import{S as md,i as fd,s as gd,e as n,k as p,w as k,t as i,M as _d,c as s,d as o,m as h,a,x as T,h as l,b as c,G as e,g as _,y,q as w,o as $,B as V,v as vd,L as Gt}from"../../chunks/vendor-hf-doc-builder.js";import{T as Eo}from"../../chunks/Tip-hf-doc-builder.js";import{D as j}from"../../chunks/Docstring-hf-doc-builder.js";import{C as Ke}from"../../chunks/CodeBlock-hf-doc-builder.js";import{I as G}from"../../chunks/IconCopyLink-hf-doc-builder.js";import{E as Kt}from"../../chunks/ExampleCodeBlock-hf-doc-builder.js";function bd(B){let d,b,g,m,v;return m=new Ke({props:{code:`from transformers import VisualBertConfig, VisualBertModel

# Initializing a VisualBERT visualbert-vqa-coco-pre style configuration
configuration = VisualBertConfig.from_pretrained("uclanlp/visualbert-vqa-coco-pre")

# Initializing a model (with random weights) from the visualbert-vqa-coco-pre style configuration
model = VisualBertModel(configuration)

# Accessing the model configuration
configuration = model.config`,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"),b=i("Example:"),g=p(),k(m.$$.fragment)},l(r){d=s(r,"P",{});var u=a(d);b=l(u,"Example:"),u.forEach(o),g=h(r),T(m.$$.fragment,r)},m(r,u){_(r,d,u),e(d,b),_(r,g,u),y(m,r,u),v=!0},p:Gt,i(r){v||(w(m.$$.fragment,r),v=!0)},o(r){$(m.$$.fragment,r),v=!1},d(r){r&&o(d),r&&o(g),V(m,r)}}}function kd(B){let d,b,g,m,v;return{c(){d=n("p"),b=i("Although the recipe for forward pass needs to be defined within this function, one should call the "),g=n("code"),m=i("Module"),v=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 u=a(d);b=l(u,"Although the recipe for forward pass needs to be defined within this function, one should call the "),g=s(u,"CODE",{});var z=a(g);m=l(z,"Module"),z.forEach(o),v=l(u,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),u.forEach(o)},m(r,u){_(r,d,u),e(d,b),e(d,g),e(g,m),e(d,v)},d(r){r&&o(d)}}}function Td(B){let d,b,g,m,v;return m=new Ke({props:{code:`# Assumption: *get_visual_embeddings(image)* gets the visual embeddings of the image.
from transformers import AutoTokenizer, VisualBertModel
import torch

tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
model = VisualBertModel.from_pretrained("uclanlp/visualbert-vqa-coco-pre")

inputs = tokenizer("The capital of France is Paris.", return_tensors="pt")
visual_embeds = get_visual_embeddings(image).unsqueeze(0)
visual_token_type_ids = torch.ones(visual_embeds.shape[:-1], dtype=torch.long)
visual_attention_mask = torch.ones(visual_embeds.shape[:-1], dtype=torch.float)

inputs.update(
    {
        "visual_embeds": visual_embeds,
        "visual_token_type_ids": visual_token_type_ids,
        "visual_attention_mask": visual_attention_mask,
    }
)

outputs = model(**inputs)

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

tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
model = VisualBertForPreTraining.from_pretrained("uclanlp/visualbert-vqa-coco-pre")

inputs = tokenizer("The capital of France is [MASK].", return_tensors="pt")
visual_embeds = get_visual_embeddings(image).unsqueeze(0)
visual_token_type_ids = torch.ones(visual_embeds.shape[:-1], dtype=torch.long)
visual_attention_mask = torch.ones(visual_embeds.shape[:-1], dtype=torch.float)

inputs.update(
    {
        "visual_embeds": visual_embeds,
        "visual_token_type_ids": visual_token_type_ids,
        "visual_attention_mask": visual_attention_mask,
    }
)
max_length = inputs["input_ids"].shape[-1] + visual_embeds.shape[-2]
labels = tokenizer(
    "The capital of France is Paris.", return_tensors="pt", padding="max_length", max_length=max_length
)["input_ids"]
sentence_image_labels = torch.tensor(1).unsqueeze(0)  # Batch_size


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

tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
model = VisualBertForQuestionAnswering.from_pretrained("uclanlp/visualbert-vqa")

text = "Who is eating the apple?"
inputs = tokenizer(text, return_tensors="pt")
visual_embeds = get_visual_embeddings(image).unsqueeze(0)
visual_token_type_ids = torch.ones(visual_embeds.shape[:-1], dtype=torch.long)
visual_attention_mask = torch.ones(visual_embeds.shape[:-1], dtype=torch.float)

inputs.update(
    {
        "visual_embeds": visual_embeds,
        "visual_token_type_ids": visual_token_type_ids,
        "visual_attention_mask": visual_attention_mask,
    }
)

labels = torch.tensor([[0.0, 1.0]]).unsqueeze(0)  # Batch size 1, Num labels 2

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

tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
model = VisualBertForMultipleChoice.from_pretrained("uclanlp/visualbert-vcr")

prompt = "In Italy, pizza served in formal settings, such as at a restaurant, is presented unsliced."
choice0 = "It is eaten with a fork and a knife."
choice1 = "It is eaten while held in the hand."

visual_embeds = get_visual_embeddings(image)
# (batch_size, num_choices, visual_seq_length, visual_embedding_dim)
visual_embeds = visual_embeds.expand(1, 2, *visual_embeds.shape)
visual_token_type_ids = torch.ones(visual_embeds.shape[:-1], dtype=torch.long)
visual_attention_mask = torch.ones(visual_embeds.shape[:-1], dtype=torch.float)

labels = torch.tensor(0).unsqueeze(0)  # choice0 is correct (according to Wikipedia ;)), batch size 1

encoding = tokenizer([[prompt, prompt], [choice0, choice1]], return_tensors="pt", padding=True)
# batch size is 1
inputs_dict = {k: v.unsqueeze(0) for k, v in encoding.items()}
inputs_dict.update(
    {
        "visual_embeds": visual_embeds,
        "visual_attention_mask": visual_attention_mask,
        "visual_token_type_ids": visual_token_type_ids,
        "labels": labels,
    }
)
outputs = model(**inputs_dict)

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

tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
model = VisualBertForVisualReasoning.from_pretrained("uclanlp/visualbert-nlvr2")

text = "Who is eating the apple?"
inputs = tokenizer(text, return_tensors="pt")
visual_embeds = get_visual_embeddings(image).unsqueeze(0)
visual_token_type_ids = torch.ones(visual_embeds.shape[:-1], dtype=torch.long)
visual_attention_mask = torch.ones(visual_embeds.shape[:-1], dtype=torch.float)

inputs.update(
    {
        "visual_embeds": visual_embeds,
        "visual_token_type_ids": visual_token_type_ids,
        "visual_attention_mask": visual_attention_mask,
    }
)

labels = torch.tensor(1).unsqueeze(0)  # Batch size 1, Num choices 2

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

tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
model = VisualBertForRegionToPhraseAlignment.from_pretrained("uclanlp/visualbert-vqa-coco-pre")

text = "Who is eating the apple?"
inputs = tokenizer(text, return_tensors="pt")
visual_embeds = get_visual_embeddings(image).unsqueeze(0)
visual_token_type_ids = torch.ones(visual_embeds.shape[:-1], dtype=torch.long)
visual_attention_mask = torch.ones(visual_embeds.shape[:-1], dtype=torch.float)
region_to_phrase_position = torch.ones((1, inputs["input_ids"].shape[-1] + visual_embeds.shape[-2]))

inputs.update(
    {
        "region_to_phrase_position": region_to_phrase_position,
        "visual_embeds": visual_embeds,
        "visual_token_type_ids": visual_token_type_ids,
        "visual_attention_mask": visual_attention_mask,
    }
)

labels = torch.ones(
    (1, inputs["input_ids"].shape[-1] + visual_embeds.shape[-2], visual_embeds.shape[-2])
)  # Batch size 1

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

model = VisualBertModel.from_pretrained("uclanlp/visualbert-vqa-coco-pre")
tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")

inputs = tokenizer("What is the man eating?", return_tensors="pt")
# this is a custom function that returns the visual embeddings given the image path
visual_embeds = get_visual_embeddings(image_path)

visual_token_type_ids = torch.ones(visual_embeds.shape[:-1], dtype=torch.long)
visual_attention_mask = torch.ones(visual_embeds.shape[:-1], dtype=torch.float)
inputs.update(
    {
        "visual_embeds": visual_embeds,
        "visual_token_type_ids": visual_token_type_ids,
        "visual_attention_mask": visual_attention_mask,
    }
)
outputs = model(**inputs)
last_hidden_state = outputs.last_hidden_state`,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 G({}),at=new j({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/main/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/main/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/main/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/main/src/transformers/models/visual_bert/configuration_visual_bert.py#L43"}}),ye=new Kt({props:{anchor:"transformers.VisualBertConfig.example",$$slots:{default:[bd]},$$scope:{ctx:B}}}),it=new G({}),lt=new j({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/main/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/main/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/visual_bert/modeling_visual_bert.py#L677"}}),mt=new j({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/main/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/main/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/main/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</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/main/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/visual_bert/modeling_visual_bert.py#L716",returnDescription:`
<p>A <a
  href="/docs/transformers/main/en/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPooling"
>transformers.modeling_outputs.BaseModelOutputWithPooling</a> or a tuple of
<code>torch.FloatTensor</code> (if <code>return_dict=False</code> is passed or when <code>config.return_dict=False</code>) comprising various
elements depending on the configuration (<a
  href="/docs/transformers/main/en/model_doc/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/main/en/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPooling"
>transformers.modeling_outputs.BaseModelOutputWithPooling</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),$e=new Eo({props:{$$slots:{default:[kd]},$$scope:{ctx:B}}}),Ve=new Kt({props:{anchor:"transformers.VisualBertModel.forward.example",$$slots:{default:[Td]},$$scope:{ctx:B}}}),ft=new G({}),gt=new j({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/main/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/main/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/visual_bert/modeling_visual_bert.py#L873"}}),kt=new j({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/main/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/main/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/main/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</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/main/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/main/src/transformers/models/visual_bert/modeling_visual_bert.py#L891",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/main/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>
`}}),ze=new Eo({props:{$$slots:{default:[yd]},$$scope:{ctx:B}}}),qe=new Kt({props:{anchor:"transformers.VisualBertForPreTraining.forward.example",$$slots:{default:[wd]},$$scope:{ctx:B}}}),Tt=new G({}),yt=new j({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/main/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/main/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/visual_bert/modeling_visual_bert.py#L1177"}}),Bt=new j({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/main/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/main/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/main/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</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/main/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/main/src/transformers/models/visual_bert/modeling_visual_bert.py#L1189",returnDescription:`
<p>A <a
  href="/docs/transformers/main/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/main/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/main/en/main_classes/output#transformers.modeling_outputs.SequenceClassifierOutput"
>transformers.modeling_outputs.SequenceClassifierOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),Fe=new Eo({props:{$$slots:{default:[$d]},$$scope:{ctx:B}}}),Pe=new Kt({props:{anchor:"transformers.VisualBertForQuestionAnswering.forward.example",$$slots:{default:[Vd]},$$scope:{ctx:B}}}),zt=new G({}),qt=new j({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/main/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/main/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/visual_bert/modeling_visual_bert.py#L1024"}}),Et=new j({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/main/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/main/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/main/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</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/main/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/main/src/transformers/models/visual_bert/modeling_visual_bert.py#L1035",returnDescription:`
<p>A <a
  href="/docs/transformers/main/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/main/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/main/en/main_classes/output#transformers.modeling_outputs.MultipleChoiceModelOutput"
>transformers.modeling_outputs.MultipleChoiceModelOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),Me=new Eo({props:{$$slots:{default:[Bd]},$$scope:{ctx:B}}}),Ae=new Kt({props:{anchor:"transformers.VisualBertForMultipleChoice.forward.example",$$slots:{default:[zd]},$$scope:{ctx:B}}}),Mt=new G({}),At=new j({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/main/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/main/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/visual_bert/modeling_visual_bert.py#L1303"}}),Ot=new j({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/main/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/main/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/main/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</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/main/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/main/src/transformers/models/visual_bert/modeling_visual_bert.py#L1315",returnDescription:`
<p>A <a
  href="/docs/transformers/main/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/main/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/main/en/main_classes/output#transformers.modeling_outputs.SequenceClassifierOutput"
>transformers.modeling_outputs.SequenceClassifierOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),Ce=new Eo({props:{$$slots:{default:[qd]},$$scope:{ctx:B}}}),Le=new Kt({props:{anchor:"transformers.VisualBertForVisualReasoning.forward.example",$$slots:{default:[xd]},$$scope:{ctx:B}}}),Rt=new G({}),Nt=new j({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/main/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/main/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/visual_bert/modeling_visual_bert.py#L1463"}}),Wt=new j({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/main/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/main/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/main/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</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/main/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/main/src/transformers/models/visual_bert/modeling_visual_bert.py#L1477",returnDescription:`
<p>A <a
  href="/docs/transformers/main/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/main/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/main/en/main_classes/output#transformers.modeling_outputs.SequenceClassifierOutput"
>transformers.modeling_outputs.SequenceClassifierOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
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