import{S as qn,i as Nn,s as Sn,e as a,k as c,w as h,t as s,M as Bn,c as o,d as r,m as d,a as n,x as g,h as i,b as l,G as e,g as E,y as _,q as v,o as x,B as y,v as Cn,L as An}from"../../chunks/vendor-hf-doc-builder.js";import{T as Vn}from"../../chunks/Tip-hf-doc-builder.js";import{D as w}from"../../chunks/Docstring-hf-doc-builder.js";import{C as On}from"../../chunks/CodeBlock-hf-doc-builder.js";import{I as Jt}from"../../chunks/IconCopyLink-hf-doc-builder.js";import{E as jn}from"../../chunks/ExampleCodeBlock-hf-doc-builder.js";function Wn(Ke){let f,L,$,F,T;return F=new On({props:{code:`# We can't instantiate directly the base class *FeatureExtractionMixin* nor *SequenceFeatureExtractor* so let's show the examples on a
# derived class: *Wav2Vec2FeatureExtractor*
feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(
    "facebook/wav2vec2-base-960h"
)  # Download feature_extraction_config from huggingface.co and cache.
feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(
    "./test/saved_model/"
)  # E.g. feature_extractor (or model) was saved using *save_pretrained('./test/saved_model/')*
feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained("./test/saved_model/preprocessor_config.json")
feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(
    "facebook/wav2vec2-base-960h", return_attention_mask=False, foo=False
)
assert feature_extractor.return_attention_mask is False
feature_extractor, unused_kwargs = Wav2Vec2FeatureExtractor.from_pretrained(
    "facebook/wav2vec2-base-960h", return_attention_mask=False, foo=False, return_unused_kwargs=True
)
assert feature_extractor.return_attention_mask is False
assert unused_kwargs == {"foo": False}`,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>}`}}),{c(){f=a("p"),L=s("Examples:"),$=c(),h(F.$$.fragment)},l(m){f=o(m,"P",{});var P=n(f);L=i(P,"Examples:"),P.forEach(r),$=d(m),g(F.$$.fragment,m)},m(m,P){E(m,f,P),e(f,L),E(m,$,P),_(F,m,P),T=!0},p:An,i(m){T||(v(F.$$.fragment,m),T=!0)},o(m){x(F.$$.fragment,m),T=!1},d(m){m&&r(f),m&&r($),y(F,m)}}}function Rn(Ke){let f,L,$,F,T,m,P,V;return{c(){f=a("p"),L=s("If the "),$=a("code"),F=s("processed_features"),T=s(` passed are dictionary of numpy arrays, PyTorch tensors or TensorFlow tensors, the
result will use the same type unless you provide a different tensor type with `),m=a("code"),P=s("return_tensors"),V=s(`. In the case of
PyTorch tensors, you will lose the specific device of your tensors however.`)},l(O){f=o(O,"P",{});var k=n(f);L=i(k,"If the "),$=o(k,"CODE",{});var z=n($);F=i(z,"processed_features"),z.forEach(r),T=i(k,` passed are dictionary of numpy arrays, PyTorch tensors or TensorFlow tensors, the
result will use the same type unless you provide a different tensor type with `),m=o(k,"CODE",{});var Qe=n(m);P=i(Qe,"return_tensors"),Qe.forEach(r),V=i(k,`. In the case of
PyTorch tensors, you will lose the specific device of your tensors however.`),k.forEach(r)},m(O,k){E(O,f,k),e(f,L),e(f,$),e($,F),e(f,T),e(f,m),e(m,P),e(f,V)},d(O){O&&r(f)}}}function Un(Ke){let f,L,$,F,T,m,P,V,O,k,z,Qe,lt,Pr,kr,mt,zr,Dr,Yt,j,Q,pt,xe,Mr,ut,Lr,Kt,D,ye,qr,ft,Nr,Sr,B,be,Br,N,Cr,Xe,Ar,Vr,ht,Or,jr,Ze,Wr,Rr,Ur,X,Hr,Z,Ee,Gr,W,Jr,gt,Yr,Kr,et,Qr,Xr,Qt,R,ee,_t,$e,Zr,vt,ea,Xt,S,Fe,ta,xt,ra,aa,q,we,oa,yt,na,sa,U,ia,bt,ca,da,Et,la,ma,pa,te,Zt,H,re,$t,Ie,ua,Ft,fa,er,I,Te,ha,G,ga,tt,_a,va,wt,xa,ya,ba,It,Ea,$a,ae,Pe,Fa,Tt,wa,Ia,oe,ke,Ta,ze,Pa,Pt,ka,za,tr,J,ne,kt,De,Da,zt,Ma,rr,p,Me,La,Dt,qa,Na,se,Le,Sa,qe,Ba,Mt,Ca,Aa,Va,ie,Ne,Oa,Se,ja,Lt,Wa,Ra,Ua,ce,Be,Ha,Ce,Ga,qt,Ja,Ya,Ka,de,Ae,Qa,Y,Xa,Nt,Za,eo,St,to,ro,ao,le,Ve,oo,M,no,Bt,so,io,Ct,co,lo,At,mo,po,Vt,uo,fo,ho,me,Oe,go,Ot,_o,vo,pe,je,xo,We,yo,jt,bo,Eo,$o,ue,Re,Fo,K,wo,Wt,Io,To,Rt,Po,ko,zo,fe,Ue,Do,He,Mo,Ut,Lo,qo,No,he,Ge,So,Je,Bo,Ht,Co,Ao,ar;return m=new Jt({}),xe=new Jt({}),ye=new w({props:{name:"class transformers.FeatureExtractionMixin",anchor:"transformers.FeatureExtractionMixin",parameters:[{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/feature_extraction_utils.py#L200"}}),be=new w({props:{name:"from_pretrained",anchor:"transformers.FeatureExtractionMixin.from_pretrained",parameters:[{name:"pretrained_model_name_or_path",val:": typing.Union[str, os.PathLike]"},{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. Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or
namespaced under a user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>a path to a <em>directory</em> containing a feature extractor file saved using the
<a href="/docs/transformers/pr_19907/en/internal/image_processing_utils#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.use_auth_token",description:`<strong>use_auth_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:"use_auth_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/vr_19907/src/transformers/feature_extraction_utils.py#L224",returnDescription:`
<p>A feature extractor of type <a
  href="/docs/transformers/pr_19907/en/internal/image_processing_utils#transformers.FeatureExtractionMixin"
>FeatureExtractionMixin</a>.</p>
`}}),X=new jn({props:{anchor:"transformers.FeatureExtractionMixin.from_pretrained.example",$$slots:{default:[Wn]},$$scope:{ctx:Ke}}}),Ee=new w({props:{name:"save_pretrained",anchor:"transformers.FeatureExtractionMixin.save_pretrained",parameters:[{name:"save_directory",val:": typing.Union[str, os.PathLike]"},{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).
kwargs &#x2014;
Additional key word arguments passed along to the <a href="/docs/transformers/pr_19907/en/internal/image_processing_utils#transformers.FeatureExtractionMixin.push_to_hub">push_to_hub()</a> method.`,name:"push_to_hub"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/feature_extraction_utils.py#L310"}}),$e=new Jt({}),Fe=new w({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 per second (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/vr_19907/src/transformers/feature_extraction_sequence_utils.py#L29"}}),we=new w({props:{name:"pad",anchor:"transformers.SequenceFeatureExtractor.pad",parameters:[{name:"processed_features",val:": typing.Union[transformers.feature_extraction_utils.BatchFeature, typing.List[transformers.feature_extraction_utils.BatchFeature], typing.Dict[str, transformers.feature_extraction_utils.BatchFeature], typing.Dict[str, typing.List[transformers.feature_extraction_utils.BatchFeature]], typing.List[typing.Dict[str, transformers.feature_extraction_utils.BatchFeature]]]"},{name:"padding",val:": typing.Union[bool, str, transformers.utils.generic.PaddingStrategy] = True"},{name:"max_length",val:": typing.Optional[int] = None"},{name:"truncation",val:": bool = False"},{name:"pad_to_multiple_of",val:": typing.Optional[int] = None"},{name:"return_attention_mask",val:": typing.Optional[bool] = None"},{name:"return_tensors",val:": typing.Union[str, transformers.utils.generic.TensorType, NoneType] = None"}],parametersDescription:[{anchor:"transformers.SequenceFeatureExtractor.pad.processed_features",description:`<strong>processed_features</strong> (<a href="/docs/transformers/pr_19907/en/main_classes/feature_extractor#transformers.BatchFeature">BatchFeature</a>, list of <a href="/docs/transformers/pr_19907/en/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/pr_19907/en/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/pr_19907/en/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/pr_19907/en/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</p>
<blockquote>
<p>= 7.5 (Volta), or on TPUs which benefit from having sequence lengths be a multiple of 128.</p>
</blockquote>`,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/pr_19907/en/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/vr_19907/src/transformers/feature_extraction_sequence_utils.py#L52"}}),te=new Vn({props:{$$slots:{default:[Rn]},$$scope:{ctx:Ke}}}),Ie=new Jt({}),Te=new w({props:{name:"class transformers.BatchFeature",anchor:"transformers.BatchFeature",parameters:[{name:"data",val:": typing.Union[typing.Dict[str, typing.Any], NoneType] = None"},{name:"tensor_type",val:": typing.Union[NoneType, str, transformers.utils.generic.TensorType] = None"}],parametersDescription:[{anchor:"transformers.BatchFeature.data",description:`<strong>data</strong> (<code>dict</code>) &#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/vr_19907/src/transformers/feature_extraction_utils.py#L58"}}),Pe=new w({props:{name:"convert_to_tensors",anchor:"transformers.BatchFeature.convert_to_tensors",parameters:[{name:"tensor_type",val:": typing.Union[str, transformers.utils.generic.TensorType, NoneType] = None"}],parametersDescription:[{anchor:"transformers.BatchFeature.convert_to_tensors.tensor_type",description:`<strong>tensor_type</strong> (<code>str</code> or <a href="/docs/transformers/pr_19907/en/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/pr_19907/en/internal/file_utils#transformers.TensorType">TensorType</a>. If
<code>None</code>, no modification is done.`,name:"tensor_type"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/feature_extraction_utils.py#L112"}}),ke=new w({props:{name:"to",anchor:"transformers.BatchFeature.to",parameters:[{name:"device",val:": typing.Union[str, ForwardRef('torch.device')]"}],parametersDescription:[{anchor:"transformers.BatchFeature.to.device",description:"<strong>device</strong> (<code>str</code> or <code>torch.device</code>) &#x2014; The device to put the tensors on.",name:"device"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/feature_extraction_utils.py#L177",returnDescription:`
<p>The same instance after modification.</p>
`,returnType:`
<p><a
  href="/docs/transformers/pr_19907/en/main_classes/feature_extractor#transformers.BatchFeature"
>BatchFeature</a></p>
`}}),De=new Jt({}),Me=new w({props:{name:"class transformers.ImageFeatureExtractionMixin",anchor:"transformers.ImageFeatureExtractionMixin",parameters:[],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/image_utils.py#L192"}}),Le=new w({props:{name:"center_crop",anchor:"transformers.ImageFeatureExtractionMixin.center_crop",parameters:[{name:"image",val:""},{name:"size",val:""}],parametersDescription:[{anchor:"transformers.ImageFeatureExtractionMixin.center_crop.image",description:`<strong>image</strong> (<code>PIL.Image.Image</code> or <code>np.ndarray</code> or <code>torch.Tensor</code> of shape (n_channels, height, width) or (height, width, n_channels)) &#x2014;
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/vr_19907/src/transformers/image_utils.py#L420",returnDescription:`
<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:`
<p>new_image</p>
`}}),Ne=new w({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/vr_19907/src/transformers/image_utils.py#L234"}}),Be=new w({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/vr_19907/src/transformers/image_utils.py#L287"}}),Ae=new w({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/vr_19907/src/transformers/image_utils.py#L495"}}),Ve=new w({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;
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