Add swin transformer v2 (huggingface#17469)

* Add files generated using transformer-cli add-new-model-like command * Add changes for swinv2 attention and forward method * Add fixes * Add modifications for weight conversion and remaining args in swin model * Add changes for patchmerging * Add changes for SwinV2selfattention * Update conversion script * Add final fixes for the swin_v2 model * Add changes for conversion script for pretrained window size case * Add pretrained window size value from config in SwinV2Encoder class * Make fixup * Add swinv2 to models_not_in_readme to utils/check_copies.py * Modify Swinv2v2 to Swin Transformer V2 * Remove copied from, to run make fixup command * Add updates to swinv2tf from main branch * Add pretrained_window_size to config, to make tests pass * Add modified weights from nandwalritik profile for swinv2 * Update model weights from swinv2 from nandwalritik profile * Add fix for build_pr_documentation CI fix * Add fixes for weight conversion * Add change to make input with padding work * Add fixes for test cases * Add few changes from swin to swinv2 to pass test cases * Remove tests for tensorflow as swinv2 for TF is not added yet * Overide test_pt_tf_model_equivalence function as TF implementation for swinv2 is not added yet * Add modeling_tf_swinv2 to _ignore_modules as test file is removed for this one right now. * Update docs url for swinv2 in README.md Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com> * Undo changes for check_repo * Update url in readme.md * Remove overrided function to test pt_tf_model_equivalence * Remove TF model imports for Swinv2 as its not implemented in this PR * Add changes for index.mdx * Add swinv2 papers link,abstract and contributors details * Rename cpb_mlp to continous_position_bias_mlp * Add tips for swinv2 model * Update src/transformers/models/swinv2/configuration_swinv2.py Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com> * Update src/transformers/models/swinv2/configuration_swinv2.py Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com> * Fix indentation for docstring example in src/transformers/models/swinv2/configuration_swinv2.py Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com> * Update import order in src/transformers/models/swinv2/configuration_swinv2.py Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com> * Add copyright statements in weights conversion script. Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com> * Remove Swinv2 from models_not_in_readme * Reformat code * Remove TF implementation file for swinv2 * Update start docstring. Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com> * Add changes for docstring * Update orgname for weights to microsoft * Remove to_2tuple function * Add copied from statements wherever applicable * Add copied from to Swinv2ForMaskedImageModelling class * Reformat code. Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com> * Add unittest.skip(with reason.) for test_inputs_embeds test case. Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com> * Add updates for test_modeling_swinv2.py * Add @unittest.skip() annotation for clarity to create_and_test_config_common_properties function * Add continuous_position_bias_mlp parameter to conversion script * Add test for testing masked_image_modelling for swinv2 * Update Swinv2 to Swin Transformer v2 in docs/source/en/model_doc/swinv2.mdx Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com> * Update Swinv2 to Swin Transformer v2 in docs/source/en/model_doc/swinv2.mdx Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com> * Update docs/source/en/model_doc/swinv2.mdx Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com> * Update docs/source/en/model_doc/swinv2.mdx Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com> * Add suggested changes * Add copied from to forward methods of Swinv2Stage and Swinv2Encoder * Add push_to_hub flag to weight conversion script * Change order or Swinv2DropPath class * Add id2label mapping for imagenet 21k * Add updated url for SwinV2 functions and classes used in implementation * Update input_feature dimensions format, mentioned in comments. Co-authored-by: Alara Dirik <8944735+alaradirik@users.noreply.github.com> * Add suggested changes for modeling_swin2.py * Update docs * Remove create_and_test_config_common_properties function, as test_model_common_attributes is sufficient. * Fix indentation. Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * Add changes for making Nit objects in code style * Add suggested changes * Add suggested changes for test_modelling_swinv2 * make fix-copies * Update docs/source/en/model_doc/swinv2.mdx Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com> Co-authored-by: Alara Dirik <8944735+alaradirik@users.noreply.github.com> Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
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diff --git a/README.md b/README.md
@@ -356,6 +356,7 @@ Current number of checkpoints: ![](https://img.shields.io/endpoint?url=https://h
 1. **[Splinter](https://huggingface.co/docs/transformers/model_doc/splinter)** (from Tel Aviv University), released together with the paper [Few-Shot Question Answering by Pretraining Span Selection](https://arxiv.org/abs/2101.00438) by Ori Ram, Yuval Kirstain, Jonathan Berant, Amir Globerson, Omer Levy.
 1. **[SqueezeBERT](https://huggingface.co/docs/transformers/model_doc/squeezebert)** (from Berkeley) released with the paper [SqueezeBERT: What can computer vision teach NLP about efficient neural networks?](https://arxiv.org/abs/2006.11316) by Forrest N. Iandola, Albert E. Shaw, Ravi Krishna, and Kurt W. Keutzer.
 1. **[Swin Transformer](https://huggingface.co/docs/transformers/model_doc/swin)** (from Microsoft) released with the paper [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://arxiv.org/abs/2103.14030) by Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, Baining Guo.
+1. **[Swin Transformer V2](https://huggingface.co/docs/transformers/main/model_doc/swinv2)** (from Microsoft) released with the paper [Swin Transformer V2: Scaling Up Capacity and Resolution](https://arxiv.org/abs/2111.09883) by Ze Liu, Han Hu, Yutong Lin, Zhuliang Yao, Zhenda Xie, Yixuan Wei, Jia Ning, Yue Cao, Zheng Zhang, Li Dong, Furu Wei, Baining Guo.
 1. **[T5](https://huggingface.co/docs/transformers/model_doc/t5)** (from Google AI) released with the paper [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://arxiv.org/abs/1910.10683) by Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu.
 1. **[T5v1.1](https://huggingface.co/docs/transformers/model_doc/t5v1.1)** (from Google AI) released in the repository [google-research/text-to-text-transfer-transformer](https://github.com/google-research/text-to-text-transfer-transformer/blob/main/released_checkpoints.md#t511) by Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu.
 1. **[TAPAS](https://huggingface.co/docs/transformers/model_doc/tapas)** (from Google AI) released with the paper [TAPAS: Weakly Supervised Table Parsing via Pre-training](https://arxiv.org/abs/2004.02349) by Jonathan Herzig, Paweł Krzysztof Nowak, Thomas Müller, Francesco Piccinno and Julian Martin Eisenschlos.

diff --git a/README_ko.md b/README_ko.md
@@ -312,6 +312,7 @@ Flax, PyTorch, TensorFlow 설치 페이지에서 이들을 conda로 설치하는
 1. **[Splinter](https://huggingface.co/docs/transformers/model_doc/splinter)** (from Tel Aviv University), released together with the paper [Few-Shot Question Answering by Pretraining Span Selection](https://arxiv.org/abs/2101.00438) by Ori Ram, Yuval Kirstain, Jonathan Berant, Amir Globerson, Omer Levy.
 1. **[SqueezeBERT](https://huggingface.co/docs/transformers/model_doc/squeezebert)** (from Berkeley) released with the paper [SqueezeBERT: What can computer vision teach NLP about efficient neural networks?](https://arxiv.org/abs/2006.11316) by Forrest N. Iandola, Albert E. Shaw, Ravi Krishna, and Kurt W. Keutzer.
 1. **[Swin Transformer](https://huggingface.co/docs/transformers/model_doc/swin)** (from Microsoft) released with the paper [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://arxiv.org/abs/2103.14030) by Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, Baining Guo.
+1. **[Swin Transformer V2](https://huggingface.co/docs/transformers/main/model_doc/swinv2)** (from Microsoft) released with the paper [Swin Transformer V2: Scaling Up Capacity and Resolution](https://arxiv.org/abs/2111.09883) by Ze Liu, Han Hu, Yutong Lin, Zhuliang Yao, Zhenda Xie, Yixuan Wei, Jia Ning, Yue Cao, Zheng Zhang, Li Dong, Furu Wei, Baining Guo.
 1. **[T5](https://huggingface.co/docs/transformers/model_doc/t5)** (from Google AI) released with the paper [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://arxiv.org/abs/1910.10683) by Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu.
 1. **[T5v1.1](https://huggingface.co/docs/transformers/model_doc/t5v1.1)** (from Google AI) released in the repository [google-research/text-to-text-transfer-transformer](https://github.com/google-research/text-to-text-transfer-transformer/blob/main/released_checkpoints.md#t511) by Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu.
 1. **[TAPAS](https://huggingface.co/docs/transformers/model_doc/tapas)** (from Google AI) released with the paper [TAPAS: Weakly Supervised Table Parsing via Pre-training](https://arxiv.org/abs/2004.02349) by Jonathan Herzig, Paweł Krzysztof Nowak, Thomas Müller, Francesco Piccinno and Julian Martin Eisenschlos.

diff --git a/README_zh-hans.md b/README_zh-hans.md
@@ -336,6 +336,7 @@ conda install -c huggingface transformers
 1. **[Splinter](https://huggingface.co/docs/transformers/model_doc/splinter)** (来自 Tel Aviv University) 伴随论文 [Few-Shot Question Answering by Pretraining Span Selection](https://arxiv.org/abs/2101.00438) 由 Ori Ram, Yuval Kirstain, Jonathan Berant, Amir Globerson, Omer Levy 发布。
 1. **[SqueezeBERT](https://huggingface.co/docs/transformers/model_doc/squeezebert)** (来自 Berkeley) 伴随论文 [SqueezeBERT: What can computer vision teach NLP about efficient neural networks?](https://arxiv.org/abs/2006.11316) 由 Forrest N. Iandola, Albert E. Shaw, Ravi Krishna, and Kurt W. Keutzer 发布。
 1. **[Swin Transformer](https://huggingface.co/docs/transformers/model_doc/swin)** (来自 Microsoft) 伴随论文 [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://arxiv.org/abs/2103.14030) 由 Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, Baining Guo 发布。
+1. **[Swin Transformer V2](https://huggingface.co/docs/transformers/main/model_doc/swinv2)** (来自 Microsoft) 伴随论文 [Swin Transformer V2: Scaling Up Capacity and Resolution](https://arxiv.org/abs/2111.09883) 由 Ze Liu, Han Hu, Yutong Lin, Zhuliang Yao, Zhenda Xie, Yixuan Wei, Jia Ning, Yue Cao, Zheng Zhang, Li Dong, Furu Wei, Baining Guo 发布。
 1. **[T5](https://huggingface.co/docs/transformers/model_doc/t5)** (来自 Google AI) 伴随论文 [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://arxiv.org/abs/1910.10683) 由 Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu 发布。
 1. **[T5v1.1](https://huggingface.co/docs/transformers/model_doc/t5v1.1)** (来自 Google AI) 伴随论文 [google-research/text-to-text-transfer-transformer](https://github.com/google-research/text-to-text-transfer-transformer/blob/main/released_checkpoints.md#t511) 由 Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu 发布。
 1. **[TAPAS](https://huggingface.co/docs/transformers/model_doc/tapas)** (来自 Google AI) 伴随论文 [TAPAS: Weakly Supervised Table Parsing via Pre-training](https://arxiv.org/abs/2004.02349) 由 Jonathan Herzig, Paweł Krzysztof Nowak, Thomas Müller, Francesco Piccinno and Julian Martin Eisenschlos 发布。

diff --git a/README_zh-hant.md b/README_zh-hant.md
@@ -348,6 +348,7 @@ conda install -c huggingface transformers
 1. **[Splinter](https://huggingface.co/docs/transformers/model_doc/splinter)** (from Tel Aviv University) released with the paper [Few-Shot Question Answering by Pretraining Span Selection](https://arxiv.org/abs/2101.00438) by Ori Ram, Yuval Kirstain, Jonathan Berant, Amir Globerson, Omer Levy.
 1. **[SqueezeBERT](https://huggingface.co/docs/transformers/model_doc/squeezebert)** (from Berkeley) released with the paper [SqueezeBERT: What can computer vision teach NLP about efficient neural networks?](https://arxiv.org/abs/2006.11316) by Forrest N. Iandola, Albert E. Shaw, Ravi Krishna, and Kurt W. Keutzer.
 1. **[Swin Transformer](https://huggingface.co/docs/transformers/model_doc/swin)** (from Microsoft) released with the paper [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://arxiv.org/abs/2103.14030) by Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, Baining Guo.
+1. **[Swin Transformer V2](https://huggingface.co/docs/transformers/main/model_doc/swinv2)** (from Microsoft) released with the paper [Swin Transformer V2: Scaling Up Capacity and Resolution](https://arxiv.org/abs/2111.09883) by Ze Liu, Han Hu, Yutong Lin, Zhuliang Yao, Zhenda Xie, Yixuan Wei, Jia Ning, Yue Cao, Zheng Zhang, Li Dong, Furu Wei, Baining Guo.
 1. **[T5](https://huggingface.co/docs/transformers/model_doc/t5)** (from Google AI) released with the paper [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://arxiv.org/abs/1910.10683) by Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu.
 1. **[T5v1.1](https://huggingface.co/docs/transformers/model_doc/t5v1.1)** (from Google AI) released with the paper [google-research/text-to-text-transfer-transformer](https://github.com/google-research/text-to-text-transfer-transformer/blob/main/released_checkpoints.md#t511) by Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu.
 1. **[TAPAS](https://huggingface.co/docs/transformers/model_doc/tapas)** (from Google AI) released with the paper [TAPAS: Weakly Supervised Table Parsing via Pre-training](https://arxiv.org/abs/2004.02349) by Jonathan Herzig, Paweł Krzysztof Nowak, Thomas Müller, Francesco Piccinno and Julian Martin Eisenschlos.

diff --git a/docs/source/en/_toctree.yml b/docs/source/en/_toctree.yml
@@ -378,6 +378,8 @@
       title: SqueezeBERT
     - local: model_doc/swin
       title: Swin Transformer
+    - local: model_doc/swinv2
+      title: Swin Transformer V2
     - local: model_doc/t5
       title: T5
     - local: model_doc/t5v1.1

diff --git a/docs/source/en/index.mdx b/docs/source/en/index.mdx
@@ -154,6 +154,7 @@ The library currently contains JAX, PyTorch and TensorFlow implementations, pret
 1. **[Splinter](model_doc/splinter)** (from Tel Aviv University), released together with the paper [Few-Shot Question Answering by Pretraining Span Selection](https://arxiv.org/abs/2101.00438) by Ori Ram, Yuval Kirstain, Jonathan Berant, Amir Globerson, Omer Levy.
 1. **[SqueezeBERT](model_doc/squeezebert)** (from Berkeley) released with the paper [SqueezeBERT: What can computer vision teach NLP about efficient neural networks?](https://arxiv.org/abs/2006.11316) by Forrest N. Iandola, Albert E. Shaw, Ravi Krishna, and Kurt W. Keutzer.
 1. **[Swin Transformer](model_doc/swin)** (from Microsoft) released with the paper [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://arxiv.org/abs/2103.14030) by Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, Baining Guo.
+1. **[Swin Transformer V2](model_doc/swinv2)** (from Microsoft) released with the paper [Swin Transformer V2: Scaling Up Capacity and Resolution](https://arxiv.org/abs/2111.09883) by Ze Liu, Han Hu, Yutong Lin, Zhuliang Yao, Zhenda Xie, Yixuan Wei, Jia Ning, Yue Cao, Zheng Zhang, Li Dong, Furu Wei, Baining Guo.
 1. **[T5](model_doc/t5)** (from Google AI) released with the paper [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://arxiv.org/abs/1910.10683) by Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu.
 1. **[T5v1.1](model_doc/t5v1.1)** (from Google AI) released in the repository [google-research/text-to-text-transfer-transformer](https://github.com/google-research/text-to-text-transfer-transformer/blob/main/released_checkpoints.md#t511) by Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu.
 1. **[TAPAS](model_doc/tapas)** (from Google AI) released with the paper [TAPAS: Weakly Supervised Table Parsing via Pre-training](https://arxiv.org/abs/2004.02349) by Jonathan Herzig, Paweł Krzysztof Nowak, Thomas Müller, Francesco Piccinno and Julian Martin Eisenschlos.
@@ -289,6 +290,7 @@ Flax), PyTorch, and/or TensorFlow.
 |          Splinter           |       ✅       |       ✅       |       ✅        |         ❌         |      ❌      |
 |         SqueezeBERT         |       ✅       |       ✅       |       ✅        |         ❌         |      ❌      |
 |      Swin Transformer       |       ❌       |       ❌       |       ✅        |         ✅         |      ❌      |
+|     Swin Transformer V2     |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
 |             T5              |       ✅       |       ✅       |       ✅        |         ✅         |      ✅      |
 |            TAPAS            |       ✅       |       ❌       |       ✅        |         ✅         |      ❌      |
 |   Trajectory Transformer    |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |

diff --git a/docs/source/en/model_doc/swinv2.mdx b/docs/source/en/model_doc/swinv2.mdx
@@ -0,0 +1,47 @@
+<!--Copyright 2022 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+specific language governing permissions and limitations under the License.
+-->
+
+# Swin Transformer V2
+
+## Overview
+
+The Swin Transformer V2 model was proposed in [Swin Transformer V2: Scaling Up Capacity and Resolution](https://arxiv.org/abs/2111.09883) by Ze Liu, Han Hu, Yutong Lin, Zhuliang Yao, Zhenda Xie, Yixuan Wei, Jia Ning, Yue Cao, Zheng Zhang, Li Dong, Furu Wei, Baining Guo.
+
+The abstract from the paper is the following:
+
+*Large-scale NLP models have been shown to significantly improve the performance on language tasks with no signs of saturation. They also demonstrate amazing few-shot capabilities like that of human beings. This paper aims to explore large-scale models in computer vision. We tackle three major issues in training and application of large vision models, including training instability, resolution gaps between pre-training and fine-tuning, and hunger on labelled data. Three main techniques are proposed: 1) a residual-post-norm method combined with cosine attention to improve training stability; 2) A log-spaced continuous position bias method to effectively transfer models pre-trained using low-resolution images to downstream tasks with high-resolution inputs; 3) A self-supervised pre-training method, SimMIM, to reduce the needs of vast labeled images. Through these techniques, this paper successfully trained a 3 billion-parameter Swin Transformer V2 model, which is the largest dense vision model to date, and makes it capable of training with images of up to 1,536×1,536 resolution. It set new performance records on 4 representative vision tasks, including ImageNet-V2 image classification, COCO object detection, ADE20K semantic segmentation, and Kinetics-400 video action classification. Also note our training is much more efficient than that in Google's billion-level visual models, which consumes 40 times less labelled data and 40 times less training time.*
+
+Tips:
+- One can use the [`AutoFeatureExtractor`] API to prepare images for the model.
+
+This model was contributed by [nandwalritik](https://huggingface.co/nandwalritik).
+The original code can be found [here](https://github.com/microsoft/Swin-Transformer).
+
+
+## Swinv2Config
+
+[[autodoc]] Swinv2Config
+
+## Swinv2Model
+
+[[autodoc]] Swinv2Model
+    - forward
+
+## Swinv2ForMaskedImageModeling
+
+[[autodoc]] Swinv2ForMaskedImageModeling
+    - forward
+
+## Swinv2ForImageClassification
+
+[[autodoc]] transformers.Swinv2ForImageClassification
+    - forward
diff --git a/src/transformers/__init__.py b/src/transformers/__init__.py
@@ -313,6 +313,7 @@
     "models.splinter": ["SPLINTER_PRETRAINED_CONFIG_ARCHIVE_MAP", "SplinterConfig", "SplinterTokenizer"],
     "models.squeezebert": ["SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "SqueezeBertConfig", "SqueezeBertTokenizer"],
     "models.swin": ["SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP", "SwinConfig"],
+    "models.swinv2": ["SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP", "Swinv2Config"],
     "models.t5": ["T5_PRETRAINED_CONFIG_ARCHIVE_MAP", "T5Config"],
     "models.tapas": ["TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP", "TapasConfig", "TapasTokenizer"],
     "models.tapex": ["TapexTokenizer"],
@@ -1750,6 +1751,15 @@
             "SwinPreTrainedModel",
         ]
     )
+    _import_structure["models.swinv2"].extend(
+        [
+            "SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST",
+            "Swinv2ForImageClassification",
+            "Swinv2ForMaskedImageModeling",
+            "Swinv2Model",
+            "Swinv2PreTrainedModel",
+        ]
+    )
     _import_structure["models.t5"].extend(
         [
             "T5_PRETRAINED_MODEL_ARCHIVE_LIST",
@@ -3068,6 +3078,7 @@
     from .models.splinter import SPLINTER_PRETRAINED_CONFIG_ARCHIVE_MAP, SplinterConfig, SplinterTokenizer
     from .models.squeezebert import SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, SqueezeBertConfig, SqueezeBertTokenizer
     from .models.swin import SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinConfig
+    from .models.swinv2 import SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP, Swinv2Config
     from .models.t5 import T5_PRETRAINED_CONFIG_ARCHIVE_MAP, T5Config
     from .models.tapas import TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP, TapasConfig, TapasTokenizer
     from .models.tapex import TapexTokenizer
@@ -4265,6 +4276,13 @@
             SwinModel,
             SwinPreTrainedModel,
         )
+        from .models.swinv2 import (
+            SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST,
+            Swinv2ForImageClassification,
+            Swinv2ForMaskedImageModeling,
+            Swinv2Model,
+            Swinv2PreTrainedModel,
+        )
         from .models.t5 import (
             T5_PRETRAINED_MODEL_ARCHIVE_LIST,
             T5EncoderModel,

diff --git a/src/transformers/models/__init__.py b/src/transformers/models/__init__.py
@@ -127,6 +127,7 @@
     splinter,
     squeezebert,
     swin,
+    swinv2,
     t5,
     tapas,
     tapex,

diff --git a/src/transformers/models/auto/configuration_auto.py b/src/transformers/models/auto/configuration_auto.py
@@ -123,6 +123,7 @@
         ("splinter", "SplinterConfig"),
         ("squeezebert", "SqueezeBertConfig"),
         ("swin", "SwinConfig"),
+        ("swinv2", "Swinv2Config"),
         ("t5", "T5Config"),
         ("tapas", "TapasConfig"),
         ("trajectory_transformer", "TrajectoryTransformerConfig"),
@@ -239,6 +240,7 @@
         ("splinter", "SPLINTER_PRETRAINED_CONFIG_ARCHIVE_MAP"),
         ("squeezebert", "SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP"),
         ("swin", "SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP"),
+        ("swinv2", "SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP"),
         ("t5", "T5_PRETRAINED_CONFIG_ARCHIVE_MAP"),
         ("tapas", "TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP"),
         ("transfo-xl", "TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP"),
@@ -374,6 +376,7 @@
         ("splinter", "Splinter"),
         ("squeezebert", "SqueezeBERT"),
         ("swin", "Swin Transformer"),
+        ("swinv2", "Swin Transformer V2"),
         ("t5", "T5"),
         ("t5v1.1", "T5v1.1"),
         ("tapas", "TAPAS"),

diff --git a/src/transformers/models/auto/feature_extraction_auto.py b/src/transformers/models/auto/feature_extraction_auto.py
@@ -66,6 +66,7 @@
         ("segformer", "SegformerFeatureExtractor"),
         ("speech_to_text", "Speech2TextFeatureExtractor"),
         ("swin", "ViTFeatureExtractor"),
+        ("swinv2", "ViTFeatureExtractor"),
         ("van", "ConvNextFeatureExtractor"),
         ("vilt", "ViltFeatureExtractor"),
         ("vit", "ViTFeatureExtractor"),