import{S as hd,i as md,s as gd,e as o,k as p,w,t as l,M as ud,c as s,d as a,m as f,a as n,x as v,h as i,b as c,G as e,g as _,y as M,q as $,o as T,B as N,v as _d,L as Ot}from"../../chunks/vendor-hf-doc-builder.js";import{T as Ea}from"../../chunks/Tip-hf-doc-builder.js";import{D as k}from"../../chunks/Docstring-hf-doc-builder.js";import{C as Kt}from"../../chunks/CodeBlock-hf-doc-builder.js";import{I as H}from"../../chunks/IconCopyLink-hf-doc-builder.js";import{E as Lt}from"../../chunks/ExampleCodeBlock-hf-doc-builder.js";import{P as yd}from"../../chunks/PipelineTag-hf-doc-builder.js";function bd(F){let d,b,u,m,y;return m=new Kt({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> RegNetConfig, RegNetModel

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Initializing a RegNet regnet-y-40 style configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>configuration = RegNetConfig()
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Initializing a model from the regnet-y-40 style configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = RegNetModel(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(){d=o("p"),b=l("Example:"),u=p(),w(m.$$.fragment)},l(r){d=s(r,"P",{});var h=n(d);b=i(h,"Example:"),h.forEach(a),u=f(r),v(m.$$.fragment,r)},m(r,h){_(r,d,h),e(d,b),_(r,u,h),M(m,r,h),y=!0},p:Ot,i(r){y||($(m.$$.fragment,r),y=!0)},o(r){T(m.$$.fragment,r),y=!1},d(r){r&&a(d),r&&a(u),N(m,r)}}}function wd(F){let d,b,u,m,y;return{c(){d=o("p"),b=l("Although the recipe for forward pass needs to be defined within this function, one should call the "),u=o("code"),m=l("Module"),y=l(`
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(r){d=s(r,"P",{});var h=n(d);b=i(h,"Although the recipe for forward pass needs to be defined within this function, one should call the "),u=s(h,"CODE",{});var R=n(u);m=i(R,"Module"),R.forEach(a),y=i(h,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),h.forEach(a)},m(r,h){_(r,d,h),e(d,b),e(d,u),e(u,m),e(d,y)},d(r){r&&a(d)}}}function vd(F){let d,b,u,m,y;return m=new Kt({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, RegNetModel
<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;facebook/regnet-y-040&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = RegNetModel.from_pretrained(<span class="hljs-string">&quot;facebook/regnet-y-040&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">1088</span>, <span class="hljs-number">7</span>, <span class="hljs-number">7</span>]`}}),{c(){d=o("p"),b=l("Example:"),u=p(),w(m.$$.fragment)},l(r){d=s(r,"P",{});var h=n(d);b=i(h,"Example:"),h.forEach(a),u=f(r),v(m.$$.fragment,r)},m(r,h){_(r,d,h),e(d,b),_(r,u,h),M(m,r,h),y=!0},p:Ot,i(r){y||($(m.$$.fragment,r),y=!0)},o(r){T(m.$$.fragment,r),y=!1},d(r){r&&a(d),r&&a(u),N(m,r)}}}function Md(F){let d,b,u,m,y;return{c(){d=o("p"),b=l("Although the recipe for forward pass needs to be defined within this function, one should call the "),u=o("code"),m=l("Module"),y=l(`
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(r){d=s(r,"P",{});var h=n(d);b=i(h,"Although the recipe for forward pass needs to be defined within this function, one should call the "),u=s(h,"CODE",{});var R=n(u);m=i(R,"Module"),R.forEach(a),y=i(h,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),h.forEach(a)},m(r,h){_(r,d,h),e(d,b),e(d,u),e(u,m),e(d,y)},d(r){r&&a(d)}}}function $d(F){let d,b,u,m,y;return m=new Kt({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, RegNetForImageClassification
<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;facebook/regnet-y-040&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = RegNetForImageClassification.from_pretrained(<span class="hljs-string">&quot;facebook/regnet-y-040&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(){d=o("p"),b=l("Example:"),u=p(),w(m.$$.fragment)},l(r){d=s(r,"P",{});var h=n(d);b=i(h,"Example:"),h.forEach(a),u=f(r),v(m.$$.fragment,r)},m(r,h){_(r,d,h),e(d,b),_(r,u,h),M(m,r,h),y=!0},p:Ot,i(r){y||($(m.$$.fragment,r),y=!0)},o(r){T(m.$$.fragment,r),y=!1},d(r){r&&a(d),r&&a(u),N(m,r)}}}function Td(F){let d,b,u,m,y;return{c(){d=o("p"),b=l("Although the recipe for forward pass needs to be defined within this function, one should call the "),u=o("code"),m=l("Module"),y=l(`
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(r){d=s(r,"P",{});var h=n(d);b=i(h,"Although the recipe for forward pass needs to be defined within this function, one should call the "),u=s(h,"CODE",{});var R=n(u);m=i(R,"Module"),R.forEach(a),y=i(h,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),h.forEach(a)},m(r,h){_(r,d,h),e(d,b),e(d,u),e(u,m),e(d,y)},d(r){r&&a(d)}}}function Nd(F){let d,b,u,m,y;return m=new Kt({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, TFRegNetModel
<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;facebook/regnet-y-040&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFRegNetModel.from_pretrained(<span class="hljs-string">&quot;facebook/regnet-y-040&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = image_processor(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
<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">1088</span>, <span class="hljs-number">7</span>, <span class="hljs-number">7</span>]`}}),{c(){d=o("p"),b=l("Example:"),u=p(),w(m.$$.fragment)},l(r){d=s(r,"P",{});var h=n(d);b=i(h,"Example:"),h.forEach(a),u=f(r),v(m.$$.fragment,r)},m(r,h){_(r,d,h),e(d,b),_(r,u,h),M(m,r,h),y=!0},p:Ot,i(r){y||($(m.$$.fragment,r),y=!0)},o(r){T(m.$$.fragment,r),y=!1},d(r){r&&a(d),r&&a(u),N(m,r)}}}function Fd(F){let d,b,u,m,y;return{c(){d=o("p"),b=l("Although the recipe for forward pass needs to be defined within this function, one should call the "),u=o("code"),m=l("Module"),y=l(`
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(r){d=s(r,"P",{});var h=n(d);b=i(h,"Although the recipe for forward pass needs to be defined within this function, one should call the "),u=s(h,"CODE",{});var R=n(u);m=i(R,"Module"),R.forEach(a),y=i(h,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),h.forEach(a)},m(r,h){_(r,d,h),e(d,b),e(d,u),e(u,m),e(d,y)},d(r){r&&a(d)}}}function Rd(F){let d,b,u,m,y;return m=new Kt({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, TFRegNetForImageClassification
<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> 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;facebook/regnet-y-040&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFRegNetForImageClassification.from_pretrained(<span class="hljs-string">&quot;facebook/regnet-y-040&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = image_processor(image, return_tensors=<span class="hljs-string">&quot;tf&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </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 = <span class="hljs-built_in">int</span>(tf.math.argmax(logits, axis=-<span class="hljs-number">1</span>))
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-built_in">print</span>(model.config.id2label[predicted_label])
tabby, tabby cat`}}),{c(){d=o("p"),b=l("Example:"),u=p(),w(m.$$.fragment)},l(r){d=s(r,"P",{});var h=n(d);b=i(h,"Example:"),h.forEach(a),u=f(r),v(m.$$.fragment,r)},m(r,h){_(r,d,h),e(d,b),_(r,u,h),M(m,r,h),y=!0},p:Ot,i(r){y||($(m.$$.fragment,r),y=!0)},o(r){T(m.$$.fragment,r),y=!1},d(r){r&&a(d),r&&a(u),N(m,r)}}}function jd(F){let d,b,u,m,y;return{c(){d=o("p"),b=l("Although the recipe for forward pass needs to be defined within this function, one should call the "),u=o("code"),m=l("Module"),y=l(`
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(r){d=s(r,"P",{});var h=n(d);b=i(h,"Although the recipe for forward pass needs to be defined within this function, one should call the "),u=s(h,"CODE",{});var R=n(u);m=i(R,"Module"),R.forEach(a),y=i(h,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),h.forEach(a)},m(r,h){_(r,d,h),e(d,b),e(d,u),e(u,m),e(d,y)},d(r){r&&a(d)}}}function Jd(F){let d,b,u,m,y;return m=new Kt({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, FlaxRegNetModel
<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;facebook/regnet-y-040&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxRegNetModel.from_pretrained(<span class="hljs-string">&quot;facebook/regnet-y-040&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = image_processor(images=image, 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_states = outputs.last_hidden_state`}}),{c(){d=o("p"),b=l("Examples:"),u=p(),w(m.$$.fragment)},l(r){d=s(r,"P",{});var h=n(d);b=i(h,"Examples:"),h.forEach(a),u=f(r),v(m.$$.fragment,r)},m(r,h){_(r,d,h),e(d,b),_(r,u,h),M(m,r,h),y=!0},p:Ot,i(r){y||($(m.$$.fragment,r),y=!0)},o(r){T(m.$$.fragment,r),y=!1},d(r){r&&a(d),r&&a(u),N(m,r)}}}function Ed(F){let d,b,u,m,y;return{c(){d=o("p"),b=l("Although the recipe for forward pass needs to be defined within this function, one should call the "),u=o("code"),m=l("Module"),y=l(`
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(r){d=s(r,"P",{});var h=n(d);b=i(h,"Although the recipe for forward pass needs to be defined within this function, one should call the "),u=s(h,"CODE",{});var R=n(u);m=i(R,"Module"),R.forEach(a),y=i(h,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),h.forEach(a)},m(r,h){_(r,d,h),e(d,b),e(d,u),e(u,m),e(d,y)},d(r){r&&a(d)}}}function xd(F){let d,b,u,m,y;return m=new Kt({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, FlaxRegNetForImageClassification
<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> jax
<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;facebook/regnet-y-040&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxRegNetForImageClassification.from_pretrained(<span class="hljs-string">&quot;facebook/regnet-y-040&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = image_processor(images=image, 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>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 = jax.numpy.argmax(logits, axis=-<span class="hljs-number">1</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[predicted_class_idx.item()])`}}),{c(){d=o("p"),b=l("Example:"),u=p(),w(m.$$.fragment)},l(r){d=s(r,"P",{});var h=n(d);b=i(h,"Example:"),h.forEach(a),u=f(r),v(m.$$.fragment,r)},m(r,h){_(r,d,h),e(d,b),_(r,u,h),M(m,r,h),y=!0},p:Ot,i(r){y||($(m.$$.fragment,r),y=!0)},o(r){T(m.$$.fragment,r),y=!1},d(r){r&&a(d),r&&a(u),N(m,r)}}}function Id(F){let d,b,u,m,y,r,h,R,Ms,xo,D,he,xa,He,$s,Ia,Ts,Io,me,Ns,De,Fs,Rs,Co,ea,js,Uo,ta,Js,ko,aa,Ca,Es,Zo,oa,xs,Wo,ge,Le,Is,sa,Cs,Us,ks,ue,Zs,Oe,Ws,Ps,Ke,As,Po,E,Gs,et,zs,Vs,tt,Bs,Ss,at,Xs,Ys,ot,qs,Qs,Ao,L,_e,Ua,st,Hs,ka,Ds,Go,na,Ls,zo,nt,Vo,ye,q,ra,Os,Ks,rt,en,tn,lt,an,on,sn,la,nn,ia,rn,Bo,da,ln,So,O,be,Za,it,dn,Wa,cn,Xo,C,dt,pn,K,fn,ca,hn,mn,ct,gn,un,_n,ee,yn,pa,bn,wn,fa,vn,Mn,$n,we,Yo,te,ve,Pa,pt,Tn,Aa,Nn,qo,V,ft,Fn,ht,Rn,mt,jn,Jn,En,Z,gt,xn,ae,In,ha,Cn,Un,Ga,kn,Zn,Wn,Me,Pn,$e,Qo,oe,Te,za,ut,An,Va,Gn,Ho,U,_t,zn,Ba,Vn,Bn,yt,Sn,bt,Xn,Yn,qn,W,wt,Qn,se,Hn,ma,Dn,Ln,Sa,On,Kn,er,Ne,tr,Fe,Do,ne,Re,Xa,vt,ar,Ya,or,Lo,B,Mt,sr,qa,nr,rr,P,$t,lr,re,ir,ga,dr,cr,Qa,pr,fr,hr,je,mr,Je,Oo,le,Ee,Ha,Tt,gr,Da,ur,Ko,S,Nt,_r,La,yr,br,A,Ft,wr,ie,vr,ua,Mr,$r,Oa,Tr,Nr,Fr,xe,Rr,Ie,es,de,Ce,Ka,Rt,jr,eo,Jr,ts,j,jt,Er,to,xr,Ir,Jt,Cr,_a,Ur,kr,Zr,Et,Wr,xt,Pr,Ar,Gr,ao,zr,Vr,X,oo,It,Br,Sr,so,Ct,Xr,Yr,no,Ut,qr,Qr,ro,kt,Hr,Dr,G,Zt,Lr,ce,Or,lo,Kr,el,io,tl,al,ol,Ue,sl,ke,as,pe,Ze,co,Wt,nl,po,rl,os,J,Pt,ll,fo,il,dl,At,cl,ya,pl,fl,hl,Gt,ml,zt,gl,ul,_l,ho,yl,bl,Y,mo,Vt,wl,vl,go,Bt,Ml,$l,uo,St,Tl,Nl,_o,Xt,Fl,Rl,z,Yt,jl,fe,Jl,yo,El,xl,bo,Il,Cl,Ul,We,kl,Pe,ss;return r=new H({}),He=new H({}),st=new H({}),nt=new yd({props:{pipeline:"image-classification"}}),it=new H({}),dt=new k({props:{name:"class transformers.RegNetConfig",anchor:"transformers.RegNetConfig",parameters:[{name:"num_channels",val:" = 3"},{name:"embedding_size",val:" = 32"},{name:"hidden_sizes",val:" = [128, 192, 512, 1088]"},{name:"depths",val:" = [2, 6, 12, 2]"},{name:"groups_width",val:" = 64"},{name:"layer_type",val:" = 'y'"},{name:"hidden_act",val:" = 'relu'"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.RegNetConfig.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.RegNetConfig.embedding_size",description:`<strong>embedding_size</strong> (<code>int</code>, <em>optional</em>, defaults to 64) &#x2014;
Dimensionality (hidden size) for the embedding layer.`,name:"embedding_size"},{anchor:"transformers.RegNetConfig.hidden_sizes",description:`<strong>hidden_sizes</strong> (<code>List[int]</code>, <em>optional</em>, defaults to <code>[256, 512, 1024, 2048]</code>) &#x2014;
Dimensionality (hidden size) at each stage.`,name:"hidden_sizes"},{anchor:"transformers.RegNetConfig.depths",description:`<strong>depths</strong> (<code>List[int]</code>, <em>optional</em>, defaults to <code>[3, 4, 6, 3]</code>) &#x2014;
Depth (number of layers) for each stage.`,name:"depths"},{anchor:"transformers.RegNetConfig.layer_type",description:`<strong>layer_type</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;y&quot;</code>) &#x2014;
The layer to use, it can be either <code>&quot;x&quot; or </code>&#x201C;y&#x201D;<code>. An </code>x<code>layer is a ResNet&apos;s BottleNeck layer with</code>reduction<code>fixed to</code>1<code>. While a </code>y<code>layer is a</code>x\` but with squeeze and excitation. Please refer to the
paper for a detailed explanation of how these layers were constructed.`,name:"layer_type"},{anchor:"transformers.RegNetConfig.hidden_act",description:`<strong>hidden_act</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;relu&quot;</code>) &#x2014;
The non-linear activation function in each block. If string, <code>&quot;gelu&quot;</code>, <code>&quot;relu&quot;</code>, <code>&quot;selu&quot;</code> and <code>&quot;gelu_new&quot;</code>
are supported.`,name:"hidden_act"},{anchor:"transformers.RegNetConfig.downsample_in_first_stage",description:`<strong>downsample_in_first_stage</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
If <code>True</code>, the first stage will downsample the inputs using a <code>stride</code> of 2.`,name:"downsample_in_first_stage"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/regnet/configuration_regnet.py#L28"}}),we=new Lt({props:{anchor:"transformers.RegNetConfig.example",$$slots:{default:[bd]},$$scope:{ctx:F}}}),pt=new H({}),ft=new k({props:{name:"class transformers.RegNetModel",anchor:"transformers.RegNetModel",parameters:[{name:"config",val:""}],parametersDescription:[{anchor:"transformers.RegNetModel.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/model_doc/regnet#transformers.RegNetConfig">RegNetConfig</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/main/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/main/src/transformers/models/regnet/modeling_regnet.py#L331"}}),gt=new k({props:{name:"forward",anchor:"transformers.RegNetModel.forward",parameters:[{name:"pixel_values",val:": Tensor"},{name:"output_hidden_states",val:": typing.Optional[bool] = None"},{name:"return_dict",val:": typing.Optional[bool] = None"}],parametersDescription:[{anchor:"transformers.RegNetModel.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/main/en/model_doc/auto#transformers.AutoImageProcessor">AutoImageProcessor</a>. See
<a href="/docs/transformers/main/en/model_doc/mobilevit#transformers.MobileViTFeatureExtractor.__call__">ConvNextImageProcessor.<strong>call</strong>()</a> for details.`,name:"pixel_values"},{anchor:"transformers.RegNetModel.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.RegNetModel.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/main/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/regnet/modeling_regnet.py#L341",returnDescription:`
<p>A <code>transformers.modeling_outputs.BaseModelOutputWithPoolingAndNoAttention</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/main/en/model_doc/regnet#transformers.RegNetConfig"
>RegNetConfig</a>) and inputs.</p>
<ul>
<li>
<p><strong>last_hidden_state</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, num_channels, height, width)</code>) \u2014 Sequence of hidden-states at the output of the last layer of the model.</p>
</li>
<li>
<p><strong>pooler_output</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, hidden_size)</code>) \u2014 Last layer hidden-state after a pooling operation on the spatial dimensions.</p>
</li>
<li>
<p><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 layer) of shape <code>(batch_size, num_channels, height, width)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.</p>
</li>
</ul>
`,returnType:`
<p><code>transformers.modeling_outputs.BaseModelOutputWithPoolingAndNoAttention</code> or <code>tuple(torch.FloatTensor)</code></p>
`}}),Me=new Ea({props:{$$slots:{default:[wd]},$$scope:{ctx:F}}}),$e=new Lt({props:{anchor:"transformers.RegNetModel.forward.example",$$slots:{default:[vd]},$$scope:{ctx:F}}}),ut=new H({}),_t=new k({props:{name:"class transformers.RegNetForImageClassification",anchor:"transformers.RegNetForImageClassification",parameters:[{name:"config",val:""}],parametersDescription:[{anchor:"transformers.RegNetForImageClassification.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/model_doc/regnet#transformers.RegNetConfig">RegNetConfig</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/main/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/main/src/transformers/models/regnet/modeling_regnet.py#L385"}}),wt=new k({props:{name:"forward",anchor:"transformers.RegNetForImageClassification.forward",parameters:[{name:"pixel_values",val:": typing.Optional[torch.FloatTensor] = None"},{name:"labels",val:": typing.Optional[torch.LongTensor] = None"},{name:"output_hidden_states",val:": typing.Optional[bool] = None"},{name:"return_dict",val:": typing.Optional[bool] = None"}],parametersDescription:[{anchor:"transformers.RegNetForImageClassification.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/main/en/model_doc/auto#transformers.AutoImageProcessor">AutoImageProcessor</a>. See
<a href="/docs/transformers/main/en/model_doc/mobilevit#transformers.MobileViTFeatureExtractor.__call__">ConvNextImageProcessor.<strong>call</strong>()</a> for details.`,name:"pixel_values"},{anchor:"transformers.RegNetForImageClassification.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.RegNetForImageClassification.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/main/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"},{anchor:"transformers.RegNetForImageClassification.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 &gt; 1</code> a classification loss is computed (Cross-Entropy).`,name:"labels"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/regnet/modeling_regnet.py#L398",returnDescription:`
<p>A <a
  href="/docs/transformers/main/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/main/en/model_doc/regnet#transformers.RegNetConfig"
>RegNetConfig</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/main/en/main_classes/output#transformers.modeling_outputs.ImageClassifierOutputWithNoAttention"
>transformers.modeling_outputs.ImageClassifierOutputWithNoAttention</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),Ne=new Ea({props:{$$slots:{default:[Md]},$$scope:{ctx:F}}}),Fe=new Lt({props:{anchor:"transformers.RegNetForImageClassification.forward.example",$$slots:{default:[$d]},$$scope:{ctx:F}}}),vt=new H({}),Mt=new k({props:{name:"class transformers.TFRegNetModel",anchor:"transformers.TFRegNetModel",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFRegNetModel.This",description:`<strong>This</strong> model is a Tensorflow &#x2014;
[tf.keras.layers.Layer](https &#x2014;//<a href="http://www.tensorflow.org/api_docs/python/tf/keras/layers/Layer" rel="nofollow">www.tensorflow.org/api_docs/python/tf/keras/layers/Layer</a>) sub-class. Use it as a`,name:"This"},{anchor:"transformers.TFRegNetModel.regular",description:`<strong>regular</strong> Tensorflow Module and refer to the Tensorflow documentation for all matter related to general usage and &#x2014;
behavior. &#x2014;
config (<a href="/docs/transformers/main/en/model_doc/regnet#transformers.RegNetConfig">RegNetConfig</a>): 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/main/en/main_classes/model#transformers.TFPreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"regular"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/regnet/modeling_tf_regnet.py#L380"}}),$t=new k({props:{name:"call",anchor:"transformers.TFRegNetModel.call",parameters:[{name:"pixel_values",val:": Tensor"},{name:"output_hidden_states",val:": typing.Optional[bool] = None"},{name:"return_dict",val:": typing.Optional[bool] = None"},{name:"training",val:": bool = False"}],parametersDescription:[{anchor:"transformers.TFRegNetModel.call.pixel_values",description:`<strong>pixel_values</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, num_channels, height, width)</code>) &#x2014;
Pixel values. Pixel values can be obtained using <a href="/docs/transformers/main/en/model_doc/auto#transformers.AutoImageProcessor">AutoImageProcessor</a>. See
<code>ConveNextImageProcessor.__call__</code> for details.`,name:"pixel_values"},{anchor:"transformers.TFRegNetModel.call.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.TFRegNetModel.call.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/main/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/regnet/modeling_tf_regnet.py#L385",returnDescription:`
<p>A <code>transformers.modeling_tf_outputs.TFBaseModelOutputWithPoolingAndNoAttention</code> or a tuple of <code>tf.Tensor</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/main/en/model_doc/regnet#transformers.RegNetConfig"
>RegNetConfig</a>) and inputs.</p>
<ul>
<li>
<p><strong>last_hidden_state</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, num_channels, height, width)</code>) \u2014 Sequence of hidden-states at the output of the last layer of the model.</p>
</li>
<li>
<p><strong>pooler_output</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, hidden_size)</code>) \u2014 Last layer hidden-state after a pooling operation on the spatial dimensions.</p>
</li>
<li>
<p><strong>hidden_states</strong> (<code>tuple(tf.Tensor)</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>tf.Tensor</code> (one for the output of the embeddings, if the model has an embedding layer, + one for
the output of each layer) of shape <code>(batch_size, num_channels, height, width)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.</p>
</li>
</ul>
`,returnType:`
<p><code>transformers.modeling_tf_outputs.TFBaseModelOutputWithPoolingAndNoAttention</code> or <code>tuple(tf.Tensor)</code></p>
`}}),je=new Ea({props:{$$slots:{default:[Td]},$$scope:{ctx:F}}}),Je=new Lt({props:{anchor:"transformers.TFRegNetModel.call.example",$$slots:{default:[Nd]},$$scope:{ctx:F}}}),Tt=new H({}),Nt=new k({props:{name:"class transformers.TFRegNetForImageClassification",anchor:"transformers.TFRegNetForImageClassification",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFRegNetForImageClassification.This",description:`<strong>This</strong> model is a Tensorflow &#x2014;
[tf.keras.layers.Layer](https &#x2014;//<a href="http://www.tensorflow.org/api_docs/python/tf/keras/layers/Layer" rel="nofollow">www.tensorflow.org/api_docs/python/tf/keras/layers/Layer</a>) sub-class. Use it as a`,name:"This"},{anchor:"transformers.TFRegNetForImageClassification.regular",description:`<strong>regular</strong> Tensorflow Module and refer to the Tensorflow documentation for all matter related to general usage and &#x2014;
behavior. &#x2014;
config (<a href="/docs/transformers/main/en/model_doc/regnet#transformers.RegNetConfig">RegNetConfig</a>): 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/main/en/main_classes/model#transformers.TFPreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"regular"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/regnet/modeling_tf_regnet.py#L429"}}),Ft=new k({props:{name:"call",anchor:"transformers.TFRegNetForImageClassification.call",parameters:[{name:"pixel_values",val:": typing.Optional[tensorflow.python.framework.ops.Tensor] = None"},{name:"labels",val:": typing.Optional[tensorflow.python.framework.ops.Tensor] = None"},{name:"output_hidden_states",val:": typing.Optional[bool] = None"},{name:"return_dict",val:": typing.Optional[bool] = None"},{name:"training",val:": bool = False"}],parametersDescription:[{anchor:"transformers.TFRegNetForImageClassification.call.pixel_values",description:`<strong>pixel_values</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, num_channels, height, width)</code>) &#x2014;
Pixel values. Pixel values can be obtained using <a href="/docs/transformers/main/en/model_doc/auto#transformers.AutoImageProcessor">AutoImageProcessor</a>. See
<code>ConveNextImageProcessor.__call__</code> for details.`,name:"pixel_values"},{anchor:"transformers.TFRegNetForImageClassification.call.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.TFRegNetForImageClassification.call.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/main/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"},{anchor:"transformers.TFRegNetForImageClassification.call.labels",description:`<strong>labels</strong> (<code>tf.Tensor</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 &gt; 1</code> a classification loss is computed (Cross-Entropy).`,name:"labels"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/regnet/modeling_tf_regnet.py#L440",returnDescription:`
<p>A <a
  href="/docs/transformers/main/en/main_classes/output#transformers.modeling_tf_outputs.TFSequenceClassifierOutput"
>transformers.modeling_tf_outputs.TFSequenceClassifierOutput</a> or a tuple of <code>tf.Tensor</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/main/en/model_doc/regnet#transformers.RegNetConfig"
>RegNetConfig</a>) and inputs.</p>
<ul>
<li>
<p><strong>loss</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, )</code>, <em>optional</em>, returned when <code>labels</code> is provided) \u2014 Classification (or regression if config.num_labels==1) loss.</p>
</li>
<li>
<p><strong>logits</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, config.num_labels)</code>) \u2014 Classification (or regression if config.num_labels==1) scores (before SoftMax).</p>
</li>
<li>
<p><strong>hidden_states</strong> (<code>tuple(tf.Tensor)</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>tf.Tensor</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>.</p>
<p>Hidden-states of the model at the output of each layer plus the initial embedding outputs.</p>
</li>
<li>
<p><strong>attentions</strong> (<code>tuple(tf.Tensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) \u2014 Tuple of <code>tf.Tensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.</p>
</li>
</ul>
`,returnType:`
<p><a
  href="/docs/transformers/main/en/main_classes/output#transformers.modeling_tf_outputs.TFSequenceClassifierOutput"
>transformers.modeling_tf_outputs.TFSequenceClassifierOutput</a> or <code>tuple(tf.Tensor)</code></p>
`}}),xe=new Ea({props:{$$slots:{default:[Fd]},$$scope:{ctx:F}}}),Ie=new Lt({props:{anchor:"transformers.TFRegNetForImageClassification.call.example",$$slots:{default:[Rd]},$$scope:{ctx:F}}}),Rt=new H({}),jt=new k({props:{name:"class transformers.FlaxRegNetModel",anchor:"transformers.FlaxRegNetModel",parameters:[{name:"config",val:": RegNetConfig"},{name:"input_shape",val:" = (1, 224, 224, 3)"},{name:"seed",val:": int = 0"},{name:"dtype",val:": dtype = <class 'jax.numpy.float32'>"},{name:"_do_init",val:": bool = True"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxRegNetModel.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/model_doc/regnet#transformers.RegNetConfig">RegNetConfig</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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The data type of the computation. Can be one of <code>jax.numpy.float32</code>, <code>jax.numpy.float16</code> (on GPUs) and
<code>jax.numpy.bfloat16</code> (on TPUs).</p>
<p>This can be used to enable mixed-precision training or half-precision inference on GPUs or TPUs. If
specified all the computation will be performed with the given <code>dtype</code>.</p>
<p><strong>Note that this only specifies the dtype of the computation and does not influence the dtype of model
parameters.</strong></p>
<p>If you wish to change the dtype of the model parameters, see <a href="/docs/transformers/main/en/main_classes/model#transformers.FlaxPreTrainedModel.to_fp16">to_fp16()</a> and
<a href="/docs/transformers/main/en/main_classes/model#transformers.FlaxPreTrainedModel.to_bf16">to_bf16()</a>.`,name:"dtype"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/regnet/modeling_flax_regnet.py#L689"}}),Zt=new k({props:{name:"__call__",anchor:"transformers.FlaxRegNetModel.__call__",parameters:[{name:"pixel_values",val:""},{name:"params",val:": dict = None"},{name:"train",val:": bool = False"},{name:"output_hidden_states",val:": typing.Optional[bool] = None"},{name:"return_dict",val:": typing.Optional[bool] = None"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/regnet/modeling_flax_regnet.py#L596",returnDescription:`
<p>A <a
  href="/docs/transformers/main/en/main_classes/output#transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPooling"
>transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPooling</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 (<code>&lt;class 'transformers.models.regnet.configuration_regnet.RegNetConfig'&gt;</code>) and inputs.</p>
<ul>
<li>
<p><strong>last_hidden_state</strong> (<code>jnp.ndarray</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.</p>
</li>
<li>
<p><strong>pooler_output</strong> (<code>jnp.ndarray</code> of shape <code>(batch_size, hidden_size)</code>) \u2014 Last layer hidden-state of the first token of the sequence (classification token) further processed by a
Linear layer and a Tanh activation function. The Linear layer weights are trained from the next sentence
prediction (classification) objective during pretraining.</p>
</li>
<li>
<p><strong>hidden_states</strong> (<code>tuple(jnp.ndarray)</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>jnp.ndarray</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>.</p>
<p>Hidden-states of the model at the output of each layer plus the initial embedding outputs.</p>
</li>
<li>
<p><strong>attentions</strong> (<code>tuple(jnp.ndarray)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) \u2014 Tuple of <code>jnp.ndarray</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.</p>
</li>
</ul>
`,returnType:`
<p><a
  href="/docs/transformers/main/en/main_classes/output#transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPooling"
>transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPooling</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),Ue=new Ea({props:{$$slots:{default:[jd]},$$scope:{ctx:F}}}),ke=new Lt({props:{anchor:"transformers.FlaxRegNetModel.__call__.example",$$slots:{default:[Jd]},$$scope:{ctx:F}}}),Wt=new H({}),Pt=new k({props:{name:"class transformers.FlaxRegNetForImageClassification",anchor:"transformers.FlaxRegNetForImageClassification",parameters:[{name:"config",val:": RegNetConfig"},{name:"input_shape",val:" = (1, 224, 224, 3)"},{name:"seed",val:": int = 0"},{name:"dtype",val:": dtype = <class 'jax.numpy.float32'>"},{name:"_do_init",val:": bool = True"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxRegNetForImageClassification.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/model_doc/regnet#transformers.RegNetConfig">RegNetConfig</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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The data type of the computation. Can be one of <code>jax.numpy.float32</code>, <code>jax.numpy.float16</code> (on GPUs) and
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<p>This can be used to enable mixed-precision training or half-precision inference on GPUs or TPUs. If
specified all the computation will be performed with the given <code>dtype</code>.</p>
<p><strong>Note that this only specifies the dtype of the computation and does not influence the dtype of model
parameters.</strong></p>
<p>If you wish to change the dtype of the model parameters, see <a href="/docs/transformers/main/en/main_classes/model#transformers.FlaxPreTrainedModel.to_fp16">to_fp16()</a> and
<a href="/docs/transformers/main/en/main_classes/model#transformers.FlaxPreTrainedModel.to_bf16">to_bf16()</a>.`,name:"dtype"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/regnet/modeling_flax_regnet.py#L782"}}),Yt=new k({props:{name:"__call__",anchor:"transformers.FlaxRegNetForImageClassification.__call__",parameters:[{name:"pixel_values",val:""},{name:"params",val:": dict = None"},{name:"train",val:": bool = False"},{name:"output_hidden_states",val:": typing.Optional[bool] = None"},{name:"return_dict",val:": typing.Optional[bool] = None"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/regnet/modeling_flax_regnet.py#L596",returnDescription:`
<p>A <code>transformers.modeling_flax_outputs.FlaxImageClassifierOutputWithNoAttention</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
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<ul>
<li><strong>logits</strong> (<code>jnp.ndarray</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(jnp.ndarray)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when</li>
<li><strong><code>config.output_hidden_states=True</code>):</strong>
Tuple of <code>jnp.ndarray</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><code>transformers.modeling_flax_outputs.FlaxImageClassifierOutputWithNoAttention</code> or <code>tuple(torch.FloatTensor)</code></p>
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model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a similar configuration to that of the RegNet
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