import{S as Zt,i as Ct,s as Wt,e as n,k as u,w,t as l,M as kt,c as o,d as a,m as g,a as r,x as _,h as i,b as c,G as s,g as f,y as b,q as j,o as J,B as v,v as Bt,L as Ra}from"../../chunks/vendor-hf-doc-builder.js";import{T as It}from"../../chunks/Tip-hf-doc-builder.js";import{D as rs}from"../../chunks/Docstring-hf-doc-builder.js";import{C as Sa}from"../../chunks/CodeBlock-hf-doc-builder.js";import{I as Re}from"../../chunks/IconCopyLink-hf-doc-builder.js";import{E as Xa}from"../../chunks/ExampleCodeBlock-hf-doc-builder.js";function Et(I){let m,T,M,p,y;return p=new Sa({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> 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`}}),{c(){m=n("p"),T=l("Example:"),M=u(),w(p.$$.fragment)},l(t){m=o(t,"P",{});var h=r(m);T=i(h,"Example:"),h.forEach(a),M=g(t),_(p.$$.fragment,t)},m(t,h){f(t,m,h),s(m,T),f(t,M,h),b(p,t,h),y=!0},p:Ra,i(t){y||(j(p.$$.fragment,t),y=!0)},o(t){J(p.$$.fragment,t),y=!1},d(t){t&&a(m),t&&a(M),v(p,t)}}}function Vt(I){let m,T,M,p,y;return{c(){m=n("p"),T=l("Although the recipe for forward pass needs to be defined within this function, one should call the "),M=n("code"),p=l("Module"),y=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(t){m=o(t,"P",{});var h=r(m);T=i(h,"Although the recipe for forward pass needs to be defined within this function, one should call the "),M=o(h,"CODE",{});var W=r(M);p=i(W,"Module"),W.forEach(a),y=i(h,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),h.forEach(a)},m(t,h){f(t,m,h),s(m,T),s(m,M),s(M,p),s(m,y)},d(t){t&&a(m)}}}function Gt(I){let m,T,M,p,y;return p=new Sa({props:{code:"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",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>    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>]`}}),{c(){m=n("p"),T=l("Examples:"),M=u(),w(p.$$.fragment)},l(t){m=o(t,"P",{});var h=r(m);T=i(h,"Examples:"),h.forEach(a),M=g(t),_(p.$$.fragment,t)},m(t,h){f(t,m,h),s(m,T),f(t,M,h),b(p,t,h),y=!0},p:Ra,i(t){y||(j(p.$$.fragment,t),y=!0)},o(t){J(p.$$.fragment,t),y=!1},d(t){t&&a(m),t&&a(M),v(p,t)}}}function $t(I){let m,T,M,p,y;return{c(){m=n("p"),T=l("Although the recipe for forward pass needs to be defined within this function, one should call the "),M=n("code"),p=l("Module"),y=l(`
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the latter silently ignores them.`)},l(t){m=o(t,"P",{});var h=r(m);T=i(h,"Although the recipe for forward pass needs to be defined within this function, one should call the "),M=o(h,"CODE",{});var W=r(M);p=i(W,"Module"),W.forEach(a),y=i(h,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),h.forEach(a)},m(t,h){f(t,m,h),s(m,T),s(m,M),s(M,p),s(m,y)},d(t){t&&a(m)}}}function At(I){let m,T,M,p,y;return p=new Sa({props:{code:"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",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>    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`}}),{c(){m=n("p"),T=l("Examples:"),M=u(),w(p.$$.fragment)},l(t){m=o(t,"P",{});var h=r(m);T=i(h,"Examples:"),h.forEach(a),M=g(t),_(p.$$.fragment,t)},m(t,h){f(t,m,h),s(m,T),f(t,M,h),b(p,t,h),y=!0},p:Ra,i(t){y||(j(p.$$.fragment,t),y=!0)},o(t){J(p.$$.fragment,t),y=!1},d(t){t&&a(m),t&&a(M),v(p,t)}}}function Nt(I){let m,T,M,p,y,t,h,W,ks,ls,V,R,Se,te,Bs,Ye,Es,is,S,Vs,ne,Gs,$s,cs,ve,As,ms,Ue,oe,Ns,re,Fs,zs,ds,Ie,xs,ps,Ze,Xs,fs,E,Rs,le,Ss,Ys,ie,Qs,Hs,hs,G,Y,Qe,ce,Ps,He,qs,us,Ce,Pe,We,Ls,gs,$,Q,qe,me,Ds,Le,Ks,Ms,U,de,Os,A,ea,ke,sa,aa,pe,ta,na,oa,N,ra,Be,la,ia,Ee,ca,ma,da,H,ys,F,P,De,fe,pa,Ke,fa,Ts,k,he,ha,ue,ua,ge,ga,Ma,ya,Z,Me,Ta,z,wa,Ve,_a,ba,Oe,ja,Ja,va,q,Ua,L,ws,x,D,es,ye,Ia,ss,Za,_s,B,Te,Ca,we,Wa,_e,ka,Ba,Ea,C,be,Va,X,Ga,Ge,$a,Aa,as,Na,Fa,za,K,xa,O,bs;return t=new Re({}),te=new Re({}),ce=new Re({}),me=new Re({}),de=new rs({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 probabilitiy 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-6) &#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/v4.31.0/src/transformers/models/timesformer/configuration_timesformer.py#L28"}}),H=new Xa({props:{anchor:"transformers.TimesformerConfig.example",$$slots:{default:[Et]},$$scope:{ctx:I}}}),fe=new Re({}),he=new rs({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/v4.31.0/en/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/v4.31.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.31.0/src/transformers/models/timesformer/modeling_timesformer.py#L534"}}),Me=new rs({props:{name:"forward",anchor:"transformers.TimesformerModel.forward",parameters:[{name:"pixel_values",val:""},{name:"output_attentions",val:" = None"},{name:"output_hidden_states",val:" = None"},{name:"return_dict",val:" = 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/v4.31.0/en/model_doc/auto#transformers.AutoImageProcessor">AutoImageProcessor</a>. See
<a href="/docs/transformers/v4.31.0/en/model_doc/videomae#transformers.VideoMAEImageProcessor.preprocess">VideoMAEImageProcessor.preprocess()</a> 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
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<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>
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<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, +
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<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>
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<li>
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<p><strong>attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_heads, patch_size, sequence_length)</code>.</p>
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