import{S as hm,i as gm,s as um,e as a,k as l,w as b,t as r,M as fm,c as n,d as t,m as d,a as s,x as v,h as i,b as m,N as _m,G as e,g as h,y as w,q as I,o as P,B as M,v as Tm,L as Dn}from"../../chunks/vendor-hf-doc-builder.js";import{T as nd}from"../../chunks/Tip-hf-doc-builder.js";import{D as te}from"../../chunks/Docstring-hf-doc-builder.js";import{C as Sn}from"../../chunks/CodeBlock-hf-doc-builder.js";import{I as ve}from"../../chunks/IconCopyLink-hf-doc-builder.js";import{E as Zn}from"../../chunks/ExampleCodeBlock-hf-doc-builder.js";import{P as ym}from"../../chunks/PipelineTag-hf-doc-builder.js";function bm(k){let g,y,_,u,T;return u=new Sn({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> ImageGPTConfig, ImageGPTModel

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Initializing a ImageGPT configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>configuration = ImageGPTConfig()

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Initializing a model (with random weights) from the configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = ImageGPTModel(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(){g=a("p"),y=r("Example:"),_=l(),b(u.$$.fragment)},l(c){g=n(c,"P",{});var f=s(g);y=i(f,"Example:"),f.forEach(t),_=d(c),v(u.$$.fragment,c)},m(c,f){h(c,g,f),e(g,y),h(c,_,f),w(u,c,f),T=!0},p:Dn,i(c){T||(I(u.$$.fragment,c),T=!0)},o(c){P(u.$$.fragment,c),T=!1},d(c){c&&t(g),c&&t(_),M(u,c)}}}function vm(k){let g,y,_,u,T;return{c(){g=a("p"),y=r("Although the recipe for forward pass needs to be defined within this function, one should call the "),_=a("code"),u=r("Module"),T=r(`
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(c){g=n(c,"P",{});var f=s(g);y=i(f,"Although the recipe for forward pass needs to be defined within this function, one should call the "),_=n(f,"CODE",{});var N=s(_);u=i(N,"Module"),N.forEach(t),T=i(f,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),f.forEach(t)},m(c,f){h(c,g,f),e(g,y),e(g,_),e(_,u),e(g,T)},d(c){c&&t(g)}}}function wm(k){let g,y,_,u,T;return u=new Sn({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, ImageGPTModel
<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;openai/imagegpt-small&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = ImageGPTModel.from_pretrained(<span class="hljs-string">&quot;openai/imagegpt-small&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = image_processor(images=image, return_tensors=<span class="hljs-string">&quot;pt&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(){g=a("p"),y=r("Examples:"),_=l(),b(u.$$.fragment)},l(c){g=n(c,"P",{});var f=s(g);y=i(f,"Examples:"),f.forEach(t),_=d(c),v(u.$$.fragment,c)},m(c,f){h(c,g,f),e(g,y),h(c,_,f),w(u,c,f),T=!0},p:Dn,i(c){T||(I(u.$$.fragment,c),T=!0)},o(c){P(u.$$.fragment,c),T=!1},d(c){c&&t(g),c&&t(_),M(u,c)}}}function Im(k){let g,y,_,u,T;return{c(){g=a("p"),y=r("Although the recipe for forward pass needs to be defined within this function, one should call the "),_=a("code"),u=r("Module"),T=r(`
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(c){g=n(c,"P",{});var f=s(g);y=i(f,"Although the recipe for forward pass needs to be defined within this function, one should call the "),_=n(f,"CODE",{});var N=s(_);u=i(N,"Module"),N.forEach(t),T=i(f,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),f.forEach(t)},m(c,f){h(c,g,f),e(g,y),e(g,_),e(_,u),e(g,T)},d(c){c&&t(g)}}}function Pm(k){let g,y,_,u,T;return u=new Sn({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, ImageGPTForCausalImageModeling
<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">import</span> matplotlib.pyplot <span class="hljs-keyword">as</span> plt
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> numpy <span class="hljs-keyword">as</span> np

<span class="hljs-meta">&gt;&gt;&gt; </span>image_processor = AutoImageProcessor.from_pretrained(<span class="hljs-string">&quot;openai/imagegpt-small&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = ImageGPTForCausalImageModeling.from_pretrained(<span class="hljs-string">&quot;openai/imagegpt-small&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>device = torch.device(<span class="hljs-string">&quot;cuda&quot;</span> <span class="hljs-keyword">if</span> torch.cuda.is_available() <span class="hljs-keyword">else</span> <span class="hljs-string">&quot;cpu&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.to(device)
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># unconditional generation of 8 images</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>batch_size = <span class="hljs-number">4</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>context = torch.full((batch_size, <span class="hljs-number">1</span>), model.config.vocab_size - <span class="hljs-number">1</span>)  <span class="hljs-comment"># initialize with SOS token</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>context = context.to(device)
<span class="hljs-meta">&gt;&gt;&gt; </span>output = model.generate(
<span class="hljs-meta">... </span>    input_ids=context, max_length=model.config.n_positions + <span class="hljs-number">1</span>, temperature=<span class="hljs-number">1.0</span>, do_sample=<span class="hljs-literal">True</span>, top_k=<span class="hljs-number">40</span>
<span class="hljs-meta">... </span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>clusters = image_processor.clusters
<span class="hljs-meta">&gt;&gt;&gt; </span>height = image_processor.size[<span class="hljs-string">&quot;height&quot;</span>]
<span class="hljs-meta">&gt;&gt;&gt; </span>width = image_processor.size[<span class="hljs-string">&quot;width&quot;</span>]

<span class="hljs-meta">&gt;&gt;&gt; </span>samples = output[:, <span class="hljs-number">1</span>:].cpu().detach().numpy()
<span class="hljs-meta">&gt;&gt;&gt; </span>samples_img = [
<span class="hljs-meta">... </span>    np.reshape(np.rint(<span class="hljs-number">127.5</span> * (clusters[s] + <span class="hljs-number">1.0</span>)), [height, width, <span class="hljs-number">3</span>]).astype(np.uint8) <span class="hljs-keyword">for</span> s <span class="hljs-keyword">in</span> samples
<span class="hljs-meta">... </span>]  <span class="hljs-comment"># convert color cluster tokens back to pixels</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>f, axes = plt.subplots(<span class="hljs-number">1</span>, batch_size, dpi=<span class="hljs-number">300</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">for</span> img, ax <span class="hljs-keyword">in</span> <span class="hljs-built_in">zip</span>(samples_img, axes):
<span class="hljs-meta">... </span>    ax.axis(<span class="hljs-string">&quot;off&quot;</span>)
<span class="hljs-meta">... </span>    ax.imshow(img)`}}),{c(){g=a("p"),y=r("Examples:"),_=l(),b(u.$$.fragment)},l(c){g=n(c,"P",{});var f=s(g);y=i(f,"Examples:"),f.forEach(t),_=d(c),v(u.$$.fragment,c)},m(c,f){h(c,g,f),e(g,y),h(c,_,f),w(u,c,f),T=!0},p:Dn,i(c){T||(I(u.$$.fragment,c),T=!0)},o(c){P(u.$$.fragment,c),T=!1},d(c){c&&t(g),c&&t(_),M(u,c)}}}function Mm(k){let g,y,_,u,T;return{c(){g=a("p"),y=r("Although the recipe for forward pass needs to be defined within this function, one should call the "),_=a("code"),u=r("Module"),T=r(`
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(c){g=n(c,"P",{});var f=s(g);y=i(f,"Although the recipe for forward pass needs to be defined within this function, one should call the "),_=n(f,"CODE",{});var N=s(_);u=i(N,"Module"),N.forEach(t),T=i(f,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),f.forEach(t)},m(c,f){h(c,g,f),e(g,y),e(g,_),e(_,u),e(g,T)},d(c){c&&t(g)}}}function km(k){let g,y,_,u,T;return u=new Sn({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9JbWFnZVByb2Nlc3NvciUyQyUyMEltYWdlR1BURm9ySW1hZ2VDbGFzc2lmaWNhdGlvbiUwQWZyb20lMjBQSUwlMjBpbXBvcnQlMjBJbWFnZSUwQWltcG9ydCUyMHJlcXVlc3RzJTBBJTBBdXJsJTIwJTNEJTIwJTIyaHR0cCUzQSUyRiUyRmltYWdlcy5jb2NvZGF0YXNldC5vcmclMkZ2YWwyMDE3JTJGMDAwMDAwMDM5NzY5LmpwZyUyMiUwQWltYWdlJTIwJTNEJTIwSW1hZ2Uub3BlbihyZXF1ZXN0cy5nZXQodXJsJTJDJTIwc3RyZWFtJTNEVHJ1ZSkucmF3KSUwQSUwQWltYWdlX3Byb2Nlc3NvciUyMCUzRCUyMEF1dG9JbWFnZVByb2Nlc3Nvci5mcm9tX3ByZXRyYWluZWQoJTIyb3BlbmFpJTJGaW1hZ2VncHQtc21hbGwlMjIpJTBBbW9kZWwlMjAlM0QlMjBJbWFnZUdQVEZvckltYWdlQ2xhc3NpZmljYXRpb24uZnJvbV9wcmV0cmFpbmVkKCUyMm9wZW5haSUyRmltYWdlZ3B0LXNtYWxsJTIyKSUwQSUwQWlucHV0cyUyMCUzRCUyMGltYWdlX3Byb2Nlc3NvcihpbWFnZXMlM0RpbWFnZSUyQyUyMHJldHVybl90ZW5zb3JzJTNEJTIycHQlMjIpJTBBb3V0cHV0cyUyMCUzRCUyMG1vZGVsKCoqaW5wdXRzKSUwQWxvZ2l0cyUyMCUzRCUyMG91dHB1dHMubG9naXRz",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoImageProcessor, ImageGPTForImageClassification
<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;openai/imagegpt-small&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = ImageGPTForImageClassification.from_pretrained(<span class="hljs-string">&quot;openai/imagegpt-small&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = image_processor(images=image, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(**inputs)
<span class="hljs-meta">&gt;&gt;&gt; </span>logits = outputs.logits`}}),{c(){g=a("p"),y=r("Examples:"),_=l(),b(u.$$.fragment)},l(c){g=n(c,"P",{});var f=s(g);y=i(f,"Examples:"),f.forEach(t),_=d(c),v(u.$$.fragment,c)},m(c,f){h(c,g,f),e(g,y),h(c,_,f),w(u,c,f),T=!0},p:Dn,i(c){T||(I(u.$$.fragment,c),T=!0)},o(c){P(u.$$.fragment,c),T=!1},d(c){c&&t(g),c&&t(_),M(u,c)}}}function Gm(k){let g,y,_,u,T,c,f,N,Bn,Da,le,we,uo,Xe,Vn,fo,On,Sa,Ie,Xn,Qe,Qn,Ln,Ba,Rt,Hn,Va,At,_o,Yn,Oa,Pe,sd,Xa,Zt,Kn,Qa,A,es,Le,ts,os,He,as,ns,Ye,ss,rs,La,Dt,is,Ha,q,Ke,ls,St,ds,cs,ms,et,ps,Bt,hs,gs,us,tt,fs,To,_s,Ts,ys,ot,bs,Vt,vs,ws,Is,yo,Ps,Ya,Me,bo,G,vo,wo,Ms,ks,Io,Po,Gs,$s,Mo,ko,Es,Cs,Go,$o,xs,Fs,Eo,Co,zs,js,xo,Fo,Us,Js,$,E,zo,Ws,Ns,jo,qs,Rs,Uo,As,Zs,Jo,Ds,Ss,Wo,Bs,Vs,No,Os,Xs,C,qo,Qs,Ls,Ro,Hs,Ys,Ao,Ks,er,Zo,tr,or,Do,ar,nr,So,sr,rr,x,Bo,ir,lr,Vo,dr,cr,Oo,mr,pr,Xo,hr,gr,Qo,ur,fr,Lo,_r,Tr,F,Ho,yr,br,Yo,vr,wr,Ko,Ir,Pr,ea,Mr,kr,ta,Gr,$r,oa,Er,Cr,z,aa,xr,Fr,na,zr,jr,sa,Ur,Jr,ra,Wr,Nr,ia,qr,Rr,la,Ar,Zr,j,da,Dr,Sr,ca,Br,Vr,ma,Or,Xr,pa,Qr,Lr,ha,Hr,Yr,ga,Kr,Ka,de,ke,ua,at,ei,fa,ti,en,Ot,oi,tn,nt,on,oe,st,ai,rt,ni,si,ri,ae,Xt,ii,li,it,di,ci,lt,mi,pi,hi,Qt,gi,Lt,ui,an,Ht,fi,nn,ce,Ge,_a,dt,_i,Ta,Ti,sn,R,ct,yi,K,bi,Yt,vi,wi,ya,Ii,Pi,mt,Mi,ki,Gi,me,$i,Kt,Ei,Ci,eo,xi,Fi,zi,$e,rn,pe,Ee,ba,pt,ji,va,Ui,ln,he,ht,Ji,Ce,gt,Wi,wa,Ni,dn,ge,xe,Ia,ut,qi,Pa,Ri,cn,ee,ft,Ai,Ma,Zi,Di,Fe,_t,Si,ka,Bi,mn,ue,ze,Ga,Tt,Vi,$a,Oi,pn,U,yt,Xi,Ea,Qi,Li,bt,Hi,to,Yi,Ki,el,vt,tl,wt,ol,al,nl,Z,It,sl,fe,rl,oo,il,ll,Ca,dl,cl,ml,je,pl,Ue,hn,_e,Je,xa,Pt,hl,Fa,gl,gn,J,Mt,ul,za,fl,_l,kt,Tl,ao,yl,bl,vl,Gt,wl,$t,Il,Pl,Ml,D,Et,kl,Te,Gl,no,$l,El,ja,Cl,xl,Fl,We,zl,Ne,un,ye,qe,Ua,Ct,jl,Ja,Ul,fn,W,xt,Jl,Ft,Wl,so,Nl,ql,Rl,zt,Al,ro,Zl,Dl,Sl,jt,Bl,Ut,Vl,Ol,Xl,S,Jt,Ql,be,Ll,io,Hl,Yl,Wa,Kl,ed,td,Re,od,Ae,_n;return c=new ve({}),Xe=new ve({}),at=new ve({}),nt=new ym({props:{pipeline:"image-classification"}}),dt=new ve({}),ct=new te({props:{name:"class transformers.ImageGPTConfig",anchor:"transformers.ImageGPTConfig",parameters:[{name:"vocab_size",val:" = 513"},{name:"n_positions",val:" = 1024"},{name:"n_embd",val:" = 512"},{name:"n_layer",val:" = 24"},{name:"n_head",val:" = 8"},{name:"n_inner",val:" = None"},{name:"activation_function",val:" = 'quick_gelu'"},{name:"resid_pdrop",val:" = 0.1"},{name:"embd_pdrop",val:" = 0.1"},{name:"attn_pdrop",val:" = 0.1"},{name:"layer_norm_epsilon",val:" = 1e-05"},{name:"initializer_range",val:" = 0.02"},{name:"scale_attn_weights",val:" = True"},{name:"use_cache",val:" = True"},{name:"tie_word_embeddings",val:" = False"},{name:"scale_attn_by_inverse_layer_idx",val:" = False"},{name:"reorder_and_upcast_attn",val:" = False"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.ImageGPTConfig.vocab_size",description:`<strong>vocab_size</strong> (<code>int</code>, <em>optional</em>, defaults to 512) &#x2014;
Vocabulary size of the GPT-2 model. Defines the number of different tokens that can be represented by the
<code>inputs_ids</code> passed when calling <a href="/docs/transformers/main/en/model_doc/imagegpt#transformers.ImageGPTModel">ImageGPTModel</a> or <code>TFImageGPTModel</code>.`,name:"vocab_size"},{anchor:"transformers.ImageGPTConfig.n_positions",description:`<strong>n_positions</strong> (<code>int</code>, <em>optional</em>, defaults to 32*32) &#x2014;
The maximum sequence length that this model might ever be used with. Typically set this to something large
just in case (e.g., 512 or 1024 or 2048).`,name:"n_positions"},{anchor:"transformers.ImageGPTConfig.n_embd",description:`<strong>n_embd</strong> (<code>int</code>, <em>optional</em>, defaults to 512) &#x2014;
Dimensionality of the embeddings and hidden states.`,name:"n_embd"},{anchor:"transformers.ImageGPTConfig.n_layer",description:`<strong>n_layer</strong> (<code>int</code>, <em>optional</em>, defaults to 24) &#x2014;
Number of hidden layers in the Transformer encoder.`,name:"n_layer"},{anchor:"transformers.ImageGPTConfig.n_head",description:`<strong>n_head</strong> (<code>int</code>, <em>optional</em>, defaults to 8) &#x2014;
Number of attention heads for each attention layer in the Transformer encoder.`,name:"n_head"},{anchor:"transformers.ImageGPTConfig.n_inner",description:`<strong>n_inner</strong> (<code>int</code>, <em>optional</em>, defaults to None) &#x2014;
Dimensionality of the inner feed-forward layers. <code>None</code> will set it to 4 times n_embd`,name:"n_inner"},{anchor:"transformers.ImageGPTConfig.activation_function",description:`<strong>activation_function</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;quick_gelu&quot;</code>) &#x2014;
Activation function (can be one of the activation functions defined in src/transformers/activations.py).
Defaults to &#x201C;quick_gelu&#x201D;.`,name:"activation_function"},{anchor:"transformers.ImageGPTConfig.resid_pdrop",description:`<strong>resid_pdrop</strong> (<code>float</code>, <em>optional</em>, defaults to 0.1) &#x2014;
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.`,name:"resid_pdrop"},{anchor:"transformers.ImageGPTConfig.embd_pdrop",description:`<strong>embd_pdrop</strong> (<code>int</code>, <em>optional</em>, defaults to 0.1) &#x2014;
The dropout ratio for the embeddings.`,name:"embd_pdrop"},{anchor:"transformers.ImageGPTConfig.attn_pdrop",description:`<strong>attn_pdrop</strong> (<code>float</code>, <em>optional</em>, defaults to 0.1) &#x2014;
The dropout ratio for the attention.`,name:"attn_pdrop"},{anchor:"transformers.ImageGPTConfig.layer_norm_epsilon",description:`<strong>layer_norm_epsilon</strong> (<code>float</code>, <em>optional</em>, defaults to 1e-5) &#x2014;
The epsilon to use in the layer normalization layers.`,name:"layer_norm_epsilon"},{anchor:"transformers.ImageGPTConfig.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.ImageGPTConfig.scale_attn_weights",description:`<strong>scale_attn_weights</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Scale attention weights by dividing by sqrt(hidden_size)..`,name:"scale_attn_weights"},{anchor:"transformers.ImageGPTConfig.use_cache",description:`<strong>use_cache</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether or not the model should return the last key/values attentions (not used by all models).`,name:"use_cache"},{anchor:"transformers.ImageGPTConfig.scale_attn_by_inverse_layer_idx",description:`<strong>scale_attn_by_inverse_layer_idx</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether to additionally scale attention weights by <code>1 / layer_idx + 1</code>.`,name:"scale_attn_by_inverse_layer_idx"},{anchor:"transformers.ImageGPTConfig.reorder_and_upcast_attn",description:`<strong>reorder_and_upcast_attn</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether to scale keys (K) prior to computing attention (dot-product) and upcast attention
dot-product/softmax to float() when training with mixed precision.`,name:"reorder_and_upcast_attn"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/imagegpt/configuration_imagegpt.py#L37"}}),$e=new Zn({props:{anchor:"transformers.ImageGPTConfig.example",$$slots:{default:[bm]},$$scope:{ctx:k}}}),pt=new ve({}),ht=new te({props:{name:"class transformers.ImageGPTFeatureExtractor",anchor:"transformers.ImageGPTFeatureExtractor",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/imagegpt/feature_extraction_imagegpt.py#L26"}}),gt=new te({props:{name:"__call__",anchor:"transformers.ImageGPTFeatureExtractor.__call__",parameters:[{name:"images",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/image_processing_utils.py#L544"}}),ut=new ve({}),ft=new te({props:{name:"class transformers.ImageGPTImageProcessor",anchor:"transformers.ImageGPTImageProcessor",parameters:[{name:"clusters",val:": typing.Union[typing.List[typing.List[int]], numpy.ndarray, NoneType] = None"},{name:"do_resize",val:": bool = True"},{name:"size",val:": typing.Dict[str, int] = None"},{name:"resample",val:": Resampling = <Resampling.BILINEAR: 2>"},{name:"do_normalize",val:": bool = True"},{name:"do_color_quantize",val:": bool = True"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.ImageGPTImageProcessor.clusters",description:`<strong>clusters</strong> (<code>np.ndarray</code> or <code>List[List[int]]</code>, <em>optional</em>) &#x2014;
The color clusters to use, of shape <code>(n_clusters, 3)</code> when color quantizing. Can be overriden by <code>clusters</code>
in <code>preprocess</code>.`,name:"clusters"},{anchor:"transformers.ImageGPTImageProcessor.do_resize",description:`<strong>do_resize</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether to resize the image&#x2019;s dimensions to <code>(size[&quot;height&quot;], size[&quot;width&quot;])</code>. Can be overridden by
<code>do_resize</code> in <code>preprocess</code>.`,name:"do_resize"},{anchor:"transformers.ImageGPTImageProcessor.size",description:`<strong>size</strong> (<code>Dict[str, int]</code> <em>optional</em>, defaults to <code>{&quot;height&quot; -- 256, &quot;width&quot;: 256}</code>):
Size of the image after resizing. Can be overridden by <code>size</code> in <code>preprocess</code>.`,name:"size"},{anchor:"transformers.ImageGPTImageProcessor.resample",description:`<strong>resample</strong> (<code>PILImageResampling</code>, <em>optional</em>, defaults to <code>PILImageResampling.BICUBIC</code>) &#x2014;
Resampling filter to use if resizing the image. Can be overridden by <code>resample</code> in <code>preprocess</code>.`,name:"resample"},{anchor:"transformers.ImageGPTImageProcessor.do_normalize",description:`<strong>do_normalize</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether to normalize the image pixel value to between [-1, 1]. Can be overridden by <code>do_normalize</code> in
<code>preprocess</code>.`,name:"do_normalize"},{anchor:"transformers.ImageGPTImageProcessor.do_color_quantize",description:`<strong>do_color_quantize</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether to color quantize the image. Can be overridden by <code>do_color_quantize</code> in <code>preprocess</code>.`,name:"do_color_quantize"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/imagegpt/image_processing_imagegpt.py#L57"}}),_t=new te({props:{name:"preprocess",anchor:"transformers.ImageGPTImageProcessor.preprocess",parameters:[{name:"images",val:": typing.Union[ForwardRef('PIL.Image.Image'), numpy.ndarray, ForwardRef('torch.Tensor'), typing.List[ForwardRef('PIL.Image.Image')], typing.List[numpy.ndarray], typing.List[ForwardRef('torch.Tensor')]]"},{name:"do_resize",val:": bool = None"},{name:"size",val:": typing.Dict[str, int] = None"},{name:"resample",val:": Resampling = None"},{name:"do_normalize",val:": bool = None"},{name:"do_color_quantize",val:": typing.Optional[bool] = None"},{name:"clusters",val:": typing.Union[typing.List[typing.List[int]], numpy.ndarray, NoneType] = None"},{name:"return_tensors",val:": typing.Union[transformers.utils.generic.TensorType, str, NoneType] = None"},{name:"data_format",val:": typing.Union[str, transformers.image_utils.ChannelDimension, NoneType] = <ChannelDimension.FIRST: 'channels_first'>"},{name:"input_data_format",val:": typing.Union[str, transformers.image_utils.ChannelDimension, NoneType] = None"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.ImageGPTImageProcessor.preprocess.images",description:`<strong>images</strong> (<code>ImageInput</code>) &#x2014;
Image to preprocess.`,name:"images"},{anchor:"transformers.ImageGPTImageProcessor.preprocess.do_resize",description:`<strong>do_resize</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>self.do_resize</code>) &#x2014;
Whether to resize the image.`,name:"do_resize"},{anchor:"transformers.ImageGPTImageProcessor.preprocess.size",description:`<strong>size</strong> (<code>Dict[str, int]</code>, <em>optional</em>, defaults to <code>self.size</code>) &#x2014;
Size of the image after resizing.`,name:"size"},{anchor:"transformers.ImageGPTImageProcessor.preprocess.resample",description:`<strong>resample</strong> (<code>int</code>, <em>optional</em>, defaults to <code>self.resample</code>) &#x2014;
Resampling filter to use if resizing the image. This can be one of the enum <code>PILImageResampling</code>, Only
has an effect if <code>do_resize</code> is set to <code>True</code>.`,name:"resample"},{anchor:"transformers.ImageGPTImageProcessor.preprocess.do_normalize",description:`<strong>do_normalize</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>self.do_normalize</code>) &#x2014;
Whether to normalize the image`,name:"do_normalize"},{anchor:"transformers.ImageGPTImageProcessor.preprocess.do_color_quantize",description:`<strong>do_color_quantize</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>self.do_color_quantize</code>) &#x2014;
Whether to color quantize the image.`,name:"do_color_quantize"},{anchor:"transformers.ImageGPTImageProcessor.preprocess.clusters",description:`<strong>clusters</strong> (<code>np.ndarray</code> or <code>List[List[int]]</code>, <em>optional</em>, defaults to <code>self.clusters</code>) &#x2014;
Clusters used to quantize the image of shape <code>(n_clusters, 3)</code>. Only has an effect if
<code>do_color_quantize</code> is set to <code>True</code>.`,name:"clusters"},{anchor:"transformers.ImageGPTImageProcessor.preprocess.return_tensors",description:`<strong>return_tensors</strong> (<code>str</code> or <code>TensorType</code>, <em>optional</em>) &#x2014;
The type of tensors to return. Can be one of:<ul>
<li>Unset: Return a list of <code>np.ndarray</code>.</li>
<li><code>TensorType.TENSORFLOW</code> or <code>&apos;tf&apos;</code>: Return a batch of type <code>tf.Tensor</code>.</li>
<li><code>TensorType.PYTORCH</code> or <code>&apos;pt&apos;</code>: Return a batch of type <code>torch.Tensor</code>.</li>
<li><code>TensorType.NUMPY</code> or <code>&apos;np&apos;</code>: Return a batch of type <code>np.ndarray</code>.</li>
<li><code>TensorType.JAX</code> or <code>&apos;jax&apos;</code>: Return a batch of type <code>jax.numpy.ndarray</code>.</li>
</ul>`,name:"return_tensors"},{anchor:"transformers.ImageGPTImageProcessor.preprocess.data_format",description:`<strong>data_format</strong> (<code>ChannelDimension</code> or <code>str</code>, <em>optional</em>, defaults to <code>ChannelDimension.FIRST</code>) &#x2014;
The channel dimension format for the output image. Can be one of:<ul>
<li><code>ChannelDimension.FIRST</code>: image in (num_channels, height, width) format.</li>
<li><code>ChannelDimension.LAST</code>: image in (height, width, num_channels) format.
Only has an effect if <code>do_color_quantize</code> is set to <code>False</code>.</li>
</ul>`,name:"data_format"},{anchor:"transformers.ImageGPTImageProcessor.preprocess.input_data_format",description:`<strong>input_data_format</strong> (<code>ChannelDimension</code> or <code>str</code>, <em>optional</em>) &#x2014;
The channel dimension format for the input image. If unset, the channel dimension format is inferred
from the input image. Can be one of:<ul>
<li><code>&quot;channels_first&quot;</code> or <code>ChannelDimension.FIRST</code>: image in (num_channels, height, width) format.</li>
<li><code>&quot;channels_last&quot;</code> or <code>ChannelDimension.LAST</code>: image in (height, width, num_channels) format.</li>
<li><code>&quot;none&quot;</code> or <code>ChannelDimension.NONE</code>: image in (height, width) format.</li>
</ul>`,name:"input_data_format"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/imagegpt/image_processing_imagegpt.py#L174"}}),Tt=new ve({}),yt=new te({props:{name:"class transformers.ImageGPTModel",anchor:"transformers.ImageGPTModel",parameters:[{name:"config",val:": ImageGPTConfig"}],parametersDescription:[{anchor:"transformers.ImageGPTModel.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/model_doc/imagegpt#transformers.ImageGPTConfig">ImageGPTConfig</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/imagegpt/modeling_imagegpt.py#L616"}}),It=new te({props:{name:"forward",anchor:"transformers.ImageGPTModel.forward",parameters:[{name:"input_ids",val:": typing.Optional[torch.Tensor] = None"},{name:"past_key_values",val:": typing.Optional[typing.Tuple[typing.Tuple[torch.Tensor]]] = None"},{name:"attention_mask",val:": typing.Optional[torch.Tensor] = None"},{name:"token_type_ids",val:": typing.Optional[torch.Tensor] = None"},{name:"position_ids",val:": typing.Optional[torch.Tensor] = None"},{name:"head_mask",val:": typing.Optional[torch.Tensor] = None"},{name:"inputs_embeds",val:": typing.Optional[torch.Tensor] = None"},{name:"encoder_hidden_states",val:": typing.Optional[torch.Tensor] = None"},{name:"encoder_attention_mask",val:": typing.Optional[torch.Tensor] = None"},{name:"use_cache",val:": typing.Optional[bool] = None"},{name:"output_attentions",val:": typing.Optional[bool] = None"},{name:"output_hidden_states",val:": typing.Optional[bool] = None"},{name:"return_dict",val:": typing.Optional[bool] = None"},{name:"**kwargs",val:": typing.Any"}],parametersDescription:[{anchor:"transformers.ImageGPTModel.forward.input_ids",description:`<strong>input_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>) &#x2014;
<code>input_ids_length</code> = <code>sequence_length</code> if <code>past_key_values</code> is <code>None</code> else
<code>past_key_values[0][0].shape[-2]</code> (<code>sequence_length</code> of input past key value states). Indices of input
sequence tokens in the vocabulary.</p>
<p>If <code>past_key_values</code> is used, only <code>input_ids</code> that do not have their past calculated should be passed as
<code>input_ids</code>.</p>
<p>Indices 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/donut#transformers.DonutFeatureExtractor.__call__">ImageGPTImageProcessor.<strong>call</strong>()</a> for details.`,name:"input_ids"},{anchor:"transformers.ImageGPTModel.forward.past_key_values",description:`<strong>past_key_values</strong> (<code>Tuple[Tuple[torch.Tensor]]</code> of length <code>config.n_layers</code>) &#x2014;
Contains precomputed hidden-states (key and values in the attention blocks) as computed by the model (see
<code>past_key_values</code> output below). Can be used to speed up sequential decoding. The <code>input_ids</code> which have
their past given to this model should not be passed as <code>input_ids</code> as they have already been computed.`,name:"past_key_values"},{anchor:"transformers.ImageGPTModel.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. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.</li>
</ul>
<p><a href="../glossary#attention-mask">What are attention masks?</a>`,name:"attention_mask"},{anchor:"transformers.ImageGPTModel.forward.token_type_ids",description:`<strong>token_type_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</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>:</p>
<ul>
<li>0 corresponds to a <em>sentence A</em> token,</li>
<li>1 corresponds to a <em>sentence B</em> token.</li>
</ul>
<p><a href="../glossary#token-type-ids">What are token type IDs?</a>`,name:"token_type_ids"},{anchor:"transformers.ImageGPTModel.forward.position_ids",description:`<strong>position_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range <code>[0, config.max_position_embeddings - 1]</code>.</p>
<p><a href="../glossary#position-ids">What are position IDs?</a>`,name:"position_ids"},{anchor:"transformers.ImageGPTModel.forward.head_mask",description:`<strong>head_mask</strong> (<code>torch.FloatTensor</code> of shape <code>(num_heads,)</code> or <code>(num_layers, num_heads)</code>, <em>optional</em>) &#x2014;
Mask to nullify selected heads of the self-attention modules. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 indicates the head is <strong>not masked</strong>,</li>
<li>0 indicates the head is <strong>masked</strong>.</li>
</ul>`,name:"head_mask"},{anchor:"transformers.ImageGPTModel.forward.inputs_embeds",description:`<strong>inputs_embeds</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>, <em>optional</em>) &#x2014;
Optionally, instead of passing <code>input_ids</code> you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert <code>input_ids</code> indices into associated vectors than the
model&#x2019;s internal embedding lookup matrix.</p>
<p>If <code>past_key_values</code> is used, optionally only the last <code>inputs_embeds</code> have to be input (see
<code>past_key_values</code>).`,name:"inputs_embeds"},{anchor:"transformers.ImageGPTModel.forward.use_cache",description:`<strong>use_cache</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
If set to <code>True</code>, <code>past_key_values</code> key value states are returned and can be used to speed up decoding (see
<code>past_key_values</code>).`,name:"use_cache"},{anchor:"transformers.ImageGPTModel.forward.output_attentions",description:`<strong>output_attentions</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the attentions tensors of all attention layers. See <code>attentions</code> under returned
tensors for more detail.`,name:"output_attentions"},{anchor:"transformers.ImageGPTModel.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.ImageGPTModel.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.ImageGPTModel.forward.labels",description:`<strong>labels</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Labels for language modeling. Note that the labels <strong>are shifted</strong> inside the model, i.e. you can set
<code>labels = input_ids</code> Indices are selected in <code>[-100, 0, ..., config.vocab_size]</code> All labels set to <code>-100</code>
are ignored (masked), the loss is only computed for labels in <code>[0, ..., config.vocab_size]</code>`,name:"labels"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/imagegpt/modeling_imagegpt.py#L649",returnDescription:`
<p>A <a
  href="/docs/transformers/main/en/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPastAndCrossAttentions"
>transformers.modeling_outputs.BaseModelOutputWithPastAndCrossAttentions</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/imagegpt#transformers.ImageGPTConfig"
>ImageGPTConfig</a>) and inputs.</p>
<ul>
<li>
<p><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.</p>
<p>If <code>past_key_values</code> is used only the last hidden-state of the sequences of shape <code>(batch_size, 1, hidden_size)</code> is output.</p>
</li>
<li>
<p><strong>past_key_values</strong> (<code>tuple(tuple(torch.FloatTensor))</code>, <em>optional</em>, returned when <code>use_cache=True</code> is passed or when <code>config.use_cache=True</code>) \u2014 Tuple of <code>tuple(torch.FloatTensor)</code> of length <code>config.n_layers</code>, with each tuple having 2 tensors of shape
<code>(batch_size, num_heads, sequence_length, embed_size_per_head)</code>) and optionally if
<code>config.is_encoder_decoder=True</code> 2 additional tensors of shape <code>(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)</code>.</p>
<p>Contains pre-computed hidden-states (key and values in the self-attention blocks and optionally if
<code>config.is_encoder_decoder=True</code> in the cross-attention blocks) that can be used (see <code>past_key_values</code>
input) to speed up sequential decoding.</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, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.</p>
</li>
<li>
<p><strong>attentions</strong> (<code>tuple(torch.FloatTensor)</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>torch.FloatTensor</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>
<li>
<p><strong>cross_attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> and <code>config.add_cross_attention=True</code> is passed or when <code>config.output_attentions=True</code>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights of the decoder\u2019s cross-attention layer, after the attention softmax, used to compute the
weighted average in the cross-attention heads.</p>
</li>
</ul>
`,returnType:`
<p><a
  href="/docs/transformers/main/en/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPastAndCrossAttentions"
>transformers.modeling_outputs.BaseModelOutputWithPastAndCrossAttentions</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),je=new nd({props:{$$slots:{default:[vm]},$$scope:{ctx:k}}}),Ue=new Zn({props:{anchor:"transformers.ImageGPTModel.forward.example",$$slots:{default:[wm]},$$scope:{ctx:k}}}),Pt=new ve({}),Mt=new te({props:{name:"class transformers.ImageGPTForCausalImageModeling",anchor:"transformers.ImageGPTForCausalImageModeling",parameters:[{name:"config",val:": ImageGPTConfig"}],parametersDescription:[{anchor:"transformers.ImageGPTForCausalImageModeling.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/model_doc/imagegpt#transformers.ImageGPTConfig">ImageGPTConfig</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/imagegpt/modeling_imagegpt.py#L895"}}),Et=new te({props:{name:"forward",anchor:"transformers.ImageGPTForCausalImageModeling.forward",parameters:[{name:"input_ids",val:": typing.Optional[torch.Tensor] = None"},{name:"past_key_values",val:": typing.Optional[typing.Tuple[typing.Tuple[torch.Tensor]]] = None"},{name:"attention_mask",val:": typing.Optional[torch.Tensor] = None"},{name:"token_type_ids",val:": typing.Optional[torch.Tensor] = None"},{name:"position_ids",val:": typing.Optional[torch.Tensor] = None"},{name:"head_mask",val:": typing.Optional[torch.Tensor] = None"},{name:"inputs_embeds",val:": typing.Optional[torch.Tensor] = None"},{name:"encoder_hidden_states",val:": typing.Optional[torch.Tensor] = None"},{name:"encoder_attention_mask",val:": typing.Optional[torch.Tensor] = None"},{name:"labels",val:": typing.Optional[torch.Tensor] = None"},{name:"use_cache",val:": typing.Optional[bool] = None"},{name:"output_attentions",val:": typing.Optional[bool] = None"},{name:"output_hidden_states",val:": typing.Optional[bool] = None"},{name:"return_dict",val:": typing.Optional[bool] = None"},{name:"**kwargs",val:": typing.Any"}],parametersDescription:[{anchor:"transformers.ImageGPTForCausalImageModeling.forward.input_ids",description:`<strong>input_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>) &#x2014;
<code>input_ids_length</code> = <code>sequence_length</code> if <code>past_key_values</code> is <code>None</code> else
<code>past_key_values[0][0].shape[-2]</code> (<code>sequence_length</code> of input past key value states). Indices of input
sequence tokens in the vocabulary.</p>
<p>If <code>past_key_values</code> is used, only <code>input_ids</code> that do not have their past calculated should be passed as
<code>input_ids</code>.</p>
<p>Indices 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/donut#transformers.DonutFeatureExtractor.__call__">ImageGPTImageProcessor.<strong>call</strong>()</a> for details.`,name:"input_ids"},{anchor:"transformers.ImageGPTForCausalImageModeling.forward.past_key_values",description:`<strong>past_key_values</strong> (<code>Tuple[Tuple[torch.Tensor]]</code> of length <code>config.n_layers</code>) &#x2014;
Contains precomputed hidden-states (key and values in the attention blocks) as computed by the model (see
<code>past_key_values</code> output below). Can be used to speed up sequential decoding. The <code>input_ids</code> which have
their past given to this model should not be passed as <code>input_ids</code> as they have already been computed.`,name:"past_key_values"},{anchor:"transformers.ImageGPTForCausalImageModeling.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. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.</li>
</ul>
<p><a href="../glossary#attention-mask">What are attention masks?</a>`,name:"attention_mask"},{anchor:"transformers.ImageGPTForCausalImageModeling.forward.token_type_ids",description:`<strong>token_type_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</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>:</p>
<ul>
<li>0 corresponds to a <em>sentence A</em> token,</li>
<li>1 corresponds to a <em>sentence B</em> token.</li>
</ul>
<p><a href="../glossary#token-type-ids">What are token type IDs?</a>`,name:"token_type_ids"},{anchor:"transformers.ImageGPTForCausalImageModeling.forward.position_ids",description:`<strong>position_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range <code>[0, config.max_position_embeddings - 1]</code>.</p>
<p><a href="../glossary#position-ids">What are position IDs?</a>`,name:"position_ids"},{anchor:"transformers.ImageGPTForCausalImageModeling.forward.head_mask",description:`<strong>head_mask</strong> (<code>torch.FloatTensor</code> of shape <code>(num_heads,)</code> or <code>(num_layers, num_heads)</code>, <em>optional</em>) &#x2014;
Mask to nullify selected heads of the self-attention modules. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 indicates the head is <strong>not masked</strong>,</li>
<li>0 indicates the head is <strong>masked</strong>.</li>
</ul>`,name:"head_mask"},{anchor:"transformers.ImageGPTForCausalImageModeling.forward.inputs_embeds",description:`<strong>inputs_embeds</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>, <em>optional</em>) &#x2014;
Optionally, instead of passing <code>input_ids</code> you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert <code>input_ids</code> indices into associated vectors than the
model&#x2019;s internal embedding lookup matrix.</p>
<p>If <code>past_key_values</code> is used, optionally only the last <code>inputs_embeds</code> have to be input (see
<code>past_key_values</code>).`,name:"inputs_embeds"},{anchor:"transformers.ImageGPTForCausalImageModeling.forward.use_cache",description:`<strong>use_cache</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
If set to <code>True</code>, <code>past_key_values</code> key value states are returned and can be used to speed up decoding (see
<code>past_key_values</code>).`,name:"use_cache"},{anchor:"transformers.ImageGPTForCausalImageModeling.forward.output_attentions",description:`<strong>output_attentions</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the attentions tensors of all attention layers. See <code>attentions</code> under returned
tensors for more detail.`,name:"output_attentions"},{anchor:"transformers.ImageGPTForCausalImageModeling.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.ImageGPTForCausalImageModeling.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.ImageGPTForCausalImageModeling.forward.labels",description:`<strong>labels</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Labels for language modeling. Note that the labels <strong>are shifted</strong> inside the model, i.e. you can set
<code>labels = input_ids</code> Indices are selected in <code>[-100, 0, ..., config.vocab_size]</code> All labels set to <code>-100</code>
are ignored (masked), the loss is only computed for labels in <code>[0, ..., config.vocab_size]</code>`,name:"labels"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/imagegpt/modeling_imagegpt.py#L943",returnDescription:`
<p>A <a
  href="/docs/transformers/main/en/main_classes/output#transformers.modeling_outputs.CausalLMOutputWithCrossAttentions"
>transformers.modeling_outputs.CausalLMOutputWithCrossAttentions</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/imagegpt#transformers.ImageGPTConfig"
>ImageGPTConfig</a>) and inputs.</p>
<ul>
<li>
<p><strong>loss</strong> (<code>torch.FloatTensor</code> of shape <code>(1,)</code>, <em>optional</em>, returned when <code>labels</code> is provided) \u2014 Language modeling loss (for next-token prediction).</p>
</li>
<li>
<p><strong>logits</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, config.vocab_size)</code>) \u2014 Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).</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, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.</p>
</li>
<li>
<p><strong>attentions</strong> (<code>tuple(torch.FloatTensor)</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>torch.FloatTensor</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>
<li>
<p><strong>cross_attentions</strong> (<code>tuple(torch.FloatTensor)</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>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Cross attentions weights after the attention softmax, used to compute the weighted average in the
cross-attention heads.</p>
</li>
<li>
<p><strong>past_key_values</strong> (<code>tuple(tuple(torch.FloatTensor))</code>, <em>optional</em>, returned when <code>use_cache=True</code> is passed or when <code>config.use_cache=True</code>) \u2014 Tuple of <code>torch.FloatTensor</code> tuples of length <code>config.n_layers</code>, with each tuple containing the cached key,
value states of the self-attention and the cross-attention layers if model is used in encoder-decoder
setting. Only relevant if <code>config.is_decoder = True</code>.</p>
<p>Contains pre-computed hidden-states (key and values in the attention blocks) that can be used (see
<code>past_key_values</code> input) to speed up sequential decoding.</p>
</li>
</ul>
`,returnType:`
<p><a
  href="/docs/transformers/main/en/main_classes/output#transformers.modeling_outputs.CausalLMOutputWithCrossAttentions"
>transformers.modeling_outputs.CausalLMOutputWithCrossAttentions</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),We=new nd({props:{$$slots:{default:[Im]},$$scope:{ctx:k}}}),Ne=new Zn({props:{anchor:"transformers.ImageGPTForCausalImageModeling.forward.example",$$slots:{default:[Pm]},$$scope:{ctx:k}}}),Ct=new ve({}),xt=new te({props:{name:"class transformers.ImageGPTForImageClassification",anchor:"transformers.ImageGPTForImageClassification",parameters:[{name:"config",val:": ImageGPTConfig"}],parametersDescription:[{anchor:"transformers.ImageGPTForImageClassification.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/model_doc/imagegpt#transformers.ImageGPTConfig">ImageGPTConfig</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/imagegpt/modeling_imagegpt.py#L1086"}}),Jt=new te({props:{name:"forward",anchor:"transformers.ImageGPTForImageClassification.forward",parameters:[{name:"input_ids",val:": typing.Optional[torch.Tensor] = None"},{name:"past_key_values",val:": typing.Optional[typing.Tuple[typing.Tuple[torch.Tensor]]] = None"},{name:"attention_mask",val:": typing.Optional[torch.Tensor] = None"},{name:"token_type_ids",val:": typing.Optional[torch.Tensor] = None"},{name:"position_ids",val:": typing.Optional[torch.Tensor] = None"},{name:"head_mask",val:": typing.Optional[torch.Tensor] = None"},{name:"inputs_embeds",val:": typing.Optional[torch.Tensor] = None"},{name:"labels",val:": typing.Optional[torch.Tensor] = None"},{name:"use_cache",val:": typing.Optional[bool] = None"},{name:"output_attentions",val:": typing.Optional[bool] = None"},{name:"output_hidden_states",val:": typing.Optional[bool] = None"},{name:"return_dict",val:": typing.Optional[bool] = None"},{name:"**kwargs",val:": typing.Any"}],parametersDescription:[{anchor:"transformers.ImageGPTForImageClassification.forward.input_ids",description:`<strong>input_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>) &#x2014;
<code>input_ids_length</code> = <code>sequence_length</code> if <code>past_key_values</code> is <code>None</code> else
<code>past_key_values[0][0].shape[-2]</code> (<code>sequence_length</code> of input past key value states). Indices of input
sequence tokens in the vocabulary.</p>
<p>If <code>past_key_values</code> is used, only <code>input_ids</code> that do not have their past calculated should be passed as
<code>input_ids</code>.</p>
<p>Indices 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/donut#transformers.DonutFeatureExtractor.__call__">ImageGPTImageProcessor.<strong>call</strong>()</a> for details.`,name:"input_ids"},{anchor:"transformers.ImageGPTForImageClassification.forward.past_key_values",description:`<strong>past_key_values</strong> (<code>Tuple[Tuple[torch.Tensor]]</code> of length <code>config.n_layers</code>) &#x2014;
Contains precomputed hidden-states (key and values in the attention blocks) as computed by the model (see
<code>past_key_values</code> output below). Can be used to speed up sequential decoding. The <code>input_ids</code> which have
their past given to this model should not be passed as <code>input_ids</code> as they have already been computed.`,name:"past_key_values"},{anchor:"transformers.ImageGPTForImageClassification.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. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.</li>
</ul>
<p><a href="../glossary#attention-mask">What are attention masks?</a>`,name:"attention_mask"},{anchor:"transformers.ImageGPTForImageClassification.forward.token_type_ids",description:`<strong>token_type_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</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>:</p>
<ul>
<li>0 corresponds to a <em>sentence A</em> token,</li>
<li>1 corresponds to a <em>sentence B</em> token.</li>
</ul>
<p><a href="../glossary#token-type-ids">What are token type IDs?</a>`,name:"token_type_ids"},{anchor:"transformers.ImageGPTForImageClassification.forward.position_ids",description:`<strong>position_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range <code>[0, config.max_position_embeddings - 1]</code>.</p>
<p><a href="../glossary#position-ids">What are position IDs?</a>`,name:"position_ids"},{anchor:"transformers.ImageGPTForImageClassification.forward.head_mask",description:`<strong>head_mask</strong> (<code>torch.FloatTensor</code> of shape <code>(num_heads,)</code> or <code>(num_layers, num_heads)</code>, <em>optional</em>) &#x2014;
Mask to nullify selected heads of the self-attention modules. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 indicates the head is <strong>not masked</strong>,</li>
<li>0 indicates the head is <strong>masked</strong>.</li>
</ul>`,name:"head_mask"},{anchor:"transformers.ImageGPTForImageClassification.forward.inputs_embeds",description:`<strong>inputs_embeds</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>, <em>optional</em>) &#x2014;
Optionally, instead of passing <code>input_ids</code> you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert <code>input_ids</code> indices into associated vectors than the
model&#x2019;s internal embedding lookup matrix.</p>
<p>If <code>past_key_values</code> is used, optionally only the last <code>inputs_embeds</code> have to be input (see
<code>past_key_values</code>).`,name:"inputs_embeds"},{anchor:"transformers.ImageGPTForImageClassification.forward.use_cache",description:`<strong>use_cache</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
If set to <code>True</code>, <code>past_key_values</code> key value states are returned and can be used to speed up decoding (see
<code>past_key_values</code>).`,name:"use_cache"},{anchor:"transformers.ImageGPTForImageClassification.forward.output_attentions",description:`<strong>output_attentions</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the attentions tensors of all attention layers. See <code>attentions</code> under returned
tensors for more detail.`,name:"output_attentions"},{anchor:"transformers.ImageGPTForImageClassification.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.ImageGPTForImageClassification.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.ImageGPTForImageClassification.forward.labels",description:`<strong>labels</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size,)</code>, <em>optional</em>) &#x2014;
Labels for computing the sequence 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
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<p>A <code>transformers.modeling_outputs.SequenceClassifierOutputWithPast</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/imagegpt#transformers.ImageGPTConfig"
>ImageGPTConfig</a>) and inputs.</p>
<ul>
<li>
<p><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.</p>
</li>
<li>
<p><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).</p>
</li>
<li>
<p><strong>past_key_values</strong> (<code>tuple(tuple(torch.FloatTensor))</code>, <em>optional</em>, returned when <code>use_cache=True</code> is passed or when <code>config.use_cache=True</code>) \u2014 Tuple of <code>tuple(torch.FloatTensor)</code> of length <code>config.n_layers</code>, with each tuple having 2 tensors of shape
<code>(batch_size, num_heads, sequence_length, embed_size_per_head)</code>)</p>
<p>Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see
<code>past_key_values</code> input) to speed up sequential decoding.</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, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.</p>
</li>
<li>
<p><strong>attentions</strong> (<code>tuple(torch.FloatTensor)</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>torch.FloatTensor</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><code>transformers.modeling_outputs.SequenceClassifierOutputWithPast</code> or <code>tuple(torch.FloatTensor)</code></p>
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we find that a GPT-2 scale model learns strong image representations as measured by linear probing, fine-tuning, and
low-data classification. On CIFAR-10, we achieve 96.3% accuracy with a linear probe, outperforming a supervised Wide
ResNet, and 99.0% accuracy with full fine-tuning, matching the top supervised pre-trained models. We are also
competitive with self-supervised benchmarks on ImageNet when substituting pixels for a VQVAE encoding, achieving 69.0%
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`),Ye=a("a"),ss=r("here"),rs=r("."),La=l(),Dt=a("p"),is=r("Tips:"),Ha=l(),q=a("ul"),Ke=a("li"),ls=r("ImageGPT is almost exactly the same as "),St=a("a"),ds=r("GPT-2"),cs=r(`, with the exception that a different activation
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we find that a GPT-2 scale model learns strong image representations as measured by linear probing, fine-tuning, and
low-data classification. On CIFAR-10, we achieve 96.3% accuracy with a linear probe, outperforming a supervised Wide
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