import{s as oa,o as na,n as F}from"../chunks/scheduler.bdbef820.js";import{S as sa,i as aa,g as c,s as r,r as h,A as ra,h as p,f as a,c as i,j as L,u as f,x as C,k as J,y as l,a as d,v as u,d as _,t as T,w as b}from"../chunks/index.33f81d56.js";import{T as pt}from"../chunks/Tip.34194030.js";import{D as U}from"../chunks/Docstring.64554317.js";import{C as fe}from"../chunks/CodeBlock.362b34a4.js";import{F as ia,M as Fs}from"../chunks/Markdown.03194dea.js";import{E as He}from"../chunks/ExampleCodeBlock.4f2252c6.js";import{P as la}from"../chunks/PipelineTag.d2f354cd.js";import{H as S,E as da}from"../chunks/EditOnGithub.a9246e21.js";function ca(v){let e,m='작은 배치 크기를 사용할 때, 플래시 어텐션을 사용하면 모델이 느려지는 것을 느낄 수 있습니다.아래의 <a href="#Expected-speedups-with-Flash-Attention-and-SDPA">플래시 어텐션과 SDPA를 사용한 예상 속도 향상</a> 섹션을 참조하여 적절한 어텐션 구현을 선택하세요.';return{c(){e=c("p"),e.innerHTML=m},l(n){e=p(n,"P",{"data-svelte-h":!0}),C(e)!=="svelte-1blgue7"&&(e.innerHTML=m)},m(n,s){d(n,e,s)},p:F,d(n){n&&a(e)}}}function pa(v){let e,m="Example:",n,s,y;return s=new fe({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> CLIPConfig, CLIPModel

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Initializing a CLIPConfig with openai/clip-vit-base-patch32 style configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>configuration = CLIPConfig()

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Initializing a CLIPModel (with random weights) from the openai/clip-vit-base-patch32 style configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = CLIPModel(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 CLIPConfig from a CLIPTextConfig and a CLIPVisionConfig</span>
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> CLIPTextConfig, CLIPVisionConfig

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Initializing a CLIPText and CLIPVision configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config_text = CLIPTextConfig()
<span class="hljs-meta">&gt;&gt;&gt; </span>config_vision = CLIPVisionConfig()

<span class="hljs-meta">&gt;&gt;&gt; </span>config = CLIPConfig.from_text_vision_configs(config_text, config_vision)`,wrap:!1}}),{c(){e=c("p"),e.textContent=m,n=r(),h(s.$$.fragment)},l(o){e=p(o,"P",{"data-svelte-h":!0}),C(e)!=="svelte-11lpom8"&&(e.textContent=m),n=i(o),f(s.$$.fragment,o)},m(o,$){d(o,e,$),d(o,n,$),u(s,o,$),y=!0},p:F,i(o){y||(_(s.$$.fragment,o),y=!0)},o(o){T(s.$$.fragment,o),y=!1},d(o){o&&(a(e),a(n)),b(s,o)}}}function ma(v){let e,m="Example:",n,s,y;return s=new fe({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> CLIPTextConfig, CLIPTextModel

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Initializing a CLIPTextConfig with openai/clip-vit-base-patch32 style configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>configuration = CLIPTextConfig()

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Initializing a CLIPTextModel (with random weights) from the openai/clip-vit-base-patch32 style configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = CLIPTextModel(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(){e=c("p"),e.textContent=m,n=r(),h(s.$$.fragment)},l(o){e=p(o,"P",{"data-svelte-h":!0}),C(e)!=="svelte-11lpom8"&&(e.textContent=m),n=i(o),f(s.$$.fragment,o)},m(o,$){d(o,e,$),d(o,n,$),u(s,o,$),y=!0},p:F,i(o){y||(_(s.$$.fragment,o),y=!0)},o(o){T(s.$$.fragment,o),y=!1},d(o){o&&(a(e),a(n)),b(s,o)}}}function ga(v){let e,m="Example:",n,s,y;return s=new fe({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> CLIPVisionConfig, CLIPVisionModel

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Initializing a CLIPVisionConfig with openai/clip-vit-base-patch32 style configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>configuration = CLIPVisionConfig()

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Initializing a CLIPVisionModel (with random weights) from the openai/clip-vit-base-patch32 style configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = CLIPVisionModel(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(){e=c("p"),e.textContent=m,n=r(),h(s.$$.fragment)},l(o){e=p(o,"P",{"data-svelte-h":!0}),C(e)!=="svelte-11lpom8"&&(e.textContent=m),n=i(o),f(s.$$.fragment,o)},m(o,$){d(o,e,$),d(o,n,$),u(s,o,$),y=!0},p:F,i(o){y||(_(s.$$.fragment,o),y=!0)},o(o){T(s.$$.fragment,o),y=!1},d(o){o&&(a(e),a(n)),b(s,o)}}}function ha(v){let e,m=`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(){e=c("p"),e.innerHTML=m},l(n){e=p(n,"P",{"data-svelte-h":!0}),C(e)!=="svelte-fincs2"&&(e.innerHTML=m)},m(n,s){d(n,e,s)},p:F,d(n){n&&a(e)}}}function fa(v){let e,m="Examples:",n,s,y;return s=new fe({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, CLIPModel

<span class="hljs-meta">&gt;&gt;&gt; </span>model = CLIPModel.from_pretrained(<span class="hljs-string">&quot;openai/clip-vit-base-patch32&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>processor = AutoProcessor.from_pretrained(<span class="hljs-string">&quot;openai/clip-vit-base-patch32&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(){e=c("p"),e.textContent=m,n=r(),h(s.$$.fragment)},l(o){e=p(o,"P",{"data-svelte-h":!0}),C(e)!=="svelte-kvfsh7"&&(e.textContent=m),n=i(o),f(s.$$.fragment,o)},m(o,$){d(o,e,$),d(o,n,$),u(s,o,$),y=!0},p:F,i(o){y||(_(s.$$.fragment,o),y=!0)},o(o){T(s.$$.fragment,o),y=!1},d(o){o&&(a(e),a(n)),b(s,o)}}}function ua(v){let e,m=`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(){e=c("p"),e.innerHTML=m},l(n){e=p(n,"P",{"data-svelte-h":!0}),C(e)!=="svelte-fincs2"&&(e.innerHTML=m)},m(n,s){d(n,e,s)},p:F,d(n){n&&a(e)}}}function _a(v){let e,m="Examples:",n,s,y;return s=new fe({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, CLIPModel

<span class="hljs-meta">&gt;&gt;&gt; </span>model = CLIPModel.from_pretrained(<span class="hljs-string">&quot;openai/clip-vit-base-patch32&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;openai/clip-vit-base-patch32&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = tokenizer([<span class="hljs-string">&quot;a photo of a cat&quot;</span>, <span class="hljs-string">&quot;a photo of a dog&quot;</span>], padding=<span class="hljs-literal">True</span>, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>text_features = model.get_text_features(**inputs)`,wrap:!1}}),{c(){e=c("p"),e.textContent=m,n=r(),h(s.$$.fragment)},l(o){e=p(o,"P",{"data-svelte-h":!0}),C(e)!=="svelte-kvfsh7"&&(e.textContent=m),n=i(o),f(s.$$.fragment,o)},m(o,$){d(o,e,$),d(o,n,$),u(s,o,$),y=!0},p:F,i(o){y||(_(s.$$.fragment,o),y=!0)},o(o){T(s.$$.fragment,o),y=!1},d(o){o&&(a(e),a(n)),b(s,o)}}}function Ta(v){let e,m=`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(){e=c("p"),e.innerHTML=m},l(n){e=p(n,"P",{"data-svelte-h":!0}),C(e)!=="svelte-fincs2"&&(e.innerHTML=m)},m(n,s){d(n,e,s)},p:F,d(n){n&&a(e)}}}function ba(v){let e,m="Examples:",n,s,y;return s=new fe({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, CLIPModel

<span class="hljs-meta">&gt;&gt;&gt; </span>model = CLIPModel.from_pretrained(<span class="hljs-string">&quot;openai/clip-vit-base-patch32&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>processor = AutoProcessor.from_pretrained(<span class="hljs-string">&quot;openai/clip-vit-base-patch32&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(){e=c("p"),e.textContent=m,n=r(),h(s.$$.fragment)},l(o){e=p(o,"P",{"data-svelte-h":!0}),C(e)!=="svelte-kvfsh7"&&(e.textContent=m),n=i(o),f(s.$$.fragment,o)},m(o,$){d(o,e,$),d(o,n,$),u(s,o,$),y=!0},p:F,i(o){y||(_(s.$$.fragment,o),y=!0)},o(o){T(s.$$.fragment,o),y=!1},d(o){o&&(a(e),a(n)),b(s,o)}}}function Ma(v){let e,m=`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(){e=c("p"),e.innerHTML=m},l(n){e=p(n,"P",{"data-svelte-h":!0}),C(e)!=="svelte-fincs2"&&(e.innerHTML=m)},m(n,s){d(n,e,s)},p:F,d(n){n&&a(e)}}}function ya(v){let e,m="Examples:",n,s,y;return s=new fe({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, CLIPTextModel

<span class="hljs-meta">&gt;&gt;&gt; </span>model = CLIPTextModel.from_pretrained(<span class="hljs-string">&quot;openai/clip-vit-base-patch32&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;openai/clip-vit-base-patch32&quot;</span>)

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

<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(**inputs)
<span class="hljs-meta">&gt;&gt;&gt; </span>last_hidden_state = outputs.last_hidden_state
<span class="hljs-meta">&gt;&gt;&gt; </span>pooled_output = outputs.pooler_output  <span class="hljs-comment"># pooled (EOS token) states</span>`,wrap:!1}}),{c(){e=c("p"),e.textContent=m,n=r(),h(s.$$.fragment)},l(o){e=p(o,"P",{"data-svelte-h":!0}),C(e)!=="svelte-kvfsh7"&&(e.textContent=m),n=i(o),f(s.$$.fragment,o)},m(o,$){d(o,e,$),d(o,n,$),u(s,o,$),y=!0},p:F,i(o){y||(_(s.$$.fragment,o),y=!0)},o(o){T(s.$$.fragment,o),y=!1},d(o){o&&(a(e),a(n)),b(s,o)}}}function Ca(v){let e,m=`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(){e=c("p"),e.innerHTML=m},l(n){e=p(n,"P",{"data-svelte-h":!0}),C(e)!=="svelte-fincs2"&&(e.innerHTML=m)},m(n,s){d(n,e,s)},p:F,d(n){n&&a(e)}}}function Ia(v){let e,m="Examples:",n,s,y;return s=new fe({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, CLIPTextModelWithProjection

<span class="hljs-meta">&gt;&gt;&gt; </span>model = CLIPTextModelWithProjection.from_pretrained(<span class="hljs-string">&quot;openai/clip-vit-base-patch32&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;openai/clip-vit-base-patch32&quot;</span>)

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

<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(**inputs)
<span class="hljs-meta">&gt;&gt;&gt; </span>text_embeds = outputs.text_embeds`,wrap:!1}}),{c(){e=c("p"),e.textContent=m,n=r(),h(s.$$.fragment)},l(o){e=p(o,"P",{"data-svelte-h":!0}),C(e)!=="svelte-kvfsh7"&&(e.textContent=m),n=i(o),f(s.$$.fragment,o)},m(o,$){d(o,e,$),d(o,n,$),u(s,o,$),y=!0},p:F,i(o){y||(_(s.$$.fragment,o),y=!0)},o(o){T(s.$$.fragment,o),y=!1},d(o){o&&(a(e),a(n)),b(s,o)}}}function $a(v){let e,m=`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(){e=c("p"),e.innerHTML=m},l(n){e=p(n,"P",{"data-svelte-h":!0}),C(e)!=="svelte-fincs2"&&(e.innerHTML=m)},m(n,s){d(n,e,s)},p:F,d(n){n&&a(e)}}}function wa(v){let e,m="Examples:",n,s,y;return s=new fe({props:{code:"ZnJvbSUyMFBJTCUyMGltcG9ydCUyMEltYWdlJTBBaW1wb3J0JTIwcmVxdWVzdHMlMEFmcm9tJTIwdHJhbnNmb3JtZXJzJTIwaW1wb3J0JTIwQXV0b1Byb2Nlc3NvciUyQyUyMENMSVBWaXNpb25Nb2RlbFdpdGhQcm9qZWN0aW9uJTBBJTBBbW9kZWwlMjAlM0QlMjBDTElQVmlzaW9uTW9kZWxXaXRoUHJvamVjdGlvbi5mcm9tX3ByZXRyYWluZWQoJTIyb3BlbmFpJTJGY2xpcC12aXQtYmFzZS1wYXRjaDMyJTIyKSUwQXByb2Nlc3NvciUyMCUzRCUyMEF1dG9Qcm9jZXNzb3IuZnJvbV9wcmV0cmFpbmVkKCUyMm9wZW5haSUyRmNsaXAtdml0LWJhc2UtcGF0Y2gzMiUyMiklMEElMEF1cmwlMjAlM0QlMjAlMjJodHRwJTNBJTJGJTJGaW1hZ2VzLmNvY29kYXRhc2V0Lm9yZyUyRnZhbDIwMTclMkYwMDAwMDAwMzk3NjkuanBnJTIyJTBBaW1hZ2UlMjAlM0QlMjBJbWFnZS5vcGVuKHJlcXVlc3RzLmdldCh1cmwlMkMlMjBzdHJlYW0lM0RUcnVlKS5yYXcpJTBBJTBBaW5wdXRzJTIwJTNEJTIwcHJvY2Vzc29yKGltYWdlcyUzRGltYWdlJTJDJTIwcmV0dXJuX3RlbnNvcnMlM0QlMjJwdCUyMiklMEElMEFvdXRwdXRzJTIwJTNEJTIwbW9kZWwoKippbnB1dHMpJTBBaW1hZ2VfZW1iZWRzJTIwJTNEJTIwb3V0cHV0cy5pbWFnZV9lbWJlZHM=",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, CLIPVisionModelWithProjection

<span class="hljs-meta">&gt;&gt;&gt; </span>model = CLIPVisionModelWithProjection.from_pretrained(<span class="hljs-string">&quot;openai/clip-vit-base-patch32&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>processor = AutoProcessor.from_pretrained(<span class="hljs-string">&quot;openai/clip-vit-base-patch32&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>image_embeds = outputs.image_embeds`,wrap:!1}}),{c(){e=c("p"),e.textContent=m,n=r(),h(s.$$.fragment)},l(o){e=p(o,"P",{"data-svelte-h":!0}),C(e)!=="svelte-kvfsh7"&&(e.textContent=m),n=i(o),f(s.$$.fragment,o)},m(o,$){d(o,e,$),d(o,n,$),u(s,o,$),y=!0},p:F,i(o){y||(_(s.$$.fragment,o),y=!0)},o(o){T(s.$$.fragment,o),y=!1},d(o){o&&(a(e),a(n)),b(s,o)}}}function va(v){let e,m=`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(){e=c("p"),e.innerHTML=m},l(n){e=p(n,"P",{"data-svelte-h":!0}),C(e)!=="svelte-fincs2"&&(e.innerHTML=m)},m(n,s){d(n,e,s)},p:F,d(n){n&&a(e)}}}function xa(v){let e,m="Examples:",n,s,y;return s=new fe({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, CLIPVisionModel

<span class="hljs-meta">&gt;&gt;&gt; </span>model = CLIPVisionModel.from_pretrained(<span class="hljs-string">&quot;openai/clip-vit-base-patch32&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>processor = AutoProcessor.from_pretrained(<span class="hljs-string">&quot;openai/clip-vit-base-patch32&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(){e=c("p"),e.textContent=m,n=r(),h(s.$$.fragment)},l(o){e=p(o,"P",{"data-svelte-h":!0}),C(e)!=="svelte-kvfsh7"&&(e.textContent=m),n=i(o),f(s.$$.fragment,o)},m(o,$){d(o,e,$),d(o,n,$),u(s,o,$),y=!0},p:F,i(o){y||(_(s.$$.fragment,o),y=!0)},o(o){T(s.$$.fragment,o),y=!1},d(o){o&&(a(e),a(n)),b(s,o)}}}function ka(v){let e,m=`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(){e=c("p"),e.innerHTML=m},l(n){e=p(n,"P",{"data-svelte-h":!0}),C(e)!=="svelte-fincs2"&&(e.innerHTML=m)},m(n,s){d(n,e,s)},p:F,d(n){n&&a(e)}}}function Pa(v){let e,m="Example:",n,s,y;return s=new fe({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> AutoImageProcessor, CLIPForImageClassification
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> datasets <span class="hljs-keyword">import</span> load_dataset

<span class="hljs-meta">&gt;&gt;&gt; </span>dataset = load_dataset(<span class="hljs-string">&quot;huggingface/cats-image&quot;</span>, trust_remote_code=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>image = dataset[<span class="hljs-string">&quot;test&quot;</span>][<span class="hljs-string">&quot;image&quot;</span>][<span class="hljs-number">0</span>]

<span class="hljs-meta">&gt;&gt;&gt; </span>image_processor = AutoImageProcessor.from_pretrained(<span class="hljs-string">&quot;openai/clip-vit-base-patch32&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = CLIPForImageClassification.from_pretrained(<span class="hljs-string">&quot;openai/clip-vit-base-patch32&quot;</span>)

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

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">with</span> torch.no_grad():
<span class="hljs-meta">... </span>    logits = model(**inputs).logits

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># model predicts one of the 1000 ImageNet classes</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>predicted_label = logits.argmax(-<span class="hljs-number">1</span>).item()
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-built_in">print</span>(model.config.id2label[predicted_label])
LABEL_0`,wrap:!1}}),{c(){e=c("p"),e.textContent=m,n=r(),h(s.$$.fragment)},l(o){e=p(o,"P",{"data-svelte-h":!0}),C(e)!=="svelte-11lpom8"&&(e.textContent=m),n=i(o),f(s.$$.fragment,o)},m(o,$){d(o,e,$),d(o,n,$),u(s,o,$),y=!0},p:F,i(o){y||(_(s.$$.fragment,o),y=!0)},o(o){T(s.$$.fragment,o),y=!1},d(o){o&&(a(e),a(n)),b(s,o)}}}function La(v){let e,m,n,s,y,o,$=`This model inherits from <a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel">PreTrainedModel</a>. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)`,nt,B,st=`This model is also a PyTorch <a href="https://pytorch.org/docs/stable/nn.html#torch.nn.Module" rel="nofollow">torch.nn.Module</a> subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.`,at,z,ue,ee,j,Ne='The <a href="/docs/transformers/main/ko/model_doc/clip#transformers.CLIPModel">CLIPModel</a> forward method, overrides the <code>__call__</code> special method.',rt,E,mt,le,Ge,O,xe,te,ke,Pe='The <a href="/docs/transformers/main/ko/model_doc/clip#transformers.CLIPModel">CLIPModel</a> forward method, overrides the <code>__call__</code> special method.',Mt,_e,Le,Ce,Xe,N,Je,K,de,Ye='The <a href="/docs/transformers/main/ko/model_doc/clip#transformers.CLIPModel">CLIPModel</a> forward method, overrides the <code>__call__</code> special method.',je,Ie,De,Q,qe,oe,ae,V,it,Te,ce,Pt=`The text model from CLIP without any head or projection on top.
This model inherits from <a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel">PreTrainedModel</a>. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)`,G,Se,Ae=`This model is also a PyTorch <a href="https://pytorch.org/docs/stable/nn.html#torch.nn.Module" rel="nofollow">torch.nn.Module</a> subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.`,Ue,ne,$e,re,pe,gt='The <a href="/docs/transformers/main/ko/model_doc/clip#transformers.CLIPTextModel">CLIPTextModel</a> forward method, overrides the <code>__call__</code> special method.',me,R,Lt,Y,ze,Oe,Z,D,be,Fe,Ee,lt="CLIP Text Model with a projection layer on top (a linear layer on top of the pooled output).",Me,Ve,We=`This model inherits from <a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel">PreTrainedModel</a>. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)`,ge,w,P=`This model is also a PyTorch <a href="https://pytorch.org/docs/stable/nn.html#torch.nn.Module" rel="nofollow">torch.nn.Module</a> subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.`,ie,W,he,ye,A,we='The <a href="/docs/transformers/main/ko/model_doc/clip#transformers.CLIPTextModelWithProjection">CLIPTextModelWithProjection</a> forward method, overrides the <code>__call__</code> special method.',se,I,k,X,ht,Ke,et,H,Ze,ft,dt,Re="CLIP Vision Model with a projection layer on top (a linear layer on top of the pooled output).",to,Wt,Nn=`This model inherits from <a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel">PreTrainedModel</a>. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)`,ko,Zt,Gn=`This model is also a PyTorch <a href="https://pytorch.org/docs/stable/nn.html#torch.nn.Module" rel="nofollow">torch.nn.Module</a> subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.`,Po,tt,St,oo,Co,Ro='The <a href="/docs/transformers/main/ko/model_doc/clip#transformers.CLIPVisionModelWithProjection">CLIPVisionModelWithProjection</a> forward method, overrides the <code>__call__</code> special method.',no,so,Lo,wt,Ho,Qt,Yt,vt,Dt,ao,Io,Xo=`The vision model from CLIP without any head or projection on top.
This model inherits from <a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel">PreTrainedModel</a>. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)`,ro,$o,qo=`This model is also a PyTorch <a href="https://pytorch.org/docs/stable/nn.html#torch.nn.Module" rel="nofollow">torch.nn.Module</a> subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.`,io,yt,Be,lo,uo,wo='The <a href="/docs/transformers/main/ko/model_doc/clip#transformers.CLIPVisionModel">CLIPVisionModel</a> forward method, overrides the <code>__call__</code> special method.',un,At,_o,co,Jo,xt,jo,ve,Jt,So,Rt,En=`CLIP vision encoder with an image classification head on top (a linear layer on top of the pooled final hidden states of
the patch tokens) e.g. for ImageNet.`,Uo,Bt,Qo=`This model inherits from <a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel">PreTrainedModel</a>. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)`,Ct,Nt,_n=`This model is also a PyTorch <a href="https://pytorch.org/docs/stable/nn.html#torch.nn.Module" rel="nofollow">torch.nn.Module</a> subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.`,To,jt,po,bo,vo,Tn='The <a href="/docs/transformers/main/ko/model_doc/clip#transformers.CLIPForImageClassification">CLIPForImageClassification</a> forward method, overrides the <code>__call__</code> special method.',Ot,Ht,mo,Xt,ut;return e=new S({props:{title:"CLIPModel",local:"transformers.CLIPModel ][ transformers.CLIPModel",headingTag:"h2"}}),s=new U({props:{name:"class transformers.CLIPModel",anchor:"transformers.CLIPModel",parameters:[{name:"config",val:": CLIPConfig"}],parametersDescription:[{anchor:"transformers.CLIPModel.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/ko/model_doc/clip#transformers.CLIPConfig">CLIPConfig</a>) &#x2014; Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the <a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/clip/modeling_clip.py#L1180"}}),ue=new U({props:{name:"forward",anchor:"transformers.CLIPModel.forward",parameters:[{name:"input_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"pixel_values",val:": typing.Optional[torch.FloatTensor] = None"},{name:"attention_mask",val:": typing.Optional[torch.Tensor] = None"},{name:"position_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"return_loss",val:": typing.Optional[bool] = None"},{name:"output_attentions",val:": typing.Optional[bool] = None"},{name:"output_hidden_states",val:": typing.Optional[bool] = None"},{name:"interpolate_pos_encoding",val:": bool = False"},{name:"return_dict",val:": typing.Optional[bool] = None"}],parametersDescription:[{anchor:"transformers.CLIPModel.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.CLIPModel.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.CLIPModel.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.CLIPModel.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.CLIPModel.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.CLIPModel.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.CLIPModel.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.CLIPModel.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.CLIPModel.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/clip/modeling_clip.py#L1318",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>A <code>transformers.models.clip.modeling_clip.CLIPOutput</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.clip.configuration_clip.CLIPConfig'&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/clip#transformers.CLIPTextModel"
>CLIPTextModel</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/clip#transformers.CLIPVisionModel"
>CLIPVisionModel</a>.</li>
<li><strong>text_model_output</strong> (<code>BaseModelOutputWithPooling</code>) — The output of the <a
  href="/docs/transformers/main/ko/model_doc/clip#transformers.CLIPTextModel"
>CLIPTextModel</a>.</li>
<li><strong>vision_model_output</strong> (<code>BaseModelOutputWithPooling</code>) — The output of the <a
  href="/docs/transformers/main/ko/model_doc/clip#transformers.CLIPVisionModel"
>CLIPVisionModel</a>.</li>
</ul>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>transformers.models.clip.modeling_clip.CLIPOutput</code> or <code>tuple(torch.FloatTensor)</code></p>
`}}),E=new pt({props:{$$slots:{default:[ha]},$$scope:{ctx:v}}}),le=new He({props:{anchor:"transformers.CLIPModel.forward.example",$$slots:{default:[fa]},$$scope:{ctx:v}}}),xe=new U({props:{name:"get_text_features",anchor:"transformers.CLIPModel.get_text_features",parameters:[{name:"input_ids",val:": typing.Optional[torch.Tensor] = None"},{name:"attention_mask",val:": typing.Optional[torch.Tensor] = None"},{name:"position_ids",val:": typing.Optional[torch.Tensor] = None"},{name:"output_attentions",val:": typing.Optional[bool] = None"},{name:"output_hidden_states",val:": typing.Optional[bool] = None"},{name:"return_dict",val:": typing.Optional[bool] = None"}],parametersDescription:[{anchor:"transformers.CLIPModel.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.CLIPModel.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.CLIPModel.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.CLIPModel.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.CLIPModel.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.CLIPModel.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/clip/modeling_clip.py#L1220",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/clip#transformers.CLIPTextModel"
>CLIPTextModel</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>
`}}),_e=new pt({props:{$$slots:{default:[ua]},$$scope:{ctx:v}}}),Ce=new He({props:{anchor:"transformers.CLIPModel.get_text_features.example",$$slots:{default:[_a]},$$scope:{ctx:v}}}),Je=new U({props:{name:"get_image_features",anchor:"transformers.CLIPModel.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.CLIPModel.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.CLIPModel.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.CLIPModel.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.CLIPModel.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.CLIPModel.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/clip/modeling_clip.py#L1267",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/clip#transformers.CLIPVisionModel"
>CLIPVisionModel</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 pt({props:{$$slots:{default:[Ta]},$$scope:{ctx:v}}}),Q=new He({props:{anchor:"transformers.CLIPModel.get_image_features.example",$$slots:{default:[ba]},$$scope:{ctx:v}}}),oe=new S({props:{title:"CLIPTextModel",local:"transformers.CLIPTextModel ][ transformers.CLIPTextModel",headingTag:"h2"}}),it=new U({props:{name:"class transformers.CLIPTextModel",anchor:"transformers.CLIPTextModel",parameters:[{name:"config",val:": CLIPTextConfig"}],parametersDescription:[{anchor:"transformers.CLIPTextModel.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/ko/model_doc/clip#transformers.CLIPConfig">CLIPConfig</a>) &#x2014; Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the <a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/clip/modeling_clip.py#L999"}}),$e=new U({props:{name:"forward",anchor:"transformers.CLIPTextModel.forward",parameters:[{name:"input_ids",val:": typing.Optional[torch.Tensor] = None"},{name:"attention_mask",val:": typing.Optional[torch.Tensor] = None"},{name:"position_ids",val:": typing.Optional[torch.Tensor] = None"},{name:"output_attentions",val:": typing.Optional[bool] = None"},{name:"output_hidden_states",val:": typing.Optional[bool] = None"},{name:"return_dict",val:": typing.Optional[bool] = None"}],parametersDescription:[{anchor:"transformers.CLIPTextModel.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.CLIPTextModel.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.CLIPTextModel.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.CLIPTextModel.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.CLIPTextModel.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.CLIPTextModel.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/clip/modeling_clip.py#L1020",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.clip.configuration_clip.CLIPTextConfig'&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>
`}}),R=new pt({props:{$$slots:{default:[Ma]},$$scope:{ctx:v}}}),Y=new He({props:{anchor:"transformers.CLIPTextModel.forward.example",$$slots:{default:[ya]},$$scope:{ctx:v}}}),Oe=new S({props:{title:"CLIPTextModelWithProjection",local:"transformers.CLIPTextModelWithProjection ][ transformers.CLIPTextModelWithProjection",headingTag:"h2"}}),be=new U({props:{name:"class transformers.CLIPTextModelWithProjection",anchor:"transformers.CLIPTextModelWithProjection",parameters:[{name:"config",val:": CLIPTextConfig"}],parametersDescription:[{anchor:"transformers.CLIPTextModelWithProjection.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/ko/model_doc/clip#transformers.CLIPConfig">CLIPConfig</a>) &#x2014; Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the <a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/clip/modeling_clip.py#L1416"}}),he=new U({props:{name:"forward",anchor:"transformers.CLIPTextModelWithProjection.forward",parameters:[{name:"input_ids",val:": typing.Optional[torch.Tensor] = None"},{name:"attention_mask",val:": typing.Optional[torch.Tensor] = None"},{name:"position_ids",val:": typing.Optional[torch.Tensor] = None"},{name:"output_attentions",val:": typing.Optional[bool] = None"},{name:"output_hidden_states",val:": typing.Optional[bool] = None"},{name:"return_dict",val:": typing.Optional[bool] = None"}],parametersDescription:[{anchor:"transformers.CLIPTextModelWithProjection.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.CLIPTextModelWithProjection.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.CLIPTextModelWithProjection.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.CLIPTextModelWithProjection.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.CLIPTextModelWithProjection.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.CLIPTextModelWithProjection.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/clip/modeling_clip.py#L1444",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>A <code>transformers.models.clip.modeling_clip.CLIPTextModelOutput</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.clip.configuration_clip.CLIPTextConfig'&gt;</code>) and inputs.</p>
<ul>
<li>
<p><strong>text_embeds</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, output_dim)</code> <em>optional</em> returned when model is initialized with <code>with_projection=True</code>) — The text embeddings obtained by applying the projection layer to the pooler_output.</p>
</li>
<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>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><code>transformers.models.clip.modeling_clip.CLIPTextModelOutput</code> or <code>tuple(torch.FloatTensor)</code></p>
`}}),I=new pt({props:{$$slots:{default:[Ca]},$$scope:{ctx:v}}}),X=new He({props:{anchor:"transformers.CLIPTextModelWithProjection.forward.example",$$slots:{default:[Ia]},$$scope:{ctx:v}}}),Ke=new S({props:{title:"CLIPVisionModelWithProjection",local:"transformers.CLIPVisionModelWithProjection ][ transformers.CLIPVisionModelWithProjection",headingTag:"h2"}}),Ze=new U({props:{name:"class transformers.CLIPVisionModelWithProjection",anchor:"transformers.CLIPVisionModelWithProjection",parameters:[{name:"config",val:": CLIPVisionConfig"}],parametersDescription:[{anchor:"transformers.CLIPVisionModelWithProjection.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/ko/model_doc/clip#transformers.CLIPConfig">CLIPConfig</a>) &#x2014; Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the <a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/clip/modeling_clip.py#L1498"}}),St=new U({props:{name:"forward",anchor:"transformers.CLIPVisionModelWithProjection.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.CLIPVisionModelWithProjection.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.CLIPVisionModelWithProjection.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.CLIPVisionModelWithProjection.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.CLIPVisionModelWithProjection.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.CLIPVisionModelWithProjection.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/clip/modeling_clip.py#L1522",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>A <code>transformers.models.clip.modeling_clip.CLIPVisionModelOutput</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.clip.configuration_clip.CLIPVisionConfig'&gt;</code>) and inputs.</p>
<ul>
<li>
<p><strong>image_embeds</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, output_dim)</code> <em>optional</em> returned when model is initialized with <code>with_projection=True</code>) — The image embeddings obtained by applying the projection layer to the pooler_output.</p>
</li>
<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>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><code>transformers.models.clip.modeling_clip.CLIPVisionModelOutput</code> or <code>tuple(torch.FloatTensor)</code></p>
`}}),so=new pt({props:{$$slots:{default:[$a]},$$scope:{ctx:v}}}),wt=new He({props:{anchor:"transformers.CLIPVisionModelWithProjection.forward.example",$$slots:{default:[wa]},$$scope:{ctx:v}}}),Qt=new S({props:{title:"CLIPVisionModel",local:"transformers.CLIPVisionModel ][ transformers.CLIPVisionModel",headingTag:"h2"}}),Dt=new U({props:{name:"class transformers.CLIPVisionModel",anchor:"transformers.CLIPVisionModel",parameters:[{name:"config",val:": CLIPVisionConfig"}],parametersDescription:[{anchor:"transformers.CLIPVisionModel.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/ko/model_doc/clip#transformers.CLIPConfig">CLIPConfig</a>) &#x2014; Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the <a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/clip/modeling_clip.py#L1119"}}),Be=new U({props:{name:"forward",anchor:"transformers.CLIPVisionModel.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.CLIPVisionModel.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.CLIPVisionModel.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.CLIPVisionModel.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.CLIPVisionModel.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.CLIPVisionModel.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/clip/modeling_clip.py#L1137",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.clip.configuration_clip.CLIPVisionConfig'&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>
`}}),At=new pt({props:{$$slots:{default:[va]},$$scope:{ctx:v}}}),co=new He({props:{anchor:"transformers.CLIPVisionModel.forward.example",$$slots:{default:[xa]},$$scope:{ctx:v}}}),xt=new S({props:{title:"CLIPForImageClassification",local:"transformers.CLIPForImageClassification ][ transformers.CLIPForImageClassification",headingTag:"h2"}}),Jt=new U({props:{name:"class transformers.CLIPForImageClassification",anchor:"transformers.CLIPForImageClassification",parameters:[{name:"config",val:": CLIPConfig"}],parametersDescription:[{anchor:"transformers.CLIPForImageClassification.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/ko/model_doc/clip#transformers.CLIPConfig">CLIPConfig</a>) &#x2014; Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the <a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/clip/modeling_clip.py#L1579"}}),po=new U({props:{name:"forward",anchor:"transformers.CLIPForImageClassification.forward",parameters:[{name:"pixel_values",val:": typing.Optional[torch.Tensor] = None"},{name:"labels",val:": typing.Optional[torch.Tensor] = None"},{name:"output_attentions",val:": typing.Optional[bool] = None"},{name:"output_hidden_states",val:": typing.Optional[bool] = None"},{name:"return_dict",val:": typing.Optional[bool] = None"}],parametersDescription:[{anchor:"transformers.CLIPForImageClassification.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.CLIPForImageClassification.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.CLIPForImageClassification.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.CLIPForImageClassification.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.CLIPForImageClassification.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.CLIPForImageClassification.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.CLIPForImageClassification.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.CLIPForImageClassification.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.CLIPForImageClassification.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"},{anchor:"transformers.CLIPForImageClassification.forward.labels",description:`<strong>labels</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size,)</code>, <em>optional</em>) &#x2014;
Labels for computing the image classification/regression loss. Indices should be in <code>[0, ..., config.num_labels - 1]</code>. If <code>config.num_labels == 1</code> a regression loss is computed (Mean-Square loss), If
<code>config.num_labels &gt; 1</code> a classification loss is computed (Cross-Entropy).`,name:"labels"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/clip/modeling_clip.py#L1604",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>A <a
  href="/docs/transformers/main/ko/main_classes/output#transformers.modeling_outputs.ImageClassifierOutput"
>transformers.modeling_outputs.ImageClassifierOutput</a> or a tuple of
<code>torch.FloatTensor</code> (if <code>return_dict=False</code> is passed or when <code>config.return_dict=False</code>) comprising various
elements depending on the configuration (<a
  href="/docs/transformers/main/ko/model_doc/clip#transformers.CLIPConfig"
>CLIPConfig</a>) and inputs.</p>
<ul>
<li>
<p><strong>loss</strong> (<code>torch.FloatTensor</code> of shape <code>(1,)</code>, <em>optional</em>, returned when <code>labels</code> is provided) — Classification (or regression if config.num_labels==1) loss.</p>
</li>
<li>
<p><strong>logits</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, config.num_labels)</code>) — Classification (or regression if config.num_labels==1) scores (before SoftMax).</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 stage) of shape <code>(batch_size, sequence_length, hidden_size)</code>. Hidden-states
(also called feature maps) of the model at the output of each stage.</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, patch_size, sequence_length)</code>.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.</p>
</li>
</ul>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><a
  href="/docs/transformers/main/ko/main_classes/output#transformers.modeling_outputs.ImageClassifierOutput"
>transformers.modeling_outputs.ImageClassifierOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
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<code>model({&quot;input_ids&quot;: input_ids, &quot;token_type_ids&quot;: token_type_ids})</code></li>`,z,ue,ee=`Note that when creating models and layers with
<a href="https://keras.io/guides/making_new_layers_and_models_via_subclassing/" rel="nofollow">subclassing</a> then you don’t need to worry
about any of this, as you can just pass inputs like you would to any other Python function!`;return{c(){e=c("p"),e.innerHTML=m,n=r(),s=c("ul"),s.innerHTML=y,o=r(),$=c("p"),$.innerHTML=nt,B=r(),st=c("ul"),st.innerHTML=at,z=r(),ue=c("p"),ue.innerHTML=ee},l(j){e=p(j,"P",{"data-svelte-h":!0}),C(e)!=="svelte-1ajbfxg"&&(e.innerHTML=m),n=i(j),s=p(j,"UL",{"data-svelte-h":!0}),C(s)!=="svelte-qm1t26"&&(s.innerHTML=y),o=i(j),$=p(j,"P",{"data-svelte-h":!0}),C($)!=="svelte-1v9qsc5"&&($.innerHTML=nt),B=i(j),st=p(j,"UL",{"data-svelte-h":!0}),C(st)!=="svelte-15scerc"&&(st.innerHTML=at),z=i(j),ue=p(j,"P",{"data-svelte-h":!0}),C(ue)!=="svelte-1an3odd"&&(ue.innerHTML=ee)},m(j,Ne){d(j,e,Ne),d(j,n,Ne),d(j,s,Ne),d(j,o,Ne),d(j,$,Ne),d(j,B,Ne),d(j,st,Ne),d(j,z,Ne),d(j,ue,Ne)},p:F,d(j){j&&(a(e),a(n),a(s),a(o),a($),a(B),a(st),a(z),a(ue))}}}function Ua(v){let e,m=`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(){e=c("p"),e.innerHTML=m},l(n){e=p(n,"P",{"data-svelte-h":!0}),C(e)!=="svelte-fincs2"&&(e.innerHTML=m)},m(n,s){d(n,e,s)},p:F,d(n){n&&a(e)}}}function za(v){let e,m="Examples:",n,s,y;return s=new fe({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> tensorflow <span class="hljs-keyword">as</span> tf
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> 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, TFCLIPModel

<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFCLIPModel.from_pretrained(<span class="hljs-string">&quot;openai/clip-vit-base-patch32&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>processor = AutoProcessor.from_pretrained(<span class="hljs-string">&quot;openai/clip-vit-base-patch32&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>url = <span class="hljs-string">&quot;http://images.cocodataset.org/val2017/000000039769.jpg&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>image = Image.<span class="hljs-built_in">open</span>(requests.get(url, stream=<span class="hljs-literal">True</span>).raw)

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

<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(**inputs)
<span class="hljs-meta">&gt;&gt;&gt; </span>logits_per_image = outputs.logits_per_image  <span class="hljs-comment"># this is the image-text similarity score</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>probs = tf.nn.softmax(logits_per_image, axis=<span class="hljs-number">1</span>)  <span class="hljs-comment"># we can take the softmax to get the label probabilities</span>`,wrap:!1}}),{c(){e=c("p"),e.textContent=m,n=r(),h(s.$$.fragment)},l(o){e=p(o,"P",{"data-svelte-h":!0}),C(e)!=="svelte-kvfsh7"&&(e.textContent=m),n=i(o),f(s.$$.fragment,o)},m(o,$){d(o,e,$),d(o,n,$),u(s,o,$),y=!0},p:F,i(o){y||(_(s.$$.fragment,o),y=!0)},o(o){T(s.$$.fragment,o),y=!1},d(o){o&&(a(e),a(n)),b(s,o)}}}function Fa(v){let e,m=`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(){e=c("p"),e.innerHTML=m},l(n){e=p(n,"P",{"data-svelte-h":!0}),C(e)!=="svelte-fincs2"&&(e.innerHTML=m)},m(n,s){d(n,e,s)},p:F,d(n){n&&a(e)}}}function Va(v){let e,m="Examples:",n,s,y;return s=new fe({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, TFCLIPModel

<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFCLIPModel.from_pretrained(<span class="hljs-string">&quot;openai/clip-vit-base-patch32&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;openai/clip-vit-base-patch32&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = tokenizer([<span class="hljs-string">&quot;a photo of a cat&quot;</span>, <span class="hljs-string">&quot;a photo of a dog&quot;</span>], padding=<span class="hljs-literal">True</span>, return_tensors=<span class="hljs-string">&quot;tf&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>text_features = model.get_text_features(**inputs)`,wrap:!1}}),{c(){e=c("p"),e.textContent=m,n=r(),h(s.$$.fragment)},l(o){e=p(o,"P",{"data-svelte-h":!0}),C(e)!=="svelte-kvfsh7"&&(e.textContent=m),n=i(o),f(s.$$.fragment,o)},m(o,$){d(o,e,$),d(o,n,$),u(s,o,$),y=!0},p:F,i(o){y||(_(s.$$.fragment,o),y=!0)},o(o){T(s.$$.fragment,o),y=!1},d(o){o&&(a(e),a(n)),b(s,o)}}}function Wa(v){let e,m=`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(){e=c("p"),e.innerHTML=m},l(n){e=p(n,"P",{"data-svelte-h":!0}),C(e)!=="svelte-fincs2"&&(e.innerHTML=m)},m(n,s){d(n,e,s)},p:F,d(n){n&&a(e)}}}function Za(v){let e,m="Examples:",n,s,y;return s=new fe({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, TFCLIPModel

<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFCLIPModel.from_pretrained(<span class="hljs-string">&quot;openai/clip-vit-base-patch32&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>processor = AutoProcessor.from_pretrained(<span class="hljs-string">&quot;openai/clip-vit-base-patch32&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>url = <span class="hljs-string">&quot;http://images.cocodataset.org/val2017/000000039769.jpg&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>image = Image.<span class="hljs-built_in">open</span>(requests.get(url, stream=<span class="hljs-literal">True</span>).raw)

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

<span class="hljs-meta">&gt;&gt;&gt; </span>image_features = model.get_image_features(**inputs)`,wrap:!1}}),{c(){e=c("p"),e.textContent=m,n=r(),h(s.$$.fragment)},l(o){e=p(o,"P",{"data-svelte-h":!0}),C(e)!=="svelte-kvfsh7"&&(e.textContent=m),n=i(o),f(s.$$.fragment,o)},m(o,$){d(o,e,$),d(o,n,$),u(s,o,$),y=!0},p:F,i(o){y||(_(s.$$.fragment,o),y=!0)},o(o){T(s.$$.fragment,o),y=!1},d(o){o&&(a(e),a(n)),b(s,o)}}}function Ba(v){let e,m=`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(){e=c("p"),e.innerHTML=m},l(n){e=p(n,"P",{"data-svelte-h":!0}),C(e)!=="svelte-fincs2"&&(e.innerHTML=m)},m(n,s){d(n,e,s)},p:F,d(n){n&&a(e)}}}function Na(v){let e,m="Examples:",n,s,y;return s=new fe({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, TFCLIPTextModel

<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFCLIPTextModel.from_pretrained(<span class="hljs-string">&quot;openai/clip-vit-base-patch32&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;openai/clip-vit-base-patch32&quot;</span>)

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

<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(**inputs)
<span class="hljs-meta">&gt;&gt;&gt; </span>last_hidden_state = outputs.last_hidden_state
<span class="hljs-meta">&gt;&gt;&gt; </span>pooled_output = outputs.pooler_output  <span class="hljs-comment"># pooled (EOS token) states</span>`,wrap:!1}}),{c(){e=c("p"),e.textContent=m,n=r(),h(s.$$.fragment)},l(o){e=p(o,"P",{"data-svelte-h":!0}),C(e)!=="svelte-kvfsh7"&&(e.textContent=m),n=i(o),f(s.$$.fragment,o)},m(o,$){d(o,e,$),d(o,n,$),u(s,o,$),y=!0},p:F,i(o){y||(_(s.$$.fragment,o),y=!0)},o(o){T(s.$$.fragment,o),y=!1},d(o){o&&(a(e),a(n)),b(s,o)}}}function Ga(v){let e,m=`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(){e=c("p"),e.innerHTML=m},l(n){e=p(n,"P",{"data-svelte-h":!0}),C(e)!=="svelte-fincs2"&&(e.innerHTML=m)},m(n,s){d(n,e,s)},p:F,d(n){n&&a(e)}}}function Ea(v){let e,m="Examples:",n,s,y;return s=new fe({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, TFCLIPVisionModel

<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFCLIPVisionModel.from_pretrained(<span class="hljs-string">&quot;openai/clip-vit-base-patch32&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>processor = AutoProcessor.from_pretrained(<span class="hljs-string">&quot;openai/clip-vit-base-patch32&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>url = <span class="hljs-string">&quot;http://images.cocodataset.org/val2017/000000039769.jpg&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>image = Image.<span class="hljs-built_in">open</span>(requests.get(url, stream=<span class="hljs-literal">True</span>).raw)

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

<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(**inputs)
<span class="hljs-meta">&gt;&gt;&gt; </span>last_hidden_state = outputs.last_hidden_state
<span class="hljs-meta">&gt;&gt;&gt; </span>pooled_output = outputs.pooler_output  <span class="hljs-comment"># pooled CLS states</span>`,wrap:!1}}),{c(){e=c("p"),e.textContent=m,n=r(),h(s.$$.fragment)},l(o){e=p(o,"P",{"data-svelte-h":!0}),C(e)!=="svelte-kvfsh7"&&(e.textContent=m),n=i(o),f(s.$$.fragment,o)},m(o,$){d(o,e,$),d(o,n,$),u(s,o,$),y=!0},p:F,i(o){y||(_(s.$$.fragment,o),y=!0)},o(o){T(s.$$.fragment,o),y=!1},d(o){o&&(a(e),a(n)),b(s,o)}}}function Ra(v){let e,m,n,s,y,o,$=`This model inherits from <a href="/docs/transformers/main/ko/main_classes/model#transformers.TFPreTrainedModel">TFPreTrainedModel</a>. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)`,nt,B,st=`This model is also a <a href="https://www.tensorflow.org/api_docs/python/tf/keras/Model" rel="nofollow">keras.Model</a> subclass. Use it
as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and
behavior.`,at,z,ue,ee,j,Ne,rt,E='The <a href="/docs/transformers/main/ko/model_doc/clip#transformers.TFCLIPModel">TFCLIPModel</a> forward method, overrides the <code>__call__</code> special method.',mt,le,Ge,O,xe,te,ke,Pe,Mt,_e='The <a href="/docs/transformers/main/ko/model_doc/clip#transformers.TFCLIPModel">TFCLIPModel</a> forward method, overrides the <code>__call__</code> special method.',Le,Ce,Xe,N,Je,K,de,Ye,je,Ie='The <a href="/docs/transformers/main/ko/model_doc/clip#transformers.TFCLIPModel">TFCLIPModel</a> forward method, overrides the <code>__call__</code> special method.',De,Q,qe,oe,ae,V,it,Te,ce,Pt,G,Se,Ae,Ue,ne='The <a href="/docs/transformers/main/ko/model_doc/clip#transformers.TFCLIPTextModel">TFCLIPTextModel</a> forward method, overrides the <code>__call__</code> special method.',$e,re,pe,gt,me,R,Lt,Y,ze,Oe,Z,D,be,Fe,Ee='The <a href="/docs/transformers/main/ko/model_doc/clip#transformers.TFCLIPVisionModel">TFCLIPVisionModel</a> forward method, overrides the <code>__call__</code> special method.',lt,Me,Ve,We,ge;return e=new S({props:{title:"TFCLIPModel",local:"transformers.TFCLIPModel ][ transformers.TFCLIPModel",headingTag:"h2"}}),s=new U({props:{name:"class transformers.TFCLIPModel",anchor:"transformers.TFCLIPModel",parameters:[{name:"config",val:": CLIPConfig"},{name:"*inputs",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFCLIPModel.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/ko/model_doc/clip#transformers.CLIPConfig">CLIPConfig</a>) &#x2014; Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the <a href="/docs/transformers/main/ko/main_classes/model#transformers.TFPreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/clip/modeling_tf_clip.py#L1300"}}),z=new pt({props:{$$slots:{default:[ja]},$$scope:{ctx:v}}}),j=new U({props:{name:"call",anchor:"transformers.TFCLIPModel.call",parameters:[{name:"input_ids",val:": TFModelInputType | None = None"},{name:"pixel_values",val:": TFModelInputType | None = None"},{name:"attention_mask",val:": np.ndarray | tf.Tensor | None = None"},{name:"position_ids",val:": np.ndarray | tf.Tensor | None = None"},{name:"return_loss",val:": Optional[bool] = None"},{name:"output_attentions",val:": Optional[bool] = None"},{name:"output_hidden_states",val:": Optional[bool] = None"},{name:"return_dict",val:": Optional[bool] = None"},{name:"training",val:": bool = False"}],parametersDescription:[{anchor:"transformers.TFCLIPModel.call.input_ids",description:`<strong>input_ids</strong> (<code>np.ndarray</code>, <code>tf.Tensor</code>, <code>List[tf.Tensor]</code> \`<code>Dict[str, tf.Tensor]</code> or <code>Dict[str, np.ndarray]</code> and each example must have the shape <code>(batch_size, sequence_length)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/main/ko/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> and
<a href="/docs/transformers/main/ko/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.TFCLIPModel.call.pixel_values",description:`<strong>pixel_values</strong> (<code>np.ndarray</code>, <code>tf.Tensor</code>, <code>List[tf.Tensor]</code> <code>Dict[str, tf.Tensor]</code> or <code>Dict[str, np.ndarray]</code> and each example must have the shape <code>(batch_size, num_channels, height, width)</code>) &#x2014;
Pixel values. Pixel values can be obtained using <a href="/docs/transformers/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.TFCLIPModel.call.attention_mask",description:`<strong>attention_mask</strong> (<code>np.ndarray</code> or <code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Mask to avoid performing attention on padding token indices. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.</li>
</ul>
<p><a href="../glossary#attention-mask">What are attention masks?</a>`,name:"attention_mask"},{anchor:"transformers.TFCLIPModel.call.position_ids",description:`<strong>position_ids</strong> (<code>np.ndarray</code> or <code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range <code>[0, config.max_position_embeddings - 1]</code>.</p>
<p><a href="../glossary#position-ids">What are position IDs?</a>`,name:"position_ids"},{anchor:"transformers.TFCLIPModel.call.return_loss",description:`<strong>return_loss</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the contrastive loss.`,name:"return_loss"},{anchor:"transformers.TFCLIPModel.call.output_attentions",description:`<strong>output_attentions</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the attentions tensors of all attention layers. See <code>attentions</code> under returned
tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the
config will be used instead.`,name:"output_attentions"},{anchor:"transformers.TFCLIPModel.call.output_hidden_states",description:`<strong>output_hidden_states</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the hidden states of all layers. See <code>hidden_states</code> under returned tensors for
more detail. This argument can be used only in eager mode, in graph mode the value in the config will be
used instead.`,name:"output_hidden_states"},{anchor:"transformers.TFCLIPModel.call.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/main/ko/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple. This argument can be used in
eager mode, in graph mode the value will always be set to True.`,name:"return_dict"},{anchor:"transformers.TFCLIPModel.call.training",description:`<strong>training</strong> (<code>bool</code>, <em>optional</em>, defaults to \`False&#x201C;) &#x2014;
Whether or not to use the model in training mode (some modules like dropout modules have different
behaviors between training and evaluation).`,name:"training"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/clip/modeling_tf_clip.py#L1391",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>A <code>transformers.models.clip.modeling_tf_clip.TFCLIPOutput</code> or a tuple of <code>tf.Tensor</code> (if
<code>return_dict=False</code> is passed or when <code>config.return_dict=False</code>) comprising various elements depending on the
configuration (<code>&lt;class 'transformers.models.clip.configuration_clip.CLIPConfig'&gt;</code>) and inputs.</p>
<ul>
<li><strong>loss</strong> (<code>tf.Tensor</code> of shape <code>(1,)</code>, <em>optional</em>, returned when <code>return_loss</code> is <code>True</code>) — Contrastive loss for image-text similarity.</li>
<li><strong>logits_per_image:(<code>tf.Tensor</code></strong> 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:(<code>tf.Tensor</code></strong> 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(<code>tf.Tensor</code></strong> 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/clip#transformers.TFCLIPTextModel"
>TFCLIPTextModel</a>.</li>
<li><strong>image_embeds(<code>tf.Tensor</code></strong> 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/clip#transformers.TFCLIPVisionModel"
>TFCLIPVisionModel</a>.</li>
<li><strong>text_model_output(<code>~modeling_tf_utils.TFBaseModelOutputWithPooling</code>):</strong>
The output of the <a
  href="/docs/transformers/main/ko/model_doc/clip#transformers.TFCLIPTextModel"
>TFCLIPTextModel</a>.</li>
<li><strong>vision_model_output(<code>~modeling_tf_utils.TFBaseModelOutputWithPooling</code>):</strong>
The output of the <a
  href="/docs/transformers/main/ko/model_doc/clip#transformers.TFCLIPVisionModel"
>TFCLIPVisionModel</a>.</li>
</ul>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>transformers.models.clip.modeling_tf_clip.TFCLIPOutput</code> or <code>tuple(tf.Tensor)</code></p>
`}}),le=new pt({props:{$$slots:{default:[Ua]},$$scope:{ctx:v}}}),O=new He({props:{anchor:"transformers.TFCLIPModel.call.example",$$slots:{default:[za]},$$scope:{ctx:v}}}),ke=new U({props:{name:"get_text_features",anchor:"transformers.TFCLIPModel.get_text_features",parameters:[{name:"input_ids",val:": TFModelInputType | None = None"},{name:"attention_mask",val:": np.ndarray | tf.Tensor | None = None"},{name:"position_ids",val:": np.ndarray | tf.Tensor | None = None"},{name:"output_attentions",val:": Optional[bool] = None"},{name:"output_hidden_states",val:": Optional[bool] = None"},{name:"return_dict",val:": Optional[bool] = None"},{name:"training",val:": bool = False"}],parametersDescription:[{anchor:"transformers.TFCLIPModel.get_text_features.input_ids",description:`<strong>input_ids</strong> (<code>np.ndarray</code>, <code>tf.Tensor</code>, <code>List[tf.Tensor]</code> \`<code>Dict[str, tf.Tensor]</code> or <code>Dict[str, np.ndarray]</code> and each example must have the shape <code>(batch_size, sequence_length)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/main/ko/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> and
<a href="/docs/transformers/main/ko/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.TFCLIPModel.get_text_features.attention_mask",description:`<strong>attention_mask</strong> (<code>np.ndarray</code> or <code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Mask to avoid performing attention on padding token indices. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.</li>
</ul>
<p><a href="../glossary#attention-mask">What are attention masks?</a>`,name:"attention_mask"},{anchor:"transformers.TFCLIPModel.get_text_features.position_ids",description:`<strong>position_ids</strong> (<code>np.ndarray</code> or <code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range <code>[0, config.max_position_embeddings - 1]</code>.</p>
<p><a href="../glossary#position-ids">What are position IDs?</a>`,name:"position_ids"},{anchor:"transformers.TFCLIPModel.get_text_features.output_attentions",description:`<strong>output_attentions</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the attentions tensors of all attention layers. See <code>attentions</code> under returned
tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the
config will be used instead.`,name:"output_attentions"},{anchor:"transformers.TFCLIPModel.get_text_features.output_hidden_states",description:`<strong>output_hidden_states</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the hidden states of all layers. See <code>hidden_states</code> under returned tensors for
more detail. This argument can be used only in eager mode, in graph mode the value in the config will be
used instead.`,name:"output_hidden_states"},{anchor:"transformers.TFCLIPModel.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. This argument can be used in
eager mode, in graph mode the value will always be set to True.`,name:"return_dict"},{anchor:"transformers.TFCLIPModel.get_text_features.training",description:`<strong>training</strong> (<code>bool</code>, <em>optional</em>, defaults to \`False&#x201C;) &#x2014;
Whether or not to use the model in training mode (some modules like dropout modules have different
behaviors between training and evaluation).`,name:"training"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/clip/modeling_tf_clip.py#L1309",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/clip#transformers.TFCLIPTextModel"
>TFCLIPTextModel</a>.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p>text_features (<code>tf.Tensor</code> of shape <code>(batch_size, output_dim</code>)</p>
`}}),Ce=new pt({props:{$$slots:{default:[Fa]},$$scope:{ctx:v}}}),N=new He({props:{anchor:"transformers.TFCLIPModel.get_text_features.example",$$slots:{default:[Va]},$$scope:{ctx:v}}}),de=new U({props:{name:"get_image_features",anchor:"transformers.TFCLIPModel.get_image_features",parameters:[{name:"pixel_values",val:": TFModelInputType | None = None"},{name:"output_attentions",val:": Optional[bool] = None"},{name:"output_hidden_states",val:": Optional[bool] = None"},{name:"return_dict",val:": Optional[bool] = None"},{name:"training",val:": bool = False"}],parametersDescription:[{anchor:"transformers.TFCLIPModel.get_image_features.pixel_values",description:`<strong>pixel_values</strong> (<code>np.ndarray</code>, <code>tf.Tensor</code>, <code>List[tf.Tensor]</code> \`<code>Dict[str, tf.Tensor]</code> or <code>Dict[str, np.ndarray]</code> and each example must have the shape <code>(batch_size, num_channels, height, width)</code>) &#x2014;
Pixel values. Pixel values can be obtained using <a href="/docs/transformers/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. output_attentions (<code>bool</code>, <em>optional</em>): Whether or not to
return the attentions tensors of all attention layers. See <code>attentions</code> under returned tensors for more
detail. This argument can be used only in eager mode, in graph mode the value in the config will be used
instead.`,name:"pixel_values"},{anchor:"transformers.TFCLIPModel.get_image_features.output_hidden_states",description:`<strong>output_hidden_states</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the hidden states of all layers. See <code>hidden_states</code> under returned tensors for
more detail. This argument can be used only in eager mode, in graph mode the value in the config will be
used instead.`,name:"output_hidden_states"},{anchor:"transformers.TFCLIPModel.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. This argument can be used in
eager mode, in graph mode the value will always be set to True.`,name:"return_dict"},{anchor:"transformers.TFCLIPModel.get_image_features.training",description:`<strong>training</strong> (<code>bool</code>, <em>optional</em>, defaults to \`False&#x201C;) &#x2014;
Whether or not to use the model in training mode (some modules like dropout modules have different
behaviors between training and evaluation).`,name:"training"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/clip/modeling_tf_clip.py#L1349",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/clip#transformers.TFCLIPVisionModel"
>TFCLIPVisionModel</a>.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p>image_features (<code>tf.Tensor</code> of shape <code>(batch_size, output_dim</code>)</p>
`}}),Q=new pt({props:{$$slots:{default:[Wa]},$$scope:{ctx:v}}}),oe=new He({props:{anchor:"transformers.TFCLIPModel.get_image_features.example",$$slots:{default:[Za]},$$scope:{ctx:v}}}),V=new S({props:{title:"TFCLIPTextModel",local:"transformers.TFCLIPTextModel ][ transformers.TFCLIPTextModel",headingTag:"h2"}}),ce=new U({props:{name:"class transformers.TFCLIPTextModel",anchor:"transformers.TFCLIPTextModel",parameters:[{name:"config",val:": CLIPTextConfig"},{name:"*inputs",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/clip/modeling_tf_clip.py#L1178"}}),Se=new U({props:{name:"call",anchor:"transformers.TFCLIPTextModel.call",parameters:[{name:"input_ids",val:": TFModelInputType | None = None"},{name:"attention_mask",val:": np.ndarray | tf.Tensor | None = None"},{name:"position_ids",val:": np.ndarray | tf.Tensor | None = None"},{name:"output_attentions",val:": Optional[bool] = None"},{name:"output_hidden_states",val:": Optional[bool] = None"},{name:"return_dict",val:": Optional[bool] = None"},{name:"training",val:": Optional[bool] = False"}],parametersDescription:[{anchor:"transformers.TFCLIPTextModel.call.input_ids",description:`<strong>input_ids</strong> (<code>np.ndarray</code>, <code>tf.Tensor</code>, <code>List[tf.Tensor]</code> \`<code>Dict[str, tf.Tensor]</code> or <code>Dict[str, np.ndarray]</code> and each example must have the shape <code>(batch_size, sequence_length)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/main/ko/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> and
<a href="/docs/transformers/main/ko/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.TFCLIPTextModel.call.attention_mask",description:`<strong>attention_mask</strong> (<code>np.ndarray</code> or <code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Mask to avoid performing attention on padding token indices. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.</li>
</ul>
<p><a href="../glossary#attention-mask">What are attention masks?</a>`,name:"attention_mask"},{anchor:"transformers.TFCLIPTextModel.call.position_ids",description:`<strong>position_ids</strong> (<code>np.ndarray</code> or <code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range <code>[0, config.max_position_embeddings - 1]</code>.</p>
<p><a href="../glossary#position-ids">What are position IDs?</a>`,name:"position_ids"},{anchor:"transformers.TFCLIPTextModel.call.output_attentions",description:`<strong>output_attentions</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the attentions tensors of all attention layers. See <code>attentions</code> under returned
tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the
config will be used instead.`,name:"output_attentions"},{anchor:"transformers.TFCLIPTextModel.call.output_hidden_states",description:`<strong>output_hidden_states</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the hidden states of all layers. See <code>hidden_states</code> under returned tensors for
more detail. This argument can be used only in eager mode, in graph mode the value in the config will be
used instead.`,name:"output_hidden_states"},{anchor:"transformers.TFCLIPTextModel.call.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/main/ko/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple. This argument can be used in
eager mode, in graph mode the value will always be set to True.`,name:"return_dict"},{anchor:"transformers.TFCLIPTextModel.call.training",description:`<strong>training</strong> (<code>bool</code>, <em>optional</em>, defaults to \`False&#x201C;) &#x2014;
Whether or not to use the model in training mode (some modules like dropout modules have different
behaviors between training and evaluation).`,name:"training"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/clip/modeling_tf_clip.py#L1186",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>A <a
  href="/docs/transformers/main/ko/main_classes/output#transformers.modeling_tf_outputs.TFBaseModelOutputWithPooling"
>transformers.modeling_tf_outputs.TFBaseModelOutputWithPooling</a> or a tuple of <code>tf.Tensor</code> (if
<code>return_dict=False</code> is passed or when <code>config.return_dict=False</code>) comprising various elements depending on the
configuration (<code>&lt;class 'transformers.models.clip.configuration_clip.CLIPTextConfig'&gt;</code>) and inputs.</p>
<ul>
<li>
<p><strong>last_hidden_state</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>) — Sequence of hidden-states at the output of the last layer of the model.</p>
</li>
<li>
<p><strong>pooler_output</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, hidden_size)</code>) — Last layer hidden-state of the first token of the sequence (classification token) further processed by a
Linear layer and a Tanh activation function. The Linear layer weights are trained from the next sentence
prediction (classification) objective during pretraining.</p>
<p>This output is usually <em>not</em> a good summary of the semantic content of the input, you’re often better with
averaging or pooling the sequence of hidden-states for the whole input sequence.</p>
</li>
<li>
<p><strong>hidden_states</strong> (<code>tuple(tf.Tensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) — Tuple of <code>tf.Tensor</code> (one for the output of the embeddings + one for the output of each layer) of shape
<code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the initial embedding outputs.</p>
</li>
<li>
<p><strong>attentions</strong> (<code>tuple(tf.Tensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) — Tuple of <code>tf.Tensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.</p>
</li>
</ul>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><a
  href="/docs/transformers/main/ko/main_classes/output#transformers.modeling_tf_outputs.TFBaseModelOutputWithPooling"
>transformers.modeling_tf_outputs.TFBaseModelOutputWithPooling</a> or <code>tuple(tf.Tensor)</code></p>
`}}),re=new pt({props:{$$slots:{default:[Ba]},$$scope:{ctx:v}}}),gt=new He({props:{anchor:"transformers.TFCLIPTextModel.call.example",$$slots:{default:[Na]},$$scope:{ctx:v}}}),R=new S({props:{title:"TFCLIPVisionModel",local:"transformers.TFCLIPVisionModel ][ transformers.TFCLIPVisionModel",headingTag:"h2"}}),ze=new U({props:{name:"class transformers.TFCLIPVisionModel",anchor:"transformers.TFCLIPVisionModel",parameters:[{name:"config",val:": CLIPVisionConfig"},{name:"*inputs",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/clip/modeling_tf_clip.py#L1238"}}),D=new U({props:{name:"call",anchor:"transformers.TFCLIPVisionModel.call",parameters:[{name:"pixel_values",val:": TFModelInputType | None = None"},{name:"output_attentions",val:": Optional[bool] = None"},{name:"output_hidden_states",val:": Optional[bool] = None"},{name:"return_dict",val:": Optional[bool] = None"},{name:"training",val:": Optional[bool] = False"}],parametersDescription:[{anchor:"transformers.TFCLIPVisionModel.call.pixel_values",description:`<strong>pixel_values</strong> (<code>np.ndarray</code>, <code>tf.Tensor</code>, <code>List[tf.Tensor]</code> \`<code>Dict[str, tf.Tensor]</code> or <code>Dict[str, np.ndarray]</code> and each example must have the shape <code>(batch_size, num_channels, height, width)</code>) &#x2014;
Pixel values. Pixel values can be obtained using <a href="/docs/transformers/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. output_attentions (<code>bool</code>, <em>optional</em>): Whether or not to
return the attentions tensors of all attention layers. See <code>attentions</code> under returned tensors for more
detail. This argument can be used only in eager mode, in graph mode the value in the config will be used
instead.`,name:"pixel_values"},{anchor:"transformers.TFCLIPVisionModel.call.output_hidden_states",description:`<strong>output_hidden_states</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the hidden states of all layers. See <code>hidden_states</code> under returned tensors for
more detail. This argument can be used only in eager mode, in graph mode the value in the config will be
used instead.`,name:"output_hidden_states"},{anchor:"transformers.TFCLIPVisionModel.call.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/main/ko/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple. This argument can be used in
eager mode, in graph mode the value will always be set to True.`,name:"return_dict"},{anchor:"transformers.TFCLIPVisionModel.call.training",description:`<strong>training</strong> (<code>bool</code>, <em>optional</em>, defaults to \`False&#x201C;) &#x2014;
Whether or not to use the model in training mode (some modules like dropout modules have different
behaviors between training and evaluation).`,name:"training"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/clip/modeling_tf_clip.py#L1247",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>A <a
  href="/docs/transformers/main/ko/main_classes/output#transformers.modeling_tf_outputs.TFBaseModelOutputWithPooling"
>transformers.modeling_tf_outputs.TFBaseModelOutputWithPooling</a> or a tuple of <code>tf.Tensor</code> (if
<code>return_dict=False</code> is passed or when <code>config.return_dict=False</code>) comprising various elements depending on the
configuration (<code>&lt;class 'transformers.models.clip.configuration_clip.CLIPVisionConfig'&gt;</code>) and inputs.</p>
<ul>
<li>
<p><strong>last_hidden_state</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>) — Sequence of hidden-states at the output of the last layer of the model.</p>
</li>
<li>
<p><strong>pooler_output</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, hidden_size)</code>) — Last layer hidden-state of the first token of the sequence (classification token) further processed by a
Linear layer and a Tanh activation function. The Linear layer weights are trained from the next sentence
prediction (classification) objective during pretraining.</p>
<p>This output is usually <em>not</em> a good summary of the semantic content of the input, you’re often better with
averaging or pooling the sequence of hidden-states for the whole input sequence.</p>
</li>
<li>
<p><strong>hidden_states</strong> (<code>tuple(tf.Tensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) — Tuple of <code>tf.Tensor</code> (one for the output of the embeddings + one for the output of each layer) of shape
<code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the initial embedding outputs.</p>
</li>
<li>
<p><strong>attentions</strong> (<code>tuple(tf.Tensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) — Tuple of <code>tf.Tensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.</p>
</li>
</ul>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><a
  href="/docs/transformers/main/ko/main_classes/output#transformers.modeling_tf_outputs.TFBaseModelOutputWithPooling"
>transformers.modeling_tf_outputs.TFBaseModelOutputWithPooling</a> or <code>tuple(tf.Tensor)</code></p>
`}}),Me=new pt({props:{$$slots:{default:[Ga]},$$scope:{ctx:v}}}),We=new 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ht={};P&2&&(ht.$$scope={dirty:P,ctx:w}),We.$set(ht)},i(w){ge||(_(e.$$.fragment,w),_(s.$$.fragment,w),_(z.$$.fragment,w),_(j.$$.fragment,w),_(le.$$.fragment,w),_(O.$$.fragment,w),_(ke.$$.fragment,w),_(Ce.$$.fragment,w),_(N.$$.fragment,w),_(de.$$.fragment,w),_(Q.$$.fragment,w),_(oe.$$.fragment,w),_(V.$$.fragment,w),_(ce.$$.fragment,w),_(Se.$$.fragment,w),_(re.$$.fragment,w),_(gt.$$.fragment,w),_(R.$$.fragment,w),_(ze.$$.fragment,w),_(D.$$.fragment,w),_(Me.$$.fragment,w),_(We.$$.fragment,w),ge=!0)},o(w){T(e.$$.fragment,w),T(s.$$.fragment,w),T(z.$$.fragment,w),T(j.$$.fragment,w),T(le.$$.fragment,w),T(O.$$.fragment,w),T(ke.$$.fragment,w),T(Ce.$$.fragment,w),T(N.$$.fragment,w),T(de.$$.fragment,w),T(Q.$$.fragment,w),T(oe.$$.fragment,w),T(V.$$.fragment,w),T(ce.$$.fragment,w),T(Se.$$.fragment,w),T(re.$$.fragment,w),T(gt.$$.fragment,w),T(R.$$.fragment,w),T(ze.$$.fragment,w),T(D.$$.fragment,w),T(Me.$$.fragment,w),T(We.$$.fragment,w),ge=!1},d(w){w&&(a(m),a(n),a(ae),a(it),a(Te),a(me),a(Lt),a(Y)),b(e,w),b(s),b(z),b(j),b(le),b(O),b(ke),b(Ce),b(N),b(de),b(Q),b(oe),b(V,w),b(ce),b(Se),b(re),b(gt),b(R,w),b(ze),b(D),b(Me),b(We)}}}function Ha(v){let e,m;return e=new Fs({props:{$$slots:{default:[Ra]},$$scope:{ctx:v}}}),{c(){h(e.$$.fragment)},l(n){f(e.$$.fragment,n)},m(n,s){u(e,n,s),m=!0},p(n,s){const y={};s&2&&(y.$$scope={dirty:s,ctx:n}),e.$set(y)},i(n){m||(_(e.$$.fragment,n),m=!0)},o(n){T(e.$$.fragment,n),m=!1},d(n){b(e,n)}}}function Xa(v){let e,m=`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(){e=c("p"),e.innerHTML=m},l(n){e=p(n,"P",{"data-svelte-h":!0}),C(e)!=="svelte-fincs2"&&(e.innerHTML=m)},m(n,s){d(n,e,s)},p:F,d(n){n&&a(e)}}}function qa(v){let e,m="Example:",n,s,y;return s=new fe({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> jax
<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, FlaxCLIPModel

<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxCLIPModel.from_pretrained(<span class="hljs-string">&quot;openai/clip-vit-base-patch32&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>processor = AutoProcessor.from_pretrained(<span class="hljs-string">&quot;openai/clip-vit-base-patch32&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;np&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 = jax.nn.softmax(logits_per_image, axis=<span class="hljs-number">1</span>)  <span class="hljs-comment"># we can take the softmax to get the label probabilities</span>`,wrap:!1}}),{c(){e=c("p"),e.textContent=m,n=r(),h(s.$$.fragment)},l(o){e=p(o,"P",{"data-svelte-h":!0}),C(e)!=="svelte-11lpom8"&&(e.textContent=m),n=i(o),f(s.$$.fragment,o)},m(o,$){d(o,e,$),d(o,n,$),u(s,o,$),y=!0},p:F,i(o){y||(_(s.$$.fragment,o),y=!0)},o(o){T(s.$$.fragment,o),y=!1},d(o){o&&(a(e),a(n)),b(s,o)}}}function Sa(v){let e,m="Examples:",n,s,y;return s=new fe({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, FlaxCLIPModel

<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxCLIPModel.from_pretrained(<span class="hljs-string">&quot;openai/clip-vit-base-patch32&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;openai/clip-vit-base-patch32&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = tokenizer([<span class="hljs-string">&quot;a photo of a cat&quot;</span>, <span class="hljs-string">&quot;a photo of a dog&quot;</span>], padding=<span class="hljs-literal">True</span>, return_tensors=<span class="hljs-string">&quot;np&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>text_features = model.get_text_features(**inputs)`,wrap:!1}}),{c(){e=c("p"),e.textContent=m,n=r(),h(s.$$.fragment)},l(o){e=p(o,"P",{"data-svelte-h":!0}),C(e)!=="svelte-kvfsh7"&&(e.textContent=m),n=i(o),f(s.$$.fragment,o)},m(o,$){d(o,e,$),d(o,n,$),u(s,o,$),y=!0},p:F,i(o){y||(_(s.$$.fragment,o),y=!0)},o(o){T(s.$$.fragment,o),y=!1},d(o){o&&(a(e),a(n)),b(s,o)}}}function Qa(v){let e,m="Examples:",n,s,y;return s=new fe({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, FlaxCLIPModel

<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxCLIPModel.from_pretrained(<span class="hljs-string">&quot;openai/clip-vit-base-patch32&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>processor = AutoProcessor.from_pretrained(<span class="hljs-string">&quot;openai/clip-vit-base-patch32&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;np&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>image_features = model.get_image_features(**inputs)`,wrap:!1}}),{c(){e=c("p"),e.textContent=m,n=r(),h(s.$$.fragment)},l(o){e=p(o,"P",{"data-svelte-h":!0}),C(e)!=="svelte-kvfsh7"&&(e.textContent=m),n=i(o),f(s.$$.fragment,o)},m(o,$){d(o,e,$),d(o,n,$),u(s,o,$),y=!0},p:F,i(o){y||(_(s.$$.fragment,o),y=!0)},o(o){T(s.$$.fragment,o),y=!1},d(o){o&&(a(e),a(n)),b(s,o)}}}function Ya(v){let e,m=`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(){e=c("p"),e.innerHTML=m},l(n){e=p(n,"P",{"data-svelte-h":!0}),C(e)!=="svelte-fincs2"&&(e.innerHTML=m)},m(n,s){d(n,e,s)},p:F,d(n){n&&a(e)}}}function Da(v){let e,m="Example:",n,s,y;return s=new fe({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, FlaxCLIPTextModel

<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxCLIPTextModel.from_pretrained(<span class="hljs-string">&quot;openai/clip-vit-base-patch32&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;openai/clip-vit-base-patch32&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = tokenizer([<span class="hljs-string">&quot;a photo of a cat&quot;</span>, <span class="hljs-string">&quot;a photo of a dog&quot;</span>], padding=<span class="hljs-literal">True</span>, return_tensors=<span class="hljs-string">&quot;np&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>pooler_output = outputs.pooler_output  <span class="hljs-comment"># pooled (EOS token) states</span>`,wrap:!1}}),{c(){e=c("p"),e.textContent=m,n=r(),h(s.$$.fragment)},l(o){e=p(o,"P",{"data-svelte-h":!0}),C(e)!=="svelte-11lpom8"&&(e.textContent=m),n=i(o),f(s.$$.fragment,o)},m(o,$){d(o,e,$),d(o,n,$),u(s,o,$),y=!0},p:F,i(o){y||(_(s.$$.fragment,o),y=!0)},o(o){T(s.$$.fragment,o),y=!1},d(o){o&&(a(e),a(n)),b(s,o)}}}function Aa(v){let e,m=`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(){e=c("p"),e.innerHTML=m},l(n){e=p(n,"P",{"data-svelte-h":!0}),C(e)!=="svelte-fincs2"&&(e.innerHTML=m)},m(n,s){d(n,e,s)},p:F,d(n){n&&a(e)}}}function Oa(v){let e,m="Example:",n,s,y;return s=new fe({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, FlaxCLIPTextModelWithProjection

<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxCLIPTextModelWithProjection.from_pretrained(<span class="hljs-string">&quot;openai/clip-vit-base-patch32&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;openai/clip-vit-base-patch32&quot;</span>)

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

<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(**inputs)
<span class="hljs-meta">&gt;&gt;&gt; </span>text_embeds = outputs.text_embeds`,wrap:!1}}),{c(){e=c("p"),e.textContent=m,n=r(),h(s.$$.fragment)},l(o){e=p(o,"P",{"data-svelte-h":!0}),C(e)!=="svelte-11lpom8"&&(e.textContent=m),n=i(o),f(s.$$.fragment,o)},m(o,$){d(o,e,$),d(o,n,$),u(s,o,$),y=!0},p:F,i(o){y||(_(s.$$.fragment,o),y=!0)},o(o){T(s.$$.fragment,o),y=!1},d(o){o&&(a(e),a(n)),b(s,o)}}}function Ka(v){let e,m=`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(){e=c("p"),e.innerHTML=m},l(n){e=p(n,"P",{"data-svelte-h":!0}),C(e)!=="svelte-fincs2"&&(e.innerHTML=m)},m(n,s){d(n,e,s)},p:F,d(n){n&&a(e)}}}function er(v){let e,m="Example:",n,s,y;return s=new fe({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, FlaxCLIPVisionModel

<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxCLIPVisionModel.from_pretrained(<span class="hljs-string">&quot;openai/clip-vit-base-patch32&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>processor = AutoProcessor.from_pretrained(<span class="hljs-string">&quot;openai/clip-vit-base-patch32&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;np&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>pooler_output = outputs.pooler_output  <span class="hljs-comment"># pooled CLS states</span>`,wrap:!1}}),{c(){e=c("p"),e.textContent=m,n=r(),h(s.$$.fragment)},l(o){e=p(o,"P",{"data-svelte-h":!0}),C(e)!=="svelte-11lpom8"&&(e.textContent=m),n=i(o),f(s.$$.fragment,o)},m(o,$){d(o,e,$),d(o,n,$),u(s,o,$),y=!0},p:F,i(o){y||(_(s.$$.fragment,o),y=!0)},o(o){T(s.$$.fragment,o),y=!1},d(o){o&&(a(e),a(n)),b(s,o)}}}function tr(v){let e,m,n,s,y,o,$=`This model inherits from <a href="/docs/transformers/main/ko/main_classes/model#transformers.FlaxPreTrainedModel">FlaxPreTrainedModel</a>. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading, saving and converting weights from PyTorch models)`,nt,B,st=`This model is also a
<a href="https://flax.readthedocs.io/en/latest/api_reference/flax.linen/module.html" rel="nofollow">flax.linen.Module</a> subclass. Use it as
a regular Flax linen Module and refer to the Flax documentation for all matter related to general usage and
behavior.`,at,z,ue="Finally, this model supports inherent JAX features such as:",ee,j,Ne='<li><a href="https://jax.readthedocs.io/en/latest/jax.html#just-in-time-compilation-jit" rel="nofollow">Just-In-Time (JIT) compilation</a></li> <li><a href="https://jax.readthedocs.io/en/latest/jax.html#automatic-differentiation" rel="nofollow">Automatic Differentiation</a></li> <li><a href="https://jax.readthedocs.io/en/latest/jax.html#vectorization-vmap" rel="nofollow">Vectorization</a></li> <li><a href="https://jax.readthedocs.io/en/latest/jax.html#parallelization-pmap" rel="nofollow">Parallelization</a></li>',rt,E,mt,le,Ge,O="The <code>FlaxCLIPPreTrainedModel</code> forward method, overrides the <code>__call__</code> special method.",xe,te,ke,Pe,Mt,_e,Le,Ce,Xe,N,Je,K,de,Ye,je,Ie,De,Q,qe,oe,ae,V,it,Te,ce="The <code>FlaxCLIPTextPreTrainedModel</code> forward method, overrides the <code>__call__</code> special method.",Pt,G,Se,Ae,Ue,ne,$e,re,pe,gt,me,R,Lt,Y,ze="The <code>FlaxCLIPTextPreTrainedModel</code> forward method, overrides the <code>__call__</code> special method.",Oe,Z,D,be,Fe,Ee,lt,Me,Ve,We,ge,w,P,ie,W="The <code>FlaxCLIPVisionPreTrainedModel</code> forward method, overrides the <code>__call__</code> special method.",he,ye,A,we,se;return e=new S({props:{title:"FlaxCLIPModel",local:"transformers.FlaxCLIPModel ][ transformers.FlaxCLIPModel",headingTag:"h2"}}),s=new U({props:{name:"class transformers.FlaxCLIPModel",anchor:"transformers.FlaxCLIPModel",parameters:[{name:"config",val:": CLIPConfig"},{name:"input_shape",val:": typing.Optional[typing.Tuple] = None"},{name:"seed",val:": int = 0"},{name:"dtype",val:": dtype = <class 'jax.numpy.float32'>"},{name:"_do_init",val:": bool = True"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxCLIPModel.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/ko/model_doc/clip#transformers.CLIPConfig">CLIPConfig</a>) &#x2014; Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the <a href="/docs/transformers/main/ko/main_classes/model#transformers.FlaxPreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"},{anchor:"transformers.FlaxCLIPModel.dtype",description:`<strong>dtype</strong> (<code>jax.numpy.dtype</code>, <em>optional</em>, defaults to <code>jax.numpy.float32</code>) &#x2014;
The data type of the computation. Can be one of <code>jax.numpy.float32</code>, <code>jax.numpy.float16</code> (on GPUs) and
<code>jax.numpy.bfloat16</code> (on TPUs).</p>
<p>This can be used to enable mixed-precision training or half-precision inference on GPUs or TPUs. If
specified all the computation will be performed with the given <code>dtype</code>.</p>
<p><strong>Note that this only specifies the dtype of the computation and does not influence the dtype of model
parameters.</strong></p>
<p>If you wish to change the dtype of the model parameters, see <a href="/docs/transformers/main/ko/main_classes/model#transformers.FlaxPreTrainedModel.to_fp16">to_fp16()</a> and
<a href="/docs/transformers/main/ko/main_classes/model#transformers.FlaxPreTrainedModel.to_bf16">to_bf16()</a>.`,name:"dtype"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/clip/modeling_flax_clip.py#L1262"}}),mt=new U({props:{name:"__call__",anchor:"transformers.FlaxCLIPModel.__call__",parameters:[{name:"input_ids",val:""},{name:"pixel_values",val:""},{name:"attention_mask",val:" = None"},{name:"position_ids",val:" = None"},{name:"params",val:": dict = None"},{name:"dropout_rng",val:": <function PRNGKey at 0x7f70657d4940> = None"},{name:"train",val:": bool = False"},{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.FlaxCLIPModel.__call__.input_ids",description:`<strong>input_ids</strong> (<code>numpy.ndarray</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.FlaxCLIPModel.__call__.attention_mask",description:`<strong>attention_mask</strong> (<code>numpy.ndarray</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.FlaxCLIPModel.__call__.position_ids",description:`<strong>position_ids</strong> (<code>numpy.ndarray</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.FlaxCLIPModel.__call__.pixel_values",description:`<strong>pixel_values</strong> (<code>numpy.ndarray</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.FlaxCLIPModel.__call__.output_attentions",description:`<strong>output_attentions</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the attentions tensors of all attention layers. See <code>attentions</code> under returned
tensors for more detail.`,name:"output_attentions"},{anchor:"transformers.FlaxCLIPModel.__call__.output_hidden_states",description:`<strong>output_hidden_states</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the hidden states of all layers. See <code>hidden_states</code> under returned tensors for
more detail.`,name:"output_hidden_states"},{anchor:"transformers.FlaxCLIPModel.__call__.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/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/clip/modeling_flax_clip.py#L820",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>A <code>transformers.models.clip.modeling_flax_clip.FlaxCLIPOutput</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.clip.configuration_clip.CLIPConfig'&gt;</code>) and inputs.</p>
<ul>
<li><strong>logits_per_image:(<code>jnp.ndarray</code></strong> 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:(<code>jnp.ndarray</code></strong> 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(<code>jnp.ndarray</code></strong> 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/clip#transformers.FlaxCLIPTextModel"
>FlaxCLIPTextModel</a>.</li>
<li><strong>image_embeds(<code>jnp.ndarray</code></strong> 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/clip#transformers.FlaxCLIPVisionModel"
>FlaxCLIPVisionModel</a>.</li>
<li><strong>text_model_output(<code>FlaxBaseModelOutputWithPooling</code>):</strong>
The output of the <a
  href="/docs/transformers/main/ko/model_doc/clip#transformers.FlaxCLIPTextModel"
>FlaxCLIPTextModel</a>.</li>
<li><strong>vision_model_output(<code>FlaxBaseModelOutputWithPooling</code>):</strong>
The output of the <a
  href="/docs/transformers/main/ko/model_doc/clip#transformers.FlaxCLIPVisionModel"
>FlaxCLIPVisionModel</a>.</li>
</ul>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>transformers.models.clip.modeling_flax_clip.FlaxCLIPOutput</code> or <code>tuple(torch.FloatTensor)</code></p>
`}}),te=new pt({props:{$$slots:{default:[Xa]},$$scope:{ctx:v}}}),Pe=new He({props:{anchor:"transformers.FlaxCLIPModel.__call__.example",$$slots:{default:[qa]},$$scope:{ctx:v}}}),Le=new U({props:{name:"get_text_features",anchor:"transformers.FlaxCLIPModel.get_text_features",parameters:[{name:"input_ids",val:""},{name:"attention_mask",val:" = None"},{name:"position_ids",val:" = None"},{name:"params",val:": dict = None"},{name:"dropout_rng",val:": <function PRNGKey at 0x7f70657d4940> = None"},{name:"train",val:" = False"}],parametersDescription:[{anchor:"transformers.FlaxCLIPModel.get_text_features.input_ids",description:`<strong>input_ids</strong> (<code>numpy.ndarray</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"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/clip/modeling_flax_clip.py#L865",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/clip#transformers.FlaxCLIPTextModel"
>FlaxCLIPTextModel</a>.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p>text_features (<code>jnp.ndarray</code> of shape <code>(batch_size, output_dim</code>)</p>
`}}),Xe=new He({props:{anchor:"transformers.FlaxCLIPModel.get_text_features.example",$$slots:{default:[Sa]},$$scope:{ctx:v}}}),K=new U({props:{name:"get_image_features",anchor:"transformers.FlaxCLIPModel.get_image_features",parameters:[{name:"pixel_values",val:""},{name:"params",val:": dict = None"},{name:"dropout_rng",val:": <function PRNGKey at 0x7f70657d4940> = None"},{name:"train",val:" = False"}],parametersDescription:[{anchor:"transformers.FlaxCLIPModel.get_image_features.pixel_values",description:`<strong>pixel_values</strong> (<code>numpy.ndarray</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"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/clip/modeling_flax_clip.py#L932",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/clip#transformers.FlaxCLIPVisionModel"
>FlaxCLIPVisionModel</a></p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p>image_features (<code>jnp.ndarray</code> of shape <code>(batch_size, output_dim</code>)</p>
`}}),Ye=new He({props:{anchor:"transformers.FlaxCLIPModel.get_image_features.example",$$slots:{default:[Qa]},$$scope:{ctx:v}}}),Ie=new S({props:{title:"FlaxCLIPTextModel",local:"transformers.FlaxCLIPTextModel ][ transformers.FlaxCLIPTextModel",headingTag:"h2"}}),qe=new U({props:{name:"class transformers.FlaxCLIPTextModel",anchor:"transformers.FlaxCLIPTextModel",parameters:[{name:"config",val:": CLIPTextConfig"},{name:"input_shape",val:" = (1, 1)"},{name:"seed",val:": int = 0"},{name:"dtype",val:": dtype = <class 'jax.numpy.float32'>"},{name:"_do_init",val:": bool = True"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/clip/modeling_flax_clip.py#L1012"}}),V=new U({props:{name:"__call__",anchor:"transformers.FlaxCLIPTextModel.__call__",parameters:[{name:"input_ids",val:""},{name:"attention_mask",val:" = None"},{name:"position_ids",val:" = None"},{name:"params",val:": dict = None"},{name:"dropout_rng",val:": <function PRNGKey at 0x7f70657d4940> = None"},{name:"train",val:": bool = False"},{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.FlaxCLIPTextModel.__call__.input_ids",description:`<strong>input_ids</strong> (<code>numpy.ndarray</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.FlaxCLIPTextModel.__call__.attention_mask",description:`<strong>attention_mask</strong> (<code>numpy.ndarray</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.FlaxCLIPTextModel.__call__.position_ids",description:`<strong>position_ids</strong> (<code>numpy.ndarray</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.FlaxCLIPTextModel.__call__.output_attentions",description:`<strong>output_attentions</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the attentions tensors of all attention layers. See <code>attentions</code> under returned
tensors for more detail.`,name:"output_attentions"},{anchor:"transformers.FlaxCLIPTextModel.__call__.output_hidden_states",description:`<strong>output_hidden_states</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the hidden states of all layers. See <code>hidden_states</code> under returned tensors for
more detail.`,name:"output_hidden_states"},{anchor:"transformers.FlaxCLIPTextModel.__call__.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/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/clip/modeling_flax_clip.py#L665",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>A <a
  href="/docs/transformers/main/ko/main_classes/output#transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPooling"
>transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPooling</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.clip.configuration_clip.CLIPTextConfig'&gt;</code>) and inputs.</p>
<ul>
<li>
<p><strong>last_hidden_state</strong> (<code>jnp.ndarray</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>jnp.ndarray</code> of shape <code>(batch_size, hidden_size)</code>) — Last layer hidden-state of the first token of the sequence (classification token) further processed by a
Linear layer and a Tanh activation function. The Linear layer weights are trained from the next sentence
prediction (classification) objective during pretraining.</p>
</li>
<li>
<p><strong>hidden_states</strong> (<code>tuple(jnp.ndarray)</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>jnp.ndarray</code> (one for the output of the embeddings + one for the output of each layer) of shape
<code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the initial embedding outputs.</p>
</li>
<li>
<p><strong>attentions</strong> (<code>tuple(jnp.ndarray)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) — Tuple of <code>jnp.ndarray</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_flax_outputs.FlaxBaseModelOutputWithPooling"
>transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPooling</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),G=new pt({props:{$$slots:{default:[Ya]},$$scope:{ctx:v}}}),Ae=new He({props:{anchor:"transformers.FlaxCLIPTextModel.__call__.example",$$slots:{default:[Da]},$$scope:{ctx:v}}}),ne=new S({props:{title:"FlaxCLIPTextModelWithProjection",local:"transformers.FlaxCLIPTextModelWithProjection ][ transformers.FlaxCLIPTextModelWithProjection",headingTag:"h2"}}),pe=new U({props:{name:"class transformers.FlaxCLIPTextModelWithProjection",anchor:"transformers.FlaxCLIPTextModelWithProjection",parameters:[{name:"config",val:": CLIPTextConfig"},{name:"input_shape",val:" = (1, 1)"},{name:"seed",val:": int = 0"},{name:"dtype",val:": dtype = <class 'jax.numpy.float32'>"},{name:"_do_init",val:": bool = True"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/clip/modeling_flax_clip.py#L1083"}}),R=new U({props:{name:"__call__",anchor:"transformers.FlaxCLIPTextModelWithProjection.__call__",parameters:[{name:"input_ids",val:""},{name:"attention_mask",val:" = None"},{name:"position_ids",val:" = None"},{name:"params",val:": dict = None"},{name:"dropout_rng",val:": <function PRNGKey at 0x7f70657d4940> = None"},{name:"train",val:": bool = False"},{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.FlaxCLIPTextModelWithProjection.__call__.input_ids",description:`<strong>input_ids</strong> (<code>numpy.ndarray</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.FlaxCLIPTextModelWithProjection.__call__.attention_mask",description:`<strong>attention_mask</strong> (<code>numpy.ndarray</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.FlaxCLIPTextModelWithProjection.__call__.position_ids",description:`<strong>position_ids</strong> (<code>numpy.ndarray</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.FlaxCLIPTextModelWithProjection.__call__.output_attentions",description:`<strong>output_attentions</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the attentions tensors of all attention layers. See <code>attentions</code> under returned
tensors for more detail.`,name:"output_attentions"},{anchor:"transformers.FlaxCLIPTextModelWithProjection.__call__.output_hidden_states",description:`<strong>output_hidden_states</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the hidden states of all layers. See <code>hidden_states</code> under returned tensors for
more detail.`,name:"output_hidden_states"},{anchor:"transformers.FlaxCLIPTextModelWithProjection.__call__.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/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/clip/modeling_flax_clip.py#L665",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>A <code>transformers.models.clip.modeling_flax_clip.FlaxCLIPTextModelOutput</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.clip.configuration_clip.CLIPTextConfig'&gt;</code>) and inputs.</p>
<ul>
<li>
<p><strong>text_embeds</strong> (<code>jnp.ndarray</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/clip#transformers.FlaxCLIPTextModel"
>FlaxCLIPTextModel</a>.</p>
</li>
<li>
<p><strong>last_hidden_state</strong> (<code>jnp.ndarray</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>hidden_states</strong> (<code>tuple(jnp.ndarray)</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>jnp.ndarray</code> (one for the output of the embeddings + one for the output of each layer) of shape
<code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the initial embedding outputs.</p>
</li>
<li>
<p><strong>attentions</strong> (<code>tuple(jnp.ndarray)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) — Tuple of <code>jnp.ndarray</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><code>transformers.models.clip.modeling_flax_clip.FlaxCLIPTextModelOutput</code> or <code>tuple(torch.FloatTensor)</code></p>
`}}),Z=new pt({props:{$$slots:{default:[Aa]},$$scope:{ctx:v}}}),be=new He({props:{anchor:"transformers.FlaxCLIPTextModelWithProjection.__call__.example",$$slots:{default:[Oa]},$$scope:{ctx:v}}}),Ee=new S({props:{title:"FlaxCLIPVisionModel",local:"transformers.FlaxCLIPVisionModel ][ transformers.FlaxCLIPVisionModel",headingTag:"h2"}}),Ve=new U({props:{name:"class transformers.FlaxCLIPVisionModel",anchor:"transformers.FlaxCLIPVisionModel",parameters:[{name:"config",val:": CLIPVisionConfig"},{name:"input_shape",val:": typing.Optional[typing.Tuple] = None"},{name:"seed",val:": int = 0"},{name:"dtype",val:": dtype = <class 'jax.numpy.float32'>"},{name:"_do_init",val:": bool = True"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/clip/modeling_flax_clip.py#L1137"}}),w=new U({props:{name:"__call__",anchor:"transformers.FlaxCLIPVisionModel.__call__",parameters:[{name:"pixel_values",val:""},{name:"params",val:": dict = None"},{name:"dropout_rng",val:": <function PRNGKey at 0x7f70657d4940> = None"},{name:"train",val:": bool = False"},{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.FlaxCLIPVisionModel.__call__.pixel_values",description:`<strong>pixel_values</strong> (<code>numpy.ndarray</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.FlaxCLIPVisionModel.__call__.output_attentions",description:`<strong>output_attentions</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the attentions tensors of all attention layers. See <code>attentions</code> under returned
tensors for more detail.`,name:"output_attentions"},{anchor:"transformers.FlaxCLIPVisionModel.__call__.output_hidden_states",description:`<strong>output_hidden_states</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the hidden states of all layers. See <code>hidden_states</code> under returned tensors for
more detail.`,name:"output_hidden_states"},{anchor:"transformers.FlaxCLIPVisionModel.__call__.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/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/clip/modeling_flax_clip.py#L745",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>A <a
  href="/docs/transformers/main/ko/main_classes/output#transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPooling"
>transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPooling</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.clip.configuration_clip.CLIPVisionConfig'&gt;</code>) and inputs.</p>
<ul>
<li>
<p><strong>last_hidden_state</strong> (<code>jnp.ndarray</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>jnp.ndarray</code> of shape <code>(batch_size, hidden_size)</code>) — Last layer hidden-state of the first token of the sequence (classification token) further processed by a
Linear layer and a Tanh activation function. The Linear layer weights are trained from the next sentence
prediction (classification) objective during pretraining.</p>
</li>
<li>
<p><strong>hidden_states</strong> (<code>tuple(jnp.ndarray)</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>jnp.ndarray</code> (one for the output of the embeddings + one for the output of each layer) of shape
<code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the initial embedding outputs.</p>
</li>
<li>
<p><strong>attentions</strong> (<code>tuple(jnp.ndarray)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) — Tuple of <code>jnp.ndarray</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_flax_outputs.FlaxBaseModelOutputWithPooling"
>transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPooling</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),ye=new pt({props:{$$slots:{default:[Ka]},$$scope:{ctx:v}}}),we=new 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or(v){let e,m;return e=new Fs({props:{$$slots:{default:[tr]},$$scope:{ctx:v}}}),{c(){h(e.$$.fragment)},l(n){f(e.$$.fragment,n)},m(n,s){u(e,n,s),m=!0},p(n,s){const y={};s&2&&(y.$$scope={dirty:s,ctx:n}),e.$set(y)},i(n){m||(_(e.$$.fragment,n),m=!0)},o(n){T(e.$$.fragment,n),m=!1},d(n){b(e,n)}}}function nr(v){let e,m,n,s,y,o,$,nt,B,st=`CLIP 모델은 Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh,
Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, Ilya Sutskever가 제안한 <a href="https://arxiv.org/abs/2103.00020" rel="nofollow">자연어 지도(supervision)를 통한 전이 가능한 시각 모델 학습</a>라는 논문에서 소개되었습니다. CLIP(Contrastive Language-Image Pre-Training)은 다양한 이미지와 텍스트 쌍으로 훈련된 신경망 입니다. GPT-2와 3의 제로샷 능력과 유사하게, 해당 작업에 직접적으로 최적화하지 않고도 주어진 이미지에 대해 가장 관련성 있는 텍스트 스니펫을 예측하도록 자연어로 지시할 수 있습니다.`,at,z,ue="해당 논문의 초록입니다.",ee,j,Ne="<em>최신 컴퓨터 비전 시스템은 미리 정해진 고정된 객체 카테고리 집합을 예측하도록 훈련됩니다. 이러한 제한된 형태의 지도는 다른 시각적 개념을 지정하기 위해 추가적인 라벨링된 데이터가 필요하므로 그 일반성과 사용성을 제한합니다. 이미지 원시 텍스트에서 직접 학습하는 것은 훨씬 더 광범위한 지도 소스를 활용하는 아주 좋은 대안입니다. 이미지와 캡션을 맞추는 간단한 사전 학습 작업이, 인터넷에서 수집한 4억 쌍의 이미지-텍스트 데이터셋에서 SOTA 수준의 이미지 표현을 처음부터 효율적이고 확장 가능하게 학습하는 방법임을 확인할 수 있습니다. 사전 훈련 후, 자연어는 학습된 시각적 개념을 참조하거나 새로운 개념을 설명하는 데 사용되어 모델의 하위 작업으로의 제로샷 전이를 가능하게 합니다. 해당 논문에서는 OCR, 비디오 내 행동 인식, 지리적 위치 파악, 그리고 많은 종류의 세밀한 객체 분류 등 30개 이상의 다양한 기존 컴퓨터 비전 데이터셋에 대한 벤치마킹을 통해 이 접근 방식의 성능을 연구합니다. 이 모델은 대부분의 작업에 대해 의미 있게 전이되며, 종종 데이터셋별 훈련 없이도 완전 지도 학습 기준선과 경쟁력 있는 성능을 보입니다. 예를 들어, ImageNet에서 원래 ResNet-50의 정확도를 제로샷으로 일치시키는데, 이는 ResNet-50이 훈련된 128만 개의 훈련 예제를 전혀 사용할 필요가 없었습니다. 코드 및 사전 훈련된 모델 가중치는 이 https URL에서 공개합니다.</em>",rt,E,mt=`이 모델은 <a href="https://huggingface.co/valhalla" rel="nofollow">valhalla</a>에 의해 기여되었습니다.
원본 코드는 <a href="https://github.com/openai/CLIP" rel="nofollow">이곳</a>에서 확인할 수 있습니다.`,le,Ge,O,xe,te="CLIP은 멀티모달 비전 밒 언어 모델입니다. 이미지-텍스트 유사도 계산과 제로샷 이미지 분류에 사용될 수 있습니다. CLIP은 ViT와 유사한 트랜스포머를 사용하여 시각적 특징을 추출하고, 인과적 언어 모델을 사용하여 텍스트 특징을 추출합니다. 그 후 텍스트와 시각적 특징 모두 동일한 차원의 잠재(latent) 공간으로 투영됩니다. 투영된 이미지와 텍스트 특징 사이의 내적이 유사도 점수로 사용됩니다.",ke,Pe,Mt='트랜스포머 인코더에 이미지를 입력하기 위해, 각 이미지는 고정 크기의 겹치지 않는 패치들의 시퀀스로 분할되고, 이후 선형 임베딩됩니다. [CLS]토큰이 전체 이미지의 표현으로 추가됩니다. 저자들은 또한 절대 위치 임베딩을 추가하고, 결과로 나온 벡터 시퀀스를 표준 트랜스포머 인토더에 입력합니다. <a href="/docs/transformers/main/ko/model_doc/clip#transformers.CLIPImageProcessor">CLIPImageProcessor</a>는 모델을 위해 이미지를 리사이즈(또는 재스캐일링)하고 정규화하는데 사용될 수 있습니다.',_e,Le,Ce='<a href="/docs/transformers/main/ko/model_doc/clip#transformers.CLIPTokenizer">CLIPTokenizer</a>는 텍스트를 인코딩하는데 사용됩니다. <a href="/docs/transformers/main/ko/model_doc/clip#transformers.CLIPProcessor">CLIPProcessor</a>는 <a href="/docs/transformers/main/ko/model_doc/clip#transformers.CLIPImageProcessor">CLIPImageProcessor</a>와 <a href="/docs/transformers/main/ko/model_doc/clip#transformers.CLIPTokenizer">CLIPTokenizer</a>를 하나의 인스턴스로 감싸서 텍스트를 인코딩하고 이미지를 준비하는데 모두 사용됩니다.',Xe,N,Je='다음 예시는 <a href="/docs/transformers/main/ko/model_doc/clip#transformers.CLIPProcessor">CLIPProcessor</a>와 <a href="/docs/transformers/main/ko/model_doc/clip#transformers.CLIPModel">CLIPModel</a>을 사용하여 이미지-텍스트 유사도 점수를 얻는 방법을 보여줍니다.',K,de,Ye,je,Ie,De,Q="먼저 최신버전의 플래시 어텐션2를 설치합니다.",qe,oe,ae,V,it="플래시 어텐션2와 호환되는 하드웨어를 가지고 있는지 확인하세요. 이에 대한 자세한 내용은 flash-attn 리포지토리의 공식문서에서 확인할 수 있습니다. 또한 모델을 반정밀도(<code>torch.float16</code>)로 로드하는 것을 잊지 마세요.",Te,ce,Pt,G,Se="플래시 어텐션2를 사용해서 모델을 로드하고 구동하기 위해서 다음 스니펫을 참고하세요:",Ae,Ue,ne,$e,re,pe,gt='파이토치는 <code>torch.nn.functional</code>의 일부로 네이티브 스케일된 내적 어텐션(SPDA) 연산자를 포함하고 있습니다. 이 함수는 입력과 사용 중인 하드웨어에 따라 적용될 수 있는 여러 구현을 포함합니다. 자세한 정보는 <a href="https://pytorch.org/docs/stable/generated/torch.nn.functional.scaled_dot_product_attention.html" rel="nofollow">공식문서</a>나 <a href="https://huggingface.co/docs/transformers/main/en/perf_infer_gpu_one#pytorch-scaled-dot-product-attention" rel="nofollow">GPU 추론</a> 페이지를 참조하세요.',me,R,Lt="<code>torch&gt;=2.1.1</code>에서는 구현이 가능할 때 SDPA가 기본적으로 사용되지만, <code>from_pretrained()</code> 함수에서 <code>attn_implementation=&quot;sdpa&quot;</code>를 설정하여 SDPA를 명시적으로 사용하도록 요청할 수도 있습니다.",Y,ze,Oe,Z,D="최고의 속도향상을 위해서, 반정밀도로 모델을 로드하는 것을 추천합니다. (예를들면 <code>torch.float16</code> 또는 <code>torch.bfloat16</code>).",be,Fe,Ee,lt,Me=`로컬 벤치마크(NVIDIA A10G, PyTorch 2.3.1+cu121)에서 <code>float16</code>을 사용하여 <code>&quot;openai/clip-vit-large-patch14&quot;</code> 체크포인트로 추론을 수행했을 때, 다음과 같은 속도 향상을 확인 했습니다.
<a href="https://gist.github.com/qubvel/ac691a54e54f9fae8144275f866a7ff8" rel="nofollow">코드</a>:`,Ve,We,ge,w,P='<thead><tr><th align="right">Num text labels</th> <th align="right">Eager (s/iter)</th> <th align="right">FA2 (s/iter)</th> <th align="right">FA2 speedup</th> <th align="right">SDPA (s/iter)</th> <th align="right">SDPA speedup</th></tr></thead> <tbody><tr><td align="right">4</td> <td align="right">0.009</td> <td align="right">0.012</td> <td align="right">0.737</td> <td align="right">0.007</td> <td align="right">1.269</td></tr> <tr><td align="right">16</td> <td align="right">0.009</td> <td align="right">0.014</td> <td align="right">0.659</td> <td align="right">0.008</td> <td align="right">1.187</td></tr> <tr><td align="right">32</td> <td align="right">0.018</td> <td align="right">0.021</td> <td align="right">0.862</td> <td align="right">0.016</td> <td align="right">1.142</td></tr> <tr><td align="right">64</td> <td align="right">0.034</td> <td align="right">0.034</td> <td align="right">1.001</td> <td align="right">0.03</td> <td align="right">1.163</td></tr> <tr><td align="right">128</td> <td align="right">0.063</td> <td align="right">0.058</td> <td align="right">1.09</td> <td align="right">0.054</td> <td align="right">1.174</td></tr></tbody>',ie,W,he='<img src="https://github.com/user-attachments/assets/e9826b43-4e66-4f4c-952b-af4d90bd38eb" alt="clip_text_model_viz_3"/>',ye,A,we,se,I='<thead><tr><th align="right">Image batch size</th> <th align="right">Eager (s/iter)</th> <th align="right">FA2 (s/iter)</th> <th align="right">FA2 speedup</th> <th align="right">SDPA (s/iter)</th> <th align="right">SDPA speedup</th></tr></thead> <tbody><tr><td align="right">1</td> <td align="right">0.016</td> <td align="right">0.013</td> <td align="right">1.247</td> <td align="right">0.012</td> <td align="right">1.318</td></tr> <tr><td align="right">4</td> <td align="right">0.025</td> <td align="right">0.021</td> <td align="right">1.198</td> <td align="right">0.021</td> <td align="right">1.202</td></tr> <tr><td align="right">16</td> <td align="right">0.093</td> <td align="right">0.075</td> <td align="right">1.234</td> <td align="right">0.075</td> <td align="right">1.24</td></tr> <tr><td align="right">32</td> <td align="right">0.181</td> <td align="right">0.147</td> <td align="right">1.237</td> <td align="right">0.146</td> <td align="right">1.241</td></tr></tbody>',k,X,ht='<img src="https://github.com/user-attachments/assets/50a36206-e3b9-4adc-ac8e-926b8b071d63" alt="clip_image_model_viz_3"/>',Ke,et,H,Ze,ft='<thead><tr><th align="right">Image batch size</th> <th align="right">Num text labels</th> <th align="right">Eager (s/iter)</th> <th align="right">FA2 (s/iter)</th> <th align="right">FA2 speedup</th> <th align="right">SDPA (s/iter)</th> <th align="right">SDPA speedup</th></tr></thead> <tbody><tr><td align="right">1</td> <td align="right">4</td> <td align="right">0.025</td> <td align="right">0.026</td> <td align="right">0.954</td> <td align="right">0.02</td> <td align="right">1.217</td></tr> <tr><td align="right">1</td> <td align="right">16</td> <td align="right">0.026</td> <td align="right">0.028</td> <td align="right">0.918</td> <td align="right">0.02</td> <td align="right">1.287</td></tr> <tr><td align="right">1</td> <td align="right">64</td> <td align="right">0.042</td> <td align="right">0.046</td> <td align="right">0.906</td> <td align="right">0.036</td> <td align="right">1.167</td></tr> <tr><td align="right">4</td> <td align="right">4</td> <td align="right">0.028</td> <td align="right">0.033</td> <td align="right">0.849</td> <td align="right">0.024</td> <td align="right">1.189</td></tr> <tr><td align="right">4</td> <td align="right">16</td> <td align="right">0.034</td> <td align="right">0.035</td> <td align="right">0.955</td> <td align="right">0.029</td> <td align="right">1.169</td></tr> <tr><td align="right">4</td> <td align="right">64</td> <td align="right">0.059</td> <td align="right">0.055</td> <td align="right">1.072</td> <td align="right">0.05</td> <td align="right">1.179</td></tr> <tr><td align="right">16</td> <td align="right">4</td> <td align="right">0.096</td> <td align="right">0.088</td> <td align="right">1.091</td> <td align="right">0.078</td> <td align="right">1.234</td></tr> <tr><td align="right">16</td> <td align="right">16</td> <td align="right">0.102</td> <td align="right">0.09</td> <td align="right">1.129</td> <td align="right">0.083</td> <td align="right">1.224</td></tr> <tr><td align="right">16</td> <td align="right">64</td> <td align="right">0.127</td> <td align="right">0.11</td> <td align="right">1.157</td> <td align="right">0.105</td> <td align="right">1.218</td></tr> <tr><td align="right">32</td> <td align="right">4</td> <td align="right">0.185</td> <td align="right">0.159</td> <td align="right">1.157</td> <td align="right">0.149</td> <td align="right">1.238</td></tr> <tr><td align="right">32</td> <td align="right">16</td> <td align="right">0.19</td> <td align="right">0.162</td> <td align="right">1.177</td> <td align="right">0.154</td> <td align="right">1.233</td></tr> <tr><td align="right">32</td> <td align="right">64</td> <td align="right">0.216</td> <td align="right">0.181</td> <td align="right">1.19</td> <td align="right">0.176</td> <td align="right">1.228</td></tr></tbody>',dt,Re,to,Wt,Nn="CLIP을 시작하는 데 도움이 되는 Hugging Face와 community 자료 목록(🌎로 표시됨) 입니다.",ko,Zt,Gn=`<li><a href="https://huggingface.co/blog/fine-tune-clip-rsicd" rel="nofollow">원격 센싱 (인공위성) 이미지와 캡션을 가지고 CLIP 미세조정하기</a>:
<a href="https://github.com/201528014227051/RSICD_optimal" rel="nofollow">RSICD dataset</a>을 가지고 CLIP을 미세조정 하는 방법과 데이터 증강에 대한 성능 비교에 대한 블로그 포스트</li> <li>이 <a href="https://github.com/huggingface/transformers/tree/main/examples/pytorch/contrastive-image-text" rel="nofollow">예시 스크립트</a>는 <a href="https://cocodataset.org/#home" rel="nofollow">COCO dataset</a>를 이용한 사전학습된 비전과 텍스트와 인코더를 사용해서 CLIP같은 비전-텍스트 듀얼 모델을 어떻게 학습시키는지 보여줍니다.</li>`,Po,tt,St,oo,Co='<li>사전학습된 CLIP모델을 이미지 캡셔닝을 위한 빔서치 추론에 어떻게 활용하는지에 관한 <a href="https://colab.research.google.com/drive/1tuoAC5F4sC7qid56Z0ap-stR3rwdk0ZV?usp=sharing" rel="nofollow">노트북</a></li>',Ro,no,so="<strong>이미지 검색</strong>",Lo,wt,Ho='<li>사전학습된 CLIP모델과 MRR(Mean Reciprocal Rank) 점수 연산을 사용한 이미지 검색에 대한 <a href="https://colab.research.google.com/drive/1bLVwVKpAndpEDHqjzxVPr_9nGrSbuOQd?usp=sharing" rel="nofollow">노트북</a>. 🌎</li> <li>이미지 검색과 유사성 점수에 대해 보여주는 <a href="https://colab.research.google.com/github/deep-diver/image_search_with_natural_language/blob/main/notebooks/Image_Search_CLIP.ipynb" rel="nofollow">노트북</a>. 🌎</li> <li>Multilingual CLIP를 사용해서 이미지와 텍스트를 어떻게 같은 벡터 공간에 매핑 시키는지에 대한 <a href="https://colab.research.google.com/drive/1xO-wC_m_GNzgjIBQ4a4znvQkvDoZJvH4?usp=sharing" rel="nofollow">노트북</a>. 🌎</li> <li><a href="https://unsplash.com" rel="nofollow">Unsplash</a>와 <a href="https://www.themoviedb.org/" rel="nofollow">TMDB</a> 데이터셋을 활용한 의미론적(semantic) 이미지 검색에서 CLIP을 구동하는 방법에 대한 <a href="https://colab.research.google.com/github/vivien000/clip-demo/blob/master/clip.ipynb#scrollTo=uzdFhRGqiWkR" rel="nofollow">노트북</a>. 🌎</li>',Qt,Yt,vt="<strong>설명 가능성</strong>",Dt,ao,Io='<li>입력 토큰과 이미지 조각(segment) 사이의 유사성을 시각화 시키는 방법에 대한 <a href="https://colab.research.google.com/github/hila-chefer/Transformer-MM-Explainability/blob/main/CLIP_explainability.ipynb" rel="nofollow">노트북</a>. 🌎</li>',Xo,ro,$o="여기에 포함될 자료를 제출하고 싶으시다면 PR(Pull Request)를 열어주세요. 리뷰 해드리겠습니다! 자료는 기존 자료를 복제하는 대신 새로운 내용을 담고 있어야 합니다.",qo,io,yt,Be,lo,uo,wo,un=`<a href="/docs/transformers/main/ko/model_doc/clip#transformers.CLIPConfig">CLIPConfig</a> is the configuration class to store the configuration of a <a href="/docs/transformers/main/ko/model_doc/clip#transformers.CLIPModel">CLIPModel</a>. It is used to instantiate
a CLIP model according to the specified arguments, defining the text model and vision model configs. Instantiating
a configuration with the defaults will yield a similar configuration to that of the CLIP
<a href="https://huggingface.co/openai/clip-vit-base-patch32" rel="nofollow">openai/clip-vit-base-patch32</a> architecture.`,At,_o,co=`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.`,Jo,xt,jo,ve,Jt,So,Rt,En=`Instantiate a <a href="/docs/transformers/main/ko/model_doc/clip#transformers.CLIPConfig">CLIPConfig</a> (or a derived class) from clip text model configuration and clip vision model
configuration.`,Uo,Bt,Qo,Ct,Nt,_n,To,jt=`This is the configuration class to store the configuration of a <a href="/docs/transformers/main/ko/model_doc/clip#transformers.CLIPTextModel">CLIPTextModel</a>. It is used to instantiate a CLIP
text encoder according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults will yield a similar configuration to that of the text encoder of the CLIP
<a href="https://huggingface.co/openai/clip-vit-base-patch32" rel="nofollow">openai/clip-vit-base-patch32</a> architecture.`,po,bo,vo=`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.`,Tn,Ot,Ht,mo,Xt,ut,g,x,_t,Gt=`This is the configuration class to store the configuration of a <a href="/docs/transformers/main/ko/model_doc/clip#transformers.CLIPVisionModel">CLIPVisionModel</a>. It is used to instantiate a
CLIP vision encoder according to the specified arguments, defining the model architecture. Instantiating a
configuration with the defaults will yield a similar configuration to that of the vision encoder of the CLIP
<a href="https://huggingface.co/openai/clip-vit-base-patch32" rel="nofollow">openai/clip-vit-base-patch32</a> architecture.`,Ut,Tt,Et=`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.`,It,ot,bt,ct,kt,q,Qe,zt,xo,Rn="Construct a CLIP tokenizer. Based on byte-level Byte-Pair-Encoding.",bn,Mn,Vs=`This tokenizer inherits from <code>PreTrainedTokenizer</code> which contains most of the main methods. Users should refer to
this superclass for more information regarding those methods.`,ds,Kt,Yo,cs,yn,Ws=`Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
adding special tokens. A CLIP sequence has the following format:`,ps,Cn,Zs="<li>single sequence: <code>&lt;|startoftext|&gt; X &lt;|endoftext|&gt;</code></li>",ms,In,Bs="Pairs of sequences are not the expected use case, but they will be handled without a separator.",gs,zo,Do,hs,$n,Ns=`Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
special tokens using the tokenizer <code>prepare_for_model</code> method.`,fs,Fo,Ao,us,wn,Gs=`Create a mask from the two sequences passed. CLIP does not make use of token type ids, therefore a list of
zeros is returned.`,_s,vn,Oo,Xn,Ko,qn,Ft,en,Ts,xn,Es=`Construct a “fast” CLIP tokenizer (backed by HuggingFace’s <em>tokenizers</em> library). Based on byte-level
Byte-Pair-Encoding.`,bs,kn,Rs=`This tokenizer inherits from <code>PreTrainedTokenizerFast</code> which contains most of the main methods. Users should
refer to this superclass for more information regarding those methods.`,Ms,eo,tn,ys,Pn,Hs=`Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
adding special tokens. A CLIP sequence has the following format:`,Cs,Ln,Xs="<li>single sequence: <code>&lt;|startoftext|&gt; X &lt;|endoftext|&gt;</code></li>",Is,Jn,qs="Pairs of sequences are not the expected use case, but they will be handled without a separator.",$s,Vo,on,ws,jn,Ss=`Create a mask from the two sequences passed. CLIP does not make use of token type ids, therefore a list of
zeros is returned.`,Sn,nn,Qn,go,sn,vs,Un,Qs="Constructs a CLIP image processor.",xs,Wo,an,ks,zn,Ys="Preprocess an image or batch of images.",Yn,rn,Dn,ln,dn,An,cn,On,Vt,pn,Ps,Fn,Ds="Constructs a CLIP processor which wraps a CLIP image processor and a CLIP tokenizer into a single processor.",Ls,Vn,As=`<a href="/docs/transformers/main/ko/model_doc/clip#transformers.CLIPProcessor">CLIPProcessor</a> offers all the functionalities of <a href="/docs/transformers/main/ko/model_doc/clip#transformers.CLIPImageProcessor">CLIPImageProcessor</a> and <a href="/docs/transformers/main/ko/model_doc/clip#transformers.CLIPTokenizerFast">CLIPTokenizerFast</a>. See the
<code>__call__()</code> and <a href="/docs/transformers/main/ko/model_doc/clip#transformers.CLIPProcessor.decode">decode()</a> for more information.`,Js,Zo,mn,js,Wn,Os=`This method forwards all its arguments to CLIPTokenizerFast’s <a href="/docs/transformers/main/ko/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.batch_decode">batch_decode()</a>. Please
refer to the docstring of this method for more information.`,Us,Bo,gn,zs,Zn,Ks=`This method forwards all its arguments to CLIPTokenizerFast’s <a href="/docs/transformers/main/ko/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.decode">decode()</a>. Please refer to
the docstring of this method for more information.`,Kn,No,es,hn,ts,Hn,os;return y=new S({props:{title:"CLIP",local:"clip",headingTag:"h1"}}),$=new S({props:{title:"개요",local:"overview",headingTag:"h2"}}),Ge=new S({props:{title:"사용 팁과 예시",local:"usage-tips-and-example",headingTag:"h2"}}),de=new fe({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> CLIPProcessor, CLIPModel

<span class="hljs-meta">&gt;&gt;&gt; </span>model = CLIPModel.from_pretrained(<span class="hljs-string">&quot;openai/clip-vit-base-patch32&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>processor = CLIPProcessor.from_pretrained(<span class="hljs-string">&quot;openai/clip-vit-base-patch32&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 유사성 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"># 확률을 레이블링 하기위해서 소프트맥스를 취합니다.</span>`,wrap:!1}}),je=new S({props:{title:"CLIP과 플래시 어텐션2 결합",local:"combining-clip-and-flash-attention-2",headingTag:"h3"}}),oe=new fe({props:{code:"cGlwJTIwaW5zdGFsbCUyMC1VJTIwZmxhc2gtYXR0biUyMC0tbm8tYnVpbGQtaXNvbGF0aW9u",highlighted:"pip install -U flash-attn --no-build-isolation",wrap:!1}}),ce=new pt({props:{warning:!0,$$slots:{default:[ca]},$$scope:{ctx:v}}}),Ue=new fe({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> requests
<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">from</span> transformers <span class="hljs-keyword">import</span> CLIPProcessor, CLIPModel

<span class="hljs-meta">&gt;&gt;&gt; </span>device = <span class="hljs-string">&quot;cuda&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>torch_dtype = torch.float16

<span class="hljs-meta">&gt;&gt;&gt; </span>model = CLIPModel.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;openai/clip-vit-base-patch32&quot;</span>,
<span class="hljs-meta">... </span>    attn_implementation=<span class="hljs-string">&quot;flash_attention_2&quot;</span>,
<span class="hljs-meta">... </span>    device_map=device,
<span class="hljs-meta">... </span>    torch_dtype=torch_dtype,
<span class="hljs-meta">... </span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>processor = CLIPProcessor.from_pretrained(<span class="hljs-string">&quot;openai/clip-vit-base-patch32&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>inputs.to(device)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">with</span> torch.no_grad():
<span class="hljs-meta">... </span>    <span class="hljs-keyword">with</span> torch.autocast(device):
<span class="hljs-meta">... </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 유사성 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"># 확률을 레이블링 하기위해서 소프트맥스를 취합니다.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-built_in">print</span>(probs)
tensor([[<span class="hljs-number">0.9946</span>, <span class="hljs-number">0.0052</span>]], device=<span class="hljs-string">&#x27;cuda:0&#x27;</span>, dtype=torch.float16)`,wrap:!1}}),$e=new S({props:{title:"스케일된 내적 어텐션 (Scaled dot-product Attention(SDPA)) 사용하기",local:"using-scaled-dot-product-attention-sdpa",headingTag:"h3"}}),ze=new fe({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMENMSVBNb2RlbCUwQSUwQW1vZGVsJTIwJTNEJTIwQ0xJUE1vZGVsLmZyb21fcHJldHJhaW5lZCglMjJvcGVuYWklMkZjbGlwLXZpdC1iYXNlLXBhdGNoMzIlMjIlMkMlMjB0b3JjaF9kdHlwZSUzRHRvcmNoLmZsb2F0MTYlMkMlMjBhdHRuX2ltcGxlbWVudGF0aW9uJTNEJTIyc2RwYSUyMik=",highlighted:`<span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> CLIPModel

model = CLIPModel.from_pretrained(<span class="hljs-string">&quot;openai/clip-vit-base-patch32&quot;</span>, torch_dtype=torch.float16, attn_implementation=<span class="hljs-string">&quot;sdpa&quot;</span>)`,wrap:!1}}),Fe=new S({props:{title:"플래시 어텐션과 스케일된 내적 어텐션(SDPA)으로 인해 예상되는 속도향상",local:"expected-speedups-with-flash-attention-and-sdpa",headingTag:"h3"}}),We=new S({props:{title:"CLIPTextModel",local:"cliptextmodel",headingTag:"h4"}}),A=new S({props:{title:"CLIPVisionModel",local:"clipvisionmodel",headingTag:"h4"}}),et=new S({props:{title:"CLIPModel",local:"clipmodel",headingTag:"h4"}}),Re=new S({props:{title:"자료",local:"resources",headingTag:"h2"}}),tt=new la({props:{pipeline:"image-to-text"}}),io=new S({props:{title:"CLIPConfig",local:"transformers.CLIPConfig ][ transformers.CLIPConfig",headingTag:"h2"}}),lo=new U({props:{name:"class transformers.CLIPConfig",anchor:"transformers.CLIPConfig",parameters:[{name:"text_config",val:" = None"},{name:"vision_config",val:" = None"},{name:"projection_dim",val:" = 512"},{name:"logit_scale_init_value",val:" = 2.6592"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.CLIPConfig.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/clip#transformers.CLIPTextConfig">CLIPTextConfig</a>.`,name:"text_config"},{anchor:"transformers.CLIPConfig.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/clip#transformers.CLIPVisionConfig">CLIPVisionConfig</a>.`,name:"vision_config"},{anchor:"transformers.CLIPConfig.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.CLIPConfig.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.CLIPConfig.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/clip/configuration_clip.py#L226"}}),xt=new He({props:{anchor:"transformers.CLIPConfig.example",$$slots:{default:[pa]},$$scope:{ctx:v}}}),Jt=new U({props:{name:"from_text_vision_configs",anchor:"transformers.CLIPConfig.from_text_vision_configs",parameters:[{name:"text_config",val:": CLIPTextConfig"},{name:"vision_config",val:": CLIPVisionConfig"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/clip/configuration_clip.py#L363",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/clip#transformers.CLIPConfig"
>CLIPConfig</a></p>
`}}),Bt=new S({props:{title:"CLIPTextConfig",local:"transformers.CLIPTextConfig ][ transformers.CLIPTextConfig",headingTag:"h2"}}),Nt=new U({props:{name:"class transformers.CLIPTextConfig",anchor:"transformers.CLIPTextConfig",parameters:[{name:"vocab_size",val:" = 49408"},{name:"hidden_size",val:" = 512"},{name:"intermediate_size",val:" = 2048"},{name:"projection_dim",val:" = 512"},{name:"num_hidden_layers",val:" = 12"},{name:"num_attention_heads",val:" = 8"},{name:"max_position_embeddings",val:" = 77"},{name:"hidden_act",val:" = 'quick_gelu'"},{name:"layer_norm_eps",val:" = 1e-05"},{name:"attention_dropout",val:" = 0.0"},{name:"initializer_range",val:" = 0.02"},{name:"initializer_factor",val:" = 1.0"},{name:"pad_token_id",val:" = 1"},{name:"bos_token_id",val:" = 49406"},{name:"eos_token_id",val:" = 49407"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.CLIPTextConfig.vocab_size",description:`<strong>vocab_size</strong> (<code>int</code>, <em>optional</em>, defaults to 49408) &#x2014;
Vocabulary size of the CLIP text model. Defines the number of different tokens that can be represented by
the <code>inputs_ids</code> passed when calling <a href="/docs/transformers/main/ko/model_doc/clip#transformers.CLIPModel">CLIPModel</a>.`,name:"vocab_size"},{anchor:"transformers.CLIPTextConfig.hidden_size",description:`<strong>hidden_size</strong> (<code>int</code>, <em>optional</em>, defaults to 512) &#x2014;
Dimensionality of the encoder layers and the pooler layer.`,name:"hidden_size"},{anchor:"transformers.CLIPTextConfig.intermediate_size",description:`<strong>intermediate_size</strong> (<code>int</code>, <em>optional</em>, defaults to 2048) &#x2014;
Dimensionality of the &#x201C;intermediate&#x201D; (i.e., feed-forward) layer in the Transformer encoder.`,name:"intermediate_size"},{anchor:"transformers.CLIPTextConfig.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.CLIPTextConfig.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.CLIPTextConfig.num_attention_heads",description:`<strong>num_attention_heads</strong> (<code>int</code>, <em>optional</em>, defaults to 8) &#x2014;
Number of attention heads for each attention layer in the Transformer encoder.`,name:"num_attention_heads"},{anchor:"transformers.CLIPTextConfig.max_position_embeddings",description:`<strong>max_position_embeddings</strong> (<code>int</code>, <em>optional</em>, defaults to 77) &#x2014;
The maximum sequence length that this model might ever be used with. Typically set this to something large
just in case (e.g., 512 or 1024 or 2048).`,name:"max_position_embeddings"},{anchor:"transformers.CLIPTextConfig.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.CLIPTextConfig.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.CLIPTextConfig.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.CLIPTextConfig.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.CLIPTextConfig.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"},{anchor:"transformers.CLIPTextConfig.pad_token_id",description:`<strong>pad_token_id</strong> (<code>int</code>, <em>optional</em>, defaults to 1) &#x2014;
Padding token id.`,name:"pad_token_id"},{anchor:"transformers.CLIPTextConfig.bos_token_id",description:`<strong>bos_token_id</strong> (<code>int</code>, <em>optional</em>, defaults to 49406) &#x2014;
Beginning of stream token id.`,name:"bos_token_id"},{anchor:"transformers.CLIPTextConfig.eos_token_id",description:`<strong>eos_token_id</strong> (<code>int</code>, <em>optional</em>, defaults to 49407) &#x2014;
End of stream token id.`,name:"eos_token_id"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/clip/configuration_clip.py#L33"}}),Ot=new He({props:{anchor:"transformers.CLIPTextConfig.example",$$slots:{default:[ma]},$$scope:{ctx:v}}}),mo=new S({props:{title:"CLIPVisionConfig",local:"transformers.CLIPVisionConfig ][ transformers.CLIPVisionConfig",headingTag:"h2"}}),g=new U({props:{name:"class transformers.CLIPVisionConfig",anchor:"transformers.CLIPVisionConfig",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.CLIPVisionConfig.hidden_size",description:`<strong>hidden_size</strong> (<code>int</code>, <em>optional</em>, defaults to 768) &#x2014;
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Dimensionality of text and vision projection layers.`,name:"projection_dim"},{anchor:"transformers.CLIPVisionConfig.num_hidden_layers",description:`<strong>num_hidden_layers</strong> (<code>int</code>, <em>optional</em>, defaults to 12) &#x2014;
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The size (resolution) of each image.`,name:"image_size"},{anchor:"transformers.CLIPVisionConfig.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.CLIPVisionConfig.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.CLIPVisionConfig.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.CLIPVisionConfig.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.CLIPVisionConfig.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.CLIPVisionConfig.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/clip/configuration_clip.py#L134"}}),ot=new He({props:{anchor:"transformers.CLIPVisionConfig.example",$$slots:{default:[ga]},$$scope:{ctx:v}}}),ct=new S({props:{title:"CLIPTokenizer",local:"transformers.CLIPTokenizer ][ transformers.CLIPTokenizer",headingTag:"h2"}}),Qe=new U({props:{name:"class transformers.CLIPTokenizer",anchor:"transformers.CLIPTokenizer",parameters:[{name:"vocab_file",val:""},{name:"merges_file",val:""},{name:"errors",val:" = 'replace'"},{name:"unk_token",val:" = '<|endoftext|>'"},{name:"bos_token",val:" = '<|startoftext|>'"},{name:"eos_token",val:" = '<|endoftext|>'"},{name:"pad_token",val:" = '<|endoftext|>'"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.CLIPTokenizer.vocab_file",description:`<strong>vocab_file</strong> (<code>str</code>) &#x2014;
Path to the vocabulary file.`,name:"vocab_file"},{anchor:"transformers.CLIPTokenizer.merges_file",description:`<strong>merges_file</strong> (<code>str</code>) &#x2014;
Path to the merges file.`,name:"merges_file"},{anchor:"transformers.CLIPTokenizer.errors",description:`<strong>errors</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;replace&quot;</code>) &#x2014;
Paradigm to follow when decoding bytes to UTF-8. See
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The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
token instead.`,name:"unk_token"},{anchor:"transformers.CLIPTokenizer.bos_token",description:`<strong>bos_token</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;&lt;|startoftext|&gt;&quot;</code>) &#x2014;
The beginning of sequence token.`,name:"bos_token"},{anchor:"transformers.CLIPTokenizer.eos_token",description:`<strong>eos_token</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;&lt;|endoftext|&gt;&quot;</code>) &#x2014;
The end of sequence token.`,name:"eos_token"},{anchor:"transformers.CLIPTokenizer.pad_token",description:`<strong>pad_token</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;&lt;|endoftext|&gt;&quot;</code>) &#x2014;
The token used for padding, for example when batching sequences of different lengths.`,name:"pad_token"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/clip/tokenization_clip.py#L254"}}),Yo=new U({props:{name:"build_inputs_with_special_tokens",anchor:"transformers.CLIPTokenizer.build_inputs_with_special_tokens",parameters:[{name:"token_ids_0",val:": typing.List[int]"},{name:"token_ids_1",val:": typing.Optional[typing.List[int]] = None"}],parametersDescription:[{anchor:"transformers.CLIPTokenizer.build_inputs_with_special_tokens.token_ids_0",description:`<strong>token_ids_0</strong> (<code>List[int]</code>) &#x2014;
List of IDs to which the special tokens will be added.`,name:"token_ids_0"},{anchor:"transformers.CLIPTokenizer.build_inputs_with_special_tokens.token_ids_1",description:`<strong>token_ids_1</strong> (<code>List[int]</code>, <em>optional</em>) &#x2014;
Optional second list of IDs for sequence pairs.`,name:"token_ids_1"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/clip/tokenization_clip.py#L339",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>List of <a href="../glossary#input-ids">input IDs</a> with the appropriate special tokens.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>List[int]</code></p>
`}}),Do=new U({props:{name:"get_special_tokens_mask",anchor:"transformers.CLIPTokenizer.get_special_tokens_mask",parameters:[{name:"token_ids_0",val:": typing.List[int]"},{name:"token_ids_1",val:": typing.Optional[typing.List[int]] = None"},{name:"already_has_special_tokens",val:": bool = False"}],parametersDescription:[{anchor:"transformers.CLIPTokenizer.get_special_tokens_mask.token_ids_0",description:`<strong>token_ids_0</strong> (<code>List[int]</code>) &#x2014;
List of IDs.`,name:"token_ids_0"},{anchor:"transformers.CLIPTokenizer.get_special_tokens_mask.token_ids_1",description:`<strong>token_ids_1</strong> (<code>List[int]</code>, <em>optional</em>) &#x2014;
Optional second list of IDs for sequence pairs.`,name:"token_ids_1"},{anchor:"transformers.CLIPTokenizer.get_special_tokens_mask.already_has_special_tokens",description:`<strong>already_has_special_tokens</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not the token list is already formatted with special tokens for the model.`,name:"already_has_special_tokens"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/clip/tokenization_clip.py#L366",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>List[int]</code></p>
`}}),Ao=new U({props:{name:"create_token_type_ids_from_sequences",anchor:"transformers.CLIPTokenizer.create_token_type_ids_from_sequences",parameters:[{name:"token_ids_0",val:": typing.List[int]"},{name:"token_ids_1",val:": typing.Optional[typing.List[int]] = None"}],parametersDescription:[{anchor:"transformers.CLIPTokenizer.create_token_type_ids_from_sequences.token_ids_0",description:`<strong>token_ids_0</strong> (<code>List[int]</code>) &#x2014;
List of IDs.`,name:"token_ids_0"},{anchor:"transformers.CLIPTokenizer.create_token_type_ids_from_sequences.token_ids_1",description:`<strong>token_ids_1</strong> (<code>List[int]</code>, <em>optional</em>) &#x2014;
Optional second list of IDs for sequence pairs.`,name:"token_ids_1"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/clip/tokenization_clip.py#L394",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>List of zeros.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>List[int]</code></p>
`}}),Oo=new U({props:{name:"save_vocabulary",anchor:"transformers.CLIPTokenizer.save_vocabulary",parameters:[{name:"save_directory",val:": str"},{name:"filename_prefix",val:": typing.Optional[str] = None"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/clip/tokenization_clip.py#L489"}}),Ko=new S({props:{title:"CLIPTokenizerFast",local:"transformers.CLIPTokenizerFast ][ transformers.CLIPTokenizerFast",headingTag:"h2"}}),en=new U({props:{name:"class transformers.CLIPTokenizerFast",anchor:"transformers.CLIPTokenizerFast",parameters:[{name:"vocab_file",val:" = None"},{name:"merges_file",val:" = None"},{name:"tokenizer_file",val:" = None"},{name:"unk_token",val:" = '<|endoftext|>'"},{name:"bos_token",val:" = '<|startoftext|>'"},{name:"eos_token",val:" = '<|endoftext|>'"},{name:"pad_token",val:" = '<|endoftext|>'"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.CLIPTokenizerFast.vocab_file",description:`<strong>vocab_file</strong> (<code>str</code>, <em>optional</em>) &#x2014;
Path to the vocabulary file.`,name:"vocab_file"},{anchor:"transformers.CLIPTokenizerFast.merges_file",description:`<strong>merges_file</strong> (<code>str</code>, <em>optional</em>) &#x2014;
Path to the merges file.`,name:"merges_file"},{anchor:"transformers.CLIPTokenizerFast.tokenizer_file",description:`<strong>tokenizer_file</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The path to a tokenizer file to use instead of the vocab file.`,name:"tokenizer_file"},{anchor:"transformers.CLIPTokenizerFast.unk_token",description:`<strong>unk_token</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;&lt;|endoftext|&gt;&quot;</code>) &#x2014;
The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
token instead.`,name:"unk_token"},{anchor:"transformers.CLIPTokenizerFast.bos_token",description:`<strong>bos_token</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;&lt;|startoftext|&gt;&quot;</code>) &#x2014;
The beginning of sequence token.`,name:"bos_token"},{anchor:"transformers.CLIPTokenizerFast.eos_token",description:`<strong>eos_token</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;&lt;|endoftext|&gt;&quot;</code>) &#x2014;
The end of sequence token.`,name:"eos_token"},{anchor:"transformers.CLIPTokenizerFast.pad_token",description:`<strong>pad_token</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;&lt;|endoftext|&gt;&quot;</code>) &#x2014;
The token used for padding, for example when batching sequences of different lengths.`,name:"pad_token"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/clip/tokenization_clip_fast.py#L31"}}),tn=new U({props:{name:"build_inputs_with_special_tokens",anchor:"transformers.CLIPTokenizerFast.build_inputs_with_special_tokens",parameters:[{name:"token_ids_0",val:": typing.List[int]"},{name:"token_ids_1",val:": typing.Optional[typing.List[int]] = None"}],parametersDescription:[{anchor:"transformers.CLIPTokenizerFast.build_inputs_with_special_tokens.token_ids_0",description:`<strong>token_ids_0</strong> (<code>List[int]</code>) &#x2014;
List of IDs to which the special tokens will be added.`,name:"token_ids_0"},{anchor:"transformers.CLIPTokenizerFast.build_inputs_with_special_tokens.token_ids_1",description:`<strong>token_ids_1</strong> (<code>List[int]</code>, <em>optional</em>) &#x2014;
Optional second list of IDs for sequence pairs.`,name:"token_ids_1"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/clip/tokenization_clip_fast.py#L109",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>List of <a href="../glossary#input-ids">input IDs</a> with the appropriate special tokens.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>List[int]</code></p>
`}}),on=new U({props:{name:"create_token_type_ids_from_sequences",anchor:"transformers.CLIPTokenizerFast.create_token_type_ids_from_sequences",parameters:[{name:"token_ids_0",val:": typing.List[int]"},{name:"token_ids_1",val:": typing.Optional[typing.List[int]] = None"}],parametersDescription:[{anchor:"transformers.CLIPTokenizerFast.create_token_type_ids_from_sequences.token_ids_0",description:`<strong>token_ids_0</strong> (<code>List[int]</code>) &#x2014;
List of IDs.`,name:"token_ids_0"},{anchor:"transformers.CLIPTokenizerFast.create_token_type_ids_from_sequences.token_ids_1",description:`<strong>token_ids_1</strong> (<code>List[int]</code>, <em>optional</em>) &#x2014;
Optional second list of IDs for sequence pairs.`,name:"token_ids_1"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/clip/tokenization_clip_fast.py#L136",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>List of zeros.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>List[int]</code></p>
`}}),nn=new S({props:{title:"CLIPImageProcessor",local:"transformers.CLIPImageProcessor ][ transformers.CLIPImageProcessor",headingTag:"h2"}}),sn=new U({props:{name:"class transformers.CLIPImageProcessor",anchor:"transformers.CLIPImageProcessor",parameters:[{name:"do_resize",val:": bool = True"},{name:"size",val:": typing.Dict[str, int] = None"},{name:"resample",val:": Resampling = <Resampling.BICUBIC: 3>"},{name:"do_center_crop",val:": bool = True"},{name:"crop_size",val:": typing.Dict[str, int] = None"},{name:"do_rescale",val:": bool = True"},{name:"rescale_factor",val:": typing.Union[int, float] = 0.00392156862745098"},{name:"do_normalize",val:": bool = True"},{name:"image_mean",val:": typing.Union[float, typing.List[float], NoneType] = None"},{name:"image_std",val:": typing.Union[float, typing.List[float], NoneType] = None"},{name:"do_convert_rgb",val:": bool = True"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.CLIPImageProcessor.do_resize",description:`<strong>do_resize</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
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Size of the image after resizing. The shortest edge of the image is resized to size[&#x201C;shortest_edge&#x201D;], with
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method.`,name:"size"},{anchor:"transformers.CLIPImageProcessor.resample",description:`<strong>resample</strong> (<code>PILImageResampling</code>, <em>optional</em>, defaults to <code>Resampling.BICUBIC</code>) &#x2014;
Resampling filter to use if resizing the image. Can be overridden by <code>resample</code> in the <code>preprocess</code> method.`,name:"resample"},{anchor:"transformers.CLIPImageProcessor.do_center_crop",description:`<strong>do_center_crop</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether to center crop the image to the specified <code>crop_size</code>. Can be overridden by <code>do_center_crop</code> in the
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Size of the output image after applying <code>center_crop</code>. Can be overridden by <code>crop_size</code> in the <code>preprocess</code>
method.`,name:"crop_size"},{anchor:"transformers.CLIPImageProcessor.do_rescale",description:`<strong>do_rescale</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether to rescale the image by the specified scale <code>rescale_factor</code>. Can be overridden by <code>do_rescale</code> in
the <code>preprocess</code> method.`,name:"do_rescale"},{anchor:"transformers.CLIPImageProcessor.rescale_factor",description:`<strong>rescale_factor</strong> (<code>int</code> or <code>float</code>, <em>optional</em>, defaults to <code>1/255</code>) &#x2014;
Scale factor to use if rescaling the image. Can be overridden by <code>rescale_factor</code> in the <code>preprocess</code>
method.`,name:"rescale_factor"},{anchor:"transformers.CLIPImageProcessor.do_normalize",description:`<strong>do_normalize</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether to normalize the image. Can be overridden by <code>do_normalize</code> in the <code>preprocess</code> method.`,name:"do_normalize"},{anchor:"transformers.CLIPImageProcessor.image_mean",description:`<strong>image_mean</strong> (<code>float</code> or <code>List[float]</code>, <em>optional</em>, defaults to <code>[0.48145466, 0.4578275, 0.40821073]</code>) &#x2014;
Mean to use if normalizing the image. This is a float or list of floats the length of the number of
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Standard deviation to use if normalizing the image. This is a float or list of floats the length of the
number of channels in the image. Can be overridden by the <code>image_std</code> parameter in the <code>preprocess</code> method.
Can be overridden by the <code>image_std</code> parameter in the <code>preprocess</code> method.`,name:"image_std"},{anchor:"transformers.CLIPImageProcessor.do_convert_rgb",description:`<strong>do_convert_rgb</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether to convert the image to RGB.`,name:"do_convert_rgb"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/clip/image_processing_clip.py#L52"}}),an=new U({props:{name:"preprocess",anchor:"transformers.CLIPImageProcessor.preprocess",parameters:[{name:"images",val:": typing.Union[ForwardRef('PIL.Image.Image'), numpy.ndarray, ForwardRef('torch.Tensor'), typing.List[ForwardRef('PIL.Image.Image')], typing.List[numpy.ndarray], typing.List[ForwardRef('torch.Tensor')]]"},{name:"do_resize",val:": bool = None"},{name:"size",val:": typing.Dict[str, int] = None"},{name:"resample",val:": Resampling = None"},{name:"do_center_crop",val:": bool = None"},{name:"crop_size",val:": int = None"},{name:"do_rescale",val:": bool = None"},{name:"rescale_factor",val:": float = None"},{name:"do_normalize",val:": bool = None"},{name:"image_mean",val:": typing.Union[float, typing.List[float], NoneType] = None"},{name:"image_std",val:": typing.Union[float, typing.List[float], NoneType] = None"},{name:"do_convert_rgb",val:": bool = None"},{name:"return_tensors",val:": typing.Union[str, transformers.utils.generic.TensorType, NoneType] = None"},{name:"data_format",val:": typing.Optional[transformers.image_utils.ChannelDimension] = <ChannelDimension.FIRST: 'channels_first'>"},{name:"input_data_format",val:": typing.Union[str, transformers.image_utils.ChannelDimension, NoneType] = None"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.CLIPImageProcessor.preprocess.images",description:`<strong>images</strong> (<code>ImageInput</code>) &#x2014;
Image to preprocess. Expects a single or batch of images with pixel values ranging from 0 to 255. If
passing in images with pixel values between 0 and 1, set <code>do_rescale=False</code>.`,name:"images"},{anchor:"transformers.CLIPImageProcessor.preprocess.do_resize",description:`<strong>do_resize</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>self.do_resize</code>) &#x2014;
Whether to resize the image.`,name:"do_resize"},{anchor:"transformers.CLIPImageProcessor.preprocess.size",description:`<strong>size</strong> (<code>Dict[str, int]</code>, <em>optional</em>, defaults to <code>self.size</code>) &#x2014;
Size of the image after resizing. Shortest edge of the image is resized to size[&#x201C;shortest_edge&#x201D;], with
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Resampling filter to use if resizing the image. This can be one of the enum <code>PILImageResampling</code>. Only
has an effect if <code>do_resize</code> is set to <code>True</code>.`,name:"resample"},{anchor:"transformers.CLIPImageProcessor.preprocess.do_center_crop",description:`<strong>do_center_crop</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>self.do_center_crop</code>) &#x2014;
Whether to center crop the image.`,name:"do_center_crop"},{anchor:"transformers.CLIPImageProcessor.preprocess.crop_size",description:`<strong>crop_size</strong> (<code>Dict[str, int]</code>, <em>optional</em>, defaults to <code>self.crop_size</code>) &#x2014;
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