Add OneFormer Model (#20577)

* Add Oneformer Model

* Add OneFormer Tests

* Add UNIVERSAL_SEGMENTATION_MAPPING

* Fix config

* 🐛 Fix error encountered while writing tests

* 🔨 Fix instance segmentation post processing

* Format Files and Add Documentation

* Add Documentation mdx file

* Run make fixup

* Run make fix-copies

* Remove unnecessary code

* Format modeling_oneformer.py

* Add OneFormer to ImageSegmentationPipeline

* Format files

* Add Demo link to Readme

* Fix fomatting errors

* Fix test failures

* Update Table in index.mdx

* Fix version

* Fix style

* Remove OneFormer from TF

* Fix Imports

* Fix dummy objects

* Fix tests

* Add newline

* Remove OneFormerFeatureExtractor

* Remove CUDA Kernels

* Use AutoBackbone for Swin

* Fix description

* Use Image Processor

* Fix copies

* Fix formatting

* Fix import order

* Fix flake8 errors

* Fix doc errors

* Add Hindi Readme entry

* Update supported backbones

* Update supported backbones

* Undo Changes

* Fix type of config

* Fix isort

* Fix auto.mdx

* Fix swin config

* Replace DinatBackbone with AutoBackbone

* Use SwinBackbone

* Use SwinBackbone

* Fix conversion script

* Fix arguments

* Add argument description

* Fix style

* Add OneFormerProcessor

* Fix OneFormerProcessor Tests

* Fix mapping

* Fix imports

* Fix inits

* Fix style

* Fix comment

* Fix docstring

* Move OneFormer to MultiModal

* Fix Copies

* Remove size divisor

* Fix check_repo.py

* Fix copies

* Add Processor for Testing Pipeline

* Fix padding for tokens

* Fix variables

* Fix formatting with correct black version

* Add Image Processor Test

* Apply suggestions

* Revert common modeling

* Add check for task

* Fix conversion script

* Fix initialization order

* Fix tests

* Undo Pipeline Changes

* Fix layers in MLP

* Fix copies

* Update image paths

* Fix copies

* Apply suggestions

README_ja.md

@@ -399,6 +399,7 @@ Flax、PyTorch、TensorFlowをcondaでインストールする方法は、それ
 1. **[Nezha](https://huggingface.co/docs/transformers/model_doc/nezha)** (Huawei Noah’s Ark Lab から) Junqiu Wei, Xiaozhe Ren, Xiaoguang Li, Wenyong Huang, Yi Liao, Yasheng Wang, Jiashu Lin, Xin Jiang, Xiao Chen and Qun Liu から公開された研究論文: [NEZHA: Neural Contextualized Representation for Chinese Language Understanding](https://arxiv.org/abs/1909.00204)
 1. **[NLLB](https://huggingface.co/docs/transformers/model_doc/nllb)** (Meta から) the NLLB team から公開された研究論文: [No Language Left Behind: Scaling Human-Centered Machine Translation](https://arxiv.org/abs/2207.04672)
 1. **[Nyströmformer](https://huggingface.co/docs/transformers/model_doc/nystromformer)** (the University of Wisconsin - Madison から) Yunyang Xiong, Zhanpeng Zeng, Rudrasis Chakraborty, Mingxing Tan, Glenn Fung, Yin Li, Vikas Singh から公開された研究論文: [Nyströmformer: A Nyström-Based Algorithm for Approximating Self-Attention](https://arxiv.org/abs/2102.03902)
+1. **[OneFormer](https://huggingface.co/docs/transformers/main/model_doc/oneformer)** (SHI Labs から) Jitesh Jain, Jiachen Li, MangTik Chiu, Ali Hassani, Nikita Orlov, Humphrey Shi から公開された研究論文: [OneFormer: One Transformer to Rule Universal Image Segmentation](https://arxiv.org/abs/2211.06220)
 1. **[OPT](https://huggingface.co/docs/transformers/master/model_doc/opt)** (Meta AI から) Susan Zhang, Stephen Roller, Naman Goyal, Mikel Artetxe, Moya Chen, Shuohui Chen et al から公開された研究論文: [OPT: Open Pre-trained Transformer Language Models](https://arxiv.org/abs/2205.01068)
 1. **[OWL-ViT](https://huggingface.co/docs/transformers/model_doc/owlvit)** (Google AI から) Matthias Minderer, Alexey Gritsenko, Austin Stone, Maxim Neumann, Dirk Weissenborn, Alexey Dosovitskiy, Aravindh Mahendran, Anurag Arnab, Mostafa Dehghani, Zhuoran Shen, Xiao Wang, Xiaohua Zhai, Thomas Kipf, and Neil Houlsby から公開された研究論文: [Simple Open-Vocabulary Object Detection with Vision Transformers](https://arxiv.org/abs/2205.06230)
 1. **[Pegasus](https://huggingface.co/docs/transformers/model_doc/pegasus)** (Google から) Jingqing Zhang, Yao Zhao, Mohammad Saleh and Peter J. Liu から公開された研究論文: [PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization](https://arxiv.org/abs/1912.08777)