import{S as Vs,i as Fs,s as xs,e as a,k as p,w as E,t as r,M as Ps,c as s,d as o,m as f,a as i,x as b,h as n,b as c,N as js,G as e,g as _,y as $,q as M,o as A,B as y,v as qs,L as xo}from"../../chunks/vendor-hf-doc-builder.js";import{T as ro}from"../../chunks/Tip-hf-doc-builder.js";import{D as we}from"../../chunks/Docstring-hf-doc-builder.js";import{C as Po}from"../../chunks/CodeBlock-hf-doc-builder.js";import{I as Ft}from"../../chunks/IconCopyLink-hf-doc-builder.js";import{E as Fo}from"../../chunks/ExampleCodeBlock-hf-doc-builder.js";function Cs(F){let d,v,u,h,T;return h=new Po({props:{code:`from transformers import ViTMAEModel, ViTMAEConfig

# Initializing a ViT MAE vit-mae-base style configuration
configuration = ViTMAEConfig()

# Initializing a model from the vit-mae-base style configuration
model = ViTMAEModel(configuration)

# Accessing the model configuration
configuration = model.config`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> ViTMAEModel, ViTMAEConfig

<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 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=a("p"),v=r("Example:"),u=p(),E(h.$$.fragment)},l(l){d=s(l,"P",{});var m=i(d);v=n(m,"Example:"),m.forEach(o),u=f(l),b(h.$$.fragment,l)},m(l,m){_(l,d,m),e(d,v),_(l,u,m),$(h,l,m),T=!0},p:xo,i(l){T||(M(h.$$.fragment,l),T=!0)},o(l){A(h.$$.fragment,l),T=!1},d(l){l&&o(d),l&&o(u),y(h,l)}}}function zs(F){let d,v,u,h,T;return{c(){d=a("p"),v=r("Although the recipe for forward pass needs to be defined within this function, one should call the "),u=a("code"),h=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(l){d=s(l,"P",{});var m=i(d);v=n(m,"Although the recipe for forward pass needs to be defined within this function, one should call the "),u=s(m,"CODE",{});var V=i(u);h=n(V,"Module"),V.forEach(o),T=n(m,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),m.forEach(o)},m(l,m){_(l,d,m),e(d,v),e(d,u),e(u,h),e(d,T)},d(l){l&&o(d)}}}function Is(F){let d,v,u,h,T;return h=new Po({props:{code:`from transformers import AutoFeatureExtractor, ViTMAEModel
from PIL import Image
import requests

url = "http://images.cocodataset.org/val2017/000000039769.jpg"
image = Image.open(requests.get(url, stream=True).raw)

feature_extractor = AutoFeatureExtractor.from_pretrained("facebook/vit-mae-base")
model = ViTMAEModel.from_pretrained("facebook/vit-mae-base")

inputs = feature_extractor(images=image, return_tensors="pt")
outputs = model(**inputs)
last_hidden_states = outputs.last_hidden_state`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoFeatureExtractor, 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>feature_extractor = AutoFeatureExtractor.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 = feature_extractor(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=a("p"),v=r("Examples:"),u=p(),E(h.$$.fragment)},l(l){d=s(l,"P",{});var m=i(d);v=n(m,"Examples:"),m.forEach(o),u=f(l),b(h.$$.fragment,l)},m(l,m){_(l,d,m),e(d,v),_(l,u,m),$(h,l,m),T=!0},p:xo,i(l){T||(M(h.$$.fragment,l),T=!0)},o(l){A(h.$$.fragment,l),T=!1},d(l){l&&o(d),l&&o(u),y(h,l)}}}function Ds(F){let d,v,u,h,T;return{c(){d=a("p"),v=r("Although the recipe for forward pass needs to be defined within this function, one should call the "),u=a("code"),h=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(l){d=s(l,"P",{});var m=i(d);v=n(m,"Although the recipe for forward pass needs to be defined within this function, one should call the "),u=s(m,"CODE",{});var V=i(u);h=n(V,"Module"),V.forEach(o),T=n(m,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),m.forEach(o)},m(l,m){_(l,d,m),e(d,v),e(d,u),e(u,h),e(d,T)},d(l){l&&o(d)}}}function Ns(F){let d,v,u,h,T;return h=new Po({props:{code:`from transformers import AutoFeatureExtractor, ViTMAEForPreTraining
from PIL import Image
import requests

url = "http://images.cocodataset.org/val2017/000000039769.jpg"
image = Image.open(requests.get(url, stream=True).raw)

feature_extractor = AutoFeatureExtractor.from_pretrained("facebook/vit-mae-base")
model = ViTMAEForPreTraining.from_pretrained("facebook/vit-mae-base")

inputs = feature_extractor(images=image, return_tensors="pt")
outputs = model(**inputs)
loss = outputs.loss
mask = outputs.mask
ids_restore = outputs.ids_restore`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoFeatureExtractor, 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>feature_extractor = AutoFeatureExtractor.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 = feature_extractor(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=a("p"),v=r("Examples:"),u=p(),E(h.$$.fragment)},l(l){d=s(l,"P",{});var m=i(d);v=n(m,"Examples:"),m.forEach(o),u=f(l),b(h.$$.fragment,l)},m(l,m){_(l,d,m),e(d,v),_(l,u,m),$(h,l,m),T=!0},p:xo,i(l){T||(M(h.$$.fragment,l),T=!0)},o(l){A(h.$$.fragment,l),T=!1},d(l){l&&o(d),l&&o(u),y(h,l)}}}function Ls(F){let d,v,u,h,T,l,m,V,Y,O,k,I,q,D,J,C,Q,H;return{c(){d=a("p"),v=r("TF 2.0 models accepts two formats as inputs:"),u=p(),h=a("ul"),T=a("li"),l=r("having all inputs as keyword arguments (like PyTorch models), or"),m=p(),V=a("li"),Y=r("having all inputs as a list, tuple or dict in the first positional arguments."),O=p(),k=a("p"),I=r("This second option is useful when using "),q=a("code"),D=r("tf.keras.Model.fit"),J=r(` method which currently requires having all the
tensors in the first argument of the model call function: `),C=a("code"),Q=r("model(inputs)"),H=r(".")},l(w){d=s(w,"P",{});var x=i(d);v=n(x,"TF 2.0 models accepts two formats as inputs:"),x.forEach(o),u=f(w),h=s(w,"UL",{});var z=i(h);T=s(z,"LI",{});var ne=i(T);l=n(ne,"having all inputs as keyword arguments (like PyTorch models), or"),ne.forEach(o),m=f(z),V=s(z,"LI",{});var ae=i(V);Y=n(ae,"having all inputs as a list, tuple or dict in the first positional arguments."),ae.forEach(o),z.forEach(o),O=f(w),k=s(w,"P",{});var P=i(k);I=n(P,"This second option is useful when using "),q=s(P,"CODE",{});var K=i(q);D=n(K,"tf.keras.Model.fit"),K.forEach(o),J=n(P,` method which currently requires having all the
tensors in the first argument of the model call function: `),C=s(P,"CODE",{});var se=i(C);Q=n(se,"model(inputs)"),se.forEach(o),H=n(P,"."),P.forEach(o)},m(w,x){_(w,d,x),e(d,v),_(w,u,x),_(w,h,x),e(h,T),e(T,l),e(h,m),e(h,V),e(V,Y),_(w,O,x),_(w,k,x),e(k,I),e(k,q),e(q,D),e(k,J),e(k,C),e(C,Q),e(k,H)},d(w){w&&o(d),w&&o(u),w&&o(h),w&&o(O),w&&o(k)}}}function Os(F){let d,v,u,h,T;return{c(){d=a("p"),v=r("Although the recipe for forward pass needs to be defined within this function, one should call the "),u=a("code"),h=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(l){d=s(l,"P",{});var m=i(d);v=n(m,"Although the recipe for forward pass needs to be defined within this function, one should call the "),u=s(m,"CODE",{});var V=i(u);h=n(V,"Module"),V.forEach(o),T=n(m,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),m.forEach(o)},m(l,m){_(l,d,m),e(d,v),e(d,u),e(u,h),e(d,T)},d(l){l&&o(d)}}}function Ss(F){let d,v,u,h,T;return h=new Po({props:{code:`from transformers import AutoFeatureExtractor, TFViTMAEModel
from PIL import Image
import requests

url = "http://images.cocodataset.org/val2017/000000039769.jpg"
image = Image.open(requests.get(url, stream=True).raw)

feature_extractor = AutoFeatureExtractor.from_pretrained("facebook/vit-mae-base")
model = TFViTMAEModel.from_pretrained("facebook/vit-mae-base")

inputs = feature_extractor(images=image, return_tensors="tf")
outputs = model(**inputs)
last_hidden_states = outputs.last_hidden_state`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoFeatureExtractor, 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>feature_extractor = AutoFeatureExtractor.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 = feature_extractor(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=a("p"),v=r("Examples:"),u=p(),E(h.$$.fragment)},l(l){d=s(l,"P",{});var m=i(d);v=n(m,"Examples:"),m.forEach(o),u=f(l),b(h.$$.fragment,l)},m(l,m){_(l,d,m),e(d,v),_(l,u,m),$(h,l,m),T=!0},p:xo,i(l){T||(M(h.$$.fragment,l),T=!0)},o(l){A(h.$$.fragment,l),T=!1},d(l){l&&o(d),l&&o(u),y(h,l)}}}function Us(F){let d,v,u,h,T,l,m,V,Y,O,k,I,q,D,J,C,Q,H;return{c(){d=a("p"),v=r("TF 2.0 models accepts two formats as inputs:"),u=p(),h=a("ul"),T=a("li"),l=r("having all inputs as keyword arguments (like PyTorch models), or"),m=p(),V=a("li"),Y=r("having all inputs as a list, tuple or dict in the first positional arguments."),O=p(),k=a("p"),I=r("This second option is useful when using "),q=a("code"),D=r("tf.keras.Model.fit"),J=r(` method which currently requires having all the
tensors in the first argument of the model call function: `),C=a("code"),Q=r("model(inputs)"),H=r(".")},l(w){d=s(w,"P",{});var x=i(d);v=n(x,"TF 2.0 models accepts two formats as inputs:"),x.forEach(o),u=f(w),h=s(w,"UL",{});var z=i(h);T=s(z,"LI",{});var ne=i(T);l=n(ne,"having all inputs as keyword arguments (like PyTorch models), or"),ne.forEach(o),m=f(z),V=s(z,"LI",{});var ae=i(V);Y=n(ae,"having all inputs as a list, tuple or dict in the first positional arguments."),ae.forEach(o),z.forEach(o),O=f(w),k=s(w,"P",{});var P=i(k);I=n(P,"This second option is useful when using "),q=s(P,"CODE",{});var K=i(q);D=n(K,"tf.keras.Model.fit"),K.forEach(o),J=n(P,` method which currently requires having all the
tensors in the first argument of the model call function: `),C=s(P,"CODE",{});var se=i(C);Q=n(se,"model(inputs)"),se.forEach(o),H=n(P,"."),P.forEach(o)},m(w,x){_(w,d,x),e(d,v),_(w,u,x),_(w,h,x),e(h,T),e(T,l),e(h,m),e(h,V),e(V,Y),_(w,O,x),_(w,k,x),e(k,I),e(k,q),e(q,D),e(k,J),e(k,C),e(C,Q),e(k,H)},d(w){w&&o(d),w&&o(u),w&&o(h),w&&o(O),w&&o(k)}}}function Ws(F){let d,v,u,h,T;return{c(){d=a("p"),v=r("Although the recipe for forward pass needs to be defined within this function, one should call the "),u=a("code"),h=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(l){d=s(l,"P",{});var m=i(d);v=n(m,"Although the recipe for forward pass needs to be defined within this function, one should call the "),u=s(m,"CODE",{});var V=i(u);h=n(V,"Module"),V.forEach(o),T=n(m,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),m.forEach(o)},m(l,m){_(l,d,m),e(d,v),e(d,u),e(u,h),e(d,T)},d(l){l&&o(d)}}}function Hs(F){let d,v,u,h,T;return h=new Po({props:{code:`from transformers import AutoFeatureExtractor, TFViTMAEForPreTraining
from PIL import Image
import requests

url = "http://images.cocodataset.org/val2017/000000039769.jpg"
image = Image.open(requests.get(url, stream=True).raw)

feature_extractor = AutoFeatureExtractor.from_pretrained("facebook/vit-mae-base")
model = TFViTMAEForPreTraining.from_pretrained("facebook/vit-mae-base")

inputs = feature_extractor(images=image, return_tensors="pt")
outputs = model(**inputs)
loss = outputs.loss
mask = outputs.mask
ids_restore = outputs.ids_restore`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoFeatureExtractor, 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>feature_extractor = AutoFeatureExtractor.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 = feature_extractor(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=a("p"),v=r("Examples:"),u=p(),E(h.$$.fragment)},l(l){d=s(l,"P",{});var m=i(d);v=n(m,"Examples:"),m.forEach(o),u=f(l),b(h.$$.fragment,l)},m(l,m){_(l,d,m),e(d,v),_(l,u,m),$(h,l,m),T=!0},p:xo,i(l){T||(M(h.$$.fragment,l),T=!0)},o(l){A(h.$$.fragment,l),T=!1},d(l){l&&o(d),l&&o(u),y(h,l)}}}function Ks(F){let d,v,u,h,T,l,m,V,Y,O,k,I,q,D,J,C,Q,H,w,x,z,ne,ae,P,K,se,jo,xt,no,cr,qo,Pt,hr,Co,j,We,mr,jt,pr,fr,ur,ie,gr,qt,_r,Tr,He,vr,wr,Er,le,br,Ct,$r,Mr,Ke,Ar,yr,kr,Ge,Vr,zt,Fr,xr,Pr,Re,jr,It,qr,Cr,zr,ao,Ir,Dr,Xe,Nr,Be,Lr,Or,zo,Ee,Pa,Io,Ye,Sr,Dt,Ur,Do,S,Wr,Je,Hr,Kr,Qe,Gr,Rr,Ze,Xr,Br,et,Yr,Jr,No,de,be,so,tt,Qr,io,Zr,Lo,W,ot,en,ce,tn,Nt,on,rn,rt,nn,an,sn,he,ln,Lt,dn,cn,Ot,hn,mn,pn,$e,Oo,me,Me,lo,nt,fn,co,un,So,Z,at,gn,st,_n,it,Tn,vn,wn,G,lt,En,pe,bn,St,$n,Mn,ho,An,yn,kn,Ae,Vn,ye,Uo,fe,ke,mo,dt,Fn,po,xn,Wo,ee,ct,Pn,ht,jn,mt,qn,Cn,zn,R,pt,In,ue,Dn,Ut,Nn,Ln,fo,On,Sn,Un,Ve,Wn,Fe,Ho,ge,xe,uo,ft,Hn,go,Kn,Ko,N,ut,Gn,gt,Rn,Wt,Xn,Bn,Yn,_t,Jn,Tt,Qn,Zn,ea,Pe,ta,X,vt,oa,_e,ra,Ht,na,aa,_o,sa,ia,la,je,da,qe,Go,Te,Ce,To,wt,ca,vo,ha,Ro,L,Et,ma,bt,pa,Kt,fa,ua,ga,$t,_a,Mt,Ta,va,wa,ze,Ea,B,At,ba,ve,$a,Gt,Ma,Aa,wo,ya,ka,Va,Ie,Fa,De,Xo;return l=new Ft({}),D=new Ft({}),tt=new Ft({}),ot=new we({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:"is_encoder_decoder",val:" = False"},{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/vr_17548/src/transformers/models/vit_mae/configuration_vit_mae.py#L29"}}),$e=new Fo({props:{anchor:"transformers.ViTMAEConfig.example",$$slots:{default:[Cs]},$$scope:{ctx:F}}}),nt=new Ft({}),at=new we({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/pr_17548/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/pr_17548/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/vr_17548/src/transformers/models/vit_mae/modeling_vit_mae.py#L646"}}),lt=new we({props:{name:"forward",anchor:"transformers.ViTMAEModel.forward",parameters:[{name:"pixel_values",val:" = None"},{name:"noise",val:" = None"},{name:"head_mask",val:" = None"},{name:"output_attentions",val:" = None"},{name:"output_hidden_states",val:" = None"},{name:"return_dict",val:" = 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/pr_17548/en/model_doc/auto#transformers.AutoFeatureExtractor">AutoFeatureExtractor</a>. See
<code>AutoFeatureExtractor.__call__()</code> 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/pr_17548/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/transformers/blob/vr_17548/src/transformers/models/vit_mae/modeling_vit_mae.py#L670",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/pr_17548/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>
`}}),Ae=new ro({props:{$$slots:{default:[zs]},$$scope:{ctx:F}}}),ye=new Fo({props:{anchor:"transformers.ViTMAEModel.forward.example",$$slots:{default:[Is]},$$scope:{ctx:F}}}),dt=new Ft({}),ct=new we({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/pr_17548/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/pr_17548/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/vr_17548/src/transformers/models/vit_mae/modeling_vit_mae.py#L849"}}),pt=new we({props:{name:"forward",anchor:"transformers.ViTMAEForPreTraining.forward",parameters:[{name:"pixel_values",val:" = None"},{name:"noise",val:" = None"},{name:"head_mask",val:" = None"},{name:"output_attentions",val:" = None"},{name:"output_hidden_states",val:" = None"},{name:"return_dict",val:" = 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/pr_17548/en/model_doc/auto#transformers.AutoFeatureExtractor">AutoFeatureExtractor</a>. See
<code>AutoFeatureExtractor.__call__()</code> 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/pr_17548/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/transformers/blob/vr_17548/src/transformers/models/vit_mae/modeling_vit_mae.py#L913",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/pr_17548/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, 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>
`}}),Ve=new ro({props:{$$slots:{default:[Ds]},$$scope:{ctx:F}}}),Fe=new Fo({props:{anchor:"transformers.ViTMAEForPreTraining.forward.example",$$slots:{default:[Ns]},$$scope:{ctx:F}}}),ft=new Ft({}),ut=new we({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/pr_17548/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/pr_17548/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/vr_17548/src/transformers/models/vit_mae/modeling_tf_vit_mae.py#L793"}}),Pe=new ro({props:{$$slots:{default:[Ls]},$$scope:{ctx:F}}}),vt=new we({props:{name:"call",anchor:"transformers.TFViTMAEModel.call",parameters:[{name:"pixel_values",val:": typing.Union[typing.List[tensorflow.python.framework.ops.Tensor], typing.List[numpy.ndarray], typing.List[tensorflow.python.keras.engine.keras_tensor.KerasTensor], typing.Dict[str, tensorflow.python.framework.ops.Tensor], typing.Dict[str, numpy.ndarray], typing.Dict[str, tensorflow.python.keras.engine.keras_tensor.KerasTensor], tensorflow.python.framework.ops.Tensor, numpy.ndarray, tensorflow.python.keras.engine.keras_tensor.KerasTensor, NoneType] = None"},{name:"noise",val:": Tensor = None"},{name:"head_mask",val:": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = 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:"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/pr_17548/en/model_doc/auto#transformers.AutoFeatureExtractor">AutoFeatureExtractor</a>. See
<code>AutoFeatureExtractor.__call__()</code> 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/pr_17548/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/vr_17548/src/transformers/models/vit_mae/modeling_tf_vit_mae.py#L802",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/pr_17548/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>
`}}),je=new ro({props:{$$slots:{default:[Os]},$$scope:{ctx:F}}}),qe=new Fo({props:{anchor:"transformers.TFViTMAEModel.call.example",$$slots:{default:[Ss]},$$scope:{ctx:F}}}),wt=new Ft({}),Et=new we({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/pr_17548/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/pr_17548/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/vr_17548/src/transformers/models/vit_mae/modeling_tf_vit_mae.py#L954"}}),ze=new ro({props:{$$slots:{default:[Us]},$$scope:{ctx:F}}}),At=new we({props:{name:"call",anchor:"transformers.TFViTMAEForPreTraining.call",parameters:[{name:"pixel_values",val:": typing.Union[typing.List[tensorflow.python.framework.ops.Tensor], typing.List[numpy.ndarray], typing.List[tensorflow.python.keras.engine.keras_tensor.KerasTensor], typing.Dict[str, tensorflow.python.framework.ops.Tensor], typing.Dict[str, numpy.ndarray], typing.Dict[str, tensorflow.python.keras.engine.keras_tensor.KerasTensor], tensorflow.python.framework.ops.Tensor, numpy.ndarray, tensorflow.python.keras.engine.keras_tensor.KerasTensor, NoneType] = None"},{name:"noise",val:": Tensor = None"},{name:"head_mask",val:": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = 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:"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/pr_17548/en/model_doc/auto#transformers.AutoFeatureExtractor">AutoFeatureExtractor</a>. See
<code>AutoFeatureExtractor.__call__()</code> 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>
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<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
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