import{S as Zd,i as ec,s as tc,e as n,k as m,w as T,t as a,M as oc,c as s,d as o,m as p,a as r,x as k,h as i,b as c,N as nc,G as e,g,y,q as w,o as x,B as $,v as sc,L as qo}from"../../chunks/vendor-hf-doc-builder.js";import{T as Lo}from"../../chunks/Tip-hf-doc-builder.js";import{D as P}from"../../chunks/Docstring-hf-doc-builder.js";import{C as Oo}from"../../chunks/CodeBlock-hf-doc-builder.js";import{I as R}from"../../chunks/IconCopyLink-hf-doc-builder.js";import{E as Co}from"../../chunks/ExampleCodeBlock-hf-doc-builder.js";function rc(V){let d,b,_,u,v;return u=new Oo({props:{code:`from transformers import ViLTModel, ViLTConfig

# Initializing a ViLT dandelin/vilt-b32-mlm style configuration
configuration = ViLTConfig()

# Initializing a model from the dandelin/vilt-b32-mlm style configuration
model = ViLTModel(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> ViLTModel, ViLTConfig

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

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

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Accessing the model configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>configuration = model.config`}}),{c(){d=n("p"),b=a("Example:"),_=m(),T(u.$$.fragment)},l(l){d=s(l,"P",{});var f=r(d);b=i(f,"Example:"),f.forEach(o),_=p(l),k(u.$$.fragment,l)},m(l,f){g(l,d,f),e(d,b),g(l,_,f),y(u,l,f),v=!0},p:qo,i(l){v||(w(u.$$.fragment,l),v=!0)},o(l){x(u.$$.fragment,l),v=!1},d(l){l&&o(d),l&&o(_),$(u,l)}}}function ac(V){let d,b,_,u,v;return{c(){d=n("p"),b=a("Although the recipe for forward pass needs to be defined within this function, one should call the "),_=n("code"),u=a("Module"),v=a(`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`)},l(l){d=s(l,"P",{});var f=r(d);b=i(f,"Although the recipe for forward pass needs to be defined within this function, one should call the "),_=s(f,"CODE",{});var F=r(_);u=i(F,"Module"),F.forEach(o),v=i(f,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),f.forEach(o)},m(l,f){g(l,d,f),e(d,b),e(d,_),e(_,u),e(d,v)},d(l){l&&o(d)}}}function ic(V){let d,b,_,u,v;return u=new Oo({props:{code:`from transformers import ViltProcessor, ViltModel
from PIL import Image
import requests

# prepare image and text
url = "http://images.cocodataset.org/val2017/000000039769.jpg"
image = Image.open(requests.get(url, stream=True).raw)
text = "hello world"

processor = ViltProcessor.from_pretrained("dandelin/vilt-b32-mlm")
model = ViltModel.from_pretrained("dandelin/vilt-b32-mlm")

inputs = processor(image, text, return_tensors="pt")
outputs = model(**inputs)
last_hidden_states = outputs.last_hidden_state`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> ViltProcessor, ViltModel
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> PIL <span class="hljs-keyword">import</span> Image
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> requests

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

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

<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = processor(image, text, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(**inputs)
<span class="hljs-meta">&gt;&gt;&gt; </span>last_hidden_states = outputs.last_hidden_state`}}),{c(){d=n("p"),b=a("Examples:"),_=m(),T(u.$$.fragment)},l(l){d=s(l,"P",{});var f=r(d);b=i(f,"Examples:"),f.forEach(o),_=p(l),k(u.$$.fragment,l)},m(l,f){g(l,d,f),e(d,b),g(l,_,f),y(u,l,f),v=!0},p:qo,i(l){v||(w(u.$$.fragment,l),v=!0)},o(l){x(u.$$.fragment,l),v=!1},d(l){l&&o(d),l&&o(_),$(u,l)}}}function lc(V){let d,b,_,u,v;return{c(){d=n("p"),b=a("Although the recipe for forward pass needs to be defined within this function, one should call the "),_=n("code"),u=a("Module"),v=a(`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`)},l(l){d=s(l,"P",{});var f=r(d);b=i(f,"Although the recipe for forward pass needs to be defined within this function, one should call the "),_=s(f,"CODE",{});var F=r(_);u=i(F,"Module"),F.forEach(o),v=i(f,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),f.forEach(o)},m(l,f){g(l,d,f),e(d,b),e(d,_),e(_,u),e(d,v)},d(l){l&&o(d)}}}function dc(V){let d,b,_,u,v;return u=new Oo({props:{code:`from transformers import ViltProcessor, ViltForMaskedLM
import requests
from PIL import Image
import re
import torch

url = "http://images.cocodataset.org/val2017/000000039769.jpg"
image = Image.open(requests.get(url, stream=True).raw)
text = "a bunch of [MASK] laying on a [MASK]."

processor = ViltProcessor.from_pretrained("dandelin/vilt-b32-mlm")
model = ViltForMaskedLM.from_pretrained("dandelin/vilt-b32-mlm")

# prepare inputs
encoding = processor(image, text, return_tensors="pt")

# forward pass
outputs = model(**encoding)

tl = len(re.findall("\\[MASK\\]", text))
inferred_token = [text]

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

selected_token = ""
encoded = processor.tokenizer(inferred_token)
output = processor.decode(encoded.input_ids[0], skip_special_tokens=True)
print(output)`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> ViltProcessor, ViltForMaskedLM
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> requests
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> PIL <span class="hljs-keyword">import</span> Image
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> re
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch

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

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

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

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

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

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

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

url = "http://images.cocodataset.org/val2017/000000039769.jpg"
image = Image.open(requests.get(url, stream=True).raw)
text = "How many cats are there?"

processor = ViltProcessor.from_pretrained("dandelin/vilt-b32-finetuned-vqa")
model = ViltForQuestionAnswering.from_pretrained("dandelin/vilt-b32-finetuned-vqa")

# prepare inputs
encoding = processor(image, text, return_tensors="pt")

# forward pass
outputs = model(**encoding)
logits = outputs.logits
idx = logits.argmax(-1).item()
print("Predicted answer:", model.config.id2label[idx])`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> ViltProcessor, ViltForQuestionAnswering
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> requests
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> PIL <span class="hljs-keyword">import</span> Image

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

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

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

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># forward pass</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(**encoding)
<span class="hljs-meta">&gt;&gt;&gt; </span>logits = outputs.logits
<span class="hljs-meta">&gt;&gt;&gt; </span>idx = logits.argmax(-<span class="hljs-number">1</span>).item()
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-built_in">print</span>(<span class="hljs-string">&quot;Predicted answer:&quot;</span>, model.config.id2label[idx])
Predicted answer: <span class="hljs-number">2</span>`}}),{c(){d=n("p"),b=a("Examples:"),_=m(),T(u.$$.fragment)},l(l){d=s(l,"P",{});var f=r(d);b=i(f,"Examples:"),f.forEach(o),_=p(l),k(u.$$.fragment,l)},m(l,f){g(l,d,f),e(d,b),g(l,_,f),y(u,l,f),v=!0},p:qo,i(l){v||(w(u.$$.fragment,l),v=!0)},o(l){x(u.$$.fragment,l),v=!1},d(l){l&&o(d),l&&o(_),$(u,l)}}}function pc(V){let d,b,_,u,v;return{c(){d=n("p"),b=a("Although the recipe for forward pass needs to be defined within this function, one should call the "),_=n("code"),u=a("Module"),v=a(`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`)},l(l){d=s(l,"P",{});var f=r(d);b=i(f,"Although the recipe for forward pass needs to be defined within this function, one should call the "),_=s(f,"CODE",{});var F=r(_);u=i(F,"Module"),F.forEach(o),v=i(f,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),f.forEach(o)},m(l,f){g(l,d,f),e(d,b),e(d,_),e(_,u),e(d,v)},d(l){l&&o(d)}}}function hc(V){let d,b,_,u,v;return u=new Oo({props:{code:`from transformers import ViltProcessor, ViltForImagesAndTextClassification
import requests
from PIL import Image

image1 = Image.open(requests.get("https://lil.nlp.cornell.edu/nlvr/exs/ex0_0.jpg", stream=True).raw)
image2 = Image.open(requests.get("https://lil.nlp.cornell.edu/nlvr/exs/ex0_1.jpg", stream=True).raw)
text = "The left image contains twice the number of dogs as the right image."

processor = ViltProcessor.from_pretrained("dandelin/vilt-b32-finetuned-nlvr2")
model = ViltForImagesAndTextClassification.from_pretrained("dandelin/vilt-b32-finetuned-nlvr2")

# prepare inputs
encoding = processor([image1, image2], text, return_tensors="pt")

# forward pass
outputs = model(input_ids=encoding.input_ids, pixel_values=encoding.pixel_values.unsqueeze(0))
logits = outputs.logits
idx = logits.argmax(-1).item()
print("Predicted answer:", model.config.id2label[idx])`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> ViltProcessor, ViltForImagesAndTextClassification
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> requests
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> PIL <span class="hljs-keyword">import</span> Image

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

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

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

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># forward pass</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(input_ids=encoding.input_ids, pixel_values=encoding.pixel_values.unsqueeze(<span class="hljs-number">0</span>))
<span class="hljs-meta">&gt;&gt;&gt; </span>logits = outputs.logits
<span class="hljs-meta">&gt;&gt;&gt; </span>idx = logits.argmax(-<span class="hljs-number">1</span>).item()
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-built_in">print</span>(<span class="hljs-string">&quot;Predicted answer:&quot;</span>, model.config.id2label[idx])
Predicted answer: <span class="hljs-literal">True</span>`}}),{c(){d=n("p"),b=a("Examples:"),_=m(),T(u.$$.fragment)},l(l){d=s(l,"P",{});var f=r(d);b=i(f,"Examples:"),f.forEach(o),_=p(l),k(u.$$.fragment,l)},m(l,f){g(l,d,f),e(d,b),g(l,_,f),y(u,l,f),v=!0},p:qo,i(l){v||(w(u.$$.fragment,l),v=!0)},o(l){x(u.$$.fragment,l),v=!1},d(l){l&&o(d),l&&o(_),$(u,l)}}}function fc(V){let d,b,_,u,v;return{c(){d=n("p"),b=a("Although the recipe for forward pass needs to be defined within this function, one should call the "),_=n("code"),u=a("Module"),v=a(`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`)},l(l){d=s(l,"P",{});var f=r(d);b=i(f,"Although the recipe for forward pass needs to be defined within this function, one should call the "),_=s(f,"CODE",{});var F=r(_);u=i(F,"Module"),F.forEach(o),v=i(f,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),f.forEach(o)},m(l,f){g(l,d,f),e(d,b),e(d,_),e(_,u),e(d,v)},d(l){l&&o(d)}}}function gc(V){let d,b,_,u,v;return u=new Oo({props:{code:`from transformers import ViltProcessor, ViltForImageAndTextRetrieval
import requests
from PIL import Image

url = "http://images.cocodataset.org/val2017/000000039769.jpg"
image = Image.open(requests.get(url, stream=True).raw)
texts = ["An image of two cats chilling on a couch", "A football player scoring a goal"]

processor = ViltProcessor.from_pretrained("dandelin/vilt-b32-finetuned-coco")
model = ViltForImageAndTextRetrieval.from_pretrained("dandelin/vilt-b32-finetuned-coco")

# forward pass
scores = dict()
for text in texts:
    # prepare inputs
    encoding = processor(image, text, return_tensors="pt")
    outputs = model(**encoding)
    scores[text] = outputs.logits[0, :].item()`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> ViltProcessor, ViltForImageAndTextRetrieval
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> requests
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> PIL <span class="hljs-keyword">import</span> Image

<span class="hljs-meta">&gt;&gt;&gt; </span>url = <span class="hljs-string">&quot;http://images.cocodataset.org/val2017/000000039769.jpg&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>image = Image.<span class="hljs-built_in">open</span>(requests.get(url, stream=<span class="hljs-literal">True</span>).raw)
<span class="hljs-meta">&gt;&gt;&gt; </span>texts = [<span class="hljs-string">&quot;An image of two cats chilling on a couch&quot;</span>, <span class="hljs-string">&quot;A football player scoring a goal&quot;</span>]

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

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># forward pass</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>scores = <span class="hljs-built_in">dict</span>()
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">for</span> text <span class="hljs-keyword">in</span> texts:
<span class="hljs-meta">... </span>    <span class="hljs-comment"># prepare inputs</span>
<span class="hljs-meta">... </span>    encoding = processor(image, text, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>)
<span class="hljs-meta">... </span>    outputs = model(**encoding)
<span class="hljs-meta">... </span>    scores[text] = outputs.logits[<span class="hljs-number">0</span>, :].item()`}}),{c(){d=n("p"),b=a("Examples:"),_=m(),T(u.$$.fragment)},l(l){d=s(l,"P",{});var f=r(d);b=i(f,"Examples:"),f.forEach(o),_=p(l),k(u.$$.fragment,l)},m(l,f){g(l,d,f),e(d,b),g(l,_,f),y(u,l,f),v=!0},p:qo,i(l){v||(w(u.$$.fragment,l),v=!0)},o(l){x(u.$$.fragment,l),v=!1},d(l){l&&o(d),l&&o(_),$(u,l)}}}function uc(V){let d,b,_,u,v;return{c(){d=n("p"),b=a("Although the recipe for forward pass needs to be defined within this function, one should call the "),_=n("code"),u=a("Module"),v=a(`
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Vocabulary size of the text part of the model. Defines the number of different tokens that can be
represented by the <code>inputs_ids</code> passed when calling <a href="/docs/transformers/v4.27.1/en/model_doc/vilt#transformers.ViltModel">ViltModel</a>.`,name:"vocab_size"},{anchor:"transformers.ViltConfig.type_vocab_size",description:`<strong>type_vocab_size</strong> (<code>int</code>, <em>optional</em>, defaults to 2) &#x2014;
The vocabulary size of the <code>token_type_ids</code> passed when calling <a href="/docs/transformers/v4.27.1/en/model_doc/vilt#transformers.ViltModel">ViltModel</a>. This is used when encoding
text.`,name:"type_vocab_size"},{anchor:"transformers.ViltConfig.modality_type_vocab_size",description:`<strong>modality_type_vocab_size</strong> (<code>int</code>, <em>optional</em>, defaults to 2) &#x2014;
The vocabulary size of the modalities passed when calling <a href="/docs/transformers/v4.27.1/en/model_doc/vilt#transformers.ViltModel">ViltModel</a>. This is used after concatening the
embeddings of the text and image modalities.`,name:"modality_type_vocab_size"},{anchor:"transformers.ViltConfig.max_position_embeddings",description:`<strong>max_position_embeddings</strong> (<code>int</code>, <em>optional</em>, defaults to 40) &#x2014;
The maximum sequence length that this model might ever be used with.`,name:"max_position_embeddings"},{anchor:"transformers.ViltConfig.hidden_size",description:`<strong>hidden_size</strong> (<code>int</code>, <em>optional</em>, defaults to 768) &#x2014;
Dimensionality of the encoder layers and the pooler layer.`,name:"hidden_size"},{anchor:"transformers.ViltConfig.num_hidden_layers",description:`<strong>num_hidden_layers</strong> (<code>int</code>, <em>optional</em>, defaults to 12) &#x2014;
Number of hidden layers in the Transformer encoder.`,name:"num_hidden_layers"},{anchor:"transformers.ViltConfig.num_attention_heads",description:`<strong>num_attention_heads</strong> (<code>int</code>, <em>optional</em>, defaults to 12) &#x2014;
Number of attention heads for each attention layer in the Transformer encoder.`,name:"num_attention_heads"},{anchor:"transformers.ViltConfig.intermediate_size",description:`<strong>intermediate_size</strong> (<code>int</code>, <em>optional</em>, defaults to 3072) &#x2014;
Dimensionality of the &#x201C;intermediate&#x201D; (i.e., feed-forward) layer in the Transformer encoder.`,name:"intermediate_size"},{anchor:"transformers.ViltConfig.hidden_act",description:`<strong>hidden_act</strong> (<code>str</code> or <code>function</code>, <em>optional</em>, defaults to <code>&quot;gelu&quot;</code>) &#x2014;
The non-linear activation function (function or string) in the encoder and pooler. If string, <code>&quot;gelu&quot;</code>,
<code>&quot;relu&quot;</code>, <code>&quot;selu&quot;</code> and <code>&quot;gelu_new&quot;</code> are supported.`,name:"hidden_act"},{anchor:"transformers.ViltConfig.hidden_dropout_prob",description:`<strong>hidden_dropout_prob</strong> (<code>float</code>, <em>optional</em>, defaults to 0.1) &#x2014;
The dropout probabilitiy for all fully connected layers in the embeddings, encoder, and pooler.`,name:"hidden_dropout_prob"},{anchor:"transformers.ViltConfig.attention_probs_dropout_prob",description:`<strong>attention_probs_dropout_prob</strong> (<code>float</code>, <em>optional</em>, defaults to 0.1) &#x2014;
The dropout ratio for the attention probabilities.`,name:"attention_probs_dropout_prob"},{anchor:"transformers.ViltConfig.initializer_range",description:`<strong>initializer_range</strong> (<code>float</code>, <em>optional</em>, defaults to 0.02) &#x2014;
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.`,name:"initializer_range"},{anchor:"transformers.ViltConfig.layer_norm_eps",description:`<strong>layer_norm_eps</strong> (<code>float</code>, <em>optional</em>, defaults to 1e-12) &#x2014;
The epsilon used by the layer normalization layers.`,name:"layer_norm_eps"},{anchor:"transformers.ViltConfig.image_size",description:`<strong>image_size</strong> (<code>int</code>, <em>optional</em>, defaults to 384) &#x2014;
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The size (resolution) of each patch.`,name:"patch_size"},{anchor:"transformers.ViltConfig.num_channels",description:`<strong>num_channels</strong> (<code>int</code>, <em>optional</em>, defaults to 3) &#x2014;
The number of input channels.`,name:"num_channels"},{anchor:"transformers.ViltConfig.qkv_bias",description:`<strong>qkv_bias</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether to add a bias to the queries, keys and values.`,name:"qkv_bias"},{anchor:"transformers.ViltConfig.max_image_length",description:`<strong>max_image_length</strong> (<code>int</code>, <em>optional</em>, defaults to -1) &#x2014;
The maximum number of patches to take as input for the Transformer encoder. If set to a positive integer,
the encoder will sample <code>max_image_length</code> patches at maximum. If set to -1, will not be taken into
account.`,name:"max_image_length"},{anchor:"transformers.ViltConfig.num_images",description:`<strong>num_images</strong> (<code>int</code>, <em>optional</em>, defaults to -1) &#x2014;
The number of images to use for natural language visual reasoning. If set to a positive integer, will be
used by <a href="/docs/transformers/v4.27.1/en/model_doc/vilt#transformers.ViltForImagesAndTextClassification">ViltForImagesAndTextClassification</a> for defining the classifier head.`,name:"num_images"}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/vilt/configuration_vilt.py#L28"}}),xe=new Co({props:{anchor:"transformers.ViltConfig.example",$$slots:{default:[rc]},$$scope:{ctx:V}}}),_t=new R({}),vt=new P({props:{name:"class transformers.ViltFeatureExtractor",anchor:"transformers.ViltFeatureExtractor",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/vilt/feature_extraction_vilt.py#L26"}}),bt=new P({props:{name:"__call__",anchor:"transformers.ViltFeatureExtractor.__call__",parameters:[{name:"images",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/image_processing_utils.py#L456"}}),Tt=new R({}),kt=new P({props:{name:"class transformers.ViltImageProcessor",anchor:"transformers.ViltImageProcessor",parameters:[{name:"do_resize",val:": bool = True"},{name:"size",val:": typing.Dict[str, int] = None"},{name:"size_divisor",val:": int = 32"},{name:"resample",val:": Resampling = <Resampling.BICUBIC: 3>"},{name:"do_rescale",val:": bool = True"},{name:"rescale_factor",val:": typing.Union[int, float] = 0.00392156862745098"},{name:"do_normalize",val:": bool = True"},{name:"image_mean",val:": typing.Union[float, typing.List[float], NoneType] = None"},{name:"image_std",val:": typing.Union[float, typing.List[float], NoneType] = None"},{name:"do_pad",val:": bool = True"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.ViltImageProcessor.do_resize",description:`<strong>do_resize</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
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Resize the shorter side of the input to <code>size[&quot;shortest_edge&quot;]</code>. The longer side will be limited to under
<code>int((1333 / 800) * size[&quot;shortest_edge&quot;])</code> while preserving the aspect ratio. Only has an effect if
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The size by which to make sure both the height and width can be divided. Only has an effect if <code>do_resize</code>
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Resampling filter to use if resizing the image. Only has an effect if <code>do_resize</code> is set to <code>True</code>. Can be
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Wwhether to rescale the image by the specified scale <code>rescale_factor</code>. Can be overridden by the
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Scale factor to use if rescaling the image. Only has an effect if <code>do_rescale</code> is set to <code>True</code>. Can be
overridden by the <code>rescale_factor</code> parameter in the <code>preprocess</code> method.`,name:"rescale_factor"},{anchor:"transformers.ViltImageProcessor.do_normalize",description:`<strong>do_normalize</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether to normalize the image. Can be overridden by the <code>do_normalize</code> parameter in the <code>preprocess</code>
method. Can be overridden by the <code>do_normalize</code> parameter in the <code>preprocess</code> method.`,name:"do_normalize"},{anchor:"transformers.ViltImageProcessor.image_mean",description:`<strong>image_mean</strong> (<code>float</code> or <code>List[float]</code>, <em>optional</em>, defaults to <code>IMAGENET_STANDARD_MEAN</code>) &#x2014;
Mean to use if normalizing the image. This is a float or list of floats the length of the number of
channels in the image. Can be overridden by the <code>image_mean</code> parameter in the <code>preprocess</code> method. Can be
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Standard deviation to use if normalizing the image. This is a float or list of floats the length of the
number of channels in the image. Can be overridden by the <code>image_std</code> parameter in the <code>preprocess</code> method.
Can be overridden by the <code>image_std</code> parameter in the <code>preprocess</code> method.`,name:"image_std"},{anchor:"transformers.ViltImageProcessor.do_pad",description:`<strong>do_pad</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether to pad the image to the <code>(max_height, max_width)</code> of the images in the batch. Can be overridden by
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Controls the size of the image after <code>resize</code>. The shortest edge of the image is resized to
<code>size[&quot;shortest_edge&quot;]</code> whilst preserving the aspect ratio. If the longest edge of this resized image
is &gt; <code>int(size[&quot;shortest_edge&quot;] * (1333 / 800))</code>, then the image is resized again to make the longest
edge equal to <code>int(size[&quot;shortest_edge&quot;] * (1333 / 800))</code>.`,name:"size"},{anchor:"transformers.ViltImageProcessor.preprocess.size_divisor",description:`<strong>size_divisor</strong> (<code>int</code>, <em>optional</em>, defaults to <code>self.size_divisor</code>) &#x2014;
The image is resized to a size that is a multiple of this value.`,name:"size_divisor"},{anchor:"transformers.ViltImageProcessor.preprocess.resample",description:`<strong>resample</strong> (<code>PILImageResampling</code>, <em>optional</em>, defaults to <code>self.resample</code>) &#x2014;
Resampling filter to use if resizing the image. Only has an effect if <code>do_resize</code> is set to <code>True</code>.`,name:"resample"},{anchor:"transformers.ViltImageProcessor.preprocess.do_rescale",description:`<strong>do_rescale</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>self.do_rescale</code>) &#x2014;
Whether to rescale the image values between [0 - 1].`,name:"do_rescale"},{anchor:"transformers.ViltImageProcessor.preprocess.rescale_factor",description:`<strong>rescale_factor</strong> (<code>float</code>, <em>optional</em>, defaults to <code>self.rescale_factor</code>) &#x2014;
Rescale factor to rescale the image by if <code>do_rescale</code> is set to <code>True</code>.`,name:"rescale_factor"},{anchor:"transformers.ViltImageProcessor.preprocess.do_normalize",description:`<strong>do_normalize</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>self.do_normalize</code>) &#x2014;
Whether to normalize the image.`,name:"do_normalize"},{anchor:"transformers.ViltImageProcessor.preprocess.image_mean",description:`<strong>image_mean</strong> (<code>float</code> or <code>List[float]</code>, <em>optional</em>, defaults to <code>self.image_mean</code>) &#x2014;
Image mean to normalize the image by if <code>do_normalize</code> is set to <code>True</code>.`,name:"image_mean"},{anchor:"transformers.ViltImageProcessor.preprocess.image_std",description:`<strong>image_std</strong> (<code>float</code> or <code>List[float]</code>, <em>optional</em>, defaults to <code>self.image_std</code>) &#x2014;
Image standard deviation to normalize the image by if <code>do_normalize</code> is set to <code>True</code>.`,name:"image_std"},{anchor:"transformers.ViltImageProcessor.preprocess.do_pad",description:`<strong>do_pad</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>self.do_pad</code>) &#x2014;
Whether to pad the image to the (max_height, max_width) in the batch. If <code>True</code>, a pixel mask is also
created and returned.`,name:"do_pad"},{anchor:"transformers.ViltImageProcessor.preprocess.return_tensors",description:`<strong>return_tensors</strong> (<code>str</code> or <code>TensorType</code>, <em>optional</em>) &#x2014;
The type of tensors to return. Can be one of:<ul>
<li>Unset: Return a list of <code>np.ndarray</code>.</li>
<li><code>TensorType.TENSORFLOW</code> or <code>&apos;tf&apos;</code>: Return a batch of type <code>tf.Tensor</code>.</li>
<li><code>TensorType.PYTORCH</code> or <code>&apos;pt&apos;</code>: Return a batch of type <code>torch.Tensor</code>.</li>
<li><code>TensorType.NUMPY</code> or <code>&apos;np&apos;</code>: Return a batch of type <code>np.ndarray</code>.</li>
<li><code>TensorType.JAX</code> or <code>&apos;jax&apos;</code>: Return a batch of type <code>jax.numpy.ndarray</code>.</li>
</ul>`,name:"return_tensors"},{anchor:"transformers.ViltImageProcessor.preprocess.data_format",description:`<strong>data_format</strong> (<code>ChannelDimension</code> or <code>str</code>, <em>optional</em>, defaults to <code>ChannelDimension.FIRST</code>) &#x2014;
The channel dimension format for the output image. Can be one of:<ul>
<li><code>ChannelDimension.FIRST</code>: image in (num_channels, height, width) format.</li>
<li><code>ChannelDimension.LAST</code>: image in (height, width, num_channels) format.</li>
</ul>`,name:"data_format"}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/vilt/image_processing_vilt.py#L369"}}),wt=new R({}),xt=new P({props:{name:"class transformers.ViltProcessor",anchor:"transformers.ViltProcessor",parameters:[{name:"image_processor",val:" = None"},{name:"tokenizer",val:" = None"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.ViltProcessor.image_processor",description:`<strong>image_processor</strong> (<code>ViltImageProcessor</code>) &#x2014;
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Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the <a href="/docs/transformers/v4.27.1/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/vilt/modeling_vilt.py#L730"}}),It=new P({props:{name:"forward",anchor:"transformers.ViltModel.forward",parameters:[{name:"input_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"attention_mask",val:": typing.Optional[torch.FloatTensor] = None"},{name:"token_type_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"pixel_values",val:": typing.Optional[torch.FloatTensor] = None"},{name:"pixel_mask",val:": typing.Optional[torch.LongTensor] = None"},{name:"head_mask",val:": typing.Optional[torch.FloatTensor] = None"},{name:"inputs_embeds",val:": typing.Optional[torch.FloatTensor] = None"},{name:"image_embeds",val:": typing.Optional[torch.FloatTensor] = None"},{name:"image_token_type_idx",val:": typing.Optional[int] = 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.ViltModel.forward.input_ids",description:`<strong>input_ids</strong> (<code>torch.LongTensor</code> of shape <code>({0})</code>) &#x2014;
Indices of input sequence tokens in the vocabulary. Indices can be obtained using <a href="/docs/transformers/v4.27.1/en/model_doc/bert#transformers.BertTokenizer">BertTokenizer</a>. See
<a href="/docs/transformers/v4.27.1/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and <a href="/docs/transformers/v4.27.1/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details. <a href="../glossary#input-ids">What are input
IDs?</a>`,name:"input_ids"},{anchor:"transformers.ViltModel.forward.attention_mask",description:`<strong>attention_mask</strong> (<code>torch.FloatTensor</code> of shape <code>({0})</code>, <em>optional</em>) &#x2014;
Mask to avoid performing attention on padding token indices. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.
<a href="../glossary#attention-mask">What are attention masks?</a></li>
</ul>`,name:"attention_mask"},{anchor:"transformers.ViltModel.forward.token_type_ids",description:`<strong>token_type_ids</strong> (<code>torch.LongTensor</code> of shape <code>({0})</code>, <em>optional</em>) &#x2014;
Segment token indices to indicate first and second portions of the inputs. Indices are selected in <code>[0, 1]</code>:</p>
<ul>
<li>0 corresponds to a <em>sentence A</em> token,</li>
<li>1 corresponds to a <em>sentence B</em> token.
<a href="../glossary#token-type-ids">What are token type IDs?</a></li>
</ul>`,name:"token_type_ids"},{anchor:"transformers.ViltModel.forward.pixel_values",description:`<strong>pixel_values</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, num_channels, height, width)</code>) &#x2014;
Pixel values. Pixel values can be obtained using <a href="/docs/transformers/v4.27.1/en/model_doc/auto#transformers.AutoImageProcessor">AutoImageProcessor</a>. See
<a href="/docs/transformers/v4.27.1/en/model_doc/videomae#transformers.VideoMAEFeatureExtractor.__call__">ViltImageProcessor.<strong>call</strong>()</a> for details.`,name:"pixel_values"},{anchor:"transformers.ViltModel.forward.pixel_mask",description:`<strong>pixel_mask</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, height, width)</code>, <em>optional</em>) &#x2014;
Mask to avoid performing attention on padding pixel values. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for pixels that are real (i.e. <strong>not masked</strong>),</li>
<li>0 for pixels that are padding (i.e. <strong>masked</strong>).
<code>What are attention masks? &lt;../glossary.html#attention-mask&gt;</code>__</li>
</ul>`,name:"pixel_mask"},{anchor:"transformers.ViltModel.forward.head_mask",description:`<strong>head_mask</strong> (<code>torch.FloatTensor</code> of shape <code>(num_heads,)</code> or <code>(num_layers, num_heads)</code>, <em>optional</em>) &#x2014;
Mask to nullify selected heads of the self-attention modules. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 indicates the head is <strong>not masked</strong>,</li>
<li>0 indicates the head is <strong>masked</strong>.</li>
</ul>`,name:"head_mask"},{anchor:"transformers.ViltModel.forward.inputs_embeds",description:`<strong>inputs_embeds</strong> (<code>torch.FloatTensor</code> of shape <code>({0}, hidden_size)</code>, <em>optional</em>) &#x2014;
Optionally, instead of passing <code>input_ids</code> you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert <code>input_ids</code> indices into associated vectors than the
model&#x2019;s internal embedding lookup matrix.`,name:"inputs_embeds"},{anchor:"transformers.ViltModel.forward.image_embeds",description:`<strong>image_embeds</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, num_patches, hidden_size)</code>, <em>optional</em>) &#x2014;
Optionally, instead of passing <code>pixel_values</code>, you can choose to directly pass an embedded representation.
This is useful if you want more control over how to convert <code>pixel_values</code> into patch embeddings.`,name:"image_embeds"},{anchor:"transformers.ViltModel.forward.output_attentions",description:`<strong>output_attentions</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the attentions tensors of all attention layers. See <code>attentions</code> under returned
tensors for more detail.`,name:"output_attentions"},{anchor:"transformers.ViltModel.forward.output_hidden_states",description:`<strong>output_hidden_states</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the hidden states of all layers. See <code>hidden_states</code> under returned tensors for
more detail.`,name:"output_hidden_states"},{anchor:"transformers.ViltModel.forward.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/v4.27.1/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/vilt/modeling_vilt.py#L758",returnDescription:`
<p>A <a
  href="/docs/transformers/v4.27.1/en/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPooling"
>transformers.modeling_outputs.BaseModelOutputWithPooling</a> or a tuple of
<code>torch.FloatTensor</code> (if <code>return_dict=False</code> is passed or when <code>config.return_dict=False</code>) comprising various
elements depending on the configuration (<a
  href="/docs/transformers/v4.27.1/en/model_doc/vilt#transformers.ViltConfig"
>ViltConfig</a>) and inputs.</p>
<ul>
<li>
<p><strong>last_hidden_state</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>) \u2014 Sequence of hidden-states at the output of the last layer of the model.</p>
</li>
<li>
<p><strong>pooler_output</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, hidden_size)</code>) \u2014 Last layer hidden-state of the first token of the sequence (classification token) after further processing
through the layers used for the auxiliary pretraining task. E.g. for BERT-family of models, this returns
the classification token after processing through a linear layer and a tanh activation function. The linear
layer weights are trained from the next sentence prediction (classification) objective during pretraining.</p>
</li>
<li>
<p><strong>hidden_states</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape <code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.</p>
</li>
<li>
<p><strong>attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.</p>
</li>
</ul>
`,returnType:`
<p><a
  href="/docs/transformers/v4.27.1/en/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPooling"
>transformers.modeling_outputs.BaseModelOutputWithPooling</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),Ae=new Lo({props:{$$slots:{default:[ac]},$$scope:{ctx:V}}}),Ee=new Co({props:{anchor:"transformers.ViltModel.forward.example",$$slots:{default:[ic]},$$scope:{ctx:V}}}),Mt=new R({}),jt=new P({props:{name:"class transformers.ViltForMaskedLM",anchor:"transformers.ViltForMaskedLM",parameters:[{name:"config",val:""}],parametersDescription:[{anchor:"transformers.ViltForMaskedLM.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.27.1/en/model_doc/vilt#transformers.ViltConfig">ViltConfig</a>) &#x2014; Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the <a href="/docs/transformers/v4.27.1/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/vilt/modeling_vilt.py#L895"}}),Et=new P({props:{name:"forward",anchor:"transformers.ViltForMaskedLM.forward",parameters:[{name:"input_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"attention_mask",val:": typing.Optional[torch.FloatTensor] = None"},{name:"token_type_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"pixel_values",val:": typing.Optional[torch.FloatTensor] = None"},{name:"pixel_mask",val:": typing.Optional[torch.LongTensor] = None"},{name:"head_mask",val:": typing.Optional[torch.FloatTensor] = None"},{name:"inputs_embeds",val:": typing.Optional[torch.FloatTensor] = None"},{name:"image_embeds",val:": typing.Optional[torch.FloatTensor] = None"},{name:"labels",val:": typing.Optional[torch.LongTensor] = None"},{name:"output_attentions",val:": typing.Optional[bool] = None"},{name:"output_hidden_states",val:": typing.Optional[bool] = None"},{name:"return_dict",val:": typing.Optional[bool] = None"}],parametersDescription:[{anchor:"transformers.ViltForMaskedLM.forward.input_ids",description:`<strong>input_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary. Indices can be obtained using <a href="/docs/transformers/v4.27.1/en/model_doc/bert#transformers.BertTokenizer">BertTokenizer</a>. See
<a href="/docs/transformers/v4.27.1/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and <a href="/docs/transformers/v4.27.1/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details. <a href="../glossary#input-ids">What are input
IDs?</a>`,name:"input_ids"},{anchor:"transformers.ViltForMaskedLM.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>.
<a href="../glossary#attention-mask">What are attention masks?</a></li>
</ul>`,name:"attention_mask"},{anchor:"transformers.ViltForMaskedLM.forward.token_type_ids",description:`<strong>token_type_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Segment token indices to indicate first and second portions of the inputs. Indices are selected in <code>[0, 1]</code>:</p>
<ul>
<li>0 corresponds to a <em>sentence A</em> token,</li>
<li>1 corresponds to a <em>sentence B</em> token.
<a href="../glossary#token-type-ids">What are token type IDs?</a></li>
</ul>`,name:"token_type_ids"},{anchor:"transformers.ViltForMaskedLM.forward.pixel_values",description:`<strong>pixel_values</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, num_channels, height, width)</code>) &#x2014;
Pixel values. Pixel values can be obtained using <a href="/docs/transformers/v4.27.1/en/model_doc/auto#transformers.AutoImageProcessor">AutoImageProcessor</a>. See
<a href="/docs/transformers/v4.27.1/en/model_doc/videomae#transformers.VideoMAEFeatureExtractor.__call__">ViltImageProcessor.<strong>call</strong>()</a> for details.`,name:"pixel_values"},{anchor:"transformers.ViltForMaskedLM.forward.pixel_mask",description:`<strong>pixel_mask</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, height, width)</code>, <em>optional</em>) &#x2014;
Mask to avoid performing attention on padding pixel values. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for pixels that are real (i.e. <strong>not masked</strong>),</li>
<li>0 for pixels that are padding (i.e. <strong>masked</strong>).
<code>What are attention masks? &lt;../glossary.html#attention-mask&gt;</code>__</li>
</ul>`,name:"pixel_mask"},{anchor:"transformers.ViltForMaskedLM.forward.head_mask",description:`<strong>head_mask</strong> (<code>torch.FloatTensor</code> of shape <code>(num_heads,)</code> or <code>(num_layers, num_heads)</code>, <em>optional</em>) &#x2014;
Mask to nullify selected heads of the self-attention modules. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 indicates the head is <strong>not masked</strong>,</li>
<li>0 indicates the head is <strong>masked</strong>.</li>
</ul>`,name:"head_mask"},{anchor:"transformers.ViltForMaskedLM.forward.inputs_embeds",description:`<strong>inputs_embeds</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>, <em>optional</em>) &#x2014;
Optionally, instead of passing <code>input_ids</code> you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert <code>input_ids</code> indices into associated vectors than the
model&#x2019;s internal embedding lookup matrix.`,name:"inputs_embeds"},{anchor:"transformers.ViltForMaskedLM.forward.image_embeds",description:`<strong>image_embeds</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, num_patches, hidden_size)</code>, <em>optional</em>) &#x2014;
Optionally, instead of passing <code>pixel_values</code>, you can choose to directly pass an embedded representation.
This is useful if you want more control over how to convert <code>pixel_values</code> into patch embeddings.`,name:"image_embeds"},{anchor:"transformers.ViltForMaskedLM.forward.output_attentions",description:`<strong>output_attentions</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the attentions tensors of all attention layers. See <code>attentions</code> under returned
tensors for more detail.`,name:"output_attentions"},{anchor:"transformers.ViltForMaskedLM.forward.output_hidden_states",description:`<strong>output_hidden_states</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the hidden states of all layers. See <code>hidden_states</code> under returned tensors for
more detail.`,name:"output_hidden_states"},{anchor:"transformers.ViltForMaskedLM.forward.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/v4.27.1/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"},{anchor:"transformers.ViltForMaskedLM.forward.labels",description:`<strong>labels</strong> (<em>torch.LongTensor</em> of shape <em>(batch_size, sequence_length)</em>, <em>optional</em>) &#x2014;
Labels for computing the masked language modeling loss. Indices should be in <em>[-100, 0, &#x2026;,
config.vocab_size]</em> (see <em>input_ids</em> docstring) Tokens with indices set to <em>-100</em> are ignored (masked), the
loss is only computed for the tokens with labels in <em>[0, &#x2026;, config.vocab_size]</em>`,name:"labels"}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/vilt/modeling_vilt.py#L913",returnDescription:`
<p>A <a
  href="/docs/transformers/v4.27.1/en/main_classes/output#transformers.modeling_outputs.MaskedLMOutput"
>transformers.modeling_outputs.MaskedLMOutput</a> or a tuple of
<code>torch.FloatTensor</code> (if <code>return_dict=False</code> is passed or when <code>config.return_dict=False</code>) comprising various
elements depending on the configuration (<a
  href="/docs/transformers/v4.27.1/en/model_doc/vilt#transformers.ViltConfig"
>ViltConfig</a>) and inputs.</p>
<ul>
<li>
<p><strong>loss</strong> (<code>torch.FloatTensor</code> of shape <code>(1,)</code>, <em>optional</em>, returned when <code>labels</code> is provided) \u2014 Masked language modeling (MLM) loss.</p>
</li>
<li>
<p><strong>logits</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, config.vocab_size)</code>) \u2014 Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).</p>
</li>
<li>
<p><strong>hidden_states</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape <code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.</p>
</li>
<li>
<p><strong>attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.</p>
</li>
</ul>
`,returnType:`
<p><a
  href="/docs/transformers/v4.27.1/en/main_classes/output#transformers.modeling_outputs.MaskedLMOutput"
>transformers.modeling_outputs.MaskedLMOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),Ce=new Lo({props:{$$slots:{default:[lc]},$$scope:{ctx:V}}}),qe=new Co({props:{anchor:"transformers.ViltForMaskedLM.forward.example",$$slots:{default:[dc]},$$scope:{ctx:V}}}),Lt=new R({}),Ct=new P({props:{name:"class transformers.ViltForQuestionAnswering",anchor:"transformers.ViltForQuestionAnswering",parameters:[{name:"config",val:""}],parametersDescription:[{anchor:"transformers.ViltForQuestionAnswering.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.27.1/en/model_doc/vilt#transformers.ViltConfig">ViltConfig</a>) &#x2014; Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the <a href="/docs/transformers/v4.27.1/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/vilt/modeling_vilt.py#L1069"}}),Ot=new P({props:{name:"forward",anchor:"transformers.ViltForQuestionAnswering.forward",parameters:[{name:"input_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"attention_mask",val:": typing.Optional[torch.FloatTensor] = None"},{name:"token_type_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"pixel_values",val:": typing.Optional[torch.FloatTensor] = None"},{name:"pixel_mask",val:": typing.Optional[torch.LongTensor] = None"},{name:"head_mask",val:": typing.Optional[torch.FloatTensor] = None"},{name:"inputs_embeds",val:": typing.Optional[torch.FloatTensor] = None"},{name:"image_embeds",val:": typing.Optional[torch.FloatTensor] = None"},{name:"labels",val:": typing.Optional[torch.LongTensor] = None"},{name:"output_attentions",val:": typing.Optional[bool] = None"},{name:"output_hidden_states",val:": typing.Optional[bool] = None"},{name:"return_dict",val:": typing.Optional[bool] = None"}],parametersDescription:[{anchor:"transformers.ViltForQuestionAnswering.forward.input_ids",description:`<strong>input_ids</strong> (<code>torch.LongTensor</code> of shape <code>({0})</code>) &#x2014;
Indices of input sequence tokens in the vocabulary. Indices can be obtained using <a href="/docs/transformers/v4.27.1/en/model_doc/bert#transformers.BertTokenizer">BertTokenizer</a>. See
<a href="/docs/transformers/v4.27.1/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and <a href="/docs/transformers/v4.27.1/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details. <a href="../glossary#input-ids">What are input
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Mask to avoid performing attention on padding token indices. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.
<a href="../glossary#attention-mask">What are attention masks?</a></li>
</ul>`,name:"attention_mask"},{anchor:"transformers.ViltForQuestionAnswering.forward.token_type_ids",description:`<strong>token_type_ids</strong> (<code>torch.LongTensor</code> of shape <code>({0})</code>, <em>optional</em>) &#x2014;
Segment token indices to indicate first and second portions of the inputs. Indices are selected in <code>[0, 1]</code>:</p>
<ul>
<li>0 corresponds to a <em>sentence A</em> token,</li>
<li>1 corresponds to a <em>sentence B</em> token.
<a href="../glossary#token-type-ids">What are token type IDs?</a></li>
</ul>`,name:"token_type_ids"},{anchor:"transformers.ViltForQuestionAnswering.forward.pixel_values",description:`<strong>pixel_values</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, num_channels, height, width)</code>) &#x2014;
Pixel values. Pixel values can be obtained using <a href="/docs/transformers/v4.27.1/en/model_doc/auto#transformers.AutoImageProcessor">AutoImageProcessor</a>. See
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Mask to avoid performing attention on padding pixel values. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for pixels that are real (i.e. <strong>not masked</strong>),</li>
<li>0 for pixels that are padding (i.e. <strong>masked</strong>).
<code>What are attention masks? &lt;../glossary.html#attention-mask&gt;</code>__</li>
</ul>`,name:"pixel_mask"},{anchor:"transformers.ViltForQuestionAnswering.forward.head_mask",description:`<strong>head_mask</strong> (<code>torch.FloatTensor</code> of shape <code>(num_heads,)</code> or <code>(num_layers, num_heads)</code>, <em>optional</em>) &#x2014;
Mask to nullify selected heads of the self-attention modules. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 indicates the head is <strong>not masked</strong>,</li>
<li>0 indicates the head is <strong>masked</strong>.</li>
</ul>`,name:"head_mask"},{anchor:"transformers.ViltForQuestionAnswering.forward.inputs_embeds",description:`<strong>inputs_embeds</strong> (<code>torch.FloatTensor</code> of shape <code>({0}, hidden_size)</code>, <em>optional</em>) &#x2014;
Optionally, instead of passing <code>input_ids</code> you can choose to directly pass an embedded representation. This
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Optionally, instead of passing <code>pixel_values</code>, you can choose to directly pass an embedded representation.
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Whether or not to return the attentions tensors of all attention layers. See <code>attentions</code> under returned
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Whether or not to return the hidden states of all layers. See <code>hidden_states</code> under returned tensors for
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Labels for computing the visual question answering loss. This tensor must be either a one-hot encoding of
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<p>A <a
  href="/docs/transformers/v4.27.1/en/main_classes/output#transformers.modeling_outputs.SequenceClassifierOutput"
>transformers.modeling_outputs.SequenceClassifierOutput</a> or a tuple of
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elements depending on the configuration (<a
  href="/docs/transformers/v4.27.1/en/model_doc/vilt#transformers.ViltConfig"
>ViltConfig</a>) and inputs.</p>
<ul>
<li>
<p><strong>loss</strong> (<code>torch.FloatTensor</code> of shape <code>(1,)</code>, <em>optional</em>, returned when <code>labels</code> is provided) \u2014 Classification (or regression if config.num_labels==1) loss.</p>
</li>
<li>
<p><strong>logits</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, config.num_labels)</code>) \u2014 Classification (or regression if config.num_labels==1) scores (before SoftMax).</p>
</li>
<li>
<p><strong>hidden_states</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape <code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.</p>
</li>
<li>
<p><strong>attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.</p>
</li>
</ul>
`,returnType:`
<p><a
  href="/docs/transformers/v4.27.1/en/main_classes/output#transformers.modeling_outputs.SequenceClassifierOutput"
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<a href="/docs/transformers/v4.27.1/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and <a href="/docs/transformers/v4.27.1/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details. <a href="../glossary#input-ids">What are input
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Mask to avoid performing attention on padding token indices. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.
<a href="../glossary#attention-mask">What are attention masks?</a></li>
</ul>`,name:"attention_mask"},{anchor:"transformers.ViltForImagesAndTextClassification.token_type_ids",description:`<strong>token_type_ids</strong> (<code>torch.LongTensor</code> of shape <code>({0})</code>, <em>optional</em>) &#x2014;
Segment token indices to indicate first and second portions of the inputs. Indices are selected in <code>[0, 1]</code>:</p>
<ul>
<li>0 corresponds to a <em>sentence A</em> token,</li>
<li>1 corresponds to a <em>sentence B</em> token.
<a href="../glossary#token-type-ids">What are token type IDs?</a></li>
</ul>`,name:"token_type_ids"},{anchor:"transformers.ViltForImagesAndTextClassification.pixel_values",description:`<strong>pixel_values</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, num_images, num_channels, height, width)</code>) &#x2014;
Pixel values. Pixel values can be obtained using <a href="/docs/transformers/v4.27.1/en/model_doc/auto#transformers.AutoImageProcessor">AutoImageProcessor</a>. See
<a href="/docs/transformers/v4.27.1/en/model_doc/videomae#transformers.VideoMAEFeatureExtractor.__call__">ViltImageProcessor.<strong>call</strong>()</a> for details.`,name:"pixel_values"},{anchor:"transformers.ViltForImagesAndTextClassification.pixel_mask",description:`<strong>pixel_mask</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, num_images, height, width)</code>, <em>optional</em>) &#x2014;
Mask to avoid performing attention on padding pixel values. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for pixels that are real (i.e. <strong>not masked</strong>),</li>
<li>0 for pixels that are padding (i.e. <strong>masked</strong>).
<code>What are attention masks? &lt;../glossary.html#attention-mask&gt;</code>__</li>
</ul>`,name:"pixel_mask"},{anchor:"transformers.ViltForImagesAndTextClassification.head_mask",description:`<strong>head_mask</strong> (<code>torch.FloatTensor</code> of shape <code>(num_heads,)</code> or <code>(num_layers, num_heads)</code>, <em>optional</em>) &#x2014;
Mask to nullify selected heads of the self-attention modules. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 indicates the head is <strong>not masked</strong>,</li>
<li>0 indicates the head is <strong>masked</strong>.</li>
</ul>`,name:"head_mask"},{anchor:"transformers.ViltForImagesAndTextClassification.inputs_embeds",description:`<strong>inputs_embeds</strong> (<code>torch.FloatTensor</code> of shape <code>({0}, hidden_size)</code>, <em>optional</em>) &#x2014;
Optionally, instead of passing <code>input_ids</code> you can choose to directly pass an embedded representation. This
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model&#x2019;s internal embedding lookup matrix.`,name:"inputs_embeds"},{anchor:"transformers.ViltForImagesAndTextClassification.image_embeds",description:`<strong>image_embeds</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, num_images, num_patches, hidden_size)</code>, <em>optional</em>) &#x2014;
Optionally, instead of passing <code>pixel_values</code>, you can choose to directly pass an embedded representation.
This is useful if you want more control over how to convert <code>pixel_values</code> into patch embeddings.`,name:"image_embeds"},{anchor:"transformers.ViltForImagesAndTextClassification.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
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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.ViltForImagesAndTextClassification.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/v4.27.1/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/vilt/modeling_vilt.py#L1279"}}),Dt=new P({props:{name:"forward",anchor:"transformers.ViltForImagesAndTextClassification.forward",parameters:[{name:"input_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"attention_mask",val:": typing.Optional[torch.FloatTensor] = None"},{name:"token_type_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"pixel_values",val:": typing.Optional[torch.FloatTensor] = None"},{name:"pixel_mask",val:": typing.Optional[torch.LongTensor] = None"},{name:"head_mask",val:": typing.Optional[torch.FloatTensor] = None"},{name:"inputs_embeds",val:": typing.Optional[torch.FloatTensor] = None"},{name:"image_embeds",val:": typing.Optional[torch.FloatTensor] = None"},{name:"labels",val:": typing.Optional[torch.LongTensor] = None"},{name:"output_attentions",val:": typing.Optional[bool] = None"},{name:"output_hidden_states",val:": typing.Optional[bool] = None"},{name:"return_dict",val:": typing.Optional[bool] = None"}],parametersDescription:[{anchor:"transformers.ViltForImagesAndTextClassification.forward.input_ids",description:`<strong>input_ids</strong> (<code>torch.LongTensor</code> of shape <code>({0})</code>) &#x2014;
Indices of input sequence tokens in the vocabulary. Indices can be obtained using <a href="/docs/transformers/v4.27.1/en/model_doc/bert#transformers.BertTokenizer">BertTokenizer</a>. See
<a href="/docs/transformers/v4.27.1/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and <a href="/docs/transformers/v4.27.1/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details. <a href="../glossary#input-ids">What are input
IDs?</a>`,name:"input_ids"},{anchor:"transformers.ViltForImagesAndTextClassification.forward.attention_mask",description:`<strong>attention_mask</strong> (<code>torch.FloatTensor</code> of shape <code>({0})</code>, <em>optional</em>) &#x2014;
Mask to avoid performing attention on padding token indices. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.
<a href="../glossary#attention-mask">What are attention masks?</a></li>
</ul>`,name:"attention_mask"},{anchor:"transformers.ViltForImagesAndTextClassification.forward.token_type_ids",description:`<strong>token_type_ids</strong> (<code>torch.LongTensor</code> of shape <code>({0})</code>, <em>optional</em>) &#x2014;
Segment token indices to indicate first and second portions of the inputs. Indices are selected in <code>[0, 1]</code>:</p>
<ul>
<li>0 corresponds to a <em>sentence A</em> token,</li>
<li>1 corresponds to a <em>sentence B</em> token.
<a href="../glossary#token-type-ids">What are token type IDs?</a></li>
</ul>`,name:"token_type_ids"},{anchor:"transformers.ViltForImagesAndTextClassification.forward.pixel_values",description:`<strong>pixel_values</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, num_channels, height, width)</code>) &#x2014;
Pixel values. Pixel values can be obtained using <a href="/docs/transformers/v4.27.1/en/model_doc/auto#transformers.AutoImageProcessor">AutoImageProcessor</a>. See
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Mask to avoid performing attention on padding pixel values. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for pixels that are real (i.e. <strong>not masked</strong>),</li>
<li>0 for pixels that are padding (i.e. <strong>masked</strong>).
<code>What are attention masks? &lt;../glossary.html#attention-mask&gt;</code>__</li>
</ul>`,name:"pixel_mask"},{anchor:"transformers.ViltForImagesAndTextClassification.forward.head_mask",description:`<strong>head_mask</strong> (<code>torch.FloatTensor</code> of shape <code>(num_heads,)</code> or <code>(num_layers, num_heads)</code>, <em>optional</em>) &#x2014;
Mask to nullify selected heads of the self-attention modules. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 indicates the head is <strong>not masked</strong>,</li>
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Optionally, instead of passing <code>input_ids</code> you can choose to directly pass an embedded representation. This
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Optionally, instead of passing <code>pixel_values</code>, you can choose to directly pass an embedded representation.
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elements depending on the configuration (<a
  href="/docs/transformers/v4.27.1/en/model_doc/vilt#transformers.ViltConfig"
>ViltConfig</a>) and inputs.</p>
<ul>
<li><strong>loss</strong> (<code>torch.FloatTensor</code> of shape <code>(1,)</code>, <em>optional</em>, returned when <code>labels</code> is provided) \u2014 Classification (or regression if config.num_labels==1) loss.</li>
<li><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).</li>
<li><strong>hidden_states</strong> (<code>List[tuple(torch.FloatTensor)]</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) \u2014 List of tuples of <code>torch.FloatTensor</code> (one for each image-text pair, each tuple containing the output of
the embeddings + one for the output of each layer) of shape <code>(batch_size, sequence_length, hidden_size)</code>.
Hidden-states of the model at the output of each layer plus the initial embedding outputs.</li>
<li><strong>attentions</strong> (<code>List[tuple(torch.FloatTensor)]</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) \u2014 List of tuples of <code>torch.FloatTensor</code> (one for each image-text pair, each tuple containing the attention
weights of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>. Attentions weights after the
attention softmax, used to compute the weighted average in the self-attention heads.</li>
</ul>
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<p><code>transformers.models.vilt.modeling_vilt.ViltForImagesAndTextClassificationOutput</code> or <code>tuple(torch.FloatTensor)</code></p>
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<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>
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<p><strong>hidden_states</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for the output of the embeddings, if the model has an embedding layer, +
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<p><strong>attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
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<p><strong>loss</strong> (<code>torch.FloatTensor</code> of shape <code>(1,)</code>, <em>optional</em>, returned when <code>labels</code> is provided)  \u2014 Classification loss.</p>
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<p><strong>logits</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, config.num_labels)</code>) \u2014 Classification scores (before SoftMax).</p>
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<p><strong>hidden_states</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for the output of the embeddings, if the model has an embedding layer, +
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<p><strong>attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
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by Wonjae Kim, Bokyung Son, Ildoo Kim. ViLT incorporates text embeddings into a Vision Transformer (ViT), allowing it to have a minimal design
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Current approaches to VLP heavily rely on image feature extraction processes, most of which involve region supervision
(e.g., object detection) and the convolutional architecture (e.g., ResNet). Although disregarded in the literature, we
find it problematic in terms of both (1) efficiency/speed, that simply extracting input features requires much more
computation than the multimodal interaction steps; and (2) expressive power, as it is upper bounded to the expressive
power of the visual embedder and its predefined visual vocabulary. In this paper, we present a minimal VLP model,
Vision-and-Language Transformer (ViLT), monolithic in the sense that the processing of visual inputs is drastically
simplified to just the same convolution-free manner that we process textual inputs. We show that ViLT is up to tens of
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