import{s as Wt,o as vt,n as ye}from"../chunks/scheduler.18a86fab.js";import{S as Ct,i as Jt,g as c,s as o,r as g,A as Ut,h as d,f as a,c as r,j as B,u as f,x as $,k as I,y as m,a as l,v as u,d as h,t as b,w as M}from"../chunks/index.98837b22.js";import{T as pt}from"../chunks/Tip.77304350.js";import{D as ce}from"../chunks/Docstring.6758ea23.js";import{C as Le}from"../chunks/CodeBlock.8d0c2e8a.js";import{E as mt}from"../chunks/ExampleCodeBlock.0c42463f.js";import{P as xt}from"../chunks/PipelineTag.7749150e.js";import{H as de}from"../chunks/Heading.7e7e0c5b.js";import{E as kt}from"../chunks/index.92f616ba.js";function It(W){let s,_='For a more detailed overview please read the <a href="https://huggingface.co/blog/timm-transformers" rel="nofollow">official blog post</a> on the timm integration.';return{c(){s=c("p"),s.innerHTML=_},l(i){s=d(i,"P",{"data-svelte-h":!0}),$(s)!=="svelte-f92w4d"&&(s.innerHTML=_)},m(i,p){l(i,s,p)},p:ye,d(i){i&&a(s)}}}function Zt(W){let s,_="Example:",i,p,w;return p=new Le({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMFRpbW1XcmFwcGVyTW9kZWwlMEElMEElMjMlMjBJbml0aWFsaXppbmclMjBhJTIwdGltbSUyMG1vZGVsJTBBbW9kZWwlMjAlM0QlMjBUaW1tV3JhcHBlck1vZGVsLmZyb21fcHJldHJhaW5lZCglMjJ0aW1tJTJGcmVzbmV0MTguYTFfaW4xayUyMiklMEElMEElMjMlMjBBY2Nlc3NpbmclMjB0aGUlMjBtb2RlbCUyMGNvbmZpZ3VyYXRpb24lMEFjb25maWd1cmF0aW9uJTIwJTNEJTIwbW9kZWwuY29uZmln",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> TimmWrapperModel

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Initializing a timm model</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TimmWrapperModel.from_pretrained(<span class="hljs-string">&quot;timm/resnet18.a1_in1k&quot;</span>)

<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`,wrap:!1}}),{c(){s=c("p"),s.textContent=_,i=o(),g(p.$$.fragment)},l(n){s=d(n,"P",{"data-svelte-h":!0}),$(s)!=="svelte-11lpom8"&&(s.textContent=_),i=r(n),f(p.$$.fragment,n)},m(n,T){l(n,s,T),l(n,i,T),u(p,n,T),w=!0},p:ye,i(n){w||(h(p.$$.fragment,n),w=!0)},o(n){b(p.$$.fragment,n),w=!1},d(n){n&&(a(s),a(i)),M(p,n)}}}function Ft(W){let s,_=`Although the recipe for forward pass needs to be defined within this function, one should call the <code>Module</code>
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`;return{c(){s=c("p"),s.innerHTML=_},l(i){s=d(i,"P",{"data-svelte-h":!0}),$(s)!=="svelte-fincs2"&&(s.innerHTML=_)},m(i,p){l(i,s,p)},p:ye,d(i){i&&a(s)}}}function Et(W){let s,_="Examples:",i,p,w;return p=new Le({props:{code:"aW1wb3J0JTIwdG9yY2glMEFmcm9tJTIwUElMJTIwaW1wb3J0JTIwSW1hZ2UlMEFmcm9tJTIwdXJsbGliLnJlcXVlc3QlMjBpbXBvcnQlMjB1cmxvcGVuJTBBZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Nb2RlbCUyQyUyMEF1dG9JbWFnZVByb2Nlc3NvciUwQSUwQSUyMyUyMExvYWQlMjBpbWFnZSUwQWltYWdlJTIwJTNEJTIwSW1hZ2Uub3Blbih1cmxvcGVuKCUwQSUyMCUyMCUyMCUyMCdodHRwcyUzQSUyRiUyRmh1Z2dpbmdmYWNlLmNvJTJGZGF0YXNldHMlMkZodWdnaW5nZmFjZSUyRmRvY3VtZW50YXRpb24taW1hZ2VzJTJGcmVzb2x2ZSUyRm1haW4lMkZiZWlnbmV0cy10YXNrLWd1aWRlLnBuZyclMEEpKSUwQSUwQSUyMyUyMExvYWQlMjBtb2RlbCUyMGFuZCUyMGltYWdlJTIwcHJvY2Vzc29yJTBBY2hlY2twb2ludCUyMCUzRCUyMCUyMnRpbW0lMkZyZXNuZXQ1MC5hMV9pbjFrJTIyJTBBaW1hZ2VfcHJvY2Vzc29yJTIwJTNEJTIwQXV0b0ltYWdlUHJvY2Vzc29yLmZyb21fcHJldHJhaW5lZChjaGVja3BvaW50KSUwQW1vZGVsJTIwJTNEJTIwQXV0b01vZGVsLmZyb21fcHJldHJhaW5lZChjaGVja3BvaW50KS5ldmFsKCklMEElMEElMjMlMjBQcmVwcm9jZXNzJTIwaW1hZ2UlMEFpbnB1dHMlMjAlM0QlMjBpbWFnZV9wcm9jZXNzb3IoaW1hZ2UpJTBBJTBBJTIzJTIwRm9yd2FyZCUyMHBhc3MlMEF3aXRoJTIwdG9yY2gubm9fZ3JhZCgpJTNBJTBBJTIwJTIwJTIwJTIwb3V0cHV0cyUyMCUzRCUyMG1vZGVsKCoqaW5wdXRzKSUwQSUwQSUyMyUyMEdldCUyMHBvb2xlZCUyMG91dHB1dCUwQXBvb2xlZF9vdXRwdXQlMjAlM0QlMjBvdXRwdXRzLnBvb2xlcl9vdXRwdXQlMEElMEElMjMlMjBHZXQlMjBsYXN0JTIwaGlkZGVuJTIwc3RhdGUlMEFsYXN0X2hpZGRlbl9zdGF0ZSUyMCUzRCUyMG91dHB1dHMubGFzdF9oaWRkZW5fc3RhdGU=",highlighted:`<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> PIL <span class="hljs-keyword">import</span> Image
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> urllib.request <span class="hljs-keyword">import</span> urlopen
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoModel, AutoImageProcessor

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Load image</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>image = Image.<span class="hljs-built_in">open</span>(urlopen(
<span class="hljs-meta">... </span>    <span class="hljs-string">&#x27;https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png&#x27;</span>
<span class="hljs-meta">... </span>))

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Load model and image processor</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>checkpoint = <span class="hljs-string">&quot;timm/resnet50.a1_in1k&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>image_processor = AutoImageProcessor.from_pretrained(checkpoint)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModel.from_pretrained(checkpoint).<span class="hljs-built_in">eval</span>()

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Preprocess image</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = image_processor(image)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Forward pass</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><span class="hljs-comment"># Get pooled output</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>pooled_output = outputs.pooler_output

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Get last hidden state</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>last_hidden_state = outputs.last_hidden_state`,wrap:!1}}),{c(){s=c("p"),s.textContent=_,i=o(),g(p.$$.fragment)},l(n){s=d(n,"P",{"data-svelte-h":!0}),$(s)!=="svelte-kvfsh7"&&(s.textContent=_),i=r(n),f(p.$$.fragment,n)},m(n,T){l(n,s,T),l(n,i,T),u(p,n,T),w=!0},p:ye,i(n){w||(h(p.$$.fragment,n),w=!0)},o(n){b(p.$$.fragment,n),w=!1},d(n){n&&(a(s),a(i)),M(p,n)}}}function Bt(W){let s,_=`Although the recipe for forward pass needs to be defined within this function, one should call the <code>Module</code>
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`;return{c(){s=c("p"),s.innerHTML=_},l(i){s=d(i,"P",{"data-svelte-h":!0}),$(s)!=="svelte-fincs2"&&(s.innerHTML=_)},m(i,p){l(i,s,p)},p:ye,d(i){i&&a(s)}}}function Gt(W){let s,_="Examples:",i,p,w;return p=new Le({props:{code:"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",highlighted:`<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> PIL <span class="hljs-keyword">import</span> Image
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> urllib.request <span class="hljs-keyword">import</span> urlopen
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoModelForImageClassification, AutoImageProcessor

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Load image</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>image = Image.<span class="hljs-built_in">open</span>(urlopen(
<span class="hljs-meta">... </span>    <span class="hljs-string">&#x27;https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png&#x27;</span>
<span class="hljs-meta">... </span>))

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Load model and image processor</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>checkpoint = <span class="hljs-string">&quot;timm/resnet50.a1_in1k&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>image_processor = AutoImageProcessor.from_pretrained(checkpoint)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForImageClassification.from_pretrained(checkpoint).<span class="hljs-built_in">eval</span>()

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Preprocess image</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = image_processor(image)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Forward pass</span>
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">with</span> torch.no_grad():
<span class="hljs-meta">... </span>    logits = model(**inputs).logits

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Get top 5 predictions</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>top5_probabilities, top5_class_indices = torch.topk(logits.softmax(dim=<span class="hljs-number">1</span>) * <span class="hljs-number">100</span>, k=<span class="hljs-number">5</span>)`,wrap:!1}}),{c(){s=c("p"),s.textContent=_,i=o(),g(p.$$.fragment)},l(n){s=d(n,"P",{"data-svelte-h":!0}),$(s)!=="svelte-kvfsh7"&&(s.textContent=_),i=r(n),f(p.$$.fragment,n)},m(n,T){l(n,s,T),l(n,i,T),u(p,n,T),w=!0},p:ye,i(n){w||(h(p.$$.fragment,n),w=!0)},o(n){b(p.$$.fragment,n),w=!1},d(n){n&&(a(s),a(i)),M(p,n)}}}function Nt(W){let s,_,i,p,w,n,T,ct='<img alt="PyTorch" src="https://img.shields.io/badge/PyTorch-DE3412?style=flat&amp;logo=pytorch&amp;logoColor=white"/>',We,Q,ve,X,dt="Helper class to enable loading timm models to be used with the transformers library and its autoclasses.",Ce,P,Je,S,Ue,A,gt="A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with TimmWrapper.",xe,D,ke,q,ft='<li><a href="https://github.com/ariG23498/timm-wrapper-examples" rel="nofollow">Collection of Example Notebook</a> 🌎</li>',Ie,G,Ze,O,Fe,y,K,Qe,ge,ut="This is the configuration class to store the configuration for a timm backbone <code>TimmWrapper</code>.",Xe,fe,ht="It is used to instantiate a timm model according to the specified arguments, defining the model.",Pe,ue,bt=`Configuration objects inherit from <a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a> and can be used to control the model outputs. Read the
documentation from <a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a> for more information.`,Se,he,Mt=`Config loads imagenet label descriptions and stores them in <code>id2label</code> attribute, <code>label2id</code> attribute for default
imagenet models is set to <code>None</code> due to occlusions in the label descriptions.`,Ae,N,Ee,ee,Be,J,te,De,be,_t="Wrapper class for timm models to be used within transformers.",qe,R,se,Oe,Me,wt="Preprocess an image or batch of images.",Ge,ae,Ne,U,ne,Ke,_e,Tt="Wrapper class for timm models to be used in transformers.",et,v,oe,tt,we,$t='The <a href="/docs/transformers/main/en/model_doc/timm_wrapper#transformers.TimmWrapperModel">TimmWrapperModel</a> forward method, overrides the <code>__call__</code> special method.',st,V,at,z,Re,re,Ve,x,le,nt,Te,yt="Wrapper class for timm models to be used in transformers for image classification.",ot,C,ie,rt,$e,jt='The <a href="/docs/transformers/main/en/model_doc/timm_wrapper#transformers.TimmWrapperForImageClassification">TimmWrapperForImageClassification</a> forward method, overrides the <code>__call__</code> special method.',lt,Y,it,H,ze,pe,Ye,je,He;return w=new de({props:{title:"TimmWrapper",local:"timmwrapper",headingTag:"h1"}}),Q=new de({props:{title:"Overview",local:"overview",headingTag:"h2"}}),P=new Le({props:{code:"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",highlighted:`<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> PIL <span class="hljs-keyword">import</span> Image
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> urllib.request <span class="hljs-keyword">import</span> urlopen
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoModelForImageClassification, AutoImageProcessor

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Load image</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>image = Image.<span class="hljs-built_in">open</span>(urlopen(
<span class="hljs-meta">... </span>    <span class="hljs-string">&#x27;https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png&#x27;</span>
<span class="hljs-meta">... </span>))

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Load model and image processor</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>checkpoint = <span class="hljs-string">&quot;timm/resnet50.a1_in1k&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>image_processor = AutoImageProcessor.from_pretrained(checkpoint)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForImageClassification.from_pretrained(checkpoint).<span class="hljs-built_in">eval</span>()

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Preprocess image</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = image_processor(image)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Forward pass</span>
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">with</span> torch.no_grad():
<span class="hljs-meta">... </span>    logits = model(**inputs).logits

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Get top 5 predictions</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>top5_probabilities, top5_class_indices = torch.topk(logits.softmax(dim=<span class="hljs-number">1</span>) * <span class="hljs-number">100</span>, k=<span class="hljs-number">5</span>)`,wrap:!1}}),S=new de({props:{title:"Resources:",local:"resources",headingTag:"h2"}}),D=new xt({props:{pipeline:"image-classification"}}),G=new pt({props:{warning:!1,$$slots:{default:[It]},$$scope:{ctx:W}}}),O=new de({props:{title:"TimmWrapperConfig",local:"transformers.TimmWrapperConfig",headingTag:"h2"}}),K=new ce({props:{name:"class transformers.TimmWrapperConfig",anchor:"transformers.TimmWrapperConfig",parameters:[{name:"initializer_range",val:": float = 0.02"},{name:"do_pooling",val:": bool = True"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TimmWrapperConfig.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.TimmWrapperConfig.do_pooling",description:`<strong>do_pooling</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether to do pooling for the last_hidden_state in <code>TimmWrapperModel</code> or not.`,name:"do_pooling"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/timm_wrapper/configuration_timm_wrapper.py#L31"}}),N=new mt({props:{anchor:"transformers.TimmWrapperConfig.example",$$slots:{default:[Zt]},$$scope:{ctx:W}}}),ee=new de({props:{title:"TimmWrapperImageProcessor",local:"transformers.TimmWrapperImageProcessor",headingTag:"h2"}}),te=new ce({props:{name:"class transformers.TimmWrapperImageProcessor",anchor:"transformers.TimmWrapperImageProcessor",parameters:[{name:"pretrained_cfg",val:": typing.Dict[str, typing.Any]"},{name:"architecture",val:": typing.Optional[str] = None"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TimmWrapperImageProcessor.pretrained_cfg",description:`<strong>pretrained_cfg</strong> (<code>Dict[str, Any]</code>) &#x2014;
The configuration of the pretrained model used to resolve evaluation and
training transforms.`,name:"pretrained_cfg"},{anchor:"transformers.TimmWrapperImageProcessor.architecture",description:`<strong>architecture</strong> (<code>Optional[str]</code>, <em>optional</em>) &#x2014;
Name of the architecture of the model.`,name:"architecture"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/timm_wrapper/image_processing_timm_wrapper.py#L38"}}),se=new ce({props:{name:"preprocess",anchor:"transformers.TimmWrapperImageProcessor.preprocess",parameters:[{name:"images",val:": typing.Union[ForwardRef('PIL.Image.Image'), numpy.ndarray, ForwardRef('torch.Tensor'), list['PIL.Image.Image'], list[numpy.ndarray], list['torch.Tensor']]"},{name:"return_tensors",val:": typing.Union[str, transformers.utils.generic.TensorType, NoneType] = 'pt'"}],parametersDescription:[{anchor:"transformers.TimmWrapperImageProcessor.preprocess.images",description:`<strong>images</strong> (<code>ImageInput</code>) &#x2014;
Image to preprocess. Expects a single or batch of images`,name:"images"},{anchor:"transformers.TimmWrapperImageProcessor.preprocess.return_tensors",description:`<strong>return_tensors</strong> (<code>str</code> or <code>TensorType</code>, <em>optional</em>) &#x2014;
The type of tensors to return.`,name:"return_tensors"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/timm_wrapper/image_processing_timm_wrapper.py#L97"}}),ae=new de({props:{title:"TimmWrapperModel",local:"transformers.TimmWrapperModel",headingTag:"h2"}}),ne=new ce({props:{name:"class transformers.TimmWrapperModel",anchor:"transformers.TimmWrapperModel",parameters:[{name:"config",val:": TimmWrapperConfig"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/timm_wrapper/modeling_timm_wrapper.py#L111"}}),oe=new ce({props:{name:"forward",anchor:"transformers.TimmWrapperModel.forward",parameters:[{name:"pixel_values",val:": FloatTensor"},{name:"output_attentions",val:": typing.Optional[bool] = None"},{name:"output_hidden_states",val:": typing.Union[bool, typing.List[int], NoneType] = None"},{name:"return_dict",val:": typing.Optional[bool] = None"},{name:"do_pooling",val:": typing.Optional[bool] = None"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TimmWrapperModel.forward.pixel_values",description:`<strong>pixel_values</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, num_channels, image_size, image_size)</code>) &#x2014;
The tensors corresponding to the input images. Pixel values can be obtained using
<code>{image_processor_class}</code>. See <code>{image_processor_class}.__call__</code> for details (<code>{processor_class}</code> uses
<code>{image_processor_class}</code> for processing images).`,name:"pixel_values"},{anchor:"transformers.TimmWrapperModel.forward.output_attentions",description:`<strong>output_attentions</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the attentions tensors of all attention layers. Not compatible with timm wrapped models.`,name:"output_attentions"},{anchor:"transformers.TimmWrapperModel.forward.output_hidden_states",description:`<strong>output_hidden_states</strong> (<code>Union[bool, List[int], NoneType]</code>, <em>optional</em>) &#x2014;
Whether or not to return the hidden states of all layers. Not compatible with timm wrapped models.`,name:"output_hidden_states"},{anchor:"transformers.TimmWrapperModel.forward.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/main/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"},{anchor:"transformers.TimmWrapperModel.forward.do_pooling",description:`<strong>do_pooling</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether to do pooling for the last_hidden_state in <code>TimmWrapperModel</code> or not. If <code>None</code> is passed, the
<code>do_pooling</code> value from the config is used.`,name:"do_pooling"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/timm_wrapper/modeling_timm_wrapper.py#L122",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>A <code>transformers.models.timm_wrapper.modeling_timm_wrapper.TimmWrapperModelOutput</code> or a tuple of
<code>torch.FloatTensor</code> (if <code>return_dict=False</code> is passed or when <code>config.return_dict=False</code>) comprising various
elements depending on the configuration (<a
  href="/docs/transformers/main/en/model_doc/timm_wrapper#transformers.TimmWrapperConfig"
>TimmWrapperConfig</a>) and inputs.</p>
<ul>
<li><strong>last_hidden_state</strong> (<code>torch.FloatTensor</code>) — The last hidden state of the model, output before applying the classification head.</li>
<li><strong>pooler_output</strong> (<code>torch.FloatTensor</code>, <em>optional</em>) — The pooled output derived from the last hidden state, if applicable.</li>
<li><strong>hidden_states</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>) — A tuple containing the intermediate hidden states of the model at the output of each layer or specified layers.
Returned if <code>output_hidden_states=True</code> is set or if <code>config.output_hidden_states=True</code>.</li>
<li><strong>attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>) — A tuple containing the intermediate attention weights of the model at the output of each layer.
Returned if <code>output_attentions=True</code> is set or if <code>config.output_attentions=True</code>.
Note: Currently, Timm models do not support attentions output.</li>
</ul>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>transformers.models.timm_wrapper.modeling_timm_wrapper.TimmWrapperModelOutput</code> or <code>tuple(torch.FloatTensor)</code></p>
`}}),V=new pt({props:{$$slots:{default:[Ft]},$$scope:{ctx:W}}}),z=new mt({props:{anchor:"transformers.TimmWrapperModel.forward.example",$$slots:{default:[Et]},$$scope:{ctx:W}}}),re=new de({props:{title:"TimmWrapperForImageClassification",local:"transformers.TimmWrapperForImageClassification",headingTag:"h2"}}),le=new ce({props:{name:"class transformers.TimmWrapperForImageClassification",anchor:"transformers.TimmWrapperForImageClassification",parameters:[{name:"config",val:": TimmWrapperConfig"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/timm_wrapper/modeling_timm_wrapper.py#L221"}}),ie=new ce({props:{name:"forward",anchor:"transformers.TimmWrapperForImageClassification.forward",parameters:[{name:"pixel_values",val:": FloatTensor"},{name:"labels",val:": typing.Optional[torch.LongTensor] = None"},{name:"output_attentions",val:": typing.Optional[bool] = None"},{name:"output_hidden_states",val:": typing.Union[bool, typing.List[int], NoneType] = None"},{name:"return_dict",val:": typing.Optional[bool] = None"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TimmWrapperForImageClassification.forward.pixel_values",description:`<strong>pixel_values</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, num_channels, image_size, image_size)</code>) &#x2014;
The tensors corresponding to the input images. Pixel values can be obtained using
<code>{image_processor_class}</code>. See <code>{image_processor_class}.__call__</code> for details (<code>{processor_class}</code> uses
<code>{image_processor_class}</code> for processing images).`,name:"pixel_values"},{anchor:"transformers.TimmWrapperForImageClassification.forward.labels",description:`<strong>labels</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size,)</code>, <em>optional</em>) &#x2014;
Labels for computing the image classification/regression loss. Indices should be in <code>[0, ..., config.num_labels - 1]</code>. If <code>config.num_labels == 1</code> a regression loss is computed (Mean-Square loss), If
<code>config.num_labels &gt; 1</code> a classification loss is computed (Cross-Entropy).`,name:"labels"},{anchor:"transformers.TimmWrapperForImageClassification.forward.output_attentions",description:`<strong>output_attentions</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the attentions tensors of all attention layers. Not compatible with timm wrapped models.`,name:"output_attentions"},{anchor:"transformers.TimmWrapperForImageClassification.forward.output_hidden_states",description:`<strong>output_hidden_states</strong> (<code>Union[bool, List[int], NoneType]</code>, <em>optional</em>) &#x2014;
Whether or not to return the hidden states of all layers. Not compatible with timm wrapped models.`,name:"output_hidden_states"},{anchor:"transformers.TimmWrapperForImageClassification.forward.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/main/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.
**kwargs:
Additional keyword arguments passed along to the <code>timm</code> model forward.`,name:"return_dict"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/timm_wrapper/modeling_timm_wrapper.py#L240",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>A <a
  href="/docs/transformers/main/en/main_classes/output#transformers.modeling_outputs.ImageClassifierOutput"
>transformers.modeling_outputs.ImageClassifierOutput</a> or a tuple of
<code>torch.FloatTensor</code> (if <code>return_dict=False</code> is passed or when <code>config.return_dict=False</code>) comprising various
elements depending on the configuration (<a
  href="/docs/transformers/main/en/model_doc/timm_wrapper#transformers.TimmWrapperConfig"
>TimmWrapperConfig</a>) and inputs.</p>
<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) — Classification (or regression if config.num_labels==1) loss.</p>
</li>
<li>
<p><strong>logits</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, config.num_labels)</code>) — Classification (or regression if config.num_labels==1) scores (before SoftMax).</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>) — Tuple of <code>torch.FloatTensor</code> (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each stage) of shape <code>(batch_size, sequence_length, hidden_size)</code>. Hidden-states
(also called feature maps) of the model at the output of each stage.</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>) — 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>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><a
  href="/docs/transformers/main/en/main_classes/output#transformers.modeling_outputs.ImageClassifierOutput"
>transformers.modeling_outputs.ImageClassifierOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),Y=new pt({props:{$$slots:{default:[Bt]},$$scope:{ctx:W}}}),H=new mt({props:{anchor:"transformers.TimmWrapperForImageClassification.forward.example",$$slots:{default:[Gt]},$$scope:{ctx:W}}}),pe=new 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