import{S as on,i as rn,s as sn,e as r,k as h,w as P,t as a,M as nn,c as s,d as o,m as f,a as n,x as w,h as i,b as d,N as an,G as t,g as m,y as $,q as y,o as L,B as N,v as ln,L as ns}from"../../chunks/vendor-hf-doc-builder.js";import{T as rs}from"../../chunks/Tip-hf-doc-builder.js";import{D as K}from"../../chunks/Docstring-hf-doc-builder.js";import{C as as}from"../../chunks/CodeBlock-hf-doc-builder.js";import{I as ge}from"../../chunks/IconCopyLink-hf-doc-builder.js";import{E as ss}from"../../chunks/ExampleCodeBlock-hf-doc-builder.js";function dn(G){let p,b,g,_,v;return{c(){p=r("p"),b=a(`This is a recently introduced model so the API hasn\u2019t been tested extensively. There may be some bugs or slight
breaking changes to fix it in the future. If you see something strange, file a `),g=r("a"),_=a("Github Issue"),v=a("."),this.h()},l(l){p=s(l,"P",{});var u=n(p);b=i(u,`This is a recently introduced model so the API hasn\u2019t been tested extensively. There may be some bugs or slight
breaking changes to fix it in the future. If you see something strange, file a `),g=s(u,"A",{href:!0,rel:!0});var E=n(g);_=i(E,"Github Issue"),E.forEach(o),v=i(u,"."),u.forEach(o),this.h()},h(){d(g,"href","https://github.com/huggingface/transformers/issues/new?assignees=&labels=&template=bug-report.md&title"),d(g,"rel","nofollow")},m(l,u){m(l,p,u),t(p,b),t(p,g),t(g,_),t(p,v)},d(l){l&&o(p)}}}function cn(G){let p,b,g,_,v;return _=new as({props:{code:`from transformers import GLPNModel, GLPNConfig

# Initializing a GLPN vinvino02/glpn-kitti style configuration
configuration = GLPNConfig()

# Initializing a model from the vinvino02/glpn-kitti style configuration
model = GLPNModel(configuration)

# Accessing the model configuration
configuration = model.config`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> GLPNModel, GLPNConfig

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Initializing a GLPN vinvino02/glpn-kitti style configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>configuration = GLPNConfig()

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Initializing a model from the vinvino02/glpn-kitti style configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = GLPNModel(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`}}),{c(){p=r("p"),b=a("Example:"),g=h(),P(_.$$.fragment)},l(l){p=s(l,"P",{});var u=n(p);b=i(u,"Example:"),u.forEach(o),g=f(l),w(_.$$.fragment,l)},m(l,u){m(l,p,u),t(p,b),m(l,g,u),$(_,l,u),v=!0},p:ns,i(l){v||(y(_.$$.fragment,l),v=!0)},o(l){L(_.$$.fragment,l),v=!1},d(l){l&&o(p),l&&o(g),N(_,l)}}}function pn(G){let p,b,g,_,v;return{c(){p=r("p"),b=a("Although the recipe for forward pass needs to be defined within this function, one should call the "),g=r("code"),_=a("Module"),v=a(`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`)},l(l){p=s(l,"P",{});var u=n(p);b=i(u,"Although the recipe for forward pass needs to be defined within this function, one should call the "),g=s(u,"CODE",{});var E=n(g);_=i(E,"Module"),E.forEach(o),v=i(u,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),u.forEach(o)},m(l,u){m(l,p,u),t(p,b),t(p,g),t(g,_),t(p,v)},d(l){l&&o(p)}}}function mn(G){let p,b,g,_,v;return _=new as({props:{code:`from transformers import AutoImageProcessor, GLPNModel
import torch
from datasets import load_dataset

dataset = load_dataset("huggingface/cats-image")
image = dataset["test"]["image"][0]

image_processor = AutoImageProcessor.from_pretrained("vinvino02/glpn-kitti")
model = GLPNModel.from_pretrained("vinvino02/glpn-kitti")

inputs = image_processor(image, return_tensors="pt")

with torch.no_grad():
    outputs = model(**inputs)

last_hidden_states = outputs.last_hidden_state
list(last_hidden_states.shape)`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoImageProcessor, GLPNModel
<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;vinvino02/glpn-kitti&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = GLPNModel.from_pretrained(<span class="hljs-string">&quot;vinvino02/glpn-kitti&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">512</span>, <span class="hljs-number">15</span>, <span class="hljs-number">20</span>]`}}),{c(){p=r("p"),b=a("Example:"),g=h(),P(_.$$.fragment)},l(l){p=s(l,"P",{});var u=n(p);b=i(u,"Example:"),u.forEach(o),g=f(l),w(_.$$.fragment,l)},m(l,u){m(l,p,u),t(p,b),m(l,g,u),$(_,l,u),v=!0},p:ns,i(l){v||(y(_.$$.fragment,l),v=!0)},o(l){L(_.$$.fragment,l),v=!1},d(l){l&&o(p),l&&o(g),N(_,l)}}}function hn(G){let p,b,g,_,v;return{c(){p=r("p"),b=a("Although the recipe for forward pass needs to be defined within this function, one should call the "),g=r("code"),_=a("Module"),v=a(`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`)},l(l){p=s(l,"P",{});var u=n(p);b=i(u,"Although the recipe for forward pass needs to be defined within this function, one should call the "),g=s(u,"CODE",{});var E=n(g);_=i(E,"Module"),E.forEach(o),v=i(u,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),u.forEach(o)},m(l,u){m(l,p,u),t(p,b),t(p,g),t(g,_),t(p,v)},d(l){l&&o(p)}}}function fn(G){let p,b,g,_,v;return _=new as({props:{code:`from transformers import AutoImageProcessor, GLPNForDepthEstimation
import torch
import numpy as np
from PIL import Image
import requests

url = "http://images.cocodataset.org/val2017/000000039769.jpg"
image = Image.open(requests.get(url, stream=True).raw)

image_processor = AutoImageProcessor.from_pretrained("vinvino02/glpn-kitti")
model = GLPNForDepthEstimation.from_pretrained("vinvino02/glpn-kitti")

# prepare image for the model
inputs = image_processor(images=image, return_tensors="pt")

with torch.no_grad():
    outputs = model(**inputs)
    predicted_depth = outputs.predicted_depth

# interpolate to original size
prediction = torch.nn.functional.interpolate(
    predicted_depth.unsqueeze(1),
    size=image.size[::-1],
    mode="bicubic",
    align_corners=False,
)

# visualize the prediction
output = prediction.squeeze().cpu().numpy()
formatted = (output * 255 / np.max(output)).astype("uint8")
depth = Image.fromarray(formatted)`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoImageProcessor, GLPNForDepthEstimation
<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">import</span> numpy <span class="hljs-keyword">as</span> np
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> PIL <span class="hljs-keyword">import</span> Image
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> requests

<span class="hljs-meta">&gt;&gt;&gt; </span>url = <span class="hljs-string">&quot;http://images.cocodataset.org/val2017/000000039769.jpg&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>image = Image.<span class="hljs-built_in">open</span>(requests.get(url, stream=<span class="hljs-literal">True</span>).raw)

<span class="hljs-meta">&gt;&gt;&gt; </span>image_processor = AutoImageProcessor.from_pretrained(<span class="hljs-string">&quot;vinvino02/glpn-kitti&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = GLPNForDepthEstimation.from_pretrained(<span class="hljs-string">&quot;vinvino02/glpn-kitti&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># prepare image for the model</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = image_processor(images=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">... </span>    predicted_depth = outputs.predicted_depth

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># interpolate to original size</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>prediction = torch.nn.functional.interpolate(
<span class="hljs-meta">... </span>    predicted_depth.unsqueeze(<span class="hljs-number">1</span>),
<span class="hljs-meta">... </span>    size=image.size[::-<span class="hljs-number">1</span>],
<span class="hljs-meta">... </span>    mode=<span class="hljs-string">&quot;bicubic&quot;</span>,
<span class="hljs-meta">... </span>    align_corners=<span class="hljs-literal">False</span>,
<span class="hljs-meta">... </span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># visualize the prediction</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>output = prediction.squeeze().cpu().numpy()
<span class="hljs-meta">&gt;&gt;&gt; </span>formatted = (output * <span class="hljs-number">255</span> / np.<span class="hljs-built_in">max</span>(output)).astype(<span class="hljs-string">&quot;uint8&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>depth = Image.fromarray(formatted)`}}),{c(){p=r("p"),b=a("Examples:"),g=h(),P(_.$$.fragment)},l(l){p=s(l,"P",{});var u=n(p);b=i(u,"Examples:"),u.forEach(o),g=f(l),w(_.$$.fragment,l)},m(l,u){m(l,p,u),t(p,b),m(l,g,u),$(_,l,u),v=!0},p:ns,i(l){v||(y(_.$$.fragment,l),v=!0)},o(l){L(_.$$.fragment,l),v=!1},d(l){l&&o(p),l&&o(g),N(_,l)}}}function un(G){let p,b,g,_,v,l,u,E,Lo,St,Y,Ot,A,X,gt,_e,No,_t,Go,Rt,F,Eo,ve,To,xo,Ve,ko,Io,Wt,Be,jo,Ut,Je,vt,zo,Ht,Ke,Fo,Vt,Ye,be,Co,Xe,Ao,Do,Bt,Q,is,Jt,Z,Mo,Pe,qo,So,Kt,C,Oo,we,Ro,Wo,$e,Uo,Ho,Yt,D,ee,bt,ye,Vo,Pt,Bo,Xt,Qe,Jo,Qt,Ze,M,Ko,et,Yo,Xo,Le,Qo,Zo,Zt,q,te,wt,Ne,er,$t,tr,eo,T,Ge,or,S,rr,tt,sr,nr,Ee,ar,ir,lr,O,dr,ot,cr,pr,rt,mr,hr,fr,oe,to,R,re,yt,Te,ur,Lt,gr,oo,W,xe,_r,se,ke,vr,Nt,br,ro,U,ne,Gt,Ie,Pr,Et,wr,so,I,je,$r,Tt,yr,Lr,ae,ze,Nr,xt,Gr,no,H,ie,kt,Fe,Er,It,Tr,ao,j,Ce,xr,Ae,kr,De,Ir,jr,zr,x,Me,Fr,V,Cr,st,Ar,Dr,jt,Mr,qr,Sr,le,Or,de,io,B,ce,zt,qe,Rr,Ft,Wr,lo,z,Se,Ur,Oe,Hr,Re,Vr,Br,Jr,k,We,Kr,J,Yr,nt,Xr,Qr,Ct,Zr,es,ts,pe,os,me,co;return l=new ge({}),Y=new rs({props:{$$slots:{default:[dn]},$$scope:{ctx:G}}}),_e=new ge({}),ye=new ge({}),Ne=new ge({}),Ge=new K({props:{name:"class transformers.GLPNConfig",anchor:"transformers.GLPNConfig",parameters:[{name:"num_channels",val:" = 3"},{name:"num_encoder_blocks",val:" = 4"},{name:"depths",val:" = [2, 2, 2, 2]"},{name:"sr_ratios",val:" = [8, 4, 2, 1]"},{name:"hidden_sizes",val:" = [32, 64, 160, 256]"},{name:"patch_sizes",val:" = [7, 3, 3, 3]"},{name:"strides",val:" = [4, 2, 2, 2]"},{name:"num_attention_heads",val:" = [1, 2, 5, 8]"},{name:"mlp_ratios",val:" = [4, 4, 4, 4]"},{name:"hidden_act",val:" = 'gelu'"},{name:"hidden_dropout_prob",val:" = 0.0"},{name:"attention_probs_dropout_prob",val:" = 0.0"},{name:"initializer_range",val:" = 0.02"},{name:"drop_path_rate",val:" = 0.1"},{name:"layer_norm_eps",val:" = 1e-06"},{name:"decoder_hidden_size",val:" = 64"},{name:"max_depth",val:" = 10"},{name:"head_in_index",val:" = -1"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.GLPNConfig.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.GLPNConfig.num_encoder_blocks",description:`<strong>num_encoder_blocks</strong> (<code>int</code>, <em>optional</em>, defaults to 4) &#x2014;
The number of encoder blocks (i.e. stages in the Mix Transformer encoder).`,name:"num_encoder_blocks"},{anchor:"transformers.GLPNConfig.depths",description:`<strong>depths</strong> (<code>List[int]</code>, <em>optional</em>, defaults to <code>[2, 2, 2, 2]</code>) &#x2014;
The number of layers in each encoder block.`,name:"depths"},{anchor:"transformers.GLPNConfig.sr_ratios",description:`<strong>sr_ratios</strong> (<code>List[int]</code>, <em>optional</em>, defaults to <code>[8, 4, 2, 1]</code>) &#x2014;
Sequence reduction ratios in each encoder block.`,name:"sr_ratios"},{anchor:"transformers.GLPNConfig.hidden_sizes",description:`<strong>hidden_sizes</strong> (<code>List[int]</code>, <em>optional</em>, defaults to <code>[32, 64, 160, 256]</code>) &#x2014;
Dimension of each of the encoder blocks.`,name:"hidden_sizes"},{anchor:"transformers.GLPNConfig.patch_sizes",description:`<strong>patch_sizes</strong> (<code>List[int]</code>, <em>optional</em>, defaults to <code>[7, 3, 3, 3]</code>) &#x2014;
Patch size before each encoder block.`,name:"patch_sizes"},{anchor:"transformers.GLPNConfig.strides",description:`<strong>strides</strong> (<code>List[int]</code>, <em>optional</em>, defaults to <code>[4, 2, 2, 2]</code>) &#x2014;
Stride before each encoder block.`,name:"strides"},{anchor:"transformers.GLPNConfig.num_attention_heads",description:`<strong>num_attention_heads</strong> (<code>List[int]</code>, <em>optional</em>, defaults to <code>[1, 2, 4, 8]</code>) &#x2014;
Number of attention heads for each attention layer in each block of the Transformer encoder.`,name:"num_attention_heads"},{anchor:"transformers.GLPNConfig.mlp_ratios",description:`<strong>mlp_ratios</strong> (<code>List[int]</code>, <em>optional</em>, defaults to <code>[4, 4, 4, 4]</code>) &#x2014;
Ratio of the size of the hidden layer compared to the size of the input layer of the Mix FFNs in the
encoder blocks.`,name:"mlp_ratios"},{anchor:"transformers.GLPNConfig.hidden_act",description:`<strong>hidden_act</strong> (<code>str</code> or <code>function</code>, <em>optional</em>, defaults to <code>&quot;gelu&quot;</code>) &#x2014;
The non-linear activation function (function or string) in the encoder and pooler. 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.GLPNConfig.hidden_dropout_prob",description:`<strong>hidden_dropout_prob</strong> (<code>float</code>, <em>optional</em>, defaults to 0.0) &#x2014;
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.`,name:"hidden_dropout_prob"},{anchor:"transformers.GLPNConfig.attention_probs_dropout_prob",description:`<strong>attention_probs_dropout_prob</strong> (<code>float</code>, <em>optional</em>, defaults to 0.0) &#x2014;
The dropout ratio for the attention probabilities.`,name:"attention_probs_dropout_prob"},{anchor:"transformers.GLPNConfig.initializer_range",description:`<strong>initializer_range</strong> (<code>float</code>, <em>optional</em>, defaults to 0.02) &#x2014;
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.`,name:"initializer_range"},{anchor:"transformers.GLPNConfig.drop_path_rate",description:`<strong>drop_path_rate</strong> (<code>float</code>, <em>optional</em>, defaults to 0.1) &#x2014;
The dropout probability for stochastic depth, used in the blocks of the Transformer encoder.`,name:"drop_path_rate"},{anchor:"transformers.GLPNConfig.layer_norm_eps",description:`<strong>layer_norm_eps</strong> (<code>float</code>, <em>optional</em>, defaults to 1e-6) &#x2014;
The epsilon used by the layer normalization layers.`,name:"layer_norm_eps"},{anchor:"transformers.GLPNConfig.decoder_hidden_size",description:`<strong>decoder_hidden_size</strong> (<code>int</code>, <em>optional</em>, defaults to 32) &#x2014;
The dimension of the decoder.`,name:"decoder_hidden_size"},{anchor:"transformers.GLPNConfig.max_depth",description:`<strong>max_depth</strong> (<code>int</code>, <em>optional</em>, defaults to 10) &#x2014;
The maximum depth of the decoder.`,name:"max_depth"},{anchor:"transformers.GLPNConfig.head_in_index",description:`<strong>head_in_index</strong> (<code>int</code>, <em>optional</em>, defaults to -1) &#x2014;
The index of the features to use in the head.`,name:"head_in_index"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/glpn/configuration_glpn.py#L29"}}),oe=new ss({props:{anchor:"transformers.GLPNConfig.example",$$slots:{default:[cn]},$$scope:{ctx:G}}}),Te=new ge({}),xe=new K({props:{name:"class transformers.GLPNFeatureExtractor",anchor:"transformers.GLPNFeatureExtractor",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/glpn/feature_extraction_glpn.py#L26"}}),ke=new K({props:{name:"__call__",anchor:"transformers.GLPNFeatureExtractor.__call__",parameters:[{name:"images",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/image_processing_utils.py#L448"}}),Ie=new ge({}),je=new K({props:{name:"class transformers.GLPNImageProcessor",anchor:"transformers.GLPNImageProcessor",parameters:[{name:"do_resize",val:": bool = True"},{name:"size_divisor",val:": int = 32"},{name:"resample",val:" = <Resampling.BILINEAR: 2>"},{name:"do_rescale",val:": bool = True"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.GLPNImageProcessor.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, rounding them down to the closest multiple of
<code>size_divisor</code>. Can be overridden by <code>do_resize</code> in <code>preprocess</code>.`,name:"do_resize"},{anchor:"transformers.GLPNImageProcessor.size_divisor",description:`<strong>size_divisor</strong> (<code>int</code>, <em>optional</em>, defaults to 32) &#x2014;
When <code>do_resize</code> is <code>True</code>, images are resized so their height and width are rounded down to the closest
multiple of <code>size_divisor</code>. Can be overridden by <code>size_divisor</code> in <code>preprocess</code>.`,name:"size_divisor"},{anchor:"transformers.GLPNImageProcessor.resample",description:`<strong>resample</strong> (<code>PIL.Image</code> resampling filter, <em>optional</em>, defaults to <code>PILImageResampling.BILINEAR</code>) &#x2014;
Resampling filter to use if resizing the image. Can be overridden by <code>resample</code> in <code>preprocess</code>.`,name:"resample"},{anchor:"transformers.GLPNImageProcessor.do_rescale",description:`<strong>do_rescale</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether or not to apply the scaling factor (to make pixel values floats between 0. and 1.). Can be
overridden by <code>do_rescale</code> in <code>preprocess</code>.`,name:"do_rescale"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/glpn/image_processing_glpn.py#L34"}}),ze=new K({props:{name:"preprocess",anchor:"transformers.GLPNImageProcessor.preprocess",parameters:[{name:"images",val:": typing.Union[ForwardRef('PIL.Image.Image'), transformers.utils.generic.TensorType, typing.List[ForwardRef('PIL.Image.Image')], typing.List[transformers.utils.generic.TensorType]]"},{name:"do_resize",val:": typing.Optional[bool] = None"},{name:"size_divisor",val:": typing.Optional[int] = None"},{name:"resample",val:" = None"},{name:"do_rescale",val:": typing.Optional[bool] = None"},{name:"return_tensors",val:": typing.Union[transformers.utils.generic.TensorType, str, NoneType] = None"},{name:"data_format",val:": ChannelDimension = <ChannelDimension.FIRST: 'channels_first'>"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.GLPNImageProcessor.preprocess.images",description:`<strong>images</strong> (<code>PIL.Image.Image</code> or <code>TensorType</code> or <code>List[np.ndarray]</code> or <code>List[TensorType]</code>) &#x2014;
The image or images to preprocess.`,name:"images"},{anchor:"transformers.GLPNImageProcessor.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 input such that the (height, width) dimensions are a multiple of <code>size_divisor</code>.`,name:"do_resize"},{anchor:"transformers.GLPNImageProcessor.preprocess.size_divisor",description:`<strong>size_divisor</strong> (<code>int</code>, <em>optional</em>, defaults to <code>self.size_divisor</code>) &#x2014;
When <code>do_resize</code> is <code>True</code>, images are resized so their height and width are rounded down to the
closest multiple of <code>size_divisor</code>.`,name:"size_divisor"},{anchor:"transformers.GLPNImageProcessor.preprocess.resample",description:`<strong>resample</strong> (<code>PIL.Image</code> resampling filter, <em>optional</em>, defaults to <code>self.resample</code>) &#x2014;
<code>PIL.Image</code> resampling filter to use if resizing the image e.g. <code>PILImageResampling.BILINEAR</code>. Only has
an effect if <code>do_resize</code> is set to <code>True</code>.`,name:"resample"},{anchor:"transformers.GLPNImageProcessor.preprocess.do_rescale",description:`<strong>do_rescale</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>self.do_rescale</code>) &#x2014;
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<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>
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The channel dimension format for the output image. Can be one of:<ul>
<li><code>ChannelDimension.FIRST</code>: image in (num_channels, height, width) format.</li>
<li><code>ChannelDimension.LAST</code>: image in (height, width, num_channels) format.</li>
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>GLPNConfig</a>) and inputs.</p>
<ul>
<li>
<p><strong>last_hidden_state</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>) \u2014 Sequence of hidden-states at the output of the last layer of the model.</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>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for the output of the embeddings, if the model has an embedding layer, +
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<p>Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.</p>
</li>
<li>
<p><strong>attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
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</li>
</ul>
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<p><a
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<ul>
<li>
<p><strong>loss</strong> (<code>torch.FloatTensor</code> of shape <code>(1,)</code>, <em>optional</em>, returned when <code>labels</code> is provided) \u2014 Classification (or regression if config.num_labels==1) loss.</p>
</li>
<li>
<p><strong>predicted_depth</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, height, width)</code>) \u2014 Predicted depth for each pixel.</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>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for the output of the embeddings, if the model has an embedding layer, +
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<p>Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.</p>
</li>
<li>
<p><strong>attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_heads, patch_size, sequence_length)</code>.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.</p>
</li>
</ul>
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<p><a
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