import{S as ti,i as oi,s as ni,e as s,k as m,w as M,t as n,M as ri,c as a,d as t,m as p,a as i,x as b,h as r,b as c,N as si,G as e,g as f,y as E,q as $,o as A,B as V,v as ai,L as bn}from"../../chunks/vendor-hf-doc-builder.js";import{T as Ao}from"../../chunks/Tip-hf-doc-builder.js";import{D as Tt}from"../../chunks/Docstring-hf-doc-builder.js";import{C as En}from"../../chunks/CodeBlock-hf-doc-builder.js";import{I as Dt}from"../../chunks/IconCopyLink-hf-doc-builder.js";import{E as Mn}from"../../chunks/ExampleCodeBlock-hf-doc-builder.js";function ii(U){let d,y,u,g,T;return g=new En({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> ViTMAEConfig, ViTMAEModel

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Initializing a ViT MAE vit-mae-base style configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>configuration = ViTMAEConfig()

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Initializing a model (with random weights) from the vit-mae-base style configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = ViTMAEModel(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=s("p"),y=n("Example:"),u=m(),M(g.$$.fragment)},l(l){d=a(l,"P",{});var h=i(d);y=r(h,"Example:"),h.forEach(t),u=p(l),b(g.$$.fragment,l)},m(l,h){f(l,d,h),e(d,y),f(l,u,h),E(g,l,h),T=!0},p:bn,i(l){T||($(g.$$.fragment,l),T=!0)},o(l){A(g.$$.fragment,l),T=!1},d(l){l&&t(d),l&&t(u),V(g,l)}}}function li(U){let d,y,u,g,T;return{c(){d=s("p"),y=n("Although the recipe for forward pass needs to be defined within this function, one should call the "),u=s("code"),g=n("Module"),T=n(`
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(l){d=a(l,"P",{});var h=i(d);y=r(h,"Although the recipe for forward pass needs to be defined within this function, one should call the "),u=a(h,"CODE",{});var F=i(u);g=r(F,"Module"),F.forEach(t),T=r(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(t)},m(l,h){f(l,d,h),e(d,y),e(d,u),e(u,g),e(d,T)},d(l){l&&t(d)}}}function di(U){let d,y,u,g,T;return g=new En({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9JbWFnZVByb2Nlc3NvciUyQyUyMFZpVE1BRU1vZGVsJTBBZnJvbSUyMFBJTCUyMGltcG9ydCUyMEltYWdlJTBBaW1wb3J0JTIwcmVxdWVzdHMlMEElMEF1cmwlMjAlM0QlMjAlMjJodHRwJTNBJTJGJTJGaW1hZ2VzLmNvY29kYXRhc2V0Lm9yZyUyRnZhbDIwMTclMkYwMDAwMDAwMzk3NjkuanBnJTIyJTBBaW1hZ2UlMjAlM0QlMjBJbWFnZS5vcGVuKHJlcXVlc3RzLmdldCh1cmwlMkMlMjBzdHJlYW0lM0RUcnVlKS5yYXcpJTBBJTBBaW1hZ2VfcHJvY2Vzc29yJTIwJTNEJTIwQXV0b0ltYWdlUHJvY2Vzc29yLmZyb21fcHJldHJhaW5lZCglMjJmYWNlYm9vayUyRnZpdC1tYWUtYmFzZSUyMiklMEFtb2RlbCUyMCUzRCUyMFZpVE1BRU1vZGVsLmZyb21fcHJldHJhaW5lZCglMjJmYWNlYm9vayUyRnZpdC1tYWUtYmFzZSUyMiklMEElMEFpbnB1dHMlMjAlM0QlMjBpbWFnZV9wcm9jZXNzb3IoaW1hZ2VzJTNEaW1hZ2UlMkMlMjByZXR1cm5fdGVuc29ycyUzRCUyMnB0JTIyKSUwQW91dHB1dHMlMjAlM0QlMjBtb2RlbCgqKmlucHV0cyklMEFsYXN0X2hpZGRlbl9zdGF0ZXMlMjAlM0QlMjBvdXRwdXRzLmxhc3RfaGlkZGVuX3N0YXRl",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoImageProcessor, ViTMAEModel
<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/vit-mae-base&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = ViTMAEModel.from_pretrained(<span class="hljs-string">&quot;facebook/vit-mae-base&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(){d=s("p"),y=n("Examples:"),u=m(),M(g.$$.fragment)},l(l){d=a(l,"P",{});var h=i(d);y=r(h,"Examples:"),h.forEach(t),u=p(l),b(g.$$.fragment,l)},m(l,h){f(l,d,h),e(d,y),f(l,u,h),E(g,l,h),T=!0},p:bn,i(l){T||($(g.$$.fragment,l),T=!0)},o(l){A(g.$$.fragment,l),T=!1},d(l){l&&t(d),l&&t(u),V(g,l)}}}function ci(U){let d,y,u,g,T;return{c(){d=s("p"),y=n("Note that we provide a script to pre-train this model on custom data in our "),u=s("a"),g=n(`examples
directory`),T=n("."),this.h()},l(l){d=a(l,"P",{});var h=i(d);y=r(h,"Note that we provide a script to pre-train this model on custom data in our "),u=a(h,"A",{href:!0,rel:!0});var F=i(u);g=r(F,`examples
directory`),F.forEach(t),T=r(h,"."),h.forEach(t),this.h()},h(){c(u,"href","https://github.com/huggingface/transformers/tree/main/examples/pytorch/image-pretraining"),c(u,"rel","nofollow")},m(l,h){f(l,d,h),e(d,y),e(d,u),e(u,g),e(d,T)},d(l){l&&t(d)}}}function hi(U){let d,y,u,g,T;return{c(){d=s("p"),y=n("Although the recipe for forward pass needs to be defined within this function, one should call the "),u=s("code"),g=n("Module"),T=n(`
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(l){d=a(l,"P",{});var h=i(d);y=r(h,"Although the recipe for forward pass needs to be defined within this function, one should call the "),u=a(h,"CODE",{});var F=i(u);g=r(F,"Module"),F.forEach(t),T=r(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(t)},m(l,h){f(l,d,h),e(d,y),e(d,u),e(u,g),e(d,T)},d(l){l&&t(d)}}}function mi(U){let d,y,u,g,T;return g=new En({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, ViTMAEForPreTraining
<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/vit-mae-base&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = ViTMAEForPreTraining.from_pretrained(<span class="hljs-string">&quot;facebook/vit-mae-base&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>loss = outputs.loss
<span class="hljs-meta">&gt;&gt;&gt; </span>mask = outputs.mask
<span class="hljs-meta">&gt;&gt;&gt; </span>ids_restore = outputs.ids_restore`}}),{c(){d=s("p"),y=n("Examples:"),u=m(),M(g.$$.fragment)},l(l){d=a(l,"P",{});var h=i(d);y=r(h,"Examples:"),h.forEach(t),u=p(l),b(g.$$.fragment,l)},m(l,h){f(l,d,h),e(d,y),f(l,u,h),E(g,l,h),T=!0},p:bn,i(l){T||($(g.$$.fragment,l),T=!0)},o(l){A(g.$$.fragment,l),T=!1},d(l){l&&t(d),l&&t(u),V(g,l)}}}function pi(U){let d,y,u,g,T,l,h,F,Pe,we,I,D,H,w,je,S,Ue,Me,W,Je,G,Y,Ce,be,R,Ie,Ee,B,pe,ze,le,P,z,$e,J,X,Ze,q,We,Ne,Z,L,Q,de,Re,K,Be,ee,O,ce,te,qe,fe,C,Se,N,oe,Oe;return{c(){d=s("p"),y=n("TensorFlow models and layers in "),u=s("code"),g=n("transformers"),T=n(" accept two formats as input:"),l=m(),h=s("ul"),F=s("li"),Pe=n("having all inputs as keyword arguments (like PyTorch models), or"),we=m(),I=s("li"),D=n("having all inputs as a list, tuple or dict in the first positional argument."),H=m(),w=s("p"),je=n(`The reason the second format is supported is that Keras methods prefer this format when passing inputs to models
and layers. Because of this support, when using methods like `),S=s("code"),Ue=n("model.fit()"),Me=n(` things should \u201Cjust work\u201D for you - just
pass your inputs and labels in any format that `),W=s("code"),Je=n("model.fit()"),G=n(` supports! If, however, you want to use the second
format outside of Keras methods like `),Y=s("code"),Ce=n("fit()"),be=n(" and "),R=s("code"),Ie=n("predict()"),Ee=n(`, such as when creating your own layers or models with
the Keras `),B=s("code"),pe=n("Functional"),ze=n(` API, there are three possibilities you can use to gather all the input Tensors in the first
positional argument:`),le=m(),P=s("ul"),z=s("li"),$e=n("a single Tensor with "),J=s("code"),X=n("pixel_values"),Ze=n(" only and nothing else: "),q=s("code"),We=n("model(pixel_values)"),Ne=m(),Z=s("li"),L=n(`a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
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`),N=s("a"),oe=n("subclassing"),Oe=n(` then you don\u2019t need to worry
about any of this, as you can just pass inputs like you would to any other Python function!`),this.h()},l(v){d=a(v,"P",{});var k=i(d);y=r(k,"TensorFlow models and layers in "),u=a(k,"CODE",{});var et=i(u);g=r(et,"transformers"),et.forEach(t),T=r(k," accept two formats as input:"),k.forEach(t),l=p(v),h=a(v,"UL",{});var he=i(h);F=a(he,"LI",{});var me=i(F);Pe=r(me,"having all inputs as keyword arguments (like PyTorch models), or"),me.forEach(t),we=p(he),I=a(he,"LI",{});var yt=i(I);D=r(yt,"having all inputs as a list, tuple or dict in the first positional argument."),yt.forEach(t),he.forEach(t),H=p(v),w=a(v,"P",{});var x=i(w);je=r(x,`The reason the second format is supported is that Keras methods prefer this format when passing inputs to models
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pass your inputs and labels in any format that `),W=a(x,"CODE",{});var tt=i(W);Je=r(tt,"model.fit()"),tt.forEach(t),G=r(x,` supports! If, however, you want to use the second
format outside of Keras methods like `),Y=a(x,"CODE",{});var Ae=i(Y);Ce=r(Ae,"fit()"),Ae.forEach(t),be=r(x," and "),R=a(x,"CODE",{});var ot=i(R);Ie=r(ot,"predict()"),ot.forEach(t),Ee=r(x,`, such as when creating your own layers or models with
the Keras `),B=a(x,"CODE",{});var Ge=i(B);pe=r(Ge,"Functional"),Ge.forEach(t),ze=r(x,` API, there are three possibilities you can use to gather all the input Tensors in the first
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`),Q=a(re,"CODE",{});var _e=i(Q);de=r(_e,"model([pixel_values, attention_mask])"),_e.forEach(t),Re=r(re," or "),K=a(re,"CODE",{});var rt=i(K);Be=r(rt,"model([pixel_values, attention_mask, token_type_ids])"),rt.forEach(t),re.forEach(t),ee=p(j),O=a(j,"LI",{});var De=i(O);ce=r(De,`a dictionary with one or several input Tensors associated to the input names given in the docstring:
`),te=a(De,"CODE",{});var ve=i(te);qe=r(ve,'model({"pixel_values": pixel_values, "token_type_ids": token_type_ids})'),ve.forEach(t),De.forEach(t),j.forEach(t),fe=p(v),C=a(v,"P",{});var Te=i(C);Se=r(Te,`Note that when creating models and layers with
`),N=a(Te,"A",{href:!0,rel:!0});var st=i(N);oe=r(st,"subclassing"),st.forEach(t),Oe=r(Te,` then you don\u2019t need to worry
about any of this, as you can just pass inputs like you would to any other Python function!`),Te.forEach(t),this.h()},h(){c(N,"href","https://keras.io/guides/making_new_layers_and_models_via_subclassing/"),c(N,"rel","nofollow")},m(v,k){f(v,d,k),e(d,y),e(d,u),e(u,g),e(d,T),f(v,l,k),f(v,h,k),e(h,F),e(F,Pe),e(h,we),e(h,I),e(I,D),f(v,H,k),f(v,w,k),e(w,je),e(w,S),e(S,Ue),e(w,Me),e(w,W),e(W,Je),e(w,G),e(w,Y),e(Y,Ce),e(w,be),e(w,R),e(R,Ie),e(w,Ee),e(w,B),e(B,pe),e(w,ze),f(v,le,k),f(v,P,k),e(P,z),e(z,$e),e(z,J),e(J,X),e(z,Ze),e(z,q),e(q,We),e(P,Ne),e(P,Z),e(Z,L),e(Z,Q),e(Q,de),e(Z,Re),e(Z,K),e(K,Be),e(P,ee),e(P,O),e(O,ce),e(O,te),e(te,qe),f(v,fe,k),f(v,C,k),e(C,Se),e(C,N),e(N,oe),e(C,Oe)},d(v){v&&t(d),v&&t(l),v&&t(h),v&&t(H),v&&t(w),v&&t(le),v&&t(P),v&&t(fe),v&&t(C)}}}function fi(U){let d,y,u,g,T;return{c(){d=s("p"),y=n("Although the recipe for forward pass needs to be defined within this function, one should call the "),u=s("code"),g=n("Module"),T=n(`
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(l){d=a(l,"P",{});var h=i(d);y=r(h,"Although the recipe for forward pass needs to be defined within this function, one should call the "),u=a(h,"CODE",{});var F=i(u);g=r(F,"Module"),F.forEach(t),T=r(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(t)},m(l,h){f(l,d,h),e(d,y),e(d,u),e(u,g),e(d,T)},d(l){l&&t(d)}}}function ui(U){let d,y,u,g,T;return g=new En({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, TFViTMAEModel
<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/vit-mae-base&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFViTMAEModel.from_pretrained(<span class="hljs-string">&quot;facebook/vit-mae-base&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = image_processor(images=image, return_tensors=<span class="hljs-string">&quot;tf&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(**inputs)
<span class="hljs-meta">&gt;&gt;&gt; </span>last_hidden_states = outputs.last_hidden_state`}}),{c(){d=s("p"),y=n("Examples:"),u=m(),M(g.$$.fragment)},l(l){d=a(l,"P",{});var h=i(d);y=r(h,"Examples:"),h.forEach(t),u=p(l),b(g.$$.fragment,l)},m(l,h){f(l,d,h),e(d,y),f(l,u,h),E(g,l,h),T=!0},p:bn,i(l){T||($(g.$$.fragment,l),T=!0)},o(l){A(g.$$.fragment,l),T=!1},d(l){l&&t(d),l&&t(u),V(g,l)}}}function gi(U){let d,y,u,g,T,l,h,F,Pe,we,I,D,H,w,je,S,Ue,Me,W,Je,G,Y,Ce,be,R,Ie,Ee,B,pe,ze,le,P,z,$e,J,X,Ze,q,We,Ne,Z,L,Q,de,Re,K,Be,ee,O,ce,te,qe,fe,C,Se,N,oe,Oe;return{c(){d=s("p"),y=n("TensorFlow models and layers in "),u=s("code"),g=n("transformers"),T=n(" accept two formats as input:"),l=m(),h=s("ul"),F=s("li"),Pe=n("having all inputs as keyword arguments (like PyTorch models), or"),we=m(),I=s("li"),D=n("having all inputs as a list, tuple or dict in the first positional argument."),H=m(),w=s("p"),je=n(`The reason the second format is supported is that Keras methods prefer this format when passing inputs to models
and layers. Because of this support, when using methods like `),S=s("code"),Ue=n("model.fit()"),Me=n(` things should \u201Cjust work\u201D for you - just
pass your inputs and labels in any format that `),W=s("code"),Je=n("model.fit()"),G=n(` supports! If, however, you want to use the second
format outside of Keras methods like `),Y=s("code"),Ce=n("fit()"),be=n(" and "),R=s("code"),Ie=n("predict()"),Ee=n(`, such as when creating your own layers or models with
the Keras `),B=s("code"),pe=n("Functional"),ze=n(` API, there are three possibilities you can use to gather all the input Tensors in the first
positional argument:`),le=m(),P=s("ul"),z=s("li"),$e=n("a single Tensor with "),J=s("code"),X=n("pixel_values"),Ze=n(" only and nothing else: "),q=s("code"),We=n("model(pixel_values)"),Ne=m(),Z=s("li"),L=n(`a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
`),Q=s("code"),de=n("model([pixel_values, attention_mask])"),Re=n(" or "),K=s("code"),Be=n("model([pixel_values, attention_mask, token_type_ids])"),ee=m(),O=s("li"),ce=n(`a dictionary with one or several input Tensors associated to the input names given in the docstring:
`),te=s("code"),qe=n('model({"pixel_values": pixel_values, "token_type_ids": token_type_ids})'),fe=m(),C=s("p"),Se=n(`Note that when creating models and layers with
`),N=s("a"),oe=n("subclassing"),Oe=n(` then you don\u2019t need to worry
about any of this, as you can just pass inputs like you would to any other Python function!`),this.h()},l(v){d=a(v,"P",{});var k=i(d);y=r(k,"TensorFlow models and layers in "),u=a(k,"CODE",{});var et=i(u);g=r(et,"transformers"),et.forEach(t),T=r(k," accept two formats as input:"),k.forEach(t),l=p(v),h=a(v,"UL",{});var he=i(h);F=a(he,"LI",{});var me=i(F);Pe=r(me,"having all inputs as keyword arguments (like PyTorch models), or"),me.forEach(t),we=p(he),I=a(he,"LI",{});var yt=i(I);D=r(yt,"having all inputs as a list, tuple or dict in the first positional argument."),yt.forEach(t),he.forEach(t),H=p(v),w=a(v,"P",{});var x=i(w);je=r(x,`The reason the second format is supported is that Keras methods prefer this format when passing inputs to models
and layers. Because of this support, when using methods like `),S=a(x,"CODE",{});var ue=i(S);Ue=r(ue,"model.fit()"),ue.forEach(t),Me=r(x,` things should \u201Cjust work\u201D for you - just
pass your inputs and labels in any format that `),W=a(x,"CODE",{});var tt=i(W);Je=r(tt,"model.fit()"),tt.forEach(t),G=r(x,` supports! If, however, you want to use the second
format outside of Keras methods like `),Y=a(x,"CODE",{});var Ae=i(Y);Ce=r(Ae,"fit()"),Ae.forEach(t),be=r(x," and "),R=a(x,"CODE",{});var ot=i(R);Ie=r(ot,"predict()"),ot.forEach(t),Ee=r(x,`, such as when creating your own layers or models with
the Keras `),B=a(x,"CODE",{});var Ge=i(B);pe=r(Ge,"Functional"),Ge.forEach(t),ze=r(x,` API, there are three possibilities you can use to gather all the input Tensors in the first
positional argument:`),x.forEach(t),le=p(v),P=a(v,"UL",{});var j=i(P);z=a(j,"LI",{});var ne=i(z);$e=r(ne,"a single Tensor with "),J=a(ne,"CODE",{});var ge=i(J);X=r(ge,"pixel_values"),ge.forEach(t),Ze=r(ne," only and nothing else: "),q=a(ne,"CODE",{});var nt=i(q);We=r(nt,"model(pixel_values)"),nt.forEach(t),ne.forEach(t),Ne=p(j),Z=a(j,"LI",{});var re=i(Z);L=r(re,`a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
`),Q=a(re,"CODE",{});var _e=i(Q);de=r(_e,"model([pixel_values, attention_mask])"),_e.forEach(t),Re=r(re," or "),K=a(re,"CODE",{});var rt=i(K);Be=r(rt,"model([pixel_values, attention_mask, token_type_ids])"),rt.forEach(t),re.forEach(t),ee=p(j),O=a(j,"LI",{});var De=i(O);ce=r(De,`a dictionary with one or several input Tensors associated to the input names given in the docstring:
`),te=a(De,"CODE",{});var ve=i(te);qe=r(ve,'model({"pixel_values": pixel_values, "token_type_ids": token_type_ids})'),ve.forEach(t),De.forEach(t),j.forEach(t),fe=p(v),C=a(v,"P",{});var Te=i(C);Se=r(Te,`Note that when creating models and layers with
`),N=a(Te,"A",{href:!0,rel:!0});var st=i(N);oe=r(st,"subclassing"),st.forEach(t),Oe=r(Te,` then you don\u2019t need to worry
about any of this, as you can just pass inputs like you would to any other Python function!`),Te.forEach(t),this.h()},h(){c(N,"href","https://keras.io/guides/making_new_layers_and_models_via_subclassing/"),c(N,"rel","nofollow")},m(v,k){f(v,d,k),e(d,y),e(d,u),e(u,g),e(d,T),f(v,l,k),f(v,h,k),e(h,F),e(F,Pe),e(h,we),e(h,I),e(I,D),f(v,H,k),f(v,w,k),e(w,je),e(w,S),e(S,Ue),e(w,Me),e(w,W),e(W,Je),e(w,G),e(w,Y),e(Y,Ce),e(w,be),e(w,R),e(R,Ie),e(w,Ee),e(w,B),e(B,pe),e(w,ze),f(v,le,k),f(v,P,k),e(P,z),e(z,$e),e(z,J),e(J,X),e(z,Ze),e(z,q),e(q,We),e(P,Ne),e(P,Z),e(Z,L),e(Z,Q),e(Q,de),e(Z,Re),e(Z,K),e(K,Be),e(P,ee),e(P,O),e(O,ce),e(O,te),e(te,qe),f(v,fe,k),f(v,C,k),e(C,Se),e(C,N),e(N,oe),e(C,Oe)},d(v){v&&t(d),v&&t(l),v&&t(h),v&&t(H),v&&t(w),v&&t(le),v&&t(P),v&&t(fe),v&&t(C)}}}function _i(U){let d,y,u,g,T;return{c(){d=s("p"),y=n("Although the recipe for forward pass needs to be defined within this function, one should call the "),u=s("code"),g=n("Module"),T=n(`
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(l){d=a(l,"P",{});var h=i(d);y=r(h,"Although the recipe for forward pass needs to be defined within this function, one should call the "),u=a(h,"CODE",{});var F=i(u);g=r(F,"Module"),F.forEach(t),T=r(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(t)},m(l,h){f(l,d,h),e(d,y),e(d,u),e(u,g),e(d,T)},d(l){l&&t(d)}}}function vi(U){let d,y,u,g,T;return g=new En({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, TFViTMAEForPreTraining
<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/vit-mae-base&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFViTMAEForPreTraining.from_pretrained(<span class="hljs-string">&quot;facebook/vit-mae-base&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>loss = outputs.loss
<span class="hljs-meta">&gt;&gt;&gt; </span>mask = outputs.mask
<span class="hljs-meta">&gt;&gt;&gt; </span>ids_restore = outputs.ids_restore`}}),{c(){d=s("p"),y=n("Examples:"),u=m(),M(g.$$.fragment)},l(l){d=a(l,"P",{});var h=i(d);y=r(h,"Examples:"),h.forEach(t),u=p(l),b(g.$$.fragment,l)},m(l,h){f(l,d,h),e(d,y),f(l,u,h),E(g,l,h),T=!0},p:bn,i(l){T||($(g.$$.fragment,l),T=!0)},o(l){A(g.$$.fragment,l),T=!1},d(l){l&&t(d),l&&t(u),V(g,l)}}}function Ti(U){let d,y,u,g,T,l,h,F,Pe,we,I,D,H,w,je,S,Ue,Me,W,Je,G,Y,Ce,be,R,Ie,Ee,B,pe,ze,le,P,z,$e,J,X,Ze,q,We,Ne,Z,L,Q,de,Re,K,Be,ee,O,ce,te,qe,fe,C,Se,N,oe,Oe,v,k,et,he,me,yt,x,ue,tt,Ae,ot,Ge,j,ne,ge,nt,re,_e,rt,De,ve,Te,st,Ht,nr,rr,$n,at,wt,Go,Gt,sr,Yo,ar,An,Vo,ir,Vn,Mt,bt,ko,lr,dr,Yt,cr,hr,mr,it,pr,Fo,fr,ur,Xt,gr,_r,kn,xo,vr,Fn,lt,Et,Xo,Lt,Tr,Lo,yr,xn,ye,Qt,wr,dt,Mr,Po,br,Er,Kt,$r,Ar,Vr,ct,kr,jo,Fr,xr,Uo,Pr,jr,Ur,$t,Pn,ht,At,Qo,eo,Jr,Ko,Cr,jn,He,to,Ir,oo,zr,no,Zr,Wr,Nr,Ve,ro,Rr,mt,Br,Jo,qr,Sr,en,Or,Dr,Hr,Vt,Gr,kt,Un,pt,Ft,tn,so,Yr,on,Xr,Jn,se,ao,Lr,nn,Qr,Kr,xt,es,io,ts,lo,os,ns,rs,ke,co,ss,ft,as,Co,is,ls,rn,ds,cs,hs,Pt,ms,jt,Cn,ut,Ut,sn,ho,ps,an,fs,In,ae,mo,us,po,gs,Io,_s,vs,Ts,fo,ys,uo,ws,Ms,bs,Jt,Es,Fe,go,$s,gt,As,zo,Vs,ks,ln,Fs,xs,Ps,Ct,js,It,zn,_t,zt,dn,_o,Us,cn,Js,Zn,ie,vo,Cs,To,Is,Zo,zs,Zs,Ws,yo,Ns,wo,Rs,Bs,qs,Zt,Ss,xe,Mo,Os,vt,Ds,Wo,Hs,Gs,hn,Ys,Xs,Ls,Wt,Qs,Nt,Wn;return l=new Dt({}),w=new Dt({}),Gt=new Dt({}),Lt=new Dt({}),Qt=new Tt({props:{name:"class transformers.ViTMAEConfig",anchor:"transformers.ViTMAEConfig",parameters:[{name:"hidden_size",val:" = 768"},{name:"num_hidden_layers",val:" = 12"},{name:"num_attention_heads",val:" = 12"},{name:"intermediate_size",val:" = 3072"},{name:"hidden_act",val:" = 'gelu'"},{name:"hidden_dropout_prob",val:" = 0.0"},{name:"attention_probs_dropout_prob",val:" = 0.0"},{name:"initializer_range",val:" = 0.02"},{name:"layer_norm_eps",val:" = 1e-12"},{name:"image_size",val:" = 224"},{name:"patch_size",val:" = 16"},{name:"num_channels",val:" = 3"},{name:"qkv_bias",val:" = True"},{name:"decoder_num_attention_heads",val:" = 16"},{name:"decoder_hidden_size",val:" = 512"},{name:"decoder_num_hidden_layers",val:" = 8"},{name:"decoder_intermediate_size",val:" = 2048"},{name:"mask_ratio",val:" = 0.75"},{name:"norm_pix_loss",val:" = False"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.ViTMAEConfig.hidden_size",description:`<strong>hidden_size</strong> (<code>int</code>, <em>optional</em>, defaults to 768) &#x2014;
Dimensionality of the encoder layers and the pooler layer.`,name:"hidden_size"},{anchor:"transformers.ViTMAEConfig.num_hidden_layers",description:`<strong>num_hidden_layers</strong> (<code>int</code>, <em>optional</em>, defaults to 12) &#x2014;
Number of hidden layers in the Transformer encoder.`,name:"num_hidden_layers"},{anchor:"transformers.ViTMAEConfig.num_attention_heads",description:`<strong>num_attention_heads</strong> (<code>int</code>, <em>optional</em>, defaults to 12) &#x2014;
Number of attention heads for each attention layer in the Transformer encoder.`,name:"num_attention_heads"},{anchor:"transformers.ViTMAEConfig.intermediate_size",description:`<strong>intermediate_size</strong> (<code>int</code>, <em>optional</em>, defaults to 3072) &#x2014;
Dimensionality of the &#x201C;intermediate&#x201D; (i.e., feed-forward) layer in the Transformer encoder.`,name:"intermediate_size"},{anchor:"transformers.ViTMAEConfig.hidden_act",description:`<strong>hidden_act</strong> (<code>str</code> or <code>function</code>, <em>optional</em>, defaults to <code>&quot;gelu&quot;</code>) &#x2014;
The non-linear activation function (function or string) in the encoder and pooler. 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.ViTMAEConfig.hidden_dropout_prob",description:`<strong>hidden_dropout_prob</strong> (<code>float</code>, <em>optional</em>, defaults to 0.0) &#x2014;
The dropout probabilitiy for all fully connected layers in the embeddings, encoder, and pooler.`,name:"hidden_dropout_prob"},{anchor:"transformers.ViTMAEConfig.attention_probs_dropout_prob",description:`<strong>attention_probs_dropout_prob</strong> (<code>float</code>, <em>optional</em>, defaults to 0.0) &#x2014;
The dropout ratio for the attention probabilities.`,name:"attention_probs_dropout_prob"},{anchor:"transformers.ViTMAEConfig.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.ViTMAEConfig.layer_norm_eps",description:`<strong>layer_norm_eps</strong> (<code>float</code>, <em>optional</em>, defaults to 1e-12) &#x2014;
The epsilon used by the layer normalization layers.`,name:"layer_norm_eps"},{anchor:"transformers.ViTMAEConfig.image_size",description:`<strong>image_size</strong> (<code>int</code>, <em>optional</em>, defaults to 224) &#x2014;
The size (resolution) of each image.`,name:"image_size"},{anchor:"transformers.ViTMAEConfig.patch_size",description:`<strong>patch_size</strong> (<code>int</code>, <em>optional</em>, defaults to 16) &#x2014;
The size (resolution) of each patch.`,name:"patch_size"},{anchor:"transformers.ViTMAEConfig.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.ViTMAEConfig.qkv_bias",description:`<strong>qkv_bias</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether to add a bias to the queries, keys and values.`,name:"qkv_bias"},{anchor:"transformers.ViTMAEConfig.decoder_num_attention_heads",description:`<strong>decoder_num_attention_heads</strong> (<code>int</code>, <em>optional</em>, defaults to 12) &#x2014;
Number of attention heads for each attention layer in the decoder.`,name:"decoder_num_attention_heads"},{anchor:"transformers.ViTMAEConfig.decoder_hidden_size",description:`<strong>decoder_hidden_size</strong> (<code>int</code>, <em>optional</em>, defaults to 512) &#x2014;
Dimensionality of the decoder.`,name:"decoder_hidden_size"},{anchor:"transformers.ViTMAEConfig.decoder_num_hidden_layers",description:`<strong>decoder_num_hidden_layers</strong> (<code>int</code>, <em>optional</em>, defaults to 8) &#x2014;
Number of hidden layers in the decoder.`,name:"decoder_num_hidden_layers"},{anchor:"transformers.ViTMAEConfig.decoder_intermediate_size",description:`<strong>decoder_intermediate_size</strong> (<code>int</code>, <em>optional</em>, defaults to 2048) &#x2014;
Dimensionality of the &#x201C;intermediate&#x201D; (i.e., feed-forward) layer in the decoder.`,name:"decoder_intermediate_size"},{anchor:"transformers.ViTMAEConfig.mask_ratio",description:`<strong>mask_ratio</strong> (<code>float</code>, <em>optional</em>, defaults to 0.75) &#x2014;
The ratio of the number of masked tokens in the input sequence.`,name:"mask_ratio"},{anchor:"transformers.ViTMAEConfig.norm_pix_loss",description:`<strong>norm_pix_loss</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to train with normalized pixels (see Table 3 in the paper). Using normalized pixels improved
representation quality in the experiments of the authors.`,name:"norm_pix_loss"}],source:"https://github.com/huggingface/transformers/blob/v4.31.0/src/transformers/models/vit_mae/configuration_vit_mae.py#L29"}}),$t=new Mn({props:{anchor:"transformers.ViTMAEConfig.example",$$slots:{default:[ii]},$$scope:{ctx:U}}}),eo=new Dt({}),to=new Tt({props:{name:"class transformers.ViTMAEModel",anchor:"transformers.ViTMAEModel",parameters:[{name:"config",val:""}],parametersDescription:[{anchor:"transformers.ViTMAEModel.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.31.0/en/model_doc/vit_mae#transformers.ViTMAEConfig">ViTMAEConfig</a>) &#x2014; Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the <a href="/docs/transformers/v4.31.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.31.0/src/transformers/models/vit_mae/modeling_vit_mae.py#L637"}}),ro=new Tt({props:{name:"forward",anchor:"transformers.ViTMAEModel.forward",parameters:[{name:"pixel_values",val:": typing.Optional[torch.FloatTensor] = None"},{name:"noise",val:": typing.Optional[torch.FloatTensor] = None"},{name:"head_mask",val:": typing.Optional[torch.FloatTensor] = 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"}],parametersDescription:[{anchor:"transformers.ViTMAEModel.forward.pixel_values",description:`<strong>pixel_values</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, num_channels, height, width)</code>) &#x2014;
Pixel values. Pixel values can be obtained using <a href="/docs/transformers/v4.31.0/en/model_doc/auto#transformers.AutoImageProcessor">AutoImageProcessor</a>. See <a href="/docs/transformers/v4.31.0/en/model_doc/perceiver#transformers.PerceiverFeatureExtractor.__call__">ViTImageProcessor.<strong>call</strong>()</a>
for details.`,name:"pixel_values"},{anchor:"transformers.ViTMAEModel.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.ViTMAEModel.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.ViTMAEModel.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.ViTMAEModel.forward.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/v4.31.0/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/transformers/blob/v4.31.0/src/transformers/models/vit_mae/modeling_vit_mae.py#L661",returnDescription:`
<p>A <code>transformers.models.vit_mae.modeling_vit_mae.ViTMAEModelOutput</code> or a tuple of
<code>torch.FloatTensor</code> (if <code>return_dict=False</code> is passed or when <code>config.return_dict=False</code>) comprising various
elements depending on the configuration (<a
  href="/docs/transformers/v4.31.0/en/model_doc/vit_mae#transformers.ViTMAEConfig"
>ViTMAEConfig</a>) and inputs.</p>
<ul>
<li><strong>last_hidden_state</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>) \u2014 Sequence of hidden-states at the output of the last layer of the model.</li>
<li><strong>mask</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length)</code>) \u2014 Tensor indicating which patches are masked (1) and which are not (0).</li>
<li><strong>ids_restore</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>) \u2014 Tensor containing the original index of the (shuffled) masked patches.</li>
<li><strong>hidden_states</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for the output of the embeddings + one for the output of each layer) of
shape <code>(batch_size, sequence_length, hidden_size)</code>. Hidden-states of the model at the output of each layer
plus the initial embedding outputs.</li>
<li><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>. Attentions weights after the attention softmax, used to compute the weighted average in
the self-attention heads.</li>
</ul>
`,returnType:`
<p><code>transformers.models.vit_mae.modeling_vit_mae.ViTMAEModelOutput</code> or <code>tuple(torch.FloatTensor)</code></p>
`}}),Vt=new Ao({props:{$$slots:{default:[li]},$$scope:{ctx:U}}}),kt=new Mn({props:{anchor:"transformers.ViTMAEModel.forward.example",$$slots:{default:[di]},$$scope:{ctx:U}}}),so=new Dt({}),ao=new Tt({props:{name:"class transformers.ViTMAEForPreTraining",anchor:"transformers.ViTMAEForPreTraining",parameters:[{name:"config",val:""}],parametersDescription:[{anchor:"transformers.ViTMAEForPreTraining.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.31.0/en/model_doc/vit_mae#transformers.ViTMAEConfig">ViTMAEConfig</a>) &#x2014; Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the <a href="/docs/transformers/v4.31.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.31.0/src/transformers/models/vit_mae/modeling_vit_mae.py#L849"}}),xt=new Ao({props:{$$slots:{default:[ci]},$$scope:{ctx:U}}}),co=new Tt({props:{name:"forward",anchor:"transformers.ViTMAEForPreTraining.forward",parameters:[{name:"pixel_values",val:": typing.Optional[torch.FloatTensor] = None"},{name:"noise",val:": typing.Optional[torch.FloatTensor] = None"},{name:"head_mask",val:": typing.Optional[torch.FloatTensor] = 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"}],parametersDescription:[{anchor:"transformers.ViTMAEForPreTraining.forward.pixel_values",description:`<strong>pixel_values</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, num_channels, height, width)</code>) &#x2014;
Pixel values. Pixel values can be obtained using <a href="/docs/transformers/v4.31.0/en/model_doc/auto#transformers.AutoImageProcessor">AutoImageProcessor</a>. See <a href="/docs/transformers/v4.31.0/en/model_doc/perceiver#transformers.PerceiverFeatureExtractor.__call__">ViTImageProcessor.<strong>call</strong>()</a>
for details.`,name:"pixel_values"},{anchor:"transformers.ViTMAEForPreTraining.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.ViTMAEForPreTraining.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.ViTMAEForPreTraining.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.ViTMAEForPreTraining.forward.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/v4.31.0/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/transformers/blob/v4.31.0/src/transformers/models/vit_mae/modeling_vit_mae.py#L962",returnDescription:`
<p>A <code>transformers.models.vit_mae.modeling_vit_mae.ViTMAEForPreTrainingOutput</code> or a tuple of
<code>torch.FloatTensor</code> (if <code>return_dict=False</code> is passed or when <code>config.return_dict=False</code>) comprising various
elements depending on the configuration (<a
  href="/docs/transformers/v4.31.0/en/model_doc/vit_mae#transformers.ViTMAEConfig"
>ViTMAEConfig</a>) and inputs.</p>
<ul>
<li><strong>loss</strong> (<code>torch.FloatTensor</code> of shape <code>(1,)</code>) \u2014 Pixel reconstruction loss.</li>
<li><strong>logits</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, patch_size ** 2 * num_channels)</code>) \u2014 Pixel reconstruction logits.</li>
<li><strong>mask</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length)</code>) \u2014 Tensor indicating which patches are masked (1) and which are not (0).</li>
<li><strong>ids_restore</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>) \u2014 Tensor containing the original index of the (shuffled) masked patches.</li>
<li><strong>hidden_states</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for the output of the embeddings + one for the output of each layer) of
shape <code>(batch_size, sequence_length, hidden_size)</code>. Hidden-states of the model at the output of each layer
plus the initial embedding outputs.</li>
<li><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>. Attentions weights after the attention softmax, used to compute the weighted average in
the self-attention heads.</li>
</ul>
`,returnType:`
<p><code>transformers.models.vit_mae.modeling_vit_mae.ViTMAEForPreTrainingOutput</code> or <code>tuple(torch.FloatTensor)</code></p>
`}}),Pt=new Ao({props:{$$slots:{default:[hi]},$$scope:{ctx:U}}}),jt=new Mn({props:{anchor:"transformers.ViTMAEForPreTraining.forward.example",$$slots:{default:[mi]},$$scope:{ctx:U}}}),ho=new Dt({}),mo=new Tt({props:{name:"class transformers.TFViTMAEModel",anchor:"transformers.TFViTMAEModel",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFViTMAEModel.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.31.0/en/model_doc/vit_mae#transformers.ViTMAEConfig">ViTMAEConfig</a>) &#x2014; Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the <a href="/docs/transformers/v4.31.0/en/main_classes/model#transformers.TFPreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.31.0/src/transformers/models/vit_mae/modeling_tf_vit_mae.py#L778"}}),Jt=new Ao({props:{$$slots:{default:[pi]},$$scope:{ctx:U}}}),go=new Tt({props:{name:"call",anchor:"transformers.TFViTMAEModel.call",parameters:[{name:"pixel_values",val:": TFModelInputType | None = None"},{name:"noise",val:": tf.Tensor = None"},{name:"head_mask",val:": np.ndarray | tf.Tensor | None = None"},{name:"output_attentions",val:": Optional[bool] = None"},{name:"output_hidden_states",val:": Optional[bool] = None"},{name:"return_dict",val:": Optional[bool] = None"},{name:"training",val:": bool = False"}],parametersDescription:[{anchor:"transformers.TFViTMAEModel.call.pixel_values",description:`<strong>pixel_values</strong> (<code>np.ndarray</code>, <code>tf.Tensor</code>, <code>List[tf.Tensor]</code> \`<code>Dict[str, tf.Tensor]</code> or <code>Dict[str, np.ndarray]</code> and each example must have the shape <code>(batch_size, num_channels, height, width)</code>) &#x2014;
Pixel values. Pixel values can be obtained using <a href="/docs/transformers/v4.31.0/en/model_doc/auto#transformers.AutoImageProcessor">AutoImageProcessor</a>. See <a href="/docs/transformers/v4.31.0/en/model_doc/perceiver#transformers.PerceiverFeatureExtractor.__call__">ViTImageProcessor.<strong>call</strong>()</a>
for details.`,name:"pixel_values"},{anchor:"transformers.TFViTMAEModel.call.head_mask",description:`<strong>head_mask</strong> (<code>np.ndarray</code> or <code>tf.Tensor</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.TFViTMAEModel.call.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. This argument can be used only in eager mode, in graph mode the value in the
config will be used instead.`,name:"output_attentions"},{anchor:"transformers.TFViTMAEModel.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. This argument can be used only in eager mode, in graph mode the value in the config will be
used instead.`,name:"output_hidden_states"},{anchor:"transformers.TFViTMAEModel.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/v4.31.0/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple. This argument can be used
in eager mode, in graph mode the value will always be set to True.`,name:"return_dict"},{anchor:"transformers.TFViTMAEModel.call.training",description:`<strong>training</strong> (<code>bool</code>, <em>optional</em>, defaults to \`False&#x201C;) &#x2014;
Whether or not to use the model in training mode (some modules like dropout modules have different
behaviors between training and evaluation).`,name:"training"}],source:"https://github.com/huggingface/transformers/blob/v4.31.0/src/transformers/models/vit_mae/modeling_tf_vit_mae.py#L787",returnDescription:`
<p>A <code>transformers.models.vit_mae.modeling_tf_vit_mae.TFViTMAEModelOutput</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/v4.31.0/en/model_doc/vit_mae#transformers.ViTMAEConfig"
>ViTMAEConfig</a>) and inputs.</p>
<ul>
<li><strong>last_hidden_state</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>) \u2014 Sequence of hidden-states at the output of the last layer of the model.</li>
<li><strong>mask</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>) \u2014 Tensor indicating which patches are masked (1) and which are not (0).</li>
<li><strong>ids_restore</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>) \u2014 Tensor containing the original index of the (shuffled) masked patches.</li>
<li><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>. Hidden-states of the model at the output of each layer plus
the initial embedding outputs.</li>
<li><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>. Attentions weights after the attention softmax, used to compute the weighted average in
the self-attention heads.</li>
</ul>
`,returnType:`
<p><code>transformers.models.vit_mae.modeling_tf_vit_mae.TFViTMAEModelOutput</code> or <code>tuple(tf.Tensor)</code></p>
`}}),Ct=new Ao({props:{$$slots:{default:[fi]},$$scope:{ctx:U}}}),It=new Mn({props:{anchor:"transformers.TFViTMAEModel.call.example",$$slots:{default:[ui]},$$scope:{ctx:U}}}),_o=new Dt({}),vo=new Tt({props:{name:"class transformers.TFViTMAEForPreTraining",anchor:"transformers.TFViTMAEForPreTraining",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFViTMAEForPreTraining.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.31.0/en/model_doc/vit_mae#transformers.ViTMAEConfig">ViTMAEConfig</a>) &#x2014; Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the <a href="/docs/transformers/v4.31.0/en/main_classes/model#transformers.TFPreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.31.0/src/transformers/models/vit_mae/modeling_tf_vit_mae.py#L939"}}),Zt=new Ao({props:{$$slots:{default:[gi]},$$scope:{ctx:U}}}),Mo=new Tt({props:{name:"call",anchor:"transformers.TFViTMAEForPreTraining.call",parameters:[{name:"pixel_values",val:": TFModelInputType | None = None"},{name:"noise",val:": tf.Tensor = None"},{name:"head_mask",val:": np.ndarray | tf.Tensor | None = None"},{name:"output_attentions",val:": Optional[bool] = None"},{name:"output_hidden_states",val:": Optional[bool] = None"},{name:"return_dict",val:": Optional[bool] = None"},{name:"training",val:": bool = False"}],parametersDescription:[{anchor:"transformers.TFViTMAEForPreTraining.call.pixel_values",description:`<strong>pixel_values</strong> (<code>np.ndarray</code>, <code>tf.Tensor</code>, <code>List[tf.Tensor]</code> \`<code>Dict[str, tf.Tensor]</code> or <code>Dict[str, np.ndarray]</code> and each example must have the shape <code>(batch_size, num_channels, height, width)</code>) &#x2014;
Pixel values. Pixel values can be obtained using <a href="/docs/transformers/v4.31.0/en/model_doc/auto#transformers.AutoImageProcessor">AutoImageProcessor</a>. See <a href="/docs/transformers/v4.31.0/en/model_doc/perceiver#transformers.PerceiverFeatureExtractor.__call__">ViTImageProcessor.<strong>call</strong>()</a>
for details.`,name:"pixel_values"},{anchor:"transformers.TFViTMAEForPreTraining.call.head_mask",description:`<strong>head_mask</strong> (<code>np.ndarray</code> or <code>tf.Tensor</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.TFViTMAEForPreTraining.call.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. This argument can be used only in eager mode, in graph mode the value in the
config will be used instead.`,name:"output_attentions"},{anchor:"transformers.TFViTMAEForPreTraining.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. This argument can be used only in eager mode, in graph mode the value in the config will be
used instead.`,name:"output_hidden_states"},{anchor:"transformers.TFViTMAEForPreTraining.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/v4.31.0/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple. This argument can be used
in eager mode, in graph mode the value will always be set to True.`,name:"return_dict"},{anchor:"transformers.TFViTMAEForPreTraining.call.training",description:`<strong>training</strong> (<code>bool</code>, <em>optional</em>, defaults to \`False&#x201C;) &#x2014;
Whether or not to use the model in training mode (some modules like dropout modules have different
behaviors between training and evaluation).`,name:"training"}],source:"https://github.com/huggingface/transformers/blob/v4.31.0/src/transformers/models/vit_mae/modeling_tf_vit_mae.py#L1063",returnDescription:`
<p>A <code>transformers.models.vit_mae.modeling_tf_vit_mae.TFViTMAEForPreTrainingOutput</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/v4.31.0/en/model_doc/vit_mae#transformers.ViTMAEConfig"
>ViTMAEConfig</a>) and inputs.</p>
<ul>
<li><strong>loss</strong> (<code>tf.Tensor</code> of shape <code>(1,)</code>) \u2014 Pixel reconstruction loss.</li>
<li><strong>logits</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, sequence_length, patch_size ** 2 * num_channels)</code>) \u2014 Pixel reconstruction logits.</li>
<li><strong>mask</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>) \u2014 Tensor indicating which patches are masked (1) and which are not (0).</li>
<li><strong>ids_restore</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>) \u2014 Tensor containing the original index of the (shuffled) masked patches.</li>
<li><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>. Hidden-states of the model at the output of each layer plus
the initial embedding outputs.</li>
<li><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>. Attentions weights after the attention softmax, used to compute the weighted average in
the self-attention heads.</li>
</ul>
`,returnType:`
<p><code>transformers.models.vit_mae.modeling_tf_vit_mae.TFViTMAEForPreTrainingOutput</code> or <code>tuple(tf.Tensor)</code></p>
`}}),Wt=new Ao({props:{$$slots:{default:[_i]},$$scope:{ctx:U}}}),Nt=new Mn({props:{anchor:"transformers.TFViTMAEForPreTraining.call.example",$$slots:{default:[vi]},$$scope:{ctx:U}}}),{c(){d=s("meta"),y=m(),u=s("h1"),g=s("a"),T=s("span"),M(l.$$.fragment),h=m(),F=s("span"),Pe=n("ViTMAE"),we=m(),I=s("h2"),D=s("a"),H=s("span"),M(w.$$.fragment),je=m(),S=s("span"),Ue=n("Overview"),Me=m(),W=s("p"),Je=n("The ViTMAE model was proposed in "),G=s("a"),Y=n("Masked Autoencoders Are Scalable Vision Learners"),Ce=n(` by Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li,
Piotr Doll\xE1r, Ross Girshick. The paper shows that, by pre-training a Vision Transformer (ViT) to reconstruct pixel values for masked patches, one can get results after
fine-tuning that outperform supervised pre-training.`),be=m(),R=s("p"),Ie=n("The abstract from the paper is the following:"),Ee=m(),B=s("p"),pe=s("em"),ze=n(`This paper shows that masked autoencoders (MAE) are scalable self-supervised learners for computer vision. Our MAE approach is simple: we mask random patches of the
input image and reconstruct the missing pixels. It is based on two core designs. First, we develop an asymmetric encoder-decoder architecture, with an encoder that operates
only on the visible subset of patches (without mask tokens), along with a lightweight decoder that reconstructs the original image from the latent representation and mask
tokens. Second, we find that masking a high proportion of the input image, e.g., 75%, yields a nontrivial and meaningful self-supervisory task. Coupling these two designs
enables us to train large models efficiently and effectively: we accelerate training (by 3x or more) and improve accuracy. Our scalable approach allows for learning high-capacity
models that generalize well: e.g., a vanilla ViT-Huge model achieves the best accuracy (87.8%) among methods that use only ImageNet-1K data. Transfer performance in downstream
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only on the visible subset of patches (without mask tokens), along with a lightweight decoder that reconstructs the original image from the latent representation and mask
tokens. Second, we find that masking a high proportion of the input image, e.g., 75%, yields a nontrivial and meaningful self-supervisory task. Coupling these two designs
enables us to train large models efficiently and effectively: we accelerate training (by 3x or more) and improve accuracy. Our scalable approach allows for learning high-capacity
models that generalize well: e.g., a vanilla ViT-Huge model achieves the best accuracy (87.8%) among methods that use only ImageNet-1K data. Transfer performance in downstream
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