import{s as Yn,o as Qn,n as U}from"../chunks/scheduler.9bc65507.js";import{S as Kn,i as er,g as d,s,r as f,A as or,h as c,f as a,c as i,j as x,u as g,x as b,k as y,y as n,a as p,v as h,d as u,t as _,w as v}from"../chunks/index.707bf1b6.js";import{T as Co}from"../chunks/Tip.c2ecdbf4.js";import{D as $}from"../chunks/Docstring.17db21ae.js";import{C as ze}from"../chunks/CodeBlock.54a9f38d.js";import{E as we}from"../chunks/ExampleCodeBlock.4f515aa9.js";import{H as z}from"../chunks/Heading.342b1fa6.js";function tr(F){let o,k="Example:",l,m,T;return m=new ze({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> FlavaConfig, FlavaModel, FlavaForPreTraining

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

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Initializing a FlavaModel and FlavaForPreTraining model (with random weights) from the style configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlavaModel(configuration)
<span class="hljs-meta">&gt;&gt;&gt; </span>model_pre = FlavaForPreTraining(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>configuration_pre = model_pre.config`,wrap:!1}}),{c(){o=d("p"),o.textContent=k,l=s(),f(m.$$.fragment)},l(t){o=c(t,"P",{"data-svelte-h":!0}),b(o)!=="svelte-11lpom8"&&(o.textContent=k),l=i(t),g(m.$$.fragment,t)},m(t,M){p(t,o,M),p(t,l,M),h(m,t,M),T=!0},p:U,i(t){T||(u(m.$$.fragment,t),T=!0)},o(t){_(m.$$.fragment,t),T=!1},d(t){t&&(a(o),a(l)),v(m,t)}}}function ar(F){let o,k="Example:",l,m,T;return m=new ze({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> FlavaTextConfig, FlavaTextModel

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

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

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Accessing the model configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>configuration = model.config`,wrap:!1}}),{c(){o=d("p"),o.textContent=k,l=s(),f(m.$$.fragment)},l(t){o=c(t,"P",{"data-svelte-h":!0}),b(o)!=="svelte-11lpom8"&&(o.textContent=k),l=i(t),g(m.$$.fragment,t)},m(t,M){p(t,o,M),p(t,l,M),h(m,t,M),T=!0},p:U,i(t){T||(u(m.$$.fragment,t),T=!0)},o(t){_(m.$$.fragment,t),T=!1},d(t){t&&(a(o),a(l)),v(m,t)}}}function nr(F){let o,k="Example:",l,m,T;return m=new ze({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> FlavaImageConfig, FlavaImageModel

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

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

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Accessing the model configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>configuration = model.config`,wrap:!1}}),{c(){o=d("p"),o.textContent=k,l=s(),f(m.$$.fragment)},l(t){o=c(t,"P",{"data-svelte-h":!0}),b(o)!=="svelte-11lpom8"&&(o.textContent=k),l=i(t),g(m.$$.fragment,t)},m(t,M){p(t,o,M),p(t,l,M),h(m,t,M),T=!0},p:U,i(t){T||(u(m.$$.fragment,t),T=!0)},o(t){_(m.$$.fragment,t),T=!1},d(t){t&&(a(o),a(l)),v(m,t)}}}function rr(F){let o,k="Example:",l,m,T;return m=new ze({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> FlavaMultimodalConfig, FlavaMultimodalModel

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

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

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Accessing the model configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>configuration = model.config`,wrap:!1}}),{c(){o=d("p"),o.textContent=k,l=s(),f(m.$$.fragment)},l(t){o=c(t,"P",{"data-svelte-h":!0}),b(o)!=="svelte-11lpom8"&&(o.textContent=k),l=i(t),g(m.$$.fragment,t)},m(t,M){p(t,o,M),p(t,l,M),h(m,t,M),T=!0},p:U,i(t){T||(u(m.$$.fragment,t),T=!0)},o(t){_(m.$$.fragment,t),T=!1},d(t){t&&(a(o),a(l)),v(m,t)}}}function sr(F){let o,k=`Although the recipe for forward pass needs to be defined within this function, one should call the <code>Module</code>
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`;return{c(){o=d("p"),o.innerHTML=k},l(l){o=c(l,"P",{"data-svelte-h":!0}),b(o)!=="svelte-fincs2"&&(o.innerHTML=k)},m(l,m){p(l,o,m)},p:U,d(l){l&&a(o)}}}function ir(F){let o,k=`Although the recipe for forward pass needs to be defined within this function, one should call the <code>Module</code>
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`;return{c(){o=d("p"),o.innerHTML=k},l(l){o=c(l,"P",{"data-svelte-h":!0}),b(o)!=="svelte-fincs2"&&(o.innerHTML=k)},m(l,m){p(l,o,m)},p:U,d(l){l&&a(o)}}}function lr(F){let o,k="Examples:",l,m,T;return m=new ze({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, FlavaModel

<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlavaModel.from_pretrained(<span class="hljs-string">&quot;facebook/flava-full&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>processor = AutoProcessor.from_pretrained(<span class="hljs-string">&quot;facebook/flava-full&quot;</span>)

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

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

<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(**inputs)

<span class="hljs-meta">&gt;&gt;&gt; </span>image_embeddings = outputs.image_embeddings
<span class="hljs-meta">&gt;&gt;&gt; </span>text_embeddings = outputs.text_embeddings
<span class="hljs-meta">&gt;&gt;&gt; </span>multimodal_embeddings = outputs.multimodal_embeddings

<span class="hljs-meta">&gt;&gt;&gt; </span>outputs.image_embeddings.shape
torch.Size([<span class="hljs-number">1</span>, <span class="hljs-number">197</span>, <span class="hljs-number">768</span>])

<span class="hljs-meta">&gt;&gt;&gt; </span>text_embeddings.shape
torch.Size([<span class="hljs-number">1</span>, <span class="hljs-number">7</span>, <span class="hljs-number">768</span>])

<span class="hljs-meta">&gt;&gt;&gt; </span>multimodal_embeddings.shape
torch.Size([<span class="hljs-number">1</span>, <span class="hljs-number">205</span>, <span class="hljs-number">768</span>])`,wrap:!1}}),{c(){o=d("p"),o.textContent=k,l=s(),f(m.$$.fragment)},l(t){o=c(t,"P",{"data-svelte-h":!0}),b(o)!=="svelte-kvfsh7"&&(o.textContent=k),l=i(t),g(m.$$.fragment,t)},m(t,M){p(t,o,M),p(t,l,M),h(m,t,M),T=!0},p:U,i(t){T||(u(m.$$.fragment,t),T=!0)},o(t){_(m.$$.fragment,t),T=!1},d(t){t&&(a(o),a(l)),v(m,t)}}}function dr(F){let o,k=`Although the recipe for forward pass needs to be defined within this function, one should call the <code>Module</code>
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`;return{c(){o=d("p"),o.innerHTML=k},l(l){o=c(l,"P",{"data-svelte-h":!0}),b(o)!=="svelte-fincs2"&&(o.innerHTML=k)},m(l,m){p(l,o,m)},p:U,d(l){l&&a(o)}}}function cr(F){let o,k=`Although the recipe for forward pass needs to be defined within this function, one should call the <code>Module</code>
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`;return{c(){o=d("p"),o.innerHTML=k},l(l){o=c(l,"P",{"data-svelte-h":!0}),b(o)!=="svelte-fincs2"&&(o.innerHTML=k)},m(l,m){p(l,o,m)},p:U,d(l){l&&a(o)}}}function mr(F){let o,k=`Although the recipe for forward pass needs to be defined within this function, one should call the <code>Module</code>
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`;return{c(){o=d("p"),o.innerHTML=k},l(l){o=c(l,"P",{"data-svelte-h":!0}),b(o)!=="svelte-fincs2"&&(o.innerHTML=k)},m(l,m){p(l,o,m)},p:U,d(l){l&&a(o)}}}function pr(F){let o,k="Example:",l,m,T;return m=new ze({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, FlavaTextModel
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;facebook/flava-full&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlavaTextModel.from_pretrained(<span class="hljs-string">&quot;facebook/flava-full&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = tokenizer(<span class="hljs-string">&quot;Hello, my dog is cute&quot;</span>, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(**inputs)

<span class="hljs-meta">&gt;&gt;&gt; </span>last_hidden_states = outputs.last_hidden_state`,wrap:!1}}),{c(){o=d("p"),o.textContent=k,l=s(),f(m.$$.fragment)},l(t){o=c(t,"P",{"data-svelte-h":!0}),b(o)!=="svelte-11lpom8"&&(o.textContent=k),l=i(t),g(m.$$.fragment,t)},m(t,M){p(t,o,M),p(t,l,M),h(m,t,M),T=!0},p:U,i(t){T||(u(m.$$.fragment,t),T=!0)},o(t){_(m.$$.fragment,t),T=!1},d(t){t&&(a(o),a(l)),v(m,t)}}}function fr(F){let o,k=`Although the recipe for forward pass needs to be defined within this function, one should call the <code>Module</code>
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`;return{c(){o=d("p"),o.innerHTML=k},l(l){o=c(l,"P",{"data-svelte-h":!0}),b(o)!=="svelte-fincs2"&&(o.innerHTML=k)},m(l,m){p(l,o,m)},p:U,d(l){l&&a(o)}}}function gr(F){let o,k="Example:",l,m,T;return m=new ze({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoImageProcessor, FlavaImageModel
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> datasets <span class="hljs-keyword">import</span> load_dataset

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

<span class="hljs-meta">&gt;&gt;&gt; </span>image_processor = AutoImageProcessor.from_pretrained(<span class="hljs-string">&quot;facebook/flava-full&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlavaImageModel.from_pretrained(<span class="hljs-string">&quot;facebook/flava-full&quot;</span>)

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

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

<span class="hljs-meta">&gt;&gt;&gt; </span>last_hidden_states = outputs.last_hidden_state
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-built_in">list</span>(last_hidden_states.shape)
[<span class="hljs-number">1</span>, <span class="hljs-number">197</span>, <span class="hljs-number">768</span>]`,wrap:!1}}),{c(){o=d("p"),o.textContent=k,l=s(),f(m.$$.fragment)},l(t){o=c(t,"P",{"data-svelte-h":!0}),b(o)!=="svelte-11lpom8"&&(o.textContent=k),l=i(t),g(m.$$.fragment,t)},m(t,M){p(t,o,M),p(t,l,M),h(m,t,M),T=!0},p:U,i(t){T||(u(m.$$.fragment,t),T=!0)},o(t){_(m.$$.fragment,t),T=!1},d(t){t&&(a(o),a(l)),v(m,t)}}}function hr(F){let o,k=`Although the recipe for forward pass needs to be defined within this function, one should call the <code>Module</code>
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`;return{c(){o=d("p"),o.innerHTML=k},l(l){o=c(l,"P",{"data-svelte-h":!0}),b(o)!=="svelte-fincs2"&&(o.innerHTML=k)},m(l,m){p(l,o,m)},p:U,d(l){l&&a(o)}}}function ur(F){let o,k="Example:",l,m,T;return m=new ze({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, FlavaMultimodalModel
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;facebook/flava-full&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlavaMultimodalModel.from_pretrained(<span class="hljs-string">&quot;facebook/flava-full&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = tokenizer(<span class="hljs-string">&quot;Hello, my dog is cute&quot;</span>, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(**inputs)

<span class="hljs-meta">&gt;&gt;&gt; </span>last_hidden_states = outputs.last_hidden_state`,wrap:!1}}),{c(){o=d("p"),o.textContent=k,l=s(),f(m.$$.fragment)},l(t){o=c(t,"P",{"data-svelte-h":!0}),b(o)!=="svelte-11lpom8"&&(o.textContent=k),l=i(t),g(m.$$.fragment,t)},m(t,M){p(t,o,M),p(t,l,M),h(m,t,M),T=!0},p:U,i(t){T||(u(m.$$.fragment,t),T=!0)},o(t){_(m.$$.fragment,t),T=!1},d(t){t&&(a(o),a(l)),v(m,t)}}}function _r(F){let o,k,l,m,T,t,M,kt,Ce,cn='The FLAVA model was proposed in <a href="https://arxiv.org/abs/2112.04482" rel="nofollow">FLAVA: A Foundational Language And Vision Alignment Model</a> by Amanpreet Singh, Ronghang Hu, Vedanuj Goswami, Guillaume Couairon, Wojciech Galuba, Marcus Rohrbach, and Douwe Kiela and is accepted at CVPR 2022.',Tt,Ie,mn=`The paper aims at creating a single unified foundation model which can work across vision, language
as well as vision-and-language multimodal tasks.`,Mt,Pe,pn="The abstract from the paper is the following:",Ft,We,fn=`<em>State-of-the-art vision and vision-and-language models rely on large-scale visio-linguistic pretraining for obtaining good performance on a variety
of downstream tasks. Generally, such models are often either cross-modal (contrastive) or multi-modal
(with earlier fusion) but not both; and they often only target specific modalities or tasks. A promising
direction would be to use a single holistic universal model, as a “foundation”, that targets all modalities
at once — a true vision and language foundation model should be good at vision tasks, language tasks, and
cross- and multi-modal vision and language tasks. We introduce FLAVA as such a model and demonstrate
impressive performance on a wide range of 35 tasks spanning these target modalities.</em>`,xt,je,gn='This model was contributed by <a href="https://huggingface.co/aps" rel="nofollow">aps</a>. The original code can be found <a href="https://github.com/facebookresearch/multimodal/tree/main/examples/flava" rel="nofollow">here</a>.',yt,Ze,$t,C,Ue,oa,Io,hn=`<a href="/docs/transformers/main/en/model_doc/flava#transformers.FlavaConfig">FlavaConfig</a> is the configuration class to store the configuration of a <a href="/docs/transformers/main/en/model_doc/flava#transformers.FlavaModel">FlavaModel</a>. It is used to
instantiate FLAVA model according to the specified arguments, defining the text model, image model, image codebook
and multimodal model configs. Instantiating a configuration with the defaults will yield a similar configuration to
that of the FLAVA <a href="https://huggingface.co/facebook/flava-full" rel="nofollow">facebook/flava-full</a> architecture.`,ta,Po,un=`Configuration objects inherit from <a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a> and can be used to control the model outputs. Read the
documentation from <a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a> for more information.`,aa,ne,na,re,Je,ra,Wo,_n=`Instantiate a <a href="/docs/transformers/main/en/model_doc/flava#transformers.FlavaConfig">FlavaConfig</a> (or a derived class) from flava text model configuration, flava image model
configuration, flava multimodal model and flava codebook model configuration.`,wt,Ne,zt,I,Be,sa,jo,vn=`This is the configuration class to store the configuration of a <a href="/docs/transformers/main/en/model_doc/flava#transformers.FlavaTextModel">FlavaTextModel</a>. It is used to instantiate an
FLAVA model according to the specified arguments, defining the model architecture.`,ia,Zo,bn=`Instantiating a configuration with the defaults will yield a similar configuration to that of the FLAVA
<a href="https://huggingface.co/facebook/flava-full" rel="nofollow">facebook/flava-full</a> architecture.`,la,Uo,kn=`Configuration objects inherit from <a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a> and can be used to control the model outputs. Read the
documentation from <a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a> for more information.`,da,se,Ct,Ve,It,P,Le,ca,Jo,Tn=`This is the configuration class to store the configuration of a <a href="/docs/transformers/main/en/model_doc/flava#transformers.FlavaImageModel">FlavaImageModel</a>. It is used to instantiate an
FLAVA model according to the specified arguments, defining the model architecture.`,ma,No,Mn=`Instantiating a configuration with the defaults will yield a similar configuration to that of the FLAVA
<a href="https://huggingface.co/facebook/flava-full" rel="nofollow">facebook/flava-full</a> architecture.`,pa,Bo,Fn=`Configuration objects inherit from <a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a> and can be used to control the model outputs. Read the
documentation from <a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a> for more information.`,fa,ie,Pt,Re,Wt,W,Ge,ga,Vo,xn=`This is the configuration class to store the configuration of a <a href="/docs/transformers/main/en/model_doc/flava#transformers.FlavaMultimodalModel">FlavaMultimodalModel</a>. It is used to instantiate
an FLAVA model according to the specified arguments, defining the model architecture.`,ha,Lo,yn=`Instantiating a configuration with the defaults will yield a similar configuration to that of the FLAVA
<a href="https://huggingface.co/facebook/flava-full" rel="nofollow">facebook/flava-full</a> architecture.`,ua,Ro,$n=`Configuration objects inherit from <a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a> and can be used to control the model outputs. Read the
documentation from <a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a> for more information.`,_a,le,jt,qe,Zt,Ee,He,Ut,Ae,Jt,j,Oe,va,Go,wn="Constructs a FLAVA processor which wraps a FLAVA image processor and a FLAVA tokenizer into a single processor.",ba,qo,zn=`<a href="/docs/transformers/main/en/model_doc/flava#transformers.FlavaProcessor">FlavaProcessor</a> offers all the functionalities of <a href="/docs/transformers/main/en/model_doc/flava#transformers.FlavaImageProcessor">FlavaImageProcessor</a> and <a href="/docs/transformers/main/en/model_doc/bert#transformers.BertTokenizerFast">BertTokenizerFast</a>. See the
<code>__call__()</code> and <a href="/docs/transformers/main/en/model_doc/flava#transformers.FlavaProcessor.decode">decode()</a> for more information.`,ka,de,De,Ta,Eo,Cn=`This method forwards all its arguments to BertTokenizerFast’s <a href="/docs/transformers/main/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.batch_decode">batch_decode()</a>. Please
refer to the docstring of this method for more information.`,Ma,ce,Se,Fa,Ho,In=`This method forwards all its arguments to BertTokenizerFast’s <a href="/docs/transformers/main/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.decode">decode()</a>. Please refer to
the docstring of this method for more information.`,Nt,Xe,Bt,Ye,Qe,Vt,Ke,Lt,D,eo,xa,Ao,Pn="Constructs a Flava image processor.",ya,me,oo,$a,Oo,Wn="Preprocess an image or batch of images.",Rt,to,Gt,J,ao,wa,Do,jn="The FLAVA model for pretraining which outputs losses, embeddings, logits and transformer outputs.",za,So,Zn=`This model is a PyTorch <a href="https://pytorch.org/docs/stable/nn.html#torch.nn.Module" rel="nofollow">torch.nn.Module</a> subclass. Use it
as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.`,Ca,K,no,Ia,Xo,Un='The <a href="/docs/transformers/main/en/model_doc/flava#transformers.FlavaForPreTraining">FlavaForPreTraining</a> forward method, overrides the <code>__call__</code> special method.',Pa,pe,qt,ro,Et,Z,so,Wa,Yo,Jn=`The bare FLAVA Model transformer outputting raw hidden-states without any specific head on top.
This model is a PyTorch <a href="https://pytorch.org/docs/stable/nn.html#torch.nn.Module" rel="nofollow">torch.nn.Module</a> subclass. Use it
as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.`,ja,q,io,Za,Qo,Nn='The <a href="/docs/transformers/main/en/model_doc/flava#transformers.FlavaModel">FlavaModel</a> forward method, overrides the <code>__call__</code> special method.',Ua,fe,Ja,ge,Na,ee,lo,Ba,Ko,Bn='The <a href="/docs/transformers/main/en/model_doc/flava#transformers.FlavaModel">FlavaModel</a> forward method, overrides the <code>__call__</code> special method.',Va,he,La,oe,co,Ra,et,Vn='The <a href="/docs/transformers/main/en/model_doc/flava#transformers.FlavaModel">FlavaModel</a> forward method, overrides the <code>__call__</code> special method.',Ga,ue,Ht,mo,At,w,po,qa,ot,Ln=`The FLAVA’s image codebook model inspired from DALL-E’s original encoder. Outputs raw hidden states and can be used
to generate image tokens for an image based on DALL-E’s vocab. Used to generate labels for MIM. Use
<code>get_codebook_indices</code> to get image tokens for an image.`,Ea,tt,Rn=`This model is a PyTorch <a href="https://pytorch.org/docs/stable/nn.html#torch.nn.Module" rel="nofollow">torch.nn.Module</a> subclass. Use it
as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.`,Ha,at,fo,Aa,nt,go,Oa,rt,ho,Ot,uo,Dt,S,_o,Da,st,Gn=`The bare FLAVA Text Model transformer outputting raw hidden-states without any specific head on top.
This model is a PyTorch <a href="https://pytorch.org/docs/stable/nn.html#torch.nn.Module" rel="nofollow">torch.nn.Module</a> subclass. Use it
as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.`,Sa,E,vo,Xa,it,qn='The <a href="/docs/transformers/main/en/model_doc/flava#transformers.FlavaTextModel">FlavaTextModel</a> forward method, overrides the <code>__call__</code> special method.',Ya,_e,Qa,ve,St,bo,Xt,X,ko,Ka,lt,En=`The bare FLAVA Image Model transformer outputting raw hidden-states without any specific head on top.
This model is a PyTorch <a href="https://pytorch.org/docs/stable/nn.html#torch.nn.Module" rel="nofollow">torch.nn.Module</a> subclass. Use it
as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.`,en,H,To,on,dt,Hn='The <a href="/docs/transformers/main/en/model_doc/flava#transformers.FlavaImageModel">FlavaImageModel</a> forward method, overrides the <code>__call__</code> special method.',tn,be,an,ke,Yt,Mo,Qt,Y,Fo,nn,ct,An=`The bare FLAVA Multimodal Model transformer outputting raw hidden-states without any specific head on top.
This model is a PyTorch <a href="https://pytorch.org/docs/stable/nn.html#torch.nn.Module" rel="nofollow">torch.nn.Module</a> subclass. Use it
as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.`,rn,A,xo,sn,mt,On='The <a href="/docs/transformers/main/en/model_doc/flava#transformers.FlavaMultimodalModel">FlavaMultimodalModel</a> forward method, overrides the <code>__call__</code> special method.',ln,Te,dn,Me,Kt,_t,ea;return T=new z({props:{title:"FLAVA",local:"flava",headingTag:"h1"}}),M=new z({props:{title:"Overview",local:"overview",headingTag:"h2"}}),Ze=new z({props:{title:"FlavaConfig",local:"transformers.FlavaConfig",headingTag:"h2"}}),Ue=new $({props:{name:"class transformers.FlavaConfig",anchor:"transformers.FlavaConfig",parameters:[{name:"image_config",val:": Dict = None"},{name:"text_config",val:": Dict = None"},{name:"multimodal_config",val:": Dict = None"},{name:"image_codebook_config",val:": Dict = None"},{name:"hidden_size",val:": int = 768"},{name:"layer_norm_eps",val:": float = 1e-12"},{name:"projection_dim",val:": int = 768"},{name:"init_codebook",val:": bool = True"},{name:"logit_scale_init_value",val:": float = 2.6592"},{name:"initializer_range",val:": float = 0.02"},{name:"ce_ignore_index",val:": int = -100"},{name:"mim_weight",val:": float = 1.0"},{name:"mlm_weight",val:": float = 1.0"},{name:"global_contrastive_weight",val:": float = 1.0"},{name:"itm_weight",val:": float = 1.0"},{name:"mmm_image_weight",val:": float = 1.0"},{name:"mmm_text_weight",val:": float = 1.0"},{name:"global_backprop_contrastive",val:": bool = True"},{name:"skip_unmasked_multimodal_encoder",val:": bool = True"},{name:"return_loss",val:": bool = True"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlavaConfig.text_config",description:`<strong>text_config</strong> (<code>dict</code>, <em>optional</em>) &#x2014;
Dictionary of configuration options used to initialize <a href="/docs/transformers/main/en/model_doc/flava#transformers.FlavaTextConfig">FlavaTextConfig</a>.`,name:"text_config"},{anchor:"transformers.FlavaConfig.image_config",description:`<strong>image_config</strong> (<code>dict</code>, <em>optional</em>) &#x2014;
Dictionary of configuration options used to initialize <a href="/docs/transformers/main/en/model_doc/flava#transformers.FlavaImageConfig">FlavaImageConfig</a>.`,name:"image_config"},{anchor:"transformers.FlavaConfig.multimodal_config",description:`<strong>multimodal_config</strong> (<code>dict</code>, <em>optional</em>) &#x2014;
Dictionary of configuration options used to initialize <a href="/docs/transformers/main/en/model_doc/flava#transformers.FlavaMultimodalConfig">FlavaMultimodalConfig</a>.`,name:"multimodal_config"},{anchor:"transformers.FlavaConfig.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.FlavaConfig.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.FlavaConfig.projection_dim",description:`<strong>projection_dim</strong> (<code>int</code>, <em>optional</em>, defaults to 512) &#x2014;
Dimentionality of text and image projection layers.`,name:"projection_dim"},{anchor:"transformers.FlavaConfig.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 FLAVA/CLIP
implementation.`,name:"logit_scale_init_value"},{anchor:"transformers.FlavaConfig.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.FlavaConfig.ce_ignore_index",description:`<strong>ce_ignore_index</strong> (<code>int</code>, <em>optional</em>, defaults to -100) &#x2014;
Cross entropy index to ignore.`,name:"ce_ignore_index"},{anchor:"transformers.FlavaConfig.mim_weight",description:`<strong>mim_weight</strong> (<code>float</code>, <em>optional</em>, defaults to 1.0) &#x2014;
Weight to be assigned to MIM (Masked Image Modeling) unimodal loss`,name:"mim_weight"},{anchor:"transformers.FlavaConfig.mlm_weight",description:`<strong>mlm_weight</strong> (<code>float</code>, <em>optional</em>, defaults to 1.0) &#x2014;
Weight to be assigned to MLM (Masked Language Modeling) unimodal loss`,name:"mlm_weight"},{anchor:"transformers.FlavaConfig.global_contrastive_weight",description:`<strong>global_contrastive_weight</strong> (<code>float</code>, <em>optional</em>, defaults to 1.0) &#x2014;
Weight to be assigned to global contrastive cross-alignment loss.`,name:"global_contrastive_weight"},{anchor:"transformers.FlavaConfig.itm_weight",description:`<strong>itm_weight</strong> (<code>float</code>, <em>optional</em>, defaults to 1.0) &#x2014;
Weight to be assigned to image-text matching multimodal loss.`,name:"itm_weight"},{anchor:"transformers.FlavaConfig.mmm_image_weight",description:`<strong>mmm_image_weight</strong> (<code>float</code>, <em>optional</em>, defaults to 1.0) &#x2014;
Weight to be assigned to MMM loss&#x2019;s image part.`,name:"mmm_image_weight"},{anchor:"transformers.FlavaConfig.mmm_text_weight",description:`<strong>mmm_text_weight</strong> (<code>float</code>, <em>optional</em>, defaults to 1.0) &#x2014;
Weight to be assigned to MMM loss&#x2019;s text part.`,name:"mmm_text_weight"},{anchor:"transformers.FlavaConfig.global_backprop_contrastive",description:`<strong>global_backprop_contrastive</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether to use global backpropgation through all workers in contrastive loss.`,name:"global_backprop_contrastive"},{anchor:"transformers.FlavaConfig.skip_unmasked_multimodal_encoder",description:`<strong>skip_unmasked_multimodal_encoder</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether to skip running unmasked multimodal encoder whose outputs are not used by FLAVA losses.`,name:"skip_unmasked_multimodal_encoder"},{anchor:"transformers.FlavaConfig.return_loss",description:`<strong>return_loss</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether to return loss or not`,name:"return_loss"},{anchor:"transformers.FlavaConfig.kwargs",description:`<strong>kwargs</strong> (<em>optional</em>) &#x2014;
Dictionary of keyword arguments.`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/flava/configuration_flava.py#L467"}}),ne=new we({props:{anchor:"transformers.FlavaConfig.example",$$slots:{default:[tr]},$$scope:{ctx:F}}}),Je=new $({props:{name:"from_configs",anchor:"transformers.FlavaConfig.from_configs",parameters:[{name:"image_config",val:": FlavaImageConfig"},{name:"text_config",val:": FlavaTextConfig"},{name:"multimodal_config",val:": FlavaMultimodalConfig"},{name:"image_codebook_config",val:": FlavaImageCodebookConfig"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/flava/configuration_flava.py#L741",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


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


<p><a
  href="/docs/transformers/main/en/model_doc/flava#transformers.FlavaConfig"
>FlavaConfig</a></p>
`}}),Ne=new z({props:{title:"FlavaTextConfig",local:"transformers.FlavaTextConfig",headingTag:"h2"}}),Be=new $({props:{name:"class transformers.FlavaTextConfig",anchor:"transformers.FlavaTextConfig",parameters:[{name:"vocab_size",val:": int = 30522"},{name:"type_vocab_size",val:": int = 2"},{name:"max_position_embeddings",val:": int = 512"},{name:"position_embedding_type",val:": str = 'absolute'"},{name:"hidden_size",val:": int = 768"},{name:"num_hidden_layers",val:": int = 12"},{name:"num_attention_heads",val:": int = 12"},{name:"intermediate_size",val:": int = 3072"},{name:"hidden_act",val:": str = 'gelu'"},{name:"hidden_dropout_prob",val:": float = 0.0"},{name:"attention_probs_dropout_prob",val:": float = 0.0"},{name:"initializer_range",val:": float = 0.02"},{name:"layer_norm_eps",val:": float = 1e-12"},{name:"pad_token_id",val:": int = 0"},{name:"qkv_bias",val:": bool = True"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlavaTextConfig.vocab_size",description:`<strong>vocab_size</strong> (<code>int</code>, <em>optional</em>, defaults to 30522) &#x2014;
Vocabulary size of the BERT model. Defines the number of different tokens that can be represented by the
<code>inputs_ids</code> passed when calling <a href="/docs/transformers/main/en/model_doc/flava#transformers.FlavaTextModel">FlavaTextModel</a>.`,name:"vocab_size"},{anchor:"transformers.FlavaTextConfig.type_vocab_size",description:`<strong>type_vocab_size</strong> (<code>int</code>, <em>optional</em>, defaults to 2) &#x2014;
The vocabulary size of the <code>token_type_ids</code> passed when calling <a href="/docs/transformers/main/en/model_doc/flava#transformers.FlavaTextModel">FlavaTextModel</a>. Note that even though
text encoder allows <code>token_type_ids</code>&#x2019;s value as 2, for text-only pretraining and fine-tuning, only 1 is
used similar to RoBERTa.`,name:"type_vocab_size"},{anchor:"transformers.FlavaTextConfig.max_position_embeddings",description:`<strong>max_position_embeddings</strong> (<code>int</code>, <em>optional</em>, defaults to 512) &#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). For VL, max_length passed to model is 77.`,name:"max_position_embeddings"},{anchor:"transformers.FlavaTextConfig.position_embedding_type",description:`<strong>position_embedding_type</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;absolute&quot;</code>) &#x2014;
Type of position embedding. Choose one of <code>&quot;absolute&quot;</code>, <code>&quot;relative_key&quot;</code>, <code>&quot;relative_key_query&quot;</code>. For
positional embeddings use <code>&quot;absolute&quot;</code>. For more information on <code>&quot;relative_key&quot;</code>, please refer to
<a href="https://arxiv.org/abs/1803.02155" rel="nofollow">Self-Attention with Relative Position Representations (Shaw et al.)</a>.
For more information on <code>&quot;relative_key_query&quot;</code>, please refer to <em>Method 4</em> in <a href="https://arxiv.org/abs/2009.13658" rel="nofollow">Improve Transformer Models
with Better Relative Position Embeddings (Huang et al.)</a>.`,name:"position_embedding_type"},{anchor:"transformers.FlavaTextConfig.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.FlavaTextConfig.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.FlavaTextConfig.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.FlavaTextConfig.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.FlavaTextConfig.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> are supported.`,name:"hidden_act"},{anchor:"transformers.FlavaTextConfig.hidden_dropout_prob",description:`<strong>hidden_dropout_prob</strong> (<code>float</code>, <em>optional</em>, defaults to 0.1) &#x2014;
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.`,name:"hidden_dropout_prob"},{anchor:"transformers.FlavaTextConfig.attention_probs_dropout_prob",description:`<strong>attention_probs_dropout_prob</strong> (<code>float</code>, <em>optional</em>, defaults to 0.1) &#x2014;
The dropout ratio for the attention probabilities.`,name:"attention_probs_dropout_prob"},{anchor:"transformers.FlavaTextConfig.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.FlavaTextConfig.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.FlavaTextConfig.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.FlavaTextConfig.patch_size",description:`<strong>patch_size</strong> (<code>int</code>, <em>optional</em>, defaults to 16) &#x2014;
The size (resolution) of each patch.`,name:"patch_size"},{anchor:"transformers.FlavaTextConfig.num_channels",description:`<strong>num_channels</strong> (<code>int</code>, <em>optional</em>, defaults to 3) &#x2014;
The number of input channels.`,name:"num_channels"},{anchor:"transformers.FlavaTextConfig.qkv_bias",description:`<strong>qkv_bias</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether to add a bias to the queries, keys and values.`,name:"qkv_bias"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/flava/configuration_flava.py#L149"}}),se=new we({props:{anchor:"transformers.FlavaTextConfig.example",$$slots:{default:[ar]},$$scope:{ctx:F}}}),Ve=new z({props:{title:"FlavaImageConfig",local:"transformers.FlavaImageConfig",headingTag:"h2"}}),Le=new $({props:{name:"class transformers.FlavaImageConfig",anchor:"transformers.FlavaImageConfig",parameters:[{name:"hidden_size",val:": int = 768"},{name:"num_hidden_layers",val:": int = 12"},{name:"num_attention_heads",val:": int = 12"},{name:"intermediate_size",val:": int = 3072"},{name:"hidden_act",val:": int = 'gelu'"},{name:"hidden_dropout_prob",val:": float = 0.0"},{name:"attention_probs_dropout_prob",val:": float = 0.0"},{name:"initializer_range",val:": float = 0.02"},{name:"layer_norm_eps",val:": float = 1e-12"},{name:"image_size",val:": int = 224"},{name:"patch_size",val:": int = 16"},{name:"num_channels",val:": int = 3"},{name:"qkv_bias",val:": bool = True"},{name:"mask_token",val:": bool = True"},{name:"vocab_size",val:": int = 8192"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlavaImageConfig.hidden_size",description:`<strong>hidden_size</strong> (<code>int</code>, <em>optional</em>, defaults to 768) &#x2014;
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Number of hidden layers in the Transformer encoder.`,name:"num_hidden_layers"},{anchor:"transformers.FlavaImageConfig.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.FlavaImageConfig.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.FlavaImageConfig.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> are supported.`,name:"hidden_act"},{anchor:"transformers.FlavaImageConfig.hidden_dropout_prob",description:`<strong>hidden_dropout_prob</strong> (<code>float</code>, <em>optional</em>, defaults to 0.0) &#x2014;
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The standard deviation of the truncated_normal_initializer for initializing all weight matrices.`,name:"initializer_range"},{anchor:"transformers.FlavaImageConfig.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.FlavaImageConfig.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.FlavaImageConfig.patch_size",description:`<strong>patch_size</strong> (<code>int</code>, <em>optional</em>, defaults to 16) &#x2014;
The size (resolution) of each patch.`,name:"patch_size"},{anchor:"transformers.FlavaImageConfig.num_channels",description:`<strong>num_channels</strong> (<code>int</code>, <em>optional</em>, defaults to 3) &#x2014;
The number of input channels.`,name:"num_channels"},{anchor:"transformers.FlavaImageConfig.qkv_bias",description:`<strong>qkv_bias</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether to add a bias to the queries, keys and values.`,name:"qkv_bias"},{anchor:"transformers.FlavaImageConfig.mask_token",description:`<strong>mask_token</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether to use a mask token or not. Used in MIM (Masked Image Modeling) loss for FLAVA.`,name:"mask_token"},{anchor:"transformers.FlavaImageConfig.vocab_size",description:`<strong>vocab_size</strong> (<code>int</code>, <em>optional</em>, defaults to 8192) &#x2014;
Vocabulary size of the <a href="/docs/transformers/main/en/model_doc/flava#transformers.FlavaImageCodebook">FlavaImageCodebook</a> used in conjunction with <a href="/docs/transformers/main/en/model_doc/flava#transformers.FlavaImageModel">FlavaImageModel</a> for MIM (Masked
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Number of attention heads for each attention layer in the Transformer encoder.`,name:"num_attention_heads"},{anchor:"transformers.FlavaMultimodalConfig.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.FlavaMultimodalConfig.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> are supported.`,name:"hidden_act"},{anchor:"transformers.FlavaMultimodalConfig.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.FlavaMultimodalConfig.attention_probs_dropout_prob",description:`<strong>attention_probs_dropout_prob</strong> (<code>float</code>, <em>optional</em>, defaults to 0.0) &#x2014;
The dropout ratio for the attention probabilities.`,name:"attention_probs_dropout_prob"},{anchor:"transformers.FlavaMultimodalConfig.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.FlavaMultimodalConfig.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.FlavaMultimodalConfig.qkv_bias",description:`<strong>qkv_bias</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether to add a bias to the queries, keys and values.`,name:"qkv_bias"},{anchor:"transformers.FlavaMultimodalConfig.use_cls_token",description:`<strong>use_cls_token</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether to use an extra CLS token for multimodal settings. Usually needed by the FLAVA model.`,name:"use_cls_token"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/flava/configuration_flava.py#L279"}}),le=new we({props:{anchor:"transformers.FlavaMultimodalConfig.example",$$slots:{default:[rr]},$$scope:{ctx:F}}}),qe=new z({props:{title:"FlavaImageCodebookConfig",local:"transformers.FlavaImageCodebookConfig",headingTag:"h2"}}),He=new $({props:{name:"class transformers.FlavaImageCodebookConfig",anchor:"transformers.FlavaImageCodebookConfig",parameters:[{name:"num_groups",val:": int = 4"},{name:"input_channels",val:": int = 3"},{name:"num_blocks_per_group",val:": int = 2"},{name:"hidden_size",val:": int = 256"},{name:"vocab_size",val:": int = 8192"},{name:"freeze",val:": int = True"},{name:"initializer_range",val:": float = 0.02"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/flava/configuration_flava.py#L382"}}),Ae=new z({props:{title:"FlavaProcessor",local:"transformers.FlavaProcessor",headingTag:"h2"}}),Oe=new $({props:{name:"class transformers.FlavaProcessor",anchor:"transformers.FlavaProcessor",parameters:[{name:"image_processor",val:" = None"},{name:"tokenizer",val:" = None"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlavaProcessor.image_processor",description:'<strong>image_processor</strong> (<a href="/docs/transformers/main/en/model_doc/flava#transformers.FlavaImageProcessor">FlavaImageProcessor</a>, <em>optional</em>) &#x2014; The image processor is a required input.',name:"image_processor"},{anchor:"transformers.FlavaProcessor.tokenizer",description:'<strong>tokenizer</strong> (<a href="/docs/transformers/main/en/model_doc/bert#transformers.BertTokenizerFast">BertTokenizerFast</a>, <em>optional</em>) &#x2014; The tokenizer is a required input.',name:"tokenizer"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/flava/processing_flava.py#L28"}}),De=new $({props:{name:"batch_decode",anchor:"transformers.FlavaProcessor.batch_decode",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/flava/processing_flava.py#L131"}}),Se=new $({props:{name:"decode",anchor:"transformers.FlavaProcessor.decode",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/flava/processing_flava.py#L138"}}),Xe=new z({props:{title:"FlavaFeatureExtractor",local:"transformers.FlavaFeatureExtractor",headingTag:"h2"}}),Qe=new $({props:{name:"class transformers.FlavaFeatureExtractor",anchor:"transformers.FlavaFeatureExtractor",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/flava/feature_extraction_flava.py#L26"}}),Ke=new z({props:{title:"FlavaImageProcessor",local:"transformers.FlavaImageProcessor",headingTag:"h2"}}),eo=new $({props:{name:"class transformers.FlavaImageProcessor",anchor:"transformers.FlavaImageProcessor",parameters:[{name:"do_resize",val:": bool = True"},{name:"size",val:": Dict = None"},{name:"resample",val:": Resampling = <Resampling.BICUBIC: 3>"},{name:"do_center_crop",val:": bool = True"},{name:"crop_size",val:": Dict = None"},{name:"do_rescale",val:": bool = True"},{name:"rescale_factor",val:": Union = 0.00392156862745098"},{name:"do_normalize",val:": bool = True"},{name:"image_mean",val:": Union = None"},{name:"image_std",val:": Union = None"},{name:"return_image_mask",val:": bool = False"},{name:"input_size_patches",val:": int = 14"},{name:"total_mask_patches",val:": int = 75"},{name:"mask_group_min_patches",val:": int = 16"},{name:"mask_group_max_patches",val:": Optional = None"},{name:"mask_group_min_aspect_ratio",val:": float = 0.3"},{name:"mask_group_max_aspect_ratio",val:": Optional = None"},{name:"return_codebook_pixels",val:": bool = False"},{name:"codebook_do_resize",val:": bool = True"},{name:"codebook_size",val:": bool = None"},{name:"codebook_resample",val:": int = <Resampling.LANCZOS: 1>"},{name:"codebook_do_center_crop",val:": bool = True"},{name:"codebook_crop_size",val:": int = None"},{name:"codebook_do_rescale",val:": bool = True"},{name:"codebook_rescale_factor",val:": Union = 0.00392156862745098"},{name:"codebook_do_map_pixels",val:": bool = True"},{name:"codebook_do_normalize",val:": bool = True"},{name:"codebook_image_mean",val:": Union = None"},{name:"codebook_image_std",val:": Union = None"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlavaImageProcessor.do_resize",description:`<strong>do_resize</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether to resize the image&#x2019;s (height, width) dimensions to the specified <code>size</code>. Can be overridden by the
<code>do_resize</code> parameter in <code>preprocess</code>.`,name:"do_resize"},{anchor:"transformers.FlavaImageProcessor.size",description:`<strong>size</strong> (<code>Dict[str, int]</code> <em>optional</em>, defaults to <code>{&quot;height&quot; -- 224, &quot;width&quot;: 224}</code>):
Size of the image after resizing. Can be overridden by the <code>size</code> parameter in <code>preprocess</code>.`,name:"size"},{anchor:"transformers.FlavaImageProcessor.resample",description:`<strong>resample</strong> (<code>PILImageResampling</code>, <em>optional</em>, defaults to <code>PILImageResampling.BICUBIC</code>) &#x2014;
Resampling filter to use if resizing the image. Can be overridden by the <code>resample</code> parameter in
<code>preprocess</code>.`,name:"resample"},{anchor:"transformers.FlavaImageProcessor.do_center_crop",description:`<strong>do_center_crop</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether to center crop the images. Can be overridden by the <code>do_center_crop</code> parameter in <code>preprocess</code>.`,name:"do_center_crop"},{anchor:"transformers.FlavaImageProcessor.crop_size",description:`<strong>crop_size</strong> (<code>Dict[str, int]</code> <em>optional</em>, defaults to <code>{&quot;height&quot; -- 224, &quot;width&quot;: 224}</code>):
Size of image after the center crop <code>(crop_size[&quot;height&quot;], crop_size[&quot;width&quot;])</code>. Can be overridden by the
<code>crop_size</code> parameter in <code>preprocess</code>.`,name:"crop_size"},{anchor:"transformers.FlavaImageProcessor.do_rescale",description:`<strong>do_rescale</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether to rescale the image by the specified scale <code>rescale_factor</code>. Can be overridden by the <code>do_rescale</code>
parameter in <code>preprocess</code>.`,name:"do_rescale"},{anchor:"transformers.FlavaImageProcessor.rescale_factor",description:`<strong>rescale_factor</strong> (<code>int</code> or <code>float</code>, <em>optional</em>, defaults to <code>1/255</code>) &#x2014;
Scale factor to use if rescaling the image. Can be overridden by the <code>rescale_factor</code> parameter in
<code>preprocess</code>.`,name:"rescale_factor"},{anchor:"transformers.FlavaImageProcessor.do_normalize",description:`<strong>do_normalize</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether to normalize the image. Can be overridden by the <code>do_normalize</code> parameter in <code>preprocess</code>.`,name:"do_normalize"},{anchor:"transformers.FlavaImageProcessor.image_mean",description:`<strong>image_mean</strong> (<code>float</code> or <code>List[float]</code>, <em>optional</em>, defaults to <code>IMAGENET_STANDARD_MEAN</code>) &#x2014;
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.`,name:"image_mean"},{anchor:"transformers.FlavaImageProcessor.image_std",description:`<strong>image_std</strong> (<code>float</code> or <code>List[float]</code>, <em>optional</em>, defaults to <code>IMAGENET_STANDARD_STD</code>) &#x2014;
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.`,name:"image_std"},{anchor:"transformers.FlavaImageProcessor.return_image_mask",description:`<strong>return_image_mask</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether to return the image mask. Can be overridden by the <code>return_image_mask</code> parameter in <code>preprocess</code>.`,name:"return_image_mask"},{anchor:"transformers.FlavaImageProcessor.input_size_patches",description:`<strong>input_size_patches</strong> (<code>int</code>, <em>optional</em>, defaults to 14) &#x2014;
Number of patches in the image in height and width direction. 14x14 = 196 total patches. Can be overridden
by the <code>input_size_patches</code> parameter in <code>preprocess</code>.`,name:"input_size_patches"},{anchor:"transformers.FlavaImageProcessor.total_mask_patches",description:`<strong>total_mask_patches</strong> (<code>int</code>, <em>optional</em>, defaults to 75) &#x2014;
Total number of patches that should be masked. Can be overridden by the <code>total_mask_patches</code> parameter in
<code>preprocess</code>.`,name:"total_mask_patches"},{anchor:"transformers.FlavaImageProcessor.mask_group_min_patches",description:`<strong>mask_group_min_patches</strong> (<code>int</code>, <em>optional</em>, defaults to 16) &#x2014;
Minimum number of patches that should be masked. Can be overridden by the <code>mask_group_min_patches</code>
parameter in <code>preprocess</code>.`,name:"mask_group_min_patches"},{anchor:"transformers.FlavaImageProcessor.mask_group_max_patches",description:`<strong>mask_group_max_patches</strong> (<code>int</code>, <em>optional</em>) &#x2014;
Maximum number of patches that should be masked. Can be overridden by the <code>mask_group_max_patches</code>
parameter in <code>preprocess</code>.`,name:"mask_group_max_patches"},{anchor:"transformers.FlavaImageProcessor.mask_group_min_aspect_ratio",description:`<strong>mask_group_min_aspect_ratio</strong> (<code>float</code>, <em>optional</em>, defaults to 0.3) &#x2014;
Minimum aspect ratio of the mask window. Can be overridden by the <code>mask_group_min_aspect_ratio</code> parameter
in <code>preprocess</code>.`,name:"mask_group_min_aspect_ratio"},{anchor:"transformers.FlavaImageProcessor.mask_group_max_aspect_ratio",description:`<strong>mask_group_max_aspect_ratio</strong> (<code>float</code>, <em>optional</em>) &#x2014;
Maximum aspect ratio of the mask window. Can be overridden by the <code>mask_group_max_aspect_ratio</code> parameter
in <code>preprocess</code>.`,name:"mask_group_max_aspect_ratio"},{anchor:"transformers.FlavaImageProcessor.codebook_do_resize",description:`<strong>codebook_do_resize</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether to resize the input for codebook to a certain. Can be overridden by the <code>codebook_do_resize</code>
parameter in <code>preprocess</code>. <code>codebook_size</code>.`,name:"codebook_do_resize"},{anchor:"transformers.FlavaImageProcessor.codebook_size",description:`<strong>codebook_size</strong> (<code>Dict[str, int]</code>, <em>optional</em>, defaults to <code>{&quot;height&quot; -- 224, &quot;width&quot;: 224}</code>):
Resize the input for codebook to the given size. Can be overridden by the <code>codebook_size</code> parameter in
<code>preprocess</code>.`,name:"codebook_size"},{anchor:"transformers.FlavaImageProcessor.codebook_resample",description:`<strong>codebook_resample</strong> (<code>PILImageResampling</code>, <em>optional</em>, defaults to <code>PILImageResampling.LANCZOS</code>) &#x2014;
Resampling filter to use if resizing the codebook image. Can be overridden by the <code>codebook_resample</code>
parameter in <code>preprocess</code>.`,name:"codebook_resample"},{anchor:"transformers.FlavaImageProcessor.codebook_do_center_crop",description:`<strong>codebook_do_center_crop</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether to crop the input for codebook at the center. If the input size is smaller than
<code>codebook_crop_size</code> along any edge, the image is padded with 0&#x2019;s and then center cropped. Can be
overridden by the <code>codebook_do_center_crop</code> parameter in <code>preprocess</code>.`,name:"codebook_do_center_crop"},{anchor:"transformers.FlavaImageProcessor.codebook_crop_size",description:`<strong>codebook_crop_size</strong> (<code>Dict[str, int]</code>, <em>optional</em>, defaults to <code>{&quot;height&quot; -- 224, &quot;width&quot;: 224}</code>):
Desired output size for codebook input when applying center-cropping. Can be overridden by the
<code>codebook_crop_size</code> parameter in <code>preprocess</code>.`,name:"codebook_crop_size"},{anchor:"transformers.FlavaImageProcessor.codebook_do_rescale",description:`<strong>codebook_do_rescale</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether to rescale the input for codebook by the specified scale <code>codebook_rescale_factor</code>. Can be
overridden by the <code>codebook_do_rescale</code> parameter in <code>preprocess</code>.`,name:"codebook_do_rescale"},{anchor:"transformers.FlavaImageProcessor.codebook_rescale_factor",description:`<strong>codebook_rescale_factor</strong> (<code>int</code> or <code>float</code>, <em>optional</em>, defaults to <code>1/255</code>) &#x2014;
Defines the scale factor to use if rescaling the codebook image. Can be overridden by the
<code>codebook_rescale_factor</code> parameter in <code>preprocess</code>.`,name:"codebook_rescale_factor"},{anchor:"transformers.FlavaImageProcessor.codebook_do_map_pixels",description:`<strong>codebook_do_map_pixels</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether to map the pixel values of the codebook input to (1 - 2e)x + e. Can be overridden by the
<code>codebook_do_map_pixels</code> parameter in <code>preprocess</code>.`,name:"codebook_do_map_pixels"},{anchor:"transformers.FlavaImageProcessor.codebook_do_normalize",description:`<strong>codebook_do_normalize</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether or not to normalize the input for codebook with <code>codebook_image_mean</code> and <code>codebook_image_std</code>. Can
be overridden by the <code>codebook_do_normalize</code> parameter in <code>preprocess</code>.`,name:"codebook_do_normalize"},{anchor:"transformers.FlavaImageProcessor.codebook_image_mean",description:`<strong>codebook_image_mean</strong> (<code>Optional[Union[float, Iterable[float]]]</code>, <em>optional</em>, defaults to <code>[0, 0, 0]</code>) &#x2014;
The sequence of means for each channel, to be used when normalizing images for codebook. Can be overridden
by the <code>codebook_image_mean</code> parameter in <code>preprocess</code>.`,name:"codebook_image_mean"},{anchor:"transformers.FlavaImageProcessor.codebook_image_std",description:`<strong>codebook_image_std</strong> (<code>Optional[Union[float, Iterable[float]]]</code>, <em>optional</em>, defaults to <code>[0.5, 0.5, 0.5]</code>) &#x2014;
The sequence of standard deviations for each channel, to be used when normalizing images for codebook. Can
be overridden by the <code>codebook_image_std</code> parameter in <code>preprocess</code>.`,name:"codebook_image_std"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/flava/image_processing_flava.py#L137"}}),oo=new $({props:{name:"preprocess",anchor:"transformers.FlavaImageProcessor.preprocess",parameters:[{name:"images",val:": Union"},{name:"do_resize",val:": Optional = None"},{name:"size",val:": Dict = None"},{name:"resample",val:": Resampling = None"},{name:"do_center_crop",val:": Optional = None"},{name:"crop_size",val:": Optional = None"},{name:"do_rescale",val:": Optional = None"},{name:"rescale_factor",val:": Optional = None"},{name:"do_normalize",val:": Optional = None"},{name:"image_mean",val:": Union = None"},{name:"image_std",val:": Union = None"},{name:"return_image_mask",val:": Optional = None"},{name:"input_size_patches",val:": Optional = None"},{name:"total_mask_patches",val:": Optional = None"},{name:"mask_group_min_patches",val:": Optional = None"},{name:"mask_group_max_patches",val:": Optional = None"},{name:"mask_group_min_aspect_ratio",val:": Optional = None"},{name:"mask_group_max_aspect_ratio",val:": Optional = None"},{name:"return_codebook_pixels",val:": Optional = None"},{name:"codebook_do_resize",val:": Optional = None"},{name:"codebook_size",val:": Optional = None"},{name:"codebook_resample",val:": Optional = None"},{name:"codebook_do_center_crop",val:": Optional = None"},{name:"codebook_crop_size",val:": Optional = None"},{name:"codebook_do_rescale",val:": Optional = None"},{name:"codebook_rescale_factor",val:": Optional = None"},{name:"codebook_do_map_pixels",val:": Optional = None"},{name:"codebook_do_normalize",val:": Optional = None"},{name:"codebook_image_mean",val:": Optional = None"},{name:"codebook_image_std",val:": Optional = None"},{name:"return_tensors",val:": Union = None"},{name:"data_format",val:": ChannelDimension = <ChannelDimension.FIRST: 'channels_first'>"},{name:"input_data_format",val:": Union = None"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlavaImageProcessor.preprocess.images",description:`<strong>images</strong> (<code>ImageInput</code>) &#x2014;
Image to preprocess. Expects a single or batch of images with pixel values ranging from 0 to 255. If
passing in images with pixel values between 0 and 1, set <code>do_rescale=False</code>.`,name:"images"},{anchor:"transformers.FlavaImageProcessor.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.FlavaImageProcessor.preprocess.size",description:`<strong>size</strong> (<code>Dict[str, int]</code>, <em>optional</em>, defaults to <code>self.size</code>) &#x2014;
Size of the image.`,name:"size"},{anchor:"transformers.FlavaImageProcessor.preprocess.resample",description:`<strong>resample</strong> (<code>int</code>, <em>optional</em>, defaults to <code>self.resample</code>) &#x2014;
Resampling filter to use if resizing the image. This can be one of the enum <code>PILImageResampling</code>, Only
has an effect if <code>do_resize</code> is set to <code>True</code>.`,name:"resample"},{anchor:"transformers.FlavaImageProcessor.preprocess.do_center_crop",description:`<strong>do_center_crop</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>self.do_center_crop</code>) &#x2014;
Whether to center crop the image.`,name:"do_center_crop"},{anchor:"transformers.FlavaImageProcessor.preprocess.crop_size",description:`<strong>crop_size</strong> (<code>Dict[str, int]</code>, <em>optional</em>, defaults to <code>self.crop_size</code>) &#x2014;
Size of the center crop. Only has an effect if <code>do_center_crop</code> is set to <code>True</code>.`,name:"crop_size"},{anchor:"transformers.FlavaImageProcessor.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.FlavaImageProcessor.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.FlavaImageProcessor.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.FlavaImageProcessor.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.`,name:"image_mean"},{anchor:"transformers.FlavaImageProcessor.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.`,name:"image_std"},{anchor:"transformers.FlavaImageProcessor.preprocess.return_image_mask",description:`<strong>return_image_mask</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>self.return_image_mask</code>) &#x2014;
Whether to return the image mask.`,name:"return_image_mask"},{anchor:"transformers.FlavaImageProcessor.preprocess.input_size_patches",description:`<strong>input_size_patches</strong> (<code>int</code>, <em>optional</em>, defaults to <code>self.input_size_patches</code>) &#x2014;
Size of the patches to extract from the image.`,name:"input_size_patches"},{anchor:"transformers.FlavaImageProcessor.preprocess.total_mask_patches",description:`<strong>total_mask_patches</strong> (<code>int</code>, <em>optional</em>, defaults to <code>self.total_mask_patches</code>) &#x2014;
Total number of patches to extract from the image.`,name:"total_mask_patches"},{anchor:"transformers.FlavaImageProcessor.preprocess.mask_group_min_patches",description:`<strong>mask_group_min_patches</strong> (<code>int</code>, <em>optional</em>, defaults to <code>self.mask_group_min_patches</code>) &#x2014;
Minimum number of patches to extract from the image.`,name:"mask_group_min_patches"},{anchor:"transformers.FlavaImageProcessor.preprocess.mask_group_max_patches",description:`<strong>mask_group_max_patches</strong> (<code>int</code>, <em>optional</em>, defaults to <code>self.mask_group_max_patches</code>) &#x2014;
Maximum number of patches to extract from the image.`,name:"mask_group_max_patches"},{anchor:"transformers.FlavaImageProcessor.preprocess.mask_group_min_aspect_ratio",description:`<strong>mask_group_min_aspect_ratio</strong> (<code>float</code>, <em>optional</em>, defaults to <code>self.mask_group_min_aspect_ratio</code>) &#x2014;
Minimum aspect ratio of the patches to extract from the image.`,name:"mask_group_min_aspect_ratio"},{anchor:"transformers.FlavaImageProcessor.preprocess.mask_group_max_aspect_ratio",description:`<strong>mask_group_max_aspect_ratio</strong> (<code>float</code>, <em>optional</em>, defaults to <code>self.mask_group_max_aspect_ratio</code>) &#x2014;
Maximum aspect ratio of the patches to extract from the image.`,name:"mask_group_max_aspect_ratio"},{anchor:"transformers.FlavaImageProcessor.preprocess.return_codebook_pixels",description:`<strong>return_codebook_pixels</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>self.return_codebook_pixels</code>) &#x2014;
Whether to return the codebook pixels.`,name:"return_codebook_pixels"},{anchor:"transformers.FlavaImageProcessor.preprocess.codebook_do_resize",description:`<strong>codebook_do_resize</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>self.codebook_do_resize</code>) &#x2014;
Whether to resize the codebook pixels.`,name:"codebook_do_resize"},{anchor:"transformers.FlavaImageProcessor.preprocess.codebook_size",description:`<strong>codebook_size</strong> (<code>Dict[str, int]</code>, <em>optional</em>, defaults to <code>self.codebook_size</code>) &#x2014;
Size of the codebook pixels.`,name:"codebook_size"},{anchor:"transformers.FlavaImageProcessor.preprocess.codebook_resample",description:`<strong>codebook_resample</strong> (<code>int</code>, <em>optional</em>, defaults to <code>self.codebook_resample</code>) &#x2014;
Resampling filter to use if resizing the codebook pixels. This can be one of the enum
<code>PILImageResampling</code>, Only has an effect if <code>codebook_do_resize</code> is set to <code>True</code>.`,name:"codebook_resample"},{anchor:"transformers.FlavaImageProcessor.preprocess.codebook_do_center_crop",description:`<strong>codebook_do_center_crop</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>self.codebook_do_center_crop</code>) &#x2014;
Whether to center crop the codebook pixels.`,name:"codebook_do_center_crop"},{anchor:"transformers.FlavaImageProcessor.preprocess.codebook_crop_size",description:`<strong>codebook_crop_size</strong> (<code>Dict[str, int]</code>, <em>optional</em>, defaults to <code>self.codebook_crop_size</code>) &#x2014;
Size of the center crop of the codebook pixels. Only has an effect if <code>codebook_do_center_crop</code> is set
to <code>True</code>.`,name:"codebook_crop_size"},{anchor:"transformers.FlavaImageProcessor.preprocess.codebook_do_rescale",description:`<strong>codebook_do_rescale</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>self.codebook_do_rescale</code>) &#x2014;
Whether to rescale the codebook pixels values between [0 - 1].`,name:"codebook_do_rescale"},{anchor:"transformers.FlavaImageProcessor.preprocess.codebook_rescale_factor",description:`<strong>codebook_rescale_factor</strong> (<code>float</code>, <em>optional</em>, defaults to <code>self.codebook_rescale_factor</code>) &#x2014;
Rescale factor to rescale the codebook pixels by if <code>codebook_do_rescale</code> is set to <code>True</code>.`,name:"codebook_rescale_factor"},{anchor:"transformers.FlavaImageProcessor.preprocess.codebook_do_map_pixels",description:`<strong>codebook_do_map_pixels</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>self.codebook_do_map_pixels</code>) &#x2014;
Whether to map the codebook pixels values.`,name:"codebook_do_map_pixels"},{anchor:"transformers.FlavaImageProcessor.preprocess.codebook_do_normalize",description:`<strong>codebook_do_normalize</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>self.codebook_do_normalize</code>) &#x2014;
Whether to normalize the codebook pixels.`,name:"codebook_do_normalize"},{anchor:"transformers.FlavaImageProcessor.preprocess.codebook_image_mean",description:`<strong>codebook_image_mean</strong> (<code>float</code> or <code>List[float]</code>, <em>optional</em>, defaults to <code>self.codebook_image_mean</code>) &#x2014;
Codebook pixels mean to normalize the codebook pixels by if <code>codebook_do_normalize</code> is set to <code>True</code>.`,name:"codebook_image_mean"},{anchor:"transformers.FlavaImageProcessor.preprocess.codebook_image_std",description:`<strong>codebook_image_std</strong> (<code>float</code> or <code>List[float]</code>, <em>optional</em>, defaults to <code>self.codebook_image_std</code>) &#x2014;
Codebook pixels standard deviation to normalize the codebook pixels by if <code>codebook_do_normalize</code> is
set to <code>True</code>.`,name:"codebook_image_std"},{anchor:"transformers.FlavaImageProcessor.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.FlavaImageProcessor.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"},{anchor:"transformers.FlavaImageProcessor.preprocess.input_data_format",description:`<strong>input_data_format</strong> (<code>ChannelDimension</code> or <code>str</code>, <em>optional</em>) &#x2014;
The channel dimension format for the input image. If unset, the channel dimension format is inferred
from the input image. Can be one of:<ul>
<li><code>&quot;channels_first&quot;</code> or <code>ChannelDimension.FIRST</code>: image in (num_channels, height, width) format.</li>
<li><code>&quot;channels_last&quot;</code> or <code>ChannelDimension.LAST</code>: image in (height, width, num_channels) format.</li>
<li><code>&quot;none&quot;</code> or <code>ChannelDimension.NONE</code>: image in (height, width) format.</li>
</ul>`,name:"input_data_format"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/flava/image_processing_flava.py#L489"}}),to=new z({props:{title:"FlavaForPreTraining",local:"transformers.FlavaForPreTraining",headingTag:"h2"}}),ao=new $({props:{name:"class transformers.FlavaForPreTraining",anchor:"transformers.FlavaForPreTraining",parameters:[{name:"config",val:": FlavaConfig"},{name:"image_codebook",val:": Optional = None"}],parametersDescription:[{anchor:"transformers.FlavaForPreTraining.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/model_doc/flava#transformers.FlavaConfig">FlavaConfig</a>) &#x2014; Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the <a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"},{anchor:"transformers.FlavaForPreTraining.image_codebook",description:`<strong>image_codebook</strong> (<code>nn.Module</code>) &#x2014; If passed, the image codebook will be set to this. Otherwise. it will
be initialized using the image_codebook_config defined in the config first as the first parameter.`,name:"image_codebook"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/flava/modeling_flava.py#L1723"}}),no=new $({props:{name:"forward",anchor:"transformers.FlavaForPreTraining.forward",parameters:[{name:"input_ids",val:": Optional = None"},{name:"input_ids_masked",val:": Optional = None"},{name:"pixel_values",val:": Optional = None"},{name:"codebook_pixel_values",val:": Optional = None"},{name:"attention_mask",val:": Optional = None"},{name:"token_type_ids",val:": Optional = None"},{name:"bool_masked_pos",val:": Optional = None"},{name:"position_ids",val:": Optional = None"},{name:"image_attention_mask",val:": Optional = None"},{name:"skip_unmasked_multimodal_encoder",val:": bool = None"},{name:"mlm_labels",val:": Optional = None"},{name:"mim_labels",val:": Optional = None"},{name:"itm_labels",val:": Optional = None"},{name:"output_attentions",val:": Optional = None"},{name:"output_hidden_states",val:": bool = True"},{name:"return_dict",val:": Optional = None"},{name:"return_loss",val:": Optional = None"}],parametersDescription:[{anchor:"transformers.FlavaForPreTraining.forward.input_ids_masked",description:`<strong>input_ids_masked</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, text_seq_len)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary. These ones are the masked version of the original task
to be used with MLM. Indices can be obtained using <a href="/docs/transformers/main/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a> along with
<code>DataCollatorForMaskedLanguageModeling</code>. See <a href="/docs/transformers/main/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/main/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_masked"},{anchor:"transformers.FlavaForPreTraining.forward.input_ids",description:`<strong>input_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, text_seq_len)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary. Indices can be obtained using <a href="/docs/transformers/main/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See
<a href="/docs/transformers/main/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and <a href="/docs/transformers/main/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.FlavaForPreTraining.forward.token_type_ids",description:`<strong>token_type_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, text_seq_len)</code>, <em>optional</em>) &#x2014;
Segment token indices to indicate first and second portions of the inputs. Indices are selected in <code>[0, 1]</code>:</p>
<ul>
<li>0 corresponds to a <em>sentence A</em> token,</li>
<li>1 corresponds to a <em>sentence B</em> token.
<a href="../glossary#token-type-ids">What are token type IDs?</a></li>
</ul>`,name:"token_type_ids"},{anchor:"transformers.FlavaForPreTraining.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. Pixel values can be obtained using <a href="/docs/transformers/main/en/model_doc/auto#transformers.AutoImageProcessor">AutoImageProcessor</a>. See
<a href="/docs/transformers/main/en/model_doc/vit#transformers.ViTFeatureExtractor.__call__">FlavaImageProcessor.<strong>call</strong>()</a> for details.`,name:"pixel_values"},{anchor:"transformers.FlavaForPreTraining.forward.bool_masked_pos",description:`<strong>bool_masked_pos</strong> (<code>torch.BoolTensor</code> of shape <code>(batch_size, image_num_patches)</code>) &#x2014;
Boolean masked positions. Indicates which patches are masked (1) and which aren&#x2019;t (0).`,name:"bool_masked_pos"},{anchor:"transformers.FlavaForPreTraining.forward.interpolate_pos_encoding",description:`<strong>interpolate_pos_encoding</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether to interpolate the pre-trained position encodings.`,name:"interpolate_pos_encoding"},{anchor:"transformers.FlavaForPreTraining.forward.image_attention_mask",description:`<strong>image_attention_mask</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, image_num_patches)</code>, <em>optional</em>) &#x2014;
Mask to avoid performing attention on padding token indices specifically for images. 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:"image_attention_mask"},{anchor:"transformers.FlavaForPreTraining.forward.skip_unmasked_multimodal_encoder",description:`<strong>skip_unmasked_multimodal_encoder</strong> (<em>bool</em>, <em>optional</em>) &#x2014;
Skip any calculations for multimodal encoder for unmasked inputs. FLAVA pretraining doesn&#x2019;t need unmasked
multimodal embeddings or outputs as of now.`,name:"skip_unmasked_multimodal_encoder"},{anchor:"transformers.FlavaForPreTraining.forward.mlm_labels",description:`<strong>mlm_labels</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, text_seq_len)</code>, <em>optional</em>) &#x2014;
Labels for computing the left-to-right language and multimodal masked modeling loss (next word prediction).
Indices should be in <code>[-100, 0, ..., text_config.vocab_size - 1]</code> (see <code>input_ids</code> docstring). Tokens with
indices set to <code>-100</code> are ignored (masked), the loss is only computed for the tokens with labels in <code>[0, ..., text_config.vocab_size - 1]</code>.`,name:"mlm_labels"},{anchor:"transformers.FlavaForPreTraining.forward.mim_labels",description:`<strong>mim_labels</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, image_num_patches)</code>, <em>optional</em>) &#x2014;
Labels for computing the image and multimodal masked modeling loss. Indices should be in <code>[-100, 0, ..., image_config.vocab_size - 1]</code>. Tokens with indices set to <code>-100</code> are ignored (masked), the loss is only
computed for the tokens with labels in <code>[0, ..., image_config.vocab_size - 1]</code>. If not passed, they are
generated automatically using the image codebook assigned to the model. By default, it uses
<a href="/docs/transformers/main/en/model_doc/flava#transformers.FlavaImageCodebook">FlavaImageCodebook</a>. See <a href="/docs/transformers/main/en/model_doc/flava#transformers.FlavaImageCodebook">FlavaImageCodebook</a> to understand how to generate mim_labels.`,name:"mim_labels"},{anchor:"transformers.FlavaForPreTraining.forward.itm_labels",description:`<strong>itm_labels</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, 1)</code>, <em>optional</em>) &#x2014;
Labels for computing the image-text matching loss. 0 means the pairs don&#x2019;t match and 1 means they match.
The pairs with 0 will be skipped for calculation of MMM and global contrastive losses as well.`,name:"itm_labels"},{anchor:"transformers.FlavaForPreTraining.forward.return_loss",description:`<strong>return_loss</strong> (<code>bool</code>, <em>optional</em>, default to None) &#x2014;
Whether to return calculated loss or not.`,name:"return_loss"},{anchor:"transformers.FlavaForPreTraining.forward.attention_mask",description:`<strong>attention_mask</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, text_seq_len)</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.FlavaForPreTraining.forward.head_mask",description:`<strong>head_mask</strong> (<code>torch.FloatTensor</code> of shape <code>(num_heads,)</code> or <code>(num_layers, num_heads)</code>, <em>optional</em>) &#x2014;
Mask to nullify selected heads of the self-attention modules. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 indicates the head is <strong>not masked</strong>,</li>
<li>0 indicates the head is <strong>masked</strong>.</li>
</ul>`,name:"head_mask"},{anchor:"transformers.FlavaForPreTraining.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.FlavaForPreTraining.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.FlavaForPreTraining.forward.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/main/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.</p>
<p>Examples &#x2014;`,name:"return_dict"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/flava/modeling_flava.py#L1773",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>A <code>transformers.models.flava.modeling_flava.FlavaForPreTrainingOutput</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.flava.configuration_flava.FlavaConfig'&gt;</code>) and inputs.</p>
<ul>
<li>
<p><strong>loss</strong> (<code>torch.FloatTensor</code>, <em>optional</em>, returned when <code>return_loss</code> is True) — Total loss calculated for this model.</p>
</li>
<li>
<p><strong>loss_info</strong> (<code>FlavaLosses</code>) — Detailed info for FLAVA Pretraining losses. Check <code>FlavaLosses</code> class description for the information on
the keys.</p>
</li>
<li>
<p><strong>image_embeddings</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, output_dim)</code>, <em>optional</em>, returned when <code>pixel_values</code> are present) — The image embeddings which are basically the pooled output of <a
  href="/docs/transformers/main/en/model_doc/flava#transformers.FlavaImageModel"
>FlavaImageModel</a>.</p>
</li>
<li>
<p><strong>image_output</strong> (<code>BaseModelOutputWithPooling</code>, <em>optional</em>, returned when <code>pixel_values</code> are present) — The output of the <a
  href="/docs/transformers/main/en/model_doc/flava#transformers.FlavaImageModel"
>FlavaImageModel</a>.</p>
</li>
<li>
<p><strong>text_embeddings</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, output_dim)</code>, <em>optional</em>, returned when <code>input_ids</code> are present) — The text embeddings which are basically the pooled output of <a
  href="/docs/transformers/main/en/model_doc/flava#transformers.FlavaTextModel"
>FlavaTextModel</a>.</p>
</li>
<li>
<p><strong>text_output</strong> (<code>BaseModelOutputWithPooling</code>, <em>optional</em>, returned when <code>input_ids</code> are present) — The output of the <a
  href="/docs/transformers/main/en/model_doc/flava#transformers.FlavaTextModel"
>FlavaTextModel</a>.</p>
</li>
<li>
<p><strong>multimodal_embeddings</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, output_dim)</code>, <em>optional</em>, returned when <code>input_ids</code> and <code>pixel_values</code> are present and <code>skip_unmasked_multimodal_encoder</code> is <code>None</code> or <code>False</code>) — The multimodal embeddings which are basically the pooled output of <a
  href="/docs/transformers/main/en/model_doc/flava#transformers.FlavaTextModel"
>FlavaTextModel</a>.</p>
</li>
<li>
<p><strong>multimodal_output</strong> (<code>BaseModelOutputWithPooling</code>, returned when <code>input_ids</code> and <code>pixel_values</code> are present and <code>skip_unmasked_multimodal_encoder</code> is <code>None</code> or <code>False</code>) — The output of the <a
  href="/docs/transformers/main/en/model_doc/flava#transformers.FlavaMultimodalModel"
>FlavaMultimodalModel</a>.</p>
</li>
<li>
<p><strong>image_masked_embeddings</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, output_dim)</code>, <em>optional</em>, returned when <code>pixel_values</code> are present) — The image embeddings which are basically the pooled output of <a
  href="/docs/transformers/main/en/model_doc/flava#transformers.FlavaImageModel"
>FlavaImageModel</a>. Uses <code>bool_masked_pos</code>
to create masked images.</p>
</li>
<li>
<p><strong>image_masked_output</strong> (<code>BaseModelOutputWithPooling</code>, <em>optional</em>, returned when <code>pixel_values</code> are present) — The output of the <a
  href="/docs/transformers/main/en/model_doc/flava#transformers.FlavaImageModel"
>FlavaImageModel</a>. Uses <code>bool_masked_pos</code> to create masked images.</p>
</li>
<li>
<p><strong>text_masked_embeddings</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, output_dim)</code>, <em>optional</em>, returned when <code>input_ids_masked</code> are present) — The text embeddings which are basically the pooled output of <a
  href="/docs/transformers/main/en/model_doc/flava#transformers.FlavaTextModel"
>FlavaTextModel</a>.</p>
</li>
<li>
<p><strong>text_masked_output</strong> (<code>BaseModelOutputWithPooling</code>, <em>optional</em>, returned when <code>input_ids_masked</code> are present) — The output of the <a
  href="/docs/transformers/main/en/model_doc/flava#transformers.FlavaTextModel"
>FlavaTextModel</a>.</p>
</li>
<li>
<p><strong>multimodal_masked_embeddings</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, output_dim)</code>, <em>optional</em>, returned when <code>input_ids</code> and <code>pixel_values</code> are present) — The multimodal embeddings which are basically the pooled output of <a
  href="/docs/transformers/main/en/model_doc/flava#transformers.FlavaTextModel"
>FlavaTextModel</a>.</p>
</li>
<li>
<p><strong>multimodal_masked_output</strong> (<code>BaseModelOutputWithPooling</code>, returned when <code>input_ids_masked</code> and <code>pixel_values</code> are present) — The output of the <a
  href="/docs/transformers/main/en/model_doc/flava#transformers.FlavaMultimodalModel"
>FlavaMultimodalModel</a>.</p>
</li>
<li>
<p><strong>mim_logits</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, num_image_patches, image_vocab_size)</code> or of shape <code>(total_masked_patches, image_vocab_size)</code> , <em>optional</em>, returned when <code>pixel_values</code> are present and <code>input_ids_masked</code> are not) — The logits for MIM unimodal loss. Uses <code>book_masked_pos</code> to get masked patches. The flattened output is
returned when <code>bool_masked_pos</code> has some of the patches masked.</p>
</li>
<li>
<p><strong>mlm_logits</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, text_seq_length, text_vocab_size)</code> or of shape <code>(total_masked_seq_length, text_vocab_size)</code>, <em>optional</em>, returned when <code>input_ids_masked</code> are present and <code>pixel_values</code> are not) — The logits for MLM unimodal loss. The flattened output is returned when <code>input_ids_masked</code> has some of
the tokens masked.</p>
</li>
<li>
<p><strong>itm_logits</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, 2)</code>, <em>optional</em>, returned when <code>input_ids_masked</code> and <code>pixel_values</code> are present) — The logits for ITM loss. Note that ITM loss is calculated on masked pairs in FLAVA.</p>
</li>
<li>
<p><strong>mmm_image_logits</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, num_image_patches, image_vocab_size)</code> or of shape<code>(total_masked_patches, image_vocab_size)</code>, <em>optional</em>, returned when <code>pixel_values</code> and <code>input_ids_masked</code> are present) — The logits for MMM image multimodal loss. Uses <code>book_masked_pos</code> to get masked patches. The flattened
output is returned when <code>bool_masked_pos</code> has some of the patches masked.</p>
</li>
<li>
<p><strong>mmm_text_logits</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, text_seq_length, text_vocab_size)</code> or of shape <code>(</code>(total_masked_seq_length, text_vocab_size)<code>), *optional*, returned when </code>pixel_values<code>and</code>input_ids_masked<code>are present) -- The logits for MMM text multimodal loss. The flattened output is returned when</code>input_ids_masked\` has
some of the tokens masked.</p>
</li>
<li>
<p><strong>contrastive_logits_per_image</strong> (<code>torch.FloatTensor</code> of shape <code>(image_batch_size, text_batch_size)</code>) — The scaled dot product scores between <code>image_embeddings</code> and <code>text_embeddings</code> but passed through FLAVA’s
<code>image_projection</code> and <code>text_projection</code> layers respectively. This represents the image-text similarity
scores. This is calculated on unmasked images and texts.</p>
</li>
<li>
<p><strong>contrastive_logits_per_text</strong> (<code>torch.FloatTensor</code> of shape <code>(text_batch_size, image_batch_size)</code>) — The scaled dot product scores between <code>text_embeddings</code> and <code>image_embeddings</code> but passed through FLAVA’s
<code>text_projection</code> and <code>image_projection</code> layers respectively. This is calculated on unmasked images and
texts.</p>
</li>
</ul>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>transformers.models.flava.modeling_flava.FlavaForPreTrainingOutput</code> or <code>tuple(torch.FloatTensor)</code></p>
`}}),pe=new Co({props:{$$slots:{default:[sr]},$$scope:{ctx:F}}}),ro=new z({props:{title:"FlavaModel",local:"transformers.FlavaModel",headingTag:"h2"}}),so=new $({props:{name:"class transformers.FlavaModel",anchor:"transformers.FlavaModel",parameters:[{name:"config",val:": FlavaConfig"}],parametersDescription:[{anchor:"transformers.FlavaModel.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/model_doc/flava#transformers.FlavaConfig">FlavaConfig</a>) &#x2014; Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the <a href="/docs/transformers/main/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/main/src/transformers/models/flava/modeling_flava.py#L1178"}}),io=new $({props:{name:"forward",anchor:"transformers.FlavaModel.forward",parameters:[{name:"input_ids",val:": Optional = None"},{name:"pixel_values",val:": Optional = None"},{name:"attention_mask",val:": Optional = None"},{name:"token_type_ids",val:": Optional = None"},{name:"bool_masked_pos",val:": Optional = None"},{name:"position_ids",val:": Optional = None"},{name:"image_attention_mask",val:": Optional = None"},{name:"skip_multimodal_encoder",val:": Optional = None"},{name:"output_attentions",val:": Optional = None"},{name:"output_hidden_states",val:": bool = True"},{name:"return_dict",val:": Optional = None"}],parametersDescription:[{anchor:"transformers.FlavaModel.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. Pixel values can be obtained using <a href="/docs/transformers/main/en/model_doc/auto#transformers.AutoImageProcessor">AutoImageProcessor</a>. See
<a href="/docs/transformers/main/en/model_doc/vit#transformers.ViTFeatureExtractor.__call__">FlavaImageProcessor.<strong>call</strong>()</a> for details.`,name:"pixel_values"},{anchor:"transformers.FlavaModel.forward.bool_masked_pos",description:`<strong>bool_masked_pos</strong> (<code>torch.BoolTensor</code> of shape <code>(batch_size, image_num_patches)</code>) &#x2014;
Boolean masked positions. Indicates which patches are masked (1) and which aren&#x2019;t (0).`,name:"bool_masked_pos"},{anchor:"transformers.FlavaModel.forward.interpolate_pos_encoding",description:`<strong>interpolate_pos_encoding</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether to interpolate the pre-trained position encodings.`,name:"interpolate_pos_encoding"},{anchor:"transformers.FlavaModel.forward.input_ids",description:`<strong>input_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, image_num_patches + text_seq_len)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary. Indices can be obtained using <a href="/docs/transformers/main/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See
<a href="/docs/transformers/main/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and <a href="/docs/transformers/main/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.FlavaModel.forward.token_type_ids",description:`<strong>token_type_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, image_num_patches + text_seq_len)</code>, <em>optional</em>) &#x2014;
Segment token indices to indicate first and second portions of the inputs. Indices are selected in <code>[0, 1]</code>:</p>
<ul>
<li>0 corresponds to a <em>sentence A</em> token,</li>
<li>1 corresponds to a <em>sentence B</em> token.
<a href="../glossary#token-type-ids">What are token type IDs?</a></li>
</ul>`,name:"token_type_ids"},{anchor:"transformers.FlavaModel.forward.attention_mask",description:`<strong>attention_mask</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, image_num_patches + text_seq_len)</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.FlavaModel.forward.head_mask",description:`<strong>head_mask</strong> (<code>torch.FloatTensor</code> of shape <code>(num_heads,)</code> or <code>(num_layers, num_heads)</code>, <em>optional</em>) &#x2014;
Mask to nullify selected heads of the self-attention modules. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 indicates the head is <strong>not masked</strong>,</li>
<li>0 indicates the head is <strong>masked</strong>.</li>
</ul>`,name:"head_mask"},{anchor:"transformers.FlavaModel.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.FlavaModel.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.FlavaModel.forward.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/main/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"},{anchor:"transformers.FlavaModel.forward.skip_multimodal_encoder",description:`<strong>skip_multimodal_encoder</strong> (<em>bool</em>, <em>optional</em>) &#x2014;
Skip any calculations for multimodal encoder. Useful if multimodal encoding is not going to be used.`,name:"skip_multimodal_encoder"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/flava/modeling_flava.py#L1322",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>A <code>transformers.models.flava.modeling_flava.FlavaModelOutput</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.flava.configuration_flava.FlavaConfig'&gt;</code>) and inputs.</p>
<ul>
<li><strong>image_embeddings</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, output_dim)</code>, <em>optional</em>, returned when <code>pixel_values</code> are present) — The image embeddings which are basically the pooled output of <a
  href="/docs/transformers/main/en/model_doc/flava#transformers.FlavaImageModel"
>FlavaImageModel</a>.</li>
<li><strong>image_output</strong> (<code>BaseModelOutputWithPooling</code>, <em>optional</em>, returned when <code>pixel_values</code> are present) — The output of the <a
  href="/docs/transformers/main/en/model_doc/flava#transformers.FlavaImageModel"
>FlavaImageModel</a>.</li>
<li><strong>text_embeddings</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, output_dim)</code>, <em>optional</em>, returned when <code>input_ids</code> are present) — The text embeddings which are basically the pooled output of <a
  href="/docs/transformers/main/en/model_doc/flava#transformers.FlavaTextModel"
>FlavaTextModel</a>.</li>
<li><strong>text_output</strong> (<code>BaseModelOutputWithPooling</code>, <em>optional</em>, returned when <code>input_ids</code> are present) — The output of the <a
  href="/docs/transformers/main/en/model_doc/flava#transformers.FlavaTextModel"
>FlavaTextModel</a>.</li>
<li><strong>multimodal_embeddings</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, output_dim)</code>, <em>optional</em>, returned when <code>input_ids</code> and <code>pixel_values</code> are present and <code>skip_multimodal_encoder</code> is <code>None</code> or <code>False</code>) — The multimodal embeddings which are basically the pooled output of <a
  href="/docs/transformers/main/en/model_doc/flava#transformers.FlavaTextModel"
>FlavaTextModel</a>.</li>
<li><strong>multimodal_output</strong> (<code>BaseModelOutputWithPooling</code>, returned when <code>input_ids</code> and <code>pixel_values</code> are present and <code>skip_multimodal_encoder</code> is <code>None</code> or <code>False</code>) — The output of the <a
  href="/docs/transformers/main/en/model_doc/flava#transformers.FlavaMultimodalModel"
>FlavaMultimodalModel</a>.</li>
</ul>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>transformers.models.flava.modeling_flava.FlavaModelOutput</code> or <code>tuple(torch.FloatTensor)</code></p>
`}}),fe=new Co({props:{$$slots:{default:[ir]},$$scope:{ctx:F}}}),ge=new we({props:{anchor:"transformers.FlavaModel.forward.example",$$slots:{default:[lr]},$$scope:{ctx:F}}}),lo=new $({props:{name:"get_text_features",anchor:"transformers.FlavaModel.get_text_features",parameters:[{name:"input_ids",val:": Optional = None"},{name:"attention_mask",val:": Optional = None"},{name:"token_type_ids",val:": Optional = None"},{name:"position_ids",val:": Optional = None"},{name:"output_attentions",val:": Optional = None"},{name:"output_hidden_states",val:": Optional = None"},{name:"return_dict",val:": Optional = None"}],parametersDescription:[{anchor:"transformers.FlavaModel.get_text_features.input_ids",description:`<strong>input_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, text_seq_length)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary. Indices can be obtained using <a href="/docs/transformers/main/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See
<a href="/docs/transformers/main/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and <a href="/docs/transformers/main/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.FlavaModel.get_text_features.token_type_ids",description:`<strong>token_type_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, text_seq_length)</code>, <em>optional</em>) &#x2014;
Segment token indices to indicate first and second portions of the inputs. Indices are selected in <code>[0, 1]</code>:</p>
<ul>
<li>0 corresponds to a <em>sentence A</em> token,</li>
<li>1 corresponds to a <em>sentence B</em> token.
<a href="../glossary#token-type-ids">What are token type IDs?</a></li>
</ul>`,name:"token_type_ids"},{anchor:"transformers.FlavaModel.get_text_features.attention_mask",description:`<strong>attention_mask</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, text_seq_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.FlavaModel.get_text_features.head_mask",description:`<strong>head_mask</strong> (<code>torch.FloatTensor</code> of shape <code>(num_heads,)</code> or <code>(num_layers, num_heads)</code>, <em>optional</em>) &#x2014;
Mask to nullify selected heads of the self-attention modules. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 indicates the head is <strong>not masked</strong>,</li>
<li>0 indicates the head is <strong>masked</strong>.</li>
</ul>`,name:"head_mask"},{anchor:"transformers.FlavaModel.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.FlavaModel.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.FlavaModel.get_text_features.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/main/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/flava/modeling_flava.py#L1228"}}),he=new Co({props:{$$slots:{default:[dr]},$$scope:{ctx:F}}}),co=new $({props:{name:"get_image_features",anchor:"transformers.FlavaModel.get_image_features",parameters:[{name:"pixel_values",val:": Optional = None"},{name:"bool_masked_pos",val:": Optional = None"},{name:"interpolate_pos_encoding",val:": Optional = None"},{name:"attention_mask",val:": Optional = None"},{name:"head_mask",val:": Optional = None"},{name:"output_attentions",val:": Optional = None"},{name:"output_hidden_states",val:": Optional = None"},{name:"return_dict",val:": Optional = None"}],parametersDescription:[{anchor:"transformers.FlavaModel.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. Pixel values can be obtained using <a href="/docs/transformers/main/en/model_doc/auto#transformers.AutoImageProcessor">AutoImageProcessor</a>. See
<a href="/docs/transformers/main/en/model_doc/vit#transformers.ViTFeatureExtractor.__call__">FlavaImageProcessor.<strong>call</strong>()</a> for details.`,name:"pixel_values"},{anchor:"transformers.FlavaModel.get_image_features.bool_masked_pos",description:`<strong>bool_masked_pos</strong> (<code>torch.BoolTensor</code> of shape <code>(batch_size, image_num_patches)</code>) &#x2014;
Boolean masked positions. Indicates which patches are masked (1) and which aren&#x2019;t (0).`,name:"bool_masked_pos"},{anchor:"transformers.FlavaModel.get_image_features.interpolate_pos_encoding",description:`<strong>interpolate_pos_encoding</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether to interpolate the pre-trained position encodings.`,name:"interpolate_pos_encoding"},{anchor:"transformers.FlavaModel.get_image_features.attention_mask",description:`<strong>attention_mask</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, image_num_patches)</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.FlavaModel.get_image_features.head_mask",description:`<strong>head_mask</strong> (<code>torch.FloatTensor</code> of shape <code>(num_heads,)</code> or <code>(num_layers, num_heads)</code>, <em>optional</em>) &#x2014;
Mask to nullify selected heads of the self-attention modules. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 indicates the head is <strong>not masked</strong>,</li>
<li>0 indicates the head is <strong>masked</strong>.</li>
</ul>`,name:"head_mask"},{anchor:"transformers.FlavaModel.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.FlavaModel.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.FlavaModel.get_image_features.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/main/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/flava/modeling_flava.py#L1272"}}),ue=new Co({props:{$$slots:{default:[cr]},$$scope:{ctx:F}}}),mo=new z({props:{title:"FlavaImageCodebook",local:"transformers.FlavaImageCodebook",headingTag:"h2"}}),po=new $({props:{name:"class transformers.FlavaImageCodebook",anchor:"transformers.FlavaImageCodebook",parameters:[{name:"config",val:": FlavaImageCodebookConfig"},{name:"**kwargs",val:": Any"}],parametersDescription:[{anchor:"transformers.FlavaImageCodebook.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/model_doc/flava#transformers.FlavaImageCodebookConfig">FlavaImageCodebookConfig</a>) &#x2014; Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the <a href="/docs/transformers/main/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/main/src/transformers/models/flava/modeling_flava.py#L1509"}}),fo=new $({props:{name:"forward",anchor:"transformers.FlavaImageCodebook.forward",parameters:[{name:"pixel_values",val:": FloatTensor"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/flava/modeling_flava.py#L1599"}}),go=new $({props:{name:"get_codebook_indices",anchor:"transformers.FlavaImageCodebook.get_codebook_indices",parameters:[{name:"pixel_values",val:": Tensor"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/flava/modeling_flava.py#L1567"}}),ho=new $({props:{name:"get_codebook_probs",anchor:"transformers.FlavaImageCodebook.get_codebook_probs",parameters:[{name:"pixel_values",val:": Tensor"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/flava/modeling_flava.py#L1595"}}),uo=new z({props:{title:"FlavaTextModel",local:"transformers.FlavaTextModel",headingTag:"h2"}}),_o=new $({props:{name:"class transformers.FlavaTextModel",anchor:"transformers.FlavaTextModel",parameters:[{name:"config",val:": FlavaTextConfig"},{name:"add_pooling_layer",val:": bool = True"}],parametersDescription:[{anchor:"transformers.FlavaTextModel.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/model_doc/flava#transformers.FlavaTextConfig">FlavaTextConfig</a>) &#x2014; Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the <a href="/docs/transformers/main/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/main/src/transformers/models/flava/modeling_flava.py#L975"}}),vo=new $({props:{name:"forward",anchor:"transformers.FlavaTextModel.forward",parameters:[{name:"input_ids",val:": Optional = None"},{name:"attention_mask",val:": Optional = None"},{name:"token_type_ids",val:": Optional = None"},{name:"position_ids",val:": Optional = None"},{name:"head_mask",val:": Optional = None"},{name:"output_attentions",val:": Optional = None"},{name:"output_hidden_states",val:": Optional = None"},{name:"return_dict",val:": Optional = None"}],parametersDescription:[{anchor:"transformers.FlavaTextModel.forward.input_ids",description:`<strong>input_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, text_seq_length)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary. Indices can be obtained using <a href="/docs/transformers/main/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See
<a href="/docs/transformers/main/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and <a href="/docs/transformers/main/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.FlavaTextModel.forward.token_type_ids",description:`<strong>token_type_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, text_seq_length)</code>, <em>optional</em>) &#x2014;
Segment token indices to indicate first and second portions of the inputs. Indices are selected in <code>[0, 1]</code>:</p>
<ul>
<li>0 corresponds to a <em>sentence A</em> token,</li>
<li>1 corresponds to a <em>sentence B</em> token.
<a href="../glossary#token-type-ids">What are token type IDs?</a></li>
</ul>`,name:"token_type_ids"},{anchor:"transformers.FlavaTextModel.forward.attention_mask",description:`<strong>attention_mask</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, text_seq_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.FlavaTextModel.forward.head_mask",description:`<strong>head_mask</strong> (<code>torch.FloatTensor</code> of shape <code>(num_heads,)</code> or <code>(num_layers, num_heads)</code>, <em>optional</em>) &#x2014;
Mask to nullify selected heads of the self-attention modules. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 indicates the head is <strong>not masked</strong>,</li>
<li>0 indicates the head is <strong>masked</strong>.</li>
</ul>`,name:"head_mask"},{anchor:"transformers.FlavaTextModel.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.FlavaTextModel.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.FlavaTextModel.forward.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/main/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/flava/modeling_flava.py#L1010",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>A <a
  href="/docs/transformers/main/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 (<a
  href="/docs/transformers/main/en/model_doc/flava#transformers.FlavaTextConfig"
>FlavaTextConfig</a>) and inputs.</p>
<ul>
<li>
<p><strong>last_hidden_state</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>) — Sequence of hidden-states at the output of the last layer of the model.</p>
</li>
<li>
<p><strong>pooler_output</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, hidden_size)</code>) — Last layer hidden-state of the first token of the sequence (classification token) after further processing
through the layers used for the auxiliary pretraining task. E.g. for BERT-family of models, this returns
the classification token after processing through a linear layer and a tanh activation function. The linear
layer weights are trained from the next sentence prediction (classification) objective during pretraining.</p>
</li>
<li>
<p><strong>hidden_states</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) — Tuple of <code>torch.FloatTensor</code> (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape <code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.</p>
</li>
<li>
<p><strong>attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) — Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.</p>
</li>
</ul>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><a
  href="/docs/transformers/main/en/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPooling"
>transformers.modeling_outputs.BaseModelOutputWithPooling</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),_e=new Co({props:{$$slots:{default:[mr]},$$scope:{ctx:F}}}),ve=new we({props:{anchor:"transformers.FlavaTextModel.forward.example",$$slots:{default:[pr]},$$scope:{ctx:F}}}),bo=new z({props:{title:"FlavaImageModel",local:"transformers.FlavaImageModel",headingTag:"h2"}}),ko=new $({props:{name:"class transformers.FlavaImageModel",anchor:"transformers.FlavaImageModel",parameters:[{name:"config",val:": FlavaImageConfig"},{name:"add_pooling_layer",val:": bool = True"}],parametersDescription:[{anchor:"transformers.FlavaImageModel.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/model_doc/flava#transformers.FlavaImageConfig">FlavaImageConfig</a>) &#x2014; Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the <a href="/docs/transformers/main/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/main/src/transformers/models/flava/modeling_flava.py#L876"}}),To=new $({props:{name:"forward",anchor:"transformers.FlavaImageModel.forward",parameters:[{name:"pixel_values",val:": Optional = None"},{name:"bool_masked_pos",val:": Optional = None"},{name:"interpolate_pos_encoding",val:": Optional = None"},{name:"attention_mask",val:": Optional = None"},{name:"head_mask",val:": Optional = None"},{name:"output_attentions",val:": Optional = None"},{name:"output_hidden_states",val:": Optional = None"},{name:"return_dict",val:": Optional = None"}],parametersDescription:[{anchor:"transformers.FlavaImageModel.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. Pixel values can be obtained using <a href="/docs/transformers/main/en/model_doc/auto#transformers.AutoImageProcessor">AutoImageProcessor</a>. See
<a href="/docs/transformers/main/en/model_doc/vit#transformers.ViTFeatureExtractor.__call__">FlavaImageProcessor.<strong>call</strong>()</a> for details.`,name:"pixel_values"},{anchor:"transformers.FlavaImageModel.forward.bool_masked_pos",description:`<strong>bool_masked_pos</strong> (<code>torch.BoolTensor</code> of shape <code>(batch_size, image_num_patches)</code>) &#x2014;
Boolean masked positions. Indicates which patches are masked (1) and which aren&#x2019;t (0).`,name:"bool_masked_pos"},{anchor:"transformers.FlavaImageModel.forward.interpolate_pos_encoding",description:`<strong>interpolate_pos_encoding</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether to interpolate the pre-trained position encodings.`,name:"interpolate_pos_encoding"},{anchor:"transformers.FlavaImageModel.forward.attention_mask",description:`<strong>attention_mask</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, image_num_patches)</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.FlavaImageModel.forward.head_mask",description:`<strong>head_mask</strong> (<code>torch.FloatTensor</code> of shape <code>(num_heads,)</code> or <code>(num_layers, num_heads)</code>, <em>optional</em>) &#x2014;
Mask to nullify selected heads of the self-attention modules. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 indicates the head is <strong>not masked</strong>,</li>
<li>0 indicates the head is <strong>masked</strong>.</li>
</ul>`,name:"head_mask"},{anchor:"transformers.FlavaImageModel.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.FlavaImageModel.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.FlavaImageModel.forward.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/main/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/flava/modeling_flava.py#L913",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>A <a
  href="/docs/transformers/main/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 (<a
  href="/docs/transformers/main/en/model_doc/flava#transformers.FlavaImageConfig"
>FlavaImageConfig</a>) and inputs.</p>
<ul>
<li>
<p><strong>last_hidden_state</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>) — Sequence of hidden-states at the output of the last layer of the model.</p>
</li>
<li>
<p><strong>pooler_output</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, hidden_size)</code>) — Last layer hidden-state of the first token of the sequence (classification token) after further processing
through the layers used for the auxiliary pretraining task. E.g. for BERT-family of models, this returns
the classification token after processing through a linear layer and a tanh activation function. The linear
layer weights are trained from the next sentence prediction (classification) objective during pretraining.</p>
</li>
<li>
<p><strong>hidden_states</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) — Tuple of <code>torch.FloatTensor</code> (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape <code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.</p>
</li>
<li>
<p><strong>attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) — Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.</p>
</li>
</ul>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><a
  href="/docs/transformers/main/en/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPooling"
>transformers.modeling_outputs.BaseModelOutputWithPooling</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),be=new Co({props:{$$slots:{default:[fr]},$$scope:{ctx:F}}}),ke=new we({props:{anchor:"transformers.FlavaImageModel.forward.example",$$slots:{default:[gr]},$$scope:{ctx:F}}}),Mo=new z({props:{title:"FlavaMultimodalModel",local:"transformers.FlavaMultimodalModel",headingTag:"h2"}}),Fo=new $({props:{name:"class transformers.FlavaMultimodalModel",anchor:"transformers.FlavaMultimodalModel",parameters:[{name:"config",val:": FlavaMultimodalConfig"},{name:"add_pooling_layer",val:" = True"}],parametersDescription:[{anchor:"transformers.FlavaMultimodalModel.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/model_doc/flava#transformers.FlavaMultimodalConfig">FlavaMultimodalConfig</a>) &#x2014; Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the <a href="/docs/transformers/main/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/main/src/transformers/models/flava/modeling_flava.py#L1080"}}),xo=new $({props:{name:"forward",anchor:"transformers.FlavaMultimodalModel.forward",parameters:[{name:"hidden_states",val:": Tensor"},{name:"attention_mask",val:": Optional = None"},{name:"head_mask",val:": Optional = None"},{name:"output_attentions",val:": Optional = None"},{name:"output_hidden_states",val:": Optional = None"},{name:"return_dict",val:": Optional = None"}],parametersDescription:[{anchor:"transformers.FlavaMultimodalModel.forward.hidden_states",description:`<strong>hidden_states</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, image_num_patches + text_seq_len, hidden_size)</code>) &#x2014;
The concatenated hidden states of unimodal encoders.`,name:"hidden_states"},{anchor:"transformers.FlavaMultimodalModel.forward.attention_mask",description:`<strong>attention_mask</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, image_num_patches + text_seq_len)</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.FlavaMultimodalModel.forward.head_mask",description:`<strong>head_mask</strong> (<code>torch.FloatTensor</code> of shape <code>(num_heads,)</code> or <code>(num_layers, num_heads)</code>, <em>optional</em>) &#x2014;
Mask to nullify selected heads of the self-attention modules. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 indicates the head is <strong>not masked</strong>,</li>
<li>0 indicates the head is <strong>masked</strong>.</li>
</ul>`,name:"head_mask"},{anchor:"transformers.FlavaMultimodalModel.forward.output_attentions",description:`<strong>output_attentions</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
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tensors for more detail.`,name:"output_attentions"},{anchor:"transformers.FlavaMultimodalModel.forward.output_hidden_states",description:`<strong>output_hidden_states</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
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more detail.`,name:"output_hidden_states"},{anchor:"transformers.FlavaMultimodalModel.forward.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
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<p>A <a
  href="/docs/transformers/main/en/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPooling"
>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 (<a
  href="/docs/transformers/main/en/model_doc/flava#transformers.FlavaMultimodalConfig"
>FlavaMultimodalConfig</a>) and inputs.</p>
<ul>
<li>
<p><strong>last_hidden_state</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>) — Sequence of hidden-states at the output of the last layer of the model.</p>
</li>
<li>
<p><strong>pooler_output</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, hidden_size)</code>) — Last layer hidden-state of the first token of the sequence (classification token) after further processing
through the layers used for the auxiliary pretraining task. E.g. for BERT-family of models, this returns
the classification token after processing through a linear layer and a tanh activation function. The linear
layer weights are trained from the next sentence prediction (classification) objective during pretraining.</p>
</li>
<li>
<p><strong>hidden_states</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) — Tuple of <code>torch.FloatTensor</code> (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape <code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.</p>
</li>
<li>
<p><strong>attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) — Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.</p>
</li>
</ul>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><a
  href="/docs/transformers/main/en/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPooling"
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