import{s as ls,o as os,n as fe}from"../chunks/scheduler.bdbef820.js";import{S as rs,i as is,g as p,s as o,r as h,A as ms,h as c,f as n,c as r,j as te,u as M,x as J,k as ae,y as f,a as l,v as u,d as g,t as y,w as j}from"../chunks/index.33f81d56.js";import{T as ns}from"../chunks/Tip.34194030.js";import{D as de}from"../chunks/Docstring.b3df4217.js";import{C as Ye}from"../chunks/CodeBlock.3bad7fc9.js";import{E as Qe}from"../chunks/ExampleCodeBlock.d705c5b9.js";import{H as ne,E as ps}from"../chunks/index.474b463a.js";function cs(U){let t,T="Example:",i,m,d;return m=new Ye({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMFRpbWVzZm9ybWVyQ29uZmlnJTJDJTIwVGltZXNmb3JtZXJNb2RlbCUwQSUwQSUyMyUyMEluaXRpYWxpemluZyUyMGElMjBUaW1lU2Zvcm1lciUyMHRpbWVzZm9ybWVyLWJhc2UlMjBzdHlsZSUyMGNvbmZpZ3VyYXRpb24lMEFjb25maWd1cmF0aW9uJTIwJTNEJTIwVGltZXNmb3JtZXJDb25maWcoKSUwQSUwQSUyMyUyMEluaXRpYWxpemluZyUyMGElMjBtb2RlbCUyMGZyb20lMjB0aGUlMjBjb25maWd1cmF0aW9uJTBBbW9kZWwlMjAlM0QlMjBUaW1lc2Zvcm1lck1vZGVsKGNvbmZpZ3VyYXRpb24pJTBBJTBBJTIzJTIwQWNjZXNzaW5nJTIwdGhlJTIwbW9kZWwlMjBjb25maWd1cmF0aW9uJTBBY29uZmlndXJhdGlvbiUyMCUzRCUyMG1vZGVsLmNvbmZpZw==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> TimesformerConfig, TimesformerModel

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Initializing a TimeSformer timesformer-base style configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>configuration = TimesformerConfig()

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Initializing a model from the configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TimesformerModel(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`,wrap:!1}}),{c(){t=p("p"),t.textContent=T,i=o(),h(m.$$.fragment)},l(a){t=c(a,"P",{"data-svelte-h":!0}),J(t)!=="svelte-11lpom8"&&(t.textContent=T),i=r(a),M(m.$$.fragment,a)},m(a,w){l(a,t,w),l(a,i,w),u(m,a,w),d=!0},p:fe,i(a){d||(g(m.$$.fragment,a),d=!0)},o(a){y(m.$$.fragment,a),d=!1},d(a){a&&(n(t),n(i)),j(m,a)}}}function ds(U){let t,T=`Although the recipe for forward pass needs to be defined within this function, one should call the <code>Module</code>
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`;return{c(){t=p("p"),t.innerHTML=T},l(i){t=c(i,"P",{"data-svelte-h":!0}),J(t)!=="svelte-fincs2"&&(t.innerHTML=T)},m(i,m){l(i,t,m)},p:fe,d(i){i&&n(t)}}}function fs(U){let t,T="Examples:",i,m,d;return m=new Ye({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> 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> AutoImageProcessor, TimesformerModel
<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">4</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>image_processor = AutoImageProcessor.from_pretrained(<span class="hljs-string">&quot;MCG-NJU/videomae-base&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TimesformerModel.from_pretrained(<span class="hljs-string">&quot;facebook/timesformer-base-finetuned-k400&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># prepare video for the model</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = image_processor(<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><span class="hljs-comment"># forward pass</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
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-built_in">list</span>(last_hidden_states.shape)
[<span class="hljs-number">1</span>, <span class="hljs-number">1569</span>, <span class="hljs-number">768</span>]`,wrap:!1}}),{c(){t=p("p"),t.textContent=T,i=o(),h(m.$$.fragment)},l(a){t=c(a,"P",{"data-svelte-h":!0}),J(t)!=="svelte-kvfsh7"&&(t.textContent=T),i=r(a),M(m.$$.fragment,a)},m(a,w){l(a,t,w),l(a,i,w),u(m,a,w),d=!0},p:fe,i(a){d||(g(m.$$.fragment,a),d=!0)},o(a){y(m.$$.fragment,a),d=!1},d(a){a&&(n(t),n(i)),j(m,a)}}}function hs(U){let t,T=`Although the recipe for forward pass needs to be defined within this function, one should call the <code>Module</code>
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`;return{c(){t=p("p"),t.innerHTML=T},l(i){t=c(i,"P",{"data-svelte-h":!0}),J(t)!=="svelte-fincs2"&&(t.innerHTML=T)},m(i,m){l(i,t,m)},p:fe,d(i){i&&n(t)}}}function Ms(U){let t,T="Examples:",i,m,d;return m=new Ye({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> AutoImageProcessor, TimesformerForVideoClassification
<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>image_processor = AutoImageProcessor.from_pretrained(<span class="hljs-string">&quot;MCG-NJU/videomae-base-finetuned-kinetics&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TimesformerForVideoClassification.from_pretrained(<span class="hljs-string">&quot;facebook/timesformer-base-finetuned-k400&quot;</span>)

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

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># model predicts one of the 400 Kinetics-400 classes</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>predicted_label = logits.argmax(-<span class="hljs-number">1</span>).item()
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-built_in">print</span>(model.config.id2label[predicted_label])
eating spaghetti`,wrap:!1}}),{c(){t=p("p"),t.textContent=T,i=o(),h(m.$$.fragment)},l(a){t=c(a,"P",{"data-svelte-h":!0}),J(t)!=="svelte-kvfsh7"&&(t.textContent=T),i=r(a),M(m.$$.fragment,a)},m(a,w){l(a,t,w),l(a,i,w),u(m,a,w),d=!0},p:fe,i(a){d||(g(m.$$.fragment,a),d=!0)},o(a){y(m.$$.fragment,a),d=!1},d(a){a&&(n(t),n(i)),j(m,a)}}}function us(U){let t,T,i,m,d,a,w,he,X,Re='TimeSformer 모델은 Facebook Research에서 제안한 <a href="https://arxiv.org/abs/2102.05095" rel="nofollow">TimeSformer: Is Space-Time Attention All You Need for Video Understanding?</a>에서 소개되었습니다. 이 연구는 첫 번째 비디오 Transformer로서, 행동 인식 분야에서 중요한 이정표가 되었습니다. 또한 Transformer 기반의 비디오 이해 및 분류 논문에 많은 영감을 주었습니다.',Me,S,He="논문의 초록은 다음과 같습니다.",ue,E,Le='<em>우리는 공간과 시간에 걸쳐 셀프 어텐션만을 사용하는 합성곱이 없는(convolution-free) 비디오 분류 방법을 제안합니다. 이 방법은 “TimeSformer”라고 불리며, 표준 Transformer 아키텍처를 비디오에 적용하여 프레임 수준 패치 시퀀스로부터 직접 시공간적 특징을 학습할 수 있게 합니다. 우리의 실험적 연구는 다양한 셀프 어텐션 방식을 비교하며, 시간적 어텐션과 공간적 어텐션을 각각의 블록 내에서 별도로 적용하는 “분할 어텐션” 방식이 고려된 설계 선택 중 가장 우수한 비디오 분류 정확도를 제공한다는 것을 시사합니다. 이 혁신적인 설계에도 불구하고, TimeSformer는 Kinetics-400 및 Kinetics-600을 포함한 여러 행동 인식 벤치마크에서 최첨단 결과를 달성했으며, 현재까지 보고된 가장 높은 정확도를 기록했습니다. 마지막으로, 3D 합성곱 네트워크와 비교했을 때, TimeSformer는 더 빠르게 학습할 수 있으며, 약간의 정확도 저하를 감수하면 테스트 효율성이 크게 향상되고, 1분 이상의 긴 비디오 클립에도 적용할 수 있습니다. 코드와 모델은 다음 링크에서 확인할 수 있습니다: <a href="https://github.com/facebookresearch/TimeSformer" rel="nofollow">https URL 링크</a>.</em>',ge,z,qe=`이 모델은 <a href="https://huggingface.co/fcakyon" rel="nofollow">fcakyon</a>이 기여하였습니다.
원본 코드는 <a href="https://github.com/facebookresearch/TimeSformer" rel="nofollow">여기</a>에서 확인할 수 있습니다.`,ye,N,je,Q,Ke="다양한 사전 학습된 모델의 변형들이 있습니다. 사용하려는 데이터셋에 맞춰 사전 학습된 모델을 선택해야 합니다. 또한, 모델 크기에 따라 클립당 입력 프레임 수가 달라지므로, 사전 학습된 모델을 선택할 때 이 매개변수를 고려해야 합니다.",Te,Y,we,R,Pe='<li><a href="../tasks/video_classification">Video classification task guide</a></li>',Je,H,be,b,L,ve,le,De=`This is the configuration class to store the configuration of a <a href="/docs/transformers/main/ko/model_doc/timesformer#transformers.TimesformerModel">TimesformerModel</a>. It is used to instantiate a
TimeSformer model according to the specified arguments, defining the model architecture. Instantiating a
configuration with the defaults will yield a similar configuration to that of the TimeSformer
<a href="https://huggingface.co/facebook/timesformer-base-finetuned-k600" rel="nofollow">facebook/timesformer-base-finetuned-k600</a>
architecture.`,Ge,oe,Oe=`Configuration objects inherit from <a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a> and can be used to control the model outputs. Read the
documentation from <a href="/docs/transformers/main/ko/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a> for more information.`,ke,V,Ue,q,_e,C,K,Ve,re,es=`The bare TimeSformer Model transformer outputting raw hidden-states without any specific head on top.
This model is a PyTorch <a href="https://pytorch.org/docs/stable/nn.html#torch.nn.Module" rel="nofollow">torch.nn.Module</a> subclass. Use it
as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.`,$e,_,P,xe,ie,ss='The <a href="/docs/transformers/main/ko/model_doc/timesformer#transformers.TimesformerModel">TimesformerModel</a> forward method, overrides the <code>__call__</code> special method.',Ae,$,Fe,x,Ze,D,Ce,I,O,Xe,me,ts=`TimeSformer Model transformer with a video classification head on top (a linear layer on top of the final hidden state
of the [CLS] token) e.g. for ImageNet.
This model is a PyTorch <a href="https://pytorch.org/docs/stable/nn.html#torch.nn.Module" rel="nofollow">torch.nn.Module</a> subclass. Use it
as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.`,Se,Z,ee,Ee,pe,as='The <a href="/docs/transformers/main/ko/model_doc/timesformer#transformers.TimesformerForVideoClassification">TimesformerForVideoClassification</a> forward method, overrides the <code>__call__</code> special method.',ze,A,Ne,F,Ie,se,Be,ce,We;return d=new ne({props:{title:"TimeSformer",local:"timesformer",headingTag:"h1"}}),w=new ne({props:{title:"개요",local:"overview",headingTag:"h2"}}),N=new ne({props:{title:"사용 팁",local:"usage-tips",headingTag:"h2"}}),Y=new ne({props:{title:"리소스",local:"resources",headingTag:"h2"}}),H=new ne({props:{title:"TimesformerConfig",local:"transformers.TimesformerConfig ][ transformers.TimesformerConfig",headingTag:"h2"}}),L=new de({props:{name:"class transformers.TimesformerConfig",anchor:"transformers.TimesformerConfig",parameters:[{name:"image_size",val:" = 224"},{name:"patch_size",val:" = 16"},{name:"num_channels",val:" = 3"},{name:"num_frames",val:" = 8"},{name:"hidden_size",val:" = 768"},{name:"num_hidden_layers",val:" = 12"},{name:"num_attention_heads",val:" = 12"},{name:"intermediate_size",val:" = 3072"},{name:"hidden_act",val:" = 'gelu'"},{name:"hidden_dropout_prob",val:" = 0.0"},{name:"attention_probs_dropout_prob",val:" = 0.0"},{name:"initializer_range",val:" = 0.02"},{name:"layer_norm_eps",val:" = 1e-06"},{name:"qkv_bias",val:" = True"},{name:"attention_type",val:" = 'divided_space_time'"},{name:"drop_path_rate",val:" = 0"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TimesformerConfig.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.TimesformerConfig.patch_size",description:`<strong>patch_size</strong> (<code>int</code>, <em>optional</em>, defaults to 16) &#x2014;
The size (resolution) of each patch.`,name:"patch_size"},{anchor:"transformers.TimesformerConfig.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.TimesformerConfig.num_frames",description:`<strong>num_frames</strong> (<code>int</code>, <em>optional</em>, defaults to 8) &#x2014;
The number of frames in each video.`,name:"num_frames"},{anchor:"transformers.TimesformerConfig.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.TimesformerConfig.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.TimesformerConfig.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.TimesformerConfig.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.TimesformerConfig.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.TimesformerConfig.hidden_dropout_prob",description:`<strong>hidden_dropout_prob</strong> (<code>float</code>, <em>optional</em>, defaults to 0.0) &#x2014;
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.`,name:"hidden_dropout_prob"},{anchor:"transformers.TimesformerConfig.attention_probs_dropout_prob",description:`<strong>attention_probs_dropout_prob</strong> (<code>float</code>, <em>optional</em>, defaults to 0.0) &#x2014;
The dropout ratio for the attention probabilities.`,name:"attention_probs_dropout_prob"},{anchor:"transformers.TimesformerConfig.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.TimesformerConfig.layer_norm_eps",description:`<strong>layer_norm_eps</strong> (<code>float</code>, <em>optional</em>, defaults to 1e-06) &#x2014;
The epsilon used by the layer normalization layers.`,name:"layer_norm_eps"},{anchor:"transformers.TimesformerConfig.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.TimesformerConfig.attention_type",description:`<strong>attention_type</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;divided_space_time&quot;</code>) &#x2014;
The attention type to use. Must be one of <code>&quot;divided_space_time&quot;</code>, <code>&quot;space_only&quot;</code>, <code>&quot;joint_space_time&quot;</code>.`,name:"attention_type"},{anchor:"transformers.TimesformerConfig.drop_path_rate",description:`<strong>drop_path_rate</strong> (<code>float</code>, <em>optional</em>, defaults to 0) &#x2014;
The dropout ratio for stochastic depth.`,name:"drop_path_rate"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/timesformer/configuration_timesformer.py#L24"}}),V=new Qe({props:{anchor:"transformers.TimesformerConfig.example",$$slots:{default:[cs]},$$scope:{ctx:U}}}),q=new ne({props:{title:"TimesformerModel",local:"transformers.TimesformerModel ][ transformers.TimesformerModel",headingTag:"h2"}}),K=new de({props:{name:"class transformers.TimesformerModel",anchor:"transformers.TimesformerModel",parameters:[{name:"config",val:""}],parametersDescription:[{anchor:"transformers.TimesformerModel.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/ko/model_doc/timesformer#transformers.TimesformerConfig">TimesformerConfig</a>) &#x2014; Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the <a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/timesformer/modeling_timesformer.py#L515"}}),P=new de({props:{name:"forward",anchor:"transformers.TimesformerModel.forward",parameters:[{name:"pixel_values",val:": FloatTensor"},{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.TimesformerModel.forward.pixel_values",description:`<strong>pixel_values</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, num_frames, num_channels, height, width)</code>) &#x2014;
Pixel values. Pixel values can be obtained using <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoImageProcessor">AutoImageProcessor</a>. See
<code>VideoMAEImageProcessor.preprocess()</code> for details.`,name:"pixel_values"},{anchor:"transformers.TimesformerModel.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.TimesformerModel.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.TimesformerModel.forward.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/main/ko/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/timesformer/modeling_timesformer.py#L543",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>A <a
  href="/docs/transformers/main/ko/main_classes/output#transformers.modeling_outputs.BaseModelOutput"
>transformers.modeling_outputs.BaseModelOutput</a> or a tuple of
<code>torch.FloatTensor</code> (if <code>return_dict=False</code> is passed or when <code>config.return_dict=False</code>) comprising various
elements depending on the configuration (<a
  href="/docs/transformers/main/ko/model_doc/timesformer#transformers.TimesformerConfig"
>TimesformerConfig</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>) — Sequence of hidden-states at the output of the last layer of the model.</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>) — 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>) — 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:`<script context="module">export const metadata = 'undefined';<\/script>


<p><a
  href="/docs/transformers/main/ko/main_classes/output#transformers.modeling_outputs.BaseModelOutput"
>transformers.modeling_outputs.BaseModelOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),$=new ns({props:{$$slots:{default:[ds]},$$scope:{ctx:U}}}),x=new Qe({props:{anchor:"transformers.TimesformerModel.forward.example",$$slots:{default:[fs]},$$scope:{ctx:U}}}),D=new ne({props:{title:"TimesformerForVideoClassification",local:"transformers.TimesformerForVideoClassification ][ transformers.TimesformerForVideoClassification",headingTag:"h2"}}),O=new de({props:{name:"class transformers.TimesformerForVideoClassification",anchor:"transformers.TimesformerForVideoClassification",parameters:[{name:"config",val:""}],parametersDescription:[{anchor:"transformers.TimesformerForVideoClassification.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/ko/model_doc/timesformer#transformers.TimesformerConfig">TimesformerConfig</a>) &#x2014; Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the <a href="/docs/transformers/main/ko/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/timesformer/modeling_timesformer.py#L656"}}),ee=new de({props:{name:"forward",anchor:"transformers.TimesformerForVideoClassification.forward",parameters:[{name:"pixel_values",val:": typing.Optional[torch.Tensor] = None"},{name:"labels",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.TimesformerForVideoClassification.forward.pixel_values",description:`<strong>pixel_values</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, num_frames, num_channels, height, width)</code>) &#x2014;
Pixel values. Pixel values can be obtained using <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoImageProcessor">AutoImageProcessor</a>. See
<code>VideoMAEImageProcessor.preprocess()</code> for details.`,name:"pixel_values"},{anchor:"transformers.TimesformerForVideoClassification.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.TimesformerForVideoClassification.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.TimesformerForVideoClassification.forward.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/main/ko/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"},{anchor:"transformers.TimesformerForVideoClassification.forward.labels",description:`<strong>labels</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size,)</code>, <em>optional</em>) &#x2014;
Labels for computing the image classification/regression loss. Indices should be in <code>[0, ..., config.num_labels - 1]</code>. If <code>config.num_labels == 1</code> a regression loss is computed (Mean-Square loss), If
<code>config.num_labels &gt; 1</code> a classification loss is computed (Cross-Entropy).`,name:"labels"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/timesformer/modeling_timesformer.py#L674",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>A <a
  href="/docs/transformers/main/ko/main_classes/output#transformers.modeling_outputs.ImageClassifierOutput"
>transformers.modeling_outputs.ImageClassifierOutput</a> or a tuple of
<code>torch.FloatTensor</code> (if <code>return_dict=False</code> is passed or when <code>config.return_dict=False</code>) comprising various
elements depending on the configuration (<a
  href="/docs/transformers/main/ko/model_doc/timesformer#transformers.TimesformerConfig"
>TimesformerConfig</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) — 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>) — 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>) — 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 stage) of shape <code>(batch_size, sequence_length, hidden_size)</code>. Hidden-states
(also called feature maps) of the model at the output of each stage.</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>) — Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_heads, patch_size, 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:`<script context="module">export const metadata = 'undefined';<\/script>


<p><a
  href="/docs/transformers/main/ko/main_classes/output#transformers.modeling_outputs.ImageClassifierOutput"
>transformers.modeling_outputs.ImageClassifierOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),A=new ns({props:{$$slots:{default:[hs]},$$scope:{ctx:U}}}),F=new Qe({props:{anchor:"transformers.TimesformerForVideoClassification.forward.example",$$slots:{default:[Ms]},$$scope:{ctx:U}}}),se=new ps({props:{source:"https://github.com/huggingface/transformers/blob/main/docs/source/ko/model_doc/timesformer.md"}}),{c(){t=p("meta"),T=o(),i=p("p"),m=o(),h(d.$$.fragment),a=o(),h(w.$$.fragment),he=o(),X=p("p"),X.innerHTML=Re,Me=o(),S=p("p"),S.textContent=He,ue=o(),E=p("p"),E.innerHTML=Le,ge=o(),z=p("p"),z.innerHTML=qe,ye=o(),h(N.$$.fragment),je=o(),Q=p("p"),Q.textContent=Ke,Te=o(),h(Y.$$.fragment),we=o(),R=p("ul"),R.innerHTML=Pe,Je=o(),h(H.$$.fragment),be=o(),b=p("div"),h(L.$$.fragment),ve=o(),le=p("p"),le.innerHTML=De,Ge=o(),oe=p("p"),oe.innerHTML=Oe,ke=o(),h(V.$$.fragment),Ue=o(),h(q.$$.fragment),_e=o(),C=p("div"),h(K.$$.fragment),Ve=o(),re=p("p"),re.innerHTML=es,$e=o(),_=p("div"),h(P.$$.fragment),xe=o(),ie=p("p"),ie.innerHTML=ss,Ae=o(),h($.$$.fragment),Fe=o(),h(x.$$.fragment),Ze=o(),h(D.$$.fragment),Ce=o(),I=p("div"),h(O.$$.fragment),Xe=o(),me=p("p"),me.innerHTML=ts,Se=o(),Z=p("div"),h(ee.$$.fragment),Ee=o(),pe=p("p"),pe.innerHTML=as,ze=o(),h(A.$$.fragment),Ne=o(),h(F.$$.fragment),Ie=o(),h(se.$$.fragment),Be=o(),ce=p("p"),this.h()},l(e){const s=ms("svelte-u9bgzb",document.head);t=c(s,"META",{name:!0,content:!0}),s.forEach(n),T=r(e),i=c(e,"P",{}),te(i).forEach(n),m=r(e),M(d.$$.fragment,e),a=r(e),M(w.$$.fragment,e),he=r(e),X=c(e,"P",{"data-svelte-h":!0}),J(X)!=="svelte-o1l88b"&&(X.innerHTML=Re),Me=r(e),S=c(e,"P",{"data-svelte-h":!0}),J(S)!=="svelte-1g6d7ht"&&(S.textContent=He),ue=r(e),E=c(e,"P",{"data-svelte-h":!0}),J(E)!=="svelte-ryb9ka"&&(E.innerHTML=Le),ge=r(e),z=c(e,"P",{"data-svelte-h":!0}),J(z)!=="svelte-ptm3zu"&&(z.innerHTML=qe),ye=r(e),M(N.$$.fragment,e),je=r(e),Q=c(e,"P",{"data-svelte-h":!0}),J(Q)!=="svelte-1h3catr"&&(Q.textContent=Ke),Te=r(e),M(Y.$$.fragment,e),we=r(e),R=c(e,"UL",{"data-svelte-h":!0}),J(R)!=="svelte-gz9vpw"&&(R.innerHTML=Pe),Je=r(e),M(H.$$.fragment,e),be=r(e),b=c(e,"DIV",{class:!0});var B=te(b);M(L.$$.fragment,B),ve=r(B),le=c(B,"P",{"data-svelte-h":!0}),J(le)!=="svelte-cpy7yd"&&(le.innerHTML=De),Ge=r(B),oe=c(B,"P",{"data-svelte-h":!0}),J(oe)!=="svelte-qr3t5r"&&(oe.innerHTML=Oe),ke=r(B),M(V.$$.fragment,B),B.forEach(n),Ue=r(e),M(q.$$.fragment,e),_e=r(e),C=c(e,"DIV",{class:!0});var G=te(C);M(K.$$.fragment,G),Ve=r(G),re=c(G,"P",{"data-svelte-h":!0}),J(re)!=="svelte-gln3qs"&&(re.innerHTML=es),$e=r(G),_=c(G,"DIV",{class:!0});var W=te(_);M(P.$$.fragment,W),xe=r(W),ie=c(W,"P",{"data-svelte-h":!0}),J(ie)!=="svelte-x4vktx"&&(ie.innerHTML=ss),Ae=r(W),M($.$$.fragment,W),Fe=r(W),M(x.$$.fragment,W),W.forEach(n),G.forEach(n),Ze=r(e),M(D.$$.fragment,e),Ce=r(e),I=c(e,"DIV",{class:!0});var k=te(I);M(O.$$.fragment,k),Xe=r(k),me=c(k,"P",{"data-svelte-h":!0}),J(me)!=="svelte-1ancuo0"&&(me.innerHTML=ts),Se=r(k),Z=c(k,"DIV",{class:!0});var v=te(Z);M(ee.$$.fragment,v),Ee=r(v),pe=c(v,"P",{"data-svelte-h":!0}),J(pe)!=="svelte-1vta815"&&(pe.innerHTML=as),ze=r(v),M(A.$$.fragment,v),Ne=r(v),M(F.$$.fragment,v),v.forEach(n),k.forEach(n),Ie=r(e),M(se.$$.fragment,e),Be=r(e),ce=c(e,"P",{}),te(ce).forEach(n),this.h()},h(){ae(t,"name","hf:doc:metadata"),ae(t,"content",gs),ae(b,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),ae(_,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),ae(C,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),ae(Z,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),ae(I,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8")},m(e,s){f(document.head,t),l(e,T,s),l(e,i,s),l(e,m,s),u(d,e,s),l(e,a,s),u(w,e,s),l(e,he,s),l(e,X,s),l(e,Me,s),l(e,S,s),l(e,ue,s),l(e,E,s),l(e,ge,s),l(e,z,s),l(e,ye,s),u(N,e,s),l(e,je,s),l(e,Q,s),l(e,Te,s),u(Y,e,s),l(e,we,s),l(e,R,s),l(e,Je,s),u(H,e,s),l(e,be,s),l(e,b,s),u(L,b,null),f(b,ve),f(b,le),f(b,Ge),f(b,oe),f(b,ke),u(V,b,null),l(e,Ue,s),u(q,e,s),l(e,_e,s),l(e,C,s),u(K,C,null),f(C,Ve),f(C,re),f(C,$e),f(C,_),u(P,_,null),f(_,xe),f(_,ie),f(_,Ae),u($,_,null),f(_,Fe),u(x,_,null),l(e,Ze,s),u(D,e,s),l(e,Ce,s),l(e,I,s),u(O,I,null),f(I,Xe),f(I,me),f(I,Se),f(I,Z),u(ee,Z,null),f(Z,Ee),f(Z,pe),f(Z,ze),u(A,Z,null),f(Z,Ne),u(F,Z,null),l(e,Ie,s),u(se,e,s),l(e,Be,s),l(e,ce,s),We=!0},p(e,[s]){const B={};s&2&&(B.$$scope={dirty:s,ctx:e}),V.$set(B);const G={};s&2&&(G.$$scope={dirty:s,ctx:e}),$.$set(G);const W={};s&2&&(W.$$scope={dirty:s,ctx:e}),x.$set(W);const k={};s&2&&(k.$$scope={dirty:s,ctx:e}),A.$set(k);const v={};s&2&&(v.$$scope={dirty:s,ctx:e}),F.$set(v)},i(e){We||(g(d.$$.fragment,e),g(w.$$.fragment,e),g(N.$$.fragment,e),g(Y.$$.fragment,e),g(H.$$.fragment,e),g(L.$$.fragment,e),g(V.$$.fragment,e),g(q.$$.fragment,e),g(K.$$.fragment,e),g(P.$$.fragment,e),g($.$$.fragment,e),g(x.$$.fragment,e),g(D.$$.fragment,e),g(O.$$.fragment,e),g(ee.$$.fragment,e),g(A.$$.fragment,e),g(F.$$.fragment,e),g(se.$$.fragment,e),We=!0)},o(e){y(d.$$.fragment,e),y(w.$$.fragment,e),y(N.$$.fragment,e),y(Y.$$.fragment,e),y(H.$$.fragment,e),y(L.$$.fragment,e),y(V.$$.fragment,e),y(q.$$.fragment,e),y(K.$$.fragment,e),y(P.$$.fragment,e),y($.$$.fragment,e),y(x.$$.fragment,e),y(D.$$.fragment,e),y(O.$$.fragment,e),y(ee.$$.fragment,e),y(A.$$.fragment,e),y(F.$$.fragment,e),y(se.$$.fragment,e),We=!1},d(e){e&&(n(T),n(i),n(m),n(a),n(he),n(X),n(Me),n(S),n(ue),n(E),n(ge),n(z),n(ye),n(je),n(Q),n(Te),n(we),n(R),n(Je),n(be),n(b),n(Ue),n(_e),n(C),n(Ze),n(Ce),n(I),n(Ie),n(Be),n(ce)),n(t),j(d,e),j(w,e),j(N,e),j(Y,e),j(H,e),j(L),j(V),j(q,e),j(K),j(P),j($),j(x),j(D,e),j(O),j(ee),j(A),j(F),j(se,e)}}}const gs='{"title":"TimeSformer","local":"timesformer","sections":[{"title":"개요","local":"overview","sections":[],"depth":2},{"title":"사용 팁","local":"usage-tips","sections":[],"depth":2},{"title":"리소스","local":"resources","sections":[],"depth":2},{"title":"TimesformerConfig","local":"transformers.TimesformerConfig ][ transformers.TimesformerConfig","sections":[],"depth":2},{"title":"TimesformerModel","local":"transformers.TimesformerModel ][ transformers.TimesformerModel","sections":[],"depth":2},{"title":"TimesformerForVideoClassification","local":"transformers.TimesformerForVideoClassification ][ transformers.TimesformerForVideoClassification","sections":[],"depth":2}],"depth":1}';function ys(U){return os(()=>{new URLSearchParams(window.location.search).get("fw")}),[]}class Zs extends rs{constructor(t){super(),is(this,t,ys,us,ls,{})}}export{Zs as component};
