import{S as om,i as sm,s as am,e as s,k as d,w as y,t as n,M as nm,c as a,d as t,m,a as i,x as M,h as r,b as p,G as e,g as f,y as $,q as V,o as E,B as F,v as rm,L as ks}from"../../chunks/vendor-hf-doc-builder.js";import{T as Zt}from"../../chunks/Tip-hf-doc-builder.js";import{D as ie}from"../../chunks/Docstring-hf-doc-builder.js";import{C as $o}from"../../chunks/CodeBlock-hf-doc-builder.js";import{I as mt}from"../../chunks/IconCopyLink-hf-doc-builder.js";import{E as Cs}from"../../chunks/ExampleCodeBlock-hf-doc-builder.js";import{P as im}from"../../chunks/PipelineTag-hf-doc-builder.js";function lm(U){let c,v,b,g,T;return g=new $o({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> MobileViTConfig, MobileViTModel

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Initializing a mobilevit-small style configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>configuration = MobileViTConfig()

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Initializing a model from the mobilevit-small style configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = MobileViTModel(configuration)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Accessing the model configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>configuration = model.config`}}),{c(){c=s("p"),v=n("Example:"),b=d(),y(g.$$.fragment)},l(l){c=a(l,"P",{});var h=i(c);v=r(h,"Example:"),h.forEach(t),b=m(l),M(g.$$.fragment,l)},m(l,h){f(l,c,h),e(c,v),f(l,b,h),$(g,l,h),T=!0},p:ks,i(l){T||(V(g.$$.fragment,l),T=!0)},o(l){E(g.$$.fragment,l),T=!1},d(l){l&&t(c),l&&t(b),F(g,l)}}}function cm(U){let c,v,b,g,T;return{c(){c=s("p"),v=n("Although the recipe for forward pass needs to be defined within this function, one should call the "),b=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){c=a(l,"P",{});var h=i(c);v=r(h,"Although the recipe for forward pass needs to be defined within this function, one should call the "),b=a(h,"CODE",{});var I=i(b);g=r(I,"Module"),I.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,c,h),e(c,v),e(c,b),e(b,g),e(c,T)},d(l){l&&t(c)}}}function dm(U){let c,v,b,g,T;return g=new $o({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, MobileViTModel
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> datasets <span class="hljs-keyword">import</span> load_dataset

<span class="hljs-meta">&gt;&gt;&gt; </span>dataset = load_dataset(<span class="hljs-string">&quot;huggingface/cats-image&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>image = dataset[<span class="hljs-string">&quot;test&quot;</span>][<span class="hljs-string">&quot;image&quot;</span>][<span class="hljs-number">0</span>]

<span class="hljs-meta">&gt;&gt;&gt; </span>image_processor = AutoImageProcessor.from_pretrained(<span class="hljs-string">&quot;apple/mobilevit-small&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = MobileViTModel.from_pretrained(<span class="hljs-string">&quot;apple/mobilevit-small&quot;</span>)

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

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

<span class="hljs-meta">&gt;&gt;&gt; </span>last_hidden_states = outputs.last_hidden_state
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-built_in">list</span>(last_hidden_states.shape)
[<span class="hljs-number">1</span>, <span class="hljs-number">640</span>, <span class="hljs-number">8</span>, <span class="hljs-number">8</span>]`}}),{c(){c=s("p"),v=n("Example:"),b=d(),y(g.$$.fragment)},l(l){c=a(l,"P",{});var h=i(c);v=r(h,"Example:"),h.forEach(t),b=m(l),M(g.$$.fragment,l)},m(l,h){f(l,c,h),e(c,v),f(l,b,h),$(g,l,h),T=!0},p:ks,i(l){T||(V(g.$$.fragment,l),T=!0)},o(l){E(g.$$.fragment,l),T=!1},d(l){l&&t(c),l&&t(b),F(g,l)}}}function mm(U){let c,v,b,g,T;return{c(){c=s("p"),v=n("Although the recipe for forward pass needs to be defined within this function, one should call the "),b=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){c=a(l,"P",{});var h=i(c);v=r(h,"Although the recipe for forward pass needs to be defined within this function, one should call the "),b=a(h,"CODE",{});var I=i(b);g=r(I,"Module"),I.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,c,h),e(c,v),e(c,b),e(b,g),e(c,T)},d(l){l&&t(c)}}}function pm(U){let c,v,b,g,T;return g=new $o({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, MobileViTForImageClassification
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> datasets <span class="hljs-keyword">import</span> load_dataset

<span class="hljs-meta">&gt;&gt;&gt; </span>dataset = load_dataset(<span class="hljs-string">&quot;huggingface/cats-image&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>image = dataset[<span class="hljs-string">&quot;test&quot;</span>][<span class="hljs-string">&quot;image&quot;</span>][<span class="hljs-number">0</span>]

<span class="hljs-meta">&gt;&gt;&gt; </span>image_processor = AutoImageProcessor.from_pretrained(<span class="hljs-string">&quot;apple/mobilevit-small&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = MobileViTForImageClassification.from_pretrained(<span class="hljs-string">&quot;apple/mobilevit-small&quot;</span>)

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

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

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># model predicts one of the 1000 ImageNet classes</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>predicted_label = logits.argmax(-<span class="hljs-number">1</span>).item()
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-built_in">print</span>(model.config.id2label[predicted_label])
tabby, tabby cat`}}),{c(){c=s("p"),v=n("Example:"),b=d(),y(g.$$.fragment)},l(l){c=a(l,"P",{});var h=i(c);v=r(h,"Example:"),h.forEach(t),b=m(l),M(g.$$.fragment,l)},m(l,h){f(l,c,h),e(c,v),f(l,b,h),$(g,l,h),T=!0},p:ks,i(l){T||(V(g.$$.fragment,l),T=!0)},o(l){E(g.$$.fragment,l),T=!1},d(l){l&&t(c),l&&t(b),F(g,l)}}}function hm(U){let c,v,b,g,T;return{c(){c=s("p"),v=n("Although the recipe for forward pass needs to be defined within this function, one should call the "),b=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){c=a(l,"P",{});var h=i(c);v=r(h,"Although the recipe for forward pass needs to be defined within this function, one should call the "),b=a(h,"CODE",{});var I=i(b);g=r(I,"Module"),I.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,c,h),e(c,v),e(c,b),e(b,g),e(c,T)},d(l){l&&t(c)}}}function fm(U){let c,v,b,g,T;return g=new $o({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> requests
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> PIL <span class="hljs-keyword">import</span> Image
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoImageProcessor, MobileViTForSemanticSegmentation

<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;apple/deeplabv3-mobilevit-small&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = MobileViTForSemanticSegmentation.from_pretrained(<span class="hljs-string">&quot;apple/deeplabv3-mobilevit-small&quot;</span>)

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

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

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># logits are of shape (batch_size, num_labels, height, width)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>logits = outputs.logits`}}),{c(){c=s("p"),v=n("Examples:"),b=d(),y(g.$$.fragment)},l(l){c=a(l,"P",{});var h=i(c);v=r(h,"Examples:"),h.forEach(t),b=m(l),M(g.$$.fragment,l)},m(l,h){f(l,c,h),e(c,v),f(l,b,h),$(g,l,h),T=!0},p:ks,i(l){T||(V(g.$$.fragment,l),T=!0)},o(l){E(g.$$.fragment,l),T=!1},d(l){l&&t(c),l&&t(b),F(g,l)}}}function um(U){let c,v,b,g,T,l,h,I,Ce,be,S,ee,te,w,ke,X,Ie,_e,z,je,oe,Y,Ue,Te,R,xe,ve,G,de,Ze,se,j,Z,we,x,me,ae,q,ne,Je,J,We,O,re,Se,B,Ne,ze,A,pe,P,Pe,L,W,Re,N,he,Q;return{c(){c=s("p"),v=n("TensorFlow models and layers in "),b=s("code"),g=n("transformers"),T=n(" accept two formats as input:"),l=d(),h=s("ul"),I=s("li"),Ce=n("having all inputs as keyword arguments (like PyTorch models), or"),be=d(),S=s("li"),ee=n("having all inputs as a list, tuple or dict in the first positional argument."),te=d(),w=s("p"),ke=n(`The reason the second format is supported is that Keras methods prefer this format when passing inputs to models
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format outside of Keras methods like `),Y=s("code"),Ue=n("fit()"),Te=n(" and "),R=s("code"),xe=n("predict()"),ve=n(`, such as when creating your own layers or models with
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the Keras `),G=a(k,"CODE",{});var Ve=i(G);de=r(Ve,"Functional"),Ve.forEach(t),Ze=r(k,` API, there are three possibilities you can use to gather all the input Tensors in the first
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`),O=a(K,"CODE",{});var De=i(O);re=r(De,"model([pixel_values, attention_mask])"),De.forEach(t),Se=r(K," or "),B=a(K,"CODE",{});var Ee=i(B);Ne=r(Ee,"model([pixel_values, attention_mask, token_type_ids])"),Ee.forEach(t),K.forEach(t),ze=m(D),A=a(D,"LI",{});var Fe=i(A);pe=r(Fe,`a dictionary with one or several input Tensors associated to the input names given in the docstring:
`),P=a(Fe,"CODE",{});var fe=i(P);Pe=r(fe,'model({"pixel_values": pixel_values, "token_type_ids": token_type_ids})'),fe.forEach(t),Fe.forEach(t),D.forEach(t),L=m(_),W=a(_,"P",{});var ce=i(W);Re=r(ce,`Note that when creating models and layers with
`),N=a(ce,"A",{href:!0,rel:!0});var Ae=i(N);he=r(Ae,"subclassing"),Ae.forEach(t),Q=r(ce,` 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!`),ce.forEach(t),this.h()},h(){p(N,"href","https://keras.io/guides/making_new_layers_and_models_via_subclassing/"),p(N,"rel","nofollow")},m(_,C){f(_,c,C),e(c,v),e(c,b),e(b,g),e(c,T),f(_,l,C),f(_,h,C),e(h,I),e(I,Ce),e(h,be),e(h,S),e(S,ee),f(_,te,C),f(_,w,C),e(w,ke),e(w,X),e(X,Ie),e(w,_e),e(w,z),e(z,je),e(w,oe),e(w,Y),e(Y,Ue),e(w,Te),e(w,R),e(R,xe),e(w,ve),e(w,G),e(G,de),e(w,Ze),f(_,se,C),f(_,j,C),e(j,Z),e(Z,we),e(Z,x),e(x,me),e(Z,ae),e(Z,q),e(q,ne),e(j,Je),e(j,J),e(J,We),e(J,O),e(O,re),e(J,Se),e(J,B),e(B,Ne),e(j,ze),e(j,A),e(A,pe),e(A,P),e(P,Pe),f(_,L,C),f(_,W,C),e(W,Re),e(W,N),e(N,he),e(W,Q)},d(_){_&&t(c),_&&t(l),_&&t(h),_&&t(te),_&&t(w),_&&t(se),_&&t(j),_&&t(L),_&&t(W)}}}function gm(U){let c,v,b,g,T;return{c(){c=s("p"),v=n("Although the recipe for forward pass needs to be defined within this function, one should call the "),b=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){c=a(l,"P",{});var h=i(c);v=r(h,"Although the recipe for forward pass needs to be defined within this function, one should call the "),b=a(h,"CODE",{});var I=i(b);g=r(I,"Module"),I.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,c,h),e(c,v),e(c,b),e(b,g),e(c,T)},d(l){l&&t(c)}}}function bm(U){let c,v,b,g,T;return g=new $o({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9JbWFnZVByb2Nlc3NvciUyQyUyMFRGTW9iaWxlVmlUTW9kZWwlMEFmcm9tJTIwZGF0YXNldHMlMjBpbXBvcnQlMjBsb2FkX2RhdGFzZXQlMEElMEFkYXRhc2V0JTIwJTNEJTIwbG9hZF9kYXRhc2V0KCUyMmh1Z2dpbmdmYWNlJTJGY2F0cy1pbWFnZSUyMiklMEFpbWFnZSUyMCUzRCUyMGRhdGFzZXQlNUIlMjJ0ZXN0JTIyJTVEJTVCJTIyaW1hZ2UlMjIlNUQlNUIwJTVEJTBBJTBBaW1hZ2VfcHJvY2Vzc29yJTIwJTNEJTIwQXV0b0ltYWdlUHJvY2Vzc29yLmZyb21fcHJldHJhaW5lZCglMjJhcHBsZSUyRm1vYmlsZXZpdC1zbWFsbCUyMiklMEFtb2RlbCUyMCUzRCUyMFRGTW9iaWxlVmlUTW9kZWwuZnJvbV9wcmV0cmFpbmVkKCUyMmFwcGxlJTJGbW9iaWxldml0LXNtYWxsJTIyKSUwQSUwQWlucHV0cyUyMCUzRCUyMGltYWdlX3Byb2Nlc3NvcihpbWFnZSUyQyUyMHJldHVybl90ZW5zb3JzJTNEJTIydGYlMjIpJTBBb3V0cHV0cyUyMCUzRCUyMG1vZGVsKCoqaW5wdXRzKSUwQSUwQWxhc3RfaGlkZGVuX3N0YXRlcyUyMCUzRCUyMG91dHB1dHMubGFzdF9oaWRkZW5fc3RhdGUlMEFsaXN0KGxhc3RfaGlkZGVuX3N0YXRlcy5zaGFwZSk=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoImageProcessor, TFMobileViTModel
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> datasets <span class="hljs-keyword">import</span> load_dataset

<span class="hljs-meta">&gt;&gt;&gt; </span>dataset = load_dataset(<span class="hljs-string">&quot;huggingface/cats-image&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>image = dataset[<span class="hljs-string">&quot;test&quot;</span>][<span class="hljs-string">&quot;image&quot;</span>][<span class="hljs-number">0</span>]

<span class="hljs-meta">&gt;&gt;&gt; </span>image_processor = AutoImageProcessor.from_pretrained(<span class="hljs-string">&quot;apple/mobilevit-small&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFMobileViTModel.from_pretrained(<span class="hljs-string">&quot;apple/mobilevit-small&quot;</span>)

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

<span class="hljs-meta">&gt;&gt;&gt; </span>last_hidden_states = outputs.last_hidden_state
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-built_in">list</span>(last_hidden_states.shape)
[<span class="hljs-number">1</span>, <span class="hljs-number">640</span>, <span class="hljs-number">8</span>, <span class="hljs-number">8</span>]`}}),{c(){c=s("p"),v=n("Example:"),b=d(),y(g.$$.fragment)},l(l){c=a(l,"P",{});var h=i(c);v=r(h,"Example:"),h.forEach(t),b=m(l),M(g.$$.fragment,l)},m(l,h){f(l,c,h),e(c,v),f(l,b,h),$(g,l,h),T=!0},p:ks,i(l){T||(V(g.$$.fragment,l),T=!0)},o(l){E(g.$$.fragment,l),T=!1},d(l){l&&t(c),l&&t(b),F(g,l)}}}function _m(U){let c,v,b,g,T,l,h,I,Ce,be,S,ee,te,w,ke,X,Ie,_e,z,je,oe,Y,Ue,Te,R,xe,ve,G,de,Ze,se,j,Z,we,x,me,ae,q,ne,Je,J,We,O,re,Se,B,Ne,ze,A,pe,P,Pe,L,W,Re,N,he,Q;return{c(){c=s("p"),v=n("TensorFlow models and layers in "),b=s("code"),g=n("transformers"),T=n(" accept two formats as input:"),l=d(),h=s("ul"),I=s("li"),Ce=n("having all inputs as keyword arguments (like PyTorch models), or"),be=d(),S=s("li"),ee=n("having all inputs as a list, tuple or dict in the first positional argument."),te=d(),w=s("p"),ke=n(`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 `),z=s("code"),je=n("model.fit()"),oe=n(` supports! If, however, you want to use the second
format outside of Keras methods like `),Y=s("code"),Ue=n("fit()"),Te=n(" and "),R=s("code"),xe=n("predict()"),ve=n(`, such as when creating your own layers or models with
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`),N=s("a"),he=n("subclassing"),Q=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(_){c=a(_,"P",{});var C=i(c);v=r(C,"TensorFlow models and layers in "),b=a(C,"CODE",{});var Qe=i(b);g=r(Qe,"transformers"),Qe.forEach(t),T=r(C," accept two formats as input:"),C.forEach(t),l=m(_),h=a(_,"UL",{});var le=i(h);I=a(le,"LI",{});var ye=i(I);Ce=r(ye,"having all inputs as keyword arguments (like PyTorch models), or"),ye.forEach(t),be=m(le),S=a(le,"LI",{});var He=i(S);ee=r(He,"having all inputs as a list, tuple or dict in the first positional argument."),He.forEach(t),le.forEach(t),te=m(_),w=a(_,"P",{});var k=i(w);ke=r(k,`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 `),X=a(k,"CODE",{});var Ke=i(X);Ie=r(Ke,"model.fit()"),Ke.forEach(t),_e=r(k,` things should \u201Cjust work\u201D for you - just
pass your inputs and labels in any format that `),z=a(k,"CODE",{});var Me=i(z);je=r(Me,"model.fit()"),Me.forEach(t),oe=r(k,` supports! If, however, you want to use the second
format outside of Keras methods like `),Y=a(k,"CODE",{});var $e=i(Y);Ue=r($e,"fit()"),$e.forEach(t),Te=r(k," and "),R=a(k,"CODE",{});var et=i(R);xe=r(et,"predict()"),et.forEach(t),ve=r(k,`, such as when creating your own layers or models with
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positional argument:`),k.forEach(t),se=m(_),j=a(_,"UL",{});var D=i(j);Z=a(D,"LI",{});var H=i(Z);we=r(H,"a single Tensor with "),x=a(H,"CODE",{});var tt=i(x);me=r(tt,"pixel_values"),tt.forEach(t),ae=r(H," only and nothing else: "),q=a(H,"CODE",{});var Be=i(q);ne=r(Be,"model(pixel_values)"),Be.forEach(t),H.forEach(t),Je=m(D),J=a(D,"LI",{});var K=i(J);We=r(K,`a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
`),O=a(K,"CODE",{});var De=i(O);re=r(De,"model([pixel_values, attention_mask])"),De.forEach(t),Se=r(K," or "),B=a(K,"CODE",{});var Ee=i(B);Ne=r(Ee,"model([pixel_values, attention_mask, token_type_ids])"),Ee.forEach(t),K.forEach(t),ze=m(D),A=a(D,"LI",{});var Fe=i(A);pe=r(Fe,`a dictionary with one or several input Tensors associated to the input names given in the docstring:
`),P=a(Fe,"CODE",{});var fe=i(P);Pe=r(fe,'model({"pixel_values": pixel_values, "token_type_ids": token_type_ids})'),fe.forEach(t),Fe.forEach(t),D.forEach(t),L=m(_),W=a(_,"P",{});var ce=i(W);Re=r(ce,`Note that when creating models and layers with
`),N=a(ce,"A",{href:!0,rel:!0});var Ae=i(N);he=r(Ae,"subclassing"),Ae.forEach(t),Q=r(ce,` 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!`),ce.forEach(t),this.h()},h(){p(N,"href","https://keras.io/guides/making_new_layers_and_models_via_subclassing/"),p(N,"rel","nofollow")},m(_,C){f(_,c,C),e(c,v),e(c,b),e(b,g),e(c,T),f(_,l,C),f(_,h,C),e(h,I),e(I,Ce),e(h,be),e(h,S),e(S,ee),f(_,te,C),f(_,w,C),e(w,ke),e(w,X),e(X,Ie),e(w,_e),e(w,z),e(z,je),e(w,oe),e(w,Y),e(Y,Ue),e(w,Te),e(w,R),e(R,xe),e(w,ve),e(w,G),e(G,de),e(w,Ze),f(_,se,C),f(_,j,C),e(j,Z),e(Z,we),e(Z,x),e(x,me),e(Z,ae),e(Z,q),e(q,ne),e(j,Je),e(j,J),e(J,We),e(J,O),e(O,re),e(J,Se),e(J,B),e(B,Ne),e(j,ze),e(j,A),e(A,pe),e(A,P),e(P,Pe),f(_,L,C),f(_,W,C),e(W,Re),e(W,N),e(N,he),e(W,Q)},d(_){_&&t(c),_&&t(l),_&&t(h),_&&t(te),_&&t(w),_&&t(se),_&&t(j),_&&t(L),_&&t(W)}}}function Tm(U){let c,v,b,g,T;return{c(){c=s("p"),v=n("Although the recipe for forward pass needs to be defined within this function, one should call the "),b=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){c=a(l,"P",{});var h=i(c);v=r(h,"Although the recipe for forward pass needs to be defined within this function, one should call the "),b=a(h,"CODE",{});var I=i(b);g=r(I,"Module"),I.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,c,h),e(c,v),e(c,b),e(b,g),e(c,T)},d(l){l&&t(c)}}}function vm(U){let c,v,b,g,T;return g=new $o({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, TFMobileViTForImageClassification
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> tensorflow <span class="hljs-keyword">as</span> tf
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> datasets <span class="hljs-keyword">import</span> load_dataset

<span class="hljs-meta">&gt;&gt;&gt; </span>dataset = load_dataset(<span class="hljs-string">&quot;huggingface/cats-image&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>image = dataset[<span class="hljs-string">&quot;test&quot;</span>][<span class="hljs-string">&quot;image&quot;</span>][<span class="hljs-number">0</span>]

<span class="hljs-meta">&gt;&gt;&gt; </span>image_processor = AutoImageProcessor.from_pretrained(<span class="hljs-string">&quot;apple/mobilevit-small&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFMobileViTForImageClassification.from_pretrained(<span class="hljs-string">&quot;apple/mobilevit-small&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = image_processor(image, return_tensors=<span class="hljs-string">&quot;tf&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>logits = model(**inputs).logits

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># model predicts one of the 1000 ImageNet classes</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>predicted_label = <span class="hljs-built_in">int</span>(tf.math.argmax(logits, axis=-<span class="hljs-number">1</span>))
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-built_in">print</span>(model.config.id2label[predicted_label])
tabby, tabby cat`}}),{c(){c=s("p"),v=n("Example:"),b=d(),y(g.$$.fragment)},l(l){c=a(l,"P",{});var h=i(c);v=r(h,"Example:"),h.forEach(t),b=m(l),M(g.$$.fragment,l)},m(l,h){f(l,c,h),e(c,v),f(l,b,h),$(g,l,h),T=!0},p:ks,i(l){T||(V(g.$$.fragment,l),T=!0)},o(l){E(g.$$.fragment,l),T=!1},d(l){l&&t(c),l&&t(b),F(g,l)}}}function wm(U){let c,v,b,g,T,l,h,I,Ce,be,S,ee,te,w,ke,X,Ie,_e,z,je,oe,Y,Ue,Te,R,xe,ve,G,de,Ze,se,j,Z,we,x,me,ae,q,ne,Je,J,We,O,re,Se,B,Ne,ze,A,pe,P,Pe,L,W,Re,N,he,Q;return{c(){c=s("p"),v=n("TensorFlow models and layers in "),b=s("code"),g=n("transformers"),T=n(" accept two formats as input:"),l=d(),h=s("ul"),I=s("li"),Ce=n("having all inputs as keyword arguments (like PyTorch models), or"),be=d(),S=s("li"),ee=n("having all inputs as a list, tuple or dict in the first positional argument."),te=d(),w=s("p"),ke=n(`The reason the second format is supported is that Keras methods prefer this format when passing inputs to models
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`),N=s("a"),he=n("subclassing"),Q=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(_){c=a(_,"P",{});var C=i(c);v=r(C,"TensorFlow models and layers in "),b=a(C,"CODE",{});var Qe=i(b);g=r(Qe,"transformers"),Qe.forEach(t),T=r(C," accept two formats as input:"),C.forEach(t),l=m(_),h=a(_,"UL",{});var le=i(h);I=a(le,"LI",{});var ye=i(I);Ce=r(ye,"having all inputs as keyword arguments (like PyTorch models), or"),ye.forEach(t),be=m(le),S=a(le,"LI",{});var He=i(S);ee=r(He,"having all inputs as a list, tuple or dict in the first positional argument."),He.forEach(t),le.forEach(t),te=m(_),w=a(_,"P",{});var k=i(w);ke=r(k,`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 `),X=a(k,"CODE",{});var Ke=i(X);Ie=r(Ke,"model.fit()"),Ke.forEach(t),_e=r(k,` things should \u201Cjust work\u201D for you - just
pass your inputs and labels in any format that `),z=a(k,"CODE",{});var Me=i(z);je=r(Me,"model.fit()"),Me.forEach(t),oe=r(k,` supports! If, however, you want to use the second
format outside of Keras methods like `),Y=a(k,"CODE",{});var $e=i(Y);Ue=r($e,"fit()"),$e.forEach(t),Te=r(k," and "),R=a(k,"CODE",{});var et=i(R);xe=r(et,"predict()"),et.forEach(t),ve=r(k,`, such as when creating your own layers or models with
the Keras `),G=a(k,"CODE",{});var Ve=i(G);de=r(Ve,"Functional"),Ve.forEach(t),Ze=r(k,` API, there are three possibilities you can use to gather all the input Tensors in the first
positional argument:`),k.forEach(t),se=m(_),j=a(_,"UL",{});var D=i(j);Z=a(D,"LI",{});var H=i(Z);we=r(H,"a single Tensor with "),x=a(H,"CODE",{});var tt=i(x);me=r(tt,"pixel_values"),tt.forEach(t),ae=r(H," only and nothing else: "),q=a(H,"CODE",{});var Be=i(q);ne=r(Be,"model(pixel_values)"),Be.forEach(t),H.forEach(t),Je=m(D),J=a(D,"LI",{});var K=i(J);We=r(K,`a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
`),O=a(K,"CODE",{});var De=i(O);re=r(De,"model([pixel_values, attention_mask])"),De.forEach(t),Se=r(K," or "),B=a(K,"CODE",{});var Ee=i(B);Ne=r(Ee,"model([pixel_values, attention_mask, token_type_ids])"),Ee.forEach(t),K.forEach(t),ze=m(D),A=a(D,"LI",{});var Fe=i(A);pe=r(Fe,`a dictionary with one or several input Tensors associated to the input names given in the docstring:
`),P=a(Fe,"CODE",{});var fe=i(P);Pe=r(fe,'model({"pixel_values": pixel_values, "token_type_ids": token_type_ids})'),fe.forEach(t),Fe.forEach(t),D.forEach(t),L=m(_),W=a(_,"P",{});var ce=i(W);Re=r(ce,`Note that when creating models and layers with
`),N=a(ce,"A",{href:!0,rel:!0});var Ae=i(N);he=r(Ae,"subclassing"),Ae.forEach(t),Q=r(ce,` 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!`),ce.forEach(t),this.h()},h(){p(N,"href","https://keras.io/guides/making_new_layers_and_models_via_subclassing/"),p(N,"rel","nofollow")},m(_,C){f(_,c,C),e(c,v),e(c,b),e(b,g),e(c,T),f(_,l,C),f(_,h,C),e(h,I),e(I,Ce),e(h,be),e(h,S),e(S,ee),f(_,te,C),f(_,w,C),e(w,ke),e(w,X),e(X,Ie),e(w,_e),e(w,z),e(z,je),e(w,oe),e(w,Y),e(Y,Ue),e(w,Te),e(w,R),e(R,xe),e(w,ve),e(w,G),e(G,de),e(w,Ze),f(_,se,C),f(_,j,C),e(j,Z),e(Z,we),e(Z,x),e(x,me),e(Z,ae),e(Z,q),e(q,ne),e(j,Je),e(j,J),e(J,We),e(J,O),e(O,re),e(J,Se),e(J,B),e(B,Ne),e(j,ze),e(j,A),e(A,pe),e(A,P),e(P,Pe),f(_,L,C),f(_,W,C),e(W,Re),e(W,N),e(N,he),e(W,Q)},d(_){_&&t(c),_&&t(l),_&&t(h),_&&t(te),_&&t(w),_&&t(se),_&&t(j),_&&t(L),_&&t(W)}}}function ym(U){let c,v,b,g,T;return{c(){c=s("p"),v=n("Although the recipe for forward pass needs to be defined within this function, one should call the "),b=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){c=a(l,"P",{});var h=i(c);v=r(h,"Although the recipe for forward pass needs to be defined within this function, one should call the "),b=a(h,"CODE",{});var I=i(b);g=r(I,"Module"),I.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,c,h),e(c,v),e(c,b),e(b,g),e(c,T)},d(l){l&&t(c)}}}function Mm(U){let c,v,b,g,T;return g=new $o({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, TFMobileViTForSemanticSegmentation
<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;apple/deeplabv3-mobilevit-small&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFMobileViTForSemanticSegmentation.from_pretrained(<span class="hljs-string">&quot;apple/deeplabv3-mobilevit-small&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><span class="hljs-comment"># logits are of shape (batch_size, num_labels, height, width)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>logits = outputs.logits`}}),{c(){c=s("p"),v=n("Examples:"),b=d(),y(g.$$.fragment)},l(l){c=a(l,"P",{});var h=i(c);v=r(h,"Examples:"),h.forEach(t),b=m(l),M(g.$$.fragment,l)},m(l,h){f(l,c,h),e(c,v),f(l,b,h),$(g,l,h),T=!0},p:ks,i(l){T||(V(g.$$.fragment,l),T=!0)},o(l){E(g.$$.fragment,l),T=!1},d(l){l&&t(c),l&&t(b),F(g,l)}}}function $m(U){let c,v,b,g,T,l,h,I,Ce,be,S,ee,te,w,ke,X,Ie,_e,z,je,oe,Y,Ue,Te,R,xe,ve,G,de,Ze,se,j,Z,we,x,me,ae,q,ne,Je,J,We,O,re,Se,B,Ne,ze,A,pe,P,Pe,L,W,Re,N,he,Q,_,C,Qe,le,ye,He,k,Ke,Me,$e,et,Ve,D,H,tt,Be,K,De,Ee,Fe,fe,ce,Ae,lr,cr,on,ot,dr,Vo,mr,pr,Eo,hr,fr,Fo,ur,gr,sn,gt,Jt,ca,Co,br,da,_r,an,Is,Tr,nn,ko,rn,Wt,ft,js,vr,wr,Io,yr,Mr,jo,$r,Vr,Er,Us,Fr,xs,Cr,ln,Zs,ma,kr,cn,Js,pa,Ws,Ir,dn,Ss,jr,mn,bt,St,ha,Uo,Ur,fa,xr,pn,Xe,xo,Zr,_t,Jr,Ns,Wr,Sr,Zo,Nr,zr,Pr,Tt,Rr,zs,Gr,Br,Ps,Ar,Dr,Xr,Nt,hn,vt,zt,ua,Jo,Or,ga,Lr,fn,pt,Wo,Yr,Pt,So,qr,ba,Qr,Hr,Rt,No,Kr,zo,ei,Rs,ti,oi,un,wt,Gt,_a,Po,si,Ta,ai,gn,Oe,Ro,ni,va,ri,ii,Bt,Go,li,wa,ci,di,At,Bo,mi,Ao,pi,Gs,hi,fi,bn,yt,Dt,ya,Do,ui,Ma,gi,_n,ht,Xo,bi,Oo,_i,Lo,Ti,vi,wi,st,Yo,yi,Mt,Mi,Bs,$i,Vi,$a,Ei,Fi,Ci,Xt,ki,Ot,Tn,$t,Lt,Va,qo,Ii,Ea,ji,vn,Le,Qo,Ui,Fa,xi,Zi,Ho,Ji,Ko,Wi,Si,Ni,at,es,zi,Vt,Pi,As,Ri,Gi,Ca,Bi,Ai,Di,Yt,Xi,qt,wn,Et,Qt,ka,ts,Oi,Ia,Li,yn,Ye,os,Yi,ja,qi,Qi,ss,Hi,as,Ki,el,tl,nt,ns,ol,Ft,sl,Ds,al,nl,Ua,rl,il,ll,Ht,cl,Kt,Mn,Ct,eo,xa,rs,dl,Za,ml,$n,Ge,is,pl,ls,hl,Xs,fl,ul,gl,cs,bl,ds,_l,Tl,vl,to,wl,rt,ms,yl,kt,Ml,Os,$l,Vl,Ja,El,Fl,Cl,oo,kl,so,Vn,It,ao,Wa,ps,Il,Sa,jl,En,ue,hs,Ul,Na,xl,Zl,fs,Jl,Ls,Wl,Sl,Nl,us,zl,gs,Pl,Rl,Gl,no,Bl,it,bs,Al,jt,Dl,Ys,Xl,Ol,za,Ll,Yl,ql,ro,Ql,io,Fn,Ut,lo,Pa,_s,Hl,Ra,Kl,Cn,ge,Ts,ec,Ga,tc,oc,vs,sc,qs,ac,nc,rc,ws,ic,ys,lc,cc,dc,co,mc,lt,Ms,pc,xt,hc,Qs,fc,uc,Ba,gc,bc,_c,mo,Tc,po,kn;return l=new mt({}),w=new mt({}),Ee=new $o({props:{code:"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",highlighted:`<span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> TFMobileViTForImageClassification
<span class="hljs-keyword">import</span> tensorflow <span class="hljs-keyword">as</span> tf


model_ckpt = <span class="hljs-string">&quot;apple/mobilevit-xx-small&quot;</span>
model = TFMobileViTForImageClassification.from_pretrained(model_ckpt)

converter = tf.lite.TFLiteConverter.from_keras_model(model)
converter.optimizations = [tf.lite.Optimize.DEFAULT]
converter.target_spec.supported_ops = [
    tf.lite.OpsSet.TFLITE_BUILTINS,
    tf.lite.OpsSet.SELECT_TF_OPS,
]
tflite_model = converter.convert()
tflite_filename = model_ckpt.split(<span class="hljs-string">&quot;/&quot;</span>)[-<span class="hljs-number">1</span>] + <span class="hljs-string">&quot;.tflite&quot;</span>
<span class="hljs-keyword">with</span> <span class="hljs-built_in">open</span>(tflite_filename, <span class="hljs-string">&quot;wb&quot;</span>) <span class="hljs-keyword">as</span> f:
    f.write(tflite_model)`}}),Co=new mt({}),ko=new im({props:{pipeline:"image-classification"}}),Uo=new mt({}),xo=new ie({props:{name:"class transformers.MobileViTConfig",anchor:"transformers.MobileViTConfig",parameters:[{name:"num_channels",val:" = 3"},{name:"image_size",val:" = 256"},{name:"patch_size",val:" = 2"},{name:"hidden_sizes",val:" = [144, 192, 240]"},{name:"neck_hidden_sizes",val:" = [16, 32, 64, 96, 128, 160, 640]"},{name:"num_attention_heads",val:" = 4"},{name:"mlp_ratio",val:" = 2.0"},{name:"expand_ratio",val:" = 4.0"},{name:"hidden_act",val:" = 'silu'"},{name:"conv_kernel_size",val:" = 3"},{name:"output_stride",val:" = 32"},{name:"hidden_dropout_prob",val:" = 0.1"},{name:"attention_probs_dropout_prob",val:" = 0.0"},{name:"classifier_dropout_prob",val:" = 0.1"},{name:"initializer_range",val:" = 0.02"},{name:"layer_norm_eps",val:" = 1e-05"},{name:"qkv_bias",val:" = True"},{name:"aspp_out_channels",val:" = 256"},{name:"atrous_rates",val:" = [6, 12, 18]"},{name:"aspp_dropout_prob",val:" = 0.1"},{name:"semantic_loss_ignore_index",val:" = 255"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.MobileViTConfig.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.MobileViTConfig.image_size",description:`<strong>image_size</strong> (<code>int</code>, <em>optional</em>, defaults to 256) &#x2014;
The size (resolution) of each image.`,name:"image_size"},{anchor:"transformers.MobileViTConfig.patch_size",description:`<strong>patch_size</strong> (<code>int</code>, <em>optional</em>, defaults to 2) &#x2014;
The size (resolution) of each patch.`,name:"patch_size"},{anchor:"transformers.MobileViTConfig.hidden_sizes",description:`<strong>hidden_sizes</strong> (<code>List[int]</code>, <em>optional</em>, defaults to <code>[144, 192, 240]</code>) &#x2014;
Dimensionality (hidden size) of the Transformer encoders at each stage.`,name:"hidden_sizes"},{anchor:"transformers.MobileViTConfig.neck_hidden_sizes",description:`<strong>neck_hidden_sizes</strong> (<code>List[int]</code>, <em>optional</em>, defaults to <code>[16, 32, 64, 96, 128, 160, 640]</code>) &#x2014;
The number of channels for the feature maps of the backbone.`,name:"neck_hidden_sizes"},{anchor:"transformers.MobileViTConfig.num_attention_heads",description:`<strong>num_attention_heads</strong> (<code>int</code>, <em>optional</em>, defaults to 4) &#x2014;
Number of attention heads for each attention layer in the Transformer encoder.`,name:"num_attention_heads"},{anchor:"transformers.MobileViTConfig.mlp_ratio",description:`<strong>mlp_ratio</strong> (<code>float</code>, <em>optional</em>, defaults to 2.0) &#x2014;
The ratio of the number of channels in the output of the MLP to the number of channels in the input.`,name:"mlp_ratio"},{anchor:"transformers.MobileViTConfig.expand_ratio",description:`<strong>expand_ratio</strong> (<code>float</code>, <em>optional</em>, defaults to 4.0) &#x2014;
Expansion factor for the MobileNetv2 layers.`,name:"expand_ratio"},{anchor:"transformers.MobileViTConfig.hidden_act",description:`<strong>hidden_act</strong> (<code>str</code> or <code>function</code>, <em>optional</em>, defaults to <code>&quot;silu&quot;</code>) &#x2014;
The non-linear activation function (function or string) in the Transformer encoder and convolution layers.`,name:"hidden_act"},{anchor:"transformers.MobileViTConfig.conv_kernel_size",description:`<strong>conv_kernel_size</strong> (<code>int</code>, <em>optional</em>, defaults to 3) &#x2014;
The size of the convolutional kernel in the MobileViT layer.`,name:"conv_kernel_size"},{anchor:"transformers.MobileViTConfig.output_stride",description:`<strong>output_stride</strong> (<code>int</code>, <code>optional</code>, defaults to 32) &#x2014;
The ratio of the spatial resolution of the output to the resolution of the input image.`,name:"output_stride"},{anchor:"transformers.MobileViTConfig.hidden_dropout_prob",description:`<strong>hidden_dropout_prob</strong> (<code>float</code>, <em>optional</em>, defaults to 0.1) &#x2014;
The dropout probabilitiy for all fully connected layers in the Transformer encoder.`,name:"hidden_dropout_prob"},{anchor:"transformers.MobileViTConfig.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.MobileViTConfig.classifier_dropout_prob",description:`<strong>classifier_dropout_prob</strong> (<code>float</code>, <em>optional</em>, defaults to 0.1) &#x2014;
The dropout ratio for attached classifiers.`,name:"classifier_dropout_prob"},{anchor:"transformers.MobileViTConfig.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.MobileViTConfig.layer_norm_eps",description:`<strong>layer_norm_eps</strong> (<code>float</code>, <em>optional</em>, defaults to 1e-5) &#x2014;
The epsilon used by the layer normalization layers.`,name:"layer_norm_eps"},{anchor:"transformers.MobileViTConfig.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.MobileViTConfig.aspp_out_channels",description:`<strong>aspp_out_channels</strong> (<code>int</code>, <code>optional</code>, defaults to 256) &#x2014;
Number of output channels used in the ASPP layer for semantic segmentation.`,name:"aspp_out_channels"},{anchor:"transformers.MobileViTConfig.atrous_rates",description:`<strong>atrous_rates</strong> (<code>List[int]</code>, <em>optional</em>, defaults to <code>[6, 12, 18]</code>) &#x2014;
Dilation (atrous) factors used in the ASPP layer for semantic segmentation.`,name:"atrous_rates"},{anchor:"transformers.MobileViTConfig.aspp_dropout_prob",description:`<strong>aspp_dropout_prob</strong> (<code>float</code>, <em>optional</em>, defaults to 0.1) &#x2014;
The dropout ratio for the ASPP layer for semantic segmentation.`,name:"aspp_dropout_prob"},{anchor:"transformers.MobileViTConfig.semantic_loss_ignore_index",description:`<strong>semantic_loss_ignore_index</strong> (<code>int</code>, <em>optional</em>, defaults to 255) &#x2014;
The index that is ignored by the loss function of the semantic segmentation model.`,name:"semantic_loss_ignore_index"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/mobilevit/configuration_mobilevit.py#L46"}}),Nt=new Cs({props:{anchor:"transformers.MobileViTConfig.example",$$slots:{default:[lm]},$$scope:{ctx:U}}}),Jo=new mt({}),Wo=new ie({props:{name:"class transformers.MobileViTFeatureExtractor",anchor:"transformers.MobileViTFeatureExtractor",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/mobilevit/feature_extraction_mobilevit.py#L26"}}),So=new ie({props:{name:"__call__",anchor:"transformers.MobileViTFeatureExtractor.__call__",parameters:[{name:"images",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/image_processing_utils.py#L544"}}),No=new ie({props:{name:"post_process_semantic_segmentation",anchor:"transformers.MobileViTFeatureExtractor.post_process_semantic_segmentation",parameters:[{name:"outputs",val:""},{name:"target_sizes",val:": typing.List[typing.Tuple] = None"}],parametersDescription:[{anchor:"transformers.MobileViTFeatureExtractor.post_process_semantic_segmentation.outputs",description:`<strong>outputs</strong> (<a href="/docs/transformers/main/en/model_doc/mobilevit#transformers.MobileViTForSemanticSegmentation">MobileViTForSemanticSegmentation</a>) &#x2014;
Raw outputs of the model.`,name:"outputs"},{anchor:"transformers.MobileViTFeatureExtractor.post_process_semantic_segmentation.target_sizes",description:`<strong>target_sizes</strong> (<code>List[Tuple]</code> of length <code>batch_size</code>, <em>optional</em>) &#x2014;
List of tuples corresponding to the requested final size (height, width) of each prediction. If unset,
predictions will not be resized.`,name:"target_sizes"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/mobilevit/image_processing_mobilevit.py#L295",returnDescription:`
<p><code>List[torch.Tensor]</code> of length <code>batch_size</code>, where each item is a semantic
segmentation map of shape (height, width) corresponding to the target_sizes entry (if <code>target_sizes</code> is
specified). Each entry of each <code>torch.Tensor</code> correspond to a semantic class id.</p>
`,returnType:`
<p>semantic_segmentation</p>
`}}),Po=new mt({}),Ro=new ie({props:{name:"class transformers.MobileViTImageProcessor",anchor:"transformers.MobileViTImageProcessor",parameters:[{name:"do_resize",val:": bool = True"},{name:"size",val:": typing.Dict[str, int] = None"},{name:"resample",val:": Resampling = <Resampling.BILINEAR: 2>"},{name:"do_rescale",val:": bool = True"},{name:"rescale_factor",val:": typing.Union[int, float] = 0.00392156862745098"},{name:"do_center_crop",val:": bool = True"},{name:"crop_size",val:": typing.Dict[str, int] = None"},{name:"do_flip_channel_order",val:": bool = True"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.MobileViTImageProcessor.do_resize",description:`<strong>do_resize</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether to resize the image&#x2019;s (height, width) dimensions to the specified <code>size</code>. Can be overridden by the
<code>do_resize</code> parameter in the <code>preprocess</code> method.`,name:"do_resize"},{anchor:"transformers.MobileViTImageProcessor.size",description:`<strong>size</strong> (<code>Dict[str, int]</code> <em>optional</em>, defaults to <code>{&quot;shortest_edge&quot; -- 224}</code>):
Controls the size of the output image after resizing. Can be overridden by the <code>size</code> parameter in the
<code>preprocess</code> method.`,name:"size"},{anchor:"transformers.MobileViTImageProcessor.resample",description:`<strong>resample</strong> (<code>PILImageResampling</code>, <em>optional</em>, defaults to <code>PILImageResampling.BILINEAR</code>) &#x2014;
Defines the resampling filter to use if resizing the image. Can be overridden by the <code>resample</code> parameter
in the <code>preprocess</code> method.`,name:"resample"},{anchor:"transformers.MobileViTImageProcessor.do_rescale",description:`<strong>do_rescale</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether to rescale the image by the specified scale <code>rescale_factor</code>. Can be overridden by the <code>do_rescale</code>
parameter in the <code>preprocess</code> method.`,name:"do_rescale"},{anchor:"transformers.MobileViTImageProcessor.rescale_factor",description:`<strong>rescale_factor</strong> (<code>int</code> or <code>float</code>, <em>optional</em>, defaults to <code>1/255</code>) &#x2014;
Scale factor to use if rescaling the image. Can be overridden by the <code>rescale_factor</code> parameter in the
<code>preprocess</code> method.`,name:"rescale_factor"},{anchor:"transformers.MobileViTImageProcessor.do_center_crop",description:`<strong>do_center_crop</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether to crop the input at the center. If the input size is smaller than <code>crop_size</code> along any edge, the
image is padded with 0&#x2019;s and then center cropped. Can be overridden by the <code>do_center_crop</code> parameter in
the <code>preprocess</code> method.`,name:"do_center_crop"},{anchor:"transformers.MobileViTImageProcessor.crop_size",description:`<strong>crop_size</strong> (<code>Dict[str, int]</code>, <em>optional</em>, defaults to <code>{&quot;height&quot; -- 256, &quot;width&quot;: 256}</code>):
Desired output size <code>(size[&quot;height&quot;], size[&quot;width&quot;])</code> when applying center-cropping. Can be overridden by
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Whether to flip the color channels from RGB to BGR. Can be overridden by the <code>do_flip_channel_order</code>
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Image to preprocess.`,name:"images"},{anchor:"transformers.MobileViTImageProcessor.preprocess.do_resize",description:`<strong>do_resize</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>self.do_resize</code>) &#x2014;
Whether to resize the image.`,name:"do_resize"},{anchor:"transformers.MobileViTImageProcessor.preprocess.size",description:`<strong>size</strong> (<code>Dict[str, int]</code>, <em>optional</em>, defaults to <code>self.size</code>) &#x2014;
Size of the image after resizing.`,name:"size"},{anchor:"transformers.MobileViTImageProcessor.preprocess.resample",description:`<strong>resample</strong> (<code>int</code>, <em>optional</em>, defaults to <code>self.resample</code>) &#x2014;
Resampling filter to use if resizing the image. This can be one of the enum <code>PILImageResampling</code>, Only
has an effect if <code>do_resize</code> is set to <code>True</code>.`,name:"resample"},{anchor:"transformers.MobileViTImageProcessor.preprocess.do_rescale",description:`<strong>do_rescale</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>self.do_rescale</code>) &#x2014;
Whether to rescale the image by rescale factor.`,name:"do_rescale"},{anchor:"transformers.MobileViTImageProcessor.preprocess.rescale_factor",description:`<strong>rescale_factor</strong> (<code>float</code>, <em>optional</em>, defaults to <code>self.rescale_factor</code>) &#x2014;
Rescale factor to rescale the image by if <code>do_rescale</code> is set to <code>True</code>.`,name:"rescale_factor"},{anchor:"transformers.MobileViTImageProcessor.preprocess.do_center_crop",description:`<strong>do_center_crop</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>self.do_center_crop</code>) &#x2014;
Whether to center crop the image.`,name:"do_center_crop"},{anchor:"transformers.MobileViTImageProcessor.preprocess.crop_size",description:`<strong>crop_size</strong> (<code>Dict[str, int]</code>, <em>optional</em>, defaults to <code>self.crop_size</code>) &#x2014;
Size of the center crop if <code>do_center_crop</code> is set to <code>True</code>.`,name:"crop_size"},{anchor:"transformers.MobileViTImageProcessor.preprocess.do_flip_channel_order",description:`<strong>do_flip_channel_order</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>self.do_flip_channel_order</code>) &#x2014;
Whether to flip the channel order of the image.`,name:"do_flip_channel_order"},{anchor:"transformers.MobileViTImageProcessor.preprocess.return_tensors",description:`<strong>return_tensors</strong> (<code>str</code> or <code>TensorType</code>, <em>optional</em>) &#x2014;
The type of tensors to return. Can be one of:<ul>
<li>Unset: Return a list of <code>np.ndarray</code>.</li>
<li><code>TensorType.TENSORFLOW</code> or <code>&apos;tf&apos;</code>: Return a batch of type <code>tf.Tensor</code>.</li>
<li><code>TensorType.PYTORCH</code> or <code>&apos;pt&apos;</code>: Return a batch of type <code>torch.Tensor</code>.</li>
<li><code>TensorType.NUMPY</code> or <code>&apos;np&apos;</code>: Return a batch of type <code>np.ndarray</code>.</li>
<li><code>TensorType.JAX</code> or <code>&apos;jax&apos;</code>: Return a batch of type <code>jax.numpy.ndarray</code>.</li>
</ul>`,name:"return_tensors"},{anchor:"transformers.MobileViTImageProcessor.preprocess.data_format",description:`<strong>data_format</strong> (<code>ChannelDimension</code> or <code>str</code>, <em>optional</em>, defaults to <code>ChannelDimension.FIRST</code>) &#x2014;
The channel dimension format for the output image. Can be one of:<ul>
<li><code>ChannelDimension.FIRST</code>: image in (num_channels, height, width) format.</li>
<li><code>ChannelDimension.LAST</code>: image in (height, width, num_channels) format.</li>
</ul>`,name:"data_format"},{anchor:"transformers.MobileViTImageProcessor.preprocess.input_data_format",description:`<strong>input_data_format</strong> (<code>ChannelDimension</code> or <code>str</code>, <em>optional</em>) &#x2014;
The channel dimension format for the input image. If unset, the channel dimension format is inferred
from the input image. Can be one of:<ul>
<li><code>&quot;channels_first&quot;</code> or <code>ChannelDimension.FIRST</code>: image in (num_channels, height, width) format.</li>
<li><code>&quot;channels_last&quot;</code> or <code>ChannelDimension.LAST</code>: image in (height, width, num_channels) format.</li>
<li><code>&quot;none&quot;</code> or <code>ChannelDimension.NONE</code>: image in (height, width) format.</li>
</ul>`,name:"input_data_format"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/mobilevit/image_processing_mobilevit.py#L171"}}),Bo=new ie({props:{name:"post_process_semantic_segmentation",anchor:"transformers.MobileViTImageProcessor.post_process_semantic_segmentation",parameters:[{name:"outputs",val:""},{name:"target_sizes",val:": typing.List[typing.Tuple] = None"}],parametersDescription:[{anchor:"transformers.MobileViTImageProcessor.post_process_semantic_segmentation.outputs",description:`<strong>outputs</strong> (<a href="/docs/transformers/main/en/model_doc/mobilevit#transformers.MobileViTForSemanticSegmentation">MobileViTForSemanticSegmentation</a>) &#x2014;
Raw outputs of the model.`,name:"outputs"},{anchor:"transformers.MobileViTImageProcessor.post_process_semantic_segmentation.target_sizes",description:`<strong>target_sizes</strong> (<code>List[Tuple]</code> of length <code>batch_size</code>, <em>optional</em>) &#x2014;
List of tuples corresponding to the requested final size (height, width) of each prediction. If unset,
predictions will not be resized.`,name:"target_sizes"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/mobilevit/image_processing_mobilevit.py#L295",returnDescription:`
<p><code>List[torch.Tensor]</code> of length <code>batch_size</code>, where each item is a semantic
segmentation map of shape (height, width) corresponding to the target_sizes entry (if <code>target_sizes</code> is
specified). Each entry of each <code>torch.Tensor</code> correspond to a semantic class id.</p>
`,returnType:`
<p>semantic_segmentation</p>
`}}),Do=new mt({}),Xo=new ie({props:{name:"class transformers.MobileViTModel",anchor:"transformers.MobileViTModel",parameters:[{name:"config",val:": MobileViTConfig"},{name:"expand_output",val:": bool = True"}],parametersDescription:[{anchor:"transformers.MobileViTModel.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/model_doc/mobilevit#transformers.MobileViTConfig">MobileViTConfig</a>) &#x2014; Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the <a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/mobilevit/modeling_mobilevit.py#L708"}}),Yo=new ie({props:{name:"forward",anchor:"transformers.MobileViTModel.forward",parameters:[{name:"pixel_values",val:": typing.Optional[torch.Tensor] = None"},{name:"output_hidden_states",val:": typing.Optional[bool] = None"},{name:"return_dict",val:": typing.Optional[bool] = None"}],parametersDescription:[{anchor:"transformers.MobileViTModel.forward.pixel_values",description:`<strong>pixel_values</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, num_channels, height, width)</code>) &#x2014;
Pixel values. Pixel values can be obtained using <a href="/docs/transformers/main/en/model_doc/auto#transformers.AutoImageProcessor">AutoImageProcessor</a>. See
<a href="/docs/transformers/main/en/model_doc/donut#transformers.DonutFeatureExtractor.__call__">MobileViTImageProcessor.<strong>call</strong>()</a> for details.`,name:"pixel_values"},{anchor:"transformers.MobileViTModel.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.MobileViTModel.forward.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/main/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/mobilevit/modeling_mobilevit.py#L745",returnDescription:`
<p>A <code>transformers.modeling_outputs.BaseModelOutputWithPoolingAndNoAttention</code> or a tuple of
<code>torch.FloatTensor</code> (if <code>return_dict=False</code> is passed or when <code>config.return_dict=False</code>) comprising various
elements depending on the configuration (<a
  href="/docs/transformers/main/en/model_doc/mobilevit#transformers.MobileViTConfig"
>MobileViTConfig</a>) and inputs.</p>
<ul>
<li>
<p><strong>last_hidden_state</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, num_channels, height, width)</code>) \u2014 Sequence of hidden-states at the output of the last layer of the model.</p>
</li>
<li>
<p><strong>pooler_output</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, hidden_size)</code>) \u2014 Last layer hidden-state after a pooling operation on the spatial dimensions.</p>
</li>
<li>
<p><strong>hidden_states</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape <code>(batch_size, num_channels, height, width)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.</p>
</li>
</ul>
`,returnType:`
<p><code>transformers.modeling_outputs.BaseModelOutputWithPoolingAndNoAttention</code> or <code>tuple(torch.FloatTensor)</code></p>
`}}),Xt=new Zt({props:{$$slots:{default:[cm]},$$scope:{ctx:U}}}),Ot=new Cs({props:{anchor:"transformers.MobileViTModel.forward.example",$$slots:{default:[dm]},$$scope:{ctx:U}}}),qo=new mt({}),Qo=new ie({props:{name:"class transformers.MobileViTForImageClassification",anchor:"transformers.MobileViTForImageClassification",parameters:[{name:"config",val:": MobileViTConfig"}],parametersDescription:[{anchor:"transformers.MobileViTForImageClassification.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/model_doc/mobilevit#transformers.MobileViTConfig">MobileViTConfig</a>) &#x2014; Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the <a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/mobilevit/modeling_mobilevit.py#L802"}}),es=new ie({props:{name:"forward",anchor:"transformers.MobileViTForImageClassification.forward",parameters:[{name:"pixel_values",val:": typing.Optional[torch.Tensor] = None"},{name:"output_hidden_states",val:": typing.Optional[bool] = None"},{name:"labels",val:": typing.Optional[torch.Tensor] = None"},{name:"return_dict",val:": typing.Optional[bool] = None"}],parametersDescription:[{anchor:"transformers.MobileViTForImageClassification.forward.pixel_values",description:`<strong>pixel_values</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, num_channels, height, width)</code>) &#x2014;
Pixel values. Pixel values can be obtained using <a href="/docs/transformers/main/en/model_doc/auto#transformers.AutoImageProcessor">AutoImageProcessor</a>. See
<a href="/docs/transformers/main/en/model_doc/donut#transformers.DonutFeatureExtractor.__call__">MobileViTImageProcessor.<strong>call</strong>()</a> for details.`,name:"pixel_values"},{anchor:"transformers.MobileViTForImageClassification.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.MobileViTForImageClassification.forward.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/main/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"},{anchor:"transformers.MobileViTForImageClassification.forward.labels",description:`<strong>labels</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size,)</code>, <em>optional</em>) &#x2014;
Labels for computing the image classification/regression loss. Indices should be in <code>[0, ..., config.num_labels - 1]</code>. If <code>config.num_labels == 1</code> a regression loss is computed (Mean-Square loss). If
<code>config.num_labels &gt; 1</code> a classification loss is computed (Cross-Entropy).`,name:"labels"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/mobilevit/modeling_mobilevit.py#L818",returnDescription:`
<p>A <a
  href="/docs/transformers/main/en/main_classes/output#transformers.modeling_outputs.ImageClassifierOutputWithNoAttention"
>transformers.modeling_outputs.ImageClassifierOutputWithNoAttention</a> or a tuple of
<code>torch.FloatTensor</code> (if <code>return_dict=False</code> is passed or when <code>config.return_dict=False</code>) comprising various
elements depending on the configuration (<a
  href="/docs/transformers/main/en/model_doc/mobilevit#transformers.MobileViTConfig"
>MobileViTConfig</a>) and inputs.</p>
<ul>
<li><strong>loss</strong> (<code>torch.FloatTensor</code> of shape <code>(1,)</code>, <em>optional</em>, returned when <code>labels</code> is provided) \u2014 Classification (or regression if config.num_labels==1) loss.</li>
<li><strong>logits</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, config.num_labels)</code>) \u2014 Classification (or regression if config.num_labels==1) scores (before SoftMax).</li>
<li><strong>hidden_states</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each stage) of shape <code>(batch_size, num_channels, height, width)</code>. Hidden-states (also
called feature maps) of the model at the output of each stage.</li>
</ul>
`,returnType:`
<p><a
  href="/docs/transformers/main/en/main_classes/output#transformers.modeling_outputs.ImageClassifierOutputWithNoAttention"
>transformers.modeling_outputs.ImageClassifierOutputWithNoAttention</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),Yt=new Zt({props:{$$slots:{default:[mm]},$$scope:{ctx:U}}}),qt=new Cs({props:{anchor:"transformers.MobileViTForImageClassification.forward.example",$$slots:{default:[pm]},$$scope:{ctx:U}}}),ts=new mt({}),os=new ie({props:{name:"class transformers.MobileViTForSemanticSegmentation",anchor:"transformers.MobileViTForSemanticSegmentation",parameters:[{name:"config",val:": MobileViTConfig"}],parametersDescription:[{anchor:"transformers.MobileViTForSemanticSegmentation.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/model_doc/mobilevit#transformers.MobileViTConfig">MobileViTConfig</a>) &#x2014; Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the <a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/mobilevit/modeling_mobilevit.py#L997"}}),ns=new ie({props:{name:"forward",anchor:"transformers.MobileViTForSemanticSegmentation.forward",parameters:[{name:"pixel_values",val:": typing.Optional[torch.Tensor] = None"},{name:"labels",val:": typing.Optional[torch.Tensor] = None"},{name:"output_hidden_states",val:": typing.Optional[bool] = None"},{name:"return_dict",val:": typing.Optional[bool] = None"}],parametersDescription:[{anchor:"transformers.MobileViTForSemanticSegmentation.forward.pixel_values",description:`<strong>pixel_values</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, num_channels, height, width)</code>) &#x2014;
Pixel values. Pixel values can be obtained using <a href="/docs/transformers/main/en/model_doc/auto#transformers.AutoImageProcessor">AutoImageProcessor</a>. See
<a href="/docs/transformers/main/en/model_doc/donut#transformers.DonutFeatureExtractor.__call__">MobileViTImageProcessor.<strong>call</strong>()</a> for details.`,name:"pixel_values"},{anchor:"transformers.MobileViTForSemanticSegmentation.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.MobileViTForSemanticSegmentation.forward.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/main/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"},{anchor:"transformers.MobileViTForSemanticSegmentation.forward.labels",description:`<strong>labels</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, height, width)</code>, <em>optional</em>) &#x2014;
Ground truth semantic segmentation maps for computing the loss. Indices should be in <code>[0, ..., config.num_labels - 1]</code>. If <code>config.num_labels &gt; 1</code>, a classification loss is computed (Cross-Entropy).`,name:"labels"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/mobilevit/modeling_mobilevit.py#L1008",returnDescription:`
<p>A <a
  href="/docs/transformers/main/en/main_classes/output#transformers.modeling_outputs.SemanticSegmenterOutput"
>transformers.modeling_outputs.SemanticSegmenterOutput</a> or a tuple of
<code>torch.FloatTensor</code> (if <code>return_dict=False</code> is passed or when <code>config.return_dict=False</code>) comprising various
elements depending on the configuration (<a
  href="/docs/transformers/main/en/model_doc/mobilevit#transformers.MobileViTConfig"
>MobileViTConfig</a>) and inputs.</p>
<ul>
<li>
<p><strong>loss</strong> (<code>torch.FloatTensor</code> of shape <code>(1,)</code>, <em>optional</em>, returned when <code>labels</code> is provided) \u2014 Classification (or regression if config.num_labels==1) loss.</p>
</li>
<li>
<p><strong>logits</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, config.num_labels, logits_height, logits_width)</code>) \u2014 Classification scores for each pixel.</p>
<Tip warning={true}>
<p>The logits returned do not necessarily have the same size as the <code>pixel_values</code> passed as inputs. This is
to avoid doing two interpolations and lose some quality when a user needs to resize the logits to the
original image size as post-processing. You should always check your logits shape and resize as needed.</p>
</Tip>
</li>
<li>
<p><strong>hidden_states</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape <code>(batch_size, patch_size, hidden_size)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.</p>
</li>
<li>
<p><strong>attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_heads, patch_size, sequence_length)</code>.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.</p>
</li>
</ul>
`,returnType:`
<p><a
  href="/docs/transformers/main/en/main_classes/output#transformers.modeling_outputs.SemanticSegmenterOutput"
>transformers.modeling_outputs.SemanticSegmenterOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),Ht=new Zt({props:{$$slots:{default:[hm]},$$scope:{ctx:U}}}),Kt=new Cs({props:{anchor:"transformers.MobileViTForSemanticSegmentation.forward.example",$$slots:{default:[fm]},$$scope:{ctx:U}}}),rs=new mt({}),is=new ie({props:{name:"class transformers.TFMobileViTModel",anchor:"transformers.TFMobileViTModel",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFMobileViTModel.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/model_doc/mobilevit#transformers.MobileViTConfig">MobileViTConfig</a>) &#x2014; Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the <a href="/docs/transformers/main/en/main_classes/model#transformers.TFPreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/mobilevit/modeling_tf_mobilevit.py#L800"}}),to=new Zt({props:{$$slots:{default:[um]},$$scope:{ctx:U}}}),ms=new ie({props:{name:"call",anchor:"transformers.TFMobileViTModel.call",parameters:[{name:"pixel_values",val:": tf.Tensor | None = None"},{name:"output_hidden_states",val:": Optional[bool] = None"},{name:"return_dict",val:": Optional[bool] = None"},{name:"training",val:": bool = False"}],parametersDescription:[{anchor:"transformers.TFMobileViTModel.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/main/en/model_doc/auto#transformers.AutoImageProcessor">AutoImageProcessor</a>. See
<a href="/docs/transformers/main/en/model_doc/donut#transformers.DonutFeatureExtractor.__call__">MobileViTImageProcessor.<strong>call</strong>()</a> for details.`,name:"pixel_values"},{anchor:"transformers.TFMobileViTModel.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.TFMobileViTModel.call.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/main/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple. This argument can be used in
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<p>A <a
  href="/docs/transformers/main/en/main_classes/output#transformers.modeling_tf_outputs.TFBaseModelOutputWithPooling"
>transformers.modeling_tf_outputs.TFBaseModelOutputWithPooling</a> or a tuple of <code>tf.Tensor</code> (if
<code>return_dict=False</code> is passed or when <code>config.return_dict=False</code>) comprising various elements depending on the
configuration (<a
  href="/docs/transformers/main/en/model_doc/mobilevit#transformers.MobileViTConfig"
>MobileViTConfig</a>) and inputs.</p>
<ul>
<li>
<p><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.</p>
</li>
<li>
<p><strong>pooler_output</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, hidden_size)</code>) \u2014 Last layer hidden-state of the first token of the sequence (classification token) further processed by a
Linear layer and a Tanh activation function. The Linear layer weights are trained from the next sentence
prediction (classification) objective during pretraining.</p>
<p>This output is usually <em>not</em> a good summary of the semantic content of the input, you\u2019re often better with
averaging or pooling the sequence of hidden-states for the whole input sequence.</p>
</li>
<li>
<p><strong>hidden_states</strong> (<code>tuple(tf.Tensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) \u2014 Tuple of <code>tf.Tensor</code> (one for the output of the embeddings + one for the output of each layer) of shape
<code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the initial embedding outputs.</p>
</li>
<li>
<p><strong>attentions</strong> (<code>tuple(tf.Tensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) \u2014 Tuple of <code>tf.Tensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.</p>
</li>
</ul>
`,returnType:`
<p><a
  href="/docs/transformers/main/en/main_classes/output#transformers.modeling_tf_outputs.TFBaseModelOutputWithPooling"
>transformers.modeling_tf_outputs.TFBaseModelOutputWithPooling</a> or <code>tuple(tf.Tensor)</code></p>
`}}),oo=new Zt({props:{$$slots:{default:[gm]},$$scope:{ctx:U}}}),so=new Cs({props:{anchor:"transformers.TFMobileViTModel.call.example",$$slots:{default:[bm]},$$scope:{ctx:U}}}),ps=new mt({}),hs=new ie({props:{name:"class transformers.TFMobileViTForImageClassification",anchor:"transformers.TFMobileViTForImageClassification",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFMobileViTForImageClassification.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/model_doc/mobilevit#transformers.MobileViTConfig">MobileViTConfig</a>) &#x2014; Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
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Labels for computing the image classification/regression loss. Indices should be in <code>[0, ..., config.num_labels - 1]</code>. If <code>config.num_labels == 1</code> a regression loss is computed (Mean-Square loss). If
<code>config.num_labels &gt; 1</code> a classification loss is computed (Cross-Entropy).`,name:"labels"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/mobilevit/modeling_tf_mobilevit.py#L848",returnDescription:`
<p>A <code>transformers.modeling_tf_outputs.TFImageClassifierOutputWithNoAttention</code> or a tuple of <code>tf.Tensor</code> (if
<code>return_dict=False</code> is passed or when <code>config.return_dict=False</code>) comprising various elements depending on the
configuration (<a
  href="/docs/transformers/main/en/model_doc/mobilevit#transformers.MobileViTConfig"
>MobileViTConfig</a>) and inputs.</p>
<ul>
<li><strong>loss</strong> (<code>tf.Tensor</code> of shape <code>(1,)</code>, <em>optional</em>, returned when <code>labels</code> is provided) \u2014 Classification (or regression if config.num_labels==1) loss.</li>
<li><strong>logits</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, config.num_labels)</code>) \u2014 Classification (or regression if config.num_labels==1) scores (before SoftMax).</li>
<li><strong>hidden_states</strong> (<code>tuple(tf.Tensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) \u2014 Tuple of <code>tf.Tensor</code> (one for the output of the embeddings, if the model has an embedding layer, + one for
the output of each stage) of shape <code>(batch_size, num_channels, height, width)</code>. Hidden-states (also called
feature maps) of the model at the output of each stage.</li>
</ul>
`,returnType:`
<p><code>transformers.modeling_tf_outputs.TFImageClassifierOutputWithNoAttention</code> or <code>tuple(tf.Tensor)</code></p>
`}}),ro=new Zt({props:{$$slots:{default:[Tm]},$$scope:{ctx:U}}}),io=new Cs({props:{anchor:"transformers.TFMobileViTForImageClassification.call.example",$$slots:{default:[vm]},$$scope:{ctx:U}}}),_s=new mt({}),Ts=new ie({props:{name:"class transformers.TFMobileViTForSemanticSegmentation",anchor:"transformers.TFMobileViTForSemanticSegmentation",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFMobileViTForSemanticSegmentation.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/model_doc/mobilevit#transformers.MobileViTConfig">MobileViTConfig</a>) &#x2014; Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
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Pixel values. Pixel values can be obtained using <a href="/docs/transformers/main/en/model_doc/auto#transformers.AutoImageProcessor">AutoImageProcessor</a>. See
<a href="/docs/transformers/main/en/model_doc/donut#transformers.DonutFeatureExtractor.__call__">MobileViTImageProcessor.<strong>call</strong>()</a> for details.`,name:"pixel_values"},{anchor:"transformers.TFMobileViTForSemanticSegmentation.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.TFMobileViTForSemanticSegmentation.call.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/main/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple. This argument can be used in
eager mode, in graph mode the value will always be set to True.`,name:"return_dict"},{anchor:"transformers.TFMobileViTForSemanticSegmentation.call.labels",description:`<strong>labels</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, height, width)</code>, <em>optional</em>) &#x2014;
Ground truth semantic segmentation maps for computing the loss. Indices should be in <code>[0, ..., config.num_labels - 1]</code>. If <code>config.num_labels &gt; 1</code>, a classification loss is computed (Cross-Entropy).`,name:"labels"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/mobilevit/modeling_tf_mobilevit.py#L1039",returnDescription:`
<p>A <code>transformers.modeling_tf_outputs.TFSemanticSegmenterOutputWithNoAttention</code> or a tuple of <code>tf.Tensor</code> (if
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configuration (<a
  href="/docs/transformers/main/en/model_doc/mobilevit#transformers.MobileViTConfig"
>MobileViTConfig</a>) and inputs.</p>
<ul>
<li>
<p><strong>loss</strong> (<code>tf.Tensor</code> of shape <code>(1,)</code>, <em>optional</em>, returned when <code>labels</code> is provided) \u2014 Classification (or regression if config.num_labels==1) loss.</p>
</li>
<li>
<p><strong>logits</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, config.num_labels, logits_height, logits_width)</code>) \u2014 Classification scores for each pixel.</p>
<Tip warning={true}>
<p>The logits returned do not necessarily have the same size as the <code>pixel_values</code> passed as inputs. This is
to avoid doing two interpolations and lose some quality when a user needs to resize the logits to the
original image size as post-processing. You should always check your logits shape and resize as needed.</p>
</Tip>
</li>
<li>
<p><strong>hidden_states</strong> (<code>tuple(tf.Tensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) \u2014 Tuple of <code>tf.Tensor</code> (one for the output of the embeddings, if the model has an embedding layer, + one for
the output of each layer) of shape <code>(batch_size, patch_size, hidden_size)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.</p>
</li>
</ul>
`,returnType:`
<p><code>transformers.modeling_tf_outputs.TFSemanticSegmenterOutputWithNoAttention</code> or <code>tuple(tf.Tensor)</code></p>
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