import{s as $r,o as Tr,n as yr}from"../chunks/scheduler.9bc65507.js";import{S as Mr,i as Fr,g as n,s as r,r as d,A as wr,h as o,f as s,c as a,j as v,u as l,x,k as b,y as t,a as _,v as m,d as p,t as u,w as f}from"../chunks/index.707bf1b6.js";import{T as Ir}from"../chunks/Tip.c2ecdbf4.js";import{D as $}from"../chunks/Docstring.17db21ae.js";import{C as Er}from"../chunks/CodeBlock.54a9f38d.js";import{E as jr}from"../chunks/ExampleCodeBlock.4f515aa9.js";import{H as De}from"../chunks/Heading.342b1fa6.js";function Jr(ye){let g,L="Examples:",T,F,I;return F=new Er({props:{code:"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",highlighted:`<span class="hljs-comment"># We can&#x27;t instantiate directly the base class *FeatureExtractionMixin* nor *SequenceFeatureExtractor* so let&#x27;s show the examples on a</span>
<span class="hljs-comment"># derived class: *Wav2Vec2FeatureExtractor*</span>
feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(
    <span class="hljs-string">&quot;facebook/wav2vec2-base-960h&quot;</span>
)  <span class="hljs-comment"># Download feature_extraction_config from huggingface.co and cache.</span>
feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(
    <span class="hljs-string">&quot;./test/saved_model/&quot;</span>
)  <span class="hljs-comment"># E.g. feature_extractor (or model) was saved using *save_pretrained(&#x27;./test/saved_model/&#x27;)*</span>
feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(<span class="hljs-string">&quot;./test/saved_model/preprocessor_config.json&quot;</span>)
feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(
    <span class="hljs-string">&quot;facebook/wav2vec2-base-960h&quot;</span>, return_attention_mask=<span class="hljs-literal">False</span>, foo=<span class="hljs-literal">False</span>
)
<span class="hljs-keyword">assert</span> feature_extractor.return_attention_mask <span class="hljs-keyword">is</span> <span class="hljs-literal">False</span>
feature_extractor, unused_kwargs = Wav2Vec2FeatureExtractor.from_pretrained(
    <span class="hljs-string">&quot;facebook/wav2vec2-base-960h&quot;</span>, return_attention_mask=<span class="hljs-literal">False</span>, foo=<span class="hljs-literal">False</span>, return_unused_kwargs=<span class="hljs-literal">True</span>
)
<span class="hljs-keyword">assert</span> feature_extractor.return_attention_mask <span class="hljs-keyword">is</span> <span class="hljs-literal">False</span>
<span class="hljs-keyword">assert</span> unused_kwargs == {<span class="hljs-string">&quot;foo&quot;</span>: <span class="hljs-literal">False</span>}`,wrap:!1}}),{c(){g=n("p"),g.textContent=L,T=r(),d(F.$$.fragment)},l(y){g=o(y,"P",{"data-svelte-h":!0}),x(g)!=="svelte-kvfsh7"&&(g.textContent=L),T=a(y),l(F.$$.fragment,y)},m(y,j){_(y,g,j),_(y,T,j),m(F,y,j),I=!0},p:yr,i(y){I||(p(F.$$.fragment,y),I=!0)},o(y){u(F.$$.fragment,y),I=!1},d(y){y&&(s(g),s(T)),f(F,y)}}}function zr(ye){let g,L=`If the <code>processed_features</code> passed are dictionary of numpy arrays, PyTorch tensors or TensorFlow tensors, the
result will use the same type unless you provide a different tensor type with <code>return_tensors</code>. In the case of
PyTorch tensors, you will lose the specific device of your tensors however.`;return{c(){g=n("p"),g.innerHTML=L},l(T){g=o(T,"P",{"data-svelte-h":!0}),x(g)!=="svelte-yvqf1m"&&(g.innerHTML=L)},m(T,F){_(T,g,F)},p:yr,d(T){T&&s(g)}}}function Lr(ye){let g,L,T,F,I,y,j,Kt="フィーチャーエクストラクタは、オーディオまたはビジョンモデルのための入力フィーチャーの準備を担当しています。これには、シーケンスからのフィーチャー抽出（例：オーディオファイルの前処理からLog-Melスペクトログラムフィーチャーへの変換）、画像からのフィーチャー抽出（例：画像ファイルのクロッピング）、またパディング、正規化、そしてNumpy、PyTorch、TensorFlowテンソルへの変換も含まれます。",Xe,O,Ye,w,K,gt,$e,er=`This is a feature extraction mixin used to provide saving/loading functionality for sequential and image feature
extractors.`,ht,Z,ee,xt,Te,tr=`Instantiate a type of <a href="/docs/transformers/main/ja/main_classes/feature_extractor#transformers.FeatureExtractionMixin">FeatureExtractionMixin</a> from a feature extractor, <em>e.g.</em> a
derived class of <a href="/docs/transformers/main/ja/main_classes/feature_extractor#transformers.SequenceFeatureExtractor">SequenceFeatureExtractor</a>.`,_t,V,vt,C,te,bt,Me,rr=`Save a feature_extractor object to the directory <code>save_directory</code>, so that it can be re-loaded using the
<a href="/docs/transformers/main/ja/main_classes/feature_extractor#transformers.FeatureExtractionMixin.from_pretrained">from_pretrained()</a> class method.`,Ge,re,Se,J,ae,yt,Fe,ar="This is a general feature extraction class for speech recognition.",$t,E,ne,Tt,we,nr=`Pad input values / input vectors or a batch of input values / input vectors up to predefined length or to the
max sequence length in the batch.`,Mt,Ie,or=`Padding side (left/right) padding values are defined at the feature extractor level (with <code>self.padding_side</code>,
<code>self.padding_value</code>)`,Ft,H,Ae,oe,Qe,M,se,wt,Ee,sr='Holds the output of the <a href="/docs/transformers/main/ja/main_classes/feature_extractor#transformers.SequenceFeatureExtractor.pad">pad()</a> and feature extractor specific <code>__call__</code> methods.',It,je,ir="This class is derived from a python dictionary and can be used as a dictionary.",Et,R,ie,jt,Je,cr="Convert the inner content to tensors.",Jt,q,ce,zt,ze,dr=`Send all values to device by calling <code>v.to(*args, **kwargs)</code> (PyTorch only). This should support casting in
different <code>dtypes</code> and sending the <code>BatchFeature</code> to a different <code>device</code>.`,Oe,de,Ke,c,le,Lt,Le,lr="Mixin that contain utilities for preparing image features.",Zt,W,me,kt,Ze,mr=`Crops <code>image</code> to the given size using a center crop. Note that if the image is too small to be cropped to the
size given, it will be padded (so the returned result has the size asked).`,Pt,N,pe,Vt,ke,pr="Converts <code>PIL.Image.Image</code> to RGB format.",Ct,U,ue,Ht,Pe,ur="Expands 2-dimensional <code>image</code> to 3 dimensions.",Rt,B,fe,qt,Ve,fr=`Flips the channel order of <code>image</code> from RGB to BGR, or vice versa. Note that this will trigger a conversion of
<code>image</code> to a NumPy array if it’s a PIL Image.`,Wt,D,ge,Nt,Ce,gr=`Normalizes <code>image</code> with <code>mean</code> and <code>std</code>. Note that this will trigger a conversion of <code>image</code> to a NumPy array
if it’s a PIL Image.`,Ut,X,he,Bt,He,hr="Rescale a numpy image by scale amount",Dt,Y,xe,Xt,Re,xr="Resizes <code>image</code>. Enforces conversion of input to PIL.Image.",Yt,G,_e,Gt,qe,_r=`Returns a rotated copy of <code>image</code>. This method returns a copy of <code>image</code>, rotated the given number of degrees
counter clockwise around its centre.`,St,S,ve,At,We,vr=`Converts <code>image</code> to a numpy array. Optionally rescales it and puts the channel dimension as the first
dimension.`,Qt,A,be,Ot,Ne,br=`Converts <code>image</code> to a PIL Image. Optionally rescales it and puts the channel dimension back as the last axis if
needed.`,et,Be,tt;return I=new De({props:{title:"Feature Extractor",local:"feature-extractor",headingTag:"h1"}}),O=new De({props:{title:"FeatureExtractionMixin",local:"transformers.FeatureExtractionMixin",headingTag:"h2"}}),K=new $({props:{name:"class transformers.FeatureExtractionMixin",anchor:"transformers.FeatureExtractionMixin",parameters:[{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/feature_extraction_utils.py#L240"}}),ee=new $({props:{name:"from_pretrained",anchor:"transformers.FeatureExtractionMixin.from_pretrained",parameters:[{name:"pretrained_model_name_or_path",val:": Union"},{name:"cache_dir",val:": Union = None"},{name:"force_download",val:": bool = False"},{name:"local_files_only",val:": bool = False"},{name:"token",val:": Union = None"},{name:"revision",val:": str = 'main'"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FeatureExtractionMixin.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
This can be either:</p>
<ul>
<li>a string, the <em>model id</em> of a pretrained feature_extractor hosted inside a model repo on
huggingface.co.</li>
<li>a path to a <em>directory</em> containing a feature extractor file saved using the
<a href="/docs/transformers/main/ja/main_classes/feature_extractor#transformers.FeatureExtractionMixin.save_pretrained">save_pretrained()</a> method, e.g.,
<code>./my_model_directory/</code>.</li>
<li>a path or url to a saved feature extractor JSON <em>file</em>, e.g.,
<code>./my_model_directory/preprocessor_config.json</code>.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.FeatureExtractionMixin.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model feature extractor should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.FeatureExtractionMixin.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force to (re-)download the feature extractor files and override the cached versions
if they exist.`,name:"force_download"},{anchor:"transformers.FeatureExtractionMixin.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to delete incompletely received file. Attempts to resume the download if such a file
exists.`,name:"resume_download"},{anchor:"transformers.FeatureExtractionMixin.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}.</code> The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.FeatureExtractionMixin.from_pretrained.token",description:`<strong>token</strong> (<code>str</code> or <code>bool</code>, <em>optional</em>) &#x2014;
The token to use as HTTP bearer authorization for remote files. If <code>True</code>, or not specified, will use
the token generated when running <code>huggingface-cli login</code> (stored in <code>~/.huggingface</code>).`,name:"token"},{anchor:"transformers.FeatureExtractionMixin.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/feature_extraction_utils.py#L264",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>A feature extractor of type <a
  href="/docs/transformers/main/ja/main_classes/feature_extractor#transformers.FeatureExtractionMixin"
>FeatureExtractionMixin</a>.</p>
`}}),V=new jr({props:{anchor:"transformers.FeatureExtractionMixin.from_pretrained.example",$$slots:{default:[Jr]},$$scope:{ctx:ye}}}),te=new $({props:{name:"save_pretrained",anchor:"transformers.FeatureExtractionMixin.save_pretrained",parameters:[{name:"save_directory",val:": Union"},{name:"push_to_hub",val:": bool = False"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FeatureExtractionMixin.save_pretrained.save_directory",description:`<strong>save_directory</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Directory where the feature extractor JSON file will be saved (will be created if it does not exist).`,name:"save_directory"},{anchor:"transformers.FeatureExtractionMixin.save_pretrained.push_to_hub",description:`<strong>push_to_hub</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to push your model to the Hugging Face model hub after saving it. You can specify the
repository you want to push to with <code>repo_id</code> (will default to the name of <code>save_directory</code> in your
namespace).`,name:"push_to_hub"},{anchor:"transformers.FeatureExtractionMixin.save_pretrained.kwargs",description:`<strong>kwargs</strong> (<code>Dict[str, Any]</code>, <em>optional</em>) &#x2014;
Additional key word arguments passed along to the <a href="/docs/transformers/main/ja/main_classes/model#transformers.utils.PushToHubMixin.push_to_hub">push_to_hub()</a> method.`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/feature_extraction_utils.py#L376"}}),re=new De({props:{title:"SequenceFeatureExtractor",local:"transformers.SequenceFeatureExtractor",headingTag:"h2"}}),ae=new $({props:{name:"class transformers.SequenceFeatureExtractor",anchor:"transformers.SequenceFeatureExtractor",parameters:[{name:"feature_size",val:": int"},{name:"sampling_rate",val:": int"},{name:"padding_value",val:": float"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.SequenceFeatureExtractor.feature_size",description:`<strong>feature_size</strong> (<code>int</code>) &#x2014;
The feature dimension of the extracted features.`,name:"feature_size"},{anchor:"transformers.SequenceFeatureExtractor.sampling_rate",description:`<strong>sampling_rate</strong> (<code>int</code>) &#x2014;
The sampling rate at which the audio files should be digitalized expressed in hertz (Hz).`,name:"sampling_rate"},{anchor:"transformers.SequenceFeatureExtractor.padding_value",description:`<strong>padding_value</strong> (<code>float</code>) &#x2014;
The value that is used to fill the padding values / vectors.`,name:"padding_value"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/feature_extraction_sequence_utils.py#L29"}}),ne=new $({props:{name:"pad",anchor:"transformers.SequenceFeatureExtractor.pad",parameters:[{name:"processed_features",val:": Union"},{name:"padding",val:": Union = True"},{name:"max_length",val:": Optional = None"},{name:"truncation",val:": bool = False"},{name:"pad_to_multiple_of",val:": Optional = None"},{name:"return_attention_mask",val:": Optional = None"},{name:"return_tensors",val:": Union = None"}],parametersDescription:[{anchor:"transformers.SequenceFeatureExtractor.pad.processed_features",description:`<strong>processed_features</strong> (<a href="/docs/transformers/main/ja/main_classes/feature_extractor#transformers.BatchFeature">BatchFeature</a>, list of <a href="/docs/transformers/main/ja/main_classes/feature_extractor#transformers.BatchFeature">BatchFeature</a>, <code>Dict[str, List[float]]</code>, <code>Dict[str, List[List[float]]</code> or <code>List[Dict[str, List[float]]]</code>) &#x2014;
Processed inputs. Can represent one input (<a href="/docs/transformers/main/ja/main_classes/feature_extractor#transformers.BatchFeature">BatchFeature</a> or <code>Dict[str, List[float]]</code>) or a batch of
input values / vectors (list of <a href="/docs/transformers/main/ja/main_classes/feature_extractor#transformers.BatchFeature">BatchFeature</a>, <em>Dict[str, List[List[float]]]</em> or <em>List[Dict[str,
List[float]]]</em>) so you can use this method during preprocessing as well as in a PyTorch Dataloader
collate function.</p>
<p>Instead of <code>List[float]</code> you can have tensors (numpy arrays, PyTorch tensors or TensorFlow tensors),
see the note above for the return type.`,name:"processed_features"},{anchor:"transformers.SequenceFeatureExtractor.pad.padding",description:`<strong>padding</strong> (<code>bool</code>, <code>str</code> or <a href="/docs/transformers/main/ja/internal/file_utils#transformers.utils.PaddingStrategy">PaddingStrategy</a>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Select a strategy to pad the returned sequences (according to the model&#x2019;s padding side and padding
index) among:</p>
<ul>
<li><code>True</code> or <code>&apos;longest&apos;</code>: Pad to the longest sequence in the batch (or no padding if only a single
sequence if provided).</li>
<li><code>&apos;max_length&apos;</code>: Pad to a maximum length specified with the argument <code>max_length</code> or to the maximum
acceptable input length for the model if that argument is not provided.</li>
<li><code>False</code> or <code>&apos;do_not_pad&apos;</code> (default): No padding (i.e., can output a batch with sequences of different
lengths).</li>
</ul>`,name:"padding"},{anchor:"transformers.SequenceFeatureExtractor.pad.max_length",description:`<strong>max_length</strong> (<code>int</code>, <em>optional</em>) &#x2014;
Maximum length of the returned list and optionally padding length (see above).`,name:"max_length"},{anchor:"transformers.SequenceFeatureExtractor.pad.truncation",description:`<strong>truncation</strong> (<code>bool</code>) &#x2014;
Activates truncation to cut input sequences longer than <code>max_length</code> to <code>max_length</code>.`,name:"truncation"},{anchor:"transformers.SequenceFeatureExtractor.pad.pad_to_multiple_of",description:`<strong>pad_to_multiple_of</strong> (<code>int</code>, <em>optional</em>) &#x2014;
If set will pad the sequence to a multiple of the provided value.</p>
<p>This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability
<code>&gt;= 7.5</code> (Volta), or on TPUs which benefit from having sequence lengths be a multiple of 128.`,name:"pad_to_multiple_of"},{anchor:"transformers.SequenceFeatureExtractor.pad.return_attention_mask",description:`<strong>return_attention_mask</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether to return the attention mask. If left to the default, will return the attention mask according
to the specific feature_extractor&#x2019;s default.</p>
<p><a href="../glossary#attention-mask">What are attention masks?</a>`,name:"return_attention_mask"},{anchor:"transformers.SequenceFeatureExtractor.pad.return_tensors",description:`<strong>return_tensors</strong> (<code>str</code> or <a href="/docs/transformers/main/ja/internal/file_utils#transformers.TensorType">TensorType</a>, <em>optional</em>) &#x2014;
If set, will return tensors instead of list of python integers. Acceptable values are:</p>
<ul>
<li><code>&apos;tf&apos;</code>: Return TensorFlow <code>tf.constant</code> objects.</li>
<li><code>&apos;pt&apos;</code>: Return PyTorch <code>torch.Tensor</code> objects.</li>
<li><code>&apos;np&apos;</code>: Return Numpy <code>np.ndarray</code> objects.</li>
</ul>`,name:"return_tensors"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/feature_extraction_sequence_utils.py#L52"}}),H=new Ir({props:{$$slots:{default:[zr]},$$scope:{ctx:ye}}}),oe=new De({props:{title:"BatchFeature",local:"transformers.BatchFeature",headingTag:"h2"}}),se=new $({props:{name:"class transformers.BatchFeature",anchor:"transformers.BatchFeature",parameters:[{name:"data",val:": Optional = None"},{name:"tensor_type",val:": Union = None"}],parametersDescription:[{anchor:"transformers.BatchFeature.data",description:`<strong>data</strong> (<code>dict</code>, <em>optional</em>) &#x2014;
Dictionary of lists/arrays/tensors returned by the <strong>call</strong>/pad methods (&#x2018;input_values&#x2019;, &#x2018;attention_mask&#x2019;,
etc.).`,name:"data"},{anchor:"transformers.BatchFeature.tensor_type",description:`<strong>tensor_type</strong> (<code>Union[None, str, TensorType]</code>, <em>optional</em>) &#x2014;
You can give a tensor_type here to convert the lists of integers in PyTorch/TensorFlow/Numpy Tensors at
initialization.`,name:"tensor_type"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/feature_extraction_utils.py#L61"}}),ie=new $({props:{name:"convert_to_tensors",anchor:"transformers.BatchFeature.convert_to_tensors",parameters:[{name:"tensor_type",val:": Union = None"}],parametersDescription:[{anchor:"transformers.BatchFeature.convert_to_tensors.tensor_type",description:`<strong>tensor_type</strong> (<code>str</code> or <a href="/docs/transformers/main/ja/internal/file_utils#transformers.TensorType">TensorType</a>, <em>optional</em>) &#x2014;
The type of tensors to use. If <code>str</code>, should be one of the values of the enum <a href="/docs/transformers/main/ja/internal/file_utils#transformers.TensorType">TensorType</a>. If
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Will be passed to the <code>to(...)</code> function of the tensors.`,name:"args"},{anchor:"transformers.BatchFeature.to.kwargs",description:`<strong>kwargs</strong> (<code>Dict</code>, <em>optional</em>) &#x2014;
Will be passed to the <code>to(...)</code> function of the tensors.`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/feature_extraction_utils.py#L195",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>The same instance after modification.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><a
  href="/docs/transformers/main/ja/main_classes/feature_extractor#transformers.BatchFeature"
>BatchFeature</a></p>
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The image to resize.`,name:"image"},{anchor:"transformers.ImageFeatureExtractionMixin.center_crop.size",description:`<strong>size</strong> (<code>int</code> or <code>Tuple[int, int]</code>) &#x2014;
The size to which crop the image.`,name:"size"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/image_utils.py#L610",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>A center cropped <code>PIL.Image.Image</code> or <code>np.ndarray</code> or <code>torch.Tensor</code> of shape: (n_channels,
height, width).</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p>new_image</p>
`}}),pe=new $({props:{name:"convert_rgb",anchor:"transformers.ImageFeatureExtractionMixin.convert_rgb",parameters:[{name:"image",val:""}],parametersDescription:[{anchor:"transformers.ImageFeatureExtractionMixin.convert_rgb.image",description:`<strong>image</strong> (<code>PIL.Image.Image</code>) &#x2014;
The image to convert.`,name:"image"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/image_utils.py#L424"}}),ue=new $({props:{name:"expand_dims",anchor:"transformers.ImageFeatureExtractionMixin.expand_dims",parameters:[{name:"image",val:""}],parametersDescription:[{anchor:"transformers.ImageFeatureExtractionMixin.expand_dims.image",description:`<strong>image</strong> (<code>PIL.Image.Image</code> or <code>np.ndarray</code> or <code>torch.Tensor</code>) &#x2014;
The image to expand.`,name:"image"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/image_utils.py#L477"}}),fe=new $({props:{name:"flip_channel_order",anchor:"transformers.ImageFeatureExtractionMixin.flip_channel_order",parameters:[{name:"image",val:""}],parametersDescription:[{anchor:"transformers.ImageFeatureExtractionMixin.flip_channel_order.image",description:`<strong>image</strong> (<code>PIL.Image.Image</code> or <code>np.ndarray</code> or <code>torch.Tensor</code>) &#x2014;
The image whose color channels to flip. If <code>np.ndarray</code> or <code>torch.Tensor</code>, the channel dimension should
be first.`,name:"image"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/image_utils.py#L685"}}),ge=new $({props:{name:"normalize",anchor:"transformers.ImageFeatureExtractionMixin.normalize",parameters:[{name:"image",val:""},{name:"mean",val:""},{name:"std",val:""},{name:"rescale",val:" = False"}],parametersDescription:[{anchor:"transformers.ImageFeatureExtractionMixin.normalize.image",description:`<strong>image</strong> (<code>PIL.Image.Image</code> or <code>np.ndarray</code> or <code>torch.Tensor</code>) &#x2014;
The image to normalize.`,name:"image"},{anchor:"transformers.ImageFeatureExtractionMixin.normalize.mean",description:`<strong>mean</strong> (<code>List[float]</code> or <code>np.ndarray</code> or <code>torch.Tensor</code>) &#x2014;
The mean (per channel) to use for normalization.`,name:"mean"},{anchor:"transformers.ImageFeatureExtractionMixin.normalize.std",description:`<strong>std</strong> (<code>List[float]</code> or <code>np.ndarray</code> or <code>torch.Tensor</code>) &#x2014;
The standard deviation (per channel) to use for normalization.`,name:"std"},{anchor:"transformers.ImageFeatureExtractionMixin.normalize.rescale",description:`<strong>rescale</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to rescale the image to be between 0 and 1. If a PIL image is provided, scaling will
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The image to resize.`,name:"image"},{anchor:"transformers.ImageFeatureExtractionMixin.resize.size",description:`<strong>size</strong> (<code>int</code> or <code>Tuple[int, int]</code>) &#x2014;
The size to use for resizing the image. If <code>size</code> is a sequence like (h, w), output size will be
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<code>size</code> is an int and <code>default_to_square</code> is <code>False</code>, then smaller edge of the image will be matched to
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How to convert <code>size</code> when it is a single int. If set to <code>True</code>, the <code>size</code> will be converted to a
square (<code>size</code>,<code>size</code>). If set to <code>False</code>, will replicate
<a href="https://pytorch.org/vision/stable/transforms.html#torchvision.transforms.Resize" rel="nofollow"><code>torchvision.transforms.Resize</code></a>
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The maximum allowed for the longer edge of the resized image: if the longer edge of the image is
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that the longer edge is equal to <code>max_size</code>. As a result, <code>size</code> might be overruled, i.e the smaller
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<p>A resized <code>PIL.Image.Image</code>.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p>image</p>
`}}),_e=new $({props:{name:"rotate",anchor:"transformers.ImageFeatureExtractionMixin.rotate",parameters:[{name:"image",val:""},{name:"angle",val:""},{name:"resample",val:" = None"},{name:"expand",val:" = 0"},{name:"center",val:" = None"},{name:"translate",val:" = None"},{name:"fillcolor",val:" = None"}],parametersDescription:[{anchor:"transformers.ImageFeatureExtractionMixin.rotate.image",description:`<strong>image</strong> (<code>PIL.Image.Image</code> or <code>np.ndarray</code> or <code>torch.Tensor</code>) &#x2014;
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<p>A rotated <code>PIL.Image.Image</code>.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p>image</p>
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