnx.FeaturesManager.get_model_from_feature",parameters:[{name:"feature",val:": str"},{name:"model",val:": str"},{name:"framework",val:": str = None"},{name:"cache_dir",val:": str = None"}],parametersDescription:[{anchor:"transformers.onnx.FeaturesManager.get_model_from_feature.feature",description:`<strong>feature</strong> (<code>str</code>) &#x2014;
The feature required.`,name:"feature"},{anchor:"transformers.onnx.FeaturesManager.get_model_from_feature.model",description:`<strong>model</strong> (<code>str</code>) &#x2014;
The name of the model to export.`,name:"model"},{anchor:"transformers.onnx.FeaturesManager.get_model_from_feature.framework",description:`<strong>framework</strong> (<code>str</code>, <em>optional</em>, defaults to <code>None</code>) &#x2014;
The framework to use for the export. See <code>FeaturesManager.determine_framework</code> for the priority should
none be provided.`,name:"framework"}],source:"https://github.com/huggingface/transformers/blob/vr_22105/src/transformers/onnx/features.py#L678",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>The instance of the model.</p>
`}}),ge=new $({props:{name:"get_supported_features_for_model_type",anchor:"transformers.onnx.FeaturesManager.get_supported_features_for_model_type",parameters:[{name:"model_type",val:": str"},{name:"model_name",val:": typing.Optional[str] = None"}],parametersDescription:[{anchor:"transformers.onnx.FeaturesManager.get_supported_features_for_model_type.model_type",description:`<strong>model_type</strong> (<code>str</code>) &#x2014;
The model type to retrieve the supported features for.`,name:"model_type"},{anchor:"transformers.onnx.FeaturesManager.get_supported_features_for_model_type.model_name",description:`<strong>model_name</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The name attribute of the model object, only used for the exception message.`,name:"model_name"}],source:"https://github.com/huggingface/transformers/blob/vr_22105/src/transformers/onnx/features.py#L556",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>The dictionary mapping each feature to a corresponding OnnxConfig constructor.</p>
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