import{S as Kd,i as ec,s as tc,e as n,k as m,w as b,t as r,M as oc,c as s,d as o,m as p,a,x as T,h as i,b as c,N as nc,G as e,g as u,y as w,q as M,o as k,B as V,v as sc,L as Ao}from"../../chunks/vendor-hf-doc-builder.js";import{T as Eo}from"../../chunks/Tip-hf-doc-builder.js";import{D as j}from"../../chunks/Docstring-hf-doc-builder.js";import{C as Zo}from"../../chunks/CodeBlock-hf-doc-builder.js";import{I as B}from"../../chunks/IconCopyLink-hf-doc-builder.js";import{E as Wo}from"../../chunks/ExampleCodeBlock-hf-doc-builder.js";function ac(x){let d,y,_,g,v;return g=new Zo({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> ViLTModel, ViLTConfig

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Initializing a ViLT dandelin/vilt-b32-mlm style configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>configuration = ViLTConfig()

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Initializing a model from the dandelin/vilt-b32-mlm style configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = ViLTModel(configuration)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Accessing the model configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>configuration = model.config`}}),{c(){d=n("p"),y=r("Example:"),_=m(),b(g.$$.fragment)},l(l){d=s(l,"P",{});var f=a(d);y=i(f,"Example:"),f.forEach(o),_=p(l),T(g.$$.fragment,l)},m(l,f){u(l,d,f),e(d,y),u(l,_,f),w(g,l,f),v=!0},p:Ao,i(l){v||(M(g.$$.fragment,l),v=!0)},o(l){k(g.$$.fragment,l),v=!1},d(l){l&&o(d),l&&o(_),V(g,l)}}}function rc(x){let d,y,_,g,v;return{c(){d=n("p"),y=r("Although the recipe for forward pass needs to be defined within this function, one should call the "),_=n("code"),g=r("Module"),v=r(`
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){d=s(l,"P",{});var f=a(d);y=i(f,"Although the recipe for forward pass needs to be defined within this function, one should call the "),_=s(f,"CODE",{});var $=a(_);g=i($,"Module"),$.forEach(o),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(o)},m(l,f){u(l,d,f),e(d,y),e(d,_),e(_,g),e(d,v)},d(l){l&&o(d)}}}function ic(x){let d,y,_,g,v;return g=new Zo({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> ViltProcessor, ViltModel
<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-comment"># prepare image and text</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>url = <span class="hljs-string">&quot;http://images.cocodataset.org/val2017/000000039769.jpg&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>image = Image.<span class="hljs-built_in">open</span>(requests.get(url, stream=<span class="hljs-literal">True</span>).raw)
<span class="hljs-meta">&gt;&gt;&gt; </span>text = <span class="hljs-string">&quot;hello world&quot;</span>

<span class="hljs-meta">&gt;&gt;&gt; </span>processor = ViltProcessor.from_pretrained(<span class="hljs-string">&quot;dandelin/vilt-b32-mlm&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = ViltModel.from_pretrained(<span class="hljs-string">&quot;dandelin/vilt-b32-mlm&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = processor(image, text, 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(){d=n("p"),y=r("Examples:"),_=m(),b(g.$$.fragment)},l(l){d=s(l,"P",{});var f=a(d);y=i(f,"Examples:"),f.forEach(o),_=p(l),T(g.$$.fragment,l)},m(l,f){u(l,d,f),e(d,y),u(l,_,f),w(g,l,f),v=!0},p:Ao,i(l){v||(M(g.$$.fragment,l),v=!0)},o(l){k(g.$$.fragment,l),v=!1},d(l){l&&o(d),l&&o(_),V(g,l)}}}function lc(x){let d,y,_,g,v;return{c(){d=n("p"),y=r("Although the recipe for forward pass needs to be defined within this function, one should call the "),_=n("code"),g=r("Module"),v=r(`
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){d=s(l,"P",{});var f=a(d);y=i(f,"Although the recipe for forward pass needs to be defined within this function, one should call the "),_=s(f,"CODE",{});var $=a(_);g=i($,"Module"),$.forEach(o),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(o)},m(l,f){u(l,d,f),e(d,y),e(d,_),e(_,g),e(d,v)},d(l){l&&o(d)}}}function dc(x){let d,y,_,g,v;return g=new Zo({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMFZpbHRQcm9jZXNzb3IlMkMlMjBWaWx0Rm9yTWFza2VkTE0lMEFpbXBvcnQlMjByZXF1ZXN0cyUwQWZyb20lMjBQSUwlMjBpbXBvcnQlMjBJbWFnZSUwQWltcG9ydCUyMHJlJTBBaW1wb3J0JTIwdG9yY2glMEElMEF1cmwlMjAlM0QlMjAlMjJodHRwJTNBJTJGJTJGaW1hZ2VzLmNvY29kYXRhc2V0Lm9yZyUyRnZhbDIwMTclMkYwMDAwMDAwMzk3NjkuanBnJTIyJTBBaW1hZ2UlMjAlM0QlMjBJbWFnZS5vcGVuKHJlcXVlc3RzLmdldCh1cmwlMkMlMjBzdHJlYW0lM0RUcnVlKS5yYXcpJTBBdGV4dCUyMCUzRCUyMCUyMmElMjBidW5jaCUyMG9mJTIwJTVCTUFTSyU1RCUyMGxheWluZyUyMG9uJTIwYSUyMCU1Qk1BU0slNUQuJTIyJTBBJTBBcHJvY2Vzc29yJTIwJTNEJTIwVmlsdFByb2Nlc3Nvci5mcm9tX3ByZXRyYWluZWQoJTIyZGFuZGVsaW4lMkZ2aWx0LWIzMi1tbG0lMjIpJTBBbW9kZWwlMjAlM0QlMjBWaWx0Rm9yTWFza2VkTE0uZnJvbV9wcmV0cmFpbmVkKCUyMmRhbmRlbGluJTJGdmlsdC1iMzItbWxtJTIyKSUwQSUwQSUyMyUyMHByZXBhcmUlMjBpbnB1dHMlMEFlbmNvZGluZyUyMCUzRCUyMHByb2Nlc3NvcihpbWFnZSUyQyUyMHRleHQlMkMlMjByZXR1cm5fdGVuc29ycyUzRCUyMnB0JTIyKSUwQSUwQSUyMyUyMGZvcndhcmQlMjBwYXNzJTBBb3V0cHV0cyUyMCUzRCUyMG1vZGVsKCoqZW5jb2RpbmcpJTBBJTBBdGwlMjAlM0QlMjBsZW4ocmUuZmluZGFsbCglMjIlNUMlNUJNQVNLJTVDJTVEJTIyJTJDJTIwdGV4dCkpJTBBaW5mZXJyZWRfdG9rZW4lMjAlM0QlMjAlNUJ0ZXh0JTVEJTBBJTBBJTIzJTIwZ3JhZHVhbGx5JTIwZmlsbCUyMGluJTIwdGhlJTIwTUFTSyUyMHRva2VucyUyQyUyMG9uZSUyMGJ5JTIwb25lJTBBd2l0aCUyMHRvcmNoLm5vX2dyYWQoKSUzQSUwQSUyMCUyMCUyMCUyMGZvciUyMGklMjBpbiUyMHJhbmdlKHRsKSUzQSUwQSUyMCUyMCUyMCUyMCUyMCUyMCUyMCUyMGVuY29kZWQlMjAlM0QlMjBwcm9jZXNzb3IudG9rZW5pemVyKGluZmVycmVkX3Rva2VuKSUwQSUyMCUyMCUyMCUyMCUyMCUyMCUyMCUyMGlucHV0X2lkcyUyMCUzRCUyMHRvcmNoLnRlbnNvcihlbmNvZGVkLmlucHV0X2lkcyklMEElMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjBlbmNvZGVkJTIwJTNEJTIwZW5jb2RlZCU1QiUyMmlucHV0X2lkcyUyMiU1RCU1QjAlNUQlNUIxJTNBLTElNUQlMEElMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjBvdXRwdXRzJTIwJTNEJTIwbW9kZWwoaW5wdXRfaWRzJTNEaW5wdXRfaWRzJTJDJTIwcGl4ZWxfdmFsdWVzJTNEZW5jb2RpbmcucGl4ZWxfdmFsdWVzKSUwQSUyMCUyMCUyMCUyMCUyMCUyMCUyMCUyMG1sbV9sb2dpdHMlMjAlM0QlMjBvdXRwdXRzLmxvZ2l0cyU1QjAlNUQlMjAlMjAlMjMlMjBzaGFwZSUyMChzZXFfbGVuJTJDJTIwdm9jYWJfc2l6ZSklMEElMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjMlMjBvbmx5JTIwdGFrZSUyMGludG8lMjBhY2NvdW50JTIwdGV4dCUyMGZlYXR1cmVzJTIwKG1pbnVzJTIwQ0xTJTIwYW5kJTIwU0VQJTIwdG9rZW4pJTBBJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwbWxtX2xvZ2l0cyUyMCUzRCUyMG1sbV9sb2dpdHMlNUIxJTIwJTNBJTIwaW5wdXRfaWRzLnNoYXBlJTVCMSU1RCUyMC0lMjAxJTJDJTIwJTNBJTVEJTBBJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwbWxtX3ZhbHVlcyUyQyUyMG1sbV9pZHMlMjAlM0QlMjBtbG1fbG9naXRzLnNvZnRtYXgoZGltJTNELTEpLm1heChkaW0lM0QtMSklMEElMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjMlMjBvbmx5JTIwdGFrZSUyMGludG8lMjBhY2NvdW50JTIwdGV4dCUwQSUyMCUyMCUyMCUyMCUyMCUyMCUyMCUyMG1sbV92YWx1ZXMlNUJ0b3JjaC50ZW5zb3IoZW5jb2RlZCklMjAhJTNEJTIwMTAzJTVEJTIwJTNEJTIwMCUwQSUyMCUyMCUyMCUyMCUyMCUyMCUyMCUyMHNlbGVjdCUyMCUzRCUyMG1sbV92YWx1ZXMuYXJnbWF4KCkuaXRlbSgpJTBBJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwZW5jb2RlZCU1QnNlbGVjdCU1RCUyMCUzRCUyMG1sbV9pZHMlNUJzZWxlY3QlNUQuaXRlbSgpJTBBJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwaW5mZXJyZWRfdG9rZW4lMjAlM0QlMjAlNUJwcm9jZXNzb3IuZGVjb2RlKGVuY29kZWQpJTVEJTBBJTBBc2VsZWN0ZWRfdG9rZW4lMjAlM0QlMjAlMjIlMjIlMEFlbmNvZGVkJTIwJTNEJTIwcHJvY2Vzc29yLnRva2VuaXplcihpbmZlcnJlZF90b2tlbiklMEFvdXRwdXQlMjAlM0QlMjBwcm9jZXNzb3IuZGVjb2RlKGVuY29kZWQuaW5wdXRfaWRzJTVCMCU1RCUyQyUyMHNraXBfc3BlY2lhbF90b2tlbnMlM0RUcnVlKSUwQXByaW50KG91dHB1dCk=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> ViltProcessor, ViltForMaskedLM
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> requests
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> PIL <span class="hljs-keyword">import</span> Image
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> re
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch

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

<span class="hljs-meta">&gt;&gt;&gt; </span>processor = ViltProcessor.from_pretrained(<span class="hljs-string">&quot;dandelin/vilt-b32-mlm&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = ViltForMaskedLM.from_pretrained(<span class="hljs-string">&quot;dandelin/vilt-b32-mlm&quot;</span>)

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

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># forward pass</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(**encoding)

<span class="hljs-meta">&gt;&gt;&gt; </span>tl = <span class="hljs-built_in">len</span>(re.findall(<span class="hljs-string">&quot;\\[MASK\\]&quot;</span>, text))
<span class="hljs-meta">&gt;&gt;&gt; </span>inferred_token = [text]

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># gradually fill in the MASK tokens, one by one</span>
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">with</span> torch.no_grad():
<span class="hljs-meta">... </span>    <span class="hljs-keyword">for</span> i <span class="hljs-keyword">in</span> <span class="hljs-built_in">range</span>(tl):
<span class="hljs-meta">... </span>        encoded = processor.tokenizer(inferred_token)
<span class="hljs-meta">... </span>        input_ids = torch.tensor(encoded.input_ids)
<span class="hljs-meta">... </span>        encoded = encoded[<span class="hljs-string">&quot;input_ids&quot;</span>][<span class="hljs-number">0</span>][<span class="hljs-number">1</span>:-<span class="hljs-number">1</span>]
<span class="hljs-meta">... </span>        outputs = model(input_ids=input_ids, pixel_values=encoding.pixel_values)
<span class="hljs-meta">... </span>        mlm_logits = outputs.logits[<span class="hljs-number">0</span>]  <span class="hljs-comment"># shape (seq_len, vocab_size)</span>
<span class="hljs-meta">... </span>        <span class="hljs-comment"># only take into account text features (minus CLS and SEP token)</span>
<span class="hljs-meta">... </span>        mlm_logits = mlm_logits[<span class="hljs-number">1</span> : input_ids.shape[<span class="hljs-number">1</span>] - <span class="hljs-number">1</span>, :]
<span class="hljs-meta">... </span>        mlm_values, mlm_ids = mlm_logits.softmax(dim=-<span class="hljs-number">1</span>).<span class="hljs-built_in">max</span>(dim=-<span class="hljs-number">1</span>)
<span class="hljs-meta">... </span>        <span class="hljs-comment"># only take into account text</span>
<span class="hljs-meta">... </span>        mlm_values[torch.tensor(encoded) != <span class="hljs-number">103</span>] = <span class="hljs-number">0</span>
<span class="hljs-meta">... </span>        select = mlm_values.argmax().item()
<span class="hljs-meta">... </span>        encoded[select] = mlm_ids[select].item()
<span class="hljs-meta">... </span>        inferred_token = [processor.decode(encoded)]

<span class="hljs-meta">&gt;&gt;&gt; </span>selected_token = <span class="hljs-string">&quot;&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>encoded = processor.tokenizer(inferred_token)
<span class="hljs-meta">&gt;&gt;&gt; </span>output = processor.decode(encoded.input_ids[<span class="hljs-number">0</span>], skip_special_tokens=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-built_in">print</span>(output)
a bunch of cats laying on a couch.`}}),{c(){d=n("p"),y=r("Examples:"),_=m(),b(g.$$.fragment)},l(l){d=s(l,"P",{});var f=a(d);y=i(f,"Examples:"),f.forEach(o),_=p(l),T(g.$$.fragment,l)},m(l,f){u(l,d,f),e(d,y),u(l,_,f),w(g,l,f),v=!0},p:Ao,i(l){v||(M(g.$$.fragment,l),v=!0)},o(l){k(g.$$.fragment,l),v=!1},d(l){l&&o(d),l&&o(_),V(g,l)}}}function cc(x){let d,y,_,g,v;return{c(){d=n("p"),y=r("Although the recipe for forward pass needs to be defined within this function, one should call the "),_=n("code"),g=r("Module"),v=r(`
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){d=s(l,"P",{});var f=a(d);y=i(f,"Although the recipe for forward pass needs to be defined within this function, one should call the "),_=s(f,"CODE",{});var $=a(_);g=i($,"Module"),$.forEach(o),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(o)},m(l,f){u(l,d,f),e(d,y),e(d,_),e(_,g),e(d,v)},d(l){l&&o(d)}}}function mc(x){let d,y,_,g,v;return g=new Zo({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> ViltProcessor, ViltForQuestionAnswering
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> requests
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> PIL <span class="hljs-keyword">import</span> Image

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

<span class="hljs-meta">&gt;&gt;&gt; </span>processor = ViltProcessor.from_pretrained(<span class="hljs-string">&quot;dandelin/vilt-b32-finetuned-vqa&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = ViltForQuestionAnswering.from_pretrained(<span class="hljs-string">&quot;dandelin/vilt-b32-finetuned-vqa&quot;</span>)

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

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># forward pass</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(**encoding)
<span class="hljs-meta">&gt;&gt;&gt; </span>logits = outputs.logits
<span class="hljs-meta">&gt;&gt;&gt; </span>idx = logits.argmax(-<span class="hljs-number">1</span>).item()
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-built_in">print</span>(<span class="hljs-string">&quot;Predicted answer:&quot;</span>, model.config.id2label[idx])
Predicted answer: <span class="hljs-number">2</span>`}}),{c(){d=n("p"),y=r("Examples:"),_=m(),b(g.$$.fragment)},l(l){d=s(l,"P",{});var f=a(d);y=i(f,"Examples:"),f.forEach(o),_=p(l),T(g.$$.fragment,l)},m(l,f){u(l,d,f),e(d,y),u(l,_,f),w(g,l,f),v=!0},p:Ao,i(l){v||(M(g.$$.fragment,l),v=!0)},o(l){k(g.$$.fragment,l),v=!1},d(l){l&&o(d),l&&o(_),V(g,l)}}}function pc(x){let d,y,_,g,v;return{c(){d=n("p"),y=r("Although the recipe for forward pass needs to be defined within this function, one should call the "),_=n("code"),g=r("Module"),v=r(`
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){d=s(l,"P",{});var f=a(d);y=i(f,"Although the recipe for forward pass needs to be defined within this function, one should call the "),_=s(f,"CODE",{});var $=a(_);g=i($,"Module"),$.forEach(o),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(o)},m(l,f){u(l,d,f),e(d,y),e(d,_),e(_,g),e(d,v)},d(l){l&&o(d)}}}function hc(x){let d,y,_,g,v;return g=new Zo({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> ViltProcessor, ViltForImagesAndTextClassification
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> requests
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> PIL <span class="hljs-keyword">import</span> Image

<span class="hljs-meta">&gt;&gt;&gt; </span>image1 = Image.<span class="hljs-built_in">open</span>(requests.get(<span class="hljs-string">&quot;https://lil.nlp.cornell.edu/nlvr/exs/ex0_0.jpg&quot;</span>, stream=<span class="hljs-literal">True</span>).raw)
<span class="hljs-meta">&gt;&gt;&gt; </span>image2 = Image.<span class="hljs-built_in">open</span>(requests.get(<span class="hljs-string">&quot;https://lil.nlp.cornell.edu/nlvr/exs/ex0_1.jpg&quot;</span>, stream=<span class="hljs-literal">True</span>).raw)
<span class="hljs-meta">&gt;&gt;&gt; </span>text = <span class="hljs-string">&quot;The left image contains twice the number of dogs as the right image.&quot;</span>

<span class="hljs-meta">&gt;&gt;&gt; </span>processor = ViltProcessor.from_pretrained(<span class="hljs-string">&quot;dandelin/vilt-b32-finetuned-nlvr2&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = ViltForImagesAndTextClassification.from_pretrained(<span class="hljs-string">&quot;dandelin/vilt-b32-finetuned-nlvr2&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># prepare inputs</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>encoding = processor([image1, image2], text, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># forward pass</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(input_ids=encoding.input_ids, pixel_values=encoding.pixel_values.unsqueeze(<span class="hljs-number">0</span>))
<span class="hljs-meta">&gt;&gt;&gt; </span>logits = outputs.logits
<span class="hljs-meta">&gt;&gt;&gt; </span>idx = logits.argmax(-<span class="hljs-number">1</span>).item()
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-built_in">print</span>(<span class="hljs-string">&quot;Predicted answer:&quot;</span>, model.config.id2label[idx])
Predicted answer: <span class="hljs-literal">True</span>`}}),{c(){d=n("p"),y=r("Examples:"),_=m(),b(g.$$.fragment)},l(l){d=s(l,"P",{});var f=a(d);y=i(f,"Examples:"),f.forEach(o),_=p(l),T(g.$$.fragment,l)},m(l,f){u(l,d,f),e(d,y),u(l,_,f),w(g,l,f),v=!0},p:Ao,i(l){v||(M(g.$$.fragment,l),v=!0)},o(l){k(g.$$.fragment,l),v=!1},d(l){l&&o(d),l&&o(_),V(g,l)}}}function fc(x){let d,y,_,g,v;return{c(){d=n("p"),y=r("Although the recipe for forward pass needs to be defined within this function, one should call the "),_=n("code"),g=r("Module"),v=r(`
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){d=s(l,"P",{});var f=a(d);y=i(f,"Although the recipe for forward pass needs to be defined within this function, one should call the "),_=s(f,"CODE",{});var $=a(_);g=i($,"Module"),$.forEach(o),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(o)},m(l,f){u(l,d,f),e(d,y),e(d,_),e(_,g),e(d,v)},d(l){l&&o(d)}}}function uc(x){let d,y,_,g,v;return g=new Zo({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> ViltProcessor, ViltForImageAndTextRetrieval
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> requests
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> PIL <span class="hljs-keyword">import</span> Image

<span class="hljs-meta">&gt;&gt;&gt; </span>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>texts = [<span class="hljs-string">&quot;An image of two cats chilling on a couch&quot;</span>, <span class="hljs-string">&quot;A football player scoring a goal&quot;</span>]

<span class="hljs-meta">&gt;&gt;&gt; </span>processor = ViltProcessor.from_pretrained(<span class="hljs-string">&quot;dandelin/vilt-b32-finetuned-coco&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = ViltForImageAndTextRetrieval.from_pretrained(<span class="hljs-string">&quot;dandelin/vilt-b32-finetuned-coco&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># forward pass</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>scores = <span class="hljs-built_in">dict</span>()
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">for</span> text <span class="hljs-keyword">in</span> texts:
<span class="hljs-meta">... </span>    <span class="hljs-comment"># prepare inputs</span>
<span class="hljs-meta">... </span>    encoding = processor(image, text, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>)
<span class="hljs-meta">... </span>    outputs = model(**encoding)
<span class="hljs-meta">... </span>    scores[text] = outputs.logits[<span class="hljs-number">0</span>, :].item()`}}),{c(){d=n("p"),y=r("Examples:"),_=m(),b(g.$$.fragment)},l(l){d=s(l,"P",{});var f=a(d);y=i(f,"Examples:"),f.forEach(o),_=p(l),T(g.$$.fragment,l)},m(l,f){u(l,d,f),e(d,y),u(l,_,f),w(g,l,f),v=!0},p:Ao,i(l){v||(M(g.$$.fragment,l),v=!0)},o(l){k(g.$$.fragment,l),v=!1},d(l){l&&o(d),l&&o(_),V(g,l)}}}function gc(x){let d,y,_,g,v;return{c(){d=n("p"),y=r("Although the recipe for forward pass needs to be defined within this function, one should call the "),_=n("code"),g=r("Module"),v=r(`
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){d=s(l,"P",{});var f=a(d);y=i(f,"Although the recipe for forward pass needs to be defined within this function, one should call the "),_=s(f,"CODE",{});var $=a(_);g=i($,"Module"),$.forEach(o),v=i(f,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),f.forEach(o)},m(l,f){u(l,d,f),e(d,y),e(d,_),e(_,g),e(d,v)},d(l){l&&o(d)}}}function _c(x){let d,y,_,g,v,l,f,$,Xs,Xn,D,ye,Po,lt,Hs,Lo,Ds,Hn,be,Ys,dt,Ks,ea,Dn,Kt,ta,Yn,eo,No,oa,Kn,to,na,es,W,ct,sa,mt,aa,ra,ia,R,la,Bo,da,ca,Ro,ma,pa,oo,ha,fa,ua,Y,ga,So,_a,va,no,ya,ba,Ta,Go,wa,ts,Te,Ml,os,we,Ma,so,ka,Va,ns,Q,xa,pt,$a,ja,ht,Ia,Fa,ss,ao,Ca,as,ro,Oo,za,rs,K,Me,qo,ft,Ua,Qo,Ja,is,F,ut,Ea,ee,Wa,Xo,Aa,Za,gt,Pa,La,Na,te,Ba,io,Ra,Sa,lo,Ga,Oa,qa,ke,ls,oe,Ve,Ho,_t,Qa,Do,Xa,ds,ne,vt,Ha,xe,yt,Da,Yo,Ya,cs,se,$e,Ko,bt,Ka,en,er,ms,S,Tt,tr,tn,or,nr,je,wt,sr,on,ar,ps,ae,Ie,nn,Mt,rr,sn,ir,hs,C,kt,lr,an,dr,cr,I,co,mr,pr,mo,hr,fr,po,ur,gr,Vt,rn,_r,vr,yr,ln,br,Tr,wr,X,xt,Mr,re,kr,Fe,Vr,dn,xr,$r,jr,Ce,Ir,cn,Fr,Cr,zr,Ur,mn,Jr,fs,ie,ze,pn,$t,Er,hn,Wr,us,G,jt,Ar,It,Zr,fn,Pr,Lr,Nr,A,Ft,Br,le,Rr,ho,Sr,Gr,un,Or,qr,Qr,Ue,Xr,Je,gs,de,Ee,gn,Ct,Hr,_n,Dr,_s,z,zt,Yr,vn,Kr,ei,Ut,ti,yn,oi,ni,si,Z,Jt,ai,ce,ri,fo,ii,li,bn,di,ci,mi,We,pi,Ae,vs,me,Ze,Tn,Et,hi,wn,fi,ys,U,Wt,ui,Mn,gi,_i,At,vi,kn,yi,bi,Ti,P,Zt,wi,pe,Mi,uo,ki,Vi,Vn,xi,$i,ji,Pe,Ii,Le,bs,he,Ne,xn,Pt,Fi,$n,Ci,Ts,O,Lt,zi,jn,Ui,Ji,L,Nt,Ei,fe,Wi,go,Ai,Zi,In,Pi,Li,Ni,Be,Bi,Re,ws,ue,Se,Fn,Bt,Ri,Cn,Si,Ms,J,Rt,Gi,zn,Oi,qi,St,Qi,Un,Xi,Hi,Di,N,Gt,Yi,ge,Ki,_o,el,tl,Jn,ol,nl,sl,Ge,al,Oe,ks,_e,qe,En,Ot,rl,Wn,il,Vs,E,qt,ll,An,dl,cl,Qt,ml,Zn,pl,hl,fl,H,Xt,ul,ve,gl,vo,_l,vl,Pn,yl,bl,Tl,Qe,xs;return l=new B({}),lt=new B({}),ft=new B({}),ut=new j({props:{name:"class transformers.ViltConfig",anchor:"transformers.ViltConfig",parameters:[{name:"vocab_size",val:" = 30522"},{name:"type_vocab_size",val:" = 2"},{name:"modality_type_vocab_size",val:" = 2"},{name:"max_position_embeddings",val:" = 40"},{name:"hidden_size",val:" = 768"},{name:"num_hidden_layers",val:" = 12"},{name:"num_attention_heads",val:" = 12"},{name:"intermediate_size",val:" = 3072"},{name:"hidden_act",val:" = 'gelu'"},{name:"hidden_dropout_prob",val:" = 0.0"},{name:"attention_probs_dropout_prob",val:" = 0.0"},{name:"initializer_range",val:" = 0.02"},{name:"layer_norm_eps",val:" = 1e-12"},{name:"image_size",val:" = 384"},{name:"patch_size",val:" = 32"},{name:"num_channels",val:" = 3"},{name:"qkv_bias",val:" = True"},{name:"max_image_length",val:" = -1"},{name:"tie_word_embeddings",val:" = False"},{name:"num_images",val:" = -1"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.ViltConfig.vocab_size",description:`<strong>vocab_size</strong> (<code>int</code>, <em>optional</em>, defaults to 30522) &#x2014;
Vocabulary size of the text part of the model. Defines the number of different tokens that can be
represented by the <code>inputs_ids</code> passed when calling <a href="/docs/transformers/v4.30.0/en/model_doc/vilt#transformers.ViltModel">ViltModel</a>.`,name:"vocab_size"},{anchor:"transformers.ViltConfig.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/v4.30.0/en/model_doc/vilt#transformers.ViltModel">ViltModel</a>. This is used when encoding
text.`,name:"type_vocab_size"},{anchor:"transformers.ViltConfig.modality_type_vocab_size",description:`<strong>modality_type_vocab_size</strong> (<code>int</code>, <em>optional</em>, defaults to 2) &#x2014;
The vocabulary size of the modalities passed when calling <a href="/docs/transformers/v4.30.0/en/model_doc/vilt#transformers.ViltModel">ViltModel</a>. This is used after concatening the
embeddings of the text and image modalities.`,name:"modality_type_vocab_size"},{anchor:"transformers.ViltConfig.max_position_embeddings",description:`<strong>max_position_embeddings</strong> (<code>int</code>, <em>optional</em>, defaults to 40) &#x2014;
The maximum sequence length that this model might ever be used with.`,name:"max_position_embeddings"},{anchor:"transformers.ViltConfig.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.ViltConfig.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.ViltConfig.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.ViltConfig.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.ViltConfig.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.ViltConfig.hidden_dropout_prob",description:`<strong>hidden_dropout_prob</strong> (<code>float</code>, <em>optional</em>, defaults to 0.1) &#x2014;
The dropout probabilitiy for all fully connected layers in the embeddings, encoder, and pooler.`,name:"hidden_dropout_prob"},{anchor:"transformers.ViltConfig.attention_probs_dropout_prob",description:`<strong>attention_probs_dropout_prob</strong> (<code>float</code>, <em>optional</em>, defaults to 0.1) &#x2014;
The dropout ratio for the attention probabilities.`,name:"attention_probs_dropout_prob"},{anchor:"transformers.ViltConfig.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.ViltConfig.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.ViltConfig.image_size",description:`<strong>image_size</strong> (<code>int</code>, <em>optional</em>, defaults to 384) &#x2014;
The size (resolution) of each image.`,name:"image_size"},{anchor:"transformers.ViltConfig.patch_size",description:`<strong>patch_size</strong> (<code>int</code>, <em>optional</em>, defaults to 32) &#x2014;
The size (resolution) of each patch.`,name:"patch_size"},{anchor:"transformers.ViltConfig.num_channels",description:`<strong>num_channels</strong> (<code>int</code>, <em>optional</em>, defaults to 3) &#x2014;
The number of input channels.`,name:"num_channels"},{anchor:"transformers.ViltConfig.qkv_bias",description:`<strong>qkv_bias</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether to add a bias to the queries, keys and values.`,name:"qkv_bias"},{anchor:"transformers.ViltConfig.max_image_length",description:`<strong>max_image_length</strong> (<code>int</code>, <em>optional</em>, defaults to -1) &#x2014;
The maximum number of patches to take as input for the Transformer encoder. If set to a positive integer,
the encoder will sample <code>max_image_length</code> patches at maximum. If set to -1, will not be taken into
account.`,name:"max_image_length"},{anchor:"transformers.ViltConfig.num_images",description:`<strong>num_images</strong> (<code>int</code>, <em>optional</em>, defaults to -1) &#x2014;
The number of images to use for natural language visual reasoning. If set to a positive integer, will be
used by <a href="/docs/transformers/v4.30.0/en/model_doc/vilt#transformers.ViltForImagesAndTextClassification">ViltForImagesAndTextClassification</a> for defining the classifier head.`,name:"num_images"}],source:"https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/vilt/configuration_vilt.py#L28"}}),ke=new Wo({props:{anchor:"transformers.ViltConfig.example",$$slots:{default:[ac]},$$scope:{ctx:x}}}),_t=new B({}),vt=new j({props:{name:"class transformers.ViltFeatureExtractor",anchor:"transformers.ViltFeatureExtractor",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/vilt/feature_extraction_vilt.py#L26"}}),yt=new j({props:{name:"__call__",anchor:"transformers.ViltFeatureExtractor.__call__",parameters:[{name:"images",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/image_processing_utils.py#L462"}}),bt=new B({}),Tt=new j({props:{name:"class transformers.ViltImageProcessor",anchor:"transformers.ViltImageProcessor",parameters:[{name:"do_resize",val:": bool = True"},{name:"size",val:": typing.Dict[str, int] = None"},{name:"size_divisor",val:": int = 32"},{name:"resample",val:": Resampling = <Resampling.BICUBIC: 3>"},{name:"do_rescale",val:": bool = True"},{name:"rescale_factor",val:": typing.Union[int, float] = 0.00392156862745098"},{name:"do_normalize",val:": bool = True"},{name:"image_mean",val:": typing.Union[float, typing.List[float], NoneType] = None"},{name:"image_std",val:": typing.Union[float, typing.List[float], NoneType] = None"},{name:"do_pad",val:": bool = True"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.ViltImageProcessor.do_resize",description:`<strong>do_resize</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether to resize the image&#x2019;s (height, width) dimensions to the specified <code>size</code>. Can be overridden by the
<code>do_resize</code> parameter in the <code>preprocess</code> method.`,name:"do_resize"},{anchor:"transformers.ViltImageProcessor.size",description:`<strong>size</strong> (<code>Dict[str, int]</code> <em>optional</em>, defaults to <code>{&quot;shortest_edge&quot; -- 384}</code>):
Resize the shorter side of the input to <code>size[&quot;shortest_edge&quot;]</code>. The longer side will be limited to under
<code>int((1333 / 800) * size[&quot;shortest_edge&quot;])</code> while preserving the aspect ratio. Only has an effect if
<code>do_resize</code> is set to <code>True</code>. Can be overridden by the <code>size</code> parameter in the <code>preprocess</code> method.`,name:"size"},{anchor:"transformers.ViltImageProcessor.size_divisor",description:`<strong>size_divisor</strong> (<code>int</code>, <em>optional</em>, defaults to 32) &#x2014;
The size by which to make sure both the height and width can be divided. Only has an effect if <code>do_resize</code>
is set to <code>True</code>. Can be overridden by the <code>size_divisor</code> parameter in the <code>preprocess</code> method.`,name:"size_divisor"},{anchor:"transformers.ViltImageProcessor.resample",description:`<strong>resample</strong> (<code>PILImageResampling</code>, <em>optional</em>, defaults to <code>PILImageResampling.BICUBIC</code>) &#x2014;
Resampling filter to use if resizing the image. Only has an effect if <code>do_resize</code> is set to <code>True</code>. Can be
overridden by the <code>resample</code> parameter in the <code>preprocess</code> method.`,name:"resample"},{anchor:"transformers.ViltImageProcessor.do_rescale",description:`<strong>do_rescale</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Wwhether to rescale the image by the specified scale <code>rescale_factor</code>. Can be overridden by the
<code>do_rescale</code> parameter in the <code>preprocess</code> method.`,name:"do_rescale"},{anchor:"transformers.ViltImageProcessor.rescale_factor",description:`<strong>rescale_factor</strong> (<code>int</code> or <code>float</code>, <em>optional</em>, defaults to <code>1/255</code>) &#x2014;
Scale factor to use if rescaling the image. Only has an effect if <code>do_rescale</code> is set to <code>True</code>. Can be
overridden by the <code>rescale_factor</code> parameter in the <code>preprocess</code> method.`,name:"rescale_factor"},{anchor:"transformers.ViltImageProcessor.do_normalize",description:`<strong>do_normalize</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether to normalize the image. Can be overridden by the <code>do_normalize</code> parameter in the <code>preprocess</code>
method. Can be overridden by the <code>do_normalize</code> parameter in the <code>preprocess</code> method.`,name:"do_normalize"},{anchor:"transformers.ViltImageProcessor.image_mean",description:`<strong>image_mean</strong> (<code>float</code> or <code>List[float]</code>, <em>optional</em>, defaults to <code>IMAGENET_STANDARD_MEAN</code>) &#x2014;
Mean to use if normalizing the image. This is a float or list of floats the length of the number of
channels in the image. Can be overridden by the <code>image_mean</code> parameter in the <code>preprocess</code> method. Can be
overridden by the <code>image_mean</code> parameter in the <code>preprocess</code> method.`,name:"image_mean"},{anchor:"transformers.ViltImageProcessor.image_std",description:`<strong>image_std</strong> (<code>float</code> or <code>List[float]</code>, <em>optional</em>, defaults to <code>IMAGENET_STANDARD_STD</code>) &#x2014;
Standard deviation to use if normalizing the image. This is a float or list of floats the length of the
number of channels in the image. Can be overridden by the <code>image_std</code> parameter in the <code>preprocess</code> method.
Can be overridden by the <code>image_std</code> parameter in the <code>preprocess</code> method.`,name:"image_std"},{anchor:"transformers.ViltImageProcessor.do_pad",description:`<strong>do_pad</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether to pad the image to the <code>(max_height, max_width)</code> of the images in the batch. Can be overridden by
the <code>do_pad</code> parameter in the <code>preprocess</code> method.`,name:"do_pad"}],source:"https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/vilt/image_processing_vilt.py#L111"}}),wt=new j({props:{name:"preprocess",anchor:"transformers.ViltImageProcessor.preprocess",parameters:[{name:"images",val:": typing.Union[ForwardRef('PIL.Image.Image'), numpy.ndarray, ForwardRef('torch.Tensor'), typing.List[ForwardRef('PIL.Image.Image')], typing.List[numpy.ndarray], typing.List[ForwardRef('torch.Tensor')]]"},{name:"do_resize",val:": typing.Optional[bool] = None"},{name:"size",val:": typing.Union[typing.Dict[str, int], NoneType] = None"},{name:"size_divisor",val:": typing.Optional[int] = None"},{name:"resample",val:": Resampling = None"},{name:"do_rescale",val:": typing.Optional[bool] = None"},{name:"rescale_factor",val:": typing.Optional[float] = None"},{name:"do_normalize",val:": typing.Optional[bool] = None"},{name:"image_mean",val:": typing.Union[float, typing.List[float], NoneType] = None"},{name:"image_std",val:": typing.Union[float, typing.List[float], NoneType] = None"},{name:"do_pad",val:": typing.Optional[bool] = None"},{name:"return_tensors",val:": typing.Union[str, transformers.utils.generic.TensorType, NoneType] = None"},{name:"data_format",val:": ChannelDimension = <ChannelDimension.FIRST: 'channels_first'>"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.ViltImageProcessor.preprocess.images",description:`<strong>images</strong> (<code>ImageInput</code>) &#x2014;
Image to preprocess.`,name:"images"},{anchor:"transformers.ViltImageProcessor.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.ViltImageProcessor.preprocess.size",description:`<strong>size</strong> (<code>Dict[str, int]</code>, <em>optional</em>, defaults to <code>self.size</code>) &#x2014;
Controls the size of the image after <code>resize</code>. The shortest edge of the image is resized to
<code>size[&quot;shortest_edge&quot;]</code> whilst preserving the aspect ratio. If the longest edge of this resized image
is &gt; <code>int(size[&quot;shortest_edge&quot;] * (1333 / 800))</code>, then the image is resized again to make the longest
edge equal to <code>int(size[&quot;shortest_edge&quot;] * (1333 / 800))</code>.`,name:"size"},{anchor:"transformers.ViltImageProcessor.preprocess.size_divisor",description:`<strong>size_divisor</strong> (<code>int</code>, <em>optional</em>, defaults to <code>self.size_divisor</code>) &#x2014;
The image is resized to a size that is a multiple of this value.`,name:"size_divisor"},{anchor:"transformers.ViltImageProcessor.preprocess.resample",description:`<strong>resample</strong> (<code>PILImageResampling</code>, <em>optional</em>, defaults to <code>self.resample</code>) &#x2014;
Resampling filter to use if resizing the image. Only has an effect if <code>do_resize</code> is set to <code>True</code>.`,name:"resample"},{anchor:"transformers.ViltImageProcessor.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.ViltImageProcessor.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.ViltImageProcessor.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.ViltImageProcessor.preprocess.image_mean",description:`<strong>image_mean</strong> (<code>float</code> or <code>List[float]</code>, <em>optional</em>, defaults to <code>self.image_mean</code>) &#x2014;
Image mean to normalize the image by if <code>do_normalize</code> is set to <code>True</code>.`,name:"image_mean"},{anchor:"transformers.ViltImageProcessor.preprocess.image_std",description:`<strong>image_std</strong> (<code>float</code> or <code>List[float]</code>, <em>optional</em>, defaults to <code>self.image_std</code>) &#x2014;
Image standard deviation to normalize the image by if <code>do_normalize</code> is set to <code>True</code>.`,name:"image_std"},{anchor:"transformers.ViltImageProcessor.preprocess.do_pad",description:`<strong>do_pad</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>self.do_pad</code>) &#x2014;
Whether to pad the image to the (max_height, max_width) in the batch. If <code>True</code>, a pixel mask is also
created and returned.`,name:"do_pad"},{anchor:"transformers.ViltImageProcessor.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.ViltImageProcessor.preprocess.data_format",description:`<strong>data_format</strong> (<code>ChannelDimension</code> or <code>str</code>, <em>optional</em>, defaults to <code>ChannelDimension.FIRST</code>) &#x2014;
The channel dimension format for the output image. Can be one of:<ul>
<li><code>ChannelDimension.FIRST</code>: image in (num_channels, height, width) format.</li>
<li><code>ChannelDimension.LAST</code>: image in (height, width, num_channels) format.</li>
</ul>`,name:"data_format"}],source:"https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/vilt/image_processing_vilt.py#L369"}}),Mt=new B({}),kt=new j({props:{name:"class transformers.ViltProcessor",anchor:"transformers.ViltProcessor",parameters:[{name:"image_processor",val:" = None"},{name:"tokenizer",val:" = None"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.ViltProcessor.image_processor",description:`<strong>image_processor</strong> (<code>ViltImageProcessor</code>) &#x2014;
An instance of <a href="/docs/transformers/v4.30.0/en/model_doc/vilt#transformers.ViltImageProcessor">ViltImageProcessor</a>. The image processor is a required input.`,name:"image_processor"},{anchor:"transformers.ViltProcessor.tokenizer",description:"<strong>tokenizer</strong> (<code>BertTokenizerFast</code>) &#x2014;\nAn instance of [&#x2018;BertTokenizerFast`]. The tokenizer is a required input.",name:"tokenizer"}],source:"https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/vilt/processing_vilt.py#L27"}}),xt=new j({props:{name:"__call__",anchor:"transformers.ViltProcessor.__call__",parameters:[{name:"images",val:""},{name:"text",val:": typing.Union[str, typing.List[str], typing.List[typing.List[str]]] = None"},{name:"add_special_tokens",val:": bool = True"},{name:"padding",val:": typing.Union[bool, str, transformers.utils.generic.PaddingStrategy] = False"},{name:"truncation",val:": typing.Union[bool, str, transformers.tokenization_utils_base.TruncationStrategy] = None"},{name:"max_length",val:": typing.Optional[int] = None"},{name:"stride",val:": int = 0"},{name:"pad_to_multiple_of",val:": typing.Optional[int] = None"},{name:"return_token_type_ids",val:": typing.Optional[bool] = None"},{name:"return_attention_mask",val:": typing.Optional[bool] = None"},{name:"return_overflowing_tokens",val:": bool = False"},{name:"return_special_tokens_mask",val:": bool = False"},{name:"return_offsets_mapping",val:": bool = False"},{name:"return_length",val:": bool = False"},{name:"verbose",val:": bool = True"},{name:"return_tensors",val:": typing.Union[str, transformers.utils.generic.TensorType, NoneType] = None"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/vilt/processing_vilt.py#L62"}}),$t=new B({}),jt=new j({props:{name:"class transformers.ViltModel",anchor:"transformers.ViltModel",parameters:[{name:"config",val:""},{name:"add_pooling_layer",val:" = True"}],parametersDescription:[{anchor:"transformers.ViltModel.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.30.0/en/model_doc/vilt#transformers.ViltConfig">ViltConfig</a>) &#x2014; Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
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Indices of input sequence tokens in the vocabulary. Indices can be obtained using <a href="/docs/transformers/v4.30.0/en/model_doc/bert#transformers.BertTokenizer">BertTokenizer</a>. See
<a href="/docs/transformers/v4.30.0/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and <a href="/docs/transformers/v4.30.0/en/model_doc/speecht5#transformers.SpeechT5Tokenizer.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details. <a href="../glossary#input-ids">What are input
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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>
</ul>`,name:"attention_mask"},{anchor:"transformers.ViltModel.forward.token_type_ids",description:`<strong>token_type_ids</strong> (<code>torch.LongTensor</code> of shape <code>({0})</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.ViltModel.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/v4.30.0/en/model_doc/auto#transformers.AutoImageProcessor">AutoImageProcessor</a>. See
<a href="/docs/transformers/v4.30.0/en/model_doc/layoutlmv2#transformers.LayoutLMv2FeatureExtractor.__call__">ViltImageProcessor.<strong>call</strong>()</a> for details.`,name:"pixel_values"},{anchor:"transformers.ViltModel.forward.pixel_mask",description:`<strong>pixel_mask</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, height, width)</code>, <em>optional</em>) &#x2014;
Mask to avoid performing attention on padding pixel values. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for pixels that are real (i.e. <strong>not masked</strong>),</li>
<li>0 for pixels that are padding (i.e. <strong>masked</strong>).
<code>What are attention masks? &lt;../glossary.html#attention-mask&gt;</code>__</li>
</ul>`,name:"pixel_mask"},{anchor:"transformers.ViltModel.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.ViltModel.forward.inputs_embeds",description:`<strong>inputs_embeds</strong> (<code>torch.FloatTensor</code> of shape <code>({0}, hidden_size)</code>, <em>optional</em>) &#x2014;
Optionally, instead of passing <code>input_ids</code> you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert <code>input_ids</code> indices into associated vectors than the
model&#x2019;s internal embedding lookup matrix.`,name:"inputs_embeds"},{anchor:"transformers.ViltModel.forward.image_embeds",description:`<strong>image_embeds</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, num_patches, hidden_size)</code>, <em>optional</em>) &#x2014;
Optionally, instead of passing <code>pixel_values</code>, you can choose to directly pass an embedded representation.
This is useful if you want more control over how to convert <code>pixel_values</code> into patch embeddings.`,name:"image_embeds"},{anchor:"transformers.ViltModel.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.ViltModel.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.ViltModel.forward.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/v4.30.0/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/vilt/modeling_vilt.py#L758",returnDescription:`
<p>A <a
  href="/docs/transformers/v4.30.0/en/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPooling"
>transformers.modeling_outputs.BaseModelOutputWithPooling</a> or a tuple of
<code>torch.FloatTensor</code> (if <code>return_dict=False</code> is passed or when <code>config.return_dict=False</code>) comprising various
elements depending on the configuration (<a
  href="/docs/transformers/v4.30.0/en/model_doc/vilt#transformers.ViltConfig"
>ViltConfig</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/v4.30.0/en/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPooling"
>transformers.modeling_outputs.BaseModelOutputWithPooling</a> or <code>tuple(torch.FloatTensor)</code></p>
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Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the <a href="/docs/transformers/v4.30.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/vilt/modeling_vilt.py#L895"}}),Jt=new j({props:{name:"forward",anchor:"transformers.ViltForMaskedLM.forward",parameters:[{name:"input_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"attention_mask",val:": typing.Optional[torch.FloatTensor] = None"},{name:"token_type_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"pixel_values",val:": typing.Optional[torch.FloatTensor] = None"},{name:"pixel_mask",val:": typing.Optional[torch.LongTensor] = None"},{name:"head_mask",val:": typing.Optional[torch.FloatTensor] = None"},{name:"inputs_embeds",val:": typing.Optional[torch.FloatTensor] = None"},{name:"image_embeds",val:": typing.Optional[torch.FloatTensor] = None"},{name:"labels",val:": typing.Optional[torch.LongTensor] = None"},{name:"output_attentions",val:": typing.Optional[bool] = None"},{name:"output_hidden_states",val:": typing.Optional[bool] = None"},{name:"return_dict",val:": typing.Optional[bool] = None"}],parametersDescription:[{anchor:"transformers.ViltForMaskedLM.forward.input_ids",description:`<strong>input_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary. Indices can be obtained using <a href="/docs/transformers/v4.30.0/en/model_doc/bert#transformers.BertTokenizer">BertTokenizer</a>. See
<a href="/docs/transformers/v4.30.0/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and <a href="/docs/transformers/v4.30.0/en/model_doc/speecht5#transformers.SpeechT5Tokenizer.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details. <a href="../glossary#input-ids">What are input
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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.ViltForMaskedLM.forward.token_type_ids",description:`<strong>token_type_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_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.ViltForMaskedLM.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/v4.30.0/en/model_doc/auto#transformers.AutoImageProcessor">AutoImageProcessor</a>. See
<a href="/docs/transformers/v4.30.0/en/model_doc/layoutlmv2#transformers.LayoutLMv2FeatureExtractor.__call__">ViltImageProcessor.<strong>call</strong>()</a> for details.`,name:"pixel_values"},{anchor:"transformers.ViltForMaskedLM.forward.pixel_mask",description:`<strong>pixel_mask</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, height, width)</code>, <em>optional</em>) &#x2014;
Mask to avoid performing attention on padding pixel values. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for pixels that are real (i.e. <strong>not masked</strong>),</li>
<li>0 for pixels that are padding (i.e. <strong>masked</strong>).
<code>What are attention masks? &lt;../glossary.html#attention-mask&gt;</code>__</li>
</ul>`,name:"pixel_mask"},{anchor:"transformers.ViltForMaskedLM.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.ViltForMaskedLM.forward.inputs_embeds",description:`<strong>inputs_embeds</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>, <em>optional</em>) &#x2014;
Optionally, instead of passing <code>input_ids</code> you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert <code>input_ids</code> indices into associated vectors than the
model&#x2019;s internal embedding lookup matrix.`,name:"inputs_embeds"},{anchor:"transformers.ViltForMaskedLM.forward.image_embeds",description:`<strong>image_embeds</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, num_patches, hidden_size)</code>, <em>optional</em>) &#x2014;
Optionally, instead of passing <code>pixel_values</code>, you can choose to directly pass an embedded representation.
This is useful if you want more control over how to convert <code>pixel_values</code> into patch embeddings.`,name:"image_embeds"},{anchor:"transformers.ViltForMaskedLM.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.ViltForMaskedLM.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.ViltForMaskedLM.forward.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/v4.30.0/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"},{anchor:"transformers.ViltForMaskedLM.forward.labels",description:`<strong>labels</strong> (<em>torch.LongTensor</em> of shape <em>(batch_size, sequence_length)</em>, <em>optional</em>) &#x2014;
Labels for computing the masked language modeling loss. Indices should be in <em>[-100, 0, &#x2026;,
config.vocab_size]</em> (see <em>input_ids</em> docstring) Tokens with indices set to <em>-100</em> are ignored (masked), the
loss is only computed for the tokens with labels in <em>[0, &#x2026;, config.vocab_size]</em>`,name:"labels"}],source:"https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/vilt/modeling_vilt.py#L913",returnDescription:`
<p>A <a
  href="/docs/transformers/v4.30.0/en/main_classes/output#transformers.modeling_outputs.MaskedLMOutput"
>transformers.modeling_outputs.MaskedLMOutput</a> or a tuple of
<code>torch.FloatTensor</code> (if <code>return_dict=False</code> is passed or when <code>config.return_dict=False</code>) comprising various
elements depending on the configuration (<a
  href="/docs/transformers/v4.30.0/en/model_doc/vilt#transformers.ViltConfig"
>ViltConfig</a>) and inputs.</p>
<ul>
<li>
<p><strong>loss</strong> (<code>torch.FloatTensor</code> of shape <code>(1,)</code>, <em>optional</em>, returned when <code>labels</code> is provided) \u2014 Masked language modeling (MLM) loss.</p>
</li>
<li>
<p><strong>logits</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, config.vocab_size)</code>) \u2014 Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).</p>
</li>
<li>
<p><strong>hidden_states</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape <code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.</p>
</li>
<li>
<p><strong>attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.</p>
</li>
</ul>
`,returnType:`
<p><a
  href="/docs/transformers/v4.30.0/en/main_classes/output#transformers.modeling_outputs.MaskedLMOutput"
>transformers.modeling_outputs.MaskedLMOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
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Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the <a href="/docs/transformers/v4.30.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/vilt/modeling_vilt.py#L1071"}}),Zt=new j({props:{name:"forward",anchor:"transformers.ViltForQuestionAnswering.forward",parameters:[{name:"input_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"attention_mask",val:": typing.Optional[torch.FloatTensor] = None"},{name:"token_type_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"pixel_values",val:": typing.Optional[torch.FloatTensor] = None"},{name:"pixel_mask",val:": typing.Optional[torch.LongTensor] = None"},{name:"head_mask",val:": typing.Optional[torch.FloatTensor] = None"},{name:"inputs_embeds",val:": typing.Optional[torch.FloatTensor] = None"},{name:"image_embeds",val:": typing.Optional[torch.FloatTensor] = None"},{name:"labels",val:": typing.Optional[torch.LongTensor] = None"},{name:"output_attentions",val:": typing.Optional[bool] = None"},{name:"output_hidden_states",val:": typing.Optional[bool] = None"},{name:"return_dict",val:": typing.Optional[bool] = None"}],parametersDescription:[{anchor:"transformers.ViltForQuestionAnswering.forward.input_ids",description:`<strong>input_ids</strong> (<code>torch.LongTensor</code> of shape <code>({0})</code>) &#x2014;
Indices of input sequence tokens in the vocabulary. Indices can be obtained using <a href="/docs/transformers/v4.30.0/en/model_doc/bert#transformers.BertTokenizer">BertTokenizer</a>. See
<a href="/docs/transformers/v4.30.0/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and <a href="/docs/transformers/v4.30.0/en/model_doc/speecht5#transformers.SpeechT5Tokenizer.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details. <a href="../glossary#input-ids">What are input
IDs?</a>`,name:"input_ids"},{anchor:"transformers.ViltForQuestionAnswering.forward.attention_mask",description:`<strong>attention_mask</strong> (<code>torch.FloatTensor</code> of shape <code>({0})</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.ViltForQuestionAnswering.forward.token_type_ids",description:`<strong>token_type_ids</strong> (<code>torch.LongTensor</code> of shape <code>({0})</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>
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<a href="../glossary#token-type-ids">What are token type IDs?</a></li>
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Mask to avoid performing attention on padding pixel values. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for pixels that are real (i.e. <strong>not masked</strong>),</li>
<li>0 for pixels that are padding (i.e. <strong>masked</strong>).
<code>What are attention masks? &lt;../glossary.html#attention-mask&gt;</code>__</li>
</ul>`,name:"pixel_mask"},{anchor:"transformers.ViltForQuestionAnswering.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.ViltForQuestionAnswering.forward.inputs_embeds",description:`<strong>inputs_embeds</strong> (<code>torch.FloatTensor</code> of shape <code>({0}, hidden_size)</code>, <em>optional</em>) &#x2014;
Optionally, instead of passing <code>input_ids</code> you can choose to directly pass an embedded representation. This
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  href="/docs/transformers/v4.30.0/en/main_classes/output#transformers.modeling_outputs.SequenceClassifierOutput"
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  href="/docs/transformers/v4.30.0/en/model_doc/vilt#transformers.ViltConfig"
>ViltConfig</a>) and inputs.</p>
<ul>
<li>
<p><strong>loss</strong> (<code>torch.FloatTensor</code> of shape <code>(1,)</code>, <em>optional</em>, returned when <code>labels</code> is provided) \u2014 Classification (or regression if config.num_labels==1) loss.</p>
</li>
<li>
<p><strong>logits</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, config.num_labels)</code>) \u2014 Classification (or regression if config.num_labels==1) scores (before SoftMax).</p>
</li>
<li>
<p><strong>hidden_states</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape <code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.</p>
</li>
<li>
<p><strong>attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.</p>
</li>
</ul>
`,returnType:`
<p><a
  href="/docs/transformers/v4.30.0/en/main_classes/output#transformers.modeling_outputs.SequenceClassifierOutput"
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<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
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<a href="../glossary#attention-mask">What are attention masks?</a></li>
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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.ViltForImagesAndTextClassification.pixel_values",description:`<strong>pixel_values</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, num_images, num_channels, height, width)</code>) &#x2014;
Pixel values. Pixel values can be obtained using <a href="/docs/transformers/v4.30.0/en/model_doc/auto#transformers.AutoImageProcessor">AutoImageProcessor</a>. See
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Mask to avoid performing attention on padding pixel values. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for pixels that are real (i.e. <strong>not masked</strong>),</li>
<li>0 for pixels that are padding (i.e. <strong>masked</strong>).
<code>What are attention masks? &lt;../glossary.html#attention-mask&gt;</code>__</li>
</ul>`,name:"pixel_mask"},{anchor:"transformers.ViltForImagesAndTextClassification.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>
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Optionally, instead of passing <code>input_ids</code> you can choose to directly pass an embedded representation. This
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Optionally, instead of passing <code>pixel_values</code>, you can choose to directly pass an embedded representation.
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Whether or not to return the attentions tensors of all attention layers. See <code>attentions</code> under returned
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Whether or not to return the hidden states of all layers. See <code>hidden_states</code> under returned tensors for
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<a href="/docs/transformers/v4.30.0/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and <a href="/docs/transformers/v4.30.0/en/model_doc/speecht5#transformers.SpeechT5Tokenizer.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details. <a href="../glossary#input-ids">What are input
IDs?</a>`,name:"input_ids"},{anchor:"transformers.ViltForImagesAndTextClassification.forward.attention_mask",description:`<strong>attention_mask</strong> (<code>torch.FloatTensor</code> of shape <code>({0})</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.ViltForImagesAndTextClassification.forward.token_type_ids",description:`<strong>token_type_ids</strong> (<code>torch.LongTensor</code> of shape <code>({0})</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.ViltForImagesAndTextClassification.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/v4.30.0/en/model_doc/auto#transformers.AutoImageProcessor">AutoImageProcessor</a>. See
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Mask to avoid performing attention on padding pixel values. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for pixels that are real (i.e. <strong>not masked</strong>),</li>
<li>0 for pixels that are padding (i.e. <strong>masked</strong>).
<code>What are attention masks? &lt;../glossary.html#attention-mask&gt;</code>__</li>
</ul>`,name:"pixel_mask"},{anchor:"transformers.ViltForImagesAndTextClassification.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>
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Optionally, instead of passing <code>input_ids</code> you can choose to directly pass an embedded representation. This
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Optionally, instead of passing <code>pixel_values</code>, you can choose to directly pass an embedded representation.
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elements depending on the configuration (<a
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>ViltConfig</a>) and inputs.</p>
<ul>
<li><strong>loss</strong> (<code>torch.FloatTensor</code> of shape <code>(1,)</code>, <em>optional</em>, returned when <code>labels</code> is provided) \u2014 Classification (or regression if config.num_labels==1) loss.</li>
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<li><strong>hidden_states</strong> (<code>List[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 List of tuples of <code>torch.FloatTensor</code> (one for each image-text pair, each tuple containing the output of
the embeddings + one for the output of each layer) of shape <code>(batch_size, sequence_length, hidden_size)</code>.
Hidden-states of the model at the output of each layer plus the initial embedding outputs.</li>
<li><strong>attentions</strong> (<code>List[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 List of tuples of <code>torch.FloatTensor</code> (one for each image-text pair, each tuple containing the attention
weights of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>. Attentions weights after the
attention softmax, used to compute the weighted average in the self-attention heads.</li>
</ul>
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<p><code>transformers.models.vilt.modeling_vilt.ViltForImagesAndTextClassificationOutput</code> or <code>tuple(torch.FloatTensor)</code></p>
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Indices of input sequence tokens in the vocabulary. Indices can be obtained using <a href="/docs/transformers/v4.30.0/en/model_doc/bert#transformers.BertTokenizer">BertTokenizer</a>. See
<a href="/docs/transformers/v4.30.0/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and <a href="/docs/transformers/v4.30.0/en/model_doc/speecht5#transformers.SpeechT5Tokenizer.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details. <a href="../glossary#input-ids">What are input
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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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<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>
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Mask to avoid performing attention on padding pixel values. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for pixels that are real (i.e. <strong>not masked</strong>),</li>
<li>0 for pixels that are padding (i.e. <strong>masked</strong>).
<code>What are attention masks? &lt;../glossary.html#attention-mask&gt;</code>__</li>
</ul>`,name:"pixel_mask"},{anchor:"transformers.ViltForImageAndTextRetrieval.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.ViltForImageAndTextRetrieval.forward.inputs_embeds",description:`<strong>inputs_embeds</strong> (<code>torch.FloatTensor</code> of shape <code>({0}, hidden_size)</code>, <em>optional</em>) &#x2014;
Optionally, instead of passing <code>input_ids</code> you can choose to directly pass an embedded representation. This
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  href="/docs/transformers/v4.30.0/en/model_doc/vilt#transformers.ViltConfig"
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<ul>
<li>
<p><strong>loss</strong> (<code>torch.FloatTensor</code> of shape <code>(1,)</code>, <em>optional</em>, returned when <code>labels</code> is provided) \u2014 Classification (or regression if config.num_labels==1) loss.</p>
</li>
<li>
<p><strong>logits</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, config.num_labels)</code>) \u2014 Classification (or regression if config.num_labels==1) scores (before SoftMax).</p>
</li>
<li>
<p><strong>hidden_states</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) \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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<a href="../glossary#attention-mask">What are attention masks?</a></li>
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<ul>
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<a href="../glossary#token-type-ids">What are token type IDs?</a></li>
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<ul>
<li>1 for pixels that are real (i.e. <strong>not masked</strong>),</li>
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<code>What are attention masks? &lt;../glossary.html#attention-mask&gt;</code>__</li>
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>transformers.modeling_outputs.TokenClassifierOutput</a> or a tuple of
<code>torch.FloatTensor</code> (if <code>return_dict=False</code> is passed or when <code>config.return_dict=False</code>) comprising various
elements depending on the configuration (<a
  href="/docs/transformers/v4.30.0/en/model_doc/vilt#transformers.ViltConfig"
>ViltConfig</a>) and inputs.</p>
<ul>
<li>
<p><strong>loss</strong> (<code>torch.FloatTensor</code> of shape <code>(1,)</code>, <em>optional</em>, returned when <code>labels</code> is provided)  \u2014 Classification loss.</p>
</li>
<li>
<p><strong>logits</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, config.num_labels)</code>) \u2014 Classification scores (before SoftMax).</p>
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<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
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</li>
</ul>
`,returnType:`
<p><a
  href="/docs/transformers/v4.30.0/en/main_classes/output#transformers.modeling_outputs.TokenClassifierOutput"
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token) for image-to-text or text-to-image retrieval, e.g. MSCOCO and F30K.`),qi=m(),St=n("p"),Qi=r("This model is a PyTorch "),Un=n("code"),Xi=r("torch.nn.Module <https://pytorch.org/docs/stable/nn.html#torch.nn.Module>"),Hi=r(`_ subclass. Use
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behavior.`),fl=m(),H=n("div"),b(Xt.$$.fragment),ul=m(),ve=n("p"),gl=r("The "),vo=n("a"),_l=r("ViltForTokenClassification"),vl=r(" forward method, overrides the "),Pn=n("code"),yl=r("__call__"),bl=r(" special method."),Tl=m(),b(Qe.$$.fragment),this.h()},l(t){const h=oc('[data-svelte="svelte-1phssyn"]',document.head);d=s(h,"META",{name:!0,content:!0}),h.forEach(o),y=p(t),_=s(t,"H1",{class:!0});var Ht=a(_);g=s(Ht,"A",{id:!0,class:!0,href:!0});var Ln=a(g);v=s(Ln,"SPAN",{});var Nn=a(v);T(l.$$.fragment,Nn),Nn.forEach(o),Ln.forEach(o),f=p(Ht),$=s(Ht,"SPAN",{});var Bn=a($);Xs=i(Bn,"ViLT"),Bn.forEach(o),Ht.forEach(o),Xn=p(t),D=s(t,"H2",{class:!0});var Dt=a(D);ye=s(Dt,"A",{id:!0,class:!0,href:!0});var Rn=a(ye);Po=s(Rn,"SPAN",{});var Sn=a(Po);T(lt.$$.fragment,Sn),Sn.forEach(o),Rn.forEach(o),Hs=p(Dt),Lo=s(Dt,"SPAN",{});var Gn=a(Lo);Ds=i(Gn,"Overview"),Gn.forEach(o),Dt.forEach(o),Hn=p(t),be=s(t,"P",{});var Yt=a(be);Ys=i(Yt,"The ViLT model was proposed in "),dt=s(Yt,"A",{href:!0,rel:!0});var On=a(dt);Ks=i(On,"ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision"),On.forEach(o),ea=i(Yt,`
by Wonjae Kim, Bokyung Son, Ildoo Kim. ViLT incorporates text embeddings into a Vision Transformer (ViT), allowing it to have a minimal design
for Vision-and-Language Pre-training (VLP).`),Yt.forEach(o),Dn=p(t),Kt=s(t,"P",{});var qn=a(Kt);ta=i(qn,"The abstract from the paper is the following:"),qn.forEach(o),Yn=p(t),eo=s(t,"P",{});var Qn=a(eo);No=s(Qn,"EM",{});var kl=a(No);oa=i(kl,`Vision-and-Language Pre-training (VLP) has improved performance on various joint vision-and-language downstream tasks.
Current approaches to VLP heavily rely on image feature extraction processes, most of which involve region supervision
(e.g., object detection) and the convolutional architecture (e.g., ResNet). Although disregarded in the literature, we
find it problematic in terms of both (1) efficiency/speed, that simply extracting input features requires much more
computation than the multimodal interaction steps; and (2) expressive power, as it is upper bounded to the expressive
power of the visual embedder and its predefined visual vocabulary. In this paper, we present a minimal VLP model,
Vision-and-Language Transformer (ViLT), monolithic in the sense that the processing of visual inputs is drastically
simplified to just the same convolution-free manner that we process textual inputs. We show that ViLT is up to tens of
times faster than previous VLP models, yet with competitive or better downstream task performance.`),kl.forEach(o),Qn.forEach(o),Kn=p(t),to=s(t,"P",{});var Vl=a(to);na=i(Vl,"Tips:"),Vl.forEach(o),es=p(t),W=s(t,"UL",{});var Xe=a(W);ct=s(Xe,"LI",{});var $s=a(ct);sa=i($s,"The quickest way to get started with ViLT is by checking the "),mt=s($s,"A",{href:!0,rel:!0});var xl=a(mt);aa=i(xl,"example notebooks"),xl.forEach(o),ra=i($s,`
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which pixel values are real and which are padding. `),no=s(yo,"A",{href:!0});var Cl=a(no);ya=i(Cl,"ViltProcessor"),Cl.forEach(o),ba=i(yo," automatically creates this for you."),yo.forEach(o),Ta=p(Xe),Go=s(Xe,"LI",{});var zl=a(Go);wa=i(zl,`The design of ViLT is very similar to that of a standard Vision Transformer (ViT). The only difference is that the model includes
additional embedding layers for the language modality.`),zl.forEach(o),Xe.forEach(o),ts=p(t),Te=s(t,"IMG",{src:!0,alt:!0,width:!0}),os=p(t),we=s(t,"SMALL",{});var js=a(we);Ma=i(js,"ViLT architecture. Taken from the "),so=s(js,"A",{href:!0});var Ul=a(so);ka=i(Ul,"original paper"),Ul.forEach(o),Va=i(js,"."),js.forEach(o),ns=p(t),Q=s(t,"P",{});var bo=a(Q);xa=i(bo,"This model was contributed by "),pt=s(bo,"A",{href:!0,rel:!0});var Jl=a(pt);$a=i(Jl,"nielsr"),Jl.forEach(o),ja=i(bo,". The original code can be found "),ht=s(bo,"A",{href:!0,rel:!0});var El=a(ht);Ia=i(El,"here"),El.forEach(o),Fa=i(bo,"."),bo.forEach(o),ss=p(t),ao=s(t,"P",{});var Wl=a(ao);Ca=i(Wl,"Tips:"),Wl.forEach(o),as=p(t),ro=s(t,"UL",{});var Al=a(ro);Oo=s(Al,"LI",{});var Zl=a(Oo);za=i(Zl,"The PyTorch version of this model is only available in torch 1.10 and higher."),Zl.forEach(o),Al.forEach(o),rs=p(t),K=s(t,"H2",{class:!0});var Is=a(K);Me=s(Is,"A",{id:!0,class:!0,href:!0});var Pl=a(Me);qo=s(Pl,"SPAN",{});var Ll=a(qo);T(ft.$$.fragment,Ll),Ll.forEach(o),Pl.forEach(o),Ua=p(Is),Qo=s(Is,"SPAN",{});var Nl=a(Qo);Ja=i(Nl,"ViltConfig"),Nl.forEach(o),Is.forEach(o),is=p(t),F=s(t,"DIV",{class:!0});var De=a(F);T(ut.$$.fragment,De),Ea=p(De),ee=s(De,"P",{});var To=a(ee);Wa=i(To,"This is the configuration class to store the configuration of a "),Xo=s(To,"CODE",{});var Bl=a(Xo);Aa=i(Bl,"ViLTModel"),Bl.forEach(o),Za=i(To,`. It is used to instantiate an ViLT
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docstring of `),Vt=s(q,"A",{href:!0});var wl=a(Vt);rn=s(wl,"STRONG",{});var ld=a(rn);_r=i(ld,"call"),ld.forEach(o),vr=i(wl,"()"),wl.forEach(o),yr=i(q," and "),ln=s(q,"CODE",{});var dd=a(ln);br=i(dd,"decode()"),dd.forEach(o),Tr=i(q," for more information."),q.forEach(o),wr=p(Ye),X=s(Ye,"DIV",{class:!0});var ko=a(X);T(xt.$$.fragment,ko),Mr=p(ko),re=s(ko,"P",{});var Vo=a(re);kr=i(Vo,"This method uses "),Fe=s(Vo,"A",{href:!0});var Ws=a(Fe);Vr=i(Ws,"ViltImageProcessor."),dn=s(Ws,"STRONG",{});var cd=a(dn);xr=i(cd,"call"),cd.forEach(o),$r=i(Ws,"()"),Ws.forEach(o),jr=i(Vo,` method to prepare image(s) for the model, and
`),Ce=s(Vo,"A",{href:!0});var As=a(Ce);Ir=i(As,"BertTokenizerFast."),cn=s(As,"STRONG",{});var md=a(cn);Fr=i(md,"call"),md.forEach(o),Cr=i(As,"()"),As.forEach(o),zr=i(Vo," to prepare text for the model."),Vo.forEach(o),Ur=p(ko),mn=s(ko,"P",{});var pd=a(mn);Jr=i(pd,"Please refer to the docstring of the above two methods for more information."),pd.forEach(o),ko.forEach(o),Ye.forEach(o),fs=p(t),ie=s(t,"H2",{class:!0});var Zs=a(ie);ze=s(Zs,"A",{id:!0,class:!0,href:!0});var hd=a(ze);pn=s(hd,"SPAN",{});var fd=a(pn);T($t.$$.fragment,fd),fd.forEach(o),hd.forEach(o),Er=p(Zs),hn=s(Zs,"SPAN",{});var ud=a(hn);Wr=i(ud,"ViltModel"),ud.forEach(o),Zs.forEach(o),us=p(t),G=s(t,"DIV",{class:!0});var xo=a(G);T(jt.$$.fragment,xo),Ar=p(xo),It=s(xo,"P",{});var Ps=a(It);Zr=i(Ps,`The bare ViLT Model transformer outputting raw hidden-states without any specific head on top.
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it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.`),Ns.forEach(o),si=p(et),Z=s(et,"DIV",{class:!0});var tt=a(Z);T(Jt.$$.fragment,tt),ai=p(tt),ce=s(tt,"P",{});var jo=a(ce);ri=i(jo,"The "),fo=s(jo,"A",{href:!0});var kd=a(fo);ii=i(kd,"ViltForMaskedLM"),kd.forEach(o),li=i(jo," forward method, overrides the "),bn=s(jo,"CODE",{});var Vd=a(bn);di=i(Vd,"__call__"),Vd.forEach(o),ci=i(jo," special method."),jo.forEach(o),mi=p(tt),T(We.$$.fragment,tt),pi=p(tt),T(Ae.$$.fragment,tt),tt.forEach(o),et.forEach(o),vs=p(t),me=s(t,"H2",{class:!0});var Bs=a(me);Ze=s(Bs,"A",{id:!0,class:!0,href:!0});var xd=a(Ze);Tn=s(xd,"SPAN",{});var $d=a(Tn);T(Et.$$.fragment,$d),$d.forEach(o),xd.forEach(o),hi=p(Bs),wn=s(Bs,"SPAN",{});var jd=a(wn);fi=i(jd,"ViltForQuestionAnswering"),jd.forEach(o),Bs.forEach(o),ys=p(t),U=s(t,"DIV",{class:!0});var ot=a(U);T(Wt.$$.fragment,ot),ui=p(ot),Mn=s(ot,"P",{});var Id=a(Mn);gi=i(Id,`Vilt Model transformer with a classifier head on top (a linear layer on top of the final hidden state of the [CLS]
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