import{S as wc,i as Vc,s as $c,e as r,k as h,w as y,t as a,M as Gc,c as s,d as o,m as f,a as l,x as b,h as i,b as m,G as e,g as T,y as M,q as w,o as V,B as $,v as kc,L as se}from"../../chunks/vendor-hf-doc-builder.js";import{T as pe}from"../../chunks/Tip-hf-doc-builder.js";import{D as J}from"../../chunks/Docstring-hf-doc-builder.js";import{C as ae}from"../../chunks/CodeBlock-hf-doc-builder.js";import{I as ne}from"../../chunks/IconCopyLink-hf-doc-builder.js";import{E as re}from"../../chunks/ExampleCodeBlock-hf-doc-builder.js";function jc(G){let d,v,u,p,g;return p=new ae({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEdyb3VwVmlUVGV4dENvbmZpZyUyQyUyMEdyb3VwVmlUVGV4dE1vZGVsJTBBJTBBJTIzJTIwSW5pdGlhbGl6aW5nJTIwYSUyMEdyb3VwVmlUVGV4dE1vZGVsJTIwd2l0aCUyMG52aWRpYSUyRmdyb3Vwdml0LWdjYy15ZmNjJTIwc3R5bGUlMjBjb25maWd1cmF0aW9uJTBBY29uZmlndXJhdGlvbiUyMCUzRCUyMEdyb3VwVmlUVGV4dENvbmZpZygpJTBBJTBBbW9kZWwlMjAlM0QlMjBHcm91cFZpVFRleHRNb2RlbChjb25maWd1cmF0aW9uKSUwQSUwQSUyMyUyMEFjY2Vzc2luZyUyMHRoZSUyMG1vZGVsJTIwY29uZmlndXJhdGlvbiUwQWNvbmZpZ3VyYXRpb24lMjAlM0QlMjBtb2RlbC5jb25maWc=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> GroupViTTextConfig, GroupViTTextModel

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Initializing a GroupViTTextModel with nvidia/groupvit-gcc-yfcc style configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>configuration = GroupViTTextConfig()

<span class="hljs-meta">&gt;&gt;&gt; </span>model = GroupViTTextModel(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=r("p"),v=a("Example:"),u=h(),y(p.$$.fragment)},l(n){d=s(n,"P",{});var c=l(d);v=i(c,"Example:"),c.forEach(o),u=f(n),b(p.$$.fragment,n)},m(n,c){T(n,d,c),e(d,v),T(n,u,c),M(p,n,c),g=!0},p:se,i(n){g||(w(p.$$.fragment,n),g=!0)},o(n){V(p.$$.fragment,n),g=!1},d(n){n&&o(d),n&&o(u),$(p,n)}}}function xc(G){let d,v,u,p,g;return p=new ae({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> GroupViTVisionConfig, GroupViTVisionModel

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Initializing a GroupViTVisionModel with nvidia/groupvit-gcc-yfcc style configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>configuration = GroupViTVisionConfig()

<span class="hljs-meta">&gt;&gt;&gt; </span>model = GroupViTVisionModel(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=r("p"),v=a("Example:"),u=h(),y(p.$$.fragment)},l(n){d=s(n,"P",{});var c=l(d);v=i(c,"Example:"),c.forEach(o),u=f(n),b(p.$$.fragment,n)},m(n,c){T(n,d,c),e(d,v),T(n,u,c),M(p,n,c),g=!0},p:se,i(n){g||(w(p.$$.fragment,n),g=!0)},o(n){V(p.$$.fragment,n),g=!1},d(n){n&&o(d),n&&o(u),$(p,n)}}}function Ec(G){let d,v,u,p,g;return{c(){d=r("p"),v=a("Although the recipe for forward pass needs to be defined within this function, one should call the "),u=r("code"),p=a("Module"),g=a(`
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(n){d=s(n,"P",{});var c=l(d);v=i(c,"Although the recipe for forward pass needs to be defined within this function, one should call the "),u=s(c,"CODE",{});var k=l(u);p=i(k,"Module"),k.forEach(o),g=i(c,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),c.forEach(o)},m(n,c){T(n,d,c),e(d,v),e(d,u),e(u,p),e(d,g)},d(n){n&&o(d)}}}function Jc(G){let d,v,u,p,g;return p=new ae({props:{code:"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",highlighted:`<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><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoProcessor, GroupViTModel

<span class="hljs-meta">&gt;&gt;&gt; </span>model = GroupViTModel.from_pretrained(<span class="hljs-string">&quot;nvidia/groupvit-gcc-yfcc&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>processor = AutoProcessor.from_pretrained(<span class="hljs-string">&quot;nvidia/groupvit-gcc-yfcc&quot;</span>)

<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>inputs = processor(
<span class="hljs-meta">... </span>    text=[<span class="hljs-string">&quot;a photo of a cat&quot;</span>, <span class="hljs-string">&quot;a photo of a dog&quot;</span>], images=image, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>, padding=<span class="hljs-literal">True</span>
<span class="hljs-meta">... </span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(**inputs)
<span class="hljs-meta">&gt;&gt;&gt; </span>logits_per_image = outputs.logits_per_image  <span class="hljs-comment"># this is the image-text similarity score</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>probs = logits_per_image.softmax(dim=<span class="hljs-number">1</span>)  <span class="hljs-comment"># we can take the softmax to get the label probabilities</span>`}}),{c(){d=r("p"),v=a("Examples:"),u=h(),y(p.$$.fragment)},l(n){d=s(n,"P",{});var c=l(d);v=i(c,"Examples:"),c.forEach(o),u=f(n),b(p.$$.fragment,n)},m(n,c){T(n,d,c),e(d,v),T(n,u,c),M(p,n,c),g=!0},p:se,i(n){g||(w(p.$$.fragment,n),g=!0)},o(n){V(p.$$.fragment,n),g=!1},d(n){n&&o(d),n&&o(u),$(p,n)}}}function Uc(G){let d,v,u,p,g;return{c(){d=r("p"),v=a("Although the recipe for forward pass needs to be defined within this function, one should call the "),u=r("code"),p=a("Module"),g=a(`
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(n){d=s(n,"P",{});var c=l(d);v=i(c,"Although the recipe for forward pass needs to be defined within this function, one should call the "),u=s(c,"CODE",{});var k=l(u);p=i(k,"Module"),k.forEach(o),g=i(c,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),c.forEach(o)},m(n,c){T(n,d,c),e(d,v),e(d,u),e(u,p),e(d,g)},d(n){n&&o(d)}}}function zc(G){let d,v,u,p,g;return p=new ae({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> CLIPTokenizer, GroupViTModel

<span class="hljs-meta">&gt;&gt;&gt; </span>model = GroupViTModel.from_pretrained(<span class="hljs-string">&quot;nvidia/groupvit-gcc-yfcc&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = CLIPTokenizer.from_pretrained(<span class="hljs-string">&quot;nvidia/groupvit-gcc-yfcc&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = tokenizer([<span class="hljs-string">&quot;a photo of a cat&quot;</span>, <span class="hljs-string">&quot;a photo of a dog&quot;</span>], padding=<span class="hljs-literal">True</span>, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>text_features = model.get_text_features(**inputs)`}}),{c(){d=r("p"),v=a("Examples:"),u=h(),y(p.$$.fragment)},l(n){d=s(n,"P",{});var c=l(d);v=i(c,"Examples:"),c.forEach(o),u=f(n),b(p.$$.fragment,n)},m(n,c){T(n,d,c),e(d,v),T(n,u,c),M(p,n,c),g=!0},p:se,i(n){g||(w(p.$$.fragment,n),g=!0)},o(n){V(p.$$.fragment,n),g=!1},d(n){n&&o(d),n&&o(u),$(p,n)}}}function Cc(G){let d,v,u,p,g;return{c(){d=r("p"),v=a("Although the recipe for forward pass needs to be defined within this function, one should call the "),u=r("code"),p=a("Module"),g=a(`
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(n){d=s(n,"P",{});var c=l(d);v=i(c,"Although the recipe for forward pass needs to be defined within this function, one should call the "),u=s(c,"CODE",{});var k=l(u);p=i(k,"Module"),k.forEach(o),g=i(c,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),c.forEach(o)},m(n,c){T(n,d,c),e(d,v),e(d,u),e(u,p),e(d,g)},d(n){n&&o(d)}}}function Ic(G){let d,v,u,p,g;return p=new ae({props:{code:"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",highlighted:`<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><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoProcessor, GroupViTModel

<span class="hljs-meta">&gt;&gt;&gt; </span>model = GroupViTModel.from_pretrained(<span class="hljs-string">&quot;nvidia/groupvit-gcc-yfcc&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>processor = AutoProcessor.from_pretrained(<span class="hljs-string">&quot;nvidia/groupvit-gcc-yfcc&quot;</span>)

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

<span class="hljs-meta">&gt;&gt;&gt; </span>image_features = model.get_image_features(**inputs)`}}),{c(){d=r("p"),v=a("Examples:"),u=h(),y(p.$$.fragment)},l(n){d=s(n,"P",{});var c=l(d);v=i(c,"Examples:"),c.forEach(o),u=f(n),b(p.$$.fragment,n)},m(n,c){T(n,d,c),e(d,v),T(n,u,c),M(p,n,c),g=!0},p:se,i(n){g||(w(p.$$.fragment,n),g=!0)},o(n){V(p.$$.fragment,n),g=!1},d(n){n&&o(d),n&&o(u),$(p,n)}}}function Wc(G){let d,v,u,p,g;return{c(){d=r("p"),v=a("Although the recipe for forward pass needs to be defined within this function, one should call the "),u=r("code"),p=a("Module"),g=a(`
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(n){d=s(n,"P",{});var c=l(d);v=i(c,"Although the recipe for forward pass needs to be defined within this function, one should call the "),u=s(c,"CODE",{});var k=l(u);p=i(k,"Module"),k.forEach(o),g=i(c,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),c.forEach(o)},m(n,c){T(n,d,c),e(d,v),e(d,u),e(u,p),e(d,g)},d(n){n&&o(d)}}}function Fc(G){let d,v,u,p,g;return p=new ae({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> CLIPTokenizer, GroupViTTextModel

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = CLIPTokenizer.from_pretrained(<span class="hljs-string">&quot;nvidia/groupvit-gcc-yfcc&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = GroupViTTextModel.from_pretrained(<span class="hljs-string">&quot;nvidia/groupvit-gcc-yfcc&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = tokenizer([<span class="hljs-string">&quot;a photo of a cat&quot;</span>, <span class="hljs-string">&quot;a photo of a dog&quot;</span>], padding=<span class="hljs-literal">True</span>, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(**inputs)
<span class="hljs-meta">&gt;&gt;&gt; </span>last_hidden_state = outputs.last_hidden_state
<span class="hljs-meta">&gt;&gt;&gt; </span>pooled_output = outputs.pooler_output  <span class="hljs-comment"># pooled (EOS token) states</span>`}}),{c(){d=r("p"),v=a("Examples:"),u=h(),y(p.$$.fragment)},l(n){d=s(n,"P",{});var c=l(d);v=i(c,"Examples:"),c.forEach(o),u=f(n),b(p.$$.fragment,n)},m(n,c){T(n,d,c),e(d,v),T(n,u,c),M(p,n,c),g=!0},p:se,i(n){g||(w(p.$$.fragment,n),g=!0)},o(n){V(p.$$.fragment,n),g=!1},d(n){n&&o(d),n&&o(u),$(p,n)}}}function Zc(G){let d,v,u,p,g;return{c(){d=r("p"),v=a("Although the recipe for forward pass needs to be defined within this function, one should call the "),u=r("code"),p=a("Module"),g=a(`
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(n){d=s(n,"P",{});var c=l(d);v=i(c,"Although the recipe for forward pass needs to be defined within this function, one should call the "),u=s(c,"CODE",{});var k=l(u);p=i(k,"Module"),k.forEach(o),g=i(c,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),c.forEach(o)},m(n,c){T(n,d,c),e(d,v),e(d,u),e(u,p),e(d,g)},d(n){n&&o(d)}}}function Bc(G){let d,v,u,p,g;return p=new ae({props:{code:"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",highlighted:`<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><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoProcessor, GroupViTVisionModel

<span class="hljs-meta">&gt;&gt;&gt; </span>processor = AutoProcessor.from_pretrained(<span class="hljs-string">&quot;nvidia/groupvit-gcc-yfcc&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = GroupViTVisionModel.from_pretrained(<span class="hljs-string">&quot;nvidia/groupvit-gcc-yfcc&quot;</span>)

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

<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(**inputs)
<span class="hljs-meta">&gt;&gt;&gt; </span>last_hidden_state = outputs.last_hidden_state
<span class="hljs-meta">&gt;&gt;&gt; </span>pooled_output = outputs.pooler_output  <span class="hljs-comment"># pooled CLS states</span>`}}),{c(){d=r("p"),v=a("Examples:"),u=h(),y(p.$$.fragment)},l(n){d=s(n,"P",{});var c=l(d);v=i(c,"Examples:"),c.forEach(o),u=f(n),b(p.$$.fragment,n)},m(n,c){T(n,d,c),e(d,v),T(n,u,c),M(p,n,c),g=!0},p:se,i(n){g||(w(p.$$.fragment,n),g=!0)},o(n){V(p.$$.fragment,n),g=!1},d(n){n&&o(d),n&&o(u),$(p,n)}}}function Rc(G){let d,v,u,p,g,n,c,k,co,Ge,E,H,ie,le,po,de,uo,ct,I,ue,ce,pt,Z,W,mo,ke,ut,me,je,mt,he,B,ho,fe,ge,fo,_e,Te,P,ve,xe,Ee,go;return{c(){d=r("p"),v=a("TF 2.0 models accepts two formats as inputs:"),u=h(),p=r("ul"),g=r("li"),n=a("having all inputs as keyword arguments (like PyTorch models), or"),c=h(),k=r("li"),co=a("having all inputs as a list, tuple or dict in the first positional arguments."),Ge=h(),E=r("p"),H=a("This second option is useful when using "),ie=r("code"),le=a("tf.keras.Model.fit"),po=a(` method which currently requires having all the
tensors in the first argument of the model call function: `),de=r("code"),uo=a("model(inputs)"),ct=a("."),I=h(),ue=r("p"),ce=a(`If you choose this second option, there are three possibilities you can use to gather all the input Tensors in the
first positional argument :`),pt=h(),Z=r("ul"),W=r("li"),mo=a("a single Tensor with "),ke=r("code"),ut=a("input_ids"),me=a(" only and nothing else: "),je=r("code"),mt=a("model(input_ids)"),he=h(),B=r("li"),ho=a(`a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
`),fe=r("code"),ge=a("model([input_ids, attention_mask])"),fo=a(" or "),_e=r("code"),Te=a("model([input_ids, attention_mask, token_type_ids])"),P=h(),ve=r("li"),xe=a(`a dictionary with one or several input Tensors associated to the input names given in the docstring:
`),Ee=r("code"),go=a('model({"input_ids": input_ids, "token_type_ids": token_type_ids})')},l(j){d=s(j,"P",{});var C=l(d);v=i(C,"TF 2.0 models accepts two formats as inputs:"),C.forEach(o),u=f(j),p=s(j,"UL",{});var ht=l(p);g=s(ht,"LI",{});var _o=l(g);n=i(_o,"having all inputs as keyword arguments (like PyTorch models), or"),_o.forEach(o),c=f(ht),k=s(ht,"LI",{});var x=l(k);co=i(x,"having all inputs as a list, tuple or dict in the first positional arguments."),x.forEach(o),ht.forEach(o),Ge=f(j),E=s(j,"P",{});var ye=l(E);H=i(ye,"This second option is useful when using "),ie=s(ye,"CODE",{});var be=l(ie);le=i(be,"tf.keras.Model.fit"),be.forEach(o),po=i(ye,` method which currently requires having all the
tensors in the first argument of the model call function: `),de=s(ye,"CODE",{});var hn=l(de);uo=i(hn,"model(inputs)"),hn.forEach(o),ct=i(ye,"."),ye.forEach(o),I=f(j),ue=s(j,"P",{});var fn=l(ue);ce=i(fn,`If you choose this second option, there are three possibilities you can use to gather all the input Tensors in the
first positional argument :`),fn.forEach(o),pt=f(j),Z=s(j,"UL",{});var R=l(Z);W=s(R,"LI",{});var Je=l(W);mo=i(Je,"a single Tensor with "),ke=s(Je,"CODE",{});var gn=l(ke);ut=i(gn,"input_ids"),gn.forEach(o),me=i(Je," only and nothing else: "),je=s(Je,"CODE",{});var Me=l(je);mt=i(Me,"model(input_ids)"),Me.forEach(o),Je.forEach(o),he=f(R),B=s(R,"LI",{});var Ue=l(B);ho=i(Ue,`a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
`),fe=s(Ue,"CODE",{});var _n=l(fe);ge=i(_n,"model([input_ids, attention_mask])"),_n.forEach(o),fo=i(Ue," or "),_e=s(Ue,"CODE",{});var we=l(_e);Te=i(we,"model([input_ids, attention_mask, token_type_ids])"),we.forEach(o),Ue.forEach(o),P=f(R),ve=s(R,"LI",{});var To=l(ve);xe=i(To,`a dictionary with one or several input Tensors associated to the input names given in the docstring:
`),Ee=s(To,"CODE",{});var Tn=l(Ee);go=i(Tn,'model({"input_ids": input_ids, "token_type_ids": token_type_ids})'),Tn.forEach(o),To.forEach(o),R.forEach(o)},m(j,C){T(j,d,C),e(d,v),T(j,u,C),T(j,p,C),e(p,g),e(g,n),e(p,c),e(p,k),e(k,co),T(j,Ge,C),T(j,E,C),e(E,H),e(E,ie),e(ie,le),e(E,po),e(E,de),e(de,uo),e(E,ct),T(j,I,C),T(j,ue,C),e(ue,ce),T(j,pt,C),T(j,Z,C),e(Z,W),e(W,mo),e(W,ke),e(ke,ut),e(W,me),e(W,je),e(je,mt),e(Z,he),e(Z,B),e(B,ho),e(B,fe),e(fe,ge),e(B,fo),e(B,_e),e(_e,Te),e(Z,P),e(Z,ve),e(ve,xe),e(ve,Ee),e(Ee,go)},d(j){j&&o(d),j&&o(u),j&&o(p),j&&o(Ge),j&&o(E),j&&o(I),j&&o(ue),j&&o(pt),j&&o(Z)}}}function Nc(G){let d,v,u,p,g;return{c(){d=r("p"),v=a("Although the recipe for forward pass needs to be defined within this function, one should call the "),u=r("code"),p=a("Module"),g=a(`
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(n){d=s(n,"P",{});var c=l(d);v=i(c,"Although the recipe for forward pass needs to be defined within this function, one should call the "),u=s(c,"CODE",{});var k=l(u);p=i(k,"Module"),k.forEach(o),g=i(c,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),c.forEach(o)},m(n,c){T(n,d,c),e(d,v),e(d,u),e(u,p),e(d,g)},d(n){n&&o(d)}}}function Pc(G){let d,v,u,p,g;return p=new ae({props:{code:"ZnJvbSUyMFBJTCUyMGltcG9ydCUyMEltYWdlJTBBaW1wb3J0JTIwcmVxdWVzdHMlMEFmcm9tJTIwdHJhbnNmb3JtZXJzJTIwaW1wb3J0JTIwQXV0b1Byb2Nlc3NvciUyQyUyMFRGR3JvdXBWaVRNb2RlbCUwQWltcG9ydCUyMHRlbnNvcmZsb3clMjBhcyUyMHRmJTBBJTBBbW9kZWwlMjAlM0QlMjBURkdyb3VwVmlUTW9kZWwuZnJvbV9wcmV0cmFpbmVkKCUyMm52aWRpYSUyRmdyb3Vwdml0LWdjYy15ZmNjJTIyKSUwQXByb2Nlc3NvciUyMCUzRCUyMEF1dG9Qcm9jZXNzb3IuZnJvbV9wcmV0cmFpbmVkKCUyMm52aWRpYSUyRmdyb3Vwdml0LWdjYy15ZmNjJTIyKSUwQSUwQXVybCUyMCUzRCUyMCUyMmh0dHAlM0ElMkYlMkZpbWFnZXMuY29jb2RhdGFzZXQub3JnJTJGdmFsMjAxNyUyRjAwMDAwMDAzOTc2OS5qcGclMjIlMEFpbWFnZSUyMCUzRCUyMEltYWdlLm9wZW4ocmVxdWVzdHMuZ2V0KHVybCUyQyUyMHN0cmVhbSUzRFRydWUpLnJhdyklMEElMEFpbnB1dHMlMjAlM0QlMjBwcm9jZXNzb3IoJTBBJTIwJTIwJTIwJTIwdGV4dCUzRCU1QiUyMmElMjBwaG90byUyMG9mJTIwYSUyMGNhdCUyMiUyQyUyMCUyMmElMjBwaG90byUyMG9mJTIwYSUyMGRvZyUyMiU1RCUyQyUyMGltYWdlcyUzRGltYWdlJTJDJTIwcmV0dXJuX3RlbnNvcnMlM0QlMjJ0ZiUyMiUyQyUyMHBhZGRpbmclM0RUcnVlJTBBKSUwQSUwQW91dHB1dHMlMjAlM0QlMjBtb2RlbCgqKmlucHV0cyklMEFsb2dpdHNfcGVyX2ltYWdlJTIwJTNEJTIwb3V0cHV0cy5sb2dpdHNfcGVyX2ltYWdlJTIwJTIwJTIzJTIwdGhpcyUyMGlzJTIwdGhlJTIwaW1hZ2UtdGV4dCUyMHNpbWlsYXJpdHklMjBzY29yZSUwQXByb2JzJTIwJTNEJTIwdGYubWF0aC5zb2Z0bWF4KGxvZ2l0c19wZXJfaW1hZ2UlMkMlMjBheGlzJTNEMSklMjAlMjAlMjMlMjB3ZSUyMGNhbiUyMHRha2UlMjB0aGUlMjBzb2Z0bWF4JTIwdG8lMjBnZXQlMjB0aGUlMjBsYWJlbCUyMHByb2JhYmlsaXRpZXM=",highlighted:`<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><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoProcessor, TFGroupViTModel
<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>model = TFGroupViTModel.from_pretrained(<span class="hljs-string">&quot;nvidia/groupvit-gcc-yfcc&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>processor = AutoProcessor.from_pretrained(<span class="hljs-string">&quot;nvidia/groupvit-gcc-yfcc&quot;</span>)

<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>inputs = processor(
<span class="hljs-meta">... </span>    text=[<span class="hljs-string">&quot;a photo of a cat&quot;</span>, <span class="hljs-string">&quot;a photo of a dog&quot;</span>], images=image, return_tensors=<span class="hljs-string">&quot;tf&quot;</span>, padding=<span class="hljs-literal">True</span>
<span class="hljs-meta">... </span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(**inputs)
<span class="hljs-meta">&gt;&gt;&gt; </span>logits_per_image = outputs.logits_per_image  <span class="hljs-comment"># this is the image-text similarity score</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>probs = tf.math.softmax(logits_per_image, axis=<span class="hljs-number">1</span>)  <span class="hljs-comment"># we can take the softmax to get the label probabilities</span>`}}),{c(){d=r("p"),v=a("Examples:"),u=h(),y(p.$$.fragment)},l(n){d=s(n,"P",{});var c=l(d);v=i(c,"Examples:"),c.forEach(o),u=f(n),b(p.$$.fragment,n)},m(n,c){T(n,d,c),e(d,v),T(n,u,c),M(p,n,c),g=!0},p:se,i(n){g||(w(p.$$.fragment,n),g=!0)},o(n){V(p.$$.fragment,n),g=!1},d(n){n&&o(d),n&&o(u),$(p,n)}}}function qc(G){let d,v,u,p,g;return{c(){d=r("p"),v=a("Although the recipe for forward pass needs to be defined within this function, one should call the "),u=r("code"),p=a("Module"),g=a(`
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(n){d=s(n,"P",{});var c=l(d);v=i(c,"Although the recipe for forward pass needs to be defined within this function, one should call the "),u=s(c,"CODE",{});var k=l(u);p=i(k,"Module"),k.forEach(o),g=i(c,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),c.forEach(o)},m(n,c){T(n,d,c),e(d,v),e(d,u),e(u,p),e(d,g)},d(n){n&&o(d)}}}function Ac(G){let d,v,u,p,g;return p=new ae({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> CLIPTokenizer, TFGroupViTModel

<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFGroupViTModel.from_pretrained(<span class="hljs-string">&quot;nvidia/groupvit-gcc-yfcc&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = CLIPTokenizer.from_pretrained(<span class="hljs-string">&quot;nvidia/groupvit-gcc-yfcc&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = tokenizer([<span class="hljs-string">&quot;a photo of a cat&quot;</span>, <span class="hljs-string">&quot;a photo of a dog&quot;</span>], padding=<span class="hljs-literal">True</span>, return_tensors=<span class="hljs-string">&quot;tf&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>text_features = model.get_text_features(**inputs)`}}),{c(){d=r("p"),v=a("Examples:"),u=h(),y(p.$$.fragment)},l(n){d=s(n,"P",{});var c=l(d);v=i(c,"Examples:"),c.forEach(o),u=f(n),b(p.$$.fragment,n)},m(n,c){T(n,d,c),e(d,v),T(n,u,c),M(p,n,c),g=!0},p:se,i(n){g||(w(p.$$.fragment,n),g=!0)},o(n){V(p.$$.fragment,n),g=!1},d(n){n&&o(d),n&&o(u),$(p,n)}}}function Sc(G){let d,v,u,p,g;return{c(){d=r("p"),v=a("Although the recipe for forward pass needs to be defined within this function, one should call the "),u=r("code"),p=a("Module"),g=a(`
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(n){d=s(n,"P",{});var c=l(d);v=i(c,"Although the recipe for forward pass needs to be defined within this function, one should call the "),u=s(c,"CODE",{});var k=l(u);p=i(k,"Module"),k.forEach(o),g=i(c,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),c.forEach(o)},m(n,c){T(n,d,c),e(d,v),e(d,u),e(u,p),e(d,g)},d(n){n&&o(d)}}}function Hc(G){let d,v,u,p,g;return p=new ae({props:{code:"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",highlighted:`<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><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoProcessor, TFGroupViTModel

<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFGroupViTModel.from_pretrained(<span class="hljs-string">&quot;nvidia/groupvit-gcc-yfcc&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>processor = AutoProcessor.from_pretrained(<span class="hljs-string">&quot;nvidia/groupvit-gcc-yfcc&quot;</span>)

<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>inputs = processor(images=image, return_tensors=<span class="hljs-string">&quot;tf&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>image_features = model.get_image_features(**inputs)`}}),{c(){d=r("p"),v=a("Examples:"),u=h(),y(p.$$.fragment)},l(n){d=s(n,"P",{});var c=l(d);v=i(c,"Examples:"),c.forEach(o),u=f(n),b(p.$$.fragment,n)},m(n,c){T(n,d,c),e(d,v),T(n,u,c),M(p,n,c),g=!0},p:se,i(n){g||(w(p.$$.fragment,n),g=!0)},o(n){V(p.$$.fragment,n),g=!1},d(n){n&&o(d),n&&o(u),$(p,n)}}}function Xc(G){let d,v,u,p,g;return{c(){d=r("p"),v=a("Although the recipe for forward pass needs to be defined within this function, one should call the "),u=r("code"),p=a("Module"),g=a(`
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(n){d=s(n,"P",{});var c=l(d);v=i(c,"Although the recipe for forward pass needs to be defined within this function, one should call the "),u=s(c,"CODE",{});var k=l(u);p=i(k,"Module"),k.forEach(o),g=i(c,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),c.forEach(o)},m(n,c){T(n,d,c),e(d,v),e(d,u),e(u,p),e(d,g)},d(n){n&&o(d)}}}function Dc(G){let d,v,u,p,g;return p=new ae({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> CLIPTokenizer, TFGroupViTTextModel

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = CLIPTokenizer.from_pretrained(<span class="hljs-string">&quot;nvidia/groupvit-gcc-yfcc&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFGroupViTTextModel.from_pretrained(<span class="hljs-string">&quot;nvidia/groupvit-gcc-yfcc&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = tokenizer([<span class="hljs-string">&quot;a photo of a cat&quot;</span>, <span class="hljs-string">&quot;a photo of a dog&quot;</span>], padding=<span class="hljs-literal">True</span>, 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_state = outputs.last_hidden_state
<span class="hljs-meta">&gt;&gt;&gt; </span>pooled_output = outputs.pooler_output  <span class="hljs-comment"># pooled (EOS token) states</span>`}}),{c(){d=r("p"),v=a("Examples:"),u=h(),y(p.$$.fragment)},l(n){d=s(n,"P",{});var c=l(d);v=i(c,"Examples:"),c.forEach(o),u=f(n),b(p.$$.fragment,n)},m(n,c){T(n,d,c),e(d,v),T(n,u,c),M(p,n,c),g=!0},p:se,i(n){g||(w(p.$$.fragment,n),g=!0)},o(n){V(p.$$.fragment,n),g=!1},d(n){n&&o(d),n&&o(u),$(p,n)}}}function Yc(G){let d,v,u,p,g;return{c(){d=r("p"),v=a("Although the recipe for forward pass needs to be defined within this function, one should call the "),u=r("code"),p=a("Module"),g=a(`
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(n){d=s(n,"P",{});var c=l(d);v=i(c,"Although the recipe for forward pass needs to be defined within this function, one should call the "),u=s(c,"CODE",{});var k=l(u);p=i(k,"Module"),k.forEach(o),g=i(c,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),c.forEach(o)},m(n,c){T(n,d,c),e(d,v),e(d,u),e(u,p),e(d,g)},d(n){n&&o(d)}}}function Oc(G){let d,v,u,p,g;return p=new ae({props:{code:"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",highlighted:`<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><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoProcessor, TFGroupViTVisionModel

<span class="hljs-meta">&gt;&gt;&gt; </span>processor = AutoProcessor.from_pretrained(<span class="hljs-string">&quot;nvidia/groupvit-gcc-yfcc&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFGroupViTVisionModel.from_pretrained(<span class="hljs-string">&quot;nvidia/groupvit-gcc-yfcc&quot;</span>)

<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>inputs = processor(images=image, return_tensors=<span class="hljs-string">&quot;tf&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(**inputs)
<span class="hljs-meta">&gt;&gt;&gt; </span>last_hidden_state = outputs.last_hidden_state
<span class="hljs-meta">&gt;&gt;&gt; </span>pooled_output = outputs.pooler_output  <span class="hljs-comment"># pooled CLS states</span>`}}),{c(){d=r("p"),v=a("Examples:"),u=h(),y(p.$$.fragment)},l(n){d=s(n,"P",{});var c=l(d);v=i(c,"Examples:"),c.forEach(o),u=f(n),b(p.$$.fragment,n)},m(n,c){T(n,d,c),e(d,v),T(n,u,c),M(p,n,c),g=!0},p:se,i(n){g||(w(p.$$.fragment,n),g=!0)},o(n){V(p.$$.fragment,n),g=!1},d(n){n&&o(d),n&&o(u),$(p,n)}}}function Lc(G){let d,v,u,p,g,n,c,k,co,Ge,E,H,ie,le,po,de,uo,ct,I,ue,ce,pt,Z,W,mo,ke,ut,me,je,mt,he,B,ho,fe,ge,fo,_e,Te,P,ve,xe,Ee,go,j,C,ht,_o,x,ye,be,hn,fn,R,Je,gn,Me,Ue,_n,we,To,Tn,vo,Ns,Ps,yo,qs,As,Lr,ze,ft,nr,bo,Ss,rr,Hs,Qr,vn,Xs,Kr,gt,Mo,Ds,wo,Ys,Os,Ls,Vo,Qs,$o,Ks,ea,es,Ce,_t,sr,Go,ta,ar,oa,ts,q,ko,na,Ve,yn,ra,sa,bn,aa,ia,jo,la,da,ca,Ie,pa,Mn,ua,ma,wn,ha,fa,ga,Tt,xo,_a,Eo,Ta,Vn,va,ya,os,We,vt,ir,Jo,ba,lr,Ma,ns,A,Uo,wa,Fe,Va,$n,$a,Ga,zo,ka,ja,xa,Ze,Ea,Gn,Ja,Ua,kn,za,Ca,Ia,yt,rs,Be,bt,dr,Co,Wa,cr,Fa,ss,S,Io,Za,Re,Ba,jn,Ra,Na,Wo,Pa,qa,Aa,Ne,Sa,xn,Ha,Xa,En,Da,Ya,Oa,Mt,as,Pe,wt,pr,Fo,La,ur,Qa,is,F,Zo,Ka,Bo,ei,Ro,ti,oi,ni,X,No,ri,qe,si,Jn,ai,ii,mr,li,di,ci,Vt,pi,$t,ui,D,Po,mi,Ae,hi,Un,fi,gi,hr,_i,Ti,vi,Gt,yi,kt,bi,Y,qo,Mi,Se,wi,zn,Vi,$i,fr,Gi,ki,ji,jt,xi,xt,ls,He,Et,gr,Ao,Ei,_r,Ji,ds,Xe,So,Ui,O,Ho,zi,De,Ci,Cn,Ii,Wi,Tr,Fi,Zi,Bi,Jt,Ri,Ut,cs,Ye,zt,vr,Xo,Ni,yr,Pi,ps,Oe,Do,qi,L,Yo,Ai,Le,Si,In,Hi,Xi,br,Di,Yi,Oi,Ct,Li,It,us,Qe,Wt,Mr,Oo,Qi,wr,Ki,ms,U,Lo,el,Qo,tl,Wn,ol,nl,rl,Ko,sl,en,al,il,ll,Ft,dl,Q,tn,cl,Ke,pl,Fn,ul,ml,Vr,hl,fl,gl,Zt,_l,Bt,Tl,K,on,vl,et,yl,Zn,bl,Ml,$r,wl,Vl,$l,Rt,Gl,Nt,kl,ee,nn,jl,tt,xl,Bn,El,Jl,Gr,Ul,zl,Cl,Pt,Il,qt,hs,ot,At,kr,rn,Wl,jr,Fl,fs,nt,sn,Zl,te,an,Bl,rt,Rl,Rn,Nl,Pl,xr,ql,Al,Sl,St,Hl,Ht,gs,st,Xt,Er,ln,Xl,Jr,Dl,_s,at,dn,Yl,oe,cn,Ol,it,Ll,Nn,Ql,Kl,Ur,ed,td,od,Dt,nd,Yt,Ts;return n=new ne({}),le=new ne({}),bo=new ne({}),Go=new ne({}),ko=new J({props:{name:"class transformers.GroupViTConfig",anchor:"transformers.GroupViTConfig",parameters:[{name:"text_config",val:" = None"},{name:"vision_config",val:" = None"},{name:"projection_dim",val:" = 256"},{name:"projection_intermediate_dim",val:" = 4096"},{name:"logit_scale_init_value",val:" = 2.6592"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.GroupViTConfig.text_config",description:`<strong>text_config</strong> (<code>dict</code>, <em>optional</em>) &#x2014;
Dictionary of configuration options used to initialize <a href="/docs/transformers/v4.33.0/en/model_doc/groupvit#transformers.GroupViTTextConfig">GroupViTTextConfig</a>.`,name:"text_config"},{anchor:"transformers.GroupViTConfig.vision_config",description:`<strong>vision_config</strong> (<code>dict</code>, <em>optional</em>) &#x2014;
Dictionary of configuration options used to initialize <a href="/docs/transformers/v4.33.0/en/model_doc/groupvit#transformers.GroupViTVisionConfig">GroupViTVisionConfig</a>.`,name:"vision_config"},{anchor:"transformers.GroupViTConfig.projection_dim",description:`<strong>projection_dim</strong> (<code>int</code>, <em>optional</em>, defaults to 256) &#x2014;
Dimentionality of text and vision projection layers.`,name:"projection_dim"},{anchor:"transformers.GroupViTConfig.projection_intermediate_dim",description:`<strong>projection_intermediate_dim</strong> (<code>int</code>, <em>optional</em>, defaults to 4096) &#x2014;
Dimentionality of intermediate layer of text and vision projection layers.`,name:"projection_intermediate_dim"},{anchor:"transformers.GroupViTConfig.logit_scale_init_value",description:`<strong>logit_scale_init_value</strong> (<code>float</code>, <em>optional</em>, defaults to 2.6592) &#x2014;
The inital value of the <em>logit_scale</em> parameter. Default is used as per the original GroupViT
implementation.`,name:"logit_scale_init_value"},{anchor:"transformers.GroupViTConfig.kwargs",description:`<strong>kwargs</strong> (<em>optional</em>) &#x2014;
Dictionary of keyword arguments.`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/groupvit/configuration_groupvit.py#L271"}}),xo=new J({props:{name:"from_text_vision_configs",anchor:"transformers.GroupViTConfig.from_text_vision_configs",parameters:[{name:"text_config",val:": GroupViTTextConfig"},{name:"vision_config",val:": GroupViTVisionConfig"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/groupvit/configuration_groupvit.py#L396",returnDescription:`
<p>An instance of a configuration object</p>
`,returnType:`
<p><a
  href="/docs/transformers/v4.33.0/en/model_doc/groupvit#transformers.GroupViTConfig"
>GroupViTConfig</a></p>
`}}),Jo=new ne({}),Uo=new J({props:{name:"class transformers.GroupViTTextConfig",anchor:"transformers.GroupViTTextConfig",parameters:[{name:"vocab_size",val:" = 49408"},{name:"hidden_size",val:" = 256"},{name:"intermediate_size",val:" = 1024"},{name:"num_hidden_layers",val:" = 12"},{name:"num_attention_heads",val:" = 4"},{name:"max_position_embeddings",val:" = 77"},{name:"hidden_act",val:" = 'quick_gelu'"},{name:"layer_norm_eps",val:" = 1e-05"},{name:"dropout",val:" = 0.0"},{name:"attention_dropout",val:" = 0.0"},{name:"initializer_range",val:" = 0.02"},{name:"initializer_factor",val:" = 1.0"},{name:"pad_token_id",val:" = 1"},{name:"bos_token_id",val:" = 49406"},{name:"eos_token_id",val:" = 49407"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.GroupViTTextConfig.vocab_size",description:`<strong>vocab_size</strong> (<code>int</code>, <em>optional</em>, defaults to 49408) &#x2014;
Vocabulary size of the GroupViT text model. Defines the number of different tokens that can be represented
by the <code>inputs_ids</code> passed when calling <a href="/docs/transformers/v4.33.0/en/model_doc/groupvit#transformers.GroupViTModel">GroupViTModel</a>.`,name:"vocab_size"},{anchor:"transformers.GroupViTTextConfig.hidden_size",description:`<strong>hidden_size</strong> (<code>int</code>, <em>optional</em>, defaults to 256) &#x2014;
Dimensionality of the encoder layers and the pooler layer.`,name:"hidden_size"},{anchor:"transformers.GroupViTTextConfig.intermediate_size",description:`<strong>intermediate_size</strong> (<code>int</code>, <em>optional</em>, defaults to 1024) &#x2014;
Dimensionality of the &#x201C;intermediate&#x201D; (i.e., feed-forward) layer in the Transformer encoder.`,name:"intermediate_size"},{anchor:"transformers.GroupViTTextConfig.num_hidden_layers",description:`<strong>num_hidden_layers</strong> (<code>int</code>, <em>optional</em>, defaults to 12) &#x2014;
Number of hidden layers in the Transformer encoder.`,name:"num_hidden_layers"},{anchor:"transformers.GroupViTTextConfig.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.GroupViTTextConfig.max_position_embeddings",description:`<strong>max_position_embeddings</strong> (<code>int</code>, <em>optional</em>, defaults to 77) &#x2014;
The maximum sequence length that this model might ever be used with. Typically set this to something large
just in case (e.g., 512 or 1024 or 2048).`,name:"max_position_embeddings"},{anchor:"transformers.GroupViTTextConfig.hidden_act",description:`<strong>hidden_act</strong> (<code>str</code> or <code>function</code>, <em>optional</em>, defaults to <code>&quot;quick_gelu&quot;</code>) &#x2014;
The non-linear activation function (function or string) in the encoder and pooler. If string, <code>&quot;gelu&quot;</code>,
<code>&quot;relu&quot;</code>, <code>&quot;selu&quot;</code> and <code>&quot;gelu_new&quot;</code> \`<code>&quot;quick_gelu&quot;</code> are supported.`,name:"hidden_act"},{anchor:"transformers.GroupViTTextConfig.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.GroupViTTextConfig.attention_dropout",description:`<strong>attention_dropout</strong> (<code>float</code>, <em>optional</em>, defaults to 0.0) &#x2014;
The dropout ratio for the attention probabilities.`,name:"attention_dropout"},{anchor:"transformers.GroupViTTextConfig.dropout",description:`<strong>dropout</strong> (<code>float</code>, <em>optional</em>, defaults to 0.0) &#x2014;
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.`,name:"dropout"},{anchor:"transformers.GroupViTTextConfig.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.GroupViTTextConfig.initializer_factor",description:`<strong>initializer_factor</strong> (<code>float</code>, <em>optional</em>, defaults to 1.0) &#x2014;
A factor for initializing all weight matrices (should be kept to 1, used internally for initialization
testing).`,name:"initializer_factor"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/groupvit/configuration_groupvit.py#L38"}}),yt=new re({props:{anchor:"transformers.GroupViTTextConfig.example",$$slots:{default:[jc]},$$scope:{ctx:G}}}),Co=new ne({}),Io=new J({props:{name:"class transformers.GroupViTVisionConfig",anchor:"transformers.GroupViTVisionConfig",parameters:[{name:"hidden_size",val:" = 384"},{name:"intermediate_size",val:" = 1536"},{name:"depths",val:" = [6, 3, 3]"},{name:"num_hidden_layers",val:" = 12"},{name:"num_group_tokens",val:" = [64, 8, 0]"},{name:"num_output_groups",val:" = [64, 8, 8]"},{name:"num_attention_heads",val:" = 6"},{name:"image_size",val:" = 224"},{name:"patch_size",val:" = 16"},{name:"num_channels",val:" = 3"},{name:"hidden_act",val:" = 'gelu'"},{name:"layer_norm_eps",val:" = 1e-05"},{name:"dropout",val:" = 0.0"},{name:"attention_dropout",val:" = 0.0"},{name:"initializer_range",val:" = 0.02"},{name:"initializer_factor",val:" = 1.0"},{name:"assign_eps",val:" = 1.0"},{name:"assign_mlp_ratio",val:" = [0.5, 4]"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.GroupViTVisionConfig.hidden_size",description:`<strong>hidden_size</strong> (<code>int</code>, <em>optional</em>, defaults to 384) &#x2014;
Dimensionality of the encoder layers and the pooler layer.`,name:"hidden_size"},{anchor:"transformers.GroupViTVisionConfig.intermediate_size",description:`<strong>intermediate_size</strong> (<code>int</code>, <em>optional</em>, defaults to 1536) &#x2014;
Dimensionality of the &#x201C;intermediate&#x201D; (i.e., feed-forward) layer in the Transformer encoder.`,name:"intermediate_size"},{anchor:"transformers.GroupViTVisionConfig.depths",description:`<strong>depths</strong> (<code>List[int]</code>, <em>optional</em>, defaults to [6, 3, 3]) &#x2014;
The number of layers in each encoder block.`,name:"depths"},{anchor:"transformers.GroupViTVisionConfig.num_group_tokens",description:`<strong>num_group_tokens</strong> (<code>List[int]</code>, <em>optional</em>, defaults to [64, 8, 0]) &#x2014;
The number of group tokens for each stage.`,name:"num_group_tokens"},{anchor:"transformers.GroupViTVisionConfig.num_output_groups",description:`<strong>num_output_groups</strong> (<code>List[int]</code>, <em>optional</em>, defaults to [64, 8, 8]) &#x2014;
The number of output groups for each stage, 0 means no group.`,name:"num_output_groups"},{anchor:"transformers.GroupViTVisionConfig.num_attention_heads",description:`<strong>num_attention_heads</strong> (<code>int</code>, <em>optional</em>, defaults to 6) &#x2014;
Number of attention heads for each attention layer in the Transformer encoder.`,name:"num_attention_heads"},{anchor:"transformers.GroupViTVisionConfig.image_size",description:`<strong>image_size</strong> (<code>int</code>, <em>optional</em>, defaults to 224) &#x2014;
The size (resolution) of each image.`,name:"image_size"},{anchor:"transformers.GroupViTVisionConfig.patch_size",description:`<strong>patch_size</strong> (<code>int</code>, <em>optional</em>, defaults to 16) &#x2014;
The size (resolution) of each patch.`,name:"patch_size"},{anchor:"transformers.GroupViTVisionConfig.hidden_act",description:`<strong>hidden_act</strong> (<code>str</code> or <code>function</code>, <em>optional</em>, defaults to <code>&quot;gelu&quot;</code>) &#x2014;
The non-linear activation function (function or string) in the encoder and pooler. If string, <code>&quot;gelu&quot;</code>,
<code>&quot;relu&quot;</code>, <code>&quot;selu&quot;</code> and <code>&quot;gelu_new&quot;</code> \`<code>&quot;quick_gelu&quot;</code> are supported.`,name:"hidden_act"},{anchor:"transformers.GroupViTVisionConfig.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.GroupViTVisionConfig.dropout",description:`<strong>dropout</strong> (<code>float</code>, <em>optional</em>, defaults to 0.0) &#x2014;
The dropout probabilitiy for all fully connected layers in the embeddings, encoder, and pooler.`,name:"dropout"},{anchor:"transformers.GroupViTVisionConfig.attention_dropout",description:`<strong>attention_dropout</strong> (<code>float</code>, <em>optional</em>, defaults to 0.0) &#x2014;
The dropout ratio for the attention probabilities.`,name:"attention_dropout"},{anchor:"transformers.GroupViTVisionConfig.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.GroupViTVisionConfig.initializer_factor",description:`<strong>initializer_factor</strong> (<code>float</code>, <em>optional</em>, defaults to 1.0) &#x2014;
A factor for initializing all weight matrices (should be kept to 1, used internally for initialization
testing).`,name:"initializer_factor"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/groupvit/configuration_groupvit.py#L146"}}),Mt=new re({props:{anchor:"transformers.GroupViTVisionConfig.example",$$slots:{default:[xc]},$$scope:{ctx:G}}}),Fo=new ne({}),Zo=new J({props:{name:"class transformers.GroupViTModel",anchor:"transformers.GroupViTModel",parameters:[{name:"config",val:": GroupViTConfig"}],parametersDescription:[{anchor:"transformers.GroupViTModel.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.33.0/en/model_doc/groupvit#transformers.GroupViTConfig">GroupViTConfig</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/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/groupvit/modeling_groupvit.py#L1346"}}),No=new J({props:{name:"forward",anchor:"transformers.GroupViTModel.forward",parameters:[{name:"input_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"pixel_values",val:": typing.Optional[torch.FloatTensor] = None"},{name:"attention_mask",val:": typing.Optional[torch.Tensor] = None"},{name:"position_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"return_loss",val:": typing.Optional[bool] = None"},{name:"output_attentions",val:": typing.Optional[bool] = None"},{name:"output_hidden_states",val:": typing.Optional[bool] = None"},{name:"output_segmentation",val:": typing.Optional[bool] = None"},{name:"return_dict",val:": typing.Optional[bool] = None"}],parametersDescription:[{anchor:"transformers.GroupViTModel.forward.input_ids",description:`<strong>input_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
it.</p>
<p>Indices can be obtained using <a href="/docs/transformers/v4.33.0/en/model_doc/clip#transformers.CLIPTokenizer">CLIPTokenizer</a>. See <a href="/docs/transformers/v4.33.0/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/v4.33.0/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.GroupViTModel.forward.attention_mask",description:`<strong>attention_mask</strong> (<code>torch.Tensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Mask to avoid performing attention on padding token indices. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.</li>
</ul>
<p><a href="../glossary#attention-mask">What are attention masks?</a>`,name:"attention_mask"},{anchor:"transformers.GroupViTModel.forward.position_ids",description:`<strong>position_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range <code>[0, config.max_position_embeddings - 1]</code>.</p>
<p><a href="../glossary#position-ids">What are position IDs?</a>`,name:"position_ids"},{anchor:"transformers.GroupViTModel.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/v4.33.0/en/model_doc/auto#transformers.AutoImageProcessor">AutoImageProcessor</a>. See
<a href="/docs/transformers/v4.33.0/en/model_doc/donut#transformers.DonutFeatureExtractor.__call__">CLIPImageProcessor.<strong>call</strong>()</a> for details.`,name:"pixel_values"},{anchor:"transformers.GroupViTModel.forward.return_loss",description:`<strong>return_loss</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the contrastive loss.`,name:"return_loss"},{anchor:"transformers.GroupViTModel.forward.output_attentions",description:`<strong>output_attentions</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the attentions tensors of all attention layers. See <code>attentions</code> under returned
tensors for more detail.`,name:"output_attentions"},{anchor:"transformers.GroupViTModel.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.GroupViTModel.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/v4.33.0/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/groupvit/modeling_groupvit.py#L1488",returnDescription:`
<p>A <code>transformers.models.groupvit.modeling_groupvit.GroupViTModelOutput</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 (<code>&lt;class 'transformers.models.groupvit.configuration_groupvit.GroupViTConfig'&gt;</code>) and inputs.</p>
<ul>
<li>
<p><strong>loss</strong> (<code>torch.FloatTensor</code> of shape <code>(1,)</code>, <em>optional</em>, returned when <code>return_loss</code> is <code>True</code>) \u2014 Contrastive loss for image-text similarity.</p>
</li>
<li>
<p><strong>logits_per_image</strong> (<code>torch.FloatTensor</code> of shape <code>(image_batch_size, text_batch_size)</code>) \u2014 The scaled dot product scores between <code>image_embeds</code> and <code>text_embeds</code>. This represents the image-text
similarity scores.</p>
</li>
<li>
<p><strong>logits_per_text</strong> (<code>torch.FloatTensor</code> of shape <code>(text_batch_size, image_batch_size)</code>) \u2014 The scaled dot product scores between <code>text_embeds</code> and <code>image_embeds</code>. This represents the text-image
similarity scores.</p>
</li>
<li>
<p><strong>segmentation_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>text_embeds</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, output_dim</code>) \u2014 The text embeddings obtained by applying the projection layer to the pooled output of
<a
  href="/docs/transformers/v4.33.0/en/model_doc/groupvit#transformers.GroupViTTextModel"
>GroupViTTextModel</a>.</p>
</li>
<li>
<p><strong>image_embeds</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, output_dim</code>) \u2014 The image embeddings obtained by applying the projection layer to the pooled output of
<a
  href="/docs/transformers/v4.33.0/en/model_doc/groupvit#transformers.GroupViTVisionModel"
>GroupViTVisionModel</a>.</p>
</li>
<li>
<p><strong>text_model_output</strong> (<code>BaseModelOutputWithPooling</code>) \u2014 The output of the <a
  href="/docs/transformers/v4.33.0/en/model_doc/groupvit#transformers.GroupViTTextModel"
>GroupViTTextModel</a>.</p>
</li>
<li>
<p><strong>vision_model_output</strong> (<code>BaseModelOutputWithPooling</code>) \u2014 The output of the <a
  href="/docs/transformers/v4.33.0/en/model_doc/groupvit#transformers.GroupViTVisionModel"
>GroupViTVisionModel</a>.</p>
</li>
</ul>
`,returnType:`
<p><code>transformers.models.groupvit.modeling_groupvit.GroupViTModelOutput</code> or <code>tuple(torch.FloatTensor)</code></p>
`}}),Vt=new pe({props:{$$slots:{default:[Ec]},$$scope:{ctx:G}}}),$t=new re({props:{anchor:"transformers.GroupViTModel.forward.example",$$slots:{default:[Jc]},$$scope:{ctx:G}}}),Po=new J({props:{name:"get_text_features",anchor:"transformers.GroupViTModel.get_text_features",parameters:[{name:"input_ids",val:": typing.Optional[torch.Tensor] = None"},{name:"attention_mask",val:": typing.Optional[torch.Tensor] = None"},{name:"position_ids",val:": typing.Optional[torch.Tensor] = None"},{name:"output_attentions",val:": typing.Optional[bool] = None"},{name:"output_hidden_states",val:": typing.Optional[bool] = None"},{name:"return_dict",val:": typing.Optional[bool] = None"}],parametersDescription:[{anchor:"transformers.GroupViTModel.get_text_features.input_ids",description:`<strong>input_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
it.</p>
<p>Indices can be obtained using <a href="/docs/transformers/v4.33.0/en/model_doc/clip#transformers.CLIPTokenizer">CLIPTokenizer</a>. See <a href="/docs/transformers/v4.33.0/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/v4.33.0/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.GroupViTModel.get_text_features.attention_mask",description:`<strong>attention_mask</strong> (<code>torch.Tensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Mask to avoid performing attention on padding token indices. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.</li>
</ul>
<p><a href="../glossary#attention-mask">What are attention masks?</a>`,name:"attention_mask"},{anchor:"transformers.GroupViTModel.get_text_features.position_ids",description:`<strong>position_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range <code>[0, config.max_position_embeddings - 1]</code>.</p>
<p><a href="../glossary#position-ids">What are position IDs?</a>`,name:"position_ids"},{anchor:"transformers.GroupViTModel.get_text_features.output_attentions",description:`<strong>output_attentions</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the attentions tensors of all attention layers. See <code>attentions</code> under returned
tensors for more detail.`,name:"output_attentions"},{anchor:"transformers.GroupViTModel.get_text_features.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.GroupViTModel.get_text_features.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/v4.33.0/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/groupvit/modeling_groupvit.py#L1392",returnDescription:`
<p>The text embeddings obtained by
applying the projection layer to the pooled output of <a
  href="/docs/transformers/v4.33.0/en/model_doc/groupvit#transformers.GroupViTTextModel"
>GroupViTTextModel</a>.</p>
`,returnType:`
<p>text_features (<code>torch.FloatTensor</code> of shape <code>(batch_size, output_dim</code>)</p>
`}}),Gt=new pe({props:{$$slots:{default:[Uc]},$$scope:{ctx:G}}}),kt=new re({props:{anchor:"transformers.GroupViTModel.get_text_features.example",$$slots:{default:[zc]},$$scope:{ctx:G}}}),qo=new J({props:{name:"get_image_features",anchor:"transformers.GroupViTModel.get_image_features",parameters:[{name:"pixel_values",val:": typing.Optional[torch.FloatTensor] = None"},{name:"output_attentions",val:": typing.Optional[bool] = None"},{name:"output_hidden_states",val:": typing.Optional[bool] = None"},{name:"return_dict",val:": typing.Optional[bool] = None"}],parametersDescription:[{anchor:"transformers.GroupViTModel.get_image_features.pixel_values",description:`<strong>pixel_values</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, num_channels, height, width)</code>) &#x2014;
Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using
<a href="/docs/transformers/v4.33.0/en/model_doc/auto#transformers.AutoImageProcessor">AutoImageProcessor</a>. See <a href="/docs/transformers/v4.33.0/en/model_doc/donut#transformers.DonutFeatureExtractor.__call__">CLIPImageProcessor.<strong>call</strong>()</a> for details.`,name:"pixel_values"},{anchor:"transformers.GroupViTModel.get_image_features.output_attentions",description:`<strong>output_attentions</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the attentions tensors of all attention layers. See <code>attentions</code> under returned
tensors for more detail.`,name:"output_attentions"},{anchor:"transformers.GroupViTModel.get_image_features.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.GroupViTModel.get_image_features.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/v4.33.0/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/groupvit/modeling_groupvit.py#L1439",returnDescription:`
<p>The image embeddings obtained by
applying the projection layer to the pooled output of <a
  href="/docs/transformers/v4.33.0/en/model_doc/groupvit#transformers.GroupViTVisionModel"
>GroupViTVisionModel</a>.</p>
`,returnType:`
<p>image_features (<code>torch.FloatTensor</code> of shape <code>(batch_size, output_dim</code>)</p>
`}}),jt=new pe({props:{$$slots:{default:[Cc]},$$scope:{ctx:G}}}),xt=new re({props:{anchor:"transformers.GroupViTModel.get_image_features.example",$$slots:{default:[Ic]},$$scope:{ctx:G}}}),Ao=new ne({}),So=new J({props:{name:"class transformers.GroupViTTextModel",anchor:"transformers.GroupViTTextModel",parameters:[{name:"config",val:": GroupViTTextConfig"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/groupvit/modeling_groupvit.py#L1182"}}),Ho=new J({props:{name:"forward",anchor:"transformers.GroupViTTextModel.forward",parameters:[{name:"input_ids",val:": typing.Optional[torch.Tensor] = None"},{name:"attention_mask",val:": typing.Optional[torch.Tensor] = None"},{name:"position_ids",val:": typing.Optional[torch.Tensor] = None"},{name:"output_attentions",val:": typing.Optional[bool] = None"},{name:"output_hidden_states",val:": typing.Optional[bool] = None"},{name:"return_dict",val:": typing.Optional[bool] = None"}],parametersDescription:[{anchor:"transformers.GroupViTTextModel.forward.input_ids",description:`<strong>input_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
it.</p>
<p>Indices can be obtained using <a href="/docs/transformers/v4.33.0/en/model_doc/clip#transformers.CLIPTokenizer">CLIPTokenizer</a>. See <a href="/docs/transformers/v4.33.0/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/v4.33.0/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.GroupViTTextModel.forward.attention_mask",description:`<strong>attention_mask</strong> (<code>torch.Tensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Mask to avoid performing attention on padding token indices. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.</li>
</ul>
<p><a href="../glossary#attention-mask">What are attention masks?</a>`,name:"attention_mask"},{anchor:"transformers.GroupViTTextModel.forward.position_ids",description:`<strong>position_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range <code>[0, config.max_position_embeddings - 1]</code>.</p>
<p><a href="../glossary#position-ids">What are position IDs?</a>`,name:"position_ids"},{anchor:"transformers.GroupViTTextModel.forward.output_attentions",description:`<strong>output_attentions</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the attentions tensors of all attention layers. See <code>attentions</code> under returned
tensors for more detail.`,name:"output_attentions"},{anchor:"transformers.GroupViTTextModel.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.GroupViTTextModel.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/v4.33.0/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/groupvit/modeling_groupvit.py#L1197",returnDescription:`
<p>A <a
  href="/docs/transformers/v4.33.0/en/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPooling"
>transformers.modeling_outputs.BaseModelOutputWithPooling</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 (<code>&lt;class 'transformers.models.groupvit.configuration_groupvit.GroupViTTextConfig'&gt;</code>) and inputs.</p>
<ul>
<li>
<p><strong>last_hidden_state</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>) \u2014 Sequence of hidden-states at the output of the last layer of the model.</p>
</li>
<li>
<p><strong>pooler_output</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, hidden_size)</code>) \u2014 Last layer hidden-state of the first token of the sequence (classification token) after further processing
through the layers used for the auxiliary pretraining task. E.g. for BERT-family of models, this returns
the classification token after processing through a linear layer and a tanh activation function. The linear
layer weights are trained from the next sentence prediction (classification) objective during pretraining.</p>
</li>
<li>
<p><strong>hidden_states</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape <code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.</p>
</li>
<li>
<p><strong>attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.</p>
</li>
</ul>
`,returnType:`
<p><a
  href="/docs/transformers/v4.33.0/en/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPooling"
>transformers.modeling_outputs.BaseModelOutputWithPooling</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),Jt=new pe({props:{$$slots:{default:[Wc]},$$scope:{ctx:G}}}),Ut=new re({props:{anchor:"transformers.GroupViTTextModel.forward.example",$$slots:{default:[Fc]},$$scope:{ctx:G}}}),Xo=new ne({}),Do=new J({props:{name:"class transformers.GroupViTVisionModel",anchor:"transformers.GroupViTVisionModel",parameters:[{name:"config",val:": GroupViTVisionConfig"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/groupvit/modeling_groupvit.py#L1293"}}),Yo=new J({props:{name:"forward",anchor:"transformers.GroupViTVisionModel.forward",parameters:[{name:"pixel_values",val:": typing.Optional[torch.FloatTensor] = None"},{name:"output_attentions",val:": typing.Optional[bool] = None"},{name:"output_hidden_states",val:": typing.Optional[bool] = None"},{name:"return_dict",val:": typing.Optional[bool] = None"}],parametersDescription:[{anchor:"transformers.GroupViTVisionModel.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. Padding will be ignored by default should you provide it. Pixel values can be obtained using
<a href="/docs/transformers/v4.33.0/en/model_doc/auto#transformers.AutoImageProcessor">AutoImageProcessor</a>. See <a href="/docs/transformers/v4.33.0/en/model_doc/donut#transformers.DonutFeatureExtractor.__call__">CLIPImageProcessor.<strong>call</strong>()</a> for details.`,name:"pixel_values"},{anchor:"transformers.GroupViTVisionModel.forward.output_attentions",description:`<strong>output_attentions</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the attentions tensors of all attention layers. See <code>attentions</code> under returned
tensors for more detail.`,name:"output_attentions"},{anchor:"transformers.GroupViTVisionModel.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.GroupViTVisionModel.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/v4.33.0/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/groupvit/modeling_groupvit.py#L1306",returnDescription:`
<p>A <a
  href="/docs/transformers/v4.33.0/en/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPooling"
>transformers.modeling_outputs.BaseModelOutputWithPooling</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 (<code>&lt;class 'transformers.models.groupvit.configuration_groupvit.GroupViTVisionConfig'&gt;</code>) and inputs.</p>
<ul>
<li>
<p><strong>last_hidden_state</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>) \u2014 Sequence of hidden-states at the output of the last layer of the model.</p>
</li>
<li>
<p><strong>pooler_output</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, hidden_size)</code>) \u2014 Last layer hidden-state of the first token of the sequence (classification token) after further processing
through the layers used for the auxiliary pretraining task. E.g. for BERT-family of models, this returns
the classification token after processing through a linear layer and a tanh activation function. The linear
layer weights are trained from the next sentence prediction (classification) objective during pretraining.</p>
</li>
<li>
<p><strong>hidden_states</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape <code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.</p>
</li>
<li>
<p><strong>attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.</p>
</li>
</ul>
`,returnType:`
<p><a
  href="/docs/transformers/v4.33.0/en/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPooling"
>transformers.modeling_outputs.BaseModelOutputWithPooling</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),Ct=new pe({props:{$$slots:{default:[Zc]},$$scope:{ctx:G}}}),It=new re({props:{anchor:"transformers.GroupViTVisionModel.forward.example",$$slots:{default:[Bc]},$$scope:{ctx:G}}}),Oo=new ne({}),Lo=new J({props:{name:"class transformers.TFGroupViTModel",anchor:"transformers.TFGroupViTModel",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFGroupViTModel.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.33.0/en/model_doc/groupvit#transformers.GroupViTConfig">GroupViTConfig</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/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/groupvit/modeling_tf_groupvit.py#L1728"}}),Ft=new pe({props:{$$slots:{default:[Rc]},$$scope:{ctx:G}}}),tn=new J({props:{name:"call",anchor:"transformers.TFGroupViTModel.call",parameters:[{name:"input_ids",val:": TFModelInputType | None = None"},{name:"pixel_values",val:": TFModelInputType | None = None"},{name:"attention_mask",val:": np.ndarray | tf.Tensor | None = None"},{name:"position_ids",val:": np.ndarray | tf.Tensor | None = None"},{name:"return_loss",val:": Optional[bool] = None"},{name:"output_attentions",val:": Optional[bool] = None"},{name:"output_hidden_states",val:": Optional[bool] = None"},{name:"output_segmentation",val:": Optional[bool] = None"},{name:"return_dict",val:": Optional[bool] = None"},{name:"training",val:": bool = False"}],parametersDescription:[{anchor:"transformers.TFGroupViTModel.call.input_ids",description:`<strong>input_ids</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, sequence_length)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using <a href="/docs/transformers/v4.33.0/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/v4.33.0/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> and
<a href="/docs/transformers/v4.33.0/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.TFGroupViTModel.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/v4.33.0/en/model_doc/auto#transformers.AutoImageProcessor">AutoImageProcessor</a>. See
<a href="/docs/transformers/v4.33.0/en/model_doc/donut#transformers.DonutFeatureExtractor.__call__">CLIPImageProcessor.<strong>call</strong>()</a> for details.`,name:"pixel_values"},{anchor:"transformers.TFGroupViTModel.call.attention_mask",description:`<strong>attention_mask</strong> (<code>np.ndarray</code> or <code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Mask to avoid performing attention on padding token indices. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.</li>
</ul>
<p><a href="../glossary#attention-mask">What are attention masks?</a>`,name:"attention_mask"},{anchor:"transformers.TFGroupViTModel.call.position_ids",description:`<strong>position_ids</strong> (<code>np.ndarray</code> or <code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range <code>[0, config.max_position_embeddings - 1]</code>.</p>
<p><a href="../glossary#position-ids">What are position IDs?</a>`,name:"position_ids"},{anchor:"transformers.TFGroupViTModel.call.return_loss",description:`<strong>return_loss</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the contrastive loss.`,name:"return_loss"},{anchor:"transformers.TFGroupViTModel.call.output_attentions",description:`<strong>output_attentions</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the attentions tensors of all attention layers. See <code>attentions</code> under returned
tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the
config will be used instead.`,name:"output_attentions"},{anchor:"transformers.TFGroupViTModel.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.TFGroupViTModel.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/v4.33.0/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple. This argument can be used in
eager mode, in graph mode the value will always be set to True.`,name:"return_dict"},{anchor:"transformers.TFGroupViTModel.call.training",description:`<strong>training</strong> (<code>bool</code>, <em>optional</em>, defaults to \`False&#x201C;) &#x2014;
Whether or not to use the model in training mode (some modules like dropout modules have different
behaviors between training and evaluation).`,name:"training"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/groupvit/modeling_tf_groupvit.py#L1820",returnDescription:`
<p>A <code>transformers.models.groupvit.modeling_tf_groupvit.TFGroupViTModelOutput</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 (<code>&lt;class 'transformers.models.groupvit.configuration_groupvit.GroupViTConfig'&gt;</code>) and inputs.</p>
<ul>
<li>
<p><strong>loss</strong> (<code>tf.Tensor</code> of shape <code>(1,)</code>, <em>optional</em>, returned when <code>return_loss</code> is <code>True</code>) \u2014 Contrastive loss for image-text similarity.</p>
</li>
<li>
<p><strong>logits_per_image</strong> (<code>tf.Tensor</code> of shape <code>(image_batch_size, text_batch_size)</code>) \u2014 The scaled dot product scores between <code>image_embeds</code> and <code>text_embeds</code>. This represents the image-text
similarity scores.</p>
</li>
<li>
<p><strong>logits_per_text</strong> (<code>tf.Tensor</code> of shape <code>(text_batch_size, image_batch_size)</code>) \u2014 The scaled dot product scores between <code>text_embeds</code> and <code>image_embeds</code>. This represents the text-image
similarity scores.</p>
</li>
<li>
<p><strong>segmentation_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>text_embeds</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, output_dim</code>) \u2014 The text embeddings obtained by applying the projection layer to the pooled output of
<a
  href="/docs/transformers/v4.33.0/en/model_doc/groupvit#transformers.TFGroupViTTextModel"
>TFGroupViTTextModel</a>.</p>
</li>
<li>
<p><strong>image_embeds</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, output_dim</code>) \u2014 The image embeddings obtained by applying the projection layer to the pooled output of
<a
  href="/docs/transformers/v4.33.0/en/model_doc/groupvit#transformers.TFGroupViTVisionModel"
>TFGroupViTVisionModel</a>.</p>
</li>
<li>
<p><strong>text_model_output</strong> (<code>TFBaseModelOutputWithPooling</code>) \u2014 The output of the <a
  href="/docs/transformers/v4.33.0/en/model_doc/groupvit#transformers.TFGroupViTTextModel"
>TFGroupViTTextModel</a>.</p>
</li>
<li>
<p><strong>vision_model_output</strong> (<code>TFBaseModelOutputWithPooling</code>) \u2014 The output of the <a
  href="/docs/transformers/v4.33.0/en/model_doc/groupvit#transformers.TFGroupViTVisionModel"
>TFGroupViTVisionModel</a>.</p>
</li>
</ul>
`,returnType:`
<p><code>transformers.models.groupvit.modeling_tf_groupvit.TFGroupViTModelOutput</code> or <code>tuple(tf.Tensor)</code></p>
`}}),Zt=new pe({props:{$$slots:{default:[Nc]},$$scope:{ctx:G}}}),Bt=new re({props:{anchor:"transformers.TFGroupViTModel.call.example",$$slots:{default:[Pc]},$$scope:{ctx:G}}}),on=new J({props:{name:"get_text_features",anchor:"transformers.TFGroupViTModel.get_text_features",parameters:[{name:"input_ids",val:": TFModelInputType | None = None"},{name:"attention_mask",val:": np.ndarray | tf.Tensor | None = None"},{name:"position_ids",val:": np.ndarray | tf.Tensor | None = None"},{name:"output_attentions",val:": Optional[bool] = None"},{name:"output_hidden_states",val:": Optional[bool] = None"},{name:"return_dict",val:": Optional[bool] = None"},{name:"training",val:": bool = False"}],parametersDescription:[{anchor:"transformers.TFGroupViTModel.get_text_features.input_ids",description:`<strong>input_ids</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, sequence_length)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using <a href="/docs/transformers/v4.33.0/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/v4.33.0/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> and
<a href="/docs/transformers/v4.33.0/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.TFGroupViTModel.get_text_features.attention_mask",description:`<strong>attention_mask</strong> (<code>np.ndarray</code> or <code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Mask to avoid performing attention on padding token indices. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.</li>
</ul>
<p><a href="../glossary#attention-mask">What are attention masks?</a>`,name:"attention_mask"},{anchor:"transformers.TFGroupViTModel.get_text_features.position_ids",description:`<strong>position_ids</strong> (<code>np.ndarray</code> or <code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range <code>[0, config.max_position_embeddings - 1]</code>.</p>
<p><a href="../glossary#position-ids">What are position IDs?</a>`,name:"position_ids"},{anchor:"transformers.TFGroupViTModel.get_text_features.output_attentions",description:`<strong>output_attentions</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the attentions tensors of all attention layers. See <code>attentions</code> under returned
tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the
config will be used instead.`,name:"output_attentions"},{anchor:"transformers.TFGroupViTModel.get_text_features.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.TFGroupViTModel.get_text_features.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/v4.33.0/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple. This argument can be used in
eager mode, in graph mode the value will always be set to True.`,name:"return_dict"},{anchor:"transformers.TFGroupViTModel.get_text_features.training",description:`<strong>training</strong> (<code>bool</code>, <em>optional</em>, defaults to \`False&#x201C;) &#x2014;
Whether or not to use the model in training mode (some modules like dropout modules have different
behaviors between training and evaluation).`,name:"training"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/groupvit/modeling_tf_groupvit.py#L1736",returnDescription:`
<p>The text embeddings obtained by applying
the projection layer to the pooled output of <a
  href="/docs/transformers/v4.33.0/en/model_doc/groupvit#transformers.TFGroupViTTextModel"
>TFGroupViTTextModel</a>.</p>
`,returnType:`
<p>text_features (<code>tf.Tensor</code> of shape <code>(batch_size, output_dim</code>)</p>
`}}),Rt=new pe({props:{$$slots:{default:[qc]},$$scope:{ctx:G}}}),Nt=new re({props:{anchor:"transformers.TFGroupViTModel.get_text_features.example",$$slots:{default:[Ac]},$$scope:{ctx:G}}}),nn=new J({props:{name:"get_image_features",anchor:"transformers.TFGroupViTModel.get_image_features",parameters:[{name:"pixel_values",val:": TFModelInputType | None = None"},{name:"output_attentions",val:": Optional[bool] = None"},{name:"output_hidden_states",val:": Optional[bool] = None"},{name:"return_dict",val:": Optional[bool] = None"},{name:"training",val:": bool = False"}],parametersDescription:[{anchor:"transformers.TFGroupViTModel.get_image_features.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/v4.33.0/en/model_doc/auto#transformers.AutoImageProcessor">AutoImageProcessor</a>. See
<a href="/docs/transformers/v4.33.0/en/model_doc/donut#transformers.DonutFeatureExtractor.__call__">CLIPImageProcessor.<strong>call</strong>()</a> for details.`,name:"pixel_values"},{anchor:"transformers.TFGroupViTModel.get_image_features.output_attentions",description:`<strong>output_attentions</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the attentions tensors of all attention layers. See <code>attentions</code> under returned
tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the
config will be used instead.`,name:"output_attentions"},{anchor:"transformers.TFGroupViTModel.get_image_features.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.TFGroupViTModel.get_image_features.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/v4.33.0/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple. This argument can be used in
eager mode, in graph mode the value will always be set to True.`,name:"return_dict"},{anchor:"transformers.TFGroupViTModel.get_image_features.training",description:`<strong>training</strong> (<code>bool</code>, <em>optional</em>, defaults to \`False&#x201C;) &#x2014;
Whether or not to use the model in training mode (some modules like dropout modules have different
behaviors between training and evaluation).`,name:"training"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/groupvit/modeling_tf_groupvit.py#L1777",returnDescription:`
<p>The image embeddings obtained by applying
the projection layer to the pooled output of <a
  href="/docs/transformers/v4.33.0/en/model_doc/groupvit#transformers.TFGroupViTVisionModel"
>TFGroupViTVisionModel</a>.</p>
`,returnType:`
<p>image_features (<code>tf.Tensor</code> of shape <code>(batch_size, output_dim</code>)</p>
`}}),Pt=new pe({props:{$$slots:{default:[Sc]},$$scope:{ctx:G}}}),qt=new re({props:{anchor:"transformers.TFGroupViTModel.get_image_features.example",$$slots:{default:[Hc]},$$scope:{ctx:G}}}),rn=new ne({}),sn=new J({props:{name:"class transformers.TFGroupViTTextModel",anchor:"transformers.TFGroupViTTextModel",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/groupvit/modeling_tf_groupvit.py#L1620"}}),an=new J({props:{name:"call",anchor:"transformers.TFGroupViTTextModel.call",parameters:[{name:"input_ids",val:": TFModelInputType | None = None"},{name:"attention_mask",val:": np.ndarray | tf.Tensor | None = None"},{name:"position_ids",val:": np.ndarray | tf.Tensor | None = None"},{name:"output_attentions",val:": Optional[bool] = None"},{name:"output_hidden_states",val:": Optional[bool] = None"},{name:"return_dict",val:": Optional[bool] = None"},{name:"training",val:": bool = False"}],parametersDescription:[{anchor:"transformers.TFGroupViTTextModel.call.input_ids",description:`<strong>input_ids</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, sequence_length)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using <a href="/docs/transformers/v4.33.0/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/v4.33.0/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> and
<a href="/docs/transformers/v4.33.0/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.TFGroupViTTextModel.call.attention_mask",description:`<strong>attention_mask</strong> (<code>np.ndarray</code> or <code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Mask to avoid performing attention on padding token indices. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.</li>
</ul>
<p><a href="../glossary#attention-mask">What are attention masks?</a>`,name:"attention_mask"},{anchor:"transformers.TFGroupViTTextModel.call.position_ids",description:`<strong>position_ids</strong> (<code>np.ndarray</code> or <code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range <code>[0, config.max_position_embeddings - 1]</code>.</p>
<p><a href="../glossary#position-ids">What are position IDs?</a>`,name:"position_ids"},{anchor:"transformers.TFGroupViTTextModel.call.output_attentions",description:`<strong>output_attentions</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the attentions tensors of all attention layers. See <code>attentions</code> under returned
tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the
config will be used instead.`,name:"output_attentions"},{anchor:"transformers.TFGroupViTTextModel.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.TFGroupViTTextModel.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/v4.33.0/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><strong>hidden_states</strong> (<code>tuple(tf.Tensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) \u2014 Tuple of <code>tf.Tensor</code> (one for the output of the embeddings + one for the output of each layer) of shape
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