import{S as _i,i as yi,s as wi,e as a,k as f,w,t as l,M as Ti,c as o,d as s,m as u,a as r,x as T,h as i,b as d,N as bi,G as e,g,y as b,q as j,o as I,B as J,v as ji,L as Gt}from"../../chunks/vendor-hf-doc-builder.js";import{T as dn}from"../../chunks/Tip-hf-doc-builder.js";import{D as x}from"../../chunks/Docstring-hf-doc-builder.js";import{C as Et}from"../../chunks/CodeBlock-hf-doc-builder.js";import{I as Q}from"../../chunks/IconCopyLink-hf-doc-builder.js";import{E as $t}from"../../chunks/ExampleCodeBlock-hf-doc-builder.js";function Ii(v){let c,y,M,m,_;return m=new Et({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> XCLIPTextModel, XCLIPTextConfig

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Initializing a XCLIPTextModel with microsoft/xclip-base-patch32 style configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>configuration = XCLIPTextConfig()

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Initializing a XCLIPTextConfig from the microsoft/xclip-base-patch32 style configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = XCLIPTextModel(configuration)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Accessing the model configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>configuration = model.config`}}),{c(){c=a("p"),y=l("Example:"),M=f(),w(m.$$.fragment)},l(n){c=o(n,"P",{});var p=r(c);y=i(p,"Example:"),p.forEach(s),M=u(n),T(m.$$.fragment,n)},m(n,p){g(n,c,p),e(c,y),g(n,M,p),b(m,n,p),_=!0},p:Gt,i(n){_||(j(m.$$.fragment,n),_=!0)},o(n){I(m.$$.fragment,n),_=!1},d(n){n&&s(c),n&&s(M),J(m,n)}}}function Ji(v){let c,y,M,m,_;return m=new Et({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> XCLIPVisionModel, XCLIPVisionConfig

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Initializing a XCLIPVisionModel with microsoft/xclip-base-patch32 style configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>configuration = XCLIPVisionConfig()

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Initializing a XCLIPVisionModel model from the microsoft/xclip-base-patch32 style configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = XCLIPVisionModel(configuration)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Accessing the model configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>configuration = model.config`}}),{c(){c=a("p"),y=l("Example:"),M=f(),w(m.$$.fragment)},l(n){c=o(n,"P",{});var p=r(c);y=i(p,"Example:"),p.forEach(s),M=u(n),T(m.$$.fragment,n)},m(n,p){g(n,c,p),e(c,y),g(n,M,p),b(m,n,p),_=!0},p:Gt,i(n){_||(j(m.$$.fragment,n),_=!0)},o(n){I(m.$$.fragment,n),_=!1},d(n){n&&s(c),n&&s(M),J(m,n)}}}function vi(v){let c,y,M,m,_;return{c(){c=a("p"),y=l("Although the recipe for forward pass needs to be defined within this function, one should call the "),M=a("code"),m=l("Module"),_=l(`
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(n){c=o(n,"P",{});var p=r(c);y=i(p,"Although the recipe for forward pass needs to be defined within this function, one should call the "),M=o(p,"CODE",{});var C=r(M);m=i(C,"Module"),C.forEach(s),_=i(p,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),p.forEach(s)},m(n,p){g(n,c,p),e(c,y),e(c,M),e(M,m),e(c,_)},d(n){n&&s(c)}}}function Ci(v){let c,y,M,m,_;return m=new Et({props:{code:" ",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> av
<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">import</span> numpy <span class="hljs-keyword">as</span> np

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoProcessor, AutoModel
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> huggingface_hub <span class="hljs-keyword">import</span> hf_hub_download

<span class="hljs-meta">&gt;&gt;&gt; </span>np.random.seed(<span class="hljs-number">0</span>)


<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">def</span> <span class="hljs-title function_">read_video_pyav</span>(<span class="hljs-params">container, indices</span>):
<span class="hljs-meta">... </span>    <span class="hljs-string">&#x27;&#x27;&#x27;
<span class="hljs-meta">... </span>    Decode the video with PyAV decoder.
<span class="hljs-meta">... </span>    Args:
<span class="hljs-meta">... </span>        container (\`av.container.input.InputContainer\`): PyAV container.
<span class="hljs-meta">... </span>        indices (\`List[int]\`): List of frame indices to decode.
<span class="hljs-meta">... </span>    Returns:
<span class="hljs-meta">... </span>        result (np.ndarray): np array of decoded frames of shape (num_frames, height, width, 3).
<span class="hljs-meta">... </span>    &#x27;&#x27;&#x27;</span>
<span class="hljs-meta">... </span>    frames = []
<span class="hljs-meta">... </span>    container.seek(<span class="hljs-number">0</span>)
<span class="hljs-meta">... </span>    start_index = indices[<span class="hljs-number">0</span>]
<span class="hljs-meta">... </span>    end_index = indices[-<span class="hljs-number">1</span>]
<span class="hljs-meta">... </span>    <span class="hljs-keyword">for</span> i, frame <span class="hljs-keyword">in</span> <span class="hljs-built_in">enumerate</span>(container.decode(video=<span class="hljs-number">0</span>)):
<span class="hljs-meta">... </span>        <span class="hljs-keyword">if</span> i &gt; end_index:
<span class="hljs-meta">... </span>            <span class="hljs-keyword">break</span>
<span class="hljs-meta">... </span>        <span class="hljs-keyword">if</span> i &gt;= start_index <span class="hljs-keyword">and</span> i <span class="hljs-keyword">in</span> indices:
<span class="hljs-meta">... </span>            frames.append(frame)
<span class="hljs-meta">... </span>    <span class="hljs-keyword">return</span> np.stack([x.to_ndarray(<span class="hljs-built_in">format</span>=<span class="hljs-string">&quot;rgb24&quot;</span>) <span class="hljs-keyword">for</span> x <span class="hljs-keyword">in</span> frames])


<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">def</span> <span class="hljs-title function_">sample_frame_indices</span>(<span class="hljs-params">clip_len, frame_sample_rate, seg_len</span>):
<span class="hljs-meta">... </span>    <span class="hljs-string">&#x27;&#x27;&#x27;
<span class="hljs-meta">... </span>    Sample a given number of frame indices from the video.
<span class="hljs-meta">... </span>    Args:
<span class="hljs-meta">... </span>        clip_len (\`int\`): Total number of frames to sample.
<span class="hljs-meta">... </span>        frame_sample_rate (\`int\`): Sample every n-th frame.
<span class="hljs-meta">... </span>        seg_len (\`int\`): Maximum allowed index of sample&#x27;s last frame.
<span class="hljs-meta">... </span>    Returns:
<span class="hljs-meta">... </span>        indices (\`List[int]\`): List of sampled frame indices
<span class="hljs-meta">... </span>    &#x27;&#x27;&#x27;</span>
<span class="hljs-meta">... </span>    converted_len = <span class="hljs-built_in">int</span>(clip_len * frame_sample_rate)
<span class="hljs-meta">... </span>    end_idx = np.random.randint(converted_len, seg_len)
<span class="hljs-meta">... </span>    start_idx = end_idx - converted_len
<span class="hljs-meta">... </span>    indices = np.linspace(start_idx, end_idx, num=clip_len)
<span class="hljs-meta">... </span>    indices = np.clip(indices, start_idx, end_idx - <span class="hljs-number">1</span>).astype(np.int64)
<span class="hljs-meta">... </span>    <span class="hljs-keyword">return</span> indices


<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># video clip consists of 300 frames (10 seconds at 30 FPS)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>file_path = hf_hub_download(
<span class="hljs-meta">... </span>    repo_id=<span class="hljs-string">&quot;nielsr/video-demo&quot;</span>, filename=<span class="hljs-string">&quot;eating_spaghetti.mp4&quot;</span>, repo_type=<span class="hljs-string">&quot;dataset&quot;</span>
<span class="hljs-meta">... </span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>container = av.<span class="hljs-built_in">open</span>(file_path)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># sample 8 frames</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>indices = sample_frame_indices(clip_len=<span class="hljs-number">8</span>, frame_sample_rate=<span class="hljs-number">1</span>, seg_len=container.streams.video[<span class="hljs-number">0</span>].frames)
<span class="hljs-meta">&gt;&gt;&gt; </span>video = read_video_pyav(container, indices)

<span class="hljs-meta">&gt;&gt;&gt; </span>processor = AutoProcessor.from_pretrained(<span class="hljs-string">&quot;microsoft/xclip-base-patch32&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModel.from_pretrained(<span class="hljs-string">&quot;microsoft/xclip-base-patch32&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = processor(
<span class="hljs-meta">... </span>    text=[<span class="hljs-string">&quot;playing sports&quot;</span>, <span class="hljs-string">&quot;eating spaghetti&quot;</span>, <span class="hljs-string">&quot;go shopping&quot;</span>],
<span class="hljs-meta">... </span>    videos=<span class="hljs-built_in">list</span>(video),
<span class="hljs-meta">... </span>    return_tensors=<span class="hljs-string">&quot;pt&quot;</span>,
<span class="hljs-meta">... </span>    padding=<span class="hljs-literal">True</span>,
<span class="hljs-meta">... </span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># forward pass</span>
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">with</span> torch.no_grad():
<span class="hljs-meta">... </span>    outputs = model(**inputs)

<span class="hljs-meta">&gt;&gt;&gt; </span>logits_per_video = outputs.logits_per_video  <span class="hljs-comment"># this is the video-text similarity score</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>probs = logits_per_video.softmax(dim=<span class="hljs-number">1</span>)  <span class="hljs-comment"># we can take the softmax to get the label probabilities</span>
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-built_in">print</span>(probs)
tensor([[<span class="hljs-number">1.9496e-04</span>, <span class="hljs-number">9.9960e-01</span>, <span class="hljs-number">2.0825e-04</span>]])`}}),{c(){c=a("p"),y=l("Examples:"),M=f(),w(m.$$.fragment)},l(n){c=o(n,"P",{});var p=r(c);y=i(p,"Examples:"),p.forEach(s),M=u(n),T(m.$$.fragment,n)},m(n,p){g(n,c,p),e(c,y),g(n,M,p),b(m,n,p),_=!0},p:Gt,i(n){_||(j(m.$$.fragment,n),_=!0)},o(n){I(m.$$.fragment,n),_=!1},d(n){n&&s(c),n&&s(M),J(m,n)}}}function Ui(v){let c,y,M,m,_;return{c(){c=a("p"),y=l("Although the recipe for forward pass needs to be defined within this function, one should call the "),M=a("code"),m=l("Module"),_=l(`
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(n){c=o(n,"P",{});var p=r(c);y=i(p,"Although the recipe for forward pass needs to be defined within this function, one should call the "),M=o(p,"CODE",{});var C=r(M);m=i(C,"Module"),C.forEach(s),_=i(p,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),p.forEach(s)},m(n,p){g(n,c,p),e(c,y),e(c,M),e(M,m),e(c,_)},d(n){n&&s(c)}}}function Xi(v){let c,y,M,m,_;return m=new Et({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Ub2tlbml6ZXIlMkMlMjBBdXRvTW9kZWwlMEElMEF0b2tlbml6ZXIlMjAlM0QlMjBBdXRvVG9rZW5pemVyLmZyb21fcHJldHJhaW5lZCglMjJtaWNyb3NvZnQlMkZ4Y2xpcC1iYXNlLXBhdGNoMzIlMjIpJTBBbW9kZWwlMjAlM0QlMjBBdXRvTW9kZWwuZnJvbV9wcmV0cmFpbmVkKCUyMm1pY3Jvc29mdCUyRnhjbGlwLWJhc2UtcGF0Y2gzMiUyMiklMEElMEFpbnB1dHMlMjAlM0QlMjB0b2tlbml6ZXIoJTVCJTIyYSUyMHBob3RvJTIwb2YlMjBhJTIwY2F0JTIyJTJDJTIwJTIyYSUyMHBob3RvJTIwb2YlMjBhJTIwZG9nJTIyJTVEJTJDJTIwcGFkZGluZyUzRFRydWUlMkMlMjByZXR1cm5fdGVuc29ycyUzRCUyMnB0JTIyKSUwQXRleHRfZmVhdHVyZXMlMjAlM0QlMjBtb2RlbC5nZXRfdGV4dF9mZWF0dXJlcygqKmlucHV0cyk=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, AutoModel

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;microsoft/xclip-base-patch32&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModel.from_pretrained(<span class="hljs-string">&quot;microsoft/xclip-base-patch32&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = tokenizer([<span class="hljs-string">&quot;a photo of a cat&quot;</span>, <span class="hljs-string">&quot;a photo of a dog&quot;</span>], padding=<span class="hljs-literal">True</span>, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>text_features = model.get_text_features(**inputs)`}}),{c(){c=a("p"),y=l("Examples:"),M=f(),w(m.$$.fragment)},l(n){c=o(n,"P",{});var p=r(c);y=i(p,"Examples:"),p.forEach(s),M=u(n),T(m.$$.fragment,n)},m(n,p){g(n,c,p),e(c,y),g(n,M,p),b(m,n,p),_=!0},p:Gt,i(n){_||(j(m.$$.fragment,n),_=!0)},o(n){I(m.$$.fragment,n),_=!1},d(n){n&&s(c),n&&s(M),J(m,n)}}}function xi(v){let c,y,M,m,_;return{c(){c=a("p"),y=l("Although the recipe for forward pass needs to be defined within this function, one should call the "),M=a("code"),m=l("Module"),_=l(`
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(n){c=o(n,"P",{});var p=r(c);y=i(p,"Although the recipe for forward pass needs to be defined within this function, one should call the "),M=o(p,"CODE",{});var C=r(M);m=i(C,"Module"),C.forEach(s),_=i(p,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),p.forEach(s)},m(n,p){g(n,c,p),e(c,y),e(c,M),e(M,m),e(c,_)},d(n){n&&s(c)}}}function Zi(v){let c,y,M,m,_;return m=new Et({props:{code:" ",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> av
<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">import</span> numpy <span class="hljs-keyword">as</span> np

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoProcessor, AutoModel
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> huggingface_hub <span class="hljs-keyword">import</span> hf_hub_download

<span class="hljs-meta">&gt;&gt;&gt; </span>np.random.seed(<span class="hljs-number">0</span>)


<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">def</span> <span class="hljs-title function_">read_video_pyav</span>(<span class="hljs-params">container, indices</span>):
<span class="hljs-meta">... </span>    <span class="hljs-string">&#x27;&#x27;&#x27;
<span class="hljs-meta">... </span>    Decode the video with PyAV decoder.
<span class="hljs-meta">... </span>    Args:
<span class="hljs-meta">... </span>        container (\`av.container.input.InputContainer\`): PyAV container.
<span class="hljs-meta">... </span>        indices (\`List[int]\`): List of frame indices to decode.
<span class="hljs-meta">... </span>    Returns:
<span class="hljs-meta">... </span>        result (np.ndarray): np array of decoded frames of shape (num_frames, height, width, 3).
<span class="hljs-meta">... </span>    &#x27;&#x27;&#x27;</span>
<span class="hljs-meta">... </span>    frames = []
<span class="hljs-meta">... </span>    container.seek(<span class="hljs-number">0</span>)
<span class="hljs-meta">... </span>    start_index = indices[<span class="hljs-number">0</span>]
<span class="hljs-meta">... </span>    end_index = indices[-<span class="hljs-number">1</span>]
<span class="hljs-meta">... </span>    <span class="hljs-keyword">for</span> i, frame <span class="hljs-keyword">in</span> <span class="hljs-built_in">enumerate</span>(container.decode(video=<span class="hljs-number">0</span>)):
<span class="hljs-meta">... </span>        <span class="hljs-keyword">if</span> i &gt; end_index:
<span class="hljs-meta">... </span>            <span class="hljs-keyword">break</span>
<span class="hljs-meta">... </span>        <span class="hljs-keyword">if</span> i &gt;= start_index <span class="hljs-keyword">and</span> i <span class="hljs-keyword">in</span> indices:
<span class="hljs-meta">... </span>            frames.append(frame)
<span class="hljs-meta">... </span>    <span class="hljs-keyword">return</span> np.stack([x.to_ndarray(<span class="hljs-built_in">format</span>=<span class="hljs-string">&quot;rgb24&quot;</span>) <span class="hljs-keyword">for</span> x <span class="hljs-keyword">in</span> frames])


<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">def</span> <span class="hljs-title function_">sample_frame_indices</span>(<span class="hljs-params">clip_len, frame_sample_rate, seg_len</span>):
<span class="hljs-meta">... </span>    <span class="hljs-string">&#x27;&#x27;&#x27;
<span class="hljs-meta">... </span>    Sample a given number of frame indices from the video.
<span class="hljs-meta">... </span>    Args:
<span class="hljs-meta">... </span>        clip_len (\`int\`): Total number of frames to sample.
<span class="hljs-meta">... </span>        frame_sample_rate (\`int\`): Sample every n-th frame.
<span class="hljs-meta">... </span>        seg_len (\`int\`): Maximum allowed index of sample&#x27;s last frame.
<span class="hljs-meta">... </span>    Returns:
<span class="hljs-meta">... </span>        indices (\`List[int]\`): List of sampled frame indices
<span class="hljs-meta">... </span>    &#x27;&#x27;&#x27;</span>
<span class="hljs-meta">... </span>    converted_len = <span class="hljs-built_in">int</span>(clip_len * frame_sample_rate)
<span class="hljs-meta">... </span>    end_idx = np.random.randint(converted_len, seg_len)
<span class="hljs-meta">... </span>    start_idx = end_idx - converted_len
<span class="hljs-meta">... </span>    indices = np.linspace(start_idx, end_idx, num=clip_len)
<span class="hljs-meta">... </span>    indices = np.clip(indices, start_idx, end_idx - <span class="hljs-number">1</span>).astype(np.int64)
<span class="hljs-meta">... </span>    <span class="hljs-keyword">return</span> indices


<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># video clip consists of 300 frames (10 seconds at 30 FPS)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>file_path = hf_hub_download(
<span class="hljs-meta">... </span>    repo_id=<span class="hljs-string">&quot;nielsr/video-demo&quot;</span>, filename=<span class="hljs-string">&quot;eating_spaghetti.mp4&quot;</span>, repo_type=<span class="hljs-string">&quot;dataset&quot;</span>
<span class="hljs-meta">... </span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>container = av.<span class="hljs-built_in">open</span>(file_path)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># sample 8 frames</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>indices = sample_frame_indices(clip_len=<span class="hljs-number">8</span>, frame_sample_rate=<span class="hljs-number">1</span>, seg_len=container.streams.video[<span class="hljs-number">0</span>].frames)
<span class="hljs-meta">&gt;&gt;&gt; </span>video = read_video_pyav(container, indices)

<span class="hljs-meta">&gt;&gt;&gt; </span>processor = AutoProcessor.from_pretrained(<span class="hljs-string">&quot;microsoft/xclip-base-patch32&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModel.from_pretrained(<span class="hljs-string">&quot;microsoft/xclip-base-patch32&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = processor(videos=<span class="hljs-built_in">list</span>(video), return_tensors=<span class="hljs-string">&quot;pt&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>video_features = model.get_video_features(**inputs)`}}),{c(){c=a("p"),y=l("Examples:"),M=f(),w(m.$$.fragment)},l(n){c=o(n,"P",{});var p=r(c);y=i(p,"Examples:"),p.forEach(s),M=u(n),T(m.$$.fragment,n)},m(n,p){g(n,c,p),e(c,y),g(n,M,p),b(m,n,p),_=!0},p:Gt,i(n){_||(j(m.$$.fragment,n),_=!0)},o(n){I(m.$$.fragment,n),_=!1},d(n){n&&s(c),n&&s(M),J(m,n)}}}function Bi(v){let c,y,M,m,_;return{c(){c=a("p"),y=l("Although the recipe for forward pass needs to be defined within this function, one should call the "),M=a("code"),m=l("Module"),_=l(`
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(n){c=o(n,"P",{});var p=r(c);y=i(p,"Although the recipe for forward pass needs to be defined within this function, one should call the "),M=o(p,"CODE",{});var C=r(M);m=i(C,"Module"),C.forEach(s),_=i(p,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),p.forEach(s)},m(n,p){g(n,c,p),e(c,y),e(c,M),e(M,m),e(c,_)},d(n){n&&s(c)}}}function ki(v){let c,y,M,m,_;return m=new Et({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, XCLIPTextModel

<span class="hljs-meta">&gt;&gt;&gt; </span>model = XCLIPTextModel.from_pretrained(<span class="hljs-string">&quot;microsoft/xclip-base-patch32&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;microsoft/xclip-base-patch32&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = tokenizer([<span class="hljs-string">&quot;a photo of a cat&quot;</span>, <span class="hljs-string">&quot;a photo of a dog&quot;</span>], padding=<span class="hljs-literal">True</span>, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(**inputs)
<span class="hljs-meta">&gt;&gt;&gt; </span>last_hidden_state = outputs.last_hidden_state
<span class="hljs-meta">&gt;&gt;&gt; </span>pooled_output = outputs.pooler_output  <span class="hljs-comment"># pooled (EOS token) states</span>`}}),{c(){c=a("p"),y=l("Examples:"),M=f(),w(m.$$.fragment)},l(n){c=o(n,"P",{});var p=r(c);y=i(p,"Examples:"),p.forEach(s),M=u(n),T(m.$$.fragment,n)},m(n,p){g(n,c,p),e(c,y),g(n,M,p),b(m,n,p),_=!0},p:Gt,i(n){_||(j(m.$$.fragment,n),_=!0)},o(n){I(m.$$.fragment,n),_=!1},d(n){n&&s(c),n&&s(M),J(m,n)}}}function Pi(v){let c,y,M,m,_;return{c(){c=a("p"),y=l("Although the recipe for forward pass needs to be defined within this function, one should call the "),M=a("code"),m=l("Module"),_=l(`
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(n){c=o(n,"P",{});var p=r(c);y=i(p,"Although the recipe for forward pass needs to be defined within this function, one should call the "),M=o(p,"CODE",{});var C=r(M);m=i(C,"Module"),C.forEach(s),_=i(p,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),p.forEach(s)},m(n,p){g(n,c,p),e(c,y),e(c,M),e(M,m),e(c,_)},d(n){n&&s(c)}}}function Wi(v){let c,y,M,m,_;return m=new Et({props:{code:" ",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> av
<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">import</span> numpy <span class="hljs-keyword">as</span> np

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoProcessor, XCLIPVisionModel
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> huggingface_hub <span class="hljs-keyword">import</span> hf_hub_download

<span class="hljs-meta">&gt;&gt;&gt; </span>np.random.seed(<span class="hljs-number">0</span>)


<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">def</span> <span class="hljs-title function_">read_video_pyav</span>(<span class="hljs-params">container, indices</span>):
<span class="hljs-meta">... </span>    <span class="hljs-string">&#x27;&#x27;&#x27;
<span class="hljs-meta">... </span>    Decode the video with PyAV decoder.
<span class="hljs-meta">... </span>    Args:
<span class="hljs-meta">... </span>        container (\`av.container.input.InputContainer\`): PyAV container.
<span class="hljs-meta">... </span>        indices (\`List[int]\`): List of frame indices to decode.
<span class="hljs-meta">... </span>    Returns:
<span class="hljs-meta">... </span>        result (np.ndarray): np array of decoded frames of shape (num_frames, height, width, 3).
<span class="hljs-meta">... </span>    &#x27;&#x27;&#x27;</span>
<span class="hljs-meta">... </span>    frames = []
<span class="hljs-meta">... </span>    container.seek(<span class="hljs-number">0</span>)
<span class="hljs-meta">... </span>    start_index = indices[<span class="hljs-number">0</span>]
<span class="hljs-meta">... </span>    end_index = indices[-<span class="hljs-number">1</span>]
<span class="hljs-meta">... </span>    <span class="hljs-keyword">for</span> i, frame <span class="hljs-keyword">in</span> <span class="hljs-built_in">enumerate</span>(container.decode(video=<span class="hljs-number">0</span>)):
<span class="hljs-meta">... </span>        <span class="hljs-keyword">if</span> i &gt; end_index:
<span class="hljs-meta">... </span>            <span class="hljs-keyword">break</span>
<span class="hljs-meta">... </span>        <span class="hljs-keyword">if</span> i &gt;= start_index <span class="hljs-keyword">and</span> i <span class="hljs-keyword">in</span> indices:
<span class="hljs-meta">... </span>            frames.append(frame)
<span class="hljs-meta">... </span>    <span class="hljs-keyword">return</span> np.stack([x.to_ndarray(<span class="hljs-built_in">format</span>=<span class="hljs-string">&quot;rgb24&quot;</span>) <span class="hljs-keyword">for</span> x <span class="hljs-keyword">in</span> frames])


<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">def</span> <span class="hljs-title function_">sample_frame_indices</span>(<span class="hljs-params">clip_len, frame_sample_rate, seg_len</span>):
<span class="hljs-meta">... </span>    <span class="hljs-string">&#x27;&#x27;&#x27;
<span class="hljs-meta">... </span>    Sample a given number of frame indices from the video.
<span class="hljs-meta">... </span>    Args:
<span class="hljs-meta">... </span>        clip_len (\`int\`): Total number of frames to sample.
<span class="hljs-meta">... </span>        frame_sample_rate (\`int\`): Sample every n-th frame.
<span class="hljs-meta">... </span>        seg_len (\`int\`): Maximum allowed index of sample&#x27;s last frame.
<span class="hljs-meta">... </span>    Returns:
<span class="hljs-meta">... </span>        indices (\`List[int]\`): List of sampled frame indices
<span class="hljs-meta">... </span>    &#x27;&#x27;&#x27;</span>
<span class="hljs-meta">... </span>    converted_len = <span class="hljs-built_in">int</span>(clip_len * frame_sample_rate)
<span class="hljs-meta">... </span>    end_idx = np.random.randint(converted_len, seg_len)
<span class="hljs-meta">... </span>    start_idx = end_idx - converted_len
<span class="hljs-meta">... </span>    indices = np.linspace(start_idx, end_idx, num=clip_len)
<span class="hljs-meta">... </span>    indices = np.clip(indices, start_idx, end_idx - <span class="hljs-number">1</span>).astype(np.int64)
<span class="hljs-meta">... </span>    <span class="hljs-keyword">return</span> indices


<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># video clip consists of 300 frames (10 seconds at 30 FPS)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>file_path = hf_hub_download(
<span class="hljs-meta">... </span>    repo_id=<span class="hljs-string">&quot;nielsr/video-demo&quot;</span>, filename=<span class="hljs-string">&quot;eating_spaghetti.mp4&quot;</span>, repo_type=<span class="hljs-string">&quot;dataset&quot;</span>
<span class="hljs-meta">... </span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>container = av.<span class="hljs-built_in">open</span>(file_path)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># sample 16 frames</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>indices = sample_frame_indices(clip_len=<span class="hljs-number">8</span>, frame_sample_rate=<span class="hljs-number">1</span>, seg_len=container.streams.video[<span class="hljs-number">0</span>].frames)
<span class="hljs-meta">&gt;&gt;&gt; </span>video = read_video_pyav(container, indices)

<span class="hljs-meta">&gt;&gt;&gt; </span>processor = AutoProcessor.from_pretrained(<span class="hljs-string">&quot;microsoft/xclip-base-patch32&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = XCLIPVisionModel.from_pretrained(<span class="hljs-string">&quot;microsoft/xclip-base-patch32&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>pixel_values = processor(videos=<span class="hljs-built_in">list</span>(video), return_tensors=<span class="hljs-string">&quot;pt&quot;</span>).pixel_values

<span class="hljs-meta">&gt;&gt;&gt; </span>batch_size, num_frames, num_channels, height, width = pixel_values.shape
<span class="hljs-meta">&gt;&gt;&gt; </span>pixel_values = pixel_values.reshape(-<span class="hljs-number">1</span>, num_channels, height, width)

<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(pixel_values)
<span class="hljs-meta">&gt;&gt;&gt; </span>last_hidden_state = outputs.last_hidden_state`}}),{c(){c=a("p"),y=l("Examples:"),M=f(),w(m.$$.fragment)},l(n){c=o(n,"P",{});var p=r(c);y=i(p,"Examples:"),p.forEach(s),M=u(n),T(m.$$.fragment,n)},m(n,p){g(n,c,p),e(c,y),g(n,M,p),b(m,n,p),_=!0},p:Gt,i(n){_||(j(m.$$.fragment,n),_=!0)},o(n){I(m.$$.fragment,n),_=!1},d(n){n&&s(c),n&&s(M),J(m,n)}}}function $i(v){let c,y,M,m,_,n,p,C,aa,pn,F,ue,Us,Se,oa,Xs,ra,mn,L,la,He,ia,ca,Vt,da,pa,hn,At,ma,fn,Lt,xs,ha,un,zt,fa,gn,Nt,qe,ua,Rt,ga,Ma,Mn,ge,sl,_n,De,_a,Yt,ya,yn,z,wa,Ke,Ta,ba,Oe,ja,Ia,wn,S,Me,Zs,et,Ja,Bs,va,Tn,Qt,Ca,bn,Ft,tt,Ua,st,Xa,xa,jn,St,Za,In,H,_e,ks,nt,Ba,Ps,ka,Jn,U,at,Pa,Ws,Wa,$a,Z,Ht,Ga,Ea,qt,Va,Aa,Dt,La,za,$s,Na,Ra,Kt,Ya,Qa,Fa,ye,ot,Sa,rt,Ha,Ot,qa,Da,Ka,we,lt,Oa,it,eo,es,to,so,vn,q,Te,Gs,ct,no,Es,ao,Cn,B,dt,oo,N,ts,ro,lo,ss,io,co,pt,po,mo,ho,D,fo,ns,uo,go,as,Mo,_o,yo,be,mt,wo,ht,To,os,bo,jo,Un,K,je,Vs,ft,Io,As,Jo,Xn,k,ut,vo,O,Co,rs,Uo,Xo,gt,xo,Zo,Bo,ee,ko,ls,Po,Wo,is,$o,Go,Eo,Ie,xn,te,Je,Ls,Mt,Vo,zs,Ao,Zn,P,_t,Lo,se,zo,cs,No,Ro,yt,Yo,Qo,Fo,ne,So,ds,Ho,qo,ps,Do,Ko,Oo,ve,Bn,ae,Ce,Ns,wt,er,Rs,tr,kn,X,Tt,sr,bt,nr,jt,ar,or,rr,W,It,lr,oe,ir,ms,cr,dr,Ys,pr,mr,hr,Ue,fr,Xe,ur,$,Jt,gr,re,Mr,hs,_r,yr,Qs,wr,Tr,br,xe,jr,Ze,Ir,G,vt,Jr,le,vr,fs,Cr,Ur,Fs,Xr,xr,Zr,Be,Br,ke,Pn,ie,Pe,Ss,Ct,kr,Hs,Pr,Wn,ce,Ut,Wr,E,Xt,$r,de,Gr,us,Er,Vr,qs,Ar,Lr,zr,We,Nr,$e,$n,pe,Ge,Ds,xt,Rr,Ks,Yr,Gn,me,Zt,Qr,V,Bt,Fr,he,Sr,gs,Hr,qr,Os,Dr,Kr,Or,Ee,el,Ve,En;return n=new Q({}),Se=new Q({}),et=new Q({}),nt=new Q({}),at=new x({props:{name:"class transformers.XCLIPProcessor",anchor:"transformers.XCLIPProcessor",parameters:[{name:"image_processor",val:" = None"},{name:"tokenizer",val:" = None"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.XCLIPProcessor.image_processor",description:`<strong>image_processor</strong> (<a href="/docs/transformers/v4.33.0/en/model_doc/videomae#transformers.VideoMAEImageProcessor">VideoMAEImageProcessor</a>) &#x2014;
The image processor is a required input.`,name:"image_processor"},{anchor:"transformers.XCLIPProcessor.tokenizer",description:`<strong>tokenizer</strong> (<a href="/docs/transformers/v4.33.0/en/model_doc/clip#transformers.CLIPTokenizerFast">CLIPTokenizerFast</a>) &#x2014;
The tokenizer is a required input.`,name:"tokenizer"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/x_clip/processing_x_clip.py#L25"}}),ot=new x({props:{name:"batch_decode",anchor:"transformers.XCLIPProcessor.batch_decode",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/x_clip/processing_x_clip.py#L115"}}),lt=new x({props:{name:"decode",anchor:"transformers.XCLIPProcessor.decode",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/x_clip/processing_x_clip.py#L122"}}),ct=new Q({}),dt=new x({props:{name:"class transformers.XCLIPConfig",anchor:"transformers.XCLIPConfig",parameters:[{name:"text_config",val:" = None"},{name:"vision_config",val:" = None"},{name:"projection_dim",val:" = 512"},{name:"prompt_layers",val:" = 2"},{name:"prompt_alpha",val:" = 0.1"},{name:"prompt_hidden_act",val:" = 'quick_gelu'"},{name:"prompt_num_attention_heads",val:" = 8"},{name:"prompt_attention_dropout",val:" = 0.0"},{name:"prompt_projection_dropout",val:" = 0.0"},{name:"logit_scale_init_value",val:" = 2.6592"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.XCLIPConfig.text_config",description:`<strong>text_config</strong> (<code>dict</code>, <em>optional</em>) &#x2014;
Dictionary of configuration options used to initialize <a href="/docs/transformers/v4.33.0/en/model_doc/xclip#transformers.XCLIPTextConfig">XCLIPTextConfig</a>.`,name:"text_config"},{anchor:"transformers.XCLIPConfig.vision_config",description:`<strong>vision_config</strong> (<code>dict</code>, <em>optional</em>) &#x2014;
Dictionary of configuration options used to initialize <a href="/docs/transformers/v4.33.0/en/model_doc/xclip#transformers.XCLIPVisionConfig">XCLIPVisionConfig</a>.`,name:"vision_config"},{anchor:"transformers.XCLIPConfig.projection_dim",description:`<strong>projection_dim</strong> (<code>int</code>, <em>optional</em>, defaults to 512) &#x2014;
Dimentionality of text and vision projection layers.`,name:"projection_dim"},{anchor:"transformers.XCLIPConfig.prompt_layers",description:`<strong>prompt_layers</strong> (<code>int</code>, <em>optional</em>, defaults to 2) &#x2014;
Number of layers in the video specific prompt generator.`,name:"prompt_layers"},{anchor:"transformers.XCLIPConfig.prompt_alpha",description:`<strong>prompt_alpha</strong> (<code>float</code>, <em>optional</em>, defaults to 0.1) &#x2014;
Alpha value to use in the video specific prompt generator.`,name:"prompt_alpha"},{anchor:"transformers.XCLIPConfig.prompt_hidden_act",description:`<strong>prompt_hidden_act</strong> (<code>str</code> or <code>function</code>, <em>optional</em>, defaults to <code>&quot;quick_gelu&quot;</code>) &#x2014;
The non-linear activation function (function or string) in the video specific prompt generator. If string,
<code>&quot;gelu&quot;</code>, <code>&quot;relu&quot;</code>, <code>&quot;selu&quot;</code> and <code>&quot;gelu_new&quot;</code> \`<code>&quot;quick_gelu&quot;</code> are supported.`,name:"prompt_hidden_act"},{anchor:"transformers.XCLIPConfig.prompt_num_attention_heads",description:`<strong>prompt_num_attention_heads</strong> (<code>int</code>, <em>optional</em>, defaults to 8) &#x2014;
Number of attention heads in the cross-attention of the video specific prompt generator.`,name:"prompt_num_attention_heads"},{anchor:"transformers.XCLIPConfig.prompt_attention_dropout",description:`<strong>prompt_attention_dropout</strong> (<code>float</code>, <em>optional</em>, defaults to 0.0) &#x2014;
The dropout probability for the attention layers in the video specific prompt generator.`,name:"prompt_attention_dropout"},{anchor:"transformers.XCLIPConfig.prompt_projection_dropout",description:`<strong>prompt_projection_dropout</strong> (<code>float</code>, <em>optional</em>, defaults to 0.0) &#x2014;
The dropout probability for the projection layers in the video specific prompt generator.`,name:"prompt_projection_dropout"},{anchor:"transformers.XCLIPConfig.logit_scale_init_value",description:`<strong>logit_scale_init_value</strong> (<code>float</code>, <em>optional</em>, defaults to 2.6592) &#x2014;
The inital value of the <em>logit_scale</em> parameter. Default is used as per the original XCLIP implementation.`,name:"logit_scale_init_value"},{anchor:"transformers.XCLIPConfig.kwargs",description:`<strong>kwargs</strong> (<em>optional</em>) &#x2014;
Dictionary of keyword arguments.`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/x_clip/configuration_x_clip.py#L264"}}),mt=new x({props:{name:"from_text_vision_configs",anchor:"transformers.XCLIPConfig.from_text_vision_configs",parameters:[{name:"text_config",val:": XCLIPTextConfig"},{name:"vision_config",val:": XCLIPVisionConfig"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/x_clip/configuration_x_clip.py#L407",returnDescription:`
<p>An instance of a configuration object</p>
`,returnType:`
<p><a
  href="/docs/transformers/v4.33.0/en/model_doc/xclip#transformers.XCLIPConfig"
>XCLIPConfig</a></p>
`}}),ft=new Q({}),ut=new x({props:{name:"class transformers.XCLIPTextConfig",anchor:"transformers.XCLIPTextConfig",parameters:[{name:"vocab_size",val:" = 49408"},{name:"hidden_size",val:" = 512"},{name:"intermediate_size",val:" = 2048"},{name:"num_hidden_layers",val:" = 12"},{name:"num_attention_heads",val:" = 8"},{name:"max_position_embeddings",val:" = 77"},{name:"hidden_act",val:" = 'quick_gelu'"},{name:"layer_norm_eps",val:" = 1e-05"},{name:"attention_dropout",val:" = 0.0"},{name:"initializer_range",val:" = 0.02"},{name:"initializer_factor",val:" = 1.0"},{name:"pad_token_id",val:" = 1"},{name:"bos_token_id",val:" = 0"},{name:"eos_token_id",val:" = 2"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.XCLIPTextConfig.vocab_size",description:`<strong>vocab_size</strong> (<code>int</code>, <em>optional</em>, defaults to 49408) &#x2014;
Vocabulary size of the X-CLIP text model. Defines the number of different tokens that can be represented by
the <code>inputs_ids</code> passed when calling <a href="/docs/transformers/v4.33.0/en/model_doc/xclip#transformers.XCLIPModel">XCLIPModel</a>.`,name:"vocab_size"},{anchor:"transformers.XCLIPTextConfig.hidden_size",description:`<strong>hidden_size</strong> (<code>int</code>, <em>optional</em>, defaults to 512) &#x2014;
Dimensionality of the encoder layers and the pooler layer.`,name:"hidden_size"},{anchor:"transformers.XCLIPTextConfig.intermediate_size",description:`<strong>intermediate_size</strong> (<code>int</code>, <em>optional</em>, defaults to 2048) &#x2014;
Dimensionality of the &#x201C;intermediate&#x201D; (i.e., feed-forward) layer in the Transformer encoder.`,name:"intermediate_size"},{anchor:"transformers.XCLIPTextConfig.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.XCLIPTextConfig.num_attention_heads",description:`<strong>num_attention_heads</strong> (<code>int</code>, <em>optional</em>, defaults to 8) &#x2014;
Number of attention heads for each attention layer in the Transformer encoder.`,name:"num_attention_heads"},{anchor:"transformers.XCLIPTextConfig.max_position_embeddings",description:`<strong>max_position_embeddings</strong> (<code>int</code>, <em>optional</em>, defaults to 77) &#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.XCLIPTextConfig.hidden_act",description:`<strong>hidden_act</strong> (<code>str</code> or <code>function</code>, <em>optional</em>, defaults to <code>&quot;quick_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> \`<code>&quot;quick_gelu&quot;</code> are supported.`,name:"hidden_act"},{anchor:"transformers.XCLIPTextConfig.layer_norm_eps",description:`<strong>layer_norm_eps</strong> (<code>float</code>, <em>optional</em>, defaults to 1e-5) &#x2014;
The epsilon used by the layer normalization layers.`,name:"layer_norm_eps"},{anchor:"transformers.XCLIPTextConfig.attention_dropout",description:`<strong>attention_dropout</strong> (<code>float</code>, <em>optional</em>, defaults to 0.0) &#x2014;
The dropout ratio for the attention probabilities.`,name:"attention_dropout"},{anchor:"transformers.XCLIPTextConfig.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.XCLIPTextConfig.initializer_factor",description:`<strong>initializer_factor</strong> (\`float&#x201C;, <em>optional</em>, defaults to 1) &#x2014;
A factor for initializing all weight matrices (should be kept to 1, used internally for initialization
testing).`,name:"initializer_factor"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/x_clip/configuration_x_clip.py#L31"}}),Ie=new $t({props:{anchor:"transformers.XCLIPTextConfig.example",$$slots:{default:[Ii]},$$scope:{ctx:v}}}),Mt=new Q({}),_t=new x({props:{name:"class transformers.XCLIPVisionConfig",anchor:"transformers.XCLIPVisionConfig",parameters:[{name:"hidden_size",val:" = 768"},{name:"intermediate_size",val:" = 3072"},{name:"num_hidden_layers",val:" = 12"},{name:"num_attention_heads",val:" = 12"},{name:"mit_hidden_size",val:" = 512"},{name:"mit_intermediate_size",val:" = 2048"},{name:"mit_num_hidden_layers",val:" = 1"},{name:"mit_num_attention_heads",val:" = 8"},{name:"num_channels",val:" = 3"},{name:"image_size",val:" = 224"},{name:"patch_size",val:" = 32"},{name:"num_frames",val:" = 8"},{name:"hidden_act",val:" = 'quick_gelu'"},{name:"layer_norm_eps",val:" = 1e-05"},{name:"attention_dropout",val:" = 0.0"},{name:"initializer_range",val:" = 0.02"},{name:"initializer_factor",val:" = 1.0"},{name:"drop_path_rate",val:" = 0.0"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.XCLIPVisionConfig.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.XCLIPVisionConfig.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.XCLIPVisionConfig.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.XCLIPVisionConfig.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.XCLIPVisionConfig.mit_hidden_size",description:`<strong>mit_hidden_size</strong> (<code>int</code>, <em>optional</em>, defaults to 512) &#x2014;
Dimensionality of the encoder layers of the Multiframe Integration Transformer (MIT).`,name:"mit_hidden_size"},{anchor:"transformers.XCLIPVisionConfig.mit_intermediate_size",description:`<strong>mit_intermediate_size</strong> (<code>int</code>, <em>optional</em>, defaults to 2048) &#x2014;
Dimensionality of the &#x201C;intermediate&#x201D; (i.e., feed-forward) layer in the Multiframe Integration Transformer
(MIT).`,name:"mit_intermediate_size"},{anchor:"transformers.XCLIPVisionConfig.mit_num_hidden_layers",description:`<strong>mit_num_hidden_layers</strong> (<code>int</code>, <em>optional</em>, defaults to 1) &#x2014;
Number of hidden layers in the Multiframe Integration Transformer (MIT).`,name:"mit_num_hidden_layers"},{anchor:"transformers.XCLIPVisionConfig.mit_num_attention_heads",description:`<strong>mit_num_attention_heads</strong> (<code>int</code>, <em>optional</em>, defaults to 8) &#x2014;
Number of attention heads for each attention layer in the Multiframe Integration Transformer (MIT).`,name:"mit_num_attention_heads"},{anchor:"transformers.XCLIPVisionConfig.image_size",description:`<strong>image_size</strong> (<code>int</code>, <em>optional</em>, defaults to 224) &#x2014;
The size (resolution) of each image.`,name:"image_size"},{anchor:"transformers.XCLIPVisionConfig.patch_size",description:`<strong>patch_size</strong> (<code>int</code>, <em>optional</em>, defaults to 32) &#x2014;
The size (resolution) of each patch.`,name:"patch_size"},{anchor:"transformers.XCLIPVisionConfig.hidden_act",description:`<strong>hidden_act</strong> (<code>str</code> or <code>function</code>, <em>optional</em>, defaults to <code>&quot;quick_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>, <code>&quot;gelu_new&quot;</code> and \`<code>&quot;quick_gelu&quot;</code> are supported.`,name:"hidden_act"},{anchor:"transformers.XCLIPVisionConfig.layer_norm_eps",description:`<strong>layer_norm_eps</strong> (<code>float</code>, <em>optional</em>, defaults to 1e-5) &#x2014;
The epsilon used by the layer normalization layers.`,name:"layer_norm_eps"},{anchor:"transformers.XCLIPVisionConfig.attention_dropout",description:`<strong>attention_dropout</strong> (<code>float</code>, <em>optional</em>, defaults to 0.0) &#x2014;
The dropout ratio for the attention probabilities.`,name:"attention_dropout"},{anchor:"transformers.XCLIPVisionConfig.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.XCLIPVisionConfig.initializer_factor",description:`<strong>initializer_factor</strong> (\`float&#x201C;, <em>optional</em>, defaults to 1) &#x2014;
A factor for initializing all weight matrices (should be kept to 1, used internally for initialization
testing).`,name:"initializer_factor"},{anchor:"transformers.XCLIPVisionConfig.drop_path_rate",description:`<strong>drop_path_rate</strong> (<code>float</code>, <em>optional</em>, defaults to 0.0) &#x2014;
Stochastic depth rate.`,name:"drop_path_rate"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/x_clip/configuration_x_clip.py#L137"}}),ve=new $t({props:{anchor:"transformers.XCLIPVisionConfig.example",$$slots:{default:[Ji]},$$scope:{ctx:v}}}),wt=new Q({}),Tt=new x({props:{name:"class transformers.XCLIPModel",anchor:"transformers.XCLIPModel",parameters:[{name:"config",val:": XCLIPConfig"}],parametersDescription:[{anchor:"transformers.XCLIPModel.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.33.0/en/model_doc/xclip#transformers.XCLIPConfig">XCLIPConfig</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/v4.33.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/x_clip/modeling_x_clip.py#L1292"}}),It=new x({props:{name:"forward",anchor:"transformers.XCLIPModel.forward",parameters:[{name:"input_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"pixel_values",val:": typing.Optional[torch.FloatTensor] = None"},{name:"attention_mask",val:": typing.Optional[torch.Tensor] = None"},{name:"position_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"return_loss",val:": typing.Optional[bool] = 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.XCLIPModel.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. Padding will be ignored by default should you provide
it.</p>
<p>Indices can be obtained using <a href="/docs/transformers/v4.33.0/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/v4.33.0/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/v4.33.0/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.XCLIPModel.forward.attention_mask",description:`<strong>attention_mask</strong> (<code>torch.Tensor</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.XCLIPModel.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.XCLIPModel.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. Padding will be ignored by default should you provide it. Pixel values can be obtained using
<a href="/docs/transformers/v4.33.0/en/model_doc/auto#transformers.AutoImageProcessor">AutoImageProcessor</a>. See <a href="/docs/transformers/v4.33.0/en/model_doc/donut#transformers.DonutFeatureExtractor.__call__">CLIPImageProcessor.<strong>call</strong>()</a> for details.`,name:"pixel_values"},{anchor:"transformers.XCLIPModel.forward.return_loss",description:`<strong>return_loss</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the contrastive loss.`,name:"return_loss"},{anchor:"transformers.XCLIPModel.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.XCLIPModel.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.XCLIPModel.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/v4.33.0/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/x_clip/modeling_x_clip.py#L1501",returnDescription:`
<p>A <code>transformers.models.x_clip.modeling_x_clip.XCLIPOutput</code> or a tuple of
<code>torch.FloatTensor</code> (if <code>return_dict=False</code> is passed or when <code>config.return_dict=False</code>) comprising various
elements depending on the configuration (<code>&lt;class 'transformers.models.x_clip.configuration_x_clip.XCLIPConfig'&gt;</code>) and inputs.</p>
<ul>
<li><strong>loss</strong> (<code>torch.FloatTensor</code> of shape <code>(1,)</code>, <em>optional</em>, returned when <code>return_loss</code> is <code>True</code>) \u2014 Contrastive loss for video-text similarity.</li>
<li><strong>logits_per_video</strong> (<code>torch.FloatTensor</code> of shape <code>(video_batch_size, text_batch_size)</code>) \u2014 The scaled dot product scores between <code>video_embeds</code> and <code>text_embeds</code>. This represents the video-text
similarity scores.</li>
<li><strong>logits_per_text</strong> (<code>torch.FloatTensor</code> of shape <code>(text_batch_size, video_batch_size)</code>) \u2014 The scaled dot product scores between <code>text_embeds</code> and <code>video_embeds</code>. This represents the text-video
similarity scores.</li>
<li><strong>text_embeds(<code>torch.FloatTensor</code></strong> of shape <code>(batch_size, output_dim</code>) \u2014 The text embeddings obtained by applying the projection layer to the pooled output of <a
  href="/docs/transformers/v4.33.0/en/model_doc/xclip#transformers.XCLIPTextModel"
>XCLIPTextModel</a>.</li>
<li><strong>video_embeds(<code>torch.FloatTensor</code></strong> of shape <code>(batch_size, output_dim</code>) \u2014 The video embeddings obtained by applying the projection layer to the pooled output of
<a
  href="/docs/transformers/v4.33.0/en/model_doc/xclip#transformers.XCLIPVisionModel"
>XCLIPVisionModel</a>.</li>
<li><strong>text_model_output</strong> (<code>BaseModelOutputWithPooling</code>) \u2014 The output of the <a
  href="/docs/transformers/v4.33.0/en/model_doc/xclip#transformers.XCLIPTextModel"
>XCLIPTextModel</a>.</li>
<li><strong>vision_model_output</strong> (<code>BaseModelOutputWithPooling</code>) \u2014 The output of the <a
  href="/docs/transformers/v4.33.0/en/model_doc/xclip#transformers.XCLIPVisionModel"
>XCLIPVisionModel</a>.</li>
<li><strong>mit_output</strong> (<code>BaseModelOutputWithPooling</code>) \u2014 The output of <code>XCLIPMultiframeIntegrationTransformer</code> (MIT for short).</li>
</ul>
`,returnType:`
<p><code>transformers.models.x_clip.modeling_x_clip.XCLIPOutput</code> or <code>tuple(torch.FloatTensor)</code></p>
`}}),Ue=new dn({props:{$$slots:{default:[vi]},$$scope:{ctx:v}}}),Xe=new $t({props:{anchor:"transformers.XCLIPModel.forward.example",$$slots:{default:[Ci]},$$scope:{ctx:v}}}),Jt=new x({props:{name:"get_text_features",anchor:"transformers.XCLIPModel.get_text_features",parameters:[{name:"input_ids",val:": typing.Optional[torch.Tensor] = None"},{name:"attention_mask",val:": typing.Optional[torch.Tensor] = None"},{name:"position_ids",val:": typing.Optional[torch.Tensor] = None"},{name:"output_attentions",val:": typing.Optional[bool] = None"},{name:"output_hidden_states",val:": typing.Optional[bool] = None"},{name:"return_dict",val:": typing.Optional[bool] = None"}],parametersDescription:[{anchor:"transformers.XCLIPModel.get_text_features.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. Padding will be ignored by default should you provide
it.</p>
<p>Indices can be obtained using <a href="/docs/transformers/v4.33.0/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/v4.33.0/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/v4.33.0/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.XCLIPModel.get_text_features.attention_mask",description:`<strong>attention_mask</strong> (<code>torch.Tensor</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.XCLIPModel.get_text_features.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.XCLIPModel.get_text_features.output_attentions",description:`<strong>output_attentions</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the attentions tensors of all attention layers. See <code>attentions</code> under returned
tensors for more detail.`,name:"output_attentions"},{anchor:"transformers.XCLIPModel.get_text_features.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.XCLIPModel.get_text_features.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/v4.33.0/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/x_clip/modeling_x_clip.py#L1339",returnDescription:`
<p>The text embeddings obtained by
applying the projection layer to the pooled output of <a
  href="/docs/transformers/v4.33.0/en/model_doc/xclip#transformers.XCLIPTextModel"
>XCLIPTextModel</a>.</p>
`,returnType:`
<p>text_features (<code>torch.FloatTensor</code> of shape <code>(batch_size, output_dim</code>)</p>
`}}),xe=new dn({props:{$$slots:{default:[Ui]},$$scope:{ctx:v}}}),Ze=new $t({props:{anchor:"transformers.XCLIPModel.get_text_features.example",$$slots:{default:[Xi]},$$scope:{ctx:v}}}),vt=new x({props:{name:"get_video_features",anchor:"transformers.XCLIPModel.get_video_features",parameters:[{name:"pixel_values",val:": typing.Optional[torch.FloatTensor] = 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.XCLIPModel.get_video_features.pixel_values",description:`<strong>pixel_values</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, num_channels, height, width)</code>) &#x2014;
Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using
<a href="/docs/transformers/v4.33.0/en/model_doc/auto#transformers.AutoImageProcessor">AutoImageProcessor</a>. See <a href="/docs/transformers/v4.33.0/en/model_doc/donut#transformers.DonutFeatureExtractor.__call__">CLIPImageProcessor.<strong>call</strong>()</a> for details.`,name:"pixel_values"},{anchor:"transformers.XCLIPModel.get_video_features.output_attentions",description:`<strong>output_attentions</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the attentions tensors of all attention layers. See <code>attentions</code> under returned
tensors for more detail.`,name:"output_attentions"},{anchor:"transformers.XCLIPModel.get_video_features.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.XCLIPModel.get_video_features.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/v4.33.0/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/x_clip/modeling_x_clip.py#L1386",returnDescription:`
<p>The video embeddings obtained by
applying the projection layer to the pooled output of <a
  href="/docs/transformers/v4.33.0/en/model_doc/xclip#transformers.XCLIPVisionModel"
>XCLIPVisionModel</a> and
<code>XCLIPMultiframeIntegrationTransformer</code>.</p>
`,returnType:`
<p>video_features (<code>torch.FloatTensor</code> of shape <code>(batch_size, output_dim</code>)</p>
`}}),Be=new dn({props:{$$slots:{default:[xi]},$$scope:{ctx:v}}}),ke=new $t({props:{anchor:"transformers.XCLIPModel.get_video_features.example",$$slots:{default:[Zi]},$$scope:{ctx:v}}}),Ct=new Q({}),Ut=new x({props:{name:"class transformers.XCLIPTextModel",anchor:"transformers.XCLIPTextModel",parameters:[{name:"config",val:": XCLIPTextConfig"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/x_clip/modeling_x_clip.py#L833"}}),Xt=new x({props:{name:"forward",anchor:"transformers.XCLIPTextModel.forward",parameters:[{name:"input_ids",val:": typing.Optional[torch.Tensor] = None"},{name:"attention_mask",val:": typing.Optional[torch.Tensor] = None"},{name:"position_ids",val:": typing.Optional[torch.Tensor] = None"},{name:"output_attentions",val:": typing.Optional[bool] = None"},{name:"output_hidden_states",val:": typing.Optional[bool] = None"},{name:"return_dict",val:": typing.Optional[bool] = None"}],parametersDescription:[{anchor:"transformers.XCLIPTextModel.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. Padding will be ignored by default should you provide
it.</p>
<p>Indices can be obtained using <a href="/docs/transformers/v4.33.0/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/v4.33.0/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/v4.33.0/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.XCLIPTextModel.forward.attention_mask",description:`<strong>attention_mask</strong> (<code>torch.Tensor</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.XCLIPTextModel.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.XCLIPTextModel.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.XCLIPTextModel.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.XCLIPTextModel.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/v4.33.0/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/x_clip/modeling_x_clip.py#L848",returnDescription:`
<p>A <a
  href="/docs/transformers/v4.33.0/en/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPooling"
>transformers.modeling_outputs.BaseModelOutputWithPooling</a> or a tuple of
<code>torch.FloatTensor</code> (if <code>return_dict=False</code> is passed or when <code>config.return_dict=False</code>) comprising various
elements depending on the configuration (<code>&lt;class 'transformers.models.x_clip.configuration_x_clip.XCLIPTextConfig'&gt;</code>) 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/v4.33.0/en/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPooling"
>transformers.modeling_outputs.BaseModelOutputWithPooling</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),We=new dn({props:{$$slots:{default:[Bi]},$$scope:{ctx:v}}}),$e=new $t({props:{anchor:"transformers.XCLIPTextModel.forward.example",$$slots:{default:[ki]},$$scope:{ctx:v}}}),xt=new Q({}),Zt=new x({props:{name:"class transformers.XCLIPVisionModel",anchor:"transformers.XCLIPVisionModel",parameters:[{name:"config",val:": XCLIPVisionConfig"}],source:"https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/x_clip/modeling_x_clip.py#L1048"}}),Bt=new x({props:{name:"forward",anchor:"transformers.XCLIPVisionModel.forward",parameters:[{name:"pixel_values",val:": typing.Optional[torch.FloatTensor] = 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.XCLIPVisionModel.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. Padding will be ignored by default should you provide it. Pixel values can be obtained using
<a href="/docs/transformers/v4.33.0/en/model_doc/auto#transformers.AutoImageProcessor">AutoImageProcessor</a>. See <a href="/docs/transformers/v4.33.0/en/model_doc/donut#transformers.DonutFeatureExtractor.__call__">CLIPImageProcessor.<strong>call</strong>()</a> for details.`,name:"pixel_values"},{anchor:"transformers.XCLIPVisionModel.forward.output_attentions",description:`<strong>output_attentions</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
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<p>A <a
  href="/docs/transformers/v4.33.0/en/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPooling"
>transformers.modeling_outputs.BaseModelOutputWithPooling</a> or a tuple of
<code>torch.FloatTensor</code> (if <code>return_dict=False</code> is passed or when <code>config.return_dict=False</code>) comprising various
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<ul>
<li>
<p><strong>last_hidden_state</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>) \u2014 Sequence of hidden-states at the output of the last layer of the model.</p>
</li>
<li>
<p><strong>pooler_output</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, hidden_size)</code>) \u2014 Last layer hidden-state of the first token of the sequence (classification token) after further processing
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/v4.33.0/en/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPooling"
>transformers.modeling_outputs.BaseModelOutputWithPooling</a> or <code>tuple(torch.FloatTensor)</code></p>
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`),$s=o(A,"CODE",{});var Jl=r($s);Na=i(Jl,"__call__()"),Jl.forEach(s),Ra=i(A," and "),Kt=o(A,"A",{href:!0});var vl=r(Kt);Ya=i(vl,"decode()"),vl.forEach(s),Qa=i(A," for more information."),A.forEach(s),Fa=u(R),ye=o(R,"DIV",{class:!0});var Nn=r(ye);T(ot.$$.fragment,Nn),Sa=u(Nn),rt=o(Nn,"P",{});var Rn=r(rt);Ha=i(Rn,"This method forwards all its arguments to CLIPTokenizerFast\u2019s "),Ot=o(Rn,"A",{href:!0});var Cl=r(Ot);qa=i(Cl,"batch_decode()"),Cl.forEach(s),Da=i(Rn,`. Please
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