import{s as zt,o as Ut,n as Be}from"../chunks/scheduler.9bc65507.js";import{S as Pt,i as Jt,g as c,s as r,r as g,A as Ft,h as m,f as o,c as i,j as F,u as f,x as y,k as R,y as p,a,v as h,d as u,t as _,w as b}from"../chunks/index.707bf1b6.js";import{T as jt}from"../chunks/Tip.c2ecdbf4.js";import{D as fe}from"../chunks/Docstring.17db21ae.js";import{C as mt}from"../chunks/CodeBlock.54a9f38d.js";import{E as ct}from"../chunks/ExampleCodeBlock.4f515aa9.js";import{P as Rt}from"../chunks/PipelineTag.44585822.js";import{H as S}from"../chunks/Heading.342b1fa6.js";function Zt(B){let s,$="Example:",l,d,v;return d=new mt({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEJpdENvbmZpZyUyQyUyMEJpdE1vZGVsJTBBJTBBJTIzJTIwSW5pdGlhbGl6aW5nJTIwYSUyMEJpVCUyMGJpdC01MCUyMHN0eWxlJTIwY29uZmlndXJhdGlvbiUwQWNvbmZpZ3VyYXRpb24lMjAlM0QlMjBCaXRDb25maWcoKSUwQSUwQSUyMyUyMEluaXRpYWxpemluZyUyMGElMjBtb2RlbCUyMCh3aXRoJTIwcmFuZG9tJTIwd2VpZ2h0cyklMjBmcm9tJTIwdGhlJTIwYml0LTUwJTIwc3R5bGUlMjBjb25maWd1cmF0aW9uJTBBbW9kZWwlMjAlM0QlMjBCaXRNb2RlbChjb25maWd1cmF0aW9uKSUwQSUwQSUyMyUyMEFjY2Vzc2luZyUyMHRoZSUyMG1vZGVsJTIwY29uZmlndXJhdGlvbiUwQWNvbmZpZ3VyYXRpb24lMjAlM0QlMjBtb2RlbC5jb25maWc=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> BitConfig, BitModel

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Initializing a BiT bit-50 style configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>configuration = BitConfig()

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Initializing a model (with random weights) from the bit-50 style configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = BitModel(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(){s=c("p"),s.textContent=$,l=r(),g(d.$$.fragment)},l(n){s=m(n,"P",{"data-svelte-h":!0}),y(s)!=="svelte-11lpom8"&&(s.textContent=$),l=i(n),f(d.$$.fragment,n)},m(n,C){a(n,s,C),a(n,l,C),h(d,n,C),v=!0},p:Be,i(n){v||(u(d.$$.fragment,n),v=!0)},o(n){_(d.$$.fragment,n),v=!1},d(n){n&&(o(s),o(l)),b(d,n)}}}function Nt(B){let s,$=`Although the recipe for forward pass needs to be defined within this function, one should call the <code>Module</code>
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`;return{c(){s=c("p"),s.innerHTML=$},l(l){s=m(l,"P",{"data-svelte-h":!0}),y(s)!=="svelte-fincs2"&&(s.innerHTML=$)},m(l,d){a(l,s,d)},p:Be,d(l){l&&o(s)}}}function Wt(B){let s,$="Example:",l,d,v;return d=new mt({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> AutoImageProcessor, BitModel
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> datasets <span class="hljs-keyword">import</span> load_dataset

<span class="hljs-meta">&gt;&gt;&gt; </span>dataset = load_dataset(<span class="hljs-string">&quot;huggingface/cats-image&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>image = dataset[<span class="hljs-string">&quot;test&quot;</span>][<span class="hljs-string">&quot;image&quot;</span>][<span class="hljs-number">0</span>]

<span class="hljs-meta">&gt;&gt;&gt; </span>image_processor = AutoImageProcessor.from_pretrained(<span class="hljs-string">&quot;google/bit-50&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = BitModel.from_pretrained(<span class="hljs-string">&quot;google/bit-50&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = image_processor(image, 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">&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">2048</span>, <span class="hljs-number">7</span>, <span class="hljs-number">7</span>]`,wrap:!1}}),{c(){s=c("p"),s.textContent=$,l=r(),g(d.$$.fragment)},l(n){s=m(n,"P",{"data-svelte-h":!0}),y(s)!=="svelte-11lpom8"&&(s.textContent=$),l=i(n),f(d.$$.fragment,n)},m(n,C){a(n,s,C),a(n,l,C),h(d,n,C),v=!0},p:Be,i(n){v||(u(d.$$.fragment,n),v=!0)},o(n){_(d.$$.fragment,n),v=!1},d(n){n&&(o(s),o(l)),b(d,n)}}}function kt(B){let s,$=`Although the recipe for forward pass needs to be defined within this function, one should call the <code>Module</code>
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`;return{c(){s=c("p"),s.innerHTML=$},l(l){s=m(l,"P",{"data-svelte-h":!0}),y(s)!=="svelte-fincs2"&&(s.innerHTML=$)},m(l,d){a(l,s,d)},p:Be,d(l){l&&o(s)}}}function Et(B){let s,$="Example:",l,d,v;return d=new mt({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> AutoImageProcessor, BitForImageClassification
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> datasets <span class="hljs-keyword">import</span> load_dataset

<span class="hljs-meta">&gt;&gt;&gt; </span>dataset = load_dataset(<span class="hljs-string">&quot;huggingface/cats-image&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>image = dataset[<span class="hljs-string">&quot;test&quot;</span>][<span class="hljs-string">&quot;image&quot;</span>][<span class="hljs-number">0</span>]

<span class="hljs-meta">&gt;&gt;&gt; </span>image_processor = AutoImageProcessor.from_pretrained(<span class="hljs-string">&quot;google/bit-50&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = BitForImageClassification.from_pretrained(<span class="hljs-string">&quot;google/bit-50&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = image_processor(image, 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>    logits = model(**inputs).logits

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># model predicts one of the 1000 ImageNet 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])
tiger cat`,wrap:!1}}),{c(){s=c("p"),s.textContent=$,l=r(),g(d.$$.fragment)},l(n){s=m(n,"P",{"data-svelte-h":!0}),y(s)!=="svelte-11lpom8"&&(s.textContent=$),l=i(n),f(d.$$.fragment,n)},m(n,C){a(n,s,C),a(n,l,C),h(d,n,C),v=!0},p:Be,i(n){v||(u(d.$$.fragment,n),v=!0)},o(n){_(d.$$.fragment,n),v=!1},d(n){n&&(o(s),o(l)),b(d,n)}}}function qt(B){let s,$,l,d,v,n,C,we,X,pt=`BiT モデルは、Alexander Kolesnikov、Lucas Beyer、Xiaohua Zhai、Joan Puigcerver、Jessica Yung、Sylvain Gelly によって <a href="https://arxiv.org/abs/1912.11370" rel="nofollow">Big Transfer (BiT): General Visual Representation Learning</a> で提案されました。ニール・ホールズビー。
BiT は、<a href="resnet">ResNet</a> のようなアーキテクチャ (具体的には ResNetv2) の事前トレーニングをスケールアップするための簡単なレシピです。この方法により、転移学習が大幅に改善されます。`,xe,V,gt="論文の要約は次のとおりです。",Ie,Q,ft="*事前トレーニングされた表現の転送により、サンプル効率が向上し、視覚用のディープ ニューラル ネットワークをトレーニングする際のハイパーパラメーター調整が簡素化されます。大規模な教師ありデータセットでの事前トレーニングと、ターゲット タスクでのモデルの微調整のパラダイムを再検討します。私たちは事前トレーニングをスケールアップし、Big Transfer (BiT) と呼ぶシンプルなレシピを提案します。いくつかの慎重に選択されたコンポーネントを組み合わせ、シンプルなヒューリスティックを使用して転送することにより、20 を超えるデータセットで優れたパフォーマンスを実現します。 BiT は、クラスごとに 1 つのサンプルから合計 100 万のサンプルまで、驚くほど広範囲のデータ領域にわたって良好にパフォーマンスを発揮します。 BiT は、ILSVRC-2012 で 87.5%、CIFAR-10 で 99.4%、19 タスクの Visual Task Adaptation Benchmark (VTAB) で 76.3% のトップ 1 精度を達成しました。小規模なデータセットでは、BiT は ILSVRC-2012 (クラスあたり 10 例) で 76.8%、CIFAR-10 (クラスあたり 10 例) で 97.0% を達成しました。高い転写性能を実現する主要成分を詳細に分析※。",je,A,ze,H,ht=`<li>BiT モデルは、アーキテクチャの点で ResNetv2 と同等ですが、次の点が異なります: 1) すべてのバッチ正規化層が <a href="https://arxiv.org/abs/1803.08494" rel="nofollow">グループ正規化</a> に置き換えられます。
2) <a href="https://arxiv.org/abs/1903.10520" rel="nofollow">重みの標準化</a> は畳み込み層に使用されます。著者らは、両方の組み合わせが大きなバッチサイズでのトレーニングに役立ち、重要な効果があることを示しています。
転移学習への影響。</li>`,Ue,Y,ut=`このモデルは、<a href="https://huggingface.co/nielsr" rel="nofollow">nielsr</a> によって提供されました。
元のコードは <a href="https://github.com/google-research/big_transfer" rel="nofollow">こちら</a> にあります。`,Pe,D,Je,O,_t="BiT を始めるのに役立つ公式 Hugging Face およびコミュニティ (🌎 で示されている) リソースのリスト。",Fe,K,Re,ee,bt='<li><a href="/docs/transformers/v4.37.1/ja/model_doc/bit#transformers.BitForImageClassification">BitForImageClassification</a> は、この <a href="https://github.com/huggingface/transformers/tree/main/examples/pytorch/image-classification" rel="nofollow">サンプル スクリプト</a> および <a href="https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/image_classification.ipynb" rel="nofollow">ノートブック</a>。</li> <li>参照: <a href="../tasks/image_classification">画像分類タスク ガイド</a></li>',Ze,te,vt="ここに含めるリソースの送信に興味がある場合は、お気軽にプル リクエストを開いてください。審査させていただきます。リソースは、既存のリソースを複製するのではなく、何か新しいものを示すことが理想的です。",Ne,oe,We,M,se,Qe,he,yt=`This is the configuration class to store the configuration of a <a href="/docs/transformers/v4.37.1/ja/model_doc/bit#transformers.BitModel">BitModel</a>. It is used to instantiate an BiT
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 BiT
<a href="https://huggingface.co/google/bit-50" rel="nofollow">google/bit-50</a> architecture.`,Ae,ue,$t=`Configuration objects inherit from <a href="/docs/transformers/v4.37.1/ja/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a> and can be used to control the model outputs. Read the
documentation from <a href="/docs/transformers/v4.37.1/ja/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a> for more information.`,He,Z,ke,ne,Ee,I,ae,Ye,_e,Ct="Constructs a BiT image processor.",De,N,re,Oe,be,Mt="Preprocess an image or batch of images.",qe,ie,Le,j,le,Ke,ve,Tt=`The bare BiT model outputting raw features 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.`,et,w,de,tt,ye,Bt='The <a href="/docs/transformers/v4.37.1/ja/model_doc/bit#transformers.BitModel">BitModel</a> forward method, overrides the <code>__call__</code> special method.',ot,W,st,k,Ge,ce,Se,T,me,nt,$e,wt=`BiT Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for
ImageNet.`,at,Ce,xt=`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.`,rt,x,pe,it,Me,It='The <a href="/docs/transformers/v4.37.1/ja/model_doc/bit#transformers.BitForImageClassification">BitForImageClassification</a> forward method, overrides the <code>__call__</code> special method.',lt,E,dt,q,Xe,Te,Ve;return v=new S({props:{title:"Big Transfer (BiT)",local:"big-transfer-bit",headingTag:"h1"}}),C=new S({props:{title:"Overview",local:"overview",headingTag:"h2"}}),A=new S({props:{title:"Usage tips",local:"usage-tips",headingTag:"h2"}}),D=new S({props:{title:"Resources",local:"resources",headingTag:"h2"}}),K=new Rt({props:{pipeline:"image-classification"}}),oe=new S({props:{title:"BitConfig",local:"transformers.BitConfig",headingTag:"h2"}}),se=new fe({props:{name:"class transformers.BitConfig",anchor:"transformers.BitConfig",parameters:[{name:"num_channels",val:" = 3"},{name:"embedding_size",val:" = 64"},{name:"hidden_sizes",val:" = [256, 512, 1024, 2048]"},{name:"depths",val:" = [3, 4, 6, 3]"},{name:"layer_type",val:" = 'preactivation'"},{name:"hidden_act",val:" = 'relu'"},{name:"global_padding",val:" = None"},{name:"num_groups",val:" = 32"},{name:"drop_path_rate",val:" = 0.0"},{name:"embedding_dynamic_padding",val:" = False"},{name:"output_stride",val:" = 32"},{name:"width_factor",val:" = 1"},{name:"out_features",val:" = None"},{name:"out_indices",val:" = None"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.BitConfig.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.BitConfig.embedding_size",description:`<strong>embedding_size</strong> (<code>int</code>, <em>optional</em>, defaults to 64) &#x2014;
Dimensionality (hidden size) for the embedding layer.`,name:"embedding_size"},{anchor:"transformers.BitConfig.hidden_sizes",description:`<strong>hidden_sizes</strong> (<code>List[int]</code>, <em>optional</em>, defaults to <code>[256, 512, 1024, 2048]</code>) &#x2014;
Dimensionality (hidden size) at each stage.`,name:"hidden_sizes"},{anchor:"transformers.BitConfig.depths",description:`<strong>depths</strong> (<code>List[int]</code>, <em>optional</em>, defaults to <code>[3, 4, 6, 3]</code>) &#x2014;
Depth (number of layers) for each stage.`,name:"depths"},{anchor:"transformers.BitConfig.layer_type",description:`<strong>layer_type</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;preactivation&quot;</code>) &#x2014;
The layer to use, it can be either <code>&quot;preactivation&quot;</code> or <code>&quot;bottleneck&quot;</code>.`,name:"layer_type"},{anchor:"transformers.BitConfig.hidden_act",description:`<strong>hidden_act</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;relu&quot;</code>) &#x2014;
The non-linear activation function in each block. 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.BitConfig.global_padding",description:`<strong>global_padding</strong> (<code>str</code>, <em>optional</em>) &#x2014;
Padding strategy to use for the convolutional layers. Can be either <code>&quot;valid&quot;</code>, <code>&quot;same&quot;</code>, or <code>None</code>.`,name:"global_padding"},{anchor:"transformers.BitConfig.num_groups",description:`<strong>num_groups</strong> (<code>int</code>, <em>optional</em>, defaults to 32) &#x2014;
Number of groups used for the <code>BitGroupNormActivation</code> layers.`,name:"num_groups"},{anchor:"transformers.BitConfig.drop_path_rate",description:`<strong>drop_path_rate</strong> (<code>float</code>, <em>optional</em>, defaults to 0.0) &#x2014;
The drop path rate for the stochastic depth.`,name:"drop_path_rate"},{anchor:"transformers.BitConfig.embedding_dynamic_padding",description:`<strong>embedding_dynamic_padding</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to make use of dynamic padding for the embedding layer.`,name:"embedding_dynamic_padding"},{anchor:"transformers.BitConfig.output_stride",description:`<strong>output_stride</strong> (<code>int</code>, <em>optional</em>, defaults to 32) &#x2014;
The output stride of the model.`,name:"output_stride"},{anchor:"transformers.BitConfig.width_factor",description:`<strong>width_factor</strong> (<code>int</code>, <em>optional</em>, defaults to 1) &#x2014;
The width factor for the model.`,name:"width_factor"},{anchor:"transformers.BitConfig.out_features",description:`<strong>out_features</strong> (<code>List[str]</code>, <em>optional</em>) &#x2014;
If used as backbone, list of features to output. Can be any of <code>&quot;stem&quot;</code>, <code>&quot;stage1&quot;</code>, <code>&quot;stage2&quot;</code>, etc.
(depending on how many stages the model has). If unset and <code>out_indices</code> is set, will default to the
corresponding stages. If unset and <code>out_indices</code> is unset, will default to the last stage. Must be in the
same order as defined in the <code>stage_names</code> attribute.`,name:"out_features"},{anchor:"transformers.BitConfig.out_indices",description:`<strong>out_indices</strong> (<code>List[int]</code>, <em>optional</em>) &#x2014;
If used as backbone, list of indices of features to output. Can be any of 0, 1, 2, etc. (depending on how
many stages the model has). If unset and <code>out_features</code> is set, will default to the corresponding stages.
If unset and <code>out_features</code> is unset, will default to the last stage. Must be in the
same order as defined in the <code>stage_names</code> attribute.`,name:"out_indices"}],source:"https://github.com/huggingface/transformers/blob/v4.37.1/src/transformers/models/bit/configuration_bit.py#L29"}}),Z=new ct({props:{anchor:"transformers.BitConfig.example",$$slots:{default:[Zt]},$$scope:{ctx:B}}}),ne=new S({props:{title:"BitImageProcessor",local:"transformers.BitImageProcessor",headingTag:"h2"}}),ae=new fe({props:{name:"class transformers.BitImageProcessor",anchor:"transformers.BitImageProcessor",parameters:[{name:"do_resize",val:": bool = True"},{name:"size",val:": Dict = None"},{name:"resample",val:": Resampling = <Resampling.BICUBIC: 3>"},{name:"do_center_crop",val:": bool = True"},{name:"crop_size",val:": Dict = None"},{name:"do_rescale",val:": bool = True"},{name:"rescale_factor",val:": Union = 0.00392156862745098"},{name:"do_normalize",val:": bool = True"},{name:"image_mean",val:": Union = None"},{name:"image_std",val:": Union = None"},{name:"do_convert_rgb",val:": bool = True"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.BitImageProcessor.do_resize",description:`<strong>do_resize</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether to resize the image&#x2019;s (height, width) dimensions to the specified <code>size</code>. Can be overridden by
<code>do_resize</code> in the <code>preprocess</code> method.`,name:"do_resize"},{anchor:"transformers.BitImageProcessor.size",description:`<strong>size</strong> (<code>Dict[str, int]</code> <em>optional</em>, defaults to <code>{&quot;shortest_edge&quot; -- 224}</code>):
Size of the image after resizing. The shortest edge of the image is resized to size[&#x201C;shortest_edge&#x201D;], with
the longest edge resized to keep the input aspect ratio. Can be overridden by <code>size</code> in the <code>preprocess</code>
method.`,name:"size"},{anchor:"transformers.BitImageProcessor.resample",description:`<strong>resample</strong> (<code>PILImageResampling</code>, <em>optional</em>, defaults to <code>PILImageResampling.BICUBIC</code>) &#x2014;
Resampling filter to use if resizing the image. Can be overridden by <code>resample</code> in the <code>preprocess</code> method.`,name:"resample"},{anchor:"transformers.BitImageProcessor.do_center_crop",description:`<strong>do_center_crop</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether to center crop the image to the specified <code>crop_size</code>. Can be overridden by <code>do_center_crop</code> in the
<code>preprocess</code> method.`,name:"do_center_crop"},{anchor:"transformers.BitImageProcessor.crop_size",description:`<strong>crop_size</strong> (<code>Dict[str, int]</code> <em>optional</em>, defaults to 224) &#x2014;
Size of the output image after applying <code>center_crop</code>. Can be overridden by <code>crop_size</code> in the <code>preprocess</code>
method.`,name:"crop_size"},{anchor:"transformers.BitImageProcessor.do_rescale",description:`<strong>do_rescale</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether to rescale the image by the specified scale <code>rescale_factor</code>. Can be overridden by <code>do_rescale</code> in
the <code>preprocess</code> method.`,name:"do_rescale"},{anchor:"transformers.BitImageProcessor.rescale_factor",description:`<strong>rescale_factor</strong> (<code>int</code> or <code>float</code>, <em>optional</em>, defaults to <code>1/255</code>) &#x2014;
Scale factor to use if rescaling the image. Can be overridden by <code>rescale_factor</code> in the <code>preprocess</code>
method.
do_normalize &#x2014;
Whether to normalize the image. Can be overridden by <code>do_normalize</code> in the <code>preprocess</code> method.`,name:"rescale_factor"},{anchor:"transformers.BitImageProcessor.image_mean",description:`<strong>image_mean</strong> (<code>float</code> or <code>List[float]</code>, <em>optional</em>, defaults to <code>OPENAI_CLIP_MEAN</code>) &#x2014;
Mean to use if normalizing the image. This is a float or list of floats the length of the number of
channels in the image. Can be overridden by the <code>image_mean</code> parameter in the <code>preprocess</code> method.`,name:"image_mean"},{anchor:"transformers.BitImageProcessor.image_std",description:`<strong>image_std</strong> (<code>float</code> or <code>List[float]</code>, <em>optional</em>, defaults to <code>OPENAI_CLIP_MEAN</code>) &#x2014;
Standard deviation to use if normalizing the image. This is a float or list of floats the length of the
number of channels in the image. Can be overridden by the <code>image_std</code> parameter in the <code>preprocess</code> method.
Can be overridden by the <code>image_std</code> parameter in the <code>preprocess</code> method.`,name:"image_std"},{anchor:"transformers.BitImageProcessor.do_convert_rgb",description:`<strong>do_convert_rgb</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether to convert the image to RGB.`,name:"do_convert_rgb"}],source:"https://github.com/huggingface/transformers/blob/v4.37.1/src/transformers/models/bit/image_processing_bit.py#L50"}}),re=new fe({props:{name:"preprocess",anchor:"transformers.BitImageProcessor.preprocess",parameters:[{name:"images",val:": Union"},{name:"do_resize",val:": bool = None"},{name:"size",val:": Dict = None"},{name:"resample",val:": Resampling = None"},{name:"do_center_crop",val:": bool = None"},{name:"crop_size",val:": int = None"},{name:"do_rescale",val:": bool = None"},{name:"rescale_factor",val:": float = None"},{name:"do_normalize",val:": bool = None"},{name:"image_mean",val:": Union = None"},{name:"image_std",val:": Union = None"},{name:"do_convert_rgb",val:": bool = None"},{name:"return_tensors",val:": Union = None"},{name:"data_format",val:": Optional = <ChannelDimension.FIRST: 'channels_first'>"},{name:"input_data_format",val:": Union = None"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.BitImageProcessor.preprocess.images",description:`<strong>images</strong> (<code>ImageInput</code>) &#x2014;
Image to preprocess. Expects a single or batch of images with pixel values ranging from 0 to 255. If
passing in images with pixel values between 0 and 1, set <code>do_rescale=False</code>.`,name:"images"},{anchor:"transformers.BitImageProcessor.preprocess.do_resize",description:`<strong>do_resize</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>self.do_resize</code>) &#x2014;
Whether to resize the image.`,name:"do_resize"},{anchor:"transformers.BitImageProcessor.preprocess.size",description:`<strong>size</strong> (<code>Dict[str, int]</code>, <em>optional</em>, defaults to <code>self.size</code>) &#x2014;
Size of the image after resizing. Shortest edge of the image is resized to size[&#x201C;shortest_edge&#x201D;], with
the longest edge resized to keep the input aspect ratio.`,name:"size"},{anchor:"transformers.BitImageProcessor.preprocess.resample",description:`<strong>resample</strong> (<code>int</code>, <em>optional</em>, defaults to <code>self.resample</code>) &#x2014;
Resampling filter to use if resizing the image. This can be one of the enum <code>PILImageResampling</code>. Only
has an effect if <code>do_resize</code> is set to <code>True</code>.`,name:"resample"},{anchor:"transformers.BitImageProcessor.preprocess.do_center_crop",description:`<strong>do_center_crop</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>self.do_center_crop</code>) &#x2014;
Whether to center crop the image.`,name:"do_center_crop"},{anchor:"transformers.BitImageProcessor.preprocess.crop_size",description:`<strong>crop_size</strong> (<code>Dict[str, int]</code>, <em>optional</em>, defaults to <code>self.crop_size</code>) &#x2014;
Size of the center crop. Only has an effect if <code>do_center_crop</code> is set to <code>True</code>.`,name:"crop_size"},{anchor:"transformers.BitImageProcessor.preprocess.do_rescale",description:`<strong>do_rescale</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>self.do_rescale</code>) &#x2014;
Whether to rescale the image.`,name:"do_rescale"},{anchor:"transformers.BitImageProcessor.preprocess.rescale_factor",description:`<strong>rescale_factor</strong> (<code>float</code>, <em>optional</em>, defaults to <code>self.rescale_factor</code>) &#x2014;
Rescale factor to rescale the image by if <code>do_rescale</code> is set to <code>True</code>.`,name:"rescale_factor"},{anchor:"transformers.BitImageProcessor.preprocess.do_normalize",description:`<strong>do_normalize</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>self.do_normalize</code>) &#x2014;
Whether to normalize the image.`,name:"do_normalize"},{anchor:"transformers.BitImageProcessor.preprocess.image_mean",description:`<strong>image_mean</strong> (<code>float</code> or <code>List[float]</code>, <em>optional</em>, defaults to <code>self.image_mean</code>) &#x2014;
Image mean to use for normalization. Only has an effect if <code>do_normalize</code> is set to <code>True</code>.`,name:"image_mean"},{anchor:"transformers.BitImageProcessor.preprocess.image_std",description:`<strong>image_std</strong> (<code>float</code> or <code>List[float]</code>, <em>optional</em>, defaults to <code>self.image_std</code>) &#x2014;
Image standard deviation to use for normalization. Only has an effect if <code>do_normalize</code> is set to
<code>True</code>.`,name:"image_std"},{anchor:"transformers.BitImageProcessor.preprocess.do_convert_rgb",description:`<strong>do_convert_rgb</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>self.do_convert_rgb</code>) &#x2014;
Whether to convert the image to RGB.`,name:"do_convert_rgb"},{anchor:"transformers.BitImageProcessor.preprocess.return_tensors",description:`<strong>return_tensors</strong> (<code>str</code> or <code>TensorType</code>, <em>optional</em>) &#x2014;
The type of tensors to return. Can be one of:<ul>
<li>Unset: Return a list of <code>np.ndarray</code>.</li>
<li><code>TensorType.TENSORFLOW</code> or <code>&apos;tf&apos;</code>: Return a batch of type <code>tf.Tensor</code>.</li>
<li><code>TensorType.PYTORCH</code> or <code>&apos;pt&apos;</code>: Return a batch of type <code>torch.Tensor</code>.</li>
<li><code>TensorType.NUMPY</code> or <code>&apos;np&apos;</code>: Return a batch of type <code>np.ndarray</code>.</li>
<li><code>TensorType.JAX</code> or <code>&apos;jax&apos;</code>: Return a batch of type <code>jax.numpy.ndarray</code>.</li>
</ul>`,name:"return_tensors"},{anchor:"transformers.BitImageProcessor.preprocess.data_format",description:`<strong>data_format</strong> (<code>ChannelDimension</code> or <code>str</code>, <em>optional</em>, defaults to <code>ChannelDimension.FIRST</code>) &#x2014;
The channel dimension format for the output image. Can be one of:<ul>
<li><code>&quot;channels_first&quot;</code> or <code>ChannelDimension.FIRST</code>: image in (num_channels, height, width) format.</li>
<li><code>&quot;channels_last&quot;</code> or <code>ChannelDimension.LAST</code>: image in (height, width, num_channels) format.</li>
<li>Unset: Use the channel dimension format of the input image.</li>
</ul>`,name:"data_format"},{anchor:"transformers.BitImageProcessor.preprocess.input_data_format",description:`<strong>input_data_format</strong> (<code>ChannelDimension</code> or <code>str</code>, <em>optional</em>) &#x2014;
The channel dimension format for the input image. If unset, the channel dimension format is inferred
from the input image. Can be one of:<ul>
<li><code>&quot;channels_first&quot;</code> or <code>ChannelDimension.FIRST</code>: image in (num_channels, height, width) format.</li>
<li><code>&quot;channels_last&quot;</code> or <code>ChannelDimension.LAST</code>: image in (height, width, num_channels) format.</li>
<li><code>&quot;none&quot;</code> or <code>ChannelDimension.NONE</code>: image in (height, width) format.</li>
</ul>`,name:"input_data_format"}],source:"https://github.com/huggingface/transformers/blob/v4.37.1/src/transformers/models/bit/image_processing_bit.py#L174"}}),ie=new S({props:{title:"BitModel",local:"transformers.BitModel",headingTag:"h2"}}),le=new fe({props:{name:"class transformers.BitModel",anchor:"transformers.BitModel",parameters:[{name:"config",val:""}],parametersDescription:[{anchor:"transformers.BitModel.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.37.1/ja/model_doc/bit#transformers.BitConfig">BitConfig</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/v4.37.1/ja/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/v4.37.1/src/transformers/models/bit/modeling_bit.py#L697"}}),de=new fe({props:{name:"forward",anchor:"transformers.BitModel.forward",parameters:[{name:"pixel_values",val:": Tensor"},{name:"output_hidden_states",val:": Optional = None"},{name:"return_dict",val:": Optional = None"}],parametersDescription:[{anchor:"transformers.BitModel.forward.pixel_values",description:`<strong>pixel_values</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, num_channels, height, width)</code>) &#x2014;
Pixel values. Pixel values can be obtained using <a href="/docs/transformers/v4.37.1/ja/model_doc/auto#transformers.AutoImageProcessor">AutoImageProcessor</a>. See <a href="/docs/transformers/v4.37.1/ja/model_doc/conditional_detr#transformers.ConditionalDetrFeatureExtractor.__call__">BitImageProcessor.<strong>call</strong>()</a>
for details.`,name:"pixel_values"},{anchor:"transformers.BitModel.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.BitModel.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/v4.37.1/ja/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/transformers/blob/v4.37.1/src/transformers/models/bit/modeling_bit.py#L719",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>A <code>transformers.modeling_outputs.BaseModelOutputWithPoolingAndNoAttention</code> or a tuple of
<code>torch.FloatTensor</code> (if <code>return_dict=False</code> is passed or when <code>config.return_dict=False</code>) comprising various
elements depending on the configuration (<a
  href="/docs/transformers/v4.37.1/ja/model_doc/bit#transformers.BitConfig"
>BitConfig</a>) and inputs.</p>
<ul>
<li>
<p><strong>last_hidden_state</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, num_channels, height, width)</code>) — 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>) — Last layer hidden-state after a pooling operation on the spatial dimensions.</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, num_channels, height, width)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.</p>
</li>
</ul>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>transformers.modeling_outputs.BaseModelOutputWithPoolingAndNoAttention</code> or <code>tuple(torch.FloatTensor)</code></p>
`}}),W=new jt({props:{$$slots:{default:[Nt]},$$scope:{ctx:B}}}),k=new ct({props:{anchor:"transformers.BitModel.forward.example",$$slots:{default:[Wt]},$$scope:{ctx:B}}}),ce=new S({props:{title:"BitForImageClassification",local:"transformers.BitForImageClassification",headingTag:"h2"}}),me=new fe({props:{name:"class transformers.BitForImageClassification",anchor:"transformers.BitForImageClassification",parameters:[{name:"config",val:""}],parametersDescription:[{anchor:"transformers.BitForImageClassification.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.37.1/ja/model_doc/bit#transformers.BitConfig">BitConfig</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/v4.37.1/ja/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/v4.37.1/src/transformers/models/bit/modeling_bit.py#L757"}}),pe=new fe({props:{name:"forward",anchor:"transformers.BitForImageClassification.forward",parameters:[{name:"pixel_values",val:": Optional = None"},{name:"labels",val:": Optional = None"},{name:"output_hidden_states",val:": Optional = None"},{name:"return_dict",val:": Optional = None"}],parametersDescription:[{anchor:"transformers.BitForImageClassification.forward.pixel_values",description:`<strong>pixel_values</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, num_channels, height, width)</code>) &#x2014;
Pixel values. Pixel values can be obtained using <a href="/docs/transformers/v4.37.1/ja/model_doc/auto#transformers.AutoImageProcessor">AutoImageProcessor</a>. See <a href="/docs/transformers/v4.37.1/ja/model_doc/conditional_detr#transformers.ConditionalDetrFeatureExtractor.__call__">BitImageProcessor.<strong>call</strong>()</a>
for details.`,name:"pixel_values"},{anchor:"transformers.BitForImageClassification.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.BitForImageClassification.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/v4.37.1/ja/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"},{anchor:"transformers.BitForImageClassification.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 &gt; 1</code> a classification loss is computed (Cross-Entropy).`,name:"labels"}],source:"https://github.com/huggingface/transformers/blob/v4.37.1/src/transformers/models/bit/modeling_bit.py#L777",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>A <a
  href="/docs/transformers/v4.37.1/ja/main_classes/output#transformers.modeling_outputs.ImageClassifierOutputWithNoAttention"
>transformers.modeling_outputs.ImageClassifierOutputWithNoAttention</a> or a tuple of
<code>torch.FloatTensor</code> (if <code>return_dict=False</code> is passed or when <code>config.return_dict=False</code>) comprising various
elements depending on the configuration (<a
  href="/docs/transformers/v4.37.1/ja/model_doc/bit#transformers.BitConfig"
>BitConfig</a>) and inputs.</p>
<ul>
<li><strong>loss</strong> (<code>torch.FloatTensor</code> of shape <code>(1,)</code>, <em>optional</em>, returned when <code>labels</code> is provided) — Classification (or regression if config.num_labels==1) loss.</li>
<li><strong>logits</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, config.num_labels)</code>) — Classification (or regression if config.num_labels==1) scores (before SoftMax).</li>
<li><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, num_channels, height, width)</code>. Hidden-states (also
called feature maps) of the model at the output of each stage.</li>
</ul>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><a
  href="/docs/transformers/v4.37.1/ja/main_classes/output#transformers.modeling_outputs.ImageClassifierOutputWithNoAttention"
>transformers.modeling_outputs.ImageClassifierOutputWithNoAttention</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),E=new jt({props:{$$slots:{default:[kt]},$$scope:{ctx:B}}}),q=new ct({props:{anchor:"transformers.BitForImageClassification.forward.example",$$slots:{default:[Et]},$$scope:{ctx:B}}}),{c(){s=c("meta"),$=r(),l=c("p"),d=r(),g(v.$$.fragment),n=r(),g(C.$$.fragment),we=r(),X=c("p"),X.innerHTML=pt,xe=r(),V=c("p"),V.textContent=gt,Ie=r(),Q=c("p"),Q.textContent=ft,je=r(),g(A.$$.fragment),ze=r(),H=c("ul"),H.innerHTML=ht,Ue=r(),Y=c("p"),Y.innerHTML=ut,Pe=r(),g(D.$$.fragment),Je=r(),O=c("p"),O.textContent=_t,Fe=r(),g(K.$$.fragment),Re=r(),ee=c("ul"),ee.innerHTML=bt,Ze=r(),te=c("p"),te.textContent=vt,Ne=r(),g(oe.$$.fragment),We=r(),M=c("div"),g(se.$$.fragment),Qe=r(),he=c("p"),he.innerHTML=yt,Ae=r(),ue=c("p"),ue.innerHTML=$t,He=r(),g(Z.$$.fragment),ke=r(),g(ne.$$.fragment),Ee=r(),I=c("div"),g(ae.$$.fragment),Ye=r(),_e=c("p"),_e.textContent=Ct,De=r(),N=c("div"),g(re.$$.fragment),Oe=r(),be=c("p"),be.textContent=Mt,qe=r(),g(ie.$$.fragment),Le=r(),j=c("div"),g(le.$$.fragment),Ke=r(),ve=c("p"),ve.innerHTML=Tt,et=r(),w=c("div"),g(de.$$.fragment),tt=r(),ye=c("p"),ye.innerHTML=Bt,ot=r(),g(W.$$.fragment),st=r(),g(k.$$.fragment),Ge=r(),g(ce.$$.fragment),Se=r(),T=c("div"),g(me.$$.fragment),nt=r(),$e=c("p"),$e.textContent=wt,at=r(),Ce=c("p"),Ce.innerHTML=xt,rt=r(),x=c("div"),g(pe.$$.fragment),it=r(),Me=c("p"),Me.innerHTML=It,lt=r(),g(E.$$.fragment),dt=r(),g(q.$$.fragment),Xe=r(),Te=c("p"),this.h()},l(e){const t=Ft("svelte-u9bgzb",document.head);s=m(t,"META",{name:!0,content:!0}),t.forEach(o),$=i(e),l=m(e,"P",{}),F(l).forEach(o),d=i(e),f(v.$$.fragment,e),n=i(e),f(C.$$.fragment,e),we=i(e),X=m(e,"P",{"data-svelte-h":!0}),y(X)!=="svelte-vkgoj7"&&(X.innerHTML=pt),xe=i(e),V=m(e,"P",{"data-svelte-h":!0}),y(V)!=="svelte-1cv3nri"&&(V.textContent=gt),Ie=i(e),Q=m(e,"P",{"data-svelte-h":!0}),y(Q)!=="svelte-ubvspa"&&(Q.textContent=ft),je=i(e),f(A.$$.fragment,e),ze=i(e),H=m(e,"UL",{"data-svelte-h":!0}),y(H)!=="svelte-1xf9jem"&&(H.innerHTML=ht),Ue=i(e),Y=m(e,"P",{"data-svelte-h":!0}),y(Y)!=="svelte-ravx6a"&&(Y.innerHTML=ut),Pe=i(e),f(D.$$.fragment,e),Je=i(e),O=m(e,"P",{"data-svelte-h":!0}),y(O)!=="svelte-71u5o7"&&(O.textContent=_t),Fe=i(e),f(K.$$.fragment,e),Re=i(e),ee=m(e,"UL",{"data-svelte-h":!0}),y(ee)!=="svelte-gzr4d8"&&(ee.innerHTML=bt),Ze=i(e),te=m(e,"P",{"data-svelte-h":!0}),y(te)!=="svelte-17ytafw"&&(te.textContent=vt),Ne=i(e),f(oe.$$.fragment,e),We=i(e),M=m(e,"DIV",{class:!0});var z=F(M);f(se.$$.fragment,z),Qe=i(z),he=m(z,"P",{"data-svelte-h":!0}),y(he)!=="svelte-o6v0ng"&&(he.innerHTML=yt),Ae=i(z),ue=m(z,"P",{"data-svelte-h":!0}),y(ue)!=="svelte-16gkrqh"&&(ue.innerHTML=$t),He=i(z),f(Z.$$.fragment,z),z.forEach(o),ke=i(e),f(ne.$$.fragment,e),Ee=i(e),I=m(e,"DIV",{class:!0});var P=F(I);f(ae.$$.fragment,P),Ye=i(P),_e=m(P,"P",{"data-svelte-h":!0}),y(_e)!=="svelte-zncq1j"&&(_e.textContent=Ct),De=i(P),N=m(P,"DIV",{class:!0});var ge=F(N);f(re.$$.fragment,ge),Oe=i(ge),be=m(ge,"P",{"data-svelte-h":!0}),y(be)!=="svelte-1x3yxsa"&&(be.textContent=Mt),ge.forEach(o),P.forEach(o),qe=i(e),f(ie.$$.fragment,e),Le=i(e),j=m(e,"DIV",{class:!0});var J=F(j);f(le.$$.fragment,J),Ke=i(J),ve=m(J,"P",{"data-svelte-h":!0}),y(ve)!=="svelte-1fxsgvt"&&(ve.innerHTML=Tt),et=i(J),w=m(J,"DIV",{class:!0});var U=F(w);f(de.$$.fragment,U),tt=i(U),ye=m(U,"P",{"data-svelte-h":!0}),y(ye)!=="svelte-1dgjmzw"&&(ye.innerHTML=Bt),ot=i(U),f(W.$$.fragment,U),st=i(U),f(k.$$.fragment,U),U.forEach(o),J.forEach(o),Ge=i(e),f(ce.$$.fragment,e),Se=i(e),T=m(e,"DIV",{class:!0});var L=F(T);f(me.$$.fragment,L),nt=i(L),$e=m(L,"P",{"data-svelte-h":!0}),y($e)!=="svelte-a01xtz"&&($e.textContent=wt),at=i(L),Ce=m(L,"P",{"data-svelte-h":!0}),y(Ce)!=="svelte-1gjh92c"&&(Ce.innerHTML=xt),rt=i(L),x=m(L,"DIV",{class:!0});var G=F(x);f(pe.$$.fragment,G),it=i(G),Me=m(G,"P",{"data-svelte-h":!0}),y(Me)!=="svelte-1x5nd4c"&&(Me.innerHTML=It),lt=i(G),f(E.$$.fragment,G),dt=i(G),f(q.$$.fragment,G),G.forEach(o),L.forEach(o),Xe=i(e),Te=m(e,"P",{}),F(Te).forEach(o),this.h()},h(){R(s,"name","hf:doc:metadata"),R(s,"content",Lt),R(M,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),R(N,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),R(I,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),R(w,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),R(j,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),R(x,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),R(T,"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,t){p(document.head,s),a(e,$,t),a(e,l,t),a(e,d,t),h(v,e,t),a(e,n,t),h(C,e,t),a(e,we,t),a(e,X,t),a(e,xe,t),a(e,V,t),a(e,Ie,t),a(e,Q,t),a(e,je,t),h(A,e,t),a(e,ze,t),a(e,H,t),a(e,Ue,t),a(e,Y,t),a(e,Pe,t),h(D,e,t),a(e,Je,t),a(e,O,t),a(e,Fe,t),h(K,e,t),a(e,Re,t),a(e,ee,t),a(e,Ze,t),a(e,te,t),a(e,Ne,t),h(oe,e,t),a(e,We,t),a(e,M,t),h(se,M,null),p(M,Qe),p(M,he),p(M,Ae),p(M,ue),p(M,He),h(Z,M,null),a(e,ke,t),h(ne,e,t),a(e,Ee,t),a(e,I,t),h(ae,I,null),p(I,Ye),p(I,_e),p(I,De),p(I,N),h(re,N,null),p(N,Oe),p(N,be),a(e,qe,t),h(ie,e,t),a(e,Le,t),a(e,j,t),h(le,j,null),p(j,Ke),p(j,ve),p(j,et),p(j,w),h(de,w,null),p(w,tt),p(w,ye),p(w,ot),h(W,w,null),p(w,st),h(k,w,null),a(e,Ge,t),h(ce,e,t),a(e,Se,t),a(e,T,t),h(me,T,null),p(T,nt),p(T,$e),p(T,at),p(T,Ce),p(T,rt),p(T,x),h(pe,x,null),p(x,it),p(x,Me),p(x,lt),h(E,x,null),p(x,dt),h(q,x,null),a(e,Xe,t),a(e,Te,t),Ve=!0},p(e,[t]){const z={};t&2&&(z.$$scope={dirty:t,ctx:e}),Z.$set(z);const P={};t&2&&(P.$$scope={dirty:t,ctx:e}),W.$set(P);const ge={};t&2&&(ge.$$scope={dirty:t,ctx:e}),k.$set(ge);const J={};t&2&&(J.$$scope={dirty:t,ctx:e}),E.$set(J);const U={};t&2&&(U.$$scope={dirty:t,ctx:e}),q.$set(U)},i(e){Ve||(u(v.$$.fragment,e),u(C.$$.fragment,e),u(A.$$.fragment,e),u(D.$$.fragment,e),u(K.$$.fragment,e),u(oe.$$.fragment,e),u(se.$$.fragment,e),u(Z.$$.fragment,e),u(ne.$$.fragment,e),u(ae.$$.fragment,e),u(re.$$.fragment,e),u(ie.$$.fragment,e),u(le.$$.fragment,e),u(de.$$.fragment,e),u(W.$$.fragment,e),u(k.$$.fragment,e),u(ce.$$.fragment,e),u(me.$$.fragment,e),u(pe.$$.fragment,e),u(E.$$.fragment,e),u(q.$$.fragment,e),Ve=!0)},o(e){_(v.$$.fragment,e),_(C.$$.fragment,e),_(A.$$.fragment,e),_(D.$$.fragment,e),_(K.$$.fragment,e),_(oe.$$.fragment,e),_(se.$$.fragment,e),_(Z.$$.fragment,e),_(ne.$$.fragment,e),_(ae.$$.fragment,e),_(re.$$.fragment,e),_(ie.$$.fragment,e),_(le.$$.fragment,e),_(de.$$.fragment,e),_(W.$$.fragment,e),_(k.$$.fragment,e),_(ce.$$.fragment,e),_(me.$$.fragment,e),_(pe.$$.fragment,e),_(E.$$.fragment,e),_(q.$$.fragment,e),Ve=!1},d(e){e&&(o($),o(l),o(d),o(n),o(we),o(X),o(xe),o(V),o(Ie),o(Q),o(je),o(ze),o(H),o(Ue),o(Y),o(Pe),o(Je),o(O),o(Fe),o(Re),o(ee),o(Ze),o(te),o(Ne),o(We),o(M),o(ke),o(Ee),o(I),o(qe),o(Le),o(j),o(Ge),o(Se),o(T),o(Xe),o(Te)),o(s),b(v,e),b(C,e),b(A,e),b(D,e),b(K,e),b(oe,e),b(se),b(Z),b(ne,e),b(ae),b(re),b(ie,e),b(le),b(de),b(W),b(k),b(ce,e),b(me),b(pe),b(E),b(q)}}}const Lt='{"title":"Big Transfer (BiT)","local":"big-transfer-bit","sections":[{"title":"Overview","local":"overview","sections":[],"depth":2},{"title":"Usage tips","local":"usage-tips","sections":[],"depth":2},{"title":"Resources","local":"resources","sections":[],"depth":2},{"title":"BitConfig","local":"transformers.BitConfig","sections":[],"depth":2},{"title":"BitImageProcessor","local":"transformers.BitImageProcessor","sections":[],"depth":2},{"title":"BitModel","local":"transformers.BitModel","sections":[],"depth":2},{"title":"BitForImageClassification","local":"transformers.BitForImageClassification","sections":[],"depth":2}],"depth":1}';function Gt(B){return Ut(()=>{new URLSearchParams(window.location.search).get("fw")}),[]}class Ot extends Pt{constructor(s){super(),Jt(this,s,Gt,qt,zt,{})}}export{Ot as component};
