import{S as ol,i as sl,s as al,e as n,k as f,w as C,t as s,M as nl,c as r,d as o,m as h,a as l,x as w,h as a,b as d,G as e,g,y as T,q as M,o as $,B as k,v as rl,L as Ro}from"../../chunks/vendor-hf-doc-builder.js";import{T as mo}from"../../chunks/Tip-hf-doc-builder.js";import{D as we}from"../../chunks/Docstring-hf-doc-builder.js";import{C as Bo}from"../../chunks/CodeBlock-hf-doc-builder.js";import{I as Qe}from"../../chunks/IconCopyLink-hf-doc-builder.js";import{E as zo}from"../../chunks/ExampleCodeBlock-hf-doc-builder.js";import{P as ll}from"../../chunks/PipelineTag-hf-doc-builder.js";function il(F){let c,b,v,m,_;return m=new Bo({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> CvtConfig, CvtModel

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Initializing a Cvt msft/cvt style configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>configuration = CvtConfig()

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Initializing a model (with random weights) from the msft/cvt style configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = CvtModel(configuration)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Accessing the model configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>configuration = model.config`}}),{c(){c=n("p"),b=s("Example:"),v=f(),C(m.$$.fragment)},l(i){c=r(i,"P",{});var p=l(c);b=a(p,"Example:"),p.forEach(o),v=h(i),w(m.$$.fragment,i)},m(i,p){g(i,c,p),e(c,b),g(i,v,p),T(m,i,p),_=!0},p:Ro,i(i){_||(M(m.$$.fragment,i),_=!0)},o(i){$(m.$$.fragment,i),_=!1},d(i){i&&o(c),i&&o(v),k(m,i)}}}function cl(F){let c,b,v,m,_;return{c(){c=n("p"),b=s("Although the recipe for forward pass needs to be defined within this function, one should call the "),v=n("code"),m=s("Module"),_=s(`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`)},l(i){c=r(i,"P",{});var p=l(c);b=a(p,"Although the recipe for forward pass needs to be defined within this function, one should call the "),v=r(p,"CODE",{});var j=l(v);m=a(j,"Module"),j.forEach(o),_=a(p,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),p.forEach(o)},m(i,p){g(i,c,p),e(c,b),e(c,v),e(v,m),e(c,_)},d(i){i&&o(c)}}}function dl(F){let c,b,v,m,_;return m=new Bo({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> AutoImageProcessor, CvtModel
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch
<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;huggingface/cats-image&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>image = dataset[<span class="hljs-string">&quot;test&quot;</span>][<span class="hljs-string">&quot;image&quot;</span>][<span class="hljs-number">0</span>]

<span class="hljs-meta">&gt;&gt;&gt; </span>image_processor = AutoImageProcessor.from_pretrained(<span class="hljs-string">&quot;microsoft/cvt-13&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = CvtModel.from_pretrained(<span class="hljs-string">&quot;microsoft/cvt-13&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = image_processor(image, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">with</span> torch.no_grad():
<span class="hljs-meta">... </span>    outputs = model(**inputs)

<span class="hljs-meta">&gt;&gt;&gt; </span>last_hidden_states = outputs.last_hidden_state
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-built_in">list</span>(last_hidden_states.shape)
[<span class="hljs-number">1</span>, <span class="hljs-number">384</span>, <span class="hljs-number">14</span>, <span class="hljs-number">14</span>]`}}),{c(){c=n("p"),b=s("Example:"),v=f(),C(m.$$.fragment)},l(i){c=r(i,"P",{});var p=l(c);b=a(p,"Example:"),p.forEach(o),v=h(i),w(m.$$.fragment,i)},m(i,p){g(i,c,p),e(c,b),g(i,v,p),T(m,i,p),_=!0},p:Ro,i(i){_||(M(m.$$.fragment,i),_=!0)},o(i){$(m.$$.fragment,i),_=!1},d(i){i&&o(c),i&&o(v),k(m,i)}}}function ml(F){let c,b,v,m,_;return{c(){c=n("p"),b=s("Although the recipe for forward pass needs to be defined within this function, one should call the "),v=n("code"),m=s("Module"),_=s(`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`)},l(i){c=r(i,"P",{});var p=l(c);b=a(p,"Although the recipe for forward pass needs to be defined within this function, one should call the "),v=r(p,"CODE",{});var j=l(v);m=a(j,"Module"),j.forEach(o),_=a(p,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),p.forEach(o)},m(i,p){g(i,c,p),e(c,b),e(c,v),e(v,m),e(c,_)},d(i){i&&o(c)}}}function fl(F){let c,b,v,m,_;return m=new Bo({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> AutoImageProcessor, CvtForImageClassification
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch
<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;huggingface/cats-image&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>image = dataset[<span class="hljs-string">&quot;test&quot;</span>][<span class="hljs-string">&quot;image&quot;</span>][<span class="hljs-number">0</span>]

<span class="hljs-meta">&gt;&gt;&gt; </span>image_processor = AutoImageProcessor.from_pretrained(<span class="hljs-string">&quot;microsoft/cvt-13&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = CvtForImageClassification.from_pretrained(<span class="hljs-string">&quot;microsoft/cvt-13&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = image_processor(image, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">with</span> torch.no_grad():
<span class="hljs-meta">... </span>    logits = model(**inputs).logits

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># model predicts one of the 1000 ImageNet classes</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>predicted_label = logits.argmax(-<span class="hljs-number">1</span>).item()
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-built_in">print</span>(model.config.id2label[predicted_label])
tabby, tabby cat`}}),{c(){c=n("p"),b=s("Example:"),v=f(),C(m.$$.fragment)},l(i){c=r(i,"P",{});var p=l(c);b=a(p,"Example:"),p.forEach(o),v=h(i),w(m.$$.fragment,i)},m(i,p){g(i,c,p),e(c,b),g(i,v,p),T(m,i,p),_=!0},p:Ro,i(i){_||(M(m.$$.fragment,i),_=!0)},o(i){$(m.$$.fragment,i),_=!1},d(i){i&&o(c),i&&o(v),k(m,i)}}}function hl(F){let c,b,v,m,_,i,p,j,D,z,E,V,Z,A,O,x,K,q;return{c(){c=n("p"),b=s("TF 2.0 models accepts two formats as inputs:"),v=f(),m=n("ul"),_=n("li"),i=s("having all inputs as keyword arguments (like PyTorch models), or"),p=f(),j=n("li"),D=s("having all inputs as a list, tuple or dict in the first positional arguments."),z=f(),E=n("p"),V=s("This second option is useful when using "),Z=n("code"),A=s("tf.keras.Model.fit"),O=s(` method which currently requires having all the
tensors in the first argument of the model call function: `),x=n("code"),K=s("model(inputs)"),q=s(".")},l(y){c=r(y,"P",{});var I=l(c);b=a(I,"TF 2.0 models accepts two formats as inputs:"),I.forEach(o),v=h(y),m=r(y,"UL",{});var N=l(m);_=r(N,"LI",{});var ae=l(_);i=a(ae,"having all inputs as keyword arguments (like PyTorch models), or"),ae.forEach(o),p=h(N),j=r(N,"LI",{});var ne=l(j);D=a(ne,"having all inputs as a list, tuple or dict in the first positional arguments."),ne.forEach(o),N.forEach(o),z=h(y),E=r(y,"P",{});var U=l(E);V=a(U,"This second option is useful when using "),Z=r(U,"CODE",{});var re=l(Z);A=a(re,"tf.keras.Model.fit"),re.forEach(o),O=a(U,` method which currently requires having all the
tensors in the first argument of the model call function: `),x=r(U,"CODE",{});var le=l(x);K=a(le,"model(inputs)"),le.forEach(o),q=a(U,"."),U.forEach(o)},m(y,I){g(y,c,I),e(c,b),g(y,v,I),g(y,m,I),e(m,_),e(_,i),e(m,p),e(m,j),e(j,D),g(y,z,I),g(y,E,I),e(E,V),e(E,Z),e(Z,A),e(E,O),e(E,x),e(x,K),e(E,q)},d(y){y&&o(c),y&&o(v),y&&o(m),y&&o(z),y&&o(E)}}}function pl(F){let c,b,v,m,_;return{c(){c=n("p"),b=s("Although the recipe for forward pass needs to be defined within this function, one should call the "),v=n("code"),m=s("Module"),_=s(`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`)},l(i){c=r(i,"P",{});var p=l(c);b=a(p,"Although the recipe for forward pass needs to be defined within this function, one should call the "),v=r(p,"CODE",{});var j=l(v);m=a(j,"Module"),j.forEach(o),_=a(p,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),p.forEach(o)},m(i,p){g(i,c,p),e(c,b),e(c,v),e(v,m),e(c,_)},d(i){i&&o(c)}}}function ul(F){let c,b,v,m,_;return m=new Bo({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> AutoImageProcessor, TFCvtModel
<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>url = <span class="hljs-string">&quot;http://images.cocodataset.org/val2017/000000039769.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_processor = AutoImageProcessor.from_pretrained(<span class="hljs-string">&quot;microsoft/cvt-13&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFCvtModel.from_pretrained(<span class="hljs-string">&quot;microsoft/cvt-13&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = image_processor(images=image, return_tensors=<span class="hljs-string">&quot;tf&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(**inputs)
<span class="hljs-meta">&gt;&gt;&gt; </span>last_hidden_states = outputs.last_hidden_state`}}),{c(){c=n("p"),b=s("Examples:"),v=f(),C(m.$$.fragment)},l(i){c=r(i,"P",{});var p=l(c);b=a(p,"Examples:"),p.forEach(o),v=h(i),w(m.$$.fragment,i)},m(i,p){g(i,c,p),e(c,b),g(i,v,p),T(m,i,p),_=!0},p:Ro,i(i){_||(M(m.$$.fragment,i),_=!0)},o(i){$(m.$$.fragment,i),_=!1},d(i){i&&o(c),i&&o(v),k(m,i)}}}function gl(F){let c,b,v,m,_,i,p,j,D,z,E,V,Z,A,O,x,K,q;return{c(){c=n("p"),b=s("TF 2.0 models accepts two formats as inputs:"),v=f(),m=n("ul"),_=n("li"),i=s("having all inputs as keyword arguments (like PyTorch models), or"),p=f(),j=n("li"),D=s("having all inputs as a list, tuple or dict in the first positional arguments."),z=f(),E=n("p"),V=s("This second option is useful when using "),Z=n("code"),A=s("tf.keras.Model.fit"),O=s(` method which currently requires having all the
tensors in the first argument of the model call function: `),x=n("code"),K=s("model(inputs)"),q=s(".")},l(y){c=r(y,"P",{});var I=l(c);b=a(I,"TF 2.0 models accepts two formats as inputs:"),I.forEach(o),v=h(y),m=r(y,"UL",{});var N=l(m);_=r(N,"LI",{});var ae=l(_);i=a(ae,"having all inputs as keyword arguments (like PyTorch models), or"),ae.forEach(o),p=h(N),j=r(N,"LI",{});var ne=l(j);D=a(ne,"having all inputs as a list, tuple or dict in the first positional arguments."),ne.forEach(o),N.forEach(o),z=h(y),E=r(y,"P",{});var U=l(E);V=a(U,"This second option is useful when using "),Z=r(U,"CODE",{});var re=l(Z);A=a(re,"tf.keras.Model.fit"),re.forEach(o),O=a(U,` method which currently requires having all the
tensors in the first argument of the model call function: `),x=r(U,"CODE",{});var le=l(x);K=a(le,"model(inputs)"),le.forEach(o),q=a(U,"."),U.forEach(o)},m(y,I){g(y,c,I),e(c,b),g(y,v,I),g(y,m,I),e(m,_),e(_,i),e(m,p),e(m,j),e(j,D),g(y,z,I),g(y,E,I),e(E,V),e(E,Z),e(Z,A),e(E,O),e(E,x),e(x,K),e(E,q)},d(y){y&&o(c),y&&o(v),y&&o(m),y&&o(z),y&&o(E)}}}function vl(F){let c,b,v,m,_;return{c(){c=n("p"),b=s("Although the recipe for forward pass needs to be defined within this function, one should call the "),v=n("code"),m=s("Module"),_=s(`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`)},l(i){c=r(i,"P",{});var p=l(c);b=a(p,"Although the recipe for forward pass needs to be defined within this function, one should call the "),v=r(p,"CODE",{});var j=l(v);m=a(j,"Module"),j.forEach(o),_=a(p,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),p.forEach(o)},m(i,p){g(i,c,p),e(c,b),e(c,v),e(v,m),e(c,_)},d(i){i&&o(c)}}}function _l(F){let c,b,v,m,_;return m=new Bo({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> AutoImageProcessor, TFCvtForImageClassification
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> tensorflow <span class="hljs-keyword">as</span> tf
<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>url = <span class="hljs-string">&quot;http://images.cocodataset.org/val2017/000000039769.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_processor = AutoImageProcessor.from_pretrained(<span class="hljs-string">&quot;microsoft/cvt-13&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFCvtForImageClassification.from_pretrained(<span class="hljs-string">&quot;microsoft/cvt-13&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = image_processor(images=image, return_tensors=<span class="hljs-string">&quot;tf&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(**inputs)
<span class="hljs-meta">&gt;&gt;&gt; </span>logits = outputs.logits
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># model predicts one of the 1000 ImageNet classes</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>predicted_class_idx = tf.math.argmax(logits, axis=-<span class="hljs-number">1</span>)[<span class="hljs-number">0</span>]
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-built_in">print</span>(<span class="hljs-string">&quot;Predicted class:&quot;</span>, model.config.id2label[<span class="hljs-built_in">int</span>(predicted_class_idx)])`}}),{c(){c=n("p"),b=s("Examples:"),v=f(),C(m.$$.fragment)},l(i){c=r(i,"P",{});var p=l(c);b=a(p,"Examples:"),p.forEach(o),v=h(i),w(m.$$.fragment,i)},m(i,p){g(i,c,p),e(c,b),g(i,v,p),T(m,i,p),_=!0},p:Ro,i(i){_||(M(m.$$.fragment,i),_=!0)},o(i){$(m.$$.fragment,i),_=!1},d(i){i&&o(c),i&&o(v),k(m,i)}}}function bl(F){let c,b,v,m,_,i,p,j,D,z,E,V,Z,A,O,x,K,q,y,I,N,ae,ne,U,re,le,qo,Zt,Ms,Go,xt,fo,$s,So,Nt,ks,Lo,te,Xe,Es,Wt,js,Fs,Is,P,Us,Ye,Zs,xs,Jt,Ns,Ws,Vt,Js,Vs,At,As,Ps,Pt,zs,Rs,Bs,ie,qs,He,Gs,Ss,De,Ls,Qs,Qo,oe,Xs,Oe,Ys,Hs,Ke,Ds,Os,Xo,ce,Te,ho,et,Ks,po,ea,Yo,zt,ta,Ho,tt,Do,Me,se,Rt,oa,sa,ot,aa,na,st,ra,la,ia,Bt,ca,qt,da,Oo,Gt,ma,Ko,de,$e,uo,at,fa,go,ha,es,R,nt,pa,me,ua,St,ga,va,rt,_a,ba,ya,fe,Ca,Lt,wa,Ta,Qt,Ma,$a,ka,ke,ts,he,Ee,vo,lt,Ea,_o,ja,os,ee,it,Fa,ct,Ia,dt,Ua,Za,xa,G,mt,Na,pe,Wa,Xt,Ja,Va,bo,Aa,Pa,za,je,Ra,Fe,ss,ue,Ie,yo,ft,Ba,Co,qa,as,B,ht,Ga,wo,Sa,La,pt,Qa,ut,Xa,Ya,Ha,S,gt,Da,ge,Oa,Yt,Ka,en,To,tn,on,sn,Ue,an,Ze,ns,ve,xe,Mo,vt,nn,$o,rn,rs,W,_t,ln,ko,cn,dn,bt,mn,Ht,fn,hn,pn,yt,un,Ct,gn,vn,_n,Ne,bn,L,wt,yn,_e,Cn,Dt,wn,Tn,Eo,Mn,$n,kn,We,En,Je,ls,be,Ve,jo,Tt,jn,Fo,Fn,is,J,Mt,In,Io,Un,Zn,$t,xn,Ot,Nn,Wn,Jn,kt,Vn,Et,An,Pn,zn,Ae,Rn,Q,jt,Bn,ye,qn,Kt,Gn,Sn,Uo,Ln,Qn,Xn,Pe,Yn,ze,cs;return i=new Qe({}),A=new Qe({}),et=new Qe({}),tt=new ll({props:{pipeline:"image-classification"}}),at=new Qe({}),nt=new we({props:{name:"class transformers.CvtConfig",anchor:"transformers.CvtConfig",parameters:[{name:"num_channels",val:" = 3"},{name:"patch_sizes",val:" = [7, 3, 3]"},{name:"patch_stride",val:" = [4, 2, 2]"},{name:"patch_padding",val:" = [2, 1, 1]"},{name:"embed_dim",val:" = [64, 192, 384]"},{name:"num_heads",val:" = [1, 3, 6]"},{name:"depth",val:" = [1, 2, 10]"},{name:"mlp_ratio",val:" = [4.0, 4.0, 4.0]"},{name:"attention_drop_rate",val:" = [0.0, 0.0, 0.0]"},{name:"drop_rate",val:" = [0.0, 0.0, 0.0]"},{name:"drop_path_rate",val:" = [0.0, 0.0, 0.1]"},{name:"qkv_bias",val:" = [True, True, True]"},{name:"cls_token",val:" = [False, False, True]"},{name:"qkv_projection_method",val:" = ['dw_bn', 'dw_bn', 'dw_bn']"},{name:"kernel_qkv",val:" = [3, 3, 3]"},{name:"padding_kv",val:" = [1, 1, 1]"},{name:"stride_kv",val:" = [2, 2, 2]"},{name:"padding_q",val:" = [1, 1, 1]"},{name:"stride_q",val:" = [1, 1, 1]"},{name:"initializer_range",val:" = 0.02"},{name:"layer_norm_eps",val:" = 1e-12"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.CvtConfig.num_channels",description:`<strong>num_channels</strong> (<code>int</code>, <em>optional</em>, defaults to 3) &#x2014;
The number of input channels.`,name:"num_channels"},{anchor:"transformers.CvtConfig.patch_sizes",description:`<strong>patch_sizes</strong> (<code>List[int]</code>, <em>optional</em>, defaults to <code>[7, 3, 3]</code>) &#x2014;
The kernel size of each encoder&#x2019;s patch embedding.`,name:"patch_sizes"},{anchor:"transformers.CvtConfig.patch_stride",description:`<strong>patch_stride</strong> (<code>List[int]</code>, <em>optional</em>, defaults to <code>[4, 2, 2]</code>) &#x2014;
The stride size of each encoder&#x2019;s patch embedding.`,name:"patch_stride"},{anchor:"transformers.CvtConfig.patch_padding",description:`<strong>patch_padding</strong> (<code>List[int]</code>, <em>optional</em>, defaults to <code>[2, 1, 1]</code>) &#x2014;
The padding size of each encoder&#x2019;s patch embedding.`,name:"patch_padding"},{anchor:"transformers.CvtConfig.embed_dim",description:`<strong>embed_dim</strong> (<code>List[int]</code>, <em>optional</em>, defaults to <code>[64, 192, 384]</code>) &#x2014;
Dimension of each of the encoder blocks.`,name:"embed_dim"},{anchor:"transformers.CvtConfig.num_heads",description:`<strong>num_heads</strong> (<code>List[int]</code>, <em>optional</em>, defaults to <code>[1, 3, 6]</code>) &#x2014;
Number of attention heads for each attention layer in each block of the Transformer encoder.`,name:"num_heads"},{anchor:"transformers.CvtConfig.depth",description:`<strong>depth</strong> (<code>List[int]</code>, <em>optional</em>, defaults to <code>[1, 2, 10]</code>) &#x2014;
The number of layers in each encoder block.`,name:"depth"},{anchor:"transformers.CvtConfig.mlp_ratios",description:`<strong>mlp_ratios</strong> (<code>List[float]</code>, <em>optional</em>, defaults to <code>[4.0, 4.0, 4.0, 4.0]</code>) &#x2014;
Ratio of the size of the hidden layer compared to the size of the input layer of the Mix FFNs in the
encoder blocks.`,name:"mlp_ratios"},{anchor:"transformers.CvtConfig.attention_drop_rate",description:`<strong>attention_drop_rate</strong> (<code>List[float]</code>, <em>optional</em>, defaults to <code>[0.0, 0.0, 0.0]</code>) &#x2014;
The dropout ratio for the attention probabilities.`,name:"attention_drop_rate"},{anchor:"transformers.CvtConfig.drop_rate",description:`<strong>drop_rate</strong> (<code>List[float]</code>, <em>optional</em>, defaults to <code>[0.0, 0.0, 0.0]</code>) &#x2014;
The dropout ratio for the patch embeddings probabilities.`,name:"drop_rate"},{anchor:"transformers.CvtConfig.drop_path_rate",description:`<strong>drop_path_rate</strong> (<code>List[float]</code>, <em>optional</em>, defaults to <code>[0.0, 0.0, 0.1]</code>) &#x2014;
The dropout probability for stochastic depth, used in the blocks of the Transformer encoder.`,name:"drop_path_rate"},{anchor:"transformers.CvtConfig.qkv_bias",description:`<strong>qkv_bias</strong> (<code>List[bool]</code>, <em>optional</em>, defaults to <code>[True, True, True]</code>) &#x2014;
The bias bool for query, key and value in attentions`,name:"qkv_bias"},{anchor:"transformers.CvtConfig.cls_token",description:`<strong>cls_token</strong> (<code>List[bool]</code>, <em>optional</em>, defaults to <code>[False, False, True]</code>) &#x2014;
Whether or not to add a classification token to the output of each of the last 3 stages.`,name:"cls_token"},{anchor:"transformers.CvtConfig.qkv_projection_method",description:`<strong>qkv_projection_method</strong> (<code>List[string]</code>, <em>optional</em>, defaults to [&#x201C;dw_bn&#x201D;, &#x201C;dw_bn&#x201D;, &#x201C;dw_bn&#x201D;]\`) &#x2014;
The projection method for query, key and value Default is depth-wise convolutions with batch norm. For
Linear projection use &#x201C;avg&#x201D;.`,name:"qkv_projection_method"},{anchor:"transformers.CvtConfig.kernel_qkv",description:`<strong>kernel_qkv</strong> (<code>List[int]</code>, <em>optional</em>, defaults to <code>[3, 3, 3]</code>) &#x2014;
The kernel size for query, key and value in attention layer`,name:"kernel_qkv"},{anchor:"transformers.CvtConfig.padding_kv",description:`<strong>padding_kv</strong> (<code>List[int]</code>, <em>optional</em>, defaults to <code>[1, 1, 1]</code>) &#x2014;
The padding size for key and value in attention layer`,name:"padding_kv"},{anchor:"transformers.CvtConfig.stride_kv",description:`<strong>stride_kv</strong> (<code>List[int]</code>, <em>optional</em>, defaults to <code>[2, 2, 2]</code>) &#x2014;
The stride size for key and value in attention layer`,name:"stride_kv"},{anchor:"transformers.CvtConfig.padding_q",description:`<strong>padding_q</strong> (<code>List[int]</code>, <em>optional</em>, defaults to <code>[1, 1, 1]</code>) &#x2014;
The padding size for query in attention layer`,name:"padding_q"},{anchor:"transformers.CvtConfig.stride_q",description:`<strong>stride_q</strong> (<code>List[int]</code>, <em>optional</em>, defaults to <code>[1, 1, 1]</code>) &#x2014;
The stride size for query in attention layer`,name:"stride_q"},{anchor:"transformers.CvtConfig.initializer_range",description:`<strong>initializer_range</strong> (<code>float</code>, <em>optional</em>, defaults to 0.02) &#x2014;
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.`,name:"initializer_range"},{anchor:"transformers.CvtConfig.layer_norm_eps",description:`<strong>layer_norm_eps</strong> (<code>float</code>, <em>optional</em>, defaults to 1e-6) &#x2014;
The epsilon used by the layer normalization layers.`,name:"layer_norm_eps"}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/cvt/configuration_cvt.py#L29"}}),ke=new zo({props:{anchor:"transformers.CvtConfig.example",$$slots:{default:[il]},$$scope:{ctx:F}}}),lt=new Qe({}),it=new we({props:{name:"class transformers.CvtModel",anchor:"transformers.CvtModel",parameters:[{name:"config",val:""},{name:"add_pooling_layer",val:" = True"}],parametersDescription:[{anchor:"transformers.CvtModel.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.32.0/en/model_doc/cvt#transformers.CvtConfig">CvtConfig</a>) &#x2014; 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 <a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/cvt/modeling_cvt.py#L590"}}),mt=new we({props:{name:"forward",anchor:"transformers.CvtModel.forward",parameters:[{name:"pixel_values",val:": typing.Optional[torch.Tensor] = None"},{name:"output_hidden_states",val:": typing.Optional[bool] = None"},{name:"return_dict",val:": typing.Optional[bool] = None"}],parametersDescription:[{anchor:"transformers.CvtModel.forward.pixel_values",description:`<strong>pixel_values</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, num_channels, height, width)</code>) &#x2014;
Pixel values. Pixel values can be obtained using <a href="/docs/transformers/v4.32.0/en/model_doc/auto#transformers.AutoImageProcessor">AutoImageProcessor</a>. See <code>CvtImageProcessor.__call__</code>
for details.`,name:"pixel_values"},{anchor:"transformers.CvtModel.forward.output_hidden_states",description:`<strong>output_hidden_states</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the hidden states of all layers. See <code>hidden_states</code> under returned tensors for
more detail.`,name:"output_hidden_states"},{anchor:"transformers.CvtModel.forward.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/v4.32.0/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/cvt/modeling_cvt.py#L605",returnDescription:`
<p>A <code>transformers.models.cvt.modeling_cvt.BaseModelOutputWithCLSToken</code> or a tuple of
<code>torch.FloatTensor</code> (if <code>return_dict=False</code> is passed or when <code>config.return_dict=False</code>) comprising various
elements depending on the configuration (<a
  href="/docs/transformers/v4.32.0/en/model_doc/cvt#transformers.CvtConfig"
>CvtConfig</a>) and inputs.</p>
<ul>
<li><strong>last_hidden_state</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>) \u2014 Sequence of hidden-states at the output of the last layer of the model.</li>
<li><strong>cls_token_value</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, 1, hidden_size)</code>) \u2014 Classification token at the output of the last layer of the model.</li>
<li><strong>hidden_states</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for the output of the embeddings + one for the output of each layer) of
shape <code>(batch_size, sequence_length, hidden_size)</code>. Hidden-states of the model at the output of each layer
plus the initial embedding outputs.</li>
</ul>
`,returnType:`
<p><code>transformers.models.cvt.modeling_cvt.BaseModelOutputWithCLSToken</code> or <code>tuple(torch.FloatTensor)</code></p>
`}}),je=new mo({props:{$$slots:{default:[cl]},$$scope:{ctx:F}}}),Fe=new zo({props:{anchor:"transformers.CvtModel.forward.example",$$slots:{default:[dl]},$$scope:{ctx:F}}}),ft=new Qe({}),ht=new we({props:{name:"class transformers.CvtForImageClassification",anchor:"transformers.CvtForImageClassification",parameters:[{name:"config",val:""}],parametersDescription:[{anchor:"transformers.CvtForImageClassification.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.32.0/en/model_doc/cvt#transformers.CvtConfig">CvtConfig</a>) &#x2014; 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 <a href="/docs/transformers/v4.32.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/cvt/modeling_cvt.py#L651"}}),gt=new we({props:{name:"forward",anchor:"transformers.CvtForImageClassification.forward",parameters:[{name:"pixel_values",val:": typing.Optional[torch.Tensor] = None"},{name:"labels",val:": typing.Optional[torch.Tensor] = None"},{name:"output_hidden_states",val:": typing.Optional[bool] = None"},{name:"return_dict",val:": typing.Optional[bool] = None"}],parametersDescription:[{anchor:"transformers.CvtForImageClassification.forward.pixel_values",description:`<strong>pixel_values</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, num_channels, height, width)</code>) &#x2014;
Pixel values. Pixel values can be obtained using <a href="/docs/transformers/v4.32.0/en/model_doc/auto#transformers.AutoImageProcessor">AutoImageProcessor</a>. See <code>CvtImageProcessor.__call__</code>
for details.`,name:"pixel_values"},{anchor:"transformers.CvtForImageClassification.forward.output_hidden_states",description:`<strong>output_hidden_states</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the hidden states of all layers. See <code>hidden_states</code> under returned tensors for
more detail.`,name:"output_hidden_states"},{anchor:"transformers.CvtForImageClassification.forward.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/v4.32.0/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"},{anchor:"transformers.CvtForImageClassification.forward.labels",description:`<strong>labels</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size,)</code>, <em>optional</em>) &#x2014;
Labels for computing the image classification/regression loss. Indices should be in <code>[0, ..., config.num_labels - 1]</code>. If <code>config.num_labels == 1</code> a regression loss is computed (Mean-Square loss), If
<code>config.num_labels &gt; 1</code> a classification loss is computed (Cross-Entropy).`,name:"labels"}],source:"https://github.com/huggingface/transformers/blob/v4.32.0/src/transformers/models/cvt/modeling_cvt.py#L666",returnDescription:`
<p>A <a
  href="/docs/transformers/v4.32.0/en/main_classes/output#transformers.modeling_outputs.ImageClassifierOutputWithNoAttention"
>transformers.modeling_outputs.ImageClassifierOutputWithNoAttention</a> or a tuple of
<code>torch.FloatTensor</code> (if <code>return_dict=False</code> is passed or when <code>config.return_dict=False</code>) comprising various
elements depending on the configuration (<a
  href="/docs/transformers/v4.32.0/en/model_doc/cvt#transformers.CvtConfig"
>CvtConfig</a>) and inputs.</p>
<ul>
<li><strong>loss</strong> (<code>torch.FloatTensor</code> of shape <code>(1,)</code>, <em>optional</em>, returned when <code>labels</code> is provided) \u2014 Classification (or regression if config.num_labels==1) loss.</li>
<li><strong>logits</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, config.num_labels)</code>) \u2014 Classification (or regression if config.num_labels==1) scores (before SoftMax).</li>
<li><strong>hidden_states</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each stage) of shape <code>(batch_size, num_channels, height, width)</code>. Hidden-states (also
called feature maps) of the model at the output of each stage.</li>
</ul>
`,returnType:`
<p><a
  href="/docs/transformers/v4.32.0/en/main_classes/output#transformers.modeling_outputs.ImageClassifierOutputWithNoAttention"
>transformers.modeling_outputs.ImageClassifierOutputWithNoAttention</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),Ue=new mo({props:{$$slots:{default:[ml]},$$scope:{ctx:F}}}),Ze=new zo({props:{anchor:"transformers.CvtForImageClassification.forward.example",$$slots:{default:[fl]},$$scope:{ctx:F}}}),vt=new Qe({}),_t=new we({props:{name:"class transformers.TFCvtModel",anchor:"transformers.TFCvtModel",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFCvtModel.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.32.0/en/model_doc/cvt#transformers.CvtConfig">CvtConfig</a>) &#x2014; 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
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