import{s as en,o as tn,n as L}from"../chunks/scheduler.bdbef820.js";import{S as on,i as nn,g as p,s as l,r as f,A as sn,h as m,f as s,c as i,j as $,u as g,x as y,k as v,y as c,a as d,v as u,d as h,t as _,w as M}from"../chunks/index.33f81d56.js";import{T as ht}from"../chunks/Tip.34194030.js";import{D as P}from"../chunks/Docstring.64554317.js";import{C as D}from"../chunks/CodeBlock.362b34a4.js";import{E as ue}from"../chunks/ExampleCodeBlock.4f2252c6.js";import{H as q,E as an}from"../chunks/EditOnGithub.a9246e21.js";function rn(I){let o,b="이 모델은 <code>CLIPModel</code>을 기반으로 하므로, 원래 CLIP처럼 사용할 수 있습니다.";return{c(){o=p("p"),o.innerHTML=b},l(a){o=m(a,"P",{"data-svelte-h":!0}),y(o)!=="svelte-11bxa9e"&&(o.innerHTML=b)},m(a,r){d(a,o,r)},p:L,d(a){a&&s(o)}}}function ln(I){let o,b="Example:",a,r,C;return r=new D({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> 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)`,wrap:!1}}),{c(){o=p("p"),o.textContent=b,a=l(),f(r.$$.fragment)},l(t){o=m(t,"P",{"data-svelte-h":!0}),y(o)!=="svelte-11lpom8"&&(o.textContent=b),a=i(t),g(r.$$.fragment,t)},m(t,T){d(t,o,T),d(t,a,T),u(r,t,T),C=!0},p:L,i(t){C||(h(r.$$.fragment,t),C=!0)},o(t){_(r.$$.fragment,t),C=!1},d(t){t&&(s(o),s(a)),M(r,t)}}}function dn(I){let o,b="Examples:",a,r,C;return r=new D({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`,wrap:!1}}),{c(){o=p("p"),o.textContent=b,a=l(),f(r.$$.fragment)},l(t){o=m(t,"P",{"data-svelte-h":!0}),y(o)!=="svelte-kvfsh7"&&(o.textContent=b),a=i(t),g(r.$$.fragment,t)},m(t,T){d(t,o,T),d(t,a,T),u(r,t,T),C=!0},p:L,i(t){C||(h(r.$$.fragment,t),C=!0)},o(t){_(r.$$.fragment,t),C=!1},d(t){t&&(s(o),s(a)),M(r,t)}}}function cn(I){let o,b="Example:",a,r,C;return r=new D({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`,wrap:!1}}),{c(){o=p("p"),o.textContent=b,a=l(),f(r.$$.fragment)},l(t){o=m(t,"P",{"data-svelte-h":!0}),y(o)!=="svelte-11lpom8"&&(o.textContent=b),a=i(t),g(r.$$.fragment,t)},m(t,T){d(t,o,T),d(t,a,T),u(r,t,T),C=!0},p:L,i(t){C||(h(r.$$.fragment,t),C=!0)},o(t){_(r.$$.fragment,t),C=!1},d(t){t&&(s(o),s(a)),M(r,t)}}}function pn(I){let o,b=`Although the recipe for forward pass needs to be defined within this function, one should call the <code>Module</code>
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`;return{c(){o=p("p"),o.innerHTML=b},l(a){o=m(a,"P",{"data-svelte-h":!0}),y(o)!=="svelte-fincs2"&&(o.innerHTML=b)},m(a,r){d(a,o,r)},p:L,d(a){a&&s(o)}}}function mn(I){let o,b="Examples:",a,r,C;return r=new D({props:{code:"ZnJvbSUyMFBJTCUyMGltcG9ydCUyMEltYWdlJTBBaW1wb3J0JTIwcmVxdWVzdHMlMEFmcm9tJTIwdHJhbnNmb3JtZXJzJTIwaW1wb3J0JTIwQXV0b1Byb2Nlc3NvciUyQyUyMEFsdENMSVBNb2RlbCUwQSUwQW1vZGVsJTIwJTNEJTIwQWx0Q0xJUE1vZGVsLmZyb21fcHJldHJhaW5lZCglMjJCQUFJJTJGQWx0Q0xJUCUyMiklMEFwcm9jZXNzb3IlMjAlM0QlMjBBdXRvUHJvY2Vzc29yLmZyb21fcHJldHJhaW5lZCglMjJCQUFJJTJGQWx0Q0xJUCUyMiklMEF1cmwlMjAlM0QlMjAlMjJodHRwJTNBJTJGJTJGaW1hZ2VzLmNvY29kYXRhc2V0Lm9yZyUyRnZhbDIwMTclMkYwMDAwMDAwMzk3NjkuanBnJTIyJTBBaW1hZ2UlMjAlM0QlMjBJbWFnZS5vcGVuKHJlcXVlc3RzLmdldCh1cmwlMkMlMjBzdHJlYW0lM0RUcnVlKS5yYXcpJTBBaW5wdXRzJTIwJTNEJTIwcHJvY2Vzc29yKCUwQSUyMCUyMCUyMCUyMHRleHQlM0QlNUIlMjJhJTIwcGhvdG8lMjBvZiUyMGElMjBjYXQlMjIlMkMlMjAlMjJhJTIwcGhvdG8lMjBvZiUyMGElMjBkb2clMjIlNUQlMkMlMjBpbWFnZXMlM0RpbWFnZSUyQyUyMHJldHVybl90ZW5zb3JzJTNEJTIycHQlMjIlMkMlMjBwYWRkaW5nJTNEVHJ1ZSUwQSklMEFvdXRwdXRzJTIwJTNEJTIwbW9kZWwoKippbnB1dHMpJTBBbG9naXRzX3Blcl9pbWFnZSUyMCUzRCUyMG91dHB1dHMubG9naXRzX3Blcl9pbWFnZSUyMCUyMCUyMyUyMHRoaXMlMjBpcyUyMHRoZSUyMGltYWdlLXRleHQlMjBzaW1pbGFyaXR5JTIwc2NvcmUlMEFwcm9icyUyMCUzRCUyMGxvZ2l0c19wZXJfaW1hZ2Uuc29mdG1heChkaW0lM0QxKSUyMCUyMCUyMyUyMHdlJTIwY2FuJTIwdGFrZSUyMHRoZSUyMHNvZnRtYXglMjB0byUyMGdldCUyMHRoZSUyMGxhYmVsJTIwcHJvYmFiaWxpdGllcw==",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>`,wrap:!1}}),{c(){o=p("p"),o.textContent=b,a=l(),f(r.$$.fragment)},l(t){o=m(t,"P",{"data-svelte-h":!0}),y(o)!=="svelte-kvfsh7"&&(o.textContent=b),a=i(t),g(r.$$.fragment,t)},m(t,T){d(t,o,T),d(t,a,T),u(r,t,T),C=!0},p:L,i(t){C||(h(r.$$.fragment,t),C=!0)},o(t){_(r.$$.fragment,t),C=!1},d(t){t&&(s(o),s(a)),M(r,t)}}}function fn(I){let o,b=`Although the recipe for forward pass needs to be defined within this function, one should call the <code>Module</code>
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`;return{c(){o=p("p"),o.innerHTML=b},l(a){o=m(a,"P",{"data-svelte-h":!0}),y(o)!=="svelte-fincs2"&&(o.innerHTML=b)},m(a,r){d(a,o,r)},p:L,d(a){a&&s(o)}}}function gn(I){let o,b="Examples:",a,r,C;return r=new D({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)`,wrap:!1}}),{c(){o=p("p"),o.textContent=b,a=l(),f(r.$$.fragment)},l(t){o=m(t,"P",{"data-svelte-h":!0}),y(o)!=="svelte-kvfsh7"&&(o.textContent=b),a=i(t),g(r.$$.fragment,t)},m(t,T){d(t,o,T),d(t,a,T),u(r,t,T),C=!0},p:L,i(t){C||(h(r.$$.fragment,t),C=!0)},o(t){_(r.$$.fragment,t),C=!1},d(t){t&&(s(o),s(a)),M(r,t)}}}function un(I){let o,b=`Although the recipe for forward pass needs to be defined within this function, one should call the <code>Module</code>
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`;return{c(){o=p("p"),o.innerHTML=b},l(a){o=m(a,"P",{"data-svelte-h":!0}),y(o)!=="svelte-fincs2"&&(o.innerHTML=b)},m(a,r){d(a,o,r)},p:L,d(a){a&&s(o)}}}function hn(I){let o,b="Examples:",a,r,C;return r=new D({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)`,wrap:!1}}),{c(){o=p("p"),o.textContent=b,a=l(),f(r.$$.fragment)},l(t){o=m(t,"P",{"data-svelte-h":!0}),y(o)!=="svelte-kvfsh7"&&(o.textContent=b),a=i(t),g(r.$$.fragment,t)},m(t,T){d(t,o,T),d(t,a,T),u(r,t,T),C=!0},p:L,i(t){C||(h(r.$$.fragment,t),C=!0)},o(t){_(r.$$.fragment,t),C=!1},d(t){t&&(s(o),s(a)),M(r,t)}}}function _n(I){let o,b=`Although the recipe for forward pass needs to be defined within this function, one should call the <code>Module</code>
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`;return{c(){o=p("p"),o.innerHTML=b},l(a){o=m(a,"P",{"data-svelte-h":!0}),y(o)!=="svelte-fincs2"&&(o.innerHTML=b)},m(a,r){d(a,o,r)},p:L,d(a){a&&s(o)}}}function Mn(I){let o,b="Examples:",a,r,C;return r=new D({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>`,wrap:!1}}),{c(){o=p("p"),o.textContent=b,a=l(),f(r.$$.fragment)},l(t){o=m(t,"P",{"data-svelte-h":!0}),y(o)!=="svelte-kvfsh7"&&(o.textContent=b),a=i(t),g(r.$$.fragment,t)},m(t,T){d(t,o,T),d(t,a,T),u(r,t,T),C=!0},p:L,i(t){C||(h(r.$$.fragment,t),C=!0)},o(t){_(r.$$.fragment,t),C=!1},d(t){t&&(s(o),s(a)),M(r,t)}}}function Cn(I){let o,b=`Although the recipe for forward pass needs to be defined within this function, one should call the <code>Module</code>
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`;return{c(){o=p("p"),o.innerHTML=b},l(a){o=m(a,"P",{"data-svelte-h":!0}),y(o)!=="svelte-fincs2"&&(o.innerHTML=b)},m(a,r){d(a,o,r)},p:L,d(a){a&&s(o)}}}function bn(I){let o,b="Examples:",a,r,C;return r=new D({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, 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>`,wrap:!1}}),{c(){o=p("p"),o.textContent=b,a=l(),f(r.$$.fragment)},l(t){o=m(t,"P",{"data-svelte-h":!0}),y(o)!=="svelte-kvfsh7"&&(o.textContent=b),a=i(t),g(r.$$.fragment,t)},m(t,T){d(t,o,T),d(t,a,T),u(r,t,T),C=!0},p:L,i(t){C||(h(r.$$.fragment,t),C=!0)},o(t){_(r.$$.fragment,t),C=!1},d(t){t&&(s(o),s(a)),M(r,t)}}}function Tn(I){let o,b,a,r,C,t,T,Mt,he,Jo='AltCLIP 모델은 Zhongzhi Chen, Guang Liu, Bo-Wen Zhang, Fulong Ye, Qinghong Yang, Ledell Wu의 <a href="https://arxiv.org/abs/2211.06679v2" rel="nofollow">AltCLIP: Altering the Language Encoder in CLIP for Extended Language Capabilities</a> 논문에서 제안되었습니다. AltCLIP(CLIP의 언어 인코더를 변경하여 언어 기능 확장)은 다양한 이미지-텍스트 및 텍스트-텍스트 쌍으로 훈련된 신경망입니다. CLIP의 텍스트 인코더를 사전 훈련된 다국어 텍스트 인코더 XLM-R로 교체하여, 거의 모든 작업에서 CLIP과 유사한 성능을 얻을 수 있었으며, 원래 CLIP의 다국어 이해와 같은 기능도 확장되었습니다.',Ct,_e,Ao="논문의 초록은 다음과 같습니다:",bt,Me,jo="<em>본 연구에서는 강력한 이중 언어 멀티모달 표현 모델을 훈련하는 개념적으로 간단하고 효과적인 방법을 제시합니다. OpenAI에서 출시한 사전 훈련된 멀티모달 표현 모델 CLIP에서 시작하여, 그 텍스트 인코더를 사전 훈련된 다국어 텍스트 인코더 XLM-R로 교체하고, 교사 학습과 대조 학습으로 구성된 2단계 훈련 스키마를 통해 언어와 이미지 표현을 정렬했습니다. 우리는 광범위한 작업 평가를 통해 우리의 방법을 검증했습니다. ImageNet-CN, Flicker30k-CN, COCO-CN을 포함한 여러 작업에서 새로운 최고 성능을 달성했으며, 거의 모든 작업에서 CLIP과 유사한 성능을 얻었습니다. 이는 CLIP의 텍스트 인코더를 단순히 변경하여 다국어 이해와 같은 확장 기능을 얻을 수 있음을 시사합니다.</em>",Tt,Ce,ko='이 모델은 <a href="https://huggingface.co/jongjyh" rel="nofollow">jongjyh</a>에 의해 기여되었습니다.',yt,be,It,Te,Uo="AltCLIP의 사용법은 CLIP과 매우 유사하며, 차이점은 텍스트 인코더에 있습니다. 일반적인 어텐션 대신 양방향 어텐션을 사용하며, XLM-R의 [CLS] 토큰을 사용하여 텍스트 임베딩을 나타냅니다.",$t,ye,Bo="AltCLIP은 멀티모달 비전 및 언어 모델입니다. 이미지와 텍스트 간의 유사성 계산 및 제로샷 이미지 분류에 사용할 수 있습니다. AltCLIP은 ViT와 같은 트랜스포머를 사용하여 시각적 특징을 얻고, 양방향 언어 모델을 사용하여 텍스트 특징을 얻습니다. 이후 텍스트와 시각적 특징 모두 동일한 차원의 잠재 공간으로 투사됩니다. 투사된 이미지와 텍스트 특징 간의 내적을 유사도 점수로 사용합니다.",vt,Ie,zo='이미지를 트랜스포머 인코더에 입력하기 위해, 각 이미지를 일정한 크기의 겹치지 않는 패치 시퀀스로 분할한 뒤, 이를 선형 임베딩합니다. 전체 이미지를 나타내기 위해 [CLS] 토큰이 추가됩니다. 저자들은 절대 위치 임베딩도 추가하여 결과 벡터 시퀀스를 표준 트랜스포머 인코더에 입력합니다. <a href="/docs/transformers/main/ko/model_doc/clip#transformers.CLIPImageProcessor">CLIPImageProcessor</a>는 모델을 위해 이미지를 크기 조정하고 정규화하는 데 사용할 수 있습니다.',xt,$e,Wo='<a href="/docs/transformers/main/ko/model_doc/altclip#transformers.AltCLIPProcessor">AltCLIPProcessor</a>는 <a href="/docs/transformers/main/ko/model_doc/clip#transformers.CLIPImageProcessor">CLIPImageProcessor</a>와 <code>XLMRobertaTokenizer</code>를 하나의 인스턴스로 묶어 텍스트를 인코딩하고 이미지를 준비합니다. 다음 예제는 <a href="/docs/transformers/main/ko/model_doc/altclip#transformers.AltCLIPProcessor">AltCLIPProcessor</a>와 <a href="/docs/transformers/main/ko/model_doc/altclip#transformers.AltCLIPModel">AltCLIPModel</a>을 사용하여 이미지와 텍스트 간의 유사성 점수를 얻는 방법을 보여줍니다.',wt,ve,Pt,Y,Lt,xe,Jt,x,we,Rt,et,Vo=`This is the configuration class to store the configuration of a <a href="/docs/transformers/main/ko/model_doc/altclip#transformers.AltCLIPModel">AltCLIPModel</a>. It is used to instantiate an
AltCLIP model according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults will yield a similar configuration to that of the AltCLIP
<a href="https://huggingface.co/BAAI/AltCLIP" rel="nofollow">BAAI/AltCLIP</a> architecture.`,qt,tt,Zo=`Configuration objects inherit from <a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a> and can be used to control the model outputs. Read the
documentation from <a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a> for more information.`,Xt,O,St,K,Pe,Dt,ot,Qo=`Instantiate a <a href="/docs/transformers/main/ko/model_doc/altclip#transformers.AltCLIPConfig">AltCLIPConfig</a> (or a derived class) from altclip text model configuration and altclip vision
model configuration.`,At,Le,jt,J,Je,Yt,nt,Eo=`This is the configuration class to store the configuration of a <a href="/docs/transformers/main/ko/model_doc/altclip#transformers.AltCLIPTextModel">AltCLIPTextModel</a>. It is used to instantiate a
AltCLIP text model according to the specified arguments, defining the model architecture. Instantiating a
configuration with the defaults will yield a similar configuration to that of the AltCLIP
<a href="https://huggingface.co/BAAI/AltCLIP" rel="nofollow">BAAI/AltCLIP</a> architecture.`,Ot,st,Ho=`Configuration objects inherit from <a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a> and can be used to control the model outputs. Read the
documentation from <a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a> for more information.`,Kt,ee,kt,Ae,Ut,A,je,eo,at,Fo=`This is the configuration class to store the configuration of a <a href="/docs/transformers/main/ko/model_doc/altclip#transformers.AltCLIPModel">AltCLIPModel</a>. It is used to instantiate an
AltCLIP model according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults will yield a similar configuration to that of the AltCLIP
<a href="https://huggingface.co/BAAI/AltCLIP" rel="nofollow">BAAI/AltCLIP</a> architecture.`,to,rt,No=`Configuration objects inherit from <a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a> and can be used to control the model outputs. Read the
documentation from <a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a> for more information.`,oo,te,Bt,ke,zt,w,Ue,no,lt,Go=`Constructs a AltCLIP processor which wraps a CLIP image processor and a XLM-Roberta tokenizer into a single
processor.`,so,it,Ro=`<a href="/docs/transformers/main/ko/model_doc/altclip#transformers.AltCLIPProcessor">AltCLIPProcessor</a> offers all the functionalities of <a href="/docs/transformers/main/ko/model_doc/clip#transformers.CLIPImageProcessor">CLIPImageProcessor</a> and <code>XLMRobertaTokenizerFast</code>. See
the <code>__call__()</code> and <a href="/docs/transformers/main/ko/model_doc/altclip#transformers.AltCLIPProcessor.decode">decode()</a> for more information.`,ao,oe,Be,ro,dt,qo=`This method forwards all its arguments to XLMRobertaTokenizerFast’s <a href="/docs/transformers/main/ko/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.batch_decode">batch_decode()</a>.
Please refer to the docstring of this method for more information.`,lo,ne,ze,io,ct,Xo=`This method forwards all its arguments to XLMRobertaTokenizerFast’s <a href="/docs/transformers/main/ko/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.decode">decode()</a>. Please
refer to the docstring of this method for more information.`,Wt,We,Vt,j,Ve,co,B,Ze,po,pt,So='The <a href="/docs/transformers/main/ko/model_doc/altclip#transformers.AltCLIPModel">AltCLIPModel</a> forward method, overrides the <code>__call__</code> special method.',mo,se,fo,ae,go,z,Qe,uo,mt,Do='The <a href="/docs/transformers/main/ko/model_doc/altclip#transformers.AltCLIPModel">AltCLIPModel</a> forward method, overrides the <code>__call__</code> special method.',ho,re,_o,le,Mo,W,Ee,Co,ft,Yo='The <a href="/docs/transformers/main/ko/model_doc/altclip#transformers.AltCLIPModel">AltCLIPModel</a> forward method, overrides the <code>__call__</code> special method.',bo,ie,To,de,Zt,He,Qt,X,Fe,yo,V,Ne,Io,gt,Oo='The <a href="/docs/transformers/main/ko/model_doc/altclip#transformers.AltCLIPTextModel">AltCLIPTextModel</a> forward method, overrides the <code>__call__</code> special method.',$o,ce,vo,pe,Et,Ge,Ht,S,Re,xo,Z,qe,wo,ut,Ko='The <a href="/docs/transformers/main/ko/model_doc/altclip#transformers.AltCLIPVisionModel">AltCLIPVisionModel</a> forward method, overrides the <code>__call__</code> special method.',Po,me,Lo,fe,Ft,Xe,Nt,_t,Gt;return C=new q({props:{title:"AltCLIP",local:"altclip",headingTag:"h1"}}),T=new q({props:{title:"개요",local:"overview",headingTag:"h2"}}),be=new q({props:{title:"사용 팁과 예제",local:"usage-tips-and-example",headingTag:"h2"}}),ve=new D({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"># 이미지-텍스트 유사도 점수</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"># 라벨 마다 확률을 얻기 위해 softmax 적용</span>`,wrap:!1}}),Y=new ht({props:{$$slots:{default:[rn]},$$scope:{ctx:I}}}),xe=new q({props:{title:"AltCLIPConfig",local:"transformers.AltCLIPConfig",headingTag:"h2"}}),we=new P({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/ko/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/ko/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 768) &#x2014;
Dimensionality 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 initial value of the <em>logit_scale</em> parameter. 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#L235"}}),O=new ue({props:{anchor:"transformers.AltCLIPConfig.example",$$slots:{default:[ln]},$$scope:{ctx:I}}}),Pe=new P({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#L371",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>An instance of a configuration object</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><a
  href="/docs/transformers/main/ko/model_doc/altclip#transformers.AltCLIPConfig"
>AltCLIPConfig</a></p>
`}}),Le=new q({props:{title:"AltCLIPTextConfig",local:"transformers.AltCLIPTextConfig",headingTag:"h2"}}),Je=new P({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/ko/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 1) &#x2014;
The vocabulary size of the <code>token_type_ids</code> passed when calling <a href="/docs/transformers/main/ko/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.initializer_factor",description:`<strong>initializer_factor</strong> (<code>float</code>, <em>optional</em>, defaults to 0.02) &#x2014;
A factor for initializing all weight matrices (should be kept to 1, used internally for initialization
testing).`,name:"initializer_factor"},{anchor:"transformers.AltCLIPTextConfig.layer_norm_eps",description:`<strong>layer_norm_eps</strong> (<code>float</code>, <em>optional</em>, defaults to 1e-05) &#x2014;
The epsilon used by the layer normalization layers.`,name:"layer_norm_eps"},{anchor:"transformers.AltCLIPTextConfig.pad_token_id",description:"<strong>pad_token_id</strong> (<code>int</code>, <em>optional</em>, defaults to 1) &#x2014; The id of the <em>padding</em> token.",name:"pad_token_id"},{anchor:"transformers.AltCLIPTextConfig.bos_token_id",description:"<strong>bos_token_id</strong> (<code>int</code>, <em>optional</em>, defaults to 0) &#x2014; The id of the <em>beginning-of-sequence</em> token.",name:"bos_token_id"},{anchor:"transformers.AltCLIPTextConfig.eos_token_id",description:`<strong>eos_token_id</strong> (<code>Union[int, List[int]]</code>, <em>optional</em>, defaults to 2) &#x2014;
The id of the <em>end-of-sequence</em> token. Optionally, use a list to set multiple <em>end-of-sequence</em> tokens.`,name:"eos_token_id"},{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 dimensions 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#L24"}}),ee=new ue({props:{anchor:"transformers.AltCLIPTextConfig.example",$$slots:{default:[dn]},$$scope:{ctx:I}}}),Ae=new q({props:{title:"AltCLIPVisionConfig",local:"transformers.AltCLIPVisionConfig",headingTag:"h2"}}),je=new P({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.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.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.num_channels",description:`<strong>num_channels</strong> (<code>int</code>, <em>optional</em>, defaults to 3) &#x2014;
The number of input channels.`,name:"num_channels"},{anchor:"transformers.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-05) &#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> (<code>float</code>, <em>optional</em>, defaults to 1.0) &#x2014;
A factor for initializing all weight matrices (should be kept to 1, used internally for initialization
testing).`,name:"initializer_factor"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/altclip/configuration_altclip.py#L142"}}),te=new ue({props:{anchor:"transformers.AltCLIPVisionConfig.example",$$slots:{default:[cn]},$$scope:{ctx:I}}}),ke=new q({props:{title:"AltCLIPProcessor",local:"transformers.AltCLIPProcessor",headingTag:"h2"}}),Ue=new P({props:{name:"class transformers.AltCLIPProcessor",anchor:"transformers.AltCLIPProcessor",parameters:[{name:"image_processor",val:" = None"},{name:"tokenizer",val:" = None"}],parametersDescription:[{anchor:"transformers.AltCLIPProcessor.image_processor",description:`<strong>image_processor</strong> (<a href="/docs/transformers/main/ko/model_doc/clip#transformers.CLIPImageProcessor">CLIPImageProcessor</a>, <em>optional</em>) &#x2014;
The image processor is a required input.`,name:"image_processor"},{anchor:"transformers.AltCLIPProcessor.tokenizer",description:`<strong>tokenizer</strong> (<code>XLMRobertaTokenizerFast</code>, <em>optional</em>) &#x2014;
The tokenizer is a required input.`,name:"tokenizer"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/altclip/processing_altclip.py#L31"}}),Be=new P({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#L127"}}),ze=new P({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#L134"}}),We=new q({props:{title:"AltCLIPModel",local:"transformers.AltCLIPModel",headingTag:"h2"}}),Ve=new P({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#L1517"}}),Ze=new P({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:"interpolate_pos_encoding",val:": bool = False"},{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/ko/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/main/ko/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/main/ko/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.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/ko/model_doc/auto#transformers.AutoImageProcessor">AutoImageProcessor</a>. See <a href="/docs/transformers/main/ko/model_doc/vit#transformers.ViTFeatureExtractor.__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.interpolate_pos_encoding",description:`<strong>interpolate_pos_encoding</strong> (<code>bool</code>, <em>optional</em>, defaults <code>False</code>) &#x2014;
Whether to interpolate the pre-trained position encodings.`,name:"interpolate_pos_encoding"},{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/ko/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#L1646",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<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>) — Contrastive loss for image-text similarity.</li>
<li><strong>logits_per_image</strong> (<code>torch.FloatTensor</code> of shape <code>(image_batch_size, text_batch_size)</code>) — 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</strong> (<code>torch.FloatTensor</code> of shape <code>(text_batch_size, image_batch_size)</code>) — 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</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, output_dim</code>) — The text embeddings obtained by applying the projection layer to the pooled output of <a
  href="/docs/transformers/main/ko/model_doc/altclip#transformers.AltCLIPTextModel"
>AltCLIPTextModel</a>.</li>
<li><strong>image_embeds</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, output_dim</code>) — The image embeddings obtained by applying the projection layer to the pooled output of <a
  href="/docs/transformers/main/ko/model_doc/altclip#transformers.AltCLIPVisionModel"
>AltCLIPVisionModel</a>.</li>
<li><strong>text_model_output</strong> (<code>BaseModelOutputWithPooling</code>) — The output of the <a
  href="/docs/transformers/main/ko/model_doc/altclip#transformers.AltCLIPTextModel"
>AltCLIPTextModel</a>.</li>
<li><strong>vision_model_output</strong> (<code>BaseModelOutputWithPooling</code>) — The output of the <a
  href="/docs/transformers/main/ko/model_doc/altclip#transformers.AltCLIPVisionModel"
>AltCLIPVisionModel</a>.</li>
</ul>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>transformers.models.altclip.modeling_altclip.AltCLIPOutput</code> or <code>tuple(torch.FloatTensor)</code></p>
`}}),se=new ht({props:{$$slots:{default:[pn]},$$scope:{ctx:I}}}),ae=new ue({props:{anchor:"transformers.AltCLIPModel.forward.example",$$slots:{default:[mn]},$$scope:{ctx:I}}}),Qe=new P({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/ko/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/main/ko/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/main/ko/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.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/ko/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#L1551",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>The text embeddings obtained by
applying the projection layer to the pooled output of <a
  href="/docs/transformers/main/ko/model_doc/altclip#transformers.AltCLIPTextModel"
>AltCLIPTextModel</a>.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p>text_features (<code>torch.FloatTensor</code> of shape <code>(batch_size, output_dim</code>)</p>
`}}),re=new ht({props:{$$slots:{default:[fn]},$$scope:{ctx:I}}}),le=new ue({props:{anchor:"transformers.AltCLIPModel.get_text_features.example",$$slots:{default:[gn]},$$scope:{ctx:I}}}),Ee=new P({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:"interpolate_pos_encoding",val:": bool = False"},{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/ko/model_doc/auto#transformers.AutoImageProcessor">AutoImageProcessor</a>. See <a href="/docs/transformers/main/ko/model_doc/vit#transformers.ViTFeatureExtractor.__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.interpolate_pos_encoding",description:`<strong>interpolate_pos_encoding</strong> (<code>bool</code>, <em>optional</em>, defaults <code>False</code>) &#x2014;
Whether to interpolate the pre-trained position encodings.`,name:"interpolate_pos_encoding"},{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/ko/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#L1598",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>The image embeddings obtained by
applying the projection layer to the pooled output of <a
  href="/docs/transformers/main/ko/model_doc/altclip#transformers.AltCLIPVisionModel"
>AltCLIPVisionModel</a>.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p>image_features (<code>torch.FloatTensor</code> of shape <code>(batch_size, output_dim</code>)</p>
`}}),ie=new ht({props:{$$slots:{default:[un]},$$scope:{ctx:I}}}),de=new ue({props:{anchor:"transformers.AltCLIPModel.get_image_features.example",$$slots:{default:[hn]},$$scope:{ctx:I}}}),He=new q({props:{title:"AltCLIPTextModel",local:"transformers.AltCLIPTextModel",headingTag:"h2"}}),Fe=new P({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#L1425"}}),Ne=new P({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/ko/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/main/ko/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/main/ko/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.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/ko/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#L1444",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<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>) — 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>) — 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>) — 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>) — 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>) — 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:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>transformers.modeling_outputs.BaseModelOutputWithPoolingAndProjection</code> or <code>tuple(torch.FloatTensor)</code></p>
`}}),ce=new ht({props:{$$slots:{default:[_n]},$$scope:{ctx:I}}}),pe=new ue({props:{anchor:"transformers.AltCLIPTextModel.forward.example",$$slots:{default:[Mn]},$$scope:{ctx:I}}}),Ge=new q({props:{title:"AltCLIPVisionModel",local:"transformers.AltCLIPVisionModel",headingTag:"h2"}}),Re=new P({props:{name:"class transformers.AltCLIPVisionModel",anchor:"transformers.AltCLIPVisionModel",parameters:[{name:"config",val:": AltCLIPVisionConfig"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/altclip/modeling_altclip.py#L1192"}}),qe=new P({props:{name:"forward",anchor:"transformers.AltCLIPVisionModel.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:"interpolate_pos_encoding",val:": bool = False"},{name:"return_dict",val:": typing.Optional[bool] = None"}],parametersDescription:[{anchor:"transformers.AltCLIPVisionModel.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/ko/model_doc/auto#transformers.AutoImageProcessor">AutoImageProcessor</a>. See <a href="/docs/transformers/main/ko/model_doc/vit#transformers.ViTFeatureExtractor.__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;
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.AltCLIPVisionModel.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.AltCLIPVisionModel.forward.interpolate_pos_encoding",description:`<strong>interpolate_pos_encoding</strong> (<code>bool</code>, <em>optional</em>, defaults <code>False</code>) &#x2014;
Whether to interpolate the pre-trained position encodings.`,name:"interpolate_pos_encoding"},{anchor:"transformers.AltCLIPVisionModel.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/ko/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#L1205",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>A <a
  href="/docs/transformers/main/ko/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.altclip.configuration_altclip.AltCLIPVisionConfig'&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>) — 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>) — 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>) — 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>) — 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:`<script context="module">export const metadata = 'undefined';<\/script>


<p><a
  href="/docs/transformers/main/ko/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPooling"
>transformers.modeling_outputs.BaseModelOutputWithPooling</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),me=new ht({props:{$$slots:{default:[Cn]},$$scope:{ctx:I}}}),fe=new ue({props:{anchor:"transformers.AltCLIPVisionModel.forward.example",$$slots:{default:[bn]},$$scope:{ctx:I}}}),Xe=new 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