import{s as Vt,n as Ht,o as At}from"../chunks/scheduler.5f3e6389.js";import{S as It,i as Wt,e as a,s as o,c as p,h as Ft,a as m,d as t,b as r,f as x,g as l,j as g,k as h,l as s,m as i,n as d,t as u,o as c,p as f}from"../chunks/index.373ab25c.js";import{C as Bt,H as _e}from"../chunks/MermaidChart.svelte_svelte_type_style_lang.972976e0.js";import{D as _}from"../chunks/Docstring.3603b29b.js";function zt(vt){let y,ye,$e,Ce,E,Oe,S,Te,V,_t="Exporting a model to ONNX involves specifying:",we,H,$t=`<li>The input names.</li> <li>The output names.</li> <li>The dynamic axes. These refer to the input dimensions can be changed dynamically at runtime (e.g. a batch size or sequence length).
All other axes will be treated as static, and hence fixed at runtime.</li> <li>Dummy inputs to trace the model. This is needed in PyTorch to record the computational graph and convert it to ONNX.</li>`,Pe,A,bt=`Since this data depends on the choice of model and task, we represent it in terms of <em>configuration classes</em>. Each configuration class is associated with
a specific model architecture, and follows the naming convention <code>ArchitectureNameOnnxConfig</code>. For instance, the configuration which specifies the ONNX
export of BERT models is <code>BertOnnxConfig</code>.`,Ne,I,yt="Since many architectures share similar properties for their ONNX configuration, 🤗 Optimum adopts a 3-level class hierarchy:",De,W,Ct=`<li>Abstract and generic base classes. These handle all the fundamental features, while being agnostic to the modality (text, image, audio, etc).</li> <li>Middle-end classes. These are aware of the modality, but multiple can exist for the same modality depending on the inputs they support.
They specify which input generators should be used for the dummy inputs, but remain model-agnostic.</li> <li>Model-specific classes like the <code>BertOnnxConfig</code> mentioned above. These are the ones actually used to export models.</li>`,Le,F,ke,v,B,tt,N,z,nt,ie,Ot="Dict containing the axis definition of the input tensors to provide to the model.",ot,D,X,rt,se,Tt="Dict containing the axis definition of the output tensors to provide to the model.",it,L,j,st,ae,wt="Generates the dummy inputs necessary for tracing the model. If not explicitly specified, default input shapes are used.",qe,$,K,at,me,Pt='Inherits from <a href="/docs/optimum/pr_68/en/onnx/package_reference/configuration#optimum.exporters.onnx.OnnxConfig">OnnxConfig</a>. A base class to handle the ONNX configuration of decoder-only models.',mt,k,R,pt,pe,Nt="Fills <code>input_or_outputs</code> mapping with past_key_values dynamic axes considering the direction.",Me,b,G,lt,le,Dt='Inherits from <a href="/docs/optimum/pr_68/en/onnx/package_reference/configuration#optimum.exporters.onnx.OnnxConfigWithPast">OnnxConfigWithPast</a>. A base class to handle the ONNX configuration of encoder-decoder models.',dt,q,U,ut,de,Lt="Creates a copy of the current OnnxConfig but with a different <code>ConfigBehavior</code> and <code>use_past</code> value.",Ee,J,Se,Q,Ve,C,Y,ct,ue,kt="Handles encoder-based text architectures.",He,O,Z,ft,ce,qt="Handles decoder-based text architectures.",Ae,T,ee,gt,fe,Mt="Handles encoder-decoder-based text architectures.",Ie,te,We,w,ne,xt,ge,Et="Handles vision architectures.",Fe,oe,Be,P,re,ht,xe,St="Handles multi-modal text and vision architectures.",ze,be,Xe;return E=new Bt({props:{containerStyle:"float: right; margin-left: 10px; display: inline-flex; position: relative; z-index: 10;"}}),S=new _e({props:{title:"Configuration classes for ONNX exports",local:"configuration-classes-for-onnx-exports",headingTag:"h1"}}),F=new _e({props:{title:"Base classes",local:"optimum.exporters.onnx.OnnxConfig",headingTag:"h2"}}),B=new _({props:{name:"class optimum.exporters.onnx.OnnxConfig",anchor:"optimum.exporters.onnx.OnnxConfig",parameters:[{name:"config",val:": PretrainedConfig"},{name:"task",val:": str = 'feature-extraction'"},{name:"preprocessors",val:": list[Any] | None = None"},{name:"int_dtype",val:": str = 'int64'"},{name:"float_dtype",val:": str = 'fp32'"}],source:"https://github.com/huggingface/optimum-onnx/blob/vr_68/optimum/exporters/onnx/base.py#L92"}}),z=new _({props:{name:"inputs",anchor:"optimum.exporters.onnx.OnnxConfig.inputs",parameters:[],source:"https://github.com/huggingface/optimum-onnx/blob/vr_68/optimum/exporters/base.py#L164",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>A mapping of each input name to a mapping of axis position to the axes symbolic name.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>Dict[str, Dict[int, str]]</code></p>
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<p><code>Dict[str, Dict[int, str]]</code></p>
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The width to use in the dummy inputs for vision tasks.`,name:"image_width"},{anchor:"optimum.exporters.onnx.OnnxConfig.generate_dummy_inputs.image_height",description:`<strong>image_height</strong> (<code>int</code>, defaults to 64) &#x2014;
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<p>A dictionary mapping the input names to dummy tensors in the proper framework format.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>Dict[str, [tf.Tensor, torch.Tensor]]</code></p>
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<p><code>OnnxSeq2SeqConfigWithPast</code></p>
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