import{S as md,i as fd,s as gd,e as n,k as u,w as y,t as i,M as _d,c as s,d as o,m as h,a,x as M,h as l,b as c,G as e,g as _,y as T,q as w,o as k,B as V,v as bd,L as Ht}from"../../chunks/vendor-hf-doc-builder.js";import{T as Fo}from"../../chunks/Tip-hf-doc-builder.js";import{D as $}from"../../chunks/Docstring-hf-doc-builder.js";import{C as Se}from"../../chunks/CodeBlock-hf-doc-builder.js";import{I as H}from"../../chunks/IconCopyLink-hf-doc-builder.js";import{E as St}from"../../chunks/ExampleCodeBlock-hf-doc-builder.js";function vd(B){let d,v,g,m,b;return m=new Se({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> VisualBertConfig, VisualBertModel

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Initializing a VisualBERT visualbert-vqa-coco-pre style configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>configuration = VisualBertConfig.from_pretrained(<span class="hljs-string">&quot;uclanlp/visualbert-vqa-coco-pre&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Initializing a model (with random weights) from the visualbert-vqa-coco-pre style configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = VisualBertModel(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"),v=i("Example:"),g=u(),y(m.$$.fragment)},l(r){d=s(r,"P",{});var p=a(d);v=l(p,"Example:"),p.forEach(o),g=h(r),M(m.$$.fragment,r)},m(r,p){_(r,d,p),e(d,v),_(r,g,p),T(m,r,p),b=!0},p:Ht,i(r){b||(w(m.$$.fragment,r),b=!0)},o(r){k(m.$$.fragment,r),b=!1},d(r){r&&o(d),r&&o(g),V(m,r)}}}function yd(B){let d,v,g,m,b;return{c(){d=n("p"),v=i("Although the recipe for forward pass needs to be defined within this function, one should call the "),g=n("code"),m=i("Module"),b=i(`
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(r){d=s(r,"P",{});var p=a(d);v=l(p,"Although the recipe for forward pass needs to be defined within this function, one should call the "),g=s(p,"CODE",{});var J=a(g);m=l(J,"Module"),J.forEach(o),b=l(p,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),p.forEach(o)},m(r,p){_(r,d,p),e(d,v),e(d,g),e(g,m),e(d,b)},d(r){r&&o(d)}}}function Md(B){let d,v,g,m,b;return m=new Se({props:{code:"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",highlighted:`<span class="hljs-comment"># Assumption: *get_visual_embeddings(image)* gets the visual embeddings of the image.</span>
<span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, VisualBertModel
<span class="hljs-keyword">import</span> torch

tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;bert-base-uncased&quot;</span>)
model = VisualBertModel.from_pretrained(<span class="hljs-string">&quot;uclanlp/visualbert-vqa-coco-pre&quot;</span>)

inputs = tokenizer(<span class="hljs-string">&quot;The capital of France is Paris.&quot;</span>, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>)
visual_embeds = get_visual_embeddings(image).unsqueeze(<span class="hljs-number">0</span>)
visual_token_type_ids = torch.ones(visual_embeds.shape[:-<span class="hljs-number">1</span>], dtype=torch.long)
visual_attention_mask = torch.ones(visual_embeds.shape[:-<span class="hljs-number">1</span>], dtype=torch.<span class="hljs-built_in">float</span>)

inputs.update(
    {
        <span class="hljs-string">&quot;visual_embeds&quot;</span>: visual_embeds,
        <span class="hljs-string">&quot;visual_token_type_ids&quot;</span>: visual_token_type_ids,
        <span class="hljs-string">&quot;visual_attention_mask&quot;</span>: visual_attention_mask,
    }
)

outputs = model(**inputs)

last_hidden_states = outputs.last_hidden_state`}}),{c(){d=n("p"),v=i("Example:"),g=u(),y(m.$$.fragment)},l(r){d=s(r,"P",{});var p=a(d);v=l(p,"Example:"),p.forEach(o),g=h(r),M(m.$$.fragment,r)},m(r,p){_(r,d,p),e(d,v),_(r,g,p),T(m,r,p),b=!0},p:Ht,i(r){b||(w(m.$$.fragment,r),b=!0)},o(r){k(m.$$.fragment,r),b=!1},d(r){r&&o(d),r&&o(g),V(m,r)}}}function Td(B){let d,v,g,m,b;return{c(){d=n("p"),v=i("Although the recipe for forward pass needs to be defined within this function, one should call the "),g=n("code"),m=i("Module"),b=i(`
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(r){d=s(r,"P",{});var p=a(d);v=l(p,"Although the recipe for forward pass needs to be defined within this function, one should call the "),g=s(p,"CODE",{});var J=a(g);m=l(J,"Module"),J.forEach(o),b=l(p,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),p.forEach(o)},m(r,p){_(r,d,p),e(d,v),e(d,g),e(g,m),e(d,b)},d(r){r&&o(d)}}}function wd(B){let d,v,g,m,b;return m=new Se({props:{code:"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",highlighted:`<span class="hljs-comment"># Assumption: *get_visual_embeddings(image)* gets the visual embeddings of the image in the batch.</span>
<span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, VisualBertForPreTraining

tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;bert-base-uncased&quot;</span>)
model = VisualBertForPreTraining.from_pretrained(<span class="hljs-string">&quot;uclanlp/visualbert-vqa-coco-pre&quot;</span>)

inputs = tokenizer(<span class="hljs-string">&quot;The capital of France is [MASK].&quot;</span>, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>)
visual_embeds = get_visual_embeddings(image).unsqueeze(<span class="hljs-number">0</span>)
visual_token_type_ids = torch.ones(visual_embeds.shape[:-<span class="hljs-number">1</span>], dtype=torch.long)
visual_attention_mask = torch.ones(visual_embeds.shape[:-<span class="hljs-number">1</span>], dtype=torch.<span class="hljs-built_in">float</span>)

inputs.update(
    {
        <span class="hljs-string">&quot;visual_embeds&quot;</span>: visual_embeds,
        <span class="hljs-string">&quot;visual_token_type_ids&quot;</span>: visual_token_type_ids,
        <span class="hljs-string">&quot;visual_attention_mask&quot;</span>: visual_attention_mask,
    }
)
max_length = inputs[<span class="hljs-string">&quot;input_ids&quot;</span>].shape[-<span class="hljs-number">1</span>] + visual_embeds.shape[-<span class="hljs-number">2</span>]
labels = tokenizer(
    <span class="hljs-string">&quot;The capital of France is Paris.&quot;</span>, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>, padding=<span class="hljs-string">&quot;max_length&quot;</span>, max_length=max_length
)[<span class="hljs-string">&quot;input_ids&quot;</span>]
sentence_image_labels = torch.tensor(<span class="hljs-number">1</span>).unsqueeze(<span class="hljs-number">0</span>)  <span class="hljs-comment"># Batch_size</span>


outputs = model(**inputs, labels=labels, sentence_image_labels=sentence_image_labels)
loss = outputs.loss
prediction_logits = outputs.prediction_logits
seq_relationship_logits = outputs.seq_relationship_logits`}}),{c(){d=n("p"),v=i("Example:"),g=u(),y(m.$$.fragment)},l(r){d=s(r,"P",{});var p=a(d);v=l(p,"Example:"),p.forEach(o),g=h(r),M(m.$$.fragment,r)},m(r,p){_(r,d,p),e(d,v),_(r,g,p),T(m,r,p),b=!0},p:Ht,i(r){b||(w(m.$$.fragment,r),b=!0)},o(r){k(m.$$.fragment,r),b=!1},d(r){r&&o(d),r&&o(g),V(m,r)}}}function kd(B){let d,v,g,m,b;return{c(){d=n("p"),v=i("Although the recipe for forward pass needs to be defined within this function, one should call the "),g=n("code"),m=i("Module"),b=i(`
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(r){d=s(r,"P",{});var p=a(d);v=l(p,"Although the recipe for forward pass needs to be defined within this function, one should call the "),g=s(p,"CODE",{});var J=a(g);m=l(J,"Module"),J.forEach(o),b=l(p,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),p.forEach(o)},m(r,p){_(r,d,p),e(d,v),e(d,g),e(g,m),e(d,b)},d(r){r&&o(d)}}}function Vd(B){let d,v,g,m,b;return m=new Se({props:{code:"JTIzJTIwQXNzdW1wdGlvbiUzQSUyMCpnZXRfdmlzdWFsX2VtYmVkZGluZ3MoaW1hZ2UpKiUyMGdldHMlMjB0aGUlMjB2aXN1YWwlMjBlbWJlZGRpbmdzJTIwb2YlMjB0aGUlMjBpbWFnZSUyMGluJTIwdGhlJTIwYmF0Y2guJTBBZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Ub2tlbml6ZXIlMkMlMjBWaXN1YWxCZXJ0Rm9yUXVlc3Rpb25BbnN3ZXJpbmclMEFpbXBvcnQlMjB0b3JjaCUwQSUwQXRva2VuaXplciUyMCUzRCUyMEF1dG9Ub2tlbml6ZXIuZnJvbV9wcmV0cmFpbmVkKCUyMmJlcnQtYmFzZS11bmNhc2VkJTIyKSUwQW1vZGVsJTIwJTNEJTIwVmlzdWFsQmVydEZvclF1ZXN0aW9uQW5zd2VyaW5nLmZyb21fcHJldHJhaW5lZCglMjJ1Y2xhbmxwJTJGdmlzdWFsYmVydC12cWElMjIpJTBBJTBBdGV4dCUyMCUzRCUyMCUyMldobyUyMGlzJTIwZWF0aW5nJTIwdGhlJTIwYXBwbGUlM0YlMjIlMEFpbnB1dHMlMjAlM0QlMjB0b2tlbml6ZXIodGV4dCUyQyUyMHJldHVybl90ZW5zb3JzJTNEJTIycHQlMjIpJTBBdmlzdWFsX2VtYmVkcyUyMCUzRCUyMGdldF92aXN1YWxfZW1iZWRkaW5ncyhpbWFnZSkudW5zcXVlZXplKDApJTBBdmlzdWFsX3Rva2VuX3R5cGVfaWRzJTIwJTNEJTIwdG9yY2gub25lcyh2aXN1YWxfZW1iZWRzLnNoYXBlJTVCJTNBLTElNUQlMkMlMjBkdHlwZSUzRHRvcmNoLmxvbmcpJTBBdmlzdWFsX2F0dGVudGlvbl9tYXNrJTIwJTNEJTIwdG9yY2gub25lcyh2aXN1YWxfZW1iZWRzLnNoYXBlJTVCJTNBLTElNUQlMkMlMjBkdHlwZSUzRHRvcmNoLmZsb2F0KSUwQSUwQWlucHV0cy51cGRhdGUoJTBBJTIwJTIwJTIwJTIwJTdCJTBBJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIydmlzdWFsX2VtYmVkcyUyMiUzQSUyMHZpc3VhbF9lbWJlZHMlMkMlMEElMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjJ2aXN1YWxfdG9rZW5fdHlwZV9pZHMlMjIlM0ElMjB2aXN1YWxfdG9rZW5fdHlwZV9pZHMlMkMlMEElMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjJ2aXN1YWxfYXR0ZW50aW9uX21hc2slMjIlM0ElMjB2aXN1YWxfYXR0ZW50aW9uX21hc2slMkMlMEElMjAlMjAlMjAlMjAlN0QlMEEpJTBBJTBBbGFiZWxzJTIwJTNEJTIwdG9yY2gudGVuc29yKCU1QiU1QjAuMCUyQyUyMDEuMCU1RCU1RCkudW5zcXVlZXplKDApJTIwJTIwJTIzJTIwQmF0Y2glMjBzaXplJTIwMSUyQyUyME51bSUyMGxhYmVscyUyMDIlMEElMEFvdXRwdXRzJTIwJTNEJTIwbW9kZWwoKippbnB1dHMlMkMlMjBsYWJlbHMlM0RsYWJlbHMpJTBBbG9zcyUyMCUzRCUyMG91dHB1dHMubG9zcyUwQXNjb3JlcyUyMCUzRCUyMG91dHB1dHMubG9naXRz",highlighted:`<span class="hljs-comment"># Assumption: *get_visual_embeddings(image)* gets the visual embeddings of the image in the batch.</span>
<span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, VisualBertForQuestionAnswering
<span class="hljs-keyword">import</span> torch

tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;bert-base-uncased&quot;</span>)
model = VisualBertForQuestionAnswering.from_pretrained(<span class="hljs-string">&quot;uclanlp/visualbert-vqa&quot;</span>)

text = <span class="hljs-string">&quot;Who is eating the apple?&quot;</span>
inputs = tokenizer(text, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>)
visual_embeds = get_visual_embeddings(image).unsqueeze(<span class="hljs-number">0</span>)
visual_token_type_ids = torch.ones(visual_embeds.shape[:-<span class="hljs-number">1</span>], dtype=torch.long)
visual_attention_mask = torch.ones(visual_embeds.shape[:-<span class="hljs-number">1</span>], dtype=torch.<span class="hljs-built_in">float</span>)

inputs.update(
    {
        <span class="hljs-string">&quot;visual_embeds&quot;</span>: visual_embeds,
        <span class="hljs-string">&quot;visual_token_type_ids&quot;</span>: visual_token_type_ids,
        <span class="hljs-string">&quot;visual_attention_mask&quot;</span>: visual_attention_mask,
    }
)

labels = torch.tensor([[<span class="hljs-number">0.0</span>, <span class="hljs-number">1.0</span>]]).unsqueeze(<span class="hljs-number">0</span>)  <span class="hljs-comment"># Batch size 1, Num labels 2</span>

outputs = model(**inputs, labels=labels)
loss = outputs.loss
scores = outputs.logits`}}),{c(){d=n("p"),v=i("Example:"),g=u(),y(m.$$.fragment)},l(r){d=s(r,"P",{});var p=a(d);v=l(p,"Example:"),p.forEach(o),g=h(r),M(m.$$.fragment,r)},m(r,p){_(r,d,p),e(d,v),_(r,g,p),T(m,r,p),b=!0},p:Ht,i(r){b||(w(m.$$.fragment,r),b=!0)},o(r){k(m.$$.fragment,r),b=!1},d(r){r&&o(d),r&&o(g),V(m,r)}}}function Bd(B){let d,v,g,m,b;return{c(){d=n("p"),v=i("Although the recipe for forward pass needs to be defined within this function, one should call the "),g=n("code"),m=i("Module"),b=i(`
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(r){d=s(r,"P",{});var p=a(d);v=l(p,"Although the recipe for forward pass needs to be defined within this function, one should call the "),g=s(p,"CODE",{});var J=a(g);m=l(J,"Module"),J.forEach(o),b=l(p,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),p.forEach(o)},m(r,p){_(r,d,p),e(d,v),e(d,g),e(g,m),e(d,b)},d(r){r&&o(d)}}}function Jd(B){let d,v,g,m,b;return m=new Se({props:{code:"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",highlighted:`<span class="hljs-comment"># Assumption: *get_visual_embeddings(image)* gets the visual embeddings of the image in the batch.</span>
<span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, VisualBertForMultipleChoice
<span class="hljs-keyword">import</span> torch

tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;bert-base-uncased&quot;</span>)
model = VisualBertForMultipleChoice.from_pretrained(<span class="hljs-string">&quot;uclanlp/visualbert-vcr&quot;</span>)

prompt = <span class="hljs-string">&quot;In Italy, pizza served in formal settings, such as at a restaurant, is presented unsliced.&quot;</span>
choice0 = <span class="hljs-string">&quot;It is eaten with a fork and a knife.&quot;</span>
choice1 = <span class="hljs-string">&quot;It is eaten while held in the hand.&quot;</span>

visual_embeds = get_visual_embeddings(image)
<span class="hljs-comment"># (batch_size, num_choices, visual_seq_length, visual_embedding_dim)</span>
visual_embeds = visual_embeds.expand(<span class="hljs-number">1</span>, <span class="hljs-number">2</span>, *visual_embeds.shape)
visual_token_type_ids = torch.ones(visual_embeds.shape[:-<span class="hljs-number">1</span>], dtype=torch.long)
visual_attention_mask = torch.ones(visual_embeds.shape[:-<span class="hljs-number">1</span>], dtype=torch.<span class="hljs-built_in">float</span>)

labels = torch.tensor(<span class="hljs-number">0</span>).unsqueeze(<span class="hljs-number">0</span>)  <span class="hljs-comment"># choice0 is correct (according to Wikipedia ;)), batch size 1</span>

encoding = tokenizer([[prompt, prompt], [choice0, choice1]], return_tensors=<span class="hljs-string">&quot;pt&quot;</span>, padding=<span class="hljs-literal">True</span>)
<span class="hljs-comment"># batch size is 1</span>
inputs_dict = {k: v.unsqueeze(<span class="hljs-number">0</span>) <span class="hljs-keyword">for</span> k, v <span class="hljs-keyword">in</span> encoding.items()}
inputs_dict.update(
    {
        <span class="hljs-string">&quot;visual_embeds&quot;</span>: visual_embeds,
        <span class="hljs-string">&quot;visual_attention_mask&quot;</span>: visual_attention_mask,
        <span class="hljs-string">&quot;visual_token_type_ids&quot;</span>: visual_token_type_ids,
        <span class="hljs-string">&quot;labels&quot;</span>: labels,
    }
)
outputs = model(**inputs_dict)

loss = outputs.loss
logits = outputs.logits`}}),{c(){d=n("p"),v=i("Example:"),g=u(),y(m.$$.fragment)},l(r){d=s(r,"P",{});var p=a(d);v=l(p,"Example:"),p.forEach(o),g=h(r),M(m.$$.fragment,r)},m(r,p){_(r,d,p),e(d,v),_(r,g,p),T(m,r,p),b=!0},p:Ht,i(r){b||(w(m.$$.fragment,r),b=!0)},o(r){k(m.$$.fragment,r),b=!1},d(r){r&&o(d),r&&o(g),V(m,r)}}}function Ud(B){let d,v,g,m,b;return{c(){d=n("p"),v=i("Although the recipe for forward pass needs to be defined within this function, one should call the "),g=n("code"),m=i("Module"),b=i(`
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(r){d=s(r,"P",{});var p=a(d);v=l(p,"Although the recipe for forward pass needs to be defined within this function, one should call the "),g=s(p,"CODE",{});var J=a(g);m=l(J,"Module"),J.forEach(o),b=l(p,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),p.forEach(o)},m(r,p){_(r,d,p),e(d,v),e(d,g),e(g,m),e(d,b)},d(r){r&&o(d)}}}function jd(B){let d,v,g,m,b;return m=new Se({props:{code:"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",highlighted:`<span class="hljs-comment"># Assumption: *get_visual_embeddings(image)* gets the visual embeddings of the image in the batch.</span>
<span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, VisualBertForVisualReasoning
<span class="hljs-keyword">import</span> torch

tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;bert-base-uncased&quot;</span>)
model = VisualBertForVisualReasoning.from_pretrained(<span class="hljs-string">&quot;uclanlp/visualbert-nlvr2&quot;</span>)

text = <span class="hljs-string">&quot;Who is eating the apple?&quot;</span>
inputs = tokenizer(text, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>)
visual_embeds = get_visual_embeddings(image).unsqueeze(<span class="hljs-number">0</span>)
visual_token_type_ids = torch.ones(visual_embeds.shape[:-<span class="hljs-number">1</span>], dtype=torch.long)
visual_attention_mask = torch.ones(visual_embeds.shape[:-<span class="hljs-number">1</span>], dtype=torch.<span class="hljs-built_in">float</span>)

inputs.update(
    {
        <span class="hljs-string">&quot;visual_embeds&quot;</span>: visual_embeds,
        <span class="hljs-string">&quot;visual_token_type_ids&quot;</span>: visual_token_type_ids,
        <span class="hljs-string">&quot;visual_attention_mask&quot;</span>: visual_attention_mask,
    }
)

labels = torch.tensor(<span class="hljs-number">1</span>).unsqueeze(<span class="hljs-number">0</span>)  <span class="hljs-comment"># Batch size 1, Num choices 2</span>

outputs = model(**inputs, labels=labels)
loss = outputs.loss
scores = outputs.logits`}}),{c(){d=n("p"),v=i("Example:"),g=u(),y(m.$$.fragment)},l(r){d=s(r,"P",{});var p=a(d);v=l(p,"Example:"),p.forEach(o),g=h(r),M(m.$$.fragment,r)},m(r,p){_(r,d,p),e(d,v),_(r,g,p),T(m,r,p),b=!0},p:Ht,i(r){b||(w(m.$$.fragment,r),b=!0)},o(r){k(m.$$.fragment,r),b=!1},d(r){r&&o(d),r&&o(g),V(m,r)}}}function zd(B){let d,v,g,m,b;return{c(){d=n("p"),v=i("Although the recipe for forward pass needs to be defined within this function, one should call the "),g=n("code"),m=i("Module"),b=i(`
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(r){d=s(r,"P",{});var p=a(d);v=l(p,"Although the recipe for forward pass needs to be defined within this function, one should call the "),g=s(p,"CODE",{});var J=a(g);m=l(J,"Module"),J.forEach(o),b=l(p,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),p.forEach(o)},m(r,p){_(r,d,p),e(d,v),e(d,g),e(g,m),e(d,b)},d(r){r&&o(d)}}}function Wd(B){let d,v,g,m,b;return m=new Se({props:{code:"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",highlighted:`<span class="hljs-comment"># Assumption: *get_visual_embeddings(image)* gets the visual embeddings of the image in the batch.</span>
<span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, VisualBertForRegionToPhraseAlignment
<span class="hljs-keyword">import</span> torch

tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;bert-base-uncased&quot;</span>)
model = VisualBertForRegionToPhraseAlignment.from_pretrained(<span class="hljs-string">&quot;uclanlp/visualbert-vqa-coco-pre&quot;</span>)

text = <span class="hljs-string">&quot;Who is eating the apple?&quot;</span>
inputs = tokenizer(text, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>)
visual_embeds = get_visual_embeddings(image).unsqueeze(<span class="hljs-number">0</span>)
visual_token_type_ids = torch.ones(visual_embeds.shape[:-<span class="hljs-number">1</span>], dtype=torch.long)
visual_attention_mask = torch.ones(visual_embeds.shape[:-<span class="hljs-number">1</span>], dtype=torch.<span class="hljs-built_in">float</span>)
region_to_phrase_position = torch.ones((<span class="hljs-number">1</span>, inputs[<span class="hljs-string">&quot;input_ids&quot;</span>].shape[-<span class="hljs-number">1</span>] + visual_embeds.shape[-<span class="hljs-number">2</span>]))

inputs.update(
    {
        <span class="hljs-string">&quot;region_to_phrase_position&quot;</span>: region_to_phrase_position,
        <span class="hljs-string">&quot;visual_embeds&quot;</span>: visual_embeds,
        <span class="hljs-string">&quot;visual_token_type_ids&quot;</span>: visual_token_type_ids,
        <span class="hljs-string">&quot;visual_attention_mask&quot;</span>: visual_attention_mask,
    }
)

labels = torch.ones(
    (<span class="hljs-number">1</span>, inputs[<span class="hljs-string">&quot;input_ids&quot;</span>].shape[-<span class="hljs-number">1</span>] + visual_embeds.shape[-<span class="hljs-number">2</span>], visual_embeds.shape[-<span class="hljs-number">2</span>])
)  <span class="hljs-comment"># Batch size 1</span>

outputs = model(**inputs, labels=labels)
loss = outputs.loss
scores = outputs.logits`}}),{c(){d=n("p"),v=i("Example:"),g=u(),y(m.$$.fragment)},l(r){d=s(r,"P",{});var p=a(d);v=l(p,"Example:"),p.forEach(o),g=h(r),M(m.$$.fragment,r)},m(r,p){_(r,d,p),e(d,v),_(r,g,p),T(m,r,p),b=!0},p:Ht,i(r){b||(w(m.$$.fragment,r),b=!0)},o(r){k(m.$$.fragment,r),b=!1},d(r){r&&o(d),r&&o(g),V(m,r)}}}function Fd(B){let d,v,g,m,b,r,p,J,Js,Bn,L,me,Io,He,Us,Co,js,Jn,fe,zs,Le,Ws,Fs,Un,Lt,Is,jn,Ot,$o,Cs,zn,Dt,$s,Wn,ge,Eo,Oe,Es,Kt,Rs,Zs,xs,Ro,Zo,Xs,Fn,O,_e,xo,De,Ns,Xo,As,In,eo,Gs,Cn,to,qs,$n,be,Qs,oo,Ys,Ps,En,ve,No,no,Ke,Ss,Hs,Ls,Ao,so,et,Os,Ds,Rn,ye,Ks,ao,ea,ta,Zn,tt,xn,A,oa,ot,na,sa,nt,aa,ra,Xn,D,Me,Go,st,ia,qo,la,Nn,C,at,da,K,ca,ro,pa,ua,rt,ha,ma,fa,ee,ga,io,_a,ba,lo,va,ya,Ma,Te,An,te,we,Qo,it,Ta,Yo,wa,Gn,U,lt,ka,dt,Va,co,Ba,Ja,Ua,ct,ja,pt,za,Wa,Fa,ut,Ia,ht,Ca,$a,Ea,E,mt,Ra,oe,Za,po,xa,Xa,Po,Na,Aa,Ga,ke,qa,Ve,qn,ne,Be,So,ft,Qa,Ho,Ya,Qn,j,gt,Pa,se,Sa,Lo,Ha,La,Oo,Oa,Da,Ka,_t,er,uo,tr,or,nr,bt,sr,vt,ar,rr,ir,R,yt,lr,ae,dr,ho,cr,pr,Do,ur,hr,mr,Je,fr,Ue,Yn,re,je,Ko,Mt,gr,en,_r,Pn,z,Tt,br,tn,vr,yr,wt,Mr,mo,Tr,wr,kr,kt,Vr,Vt,Br,Jr,Ur,Z,Bt,jr,ie,zr,fo,Wr,Fr,on,Ir,Cr,$r,ze,Er,We,Sn,le,Fe,nn,Jt,Rr,sn,Zr,Hn,W,Ut,xr,an,Xr,Nr,jt,Ar,go,Gr,qr,Qr,zt,Yr,Wt,Pr,Sr,Hr,x,Ft,Lr,de,Or,_o,Dr,Kr,rn,ei,ti,oi,Ie,ni,Ce,Ln,ce,$e,ln,It,si,dn,ai,On,F,Ct,ri,cn,ii,li,$t,di,bo,ci,pi,ui,Et,hi,Rt,mi,fi,gi,X,Zt,_i,pe,bi,vo,vi,yi,pn,Mi,Ti,wi,Ee,ki,Re,Dn,ue,Ze,un,xt,Vi,hn,Bi,Kn,I,Xt,Ji,mn,Ui,ji,Nt,zi,yo,Wi,Fi,Ii,At,Ci,Gt,$i,Ei,Ri,N,qt,Zi,he,xi,Mo,Xi,Ni,fn,Ai,Gi,qi,xe,Qi,Xe,es;return r=new H({}),He=new H({}),De=new H({}),tt=new Se({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> BertTokenizer, VisualBertModel

<span class="hljs-meta">&gt;&gt;&gt; </span>model = VisualBertModel.from_pretrained(<span class="hljs-string">&quot;uclanlp/visualbert-vqa-coco-pre&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = BertTokenizer.from_pretrained(<span class="hljs-string">&quot;bert-base-uncased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = tokenizer(<span class="hljs-string">&quot;What is the man eating?&quot;</span>, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># this is a custom function that returns the visual embeddings given the image path</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>visual_embeds = get_visual_embeddings(image_path)

<span class="hljs-meta">&gt;&gt;&gt; </span>visual_token_type_ids = torch.ones(visual_embeds.shape[:-<span class="hljs-number">1</span>], dtype=torch.long)
<span class="hljs-meta">&gt;&gt;&gt; </span>visual_attention_mask = torch.ones(visual_embeds.shape[:-<span class="hljs-number">1</span>], dtype=torch.<span class="hljs-built_in">float</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>inputs.update(
<span class="hljs-meta">... </span>    {
<span class="hljs-meta">... </span>        <span class="hljs-string">&quot;visual_embeds&quot;</span>: visual_embeds,
<span class="hljs-meta">... </span>        <span class="hljs-string">&quot;visual_token_type_ids&quot;</span>: visual_token_type_ids,
<span class="hljs-meta">... </span>        <span class="hljs-string">&quot;visual_attention_mask&quot;</span>: visual_attention_mask,
<span class="hljs-meta">... </span>    }
<span class="hljs-meta">... </span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(**inputs)
<span class="hljs-meta">&gt;&gt;&gt; </span>last_hidden_state = outputs.last_hidden_state`}}),st=new H({}),at=new $({props:{name:"class transformers.VisualBertConfig",anchor:"transformers.VisualBertConfig",parameters:[{name:"vocab_size",val:" = 30522"},{name:"hidden_size",val:" = 768"},{name:"visual_embedding_dim",val:" = 512"},{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:" = 0.1"},{name:"attention_probs_dropout_prob",val:" = 0.1"},{name:"max_position_embeddings",val:" = 512"},{name:"type_vocab_size",val:" = 2"},{name:"initializer_range",val:" = 0.02"},{name:"layer_norm_eps",val:" = 1e-12"},{name:"bypass_transformer",val:" = False"},{name:"special_visual_initialize",val:" = True"},{name:"pad_token_id",val:" = 1"},{name:"bos_token_id",val:" = 0"},{name:"eos_token_id",val:" = 2"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.VisualBertConfig.vocab_size",description:`<strong>vocab_size</strong> (<code>int</code>, <em>optional</em>, defaults to 30522) &#x2014;
Vocabulary size of the VisualBERT model. Defines the number of different tokens that can be represented by
the <code>inputs_ids</code> passed when calling <a href="/docs/transformers/v4.31.0/en/model_doc/visual_bert#transformers.VisualBertModel">VisualBertModel</a>. Vocabulary size of the model. Defines the
different tokens that can be represented by the <code>inputs_ids</code> passed to the forward method of
<a href="/docs/transformers/v4.31.0/en/model_doc/visual_bert#transformers.VisualBertModel">VisualBertModel</a>.`,name:"vocab_size"},{anchor:"transformers.VisualBertConfig.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.VisualBertConfig.visual_embedding_dim",description:`<strong>visual_embedding_dim</strong> (<code>int</code>, <em>optional</em>, defaults to 512) &#x2014;
Dimensionality of the visual embeddings to be passed to the model.`,name:"visual_embedding_dim"},{anchor:"transformers.VisualBertConfig.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.VisualBertConfig.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.VisualBertConfig.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.VisualBertConfig.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.VisualBertConfig.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.VisualBertConfig.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.VisualBertConfig.max_position_embeddings",description:`<strong>max_position_embeddings</strong> (<code>int</code>, <em>optional</em>, defaults to 512) &#x2014;
The maximum sequence length that this model might ever be used with. Typically set this to something large
just in case (e.g., 512 or 1024 or 2048).`,name:"max_position_embeddings"},{anchor:"transformers.VisualBertConfig.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/v4.31.0/en/model_doc/visual_bert#transformers.VisualBertModel">VisualBertModel</a>.`,name:"type_vocab_size"},{anchor:"transformers.VisualBertConfig.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.VisualBertConfig.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.VisualBertConfig.bypass_transformer",description:`<strong>bypass_transformer</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not the model should bypass the transformer for the visual embeddings. If set to <code>True</code>, the
model directly concatenates the visual embeddings from <code>VisualBertEmbeddings</code> with text output from
transformers, and then pass it to a self-attention layer.`,name:"bypass_transformer"},{anchor:"transformers.VisualBertConfig.special_visual_initialize",description:`<strong>special_visual_initialize</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether or not the visual token type and position type embedding weights should be initialized the same as
the textual token type and positive type embeddings. When set to <code>True</code>, the weights of the textual token
type and position type embeddings are copied to the respective visual embedding layers.`,name:"special_visual_initialize"}],source:"https://github.com/huggingface/transformers/blob/v4.31.0/src/transformers/models/visual_bert/configuration_visual_bert.py#L43"}}),Te=new St({props:{anchor:"transformers.VisualBertConfig.example",$$slots:{default:[vd]},$$scope:{ctx:B}}}),it=new H({}),lt=new $({props:{name:"class transformers.VisualBertModel",anchor:"transformers.VisualBertModel",parameters:[{name:"config",val:""},{name:"add_pooling_layer",val:" = True"}],parametersDescription:[{anchor:"transformers.VisualBertModel.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.31.0/en/model_doc/visual_bert#transformers.VisualBertConfig">VisualBertConfig</a>) &#x2014; Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the <a href="/docs/transformers/v4.31.0/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/v4.31.0/src/transformers/models/visual_bert/modeling_visual_bert.py#L678"}}),mt=new $({props:{name:"forward",anchor:"transformers.VisualBertModel.forward",parameters:[{name:"input_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"attention_mask",val:": typing.Optional[torch.LongTensor] = None"},{name:"token_type_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"position_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"head_mask",val:": typing.Optional[torch.LongTensor] = None"},{name:"inputs_embeds",val:": typing.Optional[torch.FloatTensor] = None"},{name:"visual_embeds",val:": typing.Optional[torch.FloatTensor] = None"},{name:"visual_attention_mask",val:": typing.Optional[torch.LongTensor] = None"},{name:"visual_token_type_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"image_text_alignment",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.VisualBertModel.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.</p>
<p>Indices can be obtained using <a href="/docs/transformers/v4.31.0/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/v4.31.0/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/v4.31.0/en/model_doc/wav2vec2#transformers.Wav2Vec2CTCTokenizer.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.VisualBertModel.forward.attention_mask",description:`<strong>attention_mask</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Mask to avoid performing attention on padding token indices. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.</li>
</ul>
<p><a href="../glossary#attention-mask">What are attention masks?</a>`,name:"attention_mask"},{anchor:"transformers.VisualBertModel.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.</li>
</ul>
<p><a href="../glossary#token-type-ids">What are token type IDs?</a>`,name:"token_type_ids"},{anchor:"transformers.VisualBertModel.forward.position_ids",description:`<strong>position_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range <code>[0, config.max_position_embeddings - 1]</code>.</p>
<p><a href="../glossary#position-ids">What are position IDs?</a>`,name:"position_ids"},{anchor:"transformers.VisualBertModel.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.VisualBertModel.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.VisualBertModel.forward.visual_embeds",description:`<strong>visual_embeds</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, visual_seq_length, visual_embedding_dim)</code>, <em>optional</em>) &#x2014;
The embedded representation of the visual inputs, generally derived using using an object detector.`,name:"visual_embeds"},{anchor:"transformers.VisualBertModel.forward.visual_attention_mask",description:`<strong>visual_attention_mask</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, visual_seq_length)</code>, <em>optional</em>) &#x2014;
Mask to avoid performing attention on visual embeddings. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.</li>
</ul>
<p><a href="../glossary#attention-mask">What are attention masks?</a>`,name:"visual_attention_mask"},{anchor:"transformers.VisualBertModel.forward.visual_token_type_ids",description:`<strong>visual_token_type_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, visual_seq_length)</code>, <em>optional</em>) &#x2014;
Segment token indices to indicate different portions of the visual embeds.</p>
<p><a href="../glossary#token-type-ids">What are token type IDs?</a> The authors of VisualBERT set the
<em>visual_token_type_ids</em> to <em>1</em> for all tokens.`,name:"visual_token_type_ids"},{anchor:"transformers.VisualBertModel.forward.image_text_alignment",description:`<strong>image_text_alignment</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, visual_seq_length, alignment_number)</code>, <em>optional</em>) &#x2014;
Image-Text alignment uses to decide the position IDs of the visual embeddings.`,name:"image_text_alignment"},{anchor:"transformers.VisualBertModel.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.VisualBertModel.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.VisualBertModel.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/v4.31.0/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/transformers/blob/v4.31.0/src/transformers/models/visual_bert/modeling_visual_bert.py#L717",returnDescription:`
<p>A <a
  href="/docs/transformers/v4.31.0/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/v4.31.0/en/model_doc/visual_bert#transformers.VisualBertConfig"
>VisualBertConfig</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/v4.31.0/en/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPooling"
>transformers.modeling_outputs.BaseModelOutputWithPooling</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),ke=new Fo({props:{$$slots:{default:[yd]},$$scope:{ctx:B}}}),Ve=new St({props:{anchor:"transformers.VisualBertModel.forward.example",$$slots:{default:[Md]},$$scope:{ctx:B}}}),ft=new H({}),gt=new $({props:{name:"class transformers.VisualBertForPreTraining",anchor:"transformers.VisualBertForPreTraining",parameters:[{name:"config",val:""}],parametersDescription:[{anchor:"transformers.VisualBertForPreTraining.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.31.0/en/model_doc/visual_bert#transformers.VisualBertConfig">VisualBertConfig</a>) &#x2014; Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the <a href="/docs/transformers/v4.31.0/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/v4.31.0/src/transformers/models/visual_bert/modeling_visual_bert.py#L874"}}),yt=new $({props:{name:"forward",anchor:"transformers.VisualBertForPreTraining.forward",parameters:[{name:"input_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"attention_mask",val:": typing.Optional[torch.LongTensor] = None"},{name:"token_type_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"position_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"head_mask",val:": typing.Optional[torch.LongTensor] = None"},{name:"inputs_embeds",val:": typing.Optional[torch.FloatTensor] = None"},{name:"visual_embeds",val:": typing.Optional[torch.FloatTensor] = None"},{name:"visual_attention_mask",val:": typing.Optional[torch.LongTensor] = None"},{name:"visual_token_type_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"image_text_alignment",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"},{name:"labels",val:": typing.Optional[torch.LongTensor] = None"},{name:"sentence_image_labels",val:": typing.Optional[torch.LongTensor] = None"}],parametersDescription:[{anchor:"transformers.VisualBertForPreTraining.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.</p>
<p>Indices can be obtained using <a href="/docs/transformers/v4.31.0/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/v4.31.0/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/v4.31.0/en/model_doc/wav2vec2#transformers.Wav2Vec2CTCTokenizer.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.VisualBertForPreTraining.forward.attention_mask",description:`<strong>attention_mask</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Mask to avoid performing attention on padding token indices. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.</li>
</ul>
<p><a href="../glossary#attention-mask">What are attention masks?</a>`,name:"attention_mask"},{anchor:"transformers.VisualBertForPreTraining.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.</li>
</ul>
<p><a href="../glossary#token-type-ids">What are token type IDs?</a>`,name:"token_type_ids"},{anchor:"transformers.VisualBertForPreTraining.forward.position_ids",description:`<strong>position_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range <code>[0, config.max_position_embeddings - 1]</code>.</p>
<p><a href="../glossary#position-ids">What are position IDs?</a>`,name:"position_ids"},{anchor:"transformers.VisualBertForPreTraining.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.VisualBertForPreTraining.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.VisualBertForPreTraining.forward.visual_embeds",description:`<strong>visual_embeds</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, visual_seq_length, visual_embedding_dim)</code>, <em>optional</em>) &#x2014;
The embedded representation of the visual inputs, generally derived using using an object detector.`,name:"visual_embeds"},{anchor:"transformers.VisualBertForPreTraining.forward.visual_attention_mask",description:`<strong>visual_attention_mask</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, visual_seq_length)</code>, <em>optional</em>) &#x2014;
Mask to avoid performing attention on visual embeddings. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.</li>
</ul>
<p><a href="../glossary#attention-mask">What are attention masks?</a>`,name:"visual_attention_mask"},{anchor:"transformers.VisualBertForPreTraining.forward.visual_token_type_ids",description:`<strong>visual_token_type_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, visual_seq_length)</code>, <em>optional</em>) &#x2014;
Segment token indices to indicate different portions of the visual embeds.</p>
<p><a href="../glossary#token-type-ids">What are token type IDs?</a> The authors of VisualBERT set the
<em>visual_token_type_ids</em> to <em>1</em> for all tokens.`,name:"visual_token_type_ids"},{anchor:"transformers.VisualBertForPreTraining.forward.image_text_alignment",description:`<strong>image_text_alignment</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, visual_seq_length, alignment_number)</code>, <em>optional</em>) &#x2014;
Image-Text alignment uses to decide the position IDs of the visual embeddings.`,name:"image_text_alignment"},{anchor:"transformers.VisualBertForPreTraining.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.VisualBertForPreTraining.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.VisualBertForPreTraining.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/v4.31.0/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"},{anchor:"transformers.VisualBertForPreTraining.forward.labels",description:`<strong>labels</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, total_sequence_length)</code>, <em>optional</em>) &#x2014;
Labels for computing the masked language modeling loss. Indices should be in <code>[-100, 0, ..., config.vocab_size]</code> (see <code>input_ids</code> docstring) Tokens with indices set to <code>-100</code> are ignored (masked), the
loss is only computed for the tokens with labels in <code>[0, ..., config.vocab_size]</code>`,name:"labels"},{anchor:"transformers.VisualBertForPreTraining.forward.sentence_image_labels",description:`<strong>sentence_image_labels</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size,)</code>, <em>optional</em>) &#x2014;
Labels for computing the sentence-image prediction (classification) loss. Input should be a sequence pair
(see <code>input_ids</code> docstring) Indices should be in <code>[0, 1]</code>:</p>
<ul>
<li>0 indicates sequence B is a matching pair of sequence A for the given image,</li>
<li>1 indicates sequence B is a random sequence w.r.t A for the given image.</li>
</ul>`,name:"sentence_image_labels"}],source:"https://github.com/huggingface/transformers/blob/v4.31.0/src/transformers/models/visual_bert/modeling_visual_bert.py#L892",returnDescription:`
<p>A <code>transformers.models.visual_bert.modeling_visual_bert.VisualBertForPreTrainingOutput</code> or a tuple of
<code>torch.FloatTensor</code> (if <code>return_dict=False</code> is passed or when <code>config.return_dict=False</code>) comprising various
elements depending on the configuration (<a
  href="/docs/transformers/v4.31.0/en/model_doc/visual_bert#transformers.VisualBertConfig"
>VisualBertConfig</a>) and inputs.</p>
<ul>
<li>
<p><strong>loss</strong> (<em>optional</em>, returned when <code>labels</code> is provided, <code>torch.FloatTensor</code> of shape <code>(1,)</code>) \u2014 Total loss as the sum of the masked language modeling loss and the sentence-image prediction
(classification) loss.</p>
</li>
<li>
<p><strong>prediction_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>seq_relationship_logits</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, 2)</code>) \u2014 Prediction scores of the sentence-image prediction (classification) head (scores of True/False continuation
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 + one for the output of each layer) of
shape <code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the initial embedding outputs.</p>
</li>
<li>
<p><strong>attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.</p>
</li>
</ul>
`,returnType:`
<p><code>transformers.models.visual_bert.modeling_visual_bert.VisualBertForPreTrainingOutput</code> or <code>tuple(torch.FloatTensor)</code></p>
`}}),Je=new Fo({props:{$$slots:{default:[Td]},$$scope:{ctx:B}}}),Ue=new St({props:{anchor:"transformers.VisualBertForPreTraining.forward.example",$$slots:{default:[wd]},$$scope:{ctx:B}}}),Mt=new H({}),Tt=new $({props:{name:"class transformers.VisualBertForQuestionAnswering",anchor:"transformers.VisualBertForQuestionAnswering",parameters:[{name:"config",val:""}],parametersDescription:[{anchor:"transformers.VisualBertForQuestionAnswering.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.31.0/en/model_doc/visual_bert#transformers.VisualBertConfig">VisualBertConfig</a>) &#x2014; Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the <a href="/docs/transformers/v4.31.0/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/v4.31.0/src/transformers/models/visual_bert/modeling_visual_bert.py#L1178"}}),Bt=new $({props:{name:"forward",anchor:"transformers.VisualBertForQuestionAnswering.forward",parameters:[{name:"input_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"attention_mask",val:": typing.Optional[torch.LongTensor] = None"},{name:"token_type_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"position_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"head_mask",val:": typing.Optional[torch.LongTensor] = None"},{name:"inputs_embeds",val:": typing.Optional[torch.FloatTensor] = None"},{name:"visual_embeds",val:": typing.Optional[torch.FloatTensor] = None"},{name:"visual_attention_mask",val:": typing.Optional[torch.LongTensor] = None"},{name:"visual_token_type_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"image_text_alignment",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"},{name:"labels",val:": typing.Optional[torch.LongTensor] = None"}],parametersDescription:[{anchor:"transformers.VisualBertForQuestionAnswering.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.</p>
<p>Indices can be obtained using <a href="/docs/transformers/v4.31.0/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/v4.31.0/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/v4.31.0/en/model_doc/wav2vec2#transformers.Wav2Vec2CTCTokenizer.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.VisualBertForQuestionAnswering.forward.attention_mask",description:`<strong>attention_mask</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Mask to avoid performing attention on padding token indices. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.</li>
</ul>
<p><a href="../glossary#attention-mask">What are attention masks?</a>`,name:"attention_mask"},{anchor:"transformers.VisualBertForQuestionAnswering.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.</li>
</ul>
<p><a href="../glossary#token-type-ids">What are token type IDs?</a>`,name:"token_type_ids"},{anchor:"transformers.VisualBertForQuestionAnswering.forward.position_ids",description:`<strong>position_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range <code>[0, config.max_position_embeddings - 1]</code>.</p>
<p><a href="../glossary#position-ids">What are position IDs?</a>`,name:"position_ids"},{anchor:"transformers.VisualBertForQuestionAnswering.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.VisualBertForQuestionAnswering.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.VisualBertForQuestionAnswering.forward.visual_embeds",description:`<strong>visual_embeds</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, visual_seq_length, visual_embedding_dim)</code>, <em>optional</em>) &#x2014;
The embedded representation of the visual inputs, generally derived using using an object detector.`,name:"visual_embeds"},{anchor:"transformers.VisualBertForQuestionAnswering.forward.visual_attention_mask",description:`<strong>visual_attention_mask</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, visual_seq_length)</code>, <em>optional</em>) &#x2014;
Mask to avoid performing attention on visual embeddings. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.</li>
</ul>
<p><a href="../glossary#attention-mask">What are attention masks?</a>`,name:"visual_attention_mask"},{anchor:"transformers.VisualBertForQuestionAnswering.forward.visual_token_type_ids",description:`<strong>visual_token_type_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, visual_seq_length)</code>, <em>optional</em>) &#x2014;
Segment token indices to indicate different portions of the visual embeds.</p>
<p><a href="../glossary#token-type-ids">What are token type IDs?</a> The authors of VisualBERT set the
<em>visual_token_type_ids</em> to <em>1</em> for all tokens.`,name:"visual_token_type_ids"},{anchor:"transformers.VisualBertForQuestionAnswering.forward.image_text_alignment",description:`<strong>image_text_alignment</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, visual_seq_length, alignment_number)</code>, <em>optional</em>) &#x2014;
Image-Text alignment uses to decide the position IDs of the visual embeddings.`,name:"image_text_alignment"},{anchor:"transformers.VisualBertForQuestionAnswering.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.VisualBertForQuestionAnswering.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.VisualBertForQuestionAnswering.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/v4.31.0/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"},{anchor:"transformers.VisualBertForQuestionAnswering.forward.labels",description:`<strong>labels</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, total_sequence_length)</code>, <em>optional</em>) &#x2014;
Labels for computing the sequence classification/regression loss. Indices should be in <code>[0, ..., config.num_labels - 1]</code>. A KLDivLoss is computed between the labels and the returned logits.`,name:"labels"}],source:"https://github.com/huggingface/transformers/blob/v4.31.0/src/transformers/models/visual_bert/modeling_visual_bert.py#L1190",returnDescription:`
<p>A <a
  href="/docs/transformers/v4.31.0/en/main_classes/output#transformers.modeling_outputs.SequenceClassifierOutput"
>transformers.modeling_outputs.SequenceClassifierOutput</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/v4.31.0/en/model_doc/visual_bert#transformers.VisualBertConfig"
>VisualBertConfig</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/v4.31.0/en/main_classes/output#transformers.modeling_outputs.SequenceClassifierOutput"
>transformers.modeling_outputs.SequenceClassifierOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),ze=new Fo({props:{$$slots:{default:[kd]},$$scope:{ctx:B}}}),We=new St({props:{anchor:"transformers.VisualBertForQuestionAnswering.forward.example",$$slots:{default:[Vd]},$$scope:{ctx:B}}}),Jt=new H({}),Ut=new $({props:{name:"class transformers.VisualBertForMultipleChoice",anchor:"transformers.VisualBertForMultipleChoice",parameters:[{name:"config",val:""}],parametersDescription:[{anchor:"transformers.VisualBertForMultipleChoice.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.31.0/en/model_doc/visual_bert#transformers.VisualBertConfig">VisualBertConfig</a>) &#x2014; Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the <a href="/docs/transformers/v4.31.0/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/v4.31.0/src/transformers/models/visual_bert/modeling_visual_bert.py#L1025"}}),Ft=new $({props:{name:"forward",anchor:"transformers.VisualBertForMultipleChoice.forward",parameters:[{name:"input_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"attention_mask",val:": typing.Optional[torch.LongTensor] = None"},{name:"token_type_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"position_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"head_mask",val:": typing.Optional[torch.LongTensor] = None"},{name:"inputs_embeds",val:": typing.Optional[torch.FloatTensor] = None"},{name:"visual_embeds",val:": typing.Optional[torch.FloatTensor] = None"},{name:"visual_attention_mask",val:": typing.Optional[torch.LongTensor] = None"},{name:"visual_token_type_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"image_text_alignment",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"},{name:"labels",val:": typing.Optional[torch.LongTensor] = None"}],parametersDescription:[{anchor:"transformers.VisualBertForMultipleChoice.forward.input_ids",description:`<strong>input_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, num_choices, sequence_length)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using <a href="/docs/transformers/v4.31.0/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/v4.31.0/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/v4.31.0/en/model_doc/wav2vec2#transformers.Wav2Vec2CTCTokenizer.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.VisualBertForMultipleChoice.forward.attention_mask",description:`<strong>attention_mask</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, num_choices, sequence_length)</code>, <em>optional</em>) &#x2014;
Mask to avoid performing attention on padding token indices. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.</li>
</ul>
<p><a href="../glossary#attention-mask">What are attention masks?</a>`,name:"attention_mask"},{anchor:"transformers.VisualBertForMultipleChoice.forward.token_type_ids",description:`<strong>token_type_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, num_choices, 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.</li>
</ul>
<p><a href="../glossary#token-type-ids">What are token type IDs?</a>`,name:"token_type_ids"},{anchor:"transformers.VisualBertForMultipleChoice.forward.position_ids",description:`<strong>position_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, num_choices, sequence_length)</code>, <em>optional</em>) &#x2014;
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range <code>[0, config.max_position_embeddings - 1]</code>.</p>
<p><a href="../glossary#position-ids">What are position IDs?</a>`,name:"position_ids"},{anchor:"transformers.VisualBertForMultipleChoice.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.VisualBertForMultipleChoice.forward.inputs_embeds",description:`<strong>inputs_embeds</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, num_choices, 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.VisualBertForMultipleChoice.forward.visual_embeds",description:`<strong>visual_embeds</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, visual_seq_length, visual_embedding_dim)</code>, <em>optional</em>) &#x2014;
The embedded representation of the visual inputs, generally derived using using an object detector.`,name:"visual_embeds"},{anchor:"transformers.VisualBertForMultipleChoice.forward.visual_attention_mask",description:`<strong>visual_attention_mask</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, visual_seq_length)</code>, <em>optional</em>) &#x2014;
Mask to avoid performing attention on visual embeddings. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.</li>
</ul>
<p><a href="../glossary#attention-mask">What are attention masks?</a>`,name:"visual_attention_mask"},{anchor:"transformers.VisualBertForMultipleChoice.forward.visual_token_type_ids",description:`<strong>visual_token_type_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, visual_seq_length)</code>, <em>optional</em>) &#x2014;
Segment token indices to indicate different portions of the visual embeds.</p>
<p><a href="../glossary#token-type-ids">What are token type IDs?</a> The authors of VisualBERT set the
<em>visual_token_type_ids</em> to <em>1</em> for all tokens.`,name:"visual_token_type_ids"},{anchor:"transformers.VisualBertForMultipleChoice.forward.image_text_alignment",description:`<strong>image_text_alignment</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, visual_seq_length, alignment_number)</code>, <em>optional</em>) &#x2014;
Image-Text alignment uses to decide the position IDs of the visual embeddings.`,name:"image_text_alignment"},{anchor:"transformers.VisualBertForMultipleChoice.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.VisualBertForMultipleChoice.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.VisualBertForMultipleChoice.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/v4.31.0/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"},{anchor:"transformers.VisualBertForMultipleChoice.forward.labels",description:`<strong>labels</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size,)</code>, <em>optional</em>) &#x2014;
Labels for computing the multiple choice classification loss. Indices should be in <code>[0, ..., num_choices-1]</code> where <code>num_choices</code> is the size of the second dimension of the input tensors. (See
<code>input_ids</code> above)`,name:"labels"}],source:"https://github.com/huggingface/transformers/blob/v4.31.0/src/transformers/models/visual_bert/modeling_visual_bert.py#L1036",returnDescription:`
<p>A <a
  href="/docs/transformers/v4.31.0/en/main_classes/output#transformers.modeling_outputs.MultipleChoiceModelOutput"
>transformers.modeling_outputs.MultipleChoiceModelOutput</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/v4.31.0/en/model_doc/visual_bert#transformers.VisualBertConfig"
>VisualBertConfig</a>) and inputs.</p>
<ul>
<li>
<p><strong>loss</strong> (<code>torch.FloatTensor</code> of shape <em>(1,)</em>, <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, num_choices)</code>) \u2014 <em>num_choices</em> is the second dimension of the input tensors. (see <em>input_ids</em> above).</p>
<p>Classification 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/v4.31.0/en/main_classes/output#transformers.modeling_outputs.MultipleChoiceModelOutput"
>transformers.modeling_outputs.MultipleChoiceModelOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),Ie=new Fo({props:{$$slots:{default:[Bd]},$$scope:{ctx:B}}}),Ce=new St({props:{anchor:"transformers.VisualBertForMultipleChoice.forward.example",$$slots:{default:[Jd]},$$scope:{ctx:B}}}),It=new H({}),Ct=new $({props:{name:"class transformers.VisualBertForVisualReasoning",anchor:"transformers.VisualBertForVisualReasoning",parameters:[{name:"config",val:""}],parametersDescription:[{anchor:"transformers.VisualBertForVisualReasoning.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.31.0/en/model_doc/visual_bert#transformers.VisualBertConfig">VisualBertConfig</a>) &#x2014; Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the <a href="/docs/transformers/v4.31.0/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/v4.31.0/src/transformers/models/visual_bert/modeling_visual_bert.py#L1304"}}),Zt=new $({props:{name:"forward",anchor:"transformers.VisualBertForVisualReasoning.forward",parameters:[{name:"input_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"attention_mask",val:": typing.Optional[torch.LongTensor] = None"},{name:"token_type_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"position_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"head_mask",val:": typing.Optional[torch.LongTensor] = None"},{name:"inputs_embeds",val:": typing.Optional[torch.FloatTensor] = None"},{name:"visual_embeds",val:": typing.Optional[torch.FloatTensor] = None"},{name:"visual_attention_mask",val:": typing.Optional[torch.LongTensor] = None"},{name:"visual_token_type_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"image_text_alignment",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"},{name:"labels",val:": typing.Optional[torch.LongTensor] = None"}],parametersDescription:[{anchor:"transformers.VisualBertForVisualReasoning.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.</p>
<p>Indices can be obtained using <a href="/docs/transformers/v4.31.0/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/v4.31.0/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/v4.31.0/en/model_doc/wav2vec2#transformers.Wav2Vec2CTCTokenizer.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.VisualBertForVisualReasoning.forward.attention_mask",description:`<strong>attention_mask</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Mask to avoid performing attention on padding token indices. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.</li>
</ul>
<p><a href="../glossary#attention-mask">What are attention masks?</a>`,name:"attention_mask"},{anchor:"transformers.VisualBertForVisualReasoning.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.</li>
</ul>
<p><a href="../glossary#token-type-ids">What are token type IDs?</a>`,name:"token_type_ids"},{anchor:"transformers.VisualBertForVisualReasoning.forward.position_ids",description:`<strong>position_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range <code>[0, config.max_position_embeddings - 1]</code>.</p>
<p><a href="../glossary#position-ids">What are position IDs?</a>`,name:"position_ids"},{anchor:"transformers.VisualBertForVisualReasoning.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.VisualBertForVisualReasoning.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.VisualBertForVisualReasoning.forward.visual_embeds",description:`<strong>visual_embeds</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, visual_seq_length, visual_embedding_dim)</code>, <em>optional</em>) &#x2014;
The embedded representation of the visual inputs, generally derived using using an object detector.`,name:"visual_embeds"},{anchor:"transformers.VisualBertForVisualReasoning.forward.visual_attention_mask",description:`<strong>visual_attention_mask</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, visual_seq_length)</code>, <em>optional</em>) &#x2014;
Mask to avoid performing attention on visual embeddings. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.</li>
</ul>
<p><a href="../glossary#attention-mask">What are attention masks?</a>`,name:"visual_attention_mask"},{anchor:"transformers.VisualBertForVisualReasoning.forward.visual_token_type_ids",description:`<strong>visual_token_type_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, visual_seq_length)</code>, <em>optional</em>) &#x2014;
Segment token indices to indicate different portions of the visual embeds.</p>
<p><a href="../glossary#token-type-ids">What are token type IDs?</a> The authors of VisualBERT set the
<em>visual_token_type_ids</em> to <em>1</em> for all tokens.`,name:"visual_token_type_ids"},{anchor:"transformers.VisualBertForVisualReasoning.forward.image_text_alignment",description:`<strong>image_text_alignment</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, visual_seq_length, alignment_number)</code>, <em>optional</em>) &#x2014;
Image-Text alignment uses to decide the position IDs of the visual embeddings.`,name:"image_text_alignment"},{anchor:"transformers.VisualBertForVisualReasoning.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.VisualBertForVisualReasoning.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.VisualBertForVisualReasoning.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/v4.31.0/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"},{anchor:"transformers.VisualBertForVisualReasoning.forward.labels",description:`<strong>labels</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size,)</code>, <em>optional</em>) &#x2014;
Labels for computing the sequence classification/regression loss. Indices should be in <code>[0, ..., config.num_labels - 1]</code>. A classification loss is computed (Cross-Entropy) against these labels.`,name:"labels"}],source:"https://github.com/huggingface/transformers/blob/v4.31.0/src/transformers/models/visual_bert/modeling_visual_bert.py#L1316",returnDescription:`
<p>A <a
  href="/docs/transformers/v4.31.0/en/main_classes/output#transformers.modeling_outputs.SequenceClassifierOutput"
>transformers.modeling_outputs.SequenceClassifierOutput</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/v4.31.0/en/model_doc/visual_bert#transformers.VisualBertConfig"
>VisualBertConfig</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/v4.31.0/en/main_classes/output#transformers.modeling_outputs.SequenceClassifierOutput"
>transformers.modeling_outputs.SequenceClassifierOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),Ee=new Fo({props:{$$slots:{default:[Ud]},$$scope:{ctx:B}}}),Re=new St({props:{anchor:"transformers.VisualBertForVisualReasoning.forward.example",$$slots:{default:[jd]},$$scope:{ctx:B}}}),xt=new H({}),Xt=new $({props:{name:"class transformers.VisualBertForRegionToPhraseAlignment",anchor:"transformers.VisualBertForRegionToPhraseAlignment",parameters:[{name:"config",val:""}],parametersDescription:[{anchor:"transformers.VisualBertForRegionToPhraseAlignment.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.31.0/en/model_doc/visual_bert#transformers.VisualBertConfig">VisualBertConfig</a>) &#x2014; Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the <a href="/docs/transformers/v4.31.0/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/v4.31.0/src/transformers/models/visual_bert/modeling_visual_bert.py#L1464"}}),qt=new $({props:{name:"forward",anchor:"transformers.VisualBertForRegionToPhraseAlignment.forward",parameters:[{name:"input_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"attention_mask",val:": typing.Optional[torch.LongTensor] = None"},{name:"token_type_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"position_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"head_mask",val:": typing.Optional[torch.LongTensor] = None"},{name:"inputs_embeds",val:": typing.Optional[torch.FloatTensor] = None"},{name:"visual_embeds",val:": typing.Optional[torch.FloatTensor] = None"},{name:"visual_attention_mask",val:": typing.Optional[torch.LongTensor] = None"},{name:"visual_token_type_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"image_text_alignment",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"},{name:"region_to_phrase_position",val:": typing.Optional[torch.LongTensor] = None"},{name:"labels",val:": typing.Optional[torch.LongTensor] = None"}],parametersDescription:[{anchor:"transformers.VisualBertForRegionToPhraseAlignment.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.</p>
<p>Indices can be obtained using <a href="/docs/transformers/v4.31.0/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/v4.31.0/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/v4.31.0/en/model_doc/wav2vec2#transformers.Wav2Vec2CTCTokenizer.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.VisualBertForRegionToPhraseAlignment.forward.attention_mask",description:`<strong>attention_mask</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Mask to avoid performing attention on padding token indices. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.</li>
</ul>
<p><a href="../glossary#attention-mask">What are attention masks?</a>`,name:"attention_mask"},{anchor:"transformers.VisualBertForRegionToPhraseAlignment.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.</li>
</ul>
<p><a href="../glossary#token-type-ids">What are token type IDs?</a>`,name:"token_type_ids"},{anchor:"transformers.VisualBertForRegionToPhraseAlignment.forward.position_ids",description:`<strong>position_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range <code>[0, config.max_position_embeddings - 1]</code>.</p>
<p><a href="../glossary#position-ids">What are position IDs?</a>`,name:"position_ids"},{anchor:"transformers.VisualBertForRegionToPhraseAlignment.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.VisualBertForRegionToPhraseAlignment.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.VisualBertForRegionToPhraseAlignment.forward.visual_embeds",description:`<strong>visual_embeds</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, visual_seq_length, visual_embedding_dim)</code>, <em>optional</em>) &#x2014;
The embedded representation of the visual inputs, generally derived using using an object detector.`,name:"visual_embeds"},{anchor:"transformers.VisualBertForRegionToPhraseAlignment.forward.visual_attention_mask",description:`<strong>visual_attention_mask</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, visual_seq_length)</code>, <em>optional</em>) &#x2014;
Mask to avoid performing attention on visual embeddings. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.</li>
</ul>
<p><a href="../glossary#attention-mask">What are attention masks?</a>`,name:"visual_attention_mask"},{anchor:"transformers.VisualBertForRegionToPhraseAlignment.forward.visual_token_type_ids",description:`<strong>visual_token_type_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, visual_seq_length)</code>, <em>optional</em>) &#x2014;
Segment token indices to indicate different portions of the visual embeds.</p>
<p><a href="../glossary#token-type-ids">What are token type IDs?</a> The authors of VisualBERT set the
<em>visual_token_type_ids</em> to <em>1</em> for all tokens.`,name:"visual_token_type_ids"},{anchor:"transformers.VisualBertForRegionToPhraseAlignment.forward.image_text_alignment",description:`<strong>image_text_alignment</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, visual_seq_length, alignment_number)</code>, <em>optional</em>) &#x2014;
Image-Text alignment uses to decide the position IDs of the visual embeddings.`,name:"image_text_alignment"},{anchor:"transformers.VisualBertForRegionToPhraseAlignment.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.VisualBertForRegionToPhraseAlignment.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.VisualBertForRegionToPhraseAlignment.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/v4.31.0/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"},{anchor:"transformers.VisualBertForRegionToPhraseAlignment.forward.region_to_phrase_position",description:`<strong>region_to_phrase_position</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, total_sequence_length)</code>, <em>optional</em>) &#x2014;
The positions depicting the position of the image embedding corresponding to the textual tokens.`,name:"region_to_phrase_position"},{anchor:"transformers.VisualBertForRegionToPhraseAlignment.forward.labels",description:`<strong>labels</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, total_sequence_length, visual_sequence_length)</code>, <em>optional</em>) &#x2014;
Labels for computing the masked language modeling loss. KLDivLoss is computed against these labels and the
outputs from the attention layer.`,name:"labels"}],source:"https://github.com/huggingface/transformers/blob/v4.31.0/src/transformers/models/visual_bert/modeling_visual_bert.py#L1478",returnDescription:`
<p>A <a
  href="/docs/transformers/v4.31.0/en/main_classes/output#transformers.modeling_outputs.SequenceClassifierOutput"
>transformers.modeling_outputs.SequenceClassifierOutput</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/v4.31.0/en/model_doc/visual_bert#transformers.VisualBertConfig"
>VisualBertConfig</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/v4.31.0/en/main_classes/output#transformers.modeling_outputs.SequenceClassifierOutput"
>transformers.modeling_outputs.SequenceClassifierOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),xe=new Fo({props:{$$slots:{default:[zd]},$$scope:{ctx:B}}}),Xe=new St({props:{anchor:"transformers.VisualBertForRegionToPhraseAlignment.forward.example",$$slots:{default:[Wd]},$$scope:{ctx:B}}}),{c(){d=n("meta"),v=u(),g=n("h1"),m=n("a"),b=n("span"),y(r.$$.fragment),p=u(),J=n("span"),Js=i("VisualBERT"),Bn=u(),L=n("h2"),me=n("a"),Io=n("span"),y(He.$$.fragment),Us=u(),Co=n("span"),js=i("Overview"),Jn=u(),fe=n("p"),zs=i("The VisualBERT model was proposed in "),Le=n("a"),Ws=i("VisualBERT: A Simple and Performant Baseline for Vision and Language"),Fs=i(` by Liunian Harold Li, Mark Yatskar, Da Yin, Cho-Jui Hsieh, Kai-Wei Chang.
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vectors to a standard BERT model. The text input is concatenated in the front of the visual embeddings in the embedding
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(\u2018visualbert-vcr\u2019), NLVR2 (\u2018visualbert-nlvr2\u2019). Hence, if you are not working on these downstream tasks, it is
recommended that you use the pretrained checkpoints.`),os.forEach(o),Hi.forEach(o),xs=h(ts),Ro=s(ts,"LI",{});var Oi=a(Ro);Zo=s(Oi,"P",{});var Di=a(Zo);Xs=l(Di,`For the VCR task, the authors use a fine-tuned detector for generating visual embeddings, for all the checkpoints.
We do not provide the detector and its weights as a part of the package, but it will be available in the research
projects, and the states can be loaded directly into the detector provided.`),Di.forEach(o),Oi.forEach(o),ts.forEach(o),Fn=h(t),O=s(t,"H2",{class:!0});var ns=a(O);_e=s(ns,"A",{id:!0,class:!0,href:!0});var Ki=a(_e);xo=s(Ki,"SPAN",{});var el=a(xo);M(De.$$.fragment,el),el.forEach(o),Ki.forEach(o),Ns=h(ns),Xo=s(ns,"SPAN",{});var tl=a(Xo);As=l(tl,"Usage"),tl.forEach(o),ns.forEach(o),In=h(t),eo=s(t,"P",{});var ol=a(eo);Gs=l(ol,`VisualBERT is a multi-modal vision and language model. It can be used for visual question answering, multiple choice,
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vectors to a standard BERT model. The text input is concatenated in the front of the visual embeddings in the embedding
layer, and is expected to be bound by [CLS] and a [SEP] tokens, as in BERT. The segment IDs must also be set
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