import{S as Wd,i as Bd,s as Nd,e as s,k as g,w as y,t as i,M as Vd,c as r,d as o,m as f,a,x as I,h as l,b as c,N as Fd,G as e,g as _,y as C,q as w,o as M,B as T,v as Ad,L as Me}from"../../chunks/vendor-hf-doc-builder.js";import{T as Oo}from"../../chunks/Tip-hf-doc-builder.js";import{D as S}from"../../chunks/Docstring-hf-doc-builder.js";import{C as Te}from"../../chunks/CodeBlock-hf-doc-builder.js";import{I as D}from"../../chunks/IconCopyLink-hf-doc-builder.js";import{E as we}from"../../chunks/ExampleCodeBlock-hf-doc-builder.js";import{P as Dd}from"../../chunks/PipelineTag-hf-doc-builder.js";function qd(P){let d,b,u,p,v;return p=new Te({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> CLIPSegConfig, CLIPSegModel

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Initializing a CLIPSegConfig with CIDAS/clipseg-rd64 style configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>configuration = CLIPSegConfig()

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Initializing a CLIPSegModel (with random weights) from the CIDAS/clipseg-rd64 style configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = CLIPSegModel(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

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># We can also initialize a CLIPSegConfig from a CLIPSegTextConfig and a CLIPSegVisionConfig</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Initializing a CLIPSegText and CLIPSegVision configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config_text = CLIPSegTextConfig()
<span class="hljs-meta">&gt;&gt;&gt; </span>config_vision = CLIPSegVisionConfig()

<span class="hljs-meta">&gt;&gt;&gt; </span>config = CLIPSegConfig.from_text_vision_configs(config_text, config_vision)`}}),{c(){d=s("p"),b=i("Example:"),u=g(),y(p.$$.fragment)},l(n){d=r(n,"P",{});var m=a(d);b=l(m,"Example:"),m.forEach(o),u=f(n),I(p.$$.fragment,n)},m(n,m){_(n,d,m),e(d,b),_(n,u,m),C(p,n,m),v=!0},p:Me,i(n){v||(w(p.$$.fragment,n),v=!0)},o(n){M(p.$$.fragment,n),v=!1},d(n){n&&o(d),n&&o(u),T(p,n)}}}function Rd(P){let d,b,u,p,v;return p=new Te({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMENMSVBTZWdUZXh0Q29uZmlnJTJDJTIwQ0xJUFNlZ1RleHRNb2RlbCUwQSUwQSUyMyUyMEluaXRpYWxpemluZyUyMGElMjBDTElQU2VnVGV4dENvbmZpZyUyMHdpdGglMjBDSURBUyUyRmNsaXBzZWctcmQ2NCUyMHN0eWxlJTIwY29uZmlndXJhdGlvbiUwQWNvbmZpZ3VyYXRpb24lMjAlM0QlMjBDTElQU2VnVGV4dENvbmZpZygpJTBBJTBBJTIzJTIwSW5pdGlhbGl6aW5nJTIwYSUyMENMSVBTZWdUZXh0TW9kZWwlMjAod2l0aCUyMHJhbmRvbSUyMHdlaWdodHMpJTIwZnJvbSUyMHRoZSUyMENJREFTJTJGY2xpcHNlZy1yZDY0JTIwc3R5bGUlMjBjb25maWd1cmF0aW9uJTBBbW9kZWwlMjAlM0QlMjBDTElQU2VnVGV4dE1vZGVsKGNvbmZpZ3VyYXRpb24pJTBBJTBBJTIzJTIwQWNjZXNzaW5nJTIwdGhlJTIwbW9kZWwlMjBjb25maWd1cmF0aW9uJTBBY29uZmlndXJhdGlvbiUyMCUzRCUyMG1vZGVsLmNvbmZpZw==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> CLIPSegTextConfig, CLIPSegTextModel

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Initializing a CLIPSegTextConfig with CIDAS/clipseg-rd64 style configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>configuration = CLIPSegTextConfig()

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Initializing a CLIPSegTextModel (with random weights) from the CIDAS/clipseg-rd64 style configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = CLIPSegTextModel(configuration)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Accessing the model configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>configuration = model.config`}}),{c(){d=s("p"),b=i("Example:"),u=g(),y(p.$$.fragment)},l(n){d=r(n,"P",{});var m=a(d);b=l(m,"Example:"),m.forEach(o),u=f(n),I(p.$$.fragment,n)},m(n,m){_(n,d,m),e(d,b),_(n,u,m),C(p,n,m),v=!0},p:Me,i(n){v||(w(p.$$.fragment,n),v=!0)},o(n){M(p.$$.fragment,n),v=!1},d(n){n&&o(d),n&&o(u),T(p,n)}}}function Xd(P){let d,b,u,p,v;return p=new Te({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> CLIPSegVisionConfig, CLIPSegVisionModel

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Initializing a CLIPSegVisionConfig with CIDAS/clipseg-rd64 style configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>configuration = CLIPSegVisionConfig()

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Initializing a CLIPSegVisionModel (with random weights) from the CIDAS/clipseg-rd64 style configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = CLIPSegVisionModel(configuration)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Accessing the model configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>configuration = model.config`}}),{c(){d=s("p"),b=i("Example:"),u=g(),y(p.$$.fragment)},l(n){d=r(n,"P",{});var m=a(d);b=l(m,"Example:"),m.forEach(o),u=f(n),I(p.$$.fragment,n)},m(n,m){_(n,d,m),e(d,b),_(n,u,m),C(p,n,m),v=!0},p:Me,i(n){v||(w(p.$$.fragment,n),v=!0)},o(n){M(p.$$.fragment,n),v=!1},d(n){n&&o(d),n&&o(u),T(p,n)}}}function Gd(P){let d,b,u,p,v;return{c(){d=s("p"),b=i("Although the recipe for forward pass needs to be defined within this function, one should call the "),u=s("code"),p=i("Module"),v=i(`
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=r(n,"P",{});var m=a(d);b=l(m,"Although the recipe for forward pass needs to be defined within this function, one should call the "),u=r(m,"CODE",{});var $=a(u);p=l($,"Module"),$.forEach(o),v=l(m,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),m.forEach(o)},m(n,m){_(n,d,m),e(d,b),e(d,u),e(u,p),e(d,v)},d(n){n&&o(d)}}}function Qd(P){let d,b,u,p,v;return p=new Te({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, CLIPSegModel

<span class="hljs-meta">&gt;&gt;&gt; </span>processor = AutoProcessor.from_pretrained(<span class="hljs-string">&quot;CIDAS/clipseg-rd64-refined&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = CLIPSegModel.from_pretrained(<span class="hljs-string">&quot;CIDAS/clipseg-rd64-refined&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=s("p"),b=i("Examples:"),u=g(),y(p.$$.fragment)},l(n){d=r(n,"P",{});var m=a(d);b=l(m,"Examples:"),m.forEach(o),u=f(n),I(p.$$.fragment,n)},m(n,m){_(n,d,m),e(d,b),_(n,u,m),C(p,n,m),v=!0},p:Me,i(n){v||(w(p.$$.fragment,n),v=!0)},o(n){M(p.$$.fragment,n),v=!1},d(n){n&&o(d),n&&o(u),T(p,n)}}}function Hd(P){let d,b,u,p,v;return{c(){d=s("p"),b=i("Although the recipe for forward pass needs to be defined within this function, one should call the "),u=s("code"),p=i("Module"),v=i(`
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=r(n,"P",{});var m=a(d);b=l(m,"Although the recipe for forward pass needs to be defined within this function, one should call the "),u=r(m,"CODE",{});var $=a(u);p=l($,"Module"),$.forEach(o),v=l(m,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),m.forEach(o)},m(n,m){_(n,d,m),e(d,b),e(d,u),e(u,p),e(d,v)},d(n){n&&o(d)}}}function Yd(P){let d,b,u,p,v;return p=new Te({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> AutoTokenizer, CLIPSegModel

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;CIDAS/clipseg-rd64-refined&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = CLIPSegModel.from_pretrained(<span class="hljs-string">&quot;CIDAS/clipseg-rd64-refined&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=s("p"),b=i("Examples:"),u=g(),y(p.$$.fragment)},l(n){d=r(n,"P",{});var m=a(d);b=l(m,"Examples:"),m.forEach(o),u=f(n),I(p.$$.fragment,n)},m(n,m){_(n,d,m),e(d,b),_(n,u,m),C(p,n,m),v=!0},p:Me,i(n){v||(w(p.$$.fragment,n),v=!0)},o(n){M(p.$$.fragment,n),v=!1},d(n){n&&o(d),n&&o(u),T(p,n)}}}function Od(P){let d,b,u,p,v;return{c(){d=s("p"),b=i("Although the recipe for forward pass needs to be defined within this function, one should call the "),u=s("code"),p=i("Module"),v=i(`
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=r(n,"P",{});var m=a(d);b=l(m,"Although the recipe for forward pass needs to be defined within this function, one should call the "),u=r(m,"CODE",{});var $=a(u);p=l($,"Module"),$.forEach(o),v=l(m,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),m.forEach(o)},m(n,m){_(n,d,m),e(d,b),e(d,u),e(u,p),e(d,v)},d(n){n&&o(d)}}}function Kd(P){let d,b,u,p,v;return p=new Te({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, CLIPSegModel

<span class="hljs-meta">&gt;&gt;&gt; </span>processor = AutoProcessor.from_pretrained(<span class="hljs-string">&quot;CIDAS/clipseg-rd64-refined&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = CLIPSegModel.from_pretrained(<span class="hljs-string">&quot;CIDAS/clipseg-rd64-refined&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=s("p"),b=i("Examples:"),u=g(),y(p.$$.fragment)},l(n){d=r(n,"P",{});var m=a(d);b=l(m,"Examples:"),m.forEach(o),u=f(n),I(p.$$.fragment,n)},m(n,m){_(n,d,m),e(d,b),_(n,u,m),C(p,n,m),v=!0},p:Me,i(n){v||(w(p.$$.fragment,n),v=!0)},o(n){M(p.$$.fragment,n),v=!1},d(n){n&&o(d),n&&o(u),T(p,n)}}}function ec(P){let d,b,u,p,v;return{c(){d=s("p"),b=i("Although the recipe for forward pass needs to be defined within this function, one should call the "),u=s("code"),p=i("Module"),v=i(`
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=r(n,"P",{});var m=a(d);b=l(m,"Although the recipe for forward pass needs to be defined within this function, one should call the "),u=r(m,"CODE",{});var $=a(u);p=l($,"Module"),$.forEach(o),v=l(m,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),m.forEach(o)},m(n,m){_(n,d,m),e(d,b),e(d,u),e(u,p),e(d,v)},d(n){n&&o(d)}}}function tc(P){let d,b,u,p,v;return p=new Te({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> AutoTokenizer, CLIPSegTextModel

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;CIDAS/clipseg-rd64-refined&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = CLIPSegTextModel.from_pretrained(<span class="hljs-string">&quot;CIDAS/clipseg-rd64-refined&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=s("p"),b=i("Examples:"),u=g(),y(p.$$.fragment)},l(n){d=r(n,"P",{});var m=a(d);b=l(m,"Examples:"),m.forEach(o),u=f(n),I(p.$$.fragment,n)},m(n,m){_(n,d,m),e(d,b),_(n,u,m),C(p,n,m),v=!0},p:Me,i(n){v||(w(p.$$.fragment,n),v=!0)},o(n){M(p.$$.fragment,n),v=!1},d(n){n&&o(d),n&&o(u),T(p,n)}}}function oc(P){let d,b,u,p,v;return{c(){d=s("p"),b=i("Although the recipe for forward pass needs to be defined within this function, one should call the "),u=s("code"),p=i("Module"),v=i(`
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=r(n,"P",{});var m=a(d);b=l(m,"Although the recipe for forward pass needs to be defined within this function, one should call the "),u=r(m,"CODE",{});var $=a(u);p=l($,"Module"),$.forEach(o),v=l(m,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),m.forEach(o)},m(n,m){_(n,d,m),e(d,b),e(d,u),e(u,p),e(d,v)},d(n){n&&o(d)}}}function nc(P){let d,b,u,p,v;return p=new Te({props:{code:"ZnJvbSUyMFBJTCUyMGltcG9ydCUyMEltYWdlJTBBaW1wb3J0JTIwcmVxdWVzdHMlMEFmcm9tJTIwdHJhbnNmb3JtZXJzJTIwaW1wb3J0JTIwQXV0b1Byb2Nlc3NvciUyQyUyMENMSVBTZWdWaXNpb25Nb2RlbCUwQSUwQXByb2Nlc3NvciUyMCUzRCUyMEF1dG9Qcm9jZXNzb3IuZnJvbV9wcmV0cmFpbmVkKCUyMkNJREFTJTJGY2xpcHNlZy1yZDY0LXJlZmluZWQlMjIpJTBBbW9kZWwlMjAlM0QlMjBDTElQU2VnVmlzaW9uTW9kZWwuZnJvbV9wcmV0cmFpbmVkKCUyMkNJREFTJTJGY2xpcHNlZy1yZDY0LXJlZmluZWQlMjIpJTBBJTBBdXJsJTIwJTNEJTIwJTIyaHR0cCUzQSUyRiUyRmltYWdlcy5jb2NvZGF0YXNldC5vcmclMkZ2YWwyMDE3JTJGMDAwMDAwMDM5NzY5LmpwZyUyMiUwQWltYWdlJTIwJTNEJTIwSW1hZ2Uub3BlbihyZXF1ZXN0cy5nZXQodXJsJTJDJTIwc3RyZWFtJTNEVHJ1ZSkucmF3KSUwQSUwQWlucHV0cyUyMCUzRCUyMHByb2Nlc3NvcihpbWFnZXMlM0RpbWFnZSUyQyUyMHJldHVybl90ZW5zb3JzJTNEJTIycHQlMjIpJTBBJTBBb3V0cHV0cyUyMCUzRCUyMG1vZGVsKCoqaW5wdXRzKSUwQWxhc3RfaGlkZGVuX3N0YXRlJTIwJTNEJTIwb3V0cHV0cy5sYXN0X2hpZGRlbl9zdGF0ZSUwQXBvb2xlZF9vdXRwdXQlMjAlM0QlMjBvdXRwdXRzLnBvb2xlcl9vdXRwdXQlMjAlMjAlMjMlMjBwb29sZWQlMjBDTFMlMjBzdGF0ZXM=",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, CLIPSegVisionModel

<span class="hljs-meta">&gt;&gt;&gt; </span>processor = AutoProcessor.from_pretrained(<span class="hljs-string">&quot;CIDAS/clipseg-rd64-refined&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = CLIPSegVisionModel.from_pretrained(<span class="hljs-string">&quot;CIDAS/clipseg-rd64-refined&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=s("p"),b=i("Examples:"),u=g(),y(p.$$.fragment)},l(n){d=r(n,"P",{});var m=a(d);b=l(m,"Examples:"),m.forEach(o),u=f(n),I(p.$$.fragment,n)},m(n,m){_(n,d,m),e(d,b),_(n,u,m),C(p,n,m),v=!0},p:Me,i(n){v||(w(p.$$.fragment,n),v=!0)},o(n){M(p.$$.fragment,n),v=!1},d(n){n&&o(d),n&&o(u),T(p,n)}}}function sc(P){let d,b,u,p,v;return{c(){d=s("p"),b=i("Although the recipe for forward pass needs to be defined within this function, one should call the "),u=s("code"),p=i("Module"),v=i(`
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=r(n,"P",{});var m=a(d);b=l(m,"Although the recipe for forward pass needs to be defined within this function, one should call the "),u=r(m,"CODE",{});var $=a(u);p=l($,"Module"),$.forEach(o),v=l(m,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),m.forEach(o)},m(n,m){_(n,d,m),e(d,b),e(d,u),e(u,p),e(d,v)},d(n){n&&o(d)}}}function rc(P){let d,b,u,p,v;return p=new Te({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> AutoProcessor, CLIPSegForImageSegmentation
<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>processor = AutoProcessor.from_pretrained(<span class="hljs-string">&quot;CIDAS/clipseg-rd64-refined&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = CLIPSegForImageSegmentation.from_pretrained(<span class="hljs-string">&quot;CIDAS/clipseg-rd64-refined&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>texts = [<span class="hljs-string">&quot;a cat&quot;</span>, <span class="hljs-string">&quot;a remote&quot;</span>, <span class="hljs-string">&quot;a blanket&quot;</span>]
<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = processor(text=texts, images=[image] * <span class="hljs-built_in">len</span>(texts), 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>logits = outputs.logits
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-built_in">print</span>(logits.shape)
torch.Size([<span class="hljs-number">3</span>, <span class="hljs-number">352</span>, <span class="hljs-number">352</span>])`}}),{c(){d=s("p"),b=i("Examples:"),u=g(),y(p.$$.fragment)},l(n){d=r(n,"P",{});var m=a(d);b=l(m,"Examples:"),m.forEach(o),u=f(n),I(p.$$.fragment,n)},m(n,m){_(n,d,m),e(d,b),_(n,u,m),C(p,n,m),v=!0},p:Me,i(n){v||(w(p.$$.fragment,n),v=!0)},o(n){M(p.$$.fragment,n),v=!1},d(n){n&&o(d),n&&o(u),T(p,n)}}}function ac(P){let d,b,u,p,v,n,m,$,Fs,qn,O,Pe,Ko,mt,As,en,Ds,Rn,q,qs,pt,Rs,Xs,ro,Gs,Qs,Xn,ao,Hs,Gn,io,tn,Ys,Qn,lo,Os,Hn,$e,R,co,Ks,er,mo,tr,or,po,nr,sr,rr,U,go,ar,ir,on,lr,dr,nn,cr,mr,sn,pr,gr,Yn,Se,fl,On,gt,fr,fo,hr,Kn,X,ur,ft,_r,vr,ht,br,yr,es,K,Le,rn,ut,Ir,an,Cr,ts,ho,wr,os,_t,ns,uo,vt,Mr,bt,Tr,Pr,ss,ee,xe,ln,yt,$r,dn,Sr,rs,L,It,Lr,G,_o,xr,Jr,vo,jr,kr,Ct,Er,Zr,Ur,te,zr,bo,Wr,Br,yo,Nr,Vr,Fr,Je,Ar,je,wt,Dr,Mt,qr,Io,Rr,Xr,as,oe,ke,cn,Tt,Gr,mn,Qr,is,k,Pt,Hr,ne,Yr,Co,Or,Kr,$t,ea,ta,oa,se,na,wo,sa,ra,Mo,aa,ia,la,Ee,ls,re,Ze,pn,St,da,gn,ca,ds,E,Lt,ma,ae,pa,To,ga,fa,xt,ha,ua,_a,ie,va,Po,ba,ya,$o,Ia,Ca,wa,Ue,cs,le,ze,fn,Jt,Ma,hn,Ta,ms,x,jt,Pa,un,$a,Sa,j,So,La,xa,Lo,Ja,ja,xo,ka,Ea,_n,Za,Ua,Jo,za,Wa,Ba,We,kt,Na,Et,Va,jo,Fa,Aa,Da,Be,Zt,qa,Ut,Ra,ko,Xa,Ga,ps,de,Ne,vn,zt,Qa,bn,Ha,gs,J,Wt,Ya,Bt,Oa,Nt,Ka,ei,ti,z,Vt,oi,ce,ni,Eo,si,ri,yn,ai,ii,li,Ve,di,Fe,ci,W,Ft,mi,me,pi,Zo,gi,fi,In,hi,ui,_i,Ae,vi,De,bi,B,At,yi,pe,Ii,Uo,Ci,wi,Cn,Mi,Ti,Pi,qe,$i,Re,fs,ge,Xe,wn,Dt,Si,Mn,Li,hs,fe,qt,xi,N,Rt,Ji,he,ji,zo,ki,Ei,Tn,Zi,Ui,zi,Ge,Wi,Qe,us,ue,He,Pn,Xt,Bi,$n,Ni,_s,_e,Gt,Vi,V,Qt,Fi,ve,Ai,Wo,Di,qi,Sn,Ri,Xi,Gi,Ye,Qi,Oe,vs,be,Ke,Ln,Ht,Hi,xn,Yi,bs,Z,Yt,Oi,Jn,Ki,el,Ot,tl,Kt,ol,nl,sl,F,eo,rl,ye,al,Bo,il,ll,jn,dl,cl,ml,et,pl,tt,ys;return n=new D({}),mt=new D({}),ut=new D({}),_t=new Dd({props:{pipeline:"image-segmentation"}}),yt=new D({}),It=new S({props:{name:"class transformers.CLIPSegConfig",anchor:"transformers.CLIPSegConfig",parameters:[{name:"text_config",val:" = None"},{name:"vision_config",val:" = None"},{name:"projection_dim",val:" = 512"},{name:"logit_scale_init_value",val:" = 2.6592"},{name:"extract_layers",val:" = [3, 6, 9]"},{name:"reduce_dim",val:" = 64"},{name:"decoder_num_attention_heads",val:" = 4"},{name:"decoder_attention_dropout",val:" = 0.0"},{name:"decoder_hidden_act",val:" = 'quick_gelu'"},{name:"decoder_intermediate_size",val:" = 2048"},{name:"conditional_layer",val:" = 0"},{name:"use_complex_transposed_convolution",val:" = False"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.CLIPSegConfig.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/clipseg#transformers.CLIPSegTextConfig">CLIPSegTextConfig</a>.`,name:"text_config"},{anchor:"transformers.CLIPSegConfig.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/clipseg#transformers.CLIPSegVisionConfig">CLIPSegVisionConfig</a>.`,name:"vision_config"},{anchor:"transformers.CLIPSegConfig.projection_dim",description:`<strong>projection_dim</strong> (<code>int</code>, <em>optional</em>, defaults to 512) &#x2014;
Dimensionality of text and vision projection layers.`,name:"projection_dim"},{anchor:"transformers.CLIPSegConfig.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> paramter. Default is used as per the original CLIPSeg implementation.`,name:"logit_scale_init_value"},{anchor:"transformers.CLIPSegConfig.extract_layers",description:`<strong>extract_layers</strong> (<code>List[int]</code>, <em>optional</em>, defaults to [3, 6, 9]) &#x2014;
Layers to extract when forwarding the query image through the frozen visual backbone of CLIP.`,name:"extract_layers"},{anchor:"transformers.CLIPSegConfig.reduce_dim",description:`<strong>reduce_dim</strong> (<code>int</code>, <em>optional</em>, defaults to 64) &#x2014;
Dimensionality to reduce the CLIP vision embedding.`,name:"reduce_dim"},{anchor:"transformers.CLIPSegConfig.decoder_num_attention_heads",description:`<strong>decoder_num_attention_heads</strong> (<code>int</code>, <em>optional</em>, defaults to 4) &#x2014;
Number of attention heads in the decoder of CLIPSeg.`,name:"decoder_num_attention_heads"},{anchor:"transformers.CLIPSegConfig.decoder_attention_dropout",description:`<strong>decoder_attention_dropout</strong> (<code>float</code>, <em>optional</em>, defaults to 0.0) &#x2014;
The dropout ratio for the attention probabilities.`,name:"decoder_attention_dropout"},{anchor:"transformers.CLIPSegConfig.decoder_hidden_act",description:`<strong>decoder_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:"decoder_hidden_act"},{anchor:"transformers.CLIPSegConfig.decoder_intermediate_size",description:`<strong>decoder_intermediate_size</strong> (<code>int</code>, <em>optional</em>, defaults to 2048) &#x2014;
Dimensionality of the &#x201C;intermediate&#x201D; (i.e., feed-forward) layers in the Transformer decoder.`,name:"decoder_intermediate_size"},{anchor:"transformers.CLIPSegConfig.conditional_layer",description:`<strong>conditional_layer</strong> (<code>int</code>, <em>optional</em>, defaults to 0) &#x2014;
The layer to use of the Transformer encoder whose activations will be combined with the condition
embeddings using FiLM (Feature-wise Linear Modulation). If 0, the last layer is used.`,name:"conditional_layer"},{anchor:"transformers.CLIPSegConfig.use_complex_transposed_convolution",description:`<strong>use_complex_transposed_convolution</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether to use a more complex transposed convolution in the decoder, enabling more fine-grained
segmentation.`,name:"use_complex_transposed_convolution"},{anchor:"transformers.CLIPSegConfig.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/clipseg/configuration_clipseg.py#L239"}}),Je=new we({props:{anchor:"transformers.CLIPSegConfig.example",$$slots:{default:[qd]},$$scope:{ctx:P}}}),wt=new S({props:{name:"from_text_vision_configs",anchor:"transformers.CLIPSegConfig.from_text_vision_configs",parameters:[{name:"text_config",val:": CLIPSegTextConfig"},{name:"vision_config",val:": CLIPSegVisionConfig"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/clipseg/configuration_clipseg.py#L414",returnDescription:`
<p>An instance of a configuration object</p>
`,returnType:`
<p><a
  href="/docs/transformers/v4.33.0/en/model_doc/clipseg#transformers.CLIPSegConfig"
>CLIPSegConfig</a></p>
`}}),Tt=new D({}),Pt=new S({props:{name:"class transformers.CLIPSegTextConfig",anchor:"transformers.CLIPSegTextConfig",parameters:[{name:"vocab_size",val:" = 49408"},{name:"hidden_size",val:" = 512"},{name:"intermediate_size",val:" = 2048"},{name:"num_hidden_layers",val:" = 12"},{name:"num_attention_heads",val:" = 8"},{name:"max_position_embeddings",val:" = 77"},{name:"hidden_act",val:" = 'quick_gelu'"},{name:"layer_norm_eps",val:" = 1e-05"},{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.CLIPSegTextConfig.vocab_size",description:`<strong>vocab_size</strong> (<code>int</code>, <em>optional</em>, defaults to 49408) &#x2014;
Vocabulary size of the CLIPSeg 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/clipseg#transformers.CLIPSegModel">CLIPSegModel</a>.`,name:"vocab_size"},{anchor:"transformers.CLIPSegTextConfig.hidden_size",description:`<strong>hidden_size</strong> (<code>int</code>, <em>optional</em>, defaults to 512) &#x2014;
Dimensionality of the encoder layers and the pooler layer.`,name:"hidden_size"},{anchor:"transformers.CLIPSegTextConfig.intermediate_size",description:`<strong>intermediate_size</strong> (<code>int</code>, <em>optional</em>, defaults to 2048) &#x2014;
Dimensionality of the &#x201C;intermediate&#x201D; (i.e., feed-forward) layer in the Transformer encoder.`,name:"intermediate_size"},{anchor:"transformers.CLIPSegTextConfig.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.CLIPSegTextConfig.num_attention_heads",description:`<strong>num_attention_heads</strong> (<code>int</code>, <em>optional</em>, defaults to 8) &#x2014;
Number of attention heads for each attention layer in the Transformer encoder.`,name:"num_attention_heads"},{anchor:"transformers.CLIPSegTextConfig.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.CLIPSegTextConfig.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.CLIPSegTextConfig.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.CLIPSegTextConfig.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.CLIPSegTextConfig.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.CLIPSegTextConfig.initializer_factor",description:`<strong>initializer_factor</strong> (\`float&#x201C;, <em>optional</em>, defaults to 1) &#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/clipseg/configuration_clipseg.py#L31"}}),Ee=new we({props:{anchor:"transformers.CLIPSegTextConfig.example",$$slots:{default:[Rd]},$$scope:{ctx:P}}}),St=new D({}),Lt=new S({props:{name:"class transformers.CLIPSegVisionConfig",anchor:"transformers.CLIPSegVisionConfig",parameters:[{name:"hidden_size",val:" = 768"},{name:"intermediate_size",val:" = 3072"},{name:"num_hidden_layers",val:" = 12"},{name:"num_attention_heads",val:" = 12"},{name:"num_channels",val:" = 3"},{name:"image_size",val:" = 224"},{name:"patch_size",val:" = 32"},{name:"hidden_act",val:" = 'quick_gelu'"},{name:"layer_norm_eps",val:" = 1e-05"},{name:"attention_dropout",val:" = 0.0"},{name:"initializer_range",val:" = 0.02"},{name:"initializer_factor",val:" = 1.0"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.CLIPSegVisionConfig.hidden_size",description:`<strong>hidden_size</strong> (<code>int</code>, <em>optional</em>, defaults to 768) &#x2014;
Dimensionality of the encoder layers and the pooler layer.`,name:"hidden_size"},{anchor:"transformers.CLIPSegVisionConfig.intermediate_size",description:`<strong>intermediate_size</strong> (<code>int</code>, <em>optional</em>, defaults to 3072) &#x2014;
Dimensionality of the &#x201C;intermediate&#x201D; (i.e., feed-forward) layer in the Transformer encoder.`,name:"intermediate_size"},{anchor:"transformers.CLIPSegVisionConfig.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.CLIPSegVisionConfig.num_attention_heads",description:`<strong>num_attention_heads</strong> (<code>int</code>, <em>optional</em>, defaults to 12) &#x2014;
Number of attention heads for each attention layer in the Transformer encoder.`,name:"num_attention_heads"},{anchor:"transformers.CLIPSegVisionConfig.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.CLIPSegVisionConfig.patch_size",description:`<strong>patch_size</strong> (<code>int</code>, <em>optional</em>, defaults to 32) &#x2014;
The size (resolution) of each patch.`,name:"patch_size"},{anchor:"transformers.CLIPSegVisionConfig.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.CLIPSegVisionConfig.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.CLIPSegVisionConfig.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.CLIPSegVisionConfig.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.CLIPSegVisionConfig.initializer_factor",description:`<strong>initializer_factor</strong> (\`float&#x201C;, <em>optional</em>, defaults to 1) &#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/clipseg/configuration_clipseg.py#L136"}}),Ue=new we({props:{anchor:"transformers.CLIPSegVisionConfig.example",$$slots:{default:[Xd]},$$scope:{ctx:P}}}),Jt=new D({}),jt=new S({props:{name:"class transformers.CLIPSegProcessor",anchor:"transformers.CLIPSegProcessor",parameters:[{name:"image_processor",val:" = None"},{name:"tokenizer",val:" = None"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.CLIPSegProcessor.image_processor",description:`<strong>image_processor</strong> (<a href="/docs/transformers/v4.33.0/en/model_doc/vit#transformers.ViTImageProcessor">ViTImageProcessor</a>) &#x2014;
The image processor is a required input.`,name:"image_processor"},{anchor:"transformers.CLIPSegProcessor.tokenizer",description:`<strong>tokenizer</strong> (<a href="/docs/transformers/v4.33.0/en/model_doc/clip#transformers.CLIPTokenizerFast">CLIPTokenizerFast</a>) &#x2014;
The tokenizer is a required input.`,name:"tokenizer"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/clipseg/processing_clipseg.py#L25"}}),kt=new S({props:{name:"batch_decode",anchor:"transformers.CLIPSegProcessor.batch_decode",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/clipseg/processing_clipseg.py#L133"}}),Zt=new S({props:{name:"decode",anchor:"transformers.CLIPSegProcessor.decode",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/clipseg/processing_clipseg.py#L140"}}),zt=new D({}),Wt=new S({props:{name:"class transformers.CLIPSegModel",anchor:"transformers.CLIPSegModel",parameters:[{name:"config",val:": CLIPSegConfig"}],parametersDescription:[{anchor:"transformers.CLIPSegModel.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.33.0/en/model_doc/clipseg#transformers.CLIPSegConfig">CLIPSegConfig</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/clipseg/modeling_clipseg.py#L968"}}),Vt=new S({props:{name:"forward",anchor:"transformers.CLIPSegModel.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:"return_dict",val:": typing.Optional[bool] = None"}],parametersDescription:[{anchor:"transformers.CLIPSegModel.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/auto#transformers.AutoTokenizer">AutoTokenizer</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.CLIPSegModel.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.CLIPSegModel.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.CLIPSegModel.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.CLIPSegModel.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.CLIPSegModel.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.CLIPSegModel.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.CLIPSegModel.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/clipseg/modeling_clipseg.py#L1099",returnDescription:`
<p>A <code>transformers.models.clipseg.modeling_clipseg.CLIPSegOutput</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.clipseg.configuration_clipseg.CLIPSegConfig'&gt;</code>) and inputs.</p>
<ul>
<li><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.</li>
<li><strong>logits_per_image:(<code>torch.FloatTensor</code></strong> 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.</li>
<li><strong>logits_per_text:(<code>torch.FloatTensor</code></strong> 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.</li>
<li><strong>text_embeds(<code>torch.FloatTensor</code></strong> 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/clipseg#transformers.CLIPSegTextModel"
>CLIPSegTextModel</a>.</li>
<li><strong>image_embeds(<code>torch.FloatTensor</code></strong> 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/clipseg#transformers.CLIPSegVisionModel"
>CLIPSegVisionModel</a>.</li>
<li><strong>text_model_output(<code>BaseModelOutputWithPooling</code>):</strong>
The output of the <a
  href="/docs/transformers/v4.33.0/en/model_doc/clipseg#transformers.CLIPSegTextModel"
>CLIPSegTextModel</a>.</li>
<li><strong>vision_model_output(<code>BaseModelOutputWithPooling</code>):</strong>
The output of the <a
  href="/docs/transformers/v4.33.0/en/model_doc/clipseg#transformers.CLIPSegVisionModel"
>CLIPSegVisionModel</a>.</li>
</ul>
`,returnType:`
<p><code>transformers.models.clipseg.modeling_clipseg.CLIPSegOutput</code> or <code>tuple(torch.FloatTensor)</code></p>
`}}),Ve=new Oo({props:{$$slots:{default:[Gd]},$$scope:{ctx:P}}}),Fe=new we({props:{anchor:"transformers.CLIPSegModel.forward.example",$$slots:{default:[Qd]},$$scope:{ctx:P}}}),Ft=new S({props:{name:"get_text_features",anchor:"transformers.CLIPSegModel.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.CLIPSegModel.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/auto#transformers.AutoTokenizer">AutoTokenizer</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.CLIPSegModel.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.CLIPSegModel.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.CLIPSegModel.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.CLIPSegModel.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.CLIPSegModel.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/clipseg/modeling_clipseg.py#L1003",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/clipseg#transformers.CLIPSegTextModel"
>CLIPSegTextModel</a>.</p>
`,returnType:`
<p>text_features (<code>torch.FloatTensor</code> of shape <code>(batch_size, output_dim</code>)</p>
`}}),Ae=new Oo({props:{$$slots:{default:[Hd]},$$scope:{ctx:P}}}),De=new we({props:{anchor:"transformers.CLIPSegModel.get_text_features.example",$$slots:{default:[Yd]},$$scope:{ctx:P}}}),At=new S({props:{name:"get_image_features",anchor:"transformers.CLIPSegModel.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.CLIPSegModel.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.CLIPSegModel.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.CLIPSegModel.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.CLIPSegModel.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/clipseg/modeling_clipseg.py#L1050",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/clipseg#transformers.CLIPSegVisionModel"
>CLIPSegVisionModel</a>.</p>
`,returnType:`
<p>image_features (<code>torch.FloatTensor</code> of shape <code>(batch_size, output_dim</code>)</p>
`}}),qe=new Oo({props:{$$slots:{default:[Od]},$$scope:{ctx:P}}}),Re=new we({props:{anchor:"transformers.CLIPSegModel.get_image_features.example",$$slots:{default:[Kd]},$$scope:{ctx:P}}}),Dt=new D({}),qt=new S({props:{name:"class transformers.CLIPSegTextModel",anchor:"transformers.CLIPSegTextModel",parameters:[{name:"config",val:": CLIPSegTextConfig"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/clipseg/modeling_clipseg.py#L800"}}),Rt=new S({props:{name:"forward",anchor:"transformers.CLIPSegTextModel.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.CLIPSegTextModel.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/auto#transformers.AutoTokenizer">AutoTokenizer</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.CLIPSegTextModel.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.CLIPSegTextModel.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.CLIPSegTextModel.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
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Whether or not to return the hidden states of all layers. See <code>hidden_states</code> under returned tensors for
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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/clipseg/modeling_clipseg.py#L817",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.clipseg.configuration_clipseg.CLIPSegTextConfig'&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>
`}}),Ge=new Oo({props:{$$slots:{default:[ec]},$$scope:{ctx:P}}}),Qe=new we({props:{anchor:"transformers.CLIPSegTextModel.forward.example",$$slots:{default:[tc]},$$scope:{ctx:P}}}),Xt=new D({}),Gt=new S({props:{name:"class transformers.CLIPSegVisionModel",anchor:"transformers.CLIPSegVisionModel",parameters:[{name:"config",val:": CLIPSegVisionConfig"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/clipseg/modeling_clipseg.py#L915"}}),Qt=new S({props:{name:"forward",anchor:"transformers.CLIPSegVisionModel.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.CLIPSegVisionModel.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.CLIPSegVisionModel.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
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Whether or not to return the hidden states of all layers. See <code>hidden_states</code> under returned tensors for
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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/clipseg/modeling_clipseg.py#L928",returnDescription:`
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  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
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<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>
`}}),Ye=new Oo({props:{$$slots:{default:[oc]},$$scope:{ctx:P}}}),Oe=new we({props:{anchor:"transformers.CLIPSegVisionModel.forward.example",$$slots:{default:[nc]},$$scope:{ctx:P}}}),Ht=new D({}),Yt=new S({props:{name:"class transformers.CLIPSegForImageSegmentation",anchor:"transformers.CLIPSegForImageSegmentation",parameters:[{name:"config",val:": CLIPSegConfig"}],parametersDescription:[{anchor:"transformers.CLIPSegForImageSegmentation.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.33.0/en/model_doc/clipseg#transformers.CLIPSegConfig">CLIPSegConfig</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/clipseg/modeling_clipseg.py#L1356"}}),eo=new S({props:{name:"forward",anchor:"transformers.CLIPSegForImageSegmentation.forward",parameters:[{name:"input_ids",val:": typing.Optional[torch.FloatTensor] = None"},{name:"pixel_values",val:": typing.Optional[torch.FloatTensor] = None"},{name:"conditional_pixel_values",val:": typing.Optional[torch.FloatTensor] = None"},{name:"conditional_embeddings",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:"labels",val:": typing.Optional[torch.LongTensor] = 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.CLIPSegForImageSegmentation.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/auto#transformers.AutoTokenizer">AutoTokenizer</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.CLIPSegForImageSegmentation.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.CLIPSegForImageSegmentation.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.CLIPSegForImageSegmentation.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.CLIPSegForImageSegmentation.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.CLIPSegForImageSegmentation.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.CLIPSegForImageSegmentation.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.CLIPSegForImageSegmentation.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"},{anchor:"transformers.CLIPSegForImageSegmentation.forward.labels",description:`<strong>labels</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size,)</code>, <em>optional</em>) &#x2014;
Labels for computing the sequence classification/regression loss. Indices should be in <code>[0, ..., config.num_labels - 1]</code>. If <code>config.num_labels == 1</code> a regression loss is computed (Mean-Square loss), If
<code>config.num_labels &gt; 1</code> a classification loss is computed (Cross-Entropy).`,name:"labels"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/clipseg/modeling_clipseg.py#L1401",returnDescription:`
<p>A <code>transformers.models.clipseg.modeling_clipseg.CLIPSegImageSegmentationOutput</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.clipseg.configuration_clipseg.CLIPSegTextConfig'&gt;</code>) and inputs.</p>
<ul>
<li><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.
\u2026</li>
<li><strong>vision_model_output</strong> (<code>BaseModelOutputWithPooling</code>) \u2014 The output of the <a
  href="/docs/transformers/v4.33.0/en/model_doc/clipseg#transformers.CLIPSegVisionModel"
>CLIPSegVisionModel</a>.</li>
</ul>
`,returnType:`
<p><code>transformers.models.clipseg.modeling_clipseg.CLIPSegImageSegmentationOutput</code> or <code>tuple(torch.FloatTensor)</code></p>
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model for a fixed set of object classes. Incorporating additional classes or more complex queries later is expensive
as it requires re-training the model on a dataset that encompasses these expressions. Here we propose a system
that can generate image segmentations based on arbitrary
prompts at test time. A prompt can be either a text or an
image. This approach enables us to create a unified model
(trained once) for three common segmentation tasks, which
come with distinct challenges: referring expression segmentation, zero-shot segmentation and one-shot segmentation.
We build upon the CLIP model as a backbone which we extend with a transformer-based decoder that enables dense
prediction. After training on an extended version of the
PhraseCut dataset, our system generates a binary segmentation map for an image based on a free-text prompt or on
an additional image expressing the query. We analyze different variants of the latter image-based prompts in detail.
This novel hybrid input allows for dynamic adaptation not
only to the three segmentation tasks mentioned above, but
to any binary segmentation task where a text or image query
can be formulated. Finally, we find our system to adapt well
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The original code can be found `),ht=r(No,"A",{href:!0,rel:!0});var Ml=a(ht);br=l(Ml,"here"),Ml.forEach(o),yr=l(No,"."),No.forEach(o),es=f(t),K=r(t,"H2",{class:!0});var Cs=a(K);Le=r(Cs,"A",{id:!0,class:!0,href:!0});var Tl=a(Le);rn=r(Tl,"SPAN",{});var Pl=a(rn);I(ut.$$.fragment,Pl),Pl.forEach(o),Tl.forEach(o),Ir=f(Cs),an=r(Cs,"SPAN",{});var $l=a(an);Cr=l($l,"Resources"),$l.forEach(o),Cs.forEach(o),ts=f(t),ho=r(t,"P",{});var Sl=a(ho);wr=l(Sl,"A list of official Hugging Face and community (indicated by \u{1F30E}) resources to help you get started with CLIPSeg. If you\u2019re interested in submitting a resource to be included here, please feel free to open a Pull Request and we\u2019ll review it! The resource should ideally demonstrate something new instead of duplicating an existing resource."),Sl.forEach(o),os=f(t),I(_t.$$.fragment,t),ns=f(t),uo=r(t,"UL",{});var Ll=a(uo);vt=r(Ll,"LI",{});var ws=a(vt);Mr=l(ws,"A notebook that illustrates "),bt=r(ws,"A",{href:!0,rel:!0});var xl=a(bt);Tr=l(xl,"zero-shot image segmentation with CLIPSeg"),xl.forEach(o),Pr=l(ws,"."),ws.forEach(o),Ll.forEach(o),ss=f(t),ee=r(t,"H2",{class:!0});var Ms=a(ee);xe=r(Ms,"A",{id:!0,class:!0,href:!0});var Jl=a(xe);ln=r(Jl,"SPAN",{});var jl=a(ln);I(yt.$$.fragment,jl),jl.forEach(o),Jl.forEach(o),$r=f(Ms),dn=r(Ms,"SPAN",{});var kl=a(dn);Sr=l(kl,"CLIPSegConfig"),kl.forEach(o),Ms.forEach(o),rs=f(t),L=r(t,"DIV",{class:!0});var Q=a(L);I(It.$$.fragment,Q),Lr=f(Q),G=r(Q,"P",{});var so=a(G);_o=r(so,"A",{href:!0});var El=a(_o);xr=l(El,"CLIPSegConfig"),El.forEach(o),Jr=l(so," is the configuration class to store the configuration of a "),vo=r(so,"A",{href:!0});var Zl=a(vo);jr=l(Zl,"CLIPSegModel"),Zl.forEach(o),kr=l(so,`. It is used to
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