import{S as fi,i as hi,s as gi,e as s,k as h,w as v,t as l,M as ui,c as a,d as o,m as g,a as r,x as y,h as i,b as p,G as e,g as _,y as C,q as I,o as w,B as T,v as _i,L as Ke}from"../../chunks/vendor-hf-doc-builder.js";import{T as cn}from"../../chunks/Tip-hf-doc-builder.js";import{D as x}from"../../chunks/Docstring-hf-doc-builder.js";import{C as he}from"../../chunks/CodeBlock-hf-doc-builder.js";import{I as F}from"../../chunks/IconCopyLink-hf-doc-builder.js";import{E as Oe}from"../../chunks/ExampleCodeBlock-hf-doc-builder.js";function bi(P){let d,M,u,m,b;return m=new he({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEFsdENMSVBDb25maWclMkMlMjBBbHRDTElQTW9kZWwlMEElMEElMjMlMjBJbml0aWFsaXppbmclMjBhJTIwQWx0Q0xJUENvbmZpZyUyMHdpdGglMjBCQUFJJTJGQWx0Q0xJUCUyMHN0eWxlJTIwY29uZmlndXJhdGlvbiUwQWNvbmZpZ3VyYXRpb24lMjAlM0QlMjBBbHRDTElQQ29uZmlnKCklMEElMEElMjMlMjBJbml0aWFsaXppbmclMjBhJTIwQWx0Q0xJUE1vZGVsJTIwKHdpdGglMjByYW5kb20lMjB3ZWlnaHRzKSUyMGZyb20lMjB0aGUlMjBCQUFJJTJGQWx0Q0xJUCUyMHN0eWxlJTIwY29uZmlndXJhdGlvbiUwQW1vZGVsJTIwJTNEJTIwQWx0Q0xJUE1vZGVsKGNvbmZpZ3VyYXRpb24pJTBBJTBBJTIzJTIwQWNjZXNzaW5nJTIwdGhlJTIwbW9kZWwlMjBjb25maWd1cmF0aW9uJTBBY29uZmlndXJhdGlvbiUyMCUzRCUyMG1vZGVsLmNvbmZpZyUwQSUwQSUyMyUyMFdlJTIwY2FuJTIwYWxzbyUyMGluaXRpYWxpemUlMjBhJTIwQWx0Q0xJUENvbmZpZyUyMGZyb20lMjBhJTIwQWx0Q0xJUFRleHRDb25maWclMjBhbmQlMjBhJTIwQWx0Q0xJUFZpc2lvbkNvbmZpZyUwQSUwQSUyMyUyMEluaXRpYWxpemluZyUyMGElMjBBbHRDTElQVGV4dCUyMGFuZCUyMEFsdENMSVBWaXNpb24lMjBjb25maWd1cmF0aW9uJTBBY29uZmlnX3RleHQlMjAlM0QlMjBBbHRDTElQVGV4dENvbmZpZygpJTBBY29uZmlnX3Zpc2lvbiUyMCUzRCUyMEFsdENMSVBWaXNpb25Db25maWcoKSUwQSUwQWNvbmZpZyUyMCUzRCUyMEFsdENMSVBDb25maWcuZnJvbV90ZXh0X3Zpc2lvbl9jb25maWdzKGNvbmZpZ190ZXh0JTJDJTIwY29uZmlnX3Zpc2lvbik=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AltCLIPConfig, AltCLIPModel

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Initializing a AltCLIPConfig with BAAI/AltCLIP style configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>configuration = AltCLIPConfig()

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Initializing a AltCLIPModel (with random weights) from the BAAI/AltCLIP style configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AltCLIPModel(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 AltCLIPConfig from a AltCLIPTextConfig and a AltCLIPVisionConfig</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Initializing a AltCLIPText and AltCLIPVision configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config_text = AltCLIPTextConfig()
<span class="hljs-meta">&gt;&gt;&gt; </span>config_vision = AltCLIPVisionConfig()

<span class="hljs-meta">&gt;&gt;&gt; </span>config = AltCLIPConfig.from_text_vision_configs(config_text, config_vision)`}}),{c(){d=s("p"),M=l("Example:"),u=h(),v(m.$$.fragment)},l(n){d=a(n,"P",{});var c=r(d);M=i(c,"Example:"),c.forEach(o),u=g(n),y(m.$$.fragment,n)},m(n,c){_(n,d,c),e(d,M),_(n,u,c),C(m,n,c),b=!0},p:Ke,i(n){b||(I(m.$$.fragment,n),b=!0)},o(n){w(m.$$.fragment,n),b=!1},d(n){n&&o(d),n&&o(u),T(m,n)}}}function Mi(P){let d,M,u,m,b;return m=new he({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> AltCLIPTextModel, AltCLIPTextConfig

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Initializing a AltCLIPTextConfig with BAAI/AltCLIP style configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>configuration = AltCLIPTextConfig()

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Initializing a AltCLIPTextModel (with random weights) from the BAAI/AltCLIP style configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AltCLIPTextModel(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"),M=l("Examples:"),u=h(),v(m.$$.fragment)},l(n){d=a(n,"P",{});var c=r(d);M=i(c,"Examples:"),c.forEach(o),u=g(n),y(m.$$.fragment,n)},m(n,c){_(n,d,c),e(d,M),_(n,u,c),C(m,n,c),b=!0},p:Ke,i(n){b||(I(m.$$.fragment,n),b=!0)},o(n){w(m.$$.fragment,n),b=!1},d(n){n&&o(d),n&&o(u),T(m,n)}}}function vi(P){let d,M,u,m,b;return m=new he({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> AltCLIPVisionConfig, AltCLIPVisionModel

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Initializing a AltCLIPVisionConfig with BAAI/AltCLIP style configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>configuration = AltCLIPVisionConfig()

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Initializing a AltCLIPVisionModel (with random weights) from the BAAI/AltCLIP style configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AltCLIPVisionModel(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"),M=l("Example:"),u=h(),v(m.$$.fragment)},l(n){d=a(n,"P",{});var c=r(d);M=i(c,"Example:"),c.forEach(o),u=g(n),y(m.$$.fragment,n)},m(n,c){_(n,d,c),e(d,M),_(n,u,c),C(m,n,c),b=!0},p:Ke,i(n){b||(I(m.$$.fragment,n),b=!0)},o(n){w(m.$$.fragment,n),b=!1},d(n){n&&o(d),n&&o(u),T(m,n)}}}function yi(P){let d,M,u,m,b;return{c(){d=s("p"),M=l("Although the recipe for forward pass needs to be defined within this function, one should call the "),u=s("code"),m=l("Module"),b=l(`
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=a(n,"P",{});var c=r(d);M=i(c,"Although the recipe for forward pass needs to be defined within this function, one should call the "),u=a(c,"CODE",{});var A=r(u);m=i(A,"Module"),A.forEach(o),b=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){_(n,d,c),e(d,M),e(d,u),e(u,m),e(d,b)},d(n){n&&o(d)}}}function Ci(P){let d,M,u,m,b;return m=new he({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, AltCLIPModel

<span class="hljs-meta">&gt;&gt;&gt; </span>model = AltCLIPModel.from_pretrained(<span class="hljs-string">&quot;BAAI/AltCLIP&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>processor = AutoProcessor.from_pretrained(<span class="hljs-string">&quot;BAAI/AltCLIP&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"),M=l("Examples:"),u=h(),v(m.$$.fragment)},l(n){d=a(n,"P",{});var c=r(d);M=i(c,"Examples:"),c.forEach(o),u=g(n),y(m.$$.fragment,n)},m(n,c){_(n,d,c),e(d,M),_(n,u,c),C(m,n,c),b=!0},p:Ke,i(n){b||(I(m.$$.fragment,n),b=!0)},o(n){w(m.$$.fragment,n),b=!1},d(n){n&&o(d),n&&o(u),T(m,n)}}}function Ii(P){let d,M,u,m,b;return{c(){d=s("p"),M=l("Although the recipe for forward pass needs to be defined within this function, one should call the "),u=s("code"),m=l("Module"),b=l(`
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=a(n,"P",{});var c=r(d);M=i(c,"Although the recipe for forward pass needs to be defined within this function, one should call the "),u=a(c,"CODE",{});var A=r(u);m=i(A,"Module"),A.forEach(o),b=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){_(n,d,c),e(d,M),e(d,u),e(u,m),e(d,b)},d(n){n&&o(d)}}}function wi(P){let d,M,u,m,b;return m=new he({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, AltCLIPModel

<span class="hljs-meta">&gt;&gt;&gt; </span>model = AltCLIPModel.from_pretrained(<span class="hljs-string">&quot;BAAI/AltCLIP&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>processor = AutoProcessor.from_pretrained(<span class="hljs-string">&quot;BAAI/AltCLIP&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = processor(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>], 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"),M=l("Examples:"),u=h(),v(m.$$.fragment)},l(n){d=a(n,"P",{});var c=r(d);M=i(c,"Examples:"),c.forEach(o),u=g(n),y(m.$$.fragment,n)},m(n,c){_(n,d,c),e(d,M),_(n,u,c),C(m,n,c),b=!0},p:Ke,i(n){b||(I(m.$$.fragment,n),b=!0)},o(n){w(m.$$.fragment,n),b=!1},d(n){n&&o(d),n&&o(u),T(m,n)}}}function Ti(P){let d,M,u,m,b;return{c(){d=s("p"),M=l("Although the recipe for forward pass needs to be defined within this function, one should call the "),u=s("code"),m=l("Module"),b=l(`
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=a(n,"P",{});var c=r(d);M=i(c,"Although the recipe for forward pass needs to be defined within this function, one should call the "),u=a(c,"CODE",{});var A=r(u);m=i(A,"Module"),A.forEach(o),b=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){_(n,d,c),e(d,M),e(d,u),e(u,m),e(d,b)},d(n){n&&o(d)}}}function Pi(P){let d,M,u,m,b;return m=new he({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, AltCLIPModel

<span class="hljs-meta">&gt;&gt;&gt; </span>model = AltCLIPModel.from_pretrained(<span class="hljs-string">&quot;BAAI/AltCLIP&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>processor = AutoProcessor.from_pretrained(<span class="hljs-string">&quot;BAAI/AltCLIP&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"),M=l("Examples:"),u=h(),v(m.$$.fragment)},l(n){d=a(n,"P",{});var c=r(d);M=i(c,"Examples:"),c.forEach(o),u=g(n),y(m.$$.fragment,n)},m(n,c){_(n,d,c),e(d,M),_(n,u,c),C(m,n,c),b=!0},p:Ke,i(n){b||(I(m.$$.fragment,n),b=!0)},o(n){w(m.$$.fragment,n),b=!1},d(n){n&&o(d),n&&o(u),T(m,n)}}}function Ai(P){let d,M,u,m,b;return{c(){d=s("p"),M=l("Although the recipe for forward pass needs to be defined within this function, one should call the "),u=s("code"),m=l("Module"),b=l(`
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=a(n,"P",{});var c=r(d);M=i(c,"Although the recipe for forward pass needs to be defined within this function, one should call the "),u=a(c,"CODE",{});var A=r(u);m=i(A,"Module"),A.forEach(o),b=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){_(n,d,c),e(d,M),e(d,u),e(u,m),e(d,b)},d(n){n&&o(d)}}}function $i(P){let d,M,u,m,b;return m=new he({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, AltCLIPTextModel

<span class="hljs-meta">&gt;&gt;&gt; </span>model = AltCLIPTextModel.from_pretrained(<span class="hljs-string">&quot;BAAI/AltCLIP&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>processor = AutoProcessor.from_pretrained(<span class="hljs-string">&quot;BAAI/AltCLIP&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>texts = [<span class="hljs-string">&quot;it&#x27;s a cat&quot;</span>, <span class="hljs-string">&quot;it&#x27;s a dog&quot;</span>]

<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = processor(text=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>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"),M=l("Examples:"),u=h(),v(m.$$.fragment)},l(n){d=a(n,"P",{});var c=r(d);M=i(c,"Examples:"),c.forEach(o),u=g(n),y(m.$$.fragment,n)},m(n,c){_(n,d,c),e(d,M),_(n,u,c),C(m,n,c),b=!0},p:Ke,i(n){b||(I(m.$$.fragment,n),b=!0)},o(n){w(m.$$.fragment,n),b=!1},d(n){n&&o(d),n&&o(u),T(m,n)}}}function Li(P){let d,M,u,m,b;return{c(){d=s("p"),M=l("Although the recipe for forward pass needs to be defined within this function, one should call the "),u=s("code"),m=l("Module"),b=l(`
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=a(n,"P",{});var c=r(d);M=i(c,"Although the recipe for forward pass needs to be defined within this function, one should call the "),u=a(c,"CODE",{});var A=r(u);m=i(A,"Module"),A.forEach(o),b=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){_(n,d,c),e(d,M),e(d,u),e(u,m),e(d,b)},d(n){n&&o(d)}}}function Ji(P){let d,M,u,m,b;return m=new he({props:{code:"ZnJvbSUyMFBJTCUyMGltcG9ydCUyMEltYWdlJTBBaW1wb3J0JTIwcmVxdWVzdHMlMEFmcm9tJTIwdHJhbnNmb3JtZXJzJTIwaW1wb3J0JTIwQXV0b1Byb2Nlc3NvciUyQyUyMEFsdENMSVBWaXNpb25Nb2RlbCUwQSUwQW1vZGVsJTIwJTNEJTIwQWx0Q0xJUFZpc2lvbk1vZGVsLmZyb21fcHJldHJhaW5lZCglMjJCQUFJJTJGQWx0Q0xJUCUyMiklMEFwcm9jZXNzb3IlMjAlM0QlMjBBdXRvUHJvY2Vzc29yLmZyb21fcHJldHJhaW5lZCglMjJCQUFJJTJGQWx0Q0xJUCUyMiklMEElMEF1cmwlMjAlM0QlMjAlMjJodHRwJTNBJTJGJTJGaW1hZ2VzLmNvY29kYXRhc2V0Lm9yZyUyRnZhbDIwMTclMkYwMDAwMDAwMzk3NjkuanBnJTIyJTBBaW1hZ2UlMjAlM0QlMjBJbWFnZS5vcGVuKHJlcXVlc3RzLmdldCh1cmwlMkMlMjBzdHJlYW0lM0RUcnVlKS5yYXcpJTBBJTBBaW5wdXRzJTIwJTNEJTIwcHJvY2Vzc29yKGltYWdlcyUzRGltYWdlJTJDJTIwcmV0dXJuX3RlbnNvcnMlM0QlMjJwdCUyMiklMEElMEFvdXRwdXRzJTIwJTNEJTIwbW9kZWwoKippbnB1dHMpJTBBbGFzdF9oaWRkZW5fc3RhdGUlMjAlM0QlMjBvdXRwdXRzLmxhc3RfaGlkZGVuX3N0YXRlJTBBcG9vbGVkX291dHB1dCUyMCUzRCUyMG91dHB1dHMucG9vbGVyX291dHB1dCUyMCUyMCUyMyUyMHBvb2xlZCUyMENMUyUyMHN0YXRlcw==",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, AltCLIPVisionModel

<span class="hljs-meta">&gt;&gt;&gt; </span>model = AltCLIPVisionModel.from_pretrained(<span class="hljs-string">&quot;BAAI/AltCLIP&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>processor = AutoProcessor.from_pretrained(<span class="hljs-string">&quot;BAAI/AltCLIP&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"),M=l("Examples:"),u=h(),v(m.$$.fragment)},l(n){d=a(n,"P",{});var c=r(d);M=i(c,"Examples:"),c.forEach(o),u=g(n),y(m.$$.fragment,n)},m(n,c){_(n,d,c),e(d,M),_(n,u,c),C(m,n,c),b=!0},p:Ke,i(n){b||(I(m.$$.fragment,n),b=!0)},o(n){w(m.$$.fragment,n),b=!1},d(n){n&&o(d),n&&o(u),T(m,n)}}}function xi(P){let d,M,u,m,b,n,c,A,ss,pn,H,ge,Po,et,as,Ao,rs,mn,ue,ls,tt,is,ds,fn,Bt,cs,hn,zt,$o,ps,gn,X,_e,Lo,ot,ms,Jo,fs,un,Wt,hs,_n,Zt,gs,bn,be,us,Vt,_s,bs,Mn,$,Ms,Nt,vs,ys,Qt,Cs,Is,Gt,ws,Ts,Rt,Ps,As,Ft,$s,Ls,vn,nt,yn,Ht,Js,Cn,Me,xs,xo,js,ks,In,ve,Es,st,Us,Bs,wn,q,ye,jo,at,zs,ko,Ws,Tn,L,rt,Zs,S,Vs,Xt,Ns,Qs,lt,Gs,Rs,Fs,D,Hs,qt,Xs,qs,St,Ss,Ds,Ys,Ce,Os,Ie,it,Ks,dt,ea,Dt,ta,oa,Pn,Y,we,Eo,ct,na,Uo,sa,An,k,pt,aa,O,ra,Yt,la,ia,mt,da,ca,pa,K,ma,Ot,fa,ha,Kt,ga,ua,_a,Te,$n,ee,Pe,Bo,ft,ba,zo,Ma,Ln,E,ht,va,te,ya,eo,Ca,Ia,gt,wa,Ta,Pa,oe,Aa,to,$a,La,oo,Ja,xa,ja,Ae,Jn,ne,$e,Wo,ut,ka,Zo,Ea,xn,J,_t,Ua,Vo,Ba,za,j,no,Wa,Za,so,Va,Na,ao,Qa,Ga,No,Ra,Fa,ro,Ha,Xa,qa,Le,bt,Sa,Mt,Da,lo,Ya,Oa,Ka,Je,vt,er,yt,tr,io,or,nr,jn,se,xe,Qo,Ct,sr,Go,ar,kn,U,It,rr,B,wt,lr,ae,ir,co,dr,cr,Ro,pr,mr,fr,je,hr,ke,gr,z,Tt,ur,re,_r,po,br,Mr,Fo,vr,yr,Cr,Ee,Ir,Ue,wr,W,Pt,Tr,le,Pr,mo,Ar,$r,Ho,Lr,Jr,xr,Be,jr,ze,En,ie,We,Xo,At,kr,qo,Er,Un,de,$t,Ur,Z,Lt,Br,ce,zr,fo,Wr,Zr,So,Vr,Nr,Qr,Ze,Gr,Ve,Bn,pe,Ne,Do,Jt,Rr,Yo,Fr,zn,me,xt,Hr,V,jt,Xr,fe,qr,ho,Sr,Dr,Oo,Yr,Or,Kr,Qe,el,Ge,Wn;return n=new F({}),et=new F({}),ot=new F({}),nt=new he({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> AltCLIPModel, AltCLIPProcessor

<span class="hljs-meta">&gt;&gt;&gt; </span>model = AltCLIPModel.from_pretrained(<span class="hljs-string">&quot;BAAI/AltCLIP&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>processor = AltCLIPProcessor.from_pretrained(<span class="hljs-string">&quot;BAAI/AltCLIP&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(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">&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>`}}),at=new F({}),rt=new x({props:{name:"class transformers.AltCLIPConfig",anchor:"transformers.AltCLIPConfig",parameters:[{name:"text_config",val:" = None"},{name:"vision_config",val:" = None"},{name:"projection_dim",val:" = 768"},{name:"logit_scale_init_value",val:" = 2.6592"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AltCLIPConfig.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/main/en/model_doc/altclip#transformers.AltCLIPTextConfig">AltCLIPTextConfig</a>.`,name:"text_config"},{anchor:"transformers.AltCLIPConfig.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/main/en/model_doc/altclip#transformers.AltCLIPVisionConfig">AltCLIPVisionConfig</a>.`,name:"vision_config"},{anchor:"transformers.AltCLIPConfig.projection_dim",description:`<strong>projection_dim</strong> (<code>int</code>, <em>optional</em>, defaults to 512) &#x2014;
Dimentionality of text and vision projection layers.`,name:"projection_dim"},{anchor:"transformers.AltCLIPConfig.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 CLIP implementation.`,name:"logit_scale_init_value"},{anchor:"transformers.AltCLIPConfig.kwargs",description:`<strong>kwargs</strong> (<em>optional</em>) &#x2014;
Dictionary of keyword arguments.`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/altclip/configuration_altclip.py#L247"}}),Ce=new Oe({props:{anchor:"transformers.AltCLIPConfig.example",$$slots:{default:[bi]},$$scope:{ctx:P}}}),it=new x({props:{name:"from_text_vision_configs",anchor:"transformers.AltCLIPConfig.from_text_vision_configs",parameters:[{name:"text_config",val:": AltCLIPTextConfig"},{name:"vision_config",val:": AltCLIPVisionConfig"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/altclip/configuration_altclip.py#L382",returnDescription:`
<p>An instance of a configuration object</p>
`,returnType:`
<p><a
  href="/docs/transformers/main/en/model_doc/altclip#transformers.AltCLIPConfig"
>AltCLIPConfig</a></p>
`}}),ct=new F({}),pt=new x({props:{name:"class transformers.AltCLIPTextConfig",anchor:"transformers.AltCLIPTextConfig",parameters:[{name:"vocab_size",val:" = 250002"},{name:"hidden_size",val:" = 1024"},{name:"num_hidden_layers",val:" = 24"},{name:"num_attention_heads",val:" = 16"},{name:"intermediate_size",val:" = 4096"},{name:"hidden_act",val:" = 'gelu'"},{name:"hidden_dropout_prob",val:" = 0.1"},{name:"attention_probs_dropout_prob",val:" = 0.1"},{name:"max_position_embeddings",val:" = 514"},{name:"type_vocab_size",val:" = 1"},{name:"initializer_range",val:" = 0.02"},{name:"initializer_factor",val:" = 0.02"},{name:"layer_norm_eps",val:" = 1e-05"},{name:"pad_token_id",val:" = 1"},{name:"bos_token_id",val:" = 0"},{name:"eos_token_id",val:" = 2"},{name:"position_embedding_type",val:" = 'absolute'"},{name:"use_cache",val:" = True"},{name:"project_dim",val:" = 768"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AltCLIPTextConfig.vocab_size",description:`<strong>vocab_size</strong> (<code>int</code>, <em>optional</em>, defaults to 250002) &#x2014;
Vocabulary size of the AltCLIP model. Defines the number of different tokens that can be represented by the
<code>inputs_ids</code> passed when calling <a href="/docs/transformers/main/en/model_doc/altclip#transformers.AltCLIPTextModel">AltCLIPTextModel</a>.`,name:"vocab_size"},{anchor:"transformers.AltCLIPTextConfig.hidden_size",description:`<strong>hidden_size</strong> (<code>int</code>, <em>optional</em>, defaults to 1024) &#x2014;
Dimensionality of the encoder layers and the pooler layer.`,name:"hidden_size"},{anchor:"transformers.AltCLIPTextConfig.num_hidden_layers",description:`<strong>num_hidden_layers</strong> (<code>int</code>, <em>optional</em>, defaults to 24) &#x2014;
Number of hidden layers in the Transformer encoder.`,name:"num_hidden_layers"},{anchor:"transformers.AltCLIPTextConfig.num_attention_heads",description:`<strong>num_attention_heads</strong> (<code>int</code>, <em>optional</em>, defaults to 16) &#x2014;
Number of attention heads for each attention layer in the Transformer encoder.`,name:"num_attention_heads"},{anchor:"transformers.AltCLIPTextConfig.intermediate_size",description:`<strong>intermediate_size</strong> (<code>int</code>, <em>optional</em>, defaults to 4096) &#x2014;
Dimensionality of the &#x201C;intermediate&#x201D; (often named feed-forward) layer in the Transformer encoder.`,name:"intermediate_size"},{anchor:"transformers.AltCLIPTextConfig.hidden_act",description:`<strong>hidden_act</strong> (<code>str</code> or <code>Callable</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;silu&quot;</code> and <code>&quot;gelu_new&quot;</code> are supported.`,name:"hidden_act"},{anchor:"transformers.AltCLIPTextConfig.hidden_dropout_prob",description:`<strong>hidden_dropout_prob</strong> (<code>float</code>, <em>optional</em>, defaults to 0.1) &#x2014;
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.`,name:"hidden_dropout_prob"},{anchor:"transformers.AltCLIPTextConfig.attention_probs_dropout_prob",description:`<strong>attention_probs_dropout_prob</strong> (<code>float</code>, <em>optional</em>, defaults to 0.1) &#x2014;
The dropout ratio for the attention probabilities.`,name:"attention_probs_dropout_prob"},{anchor:"transformers.AltCLIPTextConfig.max_position_embeddings",description:`<strong>max_position_embeddings</strong> (<code>int</code>, <em>optional</em>, defaults to 514) &#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.AltCLIPTextConfig.type_vocab_size",description:`<strong>type_vocab_size</strong> (<code>int</code>, <em>optional</em>, defaults to 2) &#x2014;
The vocabulary size of the <code>token_type_ids</code> passed when calling <a href="/docs/transformers/main/en/model_doc/altclip#transformers.AltCLIPTextModel">AltCLIPTextModel</a>`,name:"type_vocab_size"},{anchor:"transformers.AltCLIPTextConfig.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.AltCLIPTextConfig.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.AltCLIPTextConfig.position_embedding_type",description:`<strong>position_embedding_type</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;absolute&quot;</code>) &#x2014;
Type of position embedding. Choose one of <code>&quot;absolute&quot;</code>, <code>&quot;relative_key&quot;</code>, <code>&quot;relative_key_query&quot;</code>. For
positional embeddings use <code>&quot;absolute&quot;</code>. For more information on <code>&quot;relative_key&quot;</code>, please refer to
<a href="https://arxiv.org/abs/1803.02155" rel="nofollow">Self-Attention with Relative Position Representations (Shaw et al.)</a>.
For more information on <code>&quot;relative_key_query&quot;</code>, please refer to <em>Method 4</em> in <a href="https://arxiv.org/abs/2009.13658" rel="nofollow">Improve Transformer Models
with Better Relative Position Embeddings (Huang et al.)</a>.`,name:"position_embedding_type"},{anchor:"transformers.AltCLIPTextConfig.use_cache",description:`<strong>use_cache</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether or not the model should return the last key/values attentions (not used by all models). Only
relevant if <code>config.is_decoder=True</code>.`,name:"use_cache"},{anchor:"transformers.AltCLIPTextConfig.project_dim",description:`<strong>project_dim</strong> (<code>int</code>, <em>optional</em>, defaults to 768) &#x2014;
The dimentions of the teacher model before the mapping layer.`,name:"project_dim"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/altclip/configuration_altclip.py#L31"}}),Te=new Oe({props:{anchor:"transformers.AltCLIPTextConfig.example",$$slots:{default:[Mi]},$$scope:{ctx:P}}}),ft=new F({}),ht=new x({props:{name:"class transformers.AltCLIPVisionConfig",anchor:"transformers.AltCLIPVisionConfig",parameters:[{name:"hidden_size",val:" = 768"},{name:"intermediate_size",val:" = 3072"},{name:"projection_dim",val:" = 512"},{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.AltCLIPVisionConfig.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.AltCLIPVisionConfig.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.AltCLIPVisionConfig.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.AltCLIPVisionConfig.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.AltCLIPVisionConfig.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.AltCLIPVisionConfig.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.AltCLIPVisionConfig.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.AltCLIPVisionConfig.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.AltCLIPVisionConfig.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.AltCLIPVisionConfig.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.AltCLIPVisionConfig.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/main/src/transformers/models/altclip/configuration_altclip.py#L141"}}),Ae=new Oe({props:{anchor:"transformers.AltCLIPVisionConfig.example",$$slots:{default:[vi]},$$scope:{ctx:P}}}),ut=new F({}),_t=new x({props:{name:"class transformers.AltCLIPProcessor",anchor:"transformers.AltCLIPProcessor",parameters:[{name:"image_processor",val:" = None"},{name:"tokenizer",val:" = None"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AltCLIPProcessor.image_processor",description:`<strong>image_processor</strong> (<a href="/docs/transformers/main/en/model_doc/clip#transformers.CLIPImageProcessor">CLIPImageProcessor</a>) &#x2014;
The image processor is a required input.`,name:"image_processor"},{anchor:"transformers.AltCLIPProcessor.tokenizer",description:`<strong>tokenizer</strong> (<a href="/docs/transformers/main/en/model_doc/xlm-roberta#transformers.XLMRobertaTokenizerFast">XLMRobertaTokenizerFast</a>) &#x2014;
The tokenizer is a required input.`,name:"tokenizer"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/altclip/processing_altclip.py#L24"}}),bt=new x({props:{name:"batch_decode",anchor:"transformers.AltCLIPProcessor.batch_decode",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/altclip/processing_altclip.py#L113"}}),vt=new x({props:{name:"decode",anchor:"transformers.AltCLIPProcessor.decode",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/altclip/processing_altclip.py#L120"}}),Ct=new F({}),It=new x({props:{name:"class transformers.AltCLIPModel",anchor:"transformers.AltCLIPModel",parameters:[{name:"config",val:": AltCLIPConfig"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/altclip/modeling_altclip.py#L1481"}}),wt=new x({props:{name:"forward",anchor:"transformers.AltCLIPModel.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:"token_type_ids",val:": typing.Optional[torch.Tensor] = 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.AltCLIPModel.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/main/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/main/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/main/en/model_doc/perceiver#transformers.PerceiverTokenizer.__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.AltCLIPModel.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.AltCLIPModel.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.AltCLIPModel.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/main/en/model_doc/auto#transformers.AutoImageProcessor">AutoImageProcessor</a>. See <a href="/docs/transformers/main/en/model_doc/levit#transformers.LevitFeatureExtractor.__call__">CLIPImageProcessor.<strong>call</strong>()</a> for details.`,name:"pixel_values"},{anchor:"transformers.AltCLIPModel.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.AltCLIPModel.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.AltCLIPModel.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.AltCLIPModel.forward.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/main/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/altclip/modeling_altclip.py#L1608",returnDescription:`
<p>A <code>transformers.models.altclip.modeling_altclip.AltCLIPOutput</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.altclip.configuration_altclip.AltCLIPConfig'&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/main/en/model_doc/altclip#transformers.AltCLIPTextModel"
>AltCLIPTextModel</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/main/en/model_doc/altclip#transformers.AltCLIPVisionModel"
>AltCLIPVisionModel</a>.</li>
<li><strong>text_model_output(<code>BaseModelOutputWithPooling</code>):</strong>
The output of the <a
  href="/docs/transformers/main/en/model_doc/altclip#transformers.AltCLIPTextModel"
>AltCLIPTextModel</a>.</li>
<li><strong>vision_model_output(<code>BaseModelOutputWithPooling</code>):</strong>
The output of the <a
  href="/docs/transformers/main/en/model_doc/altclip#transformers.AltCLIPVisionModel"
>AltCLIPVisionModel</a>.</li>
</ul>
`,returnType:`
<p><code>transformers.models.altclip.modeling_altclip.AltCLIPOutput</code> or <code>tuple(torch.FloatTensor)</code></p>
`}}),je=new cn({props:{$$slots:{default:[yi]},$$scope:{ctx:P}}}),ke=new Oe({props:{anchor:"transformers.AltCLIPModel.forward.example",$$slots:{default:[Ci]},$$scope:{ctx:P}}}),Tt=new x({props:{name:"get_text_features",anchor:"transformers.AltCLIPModel.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:"token_type_ids",val:" = 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.AltCLIPModel.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/main/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/main/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/main/en/model_doc/perceiver#transformers.PerceiverTokenizer.__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.AltCLIPModel.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.AltCLIPModel.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.AltCLIPModel.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.AltCLIPModel.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.AltCLIPModel.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/main/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/altclip/modeling_altclip.py#L1515",returnDescription:`
<p>The text embeddings obtained by
applying the projection layer to the pooled output of <a
  href="/docs/transformers/main/en/model_doc/altclip#transformers.AltCLIPTextModel"
>AltCLIPTextModel</a>.</p>
`,returnType:`
<p>text_features (<code>torch.FloatTensor</code> of shape <code>(batch_size, output_dim</code>)</p>
`}}),Ee=new cn({props:{$$slots:{default:[Ii]},$$scope:{ctx:P}}}),Ue=new Oe({props:{anchor:"transformers.AltCLIPModel.get_text_features.example",$$slots:{default:[wi]},$$scope:{ctx:P}}}),Pt=new x({props:{name:"get_image_features",anchor:"transformers.AltCLIPModel.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.AltCLIPModel.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/main/en/model_doc/auto#transformers.AutoImageProcessor">AutoImageProcessor</a>. See <a href="/docs/transformers/main/en/model_doc/levit#transformers.LevitFeatureExtractor.__call__">CLIPImageProcessor.<strong>call</strong>()</a> for details.`,name:"pixel_values"},{anchor:"transformers.AltCLIPModel.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.AltCLIPModel.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.AltCLIPModel.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/main/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/altclip/modeling_altclip.py#L1562",returnDescription:`
<p>The image embeddings obtained by
applying the projection layer to the pooled output of <a
  href="/docs/transformers/main/en/model_doc/altclip#transformers.AltCLIPVisionModel"
>AltCLIPVisionModel</a>.</p>
`,returnType:`
<p>image_features (<code>torch.FloatTensor</code> of shape <code>(batch_size, output_dim</code>)</p>
`}}),Be=new cn({props:{$$slots:{default:[Ti]},$$scope:{ctx:P}}}),ze=new Oe({props:{anchor:"transformers.AltCLIPModel.get_image_features.example",$$slots:{default:[Pi]},$$scope:{ctx:P}}}),At=new F({}),$t=new x({props:{name:"class transformers.AltCLIPTextModel",anchor:"transformers.AltCLIPTextModel",parameters:[{name:"config",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/altclip/modeling_altclip.py#L1389"}}),Lt=new x({props:{name:"forward",anchor:"transformers.AltCLIPTextModel.forward",parameters:[{name:"input_ids",val:": typing.Optional[torch.Tensor] = None"},{name:"attention_mask",val:": typing.Optional[torch.Tensor] = None"},{name:"token_type_ids",val:": typing.Optional[torch.Tensor] = None"},{name:"position_ids",val:": typing.Optional[torch.Tensor] = None"},{name:"head_mask",val:": typing.Optional[torch.Tensor] = None"},{name:"inputs_embeds",val:": typing.Optional[torch.Tensor] = None"},{name:"encoder_hidden_states",val:": typing.Optional[torch.Tensor] = None"},{name:"encoder_attention_mask",val:": typing.Optional[torch.Tensor] = None"},{name:"output_attentions",val:": typing.Optional[bool] = None"},{name:"return_dict",val:": typing.Optional[bool] = None"},{name:"output_hidden_states",val:": typing.Optional[bool] = None"}],parametersDescription:[{anchor:"transformers.AltCLIPTextModel.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/main/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/main/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/main/en/model_doc/perceiver#transformers.PerceiverTokenizer.__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.AltCLIPTextModel.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.AltCLIPTextModel.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.AltCLIPTextModel.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.AltCLIPTextModel.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.AltCLIPTextModel.forward.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/main/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/altclip/modeling_altclip.py#L1408",returnDescription:`
<p>A <code>transformers.modeling_outputs.BaseModelOutputWithPoolingAndProjection</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.altclip.configuration_altclip.AltCLIPTextConfig'&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>
<li>
<p><strong>projection_state</strong> (<code>tuple(torch.FloatTensor)</code>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) \u2014 Tuple of <code>torch.FloatTensor</code> of shape <code>(batch_size,config.project_dim)</code>.</p>
<p>Text embeddings before the projection layer, used to mimic the last hidden state of the teacher encoder.</p>
</li>
</ul>
`,returnType:`
<p><code>transformers.modeling_outputs.BaseModelOutputWithPoolingAndProjection</code> or <code>tuple(torch.FloatTensor)</code></p>
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Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using
<a href="/docs/transformers/main/en/model_doc/auto#transformers.AutoImageProcessor">AutoImageProcessor</a>. See <a href="/docs/transformers/main/en/model_doc/levit#transformers.LevitFeatureExtractor.__call__">CLIPImageProcessor.<strong>call</strong>()</a> for details.`,name:"pixel_values"},{anchor:"transformers.AltCLIPVisionModel.forward.output_attentions",description:`<strong>output_attentions</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
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<p>A <a
  href="/docs/transformers/main/en/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPooling"
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<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/main/en/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPooling"
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