import{S as cp,i as mp,s as pp,e as a,k as m,w as b,t as s,M as fp,c as n,d as t,m as p,a as r,x as k,h as i,b as d,G as e,g as u,y,q as F,o as T,B as w,v as hp,L as yo}from"../../chunks/vendor-hf-doc-builder.js";import{T as Ot}from"../../chunks/Tip-hf-doc-builder.js";import{D as x}from"../../chunks/Docstring-hf-doc-builder.js";import{C as Fo}from"../../chunks/CodeBlock-hf-doc-builder.js";import{I as C}from"../../chunks/IconCopyLink-hf-doc-builder.js";import{E as ko}from"../../chunks/ExampleCodeBlock-hf-doc-builder.js";function gp($){let c,M,_,g,v;return g=new Fo({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`}}),{c(){c=a("p"),M=s("Example:"),_=m(),b(g.$$.fragment)},l(l){c=n(l,"P",{});var f=r(c);M=i(f,"Example:"),f.forEach(t),_=p(l),k(g.$$.fragment,l)},m(l,f){u(l,c,f),e(c,M),u(l,_,f),y(g,l,f),v=!0},p:yo,i(l){v||(F(g.$$.fragment,l),v=!0)},o(l){T(g.$$.fragment,l),v=!1},d(l){l&&t(c),l&&t(_),w(g,l)}}}function up($){let c,M,_,g,v;return g=new Fo({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`}}),{c(){c=a("p"),M=s("Example:"),_=m(),b(g.$$.fragment)},l(l){c=n(l,"P",{});var f=r(c);M=i(f,"Example:"),f.forEach(t),_=p(l),k(g.$$.fragment,l)},m(l,f){u(l,c,f),e(c,M),u(l,_,f),y(g,l,f),v=!0},p:yo,i(l){v||(F(g.$$.fragment,l),v=!0)},o(l){T(g.$$.fragment,l),v=!1},d(l){l&&t(c),l&&t(_),w(g,l)}}}function _p($){let c,M,_,g,v;return g=new Fo({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`}}),{c(){c=a("p"),M=s("Example:"),_=m(),b(g.$$.fragment)},l(l){c=n(l,"P",{});var f=r(c);M=i(f,"Example:"),f.forEach(t),_=p(l),k(g.$$.fragment,l)},m(l,f){u(l,c,f),e(c,M),u(l,_,f),y(g,l,f),v=!0},p:yo,i(l){v||(F(g.$$.fragment,l),v=!0)},o(l){T(g.$$.fragment,l),v=!1},d(l){l&&t(c),l&&t(_),w(g,l)}}}function vp($){let c,M,_,g,v;return g=new Fo({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`}}),{c(){c=a("p"),M=s("Example:"),_=m(),b(g.$$.fragment)},l(l){c=n(l,"P",{});var f=r(c);M=i(f,"Example:"),f.forEach(t),_=p(l),k(g.$$.fragment,l)},m(l,f){u(l,c,f),e(c,M),u(l,_,f),y(g,l,f),v=!0},p:yo,i(l){v||(F(g.$$.fragment,l),v=!0)},o(l){T(g.$$.fragment,l),v=!1},d(l){l&&t(c),l&&t(_),w(g,l)}}}function bp($){let c,M,_,g,v;return{c(){c=a("p"),M=s("Although the recipe for forward pass needs to be defined within this function, one should call the "),_=a("code"),g=s("Module"),v=s(`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`)},l(l){c=n(l,"P",{});var f=r(c);M=i(f,"Although the recipe for forward pass needs to be defined within this function, one should call the "),_=n(f,"CODE",{});var I=r(_);g=i(I,"Module"),I.forEach(t),v=i(f,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),f.forEach(t)},m(l,f){u(l,c,f),e(c,M),e(c,_),e(_,g),e(c,v)},d(l){l&&t(c)}}}function kp($){let c,M,_,g,v;return{c(){c=a("p"),M=s("Although the recipe for forward pass needs to be defined within this function, one should call the "),_=a("code"),g=s("Module"),v=s(`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`)},l(l){c=n(l,"P",{});var f=r(c);M=i(f,"Although the recipe for forward pass needs to be defined within this function, one should call the "),_=n(f,"CODE",{});var I=r(_);g=i(I,"Module"),I.forEach(t),v=i(f,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),f.forEach(t)},m(l,f){u(l,c,f),e(c,M),e(c,_),e(_,g),e(c,v)},d(l){l&&t(c)}}}function yp($){let c,M,_,g,v;return g=new Fo({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>logits_per_image = outputs.contrastive_logits_per_image  <span class="hljs-comment"># this is the image-text similarity score</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>probs = logits_per_image.softmax(dim=<span class="hljs-number">1</span>)  <span class="hljs-comment"># we can take the softmax to get the label probabilities</span>`}}),{c(){c=a("p"),M=s("Examples:"),_=m(),b(g.$$.fragment)},l(l){c=n(l,"P",{});var f=r(c);M=i(f,"Examples:"),f.forEach(t),_=p(l),k(g.$$.fragment,l)},m(l,f){u(l,c,f),e(c,M),u(l,_,f),y(g,l,f),v=!0},p:yo,i(l){v||(F(g.$$.fragment,l),v=!0)},o(l){T(g.$$.fragment,l),v=!1},d(l){l&&t(c),l&&t(_),w(g,l)}}}function Fp($){let c,M,_,g,v;return{c(){c=a("p"),M=s("Although the recipe for forward pass needs to be defined within this function, one should call the "),_=a("code"),g=s("Module"),v=s(`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`)},l(l){c=n(l,"P",{});var f=r(c);M=i(f,"Although the recipe for forward pass needs to be defined within this function, one should call the "),_=n(f,"CODE",{});var I=r(_);g=i(I,"Module"),I.forEach(t),v=i(f,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),f.forEach(t)},m(l,f){u(l,c,f),e(c,M),e(c,_),e(_,g),e(c,v)},d(l){l&&t(c)}}}function Tp($){let c,M,_,g,v;return{c(){c=a("p"),M=s("Although the recipe for forward pass needs to be defined within this function, one should call the "),_=a("code"),g=s("Module"),v=s(`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`)},l(l){c=n(l,"P",{});var f=r(c);M=i(f,"Although the recipe for forward pass needs to be defined within this function, one should call the "),_=n(f,"CODE",{});var I=r(_);g=i(I,"Module"),I.forEach(t),v=i(f,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),f.forEach(t)},m(l,f){u(l,c,f),e(c,M),e(c,_),e(_,g),e(c,v)},d(l){l&&t(c)}}}function wp($){let c,M,_,g,v;return{c(){c=a("p"),M=s("Although the recipe for forward pass needs to be defined within this function, one should call the "),_=a("code"),g=s("Module"),v=s(`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`)},l(l){c=n(l,"P",{});var f=r(c);M=i(f,"Although the recipe for forward pass needs to be defined within this function, one should call the "),_=n(f,"CODE",{});var I=r(_);g=i(I,"Module"),I.forEach(t),v=i(f,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),f.forEach(t)},m(l,f){u(l,c,f),e(c,M),e(c,_),e(_,g),e(c,v)},d(l){l&&t(c)}}}function Mp($){let c,M,_,g,v;return g=new Fo({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Ub2tlbml6ZXIlMkMlMjBGbGF2YVRleHRNb2RlbCUwQWltcG9ydCUyMHRvcmNoJTBBJTBBdG9rZW5pemVyJTIwJTNEJTIwQXV0b1Rva2VuaXplci5mcm9tX3ByZXRyYWluZWQoJTIyZmFjZWJvb2slMkZmbGF2YS1mdWxsJTIyKSUwQW1vZGVsJTIwJTNEJTIwRmxhdmFUZXh0TW9kZWwuZnJvbV9wcmV0cmFpbmVkKCUyMmZhY2Vib29rJTJGZmxhdmEtZnVsbCUyMiklMEElMEFpbnB1dHMlMjAlM0QlMjB0b2tlbml6ZXIoJTIySGVsbG8lMkMlMjBteSUyMGRvZyUyMGlzJTIwY3V0ZSUyMiUyQyUyMHJldHVybl90ZW5zb3JzJTNEJTIycHQlMjIpJTBBb3V0cHV0cyUyMCUzRCUyMG1vZGVsKCoqaW5wdXRzKSUwQSUwQWxhc3RfaGlkZGVuX3N0YXRlcyUyMCUzRCUyMG91dHB1dHMubGFzdF9oaWRkZW5fc3RhdGU=",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`}}),{c(){c=a("p"),M=s("Example:"),_=m(),b(g.$$.fragment)},l(l){c=n(l,"P",{});var f=r(c);M=i(f,"Example:"),f.forEach(t),_=p(l),k(g.$$.fragment,l)},m(l,f){u(l,c,f),e(c,M),u(l,_,f),y(g,l,f),v=!0},p:yo,i(l){v||(F(g.$$.fragment,l),v=!0)},o(l){T(g.$$.fragment,l),v=!1},d(l){l&&t(c),l&&t(_),w(g,l)}}}function $p($){let c,M,_,g,v;return{c(){c=a("p"),M=s("Although the recipe for forward pass needs to be defined within this function, one should call the "),_=a("code"),g=s("Module"),v=s(`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`)},l(l){c=n(l,"P",{});var f=r(c);M=i(f,"Although the recipe for forward pass needs to be defined within this function, one should call the "),_=n(f,"CODE",{});var I=r(_);g=i(I,"Module"),I.forEach(t),v=i(f,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),f.forEach(t)},m(l,f){u(l,c,f),e(c,M),e(c,_),e(_,g),e(c,v)},d(l){l&&t(c)}}}function xp($){let c,M,_,g,v;return g=new Fo({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9JbWFnZVByb2Nlc3NvciUyQyUyMEZsYXZhSW1hZ2VNb2RlbCUwQWltcG9ydCUyMHRvcmNoJTBBZnJvbSUyMGRhdGFzZXRzJTIwaW1wb3J0JTIwbG9hZF9kYXRhc2V0JTBBJTBBZGF0YXNldCUyMCUzRCUyMGxvYWRfZGF0YXNldCglMjJodWdnaW5nZmFjZSUyRmNhdHMtaW1hZ2UlMjIpJTBBaW1hZ2UlMjAlM0QlMjBkYXRhc2V0JTVCJTIydGVzdCUyMiU1RCU1QiUyMmltYWdlJTIyJTVEJTVCMCU1RCUwQSUwQWltYWdlX3Byb2Nlc3NvciUyMCUzRCUyMEF1dG9JbWFnZVByb2Nlc3Nvci5mcm9tX3ByZXRyYWluZWQoJTIyZmFjZWJvb2slMkZmbGF2YS1mdWxsJTIyKSUwQW1vZGVsJTIwJTNEJTIwRmxhdmFJbWFnZU1vZGVsLmZyb21fcHJldHJhaW5lZCglMjJmYWNlYm9vayUyRmZsYXZhLWZ1bGwlMjIpJTBBJTBBaW5wdXRzJTIwJTNEJTIwaW1hZ2VfcHJvY2Vzc29yKGltYWdlJTJDJTIwcmV0dXJuX3RlbnNvcnMlM0QlMjJwdCUyMiklMEElMEF3aXRoJTIwdG9yY2gubm9fZ3JhZCgpJTNBJTBBJTIwJTIwJTIwJTIwb3V0cHV0cyUyMCUzRCUyMG1vZGVsKCoqaW5wdXRzKSUwQSUwQWxhc3RfaGlkZGVuX3N0YXRlcyUyMCUzRCUyMG91dHB1dHMubGFzdF9oaWRkZW5fc3RhdGUlMEFsaXN0KGxhc3RfaGlkZGVuX3N0YXRlcy5zaGFwZSk=",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>]`}}),{c(){c=a("p"),M=s("Example:"),_=m(),b(g.$$.fragment)},l(l){c=n(l,"P",{});var f=r(c);M=i(f,"Example:"),f.forEach(t),_=p(l),k(g.$$.fragment,l)},m(l,f){u(l,c,f),e(c,M),u(l,_,f),y(g,l,f),v=!0},p:yo,i(l){v||(F(g.$$.fragment,l),v=!0)},o(l){T(g.$$.fragment,l),v=!1},d(l){l&&t(c),l&&t(_),w(g,l)}}}function Ip($){let c,M,_,g,v;return{c(){c=a("p"),M=s("Although the recipe for forward pass needs to be defined within this function, one should call the "),_=a("code"),g=s("Module"),v=s(`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`)},l(l){c=n(l,"P",{});var f=r(c);M=i(f,"Although the recipe for forward pass needs to be defined within this function, one should call the "),_=n(f,"CODE",{});var I=r(_);g=i(I,"Module"),I.forEach(t),v=i(f,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),f.forEach(t)},m(l,f){u(l,c,f),e(c,M),e(c,_),e(_,g),e(c,v)},d(l){l&&t(c)}}}function zp($){let c,M,_,g,v;return g=new Fo({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`}}),{c(){c=a("p"),M=s("Example:"),_=m(),b(g.$$.fragment)},l(l){c=n(l,"P",{});var f=r(c);M=i(f,"Example:"),f.forEach(t),_=p(l),k(g.$$.fragment,l)},m(l,f){u(l,c,f),e(c,M),u(l,_,f),y(g,l,f),v=!0},p:yo,i(l){v||(F(g.$$.fragment,l),v=!0)},o(l){T(g.$$.fragment,l),v=!1},d(l){l&&t(c),l&&t(_),w(g,l)}}}function Cp($){let c,M,_,g,v,l,f,I,ls,On,se,Ae,Sa,To,ds,La,cs,Dn,We,ms,wo,ps,fs,Xn,Dt,hs,Yn,Xt,gs,Hn,Yt,Oa,us,Qn,X,_s,Mo,vs,bs,$o,ks,ys,Kn,ie,je,Da,xo,Fs,Xa,Ts,er,P,Io,ws,Y,Ht,Ms,$s,Qt,xs,Is,zo,zs,Cs,Ps,le,Es,Kt,As,Ws,ea,js,Us,Ns,Ue,Zs,Ne,Co,Js,Po,Vs,oa,Bs,Rs,or,de,Ze,Ya,Eo,Gs,Ha,qs,tr,E,Ao,Ss,Wo,Ls,ta,Os,Ds,Xs,jo,Ys,Uo,Hs,Qs,Ks,ce,ei,aa,oi,ti,na,ai,ni,ri,Je,ar,me,Ve,Qa,No,si,Ka,ii,nr,A,Zo,li,Jo,di,ra,ci,mi,pi,Vo,fi,Bo,hi,gi,ui,pe,_i,sa,vi,bi,ia,ki,yi,Fi,Be,rr,fe,Re,en,Ro,Ti,on,wi,sr,W,Go,Mi,qo,$i,la,xi,Ii,zi,So,Ci,Lo,Pi,Ei,Ai,he,Wi,da,ji,Ui,ca,Ni,Zi,Ji,Ge,ir,ge,qe,tn,Oo,Vi,an,Bi,lr,Do,Xo,dr,ue,Se,nn,Yo,Ri,rn,Gi,cr,j,Ho,qi,sn,Si,Li,N,ma,Oi,Di,pa,Xi,Yi,fa,Hi,Qi,ln,Ki,el,ha,ol,tl,al,Le,Qo,nl,Ko,rl,ga,sl,il,ll,Oe,et,dl,ot,cl,ua,ml,pl,mr,_e,De,dn,tt,fl,cn,hl,pr,at,nt,fr,ve,Xe,mn,rt,gl,pn,ul,hr,q,st,_l,fn,vl,bl,Ye,it,kl,hn,yl,gr,be,He,gn,lt,Fl,un,Tl,ur,Z,dt,wl,_n,Ml,$l,ct,xl,mt,Il,zl,Cl,H,pt,Pl,ke,El,_a,Al,Wl,vn,jl,Ul,Nl,Qe,_r,ye,Ke,bn,ft,Zl,kn,Jl,vr,U,ht,Vl,gt,Bl,ut,Rl,Gl,ql,J,_t,Sl,Fe,Ll,va,Ol,Dl,yn,Xl,Yl,Hl,eo,Ql,oo,Kl,Q,vt,ed,Te,od,ba,td,ad,Fn,nd,rd,sd,to,id,K,bt,ld,we,dd,ka,cd,md,Tn,pd,fd,hd,ao,br,Me,no,wn,kt,gd,Mn,ud,kr,z,yt,_d,Ft,vd,$n,bd,kd,yd,Tt,Fd,wt,Td,wd,Md,ya,Mt,$d,Fa,$t,xd,Ta,xt,yr,$e,ro,xn,It,Id,In,zd,Fr,S,zt,Cd,Ct,Pd,Pt,Ed,Ad,Wd,V,Et,jd,xe,Ud,wa,Nd,Zd,zn,Jd,Vd,Bd,so,Rd,io,Tr,Ie,lo,Cn,At,Gd,Pn,qd,wr,L,Wt,Sd,jt,Ld,Ut,Od,Dd,Xd,B,Nt,Yd,ze,Hd,Ma,Qd,Kd,En,ec,oc,tc,co,ac,mo,Mr,Ce,po,An,Zt,nc,Wn,rc,$r,O,Jt,sc,Vt,ic,Bt,lc,dc,cc,R,Rt,mc,Pe,pc,$a,fc,hc,jn,gc,uc,_c,fo,vc,ho,xr;return l=new C({}),To=new C({}),xo=new C({}),Io=new x({props:{name:"class transformers.FlavaConfig",anchor:"transformers.FlavaConfig",parameters:[{name:"image_config",val:": typing.Dict[str, typing.Any] = None"},{name:"text_config",val:": typing.Dict[str, typing.Any] = None"},{name:"multimodal_config",val:": typing.Dict[str, typing.Any] = None"},{name:"image_codebook_config",val:": typing.Dict[str, typing.Any] = 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"}}),Ue=new ko({props:{anchor:"transformers.FlavaConfig.example",$$slots:{default:[gp]},$$scope:{ctx:$}}}),Co=new x({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:`
<p>An instance of a configuration object</p>
`,returnType:`
<p><a
  href="/docs/transformers/main/en/model_doc/flava#transformers.FlavaConfig"
>FlavaConfig</a></p>
`}}),Eo=new C({}),Ao=new x({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;
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Whether to crop the input for codebook at the center. If the input size is smaller than
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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#L134"}}),it=new x({props:{name:"preprocess",anchor:"transformers.FlavaImageProcessor.preprocess",parameters:[{name:"images",val:": typing.Union[ForwardRef('PIL.Image.Image'), numpy.ndarray, ForwardRef('torch.Tensor'), typing.List[ForwardRef('PIL.Image.Image')], typing.List[numpy.ndarray], typing.List[ForwardRef('torch.Tensor')]]"},{name:"do_resize",val:": typing.Optional[bool] = None"},{name:"size",val:": typing.Dict[str, int] = None"},{name:"resample",val:": Resampling = None"},{name:"do_center_crop",val:": typing.Optional[bool] = None"},{name:"crop_size",val:": typing.Union[typing.Dict[str, int], NoneType] = None"},{name:"do_rescale",val:": typing.Optional[bool] = None"},{name:"rescale_factor",val:": typing.Optional[float] = None"},{name:"do_normalize",val:": typing.Optional[bool] = None"},{name:"image_mean",val:": typing.Union[float, typing.List[float], NoneType] = None"},{name:"image_std",val:": typing.Union[float, typing.List[float], NoneType] = None"},{name:"return_image_mask",val:": typing.Optional[bool] = None"},{name:"input_size_patches",val:": typing.Optional[int] = None"},{name:"total_mask_patches",val:": typing.Optional[int] = None"},{name:"mask_group_min_patches",val:": typing.Optional[int] = None"},{name:"mask_group_max_patches",val:": typing.Optional[int] = None"},{name:"mask_group_min_aspect_ratio",val:": typing.Optional[float] = None"},{name:"mask_group_max_aspect_ratio",val:": typing.Optional[float] = None"},{name:"return_codebook_pixels",val:": typing.Optional[bool] = None"},{name:"codebook_do_resize",val:": typing.Optional[bool] = None"},{name:"codebook_size",val:": typing.Union[typing.Dict[str, int], NoneType] = None"},{name:"codebook_resample",val:": typing.Optional[int] = None"},{name:"codebook_do_center_crop",val:": typing.Optional[bool] = None"},{name:"codebook_crop_size",val:": typing.Union[typing.Dict[str, int], NoneType] = None"},{name:"codebook_do_rescale",val:": typing.Optional[bool] = None"},{name:"codebook_rescale_factor",val:": typing.Optional[float] = None"},{name:"codebook_do_map_pixels",val:": typing.Optional[bool] = None"},{name:"codebook_do_normalize",val:": typing.Optional[bool] = None"},{name:"codebook_image_mean",val:": typing.Optional[typing.Iterable[float]] = None"},{name:"codebook_image_std",val:": typing.Optional[typing.Iterable[float]] = None"},{name:"return_tensors",val:": typing.Union[str, transformers.utils.generic.TensorType, NoneType] = None"},{name:"data_format",val:": ChannelDimension = <ChannelDimension.FIRST: 'channels_first'>"},{name:"input_data_format",val:": typing.Union[str, transformers.image_utils.ChannelDimension, NoneType] = None"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlavaImageProcessor.preprocess.images",description:`<strong>images</strong> (<code>ImageInput</code>) &#x2014;
Image to preprocess.`,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#L440"}}),lt=new C({}),dt=new x({props:{name:"class transformers.FlavaForPreTraining",anchor:"transformers.FlavaForPreTraining",parameters:[{name:"config",val:": FlavaConfig"},{name:"image_codebook",val:": typing.Optional[torch.nn.modules.module.Module] = 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#L1727"}}),pt=new x({props:{name:"forward",anchor:"transformers.FlavaForPreTraining.forward",parameters:[{name:"input_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"input_ids_masked",val:": typing.Optional[torch.LongTensor] = None"},{name:"pixel_values",val:": typing.Optional[torch.FloatTensor] = None"},{name:"codebook_pixel_values",val:": typing.Optional[torch.FloatTensor] = None"},{name:"attention_mask",val:": typing.Optional[torch.Tensor] = None"},{name:"token_type_ids",val:": typing.Optional[torch.Tensor] = None"},{name:"bool_masked_pos",val:": typing.Optional[torch.Tensor] = None"},{name:"position_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"image_attention_mask",val:": typing.Optional[torch.Tensor] = None"},{name:"skip_unmasked_multimodal_encoder",val:": bool = None"},{name:"mlm_labels",val:": typing.Optional[torch.Tensor] = None"},{name:"mim_labels",val:": typing.Optional[torch.Tensor] = None"},{name:"itm_labels",val:": typing.Optional[torch.Tensor] = None"},{name:"output_attentions",val:": typing.Optional[bool] = None"},{name:"output_hidden_states",val:": bool = True"},{name:"return_dict",val:": typing.Optional[bool] = None"},{name:"return_loss",val:": typing.Optional[bool] = 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/donut#transformers.DonutFeatureExtractor.__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#L1771",returnDescription:`
<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) \u2014 Total loss calculated for this model.</p>
</li>
<li>
<p><strong>loss_info</strong> (<code>FlavaLosses</code>) \u2014 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) \u2014 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) \u2014 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) \u2014 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) \u2014 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>) \u2014 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>) \u2014 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) \u2014 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) \u2014 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) \u2014 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) \u2014 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) \u2014 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) \u2014 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) \u2014 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) \u2014 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) \u2014 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) \u2014 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>) \u2014 The scaled dot product scores between <code>image_embeddings</code> and <code>text_embeddings</code> but passed through FLAVA\u2019s
<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>) \u2014 The scaled dot product scores between <code>text_embeddings</code> and <code>image_embeddings</code> but passed through FLAVA\u2019s
<code>text_projection</code> and <code>image_projection</code> layers respectively. This is calculated on unmasked images and
texts.</p>
</li>
</ul>
`,returnType:`
<p><code>transformers.models.flava.modeling_flava.FlavaForPreTrainingOutput</code> or <code>tuple(torch.FloatTensor)</code></p>
`}}),Qe=new Ot({props:{$$slots:{default:[bp]},$$scope:{ctx:$}}}),ft=new C({}),ht=new x({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#L1193"}}),_t=new x({props:{name:"forward",anchor:"transformers.FlavaModel.forward",parameters:[{name:"input_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"pixel_values",val:": typing.Optional[torch.FloatTensor] = None"},{name:"attention_mask",val:": typing.Optional[torch.Tensor] = None"},{name:"token_type_ids",val:": typing.Optional[torch.Tensor] = None"},{name:"bool_masked_pos",val:": typing.Optional[torch.Tensor] = None"},{name:"position_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"image_attention_mask",val:": typing.Optional[torch.Tensor] = None"},{name:"skip_multimodal_encoder",val:": typing.Optional[bool] = None"},{name:"output_attentions",val:": typing.Optional[bool] = None"},{name:"output_hidden_states",val:": bool = True"},{name:"return_dict",val:": typing.Optional[bool] = 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/donut#transformers.DonutFeatureExtractor.__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#L1337",returnDescription:`
<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) \u2014 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) \u2014 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) \u2014 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) \u2014 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>) \u2014 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>) \u2014 The output of the <a
  href="/docs/transformers/main/en/model_doc/flava#transformers.FlavaMultimodalModel"
>FlavaMultimodalModel</a>.</li>
</ul>
`,returnType:`
<p><code>transformers.models.flava.modeling_flava.FlavaModelOutput</code> or <code>tuple(torch.FloatTensor)</code></p>
`}}),eo=new Ot({props:{$$slots:{default:[kp]},$$scope:{ctx:$}}}),oo=new ko({props:{anchor:"transformers.FlavaModel.forward.example",$$slots:{default:[yp]},$$scope:{ctx:$}}}),vt=new x({props:{name:"get_text_features",anchor:"transformers.FlavaModel.get_text_features",parameters:[{name:"input_ids",val:": typing.Optional[torch.Tensor] = None"},{name:"attention_mask",val:": typing.Optional[torch.Tensor] = None"},{name:"token_type_ids",val:": typing.Optional[torch.Tensor] = None"},{name:"position_ids",val:": typing.Optional[torch.Tensor] = None"},{name:"output_attentions",val:": typing.Optional[bool] = None"},{name:"output_hidden_states",val:": typing.Optional[bool] = None"},{name:"return_dict",val:": typing.Optional[bool] = None"}],parametersDescription:[{anchor:"transformers.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;
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<ul>
<li>0 corresponds to a <em>sentence A</em> token,</li>
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<a href="../glossary#token-type-ids">What are token type IDs?</a></li>
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<a href="../glossary#attention-mask">What are attention masks?</a></li>
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  href="/docs/transformers/main/en/model_doc/flava#transformers.FlavaTextConfig"
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<ul>
<li>
<p><strong>last_hidden_state</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>) \u2014 Sequence of hidden-states at the output of the last layer of the model.</p>
</li>
<li>
<p><strong>pooler_output</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, hidden_size)</code>) \u2014 Last layer hidden-state of the first token of the sequence (classification token) after further processing
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layer weights are trained from the next sentence prediction (classification) objective during pretraining.</p>
</li>
<li>
<p><strong>hidden_states</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for the output of the embeddings, if the model has an embedding layer, +
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<p>Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.</p>
</li>
<li>
<p><strong>attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.</p>
</li>
</ul>
`,returnType:`
<p><a
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<ul>
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<a href="../glossary#attention-mask">What are attention masks?</a></li>
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<ul>
<li>
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</li>
<li>
<p><strong>pooler_output</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, hidden_size)</code>) \u2014 Last layer hidden-state of the first token of the sequence (classification token) after further processing
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layer weights are trained from the next sentence prediction (classification) objective during pretraining.</p>
</li>
<li>
<p><strong>hidden_states</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for the output of the embeddings, if the model has an embedding layer, +
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<p>Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.</p>
</li>
<li>
<p><strong>attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
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</li>
</ul>
`,returnType:`
<p><a
  href="/docs/transformers/main/en/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPooling"
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<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>
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<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>) \u2014 Sequence of hidden-states at the output of the last layer of the model.</p>
</li>
<li>
<p><strong>pooler_output</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, hidden_size)</code>) \u2014 Last layer hidden-state of the first token of the sequence (classification token) after further processing
through the layers used for the auxiliary pretraining task. E.g. for BERT-family of models, this returns
the classification token after processing through a linear layer and a tanh activation function. The linear
layer weights are trained from the next sentence prediction (classification) objective during pretraining.</p>
</li>
<li>
<p><strong>hidden_states</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape <code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.</p>
</li>
<li>
<p><strong>attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.</p>
</li>
</ul>
`,returnType:`
<p><a
  href="/docs/transformers/main/en/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPooling"
>transformers.modeling_outputs.BaseModelOutputWithPooling</a> or <code>tuple(torch.FloatTensor)</code></p>
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`),Bo=n(Ur,"A",{href:!0,rel:!0});var Bc=r(Bo);hi=i(Bc,"facebook/flava-full"),Bc.forEach(t),gi=i(Ur," architecture."),Ur.forEach(t),ui=p(te),pe=n(te,"P",{});var za=r(pe);_i=i(za,"Configuration objects inherit from "),sa=n(za,"A",{href:!0});var Rc=r(sa);vi=i(Rc,"PretrainedConfig"),Rc.forEach(t),bi=i(za,` and can be used to control the model outputs. Read the
documentation from `),ia=n(za,"A",{href:!0});var Gc=r(ia);ki=i(Gc,"PretrainedConfig"),Gc.forEach(t),yi=i(za," for more information."),za.forEach(t),Fi=p(te),k(Be.$$.fragment,te),te.forEach(t),rr=p(o),fe=n(o,"H2",{class:!0});var Nr=r(fe);Re=n(Nr,"A",{id:!0,class:!0,href:!0});var qc=r(Re);en=n(qc,"SPAN",{});var Sc=r(en);k(Ro.$$.fragment,Sc),Sc.forEach(t),qc.forEach(t),Ti=p(Nr),on=n(Nr,"SPAN",{});var Lc=r(on);wi=i(Lc,"FlavaMultimodalConfig"),Lc.forEach(t),Nr.forEach(t),sr=p(o),W=n(o,"DIV",{class:!0});var ae=r(W);k(Go.$$.fragment,ae),Mi=p(ae),qo=n(ae,"P",{});var Zr=r(qo);$i=i(Zr,"This is the configuration class to store the configuration of a "),la=n(Zr,"A",{href:!0});var Oc=r(la);xi=i(Oc,"FlavaMultimodalModel"),Oc.forEach(t),Ii=i(Zr,`. It is used to instantiate
an FLAVA model according to the specified arguments, defining the model architecture.`),Zr.forEach(t),zi=p(ae),So=n(ae,"P",{});var Jr=r(So);Ci=i(Jr,`Instantiating a configuration with the defaults will yield a similar configuration to that of the FLAVA
`),Lo=n(Jr,"A",{href:!0,rel:!0});var Dc=r(Lo);Pi=i(Dc,"facebook/flava-full"),Dc.forEach(t),Ei=i(Jr," architecture."),Jr.forEach(t),Ai=p(ae),he=n(ae,"P",{});var Ca=r(he);Wi=i(Ca,"Configuration objects inherit from "),da=n(Ca,"A",{href:!0});var Xc=r(da);ji=i(Xc,"PretrainedConfig"),Xc.forEach(t),Ui=i(Ca,` and can be used to control the model outputs. Read the
documentation from `),ca=n(Ca,"A",{href:!0});var Yc=r(ca);Ni=i(Yc,"PretrainedConfig"),Yc.forEach(t),Zi=i(Ca," for more information."),Ca.forEach(t),Ji=p(ae),k(Ge.$$.fragment,ae),ae.forEach(t),ir=p(o),ge=n(o,"H2",{class:!0});var Vr=r(ge);qe=n(Vr,"A",{id:!0,class:!0,href:!0});var Hc=r(qe);tn=n(Hc,"SPAN",{});var Qc=r(tn);k(Oo.$$.fragment,Qc),Qc.forEach(t),Hc.forEach(t),Vi=p(Vr),an=n(Vr,"SPAN",{});var Kc=r(an);Bi=i(Kc,"FlavaImageCodebookConfig"),Kc.forEach(t),Vr.forEach(t),lr=p(o),Do=n(o,"DIV",{class:!0});var em=r(Do);k(Xo.$$.fragment,em),em.forEach(t),dr=p(o),ue=n(o,"H2",{class:!0});var Br=r(ue);Se=n(Br,"A",{id:!0,class:!0,href:!0});var om=r(Se);nn=n(om,"SPAN",{});var tm=r(nn);k(Yo.$$.fragment,tm),tm.forEach(t),om.forEach(t),Ri=p(Br),rn=n(Br,"SPAN",{});var am=r(rn);Gi=i(am,"FlavaProcessor"),am.forEach(t),Br.forEach(t),cr=p(o),j=n(o,"DIV",{class:!0});var ne=r(j);k(Ho.$$.fragment,ne),qi=p(ne),sn=n(ne,"P",{});var nm=r(sn);Si=i(nm,"Constructs a FLAVA processor which wraps a FLAVA image processor and a FLAVA tokenizer into a single processor."),nm.forEach(t),Li=p(ne),N=n(ne,"P",{});var D=r(N);ma=n(D,"A",{href:!0});var rm=r(ma);Oi=i(rm,"FlavaProcessor"),rm.forEach(t),Di=i(D," offers all the functionalities of "),pa=n(D,"A",{href:!0});var sm=r(pa);Xi=i(sm,"FlavaImageProcessor"),sm.forEach(t),Yi=i(D," and "),fa=n(D,"A",{href:!0});var im=r(fa);Hi=i(im,"BertTokenizerFast"),im.forEach(t),Qi=i(D,`. See the
`),ln=n(D,"CODE",{});var lm=r(ln);Ki=i(lm,"__call__()"),lm.forEach(t),el=i(D," and "),ha=n(D,"A",{href:!0});var dm=r(ha);ol=i(dm,"decode()"),dm.forEach(t),tl=i(D," for more information."),D.forEach(t),al=p(ne),Le=n(ne,"DIV",{class:!0});var Rr=r(Le);k(Qo.$$.fragment,Rr),nl=p(Rr),Ko=n(Rr,"P",{});var Gr=r(Ko);rl=i(Gr,"This method forwards all its arguments to BertTokenizerFast\u2019s "),ga=n(Gr,"A",{href:!0});var cm=r(ga);sl=i(cm,"batch_decode()"),cm.forEach(t),il=i(Gr,`. Please
refer to the docstring of this method for more information.`),Gr.forEach(t),Rr.forEach(t),ll=p(ne),Oe=n(ne,"DIV",{class:!0});var qr=r(Oe);k(et.$$.fragment,qr),dl=p(qr),ot=n(qr,"P",{});var Sr=r(ot);cl=i(Sr,"This method forwards all its arguments to BertTokenizerFast\u2019s "),ua=n(Sr,"A",{href:!0});var mm=r(ua);ml=i(mm,"decode()"),mm.forEach(t),pl=i(Sr,`. Please refer to
the docstring of this method for more information.`),Sr.forEach(t),qr.forEach(t),ne.forEach(t),mr=p(o),_e=n(o,"H2",{class:!0});var Lr=r(_e);De=n(Lr,"A",{id:!0,class:!0,href:!0});var pm=r(De);dn=n(pm,"SPAN",{});var fm=r(dn);k(tt.$$.fragment,fm),fm.forEach(t),pm.forEach(t),fl=p(Lr),cn=n(Lr,"SPAN",{});var hm=r(cn);hl=i(hm,"FlavaFeatureExtractor"),hm.forEach(t),Lr.forEach(t),pr=p(o),at=n(o,"DIV",{class:!0});var gm=r(at);k(nt.$$.fragment,gm),gm.forEach(t),fr=p(o),ve=n(o,"H2",{class:!0});var Or=r(ve);Xe=n(Or,"A",{id:!0,class:!0,href:!0});var um=r(Xe);mn=n(um,"SPAN",{});var _m=r(mn);k(rt.$$.fragment,_m),_m.forEach(t),um.forEach(t),gl=p(Or),pn=n(Or,"SPAN",{});var vm=r(pn);ul=i(vm,"FlavaImageProcessor"),vm.forEach(t),Or.forEach(t),hr=p(o),q=n(o,"DIV",{class:!0});var Pa=r(q);k(st.$$.fragment,Pa),_l=p(Pa),fn=n(Pa,"P",{});var bm=r(fn);vl=i(bm,"Constructs a Flava image processor."),bm.forEach(t),bl=p(Pa),Ye=n(Pa,"DIV",{class:!0});var Dr=r(Ye);k(it.$$.fragment,Dr),kl=p(Dr),hn=n(Dr,"P",{});var km=r(hn);yl=i(km,"Preprocess an image or batch of images."),km.forEach(t),Dr.forEach(t),Pa.forEach(t),gr=p(o),be=n(o,"H2",{class:!0});var Xr=r(be);He=n(Xr,"A",{id:!0,class:!0,href:!0});var ym=r(He);gn=n(ym,"SPAN",{});var Fm=r(gn);k(lt.$$.fragment,Fm),Fm.forEach(t),ym.forEach(t),Fl=p(Xr),un=n(Xr,"SPAN",{});var Tm=r(un);Tl=i(Tm,"FlavaForPreTraining"),Tm.forEach(t),Xr.forEach(t),ur=p(o),Z=n(o,"DIV",{class:!0});var go=r(Z);k(dt.$$.fragment,go),wl=p(go),_n=n(go,"P",{});var wm=r(_n);Ml=i(wm,"The FLAVA model for pretraining which outputs losses, embeddings, logits and transformer outputs."),wm.forEach(t),$l=p(go),ct=n(go,"P",{});var Yr=r(ct);xl=i(Yr,"This model is a PyTorch "),mt=n(Yr,"A",{href:!0,rel:!0});var Mm=r(mt);Il=i(Mm,"torch.nn.Module"),Mm.forEach(t),zl=i(Yr,` subclass. Use it
as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.`),Yr.forEach(t),Cl=p(go),H=n(go,"DIV",{class:!0});var Ea=r(H);k(pt.$$.fragment,Ea),Pl=p(Ea),ke=n(Ea,"P",{});var Aa=r(ke);El=i(Aa,"The "),_a=n(Aa,"A",{href:!0});var $m=r(_a);Al=i($m,"FlavaForPreTraining"),$m.forEach(t),Wl=i(Aa," forward method, overrides the "),vn=n(Aa,"CODE",{});var xm=r(vn);jl=i(xm,"__call__"),xm.forEach(t),Ul=i(Aa," special method."),Aa.forEach(t),Nl=p(Ea),k(Qe.$$.fragment,Ea),Ea.forEach(t),go.forEach(t),_r=p(o),ye=n(o,"H2",{class:!0});var Hr=r(ye);Ke=n(Hr,"A",{id:!0,class:!0,href:!0});var Im=r(Ke);bn=n(Im,"SPAN",{});var zm=r(bn);k(ft.$$.fragment,zm),zm.forEach(t),Im.forEach(t),Zl=p(Hr),kn=n(Hr,"SPAN",{});var Cm=r(kn);Jl=i(Cm,"FlavaModel"),Cm.forEach(t),Hr.forEach(t),vr=p(o),U=n(o,"DIV",{class:!0});var re=r(U);k(ht.$$.fragment,re),Vl=p(re),gt=n(re,"P",{});var Qr=r(gt);Bl=i(Qr,`The bare FLAVA Model transformer outputting raw hidden-states without any specific head on top.
This model is a PyTorch `),ut=n(Qr,"A",{href:!0,rel:!0});var Pm=r(ut);Rl=i(Pm,"torch.nn.Module"),Pm.forEach(t),Gl=i(Qr,` subclass. Use it
as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.`),Qr.forEach(t),ql=p(re),J=n(re,"DIV",{class:!0});var uo=r(J);k(_t.$$.fragment,uo),Sl=p(uo),Fe=n(uo,"P",{});var Wa=r(Fe);Ll=i(Wa,"The "),va=n(Wa,"A",{href:!0});var Em=r(va);Ol=i(Em,"FlavaModel"),Em.forEach(t),Dl=i(Wa," forward method, overrides the "),yn=n(Wa,"CODE",{});var Am=r(yn);Xl=i(Am,"__call__"),Am.forEach(t),Yl=i(Wa," special method."),Wa.forEach(t),Hl=p(uo),k(eo.$$.fragment,uo),Ql=p(uo),k(oo.$$.fragment,uo),uo.forEach(t),Kl=p(re),Q=n(re,"DIV",{class:!0});var ja=r(Q);k(vt.$$.fragment,ja),ed=p(ja),Te=n(ja,"P",{});var Ua=r(Te);od=i(Ua,"The "),ba=n(Ua,"A",{href:!0});var Wm=r(ba);td=i(Wm,"FlavaModel"),Wm.forEach(t),ad=i(Ua," forward method, overrides the "),Fn=n(Ua,"CODE",{});var jm=r(Fn);nd=i(jm,"__call__"),jm.forEach(t),rd=i(Ua," special method."),Ua.forEach(t),sd=p(ja),k(to.$$.fragment,ja),ja.forEach(t),id=p(re),K=n(re,"DIV",{class:!0});var Na=r(K);k(bt.$$.fragment,Na),ld=p(Na),we=n(Na,"P",{});var Za=r(we);dd=i(Za,"The "),ka=n(Za,"A",{href:!0});var Um=r(ka);cd=i(Um,"FlavaModel"),Um.forEach(t),md=i(Za," forward method, overrides the "),Tn=n(Za,"CODE",{});var Nm=r(Tn);pd=i(Nm,"__call__"),Nm.forEach(t),fd=i(Za," special method."),Za.forEach(t),hd=p(Na),k(ao.$$.fragment,Na),Na.forEach(t),re.forEach(t),br=p(o),Me=n(o,"H2",{class:!0});var Kr=r(Me);no=n(Kr,"A",{id:!0,class:!0,href:!0});var Zm=r(no);wn=n(Zm,"SPAN",{});var Jm=r(wn);k(kt.$$.fragment,Jm),Jm.forEach(t),Zm.forEach(t),gd=p(Kr),Mn=n(Kr,"SPAN",{});var Vm=r(Mn);ud=i(Vm,"FlavaImageCodebook"),Vm.forEach(t),Kr.forEach(t),kr=p(o),z=n(o,"DIV",{class:!0});var G=r(z);k(yt.$$.fragment,G),_d=p(G),Ft=n(G,"P",{});var es=r(Ft);vd=i(es,`The FLAVA\u2019s image codebook model inspired from DALL-E\u2019s original encoder. Outputs raw hidden states and can be used
to generate image tokens for an image based on DALL-E\u2019s vocab. Used to generate labels for MIM. Use
`),$n=n(es,"CODE",{});var Bm=r($n);bd=i(Bm,"get_codebook_indices"),Bm.forEach(t),kd=i(es," to get image tokens for an image."),es.forEach(t),yd=p(G),Tt=n(G,"P",{});var os=r(Tt);Fd=i(os,"This model is a PyTorch "),wt=n(os,"A",{href:!0,rel:!0});var Rm=r(wt);Td=i(Rm,"torch.nn.Module"),Rm.forEach(t),wd=i(os,` subclass. Use it
as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.`),os.forEach(t),Md=p(G),ya=n(G,"DIV",{class:!0});var Gm=r(ya);k(Mt.$$.fragment,Gm),Gm.forEach(t),$d=p(G),Fa=n(G,"DIV",{class:!0});var qm=r(Fa);k($t.$$.fragment,qm),qm.forEach(t),xd=p(G),Ta=n(G,"DIV",{class:!0});var Sm=r(Ta);k(xt.$$.fragment,Sm),Sm.forEach(t),G.forEach(t),yr=p(o),$e=n(o,"H2",{class:!0});var ts=r($e);ro=n(ts,"A",{id:!0,class:!0,href:!0});var Lm=r(ro);xn=n(Lm,"SPAN",{});var Om=r(xn);k(It.$$.fragment,Om),Om.forEach(t),Lm.forEach(t),Id=p(ts),In=n(ts,"SPAN",{});var Dm=r(In);zd=i(Dm,"FlavaTextModel"),Dm.forEach(t),ts.forEach(t),Fr=p(o),S=n(o,"DIV",{class:!0});var Ja=r(S);k(zt.$$.fragment,Ja),Cd=p(Ja),Ct=n(Ja,"P",{});var as=r(Ct);Pd=i(as,`The bare FLAVA Text Model transformer outputting raw hidden-states without any specific head on top.
This model is a PyTorch `),Pt=n(as,"A",{href:!0,rel:!0});var Xm=r(Pt);Ed=i(Xm,"torch.nn.Module"),Xm.forEach(t),Ad=i(as,` subclass. Use it
as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.`),as.forEach(t),Wd=p(Ja),V=n(Ja,"DIV",{class:!0});var _o=r(V);k(Et.$$.fragment,_o),jd=p(_o),xe=n(_o,"P",{});var Va=r(xe);Ud=i(Va,"The "),wa=n(Va,"A",{href:!0});var Ym=r(wa);Nd=i(Ym,"FlavaTextModel"),Ym.forEach(t),Zd=i(Va," forward method, overrides the "),zn=n(Va,"CODE",{});var Hm=r(zn);Jd=i(Hm,"__call__"),Hm.forEach(t),Vd=i(Va," special method."),Va.forEach(t),Bd=p(_o),k(so.$$.fragment,_o),Rd=p(_o),k(io.$$.fragment,_o),_o.forEach(t),Ja.forEach(t),Tr=p(o),Ie=n(o,"H2",{class:!0});var ns=r(Ie);lo=n(ns,"A",{id:!0,class:!0,href:!0});var Qm=r(lo);Cn=n(Qm,"SPAN",{});var Km=r(Cn);k(At.$$.fragment,Km),Km.forEach(t),Qm.forEach(t),Gd=p(ns),Pn=n(ns,"SPAN",{});var ep=r(Pn);qd=i(ep,"FlavaImageModel"),ep.forEach(t),ns.forEach(t),wr=p(o),L=n(o,"DIV",{class:!0});var Ba=r(L);k(Wt.$$.fragment,Ba),Sd=p(Ba),jt=n(Ba,"P",{});var rs=r(jt);Ld=i(rs,`The bare FLAVA Image Model transformer outputting raw hidden-states without any specific head on top.
This model is a PyTorch `),Ut=n(rs,"A",{href:!0,rel:!0});var op=r(Ut);Od=i(op,"torch.nn.Module"),op.forEach(t),Dd=i(rs,` subclass. Use it
as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.`),rs.forEach(t),Xd=p(Ba),B=n(Ba,"DIV",{class:!0});var vo=r(B);k(Nt.$$.fragment,vo),Yd=p(vo),ze=n(vo,"P",{});var Ra=r(ze);Hd=i(Ra,"The "),Ma=n(Ra,"A",{href:!0});var tp=r(Ma);Qd=i(tp,"FlavaImageModel"),tp.forEach(t),Kd=i(Ra," forward method, overrides the "),En=n(Ra,"CODE",{});var ap=r(En);ec=i(ap,"__call__"),ap.forEach(t),oc=i(Ra," special method."),Ra.forEach(t),tc=p(vo),k(co.$$.fragment,vo),ac=p(vo),k(mo.$$.fragment,vo),vo.forEach(t),Ba.forEach(t),Mr=p(o),Ce=n(o,"H2",{class:!0});var ss=r(Ce);po=n(ss,"A",{id:!0,class:!0,href:!0});var np=r(po);An=n(np,"SPAN",{});var rp=r(An);k(Zt.$$.fragment,rp),rp.forEach(t),np.forEach(t),nc=p(ss),Wn=n(ss,"SPAN",{});var sp=r(Wn);rc=i(sp,"FlavaMultimodalModel"),sp.forEach(t),ss.forEach(t),$r=p(o),O=n(o,"DIV",{class:!0});var Ga=r(O);k(Jt.$$.fragment,Ga),sc=p(Ga),Vt=n(Ga,"P",{});var is=r(Vt);ic=i(is,`The bare FLAVA Multimodal Model transformer outputting raw hidden-states without any specific head on top.
This model is a PyTorch `),Bt=n(is,"A",{href:!0,rel:!0});var ip=r(Bt);lc=i(ip,"torch.nn.Module"),ip.forEach(t),dc=i(is,` subclass. Use it
as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
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