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*This model was released on 2023-08-25 and added to Hugging Face Transformers on 2023-09-26.*

# Nougat

<div class="flex flex-wrap space-x-1">
<img alt="PyTorch" src="https://img.shields.io/badge/PyTorch-DE3412?style=flat&logo=pytorch&logoColor=white">
</div>

## Overview

The Nougat model was proposed in [Nougat: Neural Optical Understanding for Academic Documents](https://huggingface.co/papers/2308.13418) by
Lukas Blecher, Guillem Cucurull, Thomas Scialom, Robert Stojnic. Nougat uses the same architecture as [Donut](donut), meaning an image Transformer
encoder and an autoregressive text Transformer decoder to translate scientific PDFs to markdown, enabling easier access to them.

The abstract from the paper is the following:

*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.*

<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/nougat_architecture.jpg"
alt="drawing" width="600"/>

<small> Nougat high-level overview. Taken from the <a href="https://huggingface.co/papers/2308.13418">original paper</a>. </small>

This model was contributed by [nielsr](https://huggingface.co/nielsr). The original code can be found
[here](https://github.com/facebookresearch/nougat).

## Usage tips

- The quickest way to get started with Nougat is by checking the [tutorial
  notebooks](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/Nougat), which show how to use the model
  at inference time as well as fine-tuning on custom data.
- Nougat is always used within the [VisionEncoderDecoder](vision-encoder-decoder) framework. The model is identical to [Donut](donut) in terms of architecture.

## Inference

Nougat's [`VisionEncoderDecoder`] model accepts images as input and makes use of
[`~generation.GenerationMixin.generate`] to autoregressively generate text given the input image.

The [`NougatImageProcessor`] class is responsible for preprocessing the input image and
[`NougatTokenizerFast`] decodes the generated target tokens to the target string. The
[`NougatProcessor`] wraps [`NougatImageProcessor`] and [`NougatTokenizerFast`] classes
into a single instance to both extract the input features and decode the predicted token ids.

- Step-by-step PDF transcription

```py
>>> from huggingface_hub import hf_hub_download
>>> import re
>>> from PIL import Image

>>> from transformers import NougatProcessor, VisionEncoderDecoderModel
from accelerate import Accelerator
>>> from datasets import load_dataset
>>> import torch

>>> processor = NougatProcessor.from_pretrained("facebook/nougat-base")
>>> model = VisionEncoderDecoderModel.from_pretrained("facebook/nougat-base")

>>> device = Accelerator().device
>>> model.to(device)  # doctest: +IGNORE_RESULT

>>> # prepare PDF image for the model
>>> filepath = hf_hub_download(repo_id="hf-internal-testing/fixtures_docvqa", filename="nougat_paper.png", repo_type="dataset")
>>> image = Image.open(filepath)
>>> pixel_values = processor(image, return_tensors="pt").pixel_values

>>> # generate transcription (here we only generate 30 tokens)
>>> outputs = model.generate(
...     pixel_values.to(device),
...     min_length=1,
...     max_new_tokens=30,
...     bad_words_ids=[[processor.tokenizer.unk_token_id]],
... )

>>> sequence = processor.batch_decode(outputs, skip_special_tokens=True)[0]
>>> sequence = processor.post_process_generation(sequence, fix_markdown=False)
>>> # note: we're using repr here such for the sake of printing the \n characters, feel free to just print the sequence
>>> print(repr(sequence))
'\n\n# Nougat: Neural Optical Understanding for Academic Documents\n\n Lukas Blecher\n\nCorrespondence to: lblecher@'
```

See the [model hub](https://huggingface.co/models?filter=nougat) to look for Nougat checkpoints.

<Tip>

The model is identical to [Donut](donut) in terms of architecture.

</Tip>

## NougatImageProcessor

[[autodoc]] NougatImageProcessor
    - preprocess

## NougatImageProcessorPil

[[autodoc]] NougatImageProcessorPil
    - preprocess

## NougatTokenizer

[[autodoc]] NougatTokenizer

## NougatTokenizerFast

[[autodoc]] NougatTokenizerFast

## NougatProcessor

[[autodoc]] NougatProcessor
    - __call__
    - from_pretrained
    - save_pretrained
    - batch_decode
    - decode
    - post_process_generation
