import{S as Kv,i as ey,s as ty,e as a,k as h,w as y,t as s,M as oy,c as l,d as o,m as f,a as i,x as T,h as r,b as p,N as ny,G as e,g as b,y as w,q as M,o as $,B,v as sy,L as S}from"../../chunks/vendor-hf-doc-builder.js";import{T as G}from"../../chunks/Tip-hf-doc-builder.js";import{D as j}from"../../chunks/Docstring-hf-doc-builder.js";import{C as q}from"../../chunks/CodeBlock-hf-doc-builder.js";import{I as F}from"../../chunks/IconCopyLink-hf-doc-builder.js";import{E as A}from"../../chunks/ExampleCodeBlock-hf-doc-builder.js";function ry(x){let d,v,u,m,_;return m=new q({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> BlipConfig, BlipModel

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Initializing a BlipConfig with Salesforce/blip-vqa-base style configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>configuration = BlipConfig()

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Initializing a BlipPModel (with random weights) from the Salesforce/blip-vqa-base style configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = BlipModel(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 BlipConfig from a BlipTextConfig and a BlipVisionConfig</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Initializing a BLIPText and BLIPVision configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config_text = BlipTextConfig()
<span class="hljs-meta">&gt;&gt;&gt; </span>config_vision = BlipVisionConfig()

<span class="hljs-meta">&gt;&gt;&gt; </span>config = BlipConfig.from_text_vision_configs(config_text, config_vision)`}}),{c(){d=a("p"),v=s("Example:"),u=h(),y(m.$$.fragment)},l(n){d=l(n,"P",{});var c=i(d);v=r(c,"Example:"),c.forEach(o),u=f(n),T(m.$$.fragment,n)},m(n,c){b(n,d,c),e(d,v),b(n,u,c),w(m,n,c),_=!0},p:S,i(n){_||(M(m.$$.fragment,n),_=!0)},o(n){$(m.$$.fragment,n),_=!1},d(n){n&&o(d),n&&o(u),B(m,n)}}}function ay(x){let d,v,u,m,_;return m=new q({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> BlipTextConfig, BlipTextModel

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Initializing a BlipTextConfig with Salesforce/blip-vqa-base style configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>configuration = BlipTextConfig()

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Initializing a BlipTextModel (with random weights) from the Salesforce/blip-vqa-base style configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = BlipTextModel(configuration)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Accessing the model configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>configuration = model.config`}}),{c(){d=a("p"),v=s("Example:"),u=h(),y(m.$$.fragment)},l(n){d=l(n,"P",{});var c=i(d);v=r(c,"Example:"),c.forEach(o),u=f(n),T(m.$$.fragment,n)},m(n,c){b(n,d,c),e(d,v),b(n,u,c),w(m,n,c),_=!0},p:S,i(n){_||(M(m.$$.fragment,n),_=!0)},o(n){$(m.$$.fragment,n),_=!1},d(n){n&&o(d),n&&o(u),B(m,n)}}}function ly(x){let d,v,u,m,_;return m=new q({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> BlipVisionConfig, BlipVisionModel

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Initializing a BlipVisionConfig with Salesforce/blip-vqa-base style configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>configuration = BlipVisionConfig()

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Initializing a BlipVisionModel (with random weights) from the Salesforce/blip-vqa-base style configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = BlipVisionModel(configuration)

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

<span class="hljs-meta">&gt;&gt;&gt; </span>model = BlipModel.from_pretrained(<span class="hljs-string">&quot;Salesforce/blip-image-captioning-base&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>processor = AutoProcessor.from_pretrained(<span class="hljs-string">&quot;Salesforce/blip-image-captioning-base&quot;</span>)

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

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

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

<span class="hljs-meta">&gt;&gt;&gt; </span>model = BlipModel.from_pretrained(<span class="hljs-string">&quot;Salesforce/blip-image-captioning-base&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>processor = AutoProcessor.from_pretrained(<span class="hljs-string">&quot;Salesforce/blip-image-captioning-base&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = processor(text=[<span class="hljs-string">&quot;a photo of a cat&quot;</span>, <span class="hljs-string">&quot;a photo of a dog&quot;</span>], padding=<span class="hljs-literal">True</span>, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>text_features = model.get_text_features(**inputs)`}}),{c(){d=a("p"),v=s("Examples:"),u=h(),y(m.$$.fragment)},l(n){d=l(n,"P",{});var c=i(d);v=r(c,"Examples:"),c.forEach(o),u=f(n),T(m.$$.fragment,n)},m(n,c){b(n,d,c),e(d,v),b(n,u,c),w(m,n,c),_=!0},p:S,i(n){_||(M(m.$$.fragment,n),_=!0)},o(n){$(m.$$.fragment,n),_=!1},d(n){n&&o(d),n&&o(u),B(m,n)}}}function my(x){let d,v,u,m,_;return{c(){d=a("p"),v=s("Although the recipe for forward pass needs to be defined within this function, one should call the "),u=a("code"),m=s("Module"),_=s(`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`)},l(n){d=l(n,"P",{});var c=i(d);v=r(c,"Although the recipe for forward pass needs to be defined within this function, one should call the "),u=l(c,"CODE",{});var J=i(u);m=r(J,"Module"),J.forEach(o),_=r(c,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),c.forEach(o)},m(n,c){b(n,d,c),e(d,v),e(d,u),e(u,m),e(d,_)},d(n){n&&o(d)}}}function hy(x){let d,v,u,m,_;return m=new q({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, BlipModel

<span class="hljs-meta">&gt;&gt;&gt; </span>model = BlipModel.from_pretrained(<span class="hljs-string">&quot;Salesforce/blip-image-captioning-base&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>processor = AutoProcessor.from_pretrained(<span class="hljs-string">&quot;Salesforce/blip-image-captioning-base&quot;</span>)

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

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

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

<span class="hljs-meta">&gt;&gt;&gt; </span>processor = AutoProcessor.from_pretrained(<span class="hljs-string">&quot;Salesforce/blip-image-captioning-base&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = BlipForConditionalGeneration.from_pretrained(<span class="hljs-string">&quot;Salesforce/blip-image-captioning-base&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>text = <span class="hljs-string">&quot;A picture of&quot;</span>

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

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

<span class="hljs-meta">&gt;&gt;&gt; </span>model = BlipForImageTextRetrieval.from_pretrained(<span class="hljs-string">&quot;Salesforce/blip-itm-base-coco&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>processor = AutoProcessor.from_pretrained(<span class="hljs-string">&quot;Salesforce/blip-itm-base-coco&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>text = <span class="hljs-string">&quot;an image of a cat&quot;</span>

<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = processor(images=image, text=text, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(**inputs)`}}),{c(){d=a("p"),v=s("Examples:"),u=h(),y(m.$$.fragment)},l(n){d=l(n,"P",{});var c=i(d);v=r(c,"Examples:"),c.forEach(o),u=f(n),T(m.$$.fragment,n)},m(n,c){b(n,d,c),e(d,v),b(n,u,c),w(m,n,c),_=!0},p:S,i(n){_||(M(m.$$.fragment,n),_=!0)},o(n){$(m.$$.fragment,n),_=!1},d(n){n&&o(d),n&&o(u),B(m,n)}}}function vy(x){let d,v,u,m,_;return{c(){d=a("p"),v=s("Although the recipe for forward pass needs to be defined within this function, one should call the "),u=a("code"),m=s("Module"),_=s(`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`)},l(n){d=l(n,"P",{});var c=i(d);v=r(c,"Although the recipe for forward pass needs to be defined within this function, one should call the "),u=l(c,"CODE",{});var J=i(u);m=r(J,"Module"),J.forEach(o),_=r(c,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),c.forEach(o)},m(n,c){b(n,d,c),e(d,v),e(d,u),e(u,m),e(d,_)},d(n){n&&o(d)}}}function yy(x){let d,v,u,m,_;return m=new q({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, BlipForQuestionAnswering

<span class="hljs-meta">&gt;&gt;&gt; </span>model = BlipForQuestionAnswering.from_pretrained(<span class="hljs-string">&quot;Salesforce/blip-vqa-base&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>processor = AutoProcessor.from_pretrained(<span class="hljs-string">&quot;Salesforce/blip-vqa-base&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><span class="hljs-comment"># training</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>text = <span class="hljs-string">&quot;How many cats are in the picture?&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>label = <span class="hljs-string">&quot;2&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = processor(images=image, text=text, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>labels = processor(text=label, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>).input_ids

<span class="hljs-meta">&gt;&gt;&gt; </span>inputs[<span class="hljs-string">&quot;labels&quot;</span>] = labels
<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(**inputs)
<span class="hljs-meta">&gt;&gt;&gt; </span>loss = outputs.loss
<span class="hljs-meta">&gt;&gt;&gt; </span>loss.backward()

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># inference</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>text = <span class="hljs-string">&quot;How many cats are in the picture?&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = processor(images=image, text=text, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model.generate(**inputs)
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-built_in">print</span>(processor.decode(outputs[<span class="hljs-number">0</span>], skip_special_tokens=<span class="hljs-literal">True</span>))
<span class="hljs-number">2</span>`}}),{c(){d=a("p"),v=s("Examples:"),u=h(),y(m.$$.fragment)},l(n){d=l(n,"P",{});var c=i(d);v=r(c,"Examples:"),c.forEach(o),u=f(n),T(m.$$.fragment,n)},m(n,c){b(n,d,c),e(d,v),b(n,u,c),w(m,n,c),_=!0},p:S,i(n){_||(M(m.$$.fragment,n),_=!0)},o(n){$(m.$$.fragment,n),_=!1},d(n){n&&o(d),n&&o(u),B(m,n)}}}function Ty(x){let d,v,u,m,_;return{c(){d=a("p"),v=s("Although the recipe for forward pass needs to be defined within this function, one should call the "),u=a("code"),m=s("Module"),_=s(`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`)},l(n){d=l(n,"P",{});var c=i(d);v=r(c,"Although the recipe for forward pass needs to be defined within this function, one should call the "),u=l(c,"CODE",{});var J=i(u);m=r(J,"Module"),J.forEach(o),_=r(c,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),c.forEach(o)},m(n,c){b(n,d,c),e(d,v),e(d,u),e(u,m),e(d,_)},d(n){n&&o(d)}}}function wy(x){let d,v,u,m,_;return m=new q({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, TFBlipModel

<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFBlipModel.from_pretrained(<span class="hljs-string">&quot;Salesforce/blip-image-captioning-base&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>processor = AutoProcessor.from_pretrained(<span class="hljs-string">&quot;Salesforce/blip-image-captioning-base&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>`}}),{c(){d=a("p"),v=s("Examples:"),u=h(),y(m.$$.fragment)},l(n){d=l(n,"P",{});var c=i(d);v=r(c,"Examples:"),c.forEach(o),u=f(n),T(m.$$.fragment,n)},m(n,c){b(n,d,c),e(d,v),b(n,u,c),w(m,n,c),_=!0},p:S,i(n){_||(M(m.$$.fragment,n),_=!0)},o(n){$(m.$$.fragment,n),_=!1},d(n){n&&o(d),n&&o(u),B(m,n)}}}function My(x){let d,v,u,m,_;return{c(){d=a("p"),v=s("Although the recipe for forward pass needs to be defined within this function, one should call the "),u=a("code"),m=s("Module"),_=s(`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`)},l(n){d=l(n,"P",{});var c=i(d);v=r(c,"Although the recipe for forward pass needs to be defined within this function, one should call the "),u=l(c,"CODE",{});var J=i(u);m=r(J,"Module"),J.forEach(o),_=r(c,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),c.forEach(o)},m(n,c){b(n,d,c),e(d,v),e(d,u),e(u,m),e(d,_)},d(n){n&&o(d)}}}function $y(x){let d,v,u,m,_;return m=new q({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoProcessor, TFBlipModel

<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFBlipModel.from_pretrained(<span class="hljs-string">&quot;Salesforce/blip-image-captioning-base&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>processor = AutoProcessor.from_pretrained(<span class="hljs-string">&quot;Salesforce/blip-image-captioning-base&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = processor(text=[<span class="hljs-string">&quot;a photo of a cat&quot;</span>, <span class="hljs-string">&quot;a photo of a dog&quot;</span>], padding=<span class="hljs-literal">True</span>, return_tensors=<span class="hljs-string">&quot;tf&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>text_features = model.get_text_features(**inputs)`}}),{c(){d=a("p"),v=s("Examples:"),u=h(),y(m.$$.fragment)},l(n){d=l(n,"P",{});var c=i(d);v=r(c,"Examples:"),c.forEach(o),u=f(n),T(m.$$.fragment,n)},m(n,c){b(n,d,c),e(d,v),b(n,u,c),w(m,n,c),_=!0},p:S,i(n){_||(M(m.$$.fragment,n),_=!0)},o(n){$(m.$$.fragment,n),_=!1},d(n){n&&o(d),n&&o(u),B(m,n)}}}function By(x){let d,v,u,m,_;return{c(){d=a("p"),v=s("Although the recipe for forward pass needs to be defined within this function, one should call the "),u=a("code"),m=s("Module"),_=s(`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`)},l(n){d=l(n,"P",{});var c=i(d);v=r(c,"Although the recipe for forward pass needs to be defined within this function, one should call the "),u=l(c,"CODE",{});var J=i(u);m=r(J,"Module"),J.forEach(o),_=r(c,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),c.forEach(o)},m(n,c){b(n,d,c),e(d,v),e(d,u),e(u,m),e(d,_)},d(n){n&&o(d)}}}function xy(x){let d,v,u,m,_;return m=new q({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, TFBlipModel

<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFBlipModel.from_pretrained(<span class="hljs-string">&quot;Salesforce/blip-image-captioning-base&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>processor = AutoProcessor.from_pretrained(<span class="hljs-string">&quot;Salesforce/blip-image-captioning-base&quot;</span>)

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

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

<span class="hljs-meta">&gt;&gt;&gt; </span>image_features = model.get_image_features(**inputs)`}}),{c(){d=a("p"),v=s("Examples:"),u=h(),y(m.$$.fragment)},l(n){d=l(n,"P",{});var c=i(d);v=r(c,"Examples:"),c.forEach(o),u=f(n),T(m.$$.fragment,n)},m(n,c){b(n,d,c),e(d,v),b(n,u,c),w(m,n,c),_=!0},p:S,i(n){_||(M(m.$$.fragment,n),_=!0)},o(n){$(m.$$.fragment,n),_=!1},d(n){n&&o(d),n&&o(u),B(m,n)}}}function Jy(x){let d,v,u,m,_;return{c(){d=a("p"),v=s("Although the recipe for forward pass needs to be defined within this function, one should call the "),u=a("code"),m=s("Module"),_=s(`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`)},l(n){d=l(n,"P",{});var c=i(d);v=r(c,"Although the recipe for forward pass needs to be defined within this function, one should call the "),u=l(c,"CODE",{});var J=i(u);m=r(J,"Module"),J.forEach(o),_=r(c,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),c.forEach(o)},m(n,c){b(n,d,c),e(d,v),e(d,u),e(u,m),e(d,_)},d(n){n&&o(d)}}}function jy(x){let d,v,u,m,_;return{c(){d=a("p"),v=s("Although the recipe for forward pass needs to be defined within this function, one should call the "),u=a("code"),m=s("Module"),_=s(`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`)},l(n){d=l(n,"P",{});var c=i(d);v=r(c,"Although the recipe for forward pass needs to be defined within this function, one should call the "),u=l(c,"CODE",{});var J=i(u);m=r(J,"Module"),J.forEach(o),_=r(c,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),c.forEach(o)},m(n,c){b(n,d,c),e(d,v),e(d,u),e(u,m),e(d,_)},d(n){n&&o(d)}}}function Ey(x){let d,v,u,m,_;return{c(){d=a("p"),v=s("Although the recipe for forward pass needs to be defined within this function, one should call the "),u=a("code"),m=s("Module"),_=s(`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`)},l(n){d=l(n,"P",{});var c=i(d);v=r(c,"Although the recipe for forward pass needs to be defined within this function, one should call the "),u=l(c,"CODE",{});var J=i(u);m=r(J,"Module"),J.forEach(o),_=r(c,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),c.forEach(o)},m(n,c){b(n,d,c),e(d,v),e(d,u),e(u,m),e(d,_)},d(n){n&&o(d)}}}function Iy(x){let d,v,u,m,_;return m=new q({props:{code:"ZnJvbSUyMFBJTCUyMGltcG9ydCUyMEltYWdlJTBBaW1wb3J0JTIwcmVxdWVzdHMlMEFmcm9tJTIwdHJhbnNmb3JtZXJzJTIwaW1wb3J0JTIwQXV0b1Byb2Nlc3NvciUyQyUyMFRGQmxpcEZvckNvbmRpdGlvbmFsR2VuZXJhdGlvbiUwQSUwQXByb2Nlc3NvciUyMCUzRCUyMEF1dG9Qcm9jZXNzb3IuZnJvbV9wcmV0cmFpbmVkKCUyMlNhbGVzZm9yY2UlMkZibGlwLWltYWdlLWNhcHRpb25pbmctYmFzZSUyMiklMEFtb2RlbCUyMCUzRCUyMFRGQmxpcEZvckNvbmRpdGlvbmFsR2VuZXJhdGlvbi5mcm9tX3ByZXRyYWluZWQoJTIyU2FsZXNmb3JjZSUyRmJsaXAtaW1hZ2UtY2FwdGlvbmluZy1iYXNlJTIyKSUwQSUwQXVybCUyMCUzRCUyMCUyMmh0dHAlM0ElMkYlMkZpbWFnZXMuY29jb2RhdGFzZXQub3JnJTJGdmFsMjAxNyUyRjAwMDAwMDAzOTc2OS5qcGclMjIlMEFpbWFnZSUyMCUzRCUyMEltYWdlLm9wZW4ocmVxdWVzdHMuZ2V0KHVybCUyQyUyMHN0cmVhbSUzRFRydWUpLnJhdyklMEF0ZXh0JTIwJTNEJTIwJTIyQSUyMHBpY3R1cmUlMjBvZiUyMiUwQSUwQWlucHV0cyUyMCUzRCUyMHByb2Nlc3NvcihpbWFnZXMlM0RpbWFnZSUyQyUyMHRleHQlM0R0ZXh0JTJDJTIwcmV0dXJuX3RlbnNvcnMlM0QlMjJ0ZiUyMiklMEElMEFvdXRwdXRzJTIwJTNEJTIwbW9kZWwoKippbnB1dHMp",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, TFBlipForConditionalGeneration

<span class="hljs-meta">&gt;&gt;&gt; </span>processor = AutoProcessor.from_pretrained(<span class="hljs-string">&quot;Salesforce/blip-image-captioning-base&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFBlipForConditionalGeneration.from_pretrained(<span class="hljs-string">&quot;Salesforce/blip-image-captioning-base&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>text = <span class="hljs-string">&quot;A picture of&quot;</span>

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

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

<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFBlipForImageTextRetrieval.from_pretrained(<span class="hljs-string">&quot;Salesforce/blip-itm-base-coco&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>processor = AutoProcessor.from_pretrained(<span class="hljs-string">&quot;Salesforce/blip-itm-base-coco&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>text = <span class="hljs-string">&quot;an image of a cat&quot;</span>

<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = processor(images=image, text=text, return_tensors=<span class="hljs-string">&quot;tf&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(**inputs)`}}),{c(){d=a("p"),v=s("Examples:"),u=h(),y(m.$$.fragment)},l(n){d=l(n,"P",{});var c=i(d);v=r(c,"Examples:"),c.forEach(o),u=f(n),T(m.$$.fragment,n)},m(n,c){b(n,d,c),e(d,v),b(n,u,c),w(m,n,c),_=!0},p:S,i(n){_||(M(m.$$.fragment,n),_=!0)},o(n){$(m.$$.fragment,n),_=!1},d(n){n&&o(d),n&&o(u),B(m,n)}}}function Uy(x){let d,v,u,m,_;return{c(){d=a("p"),v=s("Although the recipe for forward pass needs to be defined within this function, one should call the "),u=a("code"),m=s("Module"),_=s(`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`)},l(n){d=l(n,"P",{});var c=i(d);v=r(c,"Although the recipe for forward pass needs to be defined within this function, one should call the "),u=l(c,"CODE",{});var J=i(u);m=r(J,"Module"),J.forEach(o),_=r(c,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),c.forEach(o)},m(n,c){b(n,d,c),e(d,v),e(d,u),e(u,m),e(d,_)},d(n){n&&o(d)}}}function Cy(x){let d,v,u,m,_;return m=new q({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, TFBlipForQuestionAnswering

<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFBlipForQuestionAnswering.from_pretrained(<span class="hljs-string">&quot;Salesforce/blip-vqa-base&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>processor = AutoProcessor.from_pretrained(<span class="hljs-string">&quot;Salesforce/blip-vqa-base&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><span class="hljs-comment"># training</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>text = <span class="hljs-string">&quot;How many cats are in the picture?&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>label = <span class="hljs-string">&quot;2&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = processor(images=image, text=text, return_tensors=<span class="hljs-string">&quot;tf&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>labels = processor(text=label, return_tensors=<span class="hljs-string">&quot;tf&quot;</span>).input_ids

<span class="hljs-meta">&gt;&gt;&gt; </span>inputs[<span class="hljs-string">&quot;labels&quot;</span>] = labels
<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(**inputs)
<span class="hljs-meta">&gt;&gt;&gt; </span>loss = outputs.loss

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># inference</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>text = <span class="hljs-string">&quot;How many cats are in the picture?&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = processor(images=image, text=text, return_tensors=<span class="hljs-string">&quot;tf&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model.generate(**inputs)
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-built_in">print</span>(processor.decode(outputs[<span class="hljs-number">0</span>], skip_special_tokens=<span class="hljs-literal">True</span>))
<span class="hljs-number">2</span>`}}),{c(){d=a("p"),v=s("Examples:"),u=h(),y(m.$$.fragment)},l(n){d=l(n,"P",{});var c=i(d);v=r(c,"Examples:"),c.forEach(o),u=f(n),T(m.$$.fragment,n)},m(n,c){b(n,d,c),e(d,v),b(n,u,c),w(m,n,c),_=!0},p:S,i(n){_||(M(m.$$.fragment,n),_=!0)},o(n){$(m.$$.fragment,n),_=!1},d(n){n&&o(d),n&&o(u),B(m,n)}}}function zy(x){let d,v,u,m,_,n,c,J,Nd,ni,Ce,wt,Yr,Zo,Pd,Dr,Zd,si,Mt,Vd,Vo,Rd,Gd,ri,Fs,Ad,ai,_e,Or,Sd,qd,Kr,Xd,Qd,ea,Ld,li,ks,Hd,ii,Us,ta,Yd,di,Cs,zs,l_,ci,be,Dd,Ro,Od,Kd,Go,ec,tc,pi,ze,$t,oa,Ao,oc,na,nc,mi,Ws,Ns,So,sc,rc,hi,We,Bt,sa,qo,ac,ra,lc,fi,U,Xo,ic,ve,Ps,dc,cc,Zs,pc,mc,Qo,hc,fc,uc,Ne,gc,Vs,_c,bc,Rs,vc,yc,Tc,xt,wc,Jt,Lo,Mc,Ho,$c,Gs,Bc,xc,ui,Pe,jt,aa,Yo,Jc,la,jc,gi,Q,Do,Ec,me,Ic,As,Fc,kc,ia,Uc,Cc,Oo,zc,Wc,Nc,Ze,Pc,Ss,Zc,Vc,qs,Rc,Gc,Ac,Et,_i,Ve,It,da,Ko,Sc,ca,qc,bi,L,en,Xc,Re,Qc,Xs,Lc,Hc,tn,Yc,Dc,Oc,Ge,Kc,Qs,ep,tp,Ls,op,np,sp,Ft,vi,Ae,kt,pa,on,rp,ma,ap,yi,C,nn,lp,ha,ip,dp,X,Hs,cp,pp,Ys,mp,hp,Ds,fp,up,fa,gp,_p,Os,bp,vp,yp,Ut,sn,Tp,rn,wp,Ks,Mp,$p,Bp,Ct,an,xp,ln,Jp,er,jp,Ep,Ti,Se,zt,ua,dn,Ip,ga,Fp,wi,he,cn,kp,_a,Up,Cp,Wt,pn,zp,ba,Wp,Mi,qe,Nt,va,mn,Np,ya,Pp,$i,k,hn,Zp,fn,Vp,tr,Rp,Gp,Ap,un,Sp,gn,qp,Xp,Qp,O,_n,Lp,Xe,Hp,or,Yp,Dp,Ta,Op,Kp,em,Pt,tm,Zt,om,K,bn,nm,Qe,sm,nr,rm,am,wa,lm,im,dm,Vt,cm,Rt,pm,ee,vn,mm,Le,hm,sr,fm,um,Ma,gm,_m,bm,Gt,vm,At,Bi,He,St,$a,yn,ym,Ba,Tm,xi,fe,Tn,wm,H,Mm,wn,$m,Bm,xa,xm,Jm,Ja,jm,Em,ja,Im,Fm,km,ye,Mn,Um,z,Cm,Ea,zm,Wm,Ia,Nm,Pm,Fa,Zm,Vm,ka,Rm,Gm,Ua,Am,Sm,qm,$n,Bn,Xm,Ca,Qm,Lm,Hm,E,Ym,za,Dm,Om,Wa,Km,eh,Na,th,oh,Pa,nh,sh,Za,rh,ah,Va,lh,ih,Ra,dh,ch,Ga,ph,mh,Aa,hh,fh,Sa,uh,gh,qa,_h,bh,Xa,vh,yh,Qa,Th,wh,Ji,Ye,qt,La,xn,Mh,Ha,$h,ji,De,Jn,Bh,Te,jn,xh,Oe,Jh,rr,jh,Eh,Ya,Ih,Fh,kh,Xt,Ei,Ke,Qt,Da,En,Uh,Oa,Ch,Ii,W,In,zh,Fn,Wh,Ka,Nh,Ph,Zh,kn,Vh,ar,Rh,Gh,Ah,Un,Sh,Cn,qh,Xh,Qh,te,zn,Lh,et,Hh,lr,Yh,Dh,el,Oh,Kh,ef,Lt,tf,Ht,Fi,tt,Yt,tl,Wn,of,ol,nf,ki,N,Nn,sf,nl,rf,af,Pn,lf,ir,df,cf,pf,Zn,mf,Vn,hf,ff,uf,oe,Rn,gf,ot,_f,dr,bf,vf,sl,yf,Tf,wf,Dt,Mf,Ot,Ui,nt,Kt,rl,Gn,$f,al,Bf,Ci,P,An,xf,ll,Jf,jf,Sn,Ef,cr,If,Ff,kf,qn,Uf,Xn,Cf,zf,Wf,ne,Qn,Nf,st,Pf,pr,Zf,Vf,il,Rf,Gf,Af,eo,Sf,to,zi,rt,oo,dl,Ln,qf,cl,Xf,Wi,Y,Hn,Qf,se,Yn,Lf,at,Hf,mr,Yf,Df,pl,Of,Kf,eu,no,tu,so,ou,re,Dn,nu,lt,su,hr,ru,au,ml,lu,iu,du,ro,cu,ao,pu,ae,On,mu,it,hu,fr,fu,uu,hl,gu,_u,bu,lo,vu,io,Ni,dt,co,fl,Kn,yu,ul,Tu,Pi,ue,es,wu,D,Mu,ts,$u,Bu,gl,xu,Ju,_l,ju,Eu,bl,Iu,Fu,ku,we,os,Uu,ct,Cu,ur,zu,Wu,vl,Nu,Pu,Zu,po,Zi,pt,mo,yl,ns,Vu,Tl,Ru,Vi,mt,ss,Gu,Me,rs,Au,ht,Su,gr,qu,Xu,wl,Qu,Lu,Hu,ho,Ri,ft,fo,Ml,as,Yu,$l,Du,Gi,Z,ls,Ou,is,Ku,Bl,eg,tg,og,ds,ng,_r,sg,rg,ag,cs,lg,ps,ig,dg,cg,le,ms,pg,ut,mg,br,hg,fg,xl,ug,gg,_g,uo,bg,go,Ai,gt,_o,Jl,hs,vg,jl,yg,Si,V,fs,Tg,El,wg,Mg,us,$g,vr,Bg,xg,Jg,gs,jg,_s,Eg,Ig,Fg,ie,bs,kg,_t,Ug,yr,Cg,zg,Il,Wg,Ng,Pg,bo,Zg,vo,qi,bt,yo,Fl,vs,Vg,kl,Rg,Xi,R,ys,Gg,Ul,Ag,Sg,Ts,qg,Tr,Xg,Qg,Lg,ws,Hg,Ms,Yg,Dg,Og,de,$s,Kg,vt,e_,wr,t_,o_,Cl,n_,s_,r_,To,a_,wo,Qi;return n=new F({}),Zo=new F({}),Ao=new F({}),qo=new F({}),Xo=new j({props:{name:"class transformers.BlipConfig",anchor:"transformers.BlipConfig",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:"image_text_hidden_size",val:" = 256"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.BlipConfig.text_config",description:`<strong>text_config</strong> (<code>dict</code>, <em>optional</em>) &#x2014;
Dictionary of configuration options used to initialize <a href="/docs/transformers/v4.30.0/en/model_doc/blip#transformers.BlipTextConfig">BlipTextConfig</a>.`,name:"text_config"},{anchor:"transformers.BlipConfig.vision_config",description:`<strong>vision_config</strong> (<code>dict</code>, <em>optional</em>) &#x2014;
Dictionary of configuration options used to initialize <a href="/docs/transformers/v4.30.0/en/model_doc/blip#transformers.BlipVisionConfig">BlipVisionConfig</a>.`,name:"vision_config"},{anchor:"transformers.BlipConfig.projection_dim",description:`<strong>projection_dim</strong> (<code>int</code>, <em>optional</em>, defaults to 512) &#x2014;
Dimentionality of text and vision projection layers.`,name:"projection_dim"},{anchor:"transformers.BlipConfig.logit_scale_init_value",description:`<strong>logit_scale_init_value</strong> (<code>float</code>, <em>optional</em>, defaults to 2.6592) &#x2014;
The inital value of the <em>logit_scale</em> paramter. Default is used as per the original BLIP implementation.`,name:"logit_scale_init_value"},{anchor:"transformers.BlipConfig.image_text_hidden_size",description:`<strong>image_text_hidden_size</strong> (<code>int</code>, <em>optional</em>, defaults to 768) &#x2014;
Dimentionality of the hidden state of the image-text fusion layer.`,name:"image_text_hidden_size"},{anchor:"transformers.BlipConfig.kwargs",description:`<strong>kwargs</strong> (<em>optional</em>) &#x2014;
Dictionary of keyword arguments.`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/blip/configuration_blip.py#L278"}}),xt=new A({props:{anchor:"transformers.BlipConfig.example",$$slots:{default:[ry]},$$scope:{ctx:x}}}),Lo=new j({props:{name:"from_text_vision_configs",anchor:"transformers.BlipConfig.from_text_vision_configs",parameters:[{name:"text_config",val:": BlipTextConfig"},{name:"vision_config",val:": BlipVisionConfig"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/blip/configuration_blip.py#L358",returnDescription:`
<p>An instance of a configuration object</p>
`,returnType:`
<p><a
  href="/docs/transformers/v4.30.0/en/model_doc/blip#transformers.BlipConfig"
>BlipConfig</a></p>
`}}),Yo=new F({}),Do=new j({props:{name:"class transformers.BlipTextConfig",anchor:"transformers.BlipTextConfig",parameters:[{name:"vocab_size",val:" = 30524"},{name:"hidden_size",val:" = 768"},{name:"encoder_hidden_size",val:" = 768"},{name:"intermediate_size",val:" = 3072"},{name:"projection_dim",val:" = 768"},{name:"num_hidden_layers",val:" = 12"},{name:"num_attention_heads",val:" = 8"},{name:"max_position_embeddings",val:" = 512"},{name:"hidden_act",val:" = 'gelu'"},{name:"layer_norm_eps",val:" = 1e-12"},{name:"hidden_dropout_prob",val:" = 0.0"},{name:"attention_probs_dropout_prob",val:" = 0.0"},{name:"initializer_range",val:" = 0.02"},{name:"bos_token_id",val:" = 30522"},{name:"eos_token_id",val:" = 2"},{name:"pad_token_id",val:" = 0"},{name:"sep_token_id",val:" = 102"},{name:"is_decoder",val:" = True"},{name:"use_cache",val:" = True"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.BlipTextConfig.vocab_size",description:`<strong>vocab_size</strong> (<code>int</code>, <em>optional</em>, defaults to 30522) &#x2014;
Vocabulary size of the <code>Blip</code> text model. Defines the number of different tokens that can be represented by
the <code>inputs_ids</code> passed when calling <a href="/docs/transformers/v4.30.0/en/model_doc/blip#transformers.BlipModel">BlipModel</a>.`,name:"vocab_size"},{anchor:"transformers.BlipTextConfig.hidden_size",description:`<strong>hidden_size</strong> (<code>int</code>, <em>optional</em>, defaults to 768) &#x2014;
Dimensionality of the encoder layers and the pooler layer.`,name:"hidden_size"},{anchor:"transformers.BlipTextConfig.encoder_hidden_size",description:`<strong>encoder_hidden_size</strong> (<code>int</code>, <em>optional</em>, defaults to 768) &#x2014;
Dimensionality of the encoder layers from the vision model.`,name:"encoder_hidden_size"},{anchor:"transformers.BlipTextConfig.intermediate_size",description:`<strong>intermediate_size</strong> (<code>int</code>, <em>optional</em>, defaults to 3072) &#x2014;
Dimensionality of the &#x201C;intermediate&#x201D; (i.e., feed-forward) layer in the Transformer encoder.`,name:"intermediate_size"},{anchor:"transformers.BlipTextConfig.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.BlipTextConfig.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.BlipTextConfig.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.BlipTextConfig.hidden_act",description:`<strong>hidden_act</strong> (<code>str</code> or <code>function</code>, <em>optional</em>, defaults to <code>&quot;gelu&quot;</code>) &#x2014;
The non-linear activation function (function or string) in the encoder and pooler. If string, <code>&quot;gelu&quot;</code>,
<code>&quot;relu&quot;</code>, <code>&quot;selu&quot;</code> and <code>&quot;gelu_new&quot;</code> \`<code>&quot;gelu&quot;</code> are supported.`,name:"hidden_act"},{anchor:"transformers.BlipTextConfig.layer_norm_eps",description:`<strong>layer_norm_eps</strong> (<code>float</code>, <em>optional</em>, defaults to 1e-12) &#x2014;
The epsilon used by the layer normalization layers.`,name:"layer_norm_eps"},{anchor:"transformers.BlipTextConfig.hidden_dropout_prob",description:`<strong>hidden_dropout_prob</strong> (<code>float</code>, <em>optional</em>, defaults to 0.0) &#x2014;
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.`,name:"hidden_dropout_prob"},{anchor:"transformers.BlipTextConfig.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.BlipTextConfig.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.BlipTextConfig.bos_token_id",description:`<strong>bos_token_id</strong> (<code>int</code>, <em>optional</em>, defaults to 30522) &#x2014;
The id of the <code>beginning-of-sequence</code> token.`,name:"bos_token_id"},{anchor:"transformers.BlipTextConfig.eos_token_id",description:`<strong>eos_token_id</strong> (<code>int</code>, <em>optional</em>, defaults to 2) &#x2014;
The id of the <code>end-of-sequence</code> token.`,name:"eos_token_id"},{anchor:"transformers.BlipTextConfig.pad_token_id",description:`<strong>pad_token_id</strong> (<code>int</code>, <em>optional</em>, defaults to 0) &#x2014;
The id of the <code>padding</code> token.`,name:"pad_token_id"},{anchor:"transformers.BlipTextConfig.sep_token_id",description:`<strong>sep_token_id</strong> (<code>int</code>, <em>optional</em>, defaults to 102) &#x2014;
The id of the <code>separator</code> token.`,name:"sep_token_id"},{anchor:"transformers.BlipTextConfig.is_decoder",description:`<strong>is_decoder</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether the model is used as a decoder.`,name:"is_decoder"},{anchor:"transformers.BlipTextConfig.use_cache",description:`<strong>use_cache</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether or not the model should return the last key/values attentions (not used by all models).`,name:"use_cache"}],source:"https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/blip/configuration_blip.py#L47"}}),Et=new A({props:{anchor:"transformers.BlipTextConfig.example",$$slots:{default:[ay]},$$scope:{ctx:x}}}),Ko=new F({}),en=new j({props:{name:"class transformers.BlipVisionConfig",anchor:"transformers.BlipVisionConfig",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:" = 384"},{name:"patch_size",val:" = 16"},{name:"hidden_act",val:" = 'gelu'"},{name:"layer_norm_eps",val:" = 1e-05"},{name:"attention_dropout",val:" = 0.0"},{name:"initializer_range",val:" = 1e-10"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.BlipVisionConfig.hidden_size",description:`<strong>hidden_size</strong> (<code>int</code>, <em>optional</em>, defaults to 768) &#x2014;
Dimensionality of the encoder layers and the pooler layer.`,name:"hidden_size"},{anchor:"transformers.BlipVisionConfig.intermediate_size",description:`<strong>intermediate_size</strong> (<code>int</code>, <em>optional</em>, defaults to 3072) &#x2014;
Dimensionality of the &#x201C;intermediate&#x201D; (i.e., feed-forward) layer in the Transformer encoder.`,name:"intermediate_size"},{anchor:"transformers.BlipVisionConfig.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.BlipVisionConfig.num_attention_heads",description:`<strong>num_attention_heads</strong> (<code>int</code>, <em>optional</em>, defaults to 12) &#x2014;
Number of attention heads for each attention layer in the Transformer encoder.`,name:"num_attention_heads"},{anchor:"transformers.BlipVisionConfig.image_size",description:`<strong>image_size</strong> (<code>int</code>, <em>optional</em>, defaults to 224) &#x2014;
The size (resolution) of each image.`,name:"image_size"},{anchor:"transformers.BlipVisionConfig.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.BlipVisionConfig.hidden_act",description:`<strong>hidden_act</strong> (<code>str</code> or <code>function</code>, <em>optional</em>, defaults to <code>&quot;gelu&quot;</code>) &#x2014;
The non-linear activation function (function or string) in the encoder and pooler. If string, <code>&quot;gelu&quot;</code>,
<code>&quot;relu&quot;</code>, <code>&quot;selu&quot;</code> and <code>&quot;gelu_new&quot;</code> \`<code>&quot;gelu&quot;</code> are supported.`,name:"hidden_act"},{anchor:"transformers.BlipVisionConfig.layer_norm_eps",description:`<strong>layer_norm_eps</strong> (<code>float</code>, <em>optional</em>, defaults to 1e-5) &#x2014;
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Size of the output image after resizing. Can be overridden by the <code>size</code> parameter in the <code>preprocess</code>
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Resampling filter to use if resizing the image. Only has an effect if <code>do_resize</code> is set to <code>True</code>. Can be
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Wwhether to rescale the image by the specified scale <code>rescale_factor</code>. Can be overridden by the
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Scale factor to use if rescaling the image. Only has an effect if <code>do_rescale</code> is set to <code>True</code>. Can be
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Whether to normalize the image. Can be overridden by the <code>do_normalize</code> parameter in the <code>preprocess</code>
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Mean 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_mean</code> parameter in the <code>preprocess</code> method. Can be
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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.BlipImageProcessor.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/v4.30.0/src/transformers/models/blip/image_processing_blip.py#L43"}}),pn=new j({props:{name:"preprocess",anchor:"transformers.BlipImageProcessor.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:": typing.Optional[bool] = None"},{name:"size",val:": typing.Union[typing.Dict[str, int], NoneType] = None"},{name:"resample",val:": Resampling = None"},{name:"do_rescale",val:": typing.Optional[bool] = None"},{name:"rescale_factor",val:": typing.Optional[float] = None"},{name:"do_normalize",val:": typing.Optional[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:"return_tensors",val:": typing.Union[str, transformers.utils.generic.TensorType, NoneType] = None"},{name:"do_convert_rgb",val:": bool = None"},{name:"data_format",val:": ChannelDimension = <ChannelDimension.FIRST: 'channels_first'>"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.BlipImageProcessor.preprocess.images",description:`<strong>images</strong> (<code>ImageInput</code>) &#x2014;
Image to preprocess.`,name:"images"},{anchor:"transformers.BlipImageProcessor.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.BlipImageProcessor.preprocess.size",description:`<strong>size</strong> (<code>Dict[str, int]</code>, <em>optional</em>, defaults to <code>self.size</code>) &#x2014;
Controls the size of the image after <code>resize</code>. The shortest edge of the image is resized to
<code>size[&quot;shortest_edge&quot;]</code> whilst preserving the aspect ratio. If the longest edge of this resized image
is &gt; <code>int(size[&quot;shortest_edge&quot;] * (1333 / 800))</code>, then the image is resized again to make the longest
edge equal to <code>int(size[&quot;shortest_edge&quot;] * (1333 / 800))</code>.`,name:"size"},{anchor:"transformers.BlipImageProcessor.preprocess.resample",description:`<strong>resample</strong> (<code>PILImageResampling</code>, <em>optional</em>, defaults to <code>self.resample</code>) &#x2014;
Resampling filter to use if resizing the image. Only has an effect if <code>do_resize</code> is set to <code>True</code>.`,name:"resample"},{anchor:"transformers.BlipImageProcessor.preprocess.do_rescale",description:`<strong>do_rescale</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>self.do_rescale</code>) &#x2014;
Whether to rescale the image values between [0 - 1].`,name:"do_rescale"},{anchor:"transformers.BlipImageProcessor.preprocess.rescale_factor",description:`<strong>rescale_factor</strong> (<code>float</code>, <em>optional</em>, defaults to <code>self.rescale_factor</code>) &#x2014;
Rescale factor to rescale the image by if <code>do_rescale</code> is set to <code>True</code>.`,name:"rescale_factor"},{anchor:"transformers.BlipImageProcessor.preprocess.do_normalize",description:`<strong>do_normalize</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>self.do_normalize</code>) &#x2014;
Whether to normalize the image.`,name:"do_normalize"},{anchor:"transformers.BlipImageProcessor.preprocess.image_mean",description:`<strong>image_mean</strong> (<code>float</code> or <code>List[float]</code>, <em>optional</em>, defaults to <code>self.image_mean</code>) &#x2014;
Image mean to normalize the image by if <code>do_normalize</code> is set to <code>True</code>.`,name:"image_mean"},{anchor:"transformers.BlipImageProcessor.preprocess.image_std",description:`<strong>image_std</strong> (<code>float</code> or <code>List[float]</code>, <em>optional</em>, defaults to <code>self.image_std</code>) &#x2014;
Image standard deviation to normalize the image by if <code>do_normalize</code> is set to <code>True</code>.`,name:"image_std"},{anchor:"transformers.BlipImageProcessor.preprocess.do_convert_rgb",description:`<strong>do_convert_rgb</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>self.do_convert_rgb</code>) &#x2014;
Whether to convert the image to RGB.`,name:"do_convert_rgb"},{anchor:"transformers.BlipImageProcessor.preprocess.return_tensors",description:`<strong>return_tensors</strong> (<code>str</code> or <code>TensorType</code>, <em>optional</em>) &#x2014;
The type of tensors to return. Can be one of:<ul>
<li>Unset: Return a list of <code>np.ndarray</code>.</li>
<li><code>TensorType.TENSORFLOW</code> or <code>&apos;tf&apos;</code>: Return a batch of type <code>tf.Tensor</code>.</li>
<li><code>TensorType.PYTORCH</code> or <code>&apos;pt&apos;</code>: Return a batch of type <code>torch.Tensor</code>.</li>
<li><code>TensorType.NUMPY</code> or <code>&apos;np&apos;</code>: Return a batch of type <code>np.ndarray</code>.</li>
<li><code>TensorType.JAX</code> or <code>&apos;jax&apos;</code>: Return a batch of type <code>jax.numpy.ndarray</code>.</li>
</ul>`,name:"return_tensors"},{anchor:"transformers.BlipImageProcessor.preprocess.data_format",description:`<strong>data_format</strong> (<code>ChannelDimension</code> or <code>str</code>, <em>optional</em>, defaults to <code>ChannelDimension.FIRST</code>) &#x2014;
The channel dimension format for the output image. Can be one of:<ul>
<li><code>ChannelDimension.FIRST</code>: image in (num_channels, height, width) format.</li>
<li><code>ChannelDimension.LAST</code>: image in (height, width, num_channels) format.</li>
</ul>`,name:"data_format"}],source:"https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/blip/image_processing_blip.py#L183"}}),mn=new F({}),hn=new j({props:{name:"class transformers.BlipModel",anchor:"transformers.BlipModel",parameters:[{name:"config",val:": BlipConfig"}],parametersDescription:[{anchor:"transformers.BlipModel.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.30.0/en/model_doc/blip#transformers.BlipConfig">BlipConfig</a>) &#x2014; Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the <a href="/docs/transformers/v4.30.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/blip/modeling_blip.py#L717"}}),_n=new j({props:{name:"forward",anchor:"transformers.BlipModel.forward",parameters:[{name:"input_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"pixel_values",val:": typing.Optional[torch.FloatTensor] = None"},{name:"attention_mask",val:": typing.Optional[torch.Tensor] = None"},{name:"position_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"return_loss",val:": typing.Optional[bool] = None"},{name:"output_attentions",val:": typing.Optional[bool] = None"},{name:"output_hidden_states",val:": typing.Optional[bool] = None"},{name:"return_dict",val:": typing.Optional[bool] = None"}],parametersDescription:[{anchor:"transformers.BlipModel.forward.input_ids",description:`<strong>input_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
it.</p>
<p>Indices can be obtained using <a href="/docs/transformers/v4.30.0/en/model_doc/auto#transformers.AutoProcessor">AutoProcessor</a>. See <code>BlipProcessor.__call__()</code> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.BlipModel.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.BlipModel.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.BlipModel.forward.pixel_values",description:`<strong>pixel_values</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, num_channels, height, width)</code>) &#x2014;
Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using
<a href="/docs/transformers/v4.30.0/en/model_doc/blip#transformers.BlipImageProcessor">BlipImageProcessor</a>. See <a href="/docs/transformers/v4.30.0/en/model_doc/layoutlmv2#transformers.LayoutLMv2FeatureExtractor.__call__">BlipImageProcessor.<strong>call</strong>()</a> for details.`,name:"pixel_values"},{anchor:"transformers.BlipModel.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.BlipModel.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.BlipModel.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.BlipModel.forward.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/v4.30.0/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/blip/modeling_blip.py#L827",returnDescription:`
<p>A <code>transformers.models.blip.modeling_blip.BlipOutput</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.blip.configuration_blip.BlipConfig'&gt;</code>) and inputs.</p>
<ul>
<li><strong>loss</strong> (<code>torch.FloatTensor</code> of shape <code>(1,)</code>, <em>optional</em>, returned when <code>return_loss</code> is <code>True</code>) \u2014 Contrastive loss for image-text similarity.</li>
<li><strong>logits_per_image:(<code>torch.FloatTensor</code></strong> of shape <code>(image_batch_size, text_batch_size)</code>) \u2014 The scaled dot product scores between <code>image_embeds</code> and <code>text_embeds</code>. This represents the image-text
similarity scores.</li>
<li><strong>logits_per_text:(<code>torch.FloatTensor</code></strong> of shape <code>(text_batch_size, image_batch_size)</code>) \u2014 The scaled dot product scores between <code>text_embeds</code> and <code>image_embeds</code>. This represents the text-image
similarity scores.</li>
<li><strong>text_embeds(<code>torch.FloatTensor</code></strong> of shape <code>(batch_size, output_dim</code>) \u2014 The text embeddings obtained by applying the projection layer to the pooled output of <a
  href="/docs/transformers/v4.30.0/en/model_doc/blip#transformers.BlipTextModel"
>BlipTextModel</a>.</li>
<li><strong>image_embeds(<code>torch.FloatTensor</code></strong> of shape <code>(batch_size, output_dim</code>) \u2014 The image embeddings obtained by applying the projection layer to the pooled output of <a
  href="/docs/transformers/v4.30.0/en/model_doc/blip#transformers.BlipVisionModel"
>BlipVisionModel</a>.</li>
<li><strong>text_model_output(<code>BaseModelOutputWithPooling</code>):</strong>
The output of the <a
  href="/docs/transformers/v4.30.0/en/model_doc/blip#transformers.BlipTextModel"
>BlipTextModel</a>.</li>
<li><strong>vision_model_output(<code>BaseModelOutputWithPooling</code>):</strong>
The output of the <a
  href="/docs/transformers/v4.30.0/en/model_doc/blip#transformers.BlipVisionModel"
>BlipVisionModel</a>.</li>
</ul>
`,returnType:`
<p><code>transformers.models.blip.modeling_blip.BlipOutput</code> or <code>tuple(torch.FloatTensor)</code></p>
`}}),Pt=new G({props:{$$slots:{default:[iy]},$$scope:{ctx:x}}}),Zt=new A({props:{anchor:"transformers.BlipModel.forward.example",$$slots:{default:[dy]},$$scope:{ctx:x}}}),bn=new j({props:{name:"get_text_features",anchor:"transformers.BlipModel.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:"return_dict",val:": typing.Optional[bool] = None"}],parametersDescription:[{anchor:"transformers.BlipModel.get_text_features.input_ids",description:`<strong>input_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
it.</p>
<p>Indices can be obtained using <a href="/docs/transformers/v4.30.0/en/model_doc/auto#transformers.AutoProcessor">AutoProcessor</a>. See <code>BlipProcessor.__call__()</code> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.BlipModel.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.BlipModel.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.BlipModel.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.BlipModel.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.BlipModel.get_text_features.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/v4.30.0/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/blip/modeling_blip.py#L752",returnDescription:`
<p>The text embeddings obtained by
applying the projection layer to the pooled output of <a
  href="/docs/transformers/v4.30.0/en/model_doc/blip#transformers.BlipTextModel"
>BlipTextModel</a>.</p>
`,returnType:`
<p>text_features (<code>torch.FloatTensor</code> of shape <code>(batch_size, output_dim</code>)</p>
`}}),Vt=new G({props:{$$slots:{default:[cy]},$$scope:{ctx:x}}}),Rt=new A({props:{anchor:"transformers.BlipModel.get_text_features.example",$$slots:{default:[py]},$$scope:{ctx:x}}}),vn=new j({props:{name:"get_image_features",anchor:"transformers.BlipModel.get_image_features",parameters:[{name:"pixel_values",val:": typing.Optional[torch.FloatTensor] = None"},{name:"return_dict",val:": typing.Optional[bool] = None"}],parametersDescription:[{anchor:"transformers.BlipModel.get_image_features.pixel_values",description:`<strong>pixel_values</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, num_channels, height, width)</code>) &#x2014;
Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using
<a href="/docs/transformers/v4.30.0/en/model_doc/blip#transformers.BlipImageProcessor">BlipImageProcessor</a>. See <a href="/docs/transformers/v4.30.0/en/model_doc/layoutlmv2#transformers.LayoutLMv2FeatureExtractor.__call__">BlipImageProcessor.<strong>call</strong>()</a> for details.`,name:"pixel_values"},{anchor:"transformers.BlipModel.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.BlipModel.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.BlipModel.get_image_features.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/v4.30.0/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/blip/modeling_blip.py#L790",returnDescription:`
<p>The image embeddings obtained by
applying the projection layer to the pooled output of <a
  href="/docs/transformers/v4.30.0/en/model_doc/blip#transformers.BlipVisionModel"
>BlipVisionModel</a>.</p>
`,returnType:`
<p>image_features (<code>torch.FloatTensor</code> of shape <code>(batch_size, output_dim</code>)</p>
`}}),Gt=new G({props:{$$slots:{default:[my]},$$scope:{ctx:x}}}),At=new A({props:{anchor:"transformers.BlipModel.get_image_features.example",$$slots:{default:[hy]},$$scope:{ctx:x}}}),yn=new F({}),Tn=new j({props:{name:"class transformers.BlipTextModel",anchor:"transformers.BlipTextModel",parameters:[{name:"config",val:""},{name:"add_pooling_layer",val:" = True"}],source:"https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/blip/modeling_blip_text.py#L571"}}),Mn=new j({props:{name:"forward",anchor:"transformers.BlipTextModel.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:"head_mask",val:": typing.Optional[torch.Tensor] = None"},{name:"inputs_embeds",val:": typing.Optional[torch.Tensor] = None"},{name:"encoder_embeds",val:": typing.Optional[torch.Tensor] = None"},{name:"encoder_hidden_states",val:": typing.Optional[torch.Tensor] = None"},{name:"encoder_attention_mask",val:": typing.Optional[torch.Tensor] = None"},{name:"past_key_values",val:": typing.Optional[typing.List[torch.FloatTensor]] = None"},{name:"use_cache",val:": typing.Optional[bool] = None"},{name:"output_attentions",val:": typing.Optional[bool] = None"},{name:"output_hidden_states",val:": typing.Optional[bool] = None"},{name:"return_dict",val:": typing.Optional[bool] = None"},{name:"is_decoder",val:": typing.Optional[bool] = False"}],source:"https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/blip/modeling_blip_text.py#L670"}}),xn=new F({}),Jn=new j({props:{name:"class transformers.BlipVisionModel",anchor:"transformers.BlipVisionModel",parameters:[{name:"config",val:": BlipVisionConfig"}],source:"https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/blip/modeling_blip.py#L650"}}),jn=new j({props:{name:"forward",anchor:"transformers.BlipVisionModel.forward",parameters:[{name:"pixel_values",val:": typing.Optional[torch.FloatTensor] = None"},{name:"output_attentions",val:": typing.Optional[bool] = None"},{name:"output_hidden_states",val:": typing.Optional[bool] = None"},{name:"return_dict",val:": typing.Optional[bool] = None"}],parametersDescription:[{anchor:"transformers.BlipVisionModel.forward.pixel_values",description:`<strong>pixel_values</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, num_channels, height, width)</code>) &#x2014;
Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using
<a href="/docs/transformers/v4.30.0/en/model_doc/blip#transformers.BlipImageProcessor">BlipImageProcessor</a>. See <a href="/docs/transformers/v4.30.0/en/model_doc/layoutlmv2#transformers.LayoutLMv2FeatureExtractor.__call__">BlipImageProcessor.<strong>call</strong>()</a> for details.`,name:"pixel_values"},{anchor:"transformers.BlipVisionModel.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.BlipVisionModel.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.BlipVisionModel.forward.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/v4.30.0/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/blip/modeling_blip.py#L665",returnDescription:`
<p>A <a
  href="/docs/transformers/v4.30.0/en/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPooling"
>transformers.modeling_outputs.BaseModelOutputWithPooling</a> or a tuple of
<code>torch.FloatTensor</code> (if <code>return_dict=False</code> is passed or when <code>config.return_dict=False</code>) comprising various
elements depending on the configuration (<code>&lt;class 'transformers.models.blip.configuration_blip.BlipVisionConfig'&gt;</code>) and inputs.</p>
<ul>
<li>
<p><strong>last_hidden_state</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>) \u2014 Sequence of hidden-states at the output of the last layer of the model.</p>
</li>
<li>
<p><strong>pooler_output</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, hidden_size)</code>) \u2014 Last layer hidden-state of the first token of the sequence (classification token) after further processing
through the layers used for the auxiliary pretraining task. E.g. for BERT-family of models, this returns
the classification token after processing through a linear layer and a tanh activation function. The linear
layer weights are trained from the next sentence prediction (classification) objective during pretraining.</p>
</li>
<li>
<p><strong>hidden_states</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape <code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.</p>
</li>
<li>
<p><strong>attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.</p>
</li>
</ul>
`,returnType:`
<p><a
  href="/docs/transformers/v4.30.0/en/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPooling"
>transformers.modeling_outputs.BaseModelOutputWithPooling</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),Xt=new G({props:{$$slots:{default:[fy]},$$scope:{ctx:x}}}),En=new F({}),In=new j({props:{name:"class transformers.BlipForConditionalGeneration",anchor:"transformers.BlipForConditionalGeneration",parameters:[{name:"config",val:": BlipConfig"}],parametersDescription:[{anchor:"transformers.BlipForConditionalGeneration.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.30.0/en/model_doc/blip#transformers.BlipConfig">BlipConfig</a>) &#x2014; Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the <a href="/docs/transformers/v4.30.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/blip/modeling_blip.py#L930"}}),zn=new j({props:{name:"forward",anchor:"transformers.BlipForConditionalGeneration.forward",parameters:[{name:"pixel_values",val:": FloatTensor"},{name:"input_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"attention_mask",val:": typing.Optional[torch.LongTensor] = None"},{name:"output_attentions",val:": typing.Optional[bool] = None"},{name:"output_hidden_states",val:": typing.Optional[bool] = None"},{name:"labels",val:": typing.Optional[torch.LongTensor] = None"},{name:"return_dict",val:": typing.Optional[bool] = None"}],parametersDescription:[{anchor:"transformers.BlipForConditionalGeneration.forward.pixel_values",description:`<strong>pixel_values</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, num_channels, height, width)</code>) &#x2014;
Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using
<a href="/docs/transformers/v4.30.0/en/model_doc/blip#transformers.BlipImageProcessor">BlipImageProcessor</a>. See <a href="/docs/transformers/v4.30.0/en/model_doc/layoutlmv2#transformers.LayoutLMv2FeatureExtractor.__call__">BlipImageProcessor.<strong>call</strong>()</a> for details.`,name:"pixel_values"},{anchor:"transformers.BlipForConditionalGeneration.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.BlipForConditionalGeneration.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.BlipForConditionalGeneration.forward.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/v4.30.0/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/blip/modeling_blip.py#L951",returnDescription:`
<p>A <code>transformers.models.blip.modeling_blip.BlipForConditionalGenerationModelOutput</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.blip.configuration_blip.BlipVisionConfig'&gt;</code>) and inputs.</p>
<ul>
<li>
<p><strong>loss</strong> (<code>torch.FloatTensor</code>, <em>optional</em>, returned when <code>labels</code> is provided, <code>torch.FloatTensor</code> of shape <code>(1,)</code>) \u2014 Languge modeling loss from the text decoder.</p>
</li>
<li>
<p><strong>decoder_logits</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, config.vocab_size)</code>, <em>optional</em>) \u2014 Prediction scores of the language modeling head of the text decoder model.</p>
</li>
<li>
<p><strong>image_embeds</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, output_dim)</code>, <em>optional</em>) \u2014 The image embeddings obtained after applying the Vision Transformer model to the input image.</p>
</li>
<li>
<p><strong>last_hidden_state</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>, <em>optional</em>) \u2014 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>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape <code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.</p>
</li>
<li>
<p><strong>attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed) \u2014 Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.</p>
</li>
</ul>
`,returnType:`
<p><code>transformers.models.blip.modeling_blip.BlipForConditionalGenerationModelOutput</code> or <code>tuple(torch.FloatTensor)</code></p>
`}}),Lt=new G({props:{$$slots:{default:[uy]},$$scope:{ctx:x}}}),Ht=new A({props:{anchor:"transformers.BlipForConditionalGeneration.forward.example",$$slots:{default:[gy]},$$scope:{ctx:x}}}),Wn=new F({}),Nn=new j({props:{name:"class transformers.BlipForImageTextRetrieval",anchor:"transformers.BlipForImageTextRetrieval",parameters:[{name:"config",val:": BlipConfig"}],parametersDescription:[{anchor:"transformers.BlipForImageTextRetrieval.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.30.0/en/model_doc/blip#transformers.BlipConfig">BlipConfig</a>) &#x2014; Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the <a href="/docs/transformers/v4.30.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/blip/modeling_blip.py#L1324"}}),Rn=new j({props:{name:"forward",anchor:"transformers.BlipForImageTextRetrieval.forward",parameters:[{name:"input_ids",val:": LongTensor"},{name:"pixel_values",val:": FloatTensor"},{name:"use_itm_head",val:": typing.Optional[bool] = True"},{name:"attention_mask",val:": typing.Optional[torch.LongTensor] = None"},{name:"output_attentions",val:": typing.Optional[bool] = None"},{name:"output_hidden_states",val:": typing.Optional[bool] = None"},{name:"return_dict",val:": typing.Optional[bool] = None"}],parametersDescription:[{anchor:"transformers.BlipForImageTextRetrieval.forward.pixel_values",description:`<strong>pixel_values</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, num_channels, height, width)</code>) &#x2014;
Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using
<a href="/docs/transformers/v4.30.0/en/model_doc/blip#transformers.BlipImageProcessor">BlipImageProcessor</a>. See <a href="/docs/transformers/v4.30.0/en/model_doc/layoutlmv2#transformers.LayoutLMv2FeatureExtractor.__call__">BlipImageProcessor.<strong>call</strong>()</a> for details.`,name:"pixel_values"},{anchor:"transformers.BlipForImageTextRetrieval.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.BlipForImageTextRetrieval.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.BlipForImageTextRetrieval.forward.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/v4.30.0/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/blip/modeling_blip.py#L1360",returnDescription:`
<p>A <code>transformers.models.blip.modeling_blip.BlipTextVisionModelOutput</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.blip.configuration_blip.BlipVisionConfig'&gt;</code>) and inputs.</p>
<ul>
<li>
<p><strong>loss</strong> (<code>torch.FloatTensor</code> of shape <code>(1,)</code>, <em>optional</em>, returned when <code>labels</code> is provided) \u2014 Languge modeling loss from the text decoder.</p>
</li>
<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>) \u2014 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>) \u2014 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>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape <code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.</p>
</li>
<li>
<p><strong>attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.</p>
</li>
</ul>
`,returnType:`
<p><code>transformers.models.blip.modeling_blip.BlipTextVisionModelOutput</code> or <code>tuple(torch.FloatTensor)</code></p>
`}}),Dt=new G({props:{$$slots:{default:[_y]},$$scope:{ctx:x}}}),Ot=new A({props:{anchor:"transformers.BlipForImageTextRetrieval.forward.example",$$slots:{default:[by]},$$scope:{ctx:x}}}),Gn=new F({}),An=new j({props:{name:"class transformers.BlipForQuestionAnswering",anchor:"transformers.BlipForQuestionAnswering",parameters:[{name:"config",val:": BlipConfig"}],parametersDescription:[{anchor:"transformers.BlipForQuestionAnswering.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.30.0/en/model_doc/blip#transformers.BlipConfig">BlipConfig</a>) &#x2014; Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the <a href="/docs/transformers/v4.30.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/blip/modeling_blip.py#L1102"}}),Qn=new j({props:{name:"forward",anchor:"transformers.BlipForQuestionAnswering.forward",parameters:[{name:"input_ids",val:": LongTensor"},{name:"pixel_values",val:": FloatTensor"},{name:"decoder_input_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"decoder_attention_mask",val:": typing.Optional[torch.LongTensor] = None"},{name:"attention_mask",val:": typing.Optional[torch.LongTensor] = None"},{name:"output_attentions",val:": typing.Optional[bool] = None"},{name:"output_hidden_states",val:": typing.Optional[bool] = None"},{name:"labels",val:": typing.Optional[torch.LongTensor] = None"},{name:"return_dict",val:": typing.Optional[bool] = None"}],parametersDescription:[{anchor:"transformers.BlipForQuestionAnswering.forward.pixel_values",description:`<strong>pixel_values</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, num_channels, height, width)</code>) &#x2014;
Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using
<a href="/docs/transformers/v4.30.0/en/model_doc/blip#transformers.BlipImageProcessor">BlipImageProcessor</a>. See <a href="/docs/transformers/v4.30.0/en/model_doc/layoutlmv2#transformers.LayoutLMv2FeatureExtractor.__call__">BlipImageProcessor.<strong>call</strong>()</a> for details.`,name:"pixel_values"},{anchor:"transformers.BlipForQuestionAnswering.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.BlipForQuestionAnswering.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.BlipForQuestionAnswering.forward.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/v4.30.0/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/blip/modeling_blip.py#L1124",returnDescription:`
<p>A <code>transformers.models.blip.modeling_blip.BlipTextVisionModelOutput</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.blip.configuration_blip.BlipVisionConfig'&gt;</code>) and inputs.</p>
<ul>
<li>
<p><strong>loss</strong> (<code>torch.FloatTensor</code> of shape <code>(1,)</code>, <em>optional</em>, returned when <code>labels</code> is provided) \u2014 Languge modeling loss from the text decoder.</p>
</li>
<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>) \u2014 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>) \u2014 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>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape <code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.</p>
</li>
<li>
<p><strong>attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.</p>
</li>
</ul>
`,returnType:`
<p><code>transformers.models.blip.modeling_blip.BlipTextVisionModelOutput</code> or <code>tuple(torch.FloatTensor)</code></p>
`}}),eo=new G({props:{$$slots:{default:[vy]},$$scope:{ctx:x}}}),to=new A({props:{anchor:"transformers.BlipForQuestionAnswering.forward.example",$$slots:{default:[yy]},$$scope:{ctx:x}}}),Ln=new F({}),Hn=new j({props:{name:"class transformers.TFBlipModel",anchor:"transformers.TFBlipModel",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/blip/modeling_tf_blip.py#L843"}}),Yn=new j({props:{name:"call",anchor:"transformers.TFBlipModel.call",parameters:[{name:"input_ids",val:": tf.Tensor | None = None"},{name:"pixel_values",val:": tf.Tensor | None = None"},{name:"attention_mask",val:": tf.Tensor | None = None"},{name:"position_ids",val:": 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:": Optional[bool] = None"}],parametersDescription:[{anchor:"transformers.TFBlipModel.call.input_ids",description:`<strong>input_ids</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
it.</p>
<p>Indices can be obtained using <a href="/docs/transformers/v4.30.0/en/model_doc/auto#transformers.AutoProcessor">AutoProcessor</a>. See <code>BlipProcessor.__call__()</code> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.TFBlipModel.call.attention_mask",description:`<strong>attention_mask</strong> (<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.TFBlipModel.call.position_ids",description:`<strong>position_ids</strong> (<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.TFBlipModel.call.pixel_values",description:`<strong>pixel_values</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, num_channels, height, width)</code>) &#x2014;
Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using
<a href="/docs/transformers/v4.30.0/en/model_doc/blip#transformers.BlipImageProcessor">BlipImageProcessor</a>. See <a href="/docs/transformers/v4.30.0/en/model_doc/layoutlmv2#transformers.LayoutLMv2FeatureExtractor.__call__">BlipImageProcessor.<strong>call</strong>()</a> for details.`,name:"pixel_values"},{anchor:"transformers.TFBlipModel.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.TFBlipModel.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.TFBlipModel.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.TFBlipModel.call.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/v4.30.0/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/blip/modeling_tf_blip.py#L861",returnDescription:`
<p>A <code>transformers.models.blip.modeling_tf_blip.TFBlipOutput</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.blip.configuration_blip.BlipConfig'&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>) \u2014 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>) \u2014 The scaled dot product scores between <code>image_embeds</code> and <code>text_embeds</code>. This represents the image-text
similarity scores.</li>
<li><strong>logits_per_text:(<code>tf.Tensor</code></strong> of shape <code>(text_batch_size, image_batch_size)</code>) \u2014 The scaled dot product scores between <code>text_embeds</code> and <code>image_embeds</code>. This represents the text-image
similarity scores.</li>
<li><strong>text_embeds(<code>tf.Tensor</code></strong> of shape <code>(batch_size, output_dim</code>) \u2014 The text embeddings obtained by applying the projection layer to the pooled output of <a
  href="/docs/transformers/v4.30.0/en/model_doc/blip#transformers.BlipTextModel"
>BlipTextModel</a>.</li>
<li><strong>image_embeds(<code>tf.Tensor</code></strong> of shape <code>(batch_size, output_dim</code>) \u2014 The image embeddings obtained by applying the projection layer to the pooled output of <a
  href="/docs/transformers/v4.30.0/en/model_doc/blip#transformers.BlipVisionModel"
>BlipVisionModel</a>.</li>
<li><strong>text_model_output(<code>BaseModelOutputWithPooling</code>):</strong>
The output of the <a
  href="/docs/transformers/v4.30.0/en/model_doc/blip#transformers.BlipTextModel"
>BlipTextModel</a>.</li>
<li><strong>vision_model_output(<code>BaseModelOutputWithPooling</code>):</strong>
The output of the <a
  href="/docs/transformers/v4.30.0/en/model_doc/blip#transformers.BlipVisionModel"
>BlipVisionModel</a>.</li>
</ul>
`,returnType:`
<p><code>transformers.models.blip.modeling_tf_blip.TFBlipOutput</code> or <code>tuple(tf.Tensor)</code></p>
`}}),no=new G({props:{$$slots:{default:[Ty]},$$scope:{ctx:x}}}),so=new A({props:{anchor:"transformers.TFBlipModel.call.example",$$slots:{default:[wy]},$$scope:{ctx:x}}}),Dn=new j({props:{name:"get_text_features",anchor:"transformers.TFBlipModel.get_text_features",parameters:[{name:"input_ids",val:": tf.Tensor | None = None"},{name:"attention_mask",val:": tf.Tensor | None = None"},{name:"position_ids",val:": tf.Tensor | None = None"},{name:"return_dict",val:": Optional[bool] = None"}],parametersDescription:[{anchor:"transformers.TFBlipModel.get_text_features.input_ids",description:`<strong>input_ids</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
it.</p>
<p>Indices can be obtained using <a href="/docs/transformers/v4.30.0/en/model_doc/auto#transformers.AutoProcessor">AutoProcessor</a>. See <code>BlipProcessor.__call__()</code> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.TFBlipModel.get_text_features.attention_mask",description:`<strong>attention_mask</strong> (<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.TFBlipModel.get_text_features.position_ids",description:`<strong>position_ids</strong> (<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.TFBlipModel.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.TFBlipModel.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.TFBlipModel.get_text_features.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/v4.30.0/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/blip/modeling_tf_blip.py#L913",returnDescription:`
<p>The text embeddings obtained by applying
the projection layer to the pooled output of <a
  href="/docs/transformers/v4.30.0/en/model_doc/blip#transformers.TFBlipTextModel"
>TFBlipTextModel</a>.</p>
`,returnType:`
<p>text_features (<code>tf.Tensor</code> of shape <code>(batch_size, output_dim</code>)</p>
`}}),ro=new G({props:{$$slots:{default:[My]},$$scope:{ctx:x}}}),ao=new A({props:{anchor:"transformers.TFBlipModel.get_text_features.example",$$slots:{default:[$y]},$$scope:{ctx:x}}}),On=new j({props:{name:"get_image_features",anchor:"transformers.TFBlipModel.get_image_features",parameters:[{name:"pixel_values",val:": tf.Tensor | None = None"},{name:"return_dict",val:": Optional[bool] = None"}],parametersDescription:[{anchor:"transformers.TFBlipModel.get_image_features.pixel_values",description:`<strong>pixel_values</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, num_channels, height, width)</code>) &#x2014;
Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using
<a href="/docs/transformers/v4.30.0/en/model_doc/blip#transformers.BlipImageProcessor">BlipImageProcessor</a>. See <a href="/docs/transformers/v4.30.0/en/model_doc/layoutlmv2#transformers.LayoutLMv2FeatureExtractor.__call__">BlipImageProcessor.<strong>call</strong>()</a> for details.`,name:"pixel_values"},{anchor:"transformers.TFBlipModel.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.TFBlipModel.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.TFBlipModel.get_image_features.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/v4.30.0/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/blip/modeling_tf_blip.py#L951",returnDescription:`
<p>The image embeddings obtained by applying
the projection layer to the pooled output of <a
  href="/docs/transformers/v4.30.0/en/model_doc/blip#transformers.TFBlipVisionModel"
>TFBlipVisionModel</a>.</p>
`,returnType:`
<p>image_features (<code>tf.Tensor</code> of shape <code>(batch_size, output_dim</code>)</p>
`}}),lo=new G({props:{$$slots:{default:[By]},$$scope:{ctx:x}}}),io=new A({props:{anchor:"transformers.TFBlipModel.get_image_features.example",$$slots:{default:[xy]},$$scope:{ctx:x}}}),Kn=new F({}),es=new j({props:{name:"class transformers.TFBlipTextModel",anchor:"transformers.TFBlipTextModel",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/blip/modeling_tf_blip_text.py#L579"}}),os=new j({props:{name:"call",anchor:"transformers.TFBlipTextModel.call",parameters:[{name:"input_ids",val:" = None"},{name:"attention_mask",val:" = None"},{name:"position_ids",val:" = None"},{name:"head_mask",val:" = None"},{name:"inputs_embeds",val:" = None"},{name:"encoder_embeds",val:" = None"},{name:"encoder_hidden_states",val:" = None"},{name:"encoder_attention_mask",val:" = None"},{name:"past_key_values",val:" = None"},{name:"use_cache",val:" = None"},{name:"output_attentions",val:" = None"},{name:"output_hidden_states",val:" = None"},{name:"return_dict",val:" = None"},{name:"is_decoder",val:" = False"},{name:"training",val:" = None"}],parametersDescription:[{anchor:"transformers.TFBlipTextModel.call.input_ids",description:`<strong>input_ids</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
it.</p>
<p>Indices can be obtained using <a href="/docs/transformers/v4.30.0/en/model_doc/auto#transformers.AutoProcessor">AutoProcessor</a>. See <code>BlipProcessor.__call__()</code> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.TFBlipTextModel.call.attention_mask",description:`<strong>attention_mask</strong> (<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.TFBlipTextModel.call.position_ids",description:`<strong>position_ids</strong> (<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.TFBlipTextModel.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.TFBlipTextModel.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.TFBlipTextModel.call.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/v4.30.0/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"},{anchor:"transformers.TFBlipTextModel.call.encoder_hidden_states",description:`<strong>encoder_hidden_states</strong>  (<code>tf.Tensor</code>, <em>optional</em>) &#x2014;
Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
the model is configured as a decoder.`,name:"encoder_hidden_states"},{anchor:"transformers.TFBlipTextModel.call.encoder_attention_mask",description:`<strong>encoder_attention_mask</strong> (<code>tf.Tensor</code>, <em>optional</em>) &#x2014;
Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in
the cross-attention if the model is configured as a decoder. 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>`,name:"encoder_attention_mask"},{anchor:"transformers.TFBlipTextModel.call.past_key_values",description:`<strong>past_key_values</strong> (<code>tuple(tuple(tf.Tensor))</code>, <em>optional</em>) &#x2014;
Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
If <code>past_key_values</code> are used, the user can optionally input only the last <code>decoder_input_ids</code> (those that
don&#x2019;t have their past key value states given to this model) of shape <code>(batch_size, 1)</code> instead of all
<code>decoder_input_ids</code> of shape <code>(batch_size, sequence_length)</code>.`,name:"past_key_values"},{anchor:"transformers.TFBlipTextModel.call.use_cache",description:`<strong>use_cache</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
If set to <code>True</code>, <code>past_key_values</code> key value states are returned and can be used to speed up decoding (see
<code>past_key_values</code>).`,name:"use_cache"}],source:"https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/blip/modeling_tf_blip_text.py#L667"}}),po=new G({props:{$$slots:{default:[Jy]},$$scope:{ctx:x}}}),ns=new F({}),ss=new j({props:{name:"class transformers.TFBlipVisionModel",anchor:"transformers.TFBlipVisionModel",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/blip/modeling_tf_blip.py#L639"}}),rs=new j({props:{name:"call",anchor:"transformers.TFBlipVisionModel.call",parameters:[{name:"pixel_values",val:": 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] = None"}],parametersDescription:[{anchor:"transformers.TFBlipVisionModel.call.pixel_values",description:`<strong>pixel_values</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, num_channels, height, width)</code>) &#x2014;
Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using
<a href="/docs/transformers/v4.30.0/en/model_doc/blip#transformers.BlipImageProcessor">BlipImageProcessor</a>. See <a href="/docs/transformers/v4.30.0/en/model_doc/layoutlmv2#transformers.LayoutLMv2FeatureExtractor.__call__">BlipImageProcessor.<strong>call</strong>()</a> for details.`,name:"pixel_values"},{anchor:"transformers.TFBlipVisionModel.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.TFBlipVisionModel.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.TFBlipVisionModel.call.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/v4.30.0/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/blip/modeling_tf_blip.py#L662",returnDescription:`
<p>A <a
  href="/docs/transformers/v4.30.0/en/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.blip.configuration_blip.BlipVisionConfig'&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>) \u2014 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>) \u2014 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\u2019re 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>) \u2014 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>) \u2014 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:`
<p><a
  href="/docs/transformers/v4.30.0/en/main_classes/output#transformers.modeling_tf_outputs.TFBaseModelOutputWithPooling"
>transformers.modeling_tf_outputs.TFBaseModelOutputWithPooling</a> or <code>tuple(tf.Tensor)</code></p>
`}}),ho=new G({props:{$$slots:{default:[jy]},$$scope:{ctx:x}}}),as=new F({}),ls=new j({props:{name:"class transformers.TFBlipForConditionalGeneration",anchor:"transformers.TFBlipForConditionalGeneration",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFBlipForConditionalGeneration.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.30.0/en/model_doc/blip#transformers.BlipConfig">BlipConfig</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
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<ul>
<li>
<p><strong>loss</strong> (<code>tf.Tensor</code>, <em>optional</em>, returned when <code>labels</code> is provided, <code>tf.Tensor</code> of shape <code>(1,)</code>) \u2014 Languge modeling loss from the text decoder.</p>
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<li>
<p><strong>decoder_logits</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, sequence_length, config.vocab_size)</code>, <em>optional</em>) \u2014 Prediction scores of the language modeling head of the text decoder model.</p>
</li>
<li>
<p><strong>image_embeds</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, output_dim)</code>, <em>optional</em>) \u2014 The image embeddings obtained after applying the Vision Transformer model to the input image.</p>
</li>
<li>
<p><strong>last_hidden_state</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>, <em>optional</em>) \u2014 Sequence of hidden-states at the output of the last layer of the model.</p>
</li>
<li>
<p><strong>hidden_states</strong> (<code>tuple(tf.Tensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code>) \u2014 Tuple of <code>tf.Tensor</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(tf.Tensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed) \u2014 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>
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<ul>
<li>
<p><strong>itm_score</strong> (<code>tf.Tensor</code>) \u2014 The image-text similarity scores.</p>
</li>
<li>
<p><strong>loss</strong> (<code>tf.Tensor</code> of shape <code>(1,)</code>, <em>optional</em>, returned when <code>labels</code> is provided) \u2014 Languge modeling loss from the text decoder.</p>
</li>
<li>
<p><strong>image_embeds</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, output_dim)</code> <em>optional</em> returned when model is initialized with <code>with_projection=True</code>) \u2014 The image embeddings obtained by applying the projection layer to the pooler_output.</p>
</li>
<li>
<p><strong>last_hidden_state</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>) \u2014 Sequence of hidden-states at the output of the last layer of the model.</p>
</li>
<li>
<p><strong>hidden_states</strong> (<code>tuple(tf.Tensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) \u2014 Tuple of <code>tf.Tensor</code> (one for the output of the embeddings, 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>vision_pooler_output</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, hidden_size)</code>, <em>optional</em>) \u2014 Last layer hidden-state of the vision of the vision-only branch of the model.</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>) \u2014 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>
<li>
<p><strong>question_embeds</strong> (<code>tf.Tensor</code>) \u2014 The question embeddings obtained by the text projection layer.</p>
</li>
</ul>
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<ul>
<li>
<p><strong>loss</strong> (<code>tf.Tensor</code> of shape <code>(1,)</code>, <em>optional</em>, returned when <code>labels</code> is provided) \u2014 Languge modeling loss from the text decoder.</p>
</li>
<li>
<p><strong>image_embeds</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, output_dim)</code> <em>optional</em> returned when model is initialized with <code>with_projection=True</code>) \u2014 The image embeddings obtained by applying the projection layer to the pooler_output.</p>
</li>
<li>
<p><strong>last_hidden_state</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>) \u2014 Sequence of hidden-states at the output of the last layer of the model.</p>
</li>
<li>
<p><strong>hidden_states</strong> (<code>tuple(tf.Tensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) \u2014 Tuple of <code>tf.Tensor</code> (one for the output of the embeddings, 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(tf.Tensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) \u2014 Tuple of <code>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:`
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`),Qo=l(Is,"A",{href:!0,rel:!0});var h_=i(Qo);hc=r(h_,"Salesforce/blip-vqa-base"),h_.forEach(o),fc=r(Is," architecture."),Is.forEach(o),uc=f($e),Ne=l($e,"P",{});var $r=i(Ne);gc=r($r,"Configuration objects inherit from "),Vs=l($r,"A",{href:!0});var f_=i(Vs);_c=r(f_,"PretrainedConfig"),f_.forEach(o),bc=r($r,` and can be used to control the model outputs. Read the
documentation from `),Rs=l($r,"A",{href:!0});var u_=i(Rs);vc=r(u_,"PretrainedConfig"),u_.forEach(o),yc=r($r," for more information."),$r.forEach(o),Tc=f($e),T(xt.$$.fragment,$e),wc=f($e),Jt=l($e,"DIV",{class:!0});var Li=i(Jt);T(Lo.$$.fragment,Li),Mc=f(Li),Ho=l(Li,"P",{});var Hi=i(Ho);$c=r(Hi,"Instantiate a "),Gs=l(Hi,"A",{href:!0});var g_=i(Gs);Bc=r(g_,"BlipConfig"),g_.forEach(o),xc=r(Hi,` (or a derived class) from blip text model configuration and blip vision model
configuration.`),Hi.forEach(o),Li.forEach(o),$e.forEach(o),ui=f(t),Pe=l(t,"H2",{class:!0});var Yi=i(Pe);jt=l(Yi,"A",{id:!0,class:!0,href:!0});var __=i(jt);aa=l(__,"SPAN",{});var b_=i(aa);T(Yo.$$.fragment,b_),b_.forEach(o),__.forEach(o),Jc=f(Yi),la=l(Yi,"SPAN",{});var v_=i(la);jc=r(v_,"BlipTextConfig"),v_.forEach(o),Yi.forEach(o),gi=f(t),Q=l(t,"DIV",{class:!0});var Mo=i(Q);T(Do.$$.fragment,Mo),Ec=f(Mo),me=l(Mo,"P",{});var $o=i(me);Ic=r($o,"This is the configuration class to store the configuration of a "),As=l($o,"A",{href:!0});var y_=i(As);Fc=r(y_,"BlipTextModel"),y_.forEach(o),kc=r($o,`. It is used to instantiate a BLIP
text model according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults will yield a similar configuration to that of the `),ia=l($o,"CODE",{});var T_=i(ia);Uc=r(T_,"BlipText"),T_.forEach(o),Cc=r($o," used by the "),Oo=l($o,"A",{href:!0,rel:!0});var w_=i(Oo);zc=r(w_,`base
architectures`),w_.forEach(o),Wc=r($o,"."),$o.forEach(o),Nc=f(Mo),Ze=l(Mo,"P",{});var Br=i(Ze);Pc=r(Br,"Configuration objects inherit from "),Ss=l(Br,"A",{href:!0});var M_=i(Ss);Zc=r(M_,"PretrainedConfig"),M_.forEach(o),Vc=r(Br,` and can be used to control the model outputs. Read the
documentation from `),qs=l(Br,"A",{href:!0});var $_=i(qs);Rc=r($_,"PretrainedConfig"),$_.forEach(o),Gc=r(Br," for more information."),Br.forEach(o),Ac=f(Mo),T(Et.$$.fragment,Mo),Mo.forEach(o),_i=f(t),Ve=l(t,"H2",{class:!0});var Di=i(Ve);It=l(Di,"A",{id:!0,class:!0,href:!0});var B_=i(It);da=l(B_,"SPAN",{});var x_=i(da);T(Ko.$$.fragment,x_),x_.forEach(o),B_.forEach(o),Sc=f(Di),ca=l(Di,"SPAN",{});var J_=i(ca);qc=r(J_,"BlipVisionConfig"),J_.forEach(o),Di.forEach(o),bi=f(t),L=l(t,"DIV",{class:!0});var Bo=i(L);T(en.$$.fragment,Bo),Xc=f(Bo),Re=l(Bo,"P",{});var xr=i(Re);Qc=r(xr,"This is the configuration class to store the configuration of a "),Xs=l(xr,"A",{href:!0});var j_=i(Xs);Lc=r(j_,"BlipVisionModel"),j_.forEach(o),Hc=r(xr,`. It is used to instantiate a
BLIP vision model according to the specified arguments, defining the model architecture. Instantiating a
configuration defaults will yield a similar configuration to that of the Blip-base
`),tn=l(xr,"A",{href:!0,rel:!0});var E_=i(tn);Yc=r(E_,"Salesforce/blip-vqa-base"),E_.forEach(o),Dc=r(xr," architecture."),xr.forEach(o),Oc=f(Bo),Ge=l(Bo,"P",{});var Jr=i(Ge);Kc=r(Jr,"Configuration objects inherit from "),Qs=l(Jr,"A",{href:!0});var I_=i(Qs);ep=r(I_,"PretrainedConfig"),I_.forEach(o),tp=r(Jr,` and can be used to control the model outputs. Read the
documentation from `),Ls=l(Jr,"A",{href:!0});var F_=i(Ls);op=r(F_,"PretrainedConfig"),F_.forEach(o),np=r(Jr," for more information."),Jr.forEach(o),sp=f(Bo),T(Ft.$$.fragment,Bo),Bo.forEach(o),vi=f(t),Ae=l(t,"H2",{class:!0});var Oi=i(Ae);kt=l(Oi,"A",{id:!0,class:!0,href:!0});var k_=i(kt);pa=l(k_,"SPAN",{});var U_=i(pa);T(on.$$.fragment,U_),U_.forEach(o),k_.forEach(o),rp=f(Oi),ma=l(Oi,"SPAN",{});var C_=i(ma);ap=r(C_,"BlipProcessor"),C_.forEach(o),Oi.forEach(o),yi=f(t),C=l(t,"DIV",{class:!0});var Be=i(C);T(nn.$$.fragment,Be),lp=f(Be),ha=l(Be,"P",{});var z_=i(ha);ip=r(z_,"Constructs a BLIP processor which wraps a BERT tokenizer and BLIP image processor into a single processor."),z_.forEach(o),dp=f(Be),X=l(Be,"P",{});var ge=i(X);Hs=l(ge,"A",{href:!0});var W_=i(Hs);cp=r(W_,"BlipProcessor"),W_.forEach(o),pp=r(ge," offers all the functionalities of "),Ys=l(ge,"A",{href:!0});var N_=i(Ys);mp=r(N_,"BlipImageProcessor"),N_.forEach(o),hp=r(ge," and "),Ds=l(ge,"A",{href:!0});var P_=i(Ds);fp=r(P_,"BertTokenizerFast"),P_.forEach(o),up=r(ge,`. See the
docstring of `),fa=l(ge,"CODE",{});var Z_=i(fa);gp=r(Z_,"__call__()"),Z_.forEach(o),_p=r(ge," and "),Os=l(ge,"A",{href:!0});var V_=i(Os);bp=r(V_,"decode()"),V_.forEach(o),vp=r(ge," for more information."),ge.forEach(o),yp=f(Be),Ut=l(Be,"DIV",{class:!0});var Ki=i(Ut);T(sn.$$.fragment,Ki),Tp=f(Ki),rn=l(Ki,"P",{});var ed=i(rn);wp=r(ed,"This method forwards all its arguments to BertTokenizerFast\u2019s "),Ks=l(ed,"A",{href:!0});var R_=i(Ks);Mp=r(R_,"batch_decode()"),R_.forEach(o),$p=r(ed,`. Please
refer to the docstring of this method for more information.`),ed.forEach(o),Ki.forEach(o),Bp=f(Be),Ct=l(Be,"DIV",{class:!0});var td=i(Ct);T(an.$$.fragment,td),xp=f(td),ln=l(td,"P",{});var od=i(ln);Jp=r(od,"This method forwards all its arguments to BertTokenizerFast\u2019s "),er=l(od,"A",{href:!0});var G_=i(er);jp=r(G_,"decode()"),G_.forEach(o),Ep=r(od,`. Please refer to
the docstring of this method for more information.`),od.forEach(o),td.forEach(o),Be.forEach(o),Ti=f(t),Se=l(t,"H2",{class:!0});var nd=i(Se);zt=l(nd,"A",{id:!0,class:!0,href:!0});var A_=i(zt);ua=l(A_,"SPAN",{});var S_=i(ua);T(dn.$$.fragment,S_),S_.forEach(o),A_.forEach(o),Ip=f(nd),ga=l(nd,"SPAN",{});var q_=i(ga);Fp=r(q_,"BlipImageProcessor"),q_.forEach(o),nd.forEach(o),wi=f(t),he=l(t,"DIV",{class:!0});var jr=i(he);T(cn.$$.fragment,jr),kp=f(jr),_a=l(jr,"P",{});var X_=i(_a);Up=r(X_,"Constructs a BLIP image processor."),X_.forEach(o),Cp=f(jr),Wt=l(jr,"DIV",{class:!0});var sd=i(Wt);T(pn.$$.fragment,sd),zp=f(sd),ba=l(sd,"P",{});var Q_=i(ba);Wp=r(Q_,"Preprocess an image or batch of images."),Q_.forEach(o),sd.forEach(o),jr.forEach(o),Mi=f(t),qe=l(t,"H2",{class:!0});var rd=i(qe);Nt=l(rd,"A",{id:!0,class:!0,href:!0});var L_=i(Nt);va=l(L_,"SPAN",{});var H_=i(va);T(mn.$$.fragment,H_),H_.forEach(o),L_.forEach(o),Np=f(rd),ya=l(rd,"SPAN",{});var Y_=i(ya);Pp=r(Y_,"BlipModel"),Y_.forEach(o),rd.forEach(o),$i=f(t),k=l(t,"DIV",{class:!0});var ce=i(k);T(hn.$$.fragment,ce),Zp=f(ce),fn=l(ce,"P",{});var ad=i(fn);Vp=r(ad,"This model inherits from "),tr=l(ad,"A",{href:!0});var D_=i(tr);Rp=r(D_,"PreTrainedModel"),D_.forEach(o),Gp=r(ad,`. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)`),ad.forEach(o),Ap=f(ce),un=l(ce,"P",{});var ld=i(un);Sp=r(ld,"This model is also a PyTorch "),gn=l(ld,"A",{href:!0,rel:!0});var O_=i(gn);qp=r(O_,"torch.nn.Module"),O_.forEach(o),Xp=r(ld,` subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.`),ld.forEach(o),Qp=f(ce),O=l(ce,"DIV",{class:!0});var xo=i(O);T(_n.$$.fragment,xo),Lp=f(xo),Xe=l(xo,"P",{});var Er=i(Xe);Hp=r(Er,"The "),or=l(Er,"A",{href:!0});var K_=i(or);Yp=r(K_,"BlipModel"),K_.forEach(o),Dp=r(Er," forward method, overrides the "),Ta=l(Er,"CODE",{});var eb=i(Ta);Op=r(eb,"__call__"),eb.forEach(o),Kp=r(Er," special method."),Er.forEach(o),em=f(xo),T(Pt.$$.fragment,xo),tm=f(xo),T(Zt.$$.fragment,xo),xo.forEach(o),om=f(ce),K=l(ce,"DIV",{class:!0});var Jo=i(K);T(bn.$$.fragment,Jo),nm=f(Jo),Qe=l(Jo,"P",{});var Ir=i(Qe);sm=r(Ir,"The "),nr=l(Ir,"A",{href:!0});var tb=i(nr);rm=r(tb,"BlipModel"),tb.forEach(o),am=r(Ir," forward method, overrides the "),wa=l(Ir,"CODE",{});var ob=i(wa);lm=r(ob,"__call__"),ob.forEach(o),im=r(Ir," special method."),Ir.forEach(o),dm=f(Jo),T(Vt.$$.fragment,Jo),cm=f(Jo),T(Rt.$$.fragment,Jo),Jo.forEach(o),pm=f(ce),ee=l(ce,"DIV",{class:!0});var jo=i(ee);T(vn.$$.fragment,jo),mm=f(jo),Le=l(jo,"P",{});var Fr=i(Le);hm=r(Fr,"The "),sr=l(Fr,"A",{href:!0});var nb=i(sr);fm=r(nb,"BlipModel"),nb.forEach(o),um=r(Fr," forward method, overrides the "),Ma=l(Fr,"CODE",{});var sb=i(Ma);gm=r(sb,"__call__"),sb.forEach(o),_m=r(Fr," special method."),Fr.forEach(o),bm=f(jo),T(Gt.$$.fragment,jo),vm=f(jo),T(At.$$.fragment,jo),jo.forEach(o),ce.forEach(o),Bi=f(t),He=l(t,"H2",{class:!0});var id=i(He);St=l(id,"A",{id:!0,class:!0,href:!0});var rb=i(St);$a=l(rb,"SPAN",{});var ab=i($a);T(yn.$$.fragment,ab),ab.forEach(o),rb.forEach(o),ym=f(id),Ba=l(id,"SPAN",{});var lb=i(Ba);Tm=r(lb,"BlipTextModel"),lb.forEach(o),id.forEach(o),xi=f(t),fe=l(t,"DIV",{class:!0});var kr=i(fe);T(Tn.$$.fragment,kr),wm=f(kr),H=l(kr,"P",{});var xe=i(H);Mm=r(xe,`The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of
cross-attention is added between the self-attention layers, following the architecture described in `),wn=l(xe,"A",{href:!0,rel:!0});var ib=i(wn);$m=r(ib,`Attention is
all you need`),ib.forEach(o),Bm=r(xe,` by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit,
Llion Jones, Aidan N. Gomez, Lukasz Kaiser and Illia Polosukhin. argument and `),xa=l(xe,"CODE",{});var db=i(xa);xm=r(db,"is_decoder"),db.forEach(o),Jm=r(xe," set to "),Ja=l(xe,"CODE",{});var cb=i(Ja);jm=r(cb,"True"),cb.forEach(o),Em=r(xe,`; an
`),ja=l(xe,"CODE",{});var pb=i(ja);Im=r(pb,"encoder_hidden_states"),pb.forEach(o),Fm=r(xe," is then expected as an input to the forward pass."),xe.forEach(o),km=f(kr),ye=l(kr,"DIV",{class:!0});var Ur=i(ye);T(Mn.$$.fragment,Ur),Um=f(Ur),z=l(Ur,"P",{});var pe=i(z);Cm=r(pe,"encoder_hidden_states  ("),Ea=l(pe,"CODE",{});var mb=i(Ea);zm=r(mb,"torch.FloatTensor"),mb.forEach(o),Wm=r(pe,", "),Ia=l(pe,"EM",{});var hb=i(Ia);Nm=r(hb,"optional"),hb.forEach(o),Pm=r(pe,`):
Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
the model is configured as a decoder.
encoder_attention_mask (`),Fa=l(pe,"CODE",{});var fb=i(Fa);Zm=r(fb,"torch.FloatTensor"),fb.forEach(o),Vm=r(pe,", "),ka=l(pe,"EM",{});var ub=i(ka);Rm=r(ub,"optional"),ub.forEach(o),Gm=r(pe,`):
Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in
the cross-attention if the model is configured as a decoder. Mask values selected in `),Ua=l(pe,"CODE",{});var gb=i(Ua);Am=r(gb,"[0, 1]"),gb.forEach(o),Sm=r(pe,":"),pe.forEach(o),qm=f(Ur),$n=l(Ur,"UL",{});var dd=i($n);Bn=l(dd,"LI",{});var cd=i(Bn);Xm=r(cd,"1 for tokens that are "),Ca=l(cd,"STRONG",{});var _b=i(Ca);Qm=r(_b,"not masked"),_b.forEach(o),Lm=r(cd,","),cd.forEach(o),Hm=f(dd),E=l(dd,"LI",{});var I=i(E);Ym=r(I,"0 for tokens that are "),za=l(I,"STRONG",{});var bb=i(za);Dm=r(bb,"masked"),bb.forEach(o),Om=r(I,`.
past_key_values (`),Wa=l(I,"CODE",{});var vb=i(Wa);Km=r(vb,"tuple(tuple(torch.FloatTensor))"),vb.forEach(o),eh=r(I,", "),Na=l(I,"EM",{});var yb=i(Na);th=r(yb,"optional"),yb.forEach(o),oh=r(I,`):
Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
If `),Pa=l(I,"CODE",{});var Tb=i(Pa);nh=r(Tb,"past_key_values"),Tb.forEach(o),sh=r(I," are used, the user can optionally input only the last "),Za=l(I,"CODE",{});var wb=i(Za);rh=r(wb,"decoder_input_ids"),wb.forEach(o),ah=r(I,` (those that
don\u2019t have their past key value states given to this model) of shape `),Va=l(I,"CODE",{});var Mb=i(Va);lh=r(Mb,"(batch_size, 1)"),Mb.forEach(o),ih=r(I,` instead of all
`),Ra=l(I,"CODE",{});var $b=i(Ra);dh=r($b,"decoder_input_ids"),$b.forEach(o),ch=r(I," of shape "),Ga=l(I,"CODE",{});var Bb=i(Ga);ph=r(Bb,"(batch_size, sequence_length)"),Bb.forEach(o),mh=r(I,`.
use_cache (`),Aa=l(I,"CODE",{});var xb=i(Aa);hh=r(xb,"bool"),xb.forEach(o),fh=r(I,", "),Sa=l(I,"EM",{});var Jb=i(Sa);uh=r(Jb,"optional"),Jb.forEach(o),gh=r(I,`):
If set to `),qa=l(I,"CODE",{});var jb=i(qa);_h=r(jb,"True"),jb.forEach(o),bh=r(I,", "),Xa=l(I,"CODE",{});var Eb=i(Xa);vh=r(Eb,"past_key_values"),Eb.forEach(o),yh=r(I,` key value states are returned and can be used to speed up decoding (see
`),Qa=l(I,"CODE",{});var Ib=i(Qa);Th=r(Ib,"past_key_values"),Ib.forEach(o),wh=r(I,")."),I.forEach(o),dd.forEach(o),Ur.forEach(o),kr.forEach(o),Ji=f(t),Ye=l(t,"H2",{class:!0});var pd=i(Ye);qt=l(pd,"A",{id:!0,class:!0,href:!0});var Fb=i(qt);La=l(Fb,"SPAN",{});var kb=i(La);T(xn.$$.fragment,kb),kb.forEach(o),Fb.forEach(o),Mh=f(pd),Ha=l(pd,"SPAN",{});var Ub=i(Ha);$h=r(Ub,"BlipVisionModel"),Ub.forEach(o),pd.forEach(o),ji=f(t),De=l(t,"DIV",{class:!0});var md=i(De);T(Jn.$$.fragment,md),Bh=f(md),Te=l(md,"DIV",{class:!0});var Cr=i(Te);T(jn.$$.fragment,Cr),xh=f(Cr),Oe=l(Cr,"P",{});var zr=i(Oe);Jh=r(zr,"The "),rr=l(zr,"A",{href:!0});var Cb=i(rr);jh=r(Cb,"BlipVisionModel"),Cb.forEach(o),Eh=r(zr," forward method, overrides the "),Ya=l(zr,"CODE",{});var zb=i(Ya);Ih=r(zb,"__call__"),zb.forEach(o),Fh=r(zr," special method."),zr.forEach(o),kh=f(Cr),T(Xt.$$.fragment,Cr),Cr.forEach(o),md.forEach(o),Ei=f(t),Ke=l(t,"H2",{class:!0});var hd=i(Ke);Qt=l(hd,"A",{id:!0,class:!0,href:!0});var Wb=i(Qt);Da=l(Wb,"SPAN",{});var Nb=i(Da);T(En.$$.fragment,Nb),Nb.forEach(o),Wb.forEach(o),Uh=f(hd),Oa=l(hd,"SPAN",{});var Pb=i(Oa);Ch=r(Pb,"BlipForConditionalGeneration"),Pb.forEach(o),hd.forEach(o),Ii=f(t),W=l(t,"DIV",{class:!0});var Je=i(W);T(In.$$.fragment,Je),zh=f(Je),Fn=l(Je,"P",{});var fd=i(Fn);Wh=r(fd,`BLIP Model for image captioning. The model consists of a vision encoder and a text decoder. One can optionally pass
`),Ka=l(fd,"CODE",{});var Zb=i(Ka);Nh=r(Zb,"input_ids"),Zb.forEach(o),Ph=r(fd,` to the model, which serve as a text prompt, to make the text decoder continue the prompt. Otherwise,
the decoder starts generating text from the [BOS] (beginning-of-sequence) token. will start generating the caption
from the text input. If no text input is provided, the decoder will start with the [BOS] token only.`),fd.forEach(o),Zh=f(Je),kn=l(Je,"P",{});var ud=i(kn);Vh=r(ud,"This model inherits from "),ar=l(ud,"A",{href:!0});var Vb=i(ar);Rh=r(Vb,"PreTrainedModel"),Vb.forEach(o),Gh=r(ud,`. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)`),ud.forEach(o),Ah=f(Je),Un=l(Je,"P",{});var gd=i(Un);Sh=r(gd,"This model is also a PyTorch "),Cn=l(gd,"A",{href:!0,rel:!0});var Rb=i(Cn);qh=r(Rb,"torch.nn.Module"),Rb.forEach(o),Xh=r(gd,` subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.`),gd.forEach(o),Qh=f(Je),te=l(Je,"DIV",{class:!0});var Eo=i(te);T(zn.$$.fragment,Eo),Lh=f(Eo),et=l(Eo,"P",{});var Wr=i(et);Hh=r(Wr,"The "),lr=l(Wr,"A",{href:!0});var Gb=i(lr);Yh=r(Gb,"BlipForConditionalGeneration"),Gb.forEach(o),Dh=r(Wr," forward method, overrides the "),el=l(Wr,"CODE",{});var Ab=i(el);Oh=r(Ab,"__call__"),Ab.forEach(o),Kh=r(Wr," special method."),Wr.forEach(o),ef=f(Eo),T(Lt.$$.fragment,Eo),tf=f(Eo),T(Ht.$$.fragment,Eo),Eo.forEach(o),Je.forEach(o),Fi=f(t),tt=l(t,"H2",{class:!0});var _d=i(tt);Yt=l(_d,"A",{id:!0,class:!0,href:!0});var Sb=i(Yt);tl=l(Sb,"SPAN",{});var qb=i(tl);T(Wn.$$.fragment,qb),qb.forEach(o),Sb.forEach(o),of=f(_d),ol=l(_d,"SPAN",{});var Xb=i(ol);nf=r(Xb,"BlipForImageTextRetrieval"),Xb.forEach(o),_d.forEach(o),ki=f(t),N=l(t,"DIV",{class:!0});var je=i(N);T(Nn.$$.fragment,je),sf=f(je),nl=l(je,"P",{});var Qb=i(nl);rf=r(Qb,`BLIP Model with a vision and text projector, and a classification head on top. The model is used in the context of
image-text retrieval. Given an image and a text, the model returns the probability of the text being relevant to
the image.`),Qb.forEach(o),af=f(je),Pn=l(je,"P",{});var bd=i(Pn);lf=r(bd,"This model inherits from "),ir=l(bd,"A",{href:!0});var Lb=i(ir);df=r(Lb,"PreTrainedModel"),Lb.forEach(o),cf=r(bd,`. 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.)`),bd.forEach(o),pf=f(je),Zn=l(je,"P",{});var vd=i(Zn);mf=r(vd,"This model is also a PyTorch "),Vn=l(vd,"A",{href:!0,rel:!0});var Hb=i(Vn);hf=r(Hb,"torch.nn.Module"),Hb.forEach(o),ff=r(vd,` subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.`),vd.forEach(o),uf=f(je),oe=l(je,"DIV",{class:!0});var Io=i(oe);T(Rn.$$.fragment,Io),gf=f(Io),ot=l(Io,"P",{});var Nr=i(ot);_f=r(Nr,"The "),dr=l(Nr,"A",{href:!0});var Yb=i(dr);bf=r(Yb,"BlipForImageTextRetrieval"),Yb.forEach(o),vf=r(Nr," forward method, overrides the "),sl=l(Nr,"CODE",{});var Db=i(sl);yf=r(Db,"__call__"),Db.forEach(o),Tf=r(Nr," special method."),Nr.forEach(o),wf=f(Io),T(Dt.$$.fragment,Io),Mf=f(Io),T(Ot.$$.fragment,Io),Io.forEach(o),je.forEach(o),Ui=f(t),nt=l(t,"H2",{class:!0});var yd=i(nt);Kt=l(yd,"A",{id:!0,class:!0,href:!0});var Ob=i(Kt);rl=l(Ob,"SPAN",{});var Kb=i(rl);T(Gn.$$.fragment,Kb),Kb.forEach(o),Ob.forEach(o),$f=f(yd),al=l(yd,"SPAN",{});var ev=i(al);Bf=r(ev,"BlipForQuestionAnswering"),ev.forEach(o),yd.forEach(o),Ci=f(t),P=l(t,"DIV",{class:!0});var Ee=i(P);T(An.$$.fragment,Ee),xf=f(Ee),ll=l(Ee,"P",{});var tv=i(ll);Jf=r(tv,`BLIP Model for visual question answering. The model consists of a vision encoder, a text encoder as well as a text
decoder. The vision encoder will encode the input image, the text encoder will encode the input question together
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