import{S as Xa,i as Ya,s as Oa,e as o,k as m,w as u,t as s,M as Ga,c as n,d as t,m as c,a,x as _,h as i,b as d,G as e,g,y as b,q as y,o as v,B as $,v as Ha,L as Ra}from"../../chunks/vendor-hf-doc-builder.js";import{T as Qa}from"../../chunks/Tip-hf-doc-builder.js";import{D as M}from"../../chunks/Docstring-hf-doc-builder.js";import{C as Aa}from"../../chunks/CodeBlock-hf-doc-builder.js";import{I as Gn}from"../../chunks/IconCopyLink-hf-doc-builder.js";import{E as Ba}from"../../chunks/ExampleCodeBlock-hf-doc-builder.js";function Ka(Q){let h,P,x,I,E;return I=new Aa({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(){h=o("p"),P=s("Examples:"),x=m(),u(I.$$.fragment)},l(p){h=n(p,"P",{});var T=a(h);P=i(T,"Examples:"),T.forEach(t),x=c(p),_(I.$$.fragment,p)},m(p,T){g(p,h,T),e(h,P),g(p,x,T),b(I,p,T),E=!0},p:Ra,i(p){E||(y(I.$$.fragment,p),E=!0)},o(p){v(I.$$.fragment,p),E=!1},d(p){p&&t(h),p&&t(x),$(I,p)}}}function es(Q){let h,P,x,I,E;return I=new Aa({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9JbWFnZVByb2Nlc3NvciUwQSUwQWltYWdlJTIwcHJvY2Vzc29yJTIwJTNEJTIwQXV0b0ltYWdlUHJvY2Vzc29yLmZyb21fcHJldHJhaW5lZCglMjJiZXJ0LWJhc2UtY2FzZWQlMjIpJTBBJTBBJTIzJTIwUHVzaCUyMHRoZSUyMGltYWdlJTIwcHJvY2Vzc29yJTIwdG8lMjB5b3VyJTIwbmFtZXNwYWNlJTIwd2l0aCUyMHRoZSUyMG5hbWUlMjAlMjJteS1maW5ldHVuZWQtYmVydCUyMi4lMEFpbWFnZSUyMHByb2Nlc3Nvci5wdXNoX3RvX2h1YiglMjJteS1maW5ldHVuZWQtYmVydCUyMiklMEElMEElMjMlMjBQdXNoJTIwdGhlJTIwaW1hZ2UlMjBwcm9jZXNzb3IlMjB0byUyMGFuJTIwb3JnYW5pemF0aW9uJTIwd2l0aCUyMHRoZSUyMG5hbWUlMjAlMjJteS1maW5ldHVuZWQtYmVydCUyMi4lMEFpbWFnZSUyMHByb2Nlc3Nvci5wdXNoX3RvX2h1YiglMjJodWdnaW5nZmFjZSUyRm15LWZpbmV0dW5lZC1iZXJ0JTIyKQ==",highlighted:`<span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoImageProcessor

image processor = AutoImageProcessor.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)

<span class="hljs-comment"># Push the image processor to your namespace with the name &quot;my-finetuned-bert&quot;.</span>
image processor.push_to_hub(<span class="hljs-string">&quot;my-finetuned-bert&quot;</span>)

<span class="hljs-comment"># Push the image processor to an organization with the name &quot;my-finetuned-bert&quot;.</span>
image processor.push_to_hub(<span class="hljs-string">&quot;huggingface/my-finetuned-bert&quot;</span>)`}}),{c(){h=o("p"),P=s("Examples:"),x=m(),u(I.$$.fragment)},l(p){h=n(p,"P",{});var T=a(h);P=i(T,"Examples:"),T.forEach(t),x=c(p),_(I.$$.fragment,p)},m(p,T){g(p,h,T),e(h,P),g(p,x,T),b(I,p,T),E=!0},p:Ra,i(p){E||(y(I.$$.fragment,p),E=!0)},o(p){v(I.$$.fragment,p),E=!1},d(p){p&&t(h),p&&t(x),$(I,p)}}}function rs(Q){let h,P;return{c(){h=o("p"),P=s("This API is experimental and may have some slight breaking changes in the next releases.")},l(x){h=n(x,"P",{});var I=a(h);P=i(I,"This API is experimental and may have some slight breaking changes in the next releases."),I.forEach(t)},m(x,I){g(x,h,I),e(h,P)},d(x){x&&t(h)}}}function ts(Q){let h,P,x,I,E,p,T,mr,Ct,Or,Xe,Nt,Gr,Ye,Jt,Hr,S,K,cr,ue,kt,lr,Zt,Qr,V,_e,Wt,j,Lt,dr,qt,St,pr,Vt,Ft,gr,Bt,Rt,Kr,U,be,At,fr,Xt,Yt,hr,Ot,et,z,ye,Gt,ur,Ht,Qt,_r,Kt,rt,F,ve,eo,br,ro,tt,D,$e,to,C,oo,yr,no,ao,vr,so,io,$r,mo,co,lo,Ir,po,ot,B,Ie,go,N,fo,wr,ho,uo,xr,_o,bo,Mr,yo,vo,nt,R,we,$o,Tr,Io,at,A,xe,wo,X,xo,Pr,Mo,To,Er,Po,Eo,st,Y,Me,jo,J,Uo,jr,zo,Do,Ur,Co,No,zr,Jo,ko,it,O,Te,Zo,Pe,Wo,Dr,Lo,qo,mt,G,ee,Cr,Ee,So,Nr,Vo,ct,f,je,Fo,Jr,Bo,Ro,Z,Ue,Ao,ze,Xo,kr,Yo,Oo,Go,Zr,Ho,Qo,re,De,Ko,Ce,en,Oe,rn,tn,on,te,Ne,nn,Je,an,Ge,sn,mn,cn,W,ke,ln,Ze,dn,He,pn,gn,fn,oe,hn,ne,We,un,k,_n,Wr,bn,yn,Lr,vn,$n,qr,In,wn,xn,L,Le,Mn,Sr,Tn,Pn,ae,En,q,qe,jn,Se,Un,Vr,zn,Dn,Cn,se,Nn,ie,Ve,Jn,H,kn,Fr,Zn,Wn,Qe,Ln,qn,Sn,me,Fe,Vn,Br,Fn,Bn,ce,Be,Rn,Rr,An,Xn,le,Re,Yn,Ar,On,lt;return p=new Gn({}),ue=new Gn({}),_e=new M({props:{name:"transformers.image_transforms.center_crop",anchor:"transformers.image_transforms.center_crop",parameters:[{name:"image",val:": ndarray"},{name:"size",val:": typing.Tuple[int, int]"},{name:"data_format",val:": typing.Union[transformers.image_utils.ChannelDimension, str, NoneType] = None"},{name:"input_data_format",val:": typing.Union[transformers.image_utils.ChannelDimension, str, NoneType] = None"},{name:"return_numpy",val:": typing.Optional[bool] = None"}],parametersDescription:[{anchor:"transformers.image_transforms.center_crop.image",description:`<strong>image</strong> (<code>np.ndarray</code>) &#x2014;
The image to crop.`,name:"image"},{anchor:"transformers.image_transforms.center_crop.size",description:`<strong>size</strong> (<code>Tuple[int, int]</code>) &#x2014;
The target size for the cropped image.`,name:"size"},{anchor:"transformers.image_transforms.center_crop.data_format",description:`<strong>data_format</strong> (<code>str</code> or <code>ChannelDimension</code>, <em>optional</em>) &#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.
If unset, will use the inferred format of the input image.</li>
</ul>`,name:"data_format"},{anchor:"transformers.image_transforms.center_crop.input_data_format",description:`<strong>input_data_format</strong> (<code>str</code> or <code>ChannelDimension</code>, <em>optional</em>) &#x2014;
The channel dimension format for 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.
If unset, will use the inferred format of the input image.</li>
</ul>`,name:"input_data_format"},{anchor:"transformers.image_transforms.center_crop.return_numpy",description:`<strong>return_numpy</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the cropped image as a numpy array. Used for backwards compatibility with the
previous ImageFeatureExtractionMixin method.<ul>
<li>Unset: will return the same type as the input image.</li>
<li><code>True</code>: will return a numpy array.</li>
<li><code>False</code>: will return a <code>PIL.Image.Image</code> object.</li>
</ul>`,name:"return_numpy"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/image_transforms.py#L402",returnDescription:`
<p>The cropped image.</p>
`,returnType:`
<p><code>np.ndarray</code></p>
`}}),be=new M({props:{name:"transformers.image_transforms.center_to_corners_format",anchor:"transformers.image_transforms.center_to_corners_format",parameters:[{name:"bboxes_center",val:": TensorType"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/image_transforms.py#L532"}}),ye=new M({props:{name:"transformers.image_transforms.corners_to_center_format",anchor:"transformers.image_transforms.corners_to_center_format",parameters:[{name:"bboxes_corners",val:": TensorType"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/image_transforms.py#L592"}}),ve=new M({props:{name:"transformers.image_transforms.id_to_rgb",anchor:"transformers.image_transforms.id_to_rgb",parameters:[{name:"id_map",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/image_transforms.py#L626"}}),$e=new M({props:{name:"transformers.image_transforms.normalize",anchor:"transformers.image_transforms.normalize",parameters:[{name:"image",val:": ndarray"},{name:"mean",val:": typing.Union[float, typing.Iterable[float]]"},{name:"std",val:": typing.Union[float, typing.Iterable[float]]"},{name:"data_format",val:": typing.Optional[transformers.image_utils.ChannelDimension] = None"},{name:"input_data_format",val:": typing.Union[transformers.image_utils.ChannelDimension, str, NoneType] = None"}],parametersDescription:[{anchor:"transformers.image_transforms.normalize.image",description:`<strong>image</strong> (<code>np.ndarray</code>) &#x2014;
The image to normalize.`,name:"image"},{anchor:"transformers.image_transforms.normalize.mean",description:`<strong>mean</strong> (<code>float</code> or <code>Iterable[float]</code>) &#x2014;
The mean to use for normalization.`,name:"mean"},{anchor:"transformers.image_transforms.normalize.std",description:`<strong>std</strong> (<code>float</code> or <code>Iterable[float]</code>) &#x2014;
The standard deviation to use for normalization.`,name:"std"},{anchor:"transformers.image_transforms.normalize.data_format",description:`<strong>data_format</strong> (<code>ChannelDimension</code>, <em>optional</em>) &#x2014;
The channel dimension format of the output image. If unset, will use the inferred format from the input.`,name:"data_format"},{anchor:"transformers.image_transforms.normalize.input_data_format",description:`<strong>input_data_format</strong> (<code>ChannelDimension</code>, <em>optional</em>) &#x2014;
The channel dimension format of the input image. If unset, will use the inferred format from the input.`,name:"input_data_format"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/image_transforms.py#L347"}}),Ie=new M({props:{name:"transformers.image_transforms.pad",anchor:"transformers.image_transforms.pad",parameters:[{name:"image",val:": ndarray"},{name:"padding",val:": typing.Union[int, typing.Tuple[int, int], typing.Iterable[typing.Tuple[int, int]]]"},{name:"mode",val:": PaddingMode = <PaddingMode.CONSTANT: 'constant'>"},{name:"constant_values",val:": typing.Union[float, typing.Iterable[float]] = 0.0"},{name:"data_format",val:": typing.Union[transformers.image_utils.ChannelDimension, str, NoneType] = None"},{name:"input_data_format",val:": typing.Union[transformers.image_utils.ChannelDimension, str, NoneType] = None"}],parametersDescription:[{anchor:"transformers.image_transforms.pad.image",description:`<strong>image</strong> (<code>np.ndarray</code>) &#x2014;
The image to pad.`,name:"image"},{anchor:"transformers.image_transforms.pad.padding",description:`<strong>padding</strong> (<code>int</code> or <code>Tuple[int, int]</code> or <code>Iterable[Tuple[int, int]]</code>) &#x2014;
Padding to apply to the edges of the height, width axes. Can be one of three formats:<ul>
<li><code>((before_height, after_height), (before_width, after_width))</code> unique pad widths for each axis.</li>
<li><code>((before, after),)</code> yields same before and after pad for height and width.</li>
<li><code>(pad,)</code> or int is a shortcut for before = after = pad width for all axes.</li>
</ul>`,name:"padding"},{anchor:"transformers.image_transforms.pad.mode",description:`<strong>mode</strong> (<code>PaddingMode</code>) &#x2014;
The padding mode to use. Can be one of:<ul>
<li><code>&quot;constant&quot;</code>: pads with a constant value.</li>
<li><code>&quot;reflect&quot;</code>: pads with the reflection of the vector mirrored on the first and last values of the
vector along each axis.</li>
<li><code>&quot;replicate&quot;</code>: pads with the replication of the last value on the edge of the array along each axis.</li>
<li><code>&quot;symmetric&quot;</code>: pads with the reflection of the vector mirrored along the edge of the array.</li>
</ul>`,name:"mode"},{anchor:"transformers.image_transforms.pad.constant_values",description:`<strong>constant_values</strong> (<code>float</code> or <code>Iterable[float]</code>, <em>optional</em>) &#x2014;
The value to use for the padding if <code>mode</code> is <code>&quot;constant&quot;</code>.`,name:"constant_values"},{anchor:"transformers.image_transforms.pad.data_format",description:`<strong>data_format</strong> (<code>str</code> or <code>ChannelDimension</code>, <em>optional</em>) &#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.
If unset, will use same as the input image.</li>
</ul>`,name:"data_format"},{anchor:"transformers.image_transforms.pad.input_data_format",description:`<strong>input_data_format</strong> (<code>str</code> or <code>ChannelDimension</code>, <em>optional</em>) &#x2014;
The channel dimension format for 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.
If unset, will use the inferred format of the input image.</li>
</ul>`,name:"input_data_format"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/image_transforms.py#L656",returnDescription:`
<p>The padded image.</p>
`,returnType:`
<p><code>np.ndarray</code></p>
`}}),we=new M({props:{name:"transformers.image_transforms.rgb_to_id",anchor:"transformers.image_transforms.rgb_to_id",parameters:[{name:"color",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/image_transforms.py#L615"}}),xe=new M({props:{name:"transformers.image_transforms.rescale",anchor:"transformers.image_transforms.rescale",parameters:[{name:"image",val:": ndarray"},{name:"scale",val:": float"},{name:"data_format",val:": typing.Optional[transformers.image_utils.ChannelDimension] = None"},{name:"dtype",val:": dtype = <class 'numpy.float32'>"},{name:"input_data_format",val:": typing.Union[transformers.image_utils.ChannelDimension, str, NoneType] = None"}],parametersDescription:[{anchor:"transformers.image_transforms.rescale.image",description:`<strong>image</strong> (<code>np.ndarray</code>) &#x2014;
The image to rescale.`,name:"image"},{anchor:"transformers.image_transforms.rescale.scale",description:`<strong>scale</strong> (<code>float</code>) &#x2014;
The scale to use for rescaling the image.`,name:"scale"},{anchor:"transformers.image_transforms.rescale.data_format",description:`<strong>data_format</strong> (<code>ChannelDimension</code>, <em>optional</em>) &#x2014;
The channel dimension format of the image. If not provided, it will be the same as the input image.`,name:"data_format"},{anchor:"transformers.image_transforms.rescale.dtype",description:`<strong>dtype</strong> (<code>np.dtype</code>, <em>optional</em>, defaults to <code>np.float32</code>) &#x2014;
The dtype of the output image. Defaults to <code>np.float32</code>. Used for backwards compatibility with feature
extractors.`,name:"dtype"},{anchor:"transformers.image_transforms.rescale.input_data_format",description:`<strong>input_data_format</strong> (<code>ChannelDimension</code>, <em>optional</em>) &#x2014;
The channel dimension format of the input image. If not provided, it will be inferred from the input image.`,name:"input_data_format"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/image_transforms.py#L92",returnDescription:`
<p>The rescaled image.</p>
`,returnType:`
<p><code>np.ndarray</code></p>
`}}),Me=new M({props:{name:"transformers.image_transforms.resize",anchor:"transformers.image_transforms.resize",parameters:[{name:"image",val:""},{name:"size",val:": typing.Tuple[int, int]"},{name:"resample",val:": PILImageResampling = None"},{name:"reducing_gap",val:": typing.Optional[int] = None"},{name:"data_format",val:": typing.Optional[transformers.image_utils.ChannelDimension] = None"},{name:"return_numpy",val:": bool = True"},{name:"input_data_format",val:": typing.Union[transformers.image_utils.ChannelDimension, str, NoneType] = None"}],parametersDescription:[{anchor:"transformers.image_transforms.resize.image",description:`<strong>image</strong> (<code>PIL.Image.Image</code> or <code>np.ndarray</code> or <code>torch.Tensor</code>) &#x2014;
The image to resize.`,name:"image"},{anchor:"transformers.image_transforms.resize.size",description:`<strong>size</strong> (<code>Tuple[int, int]</code>) &#x2014;
The size to use for resizing the image.`,name:"size"},{anchor:"transformers.image_transforms.resize.resample",description:`<strong>resample</strong> (<code>int</code>, <em>optional</em>, defaults to <code>PILImageResampling.BILINEAR</code>) &#x2014;
The filter to user for resampling.`,name:"resample"},{anchor:"transformers.image_transforms.resize.reducing_gap",description:`<strong>reducing_gap</strong> (<code>int</code>, <em>optional</em>) &#x2014;
Apply optimization by resizing the image in two steps. The bigger <code>reducing_gap</code>, the closer the result to
the fair resampling. See corresponding Pillow documentation for more details.`,name:"reducing_gap"},{anchor:"transformers.image_transforms.resize.data_format",description:`<strong>data_format</strong> (<code>ChannelDimension</code>, <em>optional</em>) &#x2014;
The channel dimension format of the output image. If unset, will use the inferred format from the input.`,name:"data_format"},{anchor:"transformers.image_transforms.resize.return_numpy",description:`<strong>return_numpy</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether or not to return the resized image as a numpy array. If False a <code>PIL.Image.Image</code> object is
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The channel dimension format of the input image. If unset, will use the inferred format from the input.`,name:"input_data_format"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/image_transforms.py#L276",returnDescription:`
<p>The resized image.</p>
`,returnType:`
<p><code>np.ndarray</code></p>
`}}),Te=new M({props:{name:"transformers.image_transforms.to_pil_image",anchor:"transformers.image_transforms.to_pil_image",parameters:[{name:"image",val:": typing.Union[numpy.ndarray, ForwardRef('PIL.Image.Image'), ForwardRef('torch.Tensor'), ForwardRef('tf.Tensor'), ForwardRef('jnp.ndarray')]"},{name:"do_rescale",val:": typing.Optional[bool] = None"},{name:"input_data_format",val:": typing.Union[transformers.image_utils.ChannelDimension, str, NoneType] = None"}],parametersDescription:[{anchor:"transformers.image_transforms.to_pil_image.image",description:`<strong>image</strong> (<code>PIL.Image.Image</code> or <code>numpy.ndarray</code> or <code>torch.Tensor</code> or <code>tf.Tensor</code>) &#x2014;
The image to convert to the <code>PIL.Image</code> format.`,name:"image"},{anchor:"transformers.image_transforms.to_pil_image.do_rescale",description:`<strong>do_rescale</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to apply the scaling factor (to make pixel values integers between 0 and 255). Will default
to <code>True</code> if the image type is a floating type and casting to <code>int</code> would result in a loss of precision,
and <code>False</code> otherwise.`,name:"do_rescale"},{anchor:"transformers.image_transforms.to_pil_image.input_data_format",description:`<strong>input_data_format</strong> (<code>ChannelDimension</code>, <em>optional</em>) &#x2014;
The channel dimension format of the input image. If unset, will use the inferred format from the input.`,name:"input_data_format"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/image_transforms.py#L157",returnDescription:`
<p>The converted image.</p>
`,returnType:`
<p><code>PIL.Image.Image</code></p>
`}}),Ee=new Gn({}),je=new M({props:{name:"class transformers.ImageProcessingMixin",anchor:"transformers.ImageProcessingMixin",parameters:[{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/image_processing_utils.py#L68"}}),Ue=new M({props:{name:"fetch_images",anchor:"transformers.ImageProcessingMixin.fetch_images",parameters:[{name:"image_url_or_urls",val:": typing.Union[str, typing.List[str]]"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/image_processing_utils.py#L517"}}),De=new M({props:{name:"from_dict",anchor:"transformers.ImageProcessingMixin.from_dict",parameters:[{name:"image_processor_dict",val:": typing.Dict[str, typing.Any]"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.ImageProcessingMixin.from_dict.image_processor_dict",description:`<strong>image_processor_dict</strong> (<code>Dict[str, Any]</code>) &#x2014;
Dictionary that will be used to instantiate the image processor object. Such a dictionary can be
retrieved from a pretrained checkpoint by leveraging the
<a href="/docs/transformers/main/en/internal/image_processing_utils#transformers.ImageProcessingMixin.to_dict">to_dict()</a> method.`,name:"image_processor_dict"},{anchor:"transformers.ImageProcessingMixin.from_dict.kwargs",description:`<strong>kwargs</strong> (<code>Dict[str, Any]</code>) &#x2014;
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<p>The image processor object instantiated from those
parameters.</p>
`,returnType:`
<p><a
  href="/docs/transformers/main/en/internal/image_processing_utils#transformers.ImageProcessingMixin"
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`}}),Ne=new M({props:{name:"from_json_file",anchor:"transformers.ImageProcessingMixin.from_json_file",parameters:[{name:"json_file",val:": typing.Union[str, os.PathLike]"}],parametersDescription:[{anchor:"transformers.ImageProcessingMixin.from_json_file.json_file",description:`<strong>json_file</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Path to the JSON file containing the parameters.`,name:"json_file"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/image_processing_utils.py#L437",returnDescription:`
<p>The image_processor object
instantiated from that JSON file.</p>
`,returnType:`
<p>A image processor of type <a
  href="/docs/transformers/main/en/internal/image_processing_utils#transformers.ImageProcessingMixin"
>ImageProcessingMixin</a></p>
`}}),ke=new M({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[str, bool, 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;
This can be either:</p>
<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
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 image processor file saved using the
<a href="/docs/transformers/main/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;
Path to a directory in which a downloaded pretrained model image processor should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.ImageProcessingMixin.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 image processor files and override the cached versions if
they exist.`,name:"force_download"},{anchor:"transformers.ImageProcessingMixin.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.ImageProcessingMixin.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.ImageProcessingMixin.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.ImageProcessingMixin.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/image_processing_utils.py#L92",returnDescription:`
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  href="/docs/transformers/main/en/internal/image_processing_utils#transformers.ImageProcessingMixin"
>ImageProcessingMixin</a>.</p>
`}}),oe=new Ba({props:{anchor:"transformers.ImageProcessingMixin.from_pretrained.example",$$slots:{default:[Ka]},$$scope:{ctx:Q}}}),We=new M({props:{name:"get_image_processor_dict",anchor:"transformers.ImageProcessingMixin.get_image_processor_dict",parameters:[{name:"pretrained_model_name_or_path",val:": typing.Union[str, os.PathLike]"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.ImageProcessingMixin.get_image_processor_dict.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
The identifier of the pre-trained checkpoint from which we want the dictionary of parameters.`,name:"pretrained_model_name_or_path"},{anchor:"transformers.ImageProcessingMixin.get_image_processor_dict.subfolder",description:`<strong>subfolder</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;&quot;</code>) &#x2014;
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`}}),Le=new M({props:{name:"push_to_hub",anchor:"transformers.ImageProcessingMixin.push_to_hub",parameters:[{name:"repo_id",val:": str"},{name:"use_temp_dir",val:": typing.Optional[bool] = None"},{name:"commit_message",val:": typing.Optional[str] = None"},{name:"private",val:": typing.Optional[bool] = None"},{name:"token",val:": typing.Union[bool, str, NoneType] = None"},{name:"max_shard_size",val:": typing.Union[int, str, NoneType] = '10GB'"},{name:"create_pr",val:": bool = False"},{name:"safe_serialization",val:": bool = False"},{name:"**deprecated_kwargs",val:""}],parametersDescription:[{anchor:"transformers.ImageProcessingMixin.push_to_hub.repo_id",description:`<strong>repo_id</strong> (<code>str</code>) &#x2014;
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The token to use as HTTP bearer authorization for remote files. If <code>True</code>, will use the token generated
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Only applicable for models. The maximum size for a checkpoint before being sharded. Checkpoints shard
will then be each of size lower than this size. If expressed as a string, needs to be digits followed
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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
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<p>Dictionary of all the attributes that make up this image processor instance.</p>
`,returnType:`
<p><code>Dict[str, Any]</code></p>
`}}),Be=new M({props:{name:"to_json_file",anchor:"transformers.ImageProcessingMixin.to_json_file",parameters:[{name:"json_file_path",val:": typing.Union[str, os.PathLike]"}],parametersDescription:[{anchor:"transformers.ImageProcessingMixin.to_json_file.json_file_path",description:`<strong>json_file_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
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<p>String containing all the attributes that make up this feature_extractor instance in JSON format.</p>
`,returnType:`
<p><code>str</code></p>
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