import{s as Dr,c as td,u as od,g as nd,a as ad,o as Ar,d as sd,n as J}from"../chunks/scheduler.c90a44b2.js";import{S as Kr,i as ed,e as p,c as h,s as r,a as c,f as V,g as f,d as n,b as d,k as T,m,l as s,n as y,w as id,t as g,o as u,p as M,r as ld,h as rd,j as $,u as dd}from"../chunks/index.66c3f415.js";import{T as G}from"../chunks/Tip.4ffa6759.js";import{at as pd,au as cd,C as md,H as C}from"../chunks/MermaidChart.svelte_svelte_type_style_lang.0df90478.js";import{D as k}from"../chunks/Docstring.2492792d.js";import{C as U}from"../chunks/CodeBlock.434286b4.js";const{window:gd}=cd;function ud(w){let o,v,a,i,b,t,_,Le,x;i=new pd({props:{classNames:"text-smd"}});const He=w[4].default,j=td(He,w,w[3],null);return{c(){o=p("div"),v=p("a"),a=p("span"),h(i.$$.fragment),t=r(),j&&j.c(),this.h()},l(F){o=c(F,"DIV",{class:!0});var Z=V(o);v=c(Z,"A",{id:!0,class:!0,href:!0});var Ee=V(v);a=c(Ee,"SPAN",{});var Ja=V(a);f(i.$$.fragment,Ja),Ja.forEach(n),Ee.forEach(n),t=d(Z),j&&j.l(Z),Z.forEach(n),this.h()},h(){T(v,"id",w[0]),T(v,"class","header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full"),T(v,"href",b=`#${w[0]}`),T(o,"class","relative group rounded-md")},m(F,Z){m(F,o,Z),s(o,v),s(v,a),y(i,a,null),s(o,t),j&&j.m(o,null),w[5](o),_=!0,Le||(x=id(gd,"hashchange",w[2]),Le=!0)},p(F,[Z]){(!_||Z&1)&&T(v,"id",F[0]),(!_||Z&1&&b!==(b=`#${F[0]}`))&&T(v,"href",b),j&&j.p&&(!_||Z&8)&&od(j,He,F,F[3],_?ad(He,F[3],Z,null):nd(F[3]),null)},i(F){_||(g(i.$$.fragment,F),g(j,F),_=!0)},o(F){u(i.$$.fragment,F),u(j,F),_=!1},d(F){F&&n(o),M(i),j&&j.d(F),w[5](null),Le=!1,x()}}}const Yr="bg-yellow-50 dark:bg-[#494a3d]";function hd(w,o,v){let{$$slots:a={},$$scope:i}=o,{anchor:b}=o,t;function _(){const{hash:x}=window.location,He=x.substring(1);t&&t.classList.remove(...Yr.split(" ")),He===b&&t.classList.add(...Yr.split(" "))}Ar(()=>{_()});function Le(x){sd[x?"unshift":"push"](()=>{t=x,v(1,t)})}return w.$$set=x=>{"anchor"in x&&v(0,b=x.anchor),"$$scope"in x&&v(3,i=x.$$scope)},[b,t,_,i,a,Le]}class O extends Kr{constructor(o){super(),ed(this,o,hd,ud,Dr,{anchor:0})}}function fd(w){let o,v=`Although the recipe for forward pass needs to be defined within this function, one should call the <code>Module</code>
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`;return{c(){o=p("p"),o.innerHTML=v},l(a){o=c(a,"P",{"data-svelte-h":!0}),$(o)!=="svelte-fincs2"&&(o.innerHTML=v)},m(a,i){m(a,o,i)},p:J,d(a){a&&n(o)}}}function yd(w){let o,v="Example of masked language modeling using <code>transformers.pipelines</code>:",a,i,b;return i=new U({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, pipeline
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> optimum.intel <span class="hljs-keyword">import</span> OVModelForMaskedLM

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;roberta-base&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = OVModelForMaskedLM.from_pretrained(<span class="hljs-string">&quot;roberta-base&quot;</span>, export=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>mask_token = tokenizer.mask_token
<span class="hljs-meta">&gt;&gt;&gt; </span>pipe = pipeline(<span class="hljs-string">&quot;fill-mask&quot;</span>, model=model, tokenizer=tokenizer)
<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = pipe(<span class="hljs-string">&quot;The goal of life is&quot;</span> + mask_token)`,wrap:!1}}),{c(){o=p("p"),o.innerHTML=v,a=r(),h(i.$$.fragment)},l(t){o=c(t,"P",{"data-svelte-h":!0}),$(o)!=="svelte-1lv30sh"&&(o.innerHTML=v),a=d(t),f(i.$$.fragment,t)},m(t,_){m(t,o,_),m(t,a,_),y(i,t,_),b=!0},p:J,i(t){b||(g(i.$$.fragment,t),b=!0)},o(t){u(i.$$.fragment,t),b=!1},d(t){t&&(n(o),n(a)),M(i,t)}}}function Md(w){let o,v=`Although the recipe for forward pass needs to be defined within this function, one should call the <code>Module</code>
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`;return{c(){o=p("p"),o.innerHTML=v},l(a){o=c(a,"P",{"data-svelte-h":!0}),$(o)!=="svelte-fincs2"&&(o.innerHTML=v)},m(a,i){m(a,o,i)},p:J,d(a){a&&n(o)}}}function vd(w){let o,v="Example of text generation:",a,i,b;return i=new U({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Ub2tlbml6ZXIlMEFmcm9tJTIwb3B0aW11bS5pbnRlbCUyMGltcG9ydCUyME9WTW9kZWxGb3JTZXEyU2VxTE0lMEElMEF0b2tlbml6ZXIlMjAlM0QlMjBBdXRvVG9rZW5pemVyLmZyb21fcHJldHJhaW5lZCglMjJlY2hhcmxhaXglMkZ0NS1zbWFsbC1vcGVudmlubyUyMiklMEFtb2RlbCUyMCUzRCUyME9WTW9kZWxGb3JTZXEyU2VxTE0uZnJvbV9wcmV0cmFpbmVkKCUyMmVjaGFybGFpeCUyRnQ1LXNtYWxsLW9wZW52aW5vJTIyKSUwQXRleHQlMjAlM0QlMjAlMjJIZSUyMG5ldmVyJTIwd2VudCUyMG91dCUyMHdpdGhvdXQlMjBhJTIwYm9vayUyMHVuZGVyJTIwaGlzJTIwYXJtJTJDJTIwYW5kJTIwaGUlMjBvZnRlbiUyMGNhbWUlMjBiYWNrJTIwd2l0aCUyMHR3by4lMjIlMEFpbnB1dHMlMjAlM0QlMjB0b2tlbml6ZXIodGV4dCUyQyUyMHJldHVybl90ZW5zb3JzJTNEJTIycHQlMjIpJTBBZ2VuX3Rva2VucyUyMCUzRCUyMG1vZGVsLmdlbmVyYXRlKCoqaW5wdXRzKSUwQW91dHB1dHMlMjAlM0QlMjB0b2tlbml6ZXIuYmF0Y2hfZGVjb2RlKGdlbl90b2tlbnMp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> optimum.intel <span class="hljs-keyword">import</span> OVModelForSeq2SeqLM

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;echarlaix/t5-small-openvino&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = OVModelForSeq2SeqLM.from_pretrained(<span class="hljs-string">&quot;echarlaix/t5-small-openvino&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>text = <span class="hljs-string">&quot;He never went out without a book under his arm, and he often came back with two.&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = tokenizer(text, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>gen_tokens = model.generate(**inputs)
<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = tokenizer.batch_decode(gen_tokens)`,wrap:!1}}),{c(){o=p("p"),o.textContent=v,a=r(),h(i.$$.fragment)},l(t){o=c(t,"P",{"data-svelte-h":!0}),$(o)!=="svelte-1bvu0cy"&&(o.textContent=v),a=d(t),f(i.$$.fragment,t)},m(t,_){m(t,o,_),m(t,a,_),y(i,t,_),b=!0},p:J,i(t){b||(g(i.$$.fragment,t),b=!0)},o(t){u(i.$$.fragment,t),b=!1},d(t){t&&(n(o),n(a)),M(i,t)}}}function bd(w){let o,v="Example using <code>transformers.pipeline</code>:",a,i,b;return i=new U({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Ub2tlbml6ZXIlMkMlMjBwaXBlbGluZSUwQWZyb20lMjBvcHRpbXVtLmludGVsJTIwaW1wb3J0JTIwT1ZNb2RlbEZvclNlcTJTZXFMTSUwQSUwQXRva2VuaXplciUyMCUzRCUyMEF1dG9Ub2tlbml6ZXIuZnJvbV9wcmV0cmFpbmVkKCUyMmVjaGFybGFpeCUyRnQ1LXNtYWxsLW9wZW52aW5vJTIyKSUwQW1vZGVsJTIwJTNEJTIwT1ZNb2RlbEZvclNlcTJTZXFMTS5mcm9tX3ByZXRyYWluZWQoJTIyZWNoYXJsYWl4JTJGdDUtc21hbGwtb3BlbnZpbm8lMjIpJTBBcGlwZSUyMCUzRCUyMHBpcGVsaW5lKCUyMnRyYW5zbGF0aW9uX2VuX3RvX2ZyJTIyJTJDJTIwbW9kZWwlM0Rtb2RlbCUyQyUyMHRva2VuaXplciUzRHRva2VuaXplciklMEF0ZXh0JTIwJTNEJTIwJTIySGUlMjBuZXZlciUyMHdlbnQlMjBvdXQlMjB3aXRob3V0JTIwYSUyMGJvb2slMjB1bmRlciUyMGhpcyUyMGFybSUyQyUyMGFuZCUyMGhlJTIwb2Z0ZW4lMjBjYW1lJTIwYmFjayUyMHdpdGglMjB0d28uJTIyJTBBb3V0cHV0cyUyMCUzRCUyMHBpcGUodGV4dCk=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, pipeline
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> optimum.intel <span class="hljs-keyword">import</span> OVModelForSeq2SeqLM

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;echarlaix/t5-small-openvino&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = OVModelForSeq2SeqLM.from_pretrained(<span class="hljs-string">&quot;echarlaix/t5-small-openvino&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>pipe = pipeline(<span class="hljs-string">&quot;translation_en_to_fr&quot;</span>, model=model, tokenizer=tokenizer)
<span class="hljs-meta">&gt;&gt;&gt; </span>text = <span class="hljs-string">&quot;He never went out without a book under his arm, and he often came back with two.&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = pipe(text)`,wrap:!1}}),{c(){o=p("p"),o.innerHTML=v,a=r(),h(i.$$.fragment)},l(t){o=c(t,"P",{"data-svelte-h":!0}),$(o)!=="svelte-1u1xsxh"&&(o.innerHTML=v),a=d(t),f(i.$$.fragment,t)},m(t,_){m(t,o,_),m(t,a,_),y(i,t,_),b=!0},p:J,i(t){b||(g(i.$$.fragment,t),b=!0)},o(t){u(i.$$.fragment,t),b=!1},d(t){t&&(n(o),n(a)),M(i,t)}}}function _d(w){let o,v=`Although the recipe for forward pass needs to be defined within this function, one should call the <code>Module</code>
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`;return{c(){o=p("p"),o.innerHTML=v},l(a){o=c(a,"P",{"data-svelte-h":!0}),$(o)!=="svelte-fincs2"&&(o.innerHTML=v)},m(a,i){m(a,o,i)},p:J,d(a){a&&n(o)}}}function $d(w){let o,v="Example of question answering using <code>transformers.pipeline</code>:",a,i,b;return i=new U({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, pipeline
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> optimum.intel <span class="hljs-keyword">import</span> OVModelForQuestionAnswering

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;distilbert-base-cased-distilled-squad&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = OVModelForQuestionAnswering.from_pretrained(<span class="hljs-string">&quot;distilbert-base-cased-distilled-squad&quot;</span>, export=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>pipe = pipeline(<span class="hljs-string">&quot;question-answering&quot;</span>, model=model, tokenizer=tokenizer)
<span class="hljs-meta">&gt;&gt;&gt; </span>question, text = <span class="hljs-string">&quot;Who was Jim Henson?&quot;</span>, <span class="hljs-string">&quot;Jim Henson was a nice puppet&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = pipe(question, text)`,wrap:!1}}),{c(){o=p("p"),o.innerHTML=v,a=r(),h(i.$$.fragment)},l(t){o=c(t,"P",{"data-svelte-h":!0}),$(o)!=="svelte-1m7gyu4"&&(o.innerHTML=v),a=d(t),f(i.$$.fragment,t)},m(t,_){m(t,o,_),m(t,a,_),y(i,t,_),b=!0},p:J,i(t){b||(g(i.$$.fragment,t),b=!0)},o(t){u(i.$$.fragment,t),b=!1},d(t){t&&(n(o),n(a)),M(i,t)}}}function wd(w){let o,v=`Although the recipe for forward pass needs to be defined within this function, one should call the <code>Module</code>
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`;return{c(){o=p("p"),o.innerHTML=v},l(a){o=c(a,"P",{"data-svelte-h":!0}),$(o)!=="svelte-fincs2"&&(o.innerHTML=v)},m(a,i){m(a,o,i)},p:J,d(a){a&&n(o)}}}function Td(w){let o,v="Example of sequence classification using <code>transformers.pipeline</code>:",a,i,b;return i=new U({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, pipeline
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> optimum.intel <span class="hljs-keyword">import</span> OVModelForSequenceClassification

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;distilbert-base-uncased-finetuned-sst-2-english&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = OVModelForSequenceClassification.from_pretrained(<span class="hljs-string">&quot;distilbert-base-uncased-finetuned-sst-2-english&quot;</span>, export=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>pipe = pipeline(<span class="hljs-string">&quot;text-classification&quot;</span>, model=model, tokenizer=tokenizer)
<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = pipe(<span class="hljs-string">&quot;Hello, my dog is cute&quot;</span>)`,wrap:!1}}),{c(){o=p("p"),o.innerHTML=v,a=r(),h(i.$$.fragment)},l(t){o=c(t,"P",{"data-svelte-h":!0}),$(o)!=="svelte-o0vbix"&&(o.innerHTML=v),a=d(t),f(i.$$.fragment,t)},m(t,_){m(t,o,_),m(t,a,_),y(i,t,_),b=!0},p:J,i(t){b||(g(i.$$.fragment,t),b=!0)},o(t){u(i.$$.fragment,t),b=!1},d(t){t&&(n(o),n(a)),M(i,t)}}}function Vd(w){let o,v=`Although the recipe for forward pass needs to be defined within this function, one should call the <code>Module</code>
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`;return{c(){o=p("p"),o.innerHTML=v},l(a){o=c(a,"P",{"data-svelte-h":!0}),$(o)!=="svelte-fincs2"&&(o.innerHTML=v)},m(a,i){m(a,o,i)},p:J,d(a){a&&n(o)}}}function kd(w){let o,v="Example of token classification using <code>transformers.pipelines</code>:",a,i,b;return i=new U({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, pipeline
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> optimum.intel <span class="hljs-keyword">import</span> OVModelForTokenClassification

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;dslim/bert-base-NER&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = OVModelForTokenClassification.from_pretrained(<span class="hljs-string">&quot;dslim/bert-base-NER&quot;</span>, export=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>pipe = pipeline(<span class="hljs-string">&quot;token-classification&quot;</span>, model=model, tokenizer=tokenizer)
<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = pipe(<span class="hljs-string">&quot;My Name is Peter and I live in New York.&quot;</span>)`,wrap:!1}}),{c(){o=p("p"),o.innerHTML=v,a=r(),h(i.$$.fragment)},l(t){o=c(t,"P",{"data-svelte-h":!0}),$(o)!=="svelte-6ve2pe"&&(o.innerHTML=v),a=d(t),f(i.$$.fragment,t)},m(t,_){m(t,o,_),m(t,a,_),y(i,t,_),b=!0},p:J,i(t){b||(g(i.$$.fragment,t),b=!0)},o(t){u(i.$$.fragment,t),b=!1},d(t){t&&(n(o),n(a)),M(i,t)}}}function Jd(w){let o,v=`Although the recipe for forward pass needs to be defined within this function, one should call the <code>Module</code>
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`;return{c(){o=p("p"),o.innerHTML=v},l(a){o=c(a,"P",{"data-svelte-h":!0}),$(o)!=="svelte-fincs2"&&(o.innerHTML=v)},m(a,i){m(a,o,i)},p:J,d(a){a&&n(o)}}}function Cd(w){let o,v="Example of audio classification using <code>transformers.pipelines</code>:",a,i,b;return i=new U({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> datasets <span class="hljs-keyword">import</span> load_dataset
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoFeatureExtractor, pipeline
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> optimum.intel <span class="hljs-keyword">import</span> OVModelForAudioClassification

<span class="hljs-meta">&gt;&gt;&gt; </span>preprocessor = AutoFeatureExtractor.from_pretrained(<span class="hljs-string">&quot;superb/hubert-base-superb-er&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = OVModelForAudioClassification.from_pretrained(<span class="hljs-string">&quot;superb/hubert-base-superb-er&quot;</span>, export=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>pipe = pipeline(<span class="hljs-string">&quot;audio-classification&quot;</span>, model=model, feature_extractor=preprocessor)
<span class="hljs-meta">&gt;&gt;&gt; </span>dataset = load_dataset(<span class="hljs-string">&quot;superb&quot;</span>, <span class="hljs-string">&quot;ks&quot;</span>, split=<span class="hljs-string">&quot;test&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>audio_file = dataset[<span class="hljs-number">3</span>][<span class="hljs-string">&quot;audio&quot;</span>][<span class="hljs-string">&quot;array&quot;</span>]
<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = pipe(audio_file)`,wrap:!1}}),{c(){o=p("p"),o.innerHTML=v,a=r(),h(i.$$.fragment)},l(t){o=c(t,"P",{"data-svelte-h":!0}),$(o)!=="svelte-1sgckdf"&&(o.innerHTML=v),a=d(t),f(i.$$.fragment,t)},m(t,_){m(t,o,_),m(t,a,_),y(i,t,_),b=!0},p:J,i(t){b||(g(i.$$.fragment,t),b=!0)},o(t){u(i.$$.fragment,t),b=!1},d(t){t&&(n(o),n(a)),M(i,t)}}}function jd(w){let o,v=`Although the recipe for forward pass needs to be defined within this function, one should call the <code>Module</code>
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`;return{c(){o=p("p"),o.innerHTML=v},l(a){o=c(a,"P",{"data-svelte-h":!0}),$(o)!=="svelte-fincs2"&&(o.innerHTML=v)},m(a,i){m(a,o,i)},p:J,d(a){a&&n(o)}}}function Fd(w){let o,v="Example of audio frame classification:",a,i,b;return i=new U({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoFeatureExtractor
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> optimum.intel <span class="hljs-keyword">import</span> OVModelForAudioFrameClassification
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> datasets <span class="hljs-keyword">import</span> load_dataset
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch

<span class="hljs-meta">&gt;&gt;&gt; </span>dataset = load_dataset(<span class="hljs-string">&quot;hf-internal-testing/librispeech_asr_demo&quot;</span>, <span class="hljs-string">&quot;clean&quot;</span>, split=<span class="hljs-string">&quot;validation&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>dataset = dataset.sort(<span class="hljs-string">&quot;id&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>sampling_rate = dataset.features[<span class="hljs-string">&quot;audio&quot;</span>].sampling_rate

<span class="hljs-meta">&gt;&gt;&gt; </span>feature_extractor = AutoFeatureExtractor.from_pretrained(<span class="hljs-string">&quot;anton-l/wav2vec2-base-superb-sd&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model =  OVModelForAudioFrameClassification.from_pretrained(<span class="hljs-string">&quot;anton-l/wav2vec2-base-superb-sd&quot;</span>, export=<span class="hljs-literal">True</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = feature_extractor(dataset[<span class="hljs-number">0</span>][<span class="hljs-string">&quot;audio&quot;</span>][<span class="hljs-string">&quot;array&quot;</span>], return_tensors=<span class="hljs-string">&quot;pt&quot;</span>, sampling_rate=sampling_rate)
<span class="hljs-meta">&gt;&gt;&gt; </span>   logits = model(**inputs).logits

<span class="hljs-meta">&gt;&gt;&gt; </span>probabilities = torch.sigmoid(torch.as_tensor(logits)[<span class="hljs-number">0</span>])
<span class="hljs-meta">&gt;&gt;&gt; </span>labels = (probabilities &gt; <span class="hljs-number">0.5</span>).long()
<span class="hljs-meta">&gt;&gt;&gt; </span>labels[<span class="hljs-number">0</span>].tolist()`,wrap:!1}}),{c(){o=p("p"),o.textContent=v,a=r(),h(i.$$.fragment)},l(t){o=c(t,"P",{"data-svelte-h":!0}),$(o)!=="svelte-12boqdm"&&(o.textContent=v),a=d(t),f(i.$$.fragment,t)},m(t,_){m(t,o,_),m(t,a,_),y(i,t,_),b=!0},p:J,i(t){b||(g(i.$$.fragment,t),b=!0)},o(t){u(i.$$.fragment,t),b=!1},d(t){t&&(n(o),n(a)),M(i,t)}}}function xd(w){let o,v=`Although the recipe for forward pass needs to be defined within this function, one should call the <code>Module</code>
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`;return{c(){o=p("p"),o.innerHTML=v},l(a){o=c(a,"P",{"data-svelte-h":!0}),$(o)!=="svelte-fincs2"&&(o.innerHTML=v)},m(a,i){m(a,o,i)},p:J,d(a){a&&n(o)}}}function Od(w){let o,v="Example of CTC:",a,i,b;return i=new U({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoFeatureExtractor
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> optimum.intel <span class="hljs-keyword">import</span> OVModelForCTC
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> datasets <span class="hljs-keyword">import</span> load_dataset

<span class="hljs-meta">&gt;&gt;&gt; </span>dataset = load_dataset(<span class="hljs-string">&quot;hf-internal-testing/librispeech_asr_demo&quot;</span>, <span class="hljs-string">&quot;clean&quot;</span>, split=<span class="hljs-string">&quot;validation&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>dataset = dataset.sort(<span class="hljs-string">&quot;id&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>sampling_rate = dataset.features[<span class="hljs-string">&quot;audio&quot;</span>].sampling_rate

<span class="hljs-meta">&gt;&gt;&gt; </span>processor = AutoFeatureExtractor.from_pretrained(<span class="hljs-string">&quot;facebook/hubert-large-ls960-ft&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = OVModelForCTC.from_pretrained(<span class="hljs-string">&quot;facebook/hubert-large-ls960-ft&quot;</span>, export=<span class="hljs-literal">True</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># audio file is decoded on the fly</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = processor(dataset[<span class="hljs-number">0</span>][<span class="hljs-string">&quot;audio&quot;</span>][<span class="hljs-string">&quot;array&quot;</span>], sampling_rate=sampling_rate, return_tensors=<span class="hljs-string">&quot;np&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>logits = model(**inputs).logits
<span class="hljs-meta">&gt;&gt;&gt; </span>predicted_ids = np.argmax(logits, axis=-<span class="hljs-number">1</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>transcription = processor.batch_decode(predicted_ids)`,wrap:!1}}),{c(){o=p("p"),o.textContent=v,a=r(),h(i.$$.fragment)},l(t){o=c(t,"P",{"data-svelte-h":!0}),$(o)!=="svelte-mdyxnj"&&(o.textContent=v),a=d(t),f(i.$$.fragment,t)},m(t,_){m(t,o,_),m(t,a,_),y(i,t,_),b=!0},p:J,i(t){b||(g(i.$$.fragment,t),b=!0)},o(t){u(i.$$.fragment,t),b=!1},d(t){t&&(n(o),n(a)),M(i,t)}}}function Ud(w){let o,v=`Although the recipe for forward pass needs to be defined within this function, one should call the <code>Module</code>
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`;return{c(){o=p("p"),o.innerHTML=v},l(a){o=c(a,"P",{"data-svelte-h":!0}),$(o)!=="svelte-fincs2"&&(o.innerHTML=v)},m(a,i){m(a,o,i)},p:J,d(a){a&&n(o)}}}function Zd(w){let o,v="Example of Audio XVector:",a,i,b;return i=new U({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoFeatureExtractor
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> optimum.intel <span class="hljs-keyword">import</span> OVModelForAudioXVector
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> datasets <span class="hljs-keyword">import</span> load_dataset
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch

<span class="hljs-meta">&gt;&gt;&gt; </span>dataset = load_dataset(<span class="hljs-string">&quot;hf-internal-testing/librispeech_asr_demo&quot;</span>, <span class="hljs-string">&quot;clean&quot;</span>, split=<span class="hljs-string">&quot;validation&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>dataset = dataset.sort(<span class="hljs-string">&quot;id&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>sampling_rate = dataset.features[<span class="hljs-string">&quot;audio&quot;</span>].sampling_rate

<span class="hljs-meta">&gt;&gt;&gt; </span>feature_extractor = AutoFeatureExtractor.from_pretrained(<span class="hljs-string">&quot;anton-l/wav2vec2-base-superb-sv&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = OVModelForAudioXVector.from_pretrained(<span class="hljs-string">&quot;anton-l/wav2vec2-base-superb-sv&quot;</span>, export=<span class="hljs-literal">True</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># audio file is decoded on the fly</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = feature_extractor(
<span class="hljs-meta">... </span>    [d[<span class="hljs-string">&quot;array&quot;</span>] <span class="hljs-keyword">for</span> d <span class="hljs-keyword">in</span> dataset[:<span class="hljs-number">2</span>][<span class="hljs-string">&quot;audio&quot;</span>]], sampling_rate=sampling_rate, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>, padding=<span class="hljs-literal">True</span>
<span class="hljs-meta">... </span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>    embeddings = model(**inputs).embeddings

<span class="hljs-meta">&gt;&gt;&gt; </span>embeddings = torch.nn.functional.normalize(embeddings, dim=-<span class="hljs-number">1</span>).cpu()

<span class="hljs-meta">&gt;&gt;&gt; </span>cosine_sim = torch.nn.CosineSimilarity(dim=-<span class="hljs-number">1</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>similarity = cosine_sim(embeddings[<span class="hljs-number">0</span>], embeddings[<span class="hljs-number">1</span>])
<span class="hljs-meta">&gt;&gt;&gt; </span>threshold = <span class="hljs-number">0.7</span>
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">if</span> similarity &lt; threshold:
<span class="hljs-meta">... </span>    <span class="hljs-built_in">print</span>(<span class="hljs-string">&quot;Speakers are not the same!&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-built_in">round</span>(similarity.item(), <span class="hljs-number">2</span>)`,wrap:!1}}),{c(){o=p("p"),o.textContent=v,a=r(),h(i.$$.fragment)},l(t){o=c(t,"P",{"data-svelte-h":!0}),$(o)!=="svelte-1kzdm5c"&&(o.textContent=v),a=d(t),f(i.$$.fragment,t)},m(t,_){m(t,o,_),m(t,a,_),y(i,t,_),b=!0},p:J,i(t){b||(g(i.$$.fragment,t),b=!0)},o(t){u(i.$$.fragment,t),b=!1},d(t){t&&(n(o),n(a)),M(i,t)}}}function Id(w){let o,v=`Although the recipe for forward pass needs to be defined within this function, one should call the <code>Module</code>
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`;return{c(){o=p("p"),o.innerHTML=v},l(a){o=c(a,"P",{"data-svelte-h":!0}),$(o)!=="svelte-fincs2"&&(o.innerHTML=v)},m(a,i){m(a,o,i)},p:J,d(a){a&&n(o)}}}function Wd(w){let o,v="Example of text generation:",a,i,b;return i=new U({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoProcessor
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> optimum.intel <span class="hljs-keyword">import</span> OVModelForSpeechSeq2Seq
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> datasets <span class="hljs-keyword">import</span> load_dataset

<span class="hljs-meta">&gt;&gt;&gt; </span>processor = AutoProcessor.from_pretrained(<span class="hljs-string">&quot;openai/whisper-tiny&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = OVModelForSpeechSeq2Seq.from_pretrained(<span class="hljs-string">&quot;openai/whisper-tiny&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>ds = load_dataset(<span class="hljs-string">&quot;hf-internal-testing/librispeech_asr_dummy&quot;</span>, <span class="hljs-string">&quot;clean&quot;</span>, split=<span class="hljs-string">&quot;validation&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = processor.feature_extractor(ds[<span class="hljs-number">0</span>][<span class="hljs-string">&quot;audio&quot;</span>][<span class="hljs-string">&quot;array&quot;</span>], return_tensors=<span class="hljs-string">&quot;pt&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>gen_tokens = model.generate(inputs=inputs.input_features)
<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = processor.tokenizer.batch_decode(gen_tokens)`,wrap:!1}}),{c(){o=p("p"),o.textContent=v,a=r(),h(i.$$.fragment)},l(t){o=c(t,"P",{"data-svelte-h":!0}),$(o)!=="svelte-1bvu0cy"&&(o.textContent=v),a=d(t),f(i.$$.fragment,t)},m(t,_){m(t,o,_),m(t,a,_),y(i,t,_),b=!0},p:J,i(t){b||(g(i.$$.fragment,t),b=!0)},o(t){u(i.$$.fragment,t),b=!1},d(t){t&&(n(o),n(a)),M(i,t)}}}function Nd(w){let o,v="Example using <code>transformers.pipeline</code>:",a,i,b;return i=new U({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoProcessor, pipeline
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> optimum.intel <span class="hljs-keyword">import</span> OVModelForSpeechSeq2Seq
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> datasets <span class="hljs-keyword">import</span> load_dataset

<span class="hljs-meta">&gt;&gt;&gt; </span>processor = AutoProcessor.from_pretrained(<span class="hljs-string">&quot;openai/whisper-tiny&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = OVModelForSpeechSeq2Seq.from_pretrained(<span class="hljs-string">&quot;openai/whisper-tiny&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>speech_recognition = pipeline(<span class="hljs-string">&quot;automatic-speech-recognition&quot;</span>, model=model, tokenizer=processor.tokenizer, feature_extractor=processor.feature_extractor)

<span class="hljs-meta">&gt;&gt;&gt; </span>ds = load_dataset(<span class="hljs-string">&quot;hf-internal-testing/librispeech_asr_dummy&quot;</span>, <span class="hljs-string">&quot;clean&quot;</span>, split=<span class="hljs-string">&quot;validation&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>pred = speech_recognition(ds[<span class="hljs-number">0</span>][<span class="hljs-string">&quot;audio&quot;</span>][<span class="hljs-string">&quot;array&quot;</span>])`,wrap:!1}}),{c(){o=p("p"),o.innerHTML=v,a=r(),h(i.$$.fragment)},l(t){o=c(t,"P",{"data-svelte-h":!0}),$(o)!=="svelte-1u1xsxh"&&(o.innerHTML=v),a=d(t),f(i.$$.fragment,t)},m(t,_){m(t,o,_),m(t,a,_),y(i,t,_),b=!0},p:J,i(t){b||(g(i.$$.fragment,t),b=!0)},o(t){u(i.$$.fragment,t),b=!1},d(t){t&&(n(o),n(a)),M(i,t)}}}function qd(w){let o,v=`Although the recipe for forward pass needs to be defined within this function, one should call the <code>Module</code>
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`;return{c(){o=p("p"),o.innerHTML=v},l(a){o=c(a,"P",{"data-svelte-h":!0}),$(o)!=="svelte-fincs2"&&(o.innerHTML=v)},m(a,i){m(a,o,i)},p:J,d(a){a&&n(o)}}}function Gd(w){let o,v="Example of image classification using <code>transformers.pipelines</code>:",a,i,b;return i=new U({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoFeatureExtractor, pipeline
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> optimum.intel <span class="hljs-keyword">import</span> OVModelForImageClassification

<span class="hljs-meta">&gt;&gt;&gt; </span>preprocessor = AutoFeatureExtractor.from_pretrained(<span class="hljs-string">&quot;google/vit-base-patch16-224&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = OVModelForImageClassification.from_pretrained(<span class="hljs-string">&quot;google/vit-base-patch16-224&quot;</span>, export=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.reshape(batch_size=<span class="hljs-number">1</span>, sequence_length=<span class="hljs-number">3</span>, height=<span class="hljs-number">224</span>, width=<span class="hljs-number">224</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>pipe = pipeline(<span class="hljs-string">&quot;image-classification&quot;</span>, model=model, feature_extractor=preprocessor)
<span class="hljs-meta">&gt;&gt;&gt; </span>url = <span class="hljs-string">&quot;http://images.cocodataset.org/val2017/000000039769.jpg&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = pipe(url)`,wrap:!1}}),{c(){o=p("p"),o.innerHTML=v,a=r(),h(i.$$.fragment)},l(t){o=c(t,"P",{"data-svelte-h":!0}),$(o)!=="svelte-1w9p9ki"&&(o.innerHTML=v),a=d(t),f(i.$$.fragment,t)},m(t,_){m(t,o,_),m(t,a,_),y(i,t,_),b=!0},p:J,i(t){b||(g(i.$$.fragment,t),b=!0)},o(t){u(i.$$.fragment,t),b=!1},d(t){t&&(n(o),n(a)),M(i,t)}}}function Xd(w){let o,v='models hosted on <a href="https://huggingface.co/timm" rel="nofollow">HuggingFaceHub</a>. Example:',a,i,b;return i=new U({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> pipeline
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> optimum.intel.openvino.modeling_timm <span class="hljs-keyword">import</span> TimmImageProcessor
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> optimum.intel <span class="hljs-keyword">import</span> OVModelForImageClassification

<span class="hljs-meta">&gt;&gt;&gt; </span>model_id = <span class="hljs-string">&quot;timm/vit_tiny_patch16_224.augreg_in21k&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>preprocessor = TimmImageProcessor.from_pretrained(model_id)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = OVModelForImageClassification.from_pretrained(model_id, export=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>pipe = pipeline(<span class="hljs-string">&quot;image-classification&quot;</span>, model=model, feature_extractor=preprocessor)
<span class="hljs-meta">&gt;&gt;&gt; </span>url = <span class="hljs-string">&quot;http://images.cocodataset.org/val2017/000000039769.jpg&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = pipe(url)`,wrap:!1}}),{c(){o=p("p"),o.innerHTML=v,a=r(),h(i.$$.fragment)},l(t){o=c(t,"P",{"data-svelte-h":!0}),$(o)!=="svelte-1b3qe4z"&&(o.innerHTML=v),a=d(t),f(i.$$.fragment,t)},m(t,_){m(t,o,_),m(t,a,_),y(i,t,_),b=!0},p:J,i(t){b||(g(i.$$.fragment,t),b=!0)},o(t){u(i.$$.fragment,t),b=!1},d(t){t&&(n(o),n(a)),M(i,t)}}}function zd(w){let o,v=`Although the recipe for forward pass needs to be defined within this function, one should call the <code>Module</code>
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`;return{c(){o=p("p"),o.innerHTML=v},l(a){o=c(a,"P",{"data-svelte-h":!0}),$(o)!=="svelte-fincs2"&&(o.innerHTML=v)},m(a,i){m(a,o,i)},p:J,d(a){a&&n(o)}}}function Rd(w){let o,v="Example of text generation:",a,i,b;return i=new U({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Qcm9jZXNzb3IlMkMlMjBBdXRvVG9rZW5pemVyJTBBZnJvbSUyMG9wdGltdW0uaW50ZWwlMjBpbXBvcnQlMjBPVk1vZGVsRm9yVmlzaW9uMlNlcSUwQWZyb20lMjBQSUwlMjBpbXBvcnQlMjBJbWFnZSUwQWltcG9ydCUyMHJlcXVlc3RzJTBBJTBBJTBBcHJvY2Vzc29yJTIwJTNEJTIwQXV0b1Byb2Nlc3Nvci5mcm9tX3ByZXRyYWluZWQoJTIybWljcm9zb2Z0JTJGdHJvY3Itc21hbGwtaGFuZHdyaXR0ZW4lMjIpJTBBdG9rZW5pemVyJTIwJTNEJTIwQXV0b1Rva2VuaXplci5mcm9tX3ByZXRyYWluZWQoJTIybWljcm9zb2Z0JTJGdHJvY3Itc21hbGwtaGFuZHdyaXR0ZW4lMjIpJTBBbW9kZWwlMjAlM0QlMjBPVk1vZGVsRm9yVmlzaW9uMlNlcS5mcm9tX3ByZXRyYWluZWQoJTIybWljcm9zb2Z0JTJGdHJvY3Itc21hbGwtaGFuZHdyaXR0ZW4lMjIlMkMlMjBleHBvcnQlM0RUcnVlKSUwQSUwQXVybCUyMCUzRCUyMCUyMmh0dHBzJTNBJTJGJTJGZmtpLnRpYy5oZWlhLWZyLmNoJTJGc3RhdGljJTJGaW1nJTJGYTAxLTEyMi0wMi0wMC5qcGclMjIlMEFpbWFnZSUyMCUzRCUyMEltYWdlLm9wZW4ocmVxdWVzdHMuZ2V0KHVybCUyQyUyMHN0cmVhbSUzRFRydWUpLnJhdyklMEFpbnB1dHMlMjAlM0QlMjBwcm9jZXNzb3IoaW1hZ2UlMkMlMjByZXR1cm5fdGVuc29ycyUzRCUyMnB0JTIyKSUwQSUwQWdlbl90b2tlbnMlMjAlM0QlMjBtb2RlbC5nZW5lcmF0ZSgqKmlucHV0cyklMEFvdXRwdXRzJTIwJTNEJTIwdG9rZW5pemVyLmJhdGNoX2RlY29kZShnZW5fdG9rZW5zJTJDJTIwc2tpcF9zcGVjaWFsX3Rva2VucyUzRFRydWUpJTBB",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoProcessor, AutoTokenizer
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> optimum.intel <span class="hljs-keyword">import</span> OVModelForVision2Seq
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> PIL <span class="hljs-keyword">import</span> Image
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> requests


<span class="hljs-meta">&gt;&gt;&gt; </span>processor = AutoProcessor.from_pretrained(<span class="hljs-string">&quot;microsoft/trocr-small-handwritten&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;microsoft/trocr-small-handwritten&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = OVModelForVision2Seq.from_pretrained(<span class="hljs-string">&quot;microsoft/trocr-small-handwritten&quot;</span>, export=<span class="hljs-literal">True</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>url = <span class="hljs-string">&quot;https://fki.tic.heia-fr.ch/static/img/a01-122-02-00.jpg&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>image = Image.<span class="hljs-built_in">open</span>(requests.get(url, stream=<span class="hljs-literal">True</span>).raw)
<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = processor(image, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>gen_tokens = model.generate(**inputs)
<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = tokenizer.batch_decode(gen_tokens, skip_special_tokens=<span class="hljs-literal">True</span>)
`,wrap:!1}}),{c(){o=p("p"),o.textContent=v,a=r(),h(i.$$.fragment)},l(t){o=c(t,"P",{"data-svelte-h":!0}),$(o)!=="svelte-1bvu0cy"&&(o.textContent=v),a=d(t),f(i.$$.fragment,t)},m(t,_){m(t,o,_),m(t,a,_),y(i,t,_),b=!0},p:J,i(t){b||(g(i.$$.fragment,t),b=!0)},o(t){u(i.$$.fragment,t),b=!1},d(t){t&&(n(o),n(a)),M(i,t)}}}function Bd(w){let o,v="Example using <code>transformers.pipeline</code>:",a,i,b;return i=new U({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoProcessor, AutoTokenizer, pipeline
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> optimum.intel <span class="hljs-keyword">import</span> OVModelForVision2Seq
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> PIL <span class="hljs-keyword">import</span> Image
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> requests


<span class="hljs-meta">&gt;&gt;&gt; </span>processor = AutoProcessor.from_pretrained(<span class="hljs-string">&quot;microsoft/trocr-small-handwritten&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;microsoft/trocr-small-handwritten&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = OVModelForVision2Seq.from_pretrained(<span class="hljs-string">&quot;microsoft/trocr-small-handwritten&quot;</span>, export=<span class="hljs-literal">True</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>url = <span class="hljs-string">&quot;https://fki.tic.heia-fr.ch/static/img/a01-122-02-00.jpg&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>image = Image.<span class="hljs-built_in">open</span>(requests.get(url, stream=<span class="hljs-literal">True</span>).raw)

<span class="hljs-meta">&gt;&gt;&gt; </span>image_to_text = pipeline(<span class="hljs-string">&quot;image-to-text&quot;</span>, model=model, tokenizer=tokenizer, feature_extractor=processor, image_processor=processor)
<span class="hljs-meta">&gt;&gt;&gt; </span>pred = image_to_text(image)`,wrap:!1}}),{c(){o=p("p"),o.innerHTML=v,a=r(),h(i.$$.fragment)},l(t){o=c(t,"P",{"data-svelte-h":!0}),$(o)!=="svelte-1u1xsxh"&&(o.innerHTML=v),a=d(t),f(i.$$.fragment,t)},m(t,_){m(t,o,_),m(t,a,_),y(i,t,_),b=!0},p:J,i(t){b||(g(i.$$.fragment,t),b=!0)},o(t){u(i.$$.fragment,t),b=!1},d(t){t&&(n(o),n(a)),M(i,t)}}}function Sd(w){let o,v=`Although the recipe for forward pass needs to be defined within this function, one should call the <code>Module</code>
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`;return{c(){o=p("p"),o.innerHTML=v},l(a){o=c(a,"P",{"data-svelte-h":!0}),$(o)!=="svelte-fincs2"&&(o.innerHTML=v)},m(a,i){m(a,o,i)},p:J,d(a){a&&n(o)}}}function Ld(w){let o,v="Example of pix2struct:",a,i,b;return i=new U({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Qcm9jZXNzb3IlMEFmcm9tJTIwb3B0aW11bS5pbnRlbCUyMGltcG9ydCUyME9WTW9kZWxGb3JQaXgyU3RydWN0JTBBZnJvbSUyMFBJTCUyMGltcG9ydCUyMEltYWdlJTBBaW1wb3J0JTIwcmVxdWVzdHMlMEElMEFwcm9jZXNzb3IlMjAlM0QlMjBBdXRvUHJvY2Vzc29yLmZyb21fcHJldHJhaW5lZCglMjJnb29nbGUlMkZwaXgyc3RydWN0LWFpMmQtYmFzZSUyMiklMEFtb2RlbCUyMCUzRCUyME9WTW9kZWxGb3JQaXgyU3RydWN0LmZyb21fcHJldHJhaW5lZCglMjJnb29nbGUlMkZwaXgyc3RydWN0LWFpMmQtYmFzZSUyMiUyQyUyMGV4cG9ydCUzRFRydWUpJTBBJTBBdXJsJTIwJTNEJTIwJTIyaHR0cHMlM0ElMkYlMkZodWdnaW5nZmFjZS5jbyUyRmRhdGFzZXRzJTJGaHVnZ2luZ2ZhY2UlMkZkb2N1bWVudGF0aW9uLWltYWdlcyUyRnJlc29sdmUlMkZtYWluJTJGdHJhbnNmb3JtZXJzJTJGdGFza3MlMkZhaTJkLWRlbW8uanBnJTIyJTBBaW1hZ2UlMjAlM0QlMjBJbWFnZS5vcGVuKHJlcXVlc3RzLmdldCh1cmwlMkMlMjBzdHJlYW0lM0RUcnVlKS5yYXcpJTBBcXVlc3Rpb24lMjAlM0QlMjAlMjJXaGF0JTIwZG9lcyUyMHRoZSUyMGxhYmVsJTIwMTUlMjByZXByZXNlbnQlM0YlMjAoMSklMjBsYXZhJTIwKDIpJTIwY29yZSUyMCgzKSUyMHR1bm5lbCUyMCg0KSUyMGFzaCUyMGNsb3VkJTIyJTBBaW5wdXRzJTIwJTNEJTIwcHJvY2Vzc29yKGltYWdlcyUzRGltYWdlJTJDJTIwdGV4dCUzRHF1ZXN0aW9uJTJDJTIwcmV0dXJuX3RlbnNvcnMlM0QlMjJwdCUyMiklMEElMEFnZW5fdG9rZW5zJTIwJTNEJTIwbW9kZWwuZ2VuZXJhdGUoKippbnB1dHMpJTBBb3V0cHV0cyUyMCUzRCUyMHByb2Nlc3Nvci5iYXRjaF9kZWNvZGUoZ2VuX3Rva2VucyUyQyUyMHNraXBfc3BlY2lhbF90b2tlbnMlM0RUcnVlKQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoProcessor
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> optimum.intel <span class="hljs-keyword">import</span> OVModelForPix2Struct
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> PIL <span class="hljs-keyword">import</span> Image
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> requests

<span class="hljs-meta">&gt;&gt;&gt; </span>processor = AutoProcessor.from_pretrained(<span class="hljs-string">&quot;google/pix2struct-ai2d-base&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = OVModelForPix2Struct.from_pretrained(<span class="hljs-string">&quot;google/pix2struct-ai2d-base&quot;</span>, export=<span class="hljs-literal">True</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>url = <span class="hljs-string">&quot;https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>image = Image.<span class="hljs-built_in">open</span>(requests.get(url, stream=<span class="hljs-literal">True</span>).raw)
<span class="hljs-meta">&gt;&gt;&gt; </span>question = <span class="hljs-string">&quot;What does the label 15 represent? (1) lava (2) core (3) tunnel (4) ash cloud&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = processor(images=image, text=question, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>gen_tokens = model.generate(**inputs)
<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = processor.batch_decode(gen_tokens, skip_special_tokens=<span class="hljs-literal">True</span>)`,wrap:!1}}),{c(){o=p("p"),o.textContent=v,a=r(),h(i.$$.fragment)},l(t){o=c(t,"P",{"data-svelte-h":!0}),$(o)!=="svelte-11ikyn3"&&(o.textContent=v),a=d(t),f(i.$$.fragment,t)},m(t,_){m(t,o,_),m(t,a,_),y(i,t,_),b=!0},p:J,i(t){b||(g(i.$$.fragment,t),b=!0)},o(t){u(i.$$.fragment,t),b=!1},d(t){t&&(n(o),n(a)),M(i,t)}}}function Hd(w){let o,v=`Although the recipe for forward pass needs to be defined within this function, one should call the <code>Module</code>
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`;return{c(){o=p("p"),o.innerHTML=v},l(a){o=c(a,"P",{"data-svelte-h":!0}),$(o)!=="svelte-fincs2"&&(o.innerHTML=v)},m(a,i){m(a,o,i)},p:J,d(a){a&&n(o)}}}function Ed(w){let o,v="Example of custom tasks (e.g. a sentence transformers with a pooler head):",a,i,b;return i=new U({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> optimum.intel <span class="hljs-keyword">import</span> OVModelForCustomTasks

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;IlyasMoutawwakil/sbert-all-MiniLM-L6-v2-with-pooler&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = OVModelForCustomTasks.from_pretrained(<span class="hljs-string">&quot;IlyasMoutawwakil/sbert-all-MiniLM-L6-v2-with-pooler&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = tokenizer(<span class="hljs-string">&quot;I love burritos!&quot;</span>, return_tensors=<span class="hljs-string">&quot;np&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(**inputs)
<span class="hljs-meta">&gt;&gt;&gt; </span>last_hidden_state = outputs.last_hidden_state
<span class="hljs-meta">&gt;&gt;&gt; </span>pooler_output = outputs.pooler_output`,wrap:!1}}),{c(){o=p("p"),o.textContent=v,a=r(),h(i.$$.fragment)},l(t){o=c(t,"P",{"data-svelte-h":!0}),$(o)!=="svelte-1xuxpxx"&&(o.textContent=v),a=d(t),f(i.$$.fragment,t)},m(t,_){m(t,o,_),m(t,a,_),y(i,t,_),b=!0},p:J,i(t){b||(g(i.$$.fragment,t),b=!0)},o(t){u(i.$$.fragment,t),b=!1},d(t){t&&(n(o),n(a)),M(i,t)}}}function Pd(w){let o,v=`Although the recipe for forward pass needs to be defined within this function, one should call the <code>Module</code>
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`;return{c(){o=p("p"),o.innerHTML=v},l(a){o=c(a,"P",{"data-svelte-h":!0}),$(o)!=="svelte-fincs2"&&(o.innerHTML=v)},m(a,i){m(a,o,i)},p:J,d(a){a&&n(o)}}}function Qd(w){let o,v="Example of feature extraction using <code>transformers.pipelines</code>:",a,i,b;return i=new U({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, pipeline
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> optimum.intel <span class="hljs-keyword">import</span> OVModelForFeatureExtraction

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;sentence-transformers/all-MiniLM-L6-v2&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = OVModelForFeatureExtraction.from_pretrained(<span class="hljs-string">&quot;sentence-transformers/all-MiniLM-L6-v2&quot;</span>, export=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>pipe = pipeline(<span class="hljs-string">&quot;feature-extraction&quot;</span>, model=model, tokenizer=tokenizer)
<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = pipe(<span class="hljs-string">&quot;My Name is Peter and I live in New York.&quot;</span>)`,wrap:!1}}),{c(){o=p("p"),o.innerHTML=v,a=r(),h(i.$$.fragment)},l(t){o=c(t,"P",{"data-svelte-h":!0}),$(o)!=="svelte-9brije"&&(o.innerHTML=v),a=d(t),f(i.$$.fragment,t)},m(t,_){m(t,o,_),m(t,a,_),y(i,t,_),b=!0},p:J,i(t){b||(g(i.$$.fragment,t),b=!0)},o(t){u(i.$$.fragment,t),b=!1},d(t){t&&(n(o),n(a)),M(i,t)}}}function Yd(w){let o,v,a,i,b,t,_,Le,x,He,j,F,Z,Ee,Ja="Base OVModel class.",Is,Ae,Wt,Ws,gn,Xl="Instantiate a pretrained model from a pre-trained model configuration.",Ns,Ke,Nt,qs,un,zl="Propagates the given input shapes on the model’s layers, fixing the inputs shapes of the model.",xa,qt,Oa,Gt,Rl="The following classes are available for the following natural language processing tasks.",Ua,Xt,Za,I,zt,Gs,hn,Bl=`OpenVINO Model with a causal language modeling head on top (linear layer with weights tied to the input
embeddings).`,Xs,fn,Sl=`This model inherits from <code>optimum.intel.openvino.modeling.OVBaseModel</code>. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving)`,zs,yn,Rt,Rs,Mn,Bt,Ia,St,Wa,S,Lt,Bs,vn,Ll="OpenVINO Model with a MaskedLMOutput for masked language modeling tasks.",Ss,bn,Hl=`This model inherits from <code>optimum.intel.openvino.modeling.OVBaseModel</code>. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving)`,Ls,ie,Ht,Hs,_n,El='The <a href="/docs/optimum.intel/v1.26.0/en/openvino/reference#optimum.intel.OVModelForMaskedLM">OVModelForMaskedLM</a> forward method, overrides the <code>__call__</code> special method.',Es,et,Ps,tt,Na,Et,qa,ye,Pt,Qs,$n,Pl="Sequence-to-sequence model with a language modeling head for OpenVINO inference.",Ys,X,Qt,Ds,wn,Ql='The <a href="/docs/optimum.intel/v1.26.0/en/openvino/reference#optimum.intel.OVModelForSeq2SeqLM">OVModelForSeq2SeqLM</a> forward method, overrides the <code>__call__</code> special method.',As,ot,Ks,nt,ei,at,Ga,Yt,Xa,L,Dt,ti,Tn,Yl="OpenVINO Model with a QuestionAnsweringModelOutput for extractive question-answering tasks.",oi,Vn,Dl=`This model inherits from <code>optimum.intel.openvino.modeling.OVBaseModel</code>. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving)`,ni,le,At,ai,kn,Al='The <a href="/docs/optimum.intel/v1.26.0/en/openvino/reference#optimum.intel.OVModelForQuestionAnswering">OVModelForQuestionAnswering</a> forward method, overrides the <code>__call__</code> special method.',si,st,ii,it,za,Kt,Ra,H,eo,li,Jn,Kl="OpenVINO Model with a SequenceClassifierOutput for sequence classification tasks.",ri,Cn,er=`This model inherits from <code>optimum.intel.openvino.modeling.OVBaseModel</code>. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving)`,di,re,to,pi,jn,tr='The <a href="/docs/optimum.intel/v1.26.0/en/openvino/reference#optimum.intel.OVModelForSequenceClassification">OVModelForSequenceClassification</a> forward method, overrides the <code>__call__</code> special method.',ci,lt,mi,rt,Ba,oo,Sa,E,no,gi,Fn,or="OpenVINO Model with a TokenClassifierOutput for token classification tasks.",ui,xn,nr=`This model inherits from <code>optimum.intel.openvino.modeling.OVBaseModel</code>. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving)`,hi,de,ao,fi,On,ar='The <a href="/docs/optimum.intel/v1.26.0/en/openvino/reference#optimum.intel.OVModelForTokenClassification">OVModelForTokenClassification</a> forward method, overrides the <code>__call__</code> special method.',yi,dt,Mi,pt,La,so,Ha,io,sr="The following classes are available for the following audio tasks.",Ea,lo,Pa,P,ro,vi,Un,ir="OpenVINO Model with a SequenceClassifierOutput for audio classification tasks.",bi,Zn,lr=`This model inherits from <code>optimum.intel.openvino.modeling.OVBaseModel</code>. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving)`,_i,pe,po,$i,In,rr='The <a href="/docs/optimum.intel/v1.26.0/en/openvino/reference#optimum.intel.OVModelForAudioClassification">OVModelForAudioClassification</a> forward method, overrides the <code>__call__</code> special method.',wi,ct,Ti,mt,Qa,co,Ya,W,mo,Vi,Wn,dr="OpenVINO Model for with a frame classification head on top for tasks like Speaker Diarization.",ki,Nn,pr=`This model inherits from <code>optimum.intel.openvino.modeling.OVBaseModel</code>. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving)`,Ji,qn,cr="Audio Frame Classification model for OpenVINO.",Ci,ce,go,ji,Gn,mr='The <a href="/docs/optimum.intel/v1.26.0/en/openvino/reference#optimum.intel.OVModelForAudioFrameClassification">OVModelForAudioFrameClassification</a> forward method, overrides the <code>__call__</code> special method.',Fi,gt,xi,ut,Da,uo,Aa,N,ho,Oi,Xn,gr="Onnx Model with a language modeling head on top for Connectionist Temporal Classification (CTC).",Ui,zn,ur=`This model inherits from <code>optimum.intel.openvino.modeling.OVBaseModel</code>. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving)`,Zi,Rn,hr="CTC model for OpenVINO.",Ii,me,fo,Wi,Bn,fr='The <a href="/docs/optimum.intel/v1.26.0/en/openvino/reference#optimum.intel.OVModelForCTC">OVModelForCTC</a> forward method, overrides the <code>__call__</code> special method.',Ni,ht,qi,ft,Ka,yo,es,q,Mo,Gi,Sn,yr="Onnx Model with an XVector feature extraction head on top for tasks like Speaker Verification.",Xi,Ln,Mr=`This model inherits from <code>optimum.intel.openvino.modeling.OVBaseModel</code>. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving)`,zi,Hn,vr="Audio XVector model for OpenVINO.",Ri,ge,vo,Bi,En,br='The <a href="/docs/optimum.intel/v1.26.0/en/openvino/reference#optimum.intel.OVModelForAudioXVector">OVModelForAudioXVector</a> forward method, overrides the <code>__call__</code> special method.',Si,yt,Li,Mt,ts,bo,os,Me,_o,Hi,Pn,_r="Speech Sequence-to-sequence model with a language modeling head for OpenVINO inference. This class officially supports whisper, speech_to_text.",Ei,z,$o,Pi,Qn,$r='The <a href="/docs/optimum.intel/v1.26.0/en/openvino/reference#optimum.intel.OVModelForSpeechSeq2Seq">OVModelForSpeechSeq2Seq</a> forward method, overrides the <code>__call__</code> special method.',Qi,vt,Yi,bt,Di,_t,ns,wo,as,To,wr="The following classes are available for the following computer vision tasks.",ss,Vo,is,Q,ko,Ai,Yn,Tr="OpenVINO Model with a ImageClassifierOutput for image classification tasks.",Ki,Dn,Vr=`This model inherits from <code>optimum.intel.openvino.modeling.OVBaseModel</code>. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving)`,el,R,Jo,tl,An,kr='The <a href="/docs/optimum.intel/v1.26.0/en/openvino/reference#optimum.intel.OVModelForImageClassification">OVModelForImageClassification</a> forward method, overrides the <code>__call__</code> special method.',ol,$t,nl,wt,al,Tt,ls,Co,rs,jo,Jr="The following classes are available for the following multimodal tasks.",ds,Fo,ps,ve,xo,sl,Kn,Cr="VisionEncoderDecoder Sequence-to-sequence model with a language modeling head for OpenVINO inference.",il,B,Oo,ll,ea,jr='The <a href="/docs/optimum.intel/v1.26.0/en/openvino/reference#optimum.intel.OVModelForVision2Seq">OVModelForVision2Seq</a> forward method, overrides the <code>__call__</code> special method.',rl,Vt,dl,kt,pl,Jt,cs,Uo,ms,be,Zo,cl,ta,Fr="Pix2Struct model with a language modeling head for OpenVINO inference.",ml,ue,Io,gl,oa,xr='The <a href="/docs/optimum.intel/v1.26.0/en/openvino/reference#optimum.intel.OVModelForPix2Struct">OVModelForPix2Struct</a> forward method, overrides the <code>__call__</code> special method.',ul,Ct,hl,jt,gs,Wo,us,No,hs,Y,qo,fl,na,Or="OpenVINO Model for custom tasks. It can be used to leverage the inference acceleration for any single-file OpenVINO model, that may use custom inputs and outputs.",yl,aa,Ur=`This model inherits from <code>optimum.intel.openvino.modeling.OVBaseModel</code>. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving)`,Ml,he,Go,vl,sa,Zr='The <a href="/docs/optimum.intel/v1.26.0/en/openvino/reference#optimum.intel.OVModelForCustomTasks">OVModelForCustomTasks</a> forward method, overrides the <code>__call__</code> special method.',bl,Ft,_l,xt,fs,Xo,ys,D,zo,$l,ia,Ir="OpenVINO Model with a BaseModelOutput for feature extraction tasks.",wl,la,Wr=`This model inherits from <code>optimum.intel.openvino.modeling.OVBaseModel</code>. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving)`,Tl,fe,Ro,Vl,ra,Nr='The <a href="/docs/optimum.intel/v1.26.0/en/openvino/reference#optimum.intel.OVModelForFeatureExtraction">OVModelForFeatureExtraction</a> forward method, overrides the <code>__call__</code> special method.',kl,Ot,Jl,Ut,Ms,Bo,vs,So,bs,_e,Lo,Cl,da,qr='OpenVINO-powered stable diffusion pipeline corresponding to <a href="https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/stable_diffusion#diffusers.StableDiffusionPipeline" rel="nofollow">diffusers.StableDiffusionPipeline</a>.',jl,pa,Ho,_s,Eo,$s,$e,Po,Fl,ca,Gr='OpenVINO-powered stable diffusion pipeline corresponding to <a href="https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/stable_diffusion_xl#diffusers.StableDiffusionXLPipeline" rel="nofollow">diffusers.StableDiffusionXLPipeline</a>.',xl,ma,Qo,ws,Yo,Ts,we,Do,Ol,ga,Xr='OpenVINO-powered stable diffusion pipeline corresponding to <a href="https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/latent_consistency#diffusers.LatentConsistencyModelPipeline" rel="nofollow">diffusers.LatentConsistencyModelPipeline</a>.',Ul,ua,Ao,Vs,Ko,ks,en,Js,Te,tn,Zl,ha,zr='OpenVINO-powered stable diffusion pipeline corresponding to <a href="https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/stable_diffusion_img2img#diffusers.StableDiffusionImg2ImgPipeline" rel="nofollow">diffusers.StableDiffusionImg2ImgPipeline</a>.',Il,fa,on,Cs,nn,js,Ve,an,Wl,ya,Rr='OpenVINO-powered stable diffusion pipeline corresponding to <a href="https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/stable_diffusion_xl#diffusers.StableDiffusionXLImg2ImgPipeline" rel="nofollow">diffusers.StableDiffusionXLImg2ImgPipeline</a>.',Nl,Ma,sn,Fs,ln,xs,rn,Os,ke,dn,ql,va,Br='OpenVINO-powered stable diffusion pipeline corresponding to <a href="https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/stable_diffusion_inpaint#diffusers.StableDiffusionInpaintPipeline" rel="nofollow">diffusers.StableDiffusionInpaintPipeline</a>.',Gl,ba,pn,Us,Ca,Zs;return b=new md({props:{containerStyle:"float: right; margin-left: 10px; display: inline-flex; position: relative; z-index: 10;"}}),_=new C({props:{title:"Models",local:"models",headingTag:"h1"}}),x=new C({props:{title:"Generic model classes",local:"optimum.intel.openvino.modeling_base.OVBaseModel",headingTag:"h2"}}),F=new k({props:{name:"class optimum.intel.openvino.modeling_base.OVBaseModel",anchor:"optimum.intel.openvino.modeling_base.OVBaseModel",parameters:[{name:"model",val:": Model"},{name:"config",val:": PretrainedConfig = None"},{name:"device",val:": str = 'CPU'"},{name:"dynamic_shapes",val:": bool = True"},{name:"ov_config",val:": typing.Optional[typing.Dict[str, str]] = None"},{name:"model_save_dir",val:": typing.Union[str, pathlib.Path, optimum.intel.openvino.utils.TemporaryDirectory, NoneType] = None"},{name:"quantization_config",val:": typing.Union[optimum.intel.openvino.configuration.OVWeightQuantizationConfig, typing.Dict, NoneType] = None"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/optimum-intel/blob/v1.26.0/optimum/intel/openvino/modeling_base.py#L65"}}),Wt=new k({props:{name:"from_pretrained",anchor:"optimum.intel.openvino.modeling_base.OVBaseModel.from_pretrained",parameters:[{name:"model_id",val:": typing.Union[str, pathlib.Path]"},{name:"export",val:": bool = False"},{name:"force_download",val:": bool = False"},{name:"use_auth_token",val:": typing.Union[bool, str, NoneType] = None"},{name:"token",val:": typing.Union[bool, str, NoneType] = None"},{name:"cache_dir",val:": str = '/home/runner/.cache/huggingface/hub'"},{name:"subfolder",val:": str = ''"},{name:"config",val:": typing.Optional[transformers.configuration_utils.PretrainedConfig] = None"},{name:"local_files_only",val:": bool = False"},{name:"trust_remote_code",val:": bool = False"},{name:"revision",val:": typing.Optional[str] = None"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"optimum.intel.openvino.modeling_base.OVBaseModel.from_pretrained.model_id",description:`<strong>model_id</strong> (<code>Union[str, Path]</code>) &#x2014;
Can be either:
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing a model saved using <code>~OptimizedModel.save_pretrained</code>,
e.g., <code>./my_model_directory/</code>.</li>
</ul>`,name:"model_id"},{anchor:"optimum.intel.openvino.modeling_base.OVBaseModel.from_pretrained.export",description:`<strong>export</strong> (<code>bool</code>, defaults to <code>False</code>) &#x2014;
Defines whether the provided <code>model_id</code> needs to be exported to the targeted format.`,name:"export"},{anchor:"optimum.intel.openvino.modeling_base.OVBaseModel.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, defaults to <code>True</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.`,name:"force_download"},{anchor:"optimum.intel.openvino.modeling_base.OVBaseModel.from_pretrained.token",description:`<strong>token</strong> (<code>Optional[Union[bool,str]]</code>, defaults to <code>None</code>) &#x2014;
The token to use as HTTP bearer authorization for remote files. If <code>True</code>, will use the token generated
when running <code>huggingface-cli login</code> (stored in <code>huggingface_hub.constants.HF_TOKEN_PATH</code>).`,name:"token"},{anchor:"optimum.intel.openvino.modeling_base.OVBaseModel.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>Optional[str]</code>, defaults to <code>None</code>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"optimum.intel.openvino.modeling_base.OVBaseModel.from_pretrained.subfolder",description:`<strong>subfolder</strong> (<code>str</code>, defaults to <code>&quot;&quot;</code>) &#x2014;
In case the relevant files are located inside a subfolder of the model repo either locally or on huggingface.co, you can
specify the folder name here.`,name:"subfolder"},{anchor:"optimum.intel.openvino.modeling_base.OVBaseModel.from_pretrained.config",description:`<strong>config</strong> (<code>Optional[transformers.PretrainedConfig]</code>, defaults to <code>None</code>) &#x2014;
The model configuration.`,name:"config"},{anchor:"optimum.intel.openvino.modeling_base.OVBaseModel.from_pretrained.local_files_only",description:`<strong>local_files_only</strong> (<code>Optional[bool]</code>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (i.e., do not try to download the model).`,name:"local_files_only"},{anchor:"optimum.intel.openvino.modeling_base.OVBaseModel.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom code defined on the Hub in their own modeling. This option should only be set
to <code>True</code> for repositories you trust and in which you have read the code, as it will execute code present on
the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"optimum.intel.openvino.modeling_base.OVBaseModel.from_pretrained.revision",description:`<strong>revision</strong> (<code>Optional[str]</code>, defaults to <code>None</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"}],source:"https://github.com/huggingface/optimum-intel/blob/v1.26.0/optimum/intel/openvino/modeling_base.py#L437"}}),Nt=new k({props:{name:"reshape",anchor:"optimum.intel.openvino.modeling_base.OVBaseModel.reshape",parameters:[{name:"batch_size",val:": int"},{name:"sequence_length",val:": int"},{name:"height",val:": int = None"},{name:"width",val:": int = None"}],parametersDescription:[{anchor:"optimum.intel.openvino.modeling_base.OVBaseModel.reshape.batch_size",description:`<strong>batch_size</strong> (<code>int</code>) &#x2014;
The batch size.`,name:"batch_size"},{anchor:"optimum.intel.openvino.modeling_base.OVBaseModel.reshape.sequence_length",description:`<strong>sequence_length</strong> (<code>int</code>) &#x2014;
The sequence length or number of channels.`,name:"sequence_length"},{anchor:"optimum.intel.openvino.modeling_base.OVBaseModel.reshape.height",description:`<strong>height</strong> (<code>int</code>, <em>optional</em>) &#x2014;
The image height.`,name:"height"},{anchor:"optimum.intel.openvino.modeling_base.OVBaseModel.reshape.width",description:`<strong>width</strong> (<code>int</code>, <em>optional</em>) &#x2014;
The image width.`,name:"width"}],source:"https://github.com/huggingface/optimum-intel/blob/v1.26.0/optimum/intel/openvino/modeling_base.py#L732"}}),qt=new C({props:{title:"Natural Language Processing",local:"natural-language-processing",headingTag:"h2"}}),Xt=new C({props:{title:"OVModelForCausalLM",local:"optimum.intel.OVModelForCausalLM",headingTag:"h3"}}),zt=new k({props:{name:"class optimum.intel.OVModelForCausalLM",anchor:"optimum.intel.OVModelForCausalLM",parameters:[{name:"model",val:": Model"},{name:"config",val:": PretrainedConfig = None"},{name:"device",val:": str = 'CPU'"},{name:"dynamic_shapes",val:": bool = None"},{name:"ov_config",val:": typing.Optional[typing.Dict[str, str]] = None"},{name:"model_save_dir",val:": typing.Union[str, pathlib.Path, optimum.intel.openvino.utils.TemporaryDirectory, NoneType] = None"},{name:"quantization_config",val:": typing.Union[optimum.intel.openvino.configuration.OVWeightQuantizationConfig, typing.Dict, NoneType] = None"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"optimum.intel.OVModelForCausalLM.model",description:"<strong>model</strong> (<code>openvino.Model</code>) &#x2014; is the main class used to run OpenVINO Runtime inference.",name:"model"},{anchor:"optimum.intel.OVModelForCausalLM.config",description:`<strong>config</strong> (<code>transformers.PretrainedConfig</code>) &#x2014; <a href="https://huggingface.co/docs/transformers/main_classes/configuration#transformers.PretrainedConfig" rel="nofollow">PretrainedConfig</a>
is the Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the configuration.
Check out the <code>~intel.openvino.modeling.OVBaseModel.from_pretrained</code> method to load the model weights.`,name:"config"},{anchor:"optimum.intel.OVModelForCausalLM.device",description:`<strong>device</strong> (<code>str</code>, defaults to <code>&quot;CPU&quot;</code>) &#x2014;
The device type for which the model will be optimized for. The resulting compiled model will contains nodes specific to this device.`,name:"device"},{anchor:"optimum.intel.OVModelForCausalLM.dynamic_shapes",description:`<strong>dynamic_shapes</strong> (<code>bool</code>, defaults to <code>True</code>) &#x2014;
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<a href="https://huggingface.co/docs/transformers/glossary#input-ids" rel="nofollow">What are input IDs?</a>`,name:"input_ids"},{anchor:"optimum.intel.OVModelForMaskedLM.forward.attention_mask",description:`<strong>attention_mask</strong> (<code>torch.Tensor</code>), <em>optional</em>) &#x2014;
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<a href="https://huggingface.co/docs/transformers/glossary#attention-mask" rel="nofollow">What are attention masks?</a></li>
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<a href="https://huggingface.co/docs/transformers/glossary#token-type-ids" rel="nofollow">What are token type IDs?</a></li>
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<a href="https://huggingface.co/docs/transformers/main_classes/configuration#transformers.PretrainedConfig" rel="nofollow">PretrainedConfig</a>
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Disable the model compilation during the loading step when set to <code>False</code>.
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<a href="https://huggingface.co/docs/transformers/glossary#input-ids" rel="nofollow">What are input IDs?</a>`,name:"input_ids"},{anchor:"optimum.intel.OVModelForQuestionAnswering.forward.attention_mask",description:`<strong>attention_mask</strong> (<code>torch.Tensor</code>), <em>optional</em>) &#x2014;
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<ul>
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<li>0 for tokens that are <strong>masked</strong>.
<a href="https://huggingface.co/docs/transformers/glossary#attention-mask" rel="nofollow">What are attention masks?</a></li>
</ul>`,name:"attention_mask"},{anchor:"optimum.intel.OVModelForQuestionAnswering.forward.token_type_ids",description:`<strong>token_type_ids</strong> (<code>torch.Tensor</code>, <em>optional</em>) &#x2014;
Segment token indices to indicate first and second portions of the inputs. Indices are selected in <code>[0, 1]</code>:
<ul>
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<li>0 for tokens that are <strong>sentence B</strong>.
<a href="https://huggingface.co/docs/transformers/glossary#token-type-ids" rel="nofollow">What are token type IDs?</a></li>
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Disable the model compilation during the loading step when set to <code>False</code>.
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<a href="https://huggingface.co/docs/transformers/glossary#input-ids" rel="nofollow">What are input IDs?</a>`,name:"input_ids"},{anchor:"optimum.intel.OVModelForSequenceClassification.forward.attention_mask",description:`<strong>attention_mask</strong> (<code>torch.Tensor</code>), <em>optional</em>) &#x2014;
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<a href="https://huggingface.co/docs/transformers/glossary#attention-mask" rel="nofollow">What are attention masks?</a></li>
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<a href="https://huggingface.co/docs/transformers/glossary#token-type-ids" rel="nofollow">What are token type IDs?</a></li>
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Indices of input sequence tokens in the vocabulary.
Indices can be obtained using <a href="https://huggingface.co/docs/transformers/autoclass_tutorial#autotokenizer" rel="nofollow"><code>AutoTokenizer</code></a>.
<a href="https://huggingface.co/docs/transformers/glossary#input-ids" rel="nofollow">What are input IDs?</a>`,name:"input_ids"},{anchor:"optimum.intel.OVModelForTokenClassification.forward.attention_mask",description:`<strong>attention_mask</strong> (<code>torch.Tensor</code>), <em>optional</em>) &#x2014;
Mask to avoid performing attention on padding token indices. Mask values selected in <code>[0, 1]</code>:
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.
<a href="https://huggingface.co/docs/transformers/glossary#attention-mask" rel="nofollow">What are attention masks?</a></li>
</ul>`,name:"attention_mask"},{anchor:"optimum.intel.OVModelForTokenClassification.forward.token_type_ids",description:`<strong>token_type_ids</strong> (<code>torch.Tensor</code>, <em>optional</em>) &#x2014;
Segment token indices to indicate first and second portions of the inputs. Indices are selected in <code>[0, 1]</code>:
<ul>
<li>1 for tokens that are <strong>sentence A</strong>,</li>
<li>0 for tokens that are <strong>sentence B</strong>.
<a href="https://huggingface.co/docs/transformers/glossary#token-type-ids" rel="nofollow">What are token type IDs?</a></li>
</ul>`,name:"token_type_ids"}],source:"https://github.com/huggingface/optimum-intel/blob/v1.26.0/optimum/intel/openvino/modeling.py#L294"}}),dt=new G({props:{$$slots:{default:[Vd]},$$scope:{ctx:w}}}),pt=new O({props:{anchor:"optimum.intel.OVModelForTokenClassification.forward.example",$$slots:{default:[kd]},$$scope:{ctx:w}}}),so=new C({props:{title:"Audio",local:"audio",headingTag:"h2"}}),lo=new C({props:{title:"OVModelForAudioClassification",local:"optimum.intel.OVModelForAudioClassification",headingTag:"h3"}}),ro=new k({props:{name:"class optimum.intel.OVModelForAudioClassification",anchor:"optimum.intel.OVModelForAudioClassification",parameters:[{name:"model",val:" = None"},{name:"config",val:" = None"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"optimum.intel.OVModelForAudioClassification.model",description:"<strong>model</strong> (<code>openvino.Model</code>) &#x2014; is the main class used to run OpenVINO Runtime inference.",name:"model"},{anchor:"optimum.intel.OVModelForAudioClassification.config",description:`<strong>config</strong> (<code>transformers.PretrainedConfig</code>) &#x2014; <a href="https://huggingface.co/docs/transformers/main_classes/configuration#transformers.PretrainedConfig" rel="nofollow">PretrainedConfig</a>
is the Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the configuration.
Check out the <code>~intel.openvino.modeling.OVBaseModel.from_pretrained</code> method to load the model weights.`,name:"config"},{anchor:"optimum.intel.OVModelForAudioClassification.device",description:`<strong>device</strong> (<code>str</code>, defaults to <code>&quot;CPU&quot;</code>) &#x2014;
The device type for which the model will be optimized for. The resulting compiled model will contains nodes specific to this device.`,name:"device"},{anchor:"optimum.intel.OVModelForAudioClassification.dynamic_shapes",description:`<strong>dynamic_shapes</strong> (<code>bool</code>, defaults to <code>True</code>) &#x2014;
All the model&#x2019;s dimension will be set to dynamic when set to <code>True</code>. Should be set to <code>False</code> for the model to not be dynamically reshaped by default.`,name:"dynamic_shapes"},{anchor:"optimum.intel.OVModelForAudioClassification.ov_config",description:`<strong>ov_config</strong> (<code>Optional[Dict]</code>, defaults to <code>None</code>) &#x2014;
The dictionary containing the information related to the model compilation.`,name:"ov_config"},{anchor:"optimum.intel.OVModelForAudioClassification.compile",description:`<strong>compile</strong> (<code>bool</code>, defaults to <code>True</code>) &#x2014;
Disable the model compilation during the loading step when set to <code>False</code>.
Can be useful to avoid unnecessary compilation, in the case where the model needs to be statically reshaped, the device modified or if FP16 conversion is enabled.`,name:"compile"}],source:"https://github.com/huggingface/optimum-intel/blob/v1.26.0/optimum/intel/openvino/modeling.py#L630"}}),po=new k({props:{name:"forward",anchor:"optimum.intel.OVModelForAudioClassification.forward",parameters:[{name:"input_values",val:": typing.Union[torch.Tensor, numpy.ndarray]"},{name:"attention_mask",val:": typing.Union[torch.Tensor, numpy.ndarray, NoneType] = None"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"optimum.intel.OVModelForAudioClassification.forward.input_ids",description:`<strong>input_ids</strong> (<code>torch.Tensor</code>) &#x2014;
Indices of input sequence tokens in the vocabulary.
Indices can be obtained using <a href="https://huggingface.co/docs/transformers/autoclass_tutorial#autotokenizer" rel="nofollow"><code>AutoTokenizer</code></a>.
<a href="https://huggingface.co/docs/transformers/glossary#input-ids" rel="nofollow">What are input IDs?</a>`,name:"input_ids"},{anchor:"optimum.intel.OVModelForAudioClassification.forward.attention_mask",description:`<strong>attention_mask</strong> (<code>torch.Tensor</code>), <em>optional</em>) &#x2014;
Mask to avoid performing attention on padding token indices. Mask values selected in <code>[0, 1]</code>:
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.
<a href="https://huggingface.co/docs/transformers/glossary#attention-mask" rel="nofollow">What are attention masks?</a></li>
</ul>`,name:"attention_mask"},{anchor:"optimum.intel.OVModelForAudioClassification.forward.token_type_ids",description:`<strong>token_type_ids</strong> (<code>torch.Tensor</code>, <em>optional</em>) &#x2014;
Segment token indices to indicate first and second portions of the inputs. Indices are selected in <code>[0, 1]</code>:
<ul>
<li>1 for tokens that are <strong>sentence A</strong>,</li>
<li>0 for tokens that are <strong>sentence B</strong>.
<a href="https://huggingface.co/docs/transformers/glossary#token-type-ids" rel="nofollow">What are token type IDs?</a></li>
</ul>`,name:"token_type_ids"}],source:"https://github.com/huggingface/optimum-intel/blob/v1.26.0/optimum/intel/openvino/modeling.py#L637"}}),ct=new G({props:{$$slots:{default:[Jd]},$$scope:{ctx:w}}}),mt=new O({props:{anchor:"optimum.intel.OVModelForAudioClassification.forward.example",$$slots:{default:[Cd]},$$scope:{ctx:w}}}),co=new C({props:{title:"OVModelForAudioFrameClassification",local:"optimum.intel.OVModelForAudioFrameClassification",headingTag:"h3"}}),mo=new k({props:{name:"class optimum.intel.OVModelForAudioFrameClassification",anchor:"optimum.intel.OVModelForAudioFrameClassification",parameters:[{name:"model",val:": Model"},{name:"config",val:": PretrainedConfig = None"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"optimum.intel.OVModelForAudioFrameClassification.model",description:"<strong>model</strong> (<code>openvino.Model</code>) &#x2014; is the main class used to run OpenVINO Runtime inference.",name:"model"},{anchor:"optimum.intel.OVModelForAudioFrameClassification.config",description:`<strong>config</strong> (<code>transformers.PretrainedConfig</code>) &#x2014; <a href="https://huggingface.co/docs/transformers/main_classes/configuration#transformers.PretrainedConfig" rel="nofollow">PretrainedConfig</a>
is the Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the configuration.
Check out the <code>~intel.openvino.modeling.OVBaseModel.from_pretrained</code> method to load the model weights.`,name:"config"},{anchor:"optimum.intel.OVModelForAudioFrameClassification.device",description:`<strong>device</strong> (<code>str</code>, defaults to <code>&quot;CPU&quot;</code>) &#x2014;
The device type for which the model will be optimized for. The resulting compiled model will contains nodes specific to this device.`,name:"device"},{anchor:"optimum.intel.OVModelForAudioFrameClassification.dynamic_shapes",description:`<strong>dynamic_shapes</strong> (<code>bool</code>, defaults to <code>True</code>) &#x2014;
All the model&#x2019;s dimension will be set to dynamic when set to <code>True</code>. Should be set to <code>False</code> for the model to not be dynamically reshaped by default.`,name:"dynamic_shapes"},{anchor:"optimum.intel.OVModelForAudioFrameClassification.ov_config",description:`<strong>ov_config</strong> (<code>Optional[Dict]</code>, defaults to <code>None</code>) &#x2014;
The dictionary containing the information related to the model compilation.`,name:"ov_config"},{anchor:"optimum.intel.OVModelForAudioFrameClassification.compile",description:`<strong>compile</strong> (<code>bool</code>, defaults to <code>True</code>) &#x2014;
Disable the model compilation during the loading step when set to <code>False</code>.
Can be useful to avoid unnecessary compilation, in the case where the model needs to be statically reshaped, the device modified or if FP16 conversion is enabled.`,name:"compile"}],source:"https://github.com/huggingface/optimum-intel/blob/v1.26.0/optimum/intel/openvino/modeling.py#L858"}}),go=new k({props:{name:"forward",anchor:"optimum.intel.OVModelForAudioFrameClassification.forward",parameters:[{name:"input_values",val:": typing.Optional[torch.Tensor] = None"},{name:"attention_mask",val:": typing.Optional[torch.Tensor] = None"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"optimum.intel.OVModelForAudioFrameClassification.forward.input_values",description:`<strong>input_values</strong> (<code>torch.Tensor</code> of shape <code>(batch_size, sequence_length)</code>) &#x2014;
Float values of input raw speech waveform..
Input values can be obtained from audio file loaded into an array using <a href="https://huggingface.co/docs/transformers/autoclass_tutorial#autofeatureextractor" rel="nofollow"><code>AutoFeatureExtractor</code></a>.`,name:"input_values"}],source:"https://github.com/huggingface/optimum-intel/blob/v1.26.0/optimum/intel/openvino/modeling.py#L866"}}),gt=new G({props:{$$slots:{default:[jd]},$$scope:{ctx:w}}}),ut=new O({props:{anchor:"optimum.intel.OVModelForAudioFrameClassification.forward.example",$$slots:{default:[Fd]},$$scope:{ctx:w}}}),uo=new C({props:{title:"OVModelForCTC",local:"optimum.intel.OVModelForCTC",headingTag:"h3"}}),ho=new k({props:{name:"class optimum.intel.OVModelForCTC",anchor:"optimum.intel.OVModelForCTC",parameters:[{name:"model",val:": Model"},{name:"config",val:": PretrainedConfig = None"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"optimum.intel.OVModelForCTC.model",description:"<strong>model</strong> (<code>openvino.Model</code>) &#x2014; is the main class used to run OpenVINO Runtime inference.",name:"model"},{anchor:"optimum.intel.OVModelForCTC.config",description:`<strong>config</strong> (<code>transformers.PretrainedConfig</code>) &#x2014; <a href="https://huggingface.co/docs/transformers/main_classes/configuration#transformers.PretrainedConfig" rel="nofollow">PretrainedConfig</a>
is the Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the configuration.
Check out the <code>~intel.openvino.modeling.OVBaseModel.from_pretrained</code> method to load the model weights.`,name:"config"},{anchor:"optimum.intel.OVModelForCTC.device",description:`<strong>device</strong> (<code>str</code>, defaults to <code>&quot;CPU&quot;</code>) &#x2014;
The device type for which the model will be optimized for. The resulting compiled model will contains nodes specific to this device.`,name:"device"},{anchor:"optimum.intel.OVModelForCTC.dynamic_shapes",description:`<strong>dynamic_shapes</strong> (<code>bool</code>, defaults to <code>True</code>) &#x2014;
All the model&#x2019;s dimension will be set to dynamic when set to <code>True</code>. Should be set to <code>False</code> for the model to not be dynamically reshaped by default.`,name:"dynamic_shapes"},{anchor:"optimum.intel.OVModelForCTC.ov_config",description:`<strong>ov_config</strong> (<code>Optional[Dict]</code>, defaults to <code>None</code>) &#x2014;
The dictionary containing the information related to the model compilation.`,name:"ov_config"},{anchor:"optimum.intel.OVModelForCTC.compile",description:`<strong>compile</strong> (<code>bool</code>, defaults to <code>True</code>) &#x2014;
Disable the model compilation during the loading step when set to <code>False</code>.
Can be useful to avoid unnecessary compilation, in the case where the model needs to be statically reshaped, the device modified or if FP16 conversion is enabled.`,name:"compile"}],source:"https://github.com/huggingface/optimum-intel/blob/v1.26.0/optimum/intel/openvino/modeling.py#L702"}}),fo=new k({props:{name:"forward",anchor:"optimum.intel.OVModelForCTC.forward",parameters:[{name:"input_values",val:": typing.Optional[torch.Tensor] = None"},{name:"attention_mask",val:": typing.Union[torch.Tensor, numpy.ndarray, NoneType] = None"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"optimum.intel.OVModelForCTC.forward.input_values",description:`<strong>input_values</strong> (<code>torch.Tensor</code> of shape <code>(batch_size, sequence_length)</code>) &#x2014;
Float values of input raw speech waveform..
Input values can be obtained from audio file loaded into an array using <a href="https://huggingface.co/docs/transformers/autoclass_tutorial#autofeatureextractor" rel="nofollow"><code>AutoFeatureExtractor</code></a>.`,name:"input_values"}],source:"https://github.com/huggingface/optimum-intel/blob/v1.26.0/optimum/intel/openvino/modeling.py#L710"}}),ht=new G({props:{$$slots:{default:[xd]},$$scope:{ctx:w}}}),ft=new O({props:{anchor:"optimum.intel.OVModelForCTC.forward.example",$$slots:{default:[Od]},$$scope:{ctx:w}}}),yo=new C({props:{title:"OVModelForAudioXVector",local:"optimum.intel.OVModelForAudioXVector",headingTag:"h3"}}),Mo=new k({props:{name:"class optimum.intel.OVModelForAudioXVector",anchor:"optimum.intel.OVModelForAudioXVector",parameters:[{name:"model",val:": Model"},{name:"config",val:": PretrainedConfig = None"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"optimum.intel.OVModelForAudioXVector.model",description:"<strong>model</strong> (<code>openvino.Model</code>) &#x2014; is the main class used to run OpenVINO Runtime inference.",name:"model"},{anchor:"optimum.intel.OVModelForAudioXVector.config",description:`<strong>config</strong> (<code>transformers.PretrainedConfig</code>) &#x2014; <a href="https://huggingface.co/docs/transformers/main_classes/configuration#transformers.PretrainedConfig" rel="nofollow">PretrainedConfig</a>
is the Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the configuration.
Check out the <code>~intel.openvino.modeling.OVBaseModel.from_pretrained</code> method to load the model weights.`,name:"config"},{anchor:"optimum.intel.OVModelForAudioXVector.device",description:`<strong>device</strong> (<code>str</code>, defaults to <code>&quot;CPU&quot;</code>) &#x2014;
The device type for which the model will be optimized for. The resulting compiled model will contains nodes specific to this device.`,name:"device"},{anchor:"optimum.intel.OVModelForAudioXVector.dynamic_shapes",description:`<strong>dynamic_shapes</strong> (<code>bool</code>, defaults to <code>True</code>) &#x2014;
All the model&#x2019;s dimension will be set to dynamic when set to <code>True</code>. Should be set to <code>False</code> for the model to not be dynamically reshaped by default.`,name:"dynamic_shapes"},{anchor:"optimum.intel.OVModelForAudioXVector.ov_config",description:`<strong>ov_config</strong> (<code>Optional[Dict]</code>, defaults to <code>None</code>) &#x2014;
The dictionary containing the information related to the model compilation.`,name:"ov_config"},{anchor:"optimum.intel.OVModelForAudioXVector.compile",description:`<strong>compile</strong> (<code>bool</code>, defaults to <code>True</code>) &#x2014;
Disable the model compilation during the loading step when set to <code>False</code>.
Can be useful to avoid unnecessary compilation, in the case where the model needs to be statically reshaped, the device modified or if FP16 conversion is enabled.`,name:"compile"}],source:"https://github.com/huggingface/optimum-intel/blob/v1.26.0/optimum/intel/openvino/modeling.py#L782"}}),vo=new k({props:{name:"forward",anchor:"optimum.intel.OVModelForAudioXVector.forward",parameters:[{name:"input_values",val:": typing.Optional[torch.Tensor] = None"},{name:"attention_mask",val:": typing.Optional[torch.Tensor] = None"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"optimum.intel.OVModelForAudioXVector.forward.input_values",description:`<strong>input_values</strong> (<code>torch.Tensor</code> of shape <code>(batch_size, sequence_length)</code>) &#x2014;
Float values of input raw speech waveform..
Input values can be obtained from audio file loaded into an array using <a href="https://huggingface.co/docs/transformers/autoclass_tutorial#autofeatureextractor" rel="nofollow"><code>AutoFeatureExtractor</code></a>.`,name:"input_values"}],source:"https://github.com/huggingface/optimum-intel/blob/v1.26.0/optimum/intel/openvino/modeling.py#L790"}}),yt=new G({props:{$$slots:{default:[Ud]},$$scope:{ctx:w}}}),Mt=new O({props:{anchor:"optimum.intel.OVModelForAudioXVector.forward.example",$$slots:{default:[Zd]},$$scope:{ctx:w}}}),bo=new C({props:{title:"OVModelForSpeechSeq2Seq",local:"optimum.intel.OVModelForSpeechSeq2Seq",headingTag:"h3"}}),_o=new k({props:{name:"class optimum.intel.OVModelForSpeechSeq2Seq",anchor:"optimum.intel.OVModelForSpeechSeq2Seq",parameters:[{name:"encoder",val:": Model"},{name:"decoder",val:": Model"},{name:"decoder_with_past",val:": Model = None"},{name:"config",val:": PretrainedConfig = None"},{name:"device",val:": str = 'CPU'"},{name:"dynamic_shapes",val:": bool = True"},{name:"ov_config",val:": typing.Optional[typing.Dict[str, str]] = None"},{name:"model_save_dir",val:": typing.Union[str, pathlib.Path, optimum.intel.openvino.utils.TemporaryDirectory, NoneType] = None"},{name:"quantization_config",val:": typing.Union[optimum.intel.openvino.configuration.OVWeightQuantizationConfig, typing.Dict] = None"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"optimum.intel.OVModelForSpeechSeq2Seq.encoder",description:`<strong>encoder</strong> (<code>openvino.Model</code>) &#x2014;
The OpenVINO Runtime model associated to the encoder.`,name:"encoder"},{anchor:"optimum.intel.OVModelForSpeechSeq2Seq.decoder",description:`<strong>decoder</strong> (<code>openvino.Model</code>) &#x2014;
The OpenVINO Runtime model associated to the decoder.`,name:"decoder"},{anchor:"optimum.intel.OVModelForSpeechSeq2Seq.decoder_with_past",description:`<strong>decoder_with_past</strong> (<code>openvino.Model</code>) &#x2014;
The OpenVINO Runtime model associated  to the decoder with past key values.`,name:"decoder_with_past"},{anchor:"optimum.intel.OVModelForSpeechSeq2Seq.config",description:`<strong>config</strong> (<code>transformers.PretrainedConfig</code>) &#x2014;
<a href="https://huggingface.co/docs/transformers/main_classes/configuration#transformers.PretrainedConfig" rel="nofollow">PretrainedConfig</a>
is an instance of the configuration associated to the model. Initializing with a config file does
not load the weights associated with the model, only the configuration.`,name:"config"}],source:"https://github.com/huggingface/optimum-intel/blob/v1.26.0/optimum/intel/openvino/modeling_seq2seq.py#L1298"}}),$o=new k({props:{name:"forward",anchor:"optimum.intel.OVModelForSpeechSeq2Seq.forward",parameters:[{name:"input_features",val:": typing.Optional[torch.FloatTensor] = None"},{name:"attention_mask",val:": typing.Optional[torch.LongTensor] = None"},{name:"decoder_input_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"decoder_attention_mask",val:": typing.Optional[torch.BoolTensor] = None"},{name:"encoder_outputs",val:": typing.Optional[typing.Tuple[typing.Tuple[torch.Tensor]]] = None"},{name:"past_key_values",val:": typing.Optional[typing.Tuple[typing.Tuple[torch.Tensor]]] = None"},{name:"cache_position",val:": typing.Optional[torch.LongTensor] = None"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"optimum.intel.OVModelForSpeechSeq2Seq.forward.input_features",description:`<strong>input_features</strong> (<code>torch.FloatTensor</code>) &#x2014;
Mel features extracted from the raw speech waveform.
<code>(batch_size, feature_size, encoder_sequence_length)</code>.`,name:"input_features"},{anchor:"optimum.intel.OVModelForSpeechSeq2Seq.forward.decoder_input_ids",description:`<strong>decoder_input_ids</strong> (<code>torch.LongTensor</code>) &#x2014;
Indices of decoder input sequence tokens in the vocabulary of shape <code>(batch_size, decoder_sequence_length)</code>.`,name:"decoder_input_ids"},{anchor:"optimum.intel.OVModelForSpeechSeq2Seq.forward.encoder_outputs",description:`<strong>encoder_outputs</strong> (<code>torch.FloatTensor</code>) &#x2014;
The encoder <code>last_hidden_state</code> of shape <code>(batch_size, encoder_sequence_length, hidden_size)</code>.`,name:"encoder_outputs"},{anchor:"optimum.intel.OVModelForSpeechSeq2Seq.forward.past_key_values",description:`<strong>past_key_values</strong> (<code>tuple(tuple(torch.FloatTensor), *optional*, defaults to </code>None<code>)</code> &#x2014;
Contains the precomputed key and value hidden states of the attention blocks used to speed up decoding.
The tuple is of length <code>config.n_layers</code> with each tuple having 2 tensors of shape
<code>(batch_size, num_heads, decoder_sequence_length, embed_size_per_head)</code> and 2 additional tensors of shape
<code>(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)</code>.`,name:"past_key_values"}],source:"https://github.com/huggingface/optimum-intel/blob/v1.26.0/optimum/intel/openvino/modeling_seq2seq.py#L1334"}}),vt=new G({props:{$$slots:{default:[Id]},$$scope:{ctx:w}}}),bt=new O({props:{anchor:"optimum.intel.OVModelForSpeechSeq2Seq.forward.example",$$slots:{default:[Wd]},$$scope:{ctx:w}}}),_t=new O({props:{anchor:"optimum.intel.OVModelForSpeechSeq2Seq.forward.example-2",$$slots:{default:[Nd]},$$scope:{ctx:w}}}),wo=new C({props:{title:"Computer Vision",local:"computer-vision",headingTag:"h2"}}),Vo=new C({props:{title:"OVModelForImageClassification",local:"optimum.intel.OVModelForImageClassification",headingTag:"h3"}}),ko=new k({props:{name:"class optimum.intel.OVModelForImageClassification",anchor:"optimum.intel.OVModelForImageClassification",parameters:[{name:"model",val:" = None"},{name:"config",val:" = None"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"optimum.intel.OVModelForImageClassification.model",description:"<strong>model</strong> (<code>openvino.Model</code>) &#x2014; is the main class used to run OpenVINO Runtime inference.",name:"model"},{anchor:"optimum.intel.OVModelForImageClassification.config",description:`<strong>config</strong> (<code>transformers.PretrainedConfig</code>) &#x2014; <a href="https://huggingface.co/docs/transformers/main_classes/configuration#transformers.PretrainedConfig" rel="nofollow">PretrainedConfig</a>
is the Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the configuration.
Check out the <code>~intel.openvino.modeling.OVBaseModel.from_pretrained</code> method to load the model weights.`,name:"config"},{anchor:"optimum.intel.OVModelForImageClassification.device",description:`<strong>device</strong> (<code>str</code>, defaults to <code>&quot;CPU&quot;</code>) &#x2014;
The device type for which the model will be optimized for. The resulting compiled model will contains nodes specific to this device.`,name:"device"},{anchor:"optimum.intel.OVModelForImageClassification.dynamic_shapes",description:`<strong>dynamic_shapes</strong> (<code>bool</code>, defaults to <code>True</code>) &#x2014;
All the model&#x2019;s dimension will be set to dynamic when set to <code>True</code>. Should be set to <code>False</code> for the model to not be dynamically reshaped by default.`,name:"dynamic_shapes"},{anchor:"optimum.intel.OVModelForImageClassification.ov_config",description:`<strong>ov_config</strong> (<code>Optional[Dict]</code>, defaults to <code>None</code>) &#x2014;
The dictionary containing the information related to the model compilation.`,name:"ov_config"},{anchor:"optimum.intel.OVModelForImageClassification.compile",description:`<strong>compile</strong> (<code>bool</code>, defaults to <code>True</code>) &#x2014;
Disable the model compilation during the loading step when set to <code>False</code>.
Can be useful to avoid unnecessary compilation, in the case where the model needs to be statically reshaped, the device modified or if FP16 conversion is enabled.`,name:"compile"}],source:"https://github.com/huggingface/optimum-intel/blob/v1.26.0/optimum/intel/openvino/modeling.py#L517"}}),Jo=new k({props:{name:"forward",anchor:"optimum.intel.OVModelForImageClassification.forward",parameters:[{name:"pixel_values",val:": typing.Union[torch.Tensor, numpy.ndarray]"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"optimum.intel.OVModelForImageClassification.forward.pixel_values",description:`<strong>pixel_values</strong> (<code>torch.Tensor</code>) &#x2014;
Pixel values corresponding to the images in the current batch.
Pixel values can be obtained from encoded images using <a href="https://huggingface.co/docs/transformers/autoclass_tutorial#autofeatureextractor" rel="nofollow"><code>AutoFeatureExtractor</code></a>.`,name:"pixel_values"}],source:"https://github.com/huggingface/optimum-intel/blob/v1.26.0/optimum/intel/openvino/modeling.py#L579"}}),$t=new G({props:{$$slots:{default:[qd]},$$scope:{ctx:w}}}),wt=new O({props:{anchor:"optimum.intel.OVModelForImageClassification.forward.example",$$slots:{default:[Gd]},$$scope:{ctx:w}}}),Tt=new O({props:{anchor:"optimum.intel.OVModelForImageClassification.forward.example-2",$$slots:{default:[Xd]},$$scope:{ctx:w}}}),Co=new C({props:{title:"Multimodal",local:"multimodal",headingTag:"h2"}}),Fo=new C({props:{title:"OVModelForVision2Seq",local:"optimum.intel.OVModelForVision2Seq",headingTag:"h3"}}),xo=new k({props:{name:"class optimum.intel.OVModelForVision2Seq",anchor:"optimum.intel.OVModelForVision2Seq",parameters:[{name:"encoder",val:": Model"},{name:"decoder",val:": Model"},{name:"decoder_with_past",val:": Model = None"},{name:"config",val:": PretrainedConfig = None"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"optimum.intel.OVModelForVision2Seq.encoder",description:`<strong>encoder</strong> (<code>openvino.Model</code>) &#x2014;
The OpenVINO Runtime model associated to the encoder.`,name:"encoder"},{anchor:"optimum.intel.OVModelForVision2Seq.decoder",description:`<strong>decoder</strong> (<code>openvino.Model</code>) &#x2014;
The OpenVINO Runtime model associated to the decoder.`,name:"decoder"},{anchor:"optimum.intel.OVModelForVision2Seq.decoder_with_past",description:`<strong>decoder_with_past</strong> (<code>openvino.Model</code>) &#x2014;
The OpenVINO Runtime model associated  to the decoder with past key values.`,name:"decoder_with_past"},{anchor:"optimum.intel.OVModelForVision2Seq.config",description:`<strong>config</strong> (<code>transformers.PretrainedConfig</code>) &#x2014;
<a href="https://huggingface.co/docs/transformers/main_classes/configuration#transformers.PretrainedConfig" rel="nofollow">PretrainedConfig</a>
is an instance of the configuration associated to the model. Initializing with a config file does
not load the weights associated with the model, only the configuration.`,name:"config"}],source:"https://github.com/huggingface/optimum-intel/blob/v1.26.0/optimum/intel/openvino/modeling_seq2seq.py#L1115"}}),Oo=new k({props:{name:"forward",anchor:"optimum.intel.OVModelForVision2Seq.forward",parameters:[{name:"pixel_values",val:": typing.Optional[torch.FloatTensor] = None"},{name:"attention_mask",val:": typing.Optional[torch.LongTensor] = None"},{name:"decoder_input_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"decoder_attention_mask",val:": typing.Optional[torch.BoolTensor] = None"},{name:"encoder_outputs",val:": typing.Optional[typing.Tuple[typing.Tuple[torch.Tensor]]] = None"},{name:"past_key_values",val:": typing.Optional[typing.Tuple[typing.Tuple[torch.Tensor]]] = None"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"optimum.intel.OVModelForVision2Seq.forward.pixel_values",description:`<strong>pixel_values</strong> (<code>torch.FloatTensor</code>) &#x2014;
Features extracted from an Image. This tensor should be of shape
<code>(batch_size, num_channels, height, width)</code>.`,name:"pixel_values"},{anchor:"optimum.intel.OVModelForVision2Seq.forward.decoder_input_ids",description:`<strong>decoder_input_ids</strong> (<code>torch.LongTensor</code>) &#x2014;
Indices of decoder input sequence tokens in the vocabulary of shape <code>(batch_size, decoder_sequence_length)</code>.`,name:"decoder_input_ids"},{anchor:"optimum.intel.OVModelForVision2Seq.forward.encoder_outputs",description:`<strong>encoder_outputs</strong> (<code>torch.FloatTensor</code>) &#x2014;
The encoder <code>last_hidden_state</code> of shape <code>(batch_size, encoder_sequence_length, hidden_size)</code>.`,name:"encoder_outputs"},{anchor:"optimum.intel.OVModelForVision2Seq.forward.past_key_values",description:`<strong>past_key_values</strong> (<code>tuple(tuple(torch.FloatTensor), *optional*, defaults to </code>None<code>)</code> &#x2014;
Contains the precomputed key and value hidden states of the attention blocks used to speed up decoding.
The tuple is of length <code>config.n_layers</code> with each tuple having 2 tensors of shape
<code>(batch_size, num_heads, decoder_sequence_length, embed_size_per_head)</code> and 2 additional tensors of shape
<code>(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)</code>.`,name:"past_key_values"}],source:"https://github.com/huggingface/optimum-intel/blob/v1.26.0/optimum/intel/openvino/modeling_seq2seq.py#L1162"}}),Vt=new G({props:{$$slots:{default:[zd]},$$scope:{ctx:w}}}),kt=new O({props:{anchor:"optimum.intel.OVModelForVision2Seq.forward.example",$$slots:{default:[Rd]},$$scope:{ctx:w}}}),Jt=new O({props:{anchor:"optimum.intel.OVModelForVision2Seq.forward.example-2",$$slots:{default:[Bd]},$$scope:{ctx:w}}}),Uo=new C({props:{title:"OVModelForPix2Struct",local:"optimum.intel.OVModelForPix2Struct",headingTag:"h3"}}),Zo=new k({props:{name:"class optimum.intel.OVModelForPix2Struct",anchor:"optimum.intel.OVModelForPix2Struct",parameters:[{name:"encoder",val:": Model"},{name:"decoder",val:": Model"},{name:"decoder_with_past",val:": Model = None"},{name:"config",val:": PretrainedConfig = None"},{name:"device",val:": str = 'CPU'"},{name:"dynamic_shapes",val:": bool = True"},{name:"ov_config",val:": typing.Optional[typing.Dict[str, str]] = None"},{name:"model_save_dir",val:": typing.Union[str, pathlib.Path, optimum.intel.openvino.utils.TemporaryDirectory, NoneType] = None"},{name:"quantization_config",val:": typing.Union[optimum.intel.openvino.configuration.OVWeightQuantizationConfig, typing.Dict] = None"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"optimum.intel.OVModelForPix2Struct.encoder",description:`<strong>encoder</strong> (<code>openvino.Model</code>) &#x2014;
The OpenVINO Runtime model associated to the encoder.`,name:"encoder"},{anchor:"optimum.intel.OVModelForPix2Struct.decoder",description:`<strong>decoder</strong> (<code>openvino.Model</code>) &#x2014;
The OpenVINO Runtime model associated to the decoder.`,name:"decoder"},{anchor:"optimum.intel.OVModelForPix2Struct.decoder_with_past",description:`<strong>decoder_with_past</strong> (<code>openvino.Model</code>) &#x2014;
The OpenVINO Runtime model associated  to the decoder with past key values.`,name:"decoder_with_past"},{anchor:"optimum.intel.OVModelForPix2Struct.config",description:`<strong>config</strong> (<code>transformers.PretrainedConfig</code>) &#x2014;
<a href="https://huggingface.co/docs/transformers/main_classes/configuration#transformers.PretrainedConfig" rel="nofollow">PretrainedConfig</a>
is an instance of the configuration associated to the model. Initializing with a config file does
not load the weights associated with the model, only the configuration.`,name:"config"}],source:"https://github.com/huggingface/optimum-intel/blob/v1.26.0/optimum/intel/openvino/modeling_seq2seq.py#L1213"}}),Io=new k({props:{name:"forward",anchor:"optimum.intel.OVModelForPix2Struct.forward",parameters:[{name:"flattened_patches",val:": typing.Optional[torch.FloatTensor] = None"},{name:"attention_mask",val:": typing.Optional[torch.LongTensor] = None"},{name:"decoder_input_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"decoder_attention_mask",val:": typing.Optional[torch.BoolTensor] = None"},{name:"encoder_outputs",val:": typing.Optional[typing.Tuple[typing.Tuple[torch.Tensor]]] = None"},{name:"past_key_values",val:": typing.Optional[typing.Tuple[typing.Tuple[torch.Tensor]]] = None"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"optimum.intel.OVModelForPix2Struct.forward.flattened_patches",description:`<strong>flattened_patches</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, seq_length, hidden_size)</code>) &#x2014;
Flattened pixel patches. the <code>hidden_size</code> is obtained by the following formula: <code>hidden_size</code> =
<code>num_channels</code> <em> <code>patch_size</code> </em> <code>patch_size</code>
The process of flattening the pixel patches is done by <code>Pix2StructProcessor</code>.`,name:"flattened_patches"},{anchor:"optimum.intel.OVModelForPix2Struct.forward.attention_mask",description:`<strong>attention_mask</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Mask to avoid performing attention on padding token indices.`,name:"attention_mask"},{anchor:"optimum.intel.OVModelForPix2Struct.forward.decoder_input_ids",description:`<strong>decoder_input_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, target_sequence_length)</code>, <em>optional</em>) &#x2014;
Indices of decoder input sequence tokens in the vocabulary.
Pix2StructText uses the <code>pad_token_id</code> as the starting token for <code>decoder_input_ids</code> generation. If
<code>past_key_values</code> is used, optionally only the last <code>decoder_input_ids</code> have to be input (see
<code>past_key_values</code>).`,name:"decoder_input_ids"},{anchor:"optimum.intel.OVModelForPix2Struct.forward.decoder_attention_mask",description:`<strong>decoder_attention_mask</strong> (<code>torch.BoolTensor</code> of shape <code>(batch_size, target_sequence_length)</code>, <em>optional</em>) &#x2014;
Default behavior: generate a tensor that ignores pad tokens in <code>decoder_input_ids</code>. Causal mask will also
be used by default.`,name:"decoder_attention_mask"},{anchor:"optimum.intel.OVModelForPix2Struct.forward.encoder_outputs",description:`<strong>encoder_outputs</strong> (<code>tuple(tuple(torch.FloatTensor)</code>, <em>optional</em>) &#x2014;
Tuple consists of (<code>last_hidden_state</code>, <code>optional</code>: <em>hidden_states</em>, <code>optional</code>: <em>attentions</em>)
<code>last_hidden_state</code> of shape <code>(batch_size, sequence_length, hidden_size)</code> is a sequence of hidden states at
the output of the last layer of the encoder. Used in the cross-attention of the decoder.`,name:"encoder_outputs"},{anchor:"optimum.intel.OVModelForPix2Struct.forward.past_key_values",description:`<strong>past_key_values</strong> (<code>tuple(tuple(torch.FloatTensor), *optional*, defaults to </code>None<code>)</code> &#x2014;
Contains the precomputed key and value hidden states of the attention blocks used to speed up decoding.
The tuple is of length <code>config.n_layers</code> with each tuple having 2 tensors of shape
<code>(batch_size, num_heads, decoder_sequence_length, embed_size_per_head)</code> and 2 additional tensors of shape
<code>(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)</code>.`,name:"past_key_values"}],source:"https://github.com/huggingface/optimum-intel/blob/v1.26.0/optimum/intel/openvino/modeling_seq2seq.py#L1248"}}),Ct=new G({props:{$$slots:{default:[Sd]},$$scope:{ctx:w}}}),jt=new O({props:{anchor:"optimum.intel.OVModelForPix2Struct.forward.example",$$slots:{default:[Ld]},$$scope:{ctx:w}}}),Wo=new C({props:{title:"Custom Tasks",local:"custom-tasks",headingTag:"h2"}}),No=new C({props:{title:"OVModelForCustomTasks",local:"optimum.intel.OVModelForCustomTasks",headingTag:"h3"}}),qo=new k({props:{name:"class optimum.intel.OVModelForCustomTasks",anchor:"optimum.intel.OVModelForCustomTasks",parameters:[{name:"model",val:": Model"},{name:"config",val:": PretrainedConfig = None"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"optimum.intel.OVModelForCustomTasks.model",description:"<strong>model</strong> (<code>openvino.Model</code>) &#x2014; is the main class used to run OpenVINO Runtime inference.",name:"model"},{anchor:"optimum.intel.OVModelForCustomTasks.config",description:`<strong>config</strong> (<code>transformers.PretrainedConfig</code>) &#x2014; <a href="https://huggingface.co/docs/transformers/main_classes/configuration#transformers.PretrainedConfig" rel="nofollow">PretrainedConfig</a>
is the Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the configuration.
Check out the <code>~intel.openvino.modeling.OVBaseModel.from_pretrained</code> method to load the model weights.`,name:"config"},{anchor:"optimum.intel.OVModelForCustomTasks.device",description:`<strong>device</strong> (<code>str</code>, defaults to <code>&quot;CPU&quot;</code>) &#x2014;
The device type for which the model will be optimized for. The resulting compiled model will contains nodes specific to this device.`,name:"device"},{anchor:"optimum.intel.OVModelForCustomTasks.dynamic_shapes",description:`<strong>dynamic_shapes</strong> (<code>bool</code>, defaults to <code>True</code>) &#x2014;
All the model&#x2019;s dimension will be set to dynamic when set to <code>True</code>. Should be set to <code>False</code> for the model to not be dynamically reshaped by default.`,name:"dynamic_shapes"},{anchor:"optimum.intel.OVModelForCustomTasks.ov_config",description:`<strong>ov_config</strong> (<code>Optional[Dict]</code>, defaults to <code>None</code>) &#x2014;
The dictionary containing the information related to the model compilation.`,name:"ov_config"},{anchor:"optimum.intel.OVModelForCustomTasks.compile",description:`<strong>compile</strong> (<code>bool</code>, defaults to <code>True</code>) &#x2014;
Disable the model compilation during the loading step when set to <code>False</code>.
Can be useful to avoid unnecessary compilation, in the case where the model needs to be statically reshaped, the device modified or if FP16 conversion is enabled.`,name:"compile"}],source:"https://github.com/huggingface/optimum-intel/blob/v1.26.0/optimum/intel/openvino/modeling.py#L924"}}),Go=new k({props:{name:"forward",anchor:"optimum.intel.OVModelForCustomTasks.forward",parameters:[{name:"**kwargs",val:""}],source:"https://github.com/huggingface/optimum-intel/blob/v1.26.0/optimum/intel/openvino/modeling.py#L925"}}),Ft=new G({props:{$$slots:{default:[Hd]},$$scope:{ctx:w}}}),xt=new O({props:{anchor:"optimum.intel.OVModelForCustomTasks.forward.example",$$slots:{default:[Ed]},$$scope:{ctx:w}}}),Xo=new C({props:{title:"OVModelForFeatureExtraction",local:"optimum.intel.OVModelForFeatureExtraction",headingTag:"h3"}}),zo=new k({props:{name:"class optimum.intel.OVModelForFeatureExtraction",anchor:"optimum.intel.OVModelForFeatureExtraction",parameters:[{name:"model",val:" = None"},{name:"config",val:" = None"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"optimum.intel.OVModelForFeatureExtraction.model",description:"<strong>model</strong> (<code>openvino.Model</code>) &#x2014; is the main class used to run OpenVINO Runtime inference.",name:"model"},{anchor:"optimum.intel.OVModelForFeatureExtraction.config",description:`<strong>config</strong> (<code>transformers.PretrainedConfig</code>) &#x2014; <a href="https://huggingface.co/docs/transformers/main_classes/configuration#transformers.PretrainedConfig" rel="nofollow">PretrainedConfig</a>
is the Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the configuration.
Check out the <code>~intel.openvino.modeling.OVBaseModel.from_pretrained</code> method to load the model weights.`,name:"config"},{anchor:"optimum.intel.OVModelForFeatureExtraction.device",description:`<strong>device</strong> (<code>str</code>, defaults to <code>&quot;CPU&quot;</code>) &#x2014;
The device type for which the model will be optimized for. The resulting compiled model will contains nodes specific to this device.`,name:"device"},{anchor:"optimum.intel.OVModelForFeatureExtraction.dynamic_shapes",description:`<strong>dynamic_shapes</strong> (<code>bool</code>, defaults to <code>True</code>) &#x2014;
All the model&#x2019;s dimension will be set to dynamic when set to <code>True</code>. Should be set to <code>False</code> for the model to not be dynamically reshaped by default.`,name:"dynamic_shapes"},{anchor:"optimum.intel.OVModelForFeatureExtraction.ov_config",description:`<strong>ov_config</strong> (<code>Optional[Dict]</code>, defaults to <code>None</code>) &#x2014;
The dictionary containing the information related to the model compilation.`,name:"ov_config"},{anchor:"optimum.intel.OVModelForFeatureExtraction.compile",description:`<strong>compile</strong> (<code>bool</code>, defaults to <code>True</code>) &#x2014;
Disable the model compilation during the loading step when set to <code>False</code>.
Can be useful to avoid unnecessary compilation, in the case where the model needs to be statically reshaped, the device modified or if FP16 conversion is enabled.`,name:"compile"}],source:"https://github.com/huggingface/optimum-intel/blob/v1.26.0/optimum/intel/openvino/modeling.py#L351"}}),Ro=new k({props:{name:"forward",anchor:"optimum.intel.OVModelForFeatureExtraction.forward",parameters:[{name:"input_ids",val:": typing.Union[torch.Tensor, numpy.ndarray]"},{name:"attention_mask",val:": typing.Union[torch.Tensor, numpy.ndarray]"},{name:"token_type_ids",val:": typing.Union[torch.Tensor, numpy.ndarray, NoneType] = None"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"optimum.intel.OVModelForFeatureExtraction.forward.input_ids",description:`<strong>input_ids</strong> (<code>torch.Tensor</code>) &#x2014;
Indices of input sequence tokens in the vocabulary.
Indices can be obtained using <a href="https://huggingface.co/docs/transformers/autoclass_tutorial#autotokenizer" rel="nofollow"><code>AutoTokenizer</code></a>.
<a href="https://huggingface.co/docs/transformers/glossary#input-ids" rel="nofollow">What are input IDs?</a>`,name:"input_ids"},{anchor:"optimum.intel.OVModelForFeatureExtraction.forward.attention_mask",description:`<strong>attention_mask</strong> (<code>torch.Tensor</code>), <em>optional</em>) &#x2014;
Mask to avoid performing attention on padding token indices. Mask values selected in <code>[0, 1]</code>:
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.
<a href="https://huggingface.co/docs/transformers/glossary#attention-mask" rel="nofollow">What are attention masks?</a></li>
</ul>`,name:"attention_mask"},{anchor:"optimum.intel.OVModelForFeatureExtraction.forward.token_type_ids",description:`<strong>token_type_ids</strong> (<code>torch.Tensor</code>, <em>optional</em>) &#x2014;
Segment token indices to indicate first and second portions of the inputs. Indices are selected in <code>[0, 1]</code>:
<ul>
<li>1 for tokens that are <strong>sentence A</strong>,</li>
<li>0 for tokens that are <strong>sentence B</strong>.
<a href="https://huggingface.co/docs/transformers/glossary#token-type-ids" rel="nofollow">What are token type IDs?</a></li>
</ul>`,name:"token_type_ids"}],source:"https://github.com/huggingface/optimum-intel/blob/v1.26.0/optimum/intel/openvino/modeling.py#L365"}}),Ot=new G({props:{$$slots:{default:[Pd]},$$scope:{ctx:w}}}),Ut=new O({props:{anchor:"optimum.intel.OVModelForFeatureExtraction.forward.example",$$slots:{default:[Qd]},$$scope:{ctx:w}}}),Bo=new C({props:{title:"Text-to-image",local:"text-to-image",headingTag:"h2"}}),So=new C({props:{title:"OVStableDiffusionPipeline",local:"optimum.intel.OVStableDiffusionPipeline",headingTag:"h3"}}),Lo=new k({props:{name:"class optimum.intel.OVStableDiffusionPipeline",anchor:"optimum.intel.OVStableDiffusionPipeline",parameters:[{name:"scheduler",val:": SchedulerMixin"},{name:"unet",val:": typing.Optional[openvino._ov_api.Model] = None"},{name:"vae_decoder",val:": typing.Optional[openvino._ov_api.Model] = None"},{name:"vae_encoder",val:": typing.Optional[openvino._ov_api.Model] = None"},{name:"text_encoder",val:": typing.Optional[openvino._ov_api.Model] = None"},{name:"text_encoder_2",val:": typing.Optional[openvino._ov_api.Model] = None"},{name:"text_encoder_3",val:": typing.Optional[openvino._ov_api.Model] = None"},{name:"transformer",val:": typing.Optional[openvino._ov_api.Model] = None"},{name:"tokenizer",val:": typing.Optional[transformers.models.clip.tokenization_clip.CLIPTokenizer] = None"},{name:"tokenizer_2",val:": typing.Optional[transformers.models.clip.tokenization_clip.CLIPTokenizer] = None"},{name:"tokenizer_3",val:": typing.Optional[transformers.models.clip.tokenization_clip.CLIPTokenizer] = None"},{name:"feature_extractor",val:": typing.Optional[transformers.models.clip.feature_extraction_clip.CLIPFeatureExtractor] = None"},{name:"force_zeros_for_empty_prompt",val:": bool = True"},{name:"requires_aesthetics_score",val:": bool = False"},{name:"add_watermarker",val:": typing.Optional[bool] = None"},{name:"device",val:": str = 'CPU'"},{name:"compile",val:": bool = True"},{name:"compile_only",val:": bool = False"},{name:"dynamic_shapes",val:": bool = True"},{name:"ov_config",val:": typing.Optional[typing.Dict[str, str]] = None"},{name:"model_save_dir",val:": typing.Union[str, pathlib.Path, optimum.intel.openvino.utils.TemporaryDirectory, NoneType] = None"},{name:"quantization_config",val:": typing.Union[optimum.intel.openvino.configuration.OVWeightQuantizationConfig, typing.Dict, NoneType] = None"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/optimum-intel/blob/v1.26.0/optimum/intel/openvino/modeling_diffusion.py#L1474"}}),Ho=new k({props:{name:"forward",anchor:"optimum.intel.OVStableDiffusionPipeline.forward",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/optimum-intel/blob/v1.26.0/optimum/intel/openvino/modeling_base.py#L772"}}),Eo=new C({props:{title:"OVStableDiffusionXLPipeline",local:"optimum.intel.OVStableDiffusionXLPipeline",headingTag:"h3"}}),Po=new k({props:{name:"class optimum.intel.OVStableDiffusionXLPipeline",anchor:"optimum.intel.OVStableDiffusionXLPipeline",parameters:[{name:"scheduler",val:": SchedulerMixin"},{name:"unet",val:": typing.Optional[openvino._ov_api.Model] = None"},{name:"vae_decoder",val:": typing.Optional[openvino._ov_api.Model] = None"},{name:"vae_encoder",val:": typing.Optional[openvino._ov_api.Model] = None"},{name:"text_encoder",val:": typing.Optional[openvino._ov_api.Model] = None"},{name:"text_encoder_2",val:": typing.Optional[openvino._ov_api.Model] = None"},{name:"text_encoder_3",val:": typing.Optional[openvino._ov_api.Model] = None"},{name:"transformer",val:": typing.Optional[openvino._ov_api.Model] = None"},{name:"tokenizer",val:": typing.Optional[transformers.models.clip.tokenization_clip.CLIPTokenizer] = None"},{name:"tokenizer_2",val:": typing.Optional[transformers.models.clip.tokenization_clip.CLIPTokenizer] = None"},{name:"tokenizer_3",val:": typing.Optional[transformers.models.clip.tokenization_clip.CLIPTokenizer] = None"},{name:"feature_extractor",val:": typing.Optional[transformers.models.clip.feature_extraction_clip.CLIPFeatureExtractor] = None"},{name:"force_zeros_for_empty_prompt",val:": bool = True"},{name:"requires_aesthetics_score",val:": bool = False"},{name:"add_watermarker",val:": typing.Optional[bool] = None"},{name:"device",val:": str = 'CPU'"},{name:"compile",val:": bool = True"},{name:"compile_only",val:": bool = False"},{name:"dynamic_shapes",val:": bool = True"},{name:"ov_config",val:": typing.Optional[typing.Dict[str, str]] = None"},{name:"model_save_dir",val:": typing.Union[str, pathlib.Path, optimum.intel.openvino.utils.TemporaryDirectory, NoneType] = None"},{name:"quantization_config",val:": typing.Union[optimum.intel.openvino.configuration.OVWeightQuantizationConfig, typing.Dict, NoneType] = None"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/optimum-intel/blob/v1.26.0/optimum/intel/openvino/modeling_diffusion.py#L1508"}}),Qo=new k({props:{name:"forward",anchor:"optimum.intel.OVStableDiffusionXLPipeline.forward",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/optimum-intel/blob/v1.26.0/optimum/intel/openvino/modeling_base.py#L772"}}),Yo=new C({props:{title:"OVLatentConsistencyModelPipeline",local:"optimum.intel.OVLatentConsistencyModelPipeline",headingTag:"h3"}}),Do=new k({props:{name:"class optimum.intel.OVLatentConsistencyModelPipeline",anchor:"optimum.intel.OVLatentConsistencyModelPipeline",parameters:[{name:"scheduler",val:": SchedulerMixin"},{name:"unet",val:": typing.Optional[openvino._ov_api.Model] = None"},{name:"vae_decoder",val:": typing.Optional[openvino._ov_api.Model] = None"},{name:"vae_encoder",val:": typing.Optional[openvino._ov_api.Model] = None"},{name:"text_encoder",val:": typing.Optional[openvino._ov_api.Model] = None"},{name:"text_encoder_2",val:": typing.Optional[openvino._ov_api.Model] = None"},{name:"text_encoder_3",val:": 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optimum.intel.OVStableDiffusionImg2ImgPipeline",anchor:"optimum.intel.OVStableDiffusionImg2ImgPipeline",parameters:[{name:"scheduler",val:": SchedulerMixin"},{name:"unet",val:": typing.Optional[openvino._ov_api.Model] = None"},{name:"vae_decoder",val:": typing.Optional[openvino._ov_api.Model] = None"},{name:"vae_encoder",val:": typing.Optional[openvino._ov_api.Model] = None"},{name:"text_encoder",val:": typing.Optional[openvino._ov_api.Model] = None"},{name:"text_encoder_2",val:": typing.Optional[openvino._ov_api.Model] = None"},{name:"text_encoder_3",val:": typing.Optional[openvino._ov_api.Model] = None"},{name:"transformer",val:": typing.Optional[openvino._ov_api.Model] = None"},{name:"tokenizer",val:": typing.Optional[transformers.models.clip.tokenization_clip.CLIPTokenizer] = None"},{name:"tokenizer_2",val:": typing.Optional[transformers.models.clip.tokenization_clip.CLIPTokenizer] = None"},{name:"tokenizer_3",val:": 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This class can also be used with [timm](https://github.com/huggingface/pytorch-image-models)
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This class can also be used with [timm](https://github.com/huggingface/pytorch-image-models)
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