import{S as cs,i as ls,s as ds,e as o,k as l,w as b,t as n,M as ms,c as t,d as s,m as d,a,x as M,h as i,b as c,G as e,g as h,y as w,q as $,o as I,B as x,v as ps,L as hs}from"../../chunks/vendor-hf-doc-builder.js";import{D as te}from"../../chunks/Docstring-hf-doc-builder.js";import{C as gs}from"../../chunks/CodeBlock-hf-doc-builder.js";import{I as Ye}from"../../chunks/IconCopyLink-hf-doc-builder.js";import{E as fs}from"../../chunks/ExampleCodeBlock-hf-doc-builder.js";function us(Ee){let g,F,_,f,T;return f=new gs({props:{code:"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",highlighted:`<span class="hljs-comment"># We can&#x27;t instantiate directly the base class *ImageProcessingMixin* so let&#x27;s show the examples on a</span>
<span class="hljs-comment"># derived class: *CLIPImageProcessor*</span>
image_processor = CLIPImageProcessor.from_pretrained(
    <span class="hljs-string">&quot;openai/clip-vit-base-patch32&quot;</span>
)  <span class="hljs-comment"># Download image_processing_config from huggingface.co and cache.</span>
image_processor = CLIPImageProcessor.from_pretrained(
    <span class="hljs-string">&quot;./test/saved_model/&quot;</span>
)  <span class="hljs-comment"># E.g. image processor (or model) was saved using *save_pretrained(&#x27;./test/saved_model/&#x27;)*</span>
image_processor = CLIPImageProcessor.from_pretrained(<span class="hljs-string">&quot;./test/saved_model/preprocessor_config.json&quot;</span>)
image_processor = CLIPImageProcessor.from_pretrained(
    <span class="hljs-string">&quot;openai/clip-vit-base-patch32&quot;</span>, do_normalize=<span class="hljs-literal">False</span>, foo=<span class="hljs-literal">False</span>
)
<span class="hljs-keyword">assert</span> image_processor.do_normalize <span class="hljs-keyword">is</span> <span class="hljs-literal">False</span>
image_processor, unused_kwargs = CLIPImageProcessor.from_pretrained(
    <span class="hljs-string">&quot;openai/clip-vit-base-patch32&quot;</span>, do_normalize=<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> image_processor.do_normalize <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(){g=o("p"),F=n("Examples:"),_=l(),b(f.$$.fragment)},l(m){g=t(m,"P",{});var P=a(g);F=i(P,"Examples:"),P.forEach(s),_=d(m),M(f.$$.fragment,m)},m(m,P){h(m,g,P),e(g,F),h(m,_,P),w(f,m,P),T=!0},p:hs,i(m){T||($(f.$$.fragment,m),T=!0)},o(m){I(f.$$.fragment,m),T=!1},d(m){m&&s(g),m&&s(_),x(f,m)}}}function _s(Ee){let g,F,_,f,T,m,P,pe,Le,Ue,ae,Re,je,k,W,he,L,De,ge,Ge,ke,v,R,Qe,fe,He,qe,E,D,Oe,G,Ke,ne,er,rr,sr,z,or,C,Q,tr,B,ar,ue,nr,ir,ie,cr,lr,Be,Z,X,_e,H,dr,ve,mr,Ze,u,q,pr,N,hr,ce,gr,fr,ye,ur,_r,vr,be,yr,br,A,O,Mr,Me,wr,$r,V,K,Ir,y,xr,we,Tr,Pr,$e,Er,Ur,Ie,jr,kr,xe,Br,Zr,Ne,J,S,Te,ee,Nr,Pe,Jr,Je,re,se,Fe;return m=new Ye({}),L=new Ye({}),R=new te({props:{name:"class transformers.ImageProcessingMixin",anchor:"transformers.ImageProcessingMixin",parameters:[{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.31.0/src/transformers/image_processing_utils.py#L60"}}),D=new te({props:{name:"from_pretrained",anchor:"transformers.ImageProcessingMixin.from_pretrained",parameters:[{name:"pretrained_model_name_or_path",val:": typing.Union[str, os.PathLike]"},{name:"cache_dir",val:": typing.Union[str, os.PathLike, NoneType] = None"},{name:"force_download",val:": bool = False"},{name:"local_files_only",val:": bool = False"},{name:"token",val:": typing.Union[bool, str, NoneType] = None"},{name:"revision",val:": str = 'main'"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.ImageProcessingMixin.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
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<ul>
<li>a string, the <em>model id</em> of a pretrained image_processor 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
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<li>a path to a <em>directory</em> containing a image processor file saved using the
<a href="/docs/transformers/v4.31.0/en/internal/image_processing_utils#transformers.ImageProcessingMixin.save_pretrained">save_pretrained()</a> method, e.g.,
<code>./my_model_directory/</code>.</li>
<li>a path or url to a saved image processor JSON <em>file</em>, e.g.,
<code>./my_model_directory/preprocessor_config.json</code>.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.ImageProcessingMixin.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
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The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
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identifier allowed by git.`,name:"revision"}],source:"https://github.com/huggingface/transformers/blob/v4.31.0/src/transformers/image_processing_utils.py#L84",returnDescription:`
<p>A image processor of type <a
  href="/docs/transformers/v4.31.0/en/internal/image_processing_utils#transformers.ImageProcessingMixin"
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`}}),z=new fs({props:{anchor:"transformers.ImageProcessingMixin.from_pretrained.example",$$slots:{default:[us]},$$scope:{ctx:Ee}}}),Q=new te({props:{name:"save_pretrained",anchor:"transformers.ImageProcessingMixin.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.ImageProcessingMixin.save_pretrained.save_directory",description:`<strong>save_directory</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
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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
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>BatchFeature</a></p>
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