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

# Initializing a mobilevit-small style configuration
configuration = MobileViTConfig()

# Initializing a model from the mobilevit-small style configuration
model = MobileViTModel(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> 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(){d=s("p"),T=r("Example:"),_=c(),$(g.$$.fragment)},l(l){d=a(l,"P",{});var h=i(d);T=n(h,"Example:"),h.forEach(t),_=m(l),y(g.$$.fragment,l)},m(l,h){f(l,d,h),e(d,T),f(l,_,h),M(g,l,h),v=!0},p:Is,i(l){v||(E(g.$$.fragment,l),v=!0)},o(l){V(g.$$.fragment,l),v=!1},d(l){l&&t(d),l&&t(_),k(g,l)}}}function dm(C){let d,T,_,g,v;return{c(){d=s("p"),T=r("Although the recipe for forward pass needs to be defined within this function, one should call the "),_=s("code"),g=r("Module"),v=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=a(l,"P",{});var h=i(d);T=n(h,"Although the recipe for forward pass needs to be defined within this function, one should call the "),_=a(h,"CODE",{});var P=i(_);g=n(P,"Module"),P.forEach(t),v=n(h,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),h.forEach(t)},m(l,h){f(l,d,h),e(d,T),e(d,_),e(_,g),e(d,v)},d(l){l&&t(d)}}}function cm(C){let d,T,_,g,v;return g=new Mo({props:{code:`from transformers import AutoImageProcessor, MobileViTModel
import torch
from datasets import load_dataset

dataset = load_dataset("huggingface/cats-image")
image = dataset["test"]["image"][0]

image_processor = AutoImageProcessor.from_pretrained("apple/mobilevit-small")
model = MobileViTModel.from_pretrained("apple/mobilevit-small")

inputs = image_processor(image, return_tensors="pt")

with torch.no_grad():
    outputs = model(**inputs)

last_hidden_states = outputs.last_hidden_state
list(last_hidden_states.shape)`,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(){d=s("p"),T=r("Example:"),_=c(),$(g.$$.fragment)},l(l){d=a(l,"P",{});var h=i(d);T=n(h,"Example:"),h.forEach(t),_=m(l),y(g.$$.fragment,l)},m(l,h){f(l,d,h),e(d,T),f(l,_,h),M(g,l,h),v=!0},p:Is,i(l){v||(E(g.$$.fragment,l),v=!0)},o(l){V(g.$$.fragment,l),v=!1},d(l){l&&t(d),l&&t(_),k(g,l)}}}function mm(C){let d,T,_,g,v;return{c(){d=s("p"),T=r("Although the recipe for forward pass needs to be defined within this function, one should call the "),_=s("code"),g=r("Module"),v=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=a(l,"P",{});var h=i(d);T=n(h,"Although the recipe for forward pass needs to be defined within this function, one should call the "),_=a(h,"CODE",{});var P=i(_);g=n(P,"Module"),P.forEach(t),v=n(h,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),h.forEach(t)},m(l,h){f(l,d,h),e(d,T),e(d,_),e(_,g),e(d,v)},d(l){l&&t(d)}}}function pm(C){let d,T,_,g,v;return g=new Mo({props:{code:`from transformers import AutoImageProcessor, MobileViTForImageClassification
import torch
from datasets import load_dataset

dataset = load_dataset("huggingface/cats-image")
image = dataset["test"]["image"][0]

image_processor = AutoImageProcessor.from_pretrained("apple/mobilevit-small")
model = MobileViTForImageClassification.from_pretrained("apple/mobilevit-small")

inputs = image_processor(image, return_tensors="pt")

with torch.no_grad():
    logits = model(**inputs).logits

# model predicts one of the 1000 ImageNet classes
predicted_label = logits.argmax(-1).item()
print(model.config.id2label[predicted_label])`,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(){d=s("p"),T=r("Example:"),_=c(),$(g.$$.fragment)},l(l){d=a(l,"P",{});var h=i(d);T=n(h,"Example:"),h.forEach(t),_=m(l),y(g.$$.fragment,l)},m(l,h){f(l,d,h),e(d,T),f(l,_,h),M(g,l,h),v=!0},p:Is,i(l){v||(E(g.$$.fragment,l),v=!0)},o(l){V(g.$$.fragment,l),v=!1},d(l){l&&t(d),l&&t(_),k(g,l)}}}function hm(C){let d,T,_,g,v;return{c(){d=s("p"),T=r("Although the recipe for forward pass needs to be defined within this function, one should call the "),_=s("code"),g=r("Module"),v=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=a(l,"P",{});var h=i(d);T=n(h,"Although the recipe for forward pass needs to be defined within this function, one should call the "),_=a(h,"CODE",{});var P=i(_);g=n(P,"Module"),P.forEach(t),v=n(h,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),h.forEach(t)},m(l,h){f(l,d,h),e(d,T),e(d,_),e(_,g),e(d,v)},d(l){l&&t(d)}}}function fm(C){let d,T,_,g,v;return g=new Mo({props:{code:`from transformers import AutoImageProcessor, MobileViTForSemanticSegmentation
from PIL import Image
import requests

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

image_processor = AutoImageProcessor.from_pretrained("apple/deeplabv3-mobilevit-small")
model = MobileViTForSemanticSegmentation.from_pretrained("apple/deeplabv3-mobilevit-small")

inputs = image_processor(images=image, return_tensors="pt")

with torch.no_grad():
    outputs = model(**inputs)

# logits are of shape (batch_size, num_labels, height, width)
logits = outputs.logits`,highlighted:`<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><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 = 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(){d=s("p"),T=r("Examples:"),_=c(),$(g.$$.fragment)},l(l){d=a(l,"P",{});var h=i(d);T=n(h,"Examples:"),h.forEach(t),_=m(l),y(g.$$.fragment,l)},m(l,h){f(l,d,h),e(d,T),f(l,_,h),M(g,l,h),v=!0},p:Is,i(l){v||(E(g.$$.fragment,l),v=!0)},o(l){V(g.$$.fragment,l),v=!1},d(l){l&&t(d),l&&t(_),k(g,l)}}}function um(C){let d,T,_,g,v,l,h,P,Fe,_e,O,ee,te,w,Ie,B,Pe,be,D,xe,oe,Y,Ce,ve,L,je,Te,R,ce,Se,se,x,S,we,j,me,ae,J,re,Ae,A,ze,K,ne,Oe,W,Ne,De,U,pe,q,qe,G,z,Le,N,he,X;return{c(){d=s("p"),T=r("TensorFlow models and layers in "),_=s("code"),g=r("transformers"),v=r(" accept two formats as input:"),l=c(),h=s("ul"),P=s("li"),Fe=r("having all inputs as keyword arguments (like PyTorch models), or"),_e=c(),O=s("li"),ee=r("having all inputs as a list, tuple or dict in the first positional argument."),te=c(),w=s("p"),Ie=r(`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 `),B=s("code"),Pe=r("model.fit()"),be=r(` things should \u201Cjust work\u201D for you - just
pass your inputs and labels in any format that `),D=s("code"),xe=r("model.fit()"),oe=r(` supports! If, however, you want to use the second
format outside of Keras methods like `),Y=s("code"),Ce=r("fit()"),ve=r(" and "),L=s("code"),je=r("predict()"),Te=r(`, such as when creating your own layers or models with
the Keras `),R=s("code"),ce=r("Functional"),Se=r(` API, there are three possibilities you can use to gather all the input Tensors in the first
positional argument:`),se=c(),x=s("ul"),S=s("li"),we=r("a single Tensor with "),j=s("code"),me=r("pixel_values"),ae=r(" only and nothing else: "),J=s("code"),re=r("model(pixel_values)"),Ae=c(),A=s("li"),ze=r(`a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
`),K=s("code"),ne=r("model([pixel_values, attention_mask])"),Oe=r(" or "),W=s("code"),Ne=r("model([pixel_values, attention_mask, token_type_ids])"),De=c(),U=s("li"),pe=r(`a dictionary with one or several input Tensors associated to the input names given in the docstring:
`),q=s("code"),qe=r('model({"pixel_values": pixel_values, "token_type_ids": token_type_ids})'),G=c(),z=s("p"),Le=r(`Note that when creating models and layers with
`),N=s("a"),he=r("subclassing"),X=r(` 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(b){d=a(b,"P",{});var F=i(d);T=n(F,"TensorFlow models and layers in "),_=a(F,"CODE",{});var Xe=i(_);g=n(Xe,"transformers"),Xe.forEach(t),v=n(F," accept two formats as input:"),F.forEach(t),l=m(b),h=a(b,"UL",{});var le=i(h);P=a(le,"LI",{});var $e=i(P);Fe=n($e,"having all inputs as keyword arguments (like PyTorch models), or"),$e.forEach(t),_e=m(le),O=a(le,"LI",{});var Qe=i(O);ee=n(Qe,"having all inputs as a list, tuple or dict in the first positional argument."),Qe.forEach(t),le.forEach(t),te=m(b),w=a(b,"P",{});var I=i(w);Ie=n(I,`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 `),B=a(I,"CODE",{});var Ze=i(B);Pe=n(Ze,"model.fit()"),Ze.forEach(t),be=n(I,` things should \u201Cjust work\u201D for you - just
pass your inputs and labels in any format that `),D=a(I,"CODE",{});var ye=i(D);xe=n(ye,"model.fit()"),ye.forEach(t),oe=n(I,` supports! If, however, you want to use the second
format outside of Keras methods like `),Y=a(I,"CODE",{});var Me=i(Y);Ce=n(Me,"fit()"),Me.forEach(t),ve=n(I," and "),L=a(I,"CODE",{});var et=i(L);je=n(et,"predict()"),et.forEach(t),Te=n(I,`, such as when creating your own layers or models with
the Keras `),R=a(I,"CODE",{});var Ee=i(R);ce=n(Ee,"Functional"),Ee.forEach(t),Se=n(I,` API, there are three possibilities you can use to gather all the input Tensors in the first
positional argument:`),I.forEach(t),se=m(b),x=a(b,"UL",{});var H=i(x);S=a(H,"LI",{});var Q=i(S);we=n(Q,"a single Tensor with "),j=a(Q,"CODE",{});var tt=i(j);me=n(tt,"pixel_values"),tt.forEach(t),ae=n(Q," only and nothing else: "),J=a(Q,"CODE",{});var We=i(J);re=n(We,"model(pixel_values)"),We.forEach(t),Q.forEach(t),Ae=m(H),A=a(H,"LI",{});var Z=i(A);ze=n(Z,`a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
`),K=a(Z,"CODE",{});var He=i(K);ne=n(He,"model([pixel_values, attention_mask])"),He.forEach(t),Oe=n(Z," or "),W=a(Z,"CODE",{});var Ve=i(W);Ne=n(Ve,"model([pixel_values, attention_mask, token_type_ids])"),Ve.forEach(t),Z.forEach(t),De=m(H),U=a(H,"LI",{});var ke=i(U);pe=n(ke,`a dictionary with one or several input Tensors associated to the input names given in the docstring:
`),q=a(ke,"CODE",{});var fe=i(q);qe=n(fe,'model({"pixel_values": pixel_values, "token_type_ids": token_type_ids})'),fe.forEach(t),ke.forEach(t),H.forEach(t),G=m(b),z=a(b,"P",{});var de=i(z);Le=n(de,`Note that when creating models and layers with
`),N=a(de,"A",{href:!0,rel:!0});var Ue=i(N);he=n(Ue,"subclassing"),Ue.forEach(t),X=n(de,` 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!`),de.forEach(t),this.h()},h(){p(N,"href","https://keras.io/guides/making_new_layers_and_models_via_subclassing/"),p(N,"rel","nofollow")},m(b,F){f(b,d,F),e(d,T),e(d,_),e(_,g),e(d,v),f(b,l,F),f(b,h,F),e(h,P),e(P,Fe),e(h,_e),e(h,O),e(O,ee),f(b,te,F),f(b,w,F),e(w,Ie),e(w,B),e(B,Pe),e(w,be),e(w,D),e(D,xe),e(w,oe),e(w,Y),e(Y,Ce),e(w,ve),e(w,L),e(L,je),e(w,Te),e(w,R),e(R,ce),e(w,Se),f(b,se,F),f(b,x,F),e(x,S),e(S,we),e(S,j),e(j,me),e(S,ae),e(S,J),e(J,re),e(x,Ae),e(x,A),e(A,ze),e(A,K),e(K,ne),e(A,Oe),e(A,W),e(W,Ne),e(x,De),e(x,U),e(U,pe),e(U,q),e(q,qe),f(b,G,F),f(b,z,F),e(z,Le),e(z,N),e(N,he),e(z,X)},d(b){b&&t(d),b&&t(l),b&&t(h),b&&t(te),b&&t(w),b&&t(se),b&&t(x),b&&t(G),b&&t(z)}}}function gm(C){let d,T,_,g,v;return{c(){d=s("p"),T=r("Although the recipe for forward pass needs to be defined within this function, one should call the "),_=s("code"),g=r("Module"),v=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=a(l,"P",{});var h=i(d);T=n(h,"Although the recipe for forward pass needs to be defined within this function, one should call the "),_=a(h,"CODE",{});var P=i(_);g=n(P,"Module"),P.forEach(t),v=n(h,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),h.forEach(t)},m(l,h){f(l,d,h),e(d,T),e(d,_),e(_,g),e(d,v)},d(l){l&&t(d)}}}function _m(C){let d,T,_,g,v;return g=new Mo({props:{code:`from transformers import AutoImageProcessor, TFMobileViTModel
from datasets import load_dataset

dataset = load_dataset("huggingface/cats-image")
image = dataset["test"]["image"][0]

image_processor = AutoImageProcessor.from_pretrained("apple/mobilevit-small")
model = TFMobileViTModel.from_pretrained("apple/mobilevit-small")

inputs = image_processor(image, return_tensors="tf")
outputs = model(**inputs)

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

dataset = load_dataset("huggingface/cats-image")
image = dataset["test"]["image"][0]

image_processor = AutoImageProcessor.from_pretrained("apple/mobilevit-small")
model = TFMobileViTForImageClassification.from_pretrained("apple/mobilevit-small")

inputs = image_processor(image, return_tensors="tf")
logits = model(**inputs).logits

# model predicts one of the 1000 ImageNet classes
predicted_label = int(tf.math.argmax(logits, axis=-1))
print(model.config.id2label[predicted_label])`,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(){d=s("p"),T=r("Example:"),_=c(),$(g.$$.fragment)},l(l){d=a(l,"P",{});var h=i(d);T=n(h,"Example:"),h.forEach(t),_=m(l),y(g.$$.fragment,l)},m(l,h){f(l,d,h),e(d,T),f(l,_,h),M(g,l,h),v=!0},p:Is,i(l){v||(E(g.$$.fragment,l),v=!0)},o(l){V(g.$$.fragment,l),v=!1},d(l){l&&t(d),l&&t(_),k(g,l)}}}function wm(C){let d,T,_,g,v,l,h,P,Fe,_e,O,ee,te,w,Ie,B,Pe,be,D,xe,oe,Y,Ce,ve,L,je,Te,R,ce,Se,se,x,S,we,j,me,ae,J,re,Ae,A,ze,K,ne,Oe,W,Ne,De,U,pe,q,qe,G,z,Le,N,he,X;return{c(){d=s("p"),T=r("TensorFlow models and layers in "),_=s("code"),g=r("transformers"),v=r(" accept two formats as input:"),l=c(),h=s("ul"),P=s("li"),Fe=r("having all inputs as keyword arguments (like PyTorch models), or"),_e=c(),O=s("li"),ee=r("having all inputs as a list, tuple or dict in the first positional argument."),te=c(),w=s("p"),Ie=r(`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 `),B=s("code"),Pe=r("model.fit()"),be=r(` things should \u201Cjust work\u201D for you - just
pass your inputs and labels in any format that `),D=s("code"),xe=r("model.fit()"),oe=r(` supports! If, however, you want to use the second
format outside of Keras methods like `),Y=s("code"),Ce=r("fit()"),ve=r(" and "),L=s("code"),je=r("predict()"),Te=r(`, such as when creating your own layers or models with
the Keras `),R=s("code"),ce=r("Functional"),Se=r(` API, there are three possibilities you can use to gather all the input Tensors in the first
positional argument:`),se=c(),x=s("ul"),S=s("li"),we=r("a single Tensor with "),j=s("code"),me=r("pixel_values"),ae=r(" only and nothing else: "),J=s("code"),re=r("model(pixel_values)"),Ae=c(),A=s("li"),ze=r(`a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
`),K=s("code"),ne=r("model([pixel_values, attention_mask])"),Oe=r(" or "),W=s("code"),Ne=r("model([pixel_values, attention_mask, token_type_ids])"),De=c(),U=s("li"),pe=r(`a dictionary with one or several input Tensors associated to the input names given in the docstring:
`),q=s("code"),qe=r('model({"pixel_values": pixel_values, "token_type_ids": token_type_ids})'),G=c(),z=s("p"),Le=r(`Note that when creating models and layers with
`),N=s("a"),he=r("subclassing"),X=r(` 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(b){d=a(b,"P",{});var F=i(d);T=n(F,"TensorFlow models and layers in "),_=a(F,"CODE",{});var Xe=i(_);g=n(Xe,"transformers"),Xe.forEach(t),v=n(F," accept two formats as input:"),F.forEach(t),l=m(b),h=a(b,"UL",{});var le=i(h);P=a(le,"LI",{});var $e=i(P);Fe=n($e,"having all inputs as keyword arguments (like PyTorch models), or"),$e.forEach(t),_e=m(le),O=a(le,"LI",{});var Qe=i(O);ee=n(Qe,"having all inputs as a list, tuple or dict in the first positional argument."),Qe.forEach(t),le.forEach(t),te=m(b),w=a(b,"P",{});var I=i(w);Ie=n(I,`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 `),B=a(I,"CODE",{});var Ze=i(B);Pe=n(Ze,"model.fit()"),Ze.forEach(t),be=n(I,` things should \u201Cjust work\u201D for you - just
pass your inputs and labels in any format that `),D=a(I,"CODE",{});var ye=i(D);xe=n(ye,"model.fit()"),ye.forEach(t),oe=n(I,` supports! If, however, you want to use the second
format outside of Keras methods like `),Y=a(I,"CODE",{});var Me=i(Y);Ce=n(Me,"fit()"),Me.forEach(t),ve=n(I," and "),L=a(I,"CODE",{});var et=i(L);je=n(et,"predict()"),et.forEach(t),Te=n(I,`, such as when creating your own layers or models with
the Keras `),R=a(I,"CODE",{});var Ee=i(R);ce=n(Ee,"Functional"),Ee.forEach(t),Se=n(I,` API, there are three possibilities you can use to gather all the input Tensors in the first
positional argument:`),I.forEach(t),se=m(b),x=a(b,"UL",{});var H=i(x);S=a(H,"LI",{});var Q=i(S);we=n(Q,"a single Tensor with "),j=a(Q,"CODE",{});var tt=i(j);me=n(tt,"pixel_values"),tt.forEach(t),ae=n(Q," only and nothing else: "),J=a(Q,"CODE",{});var We=i(J);re=n(We,"model(pixel_values)"),We.forEach(t),Q.forEach(t),Ae=m(H),A=a(H,"LI",{});var Z=i(A);ze=n(Z,`a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
`),K=a(Z,"CODE",{});var He=i(K);ne=n(He,"model([pixel_values, attention_mask])"),He.forEach(t),Oe=n(Z," or "),W=a(Z,"CODE",{});var Ve=i(W);Ne=n(Ve,"model([pixel_values, attention_mask, token_type_ids])"),Ve.forEach(t),Z.forEach(t),De=m(H),U=a(H,"LI",{});var ke=i(U);pe=n(ke,`a dictionary with one or several input Tensors associated to the input names given in the docstring:
`),q=a(ke,"CODE",{});var fe=i(q);qe=n(fe,'model({"pixel_values": pixel_values, "token_type_ids": token_type_ids})'),fe.forEach(t),ke.forEach(t),H.forEach(t),G=m(b),z=a(b,"P",{});var de=i(z);Le=n(de,`Note that when creating models and layers with
`),N=a(de,"A",{href:!0,rel:!0});var Ue=i(N);he=n(Ue,"subclassing"),Ue.forEach(t),X=n(de,` 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!`),de.forEach(t),this.h()},h(){p(N,"href","https://keras.io/guides/making_new_layers_and_models_via_subclassing/"),p(N,"rel","nofollow")},m(b,F){f(b,d,F),e(d,T),e(d,_),e(_,g),e(d,v),f(b,l,F),f(b,h,F),e(h,P),e(P,Fe),e(h,_e),e(h,O),e(O,ee),f(b,te,F),f(b,w,F),e(w,Ie),e(w,B),e(B,Pe),e(w,be),e(w,D),e(D,xe),e(w,oe),e(w,Y),e(Y,Ce),e(w,ve),e(w,L),e(L,je),e(w,Te),e(w,R),e(R,ce),e(w,Se),f(b,se,F),f(b,x,F),e(x,S),e(S,we),e(S,j),e(j,me),e(S,ae),e(S,J),e(J,re),e(x,Ae),e(x,A),e(A,ze),e(A,K),e(K,ne),e(A,Oe),e(A,W),e(W,Ne),e(x,De),e(x,U),e(U,pe),e(U,q),e(q,qe),f(b,G,F),f(b,z,F),e(z,Le),e(z,N),e(N,he),e(z,X)},d(b){b&&t(d),b&&t(l),b&&t(h),b&&t(te),b&&t(w),b&&t(se),b&&t(x),b&&t(G),b&&t(z)}}}function $m(C){let d,T,_,g,v;return{c(){d=s("p"),T=r("Although the recipe for forward pass needs to be defined within this function, one should call the "),_=s("code"),g=r("Module"),v=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=a(l,"P",{});var h=i(d);T=n(h,"Although the recipe for forward pass needs to be defined within this function, one should call the "),_=a(h,"CODE",{});var P=i(_);g=n(P,"Module"),P.forEach(t),v=n(h,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),h.forEach(t)},m(l,h){f(l,d,h),e(d,T),e(d,_),e(_,g),e(d,v)},d(l){l&&t(d)}}}function ym(C){let d,T,_,g,v;return g=new Mo({props:{code:`from transformers import AutoImageProcessor, TFMobileViTForSemanticSegmentation
from PIL import Image
import requests

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

image_processor = AutoImageProcessor.from_pretrained("apple/deeplabv3-mobilevit-small")
model = TFMobileViTForSemanticSegmentation.from_pretrained("apple/deeplabv3-mobilevit-small")

inputs = image_processor(images=image, return_tensors="tf")

outputs = model(**inputs)

# logits are of shape (batch_size, num_labels, height, width)
logits = outputs.logits`,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(){d=s("p"),T=r("Examples:"),_=c(),$(g.$$.fragment)},l(l){d=a(l,"P",{});var h=i(d);T=n(h,"Examples:"),h.forEach(t),_=m(l),y(g.$$.fragment,l)},m(l,h){f(l,d,h),e(d,T),f(l,_,h),M(g,l,h),v=!0},p:Is,i(l){v||(E(g.$$.fragment,l),v=!0)},o(l){V(g.$$.fragment,l),v=!1},d(l){l&&t(d),l&&t(_),k(g,l)}}}function Mm(C){let d,T,_,g,v,l,h,P,Fe,_e,O,ee,te,w,Ie,B,Pe,be,D,xe,oe,Y,Ce,ve,L,je,Te,R,ce,Se,se,x,S,we,j,me,ae,J,re,Ae,A,ze,K,ne,Oe,W,Ne,De,U,pe,q,qe,G,z,Le,N,he,X,b,F,Xe,le,$e,Qe,I,Ze,ye,Me,et,Ee,H,Q,tt,We,Z,He,Ve,ke,fe,de,Ue,ln,dn,or,ot,cn,Eo,mn,pn,Vo,hn,fn,ko,un,gn,sr,gt,At,da,Fo,_n,ca,bn,ar,Ps,vn,rr,Io,nr,zt,ft,xs,Tn,wn,Po,$n,yn,xo,Mn,En,Vn,Cs,kn,js,Fn,ir,Ss,ma,In,lr,As,pa,zs,Pn,dr,Os,xn,cr,_t,Ot,ha,Co,Cn,fa,jn,mr,Be,jo,Sn,bt,An,Ns,zn,On,So,Nn,Dn,qn,vt,Ln,Ds,Rn,Wn,qs,Un,Hn,Bn,Nt,pr,Tt,Dt,ua,Ao,Kn,ga,Gn,hr,pt,zo,Yn,qt,Oo,Jn,_a,Xn,Qn,Lt,No,Zn,Do,ei,Ls,ti,oi,fr,wt,Rt,ba,qo,si,va,ai,ur,Ke,Lo,ri,Ta,ni,ii,Wt,Ro,li,wa,di,ci,Ut,Wo,mi,Uo,pi,Rs,hi,fi,gr,$t,Ht,$a,Ho,ui,ya,gi,_r,ht,Bo,_i,Ko,bi,Go,vi,Ti,wi,st,Yo,$i,yt,yi,Ws,Mi,Ei,Ma,Vi,ki,Fi,Bt,Ii,Kt,br,Mt,Gt,Ea,Jo,Pi,Va,xi,vr,Ge,Xo,Ci,ka,ji,Si,Qo,Ai,Zo,zi,Oi,Ni,at,es,Di,Et,qi,Us,Li,Ri,Fa,Wi,Ui,Hi,Yt,Bi,Jt,Tr,Vt,Xt,Ia,ts,Ki,Pa,Gi,wr,Ye,os,Yi,xa,Ji,Xi,ss,Qi,as,Zi,el,tl,rt,rs,ol,kt,sl,Hs,al,rl,Ca,nl,il,ll,Qt,dl,Zt,$r,Ft,eo,ja,ns,cl,Sa,ml,yr,Re,is,pl,ls,hl,Bs,fl,ul,gl,ds,_l,cs,bl,vl,Tl,to,wl,nt,ms,$l,It,yl,Ks,Ml,El,Aa,Vl,kl,Fl,oo,Il,so,Mr,Pt,ao,za,ps,Pl,Oa,xl,Er,ue,hs,Cl,Na,jl,Sl,fs,Al,Gs,zl,Ol,Nl,us,Dl,gs,ql,Ll,Rl,ro,Wl,it,_s,Ul,xt,Hl,Ys,Bl,Kl,Da,Gl,Yl,Jl,no,Xl,io,Vr,Ct,lo,qa,bs,Ql,La,Zl,kr,ge,vs,ed,Ra,td,od,Ts,sd,Js,ad,rd,nd,ws,id,$s,ld,dd,cd,co,md,lt,ys,pd,jt,hd,Xs,fd,ud,Wa,gd,_d,bd,mo,vd,po,Fr;return l=new mt({}),w=new mt({}),Ve=new Mo({props:{code:`from transformers import TFMobileViTForImageClassification
import tensorflow as tf


model_ckpt = "apple/mobilevit-xx-small"
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("/")[-1] + ".tflite"
with open(tflite_filename, "wb") as f:
    f.write(tflite_model)`,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)`}}),Fo=new mt({}),Io=new im({props:{pipeline:"image-classification"}}),Co=new mt({}),jo=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/v4.27.1/src/transformers/models/mobilevit/configuration_mobilevit.py#L46"}}),Nt=new Fs({props:{anchor:"transformers.MobileViTConfig.example",$$slots:{default:[lm]},$$scope:{ctx:C}}}),Ao=new mt({}),zo=new ie({props:{name:"class transformers.MobileViTFeatureExtractor",anchor:"transformers.MobileViTFeatureExtractor",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/mobilevit/feature_extraction_mobilevit.py#L26"}}),Oo=new ie({props:{name:"__call__",anchor:"transformers.MobileViTFeatureExtractor.__call__",parameters:[{name:"images",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/image_processing_utils.py#L456"}}),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/v4.27.1/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>, <em>optional</em>) &#x2014;
A list of length <code>batch_size</code>, where each item is a <code>Tuple[int, int]</code> corresponding to the requested
final size (height, width) of each prediction. If left to None, predictions will not be resized.`,name:"target_sizes"}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/mobilevit/image_processing_mobilevit.py#L322",returnDescription:`
<p>A list 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><code>List[torch.Tensor]</code></p>
`}}),qo=new mt({}),Lo=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
the <code>crop_size</code> parameter in the <code>preprocess</code> method.`,name:"crop_size"},{anchor:"transformers.MobileViTImageProcessor.do_flip_channel_order",description:`<strong>do_flip_channel_order</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether to flip the color channels from RGB to BGR. Can be overridden by the <code>do_flip_channel_order</code>
parameter in the <code>preprocess</code> method.`,name:"do_flip_channel_order"}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/mobilevit/image_processing_mobilevit.py#L73"}}),Ro=new ie({props:{name:"preprocess",anchor:"transformers.MobileViTImageProcessor.preprocess",parameters:[{name:"images",val:": typing.Union[ForwardRef('PIL.Image.Image'), numpy.ndarray, ForwardRef('torch.Tensor'), typing.List[ForwardRef('PIL.Image.Image')], typing.List[numpy.ndarray], typing.List[ForwardRef('torch.Tensor')]]"},{name:"do_resize",val:": bool = None"},{name:"size",val:": typing.Dict[str, int] = None"},{name:"resample",val:": Resampling = None"},{name:"do_rescale",val:": bool = None"},{name:"rescale_factor",val:": float = None"},{name:"do_center_crop",val:": bool = None"},{name:"crop_size",val:": typing.Dict[str, int] = None"},{name:"do_flip_channel_order",val:": bool = None"},{name:"return_tensors",val:": typing.Union[str, transformers.utils.generic.TensorType, NoneType] = None"},{name:"data_format",val:": ChannelDimension = <ChannelDimension.FIRST: 'channels_first'>"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.MobileViTImageProcessor.preprocess.images",description:`<strong>images</strong> (<code>ImageInput</code>) &#x2014;
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"}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/mobilevit/image_processing_mobilevit.py#L220"}}),Wo=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/v4.27.1/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>, <em>optional</em>) &#x2014;
A list of length <code>batch_size</code>, where each item is a <code>Tuple[int, int]</code> corresponding to the requested
final size (height, width) of each prediction. If left to None, predictions will not be resized.`,name:"target_sizes"}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/mobilevit/image_processing_mobilevit.py#L322",returnDescription:`
<p>A list 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><code>List[torch.Tensor]</code></p>
`}}),Ho=new mt({}),Bo=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/v4.27.1/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/v4.27.1/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/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/v4.27.1/en/model_doc/auto#transformers.AutoImageProcessor">AutoImageProcessor</a>. See
<a href="/docs/transformers/v4.27.1/en/model_doc/videomae#transformers.VideoMAEFeatureExtractor.__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/v4.27.1/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/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/v4.27.1/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>
`}}),Bt=new St({props:{$$slots:{default:[dm]},$$scope:{ctx:C}}}),Kt=new Fs({props:{anchor:"transformers.MobileViTModel.forward.example",$$slots:{default:[cm]},$$scope:{ctx:C}}}),Jo=new mt({}),Xo=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/v4.27.1/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/v4.27.1/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/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/v4.27.1/en/model_doc/auto#transformers.AutoImageProcessor">AutoImageProcessor</a>. See
<a href="/docs/transformers/v4.27.1/en/model_doc/videomae#transformers.VideoMAEFeatureExtractor.__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/v4.27.1/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/v4.27.1/src/transformers/models/mobilevit/modeling_mobilevit.py#L818",returnDescription:`
<p>A <a
  href="/docs/transformers/v4.27.1/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/v4.27.1/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/v4.27.1/en/main_classes/output#transformers.modeling_outputs.ImageClassifierOutputWithNoAttention"
>transformers.modeling_outputs.ImageClassifierOutputWithNoAttention</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),Yt=new St({props:{$$slots:{default:[mm]},$$scope:{ctx:C}}}),Jt=new Fs({props:{anchor:"transformers.MobileViTForImageClassification.forward.example",$$slots:{default:[pm]},$$scope:{ctx:C}}}),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/v4.27.1/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/v4.27.1/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/mobilevit/modeling_mobilevit.py#L997"}}),rs=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/v4.27.1/en/model_doc/auto#transformers.AutoImageProcessor">AutoImageProcessor</a>. See
<a href="/docs/transformers/v4.27.1/en/model_doc/videomae#transformers.VideoMAEFeatureExtractor.__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/v4.27.1/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/v4.27.1/src/transformers/models/mobilevit/modeling_mobilevit.py#L1008",returnDescription:`
<p>A <a
  href="/docs/transformers/v4.27.1/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/v4.27.1/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/v4.27.1/en/main_classes/output#transformers.modeling_outputs.SemanticSegmenterOutput"
>transformers.modeling_outputs.SemanticSegmenterOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),Qt=new St({props:{$$slots:{default:[hm]},$$scope:{ctx:C}}}),Zt=new Fs({props:{anchor:"transformers.MobileViTForSemanticSegmentation.forward.example",$$slots:{default:[fm]},$$scope:{ctx:C}}}),ns=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/v4.27.1/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/v4.27.1/en/main_classes/model#transformers.TFPreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/mobilevit/modeling_tf_mobilevit.py#L830"}}),to=new St({props:{$$slots:{default:[um]},$$scope:{ctx:C}}}),ms=new ie({props:{name:"call",anchor:"transformers.TFMobileViTModel.call",parameters:[{name:"pixel_values",val:": typing.Optional[tensorflow.python.framework.ops.Tensor] = None"},{name:"output_hidden_states",val:": typing.Optional[bool] = None"},{name:"return_dict",val:": typing.Optional[bool] = None"},{name:"training",val:": bool = False"}],parametersDescription:[{anchor:"transformers.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/v4.27.1/en/model_doc/auto#transformers.AutoImageProcessor">AutoImageProcessor</a>. See
<a href="/docs/transformers/v4.27.1/en/model_doc/videomae#transformers.VideoMAEFeatureExtractor.__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/v4.27.1/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"}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/mobilevit/modeling_tf_mobilevit.py#L838",returnDescription:`
<p>A <a
  href="/docs/transformers/v4.27.1/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/v4.27.1/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/v4.27.1/en/main_classes/output#transformers.modeling_tf_outputs.TFBaseModelOutputWithPooling"
>transformers.modeling_tf_outputs.TFBaseModelOutputWithPooling</a> or <code>tuple(tf.Tensor)</code></p>
`}}),oo=new St({props:{$$slots:{default:[gm]},$$scope:{ctx:C}}}),so=new Fs({props:{anchor:"transformers.TFMobileViTModel.call.example",$$slots:{default:[_m]},$$scope:{ctx:C}}}),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/v4.27.1/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/v4.27.1/en/main_classes/model#transformers.TFPreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/mobilevit/modeling_tf_mobilevit.py#L873"}}),ro=new St({props:{$$slots:{default:[bm]},$$scope:{ctx:C}}}),_s=new ie({props:{name:"call",anchor:"transformers.TFMobileViTForImageClassification.call",parameters:[{name:"pixel_values",val:": typing.Optional[tensorflow.python.framework.ops.Tensor] = None"},{name:"output_hidden_states",val:": typing.Optional[bool] = None"},{name:"labels",val:": typing.Optional[tensorflow.python.framework.ops.Tensor] = None"},{name:"return_dict",val:": typing.Optional[bool] = None"},{name:"training",val:": typing.Optional[bool] = False"}],parametersDescription:[{anchor:"transformers.TFMobileViTForImageClassification.call.pixel_values",description:`<strong>pixel_values</strong> (<code>np.ndarray</code>, <code>tf.Tensor</code>, <code>List[tf.Tensor]</code>, <code>Dict[str, tf.Tensor]</code> or <code>Dict[str, np.ndarray]</code> and each example must have the shape <code>(batch_size, num_channels, height, width)</code>) &#x2014;
Pixel values. Pixel values can be obtained using <a href="/docs/transformers/v4.27.1/en/model_doc/auto#transformers.AutoImageProcessor">AutoImageProcessor</a>. See
<a href="/docs/transformers/v4.27.1/en/model_doc/videomae#transformers.VideoMAEFeatureExtractor.__call__">MobileViTImageProcessor.<strong>call</strong>()</a> for details.`,name:"pixel_values"},{anchor:"transformers.TFMobileViTForImageClassification.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.TFMobileViTForImageClassification.call.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/v4.27.1/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.TFMobileViTForImageClassification.call.labels",description:`<strong>labels</strong> (<code>tf.Tensor</code> of shape <code>(batch_size,)</code>, <em>optional</em>) &#x2014;
Labels for computing the image classification/regression loss. Indices should be in <code>[0, ..., config.num_labels - 1]</code>. If <code>config.num_labels == 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/v4.27.1/src/transformers/models/mobilevit/modeling_tf_mobilevit.py#L886",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/v4.27.1/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>
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configuration (<a
  href="/docs/transformers/v4.27.1/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>
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