import{s as Ro,f as Vo,o as qo,n as ao}from"../chunks/scheduler.9bc65507.js";import{S as Ho,i as Bo,g as s,s as n,r as m,A as Go,h as i,f as o,c as a,j as z,u as p,x as d,k as y,y as t,a as l,v as g,d as f,t as u,w as h}from"../chunks/index.707bf1b6.js";import{T as Do}from"../chunks/Tip.c2ecdbf4.js";import{D as w}from"../chunks/Docstring.17db21ae.js";import{C as Eo}from"../chunks/CodeBlock.54a9f38d.js";import{E as So}from"../chunks/ExampleCodeBlock.4f515aa9.js";import{H as Xe}from"../chunks/Heading.342b1fa6.js";function Ao(W){let c,$="Example:",k,N,M;return N=new Eo({props:{code:"Y29ycmVjdF90YWJsZXMoJTIyJTVDYmVnaW4lN0J0YWJsZSU3RCUyMCU1Q2JlZ2luJTdCdGFidWxhciU3RCU3QmwlMjBsJTdEJTIwJTI2JTIwJTVDJTIwJTVDZW5kJTdCdGFidWxhciU3RCUyMCU1Q2VuZCU3QnRhYmxlJTdEJTIyKSUwQSUyMiU1Q2JlZ2luJTdCdGFibGUlN0QlMEFhYnVsYXIlN0QlN0JsJTIwbCU3RCUyMCUyNiUyMCU1QyUyMCU1Q2VuZCU3QnRhYnVsYXIlN0QlMEFsZSU3RCUyMg==",highlighted:`correct_tables(<span class="hljs-string">&quot;\\begin{table} \\begin{tabular}{l l} &amp; \\ \\end{tabular} \\end{table}&quot;</span>)
<span class="hljs-string">&quot;\\begin{table}
abular}{l l} &amp; \\ \\end{tabular}
le}&quot;</span>`,wrap:!1}}),{c(){c=s("p"),c.textContent=$,k=n(),m(N.$$.fragment)},l(T){c=i(T,"P",{"data-svelte-h":!0}),d(c)!=="svelte-11lpom8"&&(c.textContent=$),k=a(T),p(N.$$.fragment,T)},m(T,I){l(T,c,I),l(T,k,I),g(N,T,I),M=!0},p:ao,i(T){M||(f(N.$$.fragment,T),M=!0)},o(T){u(N.$$.fragment,T),M=!1},d(T){T&&(o(c),o(k)),h(N,T)}}}function Yo(W){let c,$=`This class method is simply calling the feature extractor
<a href="/docs/transformers/main/en/main_classes/feature_extractor#transformers.FeatureExtractionMixin.from_pretrained">from_pretrained()</a>, image processor
<a href="/docs/transformers/main/en/main_classes/image_processor#transformers.ImageProcessingMixin">ImageProcessingMixin</a> and the tokenizer
<code>~tokenization_utils_base.PreTrainedTokenizer.from_pretrained</code> methods. Please refer to the docstrings of the
methods above for more information.`;return{c(){c=s("p"),c.innerHTML=$},l(k){c=i(k,"P",{"data-svelte-h":!0}),d(c)!=="svelte-1ob8jsn"&&(c.innerHTML=$)},m(k,N){l(k,c,N)},p:ao,d(k){k&&o(c)}}}function Xo(W){let c,$=`This class method is simply calling <a href="/docs/transformers/main/en/main_classes/feature_extractor#transformers.FeatureExtractionMixin.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/main/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.save_pretrained">save_pretrained()</a>. Please refer to the docstrings of the
methods above for more information.`;return{c(){c=s("p"),c.innerHTML=$},l(k){c=i(k,"P",{"data-svelte-h":!0}),d(c)!=="svelte-1qvfzw2"&&(c.innerHTML=$)},m(k,N){l(k,c,N)},p:ao,d(k){k&&o(c)}}}function Qo(W){let c,$,k,N,M,T,I,Oe,A,ro=`The Nougat model was proposed in <a href="https://arxiv.org/abs/2308.13418" rel="nofollow">Nougat: Neural Optical Understanding for Academic Documents</a> by
Lukas Blecher, Guillem Cucurull, Thomas Scialom, Robert Stojnic. Nougat uses the same architecture as <a href="donut">Donut</a>, meaning an image Transformer
encoder and an autoregressive text Transformer decoder to translate scientific PDFs to markdown, enabling easier access to them.`,Ke,Y,so="The abstract from the paper is the following:",et,X,io="<em>Scientific knowledge is predominantly stored in books and scientific journals, often in the form of PDFs. However, the PDF format leads to a loss of semantic information, particularly for mathematical expressions. We propose Nougat (Neural Optical Understanding for Academic Documents), a Visual Transformer model that performs an Optical Character Recognition (OCR) task for processing scientific documents into a markup language, and demonstrate the effectiveness of our model on a new dataset of scientific documents. The proposed approach offers a promising solution to enhance the accessibility of scientific knowledge in the digital age, by bridging the gap between human-readable documents and machine-readable text. We release the models and code to accelerate future work on scientific text recognition.</em>",tt,Z,lo,ot,Q,co='Nougat high-level overview. Taken from the <a href="https://arxiv.org/abs/2308.13418">original paper</a>.',nt,O,mo=`This model was contributed by <a href="https://huggingface.co/nielsr" rel="nofollow">nielsr</a>. The original code can be found
<a href="https://github.com/facebookresearch/nougat" rel="nofollow">here</a>.`,at,K,po="Tips:",rt,ee,go=`<li>The quickest way to get started with Nougat is by checking the <a href="https://github.com/NielsRogge/Transformers-Tutorials/tree/master/Nougat" rel="nofollow">tutorial
notebooks</a>, which show how to use the model
at inference time as well as fine-tuning on custom data.</li> <li>Nougat is always used within the <a href="vision-encoder-decoder">VisionEncoderDecoder</a> framework. The model is identical to <a href="donut">Donut</a> in terms of architecture.</li>`,st,te,it,oe,fo=`Nougat’s <code>VisionEncoderDecoder</code> model accepts images as input and makes use of
<a href="/docs/transformers/main/en/main_classes/text_generation#transformers.GenerationMixin.generate">generate()</a> to autoregressively generate text given the input image.`,lt,ne,uo=`The <a href="/docs/transformers/main/en/model_doc/nougat#transformers.NougatImageProcessor">NougatImageProcessor</a> class is responsible for preprocessing the input image and
<a href="/docs/transformers/main/en/model_doc/nougat#transformers.NougatTokenizerFast">NougatTokenizerFast</a> decodes the generated target tokens to the target string. The
<a href="/docs/transformers/main/en/model_doc/nougat#transformers.NougatProcessor">NougatProcessor</a> wraps <a href="/docs/transformers/main/en/model_doc/nougat#transformers.NougatImageProcessor">NougatImageProcessor</a> and <a href="/docs/transformers/main/en/model_doc/nougat#transformers.NougatTokenizerFast">NougatTokenizerFast</a> classes
into a single instance to both extract the input features and decode the predicted token ids.`,dt,ae,ho="<li>Step-by-step PDF transcription</li>",ct,re,mt,se,_o='See the <a href="https://huggingface.co/models?filter=nougat" rel="nofollow">model hub</a> to look for Nougat checkpoints.',pt,ie,gt,P,le,Nt,ze,bo="Constructs a Nougat image processor.",zt,D,de,wt,we,ko="Preprocess an image or batch of images.",ft,ce,ut,_,me,$t,$e,vo="Fast tokenizer for Nougat (backed by HuggingFace tokenizers library).",Mt,Me,To=`This tokenizer inherits from <a href="/docs/transformers/main/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast">PreTrainedTokenizerFast</a> which contains most of the main methods. Users should
refer to this superclass for more information regarding those methods. This class mainly adds Nougat-specific
methods for postprocessing the generated text.`,Ct,Ce,yo="Class attributes (overridden by derived classes)",It,Ie,xo=`<li><strong>vocab_files_names</strong> (<code>Dict[str, str]</code>) — A dictionary with, as keys, the <code>__init__</code> keyword name of each
vocabulary file required by the model, and as associated values, the filename for saving the associated file
(string).</li> <li><strong>pretrained_vocab_files_map</strong> (<code>Dict[str, Dict[str, str]]</code>) — A dictionary of dictionaries, with the
high-level keys being the <code>__init__</code> keyword name of each vocabulary file required by the model, the
low-level being the <code>short-cut-names</code> of the pretrained models with, as associated values, the <code>url</code> to the
associated pretrained vocabulary file.</li> <li><strong>max_model_input_sizes</strong> (<code>Dict[str, Optional[int]]</code>) — A dictionary with, as keys, the <code>short-cut-names</code>
of the pretrained models, and as associated values, the maximum length of the sequence inputs of this model,
or <code>None</code> if the model has no maximum input size.</li> <li><strong>pretrained_init_configuration</strong> (<code>Dict[str, Dict[str, Any]]</code>) — A dictionary with, as keys, the
<code>short-cut-names</code> of the pretrained models, and as associated values, a dictionary of specific arguments to
pass to the <code>__init__</code> method of the tokenizer class for this pretrained model when loading the tokenizer
with the <a href="/docs/transformers/main/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.from_pretrained">from_pretrained()</a> method.</li> <li><strong>model_input_names</strong> (<code>List[str]</code>) — A list of inputs expected in the forward pass of the model.</li> <li><strong>padding_side</strong> (<code>str</code>) — The default value for the side on which the model should have padding applied.
Should be <code>&#39;right&#39;</code> or <code>&#39;left&#39;</code>.</li> <li><strong>truncation_side</strong> (<code>str</code>) — The default value for the side on which the model should have truncation
applied. Should be <code>&#39;right&#39;</code> or <code>&#39;left&#39;</code>.</li>`,Pt,U,pe,Ut,Pe,No="Takes a generated string and fixes tables/tabulars to make them match the markdown format needed.",Ft,E,jt,C,ge,Jt,Ue,zo="Postprocess a generated text or a list of generated texts.",Lt,Fe,wo="This function can be used to perform postprocessing on generated text, such as fixing Markdown formatting.",Wt,je,$o="Postprocessing is quite slow so it is recommended to use multiprocessing to speed up the process.",Zt,R,fe,Dt,Je,Mo=`Postprocess a single generated text. Regular expressions used here are taken directly from the Nougat article
authors. These expressions are commented for clarity and tested end-to-end in most cases.`,Et,F,ue,Rt,Le,Co="Remove hallucinated or missing references from the text.",Vt,We,Io="This function identifies and removes references that are marked as missing or hallucinated from the input text.",ht,he,_t,b,_e,qt,Ze,Po="Constructs a Nougat processor which wraps a Nougat image processor and a Nougat tokenizer into a single processor.",Ht,De,Uo=`<a href="/docs/transformers/main/en/model_doc/nougat#transformers.NougatProcessor">NougatProcessor</a> offers all the functionalities of <a href="/docs/transformers/main/en/model_doc/nougat#transformers.NougatImageProcessor">NougatImageProcessor</a> and <a href="/docs/transformers/main/en/model_doc/nougat#transformers.NougatTokenizerFast">NougatTokenizerFast</a>. See the
<a href="/docs/transformers/main/en/model_doc/nougat#transformers.NougatProcessor.__call__"><strong>call</strong>()</a> and <a href="/docs/transformers/main/en/model_doc/nougat#transformers.NougatProcessor.decode">decode()</a> for more information.`,Bt,Ee,be,Gt,j,ke,St,Re,Fo="Instantiate a processor associated with a pretrained model.",At,V,Yt,J,ve,Xt,Ve,jo=`Saves the attributes of this processor (feature extractor, tokenizer…) in the specified directory so that it
can be reloaded using the <a href="/docs/transformers/main/en/model_doc/nougat#transformers.NougatProcessor.from_pretrained">from_pretrained()</a> method.`,Qt,q,Ot,H,Te,Kt,qe,Jo=`This method forwards all its arguments to NougatTokenizer’s <a href="/docs/transformers/main/en/model_doc/speecht5#transformers.SpeechT5Tokenizer.batch_decode">batch_decode()</a>. Please refer
to the docstring of this method for more information.`,eo,B,ye,to,He,Lo=`This method forwards all its arguments to NougatTokenizer’s <a href="/docs/transformers/main/en/model_doc/speecht5#transformers.SpeechT5Tokenizer.decode">decode()</a>. Please refer to
the docstring of this method for more information.`,oo,G,xe,no,Be,Wo=`This method forwards all its arguments to NougatTokenizer’s <code>~PreTrainedTokenizer.post_process_generation</code>.
Please refer to the docstring of this method for more information.`,bt,Qe,kt;return M=new Xe({props:{title:"Nougat",local:"nougat",headingTag:"h1"}}),I=new Xe({props:{title:"Overview",local:"overview",headingTag:"h2"}}),te=new Xe({props:{title:"Inference",local:"inference",headingTag:"h2"}}),re=new Eo({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> huggingface_hub <span class="hljs-keyword">import</span> hf_hub_download
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> re
<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> transformers <span class="hljs-keyword">import</span> NougatProcessor, VisionEncoderDecoderModel
<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><span class="hljs-keyword">import</span> torch

<span class="hljs-meta">&gt;&gt;&gt; </span>processor = NougatProcessor.from_pretrained(<span class="hljs-string">&quot;facebook/nougat-base&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = VisionEncoderDecoderModel.from_pretrained(<span class="hljs-string">&quot;facebook/nougat-base&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>device = <span class="hljs-string">&quot;cuda&quot;</span> <span class="hljs-keyword">if</span> torch.cuda.is_available() <span class="hljs-keyword">else</span> <span class="hljs-string">&quot;cpu&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model.to(device)
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># prepare PDF image for the model</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>filepath = hf_hub_download(repo_id=<span class="hljs-string">&quot;hf-internal-testing/fixtures_docvqa&quot;</span>, filename=<span class="hljs-string">&quot;nougat_paper.png&quot;</span>, repo_type=<span class="hljs-string">&quot;dataset&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>image = Image.<span class="hljs-built_in">open</span>(filepath)
<span class="hljs-meta">&gt;&gt;&gt; </span>pixel_values = processor(image, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>).pixel_values

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># generate transcription (here we only generate 30 tokens)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model.generate(
<span class="hljs-meta">... </span>    pixel_values.to(device),
<span class="hljs-meta">... </span>    min_length=<span class="hljs-number">1</span>,
<span class="hljs-meta">... </span>    max_new_tokens=<span class="hljs-number">30</span>,
<span class="hljs-meta">... </span>    bad_words_ids=[[processor.tokenizer.unk_token_id]],
<span class="hljs-meta">... </span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>sequence = processor.batch_decode(outputs, skip_special_tokens=<span class="hljs-literal">True</span>)[<span class="hljs-number">0</span>]
<span class="hljs-meta">&gt;&gt;&gt; </span>sequence = processor.post_process_generation(sequence, fix_markdown=<span class="hljs-literal">False</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># note: we&#x27;re using repr here such for the sake of printing the \\n characters, feel free to just print the sequence</span>
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-built_in">print</span>(<span class="hljs-built_in">repr</span>(sequence))
<span class="hljs-string">&#x27;\\n\\n# Nougat: Neural Optical Understanding for Academic Documents\\n\\n Lukas Blecher\\n\\nCorrespondence to: lblecher@&#x27;</span>`,wrap:!1}}),ie=new Xe({props:{title:"NougatImageProcessor",local:"transformers.NougatImageProcessor",headingTag:"h2"}}),le=new w({props:{name:"class transformers.NougatImageProcessor",anchor:"transformers.NougatImageProcessor",parameters:[{name:"do_crop_margin",val:": bool = True"},{name:"do_resize",val:": bool = True"},{name:"size",val:": typing.Dict[str, int] = None"},{name:"resample",val:": Resampling = <Resampling.BILINEAR: 2>"},{name:"do_thumbnail",val:": bool = True"},{name:"do_align_long_axis",val:": bool = False"},{name:"do_pad",val:": bool = True"},{name:"do_rescale",val:": bool = True"},{name:"rescale_factor",val:": typing.Union[int, float] = 0.00392156862745098"},{name:"do_normalize",val:": bool = True"},{name:"image_mean",val:": typing.Union[float, typing.List[float], NoneType] = None"},{name:"image_std",val:": typing.Union[float, typing.List[float], NoneType] = None"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.NougatImageProcessor.do_crop_margin",description:`<strong>do_crop_margin</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether to crop the image margins.`,name:"do_crop_margin"},{anchor:"transformers.NougatImageProcessor.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 to the specified <code>size</code>. Can be overridden by
<code>do_resize</code> in the <code>preprocess</code> method.`,name:"do_resize"},{anchor:"transformers.NougatImageProcessor.size",description:`<strong>size</strong> (<code>Dict[str, int]</code> <em>optional</em>, defaults to <code>{&quot;height&quot; -- 896, &quot;width&quot;: 672}</code>):
Size of the image after resizing. Can be overridden by <code>size</code> in the <code>preprocess</code> method.`,name:"size"},{anchor:"transformers.NougatImageProcessor.resample",description:`<strong>resample</strong> (<code>PILImageResampling</code>, <em>optional</em>, defaults to <code>Resampling.BILINEAR</code>) &#x2014;
Resampling filter to use if resizing the image. Can be overridden by <code>resample</code> in the <code>preprocess</code> method.`,name:"resample"},{anchor:"transformers.NougatImageProcessor.do_thumbnail",description:`<strong>do_thumbnail</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether to resize the image using thumbnail method.`,name:"do_thumbnail"},{anchor:"transformers.NougatImageProcessor.do_align_long_axis",description:`<strong>do_align_long_axis</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether to align the long axis of the image with the long axis of <code>size</code> by rotating by 90 degrees.`,name:"do_align_long_axis"},{anchor:"transformers.NougatImageProcessor.do_pad",description:`<strong>do_pad</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether to pad the images to the largest image size in the batch.`,name:"do_pad"},{anchor:"transformers.NougatImageProcessor.do_rescale",description:`<strong>do_rescale</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether to rescale the image by the specified scale <code>rescale_factor</code>. Can be overridden by the <code>do_rescale</code>
parameter in the <code>preprocess</code> method.`,name:"do_rescale"},{anchor:"transformers.NougatImageProcessor.rescale_factor",description:`<strong>rescale_factor</strong> (<code>int</code> or <code>float</code>, <em>optional</em>, defaults to <code>1/255</code>) &#x2014;
Scale factor to use if rescaling the image. Can be overridden by the <code>rescale_factor</code> parameter in the
<code>preprocess</code> method.`,name:"rescale_factor"},{anchor:"transformers.NougatImageProcessor.do_normalize",description:`<strong>do_normalize</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether to normalize the image. Can be overridden by <code>do_normalize</code> in the <code>preprocess</code> method.`,name:"do_normalize"},{anchor:"transformers.NougatImageProcessor.image_mean",description:`<strong>image_mean</strong> (<code>float</code> or <code>List[float]</code>, <em>optional</em>, defaults to <code>IMAGENET_DEFAULT_MEAN</code>) &#x2014;
Mean to use if normalizing the image. This is a float or list of floats the length of the number of
channels in the image. Can be overridden by the <code>image_mean</code> parameter in the <code>preprocess</code> method.`,name:"image_mean"},{anchor:"transformers.NougatImageProcessor.image_std",description:`<strong>image_std</strong> (<code>float</code> or <code>List[float]</code>, <em>optional</em>, defaults to <code>IMAGENET_DEFAULT_STD</code>) &#x2014;
Image standard deviation.`,name:"image_std"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/nougat/image_processing_nougat.py#L57"}}),de=new w({props:{name:"preprocess",anchor:"transformers.NougatImageProcessor.preprocess",parameters:[{name:"images",val:": typing.Union[ForwardRef('PIL.Image.Image'), numpy.ndarray, ForwardRef('torch.Tensor'), typing.List[ForwardRef('PIL.Image.Image')], typing.List[numpy.ndarray], typing.List[ForwardRef('torch.Tensor')]]"},{name:"do_crop_margin",val:": bool = None"},{name:"do_resize",val:": bool = None"},{name:"size",val:": typing.Dict[str, int] = None"},{name:"resample",val:": Resampling = None"},{name:"do_thumbnail",val:": bool = None"},{name:"do_align_long_axis",val:": bool = None"},{name:"do_pad",val:": bool = None"},{name:"do_rescale",val:": bool = None"},{name:"rescale_factor",val:": typing.Union[int, float] = None"},{name:"do_normalize",val:": bool = None"},{name:"image_mean",val:": typing.Union[float, typing.List[float], NoneType] = None"},{name:"image_std",val:": typing.Union[float, typing.List[float], NoneType] = None"},{name:"return_tensors",val:": typing.Union[str, transformers.utils.generic.TensorType, NoneType] = None"},{name:"data_format",val:": typing.Optional[transformers.image_utils.ChannelDimension] = <ChannelDimension.FIRST: 'channels_first'>"},{name:"input_data_format",val:": typing.Union[str, transformers.image_utils.ChannelDimension, NoneType] = None"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.NougatImageProcessor.preprocess.images",description:`<strong>images</strong> (<code>ImageInput</code>) &#x2014;
Image to preprocess. Expects a single or batch of images with pixel values ranging from 0 to 255.`,name:"images"},{anchor:"transformers.NougatImageProcessor.preprocess.do_crop_margin",description:`<strong>do_crop_margin</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>self.do_crop_margin</code>) &#x2014;
Whether to crop the image margins.`,name:"do_crop_margin"},{anchor:"transformers.NougatImageProcessor.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 image.`,name:"do_resize"},{anchor:"transformers.NougatImageProcessor.preprocess.size",description:`<strong>size</strong> (<code>Dict[str, int]</code>, <em>optional</em>, defaults to <code>self.size</code>) &#x2014;
Size of the image after resizing. Shortest edge of the image is resized to min(size[&#x201C;height&#x201D;],
size[&#x201C;width&#x201D;]) with the longest edge resized to keep the input aspect ratio.`,name:"size"},{anchor:"transformers.NougatImageProcessor.preprocess.resample",description:`<strong>resample</strong> (<code>int</code>, <em>optional</em>, defaults to <code>self.resample</code>) &#x2014;
Resampling filter to use if resizing the image. This can be one of the enum <code>PILImageResampling</code>. Only
has an effect if <code>do_resize</code> is set to <code>True</code>.`,name:"resample"},{anchor:"transformers.NougatImageProcessor.preprocess.do_thumbnail",description:`<strong>do_thumbnail</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>self.do_thumbnail</code>) &#x2014;
Whether to resize the image using thumbnail method.`,name:"do_thumbnail"},{anchor:"transformers.NougatImageProcessor.preprocess.do_align_long_axis",description:`<strong>do_align_long_axis</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>self.do_align_long_axis</code>) &#x2014;
Whether to align the long axis of the image with the long axis of <code>size</code> by rotating by 90 degrees.`,name:"do_align_long_axis"},{anchor:"transformers.NougatImageProcessor.preprocess.do_pad",description:`<strong>do_pad</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>self.do_pad</code>) &#x2014;
Whether to pad the images to the largest image size in the batch.`,name:"do_pad"},{anchor:"transformers.NougatImageProcessor.preprocess.do_rescale",description:`<strong>do_rescale</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>self.do_rescale</code>) &#x2014;
Whether to rescale the image by the specified scale <code>rescale_factor</code>.`,name:"do_rescale"},{anchor:"transformers.NougatImageProcessor.preprocess.rescale_factor",description:`<strong>rescale_factor</strong> (<code>int</code> or <code>float</code>, <em>optional</em>, defaults to <code>self.rescale_factor</code>) &#x2014;
Scale factor to use if rescaling the image.`,name:"rescale_factor"},{anchor:"transformers.NougatImageProcessor.preprocess.do_normalize",description:`<strong>do_normalize</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>self.do_normalize</code>) &#x2014;
Whether to normalize the image.`,name:"do_normalize"},{anchor:"transformers.NougatImageProcessor.preprocess.image_mean",description:`<strong>image_mean</strong> (<code>float</code> or <code>List[float]</code>, <em>optional</em>, defaults to <code>self.image_mean</code>) &#x2014;
Image mean to use for normalization.`,name:"image_mean"},{anchor:"transformers.NougatImageProcessor.preprocess.image_std",description:`<strong>image_std</strong> (<code>float</code> or <code>List[float]</code>, <em>optional</em>, defaults to <code>self.image_std</code>) &#x2014;
Image standard deviation to use for normalization.`,name:"image_std"},{anchor:"transformers.NougatImageProcessor.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. Can be one of:<ul>
<li>Unset: Return a list of <code>np.ndarray</code>.</li>
<li><code>TensorType.TENSORFLOW</code> or <code>&apos;tf&apos;</code>: Return a batch of type <code>tf.Tensor</code>.</li>
<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>
</ul>`,name:"return_tensors"},{anchor:"transformers.NougatImageProcessor.preprocess.data_format",description:`<strong>data_format</strong> (<code>ChannelDimension</code> or <code>str</code>, <em>optional</em>, defaults to <code>ChannelDimension.FIRST</code>) &#x2014;
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>
<li>Unset: defaults to the channel dimension format of the input image.</li>
</ul>`,name:"data_format"},{anchor:"transformers.NougatImageProcessor.preprocess.input_data_format",description:`<strong>input_data_format</strong> (<code>ChannelDimension</code> or <code>str</code>, <em>optional</em>) &#x2014;
The channel dimension format for the input image. If unset, the channel dimension format is inferred
from 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.</li>
<li><code>&quot;none&quot;</code> or <code>ChannelDimension.NONE</code>: image in (height, width) format.</li>
</ul>`,name:"input_data_format"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/nougat/image_processing_nougat.py#L358"}}),ce=new Xe({props:{title:"NougatTokenizerFast",local:"transformers.NougatTokenizerFast",headingTag:"h2"}}),me=new w({props:{name:"class transformers.NougatTokenizerFast",anchor:"transformers.NougatTokenizerFast",parameters:[{name:"vocab_file",val:" = None"},{name:"tokenizer_file",val:" = None"},{name:"clean_up_tokenization_spaces",val:" = False"},{name:"unk_token",val:" = '<unk>'"},{name:"bos_token",val:" = '<s>'"},{name:"eos_token",val:" = '</s>'"},{name:"pad_token",val:" = '<pad>'"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.NougatTokenizerFast.vocab_file",description:`<strong>vocab_file</strong> (<code>str</code>, <em>optional</em>) &#x2014;
<a href="https://github.com/google/sentencepiece" rel="nofollow">SentencePiece</a> file (generally has a .model extension) that
contains the vocabulary necessary to instantiate a tokenizer.`,name:"vocab_file"},{anchor:"transformers.NougatTokenizerFast.tokenizer_file",description:`<strong>tokenizer_file</strong> (<code>str</code>, <em>optional</em>) &#x2014;
<a href="https://github.com/huggingface/tokenizers" rel="nofollow">tokenizers</a> file (generally has a .json extension) that
contains everything needed to load the tokenizer.`,name:"tokenizer_file"},{anchor:"transformers.NougatTokenizerFast.clean_up_tokenization_spaces",description:`<strong>clean_up_tokenization_spaces</strong> (<code>str</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Wether to cleanup spaces after decoding, cleanup consists in removing potential artifacts like extra
spaces.`,name:"clean_up_tokenization_spaces"},{anchor:"transformers.NougatTokenizerFast.unk_token",description:`<strong>unk_token</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;&lt;unk&gt;&quot;</code>) &#x2014;
The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
token instead.`,name:"unk_token"},{anchor:"transformers.NougatTokenizerFast.bos_token",description:`<strong>bos_token</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;&lt;s&gt;&quot;</code>) &#x2014;
The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token.`,name:"bos_token"},{anchor:"transformers.NougatTokenizerFast.eos_token",description:`<strong>eos_token</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;&lt;/s&gt;&quot;</code>) &#x2014;
The end of sequence token.`,name:"eos_token"},{anchor:"transformers.NougatTokenizerFast.pad_token",description:`<strong>pad_token</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;&lt;pad&gt;&quot;</code>) &#x2014;
The token used for padding, for example when batching sequences of different lengths.`,name:"pad_token"},{anchor:"transformers.NougatTokenizerFast.model_max_length",description:`<strong>model_max_length</strong> (<code>int</code>, <em>optional</em>) &#x2014;
The maximum length (in number of tokens) for the inputs to the transformer model. When the tokenizer is
loaded with <a href="/docs/transformers/main/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.from_pretrained">from_pretrained()</a>, this will be set to the
value stored for the associated model in <code>max_model_input_sizes</code> (see above). If no value is provided, will
default to VERY_LARGE_INTEGER (<code>int(1e30)</code>).`,name:"model_max_length"},{anchor:"transformers.NougatTokenizerFast.padding_side",description:`<strong>padding_side</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The side on which the model should have padding applied. Should be selected between [&#x2018;right&#x2019;, &#x2018;left&#x2019;].
Default value is picked from the class attribute of the same name.`,name:"padding_side"},{anchor:"transformers.NougatTokenizerFast.truncation_side",description:`<strong>truncation_side</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The side on which the model should have truncation applied. Should be selected between [&#x2018;right&#x2019;, &#x2018;left&#x2019;].
Default value is picked from the class attribute of the same name.`,name:"truncation_side"},{anchor:"transformers.NougatTokenizerFast.chat_template",description:`<strong>chat_template</strong> (<code>str</code>, <em>optional</em>) &#x2014;
A Jinja template string that will be used to format lists of chat messages. See
<a href="https://huggingface.co/docs/transformers/chat_templating" rel="nofollow">https://huggingface.co/docs/transformers/chat_templating</a> for a full description.`,name:"chat_template"},{anchor:"transformers.NougatTokenizerFast.model_input_names",description:`<strong>model_input_names</strong> (<code>List[string]</code>, <em>optional</em>) &#x2014;
The list of inputs accepted by the forward pass of the model (like <code>&quot;token_type_ids&quot;</code> or
<code>&quot;attention_mask&quot;</code>). Default value is picked from the class attribute of the same name.`,name:"model_input_names"},{anchor:"transformers.NougatTokenizerFast.bos_token",description:`<strong>bos_token</strong> (<code>str</code> or <code>tokenizers.AddedToken</code>, <em>optional</em>) &#x2014;
A special token representing the beginning of a sentence. Will be associated to <code>self.bos_token</code> and
<code>self.bos_token_id</code>.`,name:"bos_token"},{anchor:"transformers.NougatTokenizerFast.eos_token",description:`<strong>eos_token</strong> (<code>str</code> or <code>tokenizers.AddedToken</code>, <em>optional</em>) &#x2014;
A special token representing the end of a sentence. Will be associated to <code>self.eos_token</code> and
<code>self.eos_token_id</code>.`,name:"eos_token"},{anchor:"transformers.NougatTokenizerFast.unk_token",description:`<strong>unk_token</strong> (<code>str</code> or <code>tokenizers.AddedToken</code>, <em>optional</em>) &#x2014;
A special token representing an out-of-vocabulary token. Will be associated to <code>self.unk_token</code> and
<code>self.unk_token_id</code>.`,name:"unk_token"},{anchor:"transformers.NougatTokenizerFast.sep_token",description:`<strong>sep_token</strong> (<code>str</code> or <code>tokenizers.AddedToken</code>, <em>optional</em>) &#x2014;
A special token separating two different sentences in the same input (used by BERT for instance). Will be
associated to <code>self.sep_token</code> and <code>self.sep_token_id</code>.`,name:"sep_token"},{anchor:"transformers.NougatTokenizerFast.pad_token",description:`<strong>pad_token</strong> (<code>str</code> or <code>tokenizers.AddedToken</code>, <em>optional</em>) &#x2014;
A special token used to make arrays of tokens the same size for batching purpose. Will then be ignored by
attention mechanisms or loss computation. Will be associated to <code>self.pad_token</code> and <code>self.pad_token_id</code>.`,name:"pad_token"},{anchor:"transformers.NougatTokenizerFast.cls_token",description:`<strong>cls_token</strong> (<code>str</code> or <code>tokenizers.AddedToken</code>, <em>optional</em>) &#x2014;
A special token representing the class of the input (used by BERT for instance). Will be associated to
<code>self.cls_token</code> and <code>self.cls_token_id</code>.`,name:"cls_token"},{anchor:"transformers.NougatTokenizerFast.mask_token",description:`<strong>mask_token</strong> (<code>str</code> or <code>tokenizers.AddedToken</code>, <em>optional</em>) &#x2014;
A special token representing a masked token (used by masked-language modeling pretraining objectives, like
BERT). Will be associated to <code>self.mask_token</code> and <code>self.mask_token_id</code>.`,name:"mask_token"},{anchor:"transformers.NougatTokenizerFast.additional_special_tokens",description:`<strong>additional_special_tokens</strong> (tuple or list of <code>str</code> or <code>tokenizers.AddedToken</code>, <em>optional</em>) &#x2014;
A tuple or a list of additional special tokens. Add them here to ensure they are skipped when decoding with
<code>skip_special_tokens</code> is set to True. If they are not part of the vocabulary, they will be added at the end
of the vocabulary.`,name:"additional_special_tokens"},{anchor:"transformers.NougatTokenizerFast.clean_up_tokenization_spaces",description:`<strong>clean_up_tokenization_spaces</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether or not the model should cleanup the spaces that were added when splitting the input text during the
tokenization process.`,name:"clean_up_tokenization_spaces"},{anchor:"transformers.NougatTokenizerFast.split_special_tokens",description:`<strong>split_special_tokens</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not the special tokens should be split during the tokenization process. The default behavior is
to not split special tokens. This means that if <code>&lt;s&gt;</code> is the <code>bos_token</code>, then <code>tokenizer.tokenize(&quot;&lt;s&gt;&quot;) = [&apos;&lt;s&gt;</code>]. Otherwise, if <code>split_special_tokens=True</code>, then <code>tokenizer.tokenize(&quot;&lt;s&gt;&quot;)</code> will be give <code>[&apos;&lt;&apos;, &apos;s&apos;, &apos;&gt;&apos;]</code>. This argument is only supported for <code>slow</code> tokenizers for the moment.`,name:"split_special_tokens"},{anchor:"transformers.NougatTokenizerFast.tokenizer_object",description:`<strong>tokenizer_object</strong> (<a href="https://huggingface.co/docs/tokenizers/main/en/api/tokenizer#tokenizers.Tokenizer" rel="nofollow">tokenizers.Tokenizer</a>) &#x2014;
A <a href="https://huggingface.co/docs/tokenizers/main/en/api/tokenizer#tokenizers.Tokenizer" rel="nofollow">tokenizers.Tokenizer</a> object from &#x1F917; tokenizers to instantiate from. See <a href="../fast_tokenizers">Using tokenizers from &#x1F917;
tokenizers</a> for more information.`,name:"tokenizer_object"},{anchor:"transformers.NougatTokenizerFast.tokenizer_file",description:`<strong>tokenizer_file</strong> (<code>str</code>) &#x2014;
A path to a local JSON file representing a previously serialized <a href="https://huggingface.co/docs/tokenizers/main/en/api/tokenizer#tokenizers.Tokenizer" rel="nofollow">tokenizers.Tokenizer</a> object from &#x1F917;
tokenizers.`,name:"tokenizer_file"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/nougat/tokenization_nougat_fast.py#L377"}}),pe=new w({props:{name:"correct_tables",anchor:"transformers.NougatTokenizerFast.correct_tables",parameters:[{name:"generation",val:": str"}],parametersDescription:[{anchor:"transformers.NougatTokenizerFast.correct_tables.generation",description:"<strong>generation</strong> (str) &#x2014; The generated text to be postprocessed.",name:"generation"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/nougat/tokenization_nougat_fast.py#L470",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>The postprocessed text.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p>str</p>
`}}),E=new So({props:{anchor:"transformers.NougatTokenizerFast.correct_tables.example",$$slots:{default:[Ao]},$$scope:{ctx:W}}}),ge=new w({props:{name:"post_process_generation",anchor:"transformers.NougatTokenizerFast.post_process_generation",parameters:[{name:"generation",val:": typing.Union[str, typing.List[str]]"},{name:"fix_markdown",val:": bool = True"},{name:"num_workers",val:": int = None"}],parametersDescription:[{anchor:"transformers.NougatTokenizerFast.post_process_generation.generation",description:`<strong>generation</strong> (Union[str, List[str]]) &#x2014;
The generated text or a list of generated texts.`,name:"generation"},{anchor:"transformers.NougatTokenizerFast.post_process_generation.fix_markdown",description:`<strong>fix_markdown</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether to perform Markdown formatting fixes.`,name:"fix_markdown"},{anchor:"transformers.NougatTokenizerFast.post_process_generation.num_workers",description:`<strong>num_workers</strong> (<code>int</code>, <em>optional</em>) &#x2014;
Optional number of workers to pass to leverage multiprocessing (postprocessing several texts in
parallel).`,name:"num_workers"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/nougat/tokenization_nougat_fast.py#L600",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>The postprocessed text or list of postprocessed texts.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p>Union[str, List[str]]</p>
`}}),fe=new w({props:{name:"post_process_single",anchor:"transformers.NougatTokenizerFast.post_process_single",parameters:[{name:"generation",val:": str"},{name:"fix_markdown",val:": bool = True"}],parametersDescription:[{anchor:"transformers.NougatTokenizerFast.post_process_single.generation",description:"<strong>generation</strong> (str) &#x2014; The generated text to be postprocessed.",name:"generation"},{anchor:"transformers.NougatTokenizerFast.post_process_single.fix_markdown",description:"<strong>fix_markdown</strong> (bool, optional) &#x2014; Whether to perform Markdown formatting fixes. Default is True.",name:"fix_markdown"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/nougat/tokenization_nougat_fast.py#L505",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>The postprocessed text.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p>str</p>
`}}),ue=new w({props:{name:"remove_hallucinated_references",anchor:"transformers.NougatTokenizerFast.remove_hallucinated_references",parameters:[{name:"text",val:": str"}],parametersDescription:[{anchor:"transformers.NougatTokenizerFast.remove_hallucinated_references.text",description:`<strong>text</strong> (<code>str</code>) &#x2014;
The input text containing references.`,name:"text"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/nougat/tokenization_nougat_fast.py#L440",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>The text with hallucinated references removed.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>str</code></p>
`}}),he=new Xe({props:{title:"NougatProcessor",local:"transformers.NougatProcessor",headingTag:"h2"}}),_e=new w({props:{name:"class transformers.NougatProcessor",anchor:"transformers.NougatProcessor",parameters:[{name:"image_processor",val:""},{name:"tokenizer",val:""}],parametersDescription:[{anchor:"transformers.NougatProcessor.image_processor",description:`<strong>image_processor</strong> (<a href="/docs/transformers/main/en/model_doc/nougat#transformers.NougatImageProcessor">NougatImageProcessor</a>) &#x2014;
An instance of <a href="/docs/transformers/main/en/model_doc/nougat#transformers.NougatImageProcessor">NougatImageProcessor</a>. The image processor is a required input.`,name:"image_processor"},{anchor:"transformers.NougatProcessor.tokenizer",description:`<strong>tokenizer</strong> (<a href="/docs/transformers/main/en/model_doc/nougat#transformers.NougatTokenizerFast">NougatTokenizerFast</a>) &#x2014;
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