import{S as wm,i as Tm,s as Bm,e as a,k as m,w as y,t as n,M as $m,c as s,d as o,m as f,a as l,x as M,h as r,b as c,N as km,G as e,g as _,y as w,q as T,o as B,B as $,v as jm,L as Ee}from"../../chunks/vendor-hf-doc-builder.js";import{T as hn}from"../../chunks/Tip-hf-doc-builder.js";import{D as Z}from"../../chunks/Docstring-hf-doc-builder.js";import{C as Ce}from"../../chunks/CodeBlock-hf-doc-builder.js";import{I as O}from"../../chunks/IconCopyLink-hf-doc-builder.js";import{E as Je}from"../../chunks/ExampleCodeBlock-hf-doc-builder.js";function Jm(j){let d,v,g,h,b;return h=new Ce({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> (
<span class="hljs-meta">... </span>    Blip2VisionConfig,
<span class="hljs-meta">... </span>    Blip2QFormerConfig,
<span class="hljs-meta">... </span>    OPTConfig,
<span class="hljs-meta">... </span>    Blip2Config,
<span class="hljs-meta">... </span>    Blip2ForConditionalGeneration,
<span class="hljs-meta">... </span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Initializing a Blip2Config with Salesforce/blip2-opt-2.7b style configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>configuration = Blip2Config()

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Initializing a Blip2ForConditionalGeneration (with random weights) from the Salesforce/blip2-opt-2.7b style configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = Blip2ForConditionalGeneration(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 Blip2Config from a Blip2VisionConfig, Blip2QFormerConfig and any PretrainedConfig</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Initializing BLIP-2 vision, BLIP-2 Q-Former and language model configurations</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>vision_config = Blip2VisionConfig()
<span class="hljs-meta">&gt;&gt;&gt; </span>qformer_config = Blip2QFormerConfig()
<span class="hljs-meta">&gt;&gt;&gt; </span>text_config = OPTConfig()

<span class="hljs-meta">&gt;&gt;&gt; </span>config = Blip2Config.from_text_vision_configs(vision_config, qformer_config, text_config)`}}),{c(){d=a("p"),v=n("Example:"),g=m(),y(h.$$.fragment)},l(i){d=s(i,"P",{});var p=l(d);v=r(p,"Example:"),p.forEach(o),g=f(i),M(h.$$.fragment,i)},m(i,p){_(i,d,p),e(d,v),_(i,g,p),w(h,i,p),b=!0},p:Ee,i(i){b||(T(h.$$.fragment,i),b=!0)},o(i){B(h.$$.fragment,i),b=!1},d(i){i&&o(d),i&&o(g),$(h,i)}}}function Em(j){let d,v,g,h,b;return h=new Ce({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> Blip2VisionConfig, Blip2VisionModel

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Initializing a Blip2VisionConfig with Salesforce/blip2-opt-2.7b style configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>configuration = Blip2VisionConfig()

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Initializing a Blip2VisionModel (with random weights) from the Salesforce/blip2-opt-2.7b style configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = Blip2VisionModel(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=n("Example:"),g=m(),y(h.$$.fragment)},l(i){d=s(i,"P",{});var p=l(d);v=r(p,"Example:"),p.forEach(o),g=f(i),M(h.$$.fragment,i)},m(i,p){_(i,d,p),e(d,v),_(i,g,p),w(h,i,p),b=!0},p:Ee,i(i){b||(T(h.$$.fragment,i),b=!0)},o(i){B(h.$$.fragment,i),b=!1},d(i){i&&o(d),i&&o(g),$(h,i)}}}function Cm(j){let d,v,g,h,b;return h=new Ce({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> Blip2QFormerConfig, Blip2QFormerModel

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Initializing a BLIP-2 Salesforce/blip2-opt-2.7b style configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>configuration = Blip2QFormerConfig()

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Initializing a model (with random weights) from the Salesforce/blip2-opt-2.7b style configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = Blip2QFormerModel(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=n("Examples:"),g=m(),y(h.$$.fragment)},l(i){d=s(i,"P",{});var p=l(d);v=r(p,"Examples:"),p.forEach(o),g=f(i),M(h.$$.fragment,i)},m(i,p){_(i,d,p),e(d,v),_(i,g,p),w(h,i,p),b=!0},p:Ee,i(i){b||(T(h.$$.fragment,i),b=!0)},o(i){B(h.$$.fragment,i),b=!1},d(i){i&&o(d),i&&o(g),$(h,i)}}}function Um(j){let d,v,g,h,b;return{c(){d=a("p"),v=n("Although the recipe for forward pass needs to be defined within this function, one should call the "),g=a("code"),h=n("Module"),b=n(`
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(i){d=s(i,"P",{});var p=l(d);v=r(p,"Although the recipe for forward pass needs to be defined within this function, one should call the "),g=s(p,"CODE",{});var E=l(g);h=r(E,"Module"),E.forEach(o),b=r(p,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),p.forEach(o)},m(i,p){_(i,d,p),e(d,v),e(d,g),e(g,h),e(d,b)},d(i){i&&o(d)}}}function Im(j){let d,v,g,h,b;return{c(){d=a("p"),v=n("Although the recipe for forward pass needs to be defined within this function, one should call the "),g=a("code"),h=n("Module"),b=n(`
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(i){d=s(i,"P",{});var p=l(d);v=r(p,"Although the recipe for forward pass needs to be defined within this function, one should call the "),g=s(p,"CODE",{});var E=l(g);h=r(E,"Module"),E.forEach(o),b=r(p,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),p.forEach(o)},m(i,p){_(i,d,p),e(d,v),e(d,g),e(g,h),e(d,b)},d(i){i&&o(d)}}}function Zm(j){let d,v,g,h,b;return h=new Ce({props:{code:"ZnJvbSUyMFBJTCUyMGltcG9ydCUyMEltYWdlJTBBaW1wb3J0JTIwcmVxdWVzdHMlMEFmcm9tJTIwdHJhbnNmb3JtZXJzJTIwaW1wb3J0JTIwQmxpcDJQcm9jZXNzb3IlMkMlMjBCbGlwMk1vZGVsJTBBaW1wb3J0JTIwdG9yY2glMEElMEFkZXZpY2UlMjAlM0QlMjAlMjJjdWRhJTIyJTIwaWYlMjB0b3JjaC5jdWRhLmlzX2F2YWlsYWJsZSgpJTIwZWxzZSUyMCUyMmNwdSUyMiUwQSUwQXByb2Nlc3NvciUyMCUzRCUyMEJsaXAyUHJvY2Vzc29yLmZyb21fcHJldHJhaW5lZCglMjJTYWxlc2ZvcmNlJTJGYmxpcDItb3B0LTIuN2IlMjIpJTBBbW9kZWwlMjAlM0QlMjBCbGlwMk1vZGVsLmZyb21fcHJldHJhaW5lZCglMjJTYWxlc2ZvcmNlJTJGYmxpcDItb3B0LTIuN2IlMjIlMkMlMjB0b3JjaF9kdHlwZSUzRHRvcmNoLmZsb2F0MTYpJTBBbW9kZWwudG8oZGV2aWNlKSUwQXVybCUyMCUzRCUyMCUyMmh0dHAlM0ElMkYlMkZpbWFnZXMuY29jb2RhdGFzZXQub3JnJTJGdmFsMjAxNyUyRjAwMDAwMDAzOTc2OS5qcGclMjIlMEFpbWFnZSUyMCUzRCUyMEltYWdlLm9wZW4ocmVxdWVzdHMuZ2V0KHVybCUyQyUyMHN0cmVhbSUzRFRydWUpLnJhdyklMEElMEFwcm9tcHQlMjAlM0QlMjAlMjJRdWVzdGlvbiUzQSUyMGhvdyUyMG1hbnklMjBjYXRzJTIwYXJlJTIwdGhlcmUlM0YlMjBBbnN3ZXIlM0ElMjIlMEFpbnB1dHMlMjAlM0QlMjBwcm9jZXNzb3IoaW1hZ2VzJTNEaW1hZ2UlMkMlMjB0ZXh0JTNEcHJvbXB0JTJDJTIwcmV0dXJuX3RlbnNvcnMlM0QlMjJwdCUyMikudG8oZGV2aWNlJTJDJTIwdG9yY2guZmxvYXQxNiklMEElMEFvdXRwdXRzJTIwJTNEJTIwbW9kZWwoKippbnB1dHMp",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> Blip2Processor, Blip2Model
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch

<span class="hljs-meta">&gt;&gt;&gt; </span>device = <span class="hljs-string">&quot;cuda&quot;</span> <span class="hljs-keyword">if</span> torch.cuda.is_available() <span class="hljs-keyword">else</span> <span class="hljs-string">&quot;cpu&quot;</span>

<span class="hljs-meta">&gt;&gt;&gt; </span>processor = Blip2Processor.from_pretrained(<span class="hljs-string">&quot;Salesforce/blip2-opt-2.7b&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = Blip2Model.from_pretrained(<span class="hljs-string">&quot;Salesforce/blip2-opt-2.7b&quot;</span>, torch_dtype=torch.float16)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.to(device)
<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>prompt = <span class="hljs-string">&quot;Question: how many cats are there? Answer:&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = processor(images=image, text=prompt, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>).to(device, torch.float16)

<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(**inputs)`}}),{c(){d=a("p"),v=n("Examples:"),g=m(),y(h.$$.fragment)},l(i){d=s(i,"P",{});var p=l(d);v=r(p,"Examples:"),p.forEach(o),g=f(i),M(h.$$.fragment,i)},m(i,p){_(i,d,p),e(d,v),_(i,g,p),w(h,i,p),b=!0},p:Ee,i(i){b||(T(h.$$.fragment,i),b=!0)},o(i){B(h.$$.fragment,i),b=!1},d(i){i&&o(d),i&&o(g),$(h,i)}}}function xm(j){let d,v,g,h,b;return{c(){d=a("p"),v=n("Although the recipe for forward pass needs to be defined within this function, one should call the "),g=a("code"),h=n("Module"),b=n(`
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(i){d=s(i,"P",{});var p=l(d);v=r(p,"Although the recipe for forward pass needs to be defined within this function, one should call the "),g=s(p,"CODE",{});var E=l(g);h=r(E,"Module"),E.forEach(o),b=r(p,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),p.forEach(o)},m(i,p){_(i,d,p),e(d,v),e(d,g),e(g,h),e(d,b)},d(i){i&&o(d)}}}function zm(j){let d,v,g,h,b;return h=new Ce({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, Blip2Model

<span class="hljs-meta">&gt;&gt;&gt; </span>device = <span class="hljs-string">&quot;cuda&quot;</span> <span class="hljs-keyword">if</span> torch.cuda.is_available() <span class="hljs-keyword">else</span> <span class="hljs-string">&quot;cpu&quot;</span>

<span class="hljs-meta">&gt;&gt;&gt; </span>model = Blip2Model.from_pretrained(<span class="hljs-string">&quot;Salesforce/blip2-opt-2.7b&quot;</span>, torch_dtype=torch.float16)

<span class="hljs-meta">&gt;&gt;&gt; </span>model.to(device)
<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;Salesforce/blip2-opt-2.7b&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = tokenizer([<span class="hljs-string">&quot;a photo of a cat&quot;</span>, <span class="hljs-string">&quot;a photo of a dog&quot;</span>], padding=<span class="hljs-literal">True</span>, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>).to(device)
<span class="hljs-meta">&gt;&gt;&gt; </span>text_features = model.get_text_features(**inputs)`}}),{c(){d=a("p"),v=n("Examples:"),g=m(),y(h.$$.fragment)},l(i){d=s(i,"P",{});var p=l(d);v=r(p,"Examples:"),p.forEach(o),g=f(i),M(h.$$.fragment,i)},m(i,p){_(i,d,p),e(d,v),_(i,g,p),w(h,i,p),b=!0},p:Ee,i(i){b||(T(h.$$.fragment,i),b=!0)},o(i){B(h.$$.fragment,i),b=!1},d(i){i&&o(d),i&&o(g),$(h,i)}}}function Pm(j){let d,v,g,h,b;return{c(){d=a("p"),v=n("Although the recipe for forward pass needs to be defined within this function, one should call the "),g=a("code"),h=n("Module"),b=n(`
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(i){d=s(i,"P",{});var p=l(d);v=r(p,"Although the recipe for forward pass needs to be defined within this function, one should call the "),g=s(p,"CODE",{});var E=l(g);h=r(E,"Module"),E.forEach(o),b=r(p,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),p.forEach(o)},m(i,p){_(i,d,p),e(d,v),e(d,g),e(g,h),e(d,b)},d(i){i&&o(d)}}}function Wm(j){let d,v,g,h,b;return h=new Ce({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">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, Blip2Model

<span class="hljs-meta">&gt;&gt;&gt; </span>device = <span class="hljs-string">&quot;cuda&quot;</span> <span class="hljs-keyword">if</span> torch.cuda.is_available() <span class="hljs-keyword">else</span> <span class="hljs-string">&quot;cpu&quot;</span>

<span class="hljs-meta">&gt;&gt;&gt; </span>model = Blip2Model.from_pretrained(<span class="hljs-string">&quot;Salesforce/blip2-opt-2.7b&quot;</span>, torch_dtype=torch.float16)

<span class="hljs-meta">&gt;&gt;&gt; </span>model.to(device)
<span class="hljs-meta">&gt;&gt;&gt; </span>processor = AutoProcessor.from_pretrained(<span class="hljs-string">&quot;Salesforce/blip2-opt-2.7b&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>).to(device, torch.float16)
<span class="hljs-meta">&gt;&gt;&gt; </span>image_outputs = model.get_image_features(**inputs)`}}),{c(){d=a("p"),v=n("Examples:"),g=m(),y(h.$$.fragment)},l(i){d=s(i,"P",{});var p=l(d);v=r(p,"Examples:"),p.forEach(o),g=f(i),M(h.$$.fragment,i)},m(i,p){_(i,d,p),e(d,v),_(i,g,p),w(h,i,p),b=!0},p:Ee,i(i){b||(T(h.$$.fragment,i),b=!0)},o(i){B(h.$$.fragment,i),b=!1},d(i){i&&o(d),i&&o(g),$(h,i)}}}function Fm(j){let d,v,g,h,b;return{c(){d=a("p"),v=n("Although the recipe for forward pass needs to be defined within this function, one should call the "),g=a("code"),h=n("Module"),b=n(`
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(i){d=s(i,"P",{});var p=l(d);v=r(p,"Although the recipe for forward pass needs to be defined within this function, one should call the "),g=s(p,"CODE",{});var E=l(g);h=r(E,"Module"),E.forEach(o),b=r(p,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),p.forEach(o)},m(i,p){_(i,d,p),e(d,v),e(d,g),e(g,h),e(d,b)},d(i){i&&o(d)}}}function Nm(j){let d,v,g,h,b;return h=new Ce({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">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> Blip2Processor, Blip2Model

<span class="hljs-meta">&gt;&gt;&gt; </span>device = <span class="hljs-string">&quot;cuda&quot;</span> <span class="hljs-keyword">if</span> torch.cuda.is_available() <span class="hljs-keyword">else</span> <span class="hljs-string">&quot;cpu&quot;</span>

<span class="hljs-meta">&gt;&gt;&gt; </span>processor = Blip2Processor.from_pretrained(<span class="hljs-string">&quot;Salesforce/blip2-opt-2.7b&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = Blip2Model.from_pretrained(<span class="hljs-string">&quot;Salesforce/blip2-opt-2.7b&quot;</span>, torch_dtype=torch.float16)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.to(device)
<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>).to(device, torch.float16)
<span class="hljs-meta">&gt;&gt;&gt; </span>qformer_outputs = model.get_qformer_features(**inputs)`}}),{c(){d=a("p"),v=n("Examples:"),g=m(),y(h.$$.fragment)},l(i){d=s(i,"P",{});var p=l(d);v=r(p,"Examples:"),p.forEach(o),g=f(i),M(h.$$.fragment,i)},m(i,p){_(i,d,p),e(d,v),_(i,g,p),w(h,i,p),b=!0},p:Ee,i(i){b||(T(h.$$.fragment,i),b=!0)},o(i){B(h.$$.fragment,i),b=!1},d(i){i&&o(d),i&&o(g),$(h,i)}}}function Qm(j){let d,v,g,h,b;return{c(){d=a("p"),v=n("Although the recipe for forward pass needs to be defined within this function, one should call the "),g=a("code"),h=n("Module"),b=n(`
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(i){d=s(i,"P",{});var p=l(d);v=r(p,"Although the recipe for forward pass needs to be defined within this function, one should call the "),g=s(p,"CODE",{});var E=l(g);h=r(E,"Module"),E.forEach(o),b=r(p,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),p.forEach(o)},m(i,p){_(i,d,p),e(d,v),e(d,g),e(g,h),e(d,b)},d(i){i&&o(d)}}}function Gm(j){let d,v,g,h,b;return h=new Ce({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> Blip2Processor, Blip2ForConditionalGeneration
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch

<span class="hljs-meta">&gt;&gt;&gt; </span>device = <span class="hljs-string">&quot;cuda&quot;</span> <span class="hljs-keyword">if</span> torch.cuda.is_available() <span class="hljs-keyword">else</span> <span class="hljs-string">&quot;cpu&quot;</span>

<span class="hljs-meta">&gt;&gt;&gt; </span>processor = Blip2Processor.from_pretrained(<span class="hljs-string">&quot;Salesforce/blip2-opt-2.7b&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = Blip2ForConditionalGeneration.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;Salesforce/blip2-opt-2.7b&quot;</span>, torch_dtype=torch.float16
<span class="hljs-meta">... </span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.to(device)
<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>).to(device, torch.float16)

<span class="hljs-meta">&gt;&gt;&gt; </span>generated_ids = model.generate(**inputs)
<span class="hljs-meta">&gt;&gt;&gt; </span>generated_text = processor.batch_decode(generated_ids, skip_special_tokens=<span class="hljs-literal">True</span>)[<span class="hljs-number">0</span>].strip()
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-built_in">print</span>(generated_text)
two cats laying on a couch`}}),{c(){d=a("p"),v=n("Image captioning (without providing a text prompt):"),g=m(),y(h.$$.fragment)},l(i){d=s(i,"P",{});var p=l(d);v=r(p,"Image captioning (without providing a text prompt):"),p.forEach(o),g=f(i),M(h.$$.fragment,i)},m(i,p){_(i,d,p),e(d,v),_(i,g,p),w(h,i,p),b=!0},p:Ee,i(i){b||(T(h.$$.fragment,i),b=!0)},o(i){B(h.$$.fragment,i),b=!1},d(i){i&&o(d),i&&o(g),$(h,i)}}}function qm(j){let d,v,g,h,b;return h=new Ce({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> Blip2Processor, Blip2ForConditionalGeneration
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch

<span class="hljs-meta">&gt;&gt;&gt; </span>device = <span class="hljs-string">&quot;cuda&quot;</span> <span class="hljs-keyword">if</span> torch.cuda.is_available() <span class="hljs-keyword">else</span> <span class="hljs-string">&quot;cpu&quot;</span>

<span class="hljs-meta">&gt;&gt;&gt; </span>processor = Blip2Processor.from_pretrained(<span class="hljs-string">&quot;Salesforce/blip2-opt-2.7b&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = Blip2ForConditionalGeneration.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;Salesforce/blip2-opt-2.7b&quot;</span>, torch_dtype=torch.float16
<span class="hljs-meta">... </span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.to(device)
<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>prompt = <span class="hljs-string">&quot;Question: how many cats are there? Answer:&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = processor(images=image, text=prompt, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>).to(device, torch.float16)

<span class="hljs-meta">&gt;&gt;&gt; </span>generated_ids = model.generate(**inputs)
<span class="hljs-meta">&gt;&gt;&gt; </span>generated_text = processor.batch_decode(generated_ids, skip_special_tokens=<span class="hljs-literal">True</span>)[<span class="hljs-number">0</span>].strip()
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-built_in">print</span>(generated_text)
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`,returnType:`
<p><a
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The non-linear activation function (function or string) in the encoder and pooler. If string, <code>&quot;gelu&quot;</code>,
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<ul>
<li>
<p><strong>last_hidden_state</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>) \u2014 Sequence of hidden-states at the output of the last layer of the model.</p>
</li>
<li>
<p><strong>pooler_output</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, hidden_size)</code>) \u2014 Last layer hidden-state of the first token of the sequence (classification token) after further processing
through the layers used for the auxiliary pretraining task. E.g. for BERT-family of models, this returns
the classification token after processing through a linear layer and a tanh activation function. The linear
layer weights are trained from the next sentence prediction (classification) objective during pretraining.</p>
</li>
<li>
<p><strong>hidden_states</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for the output of the embeddings, if the model has an embedding layer, +
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<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
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`}}),Re=new hn({props:{$$slots:{default:[Um]},$$scope:{ctx:j}}}),At=new O({}),Rt=new Z({props:{name:"class transformers.Blip2QFormerModel",anchor:"transformers.Blip2QFormerModel",parameters:[{name:"config",val:": Blip2QFormerConfig"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/blip_2/modeling_blip_2.py#L1006"}}),Xt=new Z({props:{name:"forward",anchor:"transformers.Blip2QFormerModel.forward",parameters:[{name:"query_embeds",val:": FloatTensor"},{name:"attention_mask",val:": typing.Optional[torch.FloatTensor] = None"},{name:"head_mask",val:": typing.Optional[torch.FloatTensor] = None"},{name:"encoder_hidden_states",val:": typing.Optional[torch.FloatTensor] = None"},{name:"encoder_attention_mask",val:": typing.Optional[torch.FloatTensor] = None"},{name:"past_key_values",val:": typing.Optional[typing.Tuple[typing.Tuple[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"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/blip_2/modeling_blip_2.py#L1081"}}),Yt=new O({}),Ot=new Z({props:{name:"class transformers.Blip2Model",anchor:"transformers.Blip2Model",parameters:[{name:"config",val:": Blip2Config"}],parametersDescription:[{anchor:"transformers.Blip2Model.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.33.0/en/model_doc/blip-2#transformers.Blip2Config">Blip2Config</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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Pixel values. Pixel values can be obtained using <a href="/docs/transformers/v4.33.0/en/model_doc/blip-2#transformers.Blip2Processor">Blip2Processor</a>. See <code>Blip2Processor.__call__()</code> for
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Indices of input sequence tokens in the vocabulary of the language model. Input tokens can optionally be
provided to serve as text prompt, which the language model can continue.</p>
<p>Indices can be obtained using <a href="/docs/transformers/v4.33.0/en/model_doc/blip-2#transformers.Blip2Processor">Blip2Processor</a>. See <code>Blip2Processor.__call__()</code> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.Blip2Model.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.Blip2Model.forward.decoder_input_ids",description:`<strong>decoder_input_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, target_sequence_length)</code>, <em>optional</em>) &#x2014;
Indices of decoder input sequence tokens in the vocabulary of the language model. Only relevant in case an
encoder-decoder language model (like T5) is used.</p>
<p>Indices can be obtained using <a href="/docs/transformers/v4.33.0/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/v4.33.0/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/v4.33.0/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details. <a href="../glossary#decoder-input-ids">What are decoder input IDs?</a>`,name:"decoder_input_ids"},{anchor:"transformers.Blip2Model.forward.decoder_attention_mask",description:`<strong>decoder_attention_mask</strong> (<code>torch.BoolTensor</code> of shape <code>(batch_size, target_sequence_length)</code>, <em>optional</em>) &#x2014;
Default behavior: generate a tensor that ignores pad tokens in <code>decoder_input_ids</code>. Causal mask will also
be used by default.</p>
<p>Only relevant in case an encoder-decoder language model (like T5) is used.`,name:"decoder_attention_mask"},{anchor:"transformers.Blip2Model.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.Blip2Model.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.Blip2Model.forward.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/v4.33.0/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/blip_2/modeling_blip_2.py#L1425",returnDescription:`
<p>A <code>transformers.models.blip_2.modeling_blip_2.Blip2ForConditionalGenerationModelOutput</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_2.configuration_blip_2.Blip2VisionConfig'&gt;</code>) and inputs.</p>
<ul>
<li><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 Language modeling loss from the language model.</li>
<li><strong>logits</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, config.vocab_size)</code>) \u2014 Prediction scores of the language modeling head of the language model.</li>
<li><strong>vision_outputs</strong> (<code>BaseModelOutputWithPooling</code>) \u2014 Outputs of the vision encoder.</li>
<li><strong>qformer_outputs</strong> (<code>BaseModelOutputWithPoolingAndCrossAttentions</code>) \u2014 Outputs of the Q-Former (Querying Transformer).</li>
<li><strong>language_model_outputs</strong> (<code>CausalLMOutputWithPast</code> or <code>Seq2SeqLMOutput</code>) \u2014 Outputs of the language model.</li>
</ul>
`,returnType:`
<p><code>transformers.models.blip_2.modeling_blip_2.Blip2ForConditionalGenerationModelOutput</code> or <code>tuple(torch.FloatTensor)</code></p>
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Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
it. Indices can be obtained using <a href="/docs/transformers/v4.33.0/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/v4.33.0/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/v4.33.0/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details. <a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.Blip2Model.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>.
<a href="../glossary#attention-mask">What are attention masks?</a></li>
</ul>`,name:"attention_mask"},{anchor:"transformers.Blip2Model.get_text_features.decoder_input_ids",description:`<strong>decoder_input_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, target_sequence_length)</code>, <em>optional</em>) &#x2014;
Indices of decoder input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using <a href="/docs/transformers/v4.33.0/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/v4.33.0/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/v4.33.0/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</p>
<p><a href="../glossary#decoder-input-ids">What are decoder input IDs?</a></p>
<p>T5 uses the <code>pad_token_id</code> as the starting token for <code>decoder_input_ids</code> generation. If <code>past_key_values</code>
is used, optionally only the last <code>decoder_input_ids</code> have to be input (see <code>past_key_values</code>).</p>
<p>To know more on how to prepare <code>decoder_input_ids</code> for pretraining take a look at <a href="./t5#training">T5
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Default behavior: generate a tensor that ignores pad tokens in <code>decoder_input_ids</code>. Causal mask will also
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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.Blip2Model.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.Blip2Model.get_text_features.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/v4.33.0/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/blip_2/modeling_blip_2.py#L1252",returnDescription:`
<p>The language model outputs. If <code>return_dict=True</code>, the output is a <code>CausalLMOutputWithPast</code> that
contains the language model logits, the past key values and the hidden states if
<code>output_hidden_states=True</code>.</p>
`,returnType:`
<p>text_outputs (<code>CausalLMOutputWithPast</code>, or <code>tuple(torch.FloatTensor)</code> if <code>return_dict=False</code>)</p>
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Pixel values. Pixel values can be obtained using <a href="/docs/transformers/v4.33.0/en/model_doc/blip-2#transformers.Blip2Processor">Blip2Processor</a>. See <code>Blip2Processor.__call__()</code> for
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Whether or not to return the attentions tensors of all attention layers. See <code>attentions</code> under returned
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Whether or not to return the hidden states of all layers. See <code>hidden_states</code> under returned tensors for
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Whether or not to return a <a href="/docs/transformers/v4.33.0/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/blip_2/modeling_blip_2.py#L1315",returnDescription:`
<p>The vision model outputs. If <code>return_dict=True</code>, the output is a <code>BaseModelOutputWithPooling</code> that
contains the image features, the pooled image features and the hidden states if
<code>output_hidden_states=True</code>.</p>
`,returnType:`
<p>vision_outputs (<code>BaseModelOutputWithPooling</code> or tuple of <code>torch.FloatTensor</code>)</p>
`}}),Ke=new hn({props:{$$slots:{default:[Pm]},$$scope:{ctx:j}}}),et=new Je({props:{anchor:"transformers.Blip2Model.get_image_features.example",$$slots:{default:[Wm]},$$scope:{ctx:j}}}),ro=new Z({props:{name:"get_qformer_features",anchor:"transformers.Blip2Model.get_qformer_features",parameters:[{name:"pixel_values",val:": typing.Optional[torch.FloatTensor] = None"},{name:"output_attentions",val:": typing.Optional[bool] = None"},{name:"output_hidden_states",val:": typing.Optional[bool] = None"},{name:"return_dict",val:": typing.Optional[bool] = None"}],parametersDescription:[{anchor:"transformers.Blip2Model.get_qformer_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. Pixel values can be obtained using <a href="/docs/transformers/v4.33.0/en/model_doc/blip-2#transformers.Blip2Processor">Blip2Processor</a>. See <code>Blip2Processor.__call__()</code> for
details.`,name:"pixel_values"},{anchor:"transformers.Blip2Model.get_qformer_features.input_ids",description:`<strong>input_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Indices of input sequence tokens in the vocabulary of the language model. Input tokens can optionally be
provided to serve as text prompt, which the language model can continue.</p>
<p>Indices can be obtained using <a href="/docs/transformers/v4.33.0/en/model_doc/blip-2#transformers.Blip2Processor">Blip2Processor</a>. See <code>Blip2Processor.__call__()</code> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.Blip2Model.get_qformer_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.Blip2Model.get_qformer_features.decoder_input_ids",description:`<strong>decoder_input_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, target_sequence_length)</code>, <em>optional</em>) &#x2014;
Indices of decoder input sequence tokens in the vocabulary of the language model. Only relevant in case an
encoder-decoder language model (like T5) is used.</p>
<p>Indices can be obtained using <a href="/docs/transformers/v4.33.0/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/v4.33.0/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/v4.33.0/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details. <a href="../glossary#decoder-input-ids">What are decoder input IDs?</a>`,name:"decoder_input_ids"},{anchor:"transformers.Blip2Model.get_qformer_features.decoder_attention_mask",description:`<strong>decoder_attention_mask</strong> (<code>torch.BoolTensor</code> of shape <code>(batch_size, target_sequence_length)</code>, <em>optional</em>) &#x2014;
Default behavior: generate a tensor that ignores pad tokens in <code>decoder_input_ids</code>. Causal mask will also
be used by default.</p>
<p>Only relevant in case an encoder-decoder language model (like T5) is used.`,name:"decoder_attention_mask"},{anchor:"transformers.Blip2Model.get_qformer_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.Blip2Model.get_qformer_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.Blip2Model.get_qformer_features.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/v4.33.0/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/blip_2/modeling_blip_2.py#L1363",returnDescription:`
<p>The vision model outputs. If <code>return_dict=True</code>, the output is a <code>BaseModelOutputWithPooling</code> that
contains the image features, the pooled image features and the hidden states if
<code>output_hidden_states=True</code>.</p>
`,returnType:`
<p>vision_outputs (<code>BaseModelOutputWithPooling</code> or tuple of <code>torch.FloatTensor</code>)</p>
`}}),tt=new hn({props:{$$slots:{default:[Fm]},$$scope:{ctx:j}}}),ot=new Je({props:{anchor:"transformers.Blip2Model.get_qformer_features.example",$$slots:{default:[Nm]},$$scope:{ctx:j}}}),ao=new O({}),so=new Z({props:{name:"class transformers.Blip2ForConditionalGeneration",anchor:"transformers.Blip2ForConditionalGeneration",parameters:[{name:"config",val:": Blip2Config"}],parametersDescription:[{anchor:"transformers.Blip2ForConditionalGeneration.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.33.0/en/model_doc/blip-2#transformers.Blip2Config">Blip2Config</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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Pixel values. Pixel values can be obtained using <a href="/docs/transformers/v4.33.0/en/model_doc/blip-2#transformers.Blip2Processor">Blip2Processor</a>. See <code>Blip2Processor.__call__()</code> for
details.`,name:"pixel_values"},{anchor:"transformers.Blip2ForConditionalGeneration.forward.input_ids",description:`<strong>input_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Indices of input sequence tokens in the vocabulary of the language model. Input tokens can optionally be
provided to serve as text prompt, which the language model can continue.</p>
<p>Indices can be obtained using <a href="/docs/transformers/v4.33.0/en/model_doc/blip-2#transformers.Blip2Processor">Blip2Processor</a>. See <code>Blip2Processor.__call__()</code> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.Blip2ForConditionalGeneration.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.Blip2ForConditionalGeneration.forward.decoder_input_ids",description:`<strong>decoder_input_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, target_sequence_length)</code>, <em>optional</em>) &#x2014;
Indices of decoder input sequence tokens in the vocabulary of the language model. Only relevant in case an
encoder-decoder language model (like T5) is used.</p>
<p>Indices can be obtained using <a href="/docs/transformers/v4.33.0/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/v4.33.0/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/v4.33.0/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details. <a href="../glossary#decoder-input-ids">What are decoder input IDs?</a>`,name:"decoder_input_ids"},{anchor:"transformers.Blip2ForConditionalGeneration.forward.decoder_attention_mask",description:`<strong>decoder_attention_mask</strong> (<code>torch.BoolTensor</code> of shape <code>(batch_size, target_sequence_length)</code>, <em>optional</em>) &#x2014;
Default behavior: generate a tensor that ignores pad tokens in <code>decoder_input_ids</code>. Causal mask will also
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<p>Only relevant in case an encoder-decoder language model (like T5) is used.`,name:"decoder_attention_mask"},{anchor:"transformers.Blip2ForConditionalGeneration.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.Blip2ForConditionalGeneration.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
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<p>A <code>transformers.models.blip_2.modeling_blip_2.Blip2ForConditionalGenerationModelOutput</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_2.configuration_blip_2.Blip2VisionConfig'&gt;</code>) and inputs.</p>
<ul>
<li><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 Language modeling loss from the language model.</li>
<li><strong>logits</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, config.vocab_size)</code>) \u2014 Prediction scores of the language modeling head of the language model.</li>
<li><strong>vision_outputs</strong> (<code>BaseModelOutputWithPooling</code>) \u2014 Outputs of the vision encoder.</li>
<li><strong>qformer_outputs</strong> (<code>BaseModelOutputWithPoolingAndCrossAttentions</code>) \u2014 Outputs of the Q-Former (Querying Transformer).</li>
<li><strong>language_model_outputs</strong> (<code>CausalLMOutputWithPast</code> or <code>Seq2SeqLMOutput</code>) \u2014 Outputs of the language model.</li>
</ul>
`,returnType:`
<p><code>transformers.models.blip_2.modeling_blip_2.Blip2ForConditionalGenerationModelOutput</code> or <code>tuple(torch.FloatTensor)</code></p>
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<p>A list of strings of length batch_size * num_captions.</p>
`,returnType:`
<p>captions (list)</p>
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Junnan Li, Dongxu Li, Silvio Savarese, Steven Hoi. BLIP-2 leverages frozen pre-trained image encoders and large language models (LLMs) by training a lightweight, 12-layer Transformer
encoder in between them, achieving state-of-the-art performance on various vision-language tasks. Most notably, BLIP-2 improves upon `),gt=s(je,"A",{href:!0,rel:!0});var kr=l(gt);qa=r(kr,"Flamingo"),kr.forEach(o),Va=r(je,`, an 80 billion parameter model, by 8.7%
on zero-shot VQAv2 with 54x fewer trainable parameters.`),je.forEach(o),Zr=f(t),bo=s(t,"P",{});var jr=l(bo);Sa=r(jr,"The abstract from the paper is the following:"),jr.forEach(o),xr=f(t),vo=s(t,"P",{});var Jr=l(vo);_n=s(Jr,"EM",{});var Er=l(_n);Aa=r(Er,"The cost of vision-and-language pre-training has become increasingly prohibitive due to end-to-end training of large-scale models. This paper proposes BLIP-2, a generic and efficient pre-training strategy that bootstraps vision-language pre-training from off-the-shelf frozen pre-trained image encoders and frozen large language models. BLIP-2 bridges the modality gap with a lightweight Querying Transformer, which is pre-trained in two stages. The first stage bootstraps vision-language representation learning from a frozen image encoder. The second stage bootstraps vision-to-language generative learning from a frozen language model. BLIP-2 achieves state-of-the-art performance on various vision-language tasks, despite having significantly fewer trainable parameters than existing methods. For example, our model outperforms Flamingo80B by 8.7% on zero-shot VQAv2 with 54x fewer trainable parameters. We also demonstrate the model\u2019s emerging capabilities of zero-shot image-to-text generation that can follow natural language instructions."),Er.forEach(o),Jr.forEach(o),zr=f(t),yo=s(t,"P",{});var Cr=l(yo);Ra=r(Cr,"Tips:"),Cr.forEach(o),Pr=f(t),Ie=s(t,"UL",{});var la=l(Ie);_t=s(la,"LI",{});var ia=l(_t);Xa=r(ia,"BLIP-2 can be used for conditional text generation given an image and an optional text prompt. At inference time, it\u2019s recommended to use the "),bn=s(ia,"CODE",{});var kc=l(bn);La=r(kc,"generate"),kc.forEach(o),Da=r(ia," method."),ia.forEach(o),Ya=f(la),bt=s(la,"LI",{});var da=l(bt);Oa=r(da,"One can use "),Mo=s(da,"A",{href:!0});var jc=l(Mo);Ha=r(jc,"Blip2Processor"),jc.forEach(o),Ka=r(da," to prepare images for the model, and decode the predicted tokens ID\u2019s back to text."),da.forEach(o),la.forEach(o),Wr=f(t),Ze=s(t,"IMG",{src:!0,alt:!0,width:!0}),Fr=f(t),vt=s(t,"SMALL",{});var Bc=l(vt);es=r(Bc,"BLIP-2 architecture. Taken from the "),wo=s(Bc,"A",{href:!0});var Jc=l(wo);ts=r(Jc,"original paper."),Jc.forEach(o),Bc.forEach(o),Nr=f(t),K=s(t,"P",{});var Ko=l(K);os=r(Ko,"This model was contributed by "),yt=s(Ko,"A",{href:!0,rel:!0});var Ec=l(yt);ns=r(Ec,"nielsr"),Ec.forEach(o),rs=r(Ko,`.
The original code can be found `),Mt=s(Ko,"A",{href:!0,rel:!0});var Cc=l(Mt);as=r(Cc,"here"),Cc.forEach(o),ss=r(Ko,"."),Ko.forEach(o),Qr=f(t),le=s(t,"H2",{class:!0});var ca=l(le);xe=s(ca,"A",{id:!0,class:!0,href:!0});var Uc=l(xe);vn=s(Uc,"SPAN",{});var Ic=l(vn);M(wt.$$.fragment,Ic),Ic.forEach(o),Uc.forEach(o),ls=f(ca),yn=s(ca,"SPAN",{});var Zc=l(yn);is=r(Zc,"Resources"),Zc.forEach(o),ca.forEach(o),Gr=f(t),To=s(t,"P",{});var xc=l(To);ds=r(xc,"A list of official Hugging Face and community (indicated by \u{1F30E}) resources to help you get started with BLIP-2."),xc.forEach(o),qr=f(t),Bo=s(t,"UL",{});var zc=l(Bo);Tt=s(zc,"LI",{});var pa=l(Tt);cs=r(pa,"Demo notebooks for BLIP-2 for image captioning, visual question answering (VQA) and chat-like conversations can be found "),Bt=s(pa,"A",{href:!0,rel:!0});var Pc=l(Bt);ps=r(Pc,"here"),Pc.forEach(o),ms=r(pa,"."),pa.forEach(o),zc.forEach(o),Vr=f(t),$o=s(t,"P",{});var Wc=l($o);fs=r(Wc,"If you\u2019re interested in submitting a resource to be included here, please feel free to open a Pull Request and we\u2019ll review it! The resource should ideally demonstrate something new instead of duplicating an existing resource."),Wc.forEach(o),Sr=f(t),ie=s(t,"H2",{class:!0});var ma=l(ie);ze=s(ma,"A",{id:!0,class:!0,href:!0});var Fc=l(ze);Mn=s(Fc,"SPAN",{});var Nc=l(Mn);M($t.$$.fragment,Nc),Nc.forEach(o),Fc.forEach(o),hs=f(ma),wn=s(ma,"SPAN",{});var Qc=l(wn);us=r(Qc,"Blip2Config"),Qc.forEach(o),ma.forEach(o),Ar=f(t),W=s(t,"DIV",{class:!0});var ne=l(W);M(kt.$$.fragment,ne),gs=f(ne),ee=s(ne,"P",{});var _o=l(ee);ko=s(_o,"A",{href:!0});var Gc=l(ko);_s=r(Gc,"Blip2Config"),Gc.forEach(o),bs=r(_o," is the configuration class to store the configuration of a "),jo=s(_o,"A",{href:!0});var qc=l(jo);vs=r(qc,"Blip2ForConditionalGeneration"),qc.forEach(o),ys=r(_o,`. It is
used to instantiate a BLIP-2 model according to the specified arguments, defining the vision model, Q-Former model
and language model configs. Instantiating a configuration with the defaults will yield a similar configuration to
that of the BLIP-2 `),jt=s(_o,"A",{href:!0,rel:!0});var Vc=l(jt);Ms=r(Vc,"Salesforce/blip2-opt-2.7b"),Vc.forEach(o),ws=r(_o," architecture."),_o.forEach(o),Ts=f(ne),de=s(ne,"P",{});var en=l(de);Bs=r(en,"Configuration objects inherit from "),Jo=s(en,"A",{href:!0});var Sc=l(Jo);$s=r(Sc,"PretrainedConfig"),Sc.forEach(o),ks=r(en,` and can be used to control the model outputs. Read the
documentation from `),Eo=s(en,"A",{href:!0});var Ac=l(Eo);js=r(Ac,"PretrainedConfig"),Ac.forEach(o),Js=r(en," for more information."),en.forEach(o),Es=f(ne),M(Pe.$$.fragment,ne),Cs=f(ne),We=s(ne,"DIV",{class:!0});var fa=l(We);M(Jt.$$.fragment,fa),Us=f(fa),Et=s(fa,"P",{});var ha=l(Et);Is=r(ha,"Instantiate a "),Co=s(ha,"A",{href:!0});var Rc=l(Co);Zs=r(Rc,"Blip2Config"),Rc.forEach(o),xs=r(ha,` (or a derived class) from a BLIP-2 vision model, Q-Former and language model
configurations.`),ha.forEach(o),fa.forEach(o),ne.forEach(o),Rr=f(t),ce=s(t,"H2",{class:!0});var ua=l(ce);Fe=s(ua,"A",{id:!0,class:!0,href:!0});var Xc=l(Fe);Tn=s(Xc,"SPAN",{});var Lc=l(Tn);M(Ct.$$.fragment,Lc),Lc.forEach(o),Xc.forEach(o),zs=f(ua),Bn=s(ua,"SPAN",{});var Dc=l(Bn);Ps=r(Dc,"Blip2VisionConfig"),Dc.forEach(o),ua.forEach(o),Xr=f(t),G=s(t,"DIV",{class:!0});var it=l(G);M(Ut.$$.fragment,it),Ws=f(it),pe=s(it,"P",{});var tn=l(pe);Fs=r(tn,"This is the configuration class to store the configuration of a "),Uo=s(tn,"A",{href:!0});var Yc=l(Uo);Ns=r(Yc,"Blip2VisionModel"),Yc.forEach(o),Qs=r(tn,`. It is used to instantiate a
BLIP-2 vision encoder according to the specified arguments, defining the model architecture. Instantiating a
configuration defaults will yield a similar configuration to that of the BLIP-2
`),It=s(tn,"A",{href:!0,rel:!0});var Oc=l(It);Gs=r(Oc,"Salesforce/blip2-opt-2.7b"),Oc.forEach(o),qs=r(tn," architecture."),tn.forEach(o),Vs=f(it),me=s(it,"P",{});var on=l(me);Ss=r(on,"Configuration objects inherit from "),Io=s(on,"A",{href:!0});var Hc=l(Io);As=r(Hc,"PretrainedConfig"),Hc.forEach(o),Rs=r(on,` and can be used to control the model outputs. Read the
documentation from `),Zo=s(on,"A",{href:!0});var Kc=l(Zo);Xs=r(Kc,"PretrainedConfig"),Kc.forEach(o),Ls=r(on," for more information."),on.forEach(o),Ds=f(it),M(Ne.$$.fragment,it),it.forEach(o),Lr=f(t),fe=s(t,"H2",{class:!0});var ga=l(fe);Qe=s(ga,"A",{id:!0,class:!0,href:!0});var ep=l(Qe);$n=s(ep,"SPAN",{});var tp=l($n);M(Zt.$$.fragment,tp),tp.forEach(o),ep.forEach(o),Ys=f(ga),kn=s(ga,"SPAN",{});var op=l(kn);Os=r(op,"Blip2QFormerConfig"),op.forEach(o),ga.forEach(o),Dr=f(t),q=s(t,"DIV",{class:!0});var dt=l(q);M(xt.$$.fragment,dt),Hs=f(dt),V=s(dt,"P",{});var re=l(V);Ks=r(re,"This is the configuration class to store the configuration of a "),xo=s(re,"A",{href:!0});var np=l(xo);el=r(np,"Blip2QFormerModel"),np.forEach(o),tl=r(re,`. It is used to instantiate a
BLIP-2 Querying Transformer (Q-Former) 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 BLIP-2
`),zt=s(re,"A",{href:!0,rel:!0});var rp=l(zt);ol=r(rp,"Salesforce/blip2-opt-2.7b"),rp.forEach(o),nl=r(re,` architecture. Configuration objects
inherit from `),zo=s(re,"A",{href:!0});var ap=l(zo);rl=r(ap,"PretrainedConfig"),ap.forEach(o),al=r(re,` and can be used to control the model outputs. Read the documentation from
`),Po=s(re,"A",{href:!0});var sp=l(Po);sl=r(sp,"PretrainedConfig"),sp.forEach(o),ll=r(re," for more information."),re.forEach(o),il=f(dt),he=s(dt,"P",{});var nn=l(he);dl=r(nn,"Note that "),Wo=s(nn,"A",{href:!0});var lp=l(Wo);cl=r(lp,"Blip2QFormerModel"),lp.forEach(o),pl=r(nn," is very similar to "),Fo=s(nn,"A",{href:!0});var ip=l(Fo);ml=r(ip,"BertLMHeadModel"),ip.forEach(o),fl=r(nn," with interleaved cross-attention."),nn.forEach(o),hl=f(dt),M(Ge.$$.fragment,dt),dt.forEach(o),Yr=f(t),ue=s(t,"H2",{class:!0});var _a=l(ue);qe=s(_a,"A",{id:!0,class:!0,href:!0});var dp=l(qe);jn=s(dp,"SPAN",{});var cp=l(jn);M(Pt.$$.fragment,cp),cp.forEach(o),dp.forEach(o),ul=f(_a),Jn=s(_a,"SPAN",{});var pp=l(Jn);gl=r(pp,"Blip2Processor"),pp.forEach(o),_a.forEach(o),Or=f(t),F=s(t,"DIV",{class:!0});var ae=l(F);M(Wt.$$.fragment,ae),_l=f(ae),En=s(ae,"P",{});var mp=l(En);bl=r(mp,"Constructs a BLIP-2 processor which wraps a BLIP image processor and an OPT/T5 tokenizer into a single processor."),mp.forEach(o),vl=f(ae),N=s(ae,"P",{});var Y=l(N);No=s(Y,"A",{href:!0});var fp=l(No);yl=r(fp,"BlipProcessor"),fp.forEach(o),Ml=r(Y," offers all the functionalities of "),Qo=s(Y,"A",{href:!0});var hp=l(Qo);wl=r(hp,"BlipImageProcessor"),hp.forEach(o),Tl=r(Y," and "),Go=s(Y,"A",{href:!0});var up=l(Go);Bl=r(up,"AutoTokenizer"),up.forEach(o),$l=r(Y,`. See the docstring
of `),Cn=s(Y,"CODE",{});var gp=l(Cn);kl=r(gp,"__call__()"),gp.forEach(o),jl=r(Y," and "),qo=s(Y,"A",{href:!0});var _p=l(qo);Jl=r(_p,"decode()"),_p.forEach(o),El=r(Y," for more information."),Y.forEach(o),Cl=f(ae),Ve=s(ae,"DIV",{class:!0});var ba=l(Ve);M(Ft.$$.fragment,ba),Ul=f(ba),Nt=s(ba,"P",{});var va=l(Nt);Il=r(va,"This method forwards all its arguments to PreTrainedTokenizer\u2019s "),Vo=s(va,"A",{href:!0});var bp=l(Vo);Zl=r(bp,"batch_decode()"),bp.forEach(o),xl=r(va,`. Please
refer to the docstring of this method for more information.`),va.forEach(o),ba.forEach(o),zl=f(ae),Se=s(ae,"DIV",{class:!0});var ya=l(Se);M(Qt.$$.fragment,ya),Pl=f(ya),Gt=s(ya,"P",{});var Ma=l(Gt);Wl=r(Ma,"This method forwards all its arguments to PreTrainedTokenizer\u2019s "),So=s(Ma,"A",{href:!0});var vp=l(So);Fl=r(vp,"decode()"),vp.forEach(o),Nl=r(Ma,`. Please refer
to the docstring of this method for more information.`),Ma.forEach(o),ya.forEach(o),ae.forEach(o),Hr=f(t),ge=s(t,"H2",{class:!0});var wa=l(ge);Ae=s(wa,"A",{id:!0,class:!0,href:!0});var yp=l(Ae);Un=s(yp,"SPAN",{});var Mp=l(Un);M(qt.$$.fragment,Mp),Mp.forEach(o),yp.forEach(o),Ql=f(wa),In=s(wa,"SPAN",{});var wp=l(In);Gl=r(wp,"Blip2VisionModel"),wp.forEach(o),wa.forEach(o),Kr=f(t),_e=s(t,"DIV",{class:!0});var Ta=l(_e);M(Vt.$$.fragment,Ta),ql=f(Ta),te=s(Ta,"DIV",{class:!0});var rn=l(te);M(St.$$.fragment,rn),Vl=f(rn),be=s(rn,"P",{});var an=l(be);Sl=r(an,"The "),Ao=s(an,"A",{href:!0});var Tp=l(Ao);Al=r(Tp,"Blip2VisionModel"),Tp.forEach(o),Rl=r(an," forward method, overrides the "),Zn=s(an,"CODE",{});var Bp=l(Zn);Xl=r(Bp,"__call__"),Bp.forEach(o),Ll=r(an," special method."),an.forEach(o),Dl=f(rn),M(Re.$$.fragment,rn),rn.forEach(o),Ta.forEach(o),ea=f(t),ve=s(t,"H2",{class:!0});var Ba=l(ve);Xe=s(Ba,"A",{id:!0,class:!0,href:!0});var $p=l(Xe);xn=s($p,"SPAN",{});var kp=l(xn);M(At.$$.fragment,kp),kp.forEach(o),$p.forEach(o),Yl=f(Ba),zn=s(Ba,"SPAN",{});var jp=l(zn);Ol=r(jp,"Blip2QFormerModel"),jp.forEach(o),Ba.forEach(o),ta=f(t),D=s(t,"DIV",{class:!0});var sn=l(D);M(Rt.$$.fragment,sn),Hl=f(sn),Pn=s(sn,"P",{});var Jp=l(Pn);Kl=r(Jp,"Querying Transformer (Q-Former), used in BLIP-2."),Jp.forEach(o),ei=f(sn),oe=s(sn,"DIV",{class:!0});var ln=l(oe);M(Xt.$$.fragment,ln),ti=f(ln),U=s(ln,"P",{});var z=l(U);oi=r(z,"encoder_hidden_states  ("),Wn=s(z,"CODE",{});var Ep=l(Wn);ni=r(Ep,"torch.FloatTensor"),Ep.forEach(o),ri=r(z," of shape "),Fn=s(z,"CODE",{});var Cp=l(Fn);ai=r(Cp,"(batch_size, sequence_length, hidden_size)"),Cp.forEach(o),si=r(z,", "),Nn=s(z,"CODE",{});var Up=l(Nn);li=r(Up,"optional"),Up.forEach(o),ii=r(z,`):
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 (`),Qn=s(z,"CODE",{});var Ip=l(Qn);di=r(Ip,"torch.FloatTensor"),Ip.forEach(o),ci=r(z," of shape "),Gn=s(z,"CODE",{});var Zp=l(Gn);pi=r(Zp,"(batch_size, sequence_length)"),Zp.forEach(o),mi=r(z,", "),qn=s(z,"CODE",{});var xp=l(qn);fi=r(xp,"optional"),xp.forEach(o),hi=r(z,`):
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 `),Vn=s(z,"CODE",{});var zp=l(Vn);ui=r(zp,"[0, 1]"),zp.forEach(o),gi=r(z,":"),z.forEach(o),_i=f(ln),Lt=s(ln,"UL",{});var $a=l(Lt);Dt=s($a,"LI",{});var ka=l(Dt);bi=r(ka,"1 for tokens that are "),Sn=s(ka,"STRONG",{});var Pp=l(Sn);vi=r(Pp,"not masked"),Pp.forEach(o),yi=r(ka,","),ka.forEach(o),Mi=f($a),k=s($a,"LI",{});var J=l(k);wi=r(J,"0 for tokens that are "),An=s(J,"STRONG",{});var Wp=l(An);Ti=r(Wp,"masked"),Wp.forEach(o),Bi=r(J,`.
past_key_values (`),Rn=s(J,"CODE",{});var Fp=l(Rn);$i=r(Fp,"tuple(tuple(torch.FloatTensor))"),Fp.forEach(o),ki=r(J," of length "),Xn=s(J,"CODE",{});var Np=l(Xn);ji=r(Np,"config.n_layers"),Np.forEach(o),Ji=r(J,` with each tuple having 4 tensors of:
shape `),Ln=s(J,"CODE",{});var Qp=l(Ln);Ei=r(Qp,"(batch_size, num_heads, sequence_length - 1, embed_size_per_head)"),Qp.forEach(o),Ci=r(J,`): Contains precomputed key and
value hidden states of the attention blocks. Can be used to speed up decoding. If `),Dn=s(J,"CODE",{});var Gp=l(Dn);Ui=r(Gp,"past_key_values"),Gp.forEach(o),Ii=r(J,` are
used, the user can optionally input only the last `),Yn=s(J,"CODE",{});var qp=l(Yn);Zi=r(qp,"decoder_input_ids"),qp.forEach(o),xi=r(J,` (those that don\u2019t have their past key
value states given to this model) of shape `),On=s(J,"CODE",{});var Vp=l(On);zi=r(Vp,"(batch_size, 1)"),Vp.forEach(o),Pi=r(J," instead of all "),Hn=s(J,"CODE",{});var Sp=l(Hn);Wi=r(Sp,"decoder_input_ids"),Sp.forEach(o),Fi=r(J,` of shape
`),Kn=s(J,"CODE",{});var Ap=l(Kn);Ni=r(Ap,"(batch_size, sequence_length)"),Ap.forEach(o),Qi=r(J,`.
use_cache (`),er=s(J,"CODE",{});var Rp=l(er);Gi=r(Rp,"bool"),Rp.forEach(o),qi=r(J,", "),tr=s(J,"CODE",{});var Xp=l(tr);Vi=r(Xp,"optional"),Xp.forEach(o),Si=r(J,`):
If set to `),or=s(J,"CODE",{});var Lp=l(or);Ai=r(Lp,"True"),Lp.forEach(o),Ri=r(J,", "),nr=s(J,"CODE",{});var Dp=l(nr);Xi=r(Dp,"past_key_values"),Dp.forEach(o),Li=r(J,` key value states are returned and can be used to speed up decoding (see
`),rr=s(J,"CODE",{});var Yp=l(rr);Di=r(Yp,"past_key_values"),Yp.forEach(o),Yi=r(J,")."),J.forEach(o),$a.forEach(o),ln.forEach(o),sn.forEach(o),oa=f(t),ye=s(t,"H2",{class:!0});var ja=l(ye);Le=s(ja,"A",{id:!0,class:!0,href:!0});var Op=l(Le);ar=s(Op,"SPAN",{});var Hp=l(ar);M(Yt.$$.fragment,Hp),Hp.forEach(o),Op.forEach(o),Oi=f(ja),sr=s(ja,"SPAN",{});var Kp=l(sr);Hi=r(Kp,"Blip2Model"),Kp.forEach(o),ja.forEach(o),na=f(t),C=s(t,"DIV",{class:!0});var P=l(C);M(Ot.$$.fragment,P),Ki=f(P),lr=s(P,"P",{});var em=l(lr);ed=r(em,`BLIP-2 Model for generating text and image features. The model consists of a vision encoder, Querying Transformer
(Q-Former) and a language model.`),em.forEach(o),td=f(P),Ht=s(P,"P",{});var Ja=l(Ht);od=r(Ja,"This model inherits from "),Ro=s(Ja,"A",{href:!0});var tm=l(Ro);nd=r(tm,"PreTrainedModel"),tm.forEach(o),rd=r(Ja,`. 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.)`),Ja.forEach(o),ad=f(P),Kt=s(P,"P",{});var Ea=l(Kt);sd=r(Ea,"This model is also a PyTorch "),eo=s(Ea,"A",{href:!0,rel:!0});var om=l(eo);ld=r(om,"torch.nn.Module"),om.forEach(o),id=r(Ea,` subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
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