import{s as cn,n as gn,o as un}from"../chunks/scheduler.25b97de1.js";import{S as hn,i as xn,g as s,s as r,r as l,A as _n,h as i,f as n,c as o,j as _,u as d,x as u,k as $,y as t,a as m,v as f,d as p,t as c,w as g}from"../chunks/index.d9030fc9.js";import{D as v}from"../chunks/Docstring.e257edda.js";import{H as he,E as $n}from"../chunks/EditOnGithub.91d95064.js";function vn(At){let O,Ie,Le,Se,V,We,z,jt=`🤗 Transformers provides a <code>transformers.onnx</code> package that enables you to
convert model checkpoints to an ONNX graph by leveraging configuration objects.`,Ve,H,Gt=`See the <a href="../serialization">guide</a> on exporting 🤗 Transformers models for more
details.`,ze,X,He,A,Ut=`We provide three abstract classes that you should inherit from, depending on the
type of model architecture you wish to export:`,Xe,j,Rt='<li>Encoder-based models inherit from <a href="/docs/transformers/main/en/main_classes/onnx#transformers.onnx.OnnxConfig">OnnxConfig</a></li> <li>Decoder-based models inherit from <a href="/docs/transformers/main/en/main_classes/onnx#transformers.onnx.OnnxConfigWithPast">OnnxConfigWithPast</a></li> <li>Encoder-decoder models inherit from <a href="/docs/transformers/main/en/main_classes/onnx#transformers.onnx.OnnxSeq2SeqConfigWithPast">OnnxSeq2SeqConfigWithPast</a></li>',Ae,G,je,h,U,gt,xe,Bt="Base class for ONNX exportable model describing metadata on how to export the model through the ONNX format.",ut,T,R,ht,_e,Jt=`Flatten any potential nested structure expanding the name of the field with the index of the element within the
structure.`,xt,k,B,_t,$e,Kt="Instantiate a OnnxConfig for a specific model",$t,P,J,vt,ve,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",Ge,Y,Ue,w,Z,Ot,q,ee,Tt,Ce,en="Fill the input_or_outputs mapping with past_key_values dynamic axes considering.",kt,N,te,Pt,we,tn="Instantiate a OnnxConfig with <code>use_past</code> attribute set to True",Re,ne,Be,re,oe,Je,ae,Ke,se,nn=`Each ONNX configuration is associated with a set of <em>features</em> that enable you
to export models for different types of topologies or tasks.`,Qe,ie,Ye,x,me,Mt,F,le,Dt,Oe,rn="Check whether or not the model has the requested features.",qt,C,de,Nt,Te,on="Determines the framework to use for the export.",Ft,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>",Et,L,fe,It,Me,mn="Gets the OnnxConfig for a model_type and feature combination.",St,E,pe,Wt,De,ln="Attempts to retrieve an AutoModel class from a feature name.",Vt,I,ce,zt,qe,dn="Attempts to retrieve a model from a model’s name and the feature to be enabled.",Ht,S,ge,Xt,Ne,fn="Tries to retrieve the feature -> OnnxConfig constructor map from the model type.",Ze,ue,et,Ee,tt;return V=new he({props:{title:"Exporting 🤗 Transformers models to ONNX",local:"exporting--transformers-models-to-onnx",headingTag:"h1"}}),X=new he({props:{title:"ONNX Configurations",local:"onnx-configurations",headingTag:"h2"}}),G=new he({props:{title:"OnnxConfig",local:"transformers.onnx.OnnxConfig",headingTag:"h3"}}),U=new v({props:{name:"class transformers.onnx.OnnxConfig",anchor:"transformers.onnx.OnnxConfig",parameters:[{name:"config",val:": PretrainedConfig"},{name:"task",val:": str = 'default'"},{name:"patching_specs",val:": List = None"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/onnx/config.py#L68"}}),R=new v({props:{name:"flatten_output_collection_property",anchor:"transformers.onnx.OnnxConfig.flatten_output_collection_property",parameters:[{name:"name",val:": str"},{name:"field",val:": Iterable"}],source:"https://github.com/huggingface/transformers/blob/main/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>
`}}),B=new v({props:{name:"from_model_config",anchor:"transformers.onnx.OnnxConfig.from_model_config",parameters:[{name:"config",val:": PretrainedConfig"},{name:"task",val:": str = 'default'"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/onnx/config.py#L127",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>OnnxConfig for this model</p>
`}}),J=new v({props:{name:"generate_dummy_inputs",anchor:"transformers.onnx.OnnxConfig.generate_dummy_inputs",parameters:[{name:"preprocessor",val:": Union"},{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:": Optional = 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.batch_size",description:`<strong>batch_size</strong> (<code>int</code>, <em>optional</em>, defaults to -1) &#x2014;
The batch size to export the model for (-1 means dynamic axis).`,name:"batch_size"},{anchor:"transformers.onnx.OnnxConfig.generate_dummy_inputs.num_choices",description:`<strong>num_choices</strong> (<code>int</code>, <em>optional</em>, defaults to -1) &#x2014;
The number of candidate answers provided for multiple choice task (-1 means dynamic axis).`,name:"num_choices"},{anchor:"transformers.onnx.OnnxConfig.generate_dummy_inputs.seq_length",description:`<strong>seq_length</strong> (<code>int</code>, <em>optional</em>, defaults to -1) &#x2014;
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;
Indicate if the input is a pair (sentence 1, sentence 2)`,name:"is_pair"},{anchor:"transformers.onnx.OnnxConfig.generate_dummy_inputs.framework",description:`<strong>framework</strong> (<code>TensorType</code>, <em>optional</em>, defaults to <code>None</code>) &#x2014;
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/main/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 v({props:{name:"generate_dummy_inputs_onnxruntime",anchor:"transformers.onnx.OnnxConfig.generate_dummy_inputs_onnxruntime",parameters:[{name:"reference_model_inputs",val:": Mapping"}],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/main/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 v({props:{name:"use_external_data_format",anchor:"transformers.onnx.OnnxConfig.use_external_data_format",parameters:[{name:"num_parameters",val:": int"}],source:"https://github.com/huggingface/transformers/blob/main/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",headingTag:"h3"}}),Z=new v({props:{name:"class transformers.onnx.OnnxConfigWithPast",anchor:"transformers.onnx.OnnxConfigWithPast",parameters:[{name:"config",val:": PretrainedConfig"},{name:"task",val:": str = 'default'"},{name:"patching_specs",val:": List = None"},{name:"use_past",val:": bool = False"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/onnx/config.py#L443"}}),ee=new v({props:{name:"fill_with_past_key_values_",anchor:"transformers.onnx.OnnxConfigWithPast.fill_with_past_key_values_",parameters:[{name:"inputs_or_outputs",val:": Mapping"},{name:"direction",val:": str"},{name:"inverted_values_shape",val:": bool = False"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/onnx/config.py#L550"}}),te=new v({props:{name:"with_past",anchor:"transformers.onnx.OnnxConfigWithPast.with_past",parameters:[{name:"config",val:": PretrainedConfig"},{name:"task",val:": str = 'default'"}],source:"https://github.com/huggingface/transformers/blob/main/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:"transformers.onnx.OnnxSeq2SeqConfigWithPast",headingTag:"h3"}}),oe=new v({props:{name:"class transformers.onnx.OnnxSeq2SeqConfigWithPast",anchor:"transformers.onnx.OnnxSeq2SeqConfigWithPast",parameters:[{name:"config",val:": PretrainedConfig"},{name:"task",val:": str = 'default'"},{name:"patching_specs",val:": List = None"},{name:"use_past",val:": bool = False"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/onnx/config.py#L590"}}),ae=new he({props:{title:"ONNX Features",local:"onnx-features",headingTag:"h2"}}),ie=new he({props:{title:"FeaturesManager",local:"transformers.onnx.FeaturesManager",headingTag:"h3"}}),me=new v({props:{name:"class transformers.onnx.FeaturesManager",anchor:"transformers.onnx.FeaturesManager",parameters:[],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/onnx/features.py#L85"}}),le=new v({props:{name:"check_supported_model_or_raise",anchor:"transformers.onnx.FeaturesManager.check_supported_model_or_raise",parameters:[{name:"model",val:": Union"},{name:"feature",val:": str = 'default'"}],source:"https://github.com/huggingface/transformers/blob/main/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>
`}}),de=new v({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/main/src/transformers/onnx/features.py#L628",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>The framework to use for the export.</p>
`}}),fe=new v({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/main/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 v({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/main/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 v({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/main/src/transformers/onnx/features.py#L678",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>The instance of the model.</p>
`}}),ge=new v({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:": Optional = 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/main/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>
`}}),ue=new 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